**2. Techniques for primary structure characterization**

Among the three major classes of biopolymers, the polysaccharides show the greatest chemical and structural variety. The nucleic acids are constructed from a handful of nucleotide bases so that the polymeric structure obtained is invariably linear. The number of amino acid building blocks used to construct the proteins is approximately twenty but, again, the proteins are always linear polymers. On the other hand, polysaccharides display a wide chemical and structural variability that is not found among the polypeptides and polynucleotides mainly due to the multiple hydroxyl functionality of the five- and sixcarbon sugars. The replacement of one or more of such sugar hydroxyl functionalities by amine, ester, carboxylate, phosphate or sulfonate groups, leads to the frequent occurrence of tree-like branching and to the huge number of possible polymeric conformations of different solution behavior. For these reasons carbohydrate analysis involves, after isolation and purification, many steps, i.e. the determination of individual monosaccharides, of anomeric linkages, of branching and sequence, of anomeric configuration and, finally, of the chain conformation [19].


*Article* **Phosphazene-Containing Epoxy Resins Based on Bisphenol F with Enhanced Heat Resistance and Mechanical Properties: Synthesis and Properties**

**Ilya V. Tarasov 1,\* , Anastasiya V. Oboishchikova 1, Roman S. Borisov 1,2, Vyacheslav V. Kireev 1 and Igor S. Sirotin 1,\***

**Abstract:** Organophosphazenes are of interest due to the combination of increased mechanical and thermal properties of polymer materials obtained with their use, however, they are characterized by a complex multi-stage synthesis. Moreover, the high viscosity of phosphazene-containing epoxy resins (PhER) makes their processing difficult. To simplify the synthesis of PhER, a one-step method was developed, and bisphenol F was chosen, which also provided a decrease in viscosity. In the current study, PhER were formed by a one-stage interaction of hexachlorocyclotriphosphazene (HCP) with bisphenol F isomers and epichlorohydrin in the presence of alkali, which was a mixture of epoxycyclophosphazenes (ECPh) with a functionality from 1 to 4 according to the results of MALDI-TOF analysis. Conventional epoxy resins based on bisphenol F, also formed during the process, showed high mechanical properties and glass transition temperature, and the reactivity of the obtained resins is similar to the base epoxy resins based on bisphenols A and F. Cured PhER had higher or the same mechanical properties compared to base epoxy resins based on bisphenol A and F, and a glass transition temperature comparable to base epoxy resins based on bisphenol F: glass transition temperature (Tg) up to 174.5 ◦C, tensile strength up to 74.5 MPa, tensile modulus up to 2050 MPa, tensile elongation at break up to 6.22%, flexural strength up to 146.6 MPa, flexural modulus up to 3630 MPa, flexural elongation at break up to 9.15%, and Izod impact strength up to 4.01 kJ/m2. Analysis of the composition of the obtained PhER was carried out by 1H and 31P NMR spectroscopy, MALDI-TOF mass spectrometry, X-ray fluorescence elemental analysis, and contained up to 3.9% phosphorus and from 1.3% to 4.2% chlorine. The temperature profile of the viscosity of the resulting epoxy resins was determined, and the viscosity at 25 ◦C ranged from 20,000 to 450,000 Pa·s, depending on the ratio of reagents. The resins studied in this work can be cured with conventional curing agents and, with a low content of the phosphazene fraction, can act as modifiers for traditional epoxy resins, being compatible with them, to increase impact strength and elasticity while maintaining the rest of the main mechanical and processing properties, and can be used as a resin component for composite materials, adhesives, and paints.

**Keywords:** epoxy resin; epoxy oligomer; phosphazene; bisphenol F; mechanical properties; glass transition temperature


*Article* **Numerical Simulation and Validation in Scrubber Wash Water Discharge from Ships**

**Yong-Seok Choi 1 and Tae-Woo Lim 2,\***

Received: 12 March 2020; Accepted: 7 April 2020; Published: 10 April 2020

**Abstract:** A regulation on the sulfur emissions of ships sailing in global sea areas has been enforced since 1 January 2020. In this new regulation, ships are required to use low-sulfur fuel oils or to install an after-treatment equipment, such as a scrubber. Open and hybrid scrubbers wash the exhaust gas using seawater and then discharge the wash water overboard. According to the regulation promulgated by the International Maritime Organization (IMO) Marine Environment Protection Committee (MEPC), the wash water must have a pH of 6.5 or higher at 4 m from the discharge point. Wash water is generally acidic, with a pH of 2.5–3.5, whereas seawater is alkaline, with a pH of approximately 8.2. The wash water is dispersed after being discharged overboard through a nozzle, and its pH is restored through dilution with the surrounding seawater. In this study, the pH was calculated by using a theoretical chemical reaction model, and then the dispersion of wash water was analyzed using CFD simulation. This study describes the process of selecting the appropriate turbulent Schmidt number in a wide range of nozzle diameters. Finally, the appropriate nozzle diameter was determined based on the initial pH of the discharged scrubber wash.

**Keywords:** CFD; dispersion; pH; turbulent Schmidt number; scrubber; wash water


**3.3 Construction materials**

Two deposits of unsaturated tropical soil (Soil 1 and Soil 2), located in Macaé, Rio de Janeiro, Brazil, were investigated, and a geotechnical characterisation was carried out in the laboratory.

Soil samples were prepared and tested according to international standards. Soil preparation, grain-size distribution, specific gravity and Atterberg limits were obtained according to ISO 23909 (2008), ISO 17892-4 (2016), ISO 17892-3 (2015), and ISO 17892-12 (2018) respectively [32, 33]. An empirical test was also carried out to check soil shrinkage during the natural drying process.

CEBs were manufactured using a mechanical hand press, from a mixture of 50% Soil 1 and 50% Soil 2, in volume. The final mixes used cement as a binder with 2% of lime and water content (CEB water absorption) next to 20% (**Figure 12a**-**c**). It

**93**

*Simple Housing Solution Project: (Re) Building in Critical Situations*

should be pointed out that water content could significantly vary within different types of soils. CEB water absorption and compressive strength were verified according to NBR 8492 (2012) (**Figure 12d**) [32, 33, 35]. Tests on wallets equivalent to two grouted columns per metre and fully grouted columns were carried out to obtain mechanical properties and wallet-block efficiency [33, 17]. The main results and

Horizontal load capacity tests were used to obtain inputs for computational models used to investigate the proposed residential model under seismic conditions, performed according to NBR 15421 (2006). The test consisted of applying a horizontal load on a wallet one metre in length (equivalent to 4 blocks per tier), a clear width of 10.5 cm, and a height one metre (equivalent to the elevation of 13 tiers of blocks and a joist at the top). All the specimens had their blocks laid manually in a ~ 1 cm thick mortar joint. The reinforced bond beams had the same width as the blocks and reflected the panel's upper confinement. Vertically, the wall had its lateral holes reinforced and grouted. In addition to these, two more intermediate holes were symmetrically reinforced and grouted, representing panels with two reinforced holes per metre. The load was manually increased and applied by a hydraulic jack placed in the middle of the joist section. The displacements were measured using linear variable differential transformer (LVDT). A load cell was used to monitor the load. Displacement and load were acquired at the same time

Housing recovery in situations where urgent construction is necessary but resources are scarce (such as post-disasters, post-conflicts, refugee settlements, and so on) is a challenging task requiring technical skills, management, and focus on the affected population. The Simple Housing Solution (SHS) methodology was developed to contribute to empowering governments, support agencies and, above all, vulnerable communities. It is presented in the course format and based on the tripod Simple Professional Design, Low-Cost Construction Technologies, and

The first class on the SHS course was held in October 2018 for an audience of 30 people: 15 Haitians living in Brazil and intending to return to their country to participate in the reconstruction process; three members of the non-governmental organisation (NGO) Engineers Without Borders Brazil; three members of the NGO Teto; three community members affected by a relocation process near UFRJ; two employees from the municipality of Niterói (Rio de Janeiro State, Brazil); two employees from the municipality of São Lourenço (Minas Gerais State, Brazil); one graduate student from the Federal University of Santa Catarina, and one refugee from the conflict in Syria. Since then, two other reduced versions of the course were offered in 2019: one at the Federal Institute of Maranhão, Brazil, on the manufacture of soil-cement bricks, during the 5th Maranhão Symposium on Civil Engineering, and another at the Federal University of Pará, Brazil, on SHS Methodology, during the 3rd Brazilian

It should be noted that the educational material developed under the SHS project is intended to be a starting point and should be evaluated and adapted to each implementation reality by the local technical assistance team. However, the material provided can greatly expedite reconstruction work, mostly because of the project's simplicity, pre-planned work, accessible construction technology, and tools developed to administer the community labour and construction works.

*DOI: http://dx.doi.org/10.5772/intechopen.94953*

extensive discussion can be found at [11].

using a data acquisition system [11, 17].

**4. Conclusion**

Community Labour System.

Congress on Disaster Risk Reduction.

*Simple Housing Solution Project: (Re) Building in Critical Situations DOI: http://dx.doi.org/10.5772/intechopen.94953*

should be pointed out that water content could significantly vary within different types of soils. CEB water absorption and compressive strength were verified according to NBR 8492 (2012) (**Figure 12d**) [32, 33, 35]. Tests on wallets equivalent to two grouted columns per metre and fully grouted columns were carried out to obtain mechanical properties and wallet-block efficiency [33, 17]. The main results and extensive discussion can be found at [11].

Horizontal load capacity tests were used to obtain inputs for computational models used to investigate the proposed residential model under seismic conditions, performed according to NBR 15421 (2006). The test consisted of applying a horizontal load on a wallet one metre in length (equivalent to 4 blocks per tier), a clear width of 10.5 cm, and a height one metre (equivalent to the elevation of 13 tiers of blocks and a joist at the top). All the specimens had their blocks laid manually in a ~ 1 cm thick mortar joint. The reinforced bond beams had the same width as the blocks and reflected the panel's upper confinement. Vertically, the wall had its lateral holes reinforced and grouted. In addition to these, two more intermediate holes were symmetrically reinforced and grouted, representing panels with two reinforced holes per metre. The load was manually increased and applied by a hydraulic jack placed in the middle of the joist section. The displacements were measured using linear variable differential transformer (LVDT). A load cell was used to monitor the load. Displacement and load were acquired at the same time using a data acquisition system [11, 17].


*4.3. Analysis of the Relationship between Foreign Exchange Reserves and Land Prices*

Through the R program regression analysis and the correlation analysis of Python, it was confirmed in Sections 4.1 and 4.2 that the most relevant factor for the land prices is the foreign exchange reserves. Next, we sought to analyze the fluctuations in foreign currency reserves and the relationship with land prices from 2019 to the present, and to investigate how the influence is applied as it is.

Figure 19, below, shows the fluctuations in foreign exchange reserves from the first quarter of 2019 to the first quarter of 2020, and the average land price fluctuations in each region's living area. The foreign exchange reserves can be checked in parentheses next to the year; the unit is 100 million dollars, and the land price is also 100 million.

As a result, we were able to confirm whether the ten factors affect land prices. Nonetheless, four factors policy, interest rate, violent crime rate, and foreign exchange reserves were found to have more influence than the other factors. In addition, the F TEST confirmed that the dependent variables of the six regional living spaces were significant to-

In Chapter 4.1, multi-regression analysis was used to analyze the independent variables affecting the dependent variables using variable data from the macroscopic point of view and variable data from the microscopic point of view; this time, we tried to find the factors that correlate with the real estate price through correlation analysis. Correlation analysis is designed to understand the degree of association between two variables, not

As shown in Figure 18, there were differences in the factors relevant to each local living zone. Compared to other factors, the economic growth rate, unemployment rate, interest rate, policy, land building regulation, and violent crime rate were found to be

gether with the independent variables.

*4.2. Python Program Data Analysis*

to explain causality.

relatively low.

**Figure 18. Figure 18.**  Relationship between real estate prices and variables. Relationship between real estate prices and variables.

The foreign currency reserve was set as an independent variable through a Python program. The representative apartment area in the living area of each region was set as the dependent variable. The results were then confirmed through correlation analysis and regression analysis. It was confirmed that, among the six regional living areas, the representative apartments in the regional living area of Samsung have a significant relationship with the fluctuation of foreign exchange reserves. The correlation was also confirmed. In addition, the fluctuation of foreign currency reserves was confirmed to be related to the land prices of each regional living area. Unfortunately, the real estate transaction is active, and it was set at 120 square yards, so there was not much data, and the relevance between foreign currency reserves and land prices increased. However, other studies confirming fluctuations in foreign exchange reserves and their relationship to the stock market have confirmed that an increase in the amount of currency has led to overinvestment in real estate and the stock market, leading to price increases [41,42]. Foreign exchange reserves in the first quarter of 2020 showed a decline due to the increase in the dollar due to the foreign exchange market stabilization measures and COVID19, but has been on the rise since. Korea's foreign exchange reserves at the end of December last year were \$443.1 billion, an increase of \$6.7 billion compared to November, indicating that securities will also have a

significant increase in return on investment, and that real estate land prices will also rise significantly. This research also confirmed the data of 2019 using the data of 2015 to 2018, but for this research, it was also confirmed that the fluctuation of real estate land prices due to the fluctuation of foreign currency reserves has a correlation [42–44]. The result of this can be valid. Figure 20, below, shows the transactions for a representative apartment in 120 square yards in the Samsung area of Gangnam-gu, where the blue line denotes the quotes for the apartment. The red dot shows the actual transaction price. The green line shows the current price of the property, and the red line at the end is the speculation that the market price will rise due to the increase in foreign currency reserves. Through the R program regression analysis and the correlation analysis of Python, it was confirmed in Sections 4.1 and 4.2 that the most relevant factor for the land prices is the foreign exchange reserves. Next, we sought to analyze the fluctuations in foreign currency reserves and the relationship with land prices from 2019 to the present, and to investigate how the influence is applied as it is. Figure 19, below, shows the fluctuations in foreign exchange reserves from the first quarter of 2019 to the first quarter of 2020, and the average land price fluctuations in each region's living area. The foreign exchange reserves can be checked in parentheses next to the year; the unit is 100 million dollars, and the land price is also 100 million.

In conclusion, the factors influencing the cost of living in each region through regression analysis in R were the amount of foreign exchange reserves, number of criminal activities, interest rates, and policies, and the factors correlated with the price of living in each region through correlation analysis in Python included the economic participation rate of the youth, rate of application of the comprehensive real estate tax bill, and amount of foreign exchange reserves, but the research results confirmed that fluctuations in foreign currency reserves are closely related to real estate land prices. As explained in Section 4.3, below, we would like to examine the impact of land prices associated with fluctuations

*Symmetry* **2021**, *13*, x FOR PEER REVIEW 19 of 25

in foreign exchange reserves in combination with the current data.

*4.3. Analysis of the Relationship between Foreign Exchange Reserves and Land Prices*

**Figure 19.** Changes in the land price of living areas in each region due to changes in the foreign exchange reserves. **Figure 19.** Changes in the land price of living areas in each region due to changes in the foreign exchange reserves. denotes the quotes for the apartment. The red dot shows the actual transaction price. The green line shows the current price of the property, and the red line at the end is the speculation that the market price will rise due to the increase in foreign currency reserves.

**Figure 20.** Prediction of changes in the market prices due to fluctuations in foreign exchange reserves. **Figure 20.** Prediction of changes in the market prices due to fluctuations in foreign exchange reserves.

#### **5. Impact on Landscape Districts of Real Estate 5. Impact on Landscape Districts of Real Estate**

tricts.

In addition to the macroscopic and microscopic perspectives in the real estate market, the landscape district is also a desirable tool for the prediction of future land prices. Landscape districts are districts required for the preservation, management, and formation of landscapes, and they are determined by the National Land Planning and Utilization Act. According to their designated purpose, landscape districts are classified into natural land-In addition to the macroscopic and microscopic perspectives in the real estate market, the landscape district is also a desirable tool for the prediction of future land prices. Landscape districts are districts required for the preservation, management, and formation of landscapes, and they are determined by the National Land Planning and Utilization Act. According to their designated purpose, landscape districts are classified into natural

scapes, urban landscapes, and waterside landscape districts. The natural landscape dis-

the natural scenery of the city. The urban landscape district is a district required for protecting and maintaining urban landscapes such as residential areas or centers, and it is a waterside landscape district and specialized landscape district. Finally the waterside landscape district is a district designed to protect and maintain the landscape around the main

There are four major factors influencing land price fluctuations in these landscape areas. First, the city planning ordinance can be changed. For example, the green belt in Haeundae-gu, Busan has recently been enhanced. Second is a case wherein changes are made through development projects such as residential land development projects. Third are the environmental factors which affect real estate or policies related to restrictions and business promotion related to the national landscape, such as the Second Framework Plan for Landscape Policy recently established in Korea. Lastly, due to factors such as capital inflow, there are various factors which can vary real estate prices, such as landscape dis-

waterfront or buildings with great cultural conservation value.

landscapes, urban landscapes, and waterside landscape districts. The natural landscape district is necessary for the protection of mountainous and hilly areas, and for maintaining the natural scenery of the city. The urban landscape district is a district required for protecting and maintaining urban landscapes such as residential areas or centers, and it is a waterside landscape district and specialized landscape district. Finally the waterside landscape district is a district designed to protect and maintain the landscape around the main waterfront or buildings with great cultural conservation value.

There are four major factors influencing land price fluctuations in these landscape areas. First, the city planning ordinance can be changed. For example, the green belt in Haeundae-gu, Busan has recently been enhanced. Second is a case wherein changes are made through development projects such as residential land development projects. Third are the environmental factors which affect real estate or policies related to restrictions and business promotion related to the national landscape, such as the Second Framework Plan for Landscape Policy recently established in Korea. Lastly, due to factors such as capital inflow, there are various factors which can vary real estate prices, such as landscape districts.

Because these landscape districts were formed in order to preserve and manage landscapes, it was difficult for landowners to retain property rights; the release of the landscape districts could affect real estate prices because it attracts investment and creates jobs in the area. Nonetheless, with the release of the landscape district, industrial factors are becoming positive, but the analysis of environmental factors does not yield good results. Because the ecosystem elements in the area are destroyed, and because there is a risk of environmental damage, the current landscape policy is called the watch landscape district or highest altitude district in order to protect the environment and urban landscape in the residential area, or to prevent overcrowding. It is a situation which sets a minimum limit for the height of a building [45].

Therefore, the current landscape district development has a policy of preventing environmental damage; for want of a better word, it is called a multifunctional landscape, providing various functions to maintain the current environmental ecosystem services [46]. To put this into the real estate market, a smart city is to be built [47–49]. In the case of the current smart city construction in the Republic of Korea, construction is roughly divided into two areas. First, in the case of the Sejong Smart City, it is a policy promoted by the nation. Before the introduction of the Sejong Smart City, it was in a remote state. After focusing on artificial-intelligence–based cities, however, the building of elements of seven major innovations—such as mobility and healthcare—was sought to transform them into nature-friendly cities [50].

The second area is the Busan Eco-Delta City. Busan City plans to build a robot city unlike Sejong City: one that can be utilized in daily life. There was also an attempt to change to a waterfront city in the future based on teenage innovation. Overseas cases include Hangzhou, which decided to realize a paper-free society by using blockchain technology, and Toronto, which decided to improve the problem of major cities and build eco-friendly cities. The purpose of introducing smart city construction is to solve various urbanization problems and build a convenient, comfortable life. In addition, the land prices in the surrounding area are likely to increase with the introduction of smart cities [51,52].

Therefore, the method of introducing a smart city in Gangnam-gu, the target area of the current study, can be explained as follows. Of the six living areas in Gangnam-gu, the southern area of Gaepo area and the western area of Suseo area are currently limited to green belt development. Therefore, it is desirable to develop a smart city that can interact with real estate and landscape in this area. Through this, various uses such as welfare, transportation, and education will be created. In addition, it would be desirable to develop real estate and landscape by benchmarking the introduction of overseas smart cities in the region. Therefore, it is to realize self-driving public transportation or to establish a nature-friendly city through a paperless society. As a result, it is expected that through the introduction of smart cities, various effects such as a nature-friendly life and a satisfying life will be enjoyed. Therefore, it is considered that the introduction of a smart city is

a way to make good use of the effect of interaction between landscape and real estate. Therefore, it is necessary to develop real estate and landscape based on the design plan for the introduction of smart cities in Korea through Figure 21 below [53]. *Symmetry* **2021**, *13*, x FOR PEER REVIEW 22 of 25

**Figure 21.** Smart city illustration. **Figure 21.** Smart city illustration.

#### **6. Discussion 6. Discussion**

Recently, people's interest in the real estate market has increased [13,14]. Among them, what attracts attention is the exploration of the factors that cause changes in real estate land prices [19]. Various factors, such as transportation access, financial stability, and stocks exist as factors which can change the land prices [26,27,54,55], but in the current study, based on macro and micro factors—which are frequently used terms in the economy—we analysed which ones have a greater influence. In addition, the technologies used for real estate Big Data analysis include artificial neural network analysis, data mining, and machine learning, but in the current study, through regression analysis and correlation analysis, we investigated which factors are influential, and explained the correlation well. As a result, it was found that the fluctuation of foreign exchange holdings among macro factors exerts influence on real estate land prices, and it was found that future real estate prices also have an influence and explain the correlation well. In addition, it was confirmed that changes in foreign exchange holdings exert an influence on changes in land prices in China and Taiwan, as well as in Korea. Based on this, it can be seen that it is an effective method to predict future land prices through changes in foreign Recently, people's interest in the real estate market has increased [13,14]. Among them, what attracts attention is the exploration of the factors that cause changes in real estate land prices [19]. Various factors, such as transportation access, financial stability, and stocks exist as factors which can change the land prices [26,27,54,55], but in the current study, based on macro and micro factors—which are frequently used terms in the economy—we analysed which ones have a greater influence. In addition, the technologies used for real estate Big Data analysis include artificial neural network analysis, data mining, and machine learning, but in the current study, through regression analysis and correlation analysis, we investigated which factors are influential, and explained the correlation well. As a result, it was found that the fluctuation of foreign exchange holdings among macro factors exerts influence on real estate land prices, and it was found that future real estate prices also have an influence and explain the correlation well. In addition, it was confirmed that changes in foreign exchange holdings exert an influence on changes in land prices in China and Taiwan, as well as in Korea. Based on this, it can be seen that it is an effective method to predict future land prices through changes in foreign exchange reserves among macro and micro factors in real estate Big Data analysis.

exchange reserves among macro and micro factors in real estate Big Data analysis. In addition to the above-described factors, it can be confirmed that the factor of the landscape is also closely related to the real estate market. Since the landscape district is a district formed for the conservation and management of the landscape, there are positive In addition to the above-described factors, it can be confirmed that the factor of the landscape is also closely related to the real estate market. Since the landscape district is a district formed for the conservation and management of the landscape, there are positive aspects for industrial factors, but negative influences for environmental factors also exist.

aspects for industrial factors, but negative influences for environmental factors also exist. In fact, in the Republic of Korea, Busan and Sejong are introducing smart cities to build a city, and technologies such as Blockchain, Big Data, and IoT are being used [36,37]. Therefore, in this paper, the factor that can interact in the landscape district and the real estate market is the establishment of a smart city. When the construction is completed, it is believed that this could also be a big factor in fluctuations in land prices. In fact, in the Republic of Korea, Busan and Sejong are introducing smart cities to build a city, and technologies such as Blockchain, Big Data, and IoT are being used [36,37]. Therefore, in this paper, the factor that can interact in the landscape district and the real estate market is the establishment of a smart city. When the construction is completed, it is believed that this could also be a big factor in fluctuations in land prices.


*4.3. Body Condition*

The examined flocks were in good condition before and after transportation, as measured by the use of the sum of the body parts that were individually scored for plumage condition and integument injuries [43]. With regard to the temporal effect (before and after transportation), we noted a slight improvement to both the plumage and integuments. The main reason is likely to be an insufficient sample size, to ensure that representative estimates and observer deviations are conceivable.

Birds lose weight overnight, even without transportation, and this has potential side effects such as increased corticosterone levels or heat stress, as results from our control flock show. Birds of the control flock were able to move around in the winter garden without access to food and water, matching the lack of these resources for transported birds. "Time in winter garden" for the control flock was 11 h, and this is thus based on the "time in plastic crates" of Variant I. Several studies describe a diurnal and seasonal weight fluctuation in wild birds (e.g., [44–47]) with amplitudes of 5–15% [44,45,47]. The weight loss of the hens of our control flock (−5.9%) falls within this range. However, a mean loss of −3.9% ± 1.8% overnight, as measured for the transported hens of Variant I, is lower than the loss measured in wild birds. Amplitudes in winter (long and colder nights) are higher than in summer [47]. The mean temperature during transport of the studied flocks was 16 ◦C, whereas the mean outside temperature for the control flock was 19.5 ◦C. Unfortunately, none of our other study experiments were performed on the control flock. Explanations therefore remain speculative. Scholtyssek et al. [48] found an greater loss of weight in broilers with increasing durations of transportation (1.3%, 2.3%, and 3.1% after transit durations of 1.5 h, 3.0 h and 4.5 h, respectively) whereas another study did not find weight differences between the control and 4 h transported treatment groups [40].


*4.1. Influence of Chemical Composition, Investment Casting Conditions, and Heat Treatment on Microstructure and Weldability*

Moulds E and O with higher casting solidification rates (1.65 ◦C/s) and standard and low Si contents, respectively, did not present any Laves phases in solution annealing state after complete HIP + solubilisation annealing heat treatment. Area percentage of Laves phase observed in moulds N, NP, and P in the solution-annealed condition ranged from 0.14 to 0.35%, without significant differences between moulds N and NP and being the residual content of Laves phase higher in mould P, that is, the heat with higher Si content and faster solidification rate. Note that this mould also had the highest amount of Laves phase in the as-cast condition (Table 2).

In moulds P, N, and NP containing residual Laves phase in the solution annealing state, the onset of ductility drop in on-heating hot ductility tests was triggered at lower temperatures, and corresponding fitting curves were shifted to the left side of the chart. This was observed in curves displayed in Figures 7a and 14, with the latter superposing the on-heating curves of the five studied heats. Note that dashed fitting lines corresponded to those three moulds. Therefore, this early ductility drop was associated with incipient melting of Laves phase. It is worth mentioning that melting of Laves phase is a fast event which does not require any significant reaction time to form the liquid as in constitutional liquation of NbC [2,6], and, consequently, it readily melts upon heating at very fast rates, as in current hot ductility tests (111 ◦C/s heating rate).

**Figure 14.** Comparison of on-heating curves in terms of area reduction percentage and testing temperature for moulds E, O, P, N, and NP.

Effective dissolution of Laves phase in alloy 718 castings prior to welding and high temperature use is pursued in industrial manufacturing processes because this phase impairs both weldability and mechanical properties [4,5]. This has been particularly shown in castings with volume fractions of secondary particles (including Laves and NbC) higher than the residual values reported in this work.

The chemical composition of remaining Laves phase was remarkably modified in moulds P, N, and NP after HIP and solubilisation treatment. The considerable enrichment in Mo could be explained by differences in diffusivities of solute elements in the austenite matrix. The dissolution kinetics of Laves phase in IN718 has been studied by [28]. In this work, the authors concluded that the back-diffusion of molybdenum in austenite is the controlling micro-mechanism for dissolution of the undesirable Laves phase. On the basis of the Johnson–Mehl–Avrami–Kolmogorov (JMAK) analysis at different temperatures, the authors determined the activation energy of 274.5 kJ/mol for the dissolution of Laves phase, which was close to the activation energy for diffusion of Mo in Ni (288 kJ/mol). This was also supported by the diffusion calculations shown in Table 9, which shows lower diffusivity values for Mo in Ni than for Nb and Ti.

**Table 9.** Parameters for diffusivity calculation of Nb, Ti, and Mo in Ni and calculated diffusivities.

It is also interesting to note that the chemical composition of remaining Laves phase in these three heats was comparable with only minor differences in Si content.

HIP and solubilisation treatment did not modify original grain size and morphology resulting from investment casting. Consequently, different cooling conditions during casting yielded different grain sizes and aspect ratios which remained in the base material employed for the weldability tests. Thus, moulds O, E, and P cooled without ceramic blanket had highly columnar grains elongated along plate thickness. On the contrary, HIP and subsequent solution annealing heat treatment was effective in reducing segregation in interdendritic regions, leading to comparable segregation ratios in the five heats (Table 5).


*3.4. EIS Analysis*

We then used EIS to examine the corrosion resistance of the ESR steel samples in 1MH2SO4 solution to determine their mechanistic and kinetic parameters and compare them with the results of potentiodynamic polarization. The Nyquist plots (Figures 5 and 6) were examined by fitting the trial results to an equivalent circuit model (Figure 7). The circuit comprises a parallel combination of a consistent phase element (CPE, Q), the charge transfer resistance (Rct) relating to the corrosion response at the metal/electrolyte interface, and the solution resistance (Rs) between the working and reference electrode [33,34]. To diminish the effects of surface anomalies in the metal, CPE (Q) was brought into the circuit instead of a pure double-layer capacitance (Cdl), allowing a more precise fit [35,36]. Among these parameters, Rct is the factor that determines the corrosion resistance of composites. Because Rct is conversely relative to icorr, a higher Rct value correlates to a lower icorr value. The impedance of CPE can be presented as follows:

$$\text{ZCPE} = \mathbf{1} / \text{YO}(\text{j}\omega)\mathbf{n},\tag{1}$$

where Y0 is the CPE constant, n is the exponent (phase shift), ω is the angular frequency, and j is the imaginary unit. CPE can represent resistance, capacitance, and inductance according to the estimations of n [37]. In all analyses, the estimated n ranged from 0.8 to 1.0, suggesting a capacitive response of CPE. From this circuit, we determined Rs, Rct, and Cdl (Tables 4 and 5).

The Nyquist plots indicated that the impedance response of the samples gradually increased along with increases in Mo concentration. However, the smallest capacitive loop in a high-frequency region was observed in the M9.8 sample in both 3.5% NaCl and 1 M H2SO4 solutions, indicating that this sample showed the highest corrosion rate. Tables 4 and 5 present all EIS parameters for all of the maraging steels. We found that the Rct value decreased gradually for samples up to M4.6, with the highest value observed for M9.8, confirming the lowest corrosion-resistance property in M9.8 sample as Rct is inversely proportional to the icorr value as mentioned earlier. Furthermore, this was supported by the potentiodynamic polarization results for M9.8. Comparing the diameters of the Nyquist plots, the curves in 3.5% NaCl solution showed increased diameters relative to those in 1MH2SO4 solution, verifying the potentiodynamic results.

**Figure 5.** Nyquist plot curves of maraging steel with different molybdenum (Mo) concentrations in 3.5% NaCl solution.

**Figure 6.** Nyquist plot curves of maraging steel with different Mo concentrations in 1 M H2SO4 solution.

**Figure 7.** Equivalent circuit diagram.

**Table 4.** Electrochemical impedance parameters of maraging-steel samples in 3.5% NaCl solution.

**Table 5.** Electrochemical impedance parameters of maraging-steel samples in1MH2SO4 solution.

#### *3.5. Scanning Electron Microscopy (SEM) Analysis*

Figures 8 and 9 show SEM images of the samples after corrosion in 3.5% NaCl solution and 1 M H2SO4 solution, respectively. It is well known that Mo affects the pitting resistance of the maraging steel by reducing pitting on the sample surface. Chloride is primarily responsible for surface pitting on materials. However, in the present study, the presence of more salt resulted in decreased corrosion resistance in NaCl solution relative to H2SO4 solution. Moreover, Mo increased the stability of the inner layers of the oxide film, resulting in minimal effect on the Ecorr value in both solutions. Furthermore, the surface of the M9.8 sample showed significant effects from both solutions relative to the other samples as a result of the decreased corrosion resistance associated with Mo.

**Figure 8.** Scanning electron microscope (SEM) micrographs of maraging-steel samples: (**a**) M0, (**b**) M2.9, (**c**) M4.6, and (**d**) M9.8 in 3.5% NaCl solution.

**Figure 9.** SEM micrographs of maraging-steel samples: (**a**) M0, (**b**) M2.9, (**c**) M4.6, and (**d**) M9.8 in 1MH2SO4 solution.

#### *3.6. Energy-Dispersive X-ray Spectroscopy (EDS) Analysis*

Figure 10 shows the results of EDS analysis of the as-received and heat-treated maraging-steel samples in 3.5% NaCl solution. The EDS was completed on the yellow boxed portion as given in the SEM images in Figure 8. The EDS analysis data are given in Table 6 in wt%. The results showed that the Mo peak increased from samples M0 to M9.8, with peaks for Ti, C, Ni, and Fe also observed. The chloride peak was higher in the M9.8 sample relative to others, which is likely related to its decreased corrosion resistance and the associated increased number of chloride ions on the M9.8 surface.

**Figure 10.** EDS spectra of the maraging steel after immersion in 3.5% NaCl solution.

**Table 6.** EDS analysis data in 3.5% NaCl solution.

Figure 11 shows the results of EDS analysis of the as-received and heat-treated maraging-steel samples in 1 M H2SO4 solution. The EDS was completed on the yellow boxed portion as given in the SEM images in Figure 10. The EDS analysis data are given in Table 7 in wt%. Similarly, peaks for Ti, C, Fe, and Ni were observed following their precipitation as carbides. Additionally, we observed an increase in the intensity of the Mo peak from samples M0–M9.8, with peaks for sulfur and oxygen ions also observed as the surface reacted with the SO4 ions present in the solution. Given the unfavorability of sulfur ions on pitting resistance, we found greater corrosive effects on the surface of the maraging-steel sample in H2SO4 solution.

**Figure 11.** EDS spectra of the maraging steel after immersion in 1.0 M H2SO4 solution.

#### *3.7. Raman Spectroscopy Analysis*

Raman spectroscopy analyses of the corroded surfaces of the samples following potentiodynamic polarization are shown in Figures 12 and 13, for both solutions. Standard normal variate (SNV) model is an effective procedure to make the output data more comparable. In this method, the spectrum mean subtraction and standard spectrum deviation procedure is used. As long as the original scale of the spectra is not interesting, this is an efficient way of removing constant baseline effects and scaling differences from

spectra. Previous studies reported that increases in the proportion of α-FeOOH and gamma \* (total mass of γ-FeOOH, β-FeOOH, and magnetite) in the corrosion products suggest a decline in the corrosion rate [38,39]. In the present study, we identified α-FeOOH (280 cm−1) as a significant phase of the corrosion product present on a superficial level, and it was more prominent in the M9.8 sample relative to the others. This suggested that a higher level of α-FeOOH in the M9.8 sample promoted an increase in the corrosion rate. Additionally, higher amounts of α-FeOOH and gamma \* (Figure 13) were observed in 1 M H2SO4 solution, indicating that the maraging steel showed a higher degree of corrosion in the presence of H2SO4 solution as compared to the NaCl solution. In the case of sulfate solution, a phase shift occurs at 310 cm−1 and a new peak is seen at 1000 cm−1. More γ-FeOOH occurs in sulfate solution at 80, 1320, and 1410 cm−1. For the same composition, only one peak at 1380 cm−1 occurs for the sample in chloride solution. The β-FeOOH phase occurs at 480 cm−1 only in the case of sulfate solution. No phase shift occurs for the δ-FeOOH phase, and it shows a peak at 400 cm−1. Only an extra peak at 610 cm−1 occurs in sulfate solution for δ- FeOOH.

**Figure 12.** Raman spectra of maraging steel with different Mo concentrations in 3.5% NaCl solution.

**Figure 13.** Raman spectra of maraging steel with different Mo concentrations in 1 M H2SO4 solution.


*Article* **Driver Cardiovascular Disease Detection Using Seismocardiogram**

**Gediminas Uskovas 1 , Algimantas Valinevicius 1 , Mindaugas Zilys 1 , Dangirutis Navikas 1 , Michal Frivaldsky 2 , Michal Prauzek 3 , Jaromir Konecny 3 and Darius Andriukaitis 1, \***

**Abstract:** This article deals with the treatment and application of cardiac biosignals, an excited accelerometer, and a gyroscope in the prevention of accidents on the road. Previously conducted studies say that the seismocardiogram is a measure of cardiac microvibration signals that allows for detecting rhythms, heart valve opening and closing disorders, and monitoring of patients' breathing. This article refers to the seismocardiogram hypothesis that the measurements of a seismocardiogram could be used to identify drivers' heart problems before they reach a critical condition and safely stop the vehicle by informing the relevant departments in a nonclinical manner. The proposed system works without an electrocardiogram, which helps to detect heart rhythms more easily. The estimation of the heart rate (HR) is calculated through automatically detected aortic valve opening (AO) peaks. The system is composed of two micro-electromechanical systems (MEMSs) to evaluate physiological parameters and eliminate the effects of external interference on the entire system. The few digital filtering methods are discussed and benchmarked to increase seismocardiogram efficiency. As a result, the fourth adaptive filter obtains the estimated HR = 65 beats per min (bmp) in a still noisy signal (SNR = −11.32 dB). In contrast with the low processing benefit (3.39 dB), 27 AO peaks were detected with a 917.56-ms peak interval mean over 1.11 s, and the calculated root mean square error (RMSE) was 0.1942 m/s 2 when the adaptive filter order is 50 and the adaptation step is equal to 0.933.

**Keywords:** arrhythmia; driving restrictions; adaptive digital filter; noninvasive method; heart rate

#### **1. Introduction**

Quality of life can be expressed by many factors, some of which are health and the opportunity to participate in favorite activities. Carrying out one's favorite work may be restricted or completely suspended due to health impairments [1]. For staff of various professions, one of the main monitoring objects is related to cardiovascular diseases, resulting in mortalities in Europe of about 4 million [2] and up to 46% of all deaths in the United States [3]. Cardiovascular diseases are a leading cause of death in the world [4], and 523.3 million people had cardiovascular disease (CVD) in 2019 [5,6]. The future prognoses of researchers are plaintive because the COVID-19 pandemic and fast aging will allegedly make the CVD numbers worse in the coming years [5,6].

Cardiovascular disease is hard to predict and can render people unfit to perform some daily or professional activities or cause them to suddenly lose control for short time periods and harm others [1,7]. This transient loss of consciousness event is called syncope and can result from cardiac conditions, mostly consisting of arrhythmic events, bradyarrhythmia, or tachyarrhythmias [8,9]. For this reason, the American Heart Association and Heart

**Citation:** Uskovas, G.; Valinevicius, A.; Zilys, M.; Navikas, D.; Frivaldsky, M.; Prauzek, M.; Konecny, J.; Andriukaitis, D. Driver Cardiovascular Disease Detection Using Seismocardiogram. *Electronics* **2022**, *11*, 484. https://doi.org/ 10.3390/electronics11030484

Academic Editor: Jose Eugenio Naranjo

Received: 15 January 2022 Accepted: 4 February 2022 Published: 7 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Rhythm Society developed recommendations to prevent a person from putting others at risk of harm and at the same time have the opportunity to work according to a society and its culture [1,7,10,11].

Athletes, pilots, drivers, and physicians are among the most prominent professions whose health statuses are regulated by strict legal provisions or directives. Arrhythmia is identified as one of the main problems of heart activity whose duration cannot exceed the prescribed time limits for electrocardiogram (ECG) short and long QT syndromes (SQTS and LQTS, respectively), namely SQRT ≤ 320 ms and LQTS ≥ 470 ms for males and LQTS ≥ 480 ms for females [12–14]. Athletes check their arrhythmic levels before competition and in training during exercise, beyond which they cannot be allowed to participate in competitions [12,15]. The international aviation agency has regulations stating that pilots must check that their arrhythmic levels do not reach certain limits which result in sinus bradycardia (<40 beats per min (bmp)), sinus tachycardia (>100 bpm at rest), or sino-atrial block (>3 s during the day or >4 s at night). Otherwise, they must reschedule flights and contact their doctors. According to the New Standards for Driving and Cardiovascular Diseases [16] and European Commission Directive 2016/1106 [17], the threshold QT interval is LQTS > 500 ms. The European Society of Cardiology (ESC) and the Canadian Cardiovascular Society (CCS) suggest driving restrictions in case of recurrent or unexplained syncope or substantial cardiovascular comorbidities, unless a definitive treatment can be ensured and controlled [18]. Moreover, the Japanese Circulation Society (JCS) describes driving restrictions for private and commercial patients with reflex syncope, which recommend restricting private driving until their symptoms are controlled and for commercial driving unless an effective treatment has been established [19]. The restrictions of activity unite these three risky professions and point out the importance of using a wide range of preventive actions before a sudden event happens. Furthermore, the population over the age of 65 continues to increase globally [20], which implies an increasing average age of active people with a higher risk of cardiovascular disease [14,21] in workplaces, driving vehicles, or traveling by plane. The acceptable risk of harm (RH) estimated by the CCS created a risk estimation formula [22]. Because aging continues, the solutions require finding and using additional diagnostic facilities that prevent sudden cardio events and protect the public from injury [23].

This article deals with the proposal to introduce a preventive diagnosis of heart disease in drivers to avoid critical health conditions that accompany road incidents. Studies of long-distance drivers' working conditions show that a driver's health is exposed to various stressors, both physical and psychological [24,25]. As a result, many working hours are lost, and there is the threat of an accident on the road due to long working hours, weak metabolism, social isolation, low control, and work shifts [14,26]. It enforces the investigations of Canadian, German, Finnish, Swiss, Japanese [14,19], Spanish [27], and Czech Republic roads [28]. Car manufacturers have a wide range of improvements that improve drivers' working conditions and facilitate vehicle management, ergonomic solutions, and the integration of various automatic control, navigation, and alert systems [9,29,30]. Most of these automotive electronic systems are related to car control and diagnostics, but a very small part of the driver and machine interfaces is oriented toward real-time driver health diagnostics, except in sports cars [31–35]. For example, [28] points out the increase in living standards and suggests using various driver response systems which can measure unobtrusively various life functions and health conditions of drivers in order to monitor their alertness.

The most popular measurement of cardiac work is the recording of an electrocardiogram, which has advanced signal processing methods [36,37], but the daily activities of patients are constrained during measurement. Alternatively, the progress of technology allows the detecting of weak cardio signals with a better diagnostic capacity without the requirement of attaching any sensor directly to the patient's skin by using a microelectromechanical system (MEMS) [38,39]. In that case, the recording of a seismocardiogram (SCG) is possible by non-invasive measurement of low-frequency vibrations

from 0 to 50 Hz of cardio cycle mechanics along the timings of corresponding cardiac events [40]. This article analyzes using the seismocardiogram method for warning a driver about possible cardiovascular disease during driving. Additionally, this article is one of the non-multiple types of research dealing with a moving patient [41] without simultaneously measuring the ECG.

The novelty of this article reflects a not yet examined location of the SCG sensor when the MEMS is integrated into the driver's safety belt for measuring the cardio mechanical vibrations. Hence, the SCG signal is affected not only by the already known respiratory and body movement artifacts but also by the movement of the car [28] and the acoustic components [42]. The aim is to obtain a useful SCG signal sufficient for heart rate calculation. Therefore, this signifies that it is necessary to select an appropriate adaptive filter algorithm and optimal settings to rapidly find an AO peak during driving. Consequently, the benchmark of the fourth adaptive digital filters has been examined with different settings.

The rest of the article is organized as follows. Section 2 discusses the related works and use methods, describes the system, and introduces SCG signal processing with adaptive filters. Section 3 presents the experimental data and analysis. Section 4 concludes the work of this paper and briefly introduces future works.

#### **2. Materials and Methods**

#### *2.1. Related Works*

Numerous researchers working on various driver response systems encounter autonomous car system requirements and advance them for safer driving. Technological progress over the past few generations let researchers set up unattached and attached sensor systems inside vehicles [28]. Researchers use galvanic, capacitive, mechanical, optical, or electromagnetic types of contact in different locations for monitoring the vital signs of a driver [43]. Based on this, a few monitoring methods and systems like video motion, a capacitive electrocardiogram, electroencephalogram, balistocardiogram, seismocardiogram, thermography, photoplethysmography, magnetic induction, and radar-based methods are possible [28,43].

An increase in the speed of signal processing allows one to return to the mechanocardiography previously proposed and analyzed by scientists, also known as a balistocardiogram, seismocardiogram, or gyrocardiogram. Unlike the most popular clinical cardiac monitoring methods such as electrocardiogram, ultrasound cardiogram, phonocardiogram, or photoplethysmography, the measurement of mechanocardiogram is oriented toward the recording and analysis of mechanical heart transmitters.

The micromechanical system (sensor), which is micro in five types—metal, semiconductor, ceramics, polymer, and composite—is used for the measurement of these twins. In this case, the greatest attention shall be paid to the characteristics of the accelerometers and their type, of which there are five: piezo resistance, receptacle, tunnel, optical, and piezoelectric [44].

A mechanocardiogram, a balistocardiogram, seismocardiogram, or gyrocardiogram may be recorded at the same time as several parameters of a heart condition, as the transmitter of the heart muscle consists of the atrial, lung, mitral, and triple valves' opening and bruising, the movement of a heart muscle caused by a sinus node, and the propagation of the heart sound. The surveyor may provide a lot of information, thus increasing the sensitivity and resilience of an MEMS to interference, reduced dimensions (0.08 g, 5 mm × 5 mm × 1.6 mm [32] and 3 mm × 3 mm × 1 mm [45]), and lowering energy costs, enabling measurements with various types of smartphones (e.g., portable phones and clocks) or specialized devices. Specialized devices have been developed according to the future anchorage site, such as a chest, a chair, or a bed [46]. The ability to see heart valve tremors in MEMS sensors has led scientists to investigate the possibility of diagnosing early-stage myocardial infarction, heart failure, atrial fibrillation, and other diseases related to cardiac valves.

Previously, the greatest attention was focused on the measurement of the balloon cardiogram when the waves of the surfaces were generated by the rise of blood pressure slopes and during the fall. Attempts have been made to monitor the work of a driver's heart by creating a special occupant evaluating the vibration of the breath at 0.13–0.73 Hz [47] and the vibration of the body due to ballistic forces [45]. Following a further examination of the seismocardiogram, the recording of mechanical fluctuations was caused by the movement of the heart and circulation through the main channels of the upper body. The mathematical model described clearly reflects the mechanical nature of the seismocardiogram signal and the calculation method that evaluates the forces operating in the vascular walls [48]. The balistocardiogram and seismocardiogram signals are not identifiable due to their mechanical twisting nature, although they can be measured with the same accelerometers. In both cases, it is necessary to properly select the location of the suitable sensor, such as the seat suitable for the balloon cardio signal and the limbs when the most suitable place to measure the seismocardiogram signal is to the left of the crude side rather than the chest. The measurement of the seismocardiogram signal shall include all three axes of the acceleration direction to obtain the desired accuracy [49].

The experimental studies described in relevant scientific articles show that the most extensive mechanocardiogram signals are recorded and analysed together with an electrocardiogram to better detect the beginning of systolic time intervals and to combine the observed deviations of the electrocardiogram and the seismocardiogram [50,51]. Such systems are not flexible and are not convenient for home use or during activity because the electrodes attached to the body must be used, which causes discomfort for patients. Therefore, this variant of cardiac disorders is more suitable for clinical use when there is a limited number of patients involved or sitting. One of the greatest advantages of such complementary systems is that it is possible to evaluate the dependency of the heart on the body. Lying on the left side is the riskiest heart disease, as the heart muscle is the most unloaded due to the mechanical pressure of the warrior in the heart [52,53].

Investigators have increasingly tried to perform non-invasive seismocardiogram or gyrocardiogram measurements with a smart phone without measuring the electrocardiogram. To be properly diagnosed, it is necessary to know the result of the systolic time intervals (from 149 ms to 1091 ms when the heartbeat drops from 220 to 30 bpm [54]) and to further analyze the signal obtained by comparing the theoretical classification requirements with the practical ones. One of the first articles on automatic identification of seismocardiogram signals appeared in 2016, with an emphasis on the statistical calculation of systolic time intervals [55]. In accordance with the method proposed in this article in 2017, Finnish scientists have applied the automatic recognition of seizures to the experimental test for atrial fibrillation [45].

The seismocardiogram signal is classified as a nonstationary signal that can lead to sudden abnormalities that indicate possible signs of disease. The analysis and processing of such biosignals is problematic, requiring a range of statistical methods, including fast Fourier transformation, wavelength theory, machine learning, decision tree, and analysis of the related and unrelated attributes [56,57]. In summary, scientists seek to automatically detect the attribute in the biosignal so that large and small data do not accumulate. The choice of methods used for analysis depends heavily on the characteristics of the signal that characterize the frequency, time intervals, number of attributes, operation principle, and purpose of interpretation. The purpose of the interpretation is to understand the real-time alerts, diagnostics, prophylactic monitoring, and planning [56]. The analysis performed is faster and more reliable in advance of a prior signal classification based on the frequency of intervals, frequency, form of attributes, and the distribution of the signal in the separate segments [58,59].

As we see in the literature analysis, measurement of the mechanocardiogram signals is not a new topic and has garnered interest from investigators since recording of the reactionary forces of Gordon's invented organism in 1877, known as a balistocardiogram, but the tests have been reduced due to the simplicity of the application of electrocardiograms and the more accurate diagnosis of cardiac diseases [60]. Thanks to technological progress, micromechanical systems have become smaller and more accurate, so researchers are again encouraged to deal with the development of mechanocardiogram signals and the extension of applications, such as the use of seismocardiogram signals for the nonclinical prevention of cardiac diseases, allowing the first signs of heart problems to be observed [61]. It is a very real topic to solve health problems during human daily activity and not in hospitals by using wearable technologies with the ability to alert users to the presence of health diseases. Moreover, this investigation is a small step in big research which requires creativity, know-how, and a wide range of knowledge from different study fields.

#### *2.2. The System Discription*

The idea of the system described in this article is based on a provision that the measurement of a driver's heart rate is carried out in a non-invasive manner without causing a sense of discomfort, avoiding ethical and private data protection aspects. Additionally, the electronic system must evaluate and predict signal changes in the noisy environment with many uncertainties, such as vibrations and body movements. The main requirement of this system is to prevent the driver from carrying out his direct function to manage the car safely.

The designed system was for the seismocardiogram measurement via an accelerometer, which was integrated in safety belt of the driver. There is a different measuring principle for measuring mechanical displacement of the chest load to that previously described in [3,7], where an accelerometer was integrated in the driver seat for measuring vibration of the back of the chest. The MEMS sensor had very good contact with the body, which was closer to the heart and provided a bigger signal amplitude of the forward chest vibration. The driver could not see the integrated MEMS sensor in the safety belt, and he or she did not have to enter any personal data in order to be in line with the requirements.

A more detailed comparison of the vital sign monitoring methods is summarized in Table 1, and the present research method is included too. The advantages and disadvantages are discussed in Table 1 based on the work in [28,43].

In order to reduce the system's interference and ensure the reliability of the diagnostics, the use of two accelerometers is described. Figure 1 shows the idea for where the accelerometers would be located.

The hardware of this system consists of two MPU9250 accelerometers, an ESP32 WROOM microcontroller, and an SD card reader.

The main accelerometer is integrated in the safety belt and is located at the chest during driving. The location of the second accelerometer is the seat of the driver. This location was chosen with respect to the need to measure the movement of the car and the movement of the driver's body. A second accelerometer was attached to the driver's seat because the chair is directly related to the driver and the weak vibrations of the car and the potholes on the road, which are measured for the acceleration amplitudes.

When measuring the seismocardiogram with an accelerometer, it is important to calculate the resultant of all accelerations of the accelerometer coordinates, because in this case, the most accurate reading of the seismocardiogram was obtained, with the evaluation being as a close to human accelerometer as possible [62]. This way, it was not necessary to accurately record the position of the accelerometer and perform precise corrections of the position in each case. Then, the driver wanted to change the angle of the seat back while driving. Therefore, the drive felt free and could concentrate attention only on the road. Moreover, the system did not require any additional adjustments before or after driving.

**Table 1.** Advantages and disadvantages of vital sign monitoring systems used in vehicles.

**Figure 1.** Driver seismocardiogram measurement system with two accelerometers. (**a**) Driver seismocardiogram measurement system inside car. (**b**) Structure of measurement data collection and processing system.

$$s(t) = \sqrt{s\_{\mathbf{x}}(t)^2 + s\_{\mathbf{y}}(t)^2 + s\_{\mathbf{z}}(t)^2} \tag{1}$$

The total acceleration (Equation (1)) required was calculated on the basis Figure 2 that a better SCG signal could be obtained instead of only the z-axis, as in other previous studies [54,63]. In addition to preparing this signal for processing, normalization of the total acceleration was performed. As a result, the power of the big vibrations reduced and impacted the input signal processing.

2

2 2

�

**Figure 2.** Resultant of three accelerometer coordinates.

This did not disturb the driver during the trip, and this system avoided personal identity regulations. The data were collected on an SD card if needed.

#### *2.3. The Signal Processing*

Adaptive filtering has many advantages and is useful for biomedical applications. Unlike other filters, an adaptive filter can self-adjust the filter coefficients to a rapidly and unpredictably changed signal. The principle of operation of the adaptive filter is defined by Equation (2) [64,65]:

> �

> > =1

$$e(n) = d(n) - y(n) \tag{2}$$

−

The operation of this filter is based on the tendency of the filter output signal *y*(*n*) to correspond as closely as possible to the affected signal *d*(*n*) through the feedback response of the error signal *e*(*n*) to the coefficients H(z) of the filter transfer function so that *e*(*n*) is zero [64,65]: � −

$$\mathbf{y}(n) = \sum\_{k=1}^{L} \mathbf{b}\_{n}(n)\mathbf{x}(n-k) \tag{3}$$

The structure shows in Figure 3 that, based on the fact that the SCG signal is measured in the frequency band of 1–20 Hz, it feeds the signal of the frequency band of interest to the adaptive filter to make the adaptive filter much more efficient and effective [66,67]. For this, a finite impulse response filter (FIR) or an infinite impulse response filter (IIR) can be used. As a result, one accelerometer can be used for measurement. Apart from the fundamental adaptive filter configuration, the frequency band is 5–45 Hz for the adaptive filter with a delay (Figure 4). Additionally, the performance for detecting AO peaks was analyzed with the FIR and IRR filters:

−

=1

**Figure 3.** Fundamental adaptive filter configuration.

**Figure 4.** Adaptive filter configuration.

The adaptive filter modifies the filter coefficients b*n*(*k*) according signal property and they can be calculated with Equation (4) [64]. The estimation accuracy of coefficients b*n*(*k*)

 − � 2

=1

≈

depends to rule how changes the convergence parameter Δ, which lie in the range

depends to rule how changes the convergence parameter ∆, which lie in the range described in Equation (5) [64].

$$\mathbf{b}\_{n}(k) = \mathbf{b}\_{n-1}(k) + \Delta e(n)\mathbf{x}(n-k) \tag{4}$$

$$0 < \Delta < \frac{1}{10LP\_{\text{x}}} \tag{5}$$

where: *L* is length of the FIR filter and *Px* power of the signal in the input. The power is calculated using Equation (6) [64]:

$$P\_\mathbf{x} \approx \frac{1}{N-1} \sum\_{n=1}^{N} \mathbf{x}^2(n) \tag{6}$$

#### **3. Results and Discussion**

The proposed system collects data without additional measurements, except for seismocardiogram detection. The main aim of this system is to monitor the heart rate with one system and, at the same time, avoid disturbing human activity during the driving process.

The duration of the interval between two peaks has a few requirements. The first requirement sets a 410-ms minimal time interval between two peaks. This requirement helps to avoid incorrectly detecting mistakes in the opening moments of the aorta. The second limitation relates to the signal's minimal level, which means that the peak has to be above this minimal level, which is equal to the total signal power. Each adaptive filter has its own minimal level, depending on the signal processing results.

#### **4. Experimental Results**

The data were collected to perform a deeper analysis of the proposed system's performance. Figure 5 shows the signal processing results, where the top diagram indicates the seismocardiogram signal in the input. The second diagram shows the adaptive filter learning processes that calculate the adaptive error. As a result, the third diagram indicates the filtered signal and looks like the driver's seismocardiogram. The fourth diagram shows that the third adaptive filter could recognize patterns of heart beats which represent the aorta opening in the seismocardiogram.

The calculated signal-to-noise ratio of the third adaptive filter (SNR = −8.0627 dB) was negative, indicating huge noise in the environment. Thus, the fourth adaptive filter was analyzed, which first filtrated the SCG signal with the IIR filter (filter order of five) and afterward with an adaptive filter with a delay (order of 5) (Figure 4). In that case, Figure 6 presents three signal processing diagrams of the fourth adaptive filter, showing the adaptation error, signal in the fourth adaptive filter output, and efficiency of the adaptive filter.

Figure 6 shows that the denoised signal y(t) in the output of the fourth adaptive filter was clearer and could mark places with similar form (black ellipse), which repeated but had different amplitudes. As a result, this partly conformed to publications of other authors and enforced the importance of continuing research by creating and developing new methods.

Figure 7 shows how the variation of the signal-to-nose ratios of the adaptive filters depended on the filter order. An interesting fact is that the signal-to-noise ratio was negative. This means that the adaptive filter did not perform as well as the filtration. Therefore, the filtrated signal had noise. This situation is not typical compared with other signals, such as with electrocardiogram measurement.

**Figure 5.** Processing of the driver's seismocardiogram signal in the third adaptive filter with sample delay order of 5 and the FIR filter in the input. (**a**) SCG signal on input. (**b**) Adaptation error of the third adaptive filter. (**c**) Adaptively filtrated signal. (**d**) The efficiency of the adaptative filter.

− On the other hand, the received signal managed to find the peak markings and heart rate calculations, while the signal had a negative value for the SNR in all adaptive filters from the first one to the fourth −7.61 dB, −7.22 dB, −8.06 dB, and −11.32 dB, respectively. The processing benefit of the fourth filter was the smallest (3.39 dB), and the others were about 7 dB.

Figure 8 shows the driver's seismocardiogram with detected peaks and a calculated heart rate of 65 beats per minute.

**Figure 6.** The fourth adaptive filter. (**a**) Adaptation error of the fourth adaptive filter. (**b**) The fourth adaptively filtrated signal. (**c**) Adaptation efficiency.

**Figure 7.** Signal-to-noise rate's dependence on the adaptive filter order.

− − − −

**Figure 8.** Driver seismocardiogram after the fourth adaptive filter and detected peaks.

Additionally, calculations have been performed to better understand the seismocardiogram signal and the behaviour of four different adaptive filter configurations. The settings of the adaptive filters and signal processing data are tabulated in Table 2.

**Table 2.** Adaptive filter processing data.

The data in the table show that increasing the minimum duration limit between pulses decreased the heart rate and coincided with the value of the standard deviation between the detected peaks, which means greater reliability for the measurement. In all cases, the root mean square error (RMSE) of the third adaptive filter was 0.0248 (m/s 2 ), and it was the smallest value compared with the other filters, indicating the stability of the signal peak level of the received signal at this adaptive filter output. The RMSE of the fourth adaptive filter (0.1942 (m/s 2 ) indicates smaller stability of the received signal peaks' levels. Hence, the seismocardiogram signal was not stable.

Figure 9 shows the benchmark of all the adaptive filter autocorrelations and presents that the signals after processing did not change enough from the input signal in the first three adaptive filters. Alternatively, the signal in the fourth was evidently changed from 5 to 20 time lags.

Additionally, the data show that the fourth filter detected 27 fewer peaks than the other 53 peaks. The standard deviation of 635.12 for the peak intervals shows that in these cases, the system did not find any peak.

Measurement of the processing duration was performed, which shows that the typical adaptive filter without delay spent the longest time in processing (57.69 s). As a result, this adaptive filter is not recommended for signal processing of seismocardiograms.

**Figure 9.** Autocorrelations of the filtered seismocardiogram signals.

#### **5. Conclusions**

A system monitoring driver cardiovascular disease has been described. Several advantages and disadvantages of the SCG were described and compared with alternative investigations. The results of the experiments show that the developed system collected sufficient data and could interpret them later using an adaptive filter. As a result, the fourth adaptive filter obtained an estimated HR = 65 beats per min (bpm) in a still-noisy signal (SNR = −11.32 dB). In contrast with the low processing benefit (3.39 dB), 27 AO peaks were detected with a 917.56-ms peak interval mean over 1.11 s, and the calculated root mean square error (RMSE) was 0.1942 m/s 2 when the adaptive filter order was 50 and the adaptation step was equal to 0.933.

Signal processing was performed using four adaptive filter algorithms and compared. The results show that the filtered signal was noisy, and more advanced adaptive filter algorithms and a sensor system with few accelerometers are needed for better signal acquisition.

The proposed system and methodology can be useful and integrated in car safety systems. For better performance, more research and improvements to the system are needed. Furthermore, future works will relate to improving the processing methods by including machine learning with an additional mathematical model of the seismocardiogram.

**Author Contributions:** Conceptualization, G.U., A.V. and D.N.; methodology, D.A., M.Z. and M.P.; software, G.U. and J.K.; validation, D.A., A.V., M.Z., M.P. and M.F.; visualization, G.U., D.N. and A.V.; investigation, D.N., G.U., D.A., A.V. and M.Z.; resources, M.Z., M.F., J.K. and M.P.; data curation, D.N., M.Z. and G.U.; writing—original draft preparation, G.U., A.V., D.A. and M.P.; writing—review and editing, D.N., D.A., J.K. and M.F.; supervision, A.V.; funding acquisition, M.Z. and D.A. All authors have read and agreed to the published version of the manuscript.

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

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

#### **References**


**1. Intelligent control based on fuzzy logic**

#### **1.1 Basic knowledge of fuzzy logic**

A set of things can be distinguished based on binary logic. The essence of the set concept is to classify or divide things according to certain attributes. The whole of all the elements of the research object is called "universe", which is represented by U, also known as "set", "entire domain" or "space". Eigen functions are an important way to represent classical sets. For fuzzy sets and fuzzy concepts, from the

perspective of set theory, the connotation of a concept is the definition of a set, and expansion is all the elements that make up a set. In people's mind, there are many concepts that are not explicitly extended, called fuzzy concepts, such as "high" and "short" to describe height [1].

The general approximation analysis of fuzzy systems is a non-linear mapping from input to output. It consists of multiple "If … Then … " rules. These rules have simple geometric features in the input and output spaces X � Y, and define many fuzzy blocks on the X � Y space. "If X is a fuzzy set A1, then Y is a fuzzy set B1." This rule corresponds to the A1 � B1 Cartesian product of the input and output spaces. The overlapping function f: X ! Y of fuzzy blocks composed of fuzzy rules can be used to approximate the function f: X ! Y with the fuzzy system F: X ! Y.

Summarizing the control behavior of the above-mentioned people is following the basic idea of feedback control. An experienced operator can summarize the principle of manual operation to control the furnace temperature. The accumulated operating experience accumulated over the years can be summarized into several rules, such as "if the furnace temperature is low, add more fuel", etc., to train young operators that he is qualified for the job and gradually replace. Similarly, several control rules summarized from operating experience can be stored in a computer, allowing the computer to imitate human control decision-making behaviors to automatically control the furnace temperature. This is the basic idea of fuzzy control.


**3. Results**

## *3.1. Cathode Compositions*

The ICP-OES and EDX analysis shows that the cathode of the cell contains 64% Ni, 35% Co and 1% Al. Two particle types, a flake shaped layered LiCoO2 particle, and a spherical LiNiO2 particle was identified on the cathode by EDX during the SEM analysis. Based on these two analyses, we conclude that the active cathode material is either an NCA/LiCoO2 mixture, a so-called blend material, or a mixture of LiNiO2 and LiCoO2. As the Al content identified by ICP-OES can also be caused by contamination during the separation of the active cathode material and the aluminum current-collector, both material combinations are possible.

## *3.2. Post-Mortem Analysis*

In this section, we first present the results of the optical PMA of the entire electrode surfaces. Figure 3 shows all electrode and separator sheets. Each number in Figure 3 corresponds to a galvanic element, 23 for the entire cell. Since the cell has been opened fully charged, most anodes are fully lithiated and, therefore, gold. As a first peculiarity in Figure 3, it is noticeable that in contrast to all other anodes, anode 1.8 and 1.12 is not fully lithiated. In the middle of the anode and cathode sheets, the pressure point of the sphere is visible. Abnormality of color can find in and around the pressure point at the anode, where a silver ring is visible. According to Cannarella et al. [4], this ring suggests the deposition of metallic lithium. This empirical observation matches the model of lithium deposition during cell charging of Tang et al. [8]. In the following, we will discuss all peculiarities of the consequence of inhomogeneous pressure on the cell stack.

Figure 4 shows three 1.8, 1.12, and 2.1 galvanic elements where the top cathode corresponds to the bottom anode. These galvanic elements are optically very different from the others in the cell after the experiment. Only the pressure point in the middle had been active Figure 4b,d,f, recognizable by the silver-colored ring in the middle of the anode as has been observed for all anodes. Also, the four dots, where the separator sticks together with the electrodes, in the corners are lithiated Figure 4b,d recognizable on the gold color around the dots. As a result of this, these dots stick on both electrodes and are therefore still active compared to the rest of the electrode. During the disassembly of the stack, no peculiarity regarding the contact of these anodes to the cathode was noticeable. A possible reason for the lack of lithiation could be that the pressure of the sphere caused the cell stack to bend so that the electrochemical contact of this electrode pair was lost during the cycling. Especially the points in Figure 4d, show a wave structure in the lithiation between the dots. Such wave structures can be created by bending and holding on to several points, so that the separator is folded in this region.

**Figure 3.** Optical post-mortem analysis after cell opening (**a**) stack 1 top (**b**) stack 2 bottom.

The galvanic element 1.12 in Figure 4c,d, which consists of an anode from stack one and a cathode from stack 2, also shows a very inhomogeneous lithium distribution around the pressure point. A possible reason for this is the transition from stack 1 to stack 2. The center of the anode 1.12 is lithiated more than the edge areas, which can be explained by the higher contact pressure in the center. In stack 2, all layers are lithiated similarly, and the pressure points have a similar appearance as well. The anodes of stack two elements 2.1 to 2.3 show an additional silver-colored diagonal line within the silver-colored ring in Figure 4f, which is very pronounced at 2.1 and almost entirely decreases up to 2.3. Also, the pressure point on the separator of galvanic element 2.1 is much darker than the other pressure point of the separator. For cathode layers 1.8 and 1.12 in Figure 4a,c, a silver-colored circle is also visible within the pressure area. The authors have no explanation for this coloration, and a change in color due to lithiation of the cathode is unknown in the literature, and the ICP-OES analysis shows no abnormality.

**Figure 4.** Optical PMA (**a**,**d**) galvanic element 1.8 (**b**,**e**) galvanic element 1.12 (**c**,**f**) galvanic element 2.1.

Figure 5 shows an example of an anode, cathode, and separator. The anode has a length of 60 mm and a width of 83.5 mm; compared to the cathode in Figure 5b, the anode is 1 mm bigger in all dimensions. Therefore, the edge of the anode becomes electrochemically inactive and does not participate in the charging and discharging process. For this reason, there is a black edge here called anode overhang. This anode overhang of the negative electrode beyond the edge of the additional positive capacitance and prevents lithium plating from occurring before the cutoff potential is reached [8].

The pressure point on the anode Figure 5a shows the anode with different color tones that corresponds to a different state of charges, and a silver-colored ring with the inner radius *Rinner* and the thickness of 1.8 mm, which indicates where the high ion flux starts and leads to lithium plating. This high current density rapidly builds a lithium metal layer on the particle surface. In this region, the anode voltage vs. Li/Li+ decreases to negative values, and metallic lithium deposited on the anode particles. Inside of this outer border region, a second ring with a very dark electrode surface has formed. As the dark color corresponds to a low state of charge, this area seems to be less active. Within the second ring, there is also a gold shimmer. One possible explanation is that the inner area was still active during cycling due to very inhomogeneous local pressure in this area. The non-transparent separator in this area supports this possibility. Nevertheless, the diffusion of lithium from active electrode parts outside the pressure area into the pressure area cannot be excluded. The initial cell voltage was 3.55 V, which corresponds to a SOC of 22%, where the graphite anode has not yet changed color [18]. Therefore, this area was lithiated during cell cycling.

The pressure point at the separator in Figure 5c has the same radius *Rout* as *Rinner*, which indicates the beginning of a transition zone of weaker pore closure, where a very high ion current density occurs. The intact separator outside the pressure area has a white color, and in the innermost edge of the pressure area, the separator becomes transparent. This transparent separator appears dark in the pictures presented here as the background behind the separator was black when the pictures made. The transparent separator indicates a pore closure as shown by Cannarella et al. [4]. Furthermore, in the pressure area, the separator becomes brighter again, where it assumed that the pores have not completely closed and that a part can still be active.

**Figure 5.** Exemplary layers of (**a**) Anode (**b**) Cathode (**c**) Separator.

#### *3.3. Scanning Electron Microscope Analysis*

To get a deeper understanding of the influence of pressure on active and passive materials on a microscopic level, the two cells were analyzed by SEM. The effects of homogeneously pressed dried electrodes compared to the effects of heterogeneously pressed cyclical cells should give an idea of how strong the effects could be. Since the pressure was adjusted so that the active materials are not damaged according to [15,16] and the experiments on the single electrode sheets, the particles should occur without damage. Considering the applied pressure is above 10 MPa, an irreversible pore closure

should occur in the [2]. Before the SEM analysis, the separator was sputtered with silver atoms to prevent static charging during measurement. For the cathode and anode, this is not necessarily due to their electrical conductivity. Figure 6 shows an SEM image of the separator in the area below the sphere, direct at the boundary, and in an area without applied pressure area. In Figure 6b,c, the open pores of the separator are visible, here the separator is fully intact. Due to the uniform lithiation in this area, it is assumed that the ion current density had to be homogeneously distributed in this area. Figure 6d,e shows a region with closed pores and only a small region with open pores as marked red in Figure 6e. This mixed region of open and closed pores confirms the observation of the PMA that the region inside the silver ring with the gold shimmer was at a higher state of charge compared to the initial state. In the dark region at the boundary of the pressure area, the pores fully closed as shown in Figure 6f,g. This dark region of fully closed pores proves that the pressure was strong enough to close the pores, which results in a very uneven ion current density distribution so that locally very high ion current densities occur. This heterogeneous pore structure leads to an uneven current distribution of the electrochemical system. According to Tang et al., this is strongly influenced by the balance of ohmic resistance from the electrolyte and kinetic resistance at the electrode interface, which is influenced by diffusion in the graphite. Uneven current distributions influence the possibility of lithium plating because of the generation of overpotential [8]. This analysis coincides with the observation in the PMA, where lithium deposition was observed in the form of a silver ring in all anodes.

**Figure 6.** SEM image from separator (**a**) General view (**b**,**c**) unpressed region (**d**,**e**) pressed region in the center under the sphere (**f**,**g**) edge of the pressed region under the sphere.

Cannarella et al. builds a model of separator pore closure within a LIB based on Tang et al. [4,8]. The results of the model match with the observations of our experiment. The simulation from Cannarella et al. predicts a ring-shaped shape of the current density distribution around the pore-closed area on the negative electrode surface. Furthermore, by including the lithium plating kinetics and varying the corresponding plating exchange current density, the model calculates that the local potential of the anode vs. Li/Li+ will be negative around the pore-closed area so that the deposition of lithium will occur. This negative potential of the anode leads to lithium plating on the anode. Lithium plating is likely to lead to increased localized mechanical stress as this deposition can lead to measurable changes in anode thickness [20]. This local increase in thickness causes additional mechanical stress and could lead to further deformation of the components in the cell. Even if the simulation of Canarella et al. [4] is only a rough estimate of the real phenomena since the kinetics of lithium plating on graphite electrodes is still poorly understood. This work here and the simulation complement each other very well and jointly support the theory and understanding of the kinetics of lithium plating.

Previous publications of Cannarella et al., Lui et al. and Peabody et al. [2,4,5] mainly investigated the influence of defects in the separator, a question that remains to be clarified: What influence does the pressure have on the active materials in a commercial cell under cycling conditions? Figure 7 shows an SEM image of the cathode. The SEM image in the pressure region Figure 7b–d shows that the pressure was so high that the particles were strongly deformed that contradicts the pressure data of Tran et al. [16] and our experiments conducted on the uncycled single electrode sheets. In contrast, in these analyses, the active material is soaked with electrolyte and (de)lithiated during the application of pressure, which induces additional higher pressure on the particle due to the volume expansion of the graphite. Therefore, it assumed that both effects could have strongly influenced on the elastic properties of the particles. Figure 7c shows that the most NCA particles crushed and the LiCoO2 flake in Figure 7d decompose in the direction of their layer structure. These cracks expose new surfaces to the electrolyte and enables an electrochemical degradation of the electrolyte, which introduced at the particle–electrolyte interface, the so-called cathode–electrolyte interface (CEI) [21]. The analogous process, as with graphite anodes, can cause lithium loss and gas formation.

**Figure 7.** SEM image of cathode (**a**) general view of the cathode (**b**–**d**) pressed region.

In the SEM analysis of the anode, two regions in Figure 8a analyzed, as these differ significantly from their morphology and structure. In Figure 8b the border of the two regions is marked with a green line. The images of the anode in the pressure region in Figure 8f–h show that the contrast of the image deteriorates due to electrostatic charging of the sample during the SEM. It seems as if a new insulating layer has formed here, since the samples are electrostatically charged during the SEM. The influence on the electrochemical kinetic could come from squeezing the electrolyte partly away into the peripheral area. From this analysis, it cannot be determined whether the mechanical load deformed the graphite flakes. A possible cause for the insulating covering layer could be the growth of the solid–electrolyte interface (SEI). This layer can grow when new particle surfaces created by particle cracking exposed to the electrolyte. Microscopic analysis of the silver ring in Figure 8c–e shows a fine structure compared to the two previously discussed regions. This microstructure could be lithium dendrites or reaction products with deposited lithium. Unfortunately, metallic lithium cannot be determined with EDX and remains unexplained for the time being.

**Figure 8.** SEM image of the anode (**a**) overview (**b**) boundary of the silver ring to the pressure area (**c**–**e**) internal pressed region (**f**–**h**) transition zone (silver ring).

An uncycled cell was analyzed microscopically using SEM to compare pristine active materials with pressed active materials from the cycled cell. Furthermore, a dried single electrode layer of anode and cathode was put under a homogeneous pressure of 30 MPa, which is higher as the pressure in the experiment with the sphere, in order to compare dried pressed electrodes with soaked, cycled electrodes from the same LIB type. In Figure 9a–c, the particles of the pristine lithiated cathode are depicted. Figure 9d–f depicts the pressed lithiated cathode, which is like the pristine cathode. No deformed or crushed particles were observed. Also, the anode in Figure 10 shows no significant change under a homogeneous pressure of 30 MPa.

**Figure 9.** SEM image of a uncycled cell (**a**–**c**) overview of a pristine lithiated cathode (**d**–**f**) pressed area of a single lithiated cathode layer with 30 MPa.

**Figure 10.** SEM image of a uncycled cell (**a**–**c**) overview of a pristine delithiated anode (**d**–**f**) pressed area of a single delithiated anode layer with 30 MPa.


**5. Conclusions**

A new landslide catalogue was prepared for Wayanad district, Kerala, India, compiling information from different data sources. The catalogue consists of landslide events that happened from 2010 to 2018 in the district. With the available data, rainfall events associated with each landslide was identified using the data from four rain gauges located at different places in the district.

The catalogue was used to determine intensity–duration thresholds on regional and local scales, which is the first attempt of its kind for the study area. Four different approaches were adopted in the study to develop intensity–duration thresholds by varying the selection of rain gauge, area considered and definition of rainfall parameters. After the analysis, it can be concluded that on a regional scale, selecting the rain gauge based on maximum average intensity (Imax) performs better than choosing the nearest rain gauge. Four separate thresholds for each polygon considered are also proposed in this study.

On a regional scale, with 5% exceedance probability, a rainfall of intensity 1mm h−1 of a one-day duration is potent enough to trigger landslides in Wayanad district. It is also observed that Mananthavady and Vythiri polygons are more susceptible to landslides than the other two regions. The intensity of rainfall of a one-day duration which can possibly trigger a landslide in the Mananthavady, Vythiri, Ambalavayal and Kuppady polygons are 0.97 mm h−1, 0.82 mm h−1, 1.26 mm h−1 and 0.75 mm h−1 respectively. The Ambalavayal polygon can be considered as a relatively less vulnerable region with a lower number of landslide events and higher threshold values.

The study emphasizes the importance of the preparation of landslide catalogues and determination of rainfall thresholds for Wayanad region. An effective LEWS is an immediate requirement in the region, and the study has to be further enhanced with state-of-the-art models developed for other parts in the world. The existing model can also be conceptually modified using precise field monitoring techniques as well. Attempts must be made to reduce the false alarms to develop an operational rainfall threshold model to function as a Landslide Early Warning System for the region.

**Author Contributions:** Conceptualization, M.T.A., S.S. and N.S.; methodology, M.T.A. and S.S.; data curation, M.T.A.; writing—original draft preparation, M.T.A. and A.R.; writing—review and editing, S.S., B.P. and N.S.; supervision, N.S. and B.P; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** Florence University, in the framework of the project SAMUELESEGONIRICATEN20.

**Acknowledgments:** The authors express their sincere gratitude to Geological Survey of India, Kerala SU, District Soil Conservation Office Wayanad and Kerala State Disaster Management Authority (KSDMA) for their support throughout the study.

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