bluuebunny/crossref_metadata_2025 huggingface dataset:
Massive Point Cloud Processing for Efficient Construction Quality Inspection and Control
Authors: Liu Zhansheng , Liu Zehong , Sun Zhe
Published: October 2024

The construction of large-scale civil infrastructures requires massive spatiotemporal data to support the management and control of scheduling, quality control, and safety monitoring. Existing artificial-intelligence-based data processing algorithms rely heavily on experienced engineers to adjust the parameters of data processing, which is inefficient and time-consuming when dealing with huge datasets. Limited studies have compared the performance of different algorithms on a unified dataset. This study proposes a framework and evaluation system for comparing different data processing policies for processing huge spatiotemporal data in construction quality control. The proposed method compares the combination of multiple types of algorithms involved in the processing of massive point cloud data. The performance of data processing strategies is evaluated through this framework, and the optimal point cloud processing strategies are explored based on registration accuracy and data fidelity. Results show that a reasonable choice of combinations of point cloud sampling, filtering, and registration algorithms can significantly improve the efficiency of point cloud data processing and satisfy engineering demands for data accuracy and completeness. The proposed method can be applied to the civil engineering problem of processing a large amount of point cloud data and selecting the optimal processing method.
Improved Speech Authenticity Detection in Chinese–English Bilingual Contexts
Authors: Tsai Cheng-Yuan , Chang Sheng-Chain , Hung Chao-Hsiang , Wang Syu-Siang , Fang Shih-Hau
Published: October 2024

The rapid evolution of voice technology has heightened the need for robust detection systems to distinguish between authentic and tampered speech. Recent competitions have significantly advanced the development of countermeasures against spoofing attacks. However, while advancements in detection technologies have been notable, existing methods often focus on a single type of tampering and language. Our contribution lies in developing an improved model that integrates an enhanced ResNet architecture with an LSTM to improve the detection of tampered audio, particularly in challenging multilingual scenarios. In the experiments, we built a hybrid dataset from self-recording Chinese speech and public VCTK2 English samples, enhanced the ResNet model generalization capabilities, and evaluated our approach using the bilingual dataset. Experiment results demonstrate that the proposed approach achieves a superior performance with an equal error rate of 11.62%, even in the face of bilingual conditions, and, more importantly, outperforms the leading models from ASVSpoof 2021 and ADD 2022 competitions. We also employed advanced tampering techniques, including CycleGAN voice conversion and auto splicing, to simulate real-world tampering scenarios and verify the effectiveness of the proposed approach.
Research on Network Security Protection Technology Based on P2AEDR in New Low-Voltage Control Scenarios for Power IoT and Other Blockchain-Based IoT Architectures
Authors: Miao Weiwei , Zhao Xinjian , Li Nianzhe , Zhang Song , Li Qianmu , Li Xiaochao
Published: October 2024

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud–edge interaction and cloud–cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures.
Effects of Mild Fatigue on Biomechanics of Single Leg Landing in Young Male Volleyball Players
Authors: Li Taisen , Kapilevich Leonid Vladimirovich , Chen Junru
Published: October 2024

Objective: To investigate the effects of mild fatigue on the biomechanics of the lower limbs of young male volleyball players while performing single leg landing tasks. Methods: A total of ten young male volleyball players were recruited as participants in this study. After the single leg landing was performed, we compared the performance between those with and without fatigue (post- and non-fatigue, respectively). Kinematics and kinetics were collected using instruments, and related captured data were imported into OpenSim to analyze the hip, knee, and ankle joints. Results: We found that the ankle dorsiflexion angle at initial contact was significantly decreased in the post-fatigue group compared to the non-fatigue one. Meanwhile, the peak gluteus maximus force, peak gluteus medius force, peak tibialis posterior force, and peak gastrocnemius force significantly increased. There were no significant differences in the hip and knee joint flexion angles as well as the quadriceps and biceps femoris long head forces between the two groups. Conclusions: Mild fatigue can affect the performance of single leg landing, and players need to control the lower limbs by generating a higher muscle force to cope with the instability induced by fatigue. In a fatigued state, following initial contact with the ground, a decreased ankle dorsiflexion angle necessitates an increase in gastrocnemius and tibialis posterior muscle force to maintain stance during landing.
Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis
Authors: Lin Haijun , Fang Jing , Zhang Junpeng , Zhang Xuhui , Piao Weiying , Liu Yukun
Published: October 2024

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely used neuroimaging technique in psychiatry, due to its non-invasive nature and cost-effectiveness. With the rise of computational psychiatry, the integration of EEG with artificial intelligence has yielded remarkable results in diagnosing depression. This review offers a comparative analysis of two predominant methodologies in research: traditional machine learning and deep learning methods. Furthermore, this review addresses key challenges in current research and suggests potential solutions. These insights aim to enhance diagnostic accuracy for depression and also foster further development in the area of computational psychiatry.
Precision Optical Metrology and Smart Sensing
Authors: Zhang Xiangchao , Wang Jian , Wang Yajun
Published: October 2024

Optics, renowned for its non-contact measurement capabilities, versatility, and high sensitivity, is recognized as a powerful tool driving the technological evolution of precision metrology [...]
What Really Matters in Old Age? A Study of Older Adults’ Perspectives on Challenging Old Age Representations
Authors: von Humboldt Sofia , Low Gail , Leal Isabel
Published: October 2024

Aim: Older age is not a homogenous or stereotypical experience. Age-stereotypical representations can often be disconnected from older people’s own experiences. Challenging the status quo is relevant for aging well. The aim of this study is to analyze older adults’ perspectives on stereotypical versus their own age representations. Method: This qualitative study included a sample of 433 older adults who were 65 to 74, 75 to 84, and 85+ years of age. Content analysis was carried out. Results: Findings from this study indicated that the great majority of the participants (88%) did not identify with stereotypical age representations. This was so among participants in the 65–74 age group in relation to Showing a sense of agency (81.1%); Feeling spiritual (73.4%); Having meaningful goals (72.2%); Holding a significant social network (70.9%); Staying tuned to the world (67.1%); and feeling physically attractive (59.8%). Participants who were 75–84 years of age clarified what physical health (81.0%) and surviving chronic illnesses (78.9%), autonomy (75.5%), being socially active (74.9%), and staying mentally active (70.1%) represent at that age. The +85 years age group drew attention to No pain or physical limitations (95.6%), Physical health (93.1%), Cognitive autonomy (87.1%), Staying tuned to society and the world (76.3%); and Mobility (72.2%). Conclusions: Participants’ favoring their own over stereotypical thinking about people their age represents inner resourcefulness and a penchant for aging well. Collaborative partnerships for sharing such wisdom around could enrich policy programs and interventions that favor inclusivity and fight ageism, fostering a more accurate perspective of what it means to be a certain ‘age’ versus simply being ‘old’.
Incidence of Elbow Injury Patterns in Japanese Adolescent Judo Players: Analysis from a Nationwide Insurance Database
Authors: Ikumi Akira , Sasaki Eiji , Sakuyama Naoki , Mikami Yasuo
Published: October 2024

Elbow injuries are common in judo, particularly among adolescents. This study investigated the incidence and patterns of three types of elbow injuries (sprain, dislocation, and fracture-dislocation) among Japanese adolescent judo players (12 to 18 years old) using data from a nationwide insurance database, covering the period from 2010 to 2019. A total of 4614 elbow injuries were recorded, with sprains being the most frequent (67.6% of cases). Female players had a 20% higher incidence of sprains than male players (6.8 vs. 5.4 per 1000 athlete-years). Severe injuries, such as dislocations and fracture-dislocations, were more common in high school players, with male middle school students exhibiting a higher rate of elbow fracture-dislocations (+15%) compared with female students. Elbow injuries frequently occurred during te-waza techniques, particularly seoi-nage, when players extended their arms to prevent being thrown. Peripheral nerve injuries, predominantly ulnar nerve injuries, accompanied elbow dislocations in 74% of the cases. This study highlights the importance of proper instruction in defensive and ukemi techniques, particularly those that discourage the use of hands to prevent falls during throws, to reduce the risk of elbow injuries.
Assessing Atlantic Kelp Forest Restoration Efforts in Southern Europe
Authors: Marques Alexandre F. S. , Sanchéz-Gallego Álvaro , Correia Rodrigo R. , Sousa-Pinto Isabel , Chemello Silvia , Louro Inês , Lemos Marco F. L. , Franco João N.
Published: October 2024

Kelp forests are essential marine ecosystems increasingly compromised by human activities. Effective reforestation strategies are urgently needed, and the “green gravel” method is a viable tool already used in some European regions. This study aimed to assess the success of this method using the native Kelp species Laminaria ochroleuca on the Portuguese coastline. Cultures of green gravel were reared until the specimens reached a size of approximately 3 cm. The gravel was then deployed at selected sites in Peniche, Berlengas, and Cascais. Over an eight-month period, scientific scuba divers monitored the integration of Kelp, along with associated fish, invertebrate, and algae communities. Nutrient availability, temperature, water movement, substrate type, and Rugosity Index (RI) were also measured. The highest success rate was 12% in Consolação, with Elefante and Galos (Berlengas) reaching 7% and 4%, respectively. By the end of the monitoring period, Cascais had no remaining Kelp on green gravel. Present data suggest that higher success is dependent on less rugged and higher RI topography. Higher grazing pressure, rougher terrain, and unexpected sedimentation appear to be the main obstacles to deployment success. Solid knowledge (biologic and topographic) on the restoration site, starting restoration actions near already established Kelp forests, and significantly scaling up restoration efforts could substantially improve the success of the green gravel method in future reforestation campaigns.
Smart Logistics Facing Industry 5.0: Research on Key Enablers and Strategic Roadmap
Authors: Hsu Chih-Hung , Cai Xue-Qing , Zhang Ting-Yi , Ji Yu-Ling
Published: October 2024

While Industry 4.0 has played a significant role in advancing smart logistics, it has yet to provide adequate solutions for widespread concerns such as human centricity, sustainability, and resilience. The emergence of Industry 5.0 addresses and complements these shortcomings of Industry 4.0. However, there is currently a notable gap in the research regarding how Industry 5.0 can drive the transformation of smart logistics. To address this gap, this study develops a strategic roadmap that offers a solution to this issue. The research is initiated by conducting a comprehensive literature review with a focus on content, identifying 13 key enablers crucial for realizing smart logistics in Industry 5.0. Subsequently, this study establishes the hierarchical relationship among these key enablers through the application of the Fuzzy Interpretative Structural Model (FISM). Following this, the study employs the Matrices Impacts Croises-Multiplication Appliance Classement (MICMAC) to compute the driving force and dependence of each enabler. The results underscore the significant roles of “Active support from the government” and “Human-centric manufacturing and logistics” as the most critical enablers for Industry 5.0. The strategic roadmap, informed by expert opinions, provides valuable insights for policymakers and implementers while explaining the methods and strategies needed to drive Industry 5.0 transformation in smart logistics. Furthermore, it determines the impact relationship between enablers and the optimal development order, facilitating their synergistic alignment. Ultimately, this strategic roadmap serves as an actionable guide for the logistics industry, steering it toward achieving smart logistics and fortifying competitiveness in the industry 5.0 era.
Greenhouse Gas Emissions from Cold Chains in Agrifood Systems
Authors: Flammini Alessandro , Adzmir Hanif , Pattison Richard , Karl Kevin , Allouche Yosr , Tubiello Francesco Nicola
Published: October 2024

Cold chains are essential components of agrifood system activities, from storage and food processing to transport, retail, and household consumption. We devise a comprehensive methodology, validated against established data, to estimate both direct (due to refrigerant use) and indirect (from energy use) greenhouse gas emissions from agrifood system cold chains, expanding previous approaches, which had focused on direct emissions only. We found that in 2022, world-total indirect emissions from energy use in cold chains were more than twice the direct component from refrigerants. This resulted in a new estimate of world-total GHG emissions from agrifood system cold chains of 1.32 Gt CO2eq in 2022, with significant growth over the past two decades (0.52 Gt CO2eq in 2000). Household consumption and food processing represented the most significant contributors to agrifood system emissions from cold chains, together representing in 2022 three-fourths of the total. These results align well with known global patterns of energy use in the refrigeration sector and highlight the importance of targeting cold chains within agrifood system with GHG emission mitigation actions.
Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves
Authors: Zheng Hexiang , Hou Hongfei , Qin Ziyuan
Published: October 2024

The precise forecasting of groundwater levels significantly influences plant growth and the sustainable management of ecosystems. Nonetheless, the non-stationary characteristics of groundwater level data often hinder the current deep learning algorithms from precisely capturing variations in groundwater levels. We used Variational Mode Decomposition (VMD) and an enhanced Transformer model to address this issue. Our objective was to develop a deep learning model called VMD-iTransformer, which aims to forecast variations in the groundwater level. This research used nine groundwater level monitoring stations located in Hangjinqi Ecological Reserve in Kubuqi Desert, China, as case studies to forecast the groundwater level over four months. To enhance the predictive performance of VMD-iTransformer, we introduced a novel approach to model the fluctuations in groundwater levels in the Kubuqi Desert region. This technique aims to achieve precise predictions of the non-stationary groundwater level conditions. Compared with the classic Transformer model, our deep learning model more effectively captured the non-stationarity of groundwater level variations and enhanced the prediction accuracy by 70% in the test set. The novelty of this deep learning model lies in its initial decomposition of multimodal signals using an adaptive approach, followed by the reconfiguration of the conventional Transformer model’s structure (via self-attention and inversion of a feed-forward neural network (FNN)) to effectively address the challenge of multivariate time prediction. Through the evaluation of the prediction results, we determined that the method had a mean absolute error (MAE) of 0.0251, a root mean square error (RMSE) of 0.0262, a mean absolute percentage error (MAPE) of 1.2811%, and a coefficient of determination (R2) of 0.9287. This study validated VMD and the iTransformer deep learning model, offering a novel modeling approach for precisely predicting fluctuations in groundwater levels in a non-stationary context, thereby aiding sustainable water resource management in ecological reserves. The VMD-iTransformer model enhances projections of the water level, facilitating the reasonable distribution of water resources and the long-term preservation of ecosystems, providing technical assistance for ecosystems’ vitality and sustainable regional development.
Bridging the Digital Divide: Empowering Rural Women Farmers Through Mobile Technology in Kerala
Authors: Udisha Omanakuttan , Ambily Philomina Illiparambil Gabriel
Published: October 2024

This research paper investigates the impact of mobile technology on empowering rural women farmers and promoting inclusive agricultural development. The primary emphasis is on how mobile technology enables rural women producers to become more self-reliant in agriculture, promotes market participation, strengthens social connections, facilitates socioeconomic integration, enhances rural quality of life, and fosters sustainable agricultural development. In addition, this study also analyses the influence of demographic factors on the use of mobile technology among rural women farmers. This study was carried out in Kerala, a renowned agricultural state in India. In Kerala, the Palakkad district is known to be the granary of Kerala due to the availability of fertile valleys, rivers, forests, and paddy fields. Data were collected from 192 rural women farmers from Palakkad via semi-structured interviews, questionnaire surveys, and focus group discussions (FGD). The data were analyzed using the paired t-test and Garratt ranking method. The results demonstrate that mobile technology markedly enhances access to agricultural information, market engagement, and social connectivity, resulting in greater empowerment for rural women farmers. Nonetheless, obstacles such as insufficient digital literacy and inadequate mobile infrastructure access persist as considerable impediments. The findings demonstrate the revolutionary potential of mobile phone use in bridging information gaps, empowering women farmers in rural areas, and advancing equitable agricultural development, as well as the barriers faced by rural women in using mobile technology.
Prosocial Behavior and Workplace Safety: Analysis of the Role of Emotional Intelligence and Perceived Organizational Support in Two UK and US Samples
Authors: Moserle Giacomo , Foti Giulia , Finstad Georgia Libera , Bazzoli Andrea , Curcuruto Matteo , Morgan Jim
Published: October 2024

Prosocial safety behavior (PSB) is a critical element of workforce participation in the promotion of safety in the workplace. This study aims to examine the influence of various antecedents on PSB. Based on an analysis of the existing literature on prosocial behaviors, we investigate whether emotional intelligence (EI) and perceived organizational support (POS) significantly influence PSB. Furthermore, we analyze the mediating role of employees’ intrinsic motivation (IM) and affective commitment (AC) in these relationships. The research was conducted by administering an online questionnaire to a large sample of 488 workers employed in safety-critical industries based in the United States and the United Kingdom with the support of an online platform (N = 346; N = 142). Statistical analyses, conducted using a multi-group approach, revealed that EI had a direct effect on PSB in the two samples analyzed. In the US sample, we found that IM mediated the influence of EI and POS on PSB. In the UK sample, none of the mediation hypotheses were supported. The study provides insights into the individual and organizational factors that promote the development of a prosocial orientation in managing workplace safety issues, revealing the importance of recognizing the role of EI as a significant person-related antecedent supporting PSB. The statistical evidence from the study suggests that organizations that desire to facilitate the expression of PSB in their workforce may consider investing in training programs to enhance the EI of their employees and designing work conditions that facilitate high levels of intrinsic motivation.
An Innovative Approach to Enhance the Durability and Sustainability of Shoe Insoles
Authors: Naseem Zohra , Zainab Iqra , Batool Syeda Rubab , Uzun Muhammet , Ioanid Alexandra , Nazeer Muhammad Anwaar
Published: October 2024

This study presents an innovative approach to designing a shoe insole with enhanced durability, sustainability, and antibacterial properties. Needle-punched non-woven recycled polyester fabrics with three different GSMs (100, 200, and 300) were developed. The composite shoe insole was developed using non-woven fabric laminated with a polyurethane sheet to enhance durability. The fabrics were treated with an antibacterial finish with three different concentrations (5%, 10%, and 15%) and subjected to 5 and 10 washing cycles. The developed composites were evaluated against their relative hand value, abrasion resistance, tensile strength, antibacterial activity, and overall moisture management capability. Overall results reveal that the developed composite shoe insole is durable, sustainable, and presents no bacterial growth, demonstrating the insole’s hygienic effectiveness.
Design and Use of a Stratum-Based Yield Predictions to Address Challenges Associated with Spatial Heterogeneity and Sample Clustering in Agricultural Fields Using Remote Sensing Data
Authors: Khechba Keltoum , Laamrani Ahmed , Belgiu Mariana , Stein Alfred , Dong Qi , Chehbouni Abdelghani
Published: October 2024

Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propose decomposing the landscape complexity into homogeneous zones using existing landform, agroecological, and climate classification datasets, and subsequently applying stratum-based ML to estimate cereal yield. This approach was tested in a heterogeneous region in northern Morocco, where wheat is the dominant crop. We compared the results of the stratum-based ML with those applied to the entire study area. Sentinel-1 and Sentinel-2 satellite imagery were used as input variables to train three ML models: Random Forest, Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression. The results showed that the XGBoost model outperformed the other assessed models. Furthermore, the stratum-based ML approach significantly improved the yield estimation accuracy, particularly when using landform classifications as homogeneous strata. For example, the accuracy of XGBoost model improved from R2 = 0.58 and RMSE = 840 kg ha−1 when the ML models were trained on data from the entire study area to R2 = 0.72 and RMSE = 809 kg ha−1 when trained in the plain area. These findings highlight that developing stratum-based ML models using landform classification as strata leads to more accurate predictions by allowing the models to better capture local environmental conditions and agricultural practices that affect crop growth.
An Integrated SIMUS–Game Theory Approach for Sustainable Decision Making—An Application for Route and Transport Operator Selection
Authors: Stoilova Svetla
Published: October 2024

The choice of management strategy for companies operating in different sectors of the economy is of great importance for their sustainable development. In many cases, companies are in competition within the scope of the same activities, meaning that the profit of one company is at the expense of the other. The choice of strategies for each of the firms in this case can be optimized using game theory for a non-cooperative game case where the two players have antagonistic interests. The aim of this research is to develop a methodology which, in non-cooperative games, accounts for the benefits of different criteria for each of the strategies of the two participants. In this research a new integrated sequential interactive model for urban systems (SIMUS)–game theory technique for decision making in the case of non-cooperative games is proposed. The methodology includes three steps. The first step consists of a determination of the strategies of both players and the selection of criteria for their assessment. In the second step the SIMUS method for multi-criteria analysis is applied to identify the benefits of the strategies for both players according to the criteria. The model formation in game theory is drawn up in the third step. The payoff matrix of the game is formed based on the benefits obtained from the SIMUS method. The strategies of both players are solved by dual linear programming. Finally, to verify the results of the new approach we apply four criteria to make a decision—Laplace’s criterion, the minimax and maximin criteria, Savage’s criterion and Hurwitz’s criterion. The new integrated SIMUS–game theory approach is applied to a real example in the transport sector. The Bulgarian transport network is investigated regarding route and transport type selection for a carriage of containers between a starting point, Sofia, and a destination, Varna, in the case of competition between railway and road operators. Two strategies for a railway operator and three strategies for a road operator are examined. The benefits of the strategies for both operators are determined using the SIMUS method, based on seven criteria representing environmental, technological, infrastructural, economic, security and safety factors. The optimal strategies for both operators are determined using the game model and dual linear programming. It is discovered that the railway operator will apply their first strategy and that the road operator will also apply their first strategy. Both players will obtain a profit if they implement their optimal strategies. The new integrated SIMUS–game theory approach can be used in different areas of research, when the strategies for both players in non-cooperatives games need to be established.
From Knowledge to Action: The Power of Green Communication and Social Media Engagement in Sustainable Food Consumption
Authors: Iliopoulou Efthymia , Koronaki Eirini , Vlachvei Aspasia , Notta Ourania
Published: October 2024

Understanding what drives sustainable consumer behavior is crucial for effective green marketing strategies. We set out to explore the mechanisms explaining the relationships between environmental attitudes, perceived environmental knowledge, and green consumption values on consumer behaviors, specifically the mediating roles of receptivity to green communication and social media engagement with environmentally friendly firms. We also investigated the moderating role of information seeking in these relationships. Data were collected through a survey of 610 Greek respondents between May and July 2024 and analyzed using IBM SPSS and AMOS version 23. Findings show significant and positive paths between green consumption values and receptivity to green communication, e-word-of-mouth intention, food behavior, and other environmental behaviors, while receptivity to green communication and social media consumers’ engagement with environmentally friendly firms has a significant and positive impact on e-word-of-mouth intention and food behavior. Findings reveal that credible and detailed communication is essential for engaging information-seeking consumers, who are more critical of green messages.
Facilitating the Production of 3D-Printed Spare Parts in the Design of Plastic Parts: A Design Requirement Review
Authors: van Oudheusden Alma , Faludi Jeremy , Balkenende Ruud
Published: October 2024

Using additive manufacturing for spare part production can ensure that spare parts are available for a long time. However, spare parts are currently not designed for additive manufacturing. This study aimed to find how the production of 3D-printed spare parts can be facilitated in the design of plastic parts. We used a literature review and illustrative case to find how the design requirements for standard injection moulded plastic parts relate to the manufacturing capabilities of additive manufacturing for spare parts. The design requirements were defined by assigning corresponding structural and material properties. These requirements were then used to construct and evaluate the capabilities of additive manufacturing compared to injection moulding. It was found that additive manufacturing is especially suitable for requirements like Accuracy, Heat resistance, and Chemical resistance. However, to fully enable 3D-printed spare parts, certain design challenges still need to be tackled. Designers should pay careful attention to the synergies and trade-offs between design requirements and the challenges that might arise from the combination of certain requirements. Also, designers should ensure products are easily reparable before considering 3D-printed spare parts. If we target these challenges in the design phase, we can facilitate 3D-printed spare parts that enable product repairability.
Sustainable Management of Agro-Food Wastes Derived from the Olive Mill and Sugar Industry and Clinoptilolite to Produce Novel Adsorptive Materials
Authors: Gkaliouri Christina Megetho , Ioannou Zacharias , Rigopoulos Nickolas , Sarris Dimitris
Published: October 2024

Novel sorbents were produced using sustainable and eco-friendly methods, aimed at minimizing environmental impact while utilizing industrial by-products and natural minerals. Olive stones and molasses derived from olive mill and sugar industries, respectively, and an abundant, natural and low-cost mineral, clinoptilolite, were combined in the following proportions: 80/20 clinoptilolite/stone, 80/10/10 clinoptilolite/stone/molasses, 50/50 stone/molasses, w/w. Then, physical carbonization (CL80OL20C, CL80OL10M10C, OL50M50C) or chemical activation (CL80OL20A, CL80OL10M10A, OL50M50A) took place. The adsorbents were characterized through Raman, FT-IR, BET and SEM-EDS analysis. The CL80OL20A material presents the highest ratio of C/O in EDS analysis and the lowest ID/IG in Raman spectroscopy. The increase in the specific surface area is as follows: OL50M50C < OL50M50A < CL80OL10M10C < CL80OL20C < CL80OL20A < CL80OL10M10A. Three applications were conducted: two with dyes (methylene blue and methyl red) in aqueous means and one in olive mill wastewaters for the removal of total phenols and their addition to rice, increasing the total phenolic content and producing novel foods. The well-fitted application of the pseudo-second order kinetic model to the experimental data has shown that chemisorption is the prevailing mechanism. The adsorbed amount of the recovered phenols to rice ranges from 0.14 to 0.93 mg/g. Consequently, olive and sugar by-products can be used as filters either to adsorb dangerous organic compounds or to recover bioactive compounds from wastewater, preventing their disposal in the environment, which could otherwise lead to severe negative effects on the ecosystems.
Consumer Risk in the Purchasing Process in the Organic Food Market
Authors: Michałowska Mariola , Kułyk Piotr , Pietrow Leonard
Published: October 2024

Risk is an inherent part of our lives and every action we take. It accompanies us in various areas of our lives. Each of us encounters risk in everyday life by undertaking and performing specific activities or actions. Risk is not always perceived by us; there are situations when we do not notice it in our actions. The main objective of this article is to identify and assess the factors influencing the level of risk in consumer purchasing decisions in the organic products market. Additionally, it aims to indicate the desired directions of actions taken by producers and suppliers of organic food to reduce the risk associated with the purchasing process perceived by consumers. The research process took into account existing scientific achievements and the survey method, chosen due to the subject and nature of the research. The survey was addressed to consumers from the Lubuskie Voivodeship and was anonymous. The conducted research shows that the greatest risk perceived by consumers is in the case of online purchases, and the way to reduce this risk is through buyer loyalty to the place of purchase. The empirical research also provides grounds to assume that there is a relationship between individual types of risk. The research shows that respondents are most afraid of safety and social risk, and the least afraid of the risk of losing too much time. Consumers who want to reduce the risk use their own experiences or positive opinions about the product. The results provide valuable information for producers and suppliers of organic food who want to build lasting relationships with consumers based on trust and loyalty. The higher the trust, the greater the customer loyalty, and the lower the perceived risk.
The Impact of Agricultural Socialization Services on the Ecological Protection of Rice Farmland in Jianghan Plain, China
Authors: Zhuo Wenjun , Zeng Zhi , Pang Xinsheng
Published: October 2024

The ecological protection of cultivated land is crucial for advancing high-quality agricultural development. This study analyzes the impact of agricultural socialization services on the ecological conservation of farmland, focusing on the reduction of chemical fertilizers and pesticides among rice farmers in the Jianghan Plain area. Utilizing data from 743 farmer household surveys conducted in 2023, the findings reveal that agricultural socialization services significantly encourage farmers to reduce chemical input usage. For every 1% increase in the level of agricultural social services, the average fertilizer use per hectare will decrease by 14% and the average pesticide use per hectare will decrease by 16.4%. The study identifies scale operation, factor substitution, and alleviation of capital constraints as mediating factors enhancing the efficacy of these services. Furthermore, heterogeneity analysis indicates that these services are more effective in reducing chemical inputs among large-scale and newer-generation farmers compared to their small-scale and older counterparts. Additionally, technology-intensive socialized services exhibit a stronger impact on reducing chemical inputs than labor-intensive services.
Financing Sustainability: Unveiling the Role of Government Debt in Carbon Reduction Performance
Authors: Yang Zhian , Liu Xiaochen , Badulescu Alina
Published: October 2024

The existing literature on government debt has predominantly focused on its influence on economic growth, with relatively limited attention paid to its ecological implications. Government debt, as an important financial tool, plays an essential role in improving the quality of economic development, yet its impact on sustainable governance remains underexplored. Against this backdrop, this paper investigates the relationship between government debt and carbon reduction using a sample of Chinese listed companies from 2010 to 2023. After excluding missing and financial firm data, our final sample includes 26,535 observations. We obtained these data from the China Security Market Accounting Research (CSMAR) database and the Wind database. This study utilizes ordinary least squares (OLS) as the baseline regression and identifies a significant positive impact of government debt on carbon emissions. Further, the moderating analysis suggests that the positive impact of government debt on carbon reduction is particularly stronger in state-owned (SOEs) and heavily polluting enterprises. To ensure the robustness of these findings, we also use fixed-effects models and the generalized method of moments (GMM), validating the consistency of the findings. This research provides critical practical and theoretical insights for regulators and adds to the prevailing body of literature on emissions reduction.
On a Class of Generalized Multivariate Hermite–Humbert Polynomials via Generalized Fibonacci Polynomials
Authors: Alam Noor , Wani Shahid Ahmad , Khan Waseem Ahmad , Kotecha Ketan , Zaidi Hasan Nihal , Gassem Fakhredine , Altaleb Anas
Published: October 2024

This paper offers a thorough examination of a unified class of Humbert’s polynomials in two variables, extending beyond well-known polynomial families such as Gegenbauer, Humbert, Legendre, Chebyshev, Pincherle, Horadam, Kinnsy, Horadam–Pethe, Djordjević, Gould, Milovanović, Djordjević, Pathan, and Khan polynomials. This study’s motivation stems from exploring polynomials that lack traditional nomenclature. This work presents various expansions for Humbert–Hermite polynomials, including those involving Hermite–Gegenbauer (or ultraspherical) polynomials and Hermite–Chebyshev polynomials. The proofs enhanced our understanding of the properties and interrelationships within this extended class of polynomials, offering valuable insights into their mathematical structure. This research consolidates existing knowledge while expanding the scope of Humbert’s polynomials, laying the groundwork for further investigation and applications in diverse mathematical fields.
Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach
Authors: Lin Yu-Cheng , Sung Bongsuk , Park Sang-Do
Published: October 2024

This study aims to introduce a novel approach for predicting China’s consumer confidence index (CCI), a key economic indicator that reflects consumers’ confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.
Association Between Prenatal Exposure to Organochlorine Pesticides and Telomere Length in Neonatal Cord Blood
Authors: Jiang Ying , Xu Ziyuan , Wang Meng , Liu Hongxiu , Li Yuanyuan , Xu Shunqing
Published: October 2024

Objectives: Environmental exposure may affect the telomere length (TL) of newborns, which is considered as an early biomarker indicating susceptibility for later life diseases. However, the effects of prenatal organochlorine pesticide (OCP) exposure on newborn TL remain unclear. This study aimed to investigate the association between prenatal exposure levels of OCPs during pregnancy and TL in neonatal cord blood. Methods: A total of 168 mother–infant pairs from a birth cohort in Wuhan, China, were included this study. The concentrations of hexachlorocyclohexanes (HCHs, including β-HCH, α-HCH, and γ-HCH), p,p’-dichlorodiphenyltrichloroethane (p,p’-DDT) and its metabolites (p,p’-dichlorodiphenyldichloroethane, p,p’-DDD; p,p’-dichlorodiphenyldichloroethylene, p,p’-DDE) were measured in cord blood. The associations between the OCPs and the TL in newborns were analyzed by a generalized linear regression model. Stratified analyses by newborn sex, maternal gestational weight gain, and pregnancy body mass index (BMI) were performed to evaluate if the associations were modified by these factors. Results: The detection rates of various OCPs ranged from 50.9% to 100.0%. The median concentration of p,p’-DDE was the highest (33.90 ng/g lipid), followed by β-HCH (8.67 ng/g lipid), and the median concentrations of the other OCPs were between 0.12 and 0.33 ng/g lipid. Among the all newborns, a two-fold increase in the γ-HCH concentration in the cord blood was significantly associated with a 0.024 (95% CI: −0.041, −0.007) decrease in the TL. After stratification by newborn sex, the inverse association between γ-HCH and the TL was only statistically significant in boys, but not in girls (P for interaction <0.05). In addition, after stratification by pre-pregnancy BMI, β-HCH and p,p’-DDT concentrations were significantly associated with a decreased TL in the overweight pre-pregnancy BMI group [−0.111 (95% CI: −0.203, −0.018) and −0.036 (95% CI: −0.049, −0.023), respectively]. Conclusions: Prenatal exposure to OCPs during pregnancy was associated with a decreased neonatal telomere length, which may be affected by the newborn sex and pre-pregnancy BMI. These findings may provide new insights into the mechanisms underlying OCP-induced adverse health effects.
Circulation and Codetections of Influenza Virus, SARS-CoV-2, Respiratory Syncytial Virus, Rhinovirus, Adenovirus, Bocavirus, and Other Respiratory Viruses During 2022–2023 Season in Latvia
Authors: Kampenusa Inara , Niedre-Otomere Baiba , Trofimova Julija , Pole Ilva , Pakarna Gatis , Savicka Oksana , Nikisins Sergejs
Published: October 2024

This retrospective study analysed the routine data obtained by multiplex real-time RT-qPCR methods for respiratory virus detection. A total of 4814 respiratory specimens collected during 1 September 2022–31 August 2023 were included in the study. A total of 38% of the specimens were positive for at least one target, with the incidence maximum (82%) for the small children (age group 0–4 years). The five dominant virus groups were rhinovirus (RV, 12%), influenza virus A (IAV, 7%), adenovirus (AdV, 6%), respiratory syncytial virus (RSV, 5%), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, 5%). The specimens with multi-detections represented 19% of the positives, unevenly distributed (n = 225, 56, 43, 24) among the age groups 0–4, 5–14, 15–64, and 65< years, respectively. The dominant virus groups in multi-positive specimens were RV (53%), AdV (43%), and bocavirus (BoV, 35%)—in mutual pairs as well as all three together—followed by RSV (21%), and IAV (15%). Our study focused on the specimens with codetections and provides an insight into the variety of the respiratory virus interactions in Latvia during the first year since pandemic-related social restriction measures were eased. The observations also emphasise the need to consider the differentiation between rhinoviruses and enteroviruses, especially for the youngest patients in the age group 0–4.
Analysis of Powassan Virus Genome Sequences from Human Cases Reveals Substantial Genetic Diversity with Implications for Molecular Assay Development
Authors: Klontz Erik H. , Chowdhury Navid , Holbrook Nolan , Solomon Isaac H. , Telford Sam R. , Aliota Matthew T. , Vogels Chantal B. F. , Grubaugh Nathan D. , Helgager Jeffrey , Hughes Holly R. , Velez Jason , Piantadosi Anne , Chiu Charles Y. , Lemieux Jacob , Branda John A.
Published: October 2024

Powassan virus (POWV) is an emerging tick-borne virus that causes severe meningoencephalitis in the United States, Canada, and Russia. Serology is generally the preferred diagnostic modality, but PCR on cerebrospinal fluid, blood, or urine has an important role, particularly in immunocompromised patients who are unable to mount a serologic response. Although the perceived poor sensitivity of PCR in the general population may be due to the biology of infection and health-seeking behavior (with short viremic periods that end before hospital presentation), limitations in assay design may also contribute. Genome sequences from clinical POWV cases are extremely scarce; PCR assay design has been informed by those available, but the numbers are limited. Larger numbers of genome sequences from tick-derived POWV are available, but it is not known if POWV genomes from human infections broadly mirror genomes from tick hosts, or if human infections are caused by a subset of more virulent strains. We obtained viral genomic data from 10 previously unpublished POWV human infections and showed that they broadly mirror the diversity of genome sequences seen in ticks, including all three major clades (lineage I, lineage II Northeast, and lineage II Midwest). These newly published clinical POWV genome sequences include the first confirmed lineage I infection in the United States, highlighting the relevance of all clades in human disease. An in silico analysis of published POWV PCR assays shows that many assays were optimized against a single clade and have mismatches that may affect their sensitivity when applied across clades. This analysis serves as a launching point for improved PCR design for clinical diagnostics and environmental surveillance.