bluuebunny/crossref_metadata_2025 huggingface dataset:
The High Electrocatalytic Performance of NiFeSe/CFP for Hydrogen Evolution Reaction Derived from a Prussian Blue Analogue
Authors: Guo Yajie , Liu Yongjie , Liu Yanrong , Zhang Chunrui , Jia Kelun , Su Jibo , Wang Ke
Published: July 2022

Non-noble-metal-based chalcogenides are promising candidates for hydrogen evolution reaction (HER) by harnessing the architectural design and the synergistic effect between the elements. Herein, a porous bimetallic selenide (NiFeSe) nanocube deposited on carbon fiber paper (NiFeSe/CFP) was synthesized through a facile selenization reaction based on Prussian blue analogues (PBAs) as precursors. The NiFeSe/CFP exhibited excellent HER activity with an overpotential of just 186 mV for a current density of 10 mA cm−2 in 1.0 M KOH at ambient temperature, similar to most of the state-of-the-art transition metal chalcogenides. The corresponding Tafel slope was calculated to be 52 mV dec−1, indicating fast discharge of the proton during the HER. Furthermore, the catalyst could endure long-term catalytic tests and showed remarkable durability. The enhanced electrocatalytic performance of NiFeSe/CFP is attributed to the unique 3D porous configuration inherited from the PBA templates, enhanced charge transfer occurring at the heterogeneous interface due to the synergistic effect between the bimetallic phases, and the high conductivity improved by the formation of amorphous carbon shells during the selenization. These findings prove that the combination of inexpensive metal–organic framework precursors and hybrid metallic compounds is a feasible way to realize the performance enhancement of non-noble-metal-based chalcogenides towards alkaline HER.
Selective Ablation of BCL11A in Epidermal Keratinocytes Alters Skin Homeostasis and Accelerates Excisional Wound Healing In Vivo
Authors: Bhattacharya Nilika , Indra Arup K. , Ganguli-Indra Gitali
Published: July 2022

Transcriptional regulator BCL11A plays a crucial role in coordinating a suite of developmental processes including skin morphogenesis, barrier functions and lipid metabolism. There is little or no reports so far documenting the role of BCL11A in postnatal adult skin homeostasis and in the physiological process of tissue repair and regeneration. The current study establishes for the first time the In Vivo role of epidermal BCL11A in maintaining adult epidermal homeostasis and as a negative regulator of cutaneous wound healing. Conditional ablation of Bcl11a in skin epidermal keratinocytes (Bcl11aep−/−mice) enhances the keratinocyte proliferation and differentiation program, suggesting its critical role in epidermal homeostasis of adult murine skin. Further, loss of keratinocytic BCL11A promotes rapid closure of excisional wounds both in a cell autonomous manner likely via accelerating wound re-epithelialization and in a non-cell autonomous manner by enhancing angiogenesis. The epidermis specific Bcl11a knockout mouse serves as a prototype to gain mechanistic understanding of various downstream pathways converging towards the manifestation of an accelerated healing phenotype upon its deletion.
Virtual Screening of Repurposed Drugs as Potential Spike Protein Inhibitors of Different SARS-CoV-2 Variants: Molecular Docking Study
Authors: Eweas Ahmad F. , Osman Hosam-Eldin H. , Naguib Ibrahim A. , Abourehab Mohammed A. S. , Abdel-Moneim Ahmed S.
Published: July 2022

Like most of the RNA viruses, SARS-CoV-2 continuously mutates. Although many mutations have an insignificant impact on the virus properties, mutations in the surface protein, especially those in the receptor-binding domain, may lead to immune or vaccine escape variants, or altered binding activities to both the cell receptor and the drugs targeting such a protein. The current study intended to assess the ability of different variants of interest (VOIs) and variants of concern (VOCs) of SARS-CoV-2 for their affinities of binding to different repurposed drugs. Seven FDA approved drugs, namely, camostat, nafamostat mesylate, fenofibrate, umifenovir, nelfinavir, cefoperazone and ceftazidime, were selected based on their reported in vitro and clinical activities against SARA-CoV-2. The S1 protein subunit from eleven different variants, including the latest highly contiguous omicron variant, were used as targets for the docking study. The docking results revealed that all tested drugs possess moderate to high binding energies to the receptor-binding domain (RBD) of the S1 protein for all different variants. Cefoperazone was found to possess the highest binding energy to the RBD of the S1 protein of all the eleven variants. Ceftazidime was the second-best drug in terms of binding affinity towards the S1 RBD of the investigated variants. On the other hand, fenofibrate showed the least binding affinity towards the RBD of the S1 protein of all eleven variants. The binding affinities of anti-spike drugs varied among different variants. Most of the interacting amino acid residues of the receptor fall within the RBD (438–506).
The Contact Angle Hysteresis Puzzle
Authors: Marmur Abraham
Published: July 2022

This short review explains the essence of the theory of contact angle hysteresis. It emphasizes the controversial points and discusses the shortcomings of contact angle hysteresis measurements. The review ends with conclusions regarding the improvements that are required to make these measurements useful for the characterization of surface wettability.
A Multi-Scale Numerical Simulation on Thermal Conductivity of Bio-Based Construction Materials
Authors: Huang Gang , Abou-Chakra Ariane , Geoffroy Sandrine , Absi Joseph
Published: July 2022

Amid increasing concern about carbon emissions and ENERGY consumption in the building industry, bio-based construction materials are one of the solutions, especially considering their excellent thermal insulation. This study aims to develop a multi-scale numerical model to analyze the effect of microstructure on the thermal conductivity of a bio-based construction material. To achieve this, the size, shape, orientation, porosity, and water saturation of the bio-aggregate were considered in this study. The results show that the thermal conductivity of the bio-based material increases significantly and nonlinearly with water saturation, in contrast to the parallel thermal conductivity of the transversely isotropic bio-aggregate, which increases linearly. The thermal conductivity of the bio-based material shows an anisotropy in different directions and it obtains a maximum at water saturation of 0.4. Analysis of inclusions with different shapes shows that the thermal conductivity in the compaction direction is almost independent of the shape, but not in the direction perpendicular to the compaction. The finite element results show that the heat flow tends to transfer along the bio-aggregate rather than across it. These findings help to better understand the effect of microstructure on thermal conductivity and then promote the application of bio-based concrete as an insulation material in buildings.
Mitochondrial Genomes of two Lycosa spiders (Araneae, Lycosidae): Genome Description and Phylogenetic Implications
Authors: Ye Wentao , Wang Jiachen , Zhao Xinyi , Liu Hongyi , Zhu Sheng
Published: July 2022

We sequenced the complete mitochondrial genomes of Lycosa shansia, and Lycosa singoriensis by combining Sanger and next-generation sequencing methods and analyzed the sequenced genomes in order to explore the phylogenetic placement and the mitogenome composition and evolution of these species. The mitochondrial genome of L. shansia was 14,638 bp, whereas that of L. singoriensis was 13,686 bp. The type of genes and direction of the coding strand present in the mitogenomes were the same as those in other species of Lycosoidea, including two ribosomal RNA genes (rRNAs), 22 transfer RNA genes (tRNAs), and 13 protein-coding genes (PCGs). The mitogenomes of the two species exhibited negative AT and positive GC skews. This indicated that the nucleotide compositions of the mitogenomes of L. singoriensis and L. shansia tended to be T and G. Both the mean and median values of Ka/Ks of ATP8 were the highest among the 13 protein-coding genes, indicating that it might have evolved more rapidly than the other protein-coding genes in both species. ATP8 may have undergone more relaxed selection constraints and accumulated more mutations. In addition, many tRNAs lacked T and D stem loops; a few had no acceptor stems. The assessed species were recovered nested within Lycosidae with high support. The present findings will be useful for future studies on the mitogenome evolution of spiders.
Performing Early Pregnancy Tests in Milk and Their Effect on Cow Welfare and Reproductive Performance Compared to Rectal Pregnancy Tests 40 to 45 Days Post Insemination
Authors: Lavon Yaniv , Friedman Shmulik , Shwimmer Adin , Falk Rama
Published: July 2022

The main pregnancy test method for dairy cows is rectal palpation performed around 40–45 days from last insemination. This is an invasive examination that disrupts the cow’s routine, potentially affecting its well-being. We examined the effectiveness of a commercial kit for performing pregnancy tests on milk samples compared to rectal palpation at 28- and 42-days post insemination. Accordingly, the purpose of the current study was to check if performing early pregnancy test instead of rectal palpation would result in better welfare and improvement of the reproductive performance, at least in part, of the cows. At 28 days, we examined the effect of early pregnancy testing on reproductive performance, especially number of days open. At 42 days, we determined the pregnancy test methods’ effects on cow welfare measures and milk production. For the day 28 experiment, cows on one farm were divided into two groups: one milk-tested for pregnancy on 28 to 30 days after insemination, and the other tested by routine veterinary examination (42 to 45 days after insemination). The milk test resulted in a 16-day reduction in days open for cows identified as non-pregnant on day 28 compared to day 42 (rectal examination), potentially improving reproductive performance on the dairy farm. For the day 42 experiment, test effects on cow welfare as seen in pedometer data and milk production were examined on two large dairy farms. Test-day milk production did not differ significantly between milk-tested and rectally palpated groups, but well-being indices of milk-tested cows improved, especially in winter months. The reason for this can be because, in the summer, cows are interrupted due to colling activities. In addition to fewer days open and economic improvement, early milk tests for pregnancy can free up the practitioner for other tasks on the dairy farm.
Maternal Mental Health Symptom Profiles and Infant Sleep: A Cross-Sectional Survey
Authors: Sandoz Vania , Lacroix Alain , Stuijfzand Suzannah , Bickle Graz Myriam , Horsch Antje
Published: July 2022

The distinct influence of different, but comorbid, maternal mental health (MMH) difficulties (postpartum depression, anxiety, childbirth-related posttraumatic stress disorder) on infant sleep is unknown, although associations between MMH and infant sleep were reported. This cross-sectional survey aimed: (1) to examine associations between MMH symptoms and infant sleep; (2) to extract data-driven maternal MMH symptom profiles from MMH symptoms; and (3) to investigate the distinct influence of these MMH symptom profiles on infant sleep when including mediators and moderators. Mothers of 3–12-month-old infants (n = 410) completed standardized questionnaires on infant sleep, maternal perception of infant negative emotionality, and MMH symptoms. Data was analyzed using: (1) simple linear regressions; (2) factor analysis; and (3) structural equation modelling. MMH symptoms were all negatively associated with nocturnal sleep duration and only postpartum depression and anxiety symptoms were associated with night waking. Three MMH symptom profiles were extracted: depressive, anxious, and birth trauma profiles. Maternal perception of infant negative emotionality mediated the associations between the depressive or anxious profiles and infant sleep but only for particular infant ages or maternal education levels. The birth trauma profile was not associated with infant sleep. The relationships between MMH and infant sleep may involve distinct mechanisms contingent on maternal symptomatology.
Cross-Sectional Study on the Comparative Assessment of Mandibular Anesthesia (Inferior Alveolar Nerve Blockage) Manual Skills Shaping among Dentists on Plastic and Biomaterial Models
Authors: Vasil’ev Yuriy , Diachkova Ekaterina , Darawsheh Hadi , Kashtanov Artem , Molotok Ekaterina , Volel Beatrice , Batov Artem , Kytko Olesya , Saleev Rinat , Saleeva Gulshat , Saleeva Laysan , Smilyk Irina , Tiunova Natalya
Published: July 2022

Background: Providing regional anesthesia skills shaping remains relevant nowadays. A number of studies show that dentists have difficulties with these working independently. The study aim is the comparative analysis of the results of mandibular anesthesia (IANB) manual-skills shaping among dentists on plastic models and cadavers. Methods: In total, 999 participants were training in the skills of mandibular anesthesia from 2017 to 2021. The participants were divided in a random way into two groups: 700 participants were trained on plastic models, and 299 were trained on the cadaver material. After a lecture on the clinical and anatomical guidelines for IANB, a demonstration of the technique was provided, with subsequent testing of the injection technique. Satisfaction with the aspects of the training was assessed using the Likert scale. Results: the analysis of average values showed that participants from the group in which the manual skills were practiced on cadavers were more satisfied with the main aspects of the training, according to the sum of the main criteria of the modified scale. Conclusions: The important advantages of cadaver educational technology are that the sensations of tissue resistance are identical to natural ones, the individuality of each object, and the possibility of the visual study of the anesthesia technique, by dissection of the needle course and the location of the anesthetic depot.
Entrepreneurship as a Neglected Pitfall in Future Finnish Teachers’ Readiness to Teach 21st Century Competencies and Financial Literacy: Expectancies, Values, and Capability
Authors: Ranta Mette , Kruskopf Milla , Kortesalmi Marilla , Kalmi Panu , Lonka Kirsti
Published: July 2022

The aim of this study was to examine entrepreneurship in the context of future Finnish teachers’ readiness to teach 21st century (broad-based) competencies. Teachers’ self-efficacy in teaching entrepreneurial skills and financial matters is vital for their pupils to actively participate and flourish in future society. The study utilized survey data of future teachers’ expectancy-values in teaching seven broad-based competencies of the current national curriculum and their financial literacy. Future teachers expressed high interest in all competencies but reported the least self-efficacy and highest cost in teaching ICT as well as working life and entrepreneurship competencies. Teaching self-efficacy (TSE) in entrepreneurial competencies was predicted by subjective evaluations of financial capability and TSE in consumer skills. Teaching STEM subjects as well as male gender were related to better objective financial knowledge. We discuss the implications of observed financial capability, lack in self-efficacy, and high experienced cost of teaching these competencies. Support for future teachers’ readiness to teach working life skills, entrepreneurship, and financial literacy through phenomenon-based school subject collaboration, formal teacher training, and digital applications are emphasized.
Real-Time 3D Mapping in Isolated Industrial Terrain with Use of Mobile Robotic Vehicle
Authors: Buratowski Tomasz , Garus Jerzy , Giergiel Mariusz , Kudriashov Andrii
Published: July 2022

Simultaneous localization and mapping (SLAM) is a dual process responsible for the ability of a robotic vehicle to build a map of its surroundings and estimate its position on that map. This paper presents the novel concept of creating a 3D map based on the adaptive Monte-Carlo location (AMCL) and the extended Kalman filter (EKF). This approach is intended for inspection or rescue operations in a closed or isolated area where there is a risk to humans. The proposed solution uses particle filters together with data from on-board sensors to estimate the local position of the robot. Its global position is determined through the Rao–Blackwellized technique. The developed system was implemented on a wheeled mobile robot equipped with a sensing system consisting of a laser scanner (LIDAR) and an inertial measurement unit (IMU), and was tested in the real conditions of an underground mine. One of the contributions of this work is to propose a low-complexity and low-cost solution to real-time 3D-map creation. The conducted experimental trials confirmed that the performance of the three-dimensional mapping was characterized by high accuracy and usefulness for recognition and inspection tasks in an unknown industrial environment.
Embedded Real-Time Implementation of Bio-Inspired Central Pattern Generator with Self-Repairing Function
Authors: Xu Jinda , Lu Meili , Zhang Zhen , Wei Xile
Published: July 2022

Both robustness and self-repairing of the rhythmic behaviors generated by central pattern generators (CPGs) play significant roles in locomotion control. Although current CPG models have been established to mimic rhythmic outputs, the mechanisms by which the self-repairing capacities of CPG systems are formed are largely unknown. In this paper, a novel bio-inspired self-repairing CPG model (BiSRP-CPG) is proposed based on the tripartite synapse, which reveals the critical role of astrocytes in the dynamic coordination of CPGs. BiSRP-CPG is implemented on the parallel FPGA platform to simulate CPG systems on real physiological scale, in which a hardware implementation method without multiplier is utilized to break the limitation of FPGA hardware resources. The experimental results verified both the robustness and self-repairing capabilities of rhythm of BiSRP-CPG in the presence of stochastic synaptic inputs and “faulty” synapse. Under the synaptic failure rate of 20%, BiSRP-CPG suffered only 10.53% performance degradation, which was much lower than the 36.84% spike loss rate of CPG networks without astrocytes. This paper provides an insight into one of the possible self-repair mechanisms of brain rhythms which can be utilized to develop autonomously fault-tolerant electronic systems.
ICT Usage for Cross-Curricular Connections in Music and Visual Arts during Emergency Remote Teaching in Slovenia
Authors: Birsa Eda , Kljun Matjaž , Kopačin Barbara
Published: July 2022

Due to the COVID-19 pandemic, the entire process of teaching and learning moved online. This forced teachers and pupils to heavily rely on information and communications technology (ICT) and make adjustments to the new mode of teaching and learning in educational institutions. We conducted a qualitative case study by interviewing 24 teachers from Slovene primary schools focusing on the implementation of cross-curricular connections in music and visual arts content with the support of ICT during the period of emergency remote teaching. We found that when planning and implementing the cross-curricular learning process, teachers insufficiently took advantage of possibilities offered by modern ICT. The manner of implementing cross-curricular connections showed uncertainties in terms of understanding their specifics, resulting in the inefficient transfer of concepts taught, the results of which were seen in pupils’ work. This might additionally show the negative influence of parental supervision on the creative thinking and expression of pupils. The present study emphasizes the lack of ICT competences on the part of all participants in the educational process. Our findings show the need to educate teachers by eliminating the uncertainties related to the implementation of distant cross-curricular connections while meaningfully applying ICT adapted to pupils’ competences.
An Aero-Engine Damage Detection Method with Low-Energy Consumption Based on Multi-Layer Contrastive Learning
Authors: Huang Xing , Li Lei , Zhang Jingsheng , Yin Dengfeng , Hu Xinjian , Du Peibing
Published: July 2022

The health of aero-engines is pivotal to the safe operation of aircraft. With increasing service time, the internal components of the engine will be damaged by threats from different sources, so it is necessary to regularly detect the damage inside the engine. At present, most of the detection methods of major airlines rely on the internal images of the engine obtained by manual use of a borescope to detect damage or traditional machine learning methods, which consume high levels of human and computational resources but have low efficiency. Artificial intelligence in various fields can achieve better performance than traditional methods, but to achieve the industrialization standard of Green AI, we need further research. Accordingly, we introduce a multi-layer contrastive learning method to a lightweight target detection model design, which is applied to real aero-engine borescope images of complex components to accomplish real-time damage detection. We intensively conduct comparative experiments to evaluate the effectiveness of our method. The verification results demonstrate that the method can help our model perform excellently compared with other available baseline models.
Spiking VGG7: Deep Convolutional Spiking Neural Network with Direct Training for Object Recognition
Authors: Xiang Shuiying , Jiang Shuqing , Liu Xiaosong , Zhang Tao , Yu Licun
Published: July 2022

We propose a deep convolutional spiking neural network (DCSNN) with direct training to classify concrete bridge damage in a real engineering environment. The leaky-integrate-and-fire (LIF) neuron model is employed in our DCSNN that is similar to VGG. Poisson encoding and convolution encoding strategies are considered. The gradient surrogate method is introduced to realize the supervised training for the DCSNN. In addition, we have examined the effect of observation time step on the network performance. The testing performance for two different spike encoding strategies are compared. The results show that the DCSNN using gradient surrogate method can achieve a performance of 97.83%, which is comparable to traditional CNN. We also present a comparison with STDP-based unsupervised learning and a converted algorithm, and the proposed DCSNN is proved to have the best performance. To demonstrate the generalization performance of the model, we also use a public dataset for comparison. This work paves the way for the practical engineering applications of the deep SNNs.
Forecasting Crude Oil Consumption in Poland Based on LSTM Recurrent Neural Network
Authors: Manowska Anna , Bluszcz Anna
Published: July 2022

Primary fuels, i.e., crude oil, natural gas, and power coal, dominate the total global demand for primary energy. Among them, crude oil plays a particularly important role due to the universality of applications and the practical lack of substitutes in transport. Crude oil is also one of the main sources of primary energy in Poland and accounts for around 30% of the energy consumed. Poland covers only 3% of its needs from domestic deposits. The rest is imported from Russia, Saudi Arabia, Nigeria, Great Britain, Kazakhstan, and Norway. Due to such a high import of raw material, Poland must anticipate future demand. On the one hand, this article aims to analyze the current (2020) and future (2040) crude oil consumption on the Polish market. The study analyzes the geopolitical and economic foundations of the functioning of the energy raw-materials market, the crude oil supply, the structure of Poland’s energy mix, and assumptions about the energy policy until 2040. On the other hand, conclusions from the research were used to build a model of crude oil consumption for the internal market. It has been also shown that the consumption of crude oil on the Polish market is a nonlinear phenomenon with a small set of statistical data, which makes it difficult to build an accurate model. This paper proposes a new model based on artificial neural networks that includes long-term memory (LSTM). The accuracy of the constructed model was assessed using the MSE, Theil, and Janus coefficients. The results show that LSTM models can be used to forecast crude oil consumption, and they cope with the nonstationary and nonlinear time series. Many important contemporary problems posed in the field of energy economy are also discussed, and it is proposed to solve them with the use of modern machine-learning tools.
Quantitative Evaluation of CO2 Storage Potential in the Offshore Atlantic Lower Cretaceous Strata, Southeastern United States
Authors: Almayahi Dawod S. , Knapp James H. , Knapp Camelia
Published: July 2022

The geological storage of CO2 in the Earth’s subsurface has the potential to significantly offset greenhouse gas emissions for safe, economical, and acceptable public use. Due to legal advantages and vast resource capacity, offshore CO2 storage provides an attractive alternative to onshore options. Although offshore Lower Cretaceous reservoirs have a vast expected storage capacity, there is a limited quantitative assessment of the offshore storage resource in the southeastern United States. This work is part of the Southeast Offshore Storage Resource Assessment (SOSRA) project, which presents a high-quality potential geological repository for CO2 in the Mid- and South Atlantic Planning Areas. This is the first comprehensive investigation and quantitative assessment of CO2 storage potential for the Lower Cretaceous section of the outer continental shelf that includes the Southeast Georgia Embayment and most of the Blake Plateau. An interpretation of 200,000 km of legacy industrial 2D seismic reflection profiles and geophysical well logs (i.e., TRANSCO 1005-1-1, COST GE-1, and EXXON 564-1) were utilized to create structure and thickness maps for the potential reservoirs and seals. We identified and assessed three target reservoirs isolated by seals based on their effective porosity values. The CO2 storage capacity of these reservoirs was theoretically calculated using the DOE-NETL equation for saline formations. The prospective storage resources are estimated between 450 and 4700 Mt of CO2, with an offshore geological efficiency factor of dolomite between 2% and 3.6% at the formation scale.
Study on the Reaction Path of -CH3 and -CHO Functional Groups during Coal Spontaneous Combustion: Quantum Chemistry and Experimental Research
Authors: Zhang Lanjun , Han Yujia , Xu Dexin , Jiang Qin , Xin Haihui , Fu Chenhui , He Wenjing
Published: July 2022

Coal spontaneous combustion (CSC) is a disaster that seriously threatens safe production in coal mines. Revealing the mechanism of CSC can provide a theoretical basis for its prevention and control. Compared with experimental research is limited by the complexity of coal molecular structure, the quantum chemical calculation method can simplify the complex molecular structure and realize the exploration of the mechanism of CSC from the micro level. In this study, toluene and phenylacetaldehyde were used as model compounds, and the quantum chemical calculation method was adopted. The reaction processes of the methyl and aldehyde groups with oxygen were investigated with the aid of the Gaussian 09 software, using the B3LYP functional and the 6-311 + G(d,p) basis set and including the D3 dispersion correction. On this basis, the generation mechanisms of CO and CO2, two important indicator gases in the process of CSC, were explored. The calculation results show that the Gibbs free energy changes and enthalpy changes in the two reaction systems are both of negative values. Accordingly, it is judged that the reactions belong to spontaneous exothermic reactions. In the reaction processes, the activation energy of CO is less than that of CO2, indicating that CO is formed more easily in the above-two reaction processes. In addition, the variations in concentrations of important oxidation products (CO and CO2) and main active functional groups (such as methyl, carboxyl and carbonyl) with temperature were revealed through a low-temperature oxidation experiment. The experimental results verify the accuracy of the above quantum chemical reaction path. Moreover, it is also found that the generation mechanisms of CO and CO2 in coal samples with different metamorphic degrees are different. To be specific, for low-rank coal (HYH), CO and CO2 mainly come from the oxidation of alkyl side chains; for high-rank coal (CQ), CO is produced by the oxidation of alkyl side chains, and CO2 is attributed to the inherent oxygen-containing structure.
A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction
Authors: Huang Longnv , Wang Qingyuan , Huang Jiehui , Chen Limin , Liang Yin , Liu Peter X. , Li Chunquan
Published: July 2022

A novel hybrid model is proposed to improve the accuracy of ultra-short-term wind speed prediction by combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the sample entropy (SE), optimized recurrent broad learning system (ORBLS), and broadened temporal convolutional network (BTCN). First, ICEEMDAN is introduced to smooth the nonlinear part of the wind speed data by decomposing the raw wind speed data into a series of sequences. Second, SE is applied to quantitatively assess the complexity of each sequence. All sequences are divided into simple sequence set and complex sequence set based on the values of SE. Third, based on the typical broad learning system (BLS), we propose ORBLS with cyclically connected enhancement nodes, which can better capture the dynamic characteristics of the wind. The improved particle swarm optimization (PSO) is used to optimize the hyper-parameters of ORBLS. Fourth, we propose BTCN by adding a dilated causal convolution layer in parallel to each residual block, which can effectively alleviate the local information loss of the temporal convolutional network (TCN) in case of insufficient time series data. Note that ORBLS and BTCN can effectively predict the simple and complex sequences, respectively. To validate the performance of the proposed model, we conducted three predictive experiments on four data sets. The experimental results show that our model obtains the best predictive results on all evaluation metrics, which fully demonstrates the accuracy and robustness of the proposed model.
Modeling and Numerical Simulation of the Inlet Velocity on Oil–Water Two-Phase Vapor Separation Efficiency by the Hydrocyclone
Authors: Zhao Shuai , Sun Jipeng , Wang Shuli , Sun Zhihui
Published: July 2022

The density of tar vapor and water vapor produced by coal pyrolysis is different. Different centrifugal forces will be generated when they flow through the hydrocyclone. The water vapor and tar vapor are divided into inner and outer layers. According to this phenomenon, the moisture in the tar can be removed. In this paper, a Eulerian gas–liquid two-phase flow model is established by numerical simulation to study the effect of inlet velocity on the separation effect of a designed hydrocyclone (split ratio 0.2). The results show that the inlet velocity and moisture content have an influence on the volume distribution characteristics, tangential velocity, axial velocity, pressure drop distribution, and separation efficiency of tar vapor and water vapor in the hydrocyclone. When the inlet velocity increases from 2.0 to 12.0 m/s, the central swirl intensity increases, and the negative pressure sweep range at the overflow outlet increases. The axial velocity increased from 2.8 to 14.9 m/s, tar vapor content at the overflow outlet decreased from 74% to 37%, and at the underflow outlet increased from 89% to 92%. When the moisture content is lower than 10%, the hydrocyclone with the split ratio of 0.20 is no longer suitable for the separation of oil–water two-phase vapor. However, when the water content is higher than 20%, the purity of tar vapor at the underflow outlet can reach 92%, and the overflow outlet needs multistage separation to realize tar purification.
Electric Vehicle Charging Model in the Urban Residential Sector
Authors: El-Hendawi Mohamed , Wang Zhanle , Paranjape Raman , Pederson Shea , Kozoriz Darcy , Fick James
Published: July 2022

Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan’s situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries.
Approximation of Fractional Order PIλDμ-Controller Transfer Function Using Chain Fractions
Authors: Marushchak Yaroslav , Mazur Damian , Kwiatkowski Bogdan , Kopchak Bohdan , Kwater Tadeusz , Koryl Maciej
Published: July 2022

The approximation of a fractional order PIλDμ-controller transfer function using a chain fraction theory is considered. Analytical expressions for the approximation of s±α components of the transfer functions of PIλDμ-controllers were obtained through the application of the chain fraction theory. Graphs of transition functions and frequency characteristics of Dμ (α = μ = 0.5) and Iλ (α = λ = −0.5) parts for five different decomposition orders were obtained and analyzed. The results showed the possibility of applying the approximation of the PIλDμ-controller transfer function by the method of chain fractions with different valuesof λ and μ. For comparison, the transfer functions with the same order polynomials, obtained by the methods of Oustaloup transformation and chain fractions, were approximated for α = ±0.5. The analysis proved the advantages of using the chain fraction method to approximate the transfer function of the PIλDμ-controller. The performed approximation opens up the possibility of developing engineering methods for the technical implementation of PIλDμ-controllers. The accuracy of the same order transfer function approximation is higher when the method of chain fractions is used. It has been established that the adequacy of the frequency characteristics of the transfer functions obtained by the chain fraction method also depends on the approximation order.
Global Wildfire Susceptibility Mapping Based on Machine Learning Models
Authors: Shmuel Assaf , Heifetz Eyal
Published: July 2022

Wildfires are a major natural hazard that lead to deforestation, carbon emissions, and loss of human and animal lives every year. Effective predictions of wildfire occurrence and burned areas are essential to forest management and firefighting. In this paper we apply various machine learning (ML) methods on a 0.25° monthly resolution global dataset of wildfires. We test the prediction accuracies of four different fire occurrence classifiers: random forest (RF), eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP) neural network, and a logistic regression. Our best ML model predicts wildfire occurrence with over 90% accuracy, compared to approximately 70% using a logistic regression. We then train ML regression models to predict the size of burned areas and obtain an MAE score of 3.13 km2, compared to 7.48 km2 using a linear regression. To the best of our knowledge, this is the first study to be conducted in such resolution on a global dataset. We use the developed models to shed light on the influence of various factors on wildfire occurrence and burned areas. We suggest building upon these results to create ML-based fire weather indices.
Physical Properties of Hydrothermally Treated Rubberwood [Hevea brasiliensis (Willd. ex A. Juss.) Müll. Arg.] in Different Buffered Media
Authors: Ali Md. Rowson , Abdullah Ummi Hani , Ashaari Zaidon , Hua Lee Seng , Hamid Norul Hisham , Kamarudin Siti Hasnah
Published: July 2022

The dimensional instability of rubberwood [Hevea brasiliensis (Willd. ex A. Juss.) Müll. Arg.] is one of the major drawbacks that limits its utilization. Therefore, treatment is needed to improve these properties. Hydrothermal treatment in different buffered media is one of the techniques that improve its dimensional stability. The physical properties of hydrothermally treated rubberwood in different buffered media (pH 4, 6, 8, 10) and tap water (pH 7.43) with different temperatures (160 °C, 180 °C and 200 °C) were studied. In this study, physical properties such as equilibrium moisture content (EMC), density (ρ), mass loss (ML), water absorption (WA), volumetric swelling coefficient (VSC), thickness swelling (TS) and anti-swelling efficiency (ASE) were investigated for both treated and untreated specimens. Both the buffered media and temperature significantly affected the physical properties. The results indicated that the EMC (%), ρ (kg/m3), ML (%), VSC (%), TS (%) of treated rubberwood samples reduced as the treatment temperature increased. With the exception of WA (%), as WA increased when the treatment temperature increased from 160 °C to 180 °C but started to decrease when the temperature was further increased to 200 °C. The research study also exhibited that hydrothermal treatment using buffered media at different temperatures enhanced the dimensional stability of the treated samples. Alkaline media gave the best results on the physical properties compared to other treatment medias.
Understanding the Long-Term Vegetation Dynamics of North Korea and Their Impact on the Thermal Environment
Authors: Lim Chul-Hee , Yeo Hyun-Chul
Published: July 2022

In response to widespread deforestation, North Korea has restored forests through national policy over the past 10 years. Here, the entire process of forest degradation and restoration was evaluated through satellite-based vegetation monitoring, and its effects were also investigated. The vegetation dynamics of North Korea were characterized from 1986 to 2021 using the Landsat satellite 5–7, after which we evaluated the effect of vegetation shifts through changes in surface temperature since the 2000s. Vegetation greenness decreased significantly from the 1980s to the 2000s but increased in recent decades due to forest restoration. During the deforestation period, vegetation in all areas of North Korea tended to decrease, which was particularly noticeable in the provinces of Pyongannam-do and Hamgyongnam-do. During the forest restoration period, increases in vegetation greenness were evident in most regions except for some high-mountainous and developing regions, and the most prominent increase was seen in Pyongyang and Pyongannam-do. According to satellite-based analyses, the land surface temperature exhibited a clear upward trend (average slope = 0.13). However, large regional differences were identified when the analysis was shortened to encompass only the last 10 years. Particularly, the correlation between the area where vegetation improved and the area where the surface temperature decreased was high (−0.32). Moreover, the observed atmospheric temperature increased due to global warming, but only the surface temperature exhibited a decreasing trend, which could be understood by the effect of vegetation restoration. Our results suggest that forest restoration can affect various sectors beyond the thermal environment due to its role as an enhancer of ecosystem services.
Starch Properties, Nutrients Profiles, In Vitro Ruminal Fermentation and Molecular Structure of Corn Processed in Different Ways
Authors: Han Chengxing , Guo Yanli , Cai Xiaofang , Yang Ruixing
Published: July 2022

Processing will improve the digestion of corn by ruminant animals. The objectives of this study were to investigate the effects of processing methods (grinding, G; steam flaking, SF; extrusion, E) on the starch properties, nutrient profiles, in vitro ruminal fermentation and molecular structure of corn. Compared with G, SF and E increased (p < 0.05) the starch content, starch gelatinization, ruminal gas production (GP, 0.5–32 h), propionic acid, starch degradability (SD), the area and height of carbohydrate peaks, and decreased (p < 0.05) starch crystallinity, content of crude protein, neutral detergent fiber and acid detergent fiber, ruminal NH3-N, the area and height of amide I and II, α-helix, and β-sheet. The total VFA (24 h, 48 h) tended to be increased by SF and E (p < 0.10). The carbohydrate peak area and height were positively (p < 0.05) correlated with GP (1–24 h) and SD. The protein molecular absorption intensity was negatively correlated with SD (p < 0.05). The change in starch properties, GP (1–24 h) and molecular structure caused by E was greater than SF (p < 0.05). These results indicated that the higher starch gelatinization and lower starch crystallinity of E corn, induced by the high temperature and pressure, enabled more fermentation and digestion in the artificial rumen. The carbohydrate and protein molecular structures were correlated with the nutritional characteristics of corn.
Research on Routing Equalization Algorithm of Inter-Satellite Partition for Low-Orbit Micro-Satellites
Authors: Cheng Hengfei , Xu Zhaobin , Guo Xiaoxu , Yang Jia , Xu Kedi , Liu Shuqin , Jin Zhonghe , Jin Xiaojun
Published: July 2022

Low-orbit micro-satellite technology has developed rapidly in recent years due to its advantages of low time delay, low cost and short research period. However, among the existing inter-satellite routing algorithms, the classical flooding and greedy algorithms and their derivatives also have some limitations. The path delay calculated by the flooding algorithm is small but the calculation is large, while the greedy algorithm is the opposite. In this paper, a balanced inter-satellite routing algorithm based on partition routing is proposed. This paper presents the simulation experiments for the following indexes of the classic inter-satellite routing algorithms and the balanced partition routing algorithm: computation complexity, single-node computation pressure, routing path delay, path delay variance (data in Topo table satisfy μ =5, σ2=10). The results reveal that the balanced partition routing algorithm achieves better performance. In this paper, two optimization directions of the balanced partition routing algorithm are simulated under conditions that the data in the Topo table satisfy μ =5, σ2= 6, σ2=10 and σ2=15, respectively, when comparing their performance indicators. The experiments show that these two optimization methods can be adapted to various application scenarios and can further reduce the hardware cost of satellite nodes.
Fishery Resource Evaluation in Shantou Seas Based on Remote Sensing and Hydroacoustics
Authors: Yin Xiaoqing , Yang Dingtian , Du Ranran
Published: July 2022

The Shantou-Taiwan shoal fishing ground in southeastern China supports a significant population of pelagic fish, which play a key role in the marine ecosystem. An acoustic survey was carried out using a digital scientific echosounder in June 2019. In this paper, the spatial distribution of pelagic fish is analyzed based on acoustic data using geostatistical analysis tools. Meanwhile, the relationship between fish density from acoustic data and sea surface environment factors were evaluated by using generalized additive models (GAMs) based on the satellite-based oceanographic data of sea surface temperature, sea surface chlorophyll-a concentration, sea surface height and sea surface wind. The results showed the following: (1) Fish density and acoustic biomass have strong spatial correlation; the optimal model for acoustic biomass is exponential and the optimal model for fish density is gaussian; based on optimal model, spatial interpolation analysis of fish density and acoustic biomass was performed using the ordinary kriging method, and the higher values of density and acoustic biomass were located in the central and eastern parts of the study area. The total fish density and acoustic biomass is 2.56 × 1010 ind. and 1908.99 m2/m, respectively. (2) In vertical distribution, fish gradually move to the middle and lower layers of water during daytime, and gather in the middle and upper layers of water at night. (3) The variance explanation rate of GAM was 88.2% which indicates that the model has an excellent fitting degree, and the results of GAM showed that longitude, sea surface temperature (SST), sea surface wind (SSW), and sea surface height (SSH) had significant effects on fish density. Results of this study were meaningful for understanding the distribution of fishery resources, and as a guide for fish management in the Shantou offshore water.
Origin and Fate of Acrolein in Foods
Authors: Jiang Kaiyu , Huang Caihuan , Liu Fu , Zheng Jie , Ou Juanying , Zhao Danyue , Ou Shiyi
Published: July 2022

Acrolein is a highly toxic agent that may promote the occurrence and development of various diseases. Acrolein is pervasive in all kinds of foods, and dietary intake is one of the main routes of human exposure to acrolein. Considering that acrolein is substantially eliminated after its formation during food processing and re-exposed in the human body after ingestion and metabolism, the origin and fate of acrolein must be traced in food. Focusing on molecular mechanisms, this review introduces the formation of acrolein in food and summarises both in vitro and in vivo fates of acrolein based on its interactions with small molecules and biomacromolecules. Future investigation of acrolein from different perspectives is also discussed.