*2.3. Seed Quality*

For the evaluation of germination ability, 3 × 50 seeds from each experimental plot (180 samples) were sown in plastic boxes between two layers of moistened (to 60% WR) filter paper. After sowing, the samples were prechilled at 7 ◦C for 3 days and placed in Sanyo growth chamber (Sanyo Electric Co., Ltd., Osaka, Japan) at constant temperature 20 ◦C. After four days, first count (germination energy) was made. The normal seedlings were counted and share in percent was evaluated. According to present International Seed Testing Association Rules [22] after eight days, the final count (germination capacity) and evaluation of normal seedlings, abnormal seedlings (AS), dead seeds (DS), and fresh ungerminated (FUS) seeds were made.

### *2.4. Fusarium DNA Quantification with Real-Time PCR*

### 2.4.1. Isolation of Total DNA from Grain

DNA was extracted according to Doyle and Doyle [23] protocol with modifications of Department of Phytopathology and Molecular Mycology UTP.

Ten grams of grain was homogenized to fine powder and 100 mg of such prepared sample was taken for DNA isolation. Samples were transferred into 2.0 mL tubes and poured with 600 μL of the extraction bu ffer containing CTAB 5.0%, EDTA 0.5 M, NaCl 5.0 M, Tris-HCl (pH 8.0) 1.0 M, β-mercaptoethanol, PVP (2.0%), and water. DNA was purified by addition of phenol:chloroform: isoamyl alcohol mixture (25:24:1) followed by centrifugation and taking the upper phase (supernatant) to the new tube, where equal volume of chloroform:isoamyl alcohol mixture (24:1) was added, mixed by inverting and centrifuged. Supernatant was taken and DNA was precipitated with cold ethanol. DNA pellet was washed with 70% cold ethanol, left to dry for 25–30 min and poured with TE bu ffer or sterile water to dissolve. Samples were stored at −20 ◦C for further analyses.


**4. Conclusions**

*Drought - Detection and Solutions*

and GPDH were induced by drought stress [49].

among the 1000 most highly expressed genes [87].

drought. Genes encoding for scavenging reactive oxygen species such as POD, PRX,

*Prunus mongolica* is a plant native to the Gobi Desert. In this species a total of 3365 differentially expressed transcripts (DETs) for water stress were identified, counting 1876 transcripts upregulated and 1489 transcripts downregulated. Among these, 42 transcripts coding for 5 aquaporin subfamilies were found. Also, 15 potential plasma membrane intrinsic proteins (PIPs) were upregulated and 1 downregulated. Interestingly, 177 transcripts related to ROS scavenging were identified. Approximately 28% of them were predicted as SOD. Several significant pathways were identified to be related to drought tolerance of *P. mongolica*. These pathways included transcription factors and plant hormone signal transduction, starch and

sucrose metabolism, and cysteine and methionine metabolism [31].

*Artemisia sphaerocephala* is a species found in sand dunes in the deserts of Northwest China. A transcriptomic analysis in this species identified 108 unigenes related to drought stress tolerance. These had homology to 17 kinases, 2 potential chaperones, 52 enzymes, 6 transporters and channels, and 3 aquaporins. Even though, transcription factor were not identified, 25 out of the 108 unigenes were

*Zygophyllum xanthoxylum* is a succulent halophythe adapted to arid environments. In this plant a total of 1723 DEGs were identified as upregulated in leaves of plants under osmotic stress (−0.5 MPa) during 24 h. Up to 53 DEGs related to ROS scavenging were also identified as upregulated, most of them encoded for glutathione S-transferase (GST) and peroxidases (POD). Also, 31 DEGs homologous to transporters were upregulated in the roots under salt and osmotic stress. However, 23 DEGs related to photosynthesis were downregulated under osmotic stress [88]. *Cleistogenes songorica* is a C4 xerophyte widely distributed in the arid regions of Northwest China. A mining study for LEA genes identified at least 44 putative LEA proteins. They were named CsLEA1 to CsLEA44 and classified into eight subfamilies. These LEAs were characterized, and two unusual LEA stress-related domains, water stress and hypersensitive response (WHy), and LEA14-like desiccationrelated proteins were detected. Most LEAs within the same family showed similar structures and properties. All the CsDehydrin proteins contained the YKS segments which are essential for keeping the capacity to adjust their conformation and maintain cellular homeostasis under stress conditions. Among the cis-regulatory elements of the LEA genes, more than three G-Boxes were registered for each *dehydrin*, *LEA\_2*, and *SMP* genes and more than two MBS for *dehydrin* and *LEA\_2*

*Prosopis juliflora* is a species native to arid lands of Mexico and widely distributed in arid and semiarid regions of Central and South America. In this species 6874 DEGs were found under salt and drought stress. Approximately 42.6% (2932 DEGs) had homology to genes with GO annotation. Among these, 1339 DEGs were upregulated, while 1596 were downregulated. More DEGs under salt and drought stress were found in roots than those of leaves. Under drought stress, there was more upregulation than downregulation in leaves, while in roots there was more downregulation. A total of 30 unigenes were recognized as exclusively responsive to drought stress. One of these genes encoding for *Arabidopsis* ortholog ABR1 was downregulated. Two upregulated genes encoded for transcription factors involved in diminishing intracellular H2O2 levels. Also, three highly upregulated DEGs encoded for "pq-loop repeat family protein transmembrane family protein" which has been assigned with a potential role in stress tolerance. Many drought-responsive genes were tissue specific. For example, 1040 genes were specifically expressed in root tissues. Up to 805 genes were commonly regulated by drought and salt stress in root tissue. Upregulated genes included those coding for dehydration-responsive

**68**

subfamily gene [89].

Drought stress tolerance is a complex quantitative trait that involves several mechanisms and multiple genes to produce the defense responses of the plants for dealing with environmental conditions of drought. Studies in model plants have found some of the main mechanisms, genes, and proteins that are activated upon drought stress. However, not enough studies have been carried out in plants native to arid lands. The new sequencing technologies and bioinformatic tools applied to some plants native to desert areas have allowed to identify many of the already characterized genes and proteins involved in drought stress tolerance. The most important is that this kind of studies has shown that myriads of drought-related transcripts are not characterized and may belong to novel genes. These may be part of the already known pathways of plants responses to drought or may participate in novel ways to tolerate drought, and after further studies may be used to improve drought tolerance in crops.


*Supporting open minds since 2005*

Forecasting Volcanic Eruptions http://dx.doi.org/10.5772/intechopen.77483 Edited by Angelo Paone and Sung-Hyo Yun

#### Contributors

Uriah Lar, Mohammed Tsalha, Isah Lekmang, Cedric Longpia, Prantik Mandal, Sang Koo Hwang, Leonel Vega, Angelo Paone

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**Abstract**

During the past decades, the landscape of speech and DTMF applications has changed from being based on proprietary platforms to being completely based on speech standards. The W3C Voice Browser Working Group played a primary goal in this change. This chapter describes that change, highlights the standards created by the W3C VBWG, and discusses the benefits that these standards have provided in many other application fields, including multi-modal interfaces.

**Keywords:** human-computer interaction, speech recognition, speech synthesis, pronunciation lexicons, natural language, spoken dialog, speech grammars, multi-modal interaction


*Article* **Study on Seismic Attenuation Based on Wave-Induced Pore Fluid Dissolution and Its Application**

**Ziqi Jin, Xuelin Zheng, Ying Shi \* and Weihong Wang**

School of Earth Sciences, Northeast Petroleum University, Daqing 163318, China **\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** Seismic wave attenuation is affected by wave-induced pore fluid dissolution. The mechanism of wave-induced pore fluid dissolution is the mutual dissolution between different fluids caused by pore fluid pressure. Compared with the traditional WIFF (wave-induced fluid flow) mechanism, the wave-induced pore fluid dissolution mechanism can predict the attenuation of the seismic frequency band and can be used in well-to-seismic calibration. Conventional methods neglect the velocity dispersion caused by the interaction between pore fluids, which will lead to errors in attenuation prediction. In this paper, we focus on accurately predicting the velocity dispersion at low porosity and permeability, which can be used in multi-scale data matching. The stretch between the synthetic data by using logging data and seismic data needs to be calibrated for more accurate interpretation. The kernel of well-to-seismic calibration is the knowledge of the velocity dispersion between the logging frequency band and seismic frequency band. We calibrate the difference between the two kinds of data by using the rock physical model. Both the model test and field data application prove the feasibility and accuracy of the proposed strategy.

**Keywords:** rock physical model; upscaling model; pore fluid dissolution


*4.4. Extensive Materials*

This subsection provides extensive descriptions for how the rules are justified using tabularised quantitative information.

Table 1 digests how the first rule was established in Section 4.1, based on the resampled, resmoothed, and mean–var. transformed data on the X axis (see Section 3.3 and Figures A7a, A8a and A9a), with the setting that wind-speed (WS) = 6 m/s. Table 2 is numerically presented on the Y axis (see Section 3.3 and Figures A7b, A8b and A9b), because rule 1 says that using the information either on the X-axis or on the Y-axis is okay.

**Table 1.** Resampled, resmoothed mean–var. transformed data on the X axis (WS = 6 m/s). (**a**) Min/var. peak/valley Stats for the 3.0-blade turbine. (**b**) Min/var. peak/valley stats for the 2.5-blade turbine. (**c**) Min/var. peak/valley stats for the 2.0-blade turbine.

**Table 2.** Resampled, resmoothed mean–var. transformed data on the Y axis (WS = 6 m/s). (**a**) Min/var. peak/valley stats for the 3.0-blade turbine. (**b**) Min/var. peak/valley stats for the 2.5-blade turbine. (**c**) Min/var. peak/valley stats for the 2.0-blade turbine.

In these tables, ID is the number of the data entry in the resmoothed and resampled dataset in 3.3, based on the retrieval of the initial 1024 signals of each data sequence in 3.2. For example, in Table 2b, the 'Mean' and 'Var.' for 'ID' = 12 means the data entries with clocks [(12 − 1) × 8, (12) × 8 − 1] in the source accelerometer data sequence (which is 0-started) have a mean value of 1.145866 and a variance of 0.154981. 'Peak' = 0 and 'Valley' = 1 means that a valley appears here (as can be seen, in this table, entries that are neither a peak nor a valley in the figure are not shown here), and this valley is having a value delimited by ['Valley Top', 'Valley Bot.'] = [0.990884, 1.300847], while the 'Valley Range' of it is 0.309963. For this identified extreme, 'Peak Top', 'Peak Bot.' and 'Peak Range' do not receive any value because they are not peaks (see also in the corresponding subfigure in Figure A8b).

Rule 1 is obvious from these tables. Along the X or Y axis, either the peak range or the valley range of the 2.5-blade turbine is far greater than the peak range or the valley range of a 3.0-blade turbine or a 2.0-blade turbine. This is more salient when a total summary for all cases and the average is given in Table 3 (e.g., when wind-speed = 6 m/s, on Y axis, 0.072865 (2.5-blade) >> 0.018713 (3.0-blade) > 0.017087 (2.0-blade); 0.187026 (2.5-blade) >> 0.01901 (3.0-blade) > 0.016757 (2.0-blade)). That is, for a 2.5-blade turbine, the variance at the peak or valley along the X or Y axis is at least triple or more than the 3.0-blade or 2.0-blade cases.

Finally, Rule 2 should also be obvious from these tables. See in Table 3. For the 3.0-blade settings, on the Z axis, from the average of all valley values to the average of all peak values (i.e., the fluctuation), the numbers are 2.034987, 3.076206, 3.185465 and 3.424772, respectively, imposing the four wind-speeds. However, for the 2.0-blade settings, on the same axis, these numbers are 1.869613, 2.928724, 3.110773 and 3.074410, respectively. All of these numbers are below those of the 3.0-blade settings (while other settings are identical) by 7% (8.8%, 5%, 2.4% and 11.4%) in average. This quantified rule can be applied, and the reason for this is related to electromechanical conversion: more electricity is produced by the wind turbine when no blade is missing.


**7. Tools, programs**

### **7.1 Features of available simulation programs, short overview**

Simulation programs are available as stand-alone versions or as integrated versions in a SCADA system. The best known and well reputated simulation programs for gas in Europe/Germany are summarized below highlighting some features:

Fig. 10. Tracking oft he calorific value in a high pressure transport network and biogas

Simulation programs are available as stand-alone versions or as integrated versions in a SCADA system. The best known and well reputated simulation programs for gas in

**7.1 Features of available simulation programs, short overview**

Europe/Germany are summarized below highlighting some features:

feedings (A, B)

**7. Tools, programs**

Table 3. Programs for simulation and gas quality parameter tracking


**5. Conclusions**

In conclusion, from our experience, the majority of children with an OFC-confirmed nut allergy have a familial history of allergy (75%) and/or concomitant allergic disorders (68%). Moreover, anaphylaxis is the first manifestation of nut allergy in a high percentage of children (48%), and the presence of anaphylaxis or severe reactions as the first clinical manifestation of a nut allergy does not differ among the various nuts. The clinical and allergological characteristics of children with nut allergies described in our study are similar to other international studies: hazelnut is the most frequent nut for referral. Over three out of four subjects have a single nut allergy. Finally, during the OFCs, the signs and symptoms involved mainly the gastrointestinal system (73%), with cases resolved spontaneously in most cases and infrequent severe reactions (19%), confirming the safety of OFCs in children with suspected food allergies. These results appear important to define a comprehensive characterization of children with nut allergies in Italy. However, the results should be confirmed by extensive data from international cohorts.

**Author Contributions:** S.T., S.B., T.A. and E.P. collected the data. S.T., S.B., M.G., G.L., L.S., G.P., C.A., E.N. and F.M. performed the investigations. S.T. and S.B. analyzed the data. S.T. and S.B. drafted the initial manuscript. S.T., S.B., M.G., G.L., L.S., G.P., C.A., E.N. and F.M. interpreted the data and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The code of the event report issued by Meyer Children's University Hospital is: IR904-18-26854.

**Informed Consent Statement:** Written informed consent was obtained from the children's parents for all procedures performed.

**Data Availability Statement:** Aggregate analyses are available on reasonable request to the corresponding author.

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


**Meet the editor**

Dr. Christian Cuadrado-Laborde received his Ph.D. degree in physics from La Plata University (UNLP, Argentina, 2005), and his Electric and Electronic Engineer degree from the San Luis University (UNSL, Argentina, 1998). Then, he joined to the most important federal research agency in Argentina (CONICET, 2005); where he is actually an Associate Researcher. He is currently also

an Associate Professor of the La Plata Catholic University (Argentina). He has served also as an Assistant Professor of the Faculty of Engineering of the UNLP. He was awarded by the Spanish Ministry of Science with a research fellow position to join to the Optical Fiber Laboratory group headed by M. V. Andrés of the Valencia University (Spain) during 2008-2009. Since then, he has a fluid collaboration with this research group. He serves also as a reviewer for several well-known journals of the Optical Society of America, the IEEE, Elsevier, SPIE, and the Electromagnetic Academy. He has published several papers in top-tier scientific journals such as Optics Letters, Laser & Photonics Reviews, Optics & Photonics News, etc. His current research interest includes fiber optics applications, photonic processing, and all-fiber lasers.

Contents

**Preface IX**

**Part 1 DSP in Communications 1**

Chapter 2 **Digital Backward Propagation:**

Nikolai Nefedov

Chapter 1 **Complex Digital Signal Processing in Telecommunications 3**  Zlatka Nikolova, Georgi Iliev,

Miglen Ovtcharov and Vladimir Poulkov

**and Non-Linear Impairments 25**

Chapter 3 **Multiple-Membership Communities Detection**

**at Perpendicular Planes 79**

Chapter 6 **High-Precision Frequency Measurement**

**Using Digital Signal Processing 115**  Ya Liu, Xiao Hui Li and Wen Li Wang

and J. L. San Emeterio

Yusuke Kanno

**A Technique to Compensate Fiber Dispersion**

Rameez Asif, Chien-Yu Lin and Bernhard Schmauss

**and Its Applications for Mobile Networks 51**

**Part 2 DSP in Monitoring, Sensing and Measurements 77**

Chapter 4 **Comparative Analysis of Three Digital Signal Processing**

**Obtained from Ultrasonic Transducers Located**

Miguel A. Rodríguez-Hernández, Antonio Ramos

Chapter 5 **In-Situ Supply-Noise Measurement in LSIs with Millivolt**

**Techniques for 2D Combination of Echographic Traces**

**Accuracy and Nanosecond-Order Time Resolution 99**


**Author details**

Yaser Alahamd1 \*, Hisham Ab Ib Swehli2 , Alaa Rahhal3 , Sundus Sardar2 , Mawahib Ali Mohammed Elhassan1 , Salma Alsamel4 and Osama Ali Ibrahim1

1 Cardiology Department, Heart Hospital, Hamad Medical Corporation, Doha, Qatar

2 Internal Medicine Department, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar

3 Pharmacy Department, Heart Hospital, Hamad Medical Corporation, Doha, Qatar

4 Military Medical Specialty Center, Doha, Qatar

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**233**

2011

*Calcium Channel Blockers*

Hypertension. 2017:**71**:e13

[2] James PA, Oparil S, Carter BL, et al. Evidence-based guideline for the management of high blood pressure in adults: Report from the panel members appointed to the eighth Joint National Committee (JNC 8). JAMA.

[3] Mancia G, Fagard R, Narkiewicz K, et al. ESH/ESC guidelines for the management of arterial hypertension: The task force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology

(ESC). Journal of Hypertens.

[4] Hart P, Bakris GL. Calcium antagonists: Do they equally protect against kidney injury? Kidney International. 2008;**73**:795

[5] Brunton LL, Hilal-Dandan R, Knollmann BC. Goodman & Gilman's:

[6] Waller D, Sampson A. Medical Pharmacology and Therapeutics. 5th ed. Amsterdam: Elsevier Limited; 2018

[7] Beale JM, Block JH. Wilson and Gisvold's Textbook of Organic Medicinal and Pharmaceutical Chemistry. 12th ed. Philadelphia: Lippincott Williams & Wilkins, a Wolters Kluwer Business;

Therapeutics, 13e. New York: McGraw-

The Pharmacological Basis of

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**5. Simulation Results**

The main purpose of this research was to reduce energy consumption and increase the network life time. In order to achieve this purpose, simulations were performed in MATLAB programming environment. Furthermore, to evaluate network stability, efficiency, and throughput of the proposed algorithm, this algorithm was compared to ECDC (2013) [24], HUCL (2015) [15], and EADUC-II (2016) [25] algorithms.

#### *5.1. Simulation Scenarios*

We also performed simulations for two different states shown in Table 2. In the first scenario, there are 100 nodes in 200 × 200 spaces and the BS is located outside the network space and located at 100 × 250. In the second scenario, the location of the BS has changed to the network center and 100 × 100 location.

In the proposed algorithm the count of alive nodes, the average of network energy and network stability, first node death (FND), half node death (HND), death of 10% and 20% of nodes (PND), and last node death (LND) during the whole simulation time were evaluated. The simulation was performed in 50 periods.

Definitions of some important concepts:

• Network lifetime: the time interval from the start of network operations to LND.

#### *5.2. Simulation Parameters*

Simulation parameters are displayed in Table 3. The optimum values of some of the parameters (for example *RLmax*) were determined from various simulation results.

**Table 3.** Parameters used in the simulation.

We performed simulations several times to determine the round numbers in a major slot (Figure 2) and major slot in data transmission stage (Figure 2). Simulation parameters, such as the nodes locations, are considered to be the same for all the nodes so that the results are reliable and solid. This simulation plays a crucial role in the proposed protocol. In this protocol, by decreasing the number of rounds, we can more accurately simulate a dynamic method which results in increased overhead and energy consumption and declined the network life span. In contrast, by increasing the number of rounds the proposed protocol gets closer to the static methods and, therefore, leads to a decrease is overhead and loss of energy in CH and, subsequently, the stability and throughput of the network reduced. Since the main aim of the proposed protocol was to increase the maximum lifetime of the network by eliminating maximum control messages and reducing overhead which leads to the reduced energy consumption of networks nodes. The number of rounds in a major slot and the number of major slots in data transmission should be optimized. In order to determine the abovementioned optimized numbers, the simulation was performed several times. Considering the structure of the proposed method, we preferred to set the amount of the mentioned parameter to the highest value of FND. According to Figures 5 and 6 we considered the count of rounds as 6 and the count of the major slot as *7*. The optimum FND average was obtained in different simulations. According to the simulation results, it can be seen that each data transmission stage consists of *7* portions of the major slot including 6 rounds, one CH rotation, and one adjustment route. Overall, during the data transmission stage, data was transmitted in 42 rounds and we aimed to remove the majority of the network overhead.

**Figure 5.** Network lifetime under different rounds in different major slots at data transmission stage for scenario #1 with Rlmax = 100 and initial energy = 1 J.

For results and considered parameters refer to Figure 2. Figure 2 demonstrates the protocol performance considering the number of rounds and major slots. According to the results of Figure 5, we set the number of rounds and a major slot in Figure 2.

One of the most important parameters in clustering is to determine the radius. The radius of the nodes in each layer of the proposed protocol is intentionally considered as different sizes. Thus, the value of *λ* in Equation (4) in the first layer is equal to 1, in the second and third layers is 1.25, and in the fourth layer is 1.75. Thus, the clusters which are closer to the BS are smaller and the CH can have more energy for relaying and routing other CH packets to the BS. To determine the *RLmax* parameter in the Equation (4) we carried out various simulation runs with different *RLmax* values to obtain the optimum value for *RLmax* parameter. Two of the important factors in IoT-based wireless nodes are the number of CH and the size of clusters, which are dependent on the radius of the nodes. The simulation results for different scenarios are shown in Figure 7.

**Figure 6.** Network lifetime under different rounds in different major slots at data transmission stage for scenario #2 with Rlmax = 100 and initial energy = 1 J.

**Figure 7.** CH generated in two scenarios.

The radius of each node must be so that the number of cluster nodes in the network is reasonable. In the paper, the number of optimal CH was considered equal to 5% of the total number of nodes in the network. In our proposed protocol, we assumed that the value of *RLmax* parameter is equal to 100 m which makes the number of CH approximately equal to 5 based on [3].

Figures 8 and 9 demonstrate the routing and clustering graph formed in one of the simulations for scenarios 1 and 2. Green nodes display CM nodes, turquoise color indicates ACH nodes, and CHs are indicated in blue color. According to these figures, clusters close to BS are smaller and as a result, they have more energy for distant CH data relay. In addition, ACH nodes cooperate with CH in data transmission. If the network area is larger, and the distance of nodes are farther, then the role of these nodes will be more effective.

**Figure 8.** Routing and clustering graph for scenario 1.

**Figure 9.** Routing and clustering graph for scenario 2.


**5. Conclusions**

As mentioned in the introduction and in the discussion, previous studies show that β-TCP is a performing bone replacement material, not only as pure material and shaped by conventional methods (e.g., freeze drying) but also as composite material (e.g., with polymers) and shaped by additive manufacturing. The resulting compressive strength is always relatively low, ruling out load-bearing clinical indications/bone defects. In contrast, our results show that we can address load-bearing bone defects using the same material as previously reported, by advancing known composites to become complexly shaped structural composites that not only unite the structural features of a real bone (dense and porous sections) but also reach similar and improved compressive strengths (of trabecular bone [44,45]), while at the same time providing degradability as given by the material. By fine-tuning the support structure design and working on composite materials to develop new structural and material composites for potential bone replacements, we might be able to further develop mechanical properties, aligning our approach with a variety of bone defects, especially long-bone and load-bearing ones. Offering the same biocompatibility, the bioceramic hybrid foams have significant mechanical advantages over the Curasan benchmark. The hybrid's failure load is 91 times higher in comparison to the commercially available β-TCP ceramic. To summarize, the compressive strength of the bone-mimicking hybrid bones was significantly enhanced, while high biocompatibility was maintained as proven on the Curasan material. At present, the BMBF-funded project "Hybrid-Bone" (03VP07633) is in progress, which builds on these results and strives for the evaluation and validation of materials, processes and hybrid scaffolds for use as compressive-strengthenhanced biodegradable jaw-bone replacements. Within the framework of this project, the comprehensive bone-forming performance tests of similar scaffolds, with a focus on hybrid foams, are carried out in animal models. The authors hope to report on these results soon.

**Author Contributions:** Conceptualization, M.A.; Data curation, M.A., S.H.L., C.S., C.F., D.W., E.S.-F. and M.S.; Investigation, C.F., D.W. and E.S.-F.; Methodology, M.A. and H.O.M.; Project administration, M.A.; Software, C.F.; Supervision, M.A.; Validation, S.H.L., C.S., C.F., D.W. and E.S.-F.; Visualization, S.H.L. and M.S.; Writing—Original draft, M.A.; Writing—Review and editing, M.A. and M.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The animal study (file number 47/18) was reviewed and approved by the Landesdirektion Leipzig, Germany. The approval date was January 15th in 2019 and the study was conducted according to Directive 2010/63/EU.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** This contribution originally was intended to be published together with Anke Bernstein, for Musculoskeletal Biomaterials of the Research Center for Tissue Replacement, Regeneration and Neogenesis (G.E.R.N.) and the Deputy Director of the G.E.R.N in the Department of Orthopaedics and Trauma Surgery at the University Hospital Freiburg in Breisgau, Germany. With our deepest regrets, Anke died in a mountaineering accident at the end of June 2021. Bernstein's research revolved around biomaterials-based therapies for repair and regeneration of musculoskeletal tissues. Bernstein was a dedicated member of the German Society for Biomaterials and held the role of the President since 2019. Anke, you will be remembered as a friendly person with a sharp mind, a great sense of humor and lots of laughter. Thank you, indeed, for walking some of the way together. We offer Anke Bernstein's family, friends and colleagues our sincere condolences on her passing. Many thanks as well to Melanie Lynn Hart for spell-checking this contribution.

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

#### **References**


**6. Conclusions**

The present study investigated the FFSP–PB problem. Since the standard HNN algorithm easily falls into local extremum and is difficult for continuous evolution, this study proposed the SAA–HNN algorithm for global optimization. It adopts the Metropolis acceptance mechanism of the simulated annealing algorithm, such that the HNN algorithm can accept the non-optimal solution. Thus, the evolutionary vitality of the HNN algorithm is enhanced, rendering it the ability to jump out of the local extremum. Consecutively, considering the influence of the public buffer on the scheduling process, the reentrant rules of the electric flat carriage and workpiece transfer rules in the public buffer for controlling the movement of the workpiece are designed according to the transit time-cost of the workpiece among the workstation, the limited buffer, and the public buffer, as well as the processing status of the workpiece during the production. This phenomenon reduces the production blockage and improves the utilization of production resources. Finally, the simulation experiment proves that the SAA–HNN algorithm, combined with the improved local scheduling rules, can solve the FFSP–PB problem.

 If these local scheduling rules coexist with the relevant local scheduling rules established for the public buffer in the present study, the complexity of the whole scheduling optimization process would increase, and certain conflicts would occur between some of the local scheduling rules. Since the problem of conflict resolution between local scheduling rules are beyond the scope of this study, the main directions of future work are divided into the following:

**Author Contributions:** Conceptualization, Z.H. and C.H.; methodology, S.L.; validation, X.D. and H.S.; writing—original draft preparation, Z.H.; writing—review and editing, C.H.; supervision, H.S.; funding acquisition, Z.H.

**Funding:** This research was funded by the Liaoning Provincial Science Foundation, China (grant number:<PHONE_NUMBER>), the Natural Science Foundation of China (grant number: 61873174), the Project of Liaoning Province Education Department, China (grant number: LJZ2017015), and the Shenyang Municipal Science and Technology Project, China (grant number: Z18-5-015).

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


**3. Graphical Targeting Approach**

The first technique for CCEP is the insight-based graphical targeting approach, called CEPA, originally developed in 2007 by Tan and Foo [12]. CEPA uses a graphical approach based on the principles of conventional PA [21]; it enables the identification of the minimum amount of low- or zero-carbon energy needed to satisfy demand-side emission constraints (target) [49]. Note that the term "zero-carbon" is usually applied to sources with very low CO2 intensity compared to fossil fuels, even if the actual magnitude is nonzero. Various applications and extensions of the CEPA methodology have been developed recently, such as applications to energy sectors in different countries (Ireland [50], New Zealand [51], USA [52], China [53], Nigeria [54], the Baltic States [55], the EU [56] and others), alternative metrics and footprints (land [13], water [15]), and other sectors, such as transport systems [16], economic systems [17] and others. Conceptual or graphical techniques applied in PA are useful tools in the preliminary stages of energy planning, energy policy [57], in graphical representation, in step-by-step user control and in the verification of results. The graphical display also provides visualization that is useful for the analysis of a problem and subsequent communication of the results. On the other hand, graphical methods are limited to relatively simple problems [12]. Another limitation could be the accuracy of the results, which depends on the quality of the graphical display [12].

All the steps of the CEPA algorithm are presented in detail by Tan and Foo [12]. The first step in the CEPA procedure is the preparation of the energy source and demand data, which are shown in Table 2. Energy sources are sorted in order of increasing emission factors (see also Table 1). The consumption described in Table 2 (in MWh/t) was obtained by multiplying the fraction of the energy source by the total consumption of electricity for the production of aluminum slugs (9.446 MWh/t). As stated previously, only one product (aluminum slugs) was considered for the graphical and algebraic approaches. Emissions (in t CO2/t) were obtained by multiplying the emission factor and total electricity consumption for 1 t of slugs. Energy demand represents the total consumption of electricity for 1 t of slugs with target emission constraints (benchmark of 0.376 t CO2/MWh [5]). Emissions (3.552 t CO2/t) were obtained by multiplying the benchmark emission factor and total electricity consumption.

The second step in the CEPA procedure is the generation of source and demand composite curves (CCs). Source curves for energy supply were obtained as CO2 emissions vs. electricity supply plots, and demand curves as CO2 emissions vs. electricity demand plots. The horizontal axis represents electricity supply or demand, while CO2 emissions are plotted as the vertical axis. The slope of each electricity source is equal to its emission factor, while the slope of electricity demand is equal to

benchmark emission factor. Fossil energy has the highest emission factor (1.015 t CO2/MWh; see also Table 1) of all electricity sources and a much steeper slope compared to renewable and nuclear energy.

**Table 2.** Data required for CEPA.

aluminum slugs. The demand curve is thus linear over the entire interval. To supply suitable energy sources that meet the CO2 emissions targets, the source CC is then

shifted to the right. The Pinch Point is where the demand curve touches the shifted source curve. On the lower end of the shifted source CC (minimum distance between origin and shifted CC) is the minimum amount of zero-carbon energy sources needed to meet the CO2 emission limit, while at the upper end (distance between the Pinch Point and the end of the shifted source CC) is excess energy from fossil sources. At the lower end, it is generally desirable to maximize the use of zeroor low-carbon sources [58], while at the upper end, it is desirable to minimize the use of the most carbon-intensive energy source. Figure 2 shows a Pinch diagram for 1 t of aluminum slug production while minimizing the zero-carbon energy source.

The minimum amount of zero-carbon energy (Figure 2) in this study is the same as the excess of the fossil source because of the same amount of electricity supply and demand. The graphical approach shows that for 1 t of aluminum slugs, about 2.14 MWh of the fossil source should be replaced with zero-carbon energy to achieve an emission limit of 3.552 t CO2. As is shown in the left-upper window of Figure 2, the zero-carbon source has a zero slope, and nuclear energy, which is the lowest carbon emission source, is shifted for the minimum zero-carbon energy source to the right.

**Figure 2.** Pinch diagram for 1 t of aluminum slugs with a minimum zero-carbon source.

**Figure 3.** Pinch diagram for 1 t of aluminum slugs with a minimum low-carbon (renewable) source.

However, it should be noted that it is virtually impossible to produce electricity with no CO2 emissions (zero-carbon electricity). Even renewable energy sources can only approach CO2 neutrality, but they do not reach it [58]. In many cases, it is more desirable to minimize the low-carbon energy source. Renewable energy is assumed to be a low-carbon energy source in this study, as it exhibits a low emission factor, with advantages such as renewability. Figure 3 shows a Pinch diagram for 1 t of aluminum slugs with a minimum low-carbon energy source (renewable energy). In the low-carbon case, about 2.22 MWh is the minimum low-carbon energy source, and the same amount of energy is the excess of the fossil source. Owing to the addition of the low-carbon source, slightly more excess energy from the fossil source with the highest emission factor is obtained compared to the zero-carbon example.

Both cases (zero- and low-carbon energy source) will also be presented using the algebraic targeting approach.