bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of the Metal Surface Texture on the Possibility of Controlling the Phase Transition of Water Droplets in the Single-Phase Regime
Authors: Glushkov Dmitriy , Orlova Evgeniya , Islamova Anastasia , Nikitin Dmitriy , Lyulin Yuriy , Feoktistov Dmitriy
Published: November 2022

We experimentally studied the influence of the texture of copper and steel surfaces on the possibility of controlling the phase transition of water droplets in the single-phase regime. The texture of metals was formed by polishing and grinding, which corresponded to the finishing treatment of heat transfer surfaces in cooling systems for energy-saturated equipment. The samples were studied by microscopy and profilometry. The texture was estimated by three-dimensional roughness parameters. It was found that, with a 2–2.5-fold increase in roughness, the wetting of copper deteriorates (the contact angle increases from 66° to 93°), whereas the wetting of steel improves (the contact angle decreases from 89° to 71°). It was experimentally proven that, among the two main factors that affect the spreading diameter (wetting and roughness), wetting is the most significant. A hypothesis was formulated regarding the reason for the increase in the contact angle of 7–10° and the drop in the decrease rate of the contact diameter during the transition from the pinning to the mixed stage of droplet evaporation. It was found that an increase in the surface area of 0.1% leads to an increase in the total droplet evaporation rate of 4–6.5%.
Impact of Optimal Feature Selection Using Hybrid Method for a Multiclass Problem in Cross Project Defect Prediction
Authors: Jalil Abeer , Faiz Rizwan Bin , Alyahya Sultan , Maddeh Mohamed
Published: November 2022

The objective of cross-project defect prediction (CPDP) is to develop a model that trains bugs on current source projects and predicts defects of target projects. Due to the complexity of projects, CPDP is a challenging task, and the precision estimated is not always trustworthy. Our goal is to predict the bugs in the new projects by training our model on the current projects for cross-projects to save time, cost, and effort. We used experimental research and the type of research is explanatory. Our research method is controlled experimentation, for which our independent variable is prediction accuracy and dependent variables are hyper-parameters which include learning rate, epochs, and dense layers of neural networks. Our research approach is quantitative as the dataset is quantitative. The design of our research is 1F1T (1 factor and 1 treatment). To obtain the results, we first carried out exploratory data analysis (EDA). Using EDA, we found that the dataset is multi-class. The dataset contains 11 different projects consisting of 28 different versions of all the projects in total. We also found that the dataset has significant issues of noise, class imbalance, and distribution gaps between different projects. We pre-processed the dataset for experimentation by resolving all these issues. To resolve the issue of noise, we removed duplication from the dataset by removing redundant rows. We then covered the data distribution gap between different sources and target projects using the min-max normalization technique. After covering the data distribution gap, we generated synthetic data using a CTGANsynthesizer to solve class imbalance issues. We solved the class imbalance issue by generating an equal number of instances, as well as an equal number of output classes. After carrying out all of these steps, we obtained normalized data. We applied the hybrid feature selection technique on the pre-processed data to optimize the feature set. We obtained significant results of an average AUC of 75.98%. From the empirical study, it was demonstrated that feature selection and hyper-parameter tuning have a significant impact on defect prediction accuracy in cross-projects.
AUV Drift Track Prediction Method Based on a Modified Neural Network
Authors: Yu Yuna , Zhang Jing , Zhang Tianchi
Published: November 2022

Since AUV works in the complex marine environment without cable and unmanned, there will be a loss of contact when an accident occurs. It is necessary to carry out research on the drift track prediction of AUV for the sake of salvage and recovery of the AUV in time. It is worth noting that the volume of AUV is small, and the drift track changes significantly when it is affected by the marine environment. Consequently, when the AUV drifts to different ocean layers, there will be a feature drift problem which will lead to a significant drop in the prediction accuracy. In this paper, a new method of AUV drift track prediction is proposed. Inspired by the human emotion modulation mechanism in psychology, a modified neural network (ECRNet) is proposed to correct the prediction error in different ocean layers. Through experimental verification, the network reduces the prediction error and achieves a better prediction performance.
Photovoltaic Energy Production Forecasting in a Short Term Horizon: Comparison between Analytical and Machine Learning Models
Authors: Etxegarai Garazi , Zapirain Irati , Camblong Haritza , Ugartemendia Juanjo , Hernandez Juan , Curea Octavian
Published: November 2022

The existing trend towards increased penetration of renewable energies in the traditional grid, and the intermittent nature of the weather conditions on which these energy sources depend, make the development of tools for the forecasting of renewable energy production more necessary than ever. Likewise, the prediction of the energy generated in these renewable production plants is key to the implementation of efficient Energy Management Systems (EMS) in buildings. These will aim both to increase the energy efficiency of the building itself, as well as to encourage self-consumption or, where appropriate, collective self-consumption (CSC). This paper presents a comparison between four different models, the former one being an analytical model and the remaining three machine learning (ML) based models. All of them will forecast the photovoltaic (PV) production curve for the next day. In order to validate these models, a case study of a PV system installed on the roof of a university building located in Bidart (France) is proposed. The model that most accurately forecasts the PV production during the period of July 2021 is the support vector regression (SVR), which has a mean R2 of 0.934 for July, being 0.97 on sunny days and 0.85 on cloudy ones. This is an improvement of 5.14%, 4.07%, and 4.18% over the nonlinear autoregressive with exogenous inputs (NARX), feedforward neural network (FFNN), and analytical model, respectively.
Ecosystem Services Assessment for Their Integration in the Analysis of Landslide Risk
Authors: Arrogante-Funes Patricia , Bruzón Adrián G. , Arrogante-Funes Fátima , Cantero Ana María , Álvarez-Ripado Ariadna , Vázquez-Jiménez René , Ramos-Bernal Rocío N.
Published: November 2022

Landslides are disasters that cause damage to anthropic activities, innumerable loss of human life, and affect the natural ecosystem and its services globally. The landslide risk evaluated by integrating susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools to address these disasters, starting with their prevention, with free spatial data and appropriate models. However, to correctly understand their interrelationships and social affection, studying the different ecosystem services that relate to them is necessary. This study is the first that has been attempted in which an integrated application methodology of ecosystem services is used to know in a systematic way if the information that ecosystem services provide is useful for landslide risk assessment. For the integration of ecosystem services into the landslide risk evaluation, (1) eight ecosystem services were chosen and mapped to improve understanding of the spatial relationships between these services in the Guerrero State (México), and (2) areas of synergies and trade-offs were identified through a principal component analysis, to understand their influence on risk analysis better. These are extracted from the models of the ARIES platform, artificial intelligence, and big data platform. Finally, (3) the similarity between the risk characteristics (susceptibility and vulnerability, already mapped by the authors) and the ecosystem services assessment was analysed. The results showed that the ecosystem services that most affect the synergy are organic carbon mass and the potential value of outdoor recreation; meanwhile, the possible removed soil mass was the most important trade-off. Furthermore, the lowest similarity value was found between landslide vulnerability and ecosystem services synergy, indicating the importance of including these ecosystem services as a source of valuable information in the risk analysis methodologies, especially with respect to risk vulnerability.
Optimal Direct Instantaneous Torque Control for SRMs Using Advanced Sliding Mode Controller
Authors: Yin Yonghua
Published: November 2022

In this paper, an optimal direct instantaneous torque control scheme using terminal sliding mode control is proposed to achieve better control performance for switched reluctance motors. To obtain faster convergence speed and better chattering suppression, the terminal sliding mode speed controller based on an advanced reaching law is proposed. Moreover, the hybrid wolf optimization algorithm is employed to tune the adjustable parameters for optimum performance. The experimental results show that the torque ripple range of the proposed scheme can be reduced by 25% and 17% under 0.3 Nm compared with PI and conventional sliding mode controllers. Meanwhile, the transient response can be improved by 50% and 31% in the initial stage, respectively. The proposed controller can perform well in aspects of speed tracking, torque ripple suppression, dynamic performance, and anti-interference capability of the system, which was validated by experimental results under different conditions.
Virtual Design and Construction (VDC) Framework: A Current Review, Update and Discussion
Authors: Del Savio Alexandre Almeida , Vidal Quincot José Francisco , Bazán Montalto Alvaro Daniel , Rischmoller Delgado Leonardo Antonio , Fischer Martin
Published: November 2022

Compared with other industries, construction has maintained one of the lowest growth rates in productivity and innovation over the last decades. Moreover, low compliance with schedule targets has remained a common issue throughout projects. Therefore, although methodologies developed have benefited productivity in design, construction and operation, meeting client and project objectives becomes challenging without properly integrating organization, information, systems and processes. This perspective introduces the Virtual Design and Construction (VDC) framework. First, this paper studies and reviews VDC since its creation at the Center for Integrated Facility Engineering (CIFE) of Stanford University in 2001. After that, a comprehensive bibliographical review of the VDC framework is presented and updated with examples of its application in construction projects, delving into its elements: Client Objectives, Project Objectives, Production Objectives, Controllable Factors, Integrated Concurrent Engineering (ICE), Building Information Modeling (BIM) and Project Production Management (PPM). Finally, the main benefits and project outcomes when applying the VDC framework are presented and discussed, followed by the conclusions and recommendations for future research.
Creating a CLOUDY-Compatible Database with CHIANTI Version 10 Data
Authors: Gunasekera Chamani M. , Chatzikos Marios , Ferland Gary J.
Published: November 2022

Atomic and molecular data are required to conduct the detailed calculations of microphysical processes performed by cloudy to predict the spectra of a theoretical model. cloudy now utilizes three atomic and molecular databases, one of which is CHIANTI version 7.1. CHIANTI version 10.0.1 is available, but its format has changed. cloudy is incompatible with the newer version. We have developed a script to convert the version 10.0.1 database into its version 7.1 format so that cloudy does not have to change every time there is a new CHIANTI version with an evolved format. This study outlines the steps taken by the script for this version format change. We have also found a modest number of significant changes to spectral line intensities/luminosities calculated by cloudy with the adoption of CHIANTI version 10.0.1. These changes are a result of improvements to collision strength data.
Crown Fire Modeling and Its Effect on Atmospheric Characteristics
Authors: Loboda Egor , Kasymov Denis , Agafontsev Mikhail , Reyno Vladimir , Lutsenko Anastasiya , Staroseltseva Asya , Perminov Vladislav , Martynov Pavel , Loboda Yuliya , Orlov Konstantin
Published: November 2022

The article is concerned with the experimental study of the crown fire effect on atmospheric transport processes: the formation of induced turbulence in the vicinity of the fire source and the transport of aerosol combustion products in the atmosphere surface layer at low altitudes. The studies were carried out in seminatural conditions on the reconstructed forest canopy. It was established that the structural characteristics of fluctuations of some atmosphere physical parameters in the case of a crown fire practically coincide with the obtained earlier values for a steppe fire. The highest concentration of aerosol combustion products was recorded at a height of 10–20 m from the ground surface. It was found that the largest number of aerosol particles formed during a crown fire had a particle diameter of 0.3 to 0.5 µm. As a result of experimental data extrapolation, it is concluded that an excess of aerosol concentration over the background value will be recorded at a distance of up to 2000 m for a given volume of burnt vegetation. It is of interest to further study these factors of the impact of wildfires on atmosphere under the conditions of a real large natural wildfire and determine the limiting distance of aerosol concentration excesses over background values.
The Characteristics of Light-Duty Passenger Vehicle Mileage and Impact Analysis in China from a Big Data Perspective
Authors: Ma Dong , Wu Xiaomeng , Sun Xin , Zhang Shaojun , Yin Hang , Ding Yan , Wu Ye
Published: November 2022

Vehicle mileage is one of the key parameters for accurately evaluating vehicle emissions and energy consumption. With the support of the national annual vehicle emission inspection networked platform in China, this study used big data methods to analyze the activity level characteristics of the light-duty passenger vehicle fleet with the highest ownership proportion. We found that the annual mileage of vehicles does not decay significantly with the increase in vehicle age, and the mileage of vehicles is relatively low in the first few years due to the run-in period, among other reasons. This study indicated that the average mileage of the private passenger car fleet is 10,300 km/yr and that of the taxi fleet was 80,000 km/yr in China in 2019, and the annual mileage dropped by 22% in 2020 due to the pandemic. Based on the vehicle mileage characteristics, the emission inventory of major pollutants from light-duty passenger vehicles in China for 2010–2020 was able to be updated, which will provide important data support for more accurate environmental and climate benefit assessments in the future.
Characteristics of VOCs Emissions from Circulating Water of Typical Petrochemical Enterprises and Their Impact on Surroundings
Authors: Fang Li , Hao Run , Xie Xiaoqi , Li Guoao , Wang Hailin
Published: November 2022

The petrochemical industry is regarded as the main source of anthropogenic VOCs emissions in China. As one of the main sources of unorganized emissions, circulating water is scarcely studied and reported. In this research, six circulating water systems (LC2X, HGLY, YJ, XJ, LC4X and LC5X) of a typical petrochemical enterprise were selected as targets to characterize VOCs emitted from such unorganized emissions. The results showed that there was a great difference in the VOCs disorganized emissions from the six circulating water systems, among which the main VOCs of HG2X, HGLY and YJ were oxygen-containing VOCs (OVOCs), accounting for about 48.0–81.2%. The main compounds of XJ, LC4X and LC5X were alkynes (89.1%), aromatic hydrocarbons (69.7%) and alkane (50.1%), respectively. TVOCs ranged from 276.0 to 23,009.6 µg·m−3. Based on POC test results, VOCs emissions of the circulating water system were 1237.5 tons, indicating further control was needed. As for their ambient impact, XJ had higher OFP contribution, and the OFP values of the six systems ranged from 823.3 to 145,739.0 µg·m−3, among which the major contributors were aromatic hydrocarbons (0.2–85.1%), OVOCs (0.1–77.2%) and alkynes (1.7–97.6%). In addition, aromatic hydrocarbons showed the largest contribution of the potential of SOA generation, which was more than 88.0%. As far as control was concerned, the replacement of an open cooling tower to closed cooling tower combined with regular POC detection will be an efficient way to control VOCs from such sources.
Energy Industry Methane Emissions Trajectory Analysis in China until 2050
Authors: Gu Alun , Zhou Sheng , Xu Shuangqing , Tong Qing
Published: November 2022

Methane (CH4) is an important greenhouse gas. There is increasing attention to CH4 abatement strategies because of its contribution to short-term warming and strong benefits of decreasing CH4 emissions. China greenhouse gas inventory methods are used to predict CH4 emissions from the energy industry and to assess the potentials of CH4 abatement policies and techniques by 2050. The NDC scenario results show using oil and gas as transitional clean energy sources instead of coal will increase CH4 emissions from oil and gas industries at least 70%, but CH4 emissions from the coal industry will decrease 45%, meaning total CH4 emissions from the energy industry will continually decrease at least 30% in 2030 compared with 2020. Energy-related CH4 emissions might peak around 2025, ahead of CO2 emission peaking. CH4 emissions will then decrease slightly and decrease markedly after 2030. Emissions in 2050 are expected to be 32% lower than emissions in 2020. In an extreme scenario, emissions may be 90% lower in 2050 than in 2020. It is suggested that the verification system for the energy industry’s CH4 emission accounting at the national level be improved and CH4 control targets in line with national emission targets and the “14th Five-Year Plan” development stage be formulated.
Risk Analysis of Green Supply Chain Using a Hybrid Multi-Criteria Decision Model: Evidence from Laptop Manufacturer Industry
Authors: Chen I-Fei , Kuo Pi-Ying , Tsaur Ruey-Chyn , Sarkar Santanu , Huang Shih-Chun
Published: November 2022

Green supply chain management has become enormously significant over the last two decades. Traditional supply chain risk management is inept at dealing with the intangible criteria related to environmental issues. Contrary to most of the previous research, which emphasized risks in merely one or two phases of the green supply chain, this study provides a systematic checklist of the cradle-to-grave approach to risk identification and prioritization using a hybrid method. Based on a world-leading Taiwanese laptop manufacturer, we first identified the risk factors of the green supply chain with respect to the components and subcomponents of Risk Priority Numbers (RPN) on the Failure Mode and Effects Analysis (FMEA). Second, we used the Analytic Network Process (ANP) to derive the relative weights of the subcomponents of RPN. Third, we combined grey relational analysis and ANP weights to derive the relative importance of each risk criterion in each risk factor in the green supply chain. The empirical results verified that our proposed method can be applied to the laptop manufacturing industry and found industry-specific green risk criteria in each factor. Therefore, following this, enterprises can control the possible risks for continuous improvement in their green activities.
Least Squares in a Data Fusion Scenario via Aggregation Operators
Authors: de Jesus Gildson Queiroz , Palmeira Eduardo Silva
Published: November 2022

In this paper, appropriate least-squares methods were developed to operate in data fusion scenarios. These methods generate optimal estimates by combining measurements from a finite collection of samples. The aggregation operators of the average type, namely, ordered weighted averaging (OWA), Choquet integral, and mixture operators, were applied to formulate the optimization problem. Numerical examples about fitting curves to a given set of points are provided to show the effectiveness of the proposed algorithms.
Rational A/B Site Ion Doping to Design Efficient and Stable Pr0.5Ba0.4Ca0.1Fe1-xCoxO3-δ Perovskites as Zinc–Air Batteries Cathode
Authors: Li Kaixin , Dong Zhanhua , Lü Zhe
Published: November 2022

The development of robust and efficient electrocatalysts for use in fuel cells and metal–air batteries has garnered a great deal of interest due to the quest for clean and renewable energy sources. In this paper, a promising Co-doped Pr0.5Ba0.4Ca0.1Fe1-xCoxO3-δ (x = 0, 0.2, 0.4, 0.6, 0.8; denoted as PBCFC-x, x = 0, 2, 4, 6, 8) with enhanced durability and electrocatalytic ORR/OER activity for zinc–air battery cathode catalysts is presented. Particularly, PBCFC-6 exhibits the best bifunctional catalytic activity in alkaline media among several materials, according to research using the RDE. The zinc–air battery with PBCFC-6 as the cathode catalyst delivered the smallest discharge–charge voltage difference at the current density of 10 mA·cm−2 and only increased by 0.031 V after 220 cycles (220 h), demonstrating its superior bifunctional catalytic activity and durability. The optimized electrochemical performance of both OER and ORR as well as stability in zinc–air batteries might result from the higher electrical conductivity, increasing concentration of adsorbed oxygen, and the greater proportion of Fe4+ (t2g3eg1) with optimal electron occupancy, owing to the partial replacement of Fe with Co.
Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering
Authors: Kalita Diana , Lyakhov Pavel
Published: November 2022

The task of determining the distance from one object to another is one of the important tasks solved in robotics systems. Conventional algorithms rely on an iterative process of predicting distance estimates, which results in an increased computational burden. Algorithms used in robotic systems should require minimal time costs, as well as be resistant to the presence of noise. To solve these problems, the paper proposes an algorithm for Kalman combination filtering with a Goldschmidt divisor and a median filter. Software simulation showed an increase in the accuracy of predicting the estimate of the developed algorithm in comparison with the traditional filtering algorithm, as well as an increase in the speed of the algorithm. The results obtained can be effectively applied in various computer vision systems.
Security and Privacy Threats and Requirements for the Centralized Contact Tracing System in Korea
Authors: Park Sungchae , Youm Heung-Youl
Published: November 2022

As COVID-19 became a pandemic worldwide, contact tracing technologies and information systems were developed for quick control of infectious diseases in both the private and public sectors. This study aims to strengthen the data subject’s security, privacy, and rights in a centralized contact tracing system adopted for a quick response to the spread of infectious diseases due to climate change, increasing cross-border movement, etc. There are several types of contact tracing systems: centralized, decentralized, and hybrid models. This study demonstrates the privacy model for a centralized contact tracing system, focusing on the case in Korea. Hence, we define security and privacy threats to the centralized contact tracing system. The threat analysis involved mapping the threats in ITU-T X.1121; in order to validate the defined threats, we used LIDDUN and STRIDE to map the threats. In addition, this study provides security requirements for each threat defined for more secure utilization of the centralized contact tracing system.
Effects of Electrical Pulse Width and Output Irradiance on Intense Pulse Light Inactivation
Authors: Xie Shuge , Shen Diya , Yuan Qing , Wu Zairui , Wang Junyuan , Li Fusheng , Han Qiuyi , Zhang Shanduan
Published: November 2022

The effects of electrical pulse width and output irradiance on the inactivation effect of intense pulse light (IPL) are studied in this paper. The measured radiant efficiency of pulsed xenon lamp can be more than 50%, and its irradiance can reach levels 100-times greater than that of a low-pressure mercury lamp. Staphylococcus aureus is used in inactivation experiments. When the irradiance and dose are both constant, there is no significant difference in inactivation efficiency when the pulse width is changed. However, a narrow pulse width corresponding to high irradiance at the same single-pulsed dose displays better inactivation effect. Experimental results are compared between the xenon lamp and low-pressure mercury lamp. The reduction factor (RF) value of the xenon lamp is more than 1.0 higher under the condition of both the same dose and irradiance. In order to achieve the same RF value, the dose of continuous-wave light must be at least three-times greater than that of pulsed light. The spectral action of the pulsed light is also studied. It is confirmed that UVC plays a major role across the whole spectrum. The experimental results show that extreme high-pulsed irradiance presents the main contributing factor behind the excellent bactericidal effect of IPL.
Antarctic Seabed Assemblages in an Ice-Shelf-Adjacent Polynya, Western Weddell Sea
Authors: Frinault Bétina A. V. , Christie Frazer D. W. , Fawcett Sarah E. , Flynn Raquel F. , Hutchinson Katherine A. , Montes Strevens Chloë M. J. , Taylor Michelle L. , Woodall Lucy C. , Barnes David K. A.
Published: November 2022

Ice shelves cover ~1.6 million km2 of the Antarctic continental shelf and are sensitive indicators of climate change. With ice-shelf retreat, aphotic marine environments transform into new open-water spaces of photo-induced primary production and associated organic matter export to the benthos. Predicting how Antarctic seafloor assemblages may develop following ice-shelf loss requires knowledge of assemblages bordering the ice-shelf margins, which are relatively undocumented. This study investigated seafloor assemblages, by taxa and functional groups, in a coastal polynya adjacent to the Larsen C Ice Shelf front, western Weddell Sea. The study area is rarely accessed, at the frontline of climate change, and located within a CCAMLR-proposed international marine protected area. Four sites, ~1 to 16 km from the ice-shelf front, were explored for megabenthic assemblages, and potential environmental drivers of assemblage structures were assessed. Faunal density increased with distance from the ice shelf, with epifaunal deposit-feeders a surrogate for overall density trends. Faunal richness did not exhibit a significant pattern with distance from the ice shelf and was most variable at sites closest to the ice-shelf front. Faunal assemblages significantly differed in composition among sites, and those nearest to the ice shelf were the most dissimilar; however, ice-shelf proximity did not emerge as a significant driver of assemblage structure. Overall, the study found a biologically-diverse and complex seafloor environment close to an ice-shelf front and provides ecological baselines for monitoring benthic ecosystem responses to environmental change, supporting marine management.
Pro-Myogenic Environment Promoted by the Synergistic Effect of Conductive Polymer Nanocomposites Combined with Extracellular Zinc Ions
Authors: Aparicio-Collado José , Molina-Mateo José , Cabanilles Constantino , Vidaurre Ana , Salesa Beatriz , Serrano-Aroca Ángel , Sabater i Serra Roser
Published: November 2022

A new strategy based on the combination of electrically conductive polymer nanocomposites and extracellular Zn2+ ions as a myogenic factor was developed to assess its ability to synergically stimulate myogenic cell response. The conductive nanocomposite was prepared with a polymeric matrix and a small amount of graphene (G) nanosheets (0.7% wt/wt) as conductive filler to produce an electrically conductive surface. The nanocomposites’ surface electrical conductivity presented values in the range of human skeletal muscle tissue. The biological evaluation of the cell environment created by the combination of the conductive surface and extracellular Zn2+ ions showed no cytotoxicity and good cell adhesion (murine C2C12 myoblasts). Amazingly, the combined strategy, cell–material interface with conductive properties and Zn bioactive ions, was found to have a pronounced synergistic effect on myoblast proliferation and the early stages of differentiation. The ratio of differentiated myoblasts cultured on the conductive nanocomposites with extracellular Zn2+ ions added in the differentiation medium (serum-deprived medium) was enhanced by more than 170% over that of non-conductive surfaces (only the polymeric matrix), and more than 120% over both conductive substrates (without extracellular Zn2+ ions) and non-conductive substrates with extracellular Zn2+. This synergistic effect was also found to increase myotube density, myotube area and diameter, and multinucleated myotube formation. MyoD-1 gene expression was also enhanced, indicating the positive effect in the early stages of myogenic differentiation. These results demonstrate the great potential of this combined strategy, which stands outs for its simplicity and robustness, for skeletal muscle tissue engineering applications.
Characterization of Indoxacarb Resistance in the Fall Armyworm: Selection, Inheritance, Cross-Resistance, Possible Biochemical Mechanisms, and Fitness Costs
Authors: Hafeez Muhammad , Li Xiaowei , Ullah Farman , Zhang Zhijun , Zhang Jinming , Huang Jun , Chen Limin , Siddiqui Junaid Ali , Ren Xiaoyun , Zhou Shuxing , Imran Muhammad , Assiri Mohammed A. , Zalucki Myron P. , Lou Yonggen , Lu Yaobin
Published: November 2022

The fall armyworm, Spodoptera frugiperda (J.E. Smith), is a voracious insect pest that is difficult to control due to resistance to insecticides and Bt proteins. We assessed cross-resistance, resistance mechanism, and fitness costs based on the life history traits of S. frugiperda. We established an S. frugiperda strain selected for resistance to indoxacarb (Ind-SEL) from a field-collected population and an unselected strain, Ind-UNSEL. Results indicated that after 24 generations of selection, the resistance to indoxacarb was increased by 472.67-fold as compared to the Ind-UNSEL. There was high cross-resistance to deltamethrin (31.23-fold) with very low or negligible cross-resistance to chlorantraniliprole, emamectin benzoate, and/or methoxyfenozide in the Ind-SEL population. Butoxide synergist increased susceptibility to indoxacarb, indicating that P450 enzymes may be involved in indoxacarb resistance. Significantly longer developmental time of larvae extended pupal duration, shorter adult longevity, and lower fecundity were observed in Ind-SEL as compared with the Ind-UNSEL population. The Net reproductive rate (R0) was the only growth parameter that differs between crosses of Ind-SEL♂ × Ind-UNSEL♀ (176 ± 46) and Ind-SEL♀ × Ind-UNSEL♂ (328 ± 57). On the other hand, all population growth parameters differ between Ind-SEL and Ind-UNSEL strains. Our work contributes to the growing body of research that demonstrates the importance of strain genetics in fitness cost experiments and helps resistance management programs make decisions.
Neuropsychological and Neurophysiological Mechanisms behind Flickering Light Stimulus Processing
Authors: Mankowska Natalia D. , Grzywinska Malgorzata , Winklewski Pawel J. , Marcinkowska Anna B.
Published: November 2022

The aim of this review is to summarise current knowledge about flickering light and the underlying processes that occur during its processing in the brain. Despite the growing interest in the topic of flickering light, its clinical applications are still not well understood. Studies using EEG indicate an appearing synchronisation of brain wave frequencies with the frequency of flickering light, and hopefully, it could be used in memory therapy, among other applications. Some researchers have focused on using the flicker test as an indicator of arousal, which may be useful in clinical studies if the background for such a relationship is described. Since flicker testing has a risk of inducing epileptic seizures, however, every effort must be made to avoid high-risk combinations, which include, for example, red-blue light flashing at 15 Hz. Future research should focus on the usage of neuroimaging methods to describe the specific neuropsychological and neurophysiological processes occurring in the brain during the processing of flickering light so that its clinical utility can be preliminarily determined and randomised clinical trials can be initiated to test existing reports.
Protein Targeting to Glycogen (PTG): A Promising Player in Glucose and Lipid Metabolism
Authors: Deng Xia , Wang Chenxi , Xia Yue , Yuan Guoyue
Published: November 2022

Protein phosphorylation and dephosphorylation are widely considered to be the key regulatory factors of cell function, and are often referred to as “molecular switches” in the regulation of cell metabolic processes. A large number of studies have shown that the phosphorylation/dephosphorylation of related signal molecules plays a key role in the regulation of liver glucose and lipid metabolism. As a new therapeutic strategy for metabolic diseases, the potential of using inhibitor-based therapies to fight diabetes has gained scientific momentum. PTG, a protein phosphatase, also known as glycogen targeting protein, is a member of the protein phosphatase 1 (PP1) family. It can play a role by catalyzing the dephosphorylation of phosphorylated protein molecules, especially regulating many aspects of glucose and lipid metabolism. In this review, we briefly summarize the role of PTG in glucose and lipid metabolism, and update its role in metabolic regulation, with special attention to glucose homeostasis and lipid metabolism.
Observational Study on the Success Rate of Osseointegration: A Prospective Analysis of 15,483 Implants in a Public Health Setting
Authors: Simão Butruz Sarkis , Costa Denis Damião , Cangussu Maria Cristina Teixeira , Sotto-Maior Bruno Salles , Devita Renan Lana , de Carvalho Jorge José , da Silva Brum Igor
Published: November 2022

The osseointegration process between the host’s bone tissue and the titanium implant is the key to success of implantology. The literature highlights the high success rate of osseointegrated implants, which is above 90%, and warns that some failures may occur, and every professional may face some inevitable failure. A longitudinal study was designed to evaluate the success rate of osseointegrated implants by taking into account the early failure of osseointegration. The study’s population included a convenience sample of all patients attending in four municipalities in the state of Paraíba, Brazil, belonging to the Brazilian Unified Health System (SUS) and those who underwent implant placements between November 2015 and November 2018 and were followed-up until March 2020. Data were extracted from the National Registry of Health Establishments (CNES), a database that contains data on all Brazilian health institutions. Of the total placed implants, 1.88% failed before prosthetic rehabilitation, corresponding to a success rate of 98.12%. The success and failure rates by anatomical region were also evaluated, which revealed, respectively, the values for the anterior maxilla (95.52% and 4.48%), posterior maxilla (97.53% and 2.47%), anterior mandible (97.13% and 2.87%), and posterior mandible (98.90% and 1.10%). We conclude that the posterior mandible performed better than the other bone types and anatomical regions. The anterior region of the maxilla was the one that presented the worst performance. Moreover, when compared, the posterior maxilla performed similarly to the anterior mandible and better than the anterior maxilla.
How Do Young and Old Spontaneously Hypertensive Rats Respond to Antihypertensive Therapy? Comparative Studies on the Effects of Combined Captopril and Nifedipine Treatment
Authors: Rassler Beate , Hawlitschek Christina , Brendel Julia , Zimmer Heinz-Gerd
Published: November 2022

Numerous studies on the effects of antihypertensive treatment in young spontaneously hypertensive rats (SHRs) have shown that early-onset therapy may effectively reduce their blood pressure (BP) even to normotensive values. In contrast, only a few studies investigated the effects of treatment started at an advanced age. These studies revealed that antihypertensive effects are lower in adult or even in senescent SHRs compared with young SHRs. Even more, prevention of cardiac sequelae of hypertension such as hypertrophy and fibrosis is less effective when treatment starts late in life. Because, in patients, combination therapies with calcium antagonists are favored, we studied the efficacy of a combination therapy with captopril and nifedipine in young and old SHRs. We directly compared the treatment effects on BP as well as on cardiac hypertrophy and remodeling between these two animal cohorts. With antihypertensive treatment, significantly lower BP values were achieved in young SHRs despite a shorter treatment period compared with old SHRs. Although treatment effects on cardiac hypertrophy were greater in old than in young SHRs, cardiac fibrosis was significantly attenuated only in young but not in old SHRs. The results emphasize the value of antihypertensive therapy and particularly accentuate the importance of an early-onset therapy. With respect to problems such as late diagnosis and poor therapy adherence, these results may have great importance for the treatment of human hypertension.
Starling-Behavior-Inspired Flocking Control of Fixed-Wing Unmanned Aerial Vehicle Swarm in Complex Environments with Dynamic Obstacles
Authors: Wu Weihuan , Zhang Xiangyin , Miao Yang
Published: November 2022

For the sake of accomplishing the rapidity, safety and consistency of obstacle avoidance for a large-scale unmanned aerial vehicle (UAV) swarm in a dynamic and unknown 3D environment, this paper proposes a flocking control algorithm that mimics the behavior of starlings. By analyzing the orderly and rapid obstacle avoidance behavior of a starling flock, a motion model inspired by a flock of starlings is built, which contains three kinds of motion patterns, including the collective pattern, evasion pattern and local-following pattern. Then, the behavior patterns of the flock of starlings are mapped on a fixed-wing UAV swarm to improve the ability of obstacle avoidance. The key contribution of this paper is collective and collision-free motion planning for UAV swarms in unknown 3D environments with dynamic obstacles. Numerous simulations are conducted in different scenarios and the results demonstrate that the proposed algorithm improves the speed, order and safety of the UAV swarm when avoiding obstacles.
Bioinspired Propulsion System for a Thunniform Robotic Fish
Authors: Mitin Iliya , Korotaev Roman , Ermolaev Artem , Mironov Vasily , Lobov Sergey A. , Kazantsev Victor B.
Published: November 2022

The paper describes a bioinspired propulsion system for a robotic fish model. The system is based on a combination of an elastic chord with a tail fin fixed on it. The tail fin is connected to a servomotor by two symmetric movable thrusts simulating muscle contractions. The propulsion system provides the oscillatory tail movement with controllable amplitude and frequency. Tail oscillations translate into the movement of the robotic fish implementing the thunniform principle of locomotion. The shape of the body and the tail fin of the robotic fish were designed using a computational model simulating a virtual body in an aquatic medium. A prototype of a robotic fish was constructed and tested in experimental conditions. Dependencies of fish velocity on the dynamic characteristics of tail oscillations were analyzed. In particular, it was found that the robot’s speed increased as the frequency of tail fin oscillations grew. We also found that for fixed frequencies, an increase in the oscillation amplitude lead to an increase in the swimming speed only up to a certain threshold. Further growth of the oscillation amplitude lead to a weak increase in speed at higher energy costs.
Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection
Authors: Wu Wei , Ma Longhua , Lian Bin , Cai Weiming , Zhao Xianghong
Published: November 2022

Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression. However, EEG signals are time-varying, and the distributions of the different subjects’ data are non-uniform, which poses a bad influence on depression detection. In this paper, the deep learning method with domain adaptation is applied to detect depression based on EEG signals. Firstly, the EEG signals are preprocessed and then transformed into pictures by two methods: the first one is to present the three channels of EEG separately in the same image, and the second one is the RGB synthesis of the three channels of EEG. Finally, the training and prediction are performed in the domain adaptation model. The results indicate that the domain adaptation model can effectively extract EEG features and obtain an average accuracy of 77.0 ± 9.7%. This paper proves that the domain adaptation method can effectively weaken the inherent differences of EEG signals, making the diagnosis of different users more accurate.
Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets
Authors: Chen Mingjing , Wu Shan , Chen Tian , Wang Changhong , Liu Guanzheng
Published: November 2022

Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild–moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.
The Mental Lexicon Features of the Hakka-Mandarin Dialect Bilingual
Authors: Chen Yao , Zhou Rong
Published: November 2022

The current study investigated the mental lexicon features of the Hakka-Mandarin dialect bilingual from two perspectives: the structural features of lexicons and the relations between lexicons. Experiment one used a semantic fluency task and complex-network analysis to observe the structural features of lexicons. Experiment two used a cross-language long-term repetition priming paradigm to explore the relations between lexicons, with three sub-experiments focusing on conceptual representation, lexical representation, and their relations, respectively. The results from experiment one showed that the dialect bilingual lexicons were small-world in nature, and the D2 (Mandarin) lexicon was better organized than the D1 (Hakka) lexicon. Experiment two found that D1 and D2 might have partially shared conceptual representations, separate lexical form representations, and partially shared lemma representations. Based on the findings, we tentatively proposed a two-layer activation model to simulate the lexicon features of dialect bilingual speakers.
Proactive Personality as a Critical Condition for Seeking Advice and Crafting Tasks in Ambiguous Roles
Authors: Shin Inyong , Kim Minwoo
Published: November 2022

In increasingly ambiguous work contexts, advice-seeking and task crafting behaviors are becoming more significant than ever before. Drawing on the uncertainty reduction theory, this study examined how role ambiguity would affect advice-seeking and task crafting. We also investigated whether a proactive personality would moderate the effects based on the capacity–willingness–opportunity model. The results, based on a two-wave design with a sample of 160 employees in South Korea, revealed that role ambiguity did not affect advice-seeking and task crafting directly. However, it was found that, as role ambiguity increased, employees with proactive personality became more involved in advice-seeking and task crafting. These findings indicate that role ambiguity serves as an opportunity for proactive employees who have the capacity and willingness to seek advice and craft tasks.
Mathematical Modelling for Predicting Thermal Properties of Selected Limestone
Authors: Sharo Abdulla A. , Rabab'ah Samer R. , Taamneh Mohammad O. , Aldeeky Hussein , Al Akhrass Haneen
Published: November 2022

Due to a lack of geotechnical and geothermal studies on Jordanian limestone, this paper aims to provide the thermal properties, including thermal conductivity, thermal diffusivity, and specific heat, using the Hot Disk Transient Plane Source (TPS) 2200 method. It also aims to provide a set of mathematical models through which the thermal properties can be indirectly predicted from the rocks’ physical and engineering properties. One hundred cylindrical rock specimens with a height of 20 cm and a diameter of 10 cm were extracted and prepared. The results showed that the thermal conductivity values ranged between (1.931–3.468) (W/(m × k)), thermal diffusivity (1.032–1.81) (mm2/s), and specific heat (1.57–2.563) ((MJ)/(m3 × K)). The results also suggest a direct relationship between conductivity and diffusivity and an inverse relationship between conductivity and specific heat. On the other hand, the results indicate the direct relationship between the conductivity and diffusivity, and the inverse relationship between the specific heat and density, hardness, sound velocity, and rock strength; the opposite happens when the rock’s porosity is considered. Simple regression, multivariate regression, and the backpropagation–artificial neural network (BP–ANN) approach were utilized to predict the thermal properties of limestone. Results indicated that the ANN model provided superior prediction performance compared to other models.