bluuebunny/crossref_metadata_2025 huggingface dataset:
An Improved Acoustic Diffusion Equation Model for Long-Channel Underground Spaces
Authors: Mou Chao , Yang Qiliang , Xing Jianchun , Chen Tao , Zou Rongwei
Published: September 2023

The acoustic diffusion equation model has been widely applied in various scenarios, but a larger prediction error exists when applied to underground spaces, showing a significantly lower characteristic of the sound pressure level in the later stage compared to field tests since underground spaces have a more closed acoustic environment. Therefore, we analyze the characteristics of underground spaces differentiating from aboveground spaces when applying the model and propose an improved model from the perspective of energy balance. The energy neglected in the calculation of the acoustic diffusion equation model is compensated in long channel underground spaces named “acoustic escape compensation”. A simulation and two field experiments are conducted to verify the effectiveness of the proposed compensation strategy in long-channel underground spaces. The mean square error is used to evaluate the differences between the classical model and the improved model, which shows a numerical improvement of 1.3 in the underground field test. The results show that the improved model is more suitable for describing underground spaces. The proposed strategy provides an effective extension of the acoustic diffusion equation model to solve the problem of sound field prediction and management in underground spaces.
Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning
Authors: Ali Aitizaz , Ali Hashim , Saeed Aamir , Ahmed Khan Aftab , Tin Ting Tin , Assam Muhammad , Ghadi Yazeed Yasin , Mohamed Heba G.
Published: September 2023

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.
Short-Term L-Citrulline Supplementation Does Not Affect Blood Pressure, Pulse Wave Reflection, or Arterial Stiffness at Rest and during Isometric Exercise in Older Males
Authors: Tryfonos Andrea , Christodoulou Filippos , Pamboris George M. , Christodoulides Stephanos , Theodorou Anastasios A.
Published: September 2023

Hypertension and arterial stiffness are significant factors contributing to cardiovascular disease. L-citrulline, a nitric oxide precursor, has been proposed as a nutritional, non-pharmacological blood pressure-lowering intervention. This study aimed to investigate the impact of L-citrulline on central and peripheral blood pressure, pulse wave reflection, and central arterial stiffness at rest and during an isometric knee extension exercise protocol. Twelve older males received 6 g of L-citrulline or a placebo for six days using a double-blind crossover design. Blood hemodynamics parameters (i.e., aortic and brachial systolic and diastolic blood pressure, mean arterial pressure, pulse pressure, heart rate), pulse wave reflection (i.e., augmented pressure, augmentation index, forward/backward wave pressure), and arterial stiffness (i.e., carotid–femoral pulse wave velocity) were measured at baseline, post-supplementation, and during isometric exercise. No significant effects of L-citrulline supplementation were observed at rest or during exercise on blood pressure, pulse wave reflection, or arterial stiffness. Both central and peripheral blood pressure were increased during the exercise, which is consistent with isometric contractions. The results of the present study do not support any blood pressure-lowering effect of short-term L-citrulline at rest or during low-intensity isometric exercise compared to the pre-exercise values in older males.
Are Late-Born Young Soccer Players Less Mature Than Their Early-Born Peers, Although No Differences in Physical and Technical Performance Are Evident?
Authors: Bezuglov Eduard , Malyakin Georgiy , Emanov Anton , Malyshev Grigory , Shoshorina Maria , Savin Evgeny , Lazarev Artemii , Morgans Ryland
Published: September 2023

The aim of the study was to compare the status of somatic maturity, anthropometry, strength, speed, and soccer-specific technical skills of players from leading youth soccer academies born in different quarters of the same calendar year. A total of 678 young male soccer players from eight leading Russian soccer academies took part in the study. The following anthropometric measures and physical characteristics were measured: height, weight, body mass index, countermovement jumps (CMJ), 5, 10, and 20 m sprints, speed dribbling, foot and body ball juggling, and short and long pass accuracy. The determination of somatic maturity as a percentage of projected adult height was collected. All subject dates of birth were divided into four quartiles according to the month of birth. The analysis of all data obtained was conducted both within the total sample and by quartiles of birth, according to the age group categories of 12–13 years, 14–15 years, and 16–17 years and the degree of somatic maturity. There was a widespread relative age effect, with 43.5% of early-born players and only 9.6% of late-born players representing the sample. Early-born players were more mature than late-born players (p < 0.001 and p < 0.001) but had no statistically significant differences in strength, speed, or soccer-specific skills.
Analysis of Machine Learning Models for Wastewater Treatment Plant Sludge Output Prediction
Authors: Shao Shuai , Fu Dianzheng , Yang Tianji , Mu Hailin , Gao Qiufeng , Zhang Yun
Published: September 2023

With China’s significant investment in wastewater treatment plants, urban sewage is effectively collected and treated, resulting in a substantial byproduct—sludge. As of 2021, a total of 2827 wastewater treatment plants have been constructed across 31 provinces, municipalities, and autonomous regions in China, with a processing capacity of 60.16 billion cubic meters. The production of dry sludge amounts to 14.229 million tons. The treatment and utilization of sludge pose significant challenges. The scientific calculation of sludge production is the basis for the reduction at the source and the design of sludge treatment and energy utilization. It is directly related to the construction scale, structure size, and equipment selection of the sludge treatment and utilization system and affects the operation and environmental management of the sludge treatment system. The wastewater treatment process using microbial metabolism is influenced by various known and unknown parameters, exhibiting highly nonlinear characteristics. These complex characteristics require the use of mathematical modeling for simulations and control. In this study, nine types of machine learning algorithms were used to establish sludge production prediction models. The extreme gradient boosting tree (XGBoost) and random forest models had the best prediction accuracies, with the former having RMSE, MAE, MAPE, and R2 values of 4.4815, 2.1169, 1.7032, 0.0415, and 0.8218, respectively. These results suggested a superiority of ensemble learning models in fitting highly nonlinear data. In addition, the contribution and influence of various input features affecting sludge output were also studied for the XGBoost model, and the daily wastewater inflow volume and surrounding temperature features had the greatest impact on sludge production. The innovation of this study lies in the application of machine learning algorithms to achieve the prediction of sludge production in wastewater treatment plants.
Prediction of Aircraft Wake Vortices under Various Crosswind Velocities Based on Convolutional Neural Networks
Authors: He Xin , Zhao Rui , Gao Haoran , Yuan Changjiang , Wang Jingyi
Published: September 2023

In order to overcome the time-consuming computational drawback of using computational fluid dynamics (CFD) for the numerical simulation of aircraft wake vortex evolution under different crosswind velocities, this paper proposes a wake vortex prediction model based on a convolutional neural network (CNN) algorithm. The study focuses on the B737-800 aircraft, and employs CFD numerical simulations to obtain the evolutionary characteristics of wake vortex parameters under crosswind velocities ranging from 0 to 7 m/s. The wake vortex velocity and Q-criterion vorticity values are collected and partitioned into mutually exclusive training and testing datasets. A CNN model is constructed, and the training dataset is used to tune hyperparameters to minimize loss and achieve accurate predictions. After saving the trained model, the desired crosswind velocity value is input to obtain the predicted wake vortex velocity and Q-criterion vorticity values. The results indicate that the convolutional neural network model exhibits an average absolute percentage error of 1.5%, which is 2.3% lower than that of the fully connected neural network model. This suggests that convolutional neural networks can enhance the accuracy of wake vortex predictions, as demonstrated in this study. Compared to traditional CFD methods, the proposed model reduces the computation time by approximately 40 times, effectively improving computational efficiency and offering valuable insight for studies involving numerous numerical simulations, such as analyzing the safety separation between aircraft wake vortices during paired approach procedures.
An Explainable Prediction Model for Aerodynamic Noise of an Engine Turbocharger Compressor Using an Ensemble Learning and Shapley Additive Explanations Approach
Authors: Huang Rong , Ni Jimin , Qiao Pengli , Wang Qiwei , Shi Xiuyong , Yin Qi
Published: September 2023

In the fields of environment and transportation, the aerodynamic noise emissions emitted from heavy-duty diesel engine turbocharger compressors are of great harm to the environment and human health, which needs to be addressed urgently. However, for the study of compressor aerodynamic noise, particularly at the full operating range, experimental or numerical simulation methods are costly or long-period, which do not match engineering requirements. To fill this gap, a method based on ensemble learning is proposed to predict aerodynamic noise. In this study, 10,773 datasets were collected to establish and normalize an aerodynamic noise dataset. Four ensemble learning algorithms (random forest, extreme gradient boosting, categorical boosting (CatBoost) and light gradient boosting machine) were applied to establish the mapping functions between the total sound pressure level (SPL) of the aerodynamic noise and the speed, mass flow rate, pressure ratio and frequency of the compressor. The results showed that, among the four models, the CatBoost model had the best prediction performance with a correlation coefficient and root mean square error of 0.984798 and 0.000628, respectively. In addition, the error between the predicted total SPL and the observed value was the smallest, at only 0.37%. Therefore, the method based on the CatBoost algorithm to predict aerodynamic noise is proposed. For different operating points of the compressor, the CatBoost model had high prediction accuracy. The noise contour cloud in the predicted MAP from the CatBoost model was better at characterizing the variation in the total SPL. The maximum and minimum total SPLs were 122.53 dB and 115.42 dB, respectively. To further interpret the model, an analysis conducted by applying the Shapley Additive Explanation algorithm showed that frequency significantly affected the SPL, while the speed, mass flow rate and pressure ratio had little effect on the SPL. Therefore, the proposed method based on the CatBoost algorithm could well predict aerodynamic noise emissions from a turbocharger compressor.
Cooperative Reference Velocity Regulation Control for Virtual Coupling Systems of Heavy-Haul Trains
Authors: Yu Xiaoquan , Li Wei , Li Shuo , Yang Yingze , Peng Jun
Published: September 2023

Virtual coupling systems are important for improving the safety and efficiency of heavy-haul trains by coordinating the velocities of heavy-haul trains. However, the set point change of reference velocity leads to unsafe train operation. To address this issue, in this paper, a cooperative reference velocity regulation control method is proposed for virtual coupling systems of heavy-haul trains, which can smoothly adjust the train velocity during the operation process. Firstly, the heavy-haul trains are characterized by a cyber-physical model, where the physical layer models the longitudinal dynamics of the trains, while the cyber layer represents the communication topology of the trains using graph theory. Secondly, a cooperative control law is designed to achieve velocity consensus and maintain a safe distance between adjacent trains. The set point modulation method is utilized to smoothly adjust the reference velocity of heavy-haul trains, suppressing velocity fluctuations of heavy-haul trains. The proposed approach is validated through extensive simulations under acceleration and deceleration conditions. Simulation results verify that the proposed method can significantly reduce the velocity fluctuations during the operation of virtual coupling systems when compared with the classical cooperative control method.
Impact of Energy Productivity and Industrial Structural Change on Energy Intensity in China: Analysis Based on Provincial Panel Data
Authors: Dai Chenyu , Liu Fengliang
Published: September 2023

Since 2000, China’s energy intensity has shown a fluctuating downward trend. Most existing studies have attributed the decline to technological progress, and only a few studies have recognized the role of industrial structure change. In this paper, a multi-region and multi-sector CGE (computable general equilibrium) model and a numerical simulation method are used to study how technological progress and structural transformation affected the energy intensity of 30 provincial-level regions in China from 2000 to 2019. The results show the following points. (1) The contribution of technological progress to the decline in energy intensity was the highest in the central region, followed by the western region, and was the lowest in the eastern region. (2) The progress of energy technology in the agriculture and industry sectors promoted the transition of energy consumption to the service sector, thereby reducing the overall energy intensity. This effect was the strongest in the eastern region, followed by the central region, and was the weakest in the western region. (3) The progress of energy technology in the service industry promoted the transition of energy consumption to industry and agriculture, thereby enhancing the energy intensity, and this effect was the strongest in the eastern region, followed by the western region, and the weakest in the central region. The conclusion of this paper provides a theoretical basis for realizing regional carbon peaking in sequence in China.
Commercial Retirement FOFs in China: Investment and Persistence Performance Analysis
Authors: Guo Yundan , Shen Li
Published: September 2023

The number and size of China’s commercial retirement Fund of Funds (FOFs) have exploded since 2018, reflecting a dearth of Chinese retirement products and widespread retirement anxiety among individual investors. Therefore, the performance of retirement FOFs continues to garner widespread interest from academia and society. This study evaluates the performance and sustainability of the investment strategies employed by China’s retirement FOFs using standard relative and absolute measures. The Sharpe ratio, Treynor ratio, and Jensen’s alpha are used as performance measurement standards, and the sustainability of performance is evaluated using the performance dichotomy, cross-sectional regression, and Spearman rank correlation coefficient methods. Target-risk FOFs for retirement are categorized into four groups: conservative, stable, balanced, and aggressive, with each group assuming progressively greater levels of risk. In evaluating fund performance, it was determined that the aggressive and stable groups of funds generated greater excess returns (as indicated by the inflation-adjusted Sharpe ratio). Additionally, the stable group of funds generated greater investment returns than the other groups (as all statistically significant alpha values for Jensen were positive). When evaluating the sustainability of fund performance, it was determined that the stable and balanced group funds exhibited the least sustainable performance. During the economic recession caused by the COVID-19 pandemic between 2020 and 2021, there were multiple fund performance ranking reversals (with significantly negative cross-sectional regression coefficients and Spearman coefficients). In the second half of 2022, the fund’s performance exhibited signs of sustainability (as indicated by significant performance dichotomy test values and positively significant Spearman coefficients). Still, this trend did not persist into 2023. Summarizing the different performance indicator results reveals that the stable group is the most worthwhile fund group to purchase among the four groups. Also, given that the historical performance of a signal fund is not sustainable, the investors should diversify their investments in this group and try to obtain the average return of the stable strategy to achieve the goal of supplementing retirement.
Post-Occupancy Evaluation in Post-Disaster Social Housing in a Hot-Humid Climate Zone in Mexico
Authors: Aguilar-Perez Yarely , Rodrigues Lucelia , Beccarelli Paolo , Tubelo Renata
Published: September 2023

In Mexico, the national fund for disasters (FONDEN) spent up to USD 800 million yearly building low-income housing for victims of highly destructive disasters. Since 2002, a total of 34 thousand new houses have been built as a response to Hurricane Isidore. However, recent research suggests that most of the FONDEN houses have been either abandoned or repurposed, which questions their suitability. In this paper, the authors sought to gain insight into occupants’ perception of post-disaster social housing in Yucatan, southern Mexico, with views of understanding their use across this housing typology. The method employed in this study was a post-occupancy evaluation (POE), investigating occupants’ satisfaction levels and thermal comfort in the homes. This was conducted through questionnaires, observation through photographic documentation, and environmental monitoring. Results revealed that 52% of occupants reported extreme dissatisfaction during warm seasons and 28% experienced dissatisfaction during cold seasons. The most used electrical appliance (84%) was found to be fans for increasing air movement. This was consistent with the results of the environmental monitoring, which demonstrated that there was thermal discomfort 67% of the time in September and 19% in December. This accounts for a warm-humid period and cool period, respectively. The results suggested that thermal discomfort may contribute to the abandonment and repurposing of these houses.
Outdoor Education, Integrated Soccer Activities, and Learning in Children with Autism Spectrum Disorder: A Project Aimed at Achieving the Sustainable Development Goals of the 2030 Agenda
Authors: Morsanuto Stefania , Peluso Cassese Francesco , Tafuri Francesco , Tafuri Domenico
Published: September 2023

This research aims to promote motor activity in children with autism spectrum disorder through the development of an adapted integrated soccer project played outdoors. The project is carried out in collaboration with the nonprofit “Smile Association” of the city of Frosinone (Lazio-Italy) and the Pegaso and Parthenope Universities. The main purpose of the Smile Association is to provide an educational and sports service in an area poorly provided with opportunities for children with intellectual disabilities, allowing them to change their predominantly sedentary condition and, through corporeality, to improve cognitive, affective, and relational processes. Participation in sports activities allows for the development of motor skills, social interactions, and cognitive stimulation. There is a need for available resources and programs dedicated exclusively to children with ASD to help them develop social, motor, and cognitive skills. One of the programs that might be helpful is a structured group play program involving physical activity. In addition, through the project, a dense social network has been activated between the third sector, sports companies, and local health authorities. The project aims to achieve the Sustainable Development Goals of the 2030 Agenda in Articles 3, 4, and 11. This paper describes the correlation between learning and motor activity. According to the hypotheses of this research, integrated soccer activity implements cognitive skills, particularly those related to memory for visual stimuli and to the theory of mind, as well as sense-motor skills. Work carried out to improve attentional skills can reduce stereotypical behaviours. The results showed that continuous and regular engagement in motor activities has positive health outcomes for children with autism in terms of reduced symptomatology and improved quality of life. A total of 108 statistical units (the control group consisted of 18 statistical units, and the sample comprised 90 participants) aged 8 to 11 years were examined. The project is aimed at girls (in compliance with Art. 5 regarding gender equality); however, the participants identified by the local health authority are predominantly male.
Foreign Direct Investments—A Perspective of Sustainability: Evidence from the Austrian and German Labor Market
Authors: Pirju Ionel Sergiu , Marchis Gabriela , Panaitescu Manuela , Prunău Nicolae Florin , Ambrozie Alisa Mihaela
Published: September 2023

In the context of the sustainability roadmap, this study presents a quantitative perspective by investigating the interconnection between foreign direct investments (FDIs) and wage dynamics to contribute to the reduction of gender inequalities. This paper focuses on Austria and Germany, two European countries with strong commitments to sustainable change, as representative cases for the analysis. The main objective is to quantify the impact of FDIs on salary trends and investigate their relationship, especially concerning gender-related aspects. This research introduces fresh insights into the existing literature by shedding light on the role of FDIs in shaping wage dynamics, particularly related to gender equality. The quantitative analysis highlights the model’s robustness, revealing that approximately 86% of wage variation is explained by the independent variable, FDIs. This statistical result suggest a linear relationship between FDIs and wages in these two countries, reaffirming the potential impact of foreign investments on wage dynamics as a base for enhancing sustainable socioeconomic progress.
The Dynamic Impacts of Economic Growth, Financial Globalization, Fossil Fuel, Renewable Energy, and Urbanization on Load Capacity Factor in Mexico
Authors: Raihan Asif , Rashid Mamunur , Voumik Liton Chandra , Akter Salma , Esquivias Miguel Angel
Published: September 2023

This study explores the interplay among economic growth, financial globalization, urbanization, fossil fuel consumption, and renewable energy usage and their combined impact on the load capacity factor in Mexico. This research employs the load capacity factor as a unique measure of ecological health, facilitating a comprehensive ecosystem assessment by sequentially evaluating biocapacity and ecological effects. Using time series data spanning from 1971 to 2018, this study employs the Autoregressive Distributed Lag (ARDL) method to analyze both long-term and short-term dynamics and cointegration. The findings reveal that economic growth, fossil fuel usage, and urbanization reduce Mexico’s load capacity factor, thereby diminishing environmental quality. In contrast, the adoption of renewable energy sources and the influence of financial globalization exhibit positive effects on the load capacity factor over the long and short term. These outcomes remain consistent even when compared with alternative estimation techniques, including dynamic ordinary least squares (DOLS), fully modified least squares (FMOLS), and canonical cointegrating regression (CCR). As a priority, Mexican policymakers should accelerate the transition to renewable energy sources, encourage sustainable urban development, and foster a more ecologically conscious economic agenda. Furthermore, promoting greener technologies can enhance the load capacity and mitigate environmental degradation. Ultimately, Mexico can establish an environment conducive to expanding sustainable investments by encouraging cross-border investments, enabling global trade in financial services, and cultivating greater integration of capital and financial markets.
Trace Element (As, Cd, Cr, Cu, Pb, Se, U) Concentrations and Health Hazards from Drinking Water and Market Rice across Lahore City, Pakistan
Authors: Imran Wania , Richardson Justin B.
Published: September 2023

Exposure to toxic concentrations of trace elements in rice and drinking water is a serious issue for millions of South Asians, due to rice serving as a large portion of their diets and the geochemical enrichment of trace elements in groundwaters. The overall goal of this study was to evaluate and compare the hazards posed from toxic trace elements through the consumption of commercially available basmati rice and public drinking water sources across Lahore, Pakistan. Drinking water samples (n = 36) were collected from publicly accessible drinking taps from eight administrative towns and the cantonment. Rice samples were obtained from 11 markets (n = 33) across Lahore between December and February 2022–2023. Market rice concentrations exceeded the World Health Organization’s (WHO) limits and the Total Hazard Quotient (THQ) values exceeded 1.0 for As, Cu, and Pb, thus indicating multielement contamination. Market rice trace element concentrations and price were not correlated. As, Se, and U concentrations in drinking water were above the WHO’s drinking water guidelines and had THQ values exceeding 1.0, showing multielement contamination. Cr, Se, and U concentrations in drinking water were greater for impoverished administrative towns compared to middle and wealthy administrative towns, highlighting socioeconomic inequities in exposure to hazardous concentrations. We conclude that the citizens of Lahore are exposed to rice and drinking water that are hazardous to human health, including As and other lesser studied trace elements.
Research on Online Sales of Low-Carbon Agricultural Products by New Urban Agricultural Business Entities: Evidence from Shanghai, China
Authors: Xu Fan , Xu Fangke , Xu Pu , Liu Min , Li Yufeng
Published: September 2023

Traditional agricultural business entities present environmental problems such as high energy consumption and high pollution. To achieve goals such as carbon capping and carbon neutrality, governments should encourage urban agricultural operators to sell low-carbon agricultural products online. This could play an important role in smoothing the connection between production and marketing, achieving industrial prosperity and promoting low-carbon agricultural development. This paper explores the formation and behavioral transformation of online sales intention by combining the theory of planned behavior, the Motivation–Opportunity–Ability (MOA) model, and the binary Probit regression model using data from 106 questionnaires. The study found that economic expectations and social norms can significantly improve online sales intention during the formation stage of online sales intention. Behavior control is not conducive to improving online sales intention. In addition, we found a gap between the willingness of urban agricultural operators to sell online and their behavior. This gap requires activation and adjustment of the opportunity and capability factors in the behavior transformation phase. Finally, we found that the strengthening of policy conditions and management capacity facilitated the transformation of urban agricultural operators’ willingness to sell online into behavior. This paper provides recommendations for the online sales of low-carbon agricultural products. While we continue to deepen urban agricultural operators’ knowledge of online sales, we should also pay attention to the creation of external opportunities that are suitable for the development of online sales, and identify differences in capacity among operators. This will provide precise services.
Analysis of Disaster Risk on Expressways for Foreign Tourists to Japan Using Rental Cars: Study Using Central Nippon Expressway Pass
Authors: Morisaki Yuma , Fujiu Makoto , Osawa Shuji
Published: September 2023

The travel patterns of foreign visitors to Japan are changing from group travel, which was the mainstream to date, to individual travel, and there has been an increase in the number of cars rented by visitors as a means of transportation. The use of rental cars improves access to rural areas and is being promoted as a means of transportation. However, if a natural disaster occurs while driving a rental car, then the individual may face difficulties owing to a lack of local support. Therefore, the traffic volume on the roads used by foreign visitors to Japan during certain periods needs to be estimated in advance as well as the natural disaster risks that exist on those roads. In this study, we utilized CEP data managed by the Central Nippon Expressway Company in Japan to identify the risk of disaster for foreign visitors to Japan who use expressways with rental cars. The CEP data has usage records of foreign visitors to Japan who used rental cars on expressways in the Chubu region of Japan during the period from 1 April 2018 to 31 March 2019. Using this data, we calculated the risk of earthquake and landslide disasters. Result of this analysis, it is clear that the risk of earthquake disasters is high on expressways in the Tokai region, and the risk of landslide disasters is high on expressways in mountainous areas.
Size Structure of Exploited Holothurian Natural Stocks in the Hellenic Seas
Authors: Vafidis Dimitris , Antoniadou Chryssanthi , Apostologamvrou Chrysoula , Voulgaris Konstantinos , Varkoulis Anastasios , Giokala Efthymia , Lolas Alexios , Roditi Kyriakoula
Published: September 2023

Size limitations are commonly applied as regulatory measures for the sustainable management of marine invertebrate fishery resources. However, the setting of biologically meaningful size limits in holothurians is puzzling, due to the limited knowledge of their biology and the great plasticity in size and weight, owing to the increased contractibility of their body, and the large quantity and variability of their coelomic fluid. To evaluate the efficiency of official size limits in Hellenic fishery regulation, the biometry of the exploited species, i.e., H. tubulosa, H. poli, H. mammata, and H. sanctori, was assessed in the main fishery grounds of the Hellenic Seas. Specimens of all four species were haphazardly collected and measured for total length and drained body weight at 46 sampling sites dispersed in the north Aegean, the Sporades, the Cyclades, the Dodecanese, and the Ionian fishery grounds. According to presented results, the official size limit of 180 g for drained weight appeared to be adequate for H. tubulosa and H. mammata. Adjustment of the relevant regulations for H. poli and H. sanctori are suggested by reduction to 140 g for the former and increment to 200 g for the latter species, to better fit their biological traits.
Pairing Superfluid–Insulator Transition Induced by Atom–Molecule Conversion in Bosonic Mixtures in Optical Lattice
Authors: Deng Haiming , Tan Zhi , Kong Chao , Ye Fuqiu , Zhong Honghua
Published: September 2023

Motivated by the recent experiment on bosonic mixtures of atoms and molecules, we investigate pairing superfluid–insulator (SI) transition for bosonic mixtures of atoms and molecules in a one-dimensional optical lattice, which is described by an extended Bose–Hubbard model with atom–molecule conservation (AMC). It is found that AMC can induce an extra pair–superfluid phase though the system does not demonstrate pair-hopping. In particular, the system may undergo several pairing SI or insulator–superfluid transitions as the detuning from the Feshbach resonance is varied from negative to positive, and the larger positive detuning can bifurcate the pair–superfluid phases into mixed superfluid phases consisting of single-atomic and pair-atomic superfluid. The calculation of the second-order Rényi entropy reveals that the discontinuity in its first-order derivative corresponds to the phase boundary of the pairing SI transition. This means that the residual entanglement in our mean-field treatment can be used to efficiently capture the signature of the pairing SI transition induced by AMC.
Deep Learning and Neural Networks: Decision-Making Implications
Authors: Taherdoost Hamed
Published: September 2023

Deep learning techniques have found applications across diverse fields, enhancing the efficiency and effectiveness of decision-making processes. The integration of these techniques underscores the significance of interdisciplinary research. In particular, decisions often rely on the output’s projected value or probability from neural networks, considering different values of the relevant output factor. This interdisciplinary review examines the impact of deep learning on decision-making systems, analyzing 25 relevant papers published between 2017 and 2022. The review highlights improved accuracy but emphasizes the need for addressing issues like interpretability, generalizability, and integration to build reliable decision support systems. Future research directions include transparency, explainability, and real-world validation, underscoring the importance of interdisciplinary collaboration for successful implementation.
Speaker Profiling Based on the Short-Term Acoustic Features of Vowels
Authors: Humayun Mohammad Ali , Shuja Junaid , Abas Pg Emeroylariffion
Published: September 2023

Speech samples can provide valuable information regarding speaker characteristics, including their social backgrounds. Accent variations with speaker backgrounds reflect corresponding acoustic features of speech, and these acoustic variations can be analyzed to assist in tracking down criminals from speech samples available as forensic evidence. Speech accent identification has recently received significant consideration in the speech forensics research community. However, most works have utilized long-term temporal modelling of acoustic features for accent classification and disregarded the stationary acoustic characteristics of particular phoneme articulations. This paper analyzes short-term acoustic features extracted from a central time window of English vowel speech segments for accent discrimination. Various feature computation techniques have been compared for the accent classification task. It has been found that using spectral features as an input gives better performance than using cepstral features, with the lower filters contributing more significantly to the classification task. Moreover, detailed analysis has been presented for time window durations and frequency bin resolution to compute short-term spectral features concerning accent discrimination. Using longer time durations generally requires higher frequency resolution to optimize classification performance. These results are significant, as they show the benefits of using spectral features for speaker profiling despite the popularity of cepstral features for other speech-related tasks.
Monitoring of Mycotoxigenic Fungi in Fish Farm Water and Fumonisins in Feeds for Farmed Colossoma macropomum
Authors: Sousa Terada-Nascimento Juliana , Vieira Dantas-Filho Jerônimo , Temponi-Santos Bruna Lucieny , Perez-Pedroti Vinícius , de Lima Pinheiro Maria Mirtes , García-Nuñez Ricardo Ysaac , Mansur Muniz Igor , Bezerra de Mira Átila , Guedes Elica Amara Cecilia , de Vargas Schons Sandro
Published: September 2023

This study aimed to evaluate the occurrence of mycotoxigenic fungi in fish farm water and mycotoxins in feeds for farmed tambaqui (Colossoma macropomum). A total of 40 samples of freshwater from fish farms and 16 samples of feed were collected and analyzed for microbiology. A total of five species of free-living fungi were identified in fish farms: Aspergillus fumigatus, Penicillium citrinum, P. implicatum, Fusarium oxysporum and Alternaria alternata. These fungi species were counted in water samples at 35.14 CFU mL−1 and 24.69 CFU mL−1 in the dry seasons. In all fish farms, there was a higher abundance of fungi species in the rainy season. During visits to the fish farmers, it was possible to verify poor feed storage conditions. Concerning mutations in blood cells, in tambaqui (C. macropomum), a total of 159 anomalies were found, and in Leptodactylus petersii, 299 anomalies were found, with higher incidences in conditions above 1.0 CFU mL−1 in log10(x+1) fungi and in the rainy season. The occurrence of mycotoxicological contamination was confirmed in 81.25% of the analyzed samples. The quantified mycotoxin was Fumonisins B1 + B2 (375 to 1418 μg kg−1). Pearson’s correlation analysis showed a significant positive correlation between Fumonisins and feed samples (r = 0.83). There was also a significant positive correlation between the abundance of fungi in water and the quantification of Fumonisins (r = 0.79). Based on the results obtained, it can be concluded that free-living fungi can be used as bioindicators of water quality in fish farms. Consequently, the lack of good management practices caused microbiological contamination of the aquatic environment.
Oral and IV Varespladib Rescue Experiments in Juvenile Pigs with Weakness Induced by Australian and Papuan Oxyuranus scutellatus Venoms
Authors: Gilliam Lyndi L. , Gilliam John , Samuel Stephen P. , Carter Rebecca W. , Ritchey Jerry , Bulfone Tommaso , Gutiérrez José María , Williams David J. , Durkin Daniela M. , Stephens Sally I. , Lewin Matthew R.
Published: September 2023

Antivenom is currently the standard-of-care treatment for snakebite envenoming, but its efficacy is limited by treatment delays, availability, and in many cases, species specificity. Many of the rapidly lethal effects of envenoming are caused by venom-derived toxins, such as phospholipase A2 (sPLA2); therefore, small molecule direct toxin inhibitors targeting these toxins may have utility as initial and adjunct therapies after envenoming. Varespladib (intravenous, IV) and varespladib-methyl (oral) have been shown to potently inhibit sPLA2s from snake venoms in murine and porcine models, thus supporting their further study as potential treatments for snakebite envenoming. In this pilot study, we tested the ability of these compounds to reverse neurotoxic effects of venom from the Australian and Papuan taipan (Oxyuranus scutellatus) subspecies in juvenile pigs (Sus domesticus). The mean survival time for control animals receiving Australian taipan venom (0.03 mg/kg, n = 3) was 331 min ± 15 min; for those receiving Papuan taipan venom (0.15 mg/kg, n = 3) it was 178 ± 31 min. Thirteen pigs received Australian taipan venom and treatment with either IV or oral varespladib (or with IV to oral transition) and all 13 survived the duration of the study (≥96 h). Eight pigs received Papuan taipan venom followed by treatment: Briefly: Two animals received antivenom immediately and survived to the end of the study. Two animals received antivenom treatment delayed 45 min from envenoming and died within 4 h. Two animals received similarly delayed antivenom treatment and were rescued by varespladib. Two animals were treated with varespladib alone after a 45-min delay. Treatment with varespladib only was effective but required repeat dosing over the course of the study. Findings highlight both the importance of early treatment and, as well, a half-life for the investigational inhibitors now in Phase II clinical trials for snakebite. Varespladib rapidly reversed weakness even when administered many hours post-envenoming and, overall, our results suggest that varespladib and varespladib-methyl could be efficacious tools in the treatment of sPLA2-induced weakness from Oxyuranus envenoming. Further clinical study as initial therapy and as potential method of rescue from some types of antivenom-resistant envenomings are supported by these data.
Recent Developments in Vaccine Design: From Live Vaccines to Recombinant Toxin Vaccines
Authors: Gupta Sonal , Pellett Sabine
Published: September 2023

Vaccines are one of the most effective strategies to prevent pathogen-induced illness in humans. The earliest vaccines were based on live inoculations with low doses of live or related pathogens, which carried a relatively high risk of developing the disease they were meant to prevent. The introduction of attenuated and killed pathogens as vaccines dramatically reduced these risks; however, attenuated live vaccines still carry a risk of reversion to a pathogenic strain capable of causing disease. This risk is completely eliminated with recombinant protein or subunit vaccines, which are atoxic and non-infectious. However, these vaccines require adjuvants and often significant optimization to induce robust T-cell responses and long-lasting immune memory. Some pathogens produce protein toxins that cause or contribute to disease. To protect against the effects of such toxins, chemically inactivated toxoid vaccines have been found to be effective. Toxoid vaccines are successfully used today at a global scale to protect against tetanus and diphtheria. Recent developments for toxoid vaccines are investigating the possibilities of utilizing recombinant protein toxins mutated to eliminate biologic activity instead of chemically inactivated toxins. Finally, one of the most contemporary approaches toward vaccine design utilizes messenger RNA (mRNA) as a vaccine candidate. This approach was used globally to protect against coronavirus disease during the COVID-19 pandemic that began in 2019, due to its advantages of quick production and scale-up, and effectiveness in eliciting a neutralizing antibody response. Nonetheless, mRNA vaccines require specialized storage and transport conditions, posing challenges for low- and middle-income countries. Among multiple available technologies for vaccine design and formulation, which technology is most appropriate? This review focuses on the considerable developments that have been made in utilizing diverse vaccine technologies with a focus on vaccines targeting bacterial toxins. We describe how advancements in vaccine technology, combined with a deeper understanding of pathogen–host interactions, offer exciting and promising avenues for the development of new and improved vaccines.
Advanced Detection Method for Dengue NS1 Protein Using Ultrasensitive ELISA with Thio-NAD Cycling
Authors: Chen Po-Kai , Chang Jyun-Hao , Ke Liang-Yin , Kao Jun-Kai , Chen Chang-Hua , Yang Rei-Cheng , Yoshimura Teruki , Ito Etsuro , Tsai Jih-Jin
Published: September 2023

Dengue fever, a mosquito-borne disease in tropical and subtropical climates caused by the dengue virus (DENV), has become a major social and economic burden in recent years. However, current primary detection methods are inadequate for early diagnosis of DENV because they are either time-consuming, expensive, or require training. Non-structural protein 1 (NS1) is secreted during DENV infection and is thus considered a suitable biomarker for the development of an early detection method. In the present study, we developed a detection method for the NS1 protein based on a previously reported thio-NAD cycling ELISA (i.e., ultrasensitive ELISA) and successfully achieved a LOD of 1.152 pg/mL. The clinical diagnosis potential of the detection system was also evaluated by using 85 patient specimens, inclusive of 60 DENV-positive and 25 DENV-negative specimens confirmed by the NAAT method. The results revealed 98.3% (59/60) sensitivity and 100% (25/25) specificity, which was in almost perfect agreement with the NAAT data with a kappa coefficient of 0.972. The present study demonstrates the diagnostic potential of using an ultrasensitive ELISA as a low-cost, easy-to-use method for the detection of DENV compared with NAAT and could be of great benefit in low-income countries.
Genotype and Phenotype Characterization of Rhinolophus sp. Sarbecoviruses from Vietnam: Implications for Coronavirus Emergence
Authors: Temmam Sarah , Tu Tran Cong , Regnault Béatrice , Bonomi Massimiliano , Chrétien Delphine , Vendramini Léa , Duong Tran Nhu , Phong Tran Vu , Yen Nguyen Thi , Anh Hoang Ngoc , Son Tran Hai , Anh Pham Tuan , Amara Faustine , Bigot Thomas , Munier Sandie , Thong Vu Dinh , van der Werf Sylvie , Nam Vu Sinh , Eloit Marc
Published: September 2023

Bats are a major reservoir of zoonotic viruses, including coronaviruses. Since the emergence of SARS-CoV in 2002/2003 in Asia, important efforts have been made to describe the diversity of Coronaviridae circulating in bats worldwide, leading to the discovery of the precursors of epidemic and pandemic sarbecoviruses in horseshoe bats. We investigated the viral communities infecting horseshoe bats living in Northern Vietnam, and report here the first identification of sarbecoviruses in Rhinolophus thomasi and Rhinolophus siamensis bats. Phylogenetic characterization of seven strains of Vietnamese sarbecoviruses identified at least three clusters of viruses. Recombination and cross-species transmission between bats seemed to constitute major drivers of virus evolution. Vietnamese sarbecoviruses were mainly enteric, therefore constituting a risk of spillover for guano collectors or people visiting caves. To evaluate the zoonotic potential of these viruses, we analyzed in silico and in vitro the ability of their RBDs to bind to mammalian ACE2s and concluded that these viruses are likely restricted to their bat hosts. The workflow applied here to characterize the spillover potential of novel sarbecoviruses is of major interest for each time a new virus is discovered, in order to concentrate surveillance efforts on high-risk interfaces.