bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Method of Wheelset Bearing Fault Diagnosis
Authors: Sun Runtao , Yang Jianwei , Yao Dechen , Wang Jinhai
Published: September 2022

During the movement of rail trains, trains are often subjected to harsh operating conditions such as variable speed and heavy loads. It is therefore vital to find a solution for the issue of rolling bearing malfunction diagnostics in such circumstances. This study proposes an adaptive technique for defect identification based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and Ramanujan subspace decomposition. MOMEDA optimally filters the signal and enhances the shock component corresponding to the defect, after which the signal is automatically decomposed into a sequence of signal components using Ramanujan subspace decomposition. The method’s benefit stems from the flawless integration of the two methods and the addition of the adaptable module. It addresses the issues that the conventional signal decomposition and subspace decomposition methods have with redundant parts and significant inaccuracies in fault feature extraction for the vibration signals under loud noise. Finally, it is evaluated through simulation and experimentation in comparison to the current widely used signal decomposition techniques. According to the findings of the envelope spectrum analysis, the novel technique can precisely extract the composite flaws that are present in the bearing, even when there is significant noise interference. Additionally, the signal-to-noise ratio (SNR) and fault defect index were introduced to quantitatively demonstrate the novel method’s denoising and potent fault extraction capabilities, respectively. The approach works well for identifying bearing faults in train wheelsets.
Fuzzy Reasoning Numerical Spiking Neural P Systems for Induction Motor Fault Diagnosis
Authors: Yin Xiu , Liu Xiyu , Sun Minghe , Dong Jianping , Zhang Gexiang
Published: September 2022

The fuzzy reasoning numerical spiking neural P systems (FRNSN P systems) are proposed by introducing the interval-valued triangular fuzzy numbers into the numerical spiking neural P systems (NSN P systems). The NSN P systems were applied to the SAT problem and the FRNSN P systems were applied to induction motor fault diagnosis. The FRNSN P system can easily model fuzzy production rules for motor faults and perform fuzzy reasoning. To perform the inference process, a FRNSN P reasoning algorithm was designed. During inference, the interval-valued triangular fuzzy numbers were used to characterize the incomplete and uncertain motor fault information. The relative preference relationship was used to estimate the severity of various faults, so as to warn and repair the motors in time when minor faults occur. The results of the case studies showed that the FRNSN P reasoning algorithm can successfully diagnose single and multiple induction motor faults and has certain advantages over other existing methods.
The Relationship between the Quality of Kindergartens’ Outdoor Physical Environment and Preschoolers’ Social Functioning
Authors: Moreira Mariana , Cordovil Rita , Lopes Frederico , Da Silva Brenda M. S. , Veiga Guida
Published: September 2022

The ability to initiate and engage in relationships is a critical landmark and predictor of children’s development and well-being. In kindergarten, children exhibit greater social participation outdoors rather than indoors. Indeed, the physical environment influences preschoolers’ social proximity. In this study, we examine the relationship between the quality of kindergartens’ outdoor physical environment and preschoolers’ social functioning. Two kindergartens in Gondomar, Portugal, were selected to participate according to different levels of their physical environment outdoors (poor and fair quality) and measured by a specific physical environment rating scale. Twenty-six children (aged 3–6, 10 boys) participated in this study. Children’s social proximity at the playground was measured through Radio Frequency Identification Devices (RFID). Mann–Whitney statistical tests were used to compare social proximity between groups. Our results showed that in the higher quality outdoor area, children spent less time alone and more time in social proximity with their peers in smaller groups (one or two children). More time was also spent in social proximity with different genders. Our study emphasizes the critical importance of reviewing kindergartens’ outdoor physical environments to support preschoolers’ social needs in a more challenging and diverse setting.
Digital Image Blending Using Inaccurate Addition
Authors: Balasubramanian Padmanabhan , Nayar Raunaq , Maskell Douglas L.
Published: September 2022

Inaccurate computing is found to be a high-speed, low-power and energy-efficient alternative to accurate computing for error-tolerant applications. In this context, this paper analyzes the usefulness of inaccurate computing for a digital image processing application, viz. digital image blending, which has been less explored. We analyze the use of inaccurate addition for image blending used in applications such as photo editing and computer graphics. For experimentation, we considered blending two digital images using accurate and inaccurate addition separately. We considered many inaccurate addition architectures which are suitable for implementation in both FPGA and ASIC design environments to perform a comparative analysis. We found that an inaccurate addition with an optimum inaccuracy produces a similar quality of blended image as obtained using accurate addition. The quality of blended images is quantified using standard metrics such as the peak signal-to-noise ratio and the structural similarity index measure. In particular, an inaccurate adder, M-HERLOA, was found to be preferable for image blending from the combined perspectives of quality of blended image, error metrics and design metrics. We implemented the accurate and inaccurate adders corresponding to an optimum inaccuracy in FPGA and ASIC design environments. We considered a Xilinx Artix-7 FPGA for an FPGA-based implementation and a 32/28 nm CMOS standard digital cell library for an ASIC type standard cell-based implementation. The results show that, for an FPGA-based implementation, M-HERLOA enables a reduction in delay by 13%, requires 50% fewer LUTs and 49% fewer registers and consumes 24.3% less on-chip power compared to the accurate high-speed FPGA adder while yielding a blended image of good quality. For an ASIC type standard cell-based implementation, M-HERLOA enables a reduction in delay by 64.6% (24.4%), requires 43% (82.4%), less area and dissipates 63% (67.8%) less power than the accurate carry ripple adder (carry look-ahead adder).
Nonlinear Dynamic System Identification in the Spectral Domain Using Particle-Bernstein Polynomials
Authors: Alessandrini Michele , Falaschetti Laura , Biagetti Giorgio , Crippa Paolo , Turchetti Claudio
Published: September 2022

System identification (SI) is the discipline of inferring mathematical models from unknown dynamic systems using the input/output observations of such systems with or without prior knowledge of some of the system parameters. Many valid algorithms are available in the literature, including Volterra series expansion, Hammerstein–Wiener models, nonlinear auto-regressive moving average model with exogenous inputs (NARMAX) and its derivatives (NARX, NARMA). Different nonlinear estimators can be used for those algorithms, such as polynomials, neural networks or wavelet networks. This paper uses a different approach, named particle-Bernstein polynomials, as an estimator for SI. Moreover, unlike the mentioned algorithms, this approach does not operate in the time domain but rather in the spectral components of the signals through the use of the discrete Karhunen–Loève transform (DKLT). Some experiments are performed to validate this approach using a publicly available dataset based on ground vibration tests recorded from a real F-16 aircraft. The experiments show better results when compared with some of the traditional algorithms, especially for large, heterogeneous datasets such as the one used. In particular, the absolute error obtained with the prosed method is 63% smaller with respect to NARX and from 42% to 62% smaller with respect to various artificial neural network-based approaches.
Improving Pneumonia Classification and Lesion Detection Using Spatial Attention Superposition and Multilayer Feature Fusion
Authors: Li Kang , Zheng Fengbo , Wu Panpan , Wang Qiuyuan , Liang Gongbo , Jiang Lifen
Published: September 2022

Pneumonia is a severe inflammation of the lung that could cause serious complications. Chest X-rays (CXRs) are commonly used to make a diagnosis of pneumonia. In this paper, we propose a deep-learning-based method with spatial attention superposition (SAS) and multilayer feature fusion (MFF) to facilitate pneumonia diagnosis based on CXRs. Specifically, an SAS module, which takes advantage of the channel and spatial attention mechanisms, was designed to identify intrinsic imaging features of pneumonia-related lesions and their locations, and an MFF module was designed to harmonize disparate features from different channels and emphasize important information. These two modules were concatenated to extract critical image features serving as the basis for pneumonia diagnosis. We further embedded the proposed modules into a baseline neural network and developed a model called SAS-MFF-YOLO to diagnose pneumonia. To validate the effectiveness of our model, extensive experiments were conducted on two CXR datasets provided by the Radiological Society of North America (RSNA) and the AI Research Institute. SAS-MFF-YOLO achieved a precision of 88.1%, a recall of 98.2% for pneumonia classification and an AP50 of 99% for lesion detection on the AI Research Institute dataset. The visualization of intermediate feature maps showed that our method could facilitate uncovering pneumonia-related lesions in CXRs. Our results demonstrated that our approach could be used to enhance the performance of the overall pneumonia detection on CXR imaging.
Crowd Anomaly Detection in Video Frames Using Fine-Tuned AlexNet Model
Authors: Khan Arfat Ahmad , Nauman Muhammad Asif , Shoaib Muhammad , Jahangir Rashid , Alroobaea Roobaea , Alsafyani Majed , Binmahfoudh Ahmed , Wechtaisong Chitapong
Published: September 2022

This study proposed an AlexNet-based crowd anomaly detection model in the video (image frames). The proposed model was comprised of four convolution layers (CLs) and three Fully Connected layers (FC). The Rectified Linear Unit (ReLU) was used as an activation function, and weights were adjusted through the backpropagation process. The first two CLs are followed by max-pool layer and batch normalization. The CLs produced features that are utilized to detect the anomaly in the image frame. The proposed model was evaluated using two parameters—Area Under the Curve (AUC) using Receiver Operator Characteristic (ROC) curve and overall accuracy. Three benchmark datasets comprised of numerous video frames with various abnormal and normal actions were used to evaluate the performance. Experimental results revealed that the proposed model outperformed other baseline studies on all three datasets and achieved 98% AUC using the ROC curve. Moreover, the proposed model achieved 95.6%, 98%, and 97% AUC on the CUHK Avenue, UCSD Ped-1, and UCSD Ped-2 datasets, respectively.
Advances in Explainable Artificial Intelligence and Edge Computing Applications
Authors: Corchado Juan M. , Ossowski Sascha , Rodríguez-González Sara , De la Prieta Fernando
Published: September 2022

Artificial Intelligence (AI) and its applications have undergone remarkable experimental development in the last decade and are now the basis for a large number of decision support systems [...]
Contemporary Study on Deep Neural Networks to Diagnose COVID-19 Using Digital Posteroanterior X-ray Images
Authors: Akbar Saad , Tariq Humera , Fahad Muhammad , Ahmed Ghufran , Syed Hassan Jamil
Published: September 2022

COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image classification during the last decade. This paper aims to examine the contributions of machine learning for the detection of COVID-19 using Deep Learning and explores the overall application of convolutional neural networks of some famous state-of-the-art deep learning pre-trained models. In this research, our objective is to explore the various image classification strategies for CXIs and the application of deep learning models for optimization and feature selection. The study presented in this article shows that the accuracy of deep learning models when detecting COVID-19 on the basis of chest X-ray images ranges from 93 percent to above 99 percent.
Sustainable Savings Applied to Operating Room Ventilation at Hospitals Located in Different Climatic Zones, through Control and Regulation Strategies
Authors: Castellanos-Antolín A. , Rey-Martínez F. J. , San José-Gallego L. J. , Arroyo-Gómez Y. , Rey-Hernández J. M. , San José-Alonso J. F.
Published: September 2022

In hospitals, operating rooms are energy-intensive spaces, due to the high flow of outside air required to achieve the necessary indoor air quality. Operating rooms demand ventilation continuously, despite periods of low daily surgical activity. However, by controlling ventilation during inactive periods in the operating room, significant energy savings can be achieved, avoiding penalties on IAQ levels. This paper evaluates the energy savings achieved by introducing ventilation flow control and regulation systems in operating rooms of hospitals located in different climatic zones within Spain. In addition, emissions and economic savings have also been evaluated. Two control and regulation strategies of the air flow to be supplied and extracted in the operating rooms, during periods of inactivity, are studied and include regulation by schedule and regulation by occupancy. Data from a 900-bed university hospital center are used as a reference to evaluate the average occupancy of the operating rooms, and the energy consumption, thus validating the model for calculating the demand of a typical operating room. The energy savings for the regulation by occupancy are 37.5%, and the regulation by schedule are 40% of the annual demand, with respect to an operating room working permanently.
Integration of Back-Up Heaters in Retrofit Heat Pump Systems: Which to Choose, Where to Place, and How to Control?
Authors: Wüllhorst Fabian , Vering Christian , Maier Laura , Müller Dirk
Published: September 2022

Back-up heaters are essential for sustainable retrofit heat pump systems to achieve low capital costs and high system temperatures. Despite its importance, current literature focuses primarily on single aspects of the interaction between the back-up heater and the heat pump system. Furthermore, influences of varying scenarios are typically not considered. This paper simultaneously investigates the impact of 18 different scenarios on the optimal answer to the questions: Which back-up heater to choose, where to place it, and how to control it? A scenario consists of boundary conditions for weather, building envelope, radiator sizing, operational envelope, and the electricity-to-gas price/emission ratio, respectively. Using annual dynamic Modelica simulations, we evaluate and assess all interdependencies based on a full factorial design. We analyze final energy consumption, thermal comfort, and back-up heater as objectives. For gas-fired back-up heaters, the optimal placement and control align with current state-of-the-art recommendations. However, for electric back-up heaters, current guideline recommendations yield up to 30% higher operational costs and emissions compared to our findings. Consequently, future studies should develop optimal design rules for sustainable retrofit heat pump systems.
Seismic Response Sensitivity Analysis of the Raft Foundation NPP Reactor Building under Different Site Conditions
Authors: Li Liang , Pan Rong , Lu Yu
Published: September 2022

The seismic sensors used for automatic seismic trip systems (ASTS) of nuclear power plants (NPPs) are mainly arranged on the raft foundation of reactor buildings; however, the arrangement principle of seismic sensors is not clear. In order to analyze the seismic response sensitivity of the raft foundation of reactor building in NPPs, this paper carried out the seismic response sensitivity research at seven positions on the raft foundation of a reactor building under three site conditions, with two sets of time histories as input. The results show that (1) the peak acceleration of the outer ring point of the raft foundation was the largest, and the peak acceleration of the center point was the smallest; (2) the peak value of the floor response spectrum at the outer ring point of the raft foundation was the largest, and the peak value at the center point was the smallest. In an earthquake, the outer ring point of the raft foundation of the reactor building was the most sensitive. It was suggested that the seismic sensors used for ASTS should be arranged at the outer ring of the raft foundation NPP reactor building, and the shutdown threshold shall be calculated according to site conditions and structural characteristics.
Time vs. Capacity—The Potential of Optimal Charging Stop Strategies for Battery Electric Trucks
Authors: Zähringer Maximilian , Wolff Sebastian , Schneider Jakob , Balke Georg , Lienkamp Markus
Published: September 2022

The decarbonization of the transport sector, and thus of road-based transport logistics, through electrification, is essential to achieve European climate targets. Battery electric trucks offer the greatest well-to-wheel potential for CO2 saving. At the same time, however, they are subject to restrictions due to charging events because of their limited range compared to conventional trucks. These restrictions can be kept to a minimum through optimal charging stop strategies. In this paper, we quantify these restrictions and show the potential of optimal strategies. The modeling of an optimal charging stop strategy is described mathematically as an optimization problem and solved by a genetic algorithm. The results show that in the case of long-distance transport using trucks with battery capacities lower than 750 kWh, a time loss is to be expected. However, this can be kept below 20 min for most battery capacities by optimal charging stops and sufficient charging infrastructure.
Multiple Initial Point Approach to Solving Power Flows for Monte Carlo Studies
Authors: Schipper Josh , McNab Sharee , Kueh Yuyin , Mukhedkar Radnya
Published: September 2022

Power flow solvers typically start from an initial point of power injection. This paper constructs a system of multiple initial points (SMIP) to enable selection of an appropriate initial point, with the objective to achieve a balanced improvement in the solution speed and accuracy, for problems with a large number of power flows. The intent is to recover time cost of forming the SMIP through the improvements to each power flow. The SMIP is tested on a time series based Monte Carlo study of Electric Vehicle (EV) hosting capacity in a low voltage distribution network, which has 5.4 million power flows. SMIP is applied to two power flow solvers: a Taylor series approximation and a Z-bus method. The accuracy of the quadratic Taylor series approximation was improved by a factor of 30 with a 27% increase in the solve time when compared against a single no-load initial point. A Z-bus solver with SMIP, limited to two iterations, gave the best performance for the EV hosting capacity case study.
Non-Renewable and Renewable Energies, and COVID-19 Pandemic: Do They Matter for China’s Environmental Sustainability?
Authors: He Yugang , Zhang Ziqian
Published: September 2022

Since the emergence of the COVID-19 pandemic, people all around the globe have seen its effects, including city closures, travel restrictions, and stringent security measures. However, the effects of the COVID-19 pandemic extend beyond people’s everyday lives. It impacts the air, water, soil, and carbon emissions as well. This article examines the effect of energy and the COVID-19 pandemic on China’s carbon dioxide emissions in light of the aforementioned context, using the daily data from 20 January 2020 and ending on 20 April 2022. Using the nonlinear autoregressive distributed lag model for empirical analysis, the findings indicate that COVID-19 pandemic confirmed cases and renewable energy advance environmental sustainability due to their negative effects on carbon dioxide emissions, whereas fossil fuel energy hinders environmental sustainability due to its positive effect on carbon dioxide emissions. Moreover, these results are also supported by the results of the frequency domain causality test and the Markow switching regression. In light of these results, there are several policy implications, such as vaccination, renewable energy utilization, and non-renewable energy alternative policies, which have been proposed in this paper.
Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix
Authors: de Oliveira Layon Mescolin , da Silva Junior Ivo Chaves , Abritta Ramon
Published: September 2022

Given the combinatorial explosion related to the operation decisions in the thermal unit commitment problem, this paper presents a new strategy to reduce the search space and to start the multi-modal optimization process. To achieve such goals, a relevance matrix is obtained to indicate how important each generating unit is at each hour of the operational planning. This matrix is generated through the results of a constructive heuristic based on sensitivity indexes that account for operational and economic characteristics of the generating units and of the system under planning. The proposed method is shown to reduce the complexity of the problem, thus decreasing the combinatorial explosion and, consequently, the computational burden. Its effectiveness is verified by performing optimizations with and without its utilization. The results achieved with the proposed space-reduction approach enable solutions that present good quality. Furthermore, these solutions are retrieved with significantly reduced processing time.
An Analytical Model for Lithium Storage in Spherical Fullerenes
Authors: Alshehri Mansoor H.
Published: September 2022

In this paper, the encapsulation of lithium atoms in spherical fullerenes of varying sizes is investigated. The 6–12 Lennard–Jones potential function and the continuum approximation, in which it is assumed that the atoms can be replaced with a uniform distribution across the surface of the molecules, are exploited to model the interaction energies between lithium atoms and spherical fullerenes. Thus, the total interaction energies can be approximated by applying surface integrations. The results show that for a lithium atom interacting inside a spherical fullerene, the interaction energies are minimized at a position that approaches the fullerene wall as the size of the fullerene increases. However, the results show that an external force would need to be applied to a lithium atom in order to overcome the repulsive energy barrier so that it can be encapsulated in CN fullerenes with a radius of less than 2 Å. The present study indicates that the optimal radius that gives the minimum energy for the storage of Li inside CN fullerenes occurs for a fullerene with a radius of ≈2.4 Å. Overall, this study provides an analytical formulation that may facilitate rapid computational results, and an application of this work is in the design of future high-energy-density batteries that utilize CN fullerenes.
Effect of Microwave Pre-Treatment of Biomass on the Thermal Oxidative Conversion of Biomass Blends Containing Pre-Treated and Raw Biomass of Different Origination in Terms of Processing Rate and Heat Production
Authors: Arshanitsa Alexandr , Jashina Lilija , Pals Matiss , Valdmanis Raimonds , Zake Maja
Published: September 2022

In this paper, the thermochemical conversion of selectively pre-treated binary blends composed of 30% microwave (MW) pre-treated biomass pellets (wheat straw, wood) and 70% of different origin raw biomass pellets (wheat straw, wood, or peat) was performed using originally developed experimental device with a heat output up to 20 kW. Biomass pellets were pre-treated at temperatures of 200 and 275 °C using a microwave laboratory scale torrefaction of the original construction. It was established that synergistic effects of interaction between the components of binary blends promote an increase in the efficiency of their thermochemical conversion by 5.8–9.4%. It was assumed that synergistic effects of thermochemical conversion can be related to an increase in surface area and porosity of pre-treated pellets. With a focus on different steps and rates of biomass degradation and heat amounts released for different types of blends, the thermal analysis (DTG/DSC) of ground blends in the temperature range of 25–650 °C was performed in air media with the heating rate of 10 °C/min. According to the thermal analysis data, the heat energy yield of ground blends meets the requirements of linear regression based on the additivity principle and the development of synergistic effects during thermochemical conversion of ground blends was not detected.
Tri-Reforming of Methane: Thermodynamics, Operating Conditions, Reactor Technology and Efficiency Evaluation—A Review
Authors: Soleimani Samira , Lehner Markus
Published: September 2022

The production of syngas with optimal energy usage, a minimal environmental impact, and an adjustable H2/CO molar ratio is possible using tri-reforming of methane (TRM). Despite the number of studies dedicated to the TRM process, this process is still in its infancy, with many technical obstacles to overcome. Except for its kinetics and catalysts, which have been reviewed elsewhere, the TRM process is evaluated thoroughly in this work. First, feasibility studies of TRM and the TRM process are presented. Second, the impacts of various operating conditions on the rate of gas conversions, syngas production, and coke formation are discussed. Third, different reactor configurations are compared. This review then goes through the energy and energetic efficiency, economic, environmental, and safety aspects of the TRM process. Finally, a research path for the future is suggested.
ENECO2Calc—A Modeling Tool for the Investigation of Energy Transition Paths toward Climate Neutrality within Municipalities
Authors: Hammerschmid Martin , Konrad Johannes , Werner Andreas , Popov Tom , Müller Stefan
Published: September 2022

The paper focuses on developing an energy-modeling tool called ENECO2Calc, which allows the determination of current ecologic and economic footprints based on calculating the final energy demand within several sectors for municipalities. Furthermore, different energy transition paths until 2050 can be investigated and compared to the business-as-usual reference scenario. ENECO2Calc is the first municipality-based energy-modeling tool that allows the development of meaningful scenarios until 2050 by considering climate policy goals and RES potentials, and it involves the mobility emission forecast tool “PROVEM”. ENECO2Calc is exclusively based on consistent statistical datasets. Additionally, the energy-modeling process was demonstrated as exemplary for the Austrian municipality St. Margareten im Rosental. For the selected municipality, three different scenarios were investigated. It could be concluded that a mix of decentral RES technologies and central cogeneration units in the heat sector, a mix of solar PV and cogeneration units in the electricity sector, and the use of synthetic biofuels coupled with a higher share of electrification in the fuel sector seemed to be most promising in the considered region. ENECO2Calc is a helpful energy-modeling tool toward climate neutrality to support municipalities in developing appropriate economic and ecological footprint strategies.
Update on the Life-Cycle GHG Emissions of Passenger Vehicles: Literature Review and Harmonization
Authors: Raugei Marco
Published: September 2022

Passenger vehicles are responsible for significant greenhouse gas (GHG) emissions, which calls for accurate and up-to-date estimates of the comparative emissions of the main types of alternative power trains, to enable evidence-based policy recommendations. This paper provides a systematic review and harmonization of the recent scientific literature on this topic. The results show that battery electric vehicles (BEVs) represent the most promising option to decarbonize the passenger vehicle fleet in all considered world regions, with up to −70% reductions in GHG emissions possible, vs. conventional internal combustion engine vehicles (ICEVs) running on petrol. Hybrid electric vehicles (HEVs and PHEVs) are less effective strategies, but they may be useful in bridging the gap between ICEVs and BEVs, especially in those markets that are harder to electrify quickly. Finally, fuel cell vehicles (FCEVs) may also be a viable option, but only if the hydrogen fuel is produced via water electrolysis using renewable energy.
The Effect of Yogurt and Kefir Starter Cultures on Bioactivity of Fermented Industrial By-Product from Cannabis sativa Production—Hemp Press Cake
Authors: Łopusiewicz Łukasz , Waszkowiak Katarzyna , Polanowska Katarzyna , Mikołajczak Beata , Śmietana Natalia , Hrebień-Filisińska Agnieszka , Sadowska Joanna , Mazurkiewicz-Zapałowicz Kinga , Drozłowska Emilia
Published: September 2022

Cannabis sativa (hemp) is a plant considered to be abundant in bioactive compounds. The increasing production of hemp oil is leaving considerable amounts of hemp press cakes (HPC), which have not been sufficiently managed so far. One of the directions of development of plant-based food is the use of by-products of the agri-food industry in accordance with the idea of zero waste and the circular economy, so the purpose of this study was to determine the possibility of HPC fermentation using yogurt and kefir cultures and to determine the effect of the type of starter on the properties of the products. In the present study, starter cultures of yogurt (YO 122) and kefir (commercial grains) were used for HPC fermentation. Changes in lactic acid bacteria (LAB) and yeast population, pH, acidity, the content of bioactive compounds by spectrophotometric methods (proteins, amino acids, polyphenols, flavonoids, reducing sugars) and antioxidant activity (DDPH, ABTS, FRAP and reducing power) were determined. The results showed that it was possible to develop high-value beverages based on HPC with high fermentation efficiency: survivability of LAB and yeast (>106 CFU/g) and acidification (pH in a range of 4.82–6.36 and 5.34–6.49 for yogurt and kefir culture, respectively). Moreover, the stability of hemp protein, with its variable free amino acid composition, antioxidant potential and presented changes in polyphenolic content, was observed during storage. The presented results show a new way to manage HPC as an oil industry residue by using it as a raw material for the development of a bioactive food product and illustrate the relationship between applied starter culture, the direction of fermentation and changes in the content of bioactive compounds.
Microbiological Characterization of Greek Galotyri Cheese PDO Products Relative to Whether They Are Marketed Fresh or Ripened
Authors: Samelis John , Kakouri Athanasia
Published: September 2022

Galotyri is the most popular traditional Greek PDO soft acid-curd cheese. This study compared the microbial numbers and types and characterized the lactic acid bacteria (LAB) biota of two artisan-type Galotyri PDO cheese varieties, one marketed fresh (Brand-K) and the other ripened (Brand-Z). Two retail batches of each cheese variety were analyzed, and a total of 102 LAB isolates were biochemically identified. LAB (7.2–9.3 log CFU/g) prevailed in all cheeses, followed by yeasts (5.8–6.8 log CFU/g). Typical starter strains of Streptococcus thermophilus and Lactobacillus delbrueckii were the most abundant species in all batches. However, the fresh Brand-K cheeses had 1–3 log units higher thermophilic starter LAB counts than the ripened Brand-Z cheeses, which contained a more diverse viable LAB biota comprising Lacticaseibacillus paracasei, Leuconostoc mesenteroides, Lentilactobacillus (L. diolivorans, L. kefiri, L. hilgardii), Pediococcus inopinatus/parvulus, few spontaneous nonstarter thermophilic streptococci and lactobacilli, and Enterococcus faecium and E. faecalis at higher subdominant levels.Conversely, the fresh Brand-K cheeses were enriched in members of the Lactiplantibacillus plantarum group; other LAB species were sporadically isolated, including Lactococcus lactis. All retail cheeses were safe (pH 3.9–4.0). No Salmonella spp. or Listeria monocytogenes were detected in 25-g samples by culture enrichment; however, Listeria innocua and coagulase-positive staphylococci (850 CFU/g) survived in one ripened batch. Gram-negative bacteria were <100 CFU/g in all cheeses. In conclusion, ripening reduced the starter LAB viability but increased the nonstarter LAB species diversity in the present Galotyri PDO market cheeses.
Nutritional Composition and Health-Promoting Properties of Amasi: A South African Fermented Milk Product
Authors: Maleke Mpho Sebabiki , Adebo Oluwafemi Ayodeji
Published: September 2022

This study investigated the effect of fermentation conditions (time and temperature) on the nutritional composition and health-promoting properties of raw milk, optimized naturally fermented amasi, and amasi fermented with starter culture. The composition of the products, including chemical analysis, amino acids, mineral composition, and fatty acids, as well as health-promoting properties (total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activity assays (2,2-Azinobis (3-Ethyl-Benzothiazone-6-Sulphonic acid) (ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH)) were investigated. The results showed that the fermentation conditions (time and temperature) significantly affected the ash contents of both fermented samples. Fermentation was also observed to have increased the contents of most of the essential and non-essential amino acids. A high concentration of glutamic acid was observed in optimized naturally fermented amasi at 32 °C (OP32, 6.30 g/100 g), followed by starter-culture-fermented amasi at 25 °C (SC25, 5.58 g/100 g) and then starter-culture-fermented amasi at 32 °C (SC32, 5.35 g/100 g), with optimized naturally fermented amasi at 32 °C (OP25, 4.46 g/100 g) having the lowest concentration. A similar trend in the composition of minerals was also observed, with potassium being the most abundant (110.1–134.5 µg/g). The TPC, TFC, and the antioxidant activities were significantly improved by fermentation, while a significant reduction in free fatty acids was recorded. In conclusion, the fermentation conditions had a significant effect on the nutritional and health-promoting properties of amasi. The results provided in this study could provide nutritional information regarding the composition of amasi, which can be beneficial to produce a high-quality product.
Screening of Poly-Glutamic Acid (PGA)-Producing Bacillus Species from Indian Fermented Soybean Foods and Characterization of PGA
Authors: Pariyar Priyambada , Yaduvanshi Puneeta Singh , Raghu Pullakhanadam , Tamang Jyoti Prakash
Published: September 2022

This present work is aimed to screen the PGA-producing Bacillus spp. from naturally fermented soybean foods of Arunachal Pradesh in India and to characterize the poly-glutamic acid (PGA) extracted from Bacillus spp. A total of 50 isolates were screened for high stickiness from 165 bacterial isolates isolated from 34 samples viz., grep chhurpi, peha/paeha, peron namsing, and peruñyaan. Based on 16S rRNA gene sequence analysis, 50 isolates were identified as Bacillus spp. Viscosity of 50 species of Bacillus were measured, out of which 7 species of Bacillus showing ≥0.03 Pa.s (30 cP) of viscosity were selected viz., Bacillus velezensis GC1-42, B. siamensis GC4-36, B. subtilis PH3-21, B. subtilis PN1-14, B. subtilis PH6-29, B. tequilensis PN9-22, and B. safensis subsp. safensis PY1-19 for PGA production. Amino acid analysis of PGA extracted from seven species of Bacillus showed high molecular weight (>600 kDa). B. safensis subsp. safensis PY1-19, isolated from peruñyaan, showed 78.9% of glutamic acid, antagonistic properties against pathogenic bacteria and had the ability to produce phytase with no hemolytic activity. Hence, this strain was selected as a potential starter to prepare peruñyaan at laboratory, and the final product showed high stickiness and viscosity with production of PGA of around 11 g/L.
Mercerization of Agricultural Waste: Sweet Clover, Buckwheat, and Rapeseed Straws
Authors: Žiganova Madara , Ābele Agnese , Iesalniece Zanda , Meri Remo Merijs
Published: September 2022

This research presents the alkali treatment effect on three types of agricultural residues: sweet clover (SCS), buckwheat (BS), and rapeseed straws (RS). The aim of the study was to find the optimal treatment conditions for each straw type, and to assess the potential of sweet clover straw as reinforcement for polymer composites in comparison to buckwheat and rapeseed. The straws were ground and treated for 15, 30, and 60 min using NaOH at concentrations of 2, 5, and 10%. To investigate the treatment results on the SCS, BS, and RS fibers, Fourier transform infrared spectroscopy, scanning electron microscopy, optical microscope, X-ray diffraction, and thermogravimetric analysis were used. Results indicate that the optimal room-temperature alkaline-treatment conditions of SCS fibers were the same as those for RS treated with 2% NaOH solution for 30 min. These conditions were milder in comparison to those used for the treatment of BS: 60 min in a 5% NaOH solution. During the treatment, noncellulosic substances were largely removed, and the aspect ratio of the fibers was increased, and the destruction temperature, crystallinity, and morphology were also affected. Consequently, SCS has promising potential for use in polymer composites.
Invasive Species Appearance and Climate Change Correspond with Dramatic Regime Shift in Thermal Guild Composition of Lake Huron Beach Fish Assemblages
Authors: Bowser Jessica , Galarowicz Tracy , Murry Brent , Johnson Jim
Published: September 2022

Lake Huron has undergone dramatic and well-documented lake-wide food web changes as a result of non-native species introductions. Coastal beaches, which serve as nursery habitats for native and introduced species, are, however, relatively poorly studied. Our objective was to assess fish assemblages of beach habitats in western Lake Huron and compare species composition pre- (1993) and post-invasion (2012) of dreissenid mussels and round goby (Neogobius melanostomus). Nearshore beach fish assemblages were sampled by nighttime beach seining during spring and summer in 1993 and 2012 in the western basin of Lake Huron along the Michigan shoreline. Catch rates were considerably higher, but there were fewer species present in 2012 than in 1993. The composition of species changed dramatically from a cold- and cool-water species assemblage in 1993 (dominated by alewife (Alosa pseudoharengus), spottail shiner (Notropis hudsonius), and lake whitefish (Coregonus clupeaformis), as well as Chinook salmon (Oncorhynchus tshawytscha) and rainbow smelt (Osmerus mordax)) to a cool- and warm-water species assemblage in 2012 (dominated by cyprinids, round goby (Nogobius melanstomus), and yellow perch (Perca flavescens)). The observed rise in catch rate and shift in species composition appears related to the introduction of invasive species as well as an on-going warming pattern in nearshore waters.
A Plea for the Integration of Fractional Differential Systems: The Initial Value Problem
Authors: Maamri Nezha , Trigeassou Jean-Claude
Published: September 2022

The usual approach to the integration of fractional order initial value problems is based on the Caputo derivative, whose initial conditions are used to formulate the classical integral equation. Thanks to an elementary counter example, we demonstrate that this technique leads to wrong free-response transients. The solution of this fundamental problem is to use the frequency-distributed model of the fractional integrator and its distributed initial conditions. Using this model, we solve the previous counter example and propose a methodology which is the generalization of the integer order approach. Finally, this technique is applied to the modeling of Fractional Differential Systems (FDS) and the formulation of their transients in the linear case. Two expressions are derived, one using the Mittag–Leffler function and a new one based on the definition of a distributed exponential function.
Effect of Household Type on the Prevalence of Metabolic Syndrome in Korea: Using Propensity Score Matching
Authors: Park Jisu , Park Ilsu
Published: September 2022

This study analyzed the effect of the household type on the prevalence of metabolic syndrome in Koreans utilizing data from the sixth, seventh, and eighth Korea National Health and Nutrition Examination Surveys conducted by the Korea Disease Control and Prevention Agency from 2015 to 2019. The demographic characteristics, metabolic syndrome characteristics according to household type, and risk factors of 25,092 subjects were identified using the Rao–Scott χ2–test and weighted multiple logistic regression results. Furthermore, to understand the effect of the household type on prevalence of metabolic syndrome, the selection bias between the groups was eliminated using the propensity score matching method. The average treatment effect for those treated for metabolic syndrome prior to propensity score matching was higher for single-person households, with 0.353 and 0.268 for single- and multi-person households, respectively. The difference was statistically significant (p < 0.0001). However, after propensity score matching, it was observed to be higher for multi-person households, with 0.290 and 0.316 for single- and multi-person households, respectively. However, the difference was not statistically significant (p < 0.1822). Difference was observed regarding the prevalence of metabolic syndrome by individual characteristics, some of which were considered in previous studies. However, the household type alone did not explain the prevalence of metabolic syndrome.
SnacktivityTM, Giant Games and Immersive Virtual Reality Exercises: A Rapid Narrative Review of These New Physical Activity Practices among Older People Living in Nursing Homes and Long-Term Care Facilities
Authors: Agbangla Nounagnon Frutueux , Séba Marie-Philippine , Bunlon Frédérique
Published: September 2022

In developed countries, the increasing number of older adults raises many public health challenges. Physical activity can enable healthy aging, as it is beneficial for both mental and physical health. Despite this, not all older adults engage in physical activity or have access to it. To counteract this, new physical practices such as SnacktivityTM, giant games and immersive virtual reality exercises are being developed. The main objective of this rapid narrative review is to summarize the effects of SnacktivityTM, giant games and immersive Virtual Reality (VR) exercise among older adults living in nursing homes and long-term care facilities. A scientific review was performed using the search engines PubMed, PsycInfo and Web of Science on 7 July 2022. Fifty-nine items are identified in total, and five items are included in the literature review. Synthesis of the studies shows that immersive virtual reality and giant games improve the motivation and enjoyment of older adults and are beneficial for their functional abilities. Furthermore, no interventional studies have tested the effect of SnacktivityTM in older adults. This review suggests that future studies should be conducted to further explore the effects of these new physical activity practices in older adults living in nursing homes or long-term care facilities.
Influence of Information Sources on Women’s Behavioural Practice towards Breast Cancer in Lagos, Nigeria
Authors: Adedoyin Precious , Adesina Evaristus , Adeyeye Babatunde , Oyesomi Kehinde , Falola Hezekiah
Published: September 2022

The global burden of breast cancer is increasing with an effect on the physical, mental and socio-economic wellbeing of the human population. Existing studies have majorly focused on the prevalent rate and clinical analysis of the disease, to the neglect of the nexus of information sources and breast cancer behaviours. This study examined the influence of information sources on perceived susceptibility, perceived severity and perceived barrier of women towards breast cancer and breast self-examination in Lagos state, Nigeria. A cross-sectional survey was employed in eliciting information from 400 women respondents randomly selected through the multistage sampling technique method. The study revealed that Internet-related channels of communication had a greater influence on susceptibility and severity perceptions of women. On the perceived barrier of women towards breast self-examination, the majority were influenced by interpersonal networks of communication (friends and relatives). Based on the findings, this study concludes that information sources play a pivotal role in the breast cancer beliefs and behavioural practices towards breast self-examination among women in Lagos state, Nigeria. It is therefore recommended that a national communication policy that will incorporate the use of information sources to strategically influence the beliefs and behavioural practices of women towards breast cancer management in Nigeria be developed.
Cross-Cultural Adaptation and Validation of the Arabic Version of the Mini-BESTest among Community-Dwelling Older Adults in Saudi Arabia
Authors: Alqahtani Bader A. , Alhowimel Ahmed S. , Alshehri Mohammed M. , Alqahtani Maha A. , Almuhaysh Abdulaziz A. , Alshakarah Ali O. , Alanazi Aljawharah , Khoja Aeshah H. , Alenazi Aqeel M.
Published: September 2022

Backgrounds: The Mini-BESTest is a clinical assessment of balance impairment; however, the translation and psychometric properties in the Arabic-speaking population have not yet been investigated. The purpose of this study was to translate into Arabic and evaluate the psychometric properties of the Mini-BESTest in Saudi community-dwelling older adults. Methods: This is a cross-sectional transcultural adaptation and validation study. A total of 144 community-dwelling older adults were enrolled (mean age = 66.2 ± 6.2 years). The translation and cross-cultural adaptation of the Mini-BESTest from English to Arabic was performed using standardized guidelines. Test–retest reliability was examined using the intraclass correlation coefficient (ICC) with one week between test and retest. The internal consistency was assessed using Cronbach’s alpha. Construct validity of the Mini-BESTest was assessed using balance such as Berg Balance Scale (BBS) and Falls Efficacy Scale International (FES-I). Results: The Arabic version of the Mini-BESTest showed good internal consistency (Cronbach’s alpha = 0.93). The scale shows excellent test–retest reliability (ICC = 0.99, 95% CI, 0.98–0.99) and excellent inter-rater reliability (ICC = 0.93, 95% CI, 0.70–0.97), which is indicative of the measure’s stability and repeatability. Mini-BESTest total scores showed an excellent inter-rater agreement. There was a significant correlation between total score of the Mini-BESTest and BBS (r = 0.72; p < 0.001). Mini-BESTest had a moderate association with FES-I. Conclusion: The Arabic version of the Mini-BESTest is a reliable and valid test for assessing balance in older adults. More research is needed to confirm the test’s reliability and validity in a specific population, such as those with neurological problems.