bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Flow Characteristics in the Gap of a Variable-Speed Pump-Turbine in Pump Mode
Authors: Wang Zhengwei , Wang Lei , Yu Shuang , Li Sainan
Published: July 2024

A variable-speed pump-turbine is the core component of a hydraulic storage and energy generation station. When the pump-turbine operates at a constant speed, its response to the power grid frequency is poor. In order to improve the hydraulic efficiency of the pumped storage unit, variable-speed units are used. However, there has been no numerical study on the effect of the rotational flow characteristics within the gap of a variable-speed pump-turbine. This paper calculates the flow characteristics within the gap of a variable-speed pump-turbine under three typical pump modes (maximum head minimum flow condition, minimum head maximum flow condition, and maximum speed condition). The research results indicate that the rotational speed significantly affects the pressure distribution, velocity distribution, and turbulent kinetic energy distribution within the crown and band gaps. The higher the speed, the larger the area of the high-pressure region before the runner inlet compared to other operating conditions, and similarly, the low-pressure area after the runner outlet is also larger than in other operating conditions. The change in speed mainly affects the internal flow field of the crown gap, with the most noticeable changes occurring in the pressure and flow velocity at the inlet and outlet of the crown gap. There is a clear trend of pressure drop and velocity increase within the gap as the speed increases. However, with the increase in speed, the pressure distribution and flow velocity within the band gap remain almost the same. In addition to speed changes, it is observed that the pressure within the gap and the flow velocity within the passages are also related to the head, especially in the condition of maximum head, where this relationship becomes more noticeable.
Transportation Machinery and Feeding Systems for Pigs in Multi-Storey Buildings: A Review
Authors: Lv Youjie , Zheng Zeyong , Zeng Jinbin , Chen Yingmei , Abdeen Mohamed Anwer , Qiu Wenlong , Wu Weibin , Luo Yuanqiang
Published: July 2024

In recent years, in order to save land resources and ensure biosecurity, multi-storey buildings for pig rearing are replacing traditional flat-floor methods in pig farms domestically and internationally. However, the precision, stability, and adaptability of pig feed transportation in multi-storey buildings have brought certain challenges to the development of large-scale pig raising. The uniqueness of this paper lies in the comprehensive review of recent research advances in various transportation machinery and feeding systems from the perspective of both pig feed transportation and feeding systems compared to other papers that singularly present pigs’ feed transportation machinery or feeding systems. In addition, this paper provides an outlook on the potential for coupling power sources for pig feed transportation and pig transportation, providing insights for future research and development. First, the paper comprehensively reviews feed transportation machinery in multi-storey pig rearing, highlighting their advantages and challenges. Then, it explores the commonly used feeding systems in large-scale pig rearing and their limitations. Finally, the paper summarizes the current issues in pig feed transportation in multi-storey buildings and examines future development trends.
A Study on the Object-Based High-Resolution Remote Sensing Image Classification of Crop Planting Structures in the Loess Plateau of Eastern Gansu Province
Authors: Yang Rui , Qi Yuan , Zhang Hui , Wang Hongwei , Zhang Jinlong , Ma Xiaofang , Zhang Juan , Ma Chao
Published: July 2024

The timely and accurate acquisition of information on the distribution of the crop planting structure in the Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in Western China, is crucial for promoting fine management of agriculture and ensuring food security. This study uses multi-temporal high-resolution remote sensing images to determine optimal segmentation scales for various crops, employing the estimation of scale parameter 2 (ESP2) tool and the Ratio of Mean Absolute Deviation to Standard Deviation (RMAS) model. The Canny edge detection algorithm is then applied for multi-scale image segmentation. By incorporating crop phenological factors and using the L1-regularized logistic regression model, we optimized 39 spatial feature factors—including spectral, textural, geometric, and index features. Within a multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model are used to classify the cropping patterns in four test areas based on the multi-scale segmented images. The results indicate that integrating the Canny edge detection algorithm with the optimal segmentation scales calculated using the ESP2 tool and RMAS model produces crop parcels with more complete boundaries and better separability. Additionally, optimizing spatial features using the L1-regularized logistic regression model, combined with phenological information, enhances classification accuracy. Within the OBIC framework, the RF classifier achieves higher accuracy in classifying cropping patterns. The overall classification accuracies for the four test areas are 91.93%, 94.92%, 89.37%, and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of the shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields.
Effects of Climate Change and Human Activities on Runoff in the Upper Reach of Jialing River, China
Authors: Shi Weizhao , He Yi , Shao Yiting
Published: July 2024

In recent years, the runoff of numerous rivers has experienced substantial changes owing to the dual influences of climate change and human activities. This study focuses on the Lixian hydrological station’s controlled basin, located in the upper reaches of the Jialing River in China. The objective is to assess and quantify the impacts of human activities and climate change on runoff variations. This study analyzed runoff variations from 1960 to 2016 and employed the Soil and Water Assessment Tool (SWAT) model, the long short-term memory (LSTM) model, and eight Budyko framework formulations to assess factors influencing runoff. Additionally, it used the patch-generating land use simulation (PLUS) and SWAT models to simulate future runoff scenarios under various conditions. The results indicate the following. (1) The study area has witnessed a significant decline in runoff (p < 0.01), while potential evapotranspiration shows a significant upward trend (p < 0.01). Precipitation displays a nonsignificant decreasing trend (p > 0.1). An abrupt change point in runoff occurred in 1994, dividing the study period into baseline and change periods. (2) The Budyko results reveal that human activities contributed 50% to 60% to runoff changes. According to the SWAT and LSTM models, the contribution rates of human activities are 63.21% and 52.22%, respectively. Human activities are thus identified as the predominant factor in the decline in runoff. (3) Human activities primarily influence runoff through land cover changes. Conservation measures led to a notable increase in forested areas from 1990 to 2010, representing the most significant change among land types. (4) Future land use scenarios suggest that the highest simulated runoff occurs under a comprehensive development scenario, while the lowest is observed under an ecological conservation scenario. Among the 32 future climate scenarios, runoff increases significantly with a 10% increase in precipitation and decreases substantially with a 15% reduction in precipitation. These findings underscore the significant impact of human activities and climate change on runoff variations in the upper reaches of the Jialing River, highlighting the importance of incorporating both factors in water resource management and planning.
Sparse-to-Dense Point Cloud Registration Based on Rotation-Invariant Features
Authors: Ma Tianjiao , Han Guangliang , Chu Yongzhi , Ren Hong
Published: July 2024

Point cloud registration is a critical problem because it is the basis of many 3D vision tasks. With the popularity of deep learning, many scholars have focused on leveraging deep neural networks to address the point cloud registration problem. However, many of these methods are still sensitive to partial overlap and differences in density distribution. For this reason, herein, we propose a method based on rotation-invariant features and using a sparse-to-dense matching strategy for robust point cloud registration. Firstly, we encode raw points as superpoints with a network combining KPConv and FPN, and their associated features are extracted. Then point pair features of these superpoints are computed and embedded into the transformer to learn the hybrid features, which makes the approach invariant to rigid transformation. Subsequently, a sparse-to-dense matching strategy is designed to address the registration problem. The correspondences of superpoints are obtained via sparse matching and then propagated to local dense points and, further, to global dense points, the byproduct of which is a series of transformation parameters. Finally, the enhanced features based on spatial consistency are repeatedly fed into the sparse-to-dense matching module to rebuild reliable correspondence, and the optimal transformation parameter is re-estimated for final alignment. Our experiments show that, with the proposed method, the inlier ratio and registration recall are effectively improved, and the performance is better than that of other point cloud registration methods on 3DMatch and ModelNet40.
Satellite Remote Sensing False Forest Fire Hotspot Excavating Based on Time-Series Features
Authors: Wang Haifeng , Zhang Gui , Yang Zhigao , Xu Haizhou , Liu Feng , Xie Shaofeng
Published: July 2024

Satellite remote sensing has become an important means of forest fire monitoring because it has the advantages of wide coverage, few ground constraints and high dynamics. When utilizing satellites for forest fire hotspot monitoring, two types of ground hotspots, agricultural and other fire hotspots can be ruled out through ground object features. False forest fire hotspots within forested areas must be excluded for a more accurate distinction between forest fires and non-forest fires. This study utilizes spatio-temporal data along with time-series classification to excavate false forest fire hotspots exhibiting temporal characteristics within forested areas and construct a dataset of such false forest fire hotspots, thereby achieving a more realistic forest fire dataset. Taking Hunan Province as the research object, this study takes the satellite ground hotspots in the forests of Hunan Province as the suspected forest fire hotspot dataset and excludes the satellite ground hotspots in the forests such as fixed heat sources, periodic heat sources and recurring heat sources which are excavated. The validity of these methods and results was then analyzed. False forest fire hotspots, from satellite ground hotspots extracted from 2019 to 2023 Himawari-8/9 satellite images, closely resemble the official release of actual forest fires data and the accuracy rate in the actual forest fire monitoring is 95.12%. This validates that the method employed in this study can improve the accuracy of satellite-based forest fire monitoring.
Delineation of Intermittent Rivers and Ephemeral Streams Using a Hybrid Method
Authors: Wang Ning , Chen Fang , Yu Bo , Zhang Haiying , Zhao Huichen , Wang Lei
Published: July 2024

Intermittent rivers and ephemeral streams are crucial for the water cycle and ecosystem services, yet they are often neglected by managers and researchers, especially in headwater areas. This oversight has caused a lack of comprehensive basemaps for these vital river systems. In headwater regions, water bodies are typically sparse and disconnected, with narrow and less distinct channels. Therefore, we propose a novel hybrid method that integrates topographic data and remote sensing imagery to delineate river networks. Our method reestablishes connectivity among sparsely distributed water bodies through topographic pairs, enhances less distinct channel features using the gamma function, and converts topographic and water indices data into a weighted graph to determine optimal channels with the A* algorithm. The topographic and water indices data are derived from the Multi-Error-Removed Improved-Terrain DEM (MERIT DEM) and an average composite of the Modified Normalized Difference Water Index (MNDWI), respectively. In the upper Lancang-Mekong River basin, our method outperformed five publicly available DEM datasets, achieving over 91% positional accuracy within a 30 m buffer. This hybrid method enhances positional accuracy and effectively connects sparse water bodies in headwater areas, offering promising applications for delineating intermittent rivers and ephemeral streams and providing baseline information for these river systems.
High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning
Authors: Yang Yue , Cermak Jan , Chen Xu , Chen Yunping , Hou Xi
Published: July 2024

Characterizing the spatial distribution of particles smaller than 10 μm (PM10) is of great importance for air quality management yet is very challenging because of the sparseness of air quality monitoring stations. In this study, we use a model-agnostic meta-learning-trained artificial neural network (MAML-ANN) to estimate the concentrations of PM10 at 60 m × 60 m spatial resolution by combining satellite-derived aerosol optical depth (AOD) with meteorological data. The network is designed to regress from the predictors at a specific time to the ground-level PM10 concentration. We utilize the ANN model to capture the time-specific nonlinearity among aerosols, meteorological conditions, and PM10, and apply MAML to enable the model to learn the nonlinearity across time from only a small number of data samples. MAML is also employed to transfer the knowledge learned from coarse spatial resolution to high spatial resolution. The MAML-ANN model is shown to accurately estimate high-resolution PM10 in Beijing, with coefficient of determination of 0.75. MAML improves the PM10 estimation performance of the ANN model compared with the baseline using pre-trained initial weights. Thus, MAML-ANN has the potential to estimate particulate matter estimation at high spatial resolution over other data-sparse, heavily polluted, and small regions.
Forty-Year Fire History Reconstruction from Landsat Data in Mediterranean Ecosystems of Algeria following International Standards
Authors: Kouachi Mostefa E. , Khairoun Amin , Moghli Aymen , Rahmani Souad , Mouillot Florent , Baeza M. Jaime , Moutahir Hassane
Published: July 2024

Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks a complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics, and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions as well as temporal and geographical coverage. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated, and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30 m spatial resolution in the typical Mediterranean ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10 m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10%; commission and omission errors < 8.20%; Dice coefficients > 91.90%; and relative biases < 3.44%. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most continuous and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution to further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades.
DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation
Authors: Wang Zhechao , Cheng Peirui , Duan Shujing , Chen Kaiqiang , Wang Zhirui , Li Xinming , Sun Xian
Published: July 2024

Collaborative perception enhances onboard perceptual capability by integrating features from other platforms, effectively mitigating the compromised accuracy caused by a restricted observational range and vulnerability to interference. However, current implementations of collaborative perception overlook the prevalent issues of both limited and low-reliability communication, as well as misaligned observations in remote sensing. To address this problem, this article presents an innovative distributed collaborative perception network (DCP-Net) specifically designed for remote sensing applications. Firstly, a self-mutual information match module is proposed to identify collaboration opportunities and select suitable partners. This module prioritizes critical collaborative features and reduces redundant transmission for better adaptation to weak communication in remote sensing. Secondly, a related feature fusion module is devised to tackle the misalignment between local and collaborative features due to the multiangle observations, improving the quality of fused features for the downstream task. We conduct extensive experiments and visualization analyses using three semantic segmentation datasets, namely Potsdam, iSAID, and DFC23. The results demonstrate that DCP-Net outperforms the existing collaborative perception methods comprehensively, improving mIoU by 2.61% to 16.89% at the highest collaboration efficiency and achieving state-of-the-art performance.
Dynamic Modeling and Analysis of Flexible-Joint Robots with Clearance
Authors: Wang Jing , Zhou Shisheng , Wu Jimei , Qing Jiajuan , Kang Tuo , Shao Mingyue
Published: July 2024

The coupling effects of flexible joints and clearance on the dynamics of a robotic system were investigated. A numerical analysis was undertaken to reveal the coupling effects between flexible joints and clearance. The nonlinear spring-damping model and Coulomb model were applied to characterize the contact characteristics of the clearance, and a model for the flexible joint was formulated using the equivalent spring theory. An accurate robot model was established based on the clearance and joint flexibility characterization. The dynamic equation of a robot was obtained according to the Newton-Euler method. A comparative analysis was performed to assess the impacts of both the joint action of clearance and flexible joints and varying joint clearance values on the performance of the robot. The results showed that the coupling effects of flexible joints and clearance had a negative impact on the system dynamic performance. The amplitudes of the dynamic responses caused by the clearance are weakened by the flexible joint, but it leads to the lag of the system response. This study served as the theoretical foundation for exploring precise control techniques in robotics research.
Sensor-Assisted Analysis of Autonomic and Cerebrovascular Dysregulation following Concussion in an Individual with a History of Ten Concussions: A Case Study
Authors: Kennedy Courtney M. , Burma Joel S. , Smirl Jonathan D.
Published: July 2024

Introduction: Concussion is known to cause transient autonomic and cerebrovascular dysregulation that generally recovers; however, few studies have focused on individuals with an extensive concussion history. Method: The case was a 26-year-old male with a history of 10 concussions, diagnosed for bipolar type II disorder, mild attention-deficit hyperactivity disorder, and a history of migraines/headaches. The case was medicated with Valproic Acid and Escitalopram. Sensor-based baseline data were collected within six months of his injury and on days 1–5, 10, and 14 post-injury. Symptom reporting, heart rate variability (HRV), neurovascular coupling (NVC), and dynamic cerebral autoregulation (dCA) assessments were completed using numerous biomedical devices (i.e., transcranial Doppler ultrasound, 3-lead electrocardiography, finger photoplethysmography). Results: Total symptom and symptom severity scores were higher for the first-week post-injury, with physical and emotional symptoms being the most impacted. The NVC response showed lowered activation in the first three days post-injury, while autonomic (HRV) and autoregulation (dCA) were impaired across all testing visits occurring in the first 14 days following his concussion. Conclusions: Despite symptom resolution, the case demonstrated ongoing autonomic and autoregulatory dysfunction. Larger samples examining individuals with an extensive history of concussion are warranted to understand the chronic physiological changes that occur following cumulative concussions through biosensing devices.
An Obstacle Detection Method Based on Longitudinal Active Vision
Authors: Shi Shuyue , Ni Juan , Kong Xiangcun , Zhu Huajian , Zhan Jiaze , Sun Qintao , Xu Yi
Published: July 2024

The types of obstacles encountered in the road environment are complex and diverse, and accurate and reliable detection of obstacles is the key to improving traffic safety. Traditional obstacle detection methods are limited by the type of samples and therefore cannot detect others comprehensively. Therefore, this paper proposes an obstacle detection method based on longitudinal active vision. The obstacles are recognized according to the height difference characteristics between the obstacle imaging points and the ground points in the image, and the obstacle detection in the target area is realized without accurately distinguishing the obstacle categories, which reduces the spatial and temporal complexity of the road environment perception. The method of this paper is compared and analyzed with the obstacle detection methods based on VIDAR (vision-IMU based detection and range method), VIDAR + MSER, and YOLOv8s. The experimental results show that the method in this paper has high detection accuracy and verifies the feasibility of obstacle detection in road environments where unknown obstacles exist.
A Comprehensive Survey on Deep Learning-Based LoRa Radio Frequency Fingerprinting Identification
Authors: Ahmed Aqeel , Quoitin Bruno , Gros Alexander , Moeyaert Veronique
Published: July 2024

LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns that requires attention. Existing device identification mechanisms use cryptography which has two major issues: (1) cryptography is hard on the device resources and (2) physical attacks might prevent them from being effective. Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. We discuss various categories of radio frequency fingerprinting techniques along with hardware imperfections that can be exploited to identify an emitter. Furthermore, we describe different deep learning algorithms implemented for the task of LoRa device classification and summarize the main approaches and results. We discuss several representations of the LoRa signal used as input to deep learning models. Additionally, we provide a thorough review of all the LoRa RF signal datasets used in the literature and summarize details about the hardware used, the type of signals collected, the features provided, availability, and size. Finally, we conclude this paper by discussing the existing challenges in deep learning-based LoRa device identification and also envisage future research directions and opportunities.
An Analysis of Occupational Hazards Based on the Physical Ergonomics Dimension to Improve the Occupational Health of Agricultural Workers: The Case in Mayo Valley, Mexico
Authors: Ramos-García Víctor Manuel , López-Leyva Josué Aarón , Balderrama-Carmona Ana Paola , Ochoa-Vázquez Iván , García-Ochoa Juan José , Espinoza-Espino Manuel de Jesús
Published: July 2024

The occupational health and safety of agricultural workers is a topic that has a direct impact on the agricultural sector worldwide. For this reason, investigations into ergonomic factors are relevant to the health and safety of agricultural workers. In this study, nine variables of the physical–ergonomic dimension were analyzed to determine which factors represent occupational risks for agricultural workers in Mayo Valley, Mexico. A sample of 200 people was considered. The sample was separated by gender and divided into groups according to age. A closed-ended survey was developed and validated to assess physical ergonomics variables using a five-level Likert scale. Using Principal Component Analysis, it was found that there are physical ergonomic variables that affect male agricultural workers more than female workers (the risk of carrying heavy objects, PE3, and the risk of performing repetitive movements, PE4). It was also found that certain physical ergonomic variables are not perceived as hazardous by agricultural workers (the risk of using inappropriate materials, PE9). In addition, various research findings are discussed to determine the implications and recommendations for improving the occupational health and safety of agricultural workers in Mayo Valley, Mexico.
Influence of Sociodemographic and Social Variables on the Relationship between Formal Years of Education and Time Spent on the Internet
Authors: Leite Ângela Maria Teixeira , Azevedo Ângela Sá , Rodrigues Anabela
Published: July 2024

This study aims to validate the impact of sociodemographic factors and differentiate the influence of social variables on the correlation between the number of years of formal education an individual has and the time they allocate to internet usage. To achieve this, we utilized a publicly available database, extracting relevant indicators for our investigation. Subsequently, we conducted analyses involving associations, regressions, and moderations among the variables under scrutiny. The results revealed statistically significant variations in daily internet usage time across different countries, residences, age groups, educational levels, and marital statuses. Factors such as living in an urban or suburban environment, being in the youth demographic, possessing a higher education, maintaining single status, having an extensive social network, holding a negative perception of health, lacking home internet access but having access at work and on the go, along with the facilitation of online communication and remote work, collectively explain the variance in daily internet usage time. The relationship between the number of years of education and the duration of internet usage is moderated by sociodemographic variables (gender, age, and marital status) as well as social variables (locations of internet usage and social contacts). These findings enable us to identify a user profile at a higher risk of developing problematic behavior in relation to internet usage, as indicated by the time invested.
Climate Change Adaptation Strategies and Its Impact on Household Vulnerability to Food Insecurity: A Micro-Level Evidence from Southwest Ethiopia
Authors: Begashaw Andualem , Ketema Mengistu , Mehare Abule , Yami Mesay , Feleke Shiferaw , Abdoulaye Tahirou
Published: July 2024

Smallholder farmers in Ethiopia face increasing challenges from climate change and variability, which threaten their food security and livelihoods. This study examines how adopting single and combined climate change adaptation practices affects their vulnerability to food insecurity in Bench Maji Zone, southwest Ethiopia. Through multistage sampling, data were gathered from 390 rural households in four climate-prone districts. The study examines the impacts resulting from both individual and combined implementations of adaptation techniques. These techniques encompass crop management practices, soil and water conservation measures, and livelihood portfolio diversification strategies. The study employed the multinomial endogenous treatment effect regression model to address selection bias and endogeneity resulting from various sources of heterogeneity, whether observed or unobserved. The results show that farmers who adopted adaptation practices were less vulnerable to food insecurity than those who did not. The study also finds that adopting multiple practices has a more significant impact than adopting single practices. Our findings suggest that implementing climate change adaptation strategies can increase the resilience of smallholder farmers in the study area and decrease their vulnerability to food insecurity. The study recommends supporting farmers in adopting these strategies through research and development, information dissemination, and collaborations among farmers, researchers, and extension services.
A Preliminary Study of 3D Printing Home Designs for Improving Efficiency and Sustainability of Indigenous Housing in Canada
Authors: Lacava Hellen , Cherrington Noah , Corrado Anthony , Bigdellou Saeide , Chen Qian
Published: July 2024

Canada has been experiencing a significant housing crisis in recent years, especially in remote and Indigenous communities, yet most of the existing construction approaches have not been rapid, sustainable, and affordable enough to meet community needs. To address this challenge, this study explores the feasibility of 3D printed (3DP) housing and develops a design that is informed by Indigenous housing requirements and is realized through a physical design prototype tailored for the implementation of 3DP homes. Site visits and community engagement were integral parts of the research to help deliver invaluable insights that guided the design process, ensuring cultural sensitivity and inclusivity. The prototyped 3DP design offers efficient and sustainable solutions customized to the unique cultural and climatic needs of Indigenous communities in Canada. The final 3DP design seamlessly integrates traditional Indigenous architectural elements, such as a circular shape inspired by pit houses, with modern construction techniques, yielding a flexible, sustainable, and culturally pertinent home design. Future research work will be focused on how the proposed 3DP design can be adapted to enable mass customization to accommodate the diverse needs and preferences of Indigenous communities across Canada.
The Sustainable Development Model of China’s Tourism-Based Poverty Alleviation Industry: Analysis of the Configuration of an Active Government, an Efficient Market and a Caring Society
Authors: Luo Wei , Zuo Shanxiang , Li Changgui
Published: July 2024

Tourism-based poverty alleviation strategies have played an important role in China’s efforts to eliminate poverty. In the post-poverty-alleviation era, the mechanism by which the government, market and society jointly promote the sustainable development of the tourism-based poverty alleviation industry requires further research. Based on data from 33 tourism-based poverty alleviation demonstration counties in China, this paper uses the fsQCA method to explore the role positioning and function of the three major entities of government, market and society. The results show that no single condition can promote the long-term development of tourism-based poverty alleviation. Four modes are proposed: government financial support, resource endowment, ecosystem tourism and multisubject coordination. The government provides a diverse and sustainable “haematopoietic” mechanism by focusing on different elements in each path. In addition, public tourism services are crucial to the long-term mechanism of tourism-based poverty alleviation.
Communicative Framework Development for Construction Risk Governance: An Analysis of Risk and Trust Perception for Organizational Sustainability
Authors: Hinsberg Katrina , Nadesan Majia , Lamanna Anthony
Published: July 2024

In the construction industry, risk governance stems from the standardization of safety policies and procedures. Safety governance is typically communicated and enforced through a top-down, command-and-control approach. Procedures and policies are created at the top level and cascaded down to the work. Individual discretion in the workplace permits a choice for worker adherence to safety procedures. Through a literature review, this study identified risk perception as a significant variable in regulatory compliance and fidelity. Individual hazard assessments and risk behaviors are often based on subjective cognitive risk perceptions rather than organizational governance policy. In the current governance system, there is a misalignment of risk perceptions and failure in trust between the workforce and the regulatory process. This study examines the impact of risk perceptions on safety compliance and promotes a more participative risk governance process for sustainable construction practices. A participative framework model is proposed to help align worker risk perceptions with organizational governance through policy creation, policy compliance and stakeholder participation and feedback. Utilizing proper discourse and cyclical communication, the proposed framework aims to increase safety fidelity, compliance, and trust in the construction industry through a more inclusive and collaborative governance strategy.
Investigation into the Factors Affecting the Green Consumption Behavior of China Rural Residents in the Context of Dual Carbon
Authors: Hua Kaiyu , Pang Xinsheng
Published: July 2024

The achievement of the dual-carbon objective and China’s green and sustainable development are both heavily reliant on green consumption. This research paper employs the Likert scale method to construct a questionnaire encompassing internal and external factors that influence green consumption behavior, as well as various types of consumption behavior. It then administers the questionnaire to 1004 rural residents residing in the eastern, central, western, and northeastern regions of China, with the objective of gathering pertinent data on green consumption behavior. Drawing from the aforementioned, structural equation modeling was employed to investigate the impact of three external factors—market incentives, social pressure, and information boost—and two internal factors—environmental awareness and psychological factors—on diverse forms of green consumption behavior. The objective was to identify the most influential factors of various green consumption behaviors to facilitate the effective promotion of green consumption. The research findings indicate that internal factors exert a more substantial influence on green consumption behaviors compared to external factors. Specifically, market incentives ranks highest among the external determinants of green consumption behavior, and environmental protection cognition rank highest among the internal determinants of green consumption behavior.
Assessment of Corporate Carbon Footprint and Energy Analysis of Transformer Industry
Authors: GÜLDÜREK Manolya , ESENBOĞA Burak
Published: July 2024

Transformers are primarily key components in power transmission and distribution systems. In the electrical industry, transformers are becoming increasingly important to increase energy efficiency and reduce environmental impact. In the process from the production to the use of transformers, various strategies and technologies are adopted to reduce the carbon footprint. To achieve decarbonization targets and a future with sustainable energy, ongoing efforts to reduce the carbon footprint of transformers need to continue. Therefore, this study aims to calculate the carbon footprint (CF) of Beta Energy in the Adana Province of Türkiye. A comprehensive inventory is being created to determine and monitor the greenhouse gas emissions of Beta Energy, a transformer manufacturer. This inventory includes direct and indirect greenhouse gas emissions from all of the company’s activities. The findings show that in 2023, the total CF of Beta Energy is equal to 1,799,482.72 tons of CO2-eq and considering the total of 6044 transformers sold in 2023, results in 297 tons of CO2-eq/transformer per product. The results show that the transformer manufacturing industry has a high carbon footprint because it is an energy-intensive process. The areas where the most carbon emissions occur in transformer production are revealed by CF hot spot analysis in this study. To minimize both current and future greenhouse gas emissions during transformer production, the measures to be taken during the R&D, production, transportation, and service stages are revealed. This study aims to establish a foundation for Beta Energy’s efforts to reduce greenhouse gas emissions by managing them effectively.
The Effects of Development-Oriented Aid on Student Development in Higher Education: The Mediating Effect of Psychological Capital
Authors: Li Xiaohong , Mu Yuxi
Published: July 2024

Recently, there has been a transition from guarantee-oriented financial aid to development-oriented aid for economically disadvantaged students. Development-oriented aid aims to enable economically disadvantaged students to acquire long-term and sustainable self-development capacity. However, to what extent and how development-oriented aid promotes student development remains unclear. Identification of the related mechanism can help both students and educators effectively apply development-oriented aid in higher education scenarios. The main objective of this study was to uncover the effect of size, pathways, and mediating factors of development-oriented aid on student development in higher education. Drawing from a sample of 1792 sponsored students at University G in China and applying the structural equation modeling method for quantitative analysis, the results indicate that development-oriented aid directly shows positive influences toward student development and indirectly impacts student development through psychological capital. These results also show that development-oriented aid directly impacts Han Chinese students’ development more than the development of ethnic minority students. The pathways through which development-oriented aid positively influences Han Chinese students and ethnic minority students vary.
Social Media as a Lens for Citizen Science: Investigating Visitor Contributions in a Forest Recreational Area
Authors: Jingu Shoma , Ogawa Yui , Yamaki Kazushige , Miyamoto Asako , Takayama Norimasa
Published: July 2024

The primary challenge in collecting biodiversity information using citizen science is to encourage a diverse range of people to participate. This is crucial in fostering a Nature Positive society. Social media approaches have the potential to engage not only nature lovers but also a wider range of citizens, including those indifferent to nature. However, current understanding of the traits of individuals who contribute to widely prevalent social media platforms is limited and insufficient. This study focuses on individuals who contribute to a forest recreational area and the photos they share on a popular social media platform; it sheds light on the untapped potential of social media as a source of citizen science data and contributor sources. We developed a taxonomy of 22 classifications for 1066 photographs that illustrate human–ecosystem interactions and identified the relationship between these subjects and the history of social media activities of the 136 photo contributors. Our findings indicate that different behavioral styles exist among different types of visitors regarding their posting behavior and associated forest recreation types. This can encourage future contributions to the recruitment of citizen scientists and the collection of biodiversity information at small spatial scales.
A Secure Protocol Authentication Method Based on the Strand Space Model for Blockchain-Based Industrial Internet of Things
Authors: Gu Huanhuan , Shang Jing , Wang Pengchuan , Mi Jingfeng , Bhattacharjya Aniruddha
Published: July 2024

The rapid development of the Industrial Internet of Things (IIoT) and its application across various sectors has led to increased interconnectivity and data sharing between devices and sensors. While this has brought convenience to users, it has also raised concerns about information security, including data security and identity authentication. IIoT devices are particularly vulnerable to attacks due to their lack of robust key management systems, efficient authentication processes, high fault tolerance, and other issues. To address these challenges, technologies such as blockchain and the formal analysis of security protocols can be utilized. And blockchain-based Industrial Internet of Things (BIIoT) is the new direction. These technologies leverage the strengths of cryptography and logical reasoning to provide secure data communication and ensure reliable identity authentication and verification, thereby becoming a crucial support for maintaining the security of the Industrial Internet. In this paper, based on the theory of the strand space attack model, we improved the Fiber Channel Password Authentication Protocol (FACP) security protocol in the network environment based on symmetric cryptography and asymmetric cryptography. Specifically, in view of the problem that the challenge value cannot reach a consensus under the symmetric cryptography system, and the subject identity cannot reach a consensus under the asymmetric cryptography system, an improved protocol is designed and implemented to meet the authentication requirements, and the corresponding attack examples are shown. Finally, the effectiveness and security of the protocol were verified by simulating different networking environments. The improved protocol has shown an increase in efficiency compared with the original protocol across three different network configurations. There was a 6.43% increase in efficiency when centralized devices were connected to centralized devices, a 5.81% increase in efficiency when centralized devices were connected to distributed devices, and a 6.32% increase in efficiency when distributed devices were connected to distributed devices. Experimental results show that this protocol can enhance the security and efficiency of communication between devices and between devices and nodes (servers, disks) in commonly used Ethernet passive optical network (EPON) environments without affecting the identity authentication function.
A Conjecture for the Clique Number of Graphs Associated with Symmetric Numerical Semigroups of Arbitrary Multiplicity and Embedding Dimension
Authors: Alali Amal S. , Binyamin Muhammad Ahsan , Mehtab Maria
Published: July 2024

A subset S of non-negative integers No is called a numerical semigroup if it is a submonoid of No and has a finite complement in No. An undirected graph G(S) associated with S is a graph having V(G(S))={vi:i∈No∖S} and E(G(S))={vivj⇔i+j∈S}. In this article, we propose a conjecture for the clique number of graphs associated with a symmetric family of numerical semigroups of arbitrary multiplicity and embedding dimension. Furthermore, we prove this conjecture for the case of arbitrary multiplicity and embedding dimension 7.
Frobenius Numbers Associated with Diophantine Triples of x2-y2=zr
Authors: Yin Ruze , Komatsu Takao
Published: July 2024

We give an explicit formula for the p-Frobenius number of triples associated with Diophantine Equations x2−y2=zr (r≥2), that is, the largest positive integer that can only be represented in p ways by combining the three integers of the solutions of Diophantine equations x2−y2=zr. This result is also a generalization because if r=2 and p=0, the (0-)Frobenius number of the Pythagorean triple has already been given. To find p-Frobenius numbers, we use geometrically easy to understand figures of the elements of the p-Apéry set, which exhibits symmetric appearances.
Impact of Train Formation on the Dynamic Responses and Operational Safety of High-Speed Trains under Non-Uniform Seismic Ground Motion
Authors: Zhong Kangming , Li Xiaojun , Zhou Zhenghua
Published: July 2024

The study of dynamic responses and operational safety of high-speed trains under seismic excitation has increasingly relied on numerical simulation as the most effective and convenient research approach. The majority of studies only focus on single-car formation, and fewer utilize train models with actual standard formation, inevitably resulting in differences in dynamic characteristics of train models and simulation results. Furthermore, trains also have different standard formations in different countries and operating scenarios. Therefore, the influences of train formation on the dynamic responses and operational safety of trains under seismic action are investigated. Hence, a detailed train/vehicle–track coupled dynamics model was established to simulate trains under non-uniform seismic ground motion. Moreover, due to wheel–rail contact simulation being the key factor constraining simulation efficiency, based on the influence pattern of train formation, whether fewer train formations can achieve the same simulation accuracy as the 8-car standard formation is also explored in this paper considering seismic wave propagation effect. Results indicate that variation in train formation can influence the dynamic responses of a coupled system significantly from both the perspective of wheel–rail interaction and vehicle kinematic responses. Moreover, the 5-car formation model can better meet the accuracy requirement and significantly improve computational efficiency compared to 8-car formation model.
Geometric Properties Connected with a Certain Multiplier Integral q−Analogue Operator
Authors: Ali Ekram E. , Oros Georgia Irina , El-Ashwah Rabha M. , Kota Wafaa Y. , Albalahi Abeer M.
Published: July 2024

The topic concerning the introduction and investigation of new classes of analytic functions using subordination techniques for obtaining certain geometric properties alongside coefficient estimates and inclusion relations is enriched by the results of the present investigation. The prolific tools of quantum calculus applied in geometric function theory are employed for the investigation of a new class of analytic functions introduced by applying a previously defined generalized q−integral operator and the concept of subordination. Investigations are conducted on the new class, including coefficient estimates, integral representation for the functions of the class, linear combinations, forms of the weighted and arithmetic means involving functions from the class, and the estimation of the integral means results.