bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Gas Accumulation on the Stability of Parallel Upward Ventilation in High-Temperature Sloped Shafts of Deep Wells
Authors: Yuan Xiaoping , Zhang Qinghua , Wang Zejun
Published: November 2024

To explore the causes and influencing factors of wind flow oscillations in high-temperature inclined aisles of deep wells under parallel upward ventilation, this study conducts a comprehensive investigation using theoretical analysis and numerical simulations. Based on the kinetic analysis of gas flow, a discriminant formula for wind flow reversal in the side branch is derived. Further analysis identifies initial wind speed and branch length as key factors influencing the reversal. Both gas pressure and thermal pressure contribute to wind flow reversal in the side branch, and the opposing directions of these pressures cause high-temperature gas to periodically flow between the two branches, resulting in wind flow oscillations. A higher initial wind speed can effectively reduce the oscillation amplitude due to increased initial kinetic energy and a larger pressure difference, but it does not extend the oscillation duration. Increasing the branch length can suppress wind flow oscillations by increasing airflow frictional resistance and damping.
Multi-Criteria Decision Making in Chemical and Process Engineering: Methods, Progress, and Potential
Authors: Wang Zhiyuan , Nabavi Seyed Reza , Rangaiah Gade Pandu
Published: November 2024

Multi-criteria decision making (MCDM) is necessary for choosing one from the available alternatives (or from the Pareto-optimal solutions obtained by multi-objective optimization), where the performance of each alternative is quantified against several criteria (or objectives). This paper presents a comprehensive review of the application of MCDM methods in chemical and process engineering. It systematically outlines the essential steps in MCDM including the various normalization, weighting, and MCDM methods that are critical to decision making. The review draws on published papers identified through a search in the Scopus database, focusing on works by authors with more contributions to the field and on highly cited papers. Each selected paper was analyzed based on the MCDM, normalization, and weighting methods used. Additionally, this paper introduces two readily available programs for performing MCDM calculations. In short, it provides insights into the MCDM steps and methods, highlights their applications in chemical and process engineering, and discusses the challenges and prospects in this area.
Evaluation of Electrical Properties and Uniformity of Single Wall Carbon Nanotube Dip-Coated Conductive Fabrics Using Convolutional Neural Network-Based Image Analysis
Authors: Kim Erin , Kim SangUn , Kim Jooyong
Published: November 2024

This study proposes a convolutional neural network (CNN)-based image analysis method to evaluate the electrical properties and uniformity of conductive fabrics treated with single-walled carbon nanotube (SWCNT) dip-coating. The conductive fabric was produced by dip-coating cotton-blended spandex with SWCNT, and the surface images were scanned and preprocessed to obtain image data, while resistance measurements were conducted to obtain labels and build the dataset. SEM analysis revealed that as the number of dip-coating cycles increased, particle density and path formation improved. The CNN model learned the relationship between surface images and resistance values, achieving a high predictive performance, with an R-squared (R²) value of 0.9422. The model demonstrated prediction accuracies of 99.1792% for the coefficient of variation (CV) of uniformly coated fabrics and 96.8877% for non-uniformly coated fabrics. Additionally, p-value analysis of all fabric samples yielded a result of 0.96044, indicating no statistically significant difference between the predicted and actual values. The proposed CNN-based model can accurately evaluate the electrical uniformity of conductive fabrics, showing potential for contributing to quality control and process optimization in production.
Improved Droop Control Strategy for Microgrids Based on Auto Disturbance Rejection Control and LSTM
Authors: Su Hongsheng , Dong Zhiwen , Wang Xingsheng
Published: November 2024

This thesis proposes an improved droop control strategy design based on active disturbance rejection control and LSTM. This strategy uses the droop control method to coordinately control the distributed generation units (DGs) in a microgrid to achieve stable operation of the microgrid system. Linear-Auto Disturbance Rejection Control (LADRC) is introduced and an improved LADRC is designed based on the error principle. A disturbance compensation link is introduced on the basis of traditional LADRC to form ILADRC and a droop control strategy is used. Instead of improving the PD controller in LADRC, an improved droop control strategy is formed, which not only achieves natural decoupling between powers, but also improves the system’s immunity and transient operation capabilities. At the same time, in order to achieve adaptive parameter tuning in the improved droop control strategy, this article introduces long short-term memory (LSTM) to form an adaptive improved droop control strategy which further improves the system’s immunity and robustness. This article builds a simulation model through the MATLAB/Simulink simulation experiment platform and tests PI control and traditional droop control. The strategy and the improved droop control strategy designed in this thesis are experimentally compared and verified, and simulation analysis and verification are conducted on the two working conditions. The simulation results clearly demonstrate the superiority of the improved droop control strategy over PI control and traditional droop control, indicating that the correctness and reliability under various working conditions are verified.
Mountain Landslide Monitoring Using a DS-InSAR Method Incorporating a Spatio-Temporal Atmospheric Phase Screen Correction Model
Authors: Guo Shipeng , Zuo Xiaoqing , Zhang Jihong , Yang Xu , Huang Cheng , Yue Xuefu
Published: November 2024

The detection of potential rural mountain landslide displacements using time-series interferometric Synthetic Aperture Radar has been challenged by both atmospheric phase screens and decoherence noise. In this study, we propose the use of a combined distributed scatterer (DS) and the Prophet_ZTD-NEF model to rapidly map the landslide surface displacements in Diqing Tibetan Autonomous Prefecture, China. We conducted tests on 28 full-resolution SENTINEL-1A images to validate the effectiveness of our methods. The conclusions are as follows: (1) Under the same sample conditions, confidence interval estimation demonstrated higher performance in identifying SHPs compared to generalized likelihood ratio test. The density of DS points was approximately eight times and five times higher than persistent scatterer interferometry and small baseline subset methods, respectively. (2) The proposed Prophet_ZTD-NEF model considers the spatial and temporal variability properties of tropospheric delays, and the root mean square error of measured values was approximately 1.19 cm instead of 1.58 cm (PZTD-NEF). (3) The proposed Prophet_ZTD-NEF method reduced the mean standard deviation of the corrected interferograms from 1.88 to 1.62 cm and improved the accuracy of the deformation velocity solution by approximately 8.27% compared to Global Position System (GPS) measurements. Finally, we summarized the driving factors contributing to landslide instability.
Novel Multicomponent Digital Care Assistant and Support Program for People After Stroke or Transient Ischaemic Attack: A Pilot Feasibility Study
Authors: Allan Liam P. , Silvera-Tawil David , Cameron Jan , Li Jane , Varnfield Marlien , Smallbon Vanessa , Bomke Julia , Olaiya Muideen T. , Lannin Natasha A. , Cadilhac Dominique A.
Published: November 2024

Evidence is increasing for digital health programs targeting the secondary prevention of stroke. We aimed to determine the feasibility of the novel Care Assistant and support Program for people after Stroke (CAPS) or transient ischaemic attack (TIA) by combining person-centred goal setting and risk-factor monitoring through a web-based clinician portal, SMS messages, a mobile application (app), and a wearable device. We conducted a 12-week mixed-methods, open-label feasibility study. Participants (6 months–3 years after stroke or TIA, access to the internet via a smartphone/tablet) were recruited via the Australian Stroke Clinical Registry. Participants set one or two secondary prevention goals with a researcher and provided access and training in technology use. Feasibility outcomes included recruitment, retention, usability, acceptability, and satisfaction. Secondary outcomes included goal attainment, health outcomes, and program costs. Following 600 invitations, 58 responded, 34/36 (94%) eligible participants commenced the program (one withdrawal; 97% retention), and 10 were interviewed. Participants (27% female, 33% TIA) generally rated the usability of the mobile application as ‘Good’ to ‘Excellent’ (System Usability Scale). Most (94%) agreed the program helped with engagement in health self-monitoring. Overall, 52 goals were set, predominantly regarding exercise (21/52), which were the most frequently achieved (9/21). At 12 weeks, participants reported significant improvements (p < 0.05) in self-efficacy (Cohen’s d = 0.40), cardiovascular health (d = 0.71), and the mental health domain of the PROMIS GH (d = 0.63). CAPS was acceptable, with good retention and engagement of participants. Evaluation of this program in a randomised controlled trial is warranted.
Reduction of Vision-Based Models for Fall Detection
Authors: Garmendia-Orbegozo Asier , Anton Miguel Angel , Nuñez-Gonzalez Jose David
Published: November 2024

Due to the limitations that falls have on humans, early detection of these becomes essential to avoid further damage. In many applications, various technologies are used to acquire accurate information from individuals such as wearable sensors, environmental sensors or cameras, but all of these require high computational resources in many cases, delaying the response of the entire system. The complexity of the models used to process the input data and detect these activities makes them almost impossible to complete on devices with limited resources, which are the ones that could offer an immediate response avoiding unnecessary communications between sensors and centralized computing centers. In this work, we chose to reduce the models to detect falls using images as input data. We proceeded to use image sequences as video frames, using data from two open source datasets, and we applied the Sparse Low Rank Method to reduce certain layers of the Convolutional Neural Networks that were the backbone of the models. Additionally, we chose to replace a convolutional block with Long Short Term Memory to consider the latest updates of these data sequences. The results showed that performance was maintained decently while significantly reducing the parameter size of the resulting models.
Overview of High-Performance Timing and Position-Sensitive MCP Detectors Utilizing Secondary Electron Emission for Mass Measurements of Exotic Nuclei at Nuclear Physics Facilities
Authors: Ge Zhuang
Published: November 2024

Timing and/or position-sensitive MCP detectors, which detect secondary electrons (SEs) emitted from a conversion foil during ion passage, are widely utilized in nuclear physics and nuclear astrophysics experiments. This review covers high-performance timing and/or position-sensitive MCP detectors that use SE emission for mass measurements of exotic nuclei at nuclear physics facilities, along with their applications in new measurement schemes. The design, principles, performance, and applications of these detectors with different arrangements of electromagnetic fields are summarized. To achieve high precision and accuracy in mass measurements of exotic nuclei using time-of-flight (TOF) and/or position (imaging) measurement methods, such as high-resolution beam-line magnetic-rigidity time-of-flight (Bρ-TOF) and in-ring isochronous mass spectrometry (IMS), foil-MCP detectors with high position and timing resolution have been introduced and simulated. Beyond TOF mass measurements, these new detector systems are also described for use in heavy ion beam trajectory monitoring and momentum measurements for both beam-line and in-ring applications. Additionally, the use of position-sensitive timing foil-MCP detectors for Penning trap mass spectrometers and multi-reflection time-of-flight (MR-TOF) mass spectrometers is proposed and discussed to improve efficiency and enhance precision.
Strategy for Accurate Detection of Six Tropane Alkaloids in Honey Using Lateral Flow Immunosensors
Authors: Sun Boyan , Wang Chuanlei , Wang Zile , Liang Jiayi , Han Ke , Zhang Shuai , Yin Chunchao , Wang Xiaomei , Liu Chujun , Feng Zhiyue , Wang Sihan , Jiang Haiyang
Published: November 2024

Honey, a widely consumed food, is susceptible to contamination by various toxic substances during production. Tropane alkaloids, with their potent neurotoxicity, are frequently found in honey. Hence, there is an acute need for rapid and effective detection methods to monitor these alkaloids. Lateral flow immunoassay (LFIA), known for its simple operation, low cost, and reliable results, holds great promise. In this study, we developed an efficient and user-friendly analytical method for the simultaneous detection of six tropane alkaloids (atropine, L-hyoscyamine, scopolamine, anisodamine, homatropine, and apoatropine) in honey based on an AuNPs lateral flow immunoassay (AuNPs-LFIA) with broad-spectrum antibodies. Under optimal conditions, the calculated detection limits were 0.22, 0.29, 0.51, 6.34, 0.30, and 0.94 ng/mL, respectively. By diluting the honey sample five times, the contaminants can be readily detected using LFIA. Semi-quantitative and quantitative analyses can be completed within 17 min. This innovative method fills the void in LFIA for detecting tropane alkaloids and serves as a valuable reference for LFIA detection of honey samples, providing a crucial strategy for the accurate detection of these important compounds.
Stakeholder Engagement and Triggers for Sustainable Development in Complex Fragile Ecosystems: Evidence from Alpine Trentino Region
Authors: Maccioni Samuele , d’Angella Francesca , De Carlo Manuela , Sfogliarini Bruno
Published: November 2024

This research digs into the dynamics of sustainable development in complex fragile ecosystems, with a focus on the Trentino alpine region in Northern Italy to identify the main triggers to foster it. In particular, this study emphasizes the critical role that Destination Management Organizations (DMOs) play in leveraging these triggers and designing sustainable development strategies. Using a Convergent Parallel Mixed Method that combines quantitative and qualitative data, this study identifies three strategies for stakeholder engagement—listen, meet, and collaborate—to align numerous actors towards common goals and encourage sustainable behaviors. Moreover, the results highlight the need to engage fringe stakeholders in sustainable destination development strategies. It also reveals the function of ‘triggers’—both internal and external—in implementing stakeholder engagement strategies. This research contributes to the literature by mapping stakeholders (core and fringe), suggesting three engagement strategies (listen, meet, and collaborate), and identifying triggers (internal and external) for destination sustainable development in complex fragile ecosystems.
Sustainable Nitrogen Management in Rice Farming: Spatial Patterns of Nitrogen Availability and Implications for Community-Level Practices
Authors: Sekiya Nobuhito , Mae Ayaka , Peter Mchuno Alfred , Anton Beno Kiwale , Eigen Tasuku , Yamayoshi Saki , Sakai Masaru , Watanabe Kunio , Kameoka Takaharu
Published: November 2024

Sustainable nitrogen management is crucial for long-term food security and environmental protection in rice farming systems. However, the spatial patterns of nitrogen availability at the community level remain poorly understood, hindering the development of effective sustainable management strategies. This study introduces a novel application of spatial autoregressive analysis to investigate available nitrogen distribution in paddy soils across a rice farming community in Kyoto, Japan. Soil samples from 61 plots, including organically farmed ones, were analyzed for available nitrogen and various physicochemical properties. Contrary to the hypothesis of high variability between adjacent plots, significant positive spatial autocorrelation in available nitrogen was observed, revealing previously unrecognized community-level patterns. The spatial Durbin model outperformed traditional regression approaches and revealed complex spatial interactions in soil properties. Water-soluble organic carbon and humus content showed strong but opposing effects, with a positive direct impact but negative spatial interaction, suggesting topography-driven accumulation processes. Water-soluble nitrogen exhibited reverse patterns with negative direct effects but positive spatial interaction, indicating potential nutrient transport through water movement. These findings highlight the importance of considering both direct and indirect spatial effects in understanding soil fertility patterns, challenging the conventional plot-by-plot management approach. This methodological advancement provides new perspectives for more effective, community-scale soil management strategies in rice farming systems. Moreover, it demonstrates an innovative approach to maximizing the value of outsourced soil analysis data, providing a model for more comprehensive utilization of such data in agricultural research. By enabling more targeted and efficient nitrogen management practices that consider both plot-level processes and landscape-scale interactions, this study potentially contributes to the development of more sustainable and resilient rice production systems.
Exploring Green HR Practices in the Construction Industry—Evidence from Poland
Authors: Moczydłowska Joanna , Sadkowska Joanna , Leszczewska Krystyna
Published: November 2024

The objective of this paper is twofold. First, it systematizes knowledge about green management practices as a manifestation of the green human resource management (HRM) concept. Second, it diagnoses the practices of green management in construction companies in Poland. The research is qualitative and was conducted in three construction companies (one large, one medium, and one small-sized). Due to the small sample size, the study is treated as a pilot study. The research concluded that although green management practices are employed in the studied companies, they are neither systemic nor satisfactory in intensity. In the large company, green management practices are visible at all stages of the human resource process except for employee appraisal. In the medium and small companies, green management is limited to the development of green knowledge and employee awareness activities. The added value of this paper is an original tool for diagnosing the level of advancement of green management practices in competence management, which can be used in various types of business entities, regardless of the sector or industry.
Promoting Sustainable Safety Work Environments: Factors Affecting Korean Workers’ Recognition of Their Right to Refuse Dangerous Work
Authors: Lee Mi-Jeong
Published: November 2024

(1) Background: The right to refuse dangerous work (RTRDW) is essential for preventing industrial accidents and protecting worker safety in Korea. However, its use remains limited in practice. This study seeks to identify the factors hindering its activation across industries such as construction, manufacturing, and services, offering a comprehensive analysis beyond previous research. (2) Methods: A survey was conducted across key industries to assess five factors—safety behavior, communication, management commitment, education and training, and education and training—using structural equation modeling (SEM) to evaluate their influence on the exercise of RTRDW. (3) Results: The SEM model showed a good fit (χ2 = 1151.333, p < 0.001, TLI = 0.978, CFI = 0.984, RMSEA = 0.05). The most significant factors influencing RTRDW were safety performance behavior and communication, while ambiguous regulations, poor training, and fear of job loss discouraged its use. (4) Conclusions: To improve RTRDW activation, clearer regulations, enhanced safety education and training, stronger management commitment, and better communication are necessary. Addressing these issues can help workers confidently exercise their right to refuse dangerous work, enhancing overall workplace safety. (5) Benefits: This study provides practical strategies for policymakers and industry leaders to promote safety, empowering workers to use RTRDW effectively and contributing to a safer work environment.
Is AI Better than Humans? Unveiling the Boundary Conditions Under Which Virtual Influencers Outperform Human Influencers in Endorsing Sustainable Products
Authors: Yan Xu , Huam Hon Tat , Sade Abu Bakar
Published: November 2024

With the rising trend toward sustainable consumption and the pivotal role of social media in the buying behavior, brands are keen to promote sustainable products through these platforms. Virtual influencers (VIs) have emerged as lower-cost and scandal-resistant alternatives to human influencers (HIs). However, their artificial nature may evoke credibility concerns, potentially undermining marketing effectiveness. Currently, it is unclear whether humans or virtual influencers achieve better endorsement outcomes for sustainable products. This study explores the effectiveness of VIs and HIs in endorsing sustainable products and the conditions under which VIs may outperform HIs. Through two studies (N = 1097), we investigate the impact of influencer type, advertising appeals, and product involvement on consumer purchase intentions and brand attitudes. Our findings reveal that, in low-involvement product scenarios with emotional appeals, HIs are more effective, while in high-involvement product scenarios featuring rational appeals, VIs exhibit superior effectiveness. These results suggest that the choice of influencer type should be tailored to the product involvement level and the nature of the advertising appeal, providing guidance for brands to enhance the impact of their green marketing strategies.
Spatial Analysis of Lung Cancer Patients and Associated Influencing Factors from the Perspective of Urban Sustainable Development: A Case Study of Jiangsu Province, China
Authors: Shi Ge , Zhang Jingran , Liu Jiahang , Xu Jinghai , Chen Yu , Wang Yutong
Published: November 2024

With global environmental changes, lung cancer has become one of the most common types of cancer worldwide, posing a significant public health challenge. Jiangsu Province, located in the eastern part of China, is an economically and socially developed region. According to the latest cancer registration data in Jiangsu Province, lung cancer ranks first in both incidence and mortality of cancer in the province. Thus, studying the spatiotemporal distribution of lung cancer cases and analyzing the influence of various factors on this distribution are crucial for the effective prevention and control of the disease in Jiangsu Province. This study takes the statistical data of lung cancer patients in Jiangsu Province in 2020 as the research object, uses Geographic Information System (GIS) visualization and spatial analysis to study the spatial distribution characteristics of lung cancer patients in Jiangsu Province, and employs the geographical detector to numerically express the impact of various environmental factors on the distribution of lung cancer patients in Jiangsu Province. The results reveal a notable spatial clustering of lung cancer cases, with high-incidence areas concentrated in Suzhou, Nanjing, and Xuzhou cities. Among the seven environmental factors examined, PM2.5, SO2, and PM10 concentration exert the most significant influence. This study employs multifactorial spatial analysis to elucidate the intricate relationships between people’s health and air quality, medical resource distribution, and lung cancer incidence in the process of pursuing sustainable development in cities and provides an important reference for the improvement in lung cancer prevention and control strategies.
Sustainable Tourism Performance Through Green Talent Management: The Mediating Power of Green Entrepreneurship and Climate
Authors: Al-Romeedy Bassam Samir , Alharethi Thaib
Published: November 2024

This study investigates how green talent management influences green performance, green entrepreneurship, and the green organizational climate. Additionally, it analyzes the impact of green entrepreneurship and green organizational climate on green performance. The research also delves into the mediating roles of green entrepreneurship and green organizational climate within the framework of dynamic capability theory in the tourism and hospitality industries. Data were gathered from employees working in travel agencies and tour operators in Saudi Arabia, with a PLS-SEM analysis conducted on 788 valid responses. The results revealed that green talent management has a positive impact on green performance, green entrepreneurship, and the green organizational climate. Additionally, the study found that both green entrepreneurship and a green organizational climate positively influence green performance. Importantly, the findings underscored the partial mediating roles of green entrepreneurship and green organizational climate in the link between green talent management and green performance. This study enriches the literature by clarifying the mechanisms through which green talent management can be utilized to enhance green performance. Theoretically, the findings expand on dynamic capability theory by emphasizing the importance of green-focused human and organizational resources as dynamic capabilities that enable environmental adaptation and sustainable competitive advantage. Practically, these insights provide actionable implications for tourism and hospitality organizations seeking to improve sustainability practices through green talent management, green entrepreneurship, and a green organizational climate, thereby reinforcing the role of dynamic capabilities in achieving sustained green performance.
A Rooftop Solar Photovoltaic Tree Solution for Small-Scale Industries
Authors: Chowdhury Sumit , Vyas Maharishi , Verma Abhishek , Jain Vinod K.
Published: November 2024

With the increase in population and the growing demands of industrialization, carbon emissions across the globe are increasing exponentially. Furthermore, the demand for clean energy from renewable sources (solar, wind, etc.) is growing at an unparalleled rate to fight against the climate change caused by these increased carbon emissions. However, at present, it is very difficult for small-scale industries in urban areas to install solar power systems due to constraints around the operation area and on rooftops. Therefore, these small-scale industries are not able to install any solar plants and, thus, are not able to reduce their carbon emissions. In the context of this problem regarding the generation of cleaner energy and reducing carbon emissions by small-scale industries in urban areas, a model of a rooftop solar photovoltaic tree (SPVT) has been proposed that may be considered by small-scale industries in the place of a conventional rooftop solar photovoltaic (SPV) system. It is also noted that various models of SPVT systems are commercially available on the market, each with their own unique features. However, no new SPVT model has been designed or provided in this paper, which simply presents simulation studies comparing a conventional rooftop SPV system and an SPVT system. The results show that a 9.12 kWp SPVT system can be installed in just 6 Sq.mt, while a 3.8 kWp conventional SPV system requires 40 Sq.mt of rooftop area. Consequently, an SPVT generates around 128% more electricity than a conventional SPV, leading to greater reductions in carbon emissions. Thus, the objective of this study is to identify the most suitable option for small-scale industries in densely populated urban areas to generate electricity and maximize carbon emission reduction.
Drought Dynamics in Sub-Saharan Africa: Impacts and Adaptation Strategies
Authors: Lombe Pedro , Carvalho Elsa , Rosa-Santos Paulo
Published: November 2024

The escalation in both frequency and severity of drought events has significantly amplified the vulnerability of numerous countries, particularly in developing ones, imposing substantial economic, environmental, and social pressures. This article presents a systematic review of drought occurrences in Sub-Saharan Africa (SSA), examining historical trends, current impacts, and projected future implications. Through this comprehensive assessment, a clear trend of intensifying drought phenomena emerges across SSA, leading to crop failures, drying of water sources, loss of pasture, food shortages, and an increase in food prices. This review also highlights the concerning potential for worsening conditions in certain regions, resulting in consequences such as migration, food insecurity, malnutrition, family disintegration, crop losses, and increased disease prevalence, notably HIV/AIDS. This study further reveals that current adaptation measures by governments and NGOs should be improved to effectively adapt to the diverse impacts of drought, and it contributes to a deeper understanding of drought dynamics in Sub-Saharan Africa and assesses its critical impacts on food security and social well-being. It also evaluates adaptation measures across different countries, highlighting their strengths and weaknesses and enabling quick identification of areas for improvement. Additionally, it informs resilience-building efforts in vulnerable communities.
Impact of Corporate Governance on Firms’ Sustainability Performance: Case Study of BIST 50 Index Companies
Authors: Lehenchuk Serhii , Zhyhlei Iryna , Ivashko Olena , Chulipa Ihor , Wit Bogdan
Published: November 2024

Purpose: the purpose of this study is to investigate whether corporate governance mechanisms and attributes influence the sustainability performance of companies included in the BIST 50 Index. Results and contributions: Regression analysis showed that there was a significant positive influence of board tenure on sustainability performance and all its types; board size on environmental performance; and a dummy variable for board evaluation externally facilitated and company size on sustainability, environmental, and social performance. A significant negative impact of director attendance at board meetings on social performance was also revealed. This study contributes to the literature on the role of corporate governance in achieving the SDGs for BIST 50 Index companies, highlighting the significant impact of its individual indicators on the achievement of sustainability performance. Methodology: The authors reviewed 45 sustainability reports of BIST 50 Index companies for 2023. Four indices—Sustainability Performance, Environmental Performance, Social Performance, and Corporate Governance Performance Indexes—were developed to characterize sustainability performance and its types based on a content analysis of sustainability disclosures. To analyze the influence of mechanisms and characteristics of the corporate governance system on sustainability performance, eight independent variables were used: board size, number of board meetings, director attendance at board meetings, board independence, board tenure, a dummy variable for board evaluation externally facilitated, a dummy variable for internal auditors present, and a dummy variable for CEO and Chair functions combined. Two control variables, company size and leverage, were used as well. Gap: Today, the scientific literature has no universal approach and understanding of how the corporate governance system should be developed to improve sustainability performance or its individual components. Relevance: Development of a corporate governance system is one of the ways to increase the level of sustainability performance of companies. Impact: The results of the study made it possible to produce several recommendations (expand the number of board members, develop an effective procedure for regular changes of general directors in company boards, introduce independent external control tools in the corporate governance systems of companies) that will lead to the achievement of SDGs 5, 8, 16.
Barriers Hindering the Successful Deployment of GLSS in Organizations: Findings from an Empirical Study
Authors: Perez-Burgoin Monserrat , Limon-Romero Jorge , Tlapa Diego , Perez-Sanchez Armando , Tortorella Guilherme , Baez-Lopez Yolanda
Published: November 2024

Given the growing interest in manufacturing’s environmental effects, understanding the impact of improvement strategies on sustainability is critical. This study focuses on the implementation of Green Lean Six Sigma (GLSS) in the Mexican manufacturing sector, an area where research is limited, particularly concerning the barriers to successful outcomes. Through the development of a structural equation model using SPSS software,,version 25, 64-bit edition, key barriers such as lack of investment, insufficient participation, ineffective infrastructure, and lack of training were identified as the primary challenges to GLSS implementation. This study also highlights the interrelationships between these barriers and their influence on project success, emphasizing the role of insufficient participation as a critical mediating factor. This insight allows for a deeper understanding of how these obstacles affect the benefits of GLSS initiatives. The findings offer valuable guidance for researchers and practitioners, providing tools to enhance the effectiveness of GLSS implementation in the manufacturing industry. The study’s marginal contribution lies in its identification of key barriers and the introduction of insufficient participation as a mediating factor, offering a more comprehensive understanding of their impact on project success and contributing to improved GLSS adoption strategies.
Industrial Organizations Adapting to the Novel EU Taxonomy While Developing Socio-Technical Systems: A Literature Review
Authors: Giudici Henri , Bento Fabio , Falk Kristin
Published: November 2024

The EU taxonomy defines criteria for economic activities committed to a net zero emission by 2050, and other environmental goals. Its overall goal is to direct investments to economic activities aligned with the European Green Deal and, thereby, classified as sustainable. For industrial organizations, there is an urgent need to develop adaptive capabilities to meet the requirements set by the new taxonomy. The present scoping review contributes by analyzing academic publications on this topic through the lens of a complex science and systems approach. It analyzes academic publications on the EU taxonomy, related to industrial organizations and their development of socio-technical systems. At the initial stage, 526 scientific publications were retrieved from three web repositories (Scopus, World of Science, and Scholar). Only eight publications matched the selection criteria and constitute the data of this study. Results indicate that there is a lack of holistic perspectives and an unbalanced system response to the EU taxonomy. Adopting a systems approach can help industrial organizations navigate the intricate dynamics of a sustainable transition, striking a balance between adhering to the EU taxonomy and maintaining market competitiveness. The findings emphasize the importance of trans-disciplinary approaches and the need for agile and adaptive ways of learning inside industrial organizations.
Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model
Authors: Yu Ru , Wang Xiaoli , Xu Xiaojun , Zhang Zhiwen
Published: November 2024

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales.
Indoor Air Quality in an Orthopedic Hospital from Romania
Authors: Moldovan Flaviu , Moldovan Liviu
Published: November 2024

Inside hospitals, there is a trend of increasing levels of air pollutants. However, only the indoor air quality in operating theaters is carefully monitored. Therefore, we set the goal of this study to evaluate the indoor air quality in areas of an orthopedics department and to compare the indoor air quality indices that characterize these areas. We used a monitoring system based on the Internet of Things with uRADMonitor model A3 sensors, with which we prospectively measured indoor air quality in the facilities of the orthopedic emergency hospital of Targu Mures in Romania, between 1 February 2023, and 31 January 2024. The primary target pollutants investigated in the emergency room, outpatient room and ward were carbon dioxide (CO2), nitrogen dioxide (NO2), volatile organic compounds (VOCs) and particles with a diameter smaller than 2.5 μm (PM2.5). We compared the effectiveness of the intervention for emergency rooms where air purifiers were working or not. The concentrations of CO2, VOCs and PM2.5 were significantly higher in the emergency room than in the outpatient room or ward. The indoor air quality was worst in winter, when the CO2, NO2 and VOC concentrations were at their highest. Air purifiers can help reduce the concentration of PM2.5 in emergency rooms. Medical staff and patients in orthopedic hospitals, especially in emergency rooms, are frequently exposed to polluted ambient air, which can affect their health. Orthopedic medical practice guidelines should address issues relating to the protection of personnel through the application of measures to improve indoor air quality.
Recent Molecular Epidemiology of Echovirus 11 Throughout North and West Africa Resulted in the First Identification of a Recombinant Strain from an Acute Flaccid Paralysis Case in West Africa
Authors: Ndiaye Ndack , Thiaw Fatou Diène , Lagare Adamou , Sinare Thérèse , Diakité Mohamed Lemine , Ngom Serigne Fallou Mbacké , Kébé Ousmane , Abdoulkader Issifi Kollo , Cissé Gassim , Dia Mohamed , Djimadoum Hermann Nodji , Neya Christelle Ouedraogo , Boubakar Rakia , Ouedraogo Issaka , Essoya Landoh Dadja , Dia Ndongo , Sall Amadou Alpha , Faye Ousmane , Faye Martin
Published: November 2024

Echovirus 11 has emerged as a major public health concern, causing sepsis in neonates in many European countries in recent years. In Africa, especially West Africa, where resources and diagnostic capacities are limited, only sporadic cases have been reported. To better understand the recent molecular epidemiology of E11 in West Africa, we characterized twenty-three echovirus 11 strains isolated through the acute flaccid paralysis and environmental surveillance systems for polio from 2013 to 2023, using high-throughput sequencing. Our data are noteworthy due to identifying for the first time a recombinant strain from an acute flaccid paralysis case and represent the first focus to date on molecular characterization of echovirus 11 in West Africa. Moreover, our data show that echovirus 11 diverged from 1970 (95% HPD range, 1961–1979) and evolved into four distinct clades, with the virus spread from West Africa to Europe, exhibiting two introductions in France around 2017, from Senegal and Guinea. Furthermore, the in silico analysis reveals four non-conservative amino acid substitutions in the VP1 sequences of the European strains associated with neonatal sepsis in newborns and a conserved amino acid motif in the VP1 protein toward enterovirus genotypes. Our data provide new insights into the epidemiology of echovirus 11 and point to the crucial need to implement specific surveillance programs targeting non-polio enteroviruses for the rapid identification of emerging or re-emerging enterovirus species, particularly in Africa.
Vaccination Against Androgen Receptor Splice Variants to Immunologically Target Prostate Cancer
Authors: Marek Robert D. , Halabi Selena , Wang Mu-En , McBane Jason , Wei Junping , Wang Tao , Yang Xiao , Liu Congxiao , Lei Gangjun , Lyerly Herbert Kim , Chen Ming , Trotter Timothy N. , Hartman Zachary C.
Published: November 2024

Background/Objectives: Androgen receptor (AR) expression and signaling are critical for the progression of prostate cancer and have been the therapeutic focus of prostate cancer for over 50 years. While a variety of agents have been developed to target this axis, many of these fail due to the emergent expression of AR RNA splice variants, such as AR-V7, that can signal independently of ligand binding. Other therapies, such as vaccination against prostate-specific antigens, have achieved FDA approvals but have fallen short of being incorporated as standard-of-care therapies for advanced prostate cancer. This may be due to the elevated level of immunosuppression observed in prostate cancer, which remains largely refractory to immune checkpoint blockade. Methods: We developed a vaccine targeting AR-V7, a common isoform associated with treatment resistance, and demonstrated its ability to elicit AR-V7-specific immunity and enable anti-tumor responses against AR-V7+ cancers in subcutaneous tumor models. Results: Our studies also revealed that AR-V7 expression conferred an immune suppressive phenotype that was significant in a non-AR-dependent prostate cancer model. Notably, in this model, we found that vaccination in combination with enzalutamide, an AR antagonist, suppressed these aggressive immune suppressive cancers and resulted in enhanced survival in comparison to control vaccinated and enzalutamide-treated mice. While anti-PD-1 immune checkpoint inhibition (ICI) alone slowed tumor growth, the majority of vaccinated mice that received anti-PD-1 therapy showed complete tumor elimination. Conclusions: Collectively, these results validate the importance of AR signaling in prostate cancer immune suppression and suggest the potential of AR-V7-specific vaccines as therapeutic strategies against prostate cancer, offering significant protective and therapeutic anti-tumor responses, even in the presence of androgen signaling inhibitors.
Heterogeneous Associations Between Pandemic-Induced Socioeconomic Hardships and COVID-19 Vaccine Uptake by Sexual Orientation and Gender Identity: A Nationally Representative Analysis in the United States
Authors: Park JungHo , Kim Byoungjun
Published: November 2024

Background/Objectives: Socioeconomic hardship during the COVID-19 pandemic was associated with lower vaccine uptake. Since the pandemic has exacerbated socioeconomic challenges faced by sexual and gender minority populations, including employment income loss, housing instability, food insufficiency, and household expense difficulty, this study investigated the disparities in COVID-19 vaccine uptake among these populations. Methods: Using the U.S. Census Bureau Household Pulse Survey, a nationally representative sample of 1,767,966 individuals (6% gay or lesbian, 4.2% bisexual, 1.6% something else, and 90.6% heterosexual respondents), we quantified the COVID-19 vaccine uptakes among sexual and gender minorities, as well as the effect measure modifications by socioeconomic hardships. Results: Despite higher vaccine uptake rates among sexual and gender minorities compared to their heterosexual counterparts, socioeconomic hardships triggered by the pandemic among these populations were associated with decreased vaccine uptake. Importantly, the effect measure modifications by socioeconomic hardships were more pronounced among sexual and gender minority status compared to heterosexual individuals. Conclusions: These results highlight the critical need to address socioeconomic hardships among sexual and gender minorities to enhance vaccine uptake, along with the pre-existing and exacerbated social and economic disadvantages during the COVID-19 pandemic.
Occupation and stress: professional burnout of GPs depending on the profile of the medical institution in situations of high epidemic risk
Authors:   FSBEI HE Samara State Medical University of the Ministry of Health of the Russian Federation, Babanov S. A. , Ostryakova N. A. ,   FSBEI HE Samara State Medical University of the Ministry of Health of the Russian Federation, Lysova M. V. ,   FSBEI HE Samara State Medical University of the Ministry of Health of the Russian Federation
Published: November 2024

Introduction. In the context of the COVID-19 pandemic, medical workers turned out to be the most vulnerable category of the population in terms of the risk of developing psycho-emotional disorders. Objective. To establish the characteristics of the psycho-emotional state among general practitioners of COVID hospitals, multidisciplinary hospitals, and polyclinics during the COVID-19 pandemic. Material and methods. The study included: group 1 – general practitioners of COVID hos pitals (n=111); group 2 – internists of multidisciplinary hospitals (n=106); group 3 – GPs working in polyclinics (n=102); group 4 – control group (healthy) (n=190). The assess ment of professional burnout was carried out by means of the Maslach Burnout Inventory, developed by American psychologists C. Maslach and S.E. Jackson, in adaptation by N.E. Vodopyanova and E.S. Starchenkova, as well as using V.V. Boyko’s questionnaire. The study utilized an integrative anxiety test to investigate the formation of psycho-emotional disorders in internists during the pandemic of the novel coronavirus infection. The quality of life was determined by means of the SF-36 questionnaire. The processing of the ob tained data was carried out using the Statistica package from StatSof t (USA). Results. It has been established that the work of general practitioners during the COVID-19 pandemic was associated with the impact of occupational stress related to caring for in fected patients on medical workers, as well as with significant changes in work regarding aspects of organization and safety, which contributed to an increase in the levels of oc cupational stress, professional burnout, and anxiety, and a decrease in the quality of life. Limitations of the study. The study has regional (Samara Region) and professional (in terms of detailing the working conditions in the studied comparison groups) limitations. Conclusions. The work of general practitioners of specialized COVID hospitals, internists of multidisciplinary hospitals, and GPs of polyclinics is associated with an increase in the levels of occupational stress, professional burnout, and anxiety, and a decrease in the quality of life. Notably, psychological changes are most pronounced in employees of specialized COVID hospitals. It is necessary to continue research on the emotional state of medical personnel in order to timely carry out preventive treatment to preserve the health of medical workers.