**5. Conclusions**

In this study, the valorization of an olive pomace-based biocomposite was evaluated through the environmental criteria, with the LCA method. The functional unit is the production of 1 m2 of a lath (building material) made of an olive pomace-based biocomposite. Two scenarios with different thermoplastic matrices (PE, PP) were assessed. In both cases, the twin-screw compounding process contributed the major burden in most of the midpoint impact categories. Compounding is mainly affected by the production of the respective polymers. When comparing the OSF/PE and OSF/PP materials, the impacts are relatively similar. Therefore, a further study at the end of life of the material should be carried out to conclude which of the proposed biocomposites is the less polluting one.

The results obtained showed that human health is the most affected area of protection; it represented 89% for both scenarios, i.e., OSF/PP and OSF/PE. The main contributors to this damage category are energy, carbon dioxide and sulfur dioxide, used and produced in the manufacturing process. Ecosystems and resources availability represented a lower contribution to the total impact. Scenarios presented values of 6% and 4.5% for ecosystems and resources availability, respectively.

The comparison of the olive pomace-based-composite with the business-as-usual scenario shows that the biocomposite released half of the pollution produced by the PVC decking, when considering the same FU. Besides, given the long lifespan of the biocomposites, the CO2 stored on it can mitigate climate change because of the delay in the carbon emissions into the technosphere.

This case study evaluates a new path for olive pomace-based composites as an alternative eco-material for the building sector, based on environmental criteria. Future works can include the use of biosourced polymer matrices, which could reduce the impact of the production of olive pomace-based composites.

**Author Contributions:** Methodology, G.E.-A.; investigation, G.E.-A., P.G.-A. and P.E.; writing— original draft, G.E.-A. and P.G.-A.; writing—review and editing, C.V., J.-P.B., P.E. and C.S.; validation, C.V., J.-P.B., P.E. and C.S.; supervision, C.V., J.-P.B. and C.S.; visualization, G.E.-A. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was carried out as part of the doctoral thesis of G. Espadas-Aldana, her work being financed by the Mexican National Council for Science and Technology (CONACYT Mexico) under the scholarship No. 471707.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data is contained within the article.

**Conflicts of Interest:** The authors declare no conflict of interest.


**10. Conclusion**

The term used to describe lifelong changes in function that follow a particular event in an earlier period of the life span is called programming. Nevertheless, the previous information may have a significance of pediatric obesity endocrine abnormality. GH-IGF-1 axis is partially responsive for the signal to enhance muscle and bone development. Growth hormone (GH) response to exercise may be weak in obesity and may not appear until later in life, especially if they affect genes that are responding to subsequent environmental responses, such as high-fat diet. I do not know how the genetic change instigated development window in the main systems. Exercise is the best way to do this when you exercise regularly, and you build stronger muscles, even if you do not work out with weights.  Fat cells of unhealthy obese were larger than those of any other group. It was swollen and full of inflammation. The collapse and filling of their fat stores were disabled and showed a closer look that their mitochondria were not functioning well causing loss of muscle power. This is due to fat cell accumulation which reduces its ability to burn fuel and produce adenosine triphosphate, or ATP, the body's energy currency. It is natural that the behavior of the human being and his attitude of life inert to be aware of the importance of physical activity and live in the healthy lifestyle system in the prenatal stages. Thus, healthy genetic modification will be inherited for future generations.


**1. Introduction**

The Non-Intrusive Appliance Load Monitoring (NIALM or NILM) [1] is a solution for the problem of collecting electrical energy consumption data more accurately than using only typical electricity meters. The methodology (also known as energy disaggregation [2]) is used for power systems analysis, in which demand for energy continuously increases. The purpose of the appliances' load identification is to provide information about the energy consumption of individual devices. This may lead to a decrease in electricity consumption and suppressing environmental pollution [3]. According to [4] the application of NIALM approaches might lead to a reduction of household energy consumption by at least 12%. Another potential application is the diagnostics of electrical appliances [5], like monitoring device degradation or detecting supply network's state in the presence of external disturbances, like voltage spikes, insulation decrease, etc. In the NILM architecture, measurements are done close to the energy meter, in contrast to intrusive systems where every socket or device is equipped with a suitable sensor [6]. When new appliances are plugged into such systems, the measurement hardware is not expanded. Acquired values are typically aggregated currents and voltages [7]. Characteristic features allowing for the identification of a particular Electrical Appliance (EA) are obtained individually during traininginthespecificdeploymentlocation.

Over the past 20 years, the topic was widely explored [7–11]. Public databases were prepared to allow for the verification of new approaches [12–14]. The main achievements are summarized, for instance, in [15].

The taxonomy of NILM methods considers multiple criteria. Firstly, they can be classified based on the frequency of the measured signals [7,8]. In [16] four types of frequency-based methods were identified: LF (Low Frequency), MF (Medium Frequency), HF (High Frequency), and EHF (Extra-High Frequency). The first one exploits the RMS of the current and voltage waveform or the amplitude of its first harmonic collected with the sampling frequency from below one to several Hz. The MF approach processes signal samples collected with a frequency from 1 kHz to dozens of kHz. The HF method operates on transients collected with a sampling frequency from dozens of kHz to several dozens of MHz. Finally, EHF methods operate on sampling frequencies above a dozen of MHz. The latter group was not investigated so far.

The second taxonomy criterion [10,11], is the moment of the appliance analysis. These can be steady-state (SS) or transient state (TS), depending on whether the waveforms are sampled during the state change or after it is already done and no transients are present in the signals. The SS-LF methods are currently the most popular because of the low cost of sensors [17] and the simple mathematical apparatus required to process data with low computational power requirements [18].

The frequency of the measured signals determines features characterizing the analyzed devices. For SS-LF methods the most commonly used parameters are the average power [19–24], reactive power [25,26], and power factor [19]. The SS-MF methods work with the amplitude [27] and phase [28] of the subsequent current harmonics. Power characteristics may be considered as well [3]. The real and imaginary parts of the current odd harmonics were used in [29]. In SS-HF approaches, disturbances not being harmonics of the fundamental component of the power grid (i.e., 50 Hz or 60 Hz) are analyzed. For example, it can be EMI noise specific for different electronic appliances [30].

Some electrical devices, when turned on, generate a short-term current pulse with an amplitude significantly exceeding their nominal supply current [31]. These transients can be used to identify the EA state [32]. Approaches using such features belong to the TS-HF group. In [32], the fundamental voltage component was filtered, from which the frequency components of the disturbances appearing at the moment of switching on the device were extracted. In [33], voltage harmonics were eliminated using Notch filters, and then the signal was analyzed using Wavelet Transform (WT). In [34] the current of devices in the transient states was recorded with the 100 kHz sampling frequency. The model of the transient state was created on the basis of the currents collected when turning the EA on. The energy spectra calculated using WT were used in [34]. Next, the energy distribution in the subbands was used to identify appliances.

The areas of HF and EHF methods, although more challenging, especially from the data acquisition point of view, rely on phenomena that cannot be observed at lower sampling rates. These give possibilities of distinguishing between different appliances, as is shown in this paper.

During our previous research [35], it was discovered that parameters of electrical signals related to the operation of a particular EA depend on other devices operating in the network at the same time. As a result, the set of devices working in the background determines features extracted from the waveforms. Such a phenomenon can be used to identify states of individual EAs.

To verify the practical application of the presented phenomenon, the HF-GEN method was developed. It includes a measurement system acquiring the supply current signal, complemented by the known impulse signal generator. To detect changes in the impulse parameters it is necessary to use the time-frequency analysis, which allows for comparing impulse signals generated when different loads are switched on. The paper also presents the data processing method used for EAs identification, based on cross-correlation of the reference signal with the analyzed signal.

The outline of the paper is as follows. Section 2 presents the method of determining the characteristics of EA. Section 3 contains the quality assessment method. Section 4 covers experimental results, while Section 5 holds conclusions and future prospects.

#### **2. HF-GEN Method for Determining the Characteristics of EA**

Known methods exploiting the analysis of the transient currents and voltages during the appliance's state change are still a minority. Most EA identification algorithms rely on the characteristic features determined in steady states of operation. The transient signal is a result of changing the state of the device (for instance, by turning it on). Electrical signals recorded at the moment of the transient state change must be analyzed. The key to detecting the EA state change is to find proper features of the impulse signal. They should clearly distinguish impulse signals appearing as a result of changes in the states of various EAs. Two problems emerge that significantly limit the applicability of such approaches. First, EAs are switched on with a random voltage phase, so the transient states of the examined EAs also have the random voltage phase. Secondly, EAs in the background influence parameters of the transient signals. Both factors affect the shape of the analyzed impulse and make assigning the specific transient to a device difficult.

In the HF-GEN method, the generated current pulse signal is introduced into the tested power network circuit. The analyzed impulse is therefore the effect of a deliberately created transition state, not related to any EA. The pulse is generated many times at regular intervals. When the EA state changes, the pulse shape also changes, because it is characteristic of the particular EA. Detection of the EA state change consists of observing corresponding changes in the pulse features, which form the EA signature. The latter should unequivocally identify the specific EA. The principle of the HF-GEN method is illustrated in Figure 1. The pulse shape changes between the appliance's "on" and "off" states.

**Figure 1.** Illustration of the HF-GEN method.

The following were the experiments' assumptions:

•the maximum frequency of measured signals is 15 MHz.

The purpose is to find changes in the pulse signal caused by the load change in the supply circuit. The load on the power circuit depends on the set of EAs connected to it. Characteristics of the impulse signal are related to the specific EA, therefore enabling identification of the moment when the particular device is turned on. The block diagram of the HF-GEN method is shown in Figure 2. The first step is the generation of the impulse signal. The generator detects the supply voltage phase and then inputs the pulse signal to the LV (Low-Voltage) circuit. In the second step, the pulse current is measured with the sampling frequency of 30 MS/s. The acquired samples are processed to select their subset acquired during 4 ms after the pulse detection. Next, cross-correlations between the samples' vector and transients patterns stored in the dictionary are calculated. A signature

characterizing the pulse signal is then prepared. Finally, the signature quality is determined. Subsequent steps are presented in detail in the sections below.

**Figure 2.** Block diagram of the HF-GEN method.

#### *2.1. Pulse Signal Generation*

The block diagram of the pulse signal generator with two connectors/ports is shown in Figure 3. The first one, the input and output port (I/O), is connected to the tested circuit of the power network with a voltage of 230 V and a frequency of 50 Hz. This port is marked as input (I) because it is used to supply the generator with voltage. It is also treated as the output port (O), because of providing the impulse current signal to the power network. The O-SYN port is used to get the synchronization signal outside the generator. It determines time instances of the pulse signal generation. The synchronization output is used to control the acquisition system. When designing the generator, the following parameters were assumed:

The pulse signal generator consists of a matching circuit (MC-GEN), an Analog-to-Digital converter (AD-GEN), a Digital Output (DO), a Relay (RE), and an Attached Load (AL). The measurement and generation system is connected to a computer (PC-GEN) on which the Control Software (CS) is running.

The pulse amplitude depends primarily on the voltage phase in which the Attached Load (AL) is connected to the power grid. The pulse amplitude is proportional to the voltage value at the moment of turning the AL on. Setting the constant voltage phase (the same each time) is the biggest challenge. The time instant must be synchronized with the phase of the supply voltage. The process is as follows: the main voltage*u*(*t*) is applied to the MC-GEN, which converts the voltage*u*(*t*) into the voltage*u*AD−GEN(*t*) whose amplitude matches the dynamic range of the AD-GEN input. The AD-GEN converts voltage*u*AD−GEN(*t*) to samples*un* with a speed of 250 kS/s. Based on the voltage samples*un*, the CS detects the supply voltage phase. As a result of its operation, the logic signal*on* is given to the input of DO, assuming a high value when the impulse signal is generated. DO converts the logic signal*on* to the voltage*u*SYN(*t*). The O-SYN synchronization output is

triggered at the right moment by a high voltage level. The main function of the RE is to apply the supply voltage to AL when a high voltage level appears at*u*SYN(*t*).

**Figure 3.** Block diagram of the pulse signal generator.

The pulse shape parameters are determined by the AL. The rise time of the pulse and its total duration depends on the AL impedance. Contrary to the tested EA, AL is a known load with a specific transmittance, temporarily connected to the supply network to change the parameters of the network. In practice, any appliance approved for use in the LV grid, for example, an energy-saving light bulb, can be used as AL. In such a situation, the pulse generator is no different than other appliances in the network. It is a typical load, connected to the network at specified intervals (e.g., 1 s) for a specific time (e.g., 40 ms).

## *2.2. Measurement Method*

In the HF-GEN method, the measured signal is the impulse in the current introduced to the tested circuit by the signal generator. The parameters of the signal change with the load of the tested network after introducing the specific EA. This fact is used to detect the change in the EA state.

The measurement system from Figure 4 consists of a transient generator (GEN), an electrical appliance energy receiver (EA), a Current-Voltage Converter (CVC), an Acquisition Card (AC), a computer (PC), software (SW), and memory (MM). The tested EA and GEN are powered from the network with an RMS voltage of 230 V and frequency of 50 Hz.

The supply network voltage*u*(*t*) is provided to GEN through the I/O terminals connected to the phase conductor L1 and the neutral conductor N. The synchronization voltage*u*SYN(*t*) is supplied from the synchronization output O-SYN of GEN to the synchronization input of the Analog-to-Digital Converter. High levels of*u*SYN(*t*) determine time instants for pulse generation. The current*i*(*t*) is converted by the CVC into*u*AD(*t*) voltage with a level adjusted to the dynamic range of the analog input of the acquisition card (AC) converter, providing samples*in*.

The voltage*u*SYN(*t*) also triggers the acquisition of current samples when a pulse is generated. The SW running on PC controls the AC operation and collects the current samples*in* storing them in MM for further analysis. Due to triggering the AC converter acquisition, the amount of data for processing is significantly reduced.

**Figure 4.** Diagram of the measuring system of the HF-GEN method.

#### *2.3. Selection of Current Samples*

The result of data acquisition is the current vector*i* = [*i*1 ... *iN*] (see Figure 5). It contains current samples recorded around (before and after) the pulse manifestation.

**Figure 5.** The sampled current vector *i*.

Due to the effectiveness of further calculations, only a selected fragment of the current vector is analyzed. This is because some fragments of the obtained current data do not contain useful information. Specifically, the current vector contains data measured prior to generating the current pulse (e.g., current vector samples from 1 to 125,000 in Figure 5). The data in this fragment of the current vector bear no information characteristic for the tested EA.

The most relevant is the fragment of the current vector near the largest pulse peak. Therefore only part of the original vector (i.e.,*i*SEL) is extracted for analysis. The vector*i* is filtered by the high-pass filter with a cut-off frequency of 1 kHz, which enables effective suppression of the 50 Hz component and its harmonics (100 Hz, 150 Hz, and so on). Then, the maximum of the high-frequency components (i.e., above 10 kHz) is found. The vector*i*SEL contains 2700 selected samples around the maximum of the high-frequency components. Figure 6 shows example of the*i*SEL vector.

**Figure 6.** Current vector*i*SEL for sample measurement data.

#### *2.4. Preparation of a Dictionary of Transients*

The dictionary of transients *D* is a set of selected fragments of the current vectors containing pulses for various appliances:

$$D = \left\{ \mathbf{i}\_{\text{DIC}}^{(1)} \, \mathbf{i}\_{\text{DIC}}^{(2)} \, \dots \, \mathbf{i}\_{\text{DIC}}^{(l\_{\text{D}})} \dots \mathbf{i}\_{\text{DIC}}^{(l\_{\text{D}})} \, \dots \, \mathbf{i}\_{\text{DIC}}^{(LDIC)} \right\},\tag{1}$$

where*i*(*l*D) DIC are the most interesting fragments of vectors *i* describing the pulse and*LDIC* is the number of examples. Figure 7 shows the method of preparing the dictionary. Samples from vector*i* are selected as in Section 2.3. Then, the initial and terminal indexes of the transition are marked, leading to the structure presented below.

**Figure 7.** Preparation of dictionary of transients.

The marking process is performed by specifying the initial*n*START and terminal*n*STOP indexes. The fragment*i*DIC is then extracted as follows:

$$\mathbf{i}\_{\text{DDC}} = \begin{bmatrix} i\_{\text{DDC},1} \dots i\_{\text{DDC},N\_{\text{DDC}}} \end{bmatrix} = \begin{Bmatrix} i\_{\text{SEL},\text{J}\_{\text{SILKT}}} \ i\_{\text{SEL},\text{J}\_{\text{SILKT}}+1} \dots \ i\_{\text{SEL},\text{J}\_{\text{SCIGP}}-1} \ i\_{\text{SEL},\text{J}\_{\text{SCIGP}}} \end{Bmatrix}, \tag{2}$$

where*N*DIC = *n*STOP − *n*START + 1 denotes the number of samples in*i*DIC.

Figure 8 shows the example of*i*SEL with the marked indices*n*START and*n*STOP (**a**) and the extracted*i*DIC (**b**).

**Figure 8.** Current vectors for the sample measurement data:*i*SEL (**a**) and*i*DIC (**b**).

For each considered EA, 10 examples of transition states were added to the dictionary. They differ in amplitude and shape. The selected number is the compromise between the variety of stored data and the computational effort required to obtain examples. An example is a current vector and corresponding category from the set*D*CAT (which cardinality determines the number of identified appliances *NEA*). Therefore, the number of vectors*i*DIC in the dictionary is*LDIC* = 10·*N*EA.

#### *2.5. Determining the Cross-Correlation*

In this stage, the maximum correlation between the measured signal*i*SEL and subsequent dictionary entries*i*DIC is found. The vector*i*SEL is longer than the current vector from the dictionary*i*DIC, so the correlation is calculated for all possible shifts between*i*DIC and*i*SEL.

The vector*i*SEL has*N*SEL = 120, 000 samples (representing the duration of 4 ms for sampling frequency *fS* = 30 MHz). The correlation will be determined many times for each transition state. Therefore, the method of determining the cross-correlation should be computationally efficient. The determination of the cross-correlation without normalization was considered due to the simplicity and efficiency of calculations. In the discussed problem, the cross-correlation without normalization cannot be used, because the elements of current vectors mainly contain a fundamental component of the current signal with a frequency of 50 Hz. On the other hand, pattern vectors only contain components with frequencies at least 200 times greater than the fundamental component. The 50 Hz frequency component significantly changes the average value of the current vector, and as a result, significantly affects the value of cross-correlation without the normalization. The measure of similarity between sample vectors based on the Pearson correlation coefficient was used. The mean and standard deviation for each fragment of the vector*i*SEL was calculated, which requires significant computational effort. Therefore, the optimized calculation method [36] was used.

As a result, vectors of correlations *r* and shifts*c* were obtained. Figures 9–11 illustrate the procedure.

**Figure 9.** Determining the correlation for sample measurement data, delay c = 18,000.

**Figure 10.** Determining the correlation for sample measurement data, delay c = 21,160.

**Figure 11.** Determining the correlation for sample measurement data, delay c = 22,000.

Figure 12a shows the cross-correlation vector*r* as a function of delay*c* for the example of measurement data. Figure 12b shows the same relationship for the vector fragment*r* with the highest correlation values.

**Figure 12.** Cross-correlation as a function of delay for an example of the measurement data; the whole correlation vector (**a**); the fragment of the vector*r* that contains the highest correlation values (**b**).

## *2.6. Signature Calculation*

The signature parameters are the maximum cross-correlation determined between the current vector*i*SEL and all current vectors*i*(*l*D) DIC from the dictionary of transients. Correlation vectors for successive current vectors*i*(*l*D) DIC are denoted as**r***l*D . The set of categories*D*CAT from the dictionary of transitions is used to name successive signature features. The idea is presented in Figure 13.

The EA signature contains maximum values of the cross-correlation between the analyzed current vector and the individual dictionary elements. Signature features are determined as the maximum absolute value of the cross-correlation*rl*D between the analyzed current vector*i*SEL and the stored current vector*i*(*l*D) DIC:

$$\text{COR}\\_x\\_y = \max |\mathbf{r}\_{\text{lp}}|\_\text{\textdegree} \tag{3}$$

where*x* ∈ {1, . . . , *N*EA}, *y* ∈ {A, B, C, D, E, F, G, H, I, J}.

The computed cross-correlation with the marked maximum value for sample measurement data are presented in Figure 14.

A signature**s***l* consists of*P*HF−COR = 10·*N*EA features, arranged in a specific order. Names of features and their acronyms are listed in Table 1.

$$\mathbf{s}\_{l} = \begin{bmatrix} s\_{l,1} \dots s\_{l,p} \dots s\_{l,P\_{\text{HF}\dots\text{COR}}} \end{bmatrix}^T. \tag{4}$$

**Figure 13.** Signature determination algorithm for the HF-COR method.

**Figure 14.** The absolute value of cross-correlation with the maximum value marked for example measurement data.

Signature vectors for individual transient states constitute successive columns of the signature array*S*:

$$\mathbf{S} = [\mathbf{s}\_1 \dots \mathbf{s}\_l \dots \mathbf{s}\_{LSP}],\tag{5}$$

where*LSP* is the total number of transients processed.

#### **3. Signature Quality Assessment Method**

The signature well describes devices if its features allow for distinguishing between them. Feature vectors for the same appliance should be similar to each other. The purpose of the signatures quality assessment is to verify if they can be used to identify appliances.

The process is presented in Figure 15. Division of available data into training and testing sets is important. The *K*-fold Cross-Validation (CV) with*K* = 10 was used here. The data set is split *K* times into training and testing subsets (with the ratio of 9:1) in such a way that each EA is represented by the single signature in the testing set. The training sets were used to extract knowledge for the intelligent classifier, while the testing ones were applied to verify their generalization abilities. The classification accuracy was averaged on all trials.

**Figure 15.** Block diagram of the signature quality assessment method.

The classifier processes signatures*S*(*k*) test to predict appliance identifiers (represented by category estimates*y*ˆ(*k*) test). The latter are compared to actual categories*y*(*k*) test so sample errors can be calculated. Among many types of candidates for classifiers, the following were selected:

For each round of the CV, each classifier is trained and tested separately (see Figure 16). This way all approaches can be compared. Also, their fusion may be applied if necessary. Each algorithm has specific advantages and hyperparameters. For instance, DT during training selects features based on which rules are constructed. This is the problem for kNN, where the subset of signature values must be manually selected or weighted. Also, the number of neighbors influences diagnostic accuracy. One CV round produces four vectors:

• actual appliances identifiers in the testing set—*y*(*k*) test,

**Figure 16.** Detailed diagram of one cross-validation attempt.

## *3.1. Decision Tree*

The DT is a tool storing knowledge in the form of a tree (Figure 17). Nodes indicated by circles represent tests on the selected feature (in our case, one of the signature parameters) and its threshold value (like *x*1 > 15). The result of the test redirects the analyzed vector of features to the node one level below until the terminal node (leaf) is reached. The leaves (rectangles) represent appliance categories. Classification of the example is then based on exploring the tree from the root (yellow node) to one of the leaves. Tests performed at each node indicate which way to take next. Generation of the DT is done using one of the machine learning algorithms like C4.5 or CART, which differ in the method of selecting tests for nodes.

**Figure 17.** Decision tree example.

## *3.2. Neural Network*

The ANN is widely used in classification. The feed-forward structures, like multilayered perceptrons or RBF networks, are the most popular. Their hyperparameters include the number of hidden layers or the number of neurons in them*s*HL. Also, the output layer category coding is important, depending on the activation functions (like sigmoidal ones or softmax). The optimal structure of ANN is then found to maximize the classification accuracy for the minimum number of neurons. Knowledge extraction is performed using gradient-based algorithms.


**4. Conclusions**

Although micronutrient malnutrition is still a challenging problem, progress has been achieved in the development of biofortified crops, either by enhancing the content of key microelements such as iron and zinc or by developing *lpa* mutants with good agronomic performance [1]. Enhancing the content of key microelements has been achieved essentially by breeding, while studies on the elucidation of the mechanisms, and therefore of the genes, involved in micronutrient uptake and efficient storage in the seed are still in progress [1–3].

Advances have been achieved in understanding the function of a number of structural genes involved in PA biosynthesis [7,8,11] and new genes, not obviously correlated to PA biosynthesis and storage, have been discovered to play a role in PA accumulation in the seed [5,6]. Furthermore, there is increasing evidence that many negative pleiotropic e ffects commonly associated with *lpa* mutants may be overcome by e fficient breeding, thus making reasonable and convenient the production of *lpa* mutants [4,9,10]. To reinforce this convenience, there are also social and economic considerations, as clearly explained in the review by Raboy [4].

In conclusion, it is quite clear that, at the moment, the most promising strategy to produce e ffective biofortified crops is by combining seed PA reduction (*lpa* mutants) with increased seed mineral content, as a number of results provide evidence showing that PA is still the main limiting factor to cations' bioavailability in the diet of humans and monogastric animals [15].

**Author Contributions:** Conceptualization E.C.; writing E.C., R.P. and F.S.; funding E.C. and F.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was co-funded by sPATIALS3 project, financed by the European Regional Development Fund under the ROP of the Lombardy Region ERDF 2014–2020—Axis I "Strengthen technological research, development and innovation"—Action 1.b.1.3 "Support for co-operative R&D activities to develop new sustainable technologies, products and services"—Call Hub.

**Acknowledgments:** We would like to deeply thank all the colleagues who contributed to this Special Issue.

**Conflicts of Interest:** The authors declare no conflict of interest.


**6. Peralkaline affinity of Kone silicic volcanics**

Kone silicic eruptives are characterized by the phenocryst assemblage of alkali feldspar, quartz, hedenbergite, aenigmatite and fayalite. This assemblage is accompanied by rare ilmenite and apatite. The appearance of aenigmatite in silicic volcanics has been attributed to reflect a peralkaline affinity [17]. Peralkaline magmas likely form when fractional crystallization removes a high proportion of plagioclase relative to mafic minerals [18]. The coexistence of aenigmatite and hednbergite is thought to be a typical feature of the mildly peralkaline silicic rocks such as comendite and trachycomendite [19].

The presence of quartz as a major phenocryst phase in Kone silicic volcanics indicates an advanced stage of fractionation (i.e., the more SiO2-rich magma). Furthermore, the SiO2 content of the glass matrix (69.3 wt.%, **Table 4**) shows a rhyolite composition. Thus, the phenocryst mineralogies reflect the SiO2 content of the magma from which they crystallized. On this basis, the alkali feldspar + hedenbergite + aenigmatite + fayalite assemblage only crystallizes in the most evolved magmas (rhyolites) with the appearance of quartz as a phenocryst phase.

On the basis of the mineral assemblage, the Kone silicic volcanics can be regarded as more mildly peralkaline rhyolite (comendite) as evidenced by the appearance of hedenbergite, which appears to cease crystallizing in strongly peralkaline rhyolite (pantellerite), generally coinciding with the crystallization of aegirine or aegirineaugite [19]. This is probably related to the low fO2, at or close to FMQ, at which the magmas evolve [20]. We conclude that the alkali feldspar + quartz + hedenbergite + aenigmatite + fayalite assemblage is essentially restricted to the mildly peralkaline rhyolite (comendite), but close to the comendite-pantellerite boundary as crystallization of aenigmatite is restricted to pantellerite [16].


**4. Conclusions**

In summary, Ag-modified TiO2 thin films were successfully anchored on bamboo material through a facile hydrothermal process, followed by an Ag mirror reaction for photoactivated antifungal coating. Upon decorating Ag NPs on anatase TiO2 thin films, the composite films showed an enlarged optical response region and improved quantum efficiency. The antifungal test results show that the Ag/TiO2 composite films grown on the bamboo surface produced a synergistic antifungal mechanism compared with pure Ag NPs or anatase TiO2 film under both light and dark conditions. However, the antifungal activity of Ag/TiO2 composite films under visible light is superior to that in the dark, owing to the transfer of photoexcited electrons from Ag to TiO2, their trapping on TiO2 under visible light illumination, and their release in the dark, which could give this photocatalyst a catalytic memory for producing •O2 −and •OH radicals in the absence of light illumination. We believe that this discovery could open a door for the continuous removal of indoor air pollutants such as VOCs, bacteria, and fungus in the day and night time.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/jof7080592/s1, Figure S1: Antifungal test. Figure S2: EDS spectrum of ATB-10. Inset images are corresponding elemental mapping results. Figure S3: XPS survey spectra of (a) original bamboo, (b) TB, and (c) ATB-10. Figure S4: SEM micrograph for a cross-sectional profile of the TiO2 thin film on bamboo substrate. Figure S5: XRD pattern of the as-prepared TB. Figure S6: High-resolution XPS spectrum of F 1s.

**Author Contributions:** Conceptualization, J.L. and J.W.; methodology, J.L. and Y.C.; software, J.L. and R.M.; formal analysis, Z.W.; investigation, S.H. and R.B.; resources, J.L.; writing—original draft preparation, J.L., R.M. and J.W.; writing—review and editing, J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** The work was financially supported by the Project of Forestry Science and Technology of the Zhejiang Province (2021SY13), Zhejiang Provincial Natural Science Foundation of China (LQ20C160002), and Fundamental Research Funds for the Central Non-profit Research Institution of CAF (CAFYBB2017MA023).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors would like to thank Teacher Yang from Shiyanjia Lab (http://www. shiyanjia.com, accessed on 28 May 2021) for the ESR analysis.

**Conflicts of Interest:** There are no conflicts to declare.

### **References**


**2. System Description**

#### *2.1. Ocean Numerical Model*

BSFS is based on a free surface implementation of the NEMO hydrodynamical model (v3.4, [7]) for the Black Sea region. The horizontal resolution is approximately 3 km (1/27◦ in zonal and 1/36◦ in meridional directions), which conforms to the mesoscale eddy-resolving scale (Rossby radius of deformation in the Black Sea is ~20 km, [8]). On the other hand, 31 unevenly distributed z-levels are used along the vertical direction, with an initial layer at about 2.5 m in depth. BSFS bathymetry is derived from GEBCO at 1 resolution [9]; a local refinement of the coastline, using a high-resolution NOAA dataset [10], is performed to represent the main coastal features of the basin.

The model is forced by momentum, water and heat fluxes interactively computed by bulk formulae implemented first for the Mediterranean Sea [11] and adapted for the Black Sea, as described in Appendix A. The analysis and forecast atmospheric fields are provided by the European Centre for Medium Range Weather Forecast (ECMWF) at 0.125◦ horizontal resolution, at 3–6 h frequency.

The momentum equation is written in flux form and is solved with a leapfrog time stepping scheme. The free surface equation uses a linearized form (i.e., the barotropic velocity field is defined with an integral from the flat surface to the bottom) and is integrated implicitly [12] with the time step of the total velocity field equal to 150 s. The advection scheme for the temperature and salinity tracers is the total variance dissipation (TVD) scheme. Horizontal eddy diffusivity is applied for tracers using a Laplacian operator, with a coefficient of 50 m2/s, while a bi-Laplacian viscosity is applied for momentum, with a coefficient of 1.0 × 108 m4/s. The vertical diffusivity and viscosity are computed from a turbulent kinetic energy (TKE) closure model, with the parameters set as in [7]. Vertical eddy viscosity and diffusivity coefficients are selected with values of 1.2 × 10−5 m2/s and 1.2 × 10−6 m2/s, respectively. The model also implements free-slip lateral boundary conditions and a classical quadratic bottom friction scheme, with a drag coefficient of 1.0 × 10−3 m2/s2.

The Bosporus Strait connects the Black Sea with the Sea of Marmara: to model it, a twolayer, narrow strait water mass exchange which maintains a relatively steady state water and salt balance in the Black Sea [13–18] is used, with a closed boundary condition. The excess precipitation and river runoff over evaporation is removed using the outflow from the strait, thus leading to a zero balance in the Black Sea [19]. Considering the horizontal average of the free surface equation [20], the net transport at the Bosporus Strait is given by:

$$\frac{1}{A}Tr\_B = -\frac{\partial}{\partial t} \langle \eta \rangle - \langle E - P - R\delta(\mathbf{x} - \mathbf{x}\_i) \rangle \tag{1}$$

where *TrB* is the net transport at the Bosporus Strait, and *A* is the basin surface area, *η* is the sea surface height, *E*, *P*, *R* are, respectively, evaporation, precipitation and runoff, the Dirac *δ* is different from zero at 72 river mouths and the triangular brackets mean horizonal basin average. First, we redefine the transport at the Bosporus Strait in terms of a "discharge" *RB* :

$$Tr\_B = L\_{i,B} H\_{i,B} \ R\_B \tag{2}$$

Thus, the discharge at the Bosporus Strait is given by:

$$R\_B = -\frac{A}{L\_{i,B}H\_{i,B}} \left[ \frac{\partial}{\partial t} \langle \eta \rangle + \langle E - P - R\delta(\mathbf{x} - \mathbf{x}\_i) \rangle \right] \tag{3}$$

where *Hi*,*B* and *Li*,*B* are depth and width in one-grid-sea-point, respectively.

We calculated the Bosporus discharge in (3) from a 10-year simulation by computing the mean free surface tendency and the mean water flux. The values of the Bosporus discharge are stored as monthly mean values and set as vertical velocity boundary conditions as done for the rivers, except with the negative sign, indicating a discharge out of the basin or a "negative river". This parametrization is quite robust for decadal long simulations that do not consider climate change trends in sea level and water fluxes.

With regard to the 72 real river runoff contributions, we use monthly mean discharge data from the SESAME dataset [21] for all rivers, including the Danube, the Dniepr, the Dniester, the Rioni, the Kizil Irmak, and the Sakarya.

#### *2.2. Data Assimilation Scheme*

The ocean model is coupled with a data assimilation system in order to produce analyses for optimal initial conditions of the forecasts. The DA ocean state vector *x* contains the temperature *T*, salinity *S* and sea level values at each model grid location. For a model setup with *n* vertical levels, this vector is defined as:

$$\mathbf{x} = (T\_{0\prime}, \dots, T\_{n\prime}S\_{0\prime}, \dots, S\_{n\prime}\,\eta) \tag{4}$$

Given an observation vector *y*0 and the background model state *xb* it is possible to define the innovation, i.e., the difference between the observations and their respective model predictions, denoted *d*:

$$d = H(\mathbf{x}\_b) - \mathbf{y}\_0 \tag{5}$$

with *H*(*xb*) being the observation operator that projects the model state onto the observation space. The aim of the DA system is to find a correction to the model state:

$$
\delta \mathbf{x} = \mathbf{x} - \mathbf{x}\_b \tag{6}
$$

This correction needs to minimize the analysis error while taking into account the error covariances of the model state vector and the observations. There are several methods to calculate such corrections; however, they can be broadly categorized as variational methods or Kalman filters. In BSFS, a 3D-variational method is used [22,23]. In this method, the corrections are derived by iteratively minimizing a cost function *J*, defined as:

$$J = \frac{1}{2} \delta \mathbf{x}^T \mathbf{B}^{-1} \delta \mathbf{x} + \frac{1}{2} (\mathbf{H} \delta \mathbf{x} - d)^T \mathbf{R}^{-1} (\mathbf{H} \delta \mathbf{x} - d) \tag{7}$$

Here the matrices **B** and **R** are the error covariance matrices of, respectively, the background state and the observations. For a number of state variables *nb*, the model covariance matrix **B** is of size *n*2 *b*. In addition, as the cost function contains **B**−1, the covariance matrix needs to be inverted. It is clear that for large numbers of state variables, this is a computationally costly calculation, which can be avoided if additional constraints are imposed on **B**, in particular if **B** is of the form:

$$\mathbf{B} = \mathbf{V}\mathbf{V}^T\tag{8}$$

with **V** an arbitrary matrix. In this case, Equation (7) can be written as:

$$J = \frac{1}{2}v^T v + \frac{1}{2}(\mathbf{H}\mathbf{V}v - d)^T \mathbf{R}^{-1} (\mathbf{H}\mathbf{V}v - d) \tag{9}$$

introducing a coordinate transformation:

$$
\delta \mathbf{x} = \mathbf{V} \mathbf{v} \tag{10}
$$

$$
v = \mathbf{V}^{-1} \delta \mathbf{x} \tag{11}$$

The minimization of the cost function can now be performed in terms of the new control variable *v*, without the need to calculate **B**−1. It is sufficient to perform the minimization in terms of *v* before transforming back to the model state space increment *δx* with (10).

As the covariance is by definition a positive definite matrix, the matrix **V** exists and could be found by performing a Cholesky decomposition on **B**. However, since the model error covariance **B** is usually derived from model state variable anomalies in a long model run, **V** is simply the anomaly matrix and **B** is already calculated according to (8).

One of the main features of OceanVar is the covariance decomposition. The matrix **V** of (8) is expressed as a product of different components:

$$\mathbf{V} = \mathbf{V\_H}\mathbf{V\_{\eta}}\mathbf{V\_{\vee}}\tag{12}$$

where **V** H is the horizontal and **V**V is the vertical component of the covariance. The horizontal component provides the correlation between neighboring grid points at each model level. This is implemented by means of a recursive filter with a radius of approximately 25 km in open sea and a gradual falloff near the coast. The vertical component is calculated from a long model simulation and decomposed using empirical orthogonal functions (EOF). This EOF decomposition significantly reduces the number of variables, *nv nx*, and reduces spurious correlations due to the finite dataset used to estimate the covariance. *Vη* is the dynamic height operator, which uses the local hydrostatic adjustment scheme [23] to transform the sea level anomaly innovations into increments for T and S in regions deeper than 1000 m.

For the BSFS system with 31 vertical levels, the vertical covariance of *T* and *S* is represented by 15 EOF. The EOF are calculated separately for each month and each grid location to adequately capture the variability of the covariance in time and in space. The DA system is used to assimilate in-situ observations of temperature and salinity from available ARGO profilers in the Black Sea, and satellite observations of sea level anomaly (SLA) and of sea surface temperature (SST). For the latter, the foundation temperature is used and assimilated only at nighttime (as in e.g., [24]). This approach limits possible biases due to the diurnal cycle of SST.

## *2.3. Operational Chain*

BSFS version here considered is EAS3 (European Analysis and forecasting System, version 3, referred also in the next sections) for the Black Sea. It implements technical interfaces with upstream data, used for model runs—including ECMWF atmospheric forcing, CMEMS in-situ and satellite observations from corresponding TACs—as well as with the CMEMS Dissemination Unit for the delivery of analysis and forecast products to users. Observations used by BSFS for assimilation and validation are summarized in Table 1.

**Table 1.** List of observations used by BSFS in the assimilation and verification steps.

The atmospheric forcing (previous day analyses and 10 days forecast starting at 00:00 UTC) is downloaded from ECMWF through Aeronautica Militare Italiana. As soon as it is available, the BSFS processing system starts (at around 7:00 UTC). The atmospheric forcing availability is a major source of delay for the oceanic forecast.

Every day the BSFS (Figure 1) produces ten days of forecast (J to J+9), one day of simulation (J−), and three days of analysis (J−4 to J−1). Once a week (on Tuesday), 14 days of analysis are produced, from J−15 to J−1, with the assimilation of all available satellite and in-situ data collected over the past weeks. Analysis produced at the weekly cycle represents the best estimation of the Black Sea state because all in-situ and satellite altimetry high quality processing data is used up to J−3. Analysis and simulation runs are forced by ECMWF atmospheric analysis fields at 6 h frequency; the forecast cycle is forced with ECMWF atmospheric forecast fields at 3 h frequency for the first 3 days and 6 h frequency for the remaining 7 days.

At the end of the forecasting cycle, post-processing involves the preparation of all the BSFS files in a format that is compliant with the CMEMS and CF standards, delivering to the CMEMS DU, and archiving of the BSFS native products at CMCC's supercomputing facilities. Every day, the forecast is released to CMEMS within less than 3 h (target delivery time: 12 UTC).

The BSFS product catalogue includes daily and hourly mean datasets, centered at midday of each J, for the Black Sea essential variables: 3D temperature ( *T*), salinity (*S*), zonal and meridional velocity components ( *U* and *V*, respectively) and 2D sea surface height (*SSH*), bottom temperature (*BottomT*) and mixed layer depth (*MLD*).

The BSFS operational chain is very similar to that of the Mediterranean Forecasting System (MFS, [25]) in terms of general setup (technical interface, analysis-simulationforecast chain). Unlike MFS, it implements a more frequent analysis cycle to provide higher quality Black Sea products using daily observations.

#### **3. Quality Assessment of the Operational System**

The aim of the product quality assessment is to monitor the analysis quality and forecast accuracy of BSFS products using quasi-independent validation assessment. Operationally, a regional website provides daily bulletins (http://bsfs.cmcc.it/, accessed on 29 December 2021) and skill scores (http://oceanlab.cmcc.it/bsfs-evaluation/, accessed on 29 December 2021). Daily bulletins consist of a collection of interactive 2D maps for visualizing the essential variables of the Black Sea (as provided in the CMEMS catalogue). Weekly skill scores are provided through the evaluation section: during the assimilation weekly cycle, as described in Section 2.3, the difference between the model analysis and the observations at the time and location of the observations (i.e., misfits) is stored to compute statistics. Two main statistical metrics for the assessment of the analysis quality are computed using the misfits: the first is the bias, given by:

$$\text{bias}(\phi) = \frac{1}{N} \sum \left( \phi\_i^M - \phi\_i^O \right) \tag{13}$$

and the Root Mean Square Error (RMSE) given by:

$$\text{RMSE}(\phi) = \sqrt{\frac{1}{N} \sum\_{i=1}^{N} \left(\phi\_i^M - \phi\_i^O\right)^2} \tag{14}$$

where *N* is the number of data used in the evaluation, *φM* is the model analysis field, and *φO* the observation.

On the other hand, to assess the quality of the forecast, we evaluated the differences of the forecast fields with respect to the analysis considered to be the best estimate of the truth. Murphy (1993, [26]), revisited by [2], defined the "forecast goodness" methodology and here we use his "quality measure" methodology that compares the forecast with the analysis and the "persistence" or the best estimate at initial time. If the difference between forecast and analysis is better than the difference between forecast and persistence, then the forecast is valuable. For a modern oceanographic forecasting system, [27] defined a metrics that we will partially follow here by computing:

• The difference between the analysis and the forecast (*AF*):

$$AF(t) = \sqrt{\frac{\sum\_{1}^{T} \left(\phi^{AN}(t) - \phi^{FC}(t)\right)^{2}}{T}} \tag{15}$$

where *φAN* and *φFC* is the temperature (salinity) daily mean forecast and analysis, respectively, at each forecast day *t* = *day*1, *day*2, ... , *day*10 (ID day as in Figure 2); *T* corresponds to the time period covered by the evaluation (here we consider a time period of 1 month). The metrics is normalized horizontally by averaging over the area and at a specific selected depth.

**Figure2.**BSFSprocessing

• The difference between the forecast and the persistence (*PF*):

 chain.

$$PF(t) = \left\langle \sqrt{\frac{\sum\_{1}^{N} \left(\phi^{FC}(t) - \phi^{AN}(t = day0)\right)^{2}}{N}} \right\rangle \tag{16}$$

Here, *φAN*(*t* = *day*0) is the persistence (ID day as in Figure 2), e.g., the initial condition for the forecast. The latter is the last time step of a simulation started from an assimilated initial condition at day J−2.

In the following subsections, we present BSFS analysis statistics for the period 2018–2019 for SST and SLA, as well as temperature and salinity at given layers: 2–5 m, 5–10 m, 10–20 m, 20–30 m, 30–50 m, 50–75 m, 75–100 m, 100–200 m, 200–500 m, and 500–1000 m. Forecast assessment is performed for 2 significant months—February and August 2020: metrics have been computed at selected depth, e.g., 2.5, 30, 150 m.

#### *3.1. Sea Surface Temperature*

The BSFS analysis sea surface temperature is assessed by comparing analysis model fields against SST satellite data remapped over the Black Sea basin at 1/16◦ spatial resolution and representative of night SST values and delivered by CMEMS (Table 1).

Figure 3 shows the time series from 2018 to 2019 of the difference between BSFS and satellite SST (BIAS, top) and RMSE (bottom). The numerical SST is slightly warmer than the measured one (+0.1 ◦C), and the error is about 0.5 ◦C.

#### *3.2. Sea Surface Height*

Figure 4 shows the RMSE for SLA in the operational period (green dotted line) and the number of observational data used, given by the sum of the available along track sea level data provided by Altika, Cryosat-2, Jason-2 and 3, Sentinel 3A and 3B. The mean value of RMSE misfits for SLA is around 2.3 cm. A decrease in the error is also shown once the number of observations increases (June 2018–December 2018; March 2019– October 2019 thanks to inclusion of Sentinel-3B data). The accuracy is comparable to the Mediterranean Sea [25], where the average error over the same period is about 3.35 cm (http://oceanlab.cmcc.it/mfs-evaluation/ accessed on 29 December 2021).

**Figure 3.** SST bias (top) and RMSE (bottom) timeseries, expressed in ◦C, at daily (thinner line) and monthly (solid line) frequency in 2018 (**a**) and 2019 (**b**).

**Figure 4.** RMSE misfit of SLA, expressed in cm, computed using available observations from Altika, Cryosat-2, Jason-2 and 3, Sentinel-3A and 3B during 2018–2019.

#### *3.3. Temperature and Salinity*

## 3.3.1. Analysis Quality

Water column properties given by BSFS are assessed after 3D temperature and salinity are validated against all available observations.

Table 2 shows the basin scale RMSE misfits at specific layers (m) for 2018–2019. In the 2–5 m surface layer, the average RMSE for temperature at the whole basin is about 0.4 ◦C (Figure 5(a1)) and 0.2 PSU (Figure 5(b1)). However, the error in temperature starts to increase in the sub-surface, from 5 m to 30 m, from 0.5 ◦C to 2 ◦C in the summer and below 0.5 ◦C in the winter (Figure 5(a2–a4)).

**Table 2.** Temperature and salinity RMSE misfits over the period 2018–2019.

The maximum error for temperature occurs in the 10–20 m layer with a value up to 2.5 ◦C computed for August–October in both 2018 and 2019 (Figure 5(a3)).

Regarding salinity, the error in the subsurface, on average, is around 0.2 PSU (Figure 5(b2–b4)), which increases from 30 m up to 100 m up to a maximum value of 0.4 PSU (Figure 5(b5–b7)). In the intermediate layers up to 1000 m, the error decreases to quasi-zero for both temperature (Figure 5(a8)) and salinity (Figure 5(b8)).

The increased error in the thermocline (10–30 m layer) and in the halocline (50–100 m layer) in the summer is likely due to a deficiency in the vertical discretization (we use only 31 z-levels) and in the vertical mixing parameterization.

**Figure 5.** *Cont*.

**Figure 5.** RMSE timeseries between BSFS analysis fields and ARGO observations for temperature (**a1**–**a8**) (expressed in ◦C) and salinity (expressed in PSU) (**b1**–**b8**). The reference layer as considered for the evaluation is written on the top of each panel.

The errors are due to (a) the Bosporus Strait being represented as closed boundary, (b) mixing processes that were not completely resolved since effects induced by waves and tides are not accounted for, and (c) simplified representation of the heat, momentum and water fluxes through bulk formulation, whose effects are particularly important especially during the summer. These are significant challenges to be considered in the development of the next generation of the Black Sea physical system.

## 3.3.2. Forecast Assessment

In Figures 6 and 7 we present the BSFS forecast quality assessment for temperature and salinity considering 2 reference months, February 2020 and August 2020, respectively. Metrics are given at different depths—2.5, 30 and 150 m. If the forecast is skillful, *AF* score should be better than *PF* score.

**Figure 6.** RMSE of *AF* (Analysis-Forecast, in blue), *PF* (Forecast-Persistence, in orange) for temperature (on the left, (**a1**–**a3**), expressed in ◦C) and salinity (on the right, (**b1**–**b3**), expressed in PSU) at 2.5 m (1), 30.0 m (2) and 150 m (3), computed for February 2020.

**Figure 7.** RMS of *AF* (Analysis-Forecast, in blue), *PF* (Forecast-Persistence, in orange) for temperature (on the left, (**a1**–**a3**), expressed in ◦C) and salinity (on the right, (**b1**–**b3**), expressed in PSU) at 2.5 m (1), 30.0 m (2) and 150 m (3), computed for August 2020.

In February 2020, *PF* is bigger than *AF* after *day*2 (Figures 6 and 7): this means that after the first day the advantage of doing the forecast is clear over persisting the initial condition. It is interesting to note that for salinity this advantage grows more rapidly, i.e., for salinity the forecast overcomes the persistence even before the first day. Over the water column, temperature and salinity analysis and forecast exhibit a similar variability: higher error reaching a maximum around 0.6◦ Celsius and 0.5 PSU at *day*10 is shown for temperature (Figure 6(a1,a2)) and salinity (Figure 6(b1,b2)) in the subsurface up to 30 m; at 150 m, both variables exhibit a lower error (never higher than 0.1◦ Celsius and 0.3 PSU over the reference 10-day period).

In August 2020, errors at 30 m are higher than the ones computed in the previous analyzed month: starting from *day*2, in particular for temperature (Figure 7(a1,a2)), *PF* shows higher values than *AF*—almost 50% higher at 30 m, with maximum error of about 2.0◦ Celsius (Figure 7(a2)). This means that during the summer the forecast adds to persistence more than during winter: the dynamical model is clearly responsible for the proper changes in water column stratification even for few days. At 30 m and in August (Figure 7(a1)) the difference between *PF* and *AF* is the largest: we assume that this is an indication of the complex dynamics involved in the mixing and the CIL dynamics. Regarding salinity (Figure 7(b1–b3)), *PF* and *AF* errors are quite similar comparing to winter and of the order of 0.3–0.4 PSU.

#### **4. Black Sea Diagnostics and Circulation Consistency**

#### *4.1. Temporal Evolution of the CIL and Stratification*

The BSFS is able to represent the formation and evolution of the cold intermediate layer (CIL), which is a typical water mass property of the Black Sea basin, and well documented in the literature ([16] for a very comprehensive literature review and updated evaluation). It is identified by the 8.0 ◦C isotherm, arising at the surface during winter, penetrating the subsurface (typically at a depth range of 50–100 m), intruding the warmer zone, and persisting over time. The time versus depth diagrams at the observation location are shown for temperature and for salinity in Figure 8, between 2015–2019. Figure 8a shows the cold event of 2017, which is also captured by observations as demonstrated by the quite low difference between the model and observations (Figure 8b). It also shows the reduction in the CIL width because of the warming period from 2018 and its perforation and partial disappearance in 2019 [16]. The major differences of about 2 ◦C occur in the upper layer and are shown in Figure 8b. Salinity stratification is well represented in Figure 9a with small differences with respect to observations, as shown in Figure 9b. It is characterized by a 2-layer structure with low salt in the CIL zone down to 100 m in depth and saltier values of about 22 PSU or more in the intermediate to deepest levels.

**Figure 8.** Time versus depth diagram for BSFS temperature in the period 2015–2019 (**a**) at the observation location and (**b**) for the computed model minus observation value.

**Figure 9.** Time versus depth diagram for BSFS salinity (PSU) in the period 2015–2019 (**a**) at the observation location and (**b**) for the computed model minus observation value.


**2. Background and methodology of data collection**

Data reported herein were collected in Portugal in Jul-Aug 2020 (Home 1) and in Scotland in Feb-Mar 2021 (Homes 2 and 3), at the invitation of the separate homeowners—usually due to the onset of a pattern of debilitating symptoms which, they claim, only began after WPP became operational in their residential areas [17]. A two-channel sound recording device was placed in and around each home with continuous data acquisition over several days. During the sound recordings, residents were asked to keep a date- and time-logged diary detailing the onset or absence of symptoms, such as sleep disruption. This onset or absence of symptoms could then be compared with changes in the sound recordings that might suggest a causal connection.

### **2.1 High resolution sound recording**

The recording equipment was a SAM Scribe Full Spectrum (FS) system (Soundscape Analytics, Palmerston North, New Zealand), Model Mk1 in Portugal and Mk2 in Scotland [18]. It is a two-channel device with sampling rates up to 44.1 kHz, that is designed to capture recordings of sonic environments with high precision, especially in the infrasonic and low-frequency bands. Data streams are delivered via USB to a Windows notebook computer and stored as uncompressed wav files to a hard disk. GPS information is stored in the files as metadata, which also include a digital signature. Each wav file corresponds to a 10-minute (600-seconds) recording of the sonic environment. The system can accurately record from 0.1–1000 Hz, as per the manufacturer frequency response of the two electret condenser microphones [19].

All measurements reported here cover the range from 0.5–1000 Hz and were captured with a sampling rate of 11.025 kHz. All recordings included a standard reference calibration tone at the start and end, produced with a Type I calibrator (part of the SAM Scribe system) at 1000 Hz/94 dB.

Calibration of the SAM Scribe system rests on 1) the manufacturer's frequencyresponse curve for the microphone and 2) calibration against a certified Larsen-Davis 831 sound level meter in the range of 6.3–1000 Hz.

### **2.2 Homes where recordings were captured**

## *2.2.1 Home 1: Portugal (the E. family)*

*Period of continuous recording:* 18 Jul 2020 (00:00)—09 Aug 2020 (10:00). *Microphone location:* At the foot of the bed in master bedroom (ground floor), tripod-mounted 1.5 m above the floor.

*Infrasound Exposure: High-Resolution Measurements Near Wind Power Plants DOI: http://dx.doi.org/10.5772/intechopen.109047*

#### **Figure 3.**

*Relative positions of Home 1 and the 19 industrial wind turbines (labeled WT in the figure) that constitute this wind power plant. (Image adapted from Google Earth).*

**Figure 3** shows the relative position of Home 1 and the WPP (19 Senvion MM92 turbines of 2 MW each, with blade length 45.2 m). The closest IWTs to the home are numbers 11, 12, 13 and 14, at 843 m, 1085 m, 648 m, and 844 m, respectively. IWT1 and IWT19 are the furthest away, at a distance of 3422 m and 2282 m, respectively.

The E. family—Mr. E. (age 63) and Mrs. E. (age 64)—have lived amid these 19 IWT since 2016. Their health deterioration has been documented by neurological medical reports.

#### *2.2.2 Home 2: Scotland (the P. family)*

*Period of continuous recording*: 24 Feb 2021 (17:30)—07 Mar 2021 (00:00).

*Microphone location:* Beside the head of the bed in an upstairs bedroom with a dormer, tripod-mounted 1.5 m above the floor.

Mrs. P documented some of her symptoms from Jul 2019 to Mar 2020. **Table 1** shows a 6-month sample (Jul–Dec 2019).

## *2.2.3 Home 3 – Scotland (The J. Family)*

*Period of continuous recording*: 20 Mar 2021 (16:20)—27 Mar 2021 (18:40). *Microphone location*: Middle of attic bedroom, tripod-mounted 1.5 m above the floor.

**Table 1.**

*Six-month sample of some of the symptoms documented by Mrs. P.*

#### **Figure 4.**

*Relative positions of Home 2 and Home 3 and the closest industrial wind turbines of wind power plants A, B and C.*

**Figure 4** shows the relative positions between Homes 2 and 3, and the three WPP located in the vicinity.

*WPP A* has 60 Gamesa G80/2000 turbines of 2 MW each, with blade diameters of 80 m. It is located 4.6 km to the west of Home 2 and 14.5 km to the southwest of Home 3. It has been operational since 2011.

*WPP B* has 96 Gamesa G114/2500 turbines of 2.5 MW each, with blade diameters of 114 m. It is located approximately 2.9 km to the south of Home 2 and 13.1 km to the south of Home 3. It has been operational since 2007.

*WPP C* has 28 Gamesa 87/2000 of 2 MW each, with blade diameters of 87 m. It is located approximately 9.5 km to the north of Home 2 and 2.1 km to the southwest of Home 3. It has been operational since 2011.

*Infrasound Exposure: High-Resolution Measurements Near Wind Power Plants DOI: http://dx.doi.org/10.5772/intechopen.109047*

#### **2.3 Wind data**

Information on wind speed and direction was retrieved for the entire period during which recordings were made.

In Portugal, data was obtained from the Portuguese Institute of Sea and Atmosphere (IPMA [20]). Data points were requested in 10-minute increments, from three distinct meteorological stations: at 58 km (altitude above sea level: 995 m), 12.5 km (altitude above sea level: 642 m) and 52.7 km (altitude above sea level: 558 m) away from the E. family home (altitude above sea level: 850 m). In Scotland, weather data was obtained from the British National Weather Institute via the Open Weather service [21] in one-hour intervals. The location for which weather data was obtained was 3.5 km away from Home 2 and 7.8 km from Home 3. Wind data was timematched to the GPS time-stamped acoustical recordings.


Preface

To unravel the nature of the emergent phenomena in strongly correlated many-body systems is one of the central challenges in theoretical and experimental research. Unexpected and exotic properties of these systems open active areas of study in the field of condensed matter physics. One of the remarkable phenomenon of these correlated systems was the discovery of superconductivity by Onnes in 1911. Since then, a lot of experimental and theoretical research has been done to describe this phenomenon. The discovery of modern superconductors, such as high-Tc, organic, two band, and some others, presented another criticism for the theoretical physicist. The possible superconductivity mechanism of these systems remains one of the most challenging problems in physics.

Most researchers believe that their superconductivity mechanism could not be explained within the BCS theory. At present, there is no mechanism to describe all the properties of these superconductors. Many different models such as the exciton model, plasmon model, bipolaron model, magnon model, spin fluctuation model etc., have been proposed. All of these models are evaluated by presenting new theoretical efforts and experimental results.

In this regard, this book includes valuable theoretical and experimental discussions about the properties of superconductors. Here you will find valuable research describing the properties of high-Tc and iron-based superconductors.

The main topics of the book are organized in five chapters, as described below.

Chapter 1 contains a brief discussion about the progress in superconductivity.

Chapter 2 discusses the experimental techniques to measure microwave absorption.

Chapter 3 deals with the electromagnetic properties of bulk high-temperature superconductors (HTS), which can be used in magnetic systems for various applications.

Chapter 4 focuses on the generalized BCS equations to discuss high Tc superconductors.

Chapter 5 covers the study of iron-pnictides using self-consistent Bogoliubov-de Gennes equations.

I hope this book is useful for theoretical and experimental researchers.

**Heshmatollah Yavari** University of Isfahan, Iran

**1**

**Chapter 1**

*Heshmatollah Yavari*

tor from a perfect conductor.

**1. Introduction**

Introductory Chapter:

of study in the field of condensed matter physics.

theory was formulated by Bardeen et al. [5].

conventional superconductors.

Superconductivity in Progress

Describing the behavior of strongly correlated systems is a challenge for every area of physics. The many-body ground state that emerges in these systems can have exotic and unexpected properties. As a remarkable of these correlated systems was the discovery of superconductivity by Onnes in 1911 [1], which opens active areas

This state is a quantum phenomenon that is displayed by certain materials under particular magnetic and temperature regimes. The Meissner effect discovered in 1933 shows that superconductivity is more than just the disappearance of resistance and it is a true thermodynamic state of matter because it leads to the superconductors as perfect diamagnetism materials [2]. This effect distinguishes a superconduc-

To describe these phenomena, a lot of theoretical works were proposed. The first phenomenological theory which is known as London theory was proposed by the London brothers [3]. This theory described well the magnetic properties and magnetic penetration depth of Type I superconductors in weak magnetic fields. By extending the London theory, Ginzburg and Landau proposed a phenomenological theory, which enables the study of Type II superconductors in a strong magnetic field [4]. By assuming the second-order phase using physical intuition and using variational principle of quantum mechanics, the Ginzburg-Landau (GL) theory allowed the calculation of macroscopic quantities of the material in the superconducting state. The most beautiful and successful theory in condensed matter physics, i.e. the microscopic theory of superconductivity which is known as BCS

The starting point of this theory was an effective Hamiltonian of fermionic quasiparticle excitations that interact via a weak phonon-mediated potential close to the Fermi surface. This attractive interaction leads to bound electron pair states known as Cooper pairs. While the single electrons are fermions, not being able to occupy the same state due to Pauli exclusion principle, Cooper pairs are no longer obliged to obey the Fermi-Dirac statistics, and like bosons, they can enter the same state. Superconductors that can be described by the BCS theory are known as

The discovery of high-temperature superconductors (HTS) in 1986 [6] presented another challenge for theoretical physicist. Their superconductivity mechanism remains one of the most challenging problems in physics. It is believed that the BCS theory alone cannot explain properties of these classes of superconductors and new theoretical concepts will be required. This unexpected discovery opened a new area in the history of superconductivity, and experimental researchers started trying to find new compounds in this class of superconductors. In the following

## **Chapter 1**


**2.3 Hagie pins**

The San Antonio -based Rockwood team described the use of a Modified Rockwood-Hagie pin for treating midshaft clavicular non-unions [15]. This device has been used successfully by a number of groups, with union rates ranging from 91.4 to 100% [16–20]. Mudd et al. [21] reported less favourable results in a small study, with an 83.3% (15/18) union rate. All authors reported that irritation from the prominent lateral end of the implant necessitated removal.

*Intramedullary Fixation of Midshaft Clavicle Fractures DOI: http://dx.doi.org/10.5772/intechopen.112256*

In our experience with this device, the inability to have the length less than 120 mm resulted in prominence of the nuts on the lateral side and often protrusion of the medial end of the screw anteriorly, causing subcuticular irritation, requiring implant removal. We have since chosen devices with more length options that can be made completely intra-osseous, avoiding a second operation to remove the implant. This is also a common problem when plates are used. As the clavicle lies in a subcutaneous plane with little soft tissue and muscular coverage, extra-osseous implants will frequently cause irritation and necessitate a second procedure for removal.

## **2.4 Knowles pins**

Knowles pins are solid threaded nails used to repair midshaft clavicular fractures [22]. Unfortunately, due to their length, one or both ends of the pin frequently protrude from the bone, causing irritation requiring removal after fracture healing.

### **2.5 Echidna pins**

Echidna pins (**Figure 1**) were developed to aid rotational and length stability [23, 24]. The retractable spikes on this intramedullary device can be deployed once in position to achieve fixation in the proximal and distal fragments. While experimental studies have demonstrated good biomechanical performance, this device has not been clinically trialled.

#### **2.6 Solid intra-medullary screws**

Khalil [25] described a medial to lateral approach that made use of a solid 6.5 mm screw, but the prominent medial head caused significant discomfort and additional surgery was needed for implant removal. Abo El Nor [26] used a similar device with

#### **Figure 1.**

*Fluoroscopy images showing intramedullary Echidna pin (a) and Herbert. Screw (b) use to manage a midshaft clavicular fracture.*

a lateral to medial insertion after retrograde drilling of the lateral fragment and achieved a 100% union rate while avoiding the medial head prominence.


Adverse Effect of Changing Climate on Maize Production

**47**

**Chapter 4**

**Abstract**

Climate Change Impacts on

*Kelvin Mulungu and John N. Ng'ombe*

production and productivity in SSA.

over comparable time periods [3].

**1. Introduction**

Sustainable Maize Production in

Maize (*Zea mays* L.) is one of the commonly grown grain crops and remains a source of staple food and food security for most countries in sub-Saharan Africa (SSA). But climate change threatens agricultural potential in SSA thereby risking food security especially that most maize production is rain-fed in these countries. Thus, numerous studies have examined impacts of climate change on maize production and productivity resulting in several adaption strategies being promoted to mitigate the negative effects of climate change. But to the best of our knowledge, there has not been any studies in literature that provide a review of impacts of climate change on maize production and productivity in SSA. This chapter therefore provides a review of empirical climate change impacts on maize production and its productivity in SSA. We chose SSA because most countries in SSA are underdeveloped and therefore more vulnerable to climate change effects. This is important because this review will provide an easier access of such results for both scholars and policy makers in search of empirical impacts of climate change on maize

**Keywords:** climate change, maize, smallholder farmers, sub-Saharan Africa

Climate change is a real phenomenon worldwide [1] as observed in the increase in atmospheric and oceanic temperature, decreased amounts of snow and ice as well as a rise in sea level [2]. The earth's surface has been warmer in past three successive decades [2] resulting higher average temperature compared to the past centuries. The term "climate change" is defined differently among different stakeholders even though the contents are similar in context. IPCC [3] defines climate change as a change in the state of the climate that can be identified by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. Based on the United Nations Framework Convention on Climate Change (UNFCCC), climate change refers to a change of climate that is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and that is in addition to natural climate variability observed

Impacts of climate change vary depending on the state of development of a region. For example, IPCC [4] suggest that rising temperatures and changing precipitation rates will most likely hamper success of rain-fed agriculture in most

Sub-Saharan Africa: A Review


*3.2. The Killing Letter and the Life-Giving Spirit According to Paul in Early Christian Judaism and in the History of Reception*

Gandhi's use of 2 Cor 3:616 as a hermeneutic principle raises the following concerns in the context of this paper:

#### 3.2.1. Paul

Paul's Statement "The letter kills, but the Spirit gives life" connects two parts: His defence of being a minister (*diakonos*) of the new covenant (vv. 1–6) and the comparison with the *diakonia* of Moses (vv. 7–11; Duff 2015, pp. 132–35; Cover 2015; Hellholm 2008). It functions as a pivot point between the two parts. Scholars have often assumed that the life-giving Spirit refers to the "new covenant" and the "killing letter" to the "old covenant" (Thrall 2004, pp. 232–37, esp. p. 234, n. 306). However, there are strong arguments to relate it not to the covenant, but to the *diakonia* of Paul, because it points both back to the letter of recommendation for his ministry (vv. 1–3) and forward to his comparison with the *diakonia* of Moses (vv. 7–11). The antithesis letter-Spirit in this context refers to the ministry, not to the covenant (Duff 2015, pp. 132–35.169.212). Nevertheless, Paul claims to be a minister of a *new covenant*. 18 Previously in the Hebrew Bible, an eschatological "new covenant" (Jeremiah) and a "heart of flesh" (Ezekiel) tried to bridge the gap between divine ethical stipulation and human violation. In this "new covenant," according to Jer 38:31–33 LXX, the laws (*nomoi*) will be written in the heart by divine intervention, resulting in an "automatic" observance of the Law in a flawless reciprocity. Ez 36:26 aimed for a purification of Israel, followed by the removal of the "heart of stone" and its replacement by a "*new heart* of flesh"(*leb¯ h. ad¯ aš¯* ) and "a *new* spirit" (*rûah. h. ădaš¯ â*). With his allusions to Jer 38 LXX and Ez 36:26 in 2 Cor 3:2–3, Paul connected these two related concepts with the keyword "new." "New" in relation to covenant does not mean a fundamental contrast to the previous or abrogated one (Rüterswörden 2006), but emphasises "the dynamic movement of continuity rather than replacement" (North, Botterweck et al. 1977, *TDOT* 4, p. 240). The concept of covenant is one of continuation and renewal, of eschatological hope and concepts of realisation.19 In the vision of Jeremiah, it is not the Torah which disappears, but the possibility to breach the covenant. For Paul, these prophetic visions had been realised in the death and resurrection of Christ.

In the second section (2 Cor 3:7–11), Paul describes the relationship between his own *diakonia* and that of Moses (Duff 2004, pp. 313–47). First, there is the connecting factor: in both ministries is *doxa* (glory). However, the *diakonia* of Moses is "a ministry of death," (v. 7) and of "condemnation" (v. 9), but Paul's ministry is one of the life-giving Spirit (v. 8) and righteousness (v. 9b). With a *qal va-h. omer* (from the lesser to the greater) argument, Paul claims that there is more glory in his than in Moses' *diakonia*. How is it possible that Paul connects Moses with death and condemnation? Duff makes here his second point by arguing that Moses' ministry concerned the situation of the Corinthians as gentiles. For them in their previous existence, Torah brought death.20 "Paul believed that from following the delivery of the Torah to Moses- all humanity (gentiles included!) would be accountable to its requirements" (Duff 2015, pp. 161–62).

The next step in Paul's argument is his own rewriting of Ex 34:29–35. The original narrative reports Moses' return from Sinai carrying the new tablets with a radiant face, due to the meeting with the *doxa* of God, and wearing a veil. Paul claims that this veil is taken away for the Corinthians. Through Christ, they see God with an unveiled face (2 Cor 3:18), while those who still read Moses21 without turning to the *kyrios* have their faces veiled.

In summary: The letter and the spirit do not refer to the covenant, but to the *diakonia* of Moses and Paul. For Paul, the visions of Jeremiah and Ezekiel had been realised in the death and resurrection of Jesus Christ. The ministry of death and condemnation ascribed to Moses refers to the idea that the Torah has been neglected by the gentiles because all humans, including Jews and gentiles, had to keep the commandments. Therefore, the ministry of Moses meant death and condemnation for the Corinthians in their previous life. In a bold reversal of the original function of the veil as a sign of protection, Paul describes its role as an obstacle for facing the *kyrios*, now taken away for the Corinthians.

The killing letter and the life-giving Spirit do not denote a literal interpretation of the Torah against a spiritual one. For the *pneuma* of V.6 refers back to the "*pneuma* of the living God," (V.3) "where the Spirit is not the true meaning of Scripture but a divine agency at work in human life" (Thrall 2004, pp. 234–35). The life-giving Spirit refers to Paul's ministry of Spirit and righteousness and the killing letter to Moses' ministry of condemnation in the sense of Duff's proposal above. The Torah itself, in its function to make humans aware of their actions, is holy and spiritual (*pneumatikos*), according to Paul (Rom 7:12.14; Hafemann 1995, pp. 438–44).

#### 3.2.2. Elements from the History of Reception

In the first half of the second century CE, 2 Cor 3:6 was an important text for Marcion and his dualistic teaching of the benevolent God of the Gospel and the malevolent Demiurge of the Old Testament (LXX). It was clear to him that "the letter that kills" was the Hebrew Bible, and that "the Spirit that gives life" referred to a Gospel that was originally based on Paul's antithesis (Dunn 2016, p. 115). Marcion's *Antitheses*, a list of passages from the Hebrew Bible that contradicted New Testament texts (von Harnack 1924, pp. 89–92; \*256–\*313), demonstrated his view.

Tertullian (155–220 CE)—one of the most powerful adversaries of Marcion—maintained the negative role of the Law and the positive one of the Gospel, but defended the one God of the Hebrew Bible and the New Testament.22 At the same time however, he stated that the Torah admittedly was a bad law—and with a dangerous shift from theory to living people—the Jews needed such laws. 23

From here on, the antithesis between Law and Gospel, the letter and the Spirit, as equivalent to old and new covenant, became a dominant element in reception history. Paula Fredriksen stated succinctly that in the second-century gentile setting, the older *intra*-Jewish polemic mutated into an *anti*-Jewish polemic (Fredriksen 2017b, p. 1). From here on, Christianity stood against Judaism.24 It resulted in the *Contra-Iudaeos* literature (Schreckenberg 1995; Krauss and Horbury 1995; Fredriksen and Irshai 2006), with the sermons of Chrysostom (386 CE) marking an oft-quoted all-time low (Meir 2019, p. 258). Of course, 2 Cor 3:6 was not the only text that contributed to the *Contra-Iudaeos* literature, but when Paul's other antithesis *sarx* ("flesh") vs. *pneuma* (Spirit) was integrated, it became a weapon in the anti-Jewish literature as well. For Augustine, the Jews belonged to carnal Israel (1 Cor 10:18) as opposed to spiritual, life-giving Christianity.25 Supersessionism was long-time part of Christian doctrine.

The big leap from antiquity to Gandhi cannot be made without mentioning Luther's name in this context. His concept of Law vs. Gospel—based on Paul's antithesis—may have been shaped primarily by and for the conflict with the Roman-Catholic Church; it also negatively impacted the relationship between Judaism and Christianity26 and, because of its profound influence on Western culture, was open to abuse with catastrophic consequences.

Finally, in Judaism itself, the letter-Spirit antithesis plays a more important role than often assumed. Huss, criticising Gershom Sholem, defends that behind the assumed dichotomy between "spiritual, vital Kabbalah and dogmatic, petrified Halakha," a Jewish adaptation of Paul's antithesis of the killing letter and the life-giving Spirit becomes visible (Huss 2021, p. 2).

## 3.2.3. Gandhi and Paul

Was Gandhi aware of the context and the history behind his own use of 2 Cor 3:6? Probably not. If concepts of the covenant between deity and believers had emphasised the religious exclusivity for one group or another, he would surely have opposed this. On the other hand, the basic concept of a mutual, conditional covenant, with the law as a guide for the practice of human behaviour and as an answer to the love of God, may well have been attractive to him.27 However, this concept forms no substantial part of Gandhi's reflections. His studies of the Bible focused mainly on the Gospels, and his readings of Pauline texts are, whether deliberately or not, coloured by later Christian reception history, including supersessionism and an antithetic theology of Torah and Gospel.

Gandhi borrowed from Paul a mantra about the killing letter and the life-giving Spirit without knowledge of the complex reasoning behind it. The similarity between the two was their situation. Both tried to reformulate their own religious heritage in a new way by claiming that the now-defended truth was already present in the scriptures. In need of a hermeneutical key, both found it in the killing letter and the life-giving Spirit. For Gandhi, it was an instrument for free faith-based and context-bound interpretation. For Paul, it was a cornerstone in the apology of his ministry as an invitation to the Corinthians to trust his *diakonia* of the Spirit.

#### **4. Gandhi, the Killing Letter and the Life-Giving Spirit: Some Examples**

How did Gandhi use the principle of the killing letter and the life-giving Spirit? He emphasised the freedom of the interpreter to distinguish between the original meaning and the later reception. For the benefit of his audience, he tried to reconcile Vedic practices with a fitting exegesis of the Gita; for himself, this was not necessary: "The teacher of the Gita did not lay down that those who came after him should always read in it only the meaning which he himself had in mind" (Gandhi 1958–1994, vol. 32, p. 212). Even in the case where Gandhi is aware that his reinterpretation of the animal sacrifice (*yajna*) as an offering with the mind differed from the original meaning, "we shall do no injustice to Vyasa28 by expanding the meaning of his words. Sons should enrich the legacy of their fathers," (Gandhi 1958–1994, vol. 32, p. 154) with Gandhi further stating "I interpret the Gita to mean that, if its central theme is *anasakti*, it also teaches *ahimsa*. Whilst we are in the flesh and tread the solid earth, we have to practise *ahimsa*. In the life beyond there is no *himsa* or *ahimsa*" (Gandhi 1958–1994, vol. 72, p. 393).

This freedom stressed the need to live in the present, practicing *ahim. sa¯* hic et nunc, not in the life beyond. History was, for Gandhi, not the dry search for "how it really was" (Ranke); it should serve the community as an instrument for the interpretation of the present and as a guideline for the future. This reconstruction is not without facts, but the facts serve a higher aim. "Moral truth," in Gandhi's case, "was higher than historical truth" (Parekh 1989, p. 164). Gandhi demonstrated this in different ways. In some cases, e.g., the message exceeded the person and history: "if one should prove that Jesus never existed, the Sermon on the Mount would still be true." In a Christmas talk, he even stated that as long as there is no peace on earth, Christ has not been born (Gandhi 1958–1994, vol. 48, pp. 438–39).

#### *4.1. A Key for the Gita*

There is no doubt that the Gita was the most important source of inspiration for Gandhi (Gandhi 1958–1994, vol. 27, p. 435, vol. 64, pp. 74–75). The recitation of the eighteen chapters of the Gita was completed within one week during morning prayers. However, in 1936, a situation emerged in which a simple key for Hinduism and the Gita was needed. Gandhi's response was that all other scriptures could be lost, but if the first mantra of the *Isha Upanishad* remained, Hinduism would be saved (Gandhi 1958–1994, vol. 64, p. 259). In his own translation, the verse reads:

"1. All this that we see in this great Universe is pervaded by God. 2. Renounce it 3. and enjoy it (or: Enjoy what He gives you) 4. Do not covet anybody's wealth or possession." (*¯ı´sa v ¯ asyamida ¯ m˙* , Isha Upanishad)

From the divine pervasion of creation, the mantra derived two relationships: First, the relationship of humans to the whole creation, stating that humans should only take from it what they really need, enjoying that portion. Second, their relationship with their neighbours, in which the desire for other people's possessions should not play any role (Palaver 2021, p. 17). For Gandhi, the Gita doctrine of uttermost detachment (Gandhi 1958–1994, vol. 84, p. 327) was revealed, for taking more from this creation than the minimum for a living meant theft. The whole Gita was, for Gandhi, a commentary on this mantra. From this verse, Hinduism in all its diverse forms took a short credo as a practical faith-based guide for everyday life (Gandhi 1958–1994, vol. 64, p. 260). The very specific context for Gandhi's hermeneutical statement was the opening of the temples of Travancore to all Hindus, regardless of their origin and caste.29 For Gandhi, it was "the great wonder of modern times." However, now he had to apply his creed of the equality of

all religions to the deeply divided modalities of Hinduism itself, regardless of birth, caste or denomination. In this religious-political minefield, he claimed that the first mantra of the *Isha Upanishad* did unite all Hindus. According to Gandhi, the Gita itself could not function in this way because the Gita was "not a book that I can place before the whole of this audience. It requires a prayerful study before the *Kamadhenu* (the cow of plenty) yields the rich milk she holds in her udder" (Gandhi 1958–1994, vol. 64, p. 258). Therefore, the mantra of the *Isha Upanishad* united all Hindus, even the *literati* and the *illiterati*. The verse attracted legendary narratives to underline its importance.30

#### *4.2. Gandhi and the Gita in 1940*

On 15 August 1940, Gandhi had an important conversation with Balasaheb Gangadhar Kher, the first chief minister of Bombay State, about questions on *ahim. sa.¯* How would Gandhi nonviolently react in the case of a foreign invasion? In his reply, Gandhi stated that he would line up a non-violent army of about 2000 men between the two combatants, describing his army allegorically with a reference to Tulsidas' Ramayan and the debate between Rama and Vibhishana on how to conquer a mighty enemy (Gandhi 1958–1994, vol. 72, p. 391). Gandhi concluded that in such a situation, faith would be needed, but that "the worst that can happen to us, is that we shall be crushed. Better to be crushed than to be vanquished".

The next question of Ker was about the central teaching of the Gita. Is it *anasakti (detachment)* or *ahim. sa¯*? Gandhi was sure. It must be *anasakti*. For that reason, he called his own translation and interpretation of the Gita "*Anasaktiyoga*," because *anasakti* transcends *ahim. sa¯*. To reach the state of detachment, one has to practise nonviolence. *Ahim. sa¯* is included in *anasakti*. Gandhi admitted that the author of the Gita probably did not inculcate *ahim. sa¯*, but he, Gandhi, did. Kher did not give up and quoted Arjuna, who was willing to practise *ahim. sa¯*: "Better I deem it, if my kinsmen strike, to face them weaponless, and bare my breast to shaft and spear, than answer blow with blow" (BG 1:46). Krishna, however, urged Arjuna to answer "blow with blow," and "that there is no greater good for a warrior than to fight in a righteous war" (BG 2:31). Gandhi denied that the focus in these passages was on *ahim. sa¯*.  This was a classical debate on the use of scriptures, on meaning and drift, on the priority of concepts, but one that was overshadowed by a world war already started, a possible threat from Japan that would materialise a year later, and the question of how to act in an India still under the British Raj.

Gandhi's answers demonstrated a shift in his reading of the Gita. In 1919, he published a short allegorical interpretation of the Gita (Satyagraha Leaflet no. 18), and contested a literal reading of Krishna's advice to Arjuna to fight his kinsmen: "Now the Bhagavad Gita is not a historical work, it is a great religious book, summing up the teaching of all religions. The poet has seized the occasion of the war between the Pandavas and the Kauravas for drawing attention to the war going on in our bodies between the forces of Good (Pandavas) and the forces of Evil (Kauravas)" (Gandhi 1958–1994, vol. 15, pp. 288–89).31 The allegorical battle returned in his "Discourses on the Gita" (1926): "But here the physical battle is only an occasion for describing the battlefield of the human body. In this view the names mentioned are not of persons but of qualities which they represent. What is described is the conflict within the human body between opposing moral tendencies imagined as distinct figures (Gandhi 1958–1994, vol. 32, p. 96; Hutchins 2018, p. 36).

Parekh demonstrated how Gandhi could not maintain this allegorical reading of the Gita, and distinguished later between two levels. The philosophical level contained general, moral principles; the historical level applied the principles to a specific situation in time and place. Only at the second level could violence be sanctioned, under certain circumstances (Parekh 1999, p. 167). In his debate with Ker, Gandhi stated, therefore, that *anasakti* as non-attachment was the highest ideal of the Gita, attachment being the major obstacle to *moksha.* Even with his new reading of the Gita, Gandhi maintained that the Gita did not

favour violence; on the contrary, "the Gita's doctrine of *anasakti* undercut the moral and psychological basis of violence" (Parekh 1999, p. 168).

Gandhi's figurative interpretation of the most important scripture of Hinduism was already needed more than thirty years earlier in his contacts with the terrorist movement. The terrorists reinterpreted Hinduism and used the Gita too to allow violence through a literal reading. The first leader of the Indian Independence Movement and radical nationalist, Tilak (1856–1920), interpreted the dialogue between Krishna and Arjuna as divine encouragement to fight one's enemies, even one's own kin. *Karma-yoga* (action), not renunciation or devotion, is the main message of the Gita according to Tilak (Mackenzie Brown 1958; Tilak 1935). To give his nonviolent *satyagraha* a scriptural base, Gandhi introduced his allegorical reading, focusing on *anasakti* as the central teaching of the Gita.


**Author details**

Masatake Kanai1,2, Shoji Mano1,2,3, Makoto Hayashi4 and Mikio Nishimura1\*

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Cell Biology, National Institute for Basic Biology, Okazaki, Japan

2 Laboratory of Biological Diversity, Department of Evolutionary Biology and Biodiversity, National Institute for Basic Biology, Okazaki, Japan

3 Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Stud‐ ies), Okazaki, Japan

4 Department of Bioscience, Nagahama Institute of Bio-Science and Technology, Nagahama, Japan


5.1.2. Effect of Varying Steam to Carbon Ratio

Figure 12 shows the change in the electrical, cogeneration, and exergy efficiencies with varying S/C ratio. For the steam methane-based system, when the S/C ratio is increased from 2 to 4.5, the electrical efficiency, exergy efficiency, and cogeneration efficiency decrease from 46.64%, 63.77%, and 42.06% to 38.36%, 62.07%, and 40.14%, respectively. This tendency occurs because a higher S/C ratio requires a considerable amount of heat to produce steam, resulting in more fuel consumption in the combustor, as can be observed in Figure 13a. The increase in parasitic power consumption for the pump and CO2 compressor is another reason for the decrease in electrical and exergy efficiencies. Regarding the cogeneration efficiency, the increased heat consumption in the CCU due to increased CO2 generation is one of the reasons. Noteworthy, when the S/C ratio increases, H2 contents in the product gas is increased, although this increase rate becomes slower, as depicted in Figure 10a. This effect compensates slightly the required fuel consumption. Nevertheless, the efficiencies decrease with S/C ratio because the increase rate of additional fuel is greater than the increase rate of hydrogen production. For the methanol-based system, the efficiencies profiles are relatively flat with increasing S/C ratio from 1.5 to 3.5, as shown in Figure 12b. This trend is attributable to the lower reforming temperature and

combustion temperature in the methanol-based system, which can be confirmed by the slight increase in fuel consumption. This trend can also be due to the trade-off between the increase in hydrogen production and the increase in parasitic power consumption with increasing S/C ratio.

**Figure 12.** Influence of S/C ratio on system efficiencies and variation of molar flow rates of CO2 and fuel.

**Figure 13.** Influence of CO2 capture ratio on system efficiencies and variation of molar flow rates of CO2 and fuel.


*8.1. Gallic Acid Grafted Polymers*

Similar to lignin, polyphenol, and quercetin polymers, gallic acid grafted polymers are ideal for targeted drug delivery, packaging, and commercial, industrial applications. Cho et al. [100] documented successful synthesis and characterization of gallic acid-graftedchitosans, which demonstrated superior antioxidant scavenging ability against various free radicals such as hydrogen peroxide (93% effectiveness) and 2,2-diphenyl-1-picrylhydrazyl (92% effectiveness) at low concentrations (50 µg/mL) [101]. The outcomes affirm the superiority of gallic acid grafted copolymers in free radical scavenging. In other studies, gallic acid grafted chitosan-casein phosphopeptide nanoparticles have demonstrated superior anticancer and antioxidant properties under simulated gastrointestinal conditions [102]. The observations made by alternatively, the incorporation of chitosan on resveratrol via free radical-induced grafting proven effective in the synthesis of resveratrol modified species [73]. Cho et al. [100] contrast with Curcio et al. [79], who documented successful free radical-induced grafting of gallic acid-chitosan and catechin-chitosan conjugates for antioxidant applications.

Considering that the in-vitro models were confined to the two free radicals (2,2 diphenyl-1-picrylhydrazyl, and H2O2, it remains unclear whether gallic acid-graftedchitosans would retain the high antioxidant scavenging ability at higher concentrations. The outcomes documented by Cho et al. are in agreement with Spizzirri et al., who documented successful synthesis of antioxidant−protein conjugates (gallic acid (GA) and catechin (CT) attached on gelatin) via grafting reaction [103]. Similar to gallic acid grafted polymers, the Gallic Acid-Catechin-Gelatin copolymers exhibited superior scavenging ability against linoleic acid peroxide hydroxyl radicals and 2,20 -diphenyl-1-picrylhydrazyl.