bluuebunny/crossref_metadata_2025 huggingface dataset:
A Sacred Place, an Enchanted Space: Crisis and Spiritual Elasticity in the Mountain of the Moon
Authors: Saraiva Clara
Published: September 2023

This article explores the notion of the “magic of a place” and the way a space attracts groups and individuals who follow various forms of vernacular or lived religion and spirituality. The space is Sintra, an “enchanted” mountain facing the westernmost point of Europe, the Roca Cape. Classified by UNESCO as Cultural Landscape, Sintra is a unique place, a “sensuous sacred geography”; its sacredness comes from its natural setting, combined with historical layers of religious use and the way these are nowadays interpreted by individuals who live spirituality as “sensational forms” (configurations of imaginations and sensations in a context of religious and spiritual traditions). Thought of as an encapsulated magical place where innumerous groups perform their ceremonies, meditations, and spiritual retreats, Sintra is a scenario where Tweed’s discussion on the sacredness of a place is highly suitable and transreligiosity and spiritual elasticity are the norm. Furthermore, through the ethnographic data presented, we will see how, within this “spiritual elasticity” directly relating to the astonishing nature of the Sintra mountain, individuals find relief for their personal crises or their collective eco-anxiety.
Introduction to the Special Issue “Plots and Rhetorical Patterns in Religious Narratives”
Authors: Vermander Benoît
Published: September 2023

This Special Issue crisscrosses three theoretical questions that the ten contributions gathered here integrate into a unified subject of investigation [...]
Resonance or Alienation: An Empirical Study on the Influencing Factors of Religious Belief Choices among China’s Generation Z
Authors: Chen Li , Zeng Sheng , Tian Zaizhen
Published: September 2023

The relationship between secularity or modernity and religious belief choices is a hot topic of academic concern. However, there has been scarce attention from the academic community regarding the religious belief choices and driving factors of China’s Generation Z, who have grown up in a secular or modern society. In this paper, we use the Centrality of Religiosity Scale (CRS) and a modified Cost and Rewards Scale to measure this group’s tendency towards “religious centrality”, and investigate the inherent driving factors using logistic regression. The results show that the religiousness of China’s Generation Z is higher than the overall level of religious beliefs in China. Their choice of religious beliefs is the result of a rational consideration and balance of “religious rewards”, but most of them are not driven by the consideration of “afterlife rewards” as the supernatural core factor of religion. Instead, they are driven by the actual benefits brought by the choice of religious beliefs and the factors that affect these benefits, such as religious socialization. This conclusion validates the opinions of Stark and Finke, as well as Hartmut Rosa, and breaks the status quo of single research model and method in previous relevant studies.
Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments
Authors: Xiao Yuhan , Liu Yufei , Luan Kai , Cheng Yuwei , Chen Xieyuanli , Lu Huimin
Published: September 2023

Robust environmental sensing and accurate object detection are crucial in enabling autonomous driving in urban environments. To achieve this goal, autonomous mobile systems commonly integrate multiple sensor modalities onboard, aiming to enhance accuracy and robustness. In this article, we focus on achieving accurate 2D object detection in urban autonomous driving scenarios. Considering the occlusion issues of using a single sensor from a single viewpoint, as well as the limitations of current vision-based approaches in bad weather conditions, we propose a novel multi-modal sensor fusion network called LRVFNet. This network effectively combines data from LiDAR, mmWave radar, and visual sensors through a deep multi-scale attention-based architecture. LRVFNet comprises three modules: a backbone responsible for generating distinct features from various sensor modalities, a feature fusion module utilizing the attention mechanism to fuse multi-modal features, and a pyramid module for object reasoning at different scales. By effectively fusing complementary information from multi-modal sensory data, LRVFNet enhances accuracy and robustness in 2D object detection. Extensive evaluations have been conducted on the public VOD dataset and the Flow dataset. The experimental results demonstrate the superior performance of our proposed LRVFNet compared to state-of-the-art baseline methods.
Terrestrial and Airborne Lidar to Quantify Shrub Cover for Canada Lynx (Lynx canadensis) Habitat Using Machine Learning
Authors: Batchelor Jonathan L. , Hudak Andrew T. , Gould Peter , Moskal L. Monika
Published: September 2023

The Canada lynx is listed as a threatened species, and as such, the identification and conservation of lynx habitats is of significant concern. Lynxes require areas with high amounts of horizontal cover made up of ground vegetation. Lidar offers a robust method of quantifying vegetation structure, and airborne lidar has been acquired across large areas of potential lynx habitat. Unfortunately, airborne lidar is often not able to directly measure understory horizontal cover due to occlusion from the upper branches. Terrestrial lidar does directly measure understory horizontal cover and can be used as training data for larger area models using airborne lidar. In this study, we acquired 168 individual terrestrial lidar scans (TLS) across 42 sites in north-central Washington state. We generated metrics from the single-scan TLS plots using depth maps, a digital cover board, and voxels. Using our TLS metrics as the training data for the airborne lidar acquired for the entire Loomis State Forest, we were able to produce a model using xgboost with 85% accuracy. We believe our study shows that single-scan TLS plots can be used effectively to quantify fine-scale forest structure elements relevant to species habitat, to then inform larger area models using airborne lidar.
Fast and Accurate Generation Method of Geometric Digital Twin Model of RC Bridge with Box Chambers Based on Terrestrial Laser Scanning
Authors: Hu Guotao , Zhou Yin , Xiang Zhongfu , Zhao Lidu , Chen Guicheng , Li Tao , Zhu Jinyu , Hu Kaixin
Published: September 2023

Digital Twin (DT) plays a crucial role in intelligent bridge management, and the geometric DT (gDT) serves as its foundation. Notably, the fast and high-precision generation of bridge gDT models has gained increasing attention. This research presents a method for generating high-precision and fast RC bridges with chambers for gDT using terrestrial laser scanning. The method begins with a proposed fast point cloud data collection technique designed specifically for bridges with internal chambers. Subsequently, Euclidean clustering and grid segmentation algorithms are developed to automatically extract contour features from the sliced point clouds. Finally, a framework based on the Dynamo–Revit reverse modelling method is introduced, enabling the automatic generation of gDT models from the identified point cloud features. To validate the feasibility and accuracy of the proposed method, a concrete variable section bridge is used. A comparison is made between the generated gDT model and the point cloud model in terms of 3D deviation, revealing a maximum deviation of 6.6 mm and an average deviation of 3 mm. These results affirm the feasibility of the proposed method.
Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model
Authors: Afshari Afshin , Vogel Julian , Chockalingam Ganesh
Published: September 2023

The analysis of the near-surface air temperature is vital for many applications such as urban heat islands and climate change studies. In particular, extreme weather events are typically localized and so should the corresponding adaptation measures. However, climate scientists are often confronted with the difficulty of providing reliable predictions at high spatial resolutions in the order of 1 km. We propose to train a convolutional neural network model to emulate the hourly high-resolution near-surface air temperature field simulated by the Weather Research and Forecasting (WRF) software over a period of 18 months. The model is driven by current and past lags of coarse SEVIRI land surface temperature fields. This nowcasting application implements a downscaling of the spatial resolution of the input by about a factor of four, while establishing a correlation between current and past land surface temperature maps and the current near-surface air temperature field. The U-Net variant that is proposed in this study uses regularization to prevent over-fitting and implements a novel space-time approach, where multiple time steps are fed into the model through 3D convolution layers. Besides LST, the model also uses urban density as additional static input to be able to predict the temperature more accurately in urban areas and to improve the generalizability of the trained model. The performance of our U-Net model is assessed via comparison to an MLR benchmark (ridge regularization). The model is shown to be superior on all performance metrics. It achieves a mean absolute error of 1.36 °C versus 1.49 °C for benchmark (a 9% relative improvement) and a root mean square error of 1.77 °C versus 1.91 °C for benchmark (a 7% relative improvement). The absolute error of the model is less than 2 °C for 77% of the prediction points versus 72% for the benchmark (a 7% relative improvement). The improvement over the benchmark is even more significant during extreme hot periods. We demonstrate the generalizability of the approach by testing the trained model on unseen spatial domains.
A Design Scenario Approach for Choosing Protection Works against Rockfall Phenomena
Authors: Taboni Battista , Umili Gessica , Ferrero Anna Maria
Published: September 2023

Proximity remote sensing techniques, both land- and drone-based, allow for a significant improvement of the quality and quantity of raw data employed in the analysis of rockfall phenomena. In particular, the large amount of data these techniques can provide allows for the use of probabilistic approaches to rock mass characterization, with particular reference to block volume and shape definition. These, in return, are key parameters required for a proper rockfall hazard assessment and the optimization of countermeasures design. This study aims at providing a sort of guide, starting from the data gathering phase to the processing, up to the implementation of the outputs in a probabilistic-based scenario, which is able to associate a probability of not being exceeded with total kinetic energy values. By doing so, we were able to introduce a new approach for the choice of design parameters and the evaluation of the effectiveness of mitigation techniques. For this purpose, a suitable case study located in Varaita Valley (Cuneo, Italy) has been selected. The area has been surveyed, and a model of the slope and a digital model of the rock faces have been defined. The results show that a 6.5 m3 block has a probability of not being exceeded of 75%; subsequent simulations show that the level of kinetic energy involved in such a rockfall is extremely high. Some mitigation techniques are discussed.
Remote Sensing Image Target Detection and Recognition Based on YOLOv5
Authors: Liu Xiaodong , Gong Wenyin , Shang Lianlian , Li Xiang , Gong Zixiang
Published: September 2023

The main task of remote sensing image target detection is to locate and classify the targets of interest in remote sensing images, which plays an important role in intelligence investigation, disaster relief, industrial application, and other fields. However, the targets in the remote sensing image scene have special problems such as special perspective, scale diversity, multi-direction, small targets, and high background complexity. In this paper, the YOLOv5 target detection algorithm is improved according to the above characteristics. Aiming at the problem of large target size span in remote sensing images, this paper uses K-Means++ clustering algorithm to eliminate the problem in which the original clustering algorithm is sensitive to initial position, noise, and outliers, and optimizes the instability caused by K-Means clustering to obtain preset anchor frames. Aiming at the redundancy of background information around the location of remote sensing image targets, the large number of small targets, and the denseness of targets, a double IoU-aware decoupling head (DDH) is introduced at the output end to replace the coupled yolo head, which eliminates the interference caused by different task sharing parameters. At the same time, the correlation between positioning accuracy and classification accuracy is improved by the IoU-aware method. The attention mechanism is introduced into the backbone network to optimize the detection and small target detection in complex backgrounds. The mAP of the improved YOLOv5 algorithm is improved by 9%, and the detection effect of small targets and dense targets is significantly improved. At the same time, the paper has achieved good results through the verification of the DIOR remote sensing data set and has also achieved good performance advantages in comparison with other models.
Concurrent Validity and Reliability of Two Portable Powermeters (Power2Max vs. PowerTap) to Measure Different Types of Efforts in Cycling
Authors: Iglesias-Pino Javier , Herrero-Molleda Alba , Saavedra-García Miguel Ángel , García-López Juan
Published: September 2023

The purpose was to assess the concurrent validity and reliability of two portable powermeters (PowerTap vs. Power2Max) in different types of cycling efforts. Ten cyclists performed two submaximal, one incremental maximal and two supramaximal sprint tests on an ergometer, while pedaling power and cadence were registered by both powermeters and a cadence sensor (GarminGSC10). During the submaximal and incremental maximal tests, significant correlations were found for power and cadence data (r = 0.992–0.997 and 0.996–0.998, respectively, p < 0.001), with a slight power underestimation by PowerTap (0.7–1.8%, p < 0.01) and a high reliability of both powermeters (p < 0.001) for measurement of power (ICC = 0.926 and 0.936, respectively) and cadence (ICC = 0.969 and 0.970, respectively). However, during the supramaximal sprint test, their agreement to measure power and cadence was weak (r = 0.850 and −0.253, p < 0.05) due to the low reliability of the cadence measurements (ICC between 0.496 and 0.736, and 0.574 and 0.664, respectively; p < 0.05) in contrast to the high reliability of the cadence sensor (ICC = 0.987–0.994). In conclusion, both powermeters are valid and reliable for measuring power and cadence during continuous cycling efforts (~100–450 W), but questionable during sprint efforts (>500 W), where they are affected by the gear ratio used (PowerTap) and by their low accuracy in cadence recording (PowerTap and Power2Max).
Fs-Laser Fabricated Miniature Fabry–Perot Interferometer in a No-Core Fiber for High-Temperature Applications
Authors: Zhu Chen , Alsalman Osamah , Huang Jie
Published: September 2023

This paper reports a fiber in-line Fabry–Perot interferometer (FPI) fabricated in a no-core fiber using the direct femtosecond laser writing technique for high-temperature sensing applications. Two in-line reflectors are directly inscribed in a no-core fiber to construct a low-finesse FPI. Fringe visibility greater than 10 dB is obtained from the reflection spectra of the fabricated no-core fiber FPIs. Temperature responses of a prototype no-core fiber FPI are characterized up to 1000 °C. The proposed configuration is compact and easy to fabricate, making it attractive for sensing applications in high-temperature harsh environments.
An Improved YOLOv5 Algorithm for Vulnerable Road User Detection
Authors: Yang Wei , Tang Xiaolin , Jiang Kongming , Fu Yang , Zhang Xinling
Published: September 2023

The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model’s convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection.
Outage Analysis of Unmanned-Aerial-Vehicle-Assisted Simultaneous Wireless Information and Power Transfer System for Industrial Emergency Applications
Authors: Cvetković Aleksandra , Blagojević Vesna , Anastasov Jelena , Pavlović Nenad T. , Milošević Miloš
Published: September 2023

In the scenario of a natural or human-induced disaster, traditional communication infrastructure is often disrupted or even completely unavailable, making the employment of emergency wireless networks highly important. In this paper, we consider an industrial Supervisory Control and Data Acquisition (SCADA) system assisted by an unmanned aerial vehicle (UAV) that restores connectivity from the master terminal unit (MTU) to the remote terminal unit (RTU). The UAV also provides power supply to the ground RTU, which transmits the signal to the end-user terminal (UT) using the harvested RF energy. The MTU-UAV and UAV-RTU channels are modeled through Nakagami-m fading, while the channel between the RTU and the UT is subject to Fisher–Snedecor composite fading. According to the channels’ characterization, the expression for evaluating the overall probability of outage events is derived. The impact of the UAV’s relative position to other terminals and the amount of harvested energy on the outage performance is investigated. In addition, the results obtained based on an independent simulation method are also provided to confirm the validity of the derived analytical results. The provided analysis shows that the position of the UAV that leads to the optimal outage system performance is highly dependent on the MTU’s output power.
AutoBar: Automatic Barrier Coverage Formation for Danger Keep Out Applications in Smart City
Authors: Shao Ying , Wang Qiwen , Lu Xingjian , Wang Zhanquan , Zhao E , Fang Shuang , Chen Jianxiong , Kong Linghe , Ghafoor Kayhan Zrar
Published: September 2023

Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection. In this paper, we study a new branch of barrier coverage, namely warning barrier coverage (WBC). Different from the classic barrier coverage, WBC has the inverse protect direction, which moves the sensors surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers. WBC holds a promising prospect in many danger keep out applications for smart cities. For example, a WBC can enclose the debris area in the sea and alarm any approaching ships in order to avoid their damaging propellers. One special feature of WBC is that the target region is usually dangerous and its boundary is previously unknown. Hence, the scattered mobile nodes need to detect the boundary and form the barrier coverage themselves. It is challenging to form these distributed sensor nodes into a barrier because a node can sense only the local information and there is no global information of the unknown region or other nodes. To this end, in response to the newly proposed issue of the formation of barrier cover, we propose a novel solution AutoBar for mobile sensor nodes to automatically form a WBC for smart cities. Notably, this is the first work to trigger the coverage problem of the alarm barrier, wherein the regional information is not pre-known. To pursue the high coverage quality, we theoretically derive the optimal distribution pattern of sensor nodes using convex theory. Based on the analysis, we design a fully distributed algorithm that enables nodes to collaboratively move toward the optimal distribution pattern. In addition, AutoBar is able to reorganize the barrier even if any node is broken. To validate the feasibility of AutoBar, we develop the prototype of the specialized mobile node, which consists of two kinds of sensors: one for boundary detection and another for visitor detection. Based on the prototype, we conduct extensive real trace-driven simulations in various smart city scenarios. Performance results demonstrate that AutoBar outperforms the existing barrier coverage strategies in terms of coverage quality, formation duration, and communication overhead.
Cryo plus Ultrasound Therapy, a Novel Rehabilitative Approach for Football Players with Acute Lateral Ankle Injury Sprain: A Pilot Randomized Controlled Trial
Authors: Ammendolia Antonio , de Sire Alessandro , Lippi Lorenzo , Ammendolia Valerio , Spanò Riccardo , Reggiani Andrea , Invernizzi Marco , Marotta Nicola
Published: September 2023

Background: Acute lateral ankle sprains are common injuries among athletes, but the optimal treatment strategies in elite athletes are still debated. This proof-of-concept study aimed to assess the impact of cryo-ultrasound therapy on the short-term recovery of football players with acute lateral ankle sprains. Methods: Semi-professional football players with grade I or II lateral ankle sprains were randomly assigned to the experimental group (receiving cryo-ultrasound therapy combined with conventional physical therapy) or control group (sham cryo-ultrasound therapy combined with conventional physical therapy). Pain intensity and physical functioning were assessed by the Numeric Rating Scale (NRS) and Foot and Ankle Disability Index (FADI) at baseline (T0) at the end of treatment (T1), after one month (T2), and two months after treatment (T3). Results: After the study intervention, significant between groups differences were reported in terms of pain relief (NRS: 4.08 ± 1.29 vs. 5.87 ± 1.19; p = 0.003) and physical function (FADI: 50.9 ± 10.3 vs. 38.3 ± 11.5; p = 0.021). However, no significant between group differences were reported at T2 and T3. No adverse effects were reported. Conclusions: Cryo-ultrasound therapy combined with conventional physical therapy can accelerate recovery and early return to sport in elite football players with acute lateral ankle sprains. While this study contributes valuable insights into the potential benefits of cryo-ultrasound therapy, further investigations with a longer follow-up are needed to validate and optimize the application of physical agent modalities in the management of ankle injuries.
Effects of Person–Environment Fit on Users’ Willingness to Contribute Knowledge in Virtual Brand Communities
Authors: Qu Li , Liu Cuiyi , Yin Jielin
Published: September 2023

Virtual brand communities and users’ knowledge contributions have become strategic resources for enterprises. It is of great significance to explore the driving mechanism of users’ willingness to contribute knowledge in virtual brand communities to promote the sustainable development of enterprises. Previous studies on the potential drivers of users’ willingness to contribute knowledge placed a great emphasis on either communities or users alone but overlooked the synergistic effects generated by both. Therefore, the purpose of this study is to investigate the effects of person–environment fit on users’ willingness to contribute knowledge. Based on two theories, we develop and test a mediation model to investigate the effects of person–environment (P–E) fit and psychological contract fulfillment on users’ willingness to contribute knowledge in virtual brand communities. Hierarchical regression methods and bootstrap analysis were used to examine data collected from 287 online survey responses. The results indicate that the three constructs of P–E fit, including values–culture (V–C) fit, needs–supplies (N–S) fit and demands–abilities (D–A) fit, have significant positive effects on users’ willingness to contribute knowledge, and transactional and relational psychological contract fulfillments partially mediate these relationships. We provide some theoretical contributions to explore users’ willingness to contribute knowledge in virtual brand communities, and practical implications are also offered. The limitation of this study is that it does not consider the influence of users’ willingness to contribute with regard to actual contribution behavior.
Interoperable, Smart, and Sustainable Urban Energy Systems
Authors: Pastor Raúl , Fraga Anabel , López-Cózar Luis
Published: September 2023

The decarbonization of cities is a priority for the European Union (EU). Fossil fuel and gas supplies need to be replaced with local renewable energy sources with a relevant increase in efficiency within 20 years in order to cap environmental, social, and economic negative impacts, and this cannot do significant harm. In this context, smart city development frameworks focused on mitigation, adaptation, and measurement not only collaborate in providing governance for sustainability but also facilitate the materialization of such sustainability along lifecycle engineering processes for decarbonization solutions. To achieve these goals, we must face the interoperability challenge and make information accessible for engineering and sustainability governance systematically. A starting point is to validate the models, data, and harmonized metadata. In this paper, the authors provide results that validate the utility of a conceptual model for interoperable smart and sustainable urban energy systems (ISSUESs). The validation is carried out for an urban energy system with a high potential for decarbonizing southern European smart cities and integrating several commercial solar technologies using academic bibliography and natural language processing (NLP) techniques.
Ecological Culture and Critical Thinking: Building of a Sustainable Future
Authors: Shutaleva Anna
Published: September 2023

The pursuit of a sustainable future necessitates the integration of critical thinking into environmental education, as it plays a crucial role in equipping individuals with the necessary skills and knowledge to address complex environmental challenges. This article aims to examine the significance of critical thinking in the educational framework for cultivating ecological culture. By exploring the relationship between critical thinking skills and sustainable practices, the study analyzes how critical thinking abilities can contribute to creating a solid foundation for a sustainable future. This article presents the findings of a sociological survey conducted between March and June 2023, focusing on assessing the development of environmental culture competencies among residents of Yekaterinburg City (N = 250). The survey utilized a Google Form as the data collection tool. Statistical analysis was performed using the Vortex program version 10.0. The study findings indicate a significant level of ownership and shared responsibility among residents, suggesting their active engagement as catalysts for positive change in fostering a sustainable and equitable society. The findings of the study revealed that the city’s ecological situation is considered one of the most urgent issues by the citizens of Yekaterinburg. The article proposes a model of ecological culture in academic disciplines and presents a structured approach to raising environmental awareness in conjunction with the integration of critical thinking into academic disciplines.
Critical Reflection in Students’ Critical Thinking Teaching and Learning Experiences
Authors: Indrašienė Valdonė , Jegelevičienė Violeta , Merfeldaitė Odeta , Penkauskienė Daiva , Pivorienė Jolanta , Railienė Asta , Sadauskas Justinas
Published: September 2023

This manuscript deals with the problematic question of how students’ critical reflection is manifested when reflecting on their experiences of learning critical thinking in higher education. Critical reflection is understood as the fusion of personal experience with new knowledge and study content, leading to a new understanding and a new sense of the meaning of oneself, one’s learning, studies, and phenomena in the wider environment. A qualitative research method was chosen to reveal the extent of critical reflection. Sixteen students participated in the research: aged 19–49 at the time of the research, from six higher education institutions, representing eight fields of study. A semi-structured interview was chosen for the research and the qualitative content analysis method for the research data analysis, allowing for valid conclusions upon an objective and systematic review of the transcribed interview text. The findings show that in reflections on the critical thinking teaching and learning experiences, students emphasize the process of interaction with others and interaction with themselves, focusing not on the external object but rather on the subject—the thinking person—and their relation to their own thinking. In reflection on teaching and learning, students emphasize applying theoretical knowledge in practice, modelled by the teacher.
The COVID-19 Pandemic and the Implementation of Sustainable Development Goals: The EU Perspective
Authors: Brzyska Joanna , Szamrej-Baran Izabela
Published: September 2023

The 2030 Agenda, a global model for sustainable development, consists of 17 Sustainable Development Goals (SDGs) that the world is to fulfil by 2030. The achievement of the SDGs has been notably impacted by the COVID-19 pandemic. The objective of this article is to analyze and assess the progress of the European Union, as represented by selected indicators, towards the Sustainable Development Goals in the EU context and the impact of the COVID-19 pandemic. A blend of research methodologies, including desk research and analysis of secondary data from the Eurostat database, was employed in our research. Additionally, statistical methods were utilized to compare the SDG implementation before and after the onset of the COVID-19 pandemic. Even before the pandemic, the EU was making unequal progress toward the SDGs, with some areas needing more concentrated attention. COVID-19 has made it considerably harder for the EU and the rest of the world to accomplish the 2030 Agenda. The study assesses whether the pandemic caused a deviation from the previously set targets. The findings offer valuable insights that can be instrumental in shaping future policies and strategies aimed at attaining the SDGs.
University-Campus-Based Zero-Carbon Action Plans for Accelerating the Zero-Carbon City Transition
Authors: Islam Md. Salman , Liu Gengyuan , Xu Duo , Chen Yu , Li Hui , Chen Caocao
Published: September 2023

After three decades of global climate initiatives, local governments’ capabilities to implement policies and solutions have not always been effective in making the urban environment more resilient and adaptive to climate change. All the previous climatic initiatives and decisions were mostly carried out by governments or affiliated actors on global or regional scales. However, the lack of notable climate actions at the community level is evident in the current crisis of urban sustainability. To drive a radical change toward a zero-carbon transition at the city scale, massive decarbonization is required at the institutional level (academic/nonacademic campus) of a city. Among all the nongovernmental actors, it is always expected that Higher Education Institutes (HEIs) would take the lead in promoting a resilient and sustainable future for the cities through their education, research, and innovation. HEIs’ multidimensional activity resembles the “small scale model of a city” interacting with different subsystems like education, administration, transport, housing, health, etc. However, the present studies were found to be mostly based on specific regions and developed countries. In addition, the previously developed methods of assessing energy consumption and CO2 emissions at the university level lack adaptability for other countries and urban settings. Following the need for a comprehensive method of evaluating energy consumption and accelerating the zero-carbon practice to a broader scale, a new framework is proposed here for a university campus. It can be implemented regardless of the campus type and geographic and weather conditions. After implementing the evaluation methods on a 753-acre campus of Rajshahi University, the campus typology and natural resources were identified. Following that, the behavior patterns of the users in terms of energy usage and waste generation were also determined. Finally, the results show that 1900.71 tons of CO2 was emitted in the academic year 2022. The per-capita CO2 emission was 0.041 tons of CO2. To boost the zero-carbon city transition, three core parameters of scaling-up methods were taken into consideration to evaluate the benefits of zero-carbon campuses. The scalability of the zero-carbon practices was evaluated based on the ideas of (1) expansion—how educating future generations about the environment can have a long-lasting impact, (2) demonstration—adopting innovative practical and technological solutions to exhibit the benefits of zero-carbon practices to society, and (3) collaboration—building strong alliances with state and nonstate actors of the city to promote sustainability through sharing knowledge, innovation, and technology.
Talent Sustainability and Development: How Talent Management Affects Employees’ Intention to Stay through Work Engagement and Perceived Organizational Support with the Moderating Role of Work–Life Balance
Authors: Weng Tzu-Chun , Shen Yun-Hsuan , Kan Te-Tzu
Published: September 2023

The pandemic and the war have caused challenges, creating a shortage of labor and talent crisis. Organizations have reviewed their work patterns to retain talent and increase employee retention. Data are collected from 344 employees working in Taiwan. This study divides the sample into “key talents” and “non-key talents” and aims to examine how talent management, work engagement, and perceived organizational support affect employees’ intention to stay. The results of this study indicate a significant positive relationship between talent management, work engagement, perceived organizational support, and the intention to stay. This study also confirmed the mediating role of work engagement and perceived organizational support in the relationship between talent management and the intention to stay. Furthermore, work–life balance exerts a notable moderating influence on the relationships among work engagement, perceived organizational support, and the intention to stay. The findings of this research reveal that when employees consider themselves “key talents”, talent management is positively and significantly correlated with the intention to stay. Companies must continuously innovate their talent management strategies to meet employees’ high expectations for a favorable work environment and development opportunities, achieving mutual benefit for both the organization and its employees.
Analysis of the Accessibility Improvement Index in Urban Areas through Heritage Buildings Used as Museums—Case Studies in the Region of Murcia (Spain)
Authors: Marín-Nicolás José , Sáez-Pérez Mª Paz , Tajani Francesco , Sica Francesco
Published: September 2023

Equality of opportunity for all people, regardless of their abilities, is a fundamental principle in contemporary society. This includes the ability to use any object, service, or environment. The analysis of universal accessibility in the built environment is a requirement to achieve the full inclusion of society as a whole, both in the urban and architectural spheres. This study is based on the analysis of the current and potential states of accessibility, which makes it possible to obtain the accessibility improvement index, a parameter that identifies how much the accessibility of a physical environment can be improved by removing architectural barriers. The methodology is applied to a sample of 25 heritage buildings used as museums to observe how they function. The results show that the feasibility of barrier removal is higher than 75% in all the buildings in the sample, reaching 100% in some cases. The results obtained are contrasted with other works and highlight the potential of expanding the analysis developed to other urban and built environments to ensure full equality of access to the physical environment.
Can Female Executives Enhance Organizational Resilience? Evidence from China during the COVID-19 Pandemic
Authors: Yang Hangsheng , Tang Min , Huang Ju
Published: September 2023

Organizational resilience is the potential ability of a system to endure adversity, sustain its existing structure, and recover quickly after a shock. However, we know little about the factors that influence organizational resilience in the context of specific emergent situations. The COVID-19 pandemic has provided a new opportunity for research on the influencing factors in organizational resilience. In this study, we explore the impact of female participation in executive teams on organizational resilience on the basis of female executives’ utility, upper echelons theory, and organizational resilience. We use 1755 listed Chinese A-share companies in Shanghai and Shenzhen as our study sample and test them by using OLS models with robust standard errors and Cox risk proportion models. The results suggest that companies with a higher proportion of female executives before the shock will experience fewer losses and will take a shorter amount of time to recover from the attack. By revealing the impact of female executives on organizational resilience, this paper fills the gap in the relevant literature and further explores the practical value and managerial implications.
Integration of Tacit and Explicit Strategies in Sustainable Livelihood Recovery: A Case Study on Project-Affected Communities of a Hydropower Plant in West Java, Indonesia
Authors: Setiawan Iwan , Sundari Ristina Siti , Asdak Chay , Kurnia Ganjar
Published: September 2023

Since the 1970s, large-scale dam construction has become a trend in developing countries. During the 1960–2020 period, 235 large-scale dams were built in Indonesia. However, all of them left a negative socio-economic impact. Explicit strategies to recover project-affected communities’ (PAC’) livelihoods have been implemented but need to be sustained. In 2011, the pumped storage innovation was adopted, and Upper Cisokan, West Java, became the pilot. The recovery of PAC livelihoods is also designed to be sustainable by integrating a “tacit and explicit strategy”. This paper aimed to determine the implementation and impact of this strategic innovation. The research was designed through a survey of 325 PAC families (975 persons) and in-depth interviews with 32 informants. Data were analyzed using descriptive statistics and dialectics. The result revealed that strategy integration could speed up the post-resettlement livelihood recovery time and collaborate with various local institutions in the academics, businessmen, community, government, media (ABCGM) scheme. In addition, increasing the number of PAC livelihoods by 155 percent and expanding the diversity of livelihoods from agricultural domination to MSMEs and the non-agricultural sector. The involvement of women and youth in livelihood recovery has also increased by 85 percent, especially in micro-, small, and medium enterprises (MSMEs) and the non-agricultural sector.
Resilient and Sustainable Housing Models against Climate Change: A Review
Authors: Ruíz Michelle A. , Mack-Vergara Yazmin L.
Published: September 2023

Most of the world’s population resides in urban areas, and it is expected that this will continue to be the case in the future. These urban areas face enormous challenges such as climate change, economic instability and inequality. Housing is considered a basic unit comprising cities, and, as such, this study contextualized the concepts of urban resilience and sustainability against climate change such that housing models that respond to these concepts could be identified. As a result of an extensive literature review, three resilient housing models and four sustainable housing models are presented and discussed with a focus on the main characteristics necessary for meeting urban resilience and sustainability against climate change. When analyzing the housing models considered resilient, the need to establish housing on safe land stood out and concrete was a common building material among these housing models. In addition, the use of alternative sources of water and energy supply was prioritized. On the other hand, sustainable housing models were developed by local entities and certified by various initiatives. In addition, these models account for the climatic specifications of their location, energy and water efficiency, and quality of the indoor environment, and promote the use of local materials and resources. The use of durable materials was considered a requirement for both resilience and sustainability. The results of this study are a contribution to the process of strengthening housing against the challenge posed by climate change in a timely and adequate manner considering both urban resilience and sustainability aspects.
Research on the Rapid Recognition Method of Electric Bicycles in Elevators Based on Machine Vision
Authors: Zhao Zhike , Li Songying , Wu Caizhang , Wei Xiaobing
Published: September 2023

People are gradually coming around to the idea of living a low-carbon lifestyle and using green transportation, and given the severe urban traffic congestion, electric bicycle commuting has taken over as the preferred mode of short-distance transportation for many. Since batteries are used to power electric bicycles, there are no greenhouse gas emissions while they are in use, which is more in line with the requirement for sustainable development around the world. The public has been increasingly concerned about the safety issues brought on by electric bicycles as a result of the industry’s quick development and the rapid increase in the number of electric bicycles worldwide. The unsafe operation of the elevator and the safety of the building have been seriously compromised by the unauthorized admission of electric bicycles into the elevator. To meet the need for fast detection and identification of electric bicycles in elevators, we designed a modified YOLOv5-based identification approach in this study. We propose the use of the EIoU loss function to address the occlusion problem in electric bicycle recognition. By considering the interaction ratio and overlap loss of the target frames, we are able to enhance localization accuracy and reduce the missed detection rate of occluded targets. Additionally, we introduce the CBAM attention mechanism in both the backbone and head of YOLOv5 to improve the expressive power of feature maps. This allows the model to prioritize important regions of the target object, leading to improved detection accuracy. Furthermore, we utilize the CARAFE operator during upsampling instead of the nearest operator in the original model. This enables our model to recover details and side information more accurately, resulting in finer sampling results. The experimental results demonstrate that our improved model achieves an mAP of 86.35 percent, a recall of 81.8 percent, and an accuracy of 88.0 percent. When compared to the original model under the same conditions, our improved YOLOv5 model shows an average detection accuracy increase of 3.49 percent, a recall increase of 5.6 percent, and an accuracy increase of 3.5 percent. Tests in application scenarios demonstrate that after putting the model on the hardware platform Jeston TX2 NX, stable and effective identification of electric bicycles can be accomplished.
The Equivalence Conditions of Optimal Feedback Control-Strategy Operators for Zero-Sum Linear Quadratic Stochastic Differential Game with Random Coefficients
Authors: Tang Chao , Liu Jinxing
Published: September 2023

From the previous work, when solving the LQ optimal control problem with random coefficients (SLQ, for short), it is remarkably shown that the solution of the backward stochastic Riccati equations is not regular enough to guarantee the robustness of the feedback control. As a generalization of SLQ, interesting questions are, “how about the situation in the differential game?”, “will the same phenomenon appear in SLQ?”. This paper will provide the answers. In this paper, we consider a closed-loop two-person zero-sum LQ stochastic differential game with random coefficients (SDG, for short) and generalize the results of Lü–Wang–Zhang into the stochastic differential game case. Under some regularity assumptions, we establish the equivalence between the existence of the robust optimal feedback control strategy operators and the solvability of the corresponding backward stochastic Riccati equations, which leads to the existence of the closed-loop saddle points. On the other hand, the problem is not closed-loop solvable if the solution of the corresponding backward stochastic Riccati equations does not have the needed regularity.
MHD Free Convection Flows for Maxwell Fluids over a Porous Plate via Novel Approach of Caputo Fractional Model
Authors: Aslam Khadeja , Zafar Azhar Ali , Shah Nehad Ali , Almutairi Bander
Published: September 2023

The ultimate goal of the article is the analysis of free convective flow of an MHD Maxwell fluid over a porous plate. The study focuses on understanding the dynamics of fluid flow over a moving plate in the presence of a magnetic field, where the magnetic lines of force can either be stationary or in motion along the plate. Further, we will investigate the heat and mass transfer characteristics of the system under specific conditions: constant species and thermal conductivity as functions of time. The study involves a symmetric temperature distribution that provides heat on both sides of the plane. Our analysis includes the study of the model for different instances of plate motion and variations in temperature. The fluid dynamics of the system are mathematically described using a system of fractional-order partial differential equations. To make the model independent of geometric units, dimensionless variables are introduced. Moreover, we employ the concept of fractional-order derivative operators in the sense of Caputo, which introduces a fractional dimension to the equations. Additionally, the integral Laplace transform and numerical algorithms are utilized to solve the problem. Finally, by using graphical analysis the contribution of physical parameters on the fluid dynamics is discussed and valuable findings are documented.