bluuebunny/crossref_metadata_2025 huggingface dataset:
Drowsiness Detection Using Ocular Indices from EEG Signal
Authors: Tarafder Sreeza , Badruddin Nasreen , Yahya Norashikin , Nasution Arbi Haza
Published: June 2022

Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10% among all classic machine learning models.
Optimizing Urban Air Pollution Detection Systems
Authors: Shakhov Vladimir , Materukhin Andrei , Sokolova Olga , Koo Insoo
Published: June 2022

Air pollution has become a serious problem in all megacities. It is necessary to continuously monitor the state of the atmosphere, but pollution data received using fixed stations are not sufficient for an accurate assessment of the aerosol pollution level of the air. Mobility in measuring devices can significantly increase the spatiotemporal resolution of the received data. Unfortunately, the quality of readings from mobile, low-cost sensors is significantly inferior to stationary sensors. This makes it necessary to evaluate the various characteristics of monitoring systems depending on the properties of the mobile sensors used. This paper presents an approach in which the time of pollution detection is considered a random variable. To the best of our knowledge, we are the first to deduce the cumulative distribution function of the pollution detection time depending on the features of the monitoring system. The obtained distribution function makes it possible to optimize some characteristics of air pollution detection systems in a smart city.
An Optimization Method for the Station Layout of a Microseismic Monitoring System in Underground Mine Engineering
Authors: Zhou Zilong , Zhao Congcong , Huang Yinghua
Published: June 2022

The layout of microseismic monitoring (MSM) station networks is very important to ensure the effectiveness of source location inversion; however, it is difficult to meet the complexity and mobility requirements of the technology in this new era. This paper proposes a network optimization method based on the geometric parameters of the proposed sensor-point database. First, according to the monitoring requirements and mine-working conditions, the overall proposed point database and model are built. Second, through the developed model, the proposed coverage area, envelope volume, effective coverage radius, and minimum energy level induction value are comprehensively calculated, and the evaluation reference index is constructed. Third, the effective maximum envelope volume is determined by taking the analyzed limit of monitoring induction energy level as the limit. Finally, the optimal design method is identified and applied to provide a sensor station layout network with the maximum energy efficiency. The method, defined as the S-V-E-R-V model, is verified by a comparison with the existing layout scheme and numerical simulation. The results show that the optimization method has strong practicability and efficiency, compared with the mine’s layout following the current method. Simulation experiments show that the optimization effect of this method meets the mine’s engineering requirements for the variability, intelligence, and high efficiency of the microseismic monitoring station network layout, and satisfies the needs of event identification and location dependent on the station network.
Rational Design of Field-Effect Sensors Using Partial Differential Equations, Bayesian Inversion, and Artificial Neural Networks
Authors: Khodadadian Amirreza , Parvizi Maryam , Teshnehlab Mohammad , Heitzinger Clemens
Published: June 2022

Silicon nanowire field-effect transistors are promising devices used to detect minute amounts of different biological species. We introduce the theoretical and computational aspects of forward and backward modeling of biosensitive sensors. Firstly, we introduce a forward system of partial differential equations to model the electrical behavior, and secondly, a backward Bayesian Markov-chain Monte-Carlo method is used to identify the unknown parameters such as the concentration of target molecules. Furthermore, we introduce a machine learning algorithm according to multilayer feed-forward neural networks. The trained model makes it possible to predict the sensor behavior based on the given parameters.
A Novel Remote Sensing Image Registration Algorithm Based on Feature Using ProbNet-RANSAC
Authors: Dong Yunyun , Liang Chenbin , Zhao Changjun
Published: June 2022

Image registration based on feature is a commonly used approach due to its robustness in complex geometric deformation and larger gray difference. However, in practical application, due to the effect of various noises, occlusions, shadows, gray differences, and even changes of image contents, the corresponding feature point set may be contaminated, which may degrade the accuracy of the transformation model estimate based on Random Sample Consensus (RANSAC). In this work, we proposed a semi-automated method to create the image registration training data, which greatly reduced the workload of labeling and made it possible to train a deep neural network. In addition, for the model estimation based on RANSAC, we determined the process according to a probabilistic perspective and presented a formulation of RANSAC with the learned guidance of hypothesis sampling. At the same time, a deep convolutional neural network of ProbNet was built to generate a sampling probability of corresponding feature points, which were then used to guide the sampling of a minimum set of RANSAC to acquire a more accurate estimation model. To illustrate the effectiveness and advantages of the proposed method, qualitative and quantitative experiments are conducted. In the qualitative experiment, the effectiveness of the proposed method was illustrated by a checkerboard visualization of image pairs before and after being registered by the proposed method. In the quantitative experiment, other three representative and popular methods of vanilla RANSAC, LMeds-RANSAC, and ProSAC-RANSAC were compared, and seven different measures were introduced to comprehensively evaluate the performance of the proposed method. The quantitative experimental result showed that the proposed method had better performance than the other methods. Furthermore, with the integration of the model estimation of the image registration into the deep-learning framework, it was possible to jointly optimize all the processes of image registration via end-to-end learning to further improve the accuracy of image registration.
Determination of the 3D Human Spine Posture from Wearable Inertial Sensors and a Multibody Model of the Spine
Authors: Michaud Florian , Lugrís Urbano , Cuadrado Javier
Published: June 2022

Determination of spine posture is of great interest for the effective prevention, evaluation, treatment and evolution monitoring of spinal disorders. Limitations of traditional imaging systems, including cost, radiation exposure (for X-ray based systems), projection volume issues and subject positioning requirements, etc., make non-invasive motion assessment tools effective alternatives for clinical and non-clinical use. In this work, a procedure was developed to obtain a subject-specific multibody model of the spine using either inertial or optical sensors and, based on this multibody model, to estimate the locations and orientations of the 17 vertebrae constituting the thoracolumbar spine. The number and calibration of the sensors, angular offsets, scaling difficulties and gender differences were addressed to achieve an accurate 3D-representation of the spine. The approach was validated by comparing the estimated positions of the sensors on 14 healthy subjects with those provided by an optical motion capture system. A mean position error of lower than 12 mm was obtained, thus showing that the proposed method can offer an effective non-invasive tool for the assessment of spine posture.
Inertial Sensor Estimation of Initial and Terminal Contact during In-Field Running
Authors: Yang Yue , Wang Li , Su Steven , Watsford Mark , Wood Lauren Marie , Duffield Rob
Published: June 2022

Given the popularity of running-based sports and the rapid development of Micro-electromechanical systems (MEMS), portable wireless sensors can provide in-field monitoring and analysis of running gait parameters during exercise. This paper proposed an intelligent analysis system from wireless micro–Inertial Measurement Unit (IMU) data to estimate contact time (CT) and flight time (FT) during running based on gyroscope and accelerometer sensors in a single location (ankle). Furthermore, a pre-processing system that detected the running period was introduced to analyse and enhance CT and FT detection accuracy and reduce noise. Results showed pre-processing successfully detected the designated running periods to remove noise of non-running periods. Furthermore, accelerometer and gyroscope algorithms showed good consistency within 95% confidence interval, and average absolute error of 31.53 ms and 24.77 ms, respectively. In turn, the combined system obtained a consistency of 84–100% agreement within tolerance values of 50 ms and 30 ms, respectively. Interestingly, both accuracy and consistency showed a decreasing trend as speed increased (36% at high-speed fore-foot strike). Successful CT and FT detection and output validation with consistency checking algorithms make in-field measurement of running gait possible using ankle-worn IMU sensors. Accordingly, accurate IMU-based gait analysis from gyroscope and accelerometer information can inform future research on in-field gait analysis.
Ultra-High-Sensitivity Humidity Fiber Sensor Based on Harmonic Vernier Effect in Cascaded FPI
Authors: Zhou Cheng , Song Yanjun , Zhou Qian , Tian Jiajun , Yao Yong
Published: June 2022

In this study, an ultra-high-sensitivity fiber humidity sensor with a chitosan film cascaded Fabry–Perot interferometer (FPI) based on the harmonic Vernier effect (HVE) is proposed and demonstrated. The proposed sensor can break the limitation of the strict optical path length matching condition in a traditional Vernier effect (TVE) FPI to achieve ultra-high sensitivity through the adjustment of the harmonic order of the HVE FPI. The intersection of the internal envelope tracking method allows spectra demodulation to no longer be limited by the size of the FSR of the FPI. The sensitivity of the proposed sensor is −83.77 nm/%RH, with a magnification of −53.98 times. This work acts as an excellent guide in the fiber sensing field for the further achievement of ultra-high sensitivity.
Physics of Composites for Low-Frequency Magnetoelectric Devices
Authors: Bichurin Mirza , Sokolov Oleg , Ivanov Sergey , Leontiev Viktor , Petrov Dmitriy , Semenov Gennady , Lobekin Vyacheslav
Published: June 2022

The article discusses the physical foundations of the application of the linear magnetoelectric (ME) effect in composites for devices in the low-frequency range, including the electromechanical resonance (EMR) region. The main theoretical expressions for the ME voltage coefficients in the case of a symmetric and asymmetric composite structure in the quasi-static and resonant modes are given. The area of EMR considered here includes longitudinal, bending, longitudinal shear, and torsional modes. Explanations are given for finding the main resonant frequencies of the modes under study. Comparison of theory and experimental results for some composites is given.
An Air Velocity Monitor for Coal Mine Ventilation Based on Vortex-Induced Triboelectric Nanogenerator
Authors: Shen Guocheng , Ma Jijie , Hu Yili , Li Jianping , Cheng Tinghai , Wen Jianming
Published: June 2022

Air velocity of coal mine ventilation is an important consideration that may cause serious damage. This paper proposes a simple, low cost and effective air velocity monitor (AVM) for coal mine ventilation. The AVM uses the lock-in characteristic of vortex-induced vibration (VIV) to sense the air velocity. Amplitude of the VIV is converted into frequency signal of a vortex-induced triboelectric nanogenerator (VI-TENG) to improve the durability. Structure of the AVM are designed, and parameters of the AVM are optimized with experiments. For the upper and lower air velocity thresholds of 3.1 and 3.6 m/s, the optimized flexible beam length, slider weight, electrode space and electrode width are 42.5 mm, 0.4 g, 0.2 mm and 0.5 mm, respectively. Experiments also show that the output frequency of the VI-TENG could represent the amplitude of VIV well with the correlation coefficient of 0.93. Durability test demonstrates that the AVM generates stable output frequency in 120,000 cycles. A prototype and its controller are fabricated. Wind tunnel tests of this prototype show that it can give alarm when the gas velocity goes above the upper threshold or below the lower threshold. The proposed AVM could be a good solution for simple and effective coal mine ventilation alarm.
Bibliometric Analysis of International Scientific Production on the Management of Happiness and Well-Being in Organizations
Authors: Jambrino-Maldonado Carmen , Rando-Cueto Dolores , Núñez-Sánchez José Manuel , Iglesias-Sanchez Patricias P. , De las Heras-Pedrosa Carlos
Published: June 2022

The interest of the scientific community and, consequently, the scientific production of topics on happiness and well-being at work, or the management of happiness in organizations, has been increasing over the years. The main objective of this bibliometric analysis is to determine the evolution of the concepts referred to in published scientific works. Bibliometric methods and techniques are used to analyze the themes and the most relevant trends, the number of papers and their citations, and the main institutions, and to highlight areas where the most research has been done on these issues. In addition to the review of the scientific literature, 312 studies are analyzed and net-mapped. The most outstanding results are the increase in the number of papers and citations during the health pandemic caused by COVID-19; the importance given to the transversality of well-being programs in corporations; and the greater frequency of research on the benefits of promoting the improvement of quality of life for work performance and its social impact.
A Look at Race, Skin Tone, and High School Students’ Perceptions of Teacher–Student Relationship Quality
Authors: Burrell-Craft Kala , Eugene Danielle R. , Nmah Juterh
Published: June 2022

Racial disparities in education have put a spotlight on the role of teachers and the school environment that is created for students. As teachers are seen as a vital element of school climate, the interactions between teachers and students can have a significant effect on students’ success. The purpose of this study was to examine the associations between race, skin tone, and teacher–student relationship (TSR) quality. Data drawn from the Fragile Families and Child Wellbeing Study included 995 ethnically and racially diverse adolescents. Hierarchical regression analyses revealed that being Black, Hispanic, or Multi-racial was significantly associated with TSRs. However, there were no between-group differences in TSRs across racial categories. Skin tone was not a significant predictor of TSRs and did not moderate the relationship between race and TSRs. Findings raise important implications for teacher training and professional development focused on culturally relevant practices that support optimal student interactions and provide promising evidence for school connectedness as an intervening mechanism in improving TSR quality, particularly for students of color.
Croatian Serb Culture, Language, and Minority Education Rights
Authors: Paravina Aleksandra
Published: June 2022

This article examines the ongoing debate on national minority rights for Croatian Serbs in the spheres of education, identity, and minority language policies. The controversy results from research on legislative acts and integration strategies in the European Union, which are predominantly focused on citizenship issues and the integration of immigrant workers and less on the constitutionally recognised language rights of minorities and the new social space created after the collapse of the former Socialist Federal Republic of Yugoslavia. A new sense of identity emerged for the Serb minority in Croatia as a result of the post-war environment. The article provides an analysis of in-depth interviews between Croatian Serbs and Croatian cultural and academic professionals through boundary-work that emphasises the importance of cultural values and legal norms for the minority, especially in minority education (Models A, B, and C).
Resistance Exercise for Improving Running Economy and Running Biomechanics and Decreasing Running-Related Injury Risk: A Narrative Review
Authors: Šuc Anja , Šarko Pija , Pleša Jernej , Kozinc Žiga
Published: June 2022

It is well-accepted that at least a certain amount of resistance exercise (RE) is recommended for most endurance athletes. In this review, we aim to summarize the evidence regarding the effects of RE on running economy, running biomechanics, and running-related injury risk in endurance runners. The evidence robustly shows that lower limb RE is effective for improving running economy and performance, with a combination of strength and plyometric training being recommended to improve RE. Isometric training is also emerging as a possible alternative to implement during periods of high overall training load. Lower limb RE may change some aspects of joint kinematics during running; however, the evidence regarding the effects on kinetics is limited. Lower limb RE may help reduce running-related injury risk, but further evidence is needed.
AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities
Authors: Pathik Nikhlesh , Gupta Rajeev Kumar , Sahu Yatendra , Sharma Ashutosh , Masud Mehedi , Baz Mohammed
Published: June 2022

As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car.
Creating Spaces for Intersubjectivity: A Sustainable Vision for Democratic Citizenship Education
Authors: Cho Hyunhee , Cho Daeyeon , Kim Junhee
Published: June 2022

Responding to the multiculturalist critiques of deliberative democratic education, Amy Gutmann and Sigal Ben-Porath suggested a more inclusive version of deliberative democratic education that emphasizes toleration, public recognition, and mutual respect. Despite its benefits and possibilities, however, their concept of democratic education fails to embrace poststructuralist ideas regarding democratic education. In the pursuit of a sustainable vision for democratic citizenship education, this study sought to conceptualize hybrid spaces wherein an ontology of plurality is woven into Gutmann and Ben-Porath’s idea of deliberative democratic education. By proposing an alternative way to integrate poststructuralist ideas such as intersubjective accounts of self-identity and human agency into the current practice of deliberative democratic education, the authors seek to promote continued dialogue on the purposes and possibilities of education for a more sustainable and democratic society.
Nitrogen Recovery from Different Livestock Slurries with an Innovative Stripping Process
Authors: Heidarzadeh Vazifehkhoran Ali , Finzi Alberto , Perazzolo Francesca , Riva Elisabetta , Ferrari Omar , Provolo Giorgio
Published: June 2022

Ammonia (NH3) emissions deriving from the management of livestock manure have a significant environmental impact, and therefore it is important to reduce them. Among the available options, the process of NH3 stripping is promising to remove NH3 from manures and digestates recovering it as a mineral fertilizer (e.g., ammonium sulfate) that is more widely adoptable on farms. The traditional stripping process takes place in batches; however, in this study, a continuous process was evaluated using a lab scale plant in which four reactors were used in series with different hydraulic retention times (HRTs) of 12 or 20 days. The NH3 recovery of each reactor was studied for the liquid fraction of pig slurry, dairy cattle slurry and digestate, applying simple headspace aeration. For 20 days of HRT, totals of 92%, 83% and 67% of NH3 were stripped from the digestate, pig slurry and dairy cattle slurry, respectively. For 12 days of HRT, total NH3 recoveries were 83%, 60% and 41% for the digestate, pig slurry and dairy cattle slurry, respectively. The inlet NH3 concentration and inlet total alkalinity had a positive and negative effect, respectively, on the specific NH3 removal rate for each reactor. Stripping NH3 on farm scale can abate NH3 emissions in response to the environmental concerns of European policies.
Energy Efficiency through the Implementation of an AI Model to Predict Room Occupancy Based on Thermal Comfort Parameters
Authors: Abdel-Razek Shahira Assem , Marie Hanaa Salem , Alshehri Ali , Elzeki Omar M.
Published: June 2022

Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.
Influence of Intersection Density on Risk Perception of Drivers in Rural Roadways: A Driving Simulator Study
Authors: Basu Samyajit , Ferrante Chiara , De Blasiis Maria Rosaria
Published: June 2022

With the aim of maintaining a decent level of accessibility, the presence of intersections, often in high numbers, is one of the typical features of rural roads. However, evidence from literature shows that increasing intersection density increases the risk of accidents. Accident analysis literature regarding intersection density primarily consists of accident prediction models which are a useful tool for measuring safety performance of roads, but the literature is lacking in terms of evaluation of driver behavior using direct measurements of driver performance. This study focuses on the influence of intersection density on the risk perception of drivers through experiments carried out with a driving simulator. A virtual driving environment of a rural roadway was constructed. The road consisted of segments featuring extra-urban and village driving environments with varying intersection density level. Participants were recruited to drive through this virtual driving environment. Various driver performance measures such as vehicle speed and brake and gas pedal usage were collected from the experiment and then processed for further analysis. Results indicate an increase in driver’s perceived risk when the intersection density increased, according with the findings from the accident prediction modeling literature. However, at the same time, this driving simulator study revealed some interesting insights about oscillating perceived risk among drivers in the case of mid-level intersection separation distances. Beyond the accident research domain, findings from this study can also be useful for engineers and transportation agencies associated with access management to make more informed decisions.
Where Will ‘Water-Energy-Food’ Research Go Next?—Visualisation Review and Prospect
Authors: Wang Jue , Ju Keyi , Wei Xiaozhuo
Published: June 2022

Research on water, energy, and food (WEF) is gradually becoming a global research hotspot in response to threats caused by the overexploitation of resources. In this study, 13,202 documents were selected from the WoS database and CiteSpace to judge frontier development in WEF research. In this study, visualisation research was carried out in 1547 papers that are most relevant to WEF research. The results show that WEF research has gradually increased during the research period, especially since 2015. The Food and Agriculture Organisation of the United Nations, Hoff, and Bazilian have the greatest influence on the promotion of WEF research and there is significant cooperation between institutions and countries. The United States, China, and the United Kingdom were the main contributors. The WEF nexus has become the most important hotspot in WEF research. In this case, 823 papers focused on the WEF nexus were selected to demonstrate the research contents, frontiers and clusters, and methods of the WEF nexus. Applied researches on the WEF nexus, such as security issues and multi-objective optimisation are current frontiers. Some new topics such as the pandemic and geopolitics have not attracted enough attention relative to their potential importance. A comprehensive data platform for the inter-department of water, energy, and food subsystems should be constructed in the near future.
The Influence of Alcohol Consumption on Tobacco Use among Urban Older Adults: Evidence from Western China in 2017
Authors: Li Chengbo , Long Chun , Zhang Mei , Zhang Luyu , Liu Mengyao , Song Meiqi , Cheng Yunfei , Chen Gong
Published: June 2022

Background: Tobacco use is a global public health concern due to its association with a myriad of health hazards and costs. Previous studies have found that the disinhibiting influence of alcohol consumption may lead to an increase in tobacco use. In this study, we investigated why alcohol consumption is associated with tobacco use among older adults in Western China, and we attempted to explain the mechanisms that underlie the link between alcohol consumption and tobacco use. Methods: In this cross-sectional study, we employed a representative sample of 812 urban individuals aged 60 and older in 2017 in Western China. Results: We found that alcohol consumption (OR = 2.816, 95%CI: 1.770–4.479) was positively associated with tobacco use. Additionally, other factors, including gender (OR = 0.023, 95%CI: 0.009–0.060), age (OR = 0.758, 95%CI: 0.634–0.906), health literacy (OR = 0.977, 95%CI: 0.959–0.995) and functional impairment (OR = 0.871, 95%CI: 0.767–0.988) were also strongly related to tobacco use. Conclusions: Consistent with the results of existing studies, our findings demonstrated the disinhibiting influence of alcohol consumption on tobacco use, which confirmed the theoretical mechanisms that underlie the alcohol–tobacco link and implied the co-occurrence and co-use of alcohol and tobacco, which are used as “social lubricants” in the Chinese context. The importance of other factors related to tobacco use were also discussed. As the tobacco epidemic is combatted, joint interventions tailored to concurrent control over alcohol and tobacco need to be performed, and corresponding male-specific and age-specific health education and the reinforcement of health literacy must be considered as important priorities.
Numerical Study and Structural Optimization of Vehicular Oil Cooler Based on 3D Impermeable Flow Model
Authors: Fu Jiahong , Hu Zhecheng , Zhang Yu , Lu Guodong
Published: June 2022

A non-uniform permeable flow numerical model of vehicular oil cooler was proposed to simulate the thermal performance of oil cooler, due to the complex internal structure of cooler and the anisotropy of coolant flow and heat transfer. By comparing the numerical simulation results with the experimental results, the maximum error of the simulation results under different working conditions is 9.2%, which indicates that the modelling method is reliable and can improve the development efficiency. On this basis, through the three-dimensional numerical simulation to establish and optimize the oil cooler’s parameters. The thermal performance under different structural oil cooler were compared using the comprehensive evaluation factor j/f. The results and the experimental data show that under the impermeable flow model can obtain good heat transfer efficiency with low flow resistance at the same time. When the cross-sectional area is 3 mm2, length of 90 mm, layer number of 11, the model accuracy was 0.6%, as the optimal structure parameters, the heat transfer increase by 47% and with the total pressure drop increased by only 30%.
Impacts of Harvesting Age and Pricing Schemes on Economic Sustainability of Cassava Farmers in Thailand under Market Uncertainty
Authors: Pannakkong Warut , Parthanadee Parthana , Buddhakulsomsiri Jirachai
Published: June 2022

This paper involves an analysis to determine appropriate cassava harvest practices and choose a pricing scheme between farmers and factories, cassava yards, and collectors in Thailand. Harvest practices represent all activities from land preparation to harvest. A key decision that governs the amount of resources required during cassava life cycle is the cassava’s harvesting age. The harvesting age can be from eight to 18 months in two patterns: fixed age, e.g., harvest every 12 months, and variable age, e.g., harvest at an age between 10 and 14 months. After harvesting, there are two common pricing schemes to consider, which are weight-based and starch-content-based. Factors that affect the two decisions made by Thai farmers at a given time are the market price, which highly varies within a season and between seasons, and yields in terms of weight and starch content, both of which change with cassava’s age and/or harvest month. Economic sustainability measure for Thai farmers is the average monthly profit that the farmers gain over cassava harvest cycle under uncertain market price. To handle uncertainties, a simulation model is constructed to imitate cassava planting activities from cultivation to harvest. The purpose is to evaluate various harvesting ages and two pricing schemes under uncertain cassava market prices. Market prices in 15 seasons (2006–2021) are grouped using the k-mean clustering into four price scenarios. As cassava grows in the simulation, the required resources are consumed until the decisions on harvesting time and pricing scheme are made with estimated selling probability under different price scenarios and uncertainty in cassava yield. Through simulation, harvesting age and pricing scheme that are most profitable and robust-to-system-variation are determined. Finally, a guideline for Thai farmers to choose a pricing scheme is developed based on the sensitivity analysis of the simulation model.
The Circular Economy Concept in the Outdoor Sporting Goods Industry: Challenges and Enablers of Current Practices among Brands and Retailers
Authors: Fuchs Michael , Hovemann Gregor
Published: June 2022

Based on an analysis of challenges and enablers of circular economy (CE) practices for outdoor brands and retailers, we aim to identify which of these CE practices are best suited for the outdoor sporting goods industry. To develop the study, we used a qualitative methodology through a content analysis of 21 expert interviews. From these interviews, we derived challenges and enablers for different CE elements in the categories reduce, circulate products, circulate materials, and shift towards renewable resources. Our findings indicate trade-offs related to the high complexity and functionality of many products, the juxtaposition of extremely technical and less technical products, and the low product return rate as challenges, while design for durability, design for repairability, and linking the degree of circularity to product types were identified as enablers. Based on our data, we argue that among circular practices reduce should form a basis upon which other aspects can build, with durability and repairability best suited for technical outdoor gear. As our study adds an industry-specific perspective, it contributes new knowledge to the CE literature. It also helps managers with the practical implications, as we argue they should be aware of potential downsides of CE elements and thus consider carefully which of those elements can help support their overall business and sustainability strategies.
Design of Decentralized Hybrid Microgrid Integrating Multiple Renewable Energy Sources with Power Quality Improvement
Authors: Jayaram Jayachandran , Srinivasan Malathi , Prabaharan Natarajan , Senjyu Tomonobu
Published: June 2022

Due to the energy crisis and exhaustion in the amount of fossil fuels left, there is an urge to increase the penetration of renewables in the grid. This paper deals with the design and control of a hybrid microgrid (HMG) in the presence of variable renewable energy sources. The DC sub-grid consists of a permanent magnet synchronous generator (PMSG) wind turbine, solar PV array with a perturb-and-observe (P&O) MPPT algorithm, boost converter, and battery energy storage system (BESS) with DC loads. The AC sub-grid consists of a PMSG wind turbine and a fuel cell with an inverter circuit synchronized to the grid to meet its load demand. A bidirectional interlinking converter (IC) connects the AC sub-grid and DC sub-grid, which facilitates power exchange between them. The decentralized control of converters allows all the renewables to operate in coordination independently without communication between them. The proposed control algorithm of the IC enables it to act as an active power filter in addition to the power exchange operation. The active power filtering feature of the IC helps to retain the power quality of the microgrid as per IEEE 519 standards by providing reactive power support and reducing the harmonic levels to less than 5%. The HMG with the proposed algorithm can operate in both grid-connected and islanded modes. While operating in grid-connected mode, power exchange between DC and AC sub-grids takes place and all the load demands are met. If it is in islanded mode, a diesel generator supports the AC sub-grid to meet the critical load demands and the BESS supports the DC microgrid. The proposed model is designed and simulated using MATLAB-SIMULINK and its results are analyzed. The efficacy of the proposed control is highlighted by comparing it with the existing controls and testing the HMG for load variations.
Deep-Learning-Based Accurate Identification of Warehouse Goods for Robot Picking Operations
Authors: Liu Huwei , Zhou Li , Zhao Junhui , Wang Fan , Yang Jianglong , Liang Kaibo , Li Zhaochan
Published: June 2022

In order to explore the application of robots in intelligent supply-chain and digital logistics, and to achieve efficient operation, energy conservation, and emission reduction in the field of warehousing and sorting, we conducted research in the field of unmanned sorting and automated warehousing. Under the guidance of the theory of sustainable development, the ESG (Environmental Social Governance) goals in the social aspect are realized through digital technology in the storage field. In the picking process of warehousing, efficient and accurate cargo identification is the premise to ensure the accuracy and timeliness of intelligent robot operation. According to the driving and grasping methods of different robot arms, the image recognition model of arbitrarily shaped objects is established by using a convolution neural network (CNN) on the basis of simulating a human hand grasping objects. The model updates the loss function value and global step size by exponential decay and moving average, realizes the identification and classification of goods, and obtains the running dynamics of the program in real time by using visual tools. In addition, combined with the different characteristics of the data set, such as shape, size, surface material, brittleness, weight, among others, different intelligent grab solutions are selected for different types of goods to realize the automatic picking of goods of any shape in the picking list. Through the application of intelligent item grabbing in the storage field, it lays a foundation for the construction of an intelligent supply-chain system, and provides a new research perspective for cooperative robots (COBOT) in the field of logistics warehousing.
Zinc Oxide Nanoparticles Induce DNA Damage in Sand Dollar Scaphechinus mirabilis Sperm
Authors: Kukla Sergey Petrovich , Chelomin Victor Pavlovich , Mazur Andrey Alexandrovich , Slobodskova Valentina Vladimirovna
Published: June 2022

Products containing nanomaterials are becoming more and more common in everyday life. Zinc oxide nanoparticles (ZnO NPs), meanwhile, are among the most widely used NPs. However, their genotoxic effect on the germ products of marine organisms is poorly understood. Therefore, the effects of ZnO NPs and zinc ions (20, 50, 100, 200 µg/L) on the sperm of sand dollar Scaphechinus mirabilis were compared. Comet assay showed that both tested pollutants caused an increase in DNA damage to 6.57 ± 2.41 and 7.42 ± 0.88% DNA in the comet tail, for zinc ions and ZnO NPs, respectively. Additionally, a different pattern was shown by the increase in DNA damage, with increasing concentration of pollutants, in different experimental groups.
Toolbox for the Extraction and Quantification of Ochratoxin A and Ochratoxin Alpha Applicable for Different Pig and Poultry Matrices
Authors: Streit Barbara , Czabany Tibor , Weingart Georg , Marchetti-Deschmann Martina , Prasad Shreenath
Published: June 2022

Ochratoxin A (OTA) is one of the major mycotoxins causing severe effects on the health of humans and animals. Ochratoxin alpha (OTα) is a metabolite of OTA, which is produced through microbial or enzymatic hydrolysis, and one of the preferred routes of OTA detoxification. The methods described here are applicable for the extraction and quantification of OTA and OTα in several pig and poultry matrices such as feed, feces/excreta, urine, plasma, dried blood spots, and tissue samples such as liver, kidney, muscle, skin, and fat. The samples are homogenized and extracted. Extraction is either based on a stepwise extraction using ethyl acetate/sodium hydrogencarbonate/ethyl acetate or an acetonitrile/water mixture. Quantitative analysis is based on reversed-phase liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Method validation was successfully performed and the linearity, limit of quantification, accuracy, precision as well as the stability of the samples, were evaluated. The analyte recovery of the spiked samples was between 80 and 120% (80–150% for spiked concentrations ≤ 1 ng/g or ng/mL) and the relative standard deviation was ≤ 15%. Therefore, we provide a toolbox for the extraction and quantification of OTA and OTα in all relevant pig and poultry matrices.
Molecular and Kinetic Models for Pore Formation of Bacillus thuringiensis Cry Toxin
Authors: Endo Haruka
Published: June 2022

Cry proteins from Bacillus thuringiensis (Bt) and other bacteria are pesticidal pore-forming toxins. Since 2010, when the ABC transporter C2 (ABCC2) was identified as a Cry1Ac protein resistant gene, our understanding of the mode of action of Cry protein has progressed substantially. ABCC2 mediates high Cry1A toxicity because of its high activity for helping pore formation. With the discovery of ABCC2, the classical killing model based on pore formation and osmotic lysis became nearly conclusive. Nevertheless, we are still far from a complete understanding of how Cry proteins form pores in the cell membrane through interactions with their host gut membrane proteins, known as receptors. Why does ABCC2 mediate pore formation with high efficiency unlike other Cry1A-binding proteins? Is the “prepore” formation indispensable for pore formation? What is the mechanism underlying the synergism between ABCC2 and the 12-cadherin domain protein? We examine potential mechanisms of pore formation via receptor interactions in this paper by merging findings from prior studies on the Cry mode of action before and after the discovery of ABC transporters as Cry protein receptors. We also attempt to explain Cry toxicity using Cry–receptor binding affinities, which successfully predicts actual Cry toxicity toward cultured cells coexpressing ABC transporters and cadherin.
Tuberculosis Mortality in Children under Fifteen Years of Age: Epidemiological Situation in Colombia, 2010–2018
Authors: Plata-Casas Laura , González-Támara Leandro , Cala-Vitery Favio
Published: June 2022

Pediatric tuberculosis is a serious infectious disease and a hidden global epidemic. The objective of this study was to describe the epidemiological situation of tuberculosis mortality in children under 15 years of age in Colombia in the period 2010–2018. A longitudinal descriptive study was conducted. The variables sex, age groups, and origin were studied. This study had 260 cases for analysis and was carried out in three phases. The first phase was the determination of the sociodemographic and clinical characteristics. The second phase was the construction of indicators by territorial entities. The third phase was stratification into four epidemiological situations according to the mortality rate and years of life lost. The median age was 7 years (range 0–14), 66.5% of cases were pulmonary tuberculosis (97.7% without bacteriological confirmation), 14.3781 years of life lost were recorded (95% CI: 142.811–168.333), and in children under 10–14 years, the loss was 110,057. Amazonas had the highest adjusted YLL rate (3979.7). In total, 36.4% of the territories had a high mortality, and 30.3% adjusted to the situation designated as 1. This is the first study that has used composite indicators to address the problem of premature mortality from childhood tuberculosis in Colombia. Our results allow us to specify that this disease remains a challenge for public health. It requires models of care and differential strategies by region. It also requires ensuring opportunities in diagnosis with sensitive methods, as well as intersectoral work for the optimal approach.
Description of a One-Year Succession of Variants of Interest and Concern of SARS-CoV-2 in Venezuela
Authors: Jaspe Rossana C. , Loureiro Carmen L. , Sulbaran Yoneira , Moros Zoila C. , D’Angelo Pierina , Hidalgo Mariana , Rodríguez Lieska , Alarcón Víctor , Aguilar Marwan , Sánchez Doneyla , Ramírez Jesús , Garzaro Domingo J. , Zambrano José Luis , Liprandi Ferdinando , Rangel Héctor R. , Pujol Flor H.
Published: June 2022

Some of the lineages of SARS-CoV-2, the new coronavirus responsible for COVID-19, exhibit higher transmissibility or partial resistance to antibody-mediated neutralization and were designated by WHO as Variants of Interests (VOIs) or Concern (VOCs). The aim of this study was to monitor the dissemination of VOIs and VOCs in Venezuela from March 2021 to February 2022. A 614 nt genomic fragment was sequenced for the detection of some relevant mutations of these variants. Their presence was confirmed by complete genome sequencing, with a correlation higher than 99% between both methodologies. After the introduction of the Gamma VOC since the beginning of the year 2021, the variants Alpha VOC and Lambda VOI were detected as early as March 2021, at a very low frequency. In contrast, the Mu VOI, detected in May 2021, was able to circulate throughout the country. After the detection of the Delta VOC in June 2021, it became the predominant circulating variant. With the arrival of the Omicron VOC in December, this variant was able to displace the Delta one in less than one month.