bluuebunny/crossref_metadata_2025 huggingface dataset:
Toward a Caribbean Genealogy of Energy: Cosmologies of Energy in Modernity’s First World
Authors: Crosson J. Brent
Published: January 2025

The story of the rise of “energy” usually centers on the Industrial Revolution and the coal-powered steam engine in nineteenth-century Western Europe. Although it often escapes notice, the Caribbean was actually the site of the first known use of a steam engine to power industrial manufacturing (on a sugar plantation) and the world’s first oil well (drilled by a US company in southern Trinidad). These “firsts” point toward energy’s roots in colonial and imperial projects of extraction in the Caribbean, revealing the centrality of race and the plantation in understanding energy capitalism and the current climate crisis. This article traces a Caribbean-attuned genealogy of “energy”. Today, energy is taken for granted as an abstract universal, but the concept was bound to specific forms of racial governance during the transition from sugar to fossil fuels as apex capitalist commodities. In tracing this genealogy, I rewrite the first two “laws of energy” as ethico-political statements on racial governance rather than descriptions of a pre-existing natural order. Adding to scholarship that has laid bare the relationship between biological sciences and race, I argue that energy sciences have also been central to sustaining (while occluding) racialized hierarchy. I then look at conceptions of energy in perhaps the world’s oldest petro-state (Trinidad, with brief comparisons to neighboring Venezuela) to elaborate Caribbean-attuned, speculative alternatives to the “laws of energy”.
Mexiletine-Induced Esophageal Ulceration: Two Case Reports and a Review of the Literature
Authors: Ghisa Matteo , Barbuscio Ilenia , Bonazzi Erica , Fassan Matteo , Barberio Brigida , Senzolo Marco , Savarino Edoardo V.
Published: January 2025

Background and Clinical Significance: Mexiletine is a class 1B antiarrhythmic drug commonly prescribed for ventricular arrhythmias and neuropathic pain. It works as a blocker of the sodium channel that modulates cardiac conduction and reduces aberrant nerve signaling. While it is generally well tolerated, gastrointestinal side effects, such as nausea, vomiting, and abdominal pain, are relatively common. Esophagitis and esophageal ulcerations have been described as rare side effects; however, they are poorly documented in the literature. Esophageal ulceration induced by oral medications, termed pill esophagitis, occurs due to prolonged contact between the medication and the esophageal mucosa. Factors contributing to this phenomenon include improper administration, such as swallowing without sufficient water, taking medication before lying down, or inherent irritant properties of the drug itself. Mexiletine-induced esophageal ulceration has not been extensively reported, making such cases clinically significant and worth investigating. In particular, the prompt diagnosis of mexiletine-induced esophageal injury is essential for timely treatment initiation or the discontinuation of the drug, preventing complications such as bleeding, strictures, or perforation. Altogether, these actions are important to prevent the onset of potentially serious complications, such as bleeding, strictures, and the perforation of the esophagus. Case Presentation: Two different patients were included in this case report on mexiletine-induced esophageal ulceration: a 78-year-old woman affected by primary dilated cardiomyopathy and atrial fibrillation with high ventricular response and a 19-year-old man affected by dilated cardiomyopathy and systemic sclerosis. Conclusions: This case report underscores the importance of recognizing mexiletine-induced esophageal ulceration, and it advocates for timely diagnosis and management to optimize patient outcomes.
Compensation for Matrix Effects in High-Dimensional Spectral Data Using Standard Addition
Authors: Khanonkin Elena , Schechter Israel , Dattner Itai
Published: January 2025

The standard addition method is widely used in analytical chemistry to compensate for matrix effects. While effective with single signals (e.g., absorbance at a single wavelength) and independent of matrix composition or blank measurements, it has limitations with high-dimensional data (e.g., full spectra). Existing methods for high-dimensional data require knowledge of the matrix composition and blank measurements, restricting their applicability. We propose a novel algorithm for standard addition that works with high-dimensional data without requiring matrix composition knowledge or blank measurements. By modifying experimental data (e.g., spectra) before applying chemometric models, the algorithm accurately determines analyte concentrations even in complex matrices like seawater or food, where blanks are unavailable. A performance evaluation shows the algorithm compensates effectively for matrix effects, outperforms previously published standard addition algorithms and direct applications of multivariate chemometric algorithms, and is robust to variations in SNR and matrix effect intensity.
Research and Application of Microwave Microstrip Transmission Line-Based Icing Detection Methods for Wind Turbine Blades
Authors: Meng Min , Zheng Xiangyuan , Wu Zhonghui , Hong Hanyu , Zhang Lei
Published: January 2025

In areas where there is high humidity and freezing rain, there is a tendency of blade icing on wind turbines. It results in energy dissipation and mechanical abrasion and also creates a safety concern due to the risk of having falling ice. Real-time online detection of icing is crucial in the enhancement of power generation efficiency and in the safety of wind turbines. The current methods of icing detection that use ultrasound, optics, vibration, and electromagnetics are already studied. But these methods have their drawbacks, including small detection ranges, low accuracy, large size, and challenges in distributed installation, making it hard to capture the real-time dynamics of the icing and de-icing processes on the wind turbine blades. To this end, this paper presents a new blade surface icing detection technique using microstrip lines. This approach uses the impact of icing state and thickness on the effective dielectric constant of the microstrip line surface. This paper presents the analysis of time-domain features of microwave signals, which facilitates the identification of both the icing state and the corresponding thickness. Simulation and experimental measurement of linear and S-shaped microstrip sensors are used in this research in order to compare the response of the sensors to the variation in the thickness of the icing layer. It is seen that for icing thickness ranging from 0 mm to 6 mm, the imaginary part of the S21 parameter of the S-shaped microstrip line has a more significant change than that of the linear microstrip line. The above experiments also confirm that the phase shift value of the S-shaped microstrip line is always higher than that of the linear microstrip line for the same variation of icing thickness, which proves that the S-shaped microstrip line is more sensitive than the linear one. Also, it was possible to establish the relationship between the phase shift values and icing thickness, which makes it possible to predict the icing thickness. The developed microwave microstrip detection technology is intended for usage in the wind turbine blade icing and similar surface detection areas. This method saves the size and thickness of icing sensors, which makes it possible to conduct measurements at various points. This is especially beneficial for usage in wind turbine blades and can be further applied in aerospace, automotive, and construction, especially the bridges.
Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods
Authors: Sasu Gabriel-Vasilică , Ciubotaru Bogdan-Iulian , Goga Nicolae , Vasilățeanu Andrei
Published: January 2025

In geriatric healthcare, missing data pose significant challenges, especially in systems used for frailty monitoring in elderly individuals. This study explores advanced imputation techniques used to enhance data quality and maintain model performance in a system designed to detect frailty insights. We introduce missing data mechanisms—Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR)—into a dataset collected from smart bracelets, simulating real-world conditions. Imputation methods, including Expectation–Maximization (EM), matrix completion, Bayesian networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Generative Adversarial Imputation Networks (GAINs), Variational Autoencoder (VAE), and GRU-D, were evaluated based on normalized Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2 metrics. The results demonstrate that KNN and SVM consistently outperform other methods across all three mechanisms due to their ability to adapt to diverse patterns of missingness. Specifically, KNN and SVM excel in MAR conditions by leveraging observed data relationships to accurately infer missing values, while their robustness to randomness enables superior performance under MCAR scenarios. In MNAR contexts, KNN and SVM effectively handle unobserved dependencies by identifying underlying patterns in the data, outperforming methods like GRU-D and VAE. These findings highlight the importance of selecting imputation methods based on the characteristics of missing data mechanisms, emphasizing the versatility and reliability of KNN and SVM in healthcare applications. This study advocates for hybrid approaches in healthcare applications like the cINnAMON project, which supports elderly individuals at risk of frailty through non-intrusive home monitoring systems.
The Inversion of SPAD Value in Pear Tree Leaves by Integrating Unmanned Aerial Vehicle Spectral Information and Textural Features
Authors: Yan Ning , Qin Yasen , Wang Haotian , Wang Qi , Hu Fangyu , Wu Yuwei , Zhang Xuedong , Li Xu
Published: January 2025

Chlorophyll is crucial for pear tree growth and fruit quality. In order to integrate the unmanned aerial vehicle (UAV) multispectral vegetation indices and textural features to realize the estimation of the SPAD value of pear leaves, this study used the UAV multispectral remote sensing images and ground measurements to extract the vegetation indices and textural features, and analyze their correlation with the SPAD value of leaves during the fruit expansion period of the pear tree. Finally, four machine learning methods, namely XGBoost, random forest (RF), back-propagation neural network (BPNN), and optimized integration algorithm (OIA), were used to construct inversion models of the SPAD value of pear trees, with different feature inputs based on vegetation indices, textural features, and their combinations, respectively. Moreover, the differences among these models were compared. The results showed the following: (1) both vegetation indices and textural features were significantly correlated with SPAD values, which were important indicators for estimating the SPAD values of pear leaves; (2) combining vegetation indices and textural features significantly improved the accuracy of SPAD value estimation compared with a single feature type; (3) the four machine learning algorithms demonstrated good predictive ability, and the OIA model outperformed the single model, with the model based on the OIA inversion model combining vegetation indices and textural features having the best accuracy, with R2 values of 0.931 and 0.877 for the training and validation sets, respectively. This study demonstrated the efficacy of integrating multiple models and features to accurately invert SPAD values, which, in turn, supported the refined management of pear orchards.
Job Stress, Burnout, and Work Ability in Tire Manufacturing: The Role of Age and Experience
Authors: Lee Jinwon , Lee Kyung-Sun , Ha Jiyeon , Hwang Jaejin
Published: January 2025

This study examined the challenges of the predominantly male, aging workforce in South Korea′s tire manufacturing industry, focusing on physical demands, job stress, and burnout. The present study surveyed 400 workers employed on the production line at a tire manufacturing company in the Republic of Korea. This study revealed a predominantly male workforce, a majority of married employees, an older age demographic, a diverse range of employment tenures with a significant portion of long-term staff, and variability in rest period durations and frequencies. An analysis of data from tire manufacturing workers revealed that high job autonomy and relationship conflict contributed to lower job satisfaction and increased stress. Generational conflicts between older, experienced workers and younger employees further exacerbated dissatisfaction. Despite the physically demanding nature of the job, no significant difference in physical ability across age groups was observed. This study also found a high burnout index, characterized by cynicism, emotional exhaustion, and decreased professional efficacy, particularly among those performing repetitive tasks. The findings suggest a need for interventions to address burnout and improve job conditions for the aging workforce.
Improvement in Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics
Authors: Raiyn Jamal , Weidl Galia
Published: January 2025

This paper proposes a new strategy for a collision avoidance system leveraging time-to-collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to traditional TTC-based approaches. The methodology is validated through extensive simulations, demonstrating a significant improvement in collision avoidance performance compared to traditional TTC-based approaches. By integrating deep learning models with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions. The use of the Gaussian model to contributes to time-to-collision (TTC) analysis by providing a probabilistic framework to quantify collision risk under uncertainty. It calculates the likelihood that TTC will fall below a critical threshold (TTC_crit), indicating a potential collision. By modeling input variations—such as sensor inaccuracies, fluctuating vehicle velocity, and unpredictable driving behavior—as a Gaussian distribution, the system can handle real-world uncertainties more effectively. This enables continuous, real-time risk prediction, allowing for dynamic and adaptive collision avoidance decisions. The Gaussian approach enhances the robustness of TTC-based systems by improving their ability to predict and prevent collisions in uncertain driving conditions.
Boron in Soil, Drainage Water and Groundwater in Long-Term Irrigated Area by Treated Wastewater in Northeast Tunisia
Authors: Fatnassi Salma , Hörmann Georg , Fohrer Nicola , Navarro-Pedreño Jose , Hachicha Mohamed
Published: January 2025

Boron (B) in soils originates from both natural and human sources, such as agriculture and industry, which contribute boron via fertilizers and irrigation water. Using treated wastewater (TWW) for irrigation is a strategy to address water scarcity in agriculture within arid areas like Tunisia; however, it introduces a risk of B contamination. Thus, the primary objective of this study is to assess the levels of B contamination throughout the entire irrigation system of the TWW “Cebala Borj-Touil” in Northeast Tunisia. To evaluate the lasting impact of TWW irrigation on B and the dynamics of physicochemical parameters across the entire system, soil samples were collected from various depths (0–150 cm) in a control area and gathered both prior to and following irrigation (two campaigns). Water samples utilized for irrigation and water released from drainage were gathered throughout an irrigation cycle. Groundwater sampling took place over two campaigns in April and September of 2018. The findings indicated that B concentrations in irrigation water were below the Tunisian standards (NT 106.03), while in drainage and groundwater, B levels exceeded the standard slightly during some periods. Throughout a three-decade cycle, the patterns of irrigation caused a notable transfer of boron from the topsoil to the groundwater. Therefore, it is essential to regularly supervise the irrigated area system and enhance the drainage system.
Technical, Tactical, and Time–Motion Match Profiles of the Forwards, Midfielders, and Defenders of a Men’s Football Serie A Team
Authors: Perrotta Rocco , Ungureanu Alexandru Nicolae , Cherubini Domenico , Brustio Paolo Riccardo , Lupo Corrado
Published: January 2025

The present study aimed to verify the (1) differences between players’ roles in relation to technical and tactical and time–motion indicators, and the (2) relationships between individual time–motion and technical and tactical indicators for each role in a men’s Italian football Serie A team. A total of 227 performances were analyzed (28 players: 8 forwards, FWs; 11 midfielders, MDs; 9 defenders, DFs). Technical and tactical indicators, such as ball possession (played balls, successful passes, successful playing patterns, lost balls, ball possession time), offensive play (total and successful dribbles, crosses, assists), and shooting (total shots, shots on target) were obtained by means of Panini Digital (DigitalSoccer Project S.r.l). In addition, a time–motion analysis included the total distance, distances covered at intensities of 16.0–19.8 km/h, 19.8–25.2 km/h, and over 25.2 km/h, the average recovery time between metabolic power peaks, and burst occurrence, the latter of which was performed by means of a 18 Hz GPS device (GPexe Pro2 system tool) worn by the players. Results showed role-specific differences: MDs covered more distance, while DFs had better ball possession. MDs and DFs had more successful playing patterns, and MDs and FWs performed more dribbles and shots. Strong correlations (p < 0.01, ρ > 0.8) were found between bursts and assists for FWs, high-intensity running and ball possession for MDs, and distance, dribbling, and shots for DFs. These findings highlight the importance of individual and tailored training programs to optimize role-specific performance demands.
The Effects of Perceptions of Economic Sustainability and Barriers on Organic Farming Implementation
Authors: Eti Hasan Selçuk
Published: January 2025

This study proposes an analysis of the impact of farmers′ demographic characteristics and their perceptions of economic sustainability and barriers on organic farming implementation in Turkey’s Thrace region. Using a mixed-methods approach, data were collected from 400 farmers through surveys and analyzed using SPSS v27 The findings revealed that age, education level, land ownership, and organic farming training were significant predictors of adoption. Perceptions of economic sustainability positively influenced adoption, while perceptions of barriers had a negative effect. The qualitative findings identified certification costs, insufficient credit opportunities, and difficulties in accessing organic inputs as the most common challenges faced by farmers. The most requested forms of support included product purchase guarantees, financial aid during certification, and fertilizer–pesticide subsidies. This study provides a foundation for developing policies and programs to promote organic farming in Turkey, contributing to the country’s sustainable agriculture goals.
The Sustainability Performance of Social Enterprises in China: The Configurational Impacts of Ecosystems and Revenue Structures
Authors: Yu Xiao-Min , Xia Hao-Yu , He Yi-Jun , Chen Hong-Yu
Published: January 2025

Despite the global development of social enterprises (SEs) over the past three decades, how to improve sustainability remains a challenging issue for most SEs. Although SE ecosystems have been recognized as crucial determinants of SE sustainability performance in the current literature, no empirical study has comprehensively examined the relationships among them. Additionally, prior studies have demonstrated that sustainability performance might vary among SEs of different revenue structures or across different contexts, suggesting that more attention should be devoted to the complexity of the causal mechanisms determining SE sustainability performance. To address these gaps in the current literature, this paper examines the complex, divergent, and asymmetric causal links among SE ecosystems, revenue structures, and the sustainability performance of SEs in China by conducting fuzzy set qualitative comparative analysis (fsQCA) of 274 typical cases of SEs. The results revealed alternative configurations for high and low levels of sustainability performance among SEs of different revenue structures. First, the fsQCA results indicated that SE sustainability performance was not determined by the impacts of single components of SE ecosystems but rather by the combined effects of multiple elements. Second, for SEs of divergent revenue structures, causal paths leading to high or low levels of sustainability performance showed notable discrepancies in terms of both number and composition. Specifically, commercial SEs receiving income mainly from market-based earned income were more likely to achieve higher levels of social and financial sustainability because of greater adaptability to SE ecosystems and less environmental dependence. Third, the impacts of different components of SE ecosystems on sustainable performance also varied with SE revenue structures. Three categories of components—policy environment, sociocultural setting, and industrial infrastructure—made more important contributions to SE sustainability performance in both the social and financial dimensions.
Hydrogen Valley in Cyprus: Insights and Strategies for Citizen Engagement
Authors: Afanasiev Andrey , Kandinskaia Olga , Eminidou Snezana , Sousanis Andreas
Published: January 2025

In remote areas or islands like Cyprus, the isolated energy system, high energy consumption in the transport sector and projected excess electricity production from solar sources create favourable conditions for establishing a hydrogen valley. But even after addressing technological, managerial, economic and financial challenges, the success of a hydrogen valley hinges on the acceptance and engagement of the local population. The role of citizens is under-researched by academia and overlooked by policymakers. Our paper’s contribution is unique data from a purposefully developed survey of Cypriot residents. The findings reveal robust support for the renewable energy transition in principle, with 90% expressing supportive views, of which 57% ‘strongly support’ the transition, and notably, middle-aged, more educated and fully employed individuals showing the strongest support. At the same time, our results show that 62% are unfamiliar with the concept of a hydrogen economy. The promising finding is that 80% of citizens are ‘very likely’ (25%) or ‘somewhat likely’ (55%) to engage in discussions or activities related to the creation of a hydrogen valley in Cyprus. Gender differences in the willingness to engage are, however, evident: 32% of males indicated they are ‘very likely’ to participate versus 23% of females. We conclude that the prevailing citizen behaviour in Cyprus is “Seeking Information”, and we make policy suggestions outlining the top ten engagement tools to foster awareness among the general population and the top ten strategies targeting active supporters of hydrogen in Cyprus to elevate their involvement to ‘Action’ and ‘Advocacy’ levels of engagement.
Enhancing Supply Chain Efficiency in India: A Sustainable Framework to Minimize Wastage Through Authentication and Contracts
Authors: Hussain S. Mahaboob , Balakrishna Akula , Naidu K. T. Narasimha , Pareek Prakash , Malviya Nishit , Reis Manuel J. C. S.
Published: January 2025

A recent study by the Food and Agriculture Organization (FAO) reveals that approximately 40% of India’s food production is wasted each year due to inefficient and fragmented supply chain management. An effective supply chain is essential to ensure the efficient use of resources and to maximize profitability. A platform is required to enhance communication, facilitate contract agreements, and streamline collaboration among stakeholders to reach their target destinations. In some cases, suppliers of raw materials often struggle to find buyers, which create gaps in the system. To address these issues, we have developed a unique methodology. We designed a platform that connects all stakeholders in the supply chain through a transparent, two-way communication channel, while ensuring high levels of authenticity and trust. Additionally, we created a comprehensive framework that includes profiles for farmers, retailers, manufacturers, and distributors. This application aims to bridge communication gaps and improve the overall efficiency of the system. To ensure the application’s integrity with the database, we conducted tests with various databases for optimal performance and authentication. During our evaluation, one of our observations on databases was that the Postgres database system utilized 48.3% of the total processing time and a latency of 10,941.3 milliseconds. At the same time, PgBouncer used 5.4% of the time consumed and required 1230.75 milliseconds before data processing started. Considering the authentication as a parameter, we also considered the Firebase (NoSQL) database for our application. It optimizes database operations to enhance the platform’s speed and efficiency, which has the potential for performance improvements in the supply chain management system.
Harnessing Industry 4.0 for SMEs: Advancing Smart Manufacturing and Logistics for Sustainable Supply Chains
Authors: Alkhodair Majid , Alkhudhayr Hanadi
Published: January 2025

The complex integration of Industry 4.0 technologies into SMEs necessitates robust frameworks to address adoption barriers and enhance sustainability. The present study investigates the impact of artificial intelligence (AI), the Internet of Things (IoT), and blockchain on smart manufacturing, logistics, and sustainability in SMEs. Using a cross-sectional design, data were collected from 300 SMEs across manufacturing, logistics, and retail sectors through purposive sampling, focusing on technology adoption, and sustainability performance from 2018 to 2022. Data were analyzed using advanced machine learning models, including XG Boost and Random Forest, alongside Recursive Feature Elimination (RFE) for dimensionality reduction and quantile regression for an inferential analysis. Findings revealed that IoT adoption improved resource utilization efficiency, while blockchain enhanced ethical sourcing—furthermore, AI-driven predictive maintenance reduced operational downtimes. XG Boost achieved a Mean Squared Error (MSE), highlighting its superior predictive capability, while Random Forest achieved perfect fitness but risked overfitting. However, adoption varied significantly across firms due to financial and technical constraints, with SMEs reporting limited access to capital and skilled labor. This study underscores the need for policy interventions and targeted support for SMEs to bridge adoption gaps. The study advances the existing body of knowledge by highlighting the synergistic benefits of integrating Industry 4.0 technologies to enhance SME sustainability. Practical implications include policy recommendations for financial incentives, technical support, and capacity-building programs, empowering SMEs with actionable insights to overcome adoption barriers and achieve sustainable growth. These findings offer industry leaders and policymakers’ actionable insights to drive SME transformation in Industry 4.0, empowering them to make a difference.
Carbon Emissions Intensity of the Transportation Sector in China: Spatiotemporal Differentiation, Trends Forecasting and Convergence Characteristics
Authors: Peng Zhimin , Li Miao
Published: January 2025

Effectively controlling the carbon emissions intensity of the transportation sector (TSCEI) is essential to promote the sustainable development of the transportation industry in China. This study, which builds upon trend analysis, the Dagum Gini coefficient, and spatial autocorrelation analysis to reveal the spatiotemporal differentiation of TSCEI, employs both traditional and spatial Markov chain to analyze the dynamic evolution of TSCEI and forecast its future development trend. Furthermore, econometric models are constructed to examine the convergence characteristics of TSCEI. The empirical results reveal the following key findings: (1) TSCEI in China has significantly declined, exhibiting a spatial distribution pattern of “higher in the north, lower in the south; higher in the west, lower in the east”. (2) Inter-regional differences are the main contributors to overall TSCEI disparities, with provincial TSCEI exhibiting positive spatial autocorrelation, primarily characterized by high–high and low–low agglomeration. (3) TSCEI tends to gradually shift from high- to low-intensity states over time, with an equilibrium probability of 90.98% for transferring to lower intensity state. Provincial TSCEI shows significant spatial spillover effects, influenced by neighboring provinces’ states. (4) TSCEI demonstrates convergence characteristics at national and regional levels, including σ convergence, absolute and conditional β convergence, with the transportation energy structure and technological progress playing a particularly prominent role in facilitating the convergence of TSCEI towards lower values. The policy implications of promoting TSCEI convergence and reducing spatial inequality are discussed.
Analysis of the Environmental and Economic Impacts of Industrial Restructuring and Identification of Key Sectors Based on an Industrial Correlation Perspective
Authors: Dong Genglin , Huang Ying , Liao Cuiping , Zhao Daiqing , Wang Peng , Sun Changlong
Published: January 2025

Systematically assessing the impact of industrial restructuring on carbon emissions and economic growth from the industrial correlation perspective holds great significance for realizing sustainable economic development. By extending the input–output analysis, this study developed a comprehensive assessment framework to evaluate the impacts of industrial restructuring on energy-related carbon emissions and economic growth within a multi-sectoral system from the industrial correlation perspective. An indicator system was established to identify key sectors for different industrial restructuring strategies. Taking Guangdong as a case, the results show that (1) the indirect impact of industrial restructuring in sectors such as equipment manufacturing and services on carbon emissions is more significant than that on economic growth, and the carbon intensity of its indirect impact is much larger than that of its direct impact; (2) industrial restructuring indirectly affects energy-related carbon emissions or economic growth mainly through a limited number of linked sectors, whereas the main linked pathways through which sector-specific industrial restructuring indirectly affects carbon emissions and economic growth are not consistent; (3) from the industrial correlation perspective, environmental benefits are higher for the service sector and lower for the construction sector; and (4) in industrial restructuring, the metal-processing sector is identified as a key sector for pursuing low-carbon transition, while the non-metallic mineral products sector is identified as a key sector for controlling production scale. The findings and framework can inform regional decisions on industrial restructuring and carbon reduction from the industrial correlation perspective.
Reevaluating Propensity to Support Sustainability
Authors: Theisz Alec Andrew , Min Aehong , Shih Patrick C.
Published: January 2025

In a world faced with ever-growing crises of climate change, economic inequality, and social injustice, sustainability has become a catch-all term to address these challenges and more. However, efforts to measure the social, environmental, and economic factors of sustainability are undermined by inconsistent understandings of the term. This research seeks to address this gap in sustainability research by constructing a wide-reaching propensity instrument that incorporates the different constructs of sustainability. A literature review informed propensity instrument construction. The first version of the instrument included 269 items, which were narrowed to 100 after an iterative process of merging, refinement, and elimination. The 100 scale items were deployed through an online survey, where 162 responses were collected to inform data analysis. Principal component analysis revealed two primary factors of Sustainable Behavior and Sustainability Attitude. After further refinement based on items’ factor-loading scores and communalities, 13 items remained that described sustainability as environmentally and socially conscious behaviors and attitudes. The third construct of sustainability, economics, was not present after such refinements, suggesting that purely economic behaviors and attitudes are disparate from individuals’ sustainability propensity. This new propensity instrument informs the understanding of sustainability and provides a tool for measuring sustainability with more breadth.
Numerical Modeling of CO2 Reduction Reactions in a Batch Cell with Different Working Electrodes
Authors: Ijaz Ahmad , Mostafayi SeyedSepehr , Esmaeilirad Mohammadreza , Asadi Mohammad , Abbasian Javad , Arastoopour Hamid
Published: January 2025

Batch cells are pivotal in advancing the foundational research of CO2 reduction by providing precise control over reaction conditions to study catalyst behavior and reaction mechanisms, generating insights that drive the development of scalable systems like flow reactors and ultimately supporting sustainability through the industrial adoption of carbon-neutral technologies. Therefore, a one-dimensional numerical model is developed to study electrochemical CO2 reduction reactions in a batch cell with three different working electrode configurations: solid electrode, glassy carbon electrode, and gas-diffusion-layer electrode. The experimental results of two Cu-based catalysts are used to obtain electrochemical kinetic parameters and to validate the numerical model. The simulation results demonstrate that both gas-diffusion-layer electrodes and glassy carbon electrodes with porous catalyst layers have superior performance over solid electrodes in terms of total current density. Furthermore, we studied the impact of the key parameters of batch cells with glassy carbon electrodes, such as boundary-layer thickness, catalyst-layer thickness, catalyst-layer porosity, electrolyte nature, and the strength of an electrolyte relative to the total current density at a fixed applied cathodic potential of −1.0 V vs. RHE.
Weigh-In-Motion Placement for Overloaded Truck Enforcement Considering Traffic Loadings and Disruptions
Authors: Jung Yunkyeong , Mizutani Daijiro , Lee Jinwoo
Published: January 2025

Overloaded trucks directly contribute to road infrastructure deterioration and undermine safety, posing significant challenges to sustainability. This makes enforcement to reduce their numbers and impacts essential. Weigh-in-motion (WIM) systems use road-embedded sensors to measure truck weights and enforce regulations. However, WIM cannot be installed on all routes, and some overloaded truck drivers can detour to avoid them instead of giving up overloading if the detour penalty is still lower than the extra profit from overloading. This paper focuses on optimal WIM location planning for overloaded truck management, incorporating a demand shift and user equilibrium model based on the utility functions of overloaded and non-overloaded trucks. The presented framework includes an upper-level problem for WIM placement and a lower-level problem for demand shifts and traffic assignments among overloaded trucks, non-overloaded trucks, and light-duty vehicles for a given WIM placement. Particularly, at the upper level, the primary objective is to minimize the traffic loadings, i.e., the expected equivalent single-axle load–kilometers per unit time, with the secondary objective of minimizing the total traffic disruptions over the target network. Simulations and sensitivity analyses are conducted through a numerical example. Consequently, this study proposes an optimal WIM placement framework that considers drivers’ utility-based route choice and social costs such as ESAL and traffic congestion.
Development of a Benchmark Model for Residential Buildings with a Mediterranean Climate: The Aero-Habitat in Algiers City
Authors: Tellache Asmaa , Lazri Youcef , Laafer Abdelkader , Attia Shady
Published: January 2025

The problem of maximizing energy efficiency in Algerian residential structures in Mediterranean climates is discussed in this article. The primary issue with North Africa’s residential building stock is the dearth of benchmark models that describe thermal comfort and energy use, which is made worse by high cooling needs and energy poverty. The principal aim of this study is to create a benchmark model that will aid in evaluating the energy performance of the existing system and to suggest a series of actions to improve efficiency and thermal comfort in the future. The technique builds a calibrated model based on a database of 284 Algiers apartments by combining modeling and empirical observations. Based on the observed U-Value wall of 0.43 W/(m2K), the average annual energy use for Archetype A is 3.70 kWh/m2, and the average annual heating energy use is 13.20 kWh/m2. The significance of this model in advancing energy efficiency and sustainability in Mediterranean climates is emphasized in the Conclusion Section. These results contribute to our understanding of the dynamics of building energy in similar global environments and evaluate the thermal comfort and the measurement of CO2 emissions in this type of building.
Discussing Food Waste Online: Current Trends in the Food Processing Industry and Future Directions
Authors: Rösler Florian , Kreyenschmidt Judith , Ritter Guido
Published: January 2025

Food loss and waste is a recurring issue of discussion that food processing companies can no longer avoid. Society, stakeholders, and directives are demanding more and more communication on sustainability issues. However, only just under half of food processing companies stated that they provide consumer information on food waste. However, consumer information is only one way of communicating about food waste. The aim of this study is to identify key themes for relevant online communication on food waste that companies should be communicating to support the German National Strategy for the Reduction of Food Waste and to determine whether companies are already using these themes online. For this purpose, national strategy experts were asked through a questionnaire which topics were relevant according to the national strategy. The websites of 105 food processing companies were analyzed using content analysis to determine the status quo of food waste communication and assessed for relevance according to the national strategy. This paper presents five prioritized clusters of topics for relevant online communication on food waste for companies. The top priorities for communication, Cluster 1, are “Business Goals”, “Business Strategy”, “Process”, “Consumer Information”, “In-house Transparency”, and “Utilization of Remaining Materials”. The status quo shows that 43.8% of the companies provided online content regarding food waste. Through theory-driven content analysis, the three most common topics were identified in relation to food waste, namely the following: “Business Goals”, “Product and Packaging”, and “Consumer Information”. Companies communicated in line with the priority clusters. However, the frequency of topics and the way they are communicated varies widely. Only “Consumer Information” and “Business Goals” from Cluster 1 are commonly communicated. During the COVID-19 pandemic and the war in Ukraine, there was a decline in publications on food waste, and afterward, more consumer-oriented content was published rather than communicating the relevant topics. Companies are expected to have two objectives in their communication: to communicate with consumers and to be transparent about their targets, data, and utilization of food waste. The results also show differences between subsectors, the occurrence of the topics over time, and examples. The findings are aimed at policymakers, researchers, and companies as a starting point for improving the consistency and transparency of food waste communication in line with the national strategy. The results are also of international interest due to the common challenges of food waste and international food companies.
Understanding User Acceptance of AI-Driven Chatbots in China’s E-Commerce: The Roles of Perceived Authenticity, Usefulness, and Risk
Authors: Marjerison Rob Kim , Dong Hang , Kim Jong-Min , Zheng Hanyi , Zhang Youran , Kuan George
Published: January 2025

This study examines users’ perceptions of Chatbots in China, with a particular focus on the factors influencing their acceptance and usage. Grounded in the Technology Acceptance Model (TAM), we analyze data from 542 online responses to explore the roles of Perceived Authenticity, usefulness, and risk in shaping user behavior toward AI-driven Chatbots. Using linear regression and mediation analyses, our findings indicate that both Perceived Authenticity and Perceived Usefulness positively impact users’ behavioral intentions, while Perceived Risk has a negative influence. Notably, Perceived Usefulness serves as a mediator between behavioral intentions and both Perceived Authenticity and Perceived Risk. These results contribute to the growing body of research on AI and e-commerce by providing empirical evidence of the key factors affecting Chatbot adoption. The study offers valuable implications for developers and marketers, suggesting that enhancing Perceived Authenticity and usefulness while addressing Perceived Risks can improve user acceptance. These insights are particularly pertinent for AI practitioners aiming to refine Chatbot technology and expand its application across various sectors.
Time-to-Fault Prediction Framework for Automated Manufacturing in Humanoid Robotics Using Deep Learning
Authors: Ali Amir R. , Kamal Hossam
Published: January 2025

Industry 4.0 is transforming predictive failure management by utilizing deep learning to enhance maintenance strategies and automate production processes. Traditional methods often fail to predict failures in time. This research addresses this issue by developing a time-to-fault prediction framework that utilizes an enhanced long short-term memory (LSTM) model to predict machine faults. The proposed method integrates real-time sensor data, including current, voltage, and temperature calibrated via ultra-sensitive optical sensing technologies based on the typical whispering gallery optical mode (WGM) to create a robust dataset. Due to the high-quality factor that these sensors exhibit, any minute change on the surrounding medium will makes a significant change on its transmission spectrum. The LSTM model trained on these data demonstrated rapid and stable convergence, outperforming other deep learning techniques with a mean absolute error (MAE) of 0.83, a root mean squared error (RMSE) of 1.62, and a coefficient of determination (R2) of 0.99. The results show the superior performance of LSTM in predicting machine failures early in real-world environments within 10 min lead time, improving productivity and reducing downtime. This framework advances smart industries by improving fault prediction in manufacturing precision robotics components, demonstrated through two humanoid robots, GUCnoid 1.0 and ARAtronica.
Evaluating the Risks of Heated Tobacco Products: Toxicological Effects on Two Selected Respiratory Bacteria and Human Lung Cells
Authors: Furnari Salvatore , Emma Rosalia , Caruso Massimo , Furneri Pio Maria , Fuochi Virginia
Published: January 2025

Heated tobacco products (THPs) are increasingly promoted as potential harm reduction tools, offering an alternative to traditional cigarettes. Despite these claims, understanding of their toxicological impact on respiratory health and associated microbial communities is limited. Comprehensive investigations are needed to elucidate the biological mechanisms and potential health implications associated with their use. Methods: This study evaluated the toxicological effects of aerosols produced by THPs (IQOS 3 Duo with Heets “Sienna Selection”) in comparison to conventional cigarette smoke (1R6F). Antibacterial activity was evaluated using Streptococcus pneumoniae and Klebsiella pneumoniae as representative species of the respiratory microbiota through agar diffusion assays and MIC/MBC determinations. Cytotoxicity was assessed in human lung fibroblast cells (MRC5) through the neutral red uptake (NRU) assay, whereas mutagenicity was investigated using the Ames test. Results: THP aerosols demonstrated the ability to inhibit the growth of both S. pneumoniae and K. pneumoniae, exerting bacteriostatic effects at lower concentrations and bactericidal effects at higher concentrations. While these antibacterial effects might initially seem beneficial against pathogens such as K. pneumoniae, they raise concerns about the potential disruption of the respiratory microbial balance, particularly in relation to S. pneumoniae. Despite these microbiological effects, THP aerosols demonstrated minimal cytotoxicity on human lung fibroblasts and lacked detectable mutagenic activity, contrasting with the significant cytotoxicity and mutagenicity caused by cigarette smoke. Conclusions: THPs present a reduced short-term toxicological profile compared with conventional cigarettes; however, their effects on respiratory microorganisms deserve attention. The observed inhibition of commensal bacteria highlights the need to explore potential changes in the microbial ecosystem that could affect respiratory health. These findings highlight the need for additional studies to evaluate the long-term effect of THP use on respiratory microbiota and the stability of the overall microbial ecosystem.