bluuebunny/crossref_metadata_2025 huggingface dataset:
A Spatial-Temporal Graph Convolutional Recurrent Network for Transportation Flow Estimation
Authors: Drosouli Ifigenia , Voulodimos Athanasios , Mastorocostas Paris , Miaoulis Georgios , Ghazanfarpour Djamchid
Published: August 2023

Accurate estimation of transportation flow is a challenging task in Intelligent Transportation Systems (ITS). Transporting data with dynamic spatial-temporal dependencies elevates transportation flow forecasting to a significant issue for operational planning, managing passenger flow, and arranging for individual travel in a smart city. The task is challenging due to the composite spatial dependency on transportation networks and the non-linear temporal dynamics with mobility conditions changing over time. To address these challenges, we propose a Spatial-Temporal Graph Convolutional Recurrent Network (ST-GCRN) that learns from both the spatial stations network data and time series of historical mobility changes in order to estimate transportation flow at a future time. The model is based on Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) in order to further improve the accuracy of transportation flow estimation. Extensive experiments on two real-world datasets of transportation flow, New York bike-sharing system and Hangzhou metro system, prove the effectiveness of the proposed model. Compared to the current state-of-the-art baselines, it decreases the estimation error by 98% in the metro system and 63% in the bike-sharing system.
Rail Corrugation Index Development by Sound-Field Excitation on the Carriage Floor of In-Service Train
Authors: Hsu Wei-Lun , Chang Chia-Ming
Published: August 2023

The steel rail and wheel in the railway system offer a high precision and smooth-running surface. Nevertheless, the point of contact between the rail and wheel presents a critical area that can give rise to rail corrugation. This phenomenon can potentially elevate sound and vibration levels in the vicinity considerably, necessitating advanced monitoring and assessment measures. Recently, many efforts have been directed towards utilizing in-service trains for evaluating rail corrugation, and the evaluation has primarily relied on axle-box acceleration (ABA). However, the ABA measurements require a higher threshold for vibration detection. This study introduces a novel approach to rail corrugation detection by carriage floor acceleration (CFA), aimed at lowering the detection threshold. The method capitalizes on the acceleration data sensed on the carriage floor, which is induced by the sound pressure (e.g., sound-field excitation) generated at the wheel–rail contact point. An exploration of the correlation between these datasets is undertaken by simultaneously measuring both ABA and CFA. Moreover, a pivotal aspect of this research is the development of the eigenfrequency rail corrugation index (E-RCI), a mechanism that culminates energy around specific eigenfrequencies by CFA. Through this index, a focused analysis of rail corrugation patterns is facilitated. The study further delves into the stability, repeatability, and sensitivity of the E-RCI via varied measurement scenarios. Ultimately, the CFA-based rail corrugation identification is verified, establishing its practical applicability and offering a distinct approach to detecting and characterizing rail corrugation phenomena. This study has introduced an innovative methodology for rail corrugation detection using CFA, with the principal objective of lowering the detection threshold. This approach offers an efficient measurement technique for identifying rail corrugation areas, thereby potentially reducing maintenance costs and enhancing efficiency within the railway industry.
Research on an Adaptive Method for the Angle Calibration of Roadside LiDAR Point Clouds
Authors: Wen Xin , Hu Jiazun , Chen Haiyu , Huang Shichun , Hu Haonan , Zhang Hui
Published: August 2023

Light Detection and Ranging (LiDAR), a laser-based technology for environmental perception, finds extensive applications in intelligent transportation. Deployed on roadsides, it provides real-time global traffic data, supporting road safety and research. To overcome accuracy issues arising from sensor misalignment and to facilitate multi-sensor fusion, this paper proposes an adaptive calibration method. The method defines an ideal coordinate system with the road’s forward direction as the X-axis and the intersection line between the vertical plane of the X-axis and the road surface plane as the Y-axis. This method utilizes the Kalman filter (KF) for trajectory smoothing and employs the random sample consensus (RANSAC) algorithm for ground fitting, obtaining the projection of the ideal coordinate system within the LiDAR system coordinate system. By comparing the two coordinate systems and calculating Euler angles, the point cloud is angle-calibrated using rotation matrices. Based on measured data from roadside LiDAR, this paper validates the calibration method. The experimental results demonstrate that the proposed method achieves high precision, with calculated Euler angle errors consistently below 1.7%.
K-XMSS and K-SPHINCS+: Enhancing Security in Next-Generation Mobile Communication and Internet Systems with Hash Based Signatures Using Korean Cryptography Algorithms
Authors: Sim Minjoo , Eum Siwoo , Song Gyeongju , Yang Yujin , Kim Wonwoong , Seo Hwajeong
Published: August 2023

As Mobile Communication and Internet Systems (MCIS) have rapidly developed, security issues related to MCIS have become increasingly important. Therefore, the development and research of security technologies for mobile communication and internet systems are actively being conducted. Hash-Based Signature (HBS) uses a hash function to construct a digital signature scheme, where its security is guaranteed by the collision resistance of the hash function used. To provide sufficient security in the post-quantum environment, the length of hash should be satisfied for the security requirement. Modern HBS can be classified into stateful and stateless schemes. Two representative stateful and stateless HBS are eXtended Merkle Signature Scheme(XMSS) and SPHINCS+, respectively. In this paper, we propose two HBS schemes: K-XMSS and K-SPHINCS+, which replace internal hash functions of XMSS and SPHINCS+ with Korean cryptography algorithms. K-XMSS is a stateful signature, while K-SPHINCS+ is its stateless counterpart. We showcase the reference implementation of K-XMSS and K-SPHINCS+ employing Lightweight Secure Hash (LSH) and two hash functions based on block ciphers (i.e., CHAM and LEA) as the internal hash function. In addition, K-XMSS and K-SPHINCS+ using Advanced Vector Extensions 2 (AVX2) have been provided, demonstrating that they can be optimized for better performance using advanced implementation techniques than previous approaches.
Clutter Effect Investigation on Co-Polarized Chipless RFID Tags and Mitigation Using Cross-Polarized Tags, Analytical Model, Simulation, and Measurement
Authors: Alam Jahangir , Khaliel Maher , Zheng Feng , Solbach Klaus , Kaiser Thomas
Published: August 2023

Chipless radio frequency identification (RFID) technology is expected to replace barcode technology due to its ability to read in non-line-of-sight (NLOS) situations, long reading range, and low cost. Currently, there is extensive research being conducted on frequency-coded (FC) co-polarized radar cross-section (RCS)-based tags, which are widely used. However, detecting co-polarized chipless RFID tags in cluttered environments is still a challenge, as confirmed by measuring two co-polarized tags in front of a perfect metal reflector (30.5cm×22.5cm). To address this challenge, a realistic mathematical model for a chipless RFID system has been developed that takes into account the characteristics of the reader and the tag, as well as reflections from cluttered objects. This extensive mathematical model developed for linear chipless RFID systems in clutter scenarios holds the potential to greatly assist researchers in their exploration of RCS-based tags. By relying solely on simulations, this model provides a tool to effectively analyze and understand RCS-based tags, ultimately simplifying the process of generating more authentic tag designs. This model has been simulated and verified with measurement results by placing a single flat metal reflector behind two co-polarized one-bit designs: a dipole array tag and a square patch tag. The results showed that the interfering signal completely overlaps the ID of the co-polarized tag, severely limiting its detectability. To solve this issue, the proposed solution involves reading the tag in cross-polarization mode by etching a diagonal slot in the square patch tag. This proposed tag provides high immunity to the environment and can be detected in front of both dielectric and metallic objects.
A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train
Authors: Malekjafarian Abdollah , Sarrabezolles Chalres-Antoine , Khan Muhammad Arslan , Golpayegani Fatemeh
Published: August 2023

In this paper, a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. An Artificial Neural Network (ANN) algorithm is developed that works with the energies of the train acceleration responses. A numerical model of a half-car train coupled with a track profile is employed to simulate the train vertical acceleration. The energy of acceleration signals measured from 100 traversing trains is used to train the ANN for healthy track conditions. The energy is calculated every 15 m along the track, each of which is called a slice. In the monitoring phase, the trained ANN is used to predict the energies of a set of train crossings. The predicted energies are compared with the simulated ones and represented as the prediction error. The damage is modeled by reducing the soil stiffness at the sub-ballast layer that represents hanging sleepers. A damage indicator (DI) based on the prediction error is proposed to visualize the differences in the predicted energies for different damage cases. In addition, a sensitivity analysis is performed where the impact of signal noise, slice sizes, and the presence of multiple damaged locations on the performance of the DI is assessed.
Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm
Authors: Qiu Shaoming , Zhao Jiancheng , Zhang Xuecui , Li Ao , Wang Yahui , Chen Fen
Published: August 2023

Sensor nodes are widely distributed in the Internet of Things and communicate with each other to form a wireless sensor network (WSN), which plays a vital role in people’s productivity and life. However, the energy of WSN nodes is limited, so this paper proposes a two-layer WSN system based on edge computing to solve the problems of high energy consumption and short life cycle of WSN data transmission and establishes wireless energy consumption and distance optimization models for sensor networks. Specifically, we propose the optimization objective of balancing load and distance factors. We adopt an improved sparrow search algorithm to evenly distribute sensor nodes in the system to reduce resource consumption, consumption, and network life. Through the simulation experiment, our method is illustrated, effectively reducing the network’s energy consumption by 26.8% and prolonging the network’s life cycle.
Advanced IoT Pressure Monitoring System for Real-Time Landfill Gas Management
Authors: Fay Cormac D. , Healy John P. , Diamond Dermot
Published: August 2023

This research presents a novel stand-alone device for the autonomous measurement of gas pressure levels on an active landfill site, which enables the real-time monitoring of gas dynamics and supports the early detection of critical events. The developed device employs advanced sensing technologies and wireless communication capabilities, enabling remote data transmission and access via the Internet. Through extensive field experiments, we demonstrate the high sampling rate of the device and its ability to detect significant events related to gas generation dynamics in landfills, such as flare shutdowns or blockages that could lead to hazardous conditions. The validation of the device’s performance against a high-end analytical system provides further evidence of its reliability and accuracy. The developed technology herein offers a cost-effective and scalable solution for environmental landfill gas monitoring and management. We expect that this research will contribute to the advancement of environmental monitoring technologies and facilitate better decision-making processes for sustainable waste management.
Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication
Authors: Lazar Razvan-Gabriel , Pauca Ovidiu , Maxim Anca , Caruntu Constantin-Florin
Published: August 2023

A suitable control architecture for connected vehicle platoons may be seen as a promising solution for today’s traffic problems, by improving road safety and traffic flow, reducing emissions and fuel consumption, and increasing driver comfort. This paper provides a comprehensive overview concerning the defining levels of a general control architecture for connected vehicle platoons, intending to illustrate the options available in terms of sensor technologies, in-vehicle networks, vehicular communication, and control solutions. Moreover, starting from the proposed control architecture, a solution that implements a Cooperative Adaptive Cruise Control (CACC) functionality for a vehicle platoon is designed. Also, two control algorithms based on the distributed model-based predictive control (DMPC) strategy and the feedback gain matrix method for the control level of the CACC functionality are proposed. The designed architecture was tested in a simulation scenario, and the obtained results show the control performances achieved using the proposed solutions suitable for the longitudinal dynamics of vehicle platoons.
Influence of Annealing on Polymer Optical Fiber Bragg Grating Inscription, Stability and Sensing: A Review
Authors: Qu Hang , Huang Weiyuan , Lin Zhoupeng , Cheng Xin , Min Rui , Teng Chuanxin , Caucheteur Christophe , Hu Xuehao
Published: August 2023

This article reviews recent research progress on the annealing effects on polymer optical fibers (POFs), which are of great importance for inscription, stability and sensing applications of fiber Bragg gratings (FBGs) in POFs due to their unique properties related to polymer molecular chains. In this review, the principle of annealing to reduce frozen-in stress in POFs drawing and different annealing timings are firstly summarized. Then, the annealing methods for POFs are introduced under several different conditions (temperature, humidity, strain, stress and solution). Afterwards, the principle of FBGs and several inscription techniques are reported. Subsequently, the annealing effects on the properties of POFs and polymer optical fiber Bragg gratings (POFBGs) quality are discussed. Finally, the influence of annealing on POFBG sensitivity is summarized. Overall, this paper provides a comprehensive overview of annealing techniques and their impact on both POFs and POFBGs. We hope that it will highlight the important progress made in this field.
Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments
Authors: Yang Xinyi , Ahemd Hafiz Usman , Huang Ying , Lu Pan
Published: August 2023

The contribution of autonomous vehicles to traffic is one of the key aspects of future ground transportation in smart cities. Autonomous vehicles are able to provide many benefits, but some benefits can only provide advantages if these vehicles comprise a large percent of on the road/driven vehicles, which may take decades. Until then, the robotic drivers in autonomous vehicles will share the same road system with human divers in a mixed-driver environment where the majority of road accidents for autonomous vehicles are associated with the operational inconsistency of human drivers. In this paper, a cumulatively anticipative car-following model (which considers cumulative influences from multiple preceding vehicles) is developed to potentially improve the safety of autonomous vehicles in mixed-driver environments that benefit from enhanced communication between the autonomous vehicles and other components in the transportation system. Through intensive simulations (200 simulations), this study comprehensively evaluates four models including the cumulative anticipative car-following model, the Wiedemann 99 model, adaptive cruise control, and the cooperative adaptive cruise control model. Across 10 scenarios and five speed limits (24.59–33.53 m/s), the cumulative anticipative car-following model consistently demonstrates superior conflict reduction, with average, maximum, and minimum conflict percentages ranging from 77.69% to 91.97% against the Wiedemann 99 model, 67.00% to 93.94% against the adaptive cruise control model, and 69.17% to 93.25% against the cooperative adaptive cruise control model. Notably, the cooperative adaptive cruise control model exhibits suboptimal performance, especially in mixed-driver settings. The cumulative anticipative car-following model also enhances vehicle mobility, reducing average stops by up to 93.54%, 91.74%, 92.04%, 88.60%, and 91.35% in comparison to the other three models at speeds of 24.59 m/s, 26.82 m/s, 29.06 m/s, 31.29 m/s, and 33.53 m/s. Overall, the cumulative anticipative car-following model holds significant potential for conflict reduction and traffic enhancement.
Las Voces de Mujercitas Empoderadas: Documenting Support for Youth with Youth Participatory Action Research
Authors: Ellington Alycia , Hice-Fromille Theresa , London Rebecca A. , Cariño Theresa M. , Otero Lynda
Published: August 2023

Youth participatory action research (YPAR) is a critical approach that engages youth as collaborative partners in research. It acknowledges the unique expertise that youth have on the adversities and assets that are present in their familiar systems, such as schools and the community. These projects are often designed to identify and address community problems; however, our projects with local youth aimed to shed light on a pre-existing community asset, Salud y Cariño, an after-school community organization, and a particular moment in time, namely the pandemic shelter-in-place. The mission and epistemologies of the organization set forth by the co-founder and Executive Director informed our partnership and guided our approach to this work. Utilizing qualitative methodologies, the authors (a faculty member, two graduate students, co-founder and director of a local non-profit, and a high school senior) collaboratively designed and implemented an interview-style documentary and photovoice projects, which garnered testimonies on participants’ experiences during the COVID-19 pandemic regarding school and the community organization. By centering the perspectives of participating Latinx girls and non-binary youth, we demonstrate the effects of this local community organization on its participants during and after the COVID lockdown, and what it means to the youth they serve. The identified themes associated with program participation during this time include the following: (1) building community and a family, (2) creating a welcoming safe space, and (3) infusing love and happiness into everyday activities. We conclude by reflecting on the process of building these collaborative projects and their implementation. Our reflections and findings contribute new insights into utilizing YPAR approaches to research and showcase leading community assets and actors.
What Digital Storytelling Means to the New Generation of Researchers
Authors: Liguori Antonia , Sung Karen Jiyun , McLaughlin Lucy , Stuttle Jennifer
Published: August 2023

A new, hybrid way of conceiving Digital Storytelling (DS) in applied research is presented here as an essential trigger to challenge, expand, and eventually re-frame the way in which DS is currently codified. The three methodological perspectives described in this paper share a common understanding of practice-as-research. They position themselves within three distinct disciplines—illustration, animation, and the creative arts in education—but have a strong commitment to interdisciplinarity. Each of them is trying to respond to a specific cultural and personal issue (e.g., sense of identity, mental health, attainment within competitive environments, etc.) and also serves as a prompt to reflect on a potential new aspect of DS as research, linked to the how, the what, and the why of these multiple and complementary applications. The intention of this paper is not to propose one alternative way of operating, but to inspire other researchers wanting to apply this approach in their work to constantly challenge any pre-conceived form and process, while prioritizing the democratization of knowledge production and informing their research process with co-design and participatory principles. The take-away message from these three case studies is that DS will be, in fact, embraced by the new generations of researchers as a sustainable practice all the more, as its many disruptions will generate spaces for co-creation and self-representation to emerge, and will stimulate everyone involved in the research process to challenge their own way of thinking and to go beyond what was codified by others and by their own practice too.
Gathering Stories: Creating Spaces for Young Women to Connect and Build Community through Multimodal Storytelling
Authors: Shackelford Laura , Kothari Ammina , vanMeenen Karen
Published: August 2023

Digital storytelling prioritizes real-time connections, story creation, contextual adaptability, multi-media expression, and accessibility. This article discusses the unrecognized affordances and value of digital storytelling practices for teens living in precarious (neo)colonial lifeworlds. We review the workshop methods developed as designers and leaders of “Gathering Stories: A Digital Storytelling Workshop for Young Women” in July 2021 to enliven and illuminate high school students’ voices while also addressing social, emotional, and affective experiences and needs during the pandemic. The article details how we co-realized spaces where teens’ lived experience of gathering and the draw of story were the driving forces for their diverse storytelling practices. Identifying positive outcomes for the first iteration of the workshop, we also identify challenges that will inform future iterations of the workshop, such as structural dimensions of intersectionality and the challenges predicative AI such as ChatGPT poses to such efforts to prioritize experiential dimensions of learning through storytelling.
Mandatory Disclosure of Corporate Social Responsibility and the Quality of Earnings Management
Authors: Fan Qunpeng , Chun Dongphil , Ban Qi , Jiang Yitong , Li Huiting , Xu Luyuan
Published: August 2023

Using the exogenous shock caused by the mandatory corporate social responsibility (CSR) information disclosure policy in 2008, this paper examines the impact of mandatory CSR information disclosure on the earnings management activities of listed firms in China from the perspective of external corporate regulation based on the Difference-in-Differences (DID) method. The results show that mandatory CSR information disclosure can significantly improve the quality of firms’ earnings management. The mechanism analysis shows that the policy’s enhancement of the effectiveness of external regulation by regulators and the media played an important role in curbing firms’ earnings management activities. The heterogeneity analysis shows that the inhibitory effect of mandatory CSR disclosure policy on firms’ earnings management activities is better in firms with lower analyst coverage and lower institutional ownership. The study further extends the mechanism of the impact of mandatory CSR disclosure on firms’ earnings management activities, and provides practical guidance on how to improve the quality of firms’ earnings management and enhance the efficiency of corporate governance.
Understanding the Environmental Security Perceptions of the European Union’s Security Actors
Authors: Kantemnidis Dimitrios , Botetzagias Iosif
Published: August 2023

In this paper, we are interested in assessing the different environmental security concepts, policies, and actions of actors involved in the Common Security and Defence Policy (CSDP). Through exploratory qualitative research, we interviewed key stakeholders who formulate both the climate security discourse and its policy outcomes. Aiming at finding the different perceptions and practices among various actors, we conducted 27 qualitative interviews with practitioners from 17 different institutions, such as EU bodies and agencies, think tanks, and ministries of defense (MoDs). This article discusses the divergence between announced environmental and climate security strategies and policies related to their actual results. Notably, the findings indicate that the effectiveness of the political directives compared with the practices and the developed capabilities around the environment–security nexus are dependent on individual initiatives and efforts that a developing community of practitioners is attempting to carry out. Our study indicates that tailored environmental security policies and actions are needed to motivate both practitioners and policymakers to develop downstream methods and programs that are suited to resolving security-related challenges associated with environmental issues and especially climate change.
Change Characteristics of Soil Organic Carbon and Soil Available Nutrients and Their Relationship in the Subalpine Shrub Zone of Qilian Mountains in China
Authors: Zhang Yue , Jia Wenxiong , Yang Le , Zhu Guofeng , Lan Xin , Luo Huifang , Yu Zhijie
Published: August 2023

Studying the spatial and temporal distribution of soil organic carbon (SOC) content in high-altitude mountainous areas and its correlation with soil nutrients provides a basis for understanding soil carbon stocks and the factors affecting the local carbon cycle. Based on soil samples collected from a semi-sunny slope and semi-shady slope in the subalpine shrub zone of the eastern Qilian Mountains from May to October 2019, we studied the temporal and spatial changes in SOC and soil available nutrients and their relationships. The results showed that SOC content and soil nutrients were greater on the semi-shady slope than on the semi-sunny slope during the growing season and decreased with an increase in soil depth in different slope directions, showing obvious surface aggregation. The soil available nitrogen (SAN) content was consistent with the SOC content and exhibited greater synchronization. SOC was significantly positively correlated with soil available nutrients in the study area during the whole growing season. However, the correlation between SOC and soil nutrients varied among the different soil layers and slope orientations. The SOC content was more obviously correlated with the SAN content in the soil layer at a depth of 30–40 cm (r = 0.67, p < 0.05) on the semi-shady slope. The SOC content was more obviously correlated with soil-available phosphorus (SAP) content in the soil layer at a depth of 30–40 cm (r = 0.57) on the semi-sunny slop. The SOC content was more obviously correlated with the SAP content in the soil layer at a depth of 60–70 cm (r = 0.55) and with the soil-available potassium (SAK) content in the soil layer at a depth of 70–80 cm (r = 0.84) on the semi-sunny slope.
Harmonizing Heritage and Artificial Neural Networks: The Role of Sustainable Tourism in UNESCO World Heritage Sites
Authors: Bozkurt Alper , Şeker Ferhat
Published: August 2023

The classification of the United Nations Educational, Scientific, and Cultural Organization (UNESCO) World Heritage Sites (WHS) is essential for promoting sustainable tourism and ensuring the long-term conservation of cultural and natural heritage sites. Therefore, two commonly used techniques for classification problems, multilayer perceptron (MLP) and radial basis function (RBF) neural networks, were utilized to define the pros and cons of their applications. Then, according to the findings, both correlation attribute evaluator (CAE) and relief attribute evaluator (RAE) identified the region and date of inscription as the most prominent features in the classification of UNESCO WHS. As a result, a trade-off condition arises when classifying a large dataset for sustainable tourism between MLP and RBF regarding evaluation time and accuracy. MLP achieves a slightly higher accuracy rate with higher processing time, while RBF achieves a slightly lower accuracy rate but with much faster evaluation time.
Impact of Video Games, Gamification, and Game-Based Learning on Sustainability Education in Higher Education
Authors: Pineda-Martínez María , Llanos-Ruiz David , Puente-Torre Paula , García-Delgado Miguel Ángel
Published: August 2023

Today, the European Union and the governments of its constituent countries are focused on the development of the Sustainable Development Goals (SDGs) and the 2030 agenda—something that has been translated into education itself. Video games, gamification, and game-based learning have become different strategies and tools to enhance the learning process and some of the growing approaches used by teachers to develop sustainable education in classrooms. This research aims to analyze the characteristics to promote sustainability in education using games and technology, specifically in terms of learning benefits for higher education. A systematic review of the literature was conducted following the PRISMA methodology. At first, 2025 documents were found; after the filtering phases, the number of articles was reduced to 9, which were subsequently analyzed in depth. The results indicated that the benefits of the use of games mediated by technologies include the following: it favors education for sustainability, and it promotes the educational inclusion and the work of various social skills, such as collaborative and cooperative work. Also, there was an increase in the number of publications between 2019 and 2023, reflecting the growing interest in the topic. However, there are some research gaps in this field.
Sun-Shading Sails in Courtyards: An Italian Case Study with RayMan
Authors: Fabbri Kristian , Antonini Ernesto , Marchi Lia
Published: August 2023

Forecasts of a drastic increase in temperatures in the coming decades are driving the adoption of design strategies and solutions to improve the livability of urban environments. Increasing attention is being paid to the thermal comfort of open spaces by both designers and researchers. Nature-based solutions and man-made devices to improve the comfort of outdoor spaces during summer are spreading, but effective, easy simulation and design support tools for this purpose are still lacking, as most of the available software such as ENVI-met or RayMan cannot model such devices. As Physiological Equivalent Temperature (PET) is one of the most relevant and comprehensive indicators of Outdoor Thermal Comfort (OTC), this study aims to investigate PET variations of different artificial shading systems and propose a simplified methodology for assessing them through analytical simulations with RayMan software. When modeling the shading elements, the trick adopted for this purpose is to associate different cloud densities with the shading provided by the screens, thus overcoming a gap that affects the software. The procedure is digitally tested in a covered courtyard case study in Bologna (Italy). Diverse options proposed by the designers for textile screening materials have been compared, showing that these reduce by at least 1 °C the PET-gauged thermal stress. Beyond specific results, the main outcome of this study is the procedure developed to simulate sun-shading sail effects on OTC by means of RayMan, which can support designers in planning effective solutions for open space livability in summertime.
Is Africa Left behind in the Global Climate Finance Architecture: Redefining Climate Vulnerability and Revamping the Climate Finance Landscape—A Comprehensive Review
Authors: Tamasiga Phemelo , Molala Malesela , Bakwena Malebogo , Nkoutchou Hugue , Onyeaka Helen
Published: August 2023

African countries contribute less than 4% of global carbon emissions and are susceptible to the repercussions of climate change due to pre-existing challenges such as poverty, unemployment, and food insecurity. The Paris Agreement underscores the need for climate finance to support resilience and low-carbon investments. However, African nations struggle to access adequate funds, hindering effective adaptation and mitigation. Against this background, a bibliometric analysis was conducted on climate finance literature for the period 2007–2023 in order to explore the publication trends, emerging themes, and future research directions. Merging 91 documents from Web of Science and 94 from Scopus yielded a dataset of 139 records. Web of Science experienced a 10.58% publication growth rate, while Scopus had a higher publication growth rate of 13.18%. The merged dataset’s publication growth rate was 13.88%, reflecting consistent contributions. The surge in publications from 2019 to 2023 points to intensified discussions on climate change and associated policies. International collaboration between authors is evident, with Web of Science at 37.76%, Scopus at 28.7%, and the merged dataset at 26.62%. Temporally, 2007–2023 saw escalating interest, especially post-2012, reflecting the evolution of climate change and renewable energy policies. Authors were ranked based on article count and fractionalized ranks, with Chirambo D being the lead author in the field of climate finance in Africa. Key articles advocated for supplementary fund integration into government budgets. The UK, USA, and Germany topped in citations, reflecting the availability of research funding, expertise, and collaborations. Leading sources included Climate Policy and Climate and Development. Keyword co-occurrence identified five emerging thematic trends, contributing to an in-depth understanding of climate finance literature’s dynamics and future directions.
Design of a Green Supply Chain Based on the Kano Model Considering Pricing
Authors: Sheikh Azadi Amir Hossein , Shamsi Nesary Vahid , Kebriyaii Omid , Khalilzadeh Mohammad , Antucheviciene Jurgita
Published: August 2023

Nowadays, the design of supply chain networks should be based on environmental issues as well as the needs of customers since the main driver of a supply chain network is customers. Continuous innovation of products requires understanding the features that are most important to customers, and product pricing should be carried out in a way that includes the satisfaction of both customers and manufacturers. This study uses the Kano model to classify product features into different categories. The design of the green supply chain network based on the Kano model has not been investigated in the literature so far. This study examines a green supply chain network including multiple manufacturers, product types, distributors, and carriers that is designed based on Kano’s conceptual model of multiple needs. In the proposed mathematical model of this paper, customer demand is a function of the selling price of the product, transportation pollution is minimized, and a solution based on the Cooperative Game Theory approach is used to solve the mathematical model using the GAMS software. One of the advantages of the proposed mathematical model in this research compared to other supply chain models is that the design needs of the supply chain network based on the Kano model (“must-be”, “one-dimensional”, “attractive” and “indifferent”) can be determined based on customer satisfaction. In addition, the price of the product can be determined according to the satisfaction of both customers and the manufacturers.
Estimating Toll Road Travel Times Using Segment-Based Data Imputation
Authors: Jedwanna Krit , Athan Chuthathip , Boonsiripant Saroch
Published: August 2023

Efficient and sustainable transportation is crucial for addressing the environmental and social challenges associated with urban mobility. Accurate estimation of travel time plays a pivotal role in traffic management and trip planning. This study focused on leveraging machine learning models to enhance travel time estimation accuracy on toll roads under diverse traffic conditions. Two models were developed for travel time estimation under a variety of traffic conditions on the Don Muang Tollway, Bangkok, Thailand: a long short-term memory (LSTM) recurrent neural network model and a support vector regression (SVR) model. Missing data were treated using the proposed segment-based data imputation method. Unlike other studies, the effects of missing input data on the travel time model performance were also analyzed. Traffic parameters, such as speed and flow, along with other relevant parameters (time of day, day of the week, holiday indicators, and a missing data indicator), were fed into each model to estimate travel time on each of the four specific routes. The LSTM and SVR results had similar performance levels based on evaluating the all-day pooled data. However, the mean absolute percentage errors were lower for LSTM during peak periods, while SVR performed slightly better during off-peak periods. Additionally, LSTM coped substantially better than SVR with unusual traffic fluctuations. The sensitivity analysis of the missing input data in this study also revealed that the LSTM model was more robust to the high degree of missing data than the SVR model.
A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes
Authors: Pan Banglong , Yu Hanming , Cheng Hongwei , Du Shuhua , Cai Shutong , Zhao Minle , Du Juan , Xie Fazhi
Published: August 2023

As particulate organic carbon (POC) from lakes plays an important role in lake ecosystem sustainability and carbon cycle, the estimation of its concentration using satellite remote sensing is of great interest. However, the high complexity and variability of lake water composition pose major challenges to the estimation algorithm of POC concentration in Class II water. This study aimed to formulate a machine-learning algorithm to predict POC concentration and compare their modeling performance. A Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) algorithm based on spectral and time sequences was proposed to construct an estimation model using the Sentinel 2 satellite images and water surface sample data of Chaohu Lake in China. As a comparison, the performances of the Backpropagation Neural Network (BP), Generalized Regression Neural Network (GRNN), and Convolutional Neural Network (CNN) models were evaluated for remote sensing inversion of POC concentration. The results show that the CNN–LSTM model obtained higher prediction precision than the BP, GRNN, and CNN models, with a coefficient of determination (R2) of 0.88, a root mean square error (RMSE) of 3.66, and residual prediction deviation (RPD) of 3.03, which are 6.02%, 22.13%, and 28.4% better than the CNN model, respectively. This indicates that CNN–LSTM effectively combines spatial and temporal information, quickly captures time-series features, strengthens the learning ability of multi-scale features, is conducive to improving estimation precision of remote sensing models, and offers good support for carbon source monitoring and assessment in lakes.
COVID-19 Perceived Risk, Travel Risk Perceptions and Hotel Staying Intention: Hotel Hygiene and Safety Practices as a Moderator
Authors: Teng Chih-Ching , Cheng Ya-Jen , Yen Wen-Shen , Shih Ping-Yu
Published: August 2023

The global hotel and tourism business has been significantly affected by the COVID-19 pandemic, prompting governments and researchers to explore ways for mitigation. Within this particular setting, this study investigation centers on Taiwan at a crucial phase of the pandemic. It utilizes the uncertainty avoidance theory and the protection motive theory to analyze the impact of perceived risk associated with COVID-19, perceptions of travel risk, and hotel hygiene and safety practices on the intention to stay in those hotels. A total of 500 valid survey responses were obtained from travelers in Taiwan during the nationwide level 3 alert for COVID-19 in 2021, via the cluster sampling technique. Using SPSS 25.0 and AMOS 22.0, the reliability and validity test as well as structure equation analysis were used to examine the proposed model. Analytical results indicate that perceived risk of COVID-19 positively affects travel risk perceptions, which in turn negatively affects hotel staying intention. Moreover, travel risk perceptions mediate the relationship between perceived risk of COVID-19 and hotel staying intention. The findings of this study indicate that there is a strong moderating effect of hotel hygiene and safety practices on the link between travel risk perceptions and hotel staying intention, highlighting the importance of these practices in influencing individuals’ decisions to remain at a hotel. The results of this study provide valuable insights into the management of tourism crises, specifically emphasizing the need for prioritizing safety and hygiene measures throughout the post-COVID era. These findings underline the importance of effectively managing perceptions of travel risks and maintaining high standards of hotel hygiene in order to enhance the likelihood of tourists’ intent to stay at a hotel. Furthermore, this study presents an in-depth strategy for hoteliers and practitioners to navigate the road to recovery, consequently facilitating the revitalization of the sector and offering valuable perspectives for future research investigations.
Using Online Customer Reviews to Understand Customers’ Experience and Satisfaction with Integrated Resorts
Authors: Yu Jun , Zhang Xiaobin , Kim Hak-Seon
Published: August 2023

With the diverse demands of customers for their accommodation experiences, integrated resorts have emerged and have been popular among tourists. Meanwhile, in the context of Tourism and Hospitality 4.0, online information collection and marketing has been one of the most effective strategies for industry operators and investors. Thus, the present study adopted online reviews generated by customers themselves to explore their experience and satisfaction with integrated resorts. With the qualitative and quantitative analyses of online customer reviews, firstly, 70 prominent keywords were extracted from the reviews, and some general understanding was obtained from the top frequent words. After conducting a semantic network analysis of the top frequent words, significant and central words in the network were identified, which were utilized as basic variables for the specific exploration of factors reflecting customers’ experience. In the meantime, four dimensions, “Amenities”, “Entertainment”, “Tourist”, and “Atmosphere”, were garnered via CONCOR analysis to illustrate the key dimensions of customers’ cognition and awareness towards resorts. At last, a quantitative analysis with an exploration factor analysis and a linear regression analysis were conducted to testify the correlation between customer experience and satisfaction. Three factors, “environment”, “staff service”, and “food service”, have a significant effect on customer satisfaction. Analytics of online customer reviews with a big volume, which differ from traditional research methods by designed structure or content, could generate more comprehensive understanding and cognition of research subjects. The findings obtained from analytics could provide baseline information for future research and industry development and promotion.
Damage Mode and Energy Consumption Characteristics of Paper-Sludge-Doped Magnesium Chloride Cement Composites
Authors: Wang Shuren , Wang Zhixiang , Gong Jian , Liu Qianqian
Published: August 2023

To reduce the pollution caused by paper sludge in the environment and overcome issues of poor water resistance and brittleness in Magnesium Oxychloride Cement (MOC), the MOC was modified by adding different dosages of paper sludge. The mechanical properties and damage modes of composite MOC materials containing paper sludge were studied by uniaxial compression tests. Under cyclic loading conditions, the damage progression of MOC composites was characterised using the tensile-shear conversion factor (Tsc) and by monitoring the energy parameters (elastic strain energy, plastic strain energy and dissipation energy). The results show that the average peak stress drop of MOC composites gradually increases with the increase in paper sludge dosage. Under uniaxial compression conditions, the Tsc of the MOC composites decreases from 0.99 to 0.44, and the damage mode is transitioned from brittle tensile damage to tensile-shear damage, X-shaped conjugate surface shear damage and finally to pure shear damage with an increase in doping. During cyclic loading conditions, the brittleness of MOC composites gradually decreases with an increase in paper sludge doping, which verifies the effect of paper sludge on the mechanical properties of MOC materials and the change in damage modes from the perspective of energy dissipation.
Stress-Relief–Anchor-Grouting, a Collaborative Control Technology for Severe Extrusion Floor Heave in a Deep Roadway: A Case Study
Authors: Shang Donghuang , Wang Meng , Li Dawei , Yu Chunsheng , Huang Shiyi , Li Jie , Wei Sijiang , Zhao Liuan
Published: August 2023

Severe extrusion floor heave is the most common type of failure of floors in deep roadways, and it is also a major problem restricting the safe and efficient mining of deep coal resources. In deep roadways, reducing floor stress is an effective means to control floor heave. In this study, the method of creating directional stress-relief zones by constructing stress-relief boreholes is applied; while the stress is released, the path of stress from the ribs transferred to the floor and to the extrusion failure path is cut off, and floor heave control is achieved. Therefore, based on the stress-boundary and rock-mass parameters of the roadway, the control effects of the borehole angle, length, diameter, and row spacing on the extrusion floor heave were studied, and the reasonable thresholds of borehole parameters were shown to ensure the stress-relief effect on the roadway. In addition, the bolt-grouting technology was used to strengthen the floor of the roadway, the broken surrounding rock was modified via grouting consolidation, the support strength of the floor was increased using high-tension bolts (cable), and there was a good floor heave control effect in the field application. On the basis of traditional floor reinforcement, the control effect of stress regulation on floor heave is fully considered in this study, and stress-relief–anchor-grouting, a collaborative control technology for floor heave in deep roadways, is developed. Based on the three factors affecting the stability of deep roadways (stress, lithology, and support), the collaborative prevention and control of severe extrusion floor heave were realized, which provides a new method for deep roadway floor heave control and has good application value.