bluuebunny/crossref_metadata_2025 huggingface dataset:
Study of the N-Paraffins Addition Effect on the Effectiveness of Depressant Additives for the Production of Low-Freezing Diesel Fuels
Authors: Titaeva Alina , Bogdanov Ilya , Titaev Kirill , Morozova Yana , Kirgina Maria
Published: January 2024

The use of depressant additives is the most common method for producing diesel fuels with improved low-temperature properties. However, the depressants effectiveness largely depends on the composition of the diesel fuel, in particular on the content of n-paraffinic hydrocarbons, which to the greatest extent determine the low-temperature properties of the fuel. The work revealed the regularities of the n-paraffins addition influence on the depressant additives effectiveness. It was found that the addition of n-paraffins to blends of diesel fuels with depressants in low concentrations (0.05–0.50% wt.) enhances the depressants effectiveness in relation to the cold filter plugging point: maximum at 6–16 °C depending on the diesel fuel sample. It is shown that the effect is observed for DF of various compositions, various depressants, and also n-paraffins of various compositions. It was established that the positive effect of adding n-paraffins increases with the heavier added n-paraffins. Recommendations have been developed for obtaining diesel fuels with improved low-temperature properties and enhancing the depressant additives effectiveness: for a sample of straight-run diesel fuel F1, it is recommended to use a blend of fuel, depressant A1 and 0.50% wt. n-paraffins, separated from heavy gasoil; for a sample of straight-run diesel fuel F2, it is recommended to use a blend of fuel, depressant A2 and 0.50% wt. n-paraffins, separated from heavy gasoil or a blend of fuel, depressant A1 and 0.05% wt. n-paraffins, separated from highly paraffinic oil fraction. The revealed patterns and the developed recommendations will allow increasing the production of low-freezing DF brands, and also offer a resource-efficient option for using heavy gas oil fractions. The results obtained in the work contribute to expanding the understanding of the mechanism of interaction between diesel fuel hydrocarbons and the active ingredients of depressant additives.
A Systematic Model to Improve Productivity in a Transformer Manufacturing Company: A Simulation Case Study
Authors: Jou Yung-Tsan , Lin Ming-Chang , Silitonga Riana Magdalena , Lu Shao-Yang , Hsu Ni-Ying
Published: January 2024

The global economy’s slow recovery has led to an increased need for transformers in organizations in recent years. An optimal strategy for production line optimization is to enhance the allocation of staff at each workstation and increase the amount of operational equipment. The focus of this study is the investigation of the transformer production line. This study carried out a comprehensive examination of manufacturing area one, manufacturing area two, and manufacturing area three, respectively. The findings revealed that the case factory requires enhancements in the allocation of its workers. The simulation approach allows for the implementation of multi-scenario evaluation and adjustment, ensuring optimal utilization of resources in the enhanced production line, hence enhancing production efficiency and total productivity. Implementing both rotational shifts and night shifts in manufacturing area one enhances the overall production efficiency of the manufacturing area. By redistributing the workforce in area two, it proved feasible to manage the production capacity of a manufacturing area and maintain the operation of the gas-phase drying furnace. With regard to the final aspect, it is imperative to enhance the processing time of preprocessing goods in order to guarantee a consistent supply of the appropriate quantity of products. This will effectively minimize production line delays and enhance overall production efficiency. These enhancement strategies aid the manufacturing company in optimizing resource allocation to enhance production efficiency and productivity.
Cross-Cultural Adaptation and Psychometric Characteristics of the Greek Functional Gait Assessment Scale in Healthy Community-Dwelling Older Adults
Authors: Lampropoulou Sofia , Kellari Anthi , Gedikoglou Ingrid A. , Kozonaki Danai Gagara , Nika Polymnia , Sakellari Vasiliki
Published: January 2024

The Functional Gait Assessment (FGA) was cross-culturally adapted into Greek, according to international guidelines. The final Greek version of the scale (FGAGR) was evaluated for its reliability and was correlated with the mini-Balance Evaluation Systems Test (mini-BESTest), the Berg Balance Scale (BBS), the Timed Up and Go (TUG) test, and the Falls Efficacy Scale-International (FES-I) questionnaire, for testing the concurrent validity. The discriminant validity between individuals reporting low and those reporting high concern about falls as well as the predictive validity in identifying people with high risk of falls were assessed. The FGAGR was characterized as comprehensible in its content and orders. Psychometric testing in 24 Greek-speaking individuals (six men and eighteen women, 66 ± 7 years old) yielded excellent test-retest (ICC = 0.976) and inter-rater reliability (ICC = 0.984), but moderate internal consistency (Cronbach’s alpha = 0.660). The FGAGR scale proved its concurrent and discriminant validity while a maximum cutoff point of 25, with sensitivity of 84% and specificity of 100%, was identified to be optimal for predicting risk of falls in the elderly. The good psychometric characteristics of the FGAGR confirm its applicability in assessing gait of Greek-speaking older adults.
A Novel Method to Identify Key Nodes in Complex Networks Based on Degree and Neighborhood Information
Authors: Zhao Na , Yang Shuangping , Wang Hao , Zhou Xinyuan , Luo Ting , Wang Jian
Published: January 2024

One key challenge within the domain of network science is accurately finding important nodes within a network. In recent years, researchers have proposed various node centrality indicators from different perspectives. However, many existing methods have their limitations. For instance, certain approaches lack a balance between time efficiency and accuracy, while the majority of research neglects the significance of local clustering coefficients, a crucial node property. Thus, this paper introduces a centrality metric called DNC (degree and neighborhood information centrality) that considers both node degree and local clustering coefficients. The combination of these two aspects provides DNC with the ability to create a more comprehensive measure of nodes’ local centrality. In addition, in order to obtain better performance in different networks, this paper sets a tunable parameter α to control the effect of neighbor information on the importance of nodes. Subsequently, the paper proceeds with a sequence of experiments, including connectivity tests, to validate the efficacy of DNC. The results of the experiments demonstrate that DNC captures more information and outperforms the other eight centrality metrics.
An Improved Empirical Wavelet Transform Filtering Method for Rail-Head Surface-Defect Magnetic-Flux Leakage Signal
Authors: Jia Yinliang , Lin Jing , Wang Ping , Zhu Yue
Published: January 2024

The rail is an important factor in railway traffic safety. Surface defects in the rail head comprise a common type of rail damage, and magnetic flux leakage (MFL) technology is applied for its detection. MFL detection is influenced by various factors, resulting in high noise and a low signal-to-noise ratio (SNR) in the collected MFL signal, which influence defect assessment. This article improves the empirical wavelet transform (EWT) to apply it to rail surface-defect MFL signal filtering. A boundary optimization method based on mutual information (MI) is proposed to reduce the boundary redundancy caused by adaptive spectrum division. A method for component selection based on MI and kurtosis is proposed to select the suitable components from the decomposed components for signal reconstruction. The experimental results show that the method can effectively filter out the interference in the MFL signal, and the effectiveness is superior to the traditional methods, such as complementary ensemble empirical mode decomposition (CEEMD) and wavelet transform (WT).
MTUW-GAN: A Multi-Teacher Knowledge Distillation Generative Adversarial Network for Underwater Image Enhancement
Authors: Zhang Tianchi , Liu Yuxuan
Published: January 2024

Underwater imagery is plagued by issues such as image blurring and color distortion, which significantly impede the detection and operational capabilities of underwater robots, specifically Autonomous Underwater Vehicles (AUVs). Previous approaches to image fusion or multi-scale feature fusion based on deep learning necessitated multi-branch image preprocessing prior to merging through fusion modules. However, these methods have intricate network structures and a high demand for computational resources, rendering them unsuitable for deployment on AUVs, which have limited resources at their disposal. To tackle these challenges, we propose a multi-teacher knowledge distillation GAN for underwater image enhancement (MTUW-GAN). Our approach entails multiple teacher networks instructing student networks simultaneously, enabling them to enhance color and detail in degraded images from various perspectives, thus achieving an image-fusion-level performance. Additionally, we employ middle layer channel distillation in conjunction with the attention mechanism to extract and transfer rich middle layer feature information from the teacher model to the student model. By eliminating multiplexed branching and fusion modules, our lightweight student model can directly generate enhanced underwater images through model compression. Furthermore, we introduce a multimodal objective enhancement function to refine the overall framework training, striking a balance between a low computational effort and high-quality image enhancement. Experimental results, obtained by comparing our method with existing approaches, demonstrate the clear advantages of our proposed method in terms of visual quality, model parameters, and real-time performance. Consequently, our method serves as an effective solution for real-time underwater image enhancement, specifically tailored for deployment on AUVs.
Comparative Analysis of Deterministic Fundamental Diagrams Representative of Continuous and Interrupted Traffic Flow on Selected Regional Road in Croatia
Authors: Jovanović Bojan , Ševrović Marko , Luburić Grgo
Published: January 2024

Since the inception of the traffic flow theory, numerous traffic flow models have been formulated by scholars in an effort to more accurately delineate the relationships between various traffic flow parameters. However, only a limited number of studies have explored the distinctions between fundamental traffic diagrams, which characterize continuous and interrupted traffic flow conditions. Addressing this research lacuna, we compared twelve “speed–density” and “flow–density” models fitted to empirical data collected under continuous and interrupted traffic flow conditions on a selected regional road in Croatia. The empirical data used to develop these models were extracted from video footage captured by an unmanned aerial vehicle on two representative road segments during characteristic peak and off-peak hours on workdays. Our analysis reveals that, depending on the selected traffic flow model and prevailing traffic flow conditions, the practical capacity of the observed regional road is estimated to be in the range from 799 to 2333 veh/h/lane. It was also discovered that the considered models reach practical capacity at a significantly different density under continuous and interrupted traffic stream conditions, i.e., between 37 and 129 veh/km/lane. The conducted t-tests underscore the need to employ distinct “speed–density” and “flow–density” regression functions for modeling continuous and interrupted traffic stream conditions.
Geochemical and Mineralogical Approaches in Unraveling Paleoweathering, Provenance, and Tectonic Setting of the Clastic Sedimentary Succession (Western Central Paratethys)
Authors: Ivančič Kristina , Brajkovič Rok , Vrabec Mirijam
Published: January 2024

Pronounced tectonic and paleogeographic changes were detected in the Alpine–Pannonian region during the Miocene at the interface between the Alps, the Dinarides, and the Pannonian Basin. To understand the major tectonic, paleogeographic, and paleoclimatic changes during this period, geochemical and mineralogical investigations were carried out on the fine-grained clastic sedimentary rocks in the Tunjice Hills. The paleoweathering indicates a cold and/or arid to a warm and humid period. The paleoclimate and the regional climatic conditions correspond well with the Middle Miocene Climatic Optimum. The mineral composition shows an abundance of quartz and calcite. Quartz is associated with detrital origin from volcanic and metamorphic rocks of the Eastern and Southern Alps and with authigenic processes in sediments. Calcite is related to authigenic origin formed in shallow marine environments and to detrital provenance from the Southern Alps. Not all discriminant functions based on major oxides provided adequate results in determining the tectonic setting. The source rocks were subjected to oceanic island arc and collision. Moreover, sedimentation was influenced by both active and passive margin settings. The former is related to the Alpine collision, which continued from the Cenozoic onward, and the latter is connected to the processes associated with the formation of the Pannonian Basin System, which began in the late Early Miocene.
Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model
Authors: Nauman Umer , Zhang Yuhong , Li Zhihui , Zhen Tong
Published: January 2024

Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud computing employ a moderate design, wherein computational resources like storage and servers are housed in a few sizable worldwide data centers. System reliability, efficiency, and low latency are all goals of virtual machine (VM) placement. Load balancing has emerged as a crucial challenge for attaining energy efficiency in a fictitious grid computing architecture where a variety of users’ workloads are distributed across several virtual machines. We propose a more effective optimization technique known as the twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, and placement cost. The proposed model’s effectiveness will be evaluated based on relocation costs, reaction times, and stability assessments. The most significant gains of the presented work are 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, and 10.62% for 20 count data of nodes for artificial bee colony–bat algorithm, ant colony optimization, crow search algorithm, krill herd, whale optimization genetic algorithm, and improved Lévy-based whale optimization algorithm, respectively.
Predicting Runoff from the Weigan River under Climate Change
Authors: Su Jingwen , Zhang Pei , Deng Xiaoya , Ren Cai , Zhang Ji , Chen Fulong , Long Aihua
Published: January 2024

With the warming and humidification process in the Northwest Arid Zone over the past 30 years, the runoff of a vast majority of rivers has been affected to different degrees. In this paper, the runoff from the Weigan River, a typical inland river in the arid zone of Northwest China, is taken as an example, and seven types of CMIP6 data are selected with the help of a SWAT model to predict the runoff volume of the Weigan River in the next 30 years under climate change. The results show that (1) the SWAT model can simulate the runoff from the Weigan River well and has good applicability in this study area. (2) With an increase in radiative forcing, the temperature, precipitation and runoff in the study area show an increasing trend. (3) Under the four radiative forcing scenarios in 2030 and 2050, the runoff from the Weigan River out of the mountain is predicted to be maintained at 25.68 to 30.89 × 108 m3, which is an increase of 1.35% to 21.91% compared with the current runoff, of which the contribution to the increase in future runoff caused by the changes in temperature and precipitation is 68.71% and 27.24%, respectively. It is important to explore the impact of climate change on the runoff from the Weigan River to understand the impact of climate change on the Northwest Arid Region scientifically and rationally, and to provide a scientific basis for evaluating the risk of climate change and formulating policies to deal with it.
Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks
Authors: Zhao Jie , Ni Yiyang , Cheng Yulun
Published: January 2024

Federated learning (FL) is a promising technique to provide intelligent services for the internet of things (IoT). By transmitting the model parameters instead of user data between the client and central server, FL greatly improves the user privacy and reduces transmission latency. However, due to the fading effects of the wireless channel, the outage of wireless transmission degenerates the learning efficiency when FL is applied in wireless IoT networks. In order to address this issue, we investigate the joint optimization of client selection and wireless resource allocation in FL-aided cellular IoT networks. By taking both the amount of training data and wireless resource consumption into consideration, we formulate the problem as a mixed integer non-linear programming to maximize the utility of the network. To solve the problem effectively, an alternative direction-based algorithm is proposed by decomposing the original problem into two sub problems. The simulation results indicate that the proposed algorithm substantially improves the FL learning performance and reduces the consumption of wireless resources compared with existing methods.
Special Issue Athletes’ Performance and Analysis in Combat Sports and Martial Arts
Authors: Rydzik Łukasz , Ambroży Tadeusz , Cynarski Wojciech J. , Czarny Wojciech , Błach Wiesław
Published: January 2024

Combat sports and martial arts have gained popularity in mainstream culture and are recognized in various realms of physical culture [...]
Impulse Characteristics of Soil Treated with Enhancement Materials and Various Moisture Contents
Authors: Muhammad Usman , Aman Fazlul , Mohamad Nor Normiza
Published: January 2024

Due to the high mineral contents of additives and enhancement materials (EMs), EMs have become popular in treating soil, which expectedly improves the electrical characteristics of soil. Much of the published work on the effectiveness of EMs is based on investigations at low magnitudes of voltage, current and frequency; however, the study of these EMs under high-impulse conditions has not been widely addressed. In this work, the experimental results of a small-mesh ground electrode without EMs and treated with EMs, where the latter was poured with a controlled amount of water, are presented. The impulse impedance was expected to change appreciably due to the ionic conduction in soil and the use of EMs with a high water content; however, the impulse impedance was found to be weak and dependent on the water content, and the inductive effect was found to be significant at the voltage and current decay times. This indicates that, even though EMs and highly wetted soil can reduce the soil resistivity and relative resistance value, special attention has to be given to the impulse characteristics of the ground electrodes, particularly at tail/decay times.
CWD-Sim: Real-Time Simulation on Grass Swaying with Controllable Wind Dynamics
Authors: Choi Namil , Sung Mankyu
Published: January 2024

In this paper, we propose algorithms for the real-time simulation of grass deformation and wind flow in complex scenes based on the Navier–Stokes fluid. Grasses play an important role in natural scenes. However, accurately simulating their deformation due to external forces such as the wind can be computationally challenging. We propose algorithms that minimize computational cost while producing visually appealing results. We do this by grouping the grass blades and then applying the same force to the group to reduce the computation time. We also use a quadratic equation to deform the blades affected by the wind force rather than using a complicated spline technique. Wind force is fully modeled by the Navier–Stokes fluid equation, and the blades react to this force as if they were being swept by the wind. We also propose the AGC interface (Arrow-Guided wind flow Control), which allows the direction and intensity of the wind to be manipulated using an arrow-shaped interface. Through this interface, users can have grass sway in response to user-defined wind forces in a real-time rate. We verified that the proposed algorithms can simulate 900% more grass blades than the compared paper’s algorithms.
Spatial Distribution Characteristics and Analysis of PM2.5 in South Korea: A Geographically Weighted Regression Analysis
Authors: Lee Ui-Jae , Kim Myeong-Ju , Kim Eun-Ji , Lee Do-Won , Lee Sang-Deok
Published: January 2024

PM2.5, a critical air pollutant, requires health-conscious management, with concentrations varying across regions due to diverse sources. This study, conducted in South Korea in 2021, employed the geographically weighted regression model to analyze the spatiotemporal correlations of PM2.5 with O3 and the normalized difference vegetation index (NDVI). Regional differences in the correlation between PM2.5 and O3 were observed, influenced by common precursors (SOx, NOx, and volatile organic compounds (VOCs)), seasonal temperature variations, and solar radiation differences. Notably, PM2.5 and O3 exhibited a heightened regression coefficient in summer, emphasizing the need for specific management targeting VOCs and NO2. The interplay between PM2.5 and NDVI revealed a negative overall impact but a positive effect in the central region of Korea, suggesting vegetation’s role in the PM2.5 concentration increase due to atmospheric stagnation caused by mountain ranges. These findings enhance our understanding of PM2.5 distribution mechanisms, highlighting the need for tailored policies in each region for effective concentration reductions.
Photocatalytic Reduction of VOCs with Ag/Ni-Doped Photocatalyst in Different Temperature and Humidity Environments
Authors: Ayturan Zeynep Cansu , Dursun Sukru
Published: January 2024

The photocatalytic oxidation (PCO) process is one of the most preferred, inexpensive, and environmentally friendly methods for VOC removal. It has been determined that this method can remove a wide range of organic pollutants. The removal of benzene and toluene pollutants, two important VOCs commonly encountered in flue gases, has been studied in the scope of this study using the photocatalytic oxidation method under UVA irradiation. For this purpose, the photocatalytic activity of the photocatalyst increased by the metal/metal doping process. Two different metals, a noble metal (Ag) and a transition metal (Ni), were used together for the doping of TiO2 nanoparticles, and the photocatalysts attached to a glass surface were prepared. Four different doping percentages were used for photocatalysts: 0.5%, 1%, 2.5%, and 5%. Several PCO experiments were conducted under different temperatures (120, 150, and 180 °C) and humidity conditions (25 and 50%). Photocatalytic oxidation experiments were carried out with artificially produced benzene and toluene gases, and the success of the system was evaluated with respect to removal efficiency calculations. The UVA light source was used for the photocatalytic experiments. The results of the study indicated that the removal efficiencies of toluene were found to be higher than those of benzene, and the most suitable conditions were determined to be 50% humidity and a 120 °C environment with the use of a 1% doped photocatalyst.
Thermospheric Mass Density Modelling during Geomagnetic Quiet and Weakly Disturbed Time
Authors: He Changyong , Li Wang , Hu Andong , Zheng Dunyong , Cai Han , Xiong Zhaohui
Published: January 2024

Atmospheric drag stands out as the predominant non-gravitational force acting on satellites in Low Earth Orbit (LEO), with altitudes below 2000 km. This drag exhibits a strong dependence on the thermospheric mass density (TMD), a parameter of vital significance in the realms of orbit determination, prediction, collision avoidance, and re-entry forecasting. A multitude of empirical TMD models have been developed, incorporating contemporary data sources, including TMD measurements obtained through onboard accelerometers on LEO satellites. This paper delves into three different TMD modelling techniques, specifically, Fourier series, spherical harmonics, and artificial neural networks (ANNs), during periods of geomagnetic quiescence. The TMD data utilised for modelling and evaluation are derived from three distinct LEO satellites: GOCE (at an altitude of approximately 250 km), CHAMP (around 400 km), and GRACE (around 500 km), spanning the years 2002 to 2013. The consistent utilisation of these TMD data sets allows for a clear performance assessment of the different modelling approaches. Subsequent research will shift its focus to TMD modelling during geomagnetic disturbances, while the present work can serve as a foundation for disentangling TMD variations stemming from geomagnetic activity. Furthermore, this study undertakes precise TMD modelling during geomagnetic quiescence using data obtained from the GRACE (at an altitude of approximately 500 km), CHAMP (around 400 km), and GOCE (roughly 250 km) satellites, covering the period from 2002 to 2013. It employs three distinct methods, namely Fourier analysis, spherical harmonics (SH) analysis, and the artificial neural network (ANN) technique, which are subsequently compared to identify the most suitable methodology for TMD modelling. Additionally, various combinations of time and coordinate representations are scrutinised within the context of TMD modelling. Our results show that the precision of low-order Fourier-based models can be enhanced by up to 10 % through the utilisation of geocentric solar magnetic coordinates. Both the Fourier- and SH-based models exhibit limitations in approximating the vertical gradient of TMD. Conversely, the ANN-based model possesses the capacity to capture vertical TMD variability without manifesting sensitivity to variations in time and coordinate inputs.
Characterizing Isotopic Composition and Trajectories of Atmospheric River Events
Authors: Greenblat Ariel T. , Allen Diana M. , Hahm W. Jesse
Published: January 2024

Landfalling atmospheric rivers (LARs) are important drivers of mid-latitude climate; however, our understanding of the water vapour sources, storm trajectories, and receiving waters of ARs is limited. This study aims to characterize LARs in southwest British Columbia by their isotopic composition and storm track trajectories and to better understand how AR-derived precipitation is manifested in watershed waters. ARs were depleted (−11.71‰ δ18O, −85.80‰ δ2H, n = 19) compared to non-ARs (−9.47‰ δ18O, −69.58‰ δ2H, n = 32) (p = 0.03); however, the difference is minimal. LAR storm tracks did not show any obvious correlation to their isotopic composition, despite the large variability in their source regions across the Pacific Ocean. The lack of correlation is attributed to mixing air parcels, thereby incorporating moisture with different isotopic compositions into the main transport mechanism. D-excess values for ARs and non-ARs were statistically similar, although seasonal differences were observed. ARs with higher d-excess were sourced from the central Pacific, whereas ARs with lower d-excess had storm tracks through the northern Pacific. Watershed water d-excess values (mean = 8.58 ± 2.97‰) were more similar to winter precipitation (mean = 10.1 ± 5.1‰), compared to summer (mean = 2.8 ± 4.3‰), likely due to their source of winter precipitation at high elevation. A greater range in AR d-excess winter values relative to summer values (3.6–16.6‰, −0.3–6.0‰, respectively) is attributed to storm track variability.
Spatiotemporal Dynamics of Ecosystem Service Balance in the Beijing-Tianjin-Hebei Region and Its Ecological Security Barrier with Inner Mongolia
Authors: Fang Yixin , Hu Richa , Meng Fanhao , Luo Min , Sa Chula , Bao Yuhai , Lei Jun , Chao Lu
Published: January 2024

In the context of the global decline in the capacity of ecosystem services (ESs) to meet increasing human demands, assessing and quantifying ESs is crucial for ecological policy formulation. To address this, our study employs an adjusted land-use matrix method and the patch-generating land-use simulation (PLUS) model for a quantitative analysis of the ES balance in the Beijing–Tianjin–Hebei–Inner Mongolia (JJJM) region from 2000 to 2020, projecting to 2040. Focusing on the JJJM region, a focal area for ecological policy exhibits significant socioeconomic disparities, revealing a synergistic interplay in the ESs balance. Areas with high vegetation cover, like forests and grasslands, demonstrate an elevated ESs balance, with Inner Mongolia having the highest total ESs balance at 71.40. Conversely, highly urbanized areas, such as Beijing and Tianjin, face deficits in the ESs balance, with Tianjin recording the lowest at 17.83. Our results show an upward trend in total ESs balance in the JJJM region (slope: 0.08 year−1). In particular, regulating services are declining (slope: −0.04 year−1), cultural services are increasing (slope: 0.08 year−1), and provisioning services remain relatively stable. Projecting to 2040, our analysis indicates a slight decline in ESs balance, attributed to Inner Mongolia’s urban expansion. This emphasizes the need for industrial transfers and proactive urbanization promotion to enhance ESs balance and support sustainable management and ecological civilization development in the JJJM region.
Exposure to Wildfires Exposures and Mental Health Problems among Firefighters: A Systematic Review
Authors: Bonita Isabelle , Halabicky Olivia M. , Liu Jianghong
Published: January 2024

Firefighters are severely impacted by climate events, yet they are an underexamined population with regard to climate change research. This systematic review aims to synthesize the existing literature on the psychological effects of wildfire events on firefighters and to discuss some of the major gaps in disaster research relating to first responders and their mental health. A thorough search of the existing literature through June 2023 on the topic of wildfires and first responder psychological health was conducted through the databases PubMed, PsychINFO, and Embase. This search yielded 13 final studies which met the exclusion and inclusion criteria for this review. The final studies consisted of populations that responded to wildfire events from four different countries (two from Israel, one from Canada, two from Greece, and eight from Australia). The data gathered by this review suggest that firefighters may experience many environmental and occupational exposures during wildfire suppression, which are linked to an increased risk of PTSD and other psychological symptoms even months after the event. This review brings to light the need for further research of the compounded effect of the environmental and psychological exposures of first responders and the potential psychological effects of those exposures.
Heuristic Ensemble Construction Methods of Automatically Designed Dispatching Rules for the Unrelated Machines Environment
Authors: Đurasević Marko , Jakobović Domagoj
Published: January 2024

Dynamic scheduling represents an important class of combinatorial optimisation problems that are usually solved with simple heuristics, the so-called dispatching rules (DRs). Designing efficient DRs is a tedious task, which is why it has been automated through the application of genetic programming (GP). Various approaches have been used to improve the results of automatically generated DRs, with ensemble learning being one of the best-known. The goal of ensemble learning is to create sets of automatically designed DRs that perform better together. One of the main problems in ensemble learning is the selection of DRs to form the ensemble. To this end, various ensemble construction methods have been proposed over the years. However, these methods are quite computationally intensive and require a lot of computation time to obtain good ensembles. Therefore, in this study, we propose several simple heuristic ensemble construction methods that can be used to construct ensembles quite efficiently and without the need to evaluate their performance. The proposed methods construct the ensembles solely based on certain properties of the individual DRs used for their construction. The experimental study shows that some of the proposed heuristic construction methods perform better than more complex state-of-the-art approaches for constructing ensembles.
The AA-Viscosity Algorithm for Fixed-Point, Generalized Equilibrium and Variational Inclusion Problems
Authors: Asghar Muhammad Waseem , Abbas Mujahid , Rouhani Behzad Djafari
Published: January 2024

The aim of this paper is to propose an inertial-type AA-viscosity algorithm for approximating the common solutions of the split variational inclusion problem, the generalized equilibrium problem and the common fixed-point problem of nonexpansive mappings. The strong convergence of an iterative sequence obtained through the proposed method is proved under some mild assumptions. Consequently, approximations of the solution of the split feasibility problem, the relaxed split feasibility problem, the split common null point problem and the split minimization problem are given. The applicability of our proposed algorithm has been illustrated with the help of a numerical example. Our iterative method was then compared graphically with different comparable methods in the existing literature.
Integer-Valued Split-BREAK Process with a General Family of Innovations and Application to Accident Count Data Modeling
Authors: Stojanović Vladica S. , Bakouch Hassan S. , Gajtanović Zorica , Almuhayfith Fatimah E. , Kuk Kristijan
Published: January 2024

This paper presents a novel count time-series model, named integer-valued Split-BREAK process of the first order, abbr. INSB(1) model. This process is examined in terms of its basic stochastic properties, such as stationarity, mean, variance and correlation structure. In addition, the marginal distribution, over-dispersion and zero-inflation properties of the INSB(1) process are also examined. To estimate the unknown parameters of the INSB(1) process, an estimation procedure based on probability generating functions (PGFs) is proposed. For the obtained estimators, their asymptotic properties, as well as the appropriate simulation study, are examined. Finally, the INSB(1) process is applied in the dynamic analysis of some real-world series, namely, the numbers of serious traffic accidents in Serbia and forest fires in Greece.
In Situ/Operando Techniques for Unraveling Mechanisms of Ionic Transport in Solid-State Lithium Indium Halide Electrolyte
Authors: Bahmani Farzaneh , Rodmyre Collin , Ly Karen , Mack Paul , White Smirnova Alevtina
Published: January 2024

Over the past years, lithium-ion solid-state batteries have demonstrated significant advancements regarding such properties as safety, long-term endurance, and energy density. Solid-state electrolytes based on lithium halides offer new opportunities due to their unique features such as a broad electrochemical stability window, high lithium-ion conductivity, and elasticity at close to melting point temperatures that could enhance lithium-ion transport at interfaces. A comparative study of lithium indium halide (Li3InCl6) electrolytes synthesized through a mechano-thermal method with varying optimization parameters revealed a significant effect of temperature and pressure on lithium-ion transport. An analysis of Electrochemical Impedance Spectroscopy (EIS) data within the temperature range of 25–100 °C revealed that the optimized Li3InCl6 electrolyte reveals high ionic conductivity, reaching 1.0 mS cm−1 at room temperature. Herein, we present the utilization of in situ/operando X-ray Photoelectron Spectroscopy (XPS) and in situ X-ray powder diffraction (XRD) to investigate the temperature-dependent behavior of the Li3InCl6 electrolyte. Confirmed by these methods, significant changes in the Li3InCl6 ionic conductivity at 70 °C were observed due to phase transformation. The observed behavior provides critical information for practical applications of the Li3InCl6 solid-state electrolyte in a broad temperature range, contributing to the enhancement of lithium-ion solid-state batteries through their improved morphology, chemical interactions, and structural integrity.
Electrospun Nanofiber Membrane for Cultured Corneal Endothelial Cell Transplantation
Authors: Song Euisun , Chen Karen M. , Margolis Mathew S. , Wungcharoen Thitima , Koh Won-Gun , Myung David
Published: January 2024

The corneal endothelium, comprising densely packed corneal endothelial cells (CECs) adhering to Descemet’s membrane (DM), plays a critical role in maintaining corneal transparency by regulating water and ion movement. CECs have limited regenerative capacity within the body, and globally, there is a shortage of donor corneas to replace damaged corneal endothelia. The development of a carrier for cultured CECs may address this worldwide clinical need. In this study we successfully manufactured a gelatin nanofiber membrane (gelNF membrane) using electrospinning, followed by crosslinking with glutaraldehyde (GA). The fabricated gelNF membrane exhibited approximately 80% transparency compared with glass and maintained a thickness of 20 µm. The gelNF membrane demonstrated desirable permeability and degradability for a Descemet’s membrane analog. Importantly, CECs cultured on the gelNF membrane at high densities showed no cytotoxic effects, and the expression of key CEC functional biomarkers was verified. To assess the potential of this gelNF membrane as a carrier for cultured CEC transplantation, we used it to conduct Descemet’s membrane endothelial keratoplasty (DMEK) on rabbit eyes. The outcomes suggest this gelNF membrane holds promise as a suitable carrier for cultured CEC transplantation, offering advantages in terms of transparency, permeability, and sufficient mechanical properties required for successful transplantation.
Effects of Hypoxia Stress on Survival, Antioxidant and Anaerobic Metabolic Enzymes, and Related Gene Expression of Red Swamp Crayfish Procambarus clarkii
Authors: Zeng Qinghui , Luo Mingzhong , Qin Lirong , Guo Chao , Liu Jiashou , Zhang Tanglin , Feng Guangpeng , Li Wei
Published: January 2024

The red swamp crayfish Procambarus clarkii is the most reared shrimp in China, but it is often affected by hypoxia stress in the process of seedling culture and adult crayfish culture. The oxygen consumption rate and asphyxiation point of juvenile crayfish (1.17 ± 0.03 g) and subadult crayfish (11.68 ± 0.11 g) at different temperatures (20, 22, 24, 26, and 28 °C) were studied. The survival, glycolysis, and expression of antioxidant genes were compared under 24 h acute hypoxia stress (1, 2, and 3 mg/L) and normal dissolved oxygen (7.5 mg/L). The results showed that the oxygen consumption rate and asphyxiation point of juvenile and subadult crayfish increased with increasing temperatures (20–28 °C). At the same temperature, the oxygen consumption rate and asphyxiation point of juvenile crayfish were significantly higher than those of subadult crayfish (p < 0.05). Within 24 h, the three hypoxia stress environments did not lead to the death of crayfish, indicating that P. clarkii has a strong ability to adapt to hypoxia. Hypoxia stress significantly affected the activities of antioxidant and anaerobic metabolic enzymes and gene expression in juvenile and subadult crayfish. The activities of the superoxide dismutase (SOD), catalase (CAT), and lactate dehydrogenase (LDH) and the content of lactic acid (LD) in the hepatopancreas of juvenile and subadult crayfish in the hypoxia stress groups increased significantly. The expression levels of SOD mRNA, CAT mRNA, Hsp70 mRNA, and crustin 4 mRNA in the hepatopancreas of juvenile and subadult crayfish in the hypoxia stress groups were significantly higher than those in the control group (p < 0.05), and the higher the degree of hypoxia stress, the higher the expression of each gene. The results showed that the antioxidant system of juvenile crayfish was more sensitive to hypoxia environments, and hypoxia stress resulted in increased stress levels in juvenile crayfish and subadult crayfish.
Red Ginseng Attenuates the Hepatic Cellular Senescence in Aged Mice
Authors: Lee Da-Yeon , Arndt Juliana , O’Connell Jennifer F. , Egan Josephine M. , Kim Yoo
Published: January 2024

Cellular senescence is defined as an irreversible cell cycle arrest accompanied by morphological and physiological alterations during aging. Red ginseng (RG), processed from fresh ginseng (Panax ginseng C.A. Meyer) with a one-time steaming and drying process, is a well-known beneficial herbal medicine showing antioxidant, anti-inflammatory, and anti-aging properties. The current study aimed to investigate the benefits of RG in alleviating hepatic cellular senescence and its adverse effects in 19-month-old aged mice. We applied two different intervention methods and durations to compare RG’s effects in a time-dependent manner: (1) oral gavage injection for 4 weeks and (2) ad libitum intervention for 14 weeks. We observed that 4-week RG administration was exerted to maintain insulin homeostasis against developing age-associated insulin insensitivity and suppressed cellular senescence pathway in the liver and primary hepatocytes. Moreover, with remarkable improvement of insulin homeostasis, 14-week RG supplementation downregulated the activation of c-Jun N-terminal kinase (JNK) and its downstream transcriptional factor nuclear factor-κB (NF-κB) in aged mice. Lastly, RG treatment significantly reduced the senescence-associated β-galactosidase (SA-β-gal)-positive cells in primary hepatocytes and ionizing radiation (IR)-exposed mouse embryonic fibroblasts (MEFs). Taken together, we suggest that RG can be a promising candidate for a senolytic substance by preventing hepatic cellular senescence.
The Role of the Brain-Derived Neurotrophic Factor in Chronic Pain: Links to Central Sensitization and Neuroinflammation
Authors: Xiong Huan-Yu , Hendrix Jolien , Schabrun Siobhan , Wyns Arne , Campenhout Jente Van , Nijs Jo , Polli Andrea
Published: January 2024

Chronic pain is sustained, in part, through the intricate process of central sensitization (CS), marked by maladaptive neuroplasticity and neuronal hyperexcitability within central pain pathways. Accumulating evidence suggests that CS is also driven by neuroinflammation in the peripheral and central nervous system. In any chronic disease, the search for perpetuating factors is crucial in identifying therapeutic targets and developing primary preventive strategies. The brain-derived neurotrophic factor (BDNF) emerges as a critical regulator of synaptic plasticity, serving as both a neurotransmitter and neuromodulator. Mounting evidence supports BDNF’s pro-nociceptive role, spanning from its pain-sensitizing capacity across multiple levels of nociceptive pathways to its intricate involvement in CS and neuroinflammation. Moreover, consistently elevated BDNF levels are observed in various chronic pain disorders. To comprehensively understand the profound impact of BDNF in chronic pain, we delve into its key characteristics, focusing on its role in underlying molecular mechanisms contributing to chronic pain. Additionally, we also explore the potential utility of BDNF as an objective biomarker for chronic pain. This discussion encompasses emerging therapeutic approaches aimed at modulating BDNF expression, offering insights into addressing the intricate complexities of chronic pain.
Advancements in Genetic and Biochemical Insights: Unraveling the Etiopathogenesis of Neurodegeneration in Parkinson’s Disease
Authors: Ratan Yashumati , Rajput Aishwarya , Pareek Ashutosh , Pareek Aaushi , Jain Vivek , Sonia Sonia , Farooqui Zeba , Kaur Ranjeet , Singh Gurjit
Published: January 2024

Parkinson’s disease (PD) is the second most prevalent neurodegenerative movement disorder worldwide, which is primarily characterized by motor impairments. Even though multiple hypotheses have been proposed over the decades that explain the pathogenesis of PD, presently, there are no cures or promising preventive therapies for PD. This could be attributed to the intricate pathophysiology of PD and the poorly understood molecular mechanism. To address these challenges comprehensively, a thorough disease model is imperative for a nuanced understanding of PD’s underlying pathogenic mechanisms. This review offers a detailed analysis of the current state of knowledge regarding the molecular mechanisms underlying the pathogenesis of PD, with a particular emphasis on the roles played by gene-based factors in the disease’s development and progression. This study includes an extensive discussion of the proteins and mutations of primary genes that are linked to PD, including α-synuclein, GBA1, LRRK2, VPS35, PINK1, DJ-1, and Parkin. Further, this review explores plausible mechanisms for DAergic neural loss, non-motor and non-dopaminergic pathologies, and the risk factors associated with PD. The present study will encourage the related research fields to understand better and analyze the current status of the biochemical mechanisms of PD, which might contribute to the design and development of efficacious and safe treatment strategies for PD in future endeavors.
Serum Brevican as a Biomarker of Cerebrovascular Disease in an Elderly Cognitively Impaired Cohort
Authors: Chia Rachel S. L. , Minta Karolina , Wu Liu-Yun , Salai Kaung H. T. , Chai Yuek Ling , Hilal Saima , Venketasubramanian Narayanaswamy , Chen Christopher P. , Chong Joyce R. , Lai Mitchell K. P.
Published: January 2024

In the brain, the extracellular matrix (ECM) composition shapes the neuronal microenvironment and can undergo substantial changes with cerebral pathology. Brevican is integral to the formation of the ECM’s neuroprotective perineuronal nets (PNNs). Decreased brevican levels were reported in vascular dementia (VaD) but not in Alzheimer’s disease (AD). However, the status of brevican in clinical cohorts with high concomitance of AD pathological burden and cerebrovascular disease (CeVD) is unclear. In this study, 32 non-cognitively impaired (NCI), 97 cognitively impaired no dementia (CIND), 46 AD, and 23 VaD participants recruited from memory clinics based in Singapore underwent neuropsychological and neuroimaging assessments, together with measurements of serum brevican. Association analyses were performed between serum brevican and neuroimaging measures of CeVDs, including white matter hyperintensities (WMHs), lacunes, cortical infarcts, and cerebral microbleeds. Using an aggregated score for CeVD burden, only CIND participants showed lower brevican levels with higher CeVD compared to those with lower CeVD burden (p = 0.006). Among the CeVD subtypes assessed, only elevated WMH burden was associated with lower brevican levels (OR = 2.7; 95% CI = 1.3–5.5). Our findings suggest that brevican deficits may play a role in early cerebrovascular damage in participants at risk of developing dementia.