bluuebunny/crossref_metadata_2025 huggingface dataset:
Electronics and Detectors for the Stellar Intensity Interferometer of the ASTRI Mini-Array Telescopes
Authors: Bonanno Giovanni , Zampieri Luca , Naletto Giampiero , Paoletti Lorenzo , Romeo Giuseppe , Bruno Pietro , Grillo Alessandro , Occhipinti Gianfranco , Timpanaro Maria Cristina , Gargano Carmelo , Fiori Michele , Rodeghiero Gabriele , Pareschi Giovanni , Scuderi Salvatore , Tosti Gino
Published: December 2023

The ASTRI Mini-Array is an international collaboration led by the Italian National Institute for Astrophysics (INAF) that will operate nine telescopes to perform Cherenkov and optical stellar intensity interferometry (SII) observations. At the focal plane of these telescopes, we are planning to install a stellar intensity interferometry instrument. Here we present the selected design, based on Silicon Photomultiplier (SiPM) detectors matching the telescope point spread function together with dedicated front-end electronics.
A Deep Reinforcement Learning Strategy for Surrounding Vehicles-Based Lane-Keeping Control
Authors: Kim Jihun , Park Sanghoon , Kim Jeesu , Yoo Jinwoo
Published: December 2023

As autonomous vehicles (AVs) are advancing to higher levels of autonomy and performance, the associated technologies are becoming increasingly diverse. Lane-keeping systems (LKS), corresponding to a key functionality of AVs, considerably enhance driver convenience. With drivers increasingly relying on autonomous driving technologies, the importance of safety features, such as fail-safe mechanisms in the event of sensor failures, has gained prominence. Therefore, this paper proposes a reinforcement learning (RL) control method for lane-keeping, which uses surrounding object information derived through LiDAR sensors instead of camera sensors for LKS. This approach uses surrounding vehicle and object information as observations for the RL framework to maintain the vehicle’s current lane. The learning environment is established by integrating simulation tools, such as IPG CarMaker, which incorporates vehicle dynamics, and MATLAB Simulink for data analysis and RL model creation. To further validate the applicability of the LiDAR sensor data in real-world settings, Gaussian noise is introduced in the virtual simulation environment to mimic sensor noise in actual operational conditions.
The Design, Fabrication, and Evaluation of a Phase-Resolved Partial Discharge Sensor Embedded in a MV-Class Bushing
Authors: Lee Gyeong-Yeol , Kim Nam-Hoon , Kim Dong-Eon , Kil Gyung-Suk , Kim Sung-Wook
Published: December 2023

This paper proposes a novel phase-resolved partial discharge (PRPD) sensor embedded in a MV-class bushing for high-accuracy insulation analysis. The design, fabrication, and evaluation of a PRPD sensor embedded in a MV-class bushing aimed to achieve the detection of partial discharge (PD) pulses that are phase-synchronized with the applied primary HV signal. A prototype PRPD sensor was composed of a flexible printed circuit board (PCB) with dual-sensing electrodes, utilizing a capacitive voltage divider (CVD) for voltage measurement, the D-dot principle for PD detection, and a signal transducer with passive elements. A PD simulator was prepared to emulate typical PD defects, i.e., a metal protrusion. The voltage measurement precision of the prototype PRPD sensor was satisfied with the accuracy class of 0.2 specified in IEC 61869-11, as the maximum corrected voltage error ratios and corrected phase errors in 80%, 100%, and 120% of the rated voltage (13.2 kilovolts (kV)) were less than 0.2% and 10 min, respectively. In addition, the prototype PRPD sensor had good linearity and high sensitivity for PD detection compared with a conventional electrical detection method. According to performance evaluation tests, the prototype PRPD sensor embedded in the MV-class bushing can measure PRPD patterns phase-synchronized with the primary voltage without any additional synchronization equipment or system. Therefore, the prototype PRPD sensor holds potential as a substitute for conventional commercial PD sensors. Consequently, this advancement could lead to the enhancement of power system monitoring and maintenance, contributing to the digitalization and minimization of power apparatus.
A Weakly Supervised Semantic Segmentation Model of Maize Seedlings and Weed Images Based on Scrawl Labels
Authors: Zhao Lulu , Zhao Yanan , Liu Ting , Deng Hanbing
Published: December 2023

The task of semantic segmentation of maize and weed images using fully supervised deep learning models requires a large number of pixel-level mask labels, and the complex morphology of the maize and weeds themselves can further increase the cost of image annotation. To solve this problem, we proposed a Scrawl Label-based Weakly Supervised Semantic Segmentation Network (SL-Net). SL-Net consists of a pseudo label generation module, encoder, and decoder. The pseudo label generation module converts scrawl labels into pseudo labels that replace manual labels that are involved in network training, improving the backbone network for feature extraction based on the DeepLab-V3+ model and using a migration learning strategy to optimize the training process. The results show that the intersection over union of the pseudo labels that are generated by the pseudo label module with the ground truth is 83.32%, and the cosine similarity is 93.55%. In the semantic segmentation testing of SL-Net for image seedling of maize plants and weeds, the mean intersection over union and average precision reached 87.30% and 94.06%, which is higher than the semantic segmentation accuracy of DeepLab-V3+ and PSPNet under weakly and fully supervised learning conditions. We conduct experiments to demonstrate the effectiveness of the proposed method.
Give Me a Sign: Using Data Gloves for Static Hand-Shape Recognition
Authors: Achenbach Philipp , Laux Sebastian , Purdack Dennis , Müller Philipp Niklas , Göbel Stefan
Published: December 2023

Human-to-human communication via the computer is mainly carried out using a keyboard or microphone. In the field of virtual reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g., silent commands during task-force training) or simply not possible (e.g., if the user has hearing loss). Data gloves help to increase immersion within VR, as they correspond to our natural interaction. At the same time, they offer the possibility of accurately capturing hand shapes, such as those used in non-verbal communication (e.g., thumbs up, okay gesture, …) and in sign language. In this paper, we present a hand-shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an outlier detection and a feature selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial data augmentation, i.e., we created new artificial data from the recorded and filtered data to augment the training data set. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. The voting meta-classifier (VL2) proved to be the most accurate, albeit slowest, classifier. A good alternative is random forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. outlier detection was proven to be an effective approach, especially in improving the classification time. Overall, we have shown that our hand-shape recognition system using data gloves is suitable for communication within VR.
Channel-Agnostic Training of Transmitter and Receiver for Wireless Communications
Authors: Davey Christopher P. , Shakeel Ismail , Deo Ravinesh C. , Salcedo-Sanz Sancho
Published: December 2023

Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all components with respect to the communications environment. In the end-to-end approach, an assumed channel model is necessary to support training of the transmitter and receiver. This limitation has motivated recent work on over-the-air training to explore disjoint training for the transmitter and receiver without an assumed channel. These methods approximate the channel through a generative adversarial model or perform gradient approximation through reinforcement learning or similar methods. However, the generative adversarial model adds complexity by requiring an additional discriminator during training, while reinforcement learning methods require multiple forward passes to approximate the gradient and are sensitive to high variance in the error signal. A third, collaborative agent-based approach relies on an echo protocol to conduct training without channel assumptions. However, the coordination between agents increases the complexity and channel usage during training. In this article, we propose a simpler approach for disjoint training in which a local receiver model approximates the remote receiver model and is used to train the local transmitter. This simplified approach performs well under several different channel conditions, has equivalent performance to end-to-end training, and is well suited to adaptation to changing channel environments.
End-to-End Solution for Analog Gauge Monitoring Using Computer Vision in an IoT Platform
Authors: Peixoto João , Sousa João , Carvalho Ricardo , Santos Gonçalo , Cardoso Ricardo , Reis Ana
Published: December 2023

The emergence of Industry 4.0 and 5.0 technologies has enabled the digital transformation of various processes and the integration of sensors with the internet. Despite these strides, many industrial sectors still rely on visual inspection of physical processes, especially those employing analog gauges. This method of monitoring introduces the risk of human errors and inefficiencies. Automating these processes has the potential, not only to boost productivity for companies, but also potentially reduce risks for workers. Therefore, this paper proposes an end-to-end solution to digitize analog gauges and monitor them using computer vision through integrating them into an IoT architecture, to tackle these problems. Our prototype device has been designed to capture images of gauges and transmit them to a remote server, where computer vision algorithms analyze the images and obtain gauge readings. These algorithms achieved adequate robustness and accuracy for industrial environments, with an average relative error of 0.95%. In addition, the gauge data were seamlessly integrated into an IoT platform leveraging computer vision and cloud computing technologies. This integration empowers users to create custom dashboards for real-time gauge monitoring, while also enabling them to set thresholds, alarms, and warnings, as needed. The proposed solution was tested and validated in a real-world industrial scenario, demonstrating the solution’s potential to be implemented in a large-scale setting to serve workers, reduce costs, and increase productivity.
Assessing the Effects of Mild Traumatic Brain Injury on Vestibular Home Exercise Performance with Wearable Sensors
Authors: Campbell Kody R. , Wilhelm Jennifer L. , Antonellis Prokopios , Scanlan Kathleen T. , Pettigrew Natalie C. , Martini Douglas N. , Chesnutt James C. , King Laurie A.
Published: December 2023

After a mild traumatic brain injury (mTBI), dizziness and balance problems are frequently reported, affecting individuals’ daily lives and functioning. Vestibular rehabilitation is a standard treatment approach for addressing these issues, but its efficacy in this population remains inconclusive. A potential reason for suboptimal outcomes is the lack of objective monitoring of exercise performance, which is crucial for therapeutic success. This study utilized wearable inertial measurement units (IMUs) to quantify exercise performance in individuals with mTBI during home-based vestibular rehabilitation exercises. Seventy-three people with mTBI and fifty healthy controls were enrolled. Vestibular exercises were performed, and IMUs measured forehead and sternum velocities and range of motions. The mTBI group demonstrated a slower forehead peak angular velocity in all exercises, which may be a compensatory strategy to manage balance issues or symptom exacerbation. Additionally, the mTBI group exhibited a larger forehead range of motion during specific exercises, potentially linked to proprioceptive deficits. These findings emphasize the usefulness of utilizing IMUs to monitor the quality of home-based vestibular exercises for individuals with mTBI and the potential for IMUs improving rehabilitation outcomes.
Zero-Velocity Update-Based GNSS/IMU Tightly Coupled Algorithm with the Constraint of the Earth’s Rotation Angular Velocity for Cableway Bracket Deformation Monitoring
Authors: Zhang Song , Zhang Qiuzhao , Yu Ruipeng , Yu Zhangjun , Zhang Chu , He Xinyue
Published: December 2023

Cableways have been widely used in industrial areas, cities, and scenic spots due to their advantages, such as being a convenient mode of transportation, time-saving, labor-saving, and low cost, as well as offering environmental protection. To ensure the safe operation of a cableway, based on the characteristic that the velocity of the cableway bracket is approximately zero in a static deformation monitoring environment, a deformation monitoring method called zero velocity update (ZUPT)-based GNSS/IMU tightly coupled algorithm with the constraint of the Earth’s rotation angular velocity was proposed. The proposed method can effectively solve the problem of a single GNSS being unable to output attitude, which is directly related to the status of wire ropes and cable cars. Meanwhile, ZUPT is used to restrain the Kalman filter’s divergence when IMU is stationary. However, the improvements of ZUPT on attitude are not obvious, so the constraint of the Earth’s rotation angular velocity was applied. The performance of the proposed method was evaluated through monitoring the cableway bracket of the Yimeng Mountain Tourism area in Shandong. Compared with the ZUPT-based GNSS/IMU tightly coupled algorithm (ZUPT-TC), the proposed method can further constrain the error accumulation of IMU while stationary and, therefore, it can provide reliable position and attitude information on cableway brackets.
Soil Organic Matter Estimation Model Integrating Spectral and Profile Features
Authors: He Shaofang , Tan Siqiao , Shen Luming , Zhou Qing
Published: December 2023

The accurate measurement of soil organic matter (SOM) is vital for maintaining soil quality. We present an innovative model for SOM prediction by integrating spectral and profile features. We use PCA, Lasso, and SCARS methods to extract important spectral features and combine them with profile data. This hybrid approach significantly improves SOM prediction across various models, including Random Forest, ExtraTrees, and XGBoost, boosting the coefficient of determination (R2) by up to 26%. Notably, the ExtraTrees model, enriched with PCA-extracted features, achieves the highest accuracy with an R2 of 0.931 and an RMSE of 0.068. Compared with single-feature models, this approach improves the R2 by 17% and 26% for PCA features of full-band spectra and profile features, respectively. Our findings highlight the potential of feature integration, especially the ExtraTrees model with PCA-extracted features and profile features, as a stable and accurate tool for SOM prediction in extensive study areas.
Integrated Sensing and Communication via Orthogonal Time Frequency Space Signaling with Hybrid Message Passing Detection and Fractional Parameter Estimation
Authors: Zhang Ji , Cai Leqi , Liu Huanyou
Published: December 2023

For the orthogonal time frequency space (OTFS) modulation, we generally multiplex symbols on a new type of carrier waveform in the delay-Doppler (DD) domain. These two parameters can be used to infer the range (R) and velocity (V) of the communication user and sensing target; thus, it is natural for the OTFS to be implemented in integrated sensing and communication (ISAC). A framework for ISAC based on OTFS modulation is proposed in this paper, in which the matched filter scheme with fractional parameter estimation is implemented for radar sensing. In addition, the hybrid message passing (MP) detection algorithm is developed for OTFS symbol demodulation. According to the simulation results, fractional DD shifts associated with multiple targets can be accurately obtained through the proposed framework. Meanwhile, the bit error rate under the proposed detector is less than 10−4 when the signal-to-noise ratio is high enough.
Fast Thermocycling in Custom Microfluidic Cartridge for Rapid Single-Molecule Droplet PCR
Authors: Takahara Hirokazu , Tanaka Hayato , Hashimoto Masahiko
Published: December 2023

The microfluidic droplet polymerase chain reaction (PCR), which enables simultaneous DNA amplification in numerous droplets, has led to the discovery of various applications that were previously deemed unattainable. Decades ago, it was demonstrated that the temperature holding periods at the denaturation and annealing stages in thermal cycles for PCR amplification could be essentially eliminated if a rapid change of temperature for an entire PCR mixture was achieved. Microfluidic devices facilitating the application of such fast thermocycling protocols have significantly reduced the time required for PCR. However, in microfluidic droplet PCR, ensuring successful amplification from single molecules within droplets has limited studies on accelerating assays through fast thermocycling. Our developed microfluidic cartridge, distinguished for its convenience in executing single-molecule droplet PCR with common laboratory equipment, features droplets positioned on a thin glass slide. We hypothesized that applying fast thermocycling to this cartridge would achieve single-molecule droplet PCR amplification. Indeed, the application of this fast protocol demonstrated successful amplification in just 22 min for 30 cycles (40 s/cycle). This breakthrough is noteworthy for its potential to expedite microfluidic droplet PCR assays, ensuring efficient single-molecule amplification within a remarkably short timeframe.
Comparison of Fluidic and Non-Fluidic Surface Plasmon Resonance Biosensor Variants for Angular and Intensity Modulation Measurements
Authors: Mrozek Piotr , Oldak Lukasz , Gorodkiewicz Ewa
Published: December 2023

Fluidic and non-fluidic surface plasmon resonance measurements were realized for the same type of sensory layer and using the same mouse IgG antibody and anti-mouse IgG antibody biomolecular system. A comparison of the thicknesses of the anti-mouse IgG antibody layers bound to the ligand at increasing analyte concentrations ranging from 0.0 μg mL−1 to 5.0 μg mL−1 in the non-fluidic and the fluidic variant showed that the thickness of the bound anti-mouse antibody layers in the fluidic variant was approximately 1.5–3 times larger than in the non-fluidic variant. The greater thicknesses of the deposited layers were also reflected in the larger increment of the resonant angle in the fluidic variant compared to the non-fluidic variant in the considered range of analyte concentrations. The choice between fluidic and non-fluidic surface plasmon resonance biosensors may be justified by the availability of analyte volume and the intended modulation technique. When working with limited analyte, non-fluidic biosensors with intensity modulation are more advantageous. For larger analyte quantities, fluidic biosensors with angular modulation are recommended, primarily due to their slightly higher sensitivity in this measurement mode.
Enhancing Short Track Speed Skating Performance through Improved DDQN Tactical Decision Model
Authors: Yang Yuanbo , Li Feimo , Chang Hongxing
Published: December 2023

This paper studies the tactical decision-making model of short track speed skating based on deep reinforcement learning, so as to improve the competitive performance of corresponding short track speed skaters. Short track speed skating, a traditional discipline in the Winter Olympics since its establishment in 1988, has consistently garnered attention. As artificial intelligence continues to advance, the utilization of deep learning methods to enhance athletes’ tactical decision-making capabilities has become increasingly prevalent. Traditional tactical decision techniques often rely on the experience and knowledge of coaches and video analysis methods that require a lot of time and effort. Consequently, this study proposes a scientific simulation environment for short track speed skating, that accurately simulates the physical attributes of the venue, the physiological fitness of the athletes, and the rules of the competition. The Double Deep Q-Network (DDQN) model is enhanced and utilized, with improvements to the reward function and the distinct description of four tactics. This enables agents to learn optimal tactical decisions in various competitive states with a simulation environment. Experimental results demonstrate that this approach effectively enhances the competition performance and physiological fitness allocation of short track speed skaters.
Improving Functional Exercises Based on Experts’ Evaluation Weights for Emergency Responses
Authors: Le Tien Hung , Pham Van Nam , Kato Takaaki
Published: December 2023

Social and economic changes in Southeast Asia have caused significant changes to Vietnam’s national emergency management structure, including expanding the role of firefighters from firefighting to rescue activities in various situations. It is essential to develop methods for designing exercises to identify and address the gaps in competencies related to the new roles assigned to Vietnamese firefighters. This study aimed to develop a quantitative method to effectively arrange exercise injects. It applies the analytic hierarchy process to determine the weights of response actions in functional exercises and uses comparison methods to establish the relationship between the weights of the action types, players’ capability targets, and disaster types. The results indicate that the action type weights change when the disaster type changes, whereas the capability weights within each action type remain constant. In this study, we developed a method to adjust the exercise structure based on response action weights and to optimize the number of injects that represent each action type. This innovative approach holds great significance in exercise design, increasing the likelihood of achieving the exercise capability targets, not only in Vietnam, but also in other countries.
Compositional Analysis of Metal(IV) Phosphate and Phosphonate Materials—Pitfalls and Best Practices
Authors: Koivula Risto , Zhang Wenzhong
Published: December 2023

Metal(IV) phosphate and phosphonates materials have increasingly found their applications in water purification, heterogeneous catalysis, drug delivery, and proton-exchange membrane fuel cells. The strong linkage between tetravalent metal cations and phosphate/phosphonate groups offers a unique bottom-up design platform, resulting in chemically stable inorganics or hybrids. Task-specific physiochemical functionalities could be deposited by modifying the phosphate/phosphonate groups before the material synthesis. The high reactivity between the metal centre and the phosphorus-containing linker, on the other hand, often leads to obtaining unordered materials (amorphous solids or coordination polymers). The chemical composition of the prepared materials is a key parameter in guiding the synthetic approach and in governing their performances. This narrative review focuses on critically summarising the traditional and advanced analytical methods for probing the composition of these materials. The reader is introduced to and guided on the advances and restrictions of different analysis techniques when probing metal(IV) phosphates/phosphonates. Both solution-based and solid-state spectroscopic techniques are covered with a focus on understanding the quantity and the linkage status of the phosphorus-containing moieties. These techniques include atomic spectroscopy, mass spectroscopy, nuclear magnetic resonance spectroscopy, X-ray-based methods, and neutron activation analysis.
The Development and Validation of Simultaneous Multi-Component Quantitative Analysis via HPLC–PDA Detection of 12 Secondary Metabolites Isolated from Drynariae Rhizoma
Authors: Ahn Jin Sung , Whang Wan Kyunn
Published: December 2023

Drynariae Rhizoma (DR) is a functional food and traditional medicine that has been widely used for bone and joint disorders for thousands of years. In this study, 14 compounds were isolated from DR, and their structures were identified using UPLC/QTOF–MS, UPLC–ESI/LTQ–Orbitrap–HRMS, and 2D NMR and compared with those obtained in previous studies. An HPLC–PDA multi-component simultaneous quantitative determination method was developed for 12 of the 14 DR-derived compounds, excluding compounds with a content <1.5 mg. The developed HPLC method was validated based on linearity (r2 ≥ 0.999), limit of detection (0.01–0.65 μg/mL), limit of quantification (0.04–1.97 μg/mL), intra-day precision and accuracy ranges (0.06–2.85% and 95.03–104.75%, respectively), and inter-day precision and accuracy ranges (0.24–2.83% and 95.75–105.75%, respectively). The developed analysis method improved the resolution of compounds 4 and 5. In addition, this is the first quantitative analysis of compounds 7, 8, and 11 and the first simultaneous quantitative analysis of 12 compounds, including compounds 4, 7, 8, 10, 11, and 14. This study developed a rapid, accurate, and economical HPLC method for performing the simultaneous quantitative analysis of 12 secondary metabolites isolated from DR.
A Comprehensive Review of the Applications of Hybrid Evaporative Cooling and Solar Energy Source Systems
Authors: Xue Tinghui , Wan Yangda , Huang Zhifeng , Chen Pinyi , Lin Jie , Chen Weidong , Liu Haibo
Published: December 2023

Recent advancements in single-stage evaporative cooling (EC) have showcased their effectiveness as an energy-efficient and sustainable air-conditioning (AC) solution. However, several challenges hinder the widespread adoption of EC in various applications. These challenges include climate sensitivity, substantial spatial requirements, and limitations in achieving desired output temperatures. To address these concerns, there has been a growing focus on integrating EC with solar energy (SE) systems. With traditional energy resources being depleted, the use of SE has gained prominence as a sustainable solution to meet future energy demands while mitigating environmental pollution. This paper presents a comprehensive review of hybrid EC–SE systems, aiming to elucidate the potential synergies, benefits, and challenges associated with this integration. The review explores the principles and mathematical approaches of various configurations of EC systems to assess their compatibility with SE sources. Furthermore, the review delves into the mathematical model of SE, encompassing both solar power generation and thermal collectors, with the aim of integrating it into the EC model. It delves into key aspects of energy consumption and performance, showcasing advancements in achieving higher efficiency and enhanced cooling capacity through the hybrid systems. Additionally, the review highlights research gaps in the existing literature, emphasizing the need for further exploration in this interdisciplinary field. In conclusion, this paper offers valuable insights into the potential of EC–SE systems to address energy and cooling requirements while promoting sustainable development.
Electrification of Last-Mile Delivery: A Fleet Management Approach with a Sustainability Perspective
Authors: Castillo Oscar , Álvarez Roberto
Published: December 2023

Light commercial vehicles that operate in last-mile deliveries are significant contributors to greenhouse gas emissions. For this reason, carbon footprint mitigation actions have become a key issue for companies involved in urban freight transport to put the organization in line with the future EU legislative framework. In this sense, the electrification of the delivery fleets is one of the actions carried out to improve the sustainability of transport operations. To this end, fleet managers have to explore several fleet renewal strategies over a finite planning horizon, evaluating different types of electric powertrains for light commercial vehicles. To address this concern, this paper presents a purpose-built analysis to assist and boost the fleet managers’ decisions when transitioning to electrified vans, intending to maximize cost savings and reduce corporate greenhouse gas emissions inventory. The model developed for this research work is a Multi-Objective Linear Programming analysis for the optimization of the total cost of ownership and the organizational transport-related emissions reported from all scope categories according to the Greenhouse Gas Protocol standards. This analysis is applied to three types of electric vans (battery electric, hydrogen fuel cell, and range extender hybrid electric/hydrogen fuel cell), and they are compared with an internal combustion van propelled with natural gas. From this perspective, the conducted research offers a novel approximation to fleet replacement problems considering organization emission reporting and long-term budgetary objectives for vehicles and their respective refueling infrastructure. The comprehensive numerical simulations carried out over different study scenarios in Spain demonstrate that the optimization approach not only shows effective fleet renewal strategies but also identifies critical factors that impact the fleet’s competitiveness, offering valuable insights for fleet managers and policymakers. The findings indicate that in Spain, battery electric and hydrogen range extender light commercial vehicles stand as a competitive option. Substituting a natural gas-powered van with an electrified alternative can reduce an organization’s inventory emissions by up to 77% and total costs by up to 24%. Additionally, this study also points out the influence of energy supply pathways and the emissions from relevant scope 3 categories.
Sustainability and Sport: An Exploratory Study on Students of Rome’s Universities
Authors: Lenzi Francesca Romana , De Falco Ciro Clemente , Iazzetta Ferdinando , Coppola Giuseppe , Capuano Maria Elena
Published: December 2023

(1) Background: The importance of sport in advancing the roadmap for the implementation of the UN 2030 Agenda was reaffirmed at the 7th Summit on Smart Cities and Sport (Lausanne, 26–28 October 2020), where it was emphasized that developing a culture of sustainability among younger generations is crucial. Given this scenario, the present study focuses on two main research questions: “Is there a diffusion of an environmentally oriented culture among university students?” and “Is there a relationship between sports’ world and environmentally oriented culture?” (2) Methods: To establish the empirical framework (279 questionnaires received), we used web-based survey techniques with a Computer-Assisted Web Interviewing (CAWI) procedure. (3) Results: The analysis results show that the sample considers environmental sustainability as a part of a more comprehensive understanding of sustainability, which reflects a sense of fear for uncertainty in the future. The second finding suggests that sport does not play a significant role in shaping awareness. (4) Conclusions: The cultural and generational background must be considered for better understanding of the research results, especially in Italy where it appears that sustainability is not considered a priority.
Scenario Analysis of Offshore Wind-Power Systems under Uncertainty
Authors: Caputo Antonio Casimiro , Federici Alessandro , Pelagagge Pacifico Marcello , Salini Paolo
Published: December 2023

Wind-energy systems are strongly affected by uncertainty and variability. Therefore, uncertainty sources should be considered during the economic evaluation of this type of system. In the literature, a framework for the economic performance assessment of wind-power systems has been proposed. Furthermore, in another contribution, the random discontinuities of political and regulatory scenarios have been included by using scenario analysis. However, the implemented models neglected the uncertainty related to disruptive events and the effect of climate change on the wind resource. To fill this gap, in this paper, climate change and disruptive events are included in a new model for evaluating the economic performance of wind turbine systems using scenario analysis. Analysis of a numerical example has been carried out to show the framework’s capabilities and to evaluate the effects of the added issues. The main results confirm previous findings on the necessity of including regulatory and political risks to achieve a proper economic evaluation. Additionally, they show that disruptive events increase the variability of the expected value of the Net Present Value (NPV). Therefore, even though climate change is expected to increase wind producibility in the numerical example location, the inclusion of disruptive events constrains the NPV growth.
The Collaboration Patterns of Institution during the COVID-19 Pandemic
Authors: Fu Chenbo , Liang Xuejiao , Min Yong , Xuan Qi , Yu Shanqing
Published: December 2023

During the COVID-19 pandemic, scientific collaboration has profoundly influenced the sustainability of human science. The pandemic has exposed the fragility of the global health system, forcing various research institutions to cooperate more actively and giving rise to a pressing inquiry regarding its implications on the patterns of scientific institution collaboration. With this in mind, we investigate 2,252,016 scientific papers between 2019 and 2020 to study this issue from an institution cooperation view. Specifically, by proposing a novel weighted cooperation network and utilizing institution cooperation freshness, we investigate different collaboration patterns and strategies when institutions respond to the pandemic. The results show that the crisis generally promotes both fresh and stable cooperation relationships among institutions, and different fields (e.g., STEM and Humanities) demonstrate different collaboration patterns. During the pandemic, institutions with a strong influence have a variety of cooperation strategies, while institutions with a low influence prefer a conservative strategy. Moreover, institutions participating in COVID-19 research are very cautious in choosing partners, which may originate from the risk of cooperation, e.g., the fluctuation of influence, and the causal inference results also verify this conclusion. Overall, our study reveals the impact of the COVID-19 pandemic on scientific research work, which may pave the way for designing policy when other global crises occur again.
Evaluating Daylighting and Preservation in Heritage Halls: Shading Retrofit Case Study at Chengdu Jiangnan Pavilion Street Site Museum
Authors: Shi Feng , Chang Bin , Li Jiaxuan , Li Xinrong , Yang Sheng , Luo Xilian
Published: December 2023

Site museums, focusing on immovable relics, are crucial for preserving unearthed artifacts by controlling their microenvironment. Artifacts are typically found in air–soil coupled environments. Current environmental control standards, designed for artifacts preserved in air, lead to diseases in soil artifacts due to inappropriate storage conditions. Taking Chengdu’s Jiangnan Guan Street as a case study, this research, through monitoring the on-site artifact environment and analyzing the correlation between diseases and the environment, proposes a tensioned membrane sunshade for the protection of artifacts under exposed roofs. Utilizing computational fluid dynamics and lighting simulations, we compared the environmental changes before and after the implementation of the plan. The results indicate: (1) direct sunlight from the exposed roof is the main cause of instability and disease in the soil–air coupled environment of the artifacts; (2) the sunshade significantly improves the storage environment of the artifacts, reducing the temperature difference at various locations from 12.8 °C to 0.3 °C and decreasing direct solar exposure by over 90%. Our proposed solution effectively improves the preservation environment of unearthed artifacts, offering new insights for the protection of the Chengdu Jiangnan Pavilion Street site.
Constructing Sustainable Learning Ecology to Overcome Burnout of Teachers: Perspective of Organizational Identity and Locus of Control
Authors: Altinay Zehra , Bicentürk Batuhan
Published: December 2023

This study examined how organizational identity, locus of control, and their interrelationships affect teacher burnout. Utilizing a quantitative survey, data were collected from 105 teachers. The Maslach Burnout Inventory measured three burnout dimensions: emotional exhaustion, depersonalization, and personal accomplishment. Locus of control was assessed via Rotter’s scale. Organizational identity was measured through the Multiple Organizational Identification Scale, assessing personal self-esteem, emotional professional identity, evaluative identification, self-classification, and team factors. PLS-SEM analysis found that external locus of control had a significant positive association with emotional exhaustion. Emotional professional identity showed robust negative relationships with depersonalization and exhaustion. Differences emerged between novice and senior teachers—identity and self-esteem were more relevant for novices, while team factors were more impactful for experienced teachers. Variations also occurred across genders, with identity and self-esteem more salient for females and team dynamics more influential for males. The study highlights the complex interplay between individual, relational, and organizational factors in shaping teacher burnout. An external locus of control may exacerbate exhaustion, while emotional professional identity seems to provide resilience. Support initiatives should account for teachers’ evolving developmental needs and gender variations in burnout experiences. Fostering internal control beliefs, strong professional identity, and tailored support based on career stage and gender can potentially buffer against burnout. This study contributes insights to guide targeted efforts to promote teacher well-being, effectiveness, and retention. Learning in the workplace instead of paying more time for education services can be considered as overcoming burnout, redesigning and implementing digital teaching for sustainable teaching and learning for both teachers and students in order to construct a better learning ecology.
Analysis and Dissolution of Potential Contradictions in Thematic Lessons from a Cultural-Historical Activity Theory Perspective
Authors: He Shengqing , Qi Chunxia
Published: December 2023

Thematic learning features sustain exploratory activities focused on a selected theme, which contributes to developing students’ core competencies, including problem solving, collaboration, and communication skills, supporting sustainable development in learning. For teachers and students, this emerging pedagogical approach may encounter various contradictions in the classroom activity system. Grounded in a cultural-historical activity theory perspective, this study examined the potential contradictions in an activity system in the context of thematic learning, as well as possible strategies for dissolving those potential contradictions and their effectiveness through a case study. Interviews with an experienced teacher were conducted to acquire information about potential contradictions, and the interview results suggested that these contradictions were perceived as potentially occurring between the students, tools, and objects. According to that information, solutions were proposed to dissolve those potential contradictions, including setting moderate learning objects, creating a sustainable exploratory learning environment, scaffolding necessary knowledge, and situating students in an interactive, communicative, and cooperative classroom environment. Finally, the dissolution of those contradictions was examined through classroom observations, with the results showing that students were able to apply their knowledge accurately and fluently to solve the problems. The contribution of this study involves adopting cultural-historical activity theory as guidance to dissolve the contradictions in classroom teaching, which expands the application of this theory compared to previous studies that focused more on the contradictions between researchers and teachers during a lesson study. This innovative application will inspire teachers to adopt this theory routinely to improve teaching, including designing instructional sequences and question chains that support students’ sustained exploration.
An Agonistic Perspective on the Challenge of Biodiversity Value Integration
Authors: Fickel Thomas
Published: December 2023

The identity-laden conflict comprising clashing biodiversity values can hinder the integration of plural biodiversity values into policy. Until now, research on the elicitation and negotiation of biodiversity values approached this task by applying an economic or a deliberative model to guide the elicitation of values and transformation knowledge regarding their negotiation. However, both models have weaknesses in generating robust and transformative outcomes, which lie in their approach to dealing with identity conflicts and their related passions and affects. To address this gap, I explain how research has used both models and discuss how an agonistic model can improve the debate. I will show that current models highlight integrating and synergising values. In contrast, the agonistic model aims at eliciting distinctive values that challenge hegemonic values and the unsustainable status quo. Thereby, it implies dealing with and utilising passions and affects within the research process. Implications and operational suggestions for biodiversity value research applying the agonistic model are outlined. These include changes in the research structure, eliciting negative attributions and marginalised or missing values, and altered communication within group valuation settings. This article is relevant to researchers in biodiversity valuation and facilitators of value negotiations that aim to achieve value integration.
Equity Evaluation of Elderly-Care Institutions Based on Ga2SFCA: The Case Study of Jinan, China
Authors: Jin Mingjiu , Deng Qingtan , Wang Siyu , Wei Luteng
Published: December 2023

With the deepening trend of aging, the issue of elderly care for the aging population has become a matter of concern for all sectors of society. Elderly-care services have become increasingly vital. Elderly-care institutions, a major component of the elderly-care system, are an important part of public facility resource provision; the spatial distribution of elderly-care institutions (ECIs) directly affects the equitable access of the elderly to public resources. To cope with the current pressure of population aging and limited resources for the elderly, this study aims to explore the relationship between population aging and the supply of ECIs in Jinan, China. Using the ArcGIS platform, this study analyzes the spatial allocation of ECIs, provides improved supply capacity modelling of ECIs based on the Gaussian Two-Step Floating Catchment Area Approach (Ga2SFCA), and evaluates the equity of ECIs in terms of accessibility, supply–demand equity, and spatial equity. The results show that there are large differences in spatial accessibility between different levels of ECIs, there is a mismatch of supply and demand, the number and size of existing ECIs do not meet the needs of the elderly, and the distribution of resources in small ECIs is highly inequitable. These results can provide a guiding direction for optimizing ECIs and urban road network planning and can also provide new insights for urban planners to assess the equity of public service facilities.
Development of a Resilience Assessment Model for Manufacturing Enterprises
Authors: De Marchi Matteo , Friedrich Fanny , Riedl Michael , Zadek Hartmut , Rauch Erwin
Published: December 2023

In today’s world, crises like the COVID-19 pandemic and ongoing global changes pose significant challenges for manufacturing companies. Resilience, the ability to withstand and recover from disruptions, is essential for survival. To make resilience actionable, the discussion introduces a four-step Circular Resilience Assessment Tool. To assess their resilience score, companies undergo a risk and vulnerability assessment, a qualitative resilience factor assessment, a suitable strategies identification phase, and a quantitative performance assessment. This tool guides companies in evaluating their resilience before, during, and after hypothetical or occurred crises. The balance among qualitative and quantitative aspects, encompassing technical, social, and organizational considerations, ensures that an omni comprehensive point of view is adopted in evaluating the overall resilience score of a company. This innovative approach empowers companies to not only survive crises but also to gain a competitive advantage and expand their market share in the long term. The work provides a thorough description of each of the four steps, accompanied by examples. The Circular Resilience Assessment Tool is designed to be as specific as necessary and as general as possible, thus making it a valuable resource for a variety of enterprises.
Location of Mountain Photovoltaic Power Station Based on Fuzzy Analytic Hierarchy Process—Taking Longyang District, Baoshan City, Yunnan Province as an Example
Authors: Li Yiping , Zhou Jingchun , Feng Zhanyong
Published: December 2023

Site selection is a key link in the early stage of constructing a photovoltaic power station and providing accurate guidance for the development of such stations. Taking Longyang District, Baoshan City, Yunnan Province, as an example, this article utilizes land-use status data from the third national land survey. The study focuses on five land-use types: idle land, bare land, shrub land, forest land, and another grassland, while excluding interfering land types such as construction land, ecological conservation areas, and cultivated land. Thirteen factors including terrain, weather, environment, and neighboring resources are considered. By employing the fuzzy analytic hierarchy process, a site selection model is constructed to analyze the suitability of photovoltaic power station locations. This study emphasizes the influence of geological disaster factors when selecting environmental factors. Given the high frequency of geological disasters in mountainous areas, these factors significantly affect the safety of later-stage photovoltaic power station operations. Previous research has paid less attention to this factor. The results indicate a high level of suitability for photovoltaic site selection in Longyang District, Baoshan City, with suitable, moderately suitable, and unsuitable areas accounting for 20.09%, 34.14%, and 45.77%, respectively. Previous studies have lacked sufficient validation of site selection outcomes. In this research, validation is conducted using areas where photovoltaic power stations have already been established and are under construction within the region. The accuracy of this site selection method is found to be 92.78%. The aim is to provide a scientific reference for site selection in mountainous areas with photovoltaic power station construction needs.