bluuebunny/crossref_metadata_2025 huggingface dataset:
Lightweight Ship Detection Network for SAR Range-Compressed Domain
Authors: Tan Xiangdong , Leng Xiangguang , Sun Zhongzhen , Luo Ru , Ji Kefeng , Kuang Gangyao
Published: September 2024

The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR imagery can significantly reduce the time and computational resources required for complete SAR imaging, enabling lightweight real-time ship detection methods to be implemented on an airborne or spaceborne SAR platform. However, there is a lack of lightweight ship detection methods specifically designed for the SAR range-compressed domain. In this paper, we propose Fast Range-Compressed Detection (FastRCDet), a novel lightweight network for ship detection in the SAR range-compressed domain. Firstly, to address the distinctive geometric characteristics of the SAR range-compressed domain, we propose a Lightweight Adaptive Network (LANet) as the backbone of the network. We introduce Arbitrary Kernel Convolution (AKConv) as a fundamental component, which enables the flexible adjustment of the receptive field shape and better adaptation to the large scale and aspect ratio characteristics of ships in the range-compressed domain. Secondly, to enhance the efficiency and simplicity of the network model further, we propose an innovative Multi-Scale Fusion Head (MSFH) module directly integrated after the backbone, eliminating the need for a neck module. This module effectively integrates features at various scales to more accurately capture detailed information about the target. Thirdly, to further enhance the network’s adaptability to ships in the range-compressed domain, we propose a novel Direction IoU (DIoU) loss function that leverages angle cost to control the convergence direction of predicted bounding boxes, thereby improving detection accuracy. Experimental results on a publicly available dataset demonstrate that FastRCDet achieves significant reductions in parameters and computational complexity compared to mainstream networks without compromising detection performance in SAR range-compressed images. FastRCDet achieves a low parameter of 2.49 M and a high detection speed of 38.02 frames per second (FPS), surpassing existing lightweight detection methods in terms of both model size and processing rate. Simultaneously, it attains an average accuracy (AP) of 77.12% in terms of its detection performance. This method provides a baseline in lightweight network design for SAR ship detection in the range-compressed domain and offers practical implications for resource-constrained embedded platforms.
Analysis of the Influence of Refraction-Parameter Deviation on Underwater Stereo-Vision Measurement with Flat Refraction Interface
Authors: Li Guanqing , Huang Shengxiang , Yin Zhi , Zheng Nanshan , Zhang Kefei
Published: September 2024

There has been substantial research on multi-medium visual measurement in fields such as underwater three-dimensional reconstruction and underwater structure monitoring. Addressing the issue where traditional air-based visual-measurement models fail due to refraction when light passes through different media, numerous studies have established refraction-imaging models based on the actual geometry of light refraction to compensate for the effects of refraction on cross-media imaging. However, the calibration of refraction parameters inevitably contains errors, leading to deviations in these parameters. To analyze the impact of refraction-parameter deviations on measurements in underwater structure visual navigation, this paper develops a dual-media stereo-vision measurement simulation model and conducts comprehensive simulation experiments. The results indicate that to achieve high-precision underwater-measurement outcomes, the calibration method for refraction parameters, the distribution of the targets in the field of view, and the distance of the target from the camera must all be meticulously designed. These findings provide guidance for the construction of underwater stereo-vision measurement systems, the calibration of refraction parameters, underwater experiments, and practical applications.
Delving into the Potential of Deep Learning Algorithms for Point Cloud Segmentation at Organ Level in Plant Phenotyping
Authors: Xie Kai , Zhu Jianzhong , Ren He , Wang Yinghua , Yang Wanneng , Chen Gang , Lin Chengda , Zhai Ruifang
Published: September 2024

Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms.
Characterization of Complex Rock Mass Discontinuities from LiDAR Point Clouds
Authors: Liu Yanan , Hua Weihua , Chen Qihao , Liu Xiuguo
Published: September 2024

The distribution and development of rock mass discontinuities in 3D space control the deformation and failure characteristics of the rock mass, which in turn affect the strength, permeability, and stability of rock masses. Therefore, it is essential to accurately and efficiently characterize these discontinuities. Light Detection and Ranging (LiDAR) now allows for fast and precise 3D data collection, which supports the creation of new methods for characterizing rock mass discontinuities. However, uneven density distribution and local surface undulations can limit the accuracy of discontinuity characterization. To address this, we propose a method for characterizing complex rock mass discontinuities based on laser point cloud data. This method is capable of processing datasets with varying densities and can reduce over-segmentation in non-planar areas. The suggested approach involves a five-stage process that includes: (1) adaptive resampling of point cloud data based on density comparison; (2) normal vector calculation using Principal Component Analysis (PCA); (3) identifying non-planar areas using a watershed-like algorithm, and determine the main discontinuity sets using Multi-threshold Mean Shift (MTMS); (4) identify single discontinuity clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN); (5) fitting discontinuity planes with Random Sample Consensus (RANSAC) and determining discontinuity orientations using analytic geometry. This method was applied to three rock slope datasets and compared with previous research results and manual measurement results. The results indicate that this method can effectively reduce over-segmentation and the characterization results have high accuracy.
Integration of a Mobile Laser Scanning System with a Forest Harvester for Accurate Localization and Tree Stem Measurements
Authors: Faitli Tamás , Hyyppä Eric , Hyyti Heikki , Hakala Teemu , Kaartinen Harri , Kukko Antero , Muhojoki Jesse , Hyyppä Juha
Published: September 2024

Automating forest machines to optimize the forest value chain requires the ability to map the surroundings of the machine and to conduct accurate measurements of nearby trees. In the near-to-medium term, integrating a forest harvester with a mobile laser scanner system may have multiple applications, including real-time assistance of the harvester operator using laser-scanner-derived tree measurements and the collection of vast amounts of training data for large-scale airborne laser scanning-based surveys at the individual tree level. In this work, we present a comprehensive processing flow for a mobile laser scanning (MLS) system mounted on a forest harvester starting from the localization of the harvester under the forest canopy followed by accurate and automatic estimation of tree attributes, such as diameter at breast height (DBH) and stem curve. To evaluate our processing flow, we recorded and processed MLS data from a commercial thinning operation on three test strips with a total driven length ranging from 270 to 447 m in a managed Finnish spruce forest stand containing a total of 658 reference trees within a distance of 15 m from the harvester trajectory. Localization reference was obtained by a robotic total station, while reference tree attributes were derived using a high-quality handheld laser scanning system. As some applications of harvester-based MLS require real-time capabilities while others do not, we investigated the positioning accuracy both for real-time localization of the harvester and after the optimization of the full trajectory. In the real-time positioning mode, the absolute localization error was on average 2.44 m, while the corresponding error after the full optimization was 0.21 m. Applying our automatic stem diameter estimation algorithm for the constructed point clouds, we measured DBH and stem curve with a root-mean-square error (RMSE) of 3.2 cm and 3.6 cm, respectively, while detecting approximately 90% of the reference trees with DBH>20 cm that were located within 15 m from the harvester trajectory. To achieve these results, we demonstrated a distance-adjusted bias correction method mitigating diameter estimation errors caused by the high beam divergence of the laser scanner used.
The Remote Effects of Typhoons on the Cold Filaments in the Southwestern South China Sea
Authors: Zhao Zezheng , Yang Shengmu , Wang Huipeng , Yuan Taikang , Ren Kaijun
Published: September 2024

Cold filaments (CFs) in the southwestern South China Sea (SCS) impact local hydrodynamics and the ecological environment. In this study, the effects of typhoons passing over the northern SCS on CFs are investigated using multi-source observational and reanalysis data. Statistical analysis of CF responses to typhoons over the past 24 years shows that during typhoon periods in the northern SCS, the CFs are intensified. We further analyze the remote effect of typhoons on the CF during Typhoon Kalmaegi in 2014, which caused a sea surface temperature (SST) drop of more than 3 °C in the CF region. The strengthened along-coast wind induced strong upwelling off the Vietnam coast. The maximum vertical velocity in the CF reaches three times the usual value. Meanwhile, influenced by the peripheral wind field of Kalmaegi, cold coastal water accumulated at the CF region due to the shafting of the offshore current. Both strong offshore currents and coastal upwellings enhanced the intensity of the CF. These findings demonstrate that typhoons not only directly affect ocean dynamic processes along their path but also present significant remote influences on coastal dynamics in other regions. This study enhances the understanding of CF evolution and sea–air interactions during extreme events.
Remote Sensing of Floodwater-Induced Subsurface Halite Dissolution in a Salt Karst System, with Implications for Landscape Evolution: The Western Shores of the Dead Sea
Authors: Baer Gidon , Gavrieli Ittai , Swaed Iyad , Nof Ran N.
Published: September 2024

We study the interrelations between salt karst and landscape evolution at the Ze’elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic aperture radar, drone photography, time-lapse cameras, and direct measurements of floodwater levels, we document floodwater recharge through riverbed sinkholes, subsurface salt dissolution, groundwater flow, and brine discharge at shoreline sinkholes during the years 2011–2023. At the Ze’elim fan, most of the surface floodwater drains into streambed sinkholes and discharges at shoreline sinkholes, whereas at the Hever fan, only a small fraction of the floodwater drains into sinkholes, while the majority flows downstream to the DS. This difference is attributed to the low-gradient stream profiles in Ze’elim, which enable water accumulation and recharge in sinkholes and their surrounding depressions, in contrast with the higher-gradient Hever profiles, which yield high-energy floods capable of carrying coarse gravel that eventually fill the sinkholes. The rapid drainage of floodwater into sinkholes also involves slope failure due to pore-pressure drop and cohesion loss within hours after each drainage event. Surface subsidence lineaments detected by InSAR indicate the presence of subsurface dissolution channels between recharge and discharge sites in the two fans and in the nearby Lynch straits. Subsidence and streambed sinkholes occur in most other fans and streams that flow to the DS; however, with the exception of Ze’elim, all other streams show only minor or no recharge along their course. This is due to either the high-gradient profiles, the gravelly sediments, the limited floods, or the lack of conditions for sinkhole development in the other streambeds. Thus, understanding the factors that govern the flood-related karst formation is of great importance for predicting landscape evolution in the DS region and elsewhere and for sinkhole hazard assessment.
Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions
Authors: Jiang Zhihao , Bakker Otto Jan , Bartolo Paulo JDS
Published: September 2024

Chronic obstructive pulmonary disease (COPD) is among prevalent occupational diseases, causing early retirement and disabilities. This paper looks into occupational-related COPD prevention and intervention in the workplace for Industry 4.0-compliant occupation health and safety management. The economic burden and other severe problems caused by COPD are introduced. Subsequently, seminal research in relevant areas is reviewed. The prospects and challenges are introduced and discussed based on critical management approaches. An initial design of an Industry 4.0-compliant occupational COPD prevention system is presented at the end.
Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method
Authors: Chen Simin , Song Shaojing , Wang Yicheng , Pan Hao , Li Fashuai , Chen Yuwei
Published: September 2024

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system’s performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management.
Variational Mode Decomposition Analysis of Electroencephalograms during General Anesthesia: Using the Grey Wolf Optimizer to Determine Hyperparameters
Authors: Kushimoto Kosuke , Obata Yurie , Yamada Tomomi , Kinoshita Mao , Akiyama Koichi , Sawa Teiji
Published: September 2024

Frequency analysis via electroencephalography (EEG) during general anesthesia is used to develop techniques for measuring anesthesia depth. Variational mode decomposition (VMD) enables mathematical optimization methods to decompose EEG signals into natural number intrinsic mode functions with distinct narrow bands. However, the analysis requires the a priori determination of hyperparameters, including the decomposition number (K) and the penalty factor (PF). In the VMD analysis of EEGs derived from a noninterventional and noninvasive retrospective observational study, we adapted the grey wolf optimizer (GWO) to determine the K and PF hyperparameters of the VMD. As a metric for optimization, we calculated the envelope function of the IMF decomposed via the VMD method and used its envelope entropy as the fitness function. The K and PF values varied in each epoch, with one epoch being the analytical unit of EEG; however, the fitness values showed convergence at an early stage in the GWO algorithm. The K value was set to 2 to capture the α wave enhancement observed during the maintenance phase of general anesthesia in intrinsic mode function 2 (IMF-2). This study suggests that using the GWO to optimize VMD hyperparameters enables the construction of a robust analytical model for examining the EEG frequency characteristics involved in the effects of general anesthesia.
Neuromuscular Load in Professional Women’s Handball: Segmentation of the Player Load and the Impacts at Group and Individual Level
Authors: Antúnez Antonio , López-Sierra Pablo , Vila-Suárez Helena , Ibáñez Sergio J.
Published: September 2024

Handball is a team sport characterised by physical interaction with other opponents. This interaction produces a high load on the players that can manifest itself in various ways, from discomfort to prolonged injuries due to tears caused by excessive load. In order to establish correct protocols for application in women’s teams, context- and gender-specific reference data must be available. For this reason, the present research aims to find out how women’s teams in European competitions prepare for decisive matches during the match week, analysing the load in a segmented way and the level of specificity that should be achieved in training. Ex post facto research was used in which a total of 17 players belonging to a women’s first division handball team in Spain participated. The variables player load and impacts extracted from the use of Wimu ProTM inertial devices were analysed. The results showed a high neuromuscular load in players at this competitive level, especially in the variable impacts, reaching values per session of up to 1000 impacts. The individuality analyses show that the load varies significantly depending on the subject, which is why it is considered essential to establish protocols for strength work and load control in the most specific way possible.
Personnel Monitoring in Shipboard Surveillance Using Improved Multi-Object Detection and Tracking Algorithm
Authors: Li Yiming , Zhang Bin , Liu Yichen , Wang Huibing , Zhang Shibo
Published: September 2024

Detecting and tracking personnel onboard is an important measure to prevent ships from being invaded by outsiders and ensure ship security. Ships are characterized by more cabins, numerous equipment, and dense personnel, so there are problems such as unpredictable personnel trajectories, frequent occlusions, and many small targets, which lead to the poor performance of existing multi-target-tracking algorithms on shipboard surveillance videos. This study conducts research in the context of onboard surveillance and proposes a multi-object detection and tracking algorithm for anti-intrusion on ships. First, this study designs the BR-YOLO network to provide high-quality object-detection results for the tracking algorithm. The shallow layers of its backbone network use the BiFormer module to capture dependencies between distant objects and reduce information loss. Second, the improved C2f module is used in the deep layer of BR-YOLO to introduce the RepGhost structure to achieve model lightweighting through reparameterization. Then, the Part OSNet network is proposed, which uses different pooling branches to focus on multi-scale features, including part-level features, thereby obtaining strong Re-ID feature representations and providing richer appearance information for personnel tracking. Finally, by integrating the appearance information for association matching, the tracking trajectory is generated in Tracking-By-Detection mode and validated on the self-constructed shipboard surveillance dataset. The experimental results show that the algorithm in this paper is effective in shipboard surveillance. Compared with the present mainstream algorithms, the MOTA, HOTP, and IDF1 are enhanced by about 10 percentage points, the MOTP is enhanced by about 7 percentage points, and IDs are also significantly reduced, which is of great practical significance for the prevention of intrusion by ship personnel.
Precursor-Based Syntheses of Mo(C,N,O)x, Molybdenum Carbide, Nitride, and Oxide Applying a Microjet Reactor
Authors: Abdirahman Mohamed Mana , Janka Oliver , Harling Susanne , Kickelbick Guido
Published: September 2024

Composite materials such as molybdenum carbides, nitrides, oxides, and mixed anionic compounds like Mo(C,N,O)x embedded in carbonaceous matrix exhibit promising potential as anode materials for lithium batteries, with a preference for fine-grained morphologies. In this study, we present a novel synthetic approach involving an inorganic–organic hybrid precursor precipitated from aqueous solutions of ammonium heptamolybdate and one of two organic species: 1,8-diaminonaphthalene (1,8-DAN) or hexamethylenediamine (HMD). The precipitation reaction can be carried out in a beaker and in a continuous process using a microjet reactor. This enables the synthesis of precursor material on the gram scale within minutes. The pyrolysis of these precursors yields mixtures of Mo(C,N,O)x, MoO2, Mo2C, Mo2N, and Mo, with the choice of organic compound significantly influencing the resulting phases and the excess carbon content in the pyrolyzed product. Notably, the pyrolysis process maintains the size and morphology of the micro- to nanometer-sized starting materials.
Smart Manufacturing and Pro-Environmental Behavior: Moderated Serial Mediation Modelling and Analysis
Authors: Aboagye-Dapaah Emily Opoku , Appiah Michael Karikari , Dagadu Joshua Caleb
Published: September 2024

Smart manufacturing encompasses a category of manufacturing that employs computer-integrated capabilities and technologies to enhance supply chain optimization, production efficiency, and recyclability. Yet, limited studies have been conducted to optimize these prospects from the perspective of low-resource countries where such innovative studies have not been adequately explored. This paper aims to examine the implications of smart manufacturing practices (Smart Procurement, Smart Supply Chain, Smart Production Planning and Control, Automation and Industrial Robot, and Supply Chain Configuration) on pro-environmental behaviour and develop a baseline moderated mediation model to explain the relationship between smart manufacturing and pro-environmental behaviour as well as the indirect effects of environmental awareness and green dynamic capability. This study employs a quantitative research approach, utilizing inductive reasoning and an explanatory design. Data from 382 manufacturing enterprises in Ghana were collected through a cross-sectional survey. We tested our hypotheses using SMART-PLS software version 3.8.9 and SEM technique. The study found a strong and positive relationship between smart manufacturing practices and pro-environmental behaviour. Additionally, the relationship between smart manufacturing and pro-environmental behaviour is influenced by green dynamic capacity and environmental orientation. The study’s implications involve the creation of a fundamental model that can assist policy makers, practitioners, and academics in understanding the connection between smart manufacturing and sustainable production in developing nations. Again, the social implication of the study includes the realisation of decent job and economic growth, responsible consumption, and production as well as actions taken to combat climate change.
Sustainable Construction through Utilization of Optimization Tools and Experimental Methods—An Editorial
Authors: Klanšek Uroš , Žula Tomaž
Published: September 2024

It is widely recognized that the adverse impacts of the construction industry on economic, social, and environmental sustainability are staggering [...]
Furnace Temperature Model Predictive Control Based on Particle Swarm Rolling Optimization for Municipal Solid Waste Incineration
Authors: Tian Hao , Tang Jian , Wang Tianzheng
Published: September 2024

Precise control of furnace temperature (FT) is crucial for the stable, efficient operation and pollution control of the municipal solid waste incineration (MSWI) process. To address the inherent nonlinearity and uncertainty of the incineration process, a FT control strategy is proposed. Firstly, by analyzing the process characteristics of the MSWI process in terms of FT control, the secondary air flow is selected as the manipulated variable to control the FT. Secondly, an FT prediction model based on the Interval Type-2 Fuzzy Broad Learning System (IT2FBLS) is developed, incorporating online parameter learning and structural learning algorithms to enhance prediction accuracy. Next, particle swarm rolling optimization (PSRO) is used to solve the optimal control law sequence to ensure optimization efficiency. Finally, the stability of the proposed method is validated using Lyapunov theory, confirming the controller’s reliability in practical applications. Experiments based on actual operational data confirm the method’s effectiveness.
Multi-Criteria Decision Making Methods for Suitable Site Selection of Concentrating Solar Power Plants
Authors: Farsi Hichem , Dizene Rabah , Flamant Gilles , Notton Gilles
Published: September 2024

The present paper deals with the application of a Multi-Criteria Evaluation approach (MCE) to carry out site selection for Concentrating Solar Power plants (CSP). As this work demonstrates, multi-criteria analysis can provide a technical–scientific decision making tool capable of justifying choices in a clear and coherent manner, particularly in the renewable energy sector. The site selection for CSP is a complex topic. Although it has a significant impact on project costs, this issue has received little attention in the literature. A practical methodology based on a multi-criteria analysis is presented. Several potential sites called alternatives are compared, using a combination of Multi-Criteria Decision Making methods (MCDM) such as the Analytical Hierarchy Process (AHP), Scoring method and Benayoun and Tergny Algorithm. The relevance of this combination is examined through a case study of Algeria, a country with a high potential for CSP projects. The comparative analysis is carried out for a set of alternatives (Kenadsa, Abdala, Naâma and Hassi R’Mel). The assessment of alternatives and their ranking are discussed according to several scenarios, both for approaches used to estimate the criteria weights (AHP, weights affected in different manner) and for the two MCDM methods chosen for the present study. The methodology provides a better understanding of the site selection process for CSP, which would encourage their large-scale deployment on the green electricity market.
Access to Sustainability in Conservation-Restoration Practices
Authors: Pinheiro Catarina , Beaumont Anna , Cardinali Francesca , Marra Annalisa , Molinari Daniela , Fife Gwendoline , Wagner Julia , Galacho Cristina , Southwick Caitlin
Published: September 2024

Cultural heritage faces significant threats from environmental challenges and unchecked development. Sustainability has made its way into the field, and there is a growing interest in seeing it thrive. The particular field of the conservation and restoration of tangible movable cultural heritage is also being improved by new concepts and treatment options more aligned with environmental standards. This article investigates the integration of sustainability into the conservation and restoration of this specific cultural heritage by leveraging the SCOPUS and BCIN databases to examine the evolution of scientific content on the topic over the past 24 years. Key trends in research include assessing greener practices in conservation and education and guidance for the promotion of sustainable practices. These themes are also championed by organizations and initiatives that disseminate this message to conservation professionals through effective and less formal communication strategies. The findings emphasize the critical value of bridging the gap between scientific research and practical application, advocating for accessible resources and collaborative efforts to advance sustainable conservation practices.
How Can Organizational Leadership Promote Environmental Behaviors through Corporate Social Responsibility Policy Adoption? The Moderating Role of Environmental Awareness
Authors: Ting Chih-Wen , Li Hung-Xin , Chen Kuei-Hsien , Lee Yue-Shi , Yen Show-Jane
Published: September 2024

Environmental behaviors are critical for the sustainability of business organizations. However, the role of organizational leadership and corporate social responsibility in predicting employees’ environmental behaviors has been underexplored. This paper takes an innovative approach to fill this gap, drawing on the upper echelons theory and organizational identification theory. It explains how the adoption of organizational leadership by top management teams can influence companies’ adoption of corporate social responsibility policy and encourage employees to engage in environmental behavior, with the moderating role of environmental awareness. The research analyze longitudinal-section data based on empirical data from a three-wave sampling of 160 chief executive officers and their employees over six months. By advancing the literature on corporate social responsibility, this paper provides a paradigm for companies to implement environmental behaviors through leadership mechanisms, thereby achieving competitive advantage and sustainable development.
Symphony or Solo: Does Convergence Exist in Environmental Taxation among EU Countries?
Authors: Li Zhengzheng , Sun Zhongyang , Wang Kaihua , Lobonț Oana-Ramona
Published: September 2024

This study sought to ascertain the existence of convergence in environmental taxation among EU countries. By employing the sequential panel selection method and the panel KSS unit root test, it is demonstrated that the environmental taxation sequences in Luxembourg, Germany, Spain, France, and Cyprus diverge, while those of the remaining 22 countries converge. Similarly, the energy tax sequences of Croatia, Spain, Germany, and France are unstable, indicating divergence, while the remaining 23 countries exhibit significant convergence. Finally, transport taxes show a convergent trend across all EU countries. The convergence of environmental taxation can be explained by the catch-up and spatial spillover effects, and the non-convergence of a few EU countries is attributed to their minimal implementation of environmental goals. This study provides recommendations for improving environmental policies.
Teachers’ Resilience Scale for Sustainability Enabled by ICT/Metaverse Learning Technologies: Factorial Structure, Reliability, and Validation
Authors: Makrakis Vassilios
Published: September 2024

A significant trend in education is the increasing recognition of the need to shift from transmissive teaching to incorporating reflexive practices associated with real-life issues in learning, curriculum, and teaching. Merging Information and Communication Technologies (ICTs) and Metaverse learning technologies in Education for Sustainability (ICT/MeEfS) is critical in responding to current sustainability crises such as climate change. This research article focuses on the factorial structure, reliability, and validity of a teachers’ ICT/MeEfS resilience scale. It examines the predictive value of teacher self-efficacy and transformative teaching beliefs in merging ICTs and education for sustainability. The respondents were 1815 in-service teachers in Indonesia, Malaysia, and Vietnam. The principal component analysis showed a two-factor model (factor 1: “personal ICT/MeEfS resilience” and factor 2: “reflexive practice”), with a significant amount of extracted variance (68.26%). The overall Cronbach’s alpha reliability analysis of the teachers’ resilience scale enabled by ICT/MeEfS was 0.90, indicating a high score and excellent internal consistency. Similarly, the stepwise multiple regression analysis revealed that the two hypothesized predictors, teacher self-efficacy and transformative teaching beliefs, significantly contributed to teachers’ ICT/MeEfS resilience, explaining 73% of its variability. The implications of the research results are discussed in terms of research and in developing the capacity of teachers to embed sustainability issues and SDGs in teaching practices, learning environments, and course curricula enabled by ICTs and Metaverse learning technologies.
Ecosystem Services Synergies and Trade-Offs from Tree Structural Perspectives: Implications for Effective Urban Green Space Management and Strategic Land Use Planning
Authors: Hintural Wencelito Palis , Woo Hee-Gyu , Choi Hyeongwon , Lee Hyo-Lim , Lim HaSu , Youn Woo Bin , Park Byung Bae
Published: September 2024

Urban green spaces (UGSs) are critical in providing essential ecosystem services (ESs) that enhance the quality of life of urban communities. This study investigated the synergies and trade-offs between structural characteristics of urban trees and their ecosystem services and their implications for urban park management within Yurim Park, Daejeon, South Korea, using the i-Tree Eco tool. The study specifically focused on regulating and supporting services, assessing diversity, air pollution removal, carbon sequestration, and avoiding runoff. A systematic review of urban park management practices complemented the empirical analysis to provide comprehensive management recommendations. The findings of a total of 305 trees from 23 species were assessed, revealing moderate species diversity and significant variations in structural attributes, such as diameter at breast height (DBH), leaf area index (LAI), and crown width (CW). These attributes were found to be strongly correlated with ES outcomes, indicating that healthier and larger trees with extensive canopies are more effective in providing benefits such as pollution removal, runoff reduction, and carbon sequestration. However, the study also identified trade-offs, particularly regarding volatile organic compound (VOC) emissions, which can contribute to ground-level ozone formation despite the trees’ pollution removal capabilities, sensitivity to water stress, requirements for shade and cooling effects, and impacts on water yield. The results highlight the importance of strategic management practices to balance these trade-offs, such as selecting low-emitting species and employing incremental pruning to enhance pollutant removal while minimizing VOC emissions. Additionally, the findings underscore the significance of tree placement and landscape patterns in optimizing year-round benefits, particularly in reducing urban heat island effects and enhancing energy efficiency in adjacent buildings. The study concludes that while urban parks like Yurim Park offer substantial ecological and environmental benefits, continuous monitoring and adaptive management are essential to maximize synergies and mitigate trade-offs. The insights provided on species selection, tree placement, and landscape design offer valuable guidance for urban planners and landscape architects aiming at enhancing the effectiveness of urban parks as nature-based solutions for sustainable urban development.
Experimental Investigation of a Water–Air Heat Recovery System
Authors: Vizitiu Robert Ștefan , Vizitiu Ștefănica Eliza , Burlacu Andrei , Abid Chérifa , Balan Marius Costel , Kaba Nicoleta Elena
Published: September 2024

The implementation of energy-saving measures has a substantial and beneficial impact on the preservation of energy resources as well as the reduction of carbon dioxide emissions. This study focuses on the design and experimental analysis of a water-to-air heat recovery system aimed at capturing waste heat from wastewater and transferring it to a fresh cold air stream using heat pipe technology. The research problem addressed in this study is the efficient recovery of low-grade thermal energy from wastewater, which is often underutilized. The prototype heat recovery unit was designed, manufactured, and tested in the laboratory to assess its performance across various operating conditions. The experimental setup included a system where the primary agent, hot water, was heated to 60 °C and circulated through the evaporator section of the heat recovery unit, while the secondary agent, fresh air, was forced through the condenser section. The system’s performance was evaluated under different air velocities, ranging from 3.5 m/s to 4.5 m/s, corresponding to airflow rates of 207.1 m3/h and 268.6 m3/h, respectively. The study employed analytical methods alongside empirical testing to determine the effectiveness of the heat recovery system, with the global heat transfer coefficient calculated for different scenarios. The efficiency of the system varied between 25% and 51.6%, depending on the temperature and speed of the fresh air stream. The most significant temperature difference observed between the inflow and outflow of the fresh air stream was 16.8 °C, resulting in a thermal output of 1553 W. Additionally, the average (mean) overall heat transfer coefficient of the unit was calculated to be 49 W/m2 K, which aligns with values reported in the literature for similar systems. The results demonstrate the potential of the designed system for practical applications in energy conservation and carbon emission reduction.
Study on Summer Microclimate Analysis and Optimization Strategies for Urban Parks in Xinjiang—A Case Study of Mingzhu Park
Authors: Zhao Zhao , Li Jie , Fu Zongchi
Published: September 2024

To investigate the impact of landscape characteristics on microclimate and thermal comfort in summer urban parks in Xinjiang, we focused on Mingzhu Park in Shihezi City. We collected microclimatic data through field measurements and analyzed the correlations among these factors, the physiological equivalent temperature (PET), and the landscape features. ENVI-met was utilized for microclimate simulations to assess the optimization effects. The results revealed that different landscape features significantly influenced the microclimate and thermal comfort. Trees and grass effectively lowered the temperature, increased humidity, reduced wind speeds, blocked solar radiation, and decreased the PET. Water bodies exposed to sunlight and without shade have a low reflectivity, leading to significant temperature increases. While evaporation can lower the surrounding temperatures, the water surface temperature remains higher than in shaded areas, raising temperatures there. The temperature, humidity, wind speed, and mean radiant temperature show significant correlations. The correlation ranking is as follows: mean radiant temperature (Tmrt) > air temperature (Ta) > relative humidity (RH) > wind speed (Va). After increasing the tree cover and designing dispersed water bodies, the average PET decreased by up to 0.67 °C, with the park experiencing the largest reduction of 1.86 °C. The PET in the eastern built-up area was reduced by 0.35 °C, and in the western built-up area, by only 0.13 °C.
When Artificial Intelligence Tools Meet “Non-Violent” Learning Environments (SDG 4.3): Crossroads with Smart Education
Authors: Okulich-Kazarin Valery , Artyukhov Artem , Skowron Łukasz , Artyukhova Nadiia , Wołowiec Tomasz
Published: September 2024

This paper continues the series of publications of our interdisciplinary research findings at the crossroads of higher education sustainability (SDG 4.3), smart education, and artificial intelligence (AI) tools. AI has begun to be used by universities to increase the quality of higher educational services. AI tools are expected to help university teachers in the teaching process. Students also use AI to help them complete their tasks. At the same time, AI may threaten Sustainable Development Goal 4 (SDG 4). In particular, this is a “blank spot” in the study of AI and non-violent learning environments (SDG 4.3). The aim of the study was to verify competing statistical hypotheses. To achieve this aim, the authors used modern, economically sound methods. The authors processed the responses of 1102 students from eight Eastern European universities using a special electronic questionnaire. The authors statistically processed the student survey results and then tested a pair of conflicting statistical hypotheses. The authors adopted a standard level (α = 0.05) of hypothesis checking. Testing statistical hypotheses led to obtaining two statistically substantiated new scientific facts: (1) The requirement for “non-violent” learning environments does not meet some students’ needs. (2) The number of these students can be up to 31.94%. Summary: The new scientific facts are helpful for further developing world pedagogical theory and practice. They are the basis for forecasting and preparing for managerial actions aimed at SDG 4.3.
Object Skill Advantage in Infants with a Hand Preference
Authors: Marcinowski Emily C. , Michel George F. , Nelson Eliza L.
Published: September 2024

How infants engage with objects changes dramatically over the first year of life. While some infants exhibit a consistent hand preference for acquiring objects during this period, others have no identifiable preference. The goal of this study was to test whether lateralization confers an advantage in the development of early object management skills. We examined whether lateralized infants show different rates of growth in how they interact with multiple objects as compared to infants without a hand preference. In a longitudinal study consisting of seven monthly visits from 6 to 12 months, 303 infants were assessed for their hand preference and object management skill (i.e., holding up to three objects). Group-Based Trajectory Modeling (GBTM) identified the following three hand preference trajectory groups: Left, Right, and No Preference (NP). A Hierarchical Generalized Linear Model (HGLM) with the NP infants as the reference group for statistical comparisons revealed that while all the infants showed similar trends in their object management skills over time, the lateralized infants had an advantage over the non-lateralized infants. The infants in the Right and Left groups transitioned from holding one to two objects more quickly relative to the NP infants. Further research is needed to determine if this early object skill advantage cascades to a more complex handling of multiple objects.
Symmetry-Based Urban Rail Transit Network Planning Using Two-Stage Robust Optimization
Authors: Huang Zhaoguo , Ma Changxi
Published: September 2024

To address the symmetry-related resilience issues of stations and lines in urban rail transit networks, we propose a two-stage robust optimization-based approach for urban rail transit network planning. In this context, resilience is conceptualized as the ability of the network to maintain its operational symmetry under normal and disruptive conditions. Firstly, we used passenger flow distributions as decision variables to construct a two-stage symmetry-based urban rail transit network planning model, aiming to simultaneously minimize the total cost and total operating time of the network while preserving its functional symmetry. Secondly, we designed a hybrid evolutionary algorithm with chromosomes having a two-layer encoding structure, where the Niched Pareto Genetic Algorithm served as the main algorithmic framework, and a Large Neighborhood Search mechanism was designed to optimize the connectivity gene layer of individuals, ensuring the symmetry of network connectivity. Finally, we conducted computational verification on randomly generated instances to confirm the effectiveness of the model and algorithm. The experimental results demonstrated that our method could find two sets of Pareto optimal solutions for cost preference and time preference, thereby preserving the operational symmetry of the network under normal and damaged conditions, as well as reducing the total operating time. This effectively improved the overall efficiency and resilience of the network. Our designed hybrid evolutionary algorithm converged to satisfactory objective values in the early iterations, exhibiting strong search and optimization performance and effectively solving the two-stage symmetry-based urban rail transit network planning model.