bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation on Thermal Comfort and Thermal Adaptive Behaviors of Rural Residents in Suibin Town, China, in Summer
Authors: Yin Qing , Zhang Yuqi , Liu Ying
Published: April 2023

With global warming, the climate is becoming hotter, even at higher latitudes. In order to clarify the thermal environment and thermal comfort level of rural residents at higher latitudes in summer, a field survey on thermal comfort was conducted in Suibin Town, China. The results show the following: (1) The correlation between the operative temperature and the mean thermal sensation score is stronger than that between the operative temperature and the thermal sensation score. Moreover, the applicability of the thermal comfort evaluation index of the operative temperature in severely cold areas was verified. The linear regression method showed that the neutral temperature is 25.1 °C and the thermal acceptable range is 22.5–27.6 °C. (2) There is a strong correlation between thermal sensation and thermal acceptability. (3) The preferred temperature in summer is 25.3 °C. Moreover, rural residents prefer cooler indoor temperatures in summer. (4) The clothing insulation of rural residents decreases with an increase in the indoor operative temperature. (5) Rural residents’ acceptance of the indoor temperature in summer is influenced by the economy, psychology, adaptive behaviors, etc. Moreover, rural residents expect cooler indoor temperatures in summer and will adapt to the thermal environment via low-cost adaptive behaviors.
Design and Numerical Energetic Analysis of a Novel Semi-Automated Biomass-Powered Multipurpose Dryer
Authors: Ezurike Benjamin O. , Abid Muhammad , Ajah Stephen A. , Okoronkwo Chukwunenye A. , Adun Humphrey , Nwawelu Udora N. , Bamisile Olusola , Zaini Juliana Hj
Published: April 2023

This work presents a new all-inclusive mathematical model that combines both processes and an energy analysis of a semi-automated biomass-powered multipurpose dryer. A mathematical model was developed and a wood sample was used to simulate the model. Energy interaction between the system and sample was established. Most importantly, the incorporation of a sensor control system ensures that, once there is an increase in thermal energy from the combustion of the biomass, a signal is passed to the temperature sensor module that controls the system’s temperature and hence shuts down the heat supply at a predetermined temperature; in this case, at 67 °C. The results of the system’s modification show that the peak temperature of the drying space and the sample was 67 °C and 56 °C, respectively, and that the maximum temperature lag witnessed by the two regimes was 10 °C. The peak temperature removal rate of the sample was 0.0066 kg/h, while the sample attained 0.4 (40%) moisture concentration of its initial value; 90% mass content removal (10% remaining mass content) of the initial mass of the sample was achieved at the end, with a simulation time of 240 s.
The Spatio-Temporal Evolution Characteristics of the Vegetation NDVI in the Northern Slope of the Tianshan Mountains at Different Spatial Scales
Authors: Fan Jie , Fan Yanmin , Cheng Junhui , Wu Hongqi , Yan Yang , Zheng Kai , Shi Mingjie , Yang Qiangjun
Published: April 2023

The purposes of this study are to reveal the spatial pattern and dynamic changes of NDVI in the northern slope of the Tianshan Mountains for an extended period and to explore whether the spatial and temporal evolution of NDVI in different spatial scales is consistent so as to provide a reasonable theoretical basis for the selection of appropriate remote sensing spatial resolution in the study area. The GIMMS NDVI remote sensing data set was used to resample the NDVI data with three spatial resolutions of 0.5 km × 0.5 km, 1 km × 1 km, and 8 km × 8 km. The Mann-Kendall method was used to analyze the spatial-temporal evolution characteristics of vegetation NDVI on the NTSM from 1981 to 2015. The results showed that the interannual variation trend and spatial distribution of vegetation NDVI were consistent at different spatial scales. The change of NDVI displayed an increasing trend with changes concentrated in the middle of the NTSM. Five distinct trends were observed: no significant change (35% of the area), significant positive change (26%), significant single peak change (15%), a significant U-shaped change relationship (12%), and significant negative change (11%). Remote sensing NDVI data with a spatial resolution of 8 km could be used to analyze the long-term interannual variation trend of vegetation NDVI on the NTSM.
Determining Service Quality Indicators to Recruit and Retain International Students in Malaysia Higher Education Institutions: Global Issues and Local Challenges
Authors: Amzat Ismail Hussein , Najimdeen Abdul Hakeem Alade , Walters Lynne M. , Yusuf Byabazaire , Padilla-Valdez Nena
Published: April 2023

International student mobility has become an effective global strategy for tertiary institutions’ economic growth, revenue, and diversity. This study aimed to provide service quality indicators that can be used to improve international student mobility services in Malaysian public universities. The sample comprised international students in Malaysia, with 1273 students from 76 countries participating. The SERVQUAL model and instrument were used, and the indicators were determined using the measurement model (MM) and Analytic Hierarchy Process (AHP). The findings of MM and AHP ranked courtesy and communication as the highest indicators, and AHP found that soft skills were required to improve service quality at Malaysian higher education institutions. These findings contribute to understanding what is needed to improve the university quality service system in Malaysian public universities. The results also apply to other universities, especially Southeast Asian countries involved in internationalisation practices. Practical implications were provided to improve internationalisation service quality at colleges and universities, with suggestions for future study.
Passengers’ Perception of Satisfaction and Its Relationship with Travel Experience Attributes: Results from an Australian Survey
Authors: Yang Jie , Shiwakoti Nirajan , Tay Richard
Published: April 2023

Rail, one of the most sustainable modes of transport, is vital in carrying mass passengers in many urban cities. Passengers’ satisfaction with railway services is mostly discussed in the context of service quality in the literature. However, limited studies have considered other attributes that may influence passengers’ satisfaction, such as their travel experience and issues encountered. This study aims to systematically model passengers’ satisfaction and its relationship with travel experience attributes. This paper makes a theoretical contribution by proposing a conceptual model that evaluates the overall satisfaction of passengers through four attribute groups, including traveller attributes, trip attributes, service attributes, and other attributes. The model is tested with the 429 valid responses collected from a passenger survey targeting Metro train users in Melbourne, Australia. Result shows that the best-fitted model is produced only when all attribute groups are considered together, for which 60% of the variation in overall satisfaction is accountable. It is found that all attribute groups have at least one variable included in the final model, and the service attribute group has the greatest influence. The best model has nine significant variables, with eight having positive associations to the overall satisfaction and one variable (GroupTravel) having a negative association. This finding suggests that consideration of other attributes is also important besides the service attributes, and hence advances our scientific understanding of train passengers’ satisfaction with train services. The public transport sector and the operators can use this knowledge to improve service and increase passenger satisfaction.
The Future Snow Potential and Snowmelt Runoff of Mesopotamian Water Tower
Authors: Şensoy Aynur , Uysal Gökçen , Doğan Y. Oğulcan , Civelek H. Soykan
Published: April 2023

Mountainous basins are frequently called “natural water towers” because they supply essential water to downstream regions for irrigation, industrial–municipal use, and hydropower generation. The possible implications of climate change on water supplies have gained prominence in recent years, particularly in snow-dominated mountainous basins. The Euphrates River, a snow-fed transboundary river that originates from the Eastern part of Türkiye with several large dam reservoirs downstream, was chosen within this scope. The study reveals the impact of climate change on two snow-dominated headwaters, namely Karasu and Murat, which have a basin area of 41,109 km2. The impact of climate change is assessed across runoff regimes and snow dynamics for future periods (2024–2099). Global Climate Model (GCM) data sets (CNRM-CM5, IPSL-CM5A, EC-EARTH, MPI-ESM-LR, NorESM1-M, HadGEM2-ES) were downscaled by Regional Circulation Models (RCMs), provided from CMIP5 EURO-CORDEX domain for climate projections under RCP4.5 and RCP8.5 scenarios. Future projections of runoff and snow variables are predicted by two conceptual hydrological models, HBV and HEC-HMS. The results indicate a dramatic shrink in snow cover extents (>65%) and snow duration (25%), a decrease in snow water equivalent (>50%), and a timely shift (up to a month) in peak runoff through early spring in the runoff hydrograph for the last future period (2075–2099). The overall assessment shows that operations of downstream water systems should be reconsidered for future changes.
Predicting Raw Milk Price Based on Depth Time Series Features for Consumer Behavior Analysis
Authors: Li Zongyu , Zuo Anmin , Li Cuixia
Published: April 2023

The dairy industry has a long supply chain that involves dairy farmers, enterprises, consumers, and the government. The stable growth of consumer groups is the driving force for the sustainable development of the dairy industry. However, in recent years, sustainable development of the dairy industry has faced great challenges due to the constant changes in the global climate environment and the increasing uncertainty of the international economic environment. Therefore, it is essential to systematically monitor and accurately predict the consumption market of dairy products to ensure that the government, dairy enterprises, and dairy farmers can share information in a timely manner and take effective measures to cope with the changes in the dairy consumption market without disturbing the normal pricing mechanism of the dairy market. The purpose of the conducted research is to systematically monitor and accurately predict the dairy product consumption market while consistently delivering dependable forecasts of consumer behavior in the dairy industry. In this paper, we proposed a raw milk price prediction framework (RMP-CPR) to analyze consumer behavior based on the relationship between milk price and dairy consumption. This study concludes that dairy consumption behavior can be predicted accurately by predicting the price of raw milk based on the proposed framework (RMP-CPR). Our research explores a new angle for studying consumer behavior. The results can assist dairy enterprises in developing accurate marketing strategies based on the forecast results of dairy consumption, thereby enhancing their competitiveness in the market. Policymakers can also use the forecast results of the development trend of the dairy consumption market to adjust corresponding policies in a timely manner. This can help to balance the interests among consumers, dairy enterprises, dairy farmers, and other relevant stakeholders and effectively maintain the sustainable and healthy development of the dairy market.
Implementing Smart Sustainable Cities in Saudi Arabia: A Framework for Citizens’ Participation towards SAUDI VISION 2030
Authors: Alamoudi Abood Khaled , Abidoye Rotimi Boluwatife , Lam Terence Y. M.
Published: April 2023

Cities in Saudi Arabia need to expand rapidly due to the rapidly growing urban population. To develop smart sustainable cities (SSC), human, social, and environmental capital investments must be expanded beyond just focusing on technology. There have been several cities that have adopted smart city labels as recognition of the advantages of smart cities. Many countries acknowledge the value of citizens’ involvement in public urban planning and decision making, but it is difficult to evaluate their impact and compare it to other factors. This study aims to develop a citizens’ participation framework, identify any additional stakeholder’s management measures (SMM) (in addition to the ones previously developed by the authors), and explain the relationship with citizens’ participation level (CPL) for driving SSC. Three rounds of the Delphi method were conducted to structure and validate the framework by the decision maker in the field of urban planning and reach a consensus of understanding the drivers of SSC. The study group was limited to 25 participants because this study focuses on the perspective of decision makers toward CP. Mean score (MS) ranking and Kendall Coefficient were used to confirm the importance of these additional stakeholders’ management measures. The results suggest three main component structures of the conceptual framework, which are SMM, CPL, and Citizens’ Participation Recruitment (CPR), which are all necessary for smart sustainable city outcomes (SSCO) for achieving the Future Sustainable Cities Plan (FSCP) within the context of Vision 2030 and government policy in Saudi Arabia. Using the proposed framework will enable all the stakeholders to gain a deeper understanding of SSC and their complex natures from a conceptual and practical standpoint. The contribution to knowledge of this study is by developing a conceptual framework that can support the implementation of SSC, and by providing an understanding the CPR standards and the involvement of citizens in urban development, which eliminates any debate regarding SSC.
Factors Affecting Flood Disaster Preparedness and Mitigation in Flood-Prone Areas in the Philippines: An Integration of Protection Motivation Theory and Theory of Planned Behavior
Authors: Kurata Yoshiki B. , Ong Ardvin Kester S. , Ang Ranice Ysabelle B. , Angeles John Karol F. , Bornilla Bianca Danielle C. , Fabia Justine Lian P.
Published: April 2023

Natural hazards are one of the destructive phenomena that pose a significant hazard to humans, property, and the economy, among others. One of the most recurring natural hazards is flooding, which is caused by typhoons, monsoons, and heavy rainfall and has been one of the main concerns of the Philippines in recent years. The study’s results will provide information on the factors affecting flood disaster preparedness by integrating the Theory of Planned Behavior (TPB) and the Protection Motivation Theory (PMT). A total of 509 individuals answered an online survey questionnaire with 52 adapted questions. Structural equation modeling (SEM) revealed that risk perception (RP), media (M), and personal experience (PE) had an effect on perceived severity (PS) and perceived vulnerability (PV), which consequently affected the attitude toward the behavior (ATB), social norms (SN), and perceived behavioral control (PBC). It was determined that ATB, SN, and PBC significantly affected adapted behavior (AB), which consequently led to intention to follow (ITF) and perceived prevention (PP). After analyzing the data, it was revealed that 56.2% of female respondents were said to be more disaster resilient compared to males. This is the first study to determine the perceived prevention of disaster preparedness and mitigation in flood-prone areas in the Philippines. The results will be beneficial to academicians and government officials in developing determining factors that affect flood disaster preparedness. Lastly, a deeper understanding of how AB is the most significant variable may be further researched to improve the paper.
Do Consumers Intend to Use Indoor Smart Farm Restaurants for a Sustainable Future? The Influence of Cognitive Drivers on Behavioral Intentions
Authors: Joo Kyuhyeon , Hwang Jinsoo
Published: April 2023

Smart farms are eco-friendly and sustainable agriculture practices that also play a crucial role in the foodservice industry. This study investigated cognitive drivers, which included biospheric value, environmental concern, problem awareness, and ascription of responsibility, in order to form consumers’ behavioral intentions in the context of indoor smart farm restaurants. The current study also investigated the differences among the four sub-dimensions of cognitive drivers, which are based on the respondents’ demographic factors. This study was performed using data from 310 participants. The study conducted multiple linear regression to test the causal relationships and t-test and one-way ANOVA to test the demographic differences. The results of the data analysis revealed that all four sub-dimensions of the cognitive drivers aid in regard to increasing behavioral intentions. Furthermore, the data analysis results showed that age and marital status were associated with differences in biospheric value, and gender was associated with differences in environmental concern and problem awareness. This study empirically identified the direct effect of cognitive drivers on consumers’ pro-environmental behavior and their demographic differences, and it also presents practical suggestions from the perspective of green marketing.
Bolstering the Impact of Social Entrepreneurship and Poverty Alleviation for Sustainable Development in Nigeria
Authors: Arejiogbe Oluwatosin Esther , Moses Chinonye Love , Salau Odunayo Paul , Onayemi Oluwakemi Oluwafunmilayo , Agada Solomon Agada , Dada Augustina Esitse , Obisesan Oluwakemi Titilope
Published: April 2023

Social entrepreneurship has gained increasing attention as a means to address social problems, including poverty, in Nigeria. Poverty is a major challenge in Nigeria, with a poverty rate of over 40%. Poverty alleviation is critical to achieving sustainable development in the country. Social entrepreneurship can play a key role in addressing poverty by creating jobs, providing access to goods and services, and promoting economic growth. Hence, this study examines bolstering the impact of social entrepreneurship and poverty alleviation for sustainable development in Nigeria. The research focuses primarily on fostering economic, social, and environmental improvements, as well as improving people’s lives. Three hypotheses were formulated to help accomplish the primary objectives of the study. The Opportunity-Based Entrepreneurship Theory, Social Network Theory, and Schumpeterian Theory of Innovation were used to explain the objective of the study. In this study, a purposive sampling technique was used, and 300 copies of structured questionnaires were administered to selected social entrepreneurs of which 262 copies were retrieved and used for the analysis. The data were analyzed using structural and measurement models, and the path coefficient was determined using PLS-SEM. The significance level was calculated using the bootstrapping method. The results from the test of hypotheses showed that; the most significant predictor of poverty alleviation is social innovation (β = 0.376, p < 0.05), followed by the social value (β = 0.314, p < 0.05), and finally, social impact (β = 0.231, p < 0.05). This study concludes that social innovation is fundamental for empowering individuals and communities to lift themselves out of poverty and achieve long-term prosperity. The study recommends that to promote sustainable social entrepreneurship and poverty alleviation in Nigeria, the government can create policies and programs that support social innovation, such as providing access to funding, business training, and mentorship. The government can also collaborate with entrepreneurs and organizations to promote their initiatives and create a supportive ecosystem for social entrepreneurship.
Can Digital Services Trade Liberalization Improve the Quality of Green Innovation of Enterprises? Evidence from China
Authors: Fang Hui , Huo Qixin , Hatim Kaouakib
Published: April 2023

Green innovation is a critical driver in achieving the goals of “carbon peaking” and “carbon neutrality”, as well as an important aid in accelerating green transformation. Does the opening-up of digital services trade, as a major component of the high-level opening-up strategy, serve to improve the quality of green innovation of Chinese enterprises at this critical juncture in China’s promotion of ecological civilization? To answer this question, this paper measures the degree of openness of digital services trade in each industry in China, and it empirically examines the impact of digital services trade liberalization on enterprises’ green innovation quality using data from A-share listed companies from 2014 to 2021. This research finds that, first, digital services trade liberalization can significantly improve the quality of green innovation of Chinese enterprises, which still holds after a series of robustness tests. Second, mechanism analysis indicates that digital services trade liberalization promotes enterprises’ green innovation quality by improving human capital level, increasing green R&D expenditure, and strengthening information resource sharing. Third, a heterogeneity test shows that the effect of digital services trade liberalization on the quality of green innovation is more prominent for state-owned enterprises, enterprises with stronger technology absorption capacity, highly competitive industries, and regions with a high intensity of environmental regulations. The study’s findings not only provide new perspectives and ideas for enterprises’ green innovation practices in the midst of the digital services trade wave but also theoretical and empirical support for the inherent self-consistency between high-level opening-up and green development.
Parameter Estimation of Fuel Cells Using a Hybrid Optimization Algorithm
Authors: Singla Manish Kumar , Gupta Jyoti , Singh Beant , Nijhawan Parag , Abdelaziz Almoataz Y. , El-Shahat Adel
Published: April 2023

Because of the current increase in energy requirement, reduction in fossil fuels, and global warming, as well as pollution, a suitable and promising alternative to the non-renewable energy sources is proton exchange membrane fuel cells. Hence, the efficiency of the renewable energy source can be increased by extracting the precise values for each of the parameters of the renewable mathematical model. Various optimization algorithms have been proposed and developed in order to estimate the parameters of proton exchange membrane fuel cells. In this manuscript, a novel hybrid algorithm, i.e., Hybrid Particle Swarm Optimization Puffer Fish (HPSOPF), based on the Particle Swarm Optimization and Puffer Fish algorithms, was proposed to estimate the proton exchange membrane fuel cell parameters. The two models were taken for the parameter estimation of proton exchange membrane fuel cells, i.e., Ballard Mark V and Avista SR-12 model. Firstly, justification of the proposed algorithm was achieved by benchmarking it on 10 functions and then a comparison of the parameter estimation results obtained using the Hybrid Particle Swarm Optimization Puffer Fish algorithm was done with other meta-heuristic algorithms, i.e., Particle Swarm Optimization, Puffer Fish algorithm, Grey Wolf Optimization, Grey Wolf Optimization Cuckoo Search, and Particle Swarm Optimization Grey Wolf Optimization. The sum of the square error was used as an evaluation metric for the performance evaluation and efficiency of the proposed algorithm. The results obtained show that the value of the sum of square error was smallest in the case of the proposed HPSOPF, while for the Ballard Mark V model it was 6.621 × 10−9 and for the Avista SR-12 model it was 5.65 × 10−8. To check the superiority and robustness of the proposed algorithm computation time, voltage–current (V–I) curve, power–current (P–I) curve, convergence curve, different operating temperature conditions, and different pressure results were obtained. From these results, it is concluded that the Hybrid Particle Swarm Optimization Puffer Fish algorithm had a better performance in comparison with the other compared algorithms. Furthermore, a non-parametric test, i.e., the Friedman Ranking Test, was performed and the results demonstrate that the efficiency and robustness of the proposed hybrid algorithm was superior.
The Effect of Green Supply Chain Management Practices on the Sustainability Performance of Turkish Shipyards
Authors: Tantan Mehmet , Akdağ Hatice Camgöz
Published: April 2023

The study explores the effect of green supply chain management (GSCM) practices on three sustainability dimensions; environmental, social, and economic in Turkish shipyards. The GSCM practices examined are green design, green purchasing, green production, green marketing, environmental management, and recycling. A research model was developed to test the relationship between six GSCM practices and the three sustainability performance dimensions by using a shipyard-level survey. Environmental uncertainty was placed in the model as a moderator variable. The authors utilized a web-based survey. Partial least squares structural equation modeling was used to test the proposed hypotheses. The research revealed that the GSCM practices were positively significant for economic and social performance but not for environmental performance. Environmental uncertainty did not have a moderating effect between GSCM practices and sustainability performances. Quality, Environmental, and Safety Certifications are the first step for GSCM practices but do not mean the company achieved environmental performance positively. The results brought into prominence the GSCM practices in improving the sustainability performance of the Turkish shipyards. While this study was the pioneer in examining the relationship between GSCM and sustainability performance in Turkish shipyards, it enhanced the understanding of GSCM practices and sustainability performances.
Deploying SDG Knowledge to Foster Young People’s Critical Values: A Study on Social Trends about SDGs in an Educational Online Activity
Authors: Feltrero Roberto , Junguitu-Angulo Leire , Osuna-Acedo Sara
Published: April 2023

Educational tasks for the development of competencies on the Sustainable Development Goals (SDGs) pose a challenge to educators due to the multidisciplinary and complex nature of these goals. It is necessary to develop innovative educational resources and tools that holistically account for this complexity and highlight the environmental and social components of these goals on an equal footing. A learning tool based on the serious games methodology, called BotBusters, is used to inoculate this type of knowledge and skills among young Europeans through a specific news verification activity. This game has been designed to collect interaction data (logs) and thus perform an analysis of players’ behavior, which allows us to understand their preferences and knowledge of the SDGs (specifically 3, 12, 13 and 15). The data collected yield interesting conclusions about the educational needs of young people in this field, showing their preferences for topics related to the environmental SDGs. This indicates that there is a need to promote awareness of the SDGs on social issues, especially those that require a more collective type of agreement and intervention.
Drought Assessment across Erbil Using Satellite Products
Authors: Mustafa Alee Mohammed , Danandeh Mehr Ali , Akdegirmen Ozgun , Nourani Vahid
Published: April 2023

In this article, meteorological and agricultural droughts across the Erbil province, Iraq, were assessed using remote sensing data and satellite products. To this end, the long-term (2000–2022) Standardized Precipitation Evapotranspiration index (SPEI) at 1- and 3-month accumulation periods (SPEI-1 and SPEI-3) as well as the Normalized Difference Vegetation Index (NDVI) across Erbil were utilized. While the former was retrieved from the global SPEI data repository, the latter was derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The spatiotemporal variations in the SPEI indices indicated that two to nine extreme drought events occurred in the province with an increasing northward pattern. An increasing trend in the long-term NDVI series was also detected, having more diversity in vegetation coverage in the northern part of the province. The relationship between the SPEI and MODIS-NDVI was found to be positive but insignificant. Thus, we concluded that short-term meteorological droughts were not the only reason for the agricultural droughts in Erbil. Furthermore, the climate characteristics related to the cumulative water balance over a previous season is not an important trigger for the spatial variation in vegetation coverage across the province.
Comparison of Anaerobic Co-Digestion of Buffalo Manure and Excess Sludge with Different Mixing Ratios under Thermophilic and Mesophilic Conditions
Authors: Zhou Youfei , Hu Weijie , Sheng Jun , Peng Cheng , Wang Tianfeng
Published: April 2023

In this study, the main aim is to evaluate the mixing ratio of co-digestion of buffalo manure (BM) and excess sludge (ES) influenced for methane yield and digestate dewaterability. Five batch experiments with different BM and ES mixing ratios were carried out under thermophilic and mesophilic conditions. The methane yield of co-digestion of BM and ES increased by 10.1–73.5% under thermophilic conditions and 87.9–153.3% under mesophilic conditions, compared with the mono-anaerobic digestion of ES under the same conditions. Shannon and Chao1 indices showed that the bacterial species of the mesophilic digesters were more abundant than that of the thermophilic digesters. With the increase in the BM proportion in the substrate, the normalized capillary suction time (NCST) and total solids (TS) of sediment (centrifugal dewatering) increased. The NCST at thermophilic temperature (8.98–12.54 s∙g−1-TS) was greater than that at the mesophilic temperatures (5.45–12.32 s∙g−1-TS). However, the TS of sediment was not directly related to the digestion temperature. This study has shown that anaerobic co-digestion of BM and ES at the appropriate ratio (BM/ES = 1:1.5) has a significant meaning in a high methane yield.
The Uniform Lipschitz Continuity of Eigenvalues of Sturm–Liouville Problems with Respect to the Weighted Function
Authors: Xu Jing , Liu Zhiwen , Qi Jiangang
Published: April 2023

The present paper is concerned with the uniform boundedness of the normalized eigenfunctions of Sturm–Liouville problems and shows that the sequence of eigenvalues is uniformly local Lipschitz continuous with respect to the weighted functions.
Verifiable Keyword Search Encryption Scheme That Supports Revocation of Attributes
Authors: Feng Tao , Miao Sirui , Liu Chunyan , Ma Rong
Published: April 2023

In recent years, searchable encryption technology and attribute encryption technology have been widely used in cloud storage environments, and attribute-based searchable encryption schemes can both achieve the retrieval of encrypted data and effectively solve the access control problem. Considering that existing attribute-based searchable encryption schemes for cloud storage only support keyword search and do not support attribute revocation, most of the schemes that support attribute revocation only consider the computational overhead of users and ignore the large amount of computational resources consumed by attribute authorization centers when updating keys. In addition, keyword search may lead to partial errors in the returned search results, leading to the wastage of computational and broadband resources. To solve these issues, this paper proposes an attribute-based searchable encryption scheme that supports attribute revocation and is verifiable. To realize fine-grained ciphertext search of encrypted data, support scenarios of dynamic changes of user attributes, and ensure that third-party servers perform the search process reliably and honestly while minimizing computation and storage costs, first, this paper implements attribute revocation with the attribute authorization center by creating a user revocation list and an attribute key revocation list. At the same time, the system updates the attribute key at the time of user search request, which effectively reduces the computational overhead. Second, a third-party auditor is introduced to ensure the correctness of the search results. Finally, the security of this paper is verified by theoretical analysis, and the efficiency and practicality of this paper are verified by comparing it to other schemes through simulation experiments.
Novel Red-Emitting BaBi2B4O10:Eu3+ Phosphors: Synthesis, Crystal Structure and Luminescence
Authors: Shablinskii Andrey P. , Povolotskiy Alexey V. , Yuriev Artem A. , Biryukov Yaroslav P. , Bubnova Rimma S. , Avdontceva Margarita S. , Janson Svetlana Yu. , Filatov Stanislav K.
Published: April 2023

The novel red-emitting BaBi2−xEuxB4O10 (x = 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6) phosphors were obtained by a crystallization from a glass. Distribution of the Eu3+ ions over cation sites were refined for x = 0.1, 0.3 and 0.4 from single-crystal X-ray diffraction data. Emission and excitation spectra of the Eu3+-doped BaBi2B4O10 phosphors were investigated for the first time, where it was shown that characteristic lines are attributed to the intraconfigurational 4f-4f transitions. The optimal concentration for the BaBi2−xEuxB4O10 phosphors is x = 0.4, after which a luminescence intensity decreases. The CIE chromaticity coordinates for the BaBi2B4O10:Eu3+ (x = 0.4) phosphor (0.65, 0.35) are close to the NTSC standard values (0.67, 0.33) for commercial red phosphor. The obtained results show that the BaBi2B4O10:Eu3+ phosphors are promising candidates for solid state lighting application.
Using Dual Attention BiLSTM to Predict Vehicle Lane Changing Maneuvers on Highway Dataset
Authors: Ashfaq Farzeen , Ghoniem Rania M. , Jhanjhi N. Z. , Khan Navid Ali , Algarni Abeer D.
Published: April 2023

In this research, we address the problem of accurately predicting lane-change maneuvers on highways. Lane-change maneuvers are a critical aspect of highway safety and traffic flow, and the accurate prediction of these maneuvers can have significant implications for both. However, current methods for lane-change prediction are limited in their ability to handle naturalistic driving scenarios and often require large amounts of labeled data. Our proposed model uses a bidirectional long short-term memory (BiLSTM) network to analyze naturalistic vehicle trajectories recorded from multiple sensors on German highways. To handle the temporal aspect of vehicle behavior, we utilized a sliding window approach, considering both the preceding and following vehicles’ trajectories. To tackle class imbalances in the data, we introduced rolling mean computed weights. Our extensive feature engineering process resulted in a comprehensive feature set to train the model. The proposed model fills the gap in the state-of-the-art lane change prediction methods and can be applied in advanced driver assistance systems (ADAS) and autonomous driving systems. Our results show that the BiLSTM-based approach with the sliding window technique effectively predicts lane changes with 86% test accuracy and a test loss of 0.325 by considering the context of the input data in both the past and future. The F1 score of 0.52, precision of 0.41, recall of 0.75, accuracy of 0.86, and AUC of 0.81 also demonstrate the model’s high ability to distinguish between the two target classes. Furthermore, the model achieved an accuracy of 83.65% with a loss value of 0.3306 on the other half of the data samples, and the validation accuracy was observed to improve over these epochs, reaching the highest validation accuracy of 92.53%. The F1 score of 0.51, precision of 0.36, recall of 0.89, accuracy of 0.82, and AUC of 0.85 on this data sample also demonstrate the model’s strong ability to identify both positive and negative classes. Overall, our proposed approach outperforms existing methods and can significantly contribute to improving highway safety and traffic flow.
Editor’s Review of Key Research Papers Published in Tomography during the Last Year
Authors: Quaia Emilio
Published: April 2023

Tomography is an open access journal dedicated to all aspects of imaging science from basic research to clinical applications and imaging trials [...]
Effects of Multiwall Carbon Nanotubes on Premature Kidney Aging: Biochemical and Histological Analysis
Authors: Kim Ji-Eun , Cho Myung-Haing
Published: April 2023

Carbon nanotubes (CNTs) have gained much attention due to their superb properties, which make them promising options for the reinforcing composite materials with desirable mechanical properties. However, little is known about the linkage between lung exposure to nanomaterials and kidney disease. In this study, we compared the effects on the kidneys and aging for two different types of multiwall carbon nanotubes (MWCNTs): pristine MWCNTs (PMWCNTs) and acid-treated MWCNTs (TMWCNTs), with TMWCNTs being the preferred form for use as a composite material due to its superior dispersion properties. We used tracheal instillation and maximum tolerated dose (MTD) for both types of CNTs. MTD was determined as a 10% weight loss dose in a 3-month subchronic study, and the appropriate dosage for 1-year exposure was 0.1 mg/mouse. Serum and kidney samples were analyzed using ELISA, Western blot, and immunohistochemistry after 6 months and 1 year of treatment. PMWCNT-administered mice showed the activation of pathways for inflammation, apoptosis, and insufficient autophagy, as well as decreased serum Klotho levels and increased serum levels of DKK-1, FGF-23, and sclerostin, while TMWCNTs did not. Our study suggests that lung exposure to PMWCNTs can induce premature kidney aging and highlights a possible toxic effect of using MWCNTs on the kidneys in the industrial field, further highlighting that dispersibility can affect the toxicity of the nanotubes.
One Health Approach to Tackle Microbial Contamination on Poultries—A Systematic Review
Authors: Gomes Bianca , Dias Marta , Cervantes Renata , Pena Pedro , Santos Joana , Vasconcelos Pinto Marta , Viegas Carla
Published: April 2023

This study reports the search of available data published regarding microbial occupational exposure assessment in poultries, following the PRISMA methodology. Air collection through filtration was the most frequently used. The most commonly used passive sampling method was material collection such as dust, cages, soils, sediment, and wastewater. Regarding assays applied, the majority of studies comprised culture-based methods, but molecular tools were also frequently used. Screening for antimicrobial susceptibility was performed only for bacteria; cytotoxicity, virological and serological assays were also performed. Most of the selected studies focused on bacteria, although fungi, endotoxins, and β-glucans were also assessed. The only study concerning fungi and mycotoxins reported the carcinogenic mycotoxin AFB1. This study gives a comprehensive overview of microbial contamination in the poultry industry, emphasizing this setting as a potential reservoir of microbial pathogens threatening human, animal, and environmental health. Additionally, this research helps to provide a sampling and analysis protocol proposal to evaluate the microbiological contamination in these facilities. Few articles were found reporting fungal contamination in poultry farms worldwide. In addition, information concerning fungal resistance profile and mycotoxin contamination remain scarce. Overall, a One Health approach should be incorporated in exposure assessments and the knowledge gaps identified in this paper should be addressed in further research.
Antitumor Profile of Combined Matricaria recutita Flower Extract and 5-Fluorouracil Chemotherapy in Sarcoma 180 In Vivo Model
Authors: Santos Sara A. , Amaral Ricardo G. , Graça Ariel S. , Gomes Silvana V. F. , Santana Fabrício P. , de Oliveira Iza B. , Andrade Luciana N. , Severino Patrícia , de Albuquerque-Júnior Ricardo L. C. , Santos Sandra L. , Souto Eliana B. , Carvalho Adriana A.
Published: April 2023

Medicinal plants have been commonly associated with chemotherapeutic treatments, as an approach to reduce the toxicological risks of classical anticancer drugs. The objective of this study was to evaluate the effects of combining the antineoplastic drug 5-fluorouracil (5-FU) with Matricaria recutita flowers extract (MRFE) to treat mice transplanted with sarcoma 180. Tumor inhibition, body and visceral mass variation, biochemical, hematological, and histopathological parameters were evaluated. The isolated 5-FU, 5-FU+MRFE 100 mg/kg/day, and 5-FU+MRFE 200 mg/kg/day reduced tumor growth; however, 5-FU+MRFE 200 mg/kg/day showed a more significant tumor reduction when compared to 5-FU alone. These results corroborated with the analysis of the tumor histopathological and immunodetection of the Ki67 antigen. In the toxicological analysis of the association 5-FU+MRFE 200 mg/kg/day, an intense loss of body mass was observed, possibly as a result of diarrhea. In addition, spleen atrophy, with a reduction in white pulp, leukopenia and thrombocytopenia, was observed in the 5-FU groups alone and associated with MRFE 200 mg/kg/day; however, there was no statistical difference between these groups. Therefore, the MRFE 200 mg/kg/day did not interfere in myelosuppressive action of 5-FU. In hematological analysis, body and visceral mass variation and biochemical parameters related to renal (urea and creatinine) and cardiac (CK-MB) function, no alteration was observed. In biochemical parameters related to liver function enzymes, there was a reduction in aspartate transaminase (AST) values in the 5-FU groups alone and associated with MRFE 200 mg/kg/day; however, there was no statistical difference between these groups. Therefore, the MRFE 200 mg/kg/day does not appear to influence enzyme reduction. The results of this study suggest that the association between the 5-FU+MRFE 200 can positively interfere with the antitumor activity, promoting the antineoplastic-induced reduction in body mass, while minimizing the toxicity of chemotherapy.
Protective Effects of Epigallocatechin-3-gallate (EGCG) against the Jellyfish Nemopilema nomurai Envenoming
Authors: Li Jie , Wang Qianqian , Zou Shuaijun , Song Juxingsi , Zhang Peipei , Wang Fan , Huang Yichao , He Qian , Zhang Liming
Published: April 2023

Jellyfish stings are the most common marine animal injuries worldwide, with approximately 150 million envenomation cases annually, and the victims may suffer from severe pain, itching, swelling, inflammation, arrhythmias, cardiac failure, or even death. Consequently, identification of effective first aid reagents for jellyfish envenoming is urgently needed. Here, we found that the polyphenol epigallocatechin-3-gallate (EGCG) markedly antagonized the hemolytic toxicity, proteolytic activity, and cardiomyocyte toxicity of the jellyfish Nemopilema nomurai venom in vitro and could prevent and treat systemic envenoming caused by N. nomurai venom in vivo. Moreover, EGCG is a natural plant active ingredient and widely used as a food additive without toxic side effects. Hence, we suppose that EGCG might be an effective antagonist against systemic envenoming induced by jellyfish venom.
Porcine Epidemic Diarrhea Virus Replication in Human Intestinal Cells Reveals Potential Susceptibility to Cross-Species Infection
Authors: Niu Zheng , Zhang Shujuan , Xu Shasha , Wang Jing , Wang Siying , Hu Xia , Zhang Li , Ren Lixin , Zhang Jingyi , Liu Xiangyang , Zhou Yang , Yang Liu , Song Zhenhui
Published: April 2023

Various coronaviruses have emerged as a result of cross-species transmission among humans and domestic animals. Porcine epidemic diarrhea virus (PEDV; family Coronaviridae, genus Alphacoronavirus) causes acute diarrhea, vomiting, dehydration, and high mortality in neonatal piglets. Porcine small intestinal epithelial cells (IPEC-J2 cells) can be used as target cells for PEDV infection. However, the origin of PEDV in pigs, the host range, and cross-species infection of PEDV remain unclear. To determine whether PEDV has the ability to infect human cells in vitro, human small intestinal epithelial cells (FHs 74 Int cells) were inoculated with PEDV LJX and PEDV CV777 strains. The results indicated that PEDV LJX, but not PEDV CV777, could infect FHs 74 Int cells. Furthermore, we observed M gene mRNA transcripts and N protein expression in infected FHs 74 Int cells. A one-step growth curve showed that the highest viral titer of PEDV occurred at 12 h post infection. Viral particles in vacuoles were observed in FHs 74 Int cells at 24 h post infection. The results proved that human small intestinal epithelial cells are susceptible to PEDV infection, suggesting the possibility of cross-species transmission of PEDV.
T Cells in Tick-Borne Flavivirus Encephalitis: A Review of Current Paradigms in Protection and Disease Pathology
Authors: Stone E. Taylor , Pinto Amelia K.
Published: April 2023

The family Flaviviridae is comprised of a diverse group of arthropod-borne viruses that are the etiological agents of globally relevant diseases in humans. Among these, infection with several of these flaviviruses—including West Nile virus (WNV), Zika virus (ZIKV), Japanese encephalitis virus (JEV), tick-borne encephalitis virus (TBEV), and Powassan virus (POWV)—can result in neuroinvasive disease presenting as meningitis or encephalitis. Factors contributing to the development and resolution of tick-borne flavivirus (TBEV, POWV) infection and neuropathology remain unclear, though many recently undertaken studies have described the virus–host interactions underlying encephalitic disease. With access to neural tissues despite the selectively permeable blood–brain barrier, T cells have emerged as one notable contributor to neuroinflammation. The goal of this review is to summarize the recent advances in tick-borne flavivirus immunology—particularly with respect to T cells—as it pertains to the development of encephalitis. We found that although T cell responses are rarely evaluated in a clinical setting, they are integral in conjunction with antibody responses to restricting the entry of TBFV into the CNS. The extent and means by which they can drive immune pathology, however, merits further study. Understanding the role of the T cell compartment in tick-borne flavivirus encephalitis is instrumental for improving vaccine safety and efficacy, and has implications for treatments and interventions for human disease.
Molecular Tools to Infer Resistance-Breaking Abilities of Rice Yellow Mottle Virus Isolates
Authors: Dossou Laurence , Pinel-Galzi Agnès , Aribi Jamel , Poulicard Nils , Albar Laurence , Fatogoma Sorho , Ndjiondjop Marie Noëlle , Koné Daouda , Hébrard Eugénie
Published: April 2023

Rice yellow mottle virus (RYMV) is a major biotic constraint to rice cultivation in Africa. RYMV shows a high genetic diversity. Viral lineages were defined according to the coat protein (CP) phylogeny. Varietal selection is considered as the most efficient way to manage RYMV. Sources of high resistance were identified mostly in accessions of the African rice species, Oryza glaberrima. Emergence of resistance-breaking (RB) genotypes was observed in controlled conditions. The RB ability was highly contrasted, depending on the resistance sources and on the RYMV lineages. A molecular marker linked to the adaptation to susceptible and resistant O. glaberrima was identified in the viral protein genome-linked (VPg). By contrast, as no molecular method was available to identify the hypervirulent lineage able to overcome all known resistance sources, plant inoculation assays were still required. Here, we designed specific RT-PCR primers to infer the RB abilities of RYMV isolates without greenhouse experiments or sequencing steps. These primers were tested and validated on 52 isolates, representative of RYMV genetic diversity. The molecular tools described in this study will contribute to optimizing the deployment strategy of resistant lines, considering the RYMV lineages identified in fields and their potential adaptability.