bluuebunny/crossref_metadata_2025 huggingface dataset:
An Improved Unscented Kalman Filter Applied to Positioning and Navigation of Autonomous Underwater Vehicles
Authors: Zhao Jinchao , Zhang Ya , Li Shizhong , Wang Jiaxuan , Fang Lingling , Ning Luoyin , Feng Jinghao , Zhang Jianwu
Published: January 2025

To enhance the positioning accuracy of autonomous underwater vehicles (AUVs), a new adaptive filtering algorithm (RHAUKF) is proposed. The most widely used filtering algorithm is the traditional Unscented Kalman Filter or the Adaptive Robust UKF (ARUKF). Excessive noise interference may cause a decrease in filtering accuracy and is highly likely to result in divergence by means of the traditional Unscented Kalman Filter, resulting in an increase in uncertainty factors during submersible mission execution. An estimation model for system noise, the adaptive Unscented Kalman Filter (UKF) algorithm was derived in light of the maximum likelihood criterion and optimized by applying the rolling-horizon estimation method, using the Newton–Raphson algorithm for the maximum likelihood estimation of noise statistics, and it was verified by simulation experiments using the Lie group inertial navigation error model. The results indicate that, compared with the UKF algorithm and the ARUKF, the improved algorithm reduces attitude angle errors by 45%, speed errors by 44%, and three-dimensional position errors by 47%. It can better cope with complex underwater environments, effectively address the problems of low filtering accuracy and even divergence, and improve the stability of submersibles.
Cross-Modal Collaboration and Robust Feature Classifier for Open-Vocabulary 3D Object Detection
Authors: Liu Hengsong , Duan Tongle
Published: January 2025

The multi-sensor fusion, such as LiDAR and camera-based 3D object detection, is a key technology in autonomous driving and robotics. However, traditional 3D detection models are limited to recognizing predefined categories and struggle with unknown or novel objects. Given the complexity of real-world environments, research into open-vocabulary 3D object detection is essential. Therefore, this paper aims to address two key issues in this area: how to localize and classify novel objects. We propose Cross-modal Collaboration and Robust Feature Classifier to improve localization accuracy and classification robustness for novel objects. The Cross-modal Collaboration involves the collaborative localization between LiDAR and camera. In this approach, 2D images provide preliminary regions of interest for novel objects in the 3D point cloud, while the 3D point cloud offers more precise positional information to the 2D images. Through iterative updates between two modalities, the preliminary region and positional information are refined, achieving the accurate localization of novel objects. The Robust Feature Classifier aims to accurately classify novel objects. To prevent them from being misidentified as background or other incorrect categories, this method maps the semantic vectors of new categories into multiple sets of visual features distinguished from the background. And it clusters these visual features based on each individual semantic vector to maintain inter-class separability. Our method achieves state-of-the-art performance on various scenarios and datasets.
A Deep Learning Approach for Mental Fatigue State Assessment
Authors: Fan Jiaxing , Dong Lin , Sun Gang , Zhou Zhize
Published: January 2025

This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.29% in identifying fatigue from original ECG data, 2D spectral characteristics and physiological information of subjects. In comparison to traditional methods, such as Support Vector Machines (SVMs) and Random Forests (RFs), as well as other deep learning methods, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), the proposed approach demonstrates significantly improved experimental outcomes. Overall, this study offers a promising solution for accurately recognizing fatigue through the analysis of physiological signals, with potential applications in sports and physical fitness training contexts.
Neural Radiance Fields for High-Fidelity Soft Tissue Reconstruction in Endoscopy
Authors: Liu Jinhua , Shi Yongsheng , Huang Dongjin , Qu Jiantao
Published: January 2025

The advancement of neural radiance fields (NeRFs) has facilitated the high-quality 3D reconstruction of complex scenes. However, for most NeRFs, reconstructing 3D tissues from endoscopy images poses significant challenges due to the occlusion of soft tissue regions by invalid pixels, deformations in soft tissue, and poor image quality, which severely limits their application in endoscopic scenarios. To address the above issues, we propose a novel framework to reconstruct high-fidelity soft tissue scenes from low-quality endoscopic images. We first construct an EndoTissue dataset of soft tissue regions in endoscopic images and fine-tune the Segment Anything Model (SAM) based on EndoTissue to obtain a potent segmentation network. Given a sequence of monocular endoscopic images, this segmentation network can quickly obtain the tissue mask images. Additionally, we incorporate tissue masks into a dynamic scene reconstruction method called Tensor4D to effectively guide the reconstruction of 3D deformable soft tissues. Finally, we propose adopting the image enhancement model EDAU-Net to improve the quality of the rendered views. The experimental results show that our method can effectively focus on the soft tissue regions in the image, achieving higher fidelity in detail and geometric structural integrity in reconstruction compared to state-of-the-art algorithms. Feedback from the user study indicates high participant scores for our method.
Computer-Aided Supporting Models of Customized Crack Propagation Sensors for Analysis and Prototyping
Authors: Kurnyta-Mazurek Paulina , Wrąbel Rafał , Kurnyta Artur
Published: January 2025

The range of sensor technologies for structural health monitoring (SHM) systems is expanding as the need for ongoing structural monitoring increases. In such a case, damage to the monitored structure elements is detected using an integrated network of sensors operating in real-time or periodically in frequent time stamps. This paper briefly introduces a new type of sensor, called a Customized Crack Propagation Sensor (CCPS), which is an alternative for crack gauges, but with enhanced functional features and customizability. Due to those characteristics, it is necessary to develop a family of computer-aided supporting models for rapid prototyping and analysis of the new designs of sensors of various shapes and configurations, which this paper presents by use of simulation tools. For a prototyping of the sensor lay out, an algorithm is elaborated, based on an application created in LabVIEW 2022 software, which generates two spreadsheets formatted by the requirements of Autodesk Inventor 2014 and COMSOL Multiphysics 5.6 software, based on data entered by the user. As a result, a tailored-in-shape CCPS layout is prepared. A parametric model of the sensor is prepared in Autodesk Inventor software, which automatically changes its geometric dimensions after changing data in an MS Excel spreadsheet. Then, the generated layout is analyzed to obtain electromechanical characteristics for defined CCPS geometry and materials used in the COMSOL Multiphysics software. Another application is devoted to purely mechanical analysis. The graphical user interface (GUI) add-on based on the Abaqus 2018 software engine is prepared for advanced mechanical analysis simulations of sensor materials in selected loading scenarios. The GUI is used for entering material libraries and the selection of loading conditions and a type of specimen, while the results of the numerical analysis are delivered through Abaqus. The main advantage of the developed GUI is the capacity for personnel inexperienced in using the Abaqus environment to perform analysis. Some results of simulation tests carried out in both COMSOL Multiphysics as well as Abaqus software are delivered in this paper, using a predefined parametric sensor model. For example, using a rigid epoxy resin for an insulating layer shows a negligible difference in the level of strain compared to the structure during a simulated tensile test, specifically in the tested layer thickness range of up to 0.3 mm. However, during bending tests, an approx. 17% change in principal strain level can be observed through the top to bottom edge of the epoxy resin layer. The adopted methodology for carrying out simulation studies assumes the parallel use of a set of various computer-aided tools. This approach allows for taking advantage of individual software environments, which allows for expanding the scope of analyses and using the developed models and applications in further research activities.
Depression Recognition Using Daily Wearable-Derived Physiological Data
Authors: Shui Xinyu , Xu Hao , Tan Shuping , Zhang Dan
Published: January 2025

The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states. The present study leverages multimodal wristband devices to collect data from fifty-eight participants clinically diagnosed with depression during their normal daytime activities over six hours. Data collected include pulse wave, skin conductance, and triaxial acceleration. For comparison, we also utilized data from fifty-eight matched healthy controls from a publicly available dataset, collected using the same devices over equivalent durations. Our aim was to identify depressive individuals through the analysis of multimodal physiological measurements derived from wearable devices in daily life scenarios. We extracted static features such as the mean, variance, skewness, and kurtosis of physiological indicators like heart rate, skin conductance, and acceleration, as well as autoregressive coefficients of these signals reflecting the temporal dynamics. Utilizing a Random Forest algorithm, we distinguished depressive and non-depressive individuals with varying classification accuracies on data aggregated over 6 h, 2 h, 30 min, and 5 min segments, as 90.0%, 84.7%, 80.1%, and 76.0%, respectively. Our results demonstrate the feasibility of using daily wearable-derived physiological data for depression recognition. The achieved classification accuracies suggest that this approach could be integrated into clinical settings for the early detection and monitoring of depressive symptoms. Future work will explore the potential of these methods for personalized interventions and real-time monitoring, offering a promising avenue for enhancing mental health care through the integration of wearable technology.
Normative Database of Spatiotemporal Gait Metrics Across Age Groups: An Observational Case–Control Study
Authors: Mobbs Lianne , Fernando Vinuja , Fonseka R. Dineth , Natarajan Pragadesh , Maharaj Monish , Mobbs Ralph J.
Published: January 2025

Introduction: Gait analysis is a vital tool in the assessment of human movement and has been widely used in clinical settings to identify potential abnormalities in individuals. However, there is a lack of consensus on the normative values for gait metrics in large populations. The primary objective of this study is to establish a normative database of spatiotemporal gait metrics across various age groups, contributing to a broader understanding of human gait dynamics. By doing so, we aim to enhance the clinical utility of gait analysis in diagnosing and managing health conditions. Methods: We conducted an observational case–control study involving 313 healthy participants. The MetaMotionC IMU by Mbientlab Inc., equipped with a triaxial accelerometer, gyroscope, and magnetometer, was used to capture gait data. The IMU was placed at the sternal angle of each participant to ensure optimal data capture during a 50 m walk along a flat, unobstructed pathway. Data were collected through a Bluetooth connection to a smartphone running a custom-developed application and subsequently analysed using IMUGaitPY, a specialised version of the GaitPY Python package. Results: The data showed that gait speeds decrease with ageing for males and females. The fastest gait speed is observed in the 41–50 age group at 1.35 ± 0.23 m/s. Males consistently exhibit faster gait speeds than females across all age groups. Step length and cadence do not have clear trends with ageing. Gait speed and step length increase consistently with height, with the tallest group (191–200 cm) walking at an average speed of 1.49 ± 0.12 m/s, with an average step length of 0.91 ± 0.05 m. Cadence, however, decreases with increasing height, with the tallest group taking 103.52 ± 5.04 steps/min on average. Conclusions: This study has established a comprehensive normative database for the spatiotemporal gait metrics of gait speed, step length, and cadence, highlighting the complexities of gait dynamics across age and sex groups and the influence of height. Our findings offer valuable reference points for clinicians to distinguish between healthy and pathological gait patterns, facilitating early detection and intervention for gait-related disorders. Moreover, this database enhances the clinical utility of gait analysis, supporting more objective diagnoses and assessments of therapeutic interventions. The normative database provides a valuable reference future research and clinical practice. It enables a more nuanced understanding of how gait evolves with age, gender, and physical stature, thus informing the development of targeted interventions to maintain mobility and prevent falls in older adults. Despite potential selection bias and the cross-sectional nature of the study, the insights gained provide a solid foundation for further longitudinal studies and diverse sampling to validate and expand upon these findings.
Characterization of RAP Signal Patterns, Temporal Relationships, and Artifact Profiles Derived from Intracranial Pressure Sensors in Acute Traumatic Neural Injury
Authors: Islam Abrar , Sainbhi Amanjyot Singh , Stein Kevin Y. , Vakitbilir Nuray , Gomez Alwyn , Silvaggio Noah , Bergmann Tobias , Hayat Mansoor , Froese Logan , Zeiler Frederick A.
Published: January 2025

Goal: Current methodologies for assessing cerebral compliance using pressure sensor technologies are prone to errors and issues with inter- and intra-observer consistency. RAP, a metric for measuring intracranial compensatory reserve (and therefore compliance), holds promise. It is derived using the moving correlation between intracranial pressure (ICP) and the pulse amplitude of ICP (AMP). RAP remains largely unexplored in cases of moderate to severe acute traumatic neural injury (also known as traumatic brain injury (TBI)). The goal of this work is to explore the general description of (a) RAP signal patterns and behaviors derived from ICP pressure transducers, (b) temporal statistical relationships, and (c) the characterization of the artifact profile. Methods: Different summary and statistical measurements were used to describe RAP’s pattern and behaviors, along with performing sub-group analyses. The autoregressive integrated moving average (ARIMA) model was employed to outline the time-series structure of RAP across different temporal resolutions using the autoregressive (p-order) and moving average orders (q-order). After leveraging the time-series structure of RAP, similar methods were applied to ICP and AMP for comparison with RAP. Finally, key features were identified to distinguish artifacts in RAP. This might involve leveraging ICP/AMP signals and statistical structures. Results: The mean and time spent within the RAP threshold ranges ([0.4, 1], (0, 0.4), and [−1, 0]) indicate that RAP exhibited high positive values, suggesting an impaired compensatory reserve in TBI patients. The median optimal ARIMA model for each resolution and each signal was determined. Autocorrelative function (ACF) and partial ACF (PACF) plots of residuals verified the adequacy of these median optimal ARIMA models. The median of residuals indicates that ARIMA performed better with the higher-resolution data. To identify artifacts, (a) ICP q-order, AMP p-order, and RAP p-order and q-order, (b) residuals of ICP, AMP, and RAP, and (c) cross-correlation between residuals of RAP and AMP proved to be useful at the minute-by-minute resolution, whereas, for the 10-min-by-10-min data resolution, only the q-order of the optimal ARIMA model of ICP and AMP served as a distinguishing factor. Conclusions: RAP signals derived from ICP pressure sensor technology displayed reproducible behaviors across this population of TBI patients. ARIMA modeling at the higher resolution provided comparatively strong accuracy, and key features were identified leveraging these models that could identify RAP artifacts. Further research is needed to enhance artifact management and broaden applicability across varied datasets.
Cloud/VPN-Based Remote Control of a Modular Production System Assisted by a Mobile Cyber–Physical Robotic System—Digital Twin Approach
Authors: Simion Georgian , Filipescu Adrian , Ionescu Dan , Filipescu Adriana
Published: January 2025

This paper deals with a “digital twin” (DT) approach for processing, reprocessing, and scrapping (P/R/S) technology running on a modular production system (MPS) assisted by a mobile cyber–physical robotic system (MCPRS). The main hardware architecture consists of four line-shaped workstations (WSs), a wheeled mobile robot (WMR) equipped with a robotic manipulator (RM) and a mobile visual servoing system (MVSS) mounted on the end effector. The system architecture integrates a hierarchical control system where each of the four WSs, in the MPS, is controlled by a Programable Logic Controller (PLC), all connected via Profibus DP to a central PLC. In addition to the connection via Profibus of the four PLCs, related to the WSs, to the main PLC, there are also the connections of other devices to the local networks, LAN Profinet and LAN Ethernet. There are the connections to the Internet, Cloud and Virtual Private Network (VPN) via WAN Ethernet by open platform communication unified architecture (OPC-UA). The overall system follows a DT approach that enables task planning through augmented reality (AR) and uses virtual reality (VR) for visualization through Synchronized Hybrid Petri Net (SHPN) simulation. Timed Petri Nets (TPNs) are used to control the processes within the MPS’s workstations. Continuous Petri Nets (CPNs) handle the movement of the MCPRS. Task planning in AR enables users to interact with the system in real time using AR technology to visualize and plan tasks. SHPN in VR is a combination of TPNs and CPNs used in the virtual representation of the system to synchronize tasks between the MPS and MCPRS. The workpiece (WP) visits stations successively as it is moved along the line for processing. If the processed WP does not pass the quality test, it is taken from the last WS and is transported, by MCPRS, to the first WS where it will be considered for reprocessing or scrapping.
Optical Gain in Eu-Doped Hybrid Nanocrystals Embedded SiO2-HfO2-ZnO Ternary Glass-Ceramic Waveguides
Authors: Ghosh Subhabrata , Turrell Sylvia , Ferrari Maurizio , N. Shivakiran Bhaktha B.
Published: January 2025

Rare-earth doped transparent glass-ceramic waveguides are playing a very crucial role in integrated optics. We fabricated ZnO-HfO2 hybrid nanocrystals embedded with 70 SiO2–(30-x) HfO2–x ZnO (x = 0, 2, 5 and 7 mol %) ternary transparent glass-ceramic waveguides doped with 1 mol % Eu-ions. The formation and size of the nanocrystals evolved with an increase in ZnO concentration in the glass-ceramic waveguides. In this context, key factors of such nanocrystals embedded active glass-ceramic waveguides were optical losses and transparency. A lab-built m-line experimental set-up was used for the characterization of the waveguides. On the other hand, optical gain measurements of the Eu-doped hybrid nanocrystals embedded glass-ceramic waveguides were performed using the variable stripe length method. The optical amplification of the waveguides was investigated on the red emission line (5D0 → 7F2) of Eu-ions pumped by a 532 nm laser in a stripe-like geometry generated by a cylindrical lens. Here, we report, the optical gain in rare-earth activated glass-ceramic waveguides with nanocrystals of varying sizes formed in the waveguides with increasing ZnO concentration.
Testing the Impact of Renewable Energy and Institutional Quality on Consumption-Based CO2 Emissions: Fresh Insights from MMQR Approach
Authors: Almulhim Abdulateif A. , Inuwa Nasiru , Chaouachi Maroua , Samour Ahmed
Published: January 2025

The motivation for this research stems from the United Nations Sustainable Development Goals (UN SDGs), specifically SDGs 7, 11, 12, and 13, which focus on the mitigation of climate change and sustainable economic development. This study examined the impact of renewable energy use, institutional quality, and production expansion on consumption-based carbon dioxide (CCO2) emissions in BRICS countries (Brazil, Russia, India, China, and South Africa) from 1996 to 2020. To achieve this, we applied advanced econometric techniques, including second-generation cointegration and unit root tests, along with the novel panel method of moments quantile regression (MMQR). The Westerlund cointegration test confirmed the presence of a long-run co-movement among renewable energy usage, economic growth, institutional quality, and environmental quality, suggesting a stable equilibrium relationship between these variables. The results from MMQR reveal that GDP has a positive and statistically significant effect on CCO2 emissions across all quantiles, indicating that economic expansion contributes to environmental degradation. In contrast, renewable energy consumption and institutional quality show negative and significant impacts on CCO2 emissions, indicating their mitigating effect on environmental deterioration. As a robustness check, the findings from fixed-effect OLS (FE-OLS), generalized method of moments (GMM), and common correlated effects mean group (CCEMG) estimations broadly confirm the results of MMQR. These findings underscore the importance of renewable energy consumption and strong institutional frameworks in promoting environmental sustainability.
The Impact of Subjective Well-Being on Sustainable Actions: Resilience as a Mediator Between Spirituality and Happiness in Future Environmental Engineers in Peru
Authors: Reyes-Perez Moises David , Facho-Cornejo Jhoselit Lisset , Li-Vega Fiorella Vanessa , López-Ñiquen Karla Elizabeth , Calonge-De la Piedra Dina Marisol , Cabanillas-Palomino Roxana , Padilla-Caballero Jesús Emilio Agustín , Jaime-Rodríguez Johan Pablo , Fernández-Altamirano Antony Esmit Franco
Published: January 2025

This study explores the mediating role of resilience in the relationship between spirituality and happiness among environmental engineering students in northern Peru, emphasizing its implications for sustainability in education and professional development. Through a quantitative cross-sectional study involving 392 students from public and private universities, two key hypotheses were tested: the direct influence of spirituality on happiness and the mediating effect of resilience. Data were gathered using validated instruments, including the Personal Spirituality Scale (SPI), Connor-Davidson Brief Resilience Scale (CD-RISC 10), and Subjective Happiness Scale (SHS). Structural equation modeling revealed that spirituality has a significant direct effect on happiness and an indirect effect through resilience. The model showed high explanatory power, with spirituality explaining 87% of the variance in resilience, and both variables accounting for 76% of the variance in happiness. These findings highlight the importance of promoting spiritual and resilience-building practices as key strategies for enhancing subjective well-being, a critical factor for preparing sustainable professionals capable of addressing complex environmental challenges. This study contributes to the understanding of how spiritual resources and resilience mechanisms can support the development of socially and psychologically sustainable future engineers.
Mariculture in Natural Environments: Tourists’ Attitudes Towards Aquaculture During Marine Tours in Oban, Scotland
Authors: Budhathoki Mausam , Pounds Alexandra , Younes Jad Abi , Baltadakis Anastasios , Little David C.
Published: January 2025

Aquaculture and tourism can both be important industries for economic development, particularly in isolated geographies endowed with natural beauty. However, spatial conflict can arise, particularly along scenic coastlines, such as that in Oban, Scotland. This area is notable for the frequent overlap between marine tours and cage culture sites, with tours passing by or near these aquaculture locations. This study aims to understand whether tourists who encounter aquaculture operations during these tours have positive attitudes toward aquaculture–tourism integration, which refers to the favourable perception and acceptance of the coexistence of aquaculture and tourism activities, and whether this exposure influences their intention to consume locally farmed aquatic products. A face-to-face survey was conducted to recruit 200 tourists who experienced marine tours in Oban, Scotland. Hierarchical cluster analysis identified three types of tourists based on tourism motivation: (1) Multiple motivations, (2) “Relaxers”, and (3) “Outgoing nature enthusiasts”. Among the three tourist segments, the “Relaxers” were particularly unfamiliar with the concept of farming aquatic food. In general, the majority of tourists were positive about being exposed to aquaculture during marine tours. The results of the structural equation modelling analysis indicated that attitudes towards being exposed to aquaculture during marine tours mediate the relationship between attitudes towards aquaculture and the intention to eat locally farmed aquatic food. Motivational tourism factors, socialisation, and learning positively and significantly influenced attitudes towards being exposed to aquaculture during marine tours. Compared to age, gender, education, income, and travel frequency, only tourists’ dietary patterns significantly influenced behavioural intention. The findings suggest that aquaculture might serve as a potential site for social knowledge exchange, which might be crucial not only for tourism to be successful but also for promoting aquaculture’s social acceptability. The results can be used to inform policymakers, marketers, and aquaculture/tourism practitioners to promote sustainable aquaculture tourism in Scotland or similar sites where tourism and aquaculture coexist.
The Nexus of Sustainability Innovation, Knowledge Application, and Entrepreneurial Success: Exploring the Role of Environmental Awareness
Authors: Krara Wessam , Alzubi Ahmad , Khadem Amir , Iyiola Kolawole
Published: January 2025

Small and medium-sized enterprises (SMEs) are pivotal in driving economic growth and sustainability, necessitating a strategic focus on innovation and knowledge application to navigate dynamic markets effectively. Grounded in the Natural Resource-Based View (NRBV) and Knowledge-Based View (KBV) theories, this study investigates the interplay between sustainability innovation, knowledge application, environmental awareness, and entrepreneurial success among SMEs in Turkey. The research adopts a cross-sectional design, using purposive sampling to survey 402 SME owners and managers from Istanbul and Ankara. Data were collected via electronic surveys and analyzed using SPSS and AMOS to validate the measurement model and test hypotheses through the PROCESS macro for mediation and moderated mediation analysis. The findings reveal that sustainability innovation significantly enhances entrepreneurial success by allowing businesses to develop products and services that address environmental and social concerns. Essentially, by integrating sustainability innovation, entrepreneurs create a growing market demand for eco-conscious solutions. Moreover, knowledge application positively influences entrepreneurial success and mediates the relationship between sustainability innovation and entrepreneurial success. Environmental awareness moderates this mediation, with the indirect effect being stronger for SMEs with higher environmental awareness. These results underscore the critical role of integrating sustainability and knowledge-driven strategies in achieving entrepreneurial success. Practical implications include fostering eco-innovation, enhancing knowledge-sharing practices, and promoting environmental awareness to achieve sustainable business growth and competitiveness in resource-constrained environments. This research provides actionable insights for policymakers and practitioners to support sustainable entrepreneurship.
Improving Crop Tolerance to Abiotic Stress for Sustainable Agriculture: Progress in Manipulating Ascorbic Acid Metabolism via Genome Editing
Authors: Rogo Ugo , Viviani Ambra , Pugliesi Claudio , Fambrini Marco , Usai Gabriele , Castellacci Marco , Simoni Samuel
Published: January 2025

Plants often encounter challenging environmental factors, including intense sunlight, drought, extreme heat, cold temperatures, salinity, excessive metals, and nutrient shortages, which can heavily affect their growth and survival. In this regard, L-ascorbic acid (AsA) is not only an essential nutrient for human health but also plays a significant role in plant responses to environmental stresses, regulating various functions during growth and development, redox signaling, and phytohormone biosynthesis. The growing need to cope with climate change, together with the advancement of CRISPR/Cas9-editing technologies, stimulated new opportunities to enhance AsA biosynthesis to improve crop stress tolerance. In this review, we discuss the biosynthesis and regulation of AsA in abiotic stress response mechanisms. We also explore the latest advancements of CRISPR/Cas9 technologies, their applications, and their challenges as tools for modifying genes associated with AsA metabolism, aiming to develop crops more tolerant and resilient to environmental changes.
Development and Valuation of Novel PLA-Based Biodegradable Packaging Materials Complemented with Food Waste of Plant and Animal Origin for Shelf-Life Extension of Selected Foods: Trends and Challenges
Authors: Lazaridis Dimitrios G. , Andritsos Nikolaos D. , Giannakas Aris E. , Karabagias Ioannis K.
Published: January 2025

Food waste and food waste by-products have gained considerable attention in recent years. Based on the principles of circular economy, these materials can be used for the preparation of novel and biodegradable packaging materials for food preservation. Among the matrices that have been well exploited, poly-lactic acid (PLA) comprises a key material to be fortified with food waste by-products, as shown by numerous studies in the recent literature. In this context, the aim of the present review was to provide an overview of the literature on the most recent trends in the use of PLA and food waste by-products to prepare films for the shelf-life extension of foods of animal or plant origin. The results showed that the use of PLA packaging films fortified with food waste by-products of plant or animal origin has greatly expanded in the last 20 years. The application of these novel packaging materials to foods has led to considerable shelf-life extension and stability. However, there is still a gap in the use of specific food waste by-products of plant origin, such as peels, seeds, or gels (i.e., onion peels, grape seed extract, grape pomace, prickly pear cladode gel) or animal food waste by-products (i.e., whey, collagen, gelatin), to prepare PLA-based packaging films. The present review, which comprises the thematic issue of an ongoing doctoral study, examines trends and challenges with regard to this topic that have not been extensively studied.
Navigating Sustainable Supply Chain Management in the Perfume Industry: Challenges and Solutions
Authors: Alhindawi Reham , Abu Nahleh Yousef , Kumar Arun
Published: January 2025

The perfume industry faces significant challenges in integrating sustainable supply chain management (SSCM) practices due to its fragmentation and the need for coordination among diverse stakeholders. This study explores these challenges and identifies potential solutions through a comprehensive literature review, a case study, and interviews with industry professionals. Findings highlight the complexity of SSCM, emphasizing the integration of environmental, social, and economic considerations. Key challenges include high implementation costs, lack of stakeholder awareness, and inadequate regulatory frameworks. Economically, the primary issue is balancing SSCM investments with short-term benefits. Socially, effective communication between stakeholders is critical, while environmentally, reducing carbon footprints and waste, particularly in transportation, is essential. The research underscores the need for a long-term vision and collaboration to successfully implement SSCM practices in the perfume industry.
Economic Policy Uncertainty and Corporate Green Technology Innovation—Evidence from Privately Listed Companies in China
Authors: Song Yiran , Wang Ying , Zhang Yuting , Lu Nan , Ge Chunbao
Published: January 2025

Green technology innovation (GTI) is an important way for enterprises to promote technological progress and enhance their market competitiveness. As investment in R and D and innovation is a high-risk, long-term investment project with high sensitivity to relevant policies, increases or drastic fluctuations in economic policy uncertainty (EPU) may have a strong impact on technological innovation. Therefore, it is necessary to examine how EPU affects GTI. In contrast to studies that have focused on the impacts of EPU on GTI in developed countries or state-owned firms in China, this study uses the mediating effect panel fixed effect model to investigate the impacts of EPU on corporate GTI and its mechanisms, based on data from China’s Shanghai and Shenzhen A-share private manufacturing listed companies from 2013 to 2022. The results indicate that an increase in EPU has a positive impact on the GTI of private enterprises and that the impact is stronger in companies with multiple major shareholders, those purchasing directors’ and officers’ liability insurance, and those with stricter environmental regulations. This conclusion remains valid after robustness checks and instrumental variable tests. Mechanism tests reveal that the increase in EPU indirectly promotes GTI by forcing private enterprises to avoid short-sighted management tendencies, increase risk-taking levels, boost environmental protection investments, and strengthen internal controls. For example, EPU will lead to increased investment in green protection, meaning that private companies are more willing to promote green transformation and market competitiveness through innovative activities. These findings provide a reference for the Chinese government to formulate targeted environmental regulation policies and financing policies to guide the green transformation of businesses; they also provide insights for enterprises in other developing countries to cope with economic policy risks and promote green technological advancement.
The Formation of Knowledge Flow Networks in the Yangtze River Delta, China: Knowledge Implicitness and Proximity Effect
Authors: Zhu Pengcheng , Chen Jianglong , Yuan Feng , Liu Weichen
Published: January 2025

Knowledge flow as the key to facilitating new technology production and diffusing innovation is crucial for achieving sustainable development. However, previous studies pay less attention to the type of knowledge in knowledge flow network construction, possibly leading to the deviation of conclusions. To fully show the panorama of knowledge flow, this study distinguishes between explicit and tacit knowledge based on the transfer of patent rights data and talent flow data, describes the spatial characteristics of flow networks and uses a multiple regression quadratic assignment procedure model to analyze the proximity mechanism of network formation in the Yangtze River Delta. We find that knowledge flow networks in the Yangtze River Delta cover a wide range but are extremely uneven, mainly concentrated along the Yangtze River and around Hangzhou Bay. In addition, the spatial structures of different types of knowledge flow networks vary. Different dimensions of proximity act in relatively consistent directions for both types of knowledge flows, with geographical and organizational proximity found to exert positive effects on facilitating knowledge flows while cognitive proximity has a negative impact. There is also a substitution effect between geographical proximity and organizational proximity, and a complementary effect with cognitive proximity. These findings provide significant implications for optimizing knowledge flow networks and promoting sustainable development.
The Impact and Mechanisms of State-Owned Shareholding on Greenwashing Behaviors in Chinese A-Share Private Enterprises
Authors: Li Xinru , Tian Zengrui , Liu Qian , Chang Beiquan
Published: January 2025

In response to the increasing global emphasis on environmental accountability, the issue of greenwashing requires urgent resolution. This research investigates how state ownership affects greenwashing behaviors in Chinese A-share private companies over the period from 2010 to 2021, utilizing resource support and supervisory governance as analytical frameworks. Empirical analysis reveals that state-owned shareholder holdings significantly inhibit greenwashing practices in private enterprises, with this result remaining robust across various sensitivity tests. Furthermore, it is demonstrated that these holdings reduce greenwashing through both resource support and supervisory governance pathways. This study enhances the scholarly understanding of how state capital impacts private firms and underscores the distinctive roles and benefits that state-owned shareholders bring to the mixed-ownership reform process. The results suggest new pathways for fostering the sustainable development of private enterprises and offer crucial insights for policymakers focused on advancing mixed-ownership reforms and ensuring corporate accountability.
Factors Influencing Electric Vehicle Charging Station Locations and Policy Implications: Empirical Lessons from Seoul Metropolitan Area in Korea
Authors: Kim Hyunjoong , Kim Gyeong Seok
Published: January 2025

The growth of electric vehicle (EV) demand in Korea is closely tied to the challenge of optimal charging station placement. Despite the quantitative increase in charging stations, their uneven spatial distribution remains a significant issue, as highlighted by several related studies. This research analyzes the factors influencing the location of public electric vehicle fast-charging stations (PEVFCSs) in Korea’s Seoul Metropolitan Area (SMA). Our analysis reveals that the SMA has yet to implement a systematic urban planning approach for PEVFCS placement. Interestingly, traffic volume is negatively correlated with PEVFCS location, contrary to expectations. Through a comprehensive diagnosis of the current situation, we offer valuable insights and lessons learned from the SMA experience. These findings contribute to the development of plausible policies for more effective PEVFCS distribution in urban areas.
ESG Strategies and Sustainable Performance in Multinational Enterprises
Authors: Chen Kao-Shan , Lin Shih-Tse , Chuang Che-Jen
Published: January 2025

This study examines the impact of ESG strategies on sustainable performance in multinational enterprises (MNEs), with a particular focus on the mediating role of internal market-oriented culture (IMOC) and the moderating effect of job crafting. To validate these key mechanisms, the study employs a stratified random sampling method to collect data from 614 employees across manufacturing, service, and technology-intensive industries. The data were analyzed using structural equation modeling (SEM) to test the hypothesized relationships. The empirical results demonstrate that ESG strategies significantly enhance sustainable organizational performance, with IMOC serving as a critical mediator linking ESG strategies to improved outcomes. Furthermore, job crafting strengthens the positive relationship between IMOC and sustainable performance, indicating that when employees proactively reshape their tasks and roles to align with ESG objectives, organizational sustainability is further enhanced. The influence of cultural context plays a crucial role in this process. The findings reveal that the effects of IMOC and job crafting are more pronounced in collectivist cultures (e.g., China and Japan), while in individualist cultures (e.g., the United States and Germany), performance improvements rely more heavily on individual incentives and feedback mechanisms. These insights provide practical guidance for MNE managers on how to tailor ESG strategies, IMOC, and job crafting approaches to specific regional and cultural settings. This study contributes to the literature by demonstrating how ESG strategies interact with internal cultural and behavioral mechanisms to drive sustainable performance in MNEs. It highlights the role of IMOC and job crafting as internal drivers that bridge ESG initiatives with tangible organizational outcomes. From a practical perspective, the findings offer actionable guidance for MNEs on how to design adaptive ESG strategies and employee engagement practices tailored to culturally diverse environments. These insights provide managers with a clear pathway to enhance employee-driven change and improve sustainable performance across global operations.
As(III) Removal via Combined Addition of Mg- and Ca-Based Adsorbents and Comparison to As(V) Removal via Those Mechanisms
Authors: Sugita Hajime , Morimoto Kazuya , Saito Takeshi , Hara Junko
Published: January 2025

Damage to human health caused by As-contaminated water can be prevented using proper As-removal techniques, such as employing excellent arsenic adsorbents. In this study, the combined addition of Mg- and Ca-based adsorbents was investigated for the efficient removal of As from contaminated water. Following a previous study on As(V), As-removal tests targeting As(III) and several additional tests, including X-ray diffraction analysis, were conducted to clarify the mechanism of the improved performance of the combined-addition As removal. Similarly as for As(V), the combined additions of both MgCO3 + CaO and MgCO3 + Ca(OH)2 improved As(III)-removal performance while inhibiting the leaching of base material components; however, they did not remove As(III) as effectively as As(V). The differences in the removal ratios of As(V) and As(III) in these combined additions were concluded to be primarily due to the different As-removal mechanisms. Mg(OH)2 and CaCO3 were generated, and As(III) was incorporated into the generated precipitate of Mg(OH)2 but not into that of CaCO3. Conversely, As(V) was incorporated into both Mg(OH)2 and CaCO3. Additionally, MgCO3 + Ca(OH)2 was evaluated as a more efficient combined-addition method because MgCO3 + Ca(OH)2 exhibited a higher As-removal ratio value than MgO + CaO. Proposals have been made to remove As(III) using activated carbon modified with heavy metals or transition elements, or concrete waste grafted with polymers, but these methods are complicated to prepare, costly, and involve the risk of leaching of harmful components. Adsorbents that use general Mg and Ca components as their base material do not contain such harmful components. The Mg- and Ca-based adsorbents are readily available and low-cost, and, best of all, there is no concern that they will leach harmful components. Therefore, widespread use of Mg- and Ca-based adsorbents as a measure against arsenic contamination could greatly contribute to a sustainable society.
How Livelihood Capital Affects Farmers’ Green Production Behavior: Analysis of Mediating Effects Based on Farmers’ Cognition
Authors: Yang Jieyu , Cui Xiujuan
Published: January 2025

In light of global climate change and sustainable agricultural growth, it is critical to look at producers’ green production methods. Enhancing the quality of agricultural goods and reducing agricultural pollution are the main goals of future agricultural growth, and this is accomplished by farmers using green production methods. Regarding the research data of 364 vegetable farmers, this study uses structural equation modeling and a mediation effect model to empirically assess the effect of livelihood capital and farmers’ cognition on their green behavior. The results show that (1) natural capital, human capital, financial capital, and social capital in the livelihood capital of vegetable growers may significantly impact producers’ green production behavior. It is not immediately clear how physical capital affects the way green manufacturing practices work. (2) Natural capital and green production behavior are completely mediated by environmental and policy cognition, while human capital and green production behavior are partly mediated by environmental and policy cognition. Human and natural capital indirectly influence farmers’ sustainable production techniques via these activities. Financial and social capital directly influence farmers’ sustainable production methods, with no mediation effect seen. (3) Farmers’ green production behavior is more significantly influenced by their cognitive behavior than by their livelihood capital. Accordingly, it is recommended that environmental education and policy promotion be strengthened, that farmers’ livelihood capital be accumulated via a variety of channels, that farmers’ subsidies for green production be increased, and that farmers’ knowledge of green production be improved. The cognitive level of farmers should also be raised. In addition to providing theoretical justification for analyzing farmers’ green production practices within the framework of sustainable agricultural development, this study also acts as a guide for pertinent government agencies to help farmers choose more ecologically friendly farming methods.