bluuebunny/crossref_metadata_2025 huggingface dataset:
Human-in-the-Loop Modeling and Bilateral Skill Transfer Control of Soft Exoskeleton
Authors: Xu Jiajun , Huang Kaizhen , Zhao Mengcheng , Liu Jinfu
Published: December 2024

Soft exoskeletons (exosuits) are expected to provide a comfortable wearing experience and compliant assistance compared with traditional rigid exoskeleton robots. In this paper, an exosuit with twisted string actuators (TSAs) is developed to provide high-strength and variable-stiffness actuation for hemiplegic patients. By formulating the analytic model of the TSA and decoding the human impedance characteristic, the human-exosuit coupled dynamic model is constructed. An adaptive impedance controller is designed to transfer the skills of the patient’s healthy limb (HL) to the bilateral impaired limb (IL) with a mirror training strategy, including the movement trajectory and stiffness profiles. A reinforcement learning (RL) algorithm is proposed to optimize the robotic assistance by adapting the impedance model parameters to the subject’s performance. Experiments are conducted to demonstrate the effectiveness and superiority of the proposed method.
An On-Machine Measuring Apparatus for Dimension and Form Errors of Deep-Hole Parts
Authors: Liang Jintao , Song Xiaotian , Wang Kaixin , Han Xiaolan
Published: December 2024

The precise measurement of inner dimensions and contour accuracy is required for deep-hole parts, particularly during the manufacturing process, to monitor quality and obtain real-time error parameters. However, on-machine measurement is challenging due to the limited inner space of deep holes. This study proposes an automatic on-machine measuring apparatus for assessing inner diameter, straightness, and roundness errors. Based on the axial-section measurement principle, an integrated measuring module was designed, including a self-centering mechanism, a diameter measuring sensor, and a positioning reference sensor, all embedded within a control system. On this basis, calculations of the inner diameter, and evaluations of the straightness and roundness errors are presented. Experimental verification is conducted on a blind deep hole with a nominal 100 mm inner diameter and 700 mm depth. Compared with measurements performed on a coordinate measuring machine (CMM), which is limited to a maximum hole depth of 300 mm, the proposed apparatus achieved full-depth on-machine measurements. Meanwhile, the measurement results were consistent with the data obtained by the CMM. The straightness error is considered less than 0.05 mm, and the roundness error is considered less than 0.015 mm. Ultimately, without requiring any additional reference platform, the proposed apparatus shows promise for measuring deep-hole parts on various machine tools, with diameters of no less than 80 mm and theoretically unlimited hole depth.
Improving Monitoring of Indoor RF-EMF Exposure Using IoT-Embedded Sensors and Kriging Techniques
Authors: Jabeur Randa , Alaerjan Alaa
Published: December 2024

Distributed wireless sensor networks (WSNs) are widely used to enhance the quality and safety of various applications. These networks consist of numerous sensor nodes, often deployed in challenging terrains where maintenance is difficult. Efficient monitoring approaches are essential to maximize the functionality and lifespan of each sensor node, thereby improving the overall performance of the WSN. In this study, we propose a method to efficiently monitor radiofrequency electromagnetic fields (RF-EMF) exposure using WSNs. Our approach leverages sensor nodes to provide real-time measurements, ensuring accurate and timely data collection. With the increasing prevalence of wireless communication systems, assessing RF-EMF exposure has become crucial due to public health concerns. Since individuals spend over 70% of their time indoors, it is vital to evaluate indoor RF-EMF exposure. However, this task is complicated by the complex indoor environments, furniture arrangements, temporal variability of exposure, numerous obstructions with unknown dielectric properties, and uncontrolled factors such as people’s movements and the random positioning of furniture and doors. To address these challenges, we employ a sensor network to monitor RF-EMF exposure limits using embedded sensors. By integrating Internet of Things-embedded sensors with advanced modeling techniques, such as kriging, we characterize and model indoor RF-EMF downlink (DL) exposure effectively. Measurements taken in several buildings within a few hundred meters of base stations equipped with multiple cellular antennas (2G, 3G, 4G, and 5G) demonstrate that the kriging technique using the spherical model provides superior RF-EMF prediction compared with the exponential model. Using the spherical model, we constructed a high-resolution coverage map for the entire corridor, showcasing the effectiveness of our approach.
Research on a Service Touchpoint Design Model Driven by Smart Technology Based on Kano–Failure Modes and Effects Analysis
Authors: Chen Jian , Li Zhihan , Wang Weiwei , Wang Yi , He Zhaoxuan
Published: December 2024

With the development of smart technology and the increasing variety of everyday products, factors influencing product service touchpoint design have become more diverse and complex. Existing service touchpoint design methods and models often focus narrowly on user research, co-design, and risk analyses, lacking a systematic approach. Consequently, they struggle to deliver solutions that align with user needs. This misalignment may result in issues such as increased cognitive load during product use, a diminished user experience, and lower evaluations of the product. In response, this paper proposes a service touchpoint design model, the “BEDFITA” model. It starts with user behavior and follows a structured, systematic process that includes understanding user behavior, recording user emotions, matching user needs, designing product functions, planning interaction experiences, designing service touchpoints, and analyzing failure risks. The Kano model is employed in the user requirement identification phase to provide more precise user requirement parameters, while FMEA is employed in the failure risk analysis phase to generate more accurate failure risk assessments. This ensures that the final service touchpoint design meets user needs and offers reliability and robustness. Finally, the feasibility and effectiveness of the proposed model are validated through a case study on the service touchpoint design of a smart desk.
Tackling Heterogeneous Light Detection and Ranging-Camera Alignment Challenges in Dynamic Environments: A Review for Object Detection
Authors: Wang Yujing , Abd Rahman Abdul Hadi , Nor Rashid Fadilla ’Atyka , Razali Mohamad Khairulamirin Md
Published: December 2024

Object detection is an essential computer vision task that identifies and locates objects within images or videos and is crucial for applications such as autonomous driving, robotics, and augmented reality. Light Detection and Ranging (LiDAR) and camera sensors are widely used for reliable object detection. These sensors produce heterogeneous data due to differences in data format, spatial resolution, and environmental responsiveness. Existing review articles on object detection predominantly focus on the statistical analysis of fusion algorithms, often overlooking the complexities of aligning data from these distinct modalities, especially dynamic environment data alignment. This paper addresses the challenges of heterogeneous LiDAR-camera alignment in dynamic environments by surveying over 20 alignment methods for three-dimensional (3D) object detection, focusing on research published between 2019 and 2024. This study introduces the core concepts of multimodal 3D object detection, emphasizing the importance of integrating data from different sensor modalities for accurate object recognition in dynamic environments. The survey then delves into a detailed comparison of recent heterogeneous alignment methods, analyzing critical approaches found in the literature, and identifying their strengths and limitations. A classification of methods for aligning heterogeneous data in 3D object detection is presented. This paper also highlights the critical challenges in aligning multimodal data, including dynamic environments, sensor fusion, scalability, and real-time processing. These limitations are thoroughly discussed, and potential future research directions are proposed to address current gaps and advance the state-of-the-art. By summarizing the latest advancements and highlighting open challenges, this survey aims to stimulate further research and innovation in heterogeneous alignment methods for multimodal 3D object detection, thereby pushing the boundaries of what is currently achievable in this rapidly evolving domain.
Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images
Authors: Praharsha Chittathuru Himala , Poulose Alwin , Badgujar Chetan
Published: December 2024

Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (Solanum lycopersicum), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and financial losses to farmers. Accurate detection and classification of tomato pests are the primary steps of integrated pest management practices, which are crucial for sustainable agriculture. This paper explores using Convolutional Neural Networks (CNNs) to classify tomato pest images automatically. Specifically, we investigate the impact of various optimizers on classification performance, including AdaDelta, AdaGrad, Adam, RMSprop, Stochastic Gradient Descent (SGD), and Nadam. A diverse dataset comprising 4263 images of eight common tomato pests was used to train and evaluate a customized CNN model. Extensive experiments were conducted to compare the performance of different optimizers in terms of classification accuracy, convergence speed, and robustness. RMSprop achieved the highest validation accuracy of 89.09%, a precision of 88%, recall of 85%, and F1 score of 86% among the optimizers, outperforming other optimizer-based CNN architectures. Additionally, conventional machine learning models such as logistic regression, random forest, naive Bayes classifier, support vector machine, decision tree classifier, and K-nearest neighbors (KNN) were applied to the tomato pest dataset. The best optimizer-based CNN architecture results were compared with these machine learning models. Furthermore, we evaluated the cross-validation results of various optimizers for tomato pest classification. The cross-validation results demonstrate that the Nadam optimizer with CNN outperformed the other optimizer-based approaches and achieved a mean accuracy of 79.12% and F1 score of 78.92%, which is 14.48% higher than the RMSprop optimizer-based approach. The state-of-the-art deep learning models such as LeNet, AlexNet, Xception, Inception, ResNet, and MobileNet were compared with the CNN-optimized approaches and validated the significance of our RMSprop and Nadam-optimized CNN approaches. Our findings provide insights into the effectiveness of each optimizer for tomato pest classification tasks, offering valuable guidance for practitioners and researchers in agricultural image analysis. This research contributes to advancing automated pest detection systems, ultimately aiding in early pest identification and proactive pest management strategies in tomato cultivation.
Addressing the Return Visit Challenge in Autonomous Flying Ad Hoc Networks Linked to a Central Station
Authors: Erkalkan Ercan , Topuz Vedat , Buldu Ali
Published: December 2024

Unmanned Aerial Vehicles (UAVs) have become essential tools across various sectors due to their versatility and advanced capabilities in autonomy, perception, and networking. Despite over a decade of experimental efforts in multi-UAV systems, substantial theoretical challenges concerning coordination mechanisms still need to be solved, particularly in maintaining network connectivity and optimizing routing. Current research has revealed the absence of an efficient algorithm tailored for the routing problem of multiple UAVs connected to a central station, especially under the constraints of maintaining constant network connectivity and minimizing the average goal revisit time. This paper proposes a heuristic routing algorithm for multiple UAV systems to address the return visit challenge in flying ad hoc networks (FANETs) linked to a central station. Our approach introduces a composite valuation function for target prioritization and a mathematical model for task assignment with relay allocation, allowing any UAV to visit various objectives and gain an advantage or incur a cost for each. We exclusively utilized a simulation environment to mimic UAV operations, assessing communication range, connectivity, and routing performance. Extensive simulations demonstrate that our routing algorithm remains efficient in the face of frequent topological alterations in the network, showing robustness against dynamic environments and superior performance compared to existing methods. This paper presents different approaches to efficiently directing UAVs and explains how heuristic algorithms can enhance our understanding and improve current methods for task assignments.
Plant Stress Detection Using a Three-Dimensional Analysis from a Single RGB Image
Authors: Pérez-Patricio Madaín , Osuna-Coutiño J. A. de Jesús , Ríos-Toledo German , Aguilar-González Abiel , Camas-Anzueto J. L. , Morales-Navarro N. A. , Velázquez-González J. Renán , Cundapí-López Luis Ángel
Published: December 2024

Plant stress detection involves the process of Identification, Classification, Quantification, and Prediction (ICQP) in crop stress. Numerous approaches exist for plant stress identification; however, a majority rely on expert personnel or invasive techniques. While expert employees demonstrate proficiency across various plants, this approach demands a substantial workforce to ensure the quality of crops. Conversely, invasive techniques entail leaf dismemberment. To overcome these challenges, an alternative is to employ image processing to interpret areas where plant geometry is observable, eliminating the dependency on skilled labor or the need for crop dismemberment. However, this alternative introduces the challenge of accurately interpreting ambiguous image features. Motivated by the latter, we propose a methodology for plant stress detection using 3D reconstruction and deep learning from a single RGB image. For that, our methodology has three steps. First, the plant recognition step provides the segmentation, location, and delimitation of the crop. Second, we propose a leaf detection analysis to classify and locate the boundaries between the different leaves. Finally, we use a Deep Neural Network (DNN) and the 3D reconstruction for plant stress detection. Experimental results are encouraging, showing that our approach has high performance under real-world scenarios. Also, the proposed methodology has 22.86% higher precision, 24.05% higher recall, and 23.45% higher F1-score than the 2D classification method.
Simulation and Prediction of Springback in Sheet Metal Bending Process Based on Embedded Control System
Authors: Xu Jinhan , Yan Jun , Huang Yan , Ding Dawei
Published: December 2024

Amidst the accelerating pace of automation in sheet metal bending, the need for small-batch, multi-varietal, efficient, and adaptable production modalities has become increasingly pronounced. To address this need and to enhance the efficacy of the bending process, this study presents the design and development of an embedded soft PLC (Programmable Logic Controller) rooted in the Codesys development platform and leveraging the ARM Cortex-A55 architecture. This controller employs the EtherCAT communication protocol to facilitate seamless and efficient interactions with fully electric servo-driven CNC (Computerized Numerical Control) bending machinery. To mitigate the challenge of bending springback errors, a finite element simulation model is constructed and refined through the application of ALE (Arbitrary Lagrangian-Eulerian) adaptive grid technology, thereby bolstering simulation precision. Subsequently, an enhanced WOA-BP (Whale Optimization Algorithm—Backpropagation) model, integrating Latin hypercube sampling and neural network techniques, is deployed to anticipate and counteract these springback errors. Experimental outcomes demonstrate that the proposed methodology effectively constrains the final forming angle deviation to within 0.3°, significantly enhancing the reliability and precision of the bending system. This achievement not only underscores the technical feasibility but also contributes to advancing the frontier of sheet metal bending automation.
Some Aspects of the Use of Carbon Dioxide as a Carrier and Makeup Gas in GC–FID Analysis
Authors: Dąbrowski Łukasz
Published: December 2024

The paper presents the possibility of using carbon dioxide as a carrier gas in capillary gas chromatography (with a stationary liquid phase) to analyze semi-volatile compounds (boiling points of up to 400 °C). Based on the experiments carried out for compounds from the group of organochlorine pesticides (OCPs), organophosphate pesticides (OPPs), polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs), the maximum volumetric flow rate (2.4 mL/min for CO2) was determined, enabling the correct separation of the tested standard mixtures (except for two compounds from the OCP group: 4,4′-DDD and Endrin aldehyde and two other pairs of compounds with Rs slightly less than 1.5). Compared to using helium as a carrier gas (and makeup), carbon dioxide produces wider (about 1.6 times) and lower (about 1.8 times) peaks of analytes; these values can vary depending on the separation efficiency of the column. Carbon dioxide can also be effectively used as a makeup gas for the FID detector. The signal increase is comparable to that obtained with helium used as makeup (on average 40–50% depending on the carrier gas). When high sensitivity and high resolution are not required, CO2 can be an alternative carrier and makeup gas to helium under the same flow conditions. The paper also describes practical aspects related to the implementation of CO2 as a carrier and makeup gas in GC.
The 288.2 K Isothermal Evaporation Experiment of Potassium Precipitation Brine in West Taijinair Salt Lake
Authors: Yang Yousheng , Wu Xiaowang , Yu Xudong , Qin Jiazheng , Su Jianjun , Quan Caixiong , Xu Pan
Published: December 2024

Rubidium and cesium are important strategic resources, and West Taijinar Salt Lake is rich in rubidium and cesium reserves, while the concentration is low and the relationship with coexisting potassium and magnesium ions is complex. In order to understand the evaporative enrichment and salt precipitation patterns of rare elements such as lithium, rubidium, cesium, and boron of the brine after potassium precipitation in West Taijinar Salt Lake, the 288.2 K isothermal evaporation experiment was carried out. The experimental results show that during the evaporation process at 288.2 K, the following salts precipitate from the brine after potassium crystallization: halite (NaCl), bischofite (MgCl2·6H2O), carnallite (KCl·MgCl2·6H2O), hexahydrite (MgSO4·6H2O), epsomite (MgSO4·7H2O), boric acid (H3BO3), and lithium sulfate monohydrate (Li2SO4·H2O). The concentrations of lithium and boron are significantly enriched, the content of Li+ was enriched from 1.7 g/L to 5.63 g/L, and the B2O3 content was enriched from 6.72 g/L to 50.78 g/L. The isomorphism phenomenon of Rb+, Cs+, and K+ makes Rb+ and Cs+ enter potassium ore to form solid solution-type carnallite ((K, Rb)MgCl3·6H2O, (K, Cs)MgCl3·6H2O)) and reduce the content of brine. This study provides data support for the development and comprehensive utilization of lithium, boron, rubidium, and cesium resources in West Taijinar Salt Lake.
Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy
Authors: Pace Leonardo , Priori Simone , Zanini Monica , Cristofori Valerio
Published: December 2024

Precision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost alternative that increases datapoints and map accuracy. This study tests and optimizes a methodology for high-detail soil mapping in a 2.5 ha hazelnut grove in Grosseto, Southern Tuscany, Italy, using both EMI sensors (GF Mini Explorer, Brno, Czech Republic) and a handheld NIR spectrometer (Neospectra Scanner, Si-Ware Systems, Menlo Park, CA, USA). In addition to two profiles selected by clustering, another 35 topsoil augerings (0–30 cm) were added. Laboratory analyses were performed on only five samples (two profiles + three samples from the augerings). Partial least square regression (PLSR) with a national spectral library, augmented by the five local samples, predicted clay, sand, organic carbon (SOC), total nitrogen (TN), and cation exchange capacity (CEC). The 37 predicted datapoints were used for spatial interpolation, using the ECa map, elevation, and DEM derivatives as covariates. Kriging with external drift (KED) was used to spatialize the results. The errors of the predictive maps were calculated using five additional validation points analyzed by conventional methods. The validation showed good accuracy of the predictive maps, particularly for SOC and TN.
Mathematical Modeling of Inhibitory Microbial Lethality Synergistic: Secondary Phytocompounds from Purple Toronjil, Temperature, and Harvest Stress Effects on Escherichia coli
Authors: Celis-Segura AyslethSacar , Reséndiz-Muñoz Juan , Delgado-Nuñez Edgar Jesús , Zamora-Gasa Víctor Manuel , Fernández-Muñoz José Luis , Cruz-Lagunas Blas , Godinez-Jaimes Flaviano , Gruintal-Santos Miguel Angel , Urbieta-Parrazales Romeo
Published: December 2024

This research investigated the inhibition of *Escherichia coli* ATCC 25922 (E. coli) bacterial growth in situ, specifically on the stems and aerial parts of *Agastache mexicana* subsp. mexicana (Amm) or “purple toronjil” and on food-grade paper, both contained within Kraft paper bags with a plastic window. The qualitative phytochemical profile of an aqueous extract of Amm revealed the presence of various compounds including alkaloids, coumarins, tannins, flavonoids, saponins, triterpenes, and sterols. The results indicate that these secondary metabolites exhibit a synergistic bactericidal effect, especially when combined with temperature and starvation stress. This was quantified using a decay equation referred to as the bacterial growth inhibition profile of E. coli (BGIPEc). Calculations, which included first derivatives, gradients based on substrate effects and temperature as well as the area under the curve of BGIPEc, demonstrated that higher temperatures led to the greater inhibition of colony forming units (CFUs), further enhanced by the presence of secondary metabolites. Additionally, a shorter half-life corresponded to a faster change rate and a lower area under the curve, indicating a reduced survival rate over time. At lower temperatures, E. coli exhibited a survival effect, which was corroborated by the preceding calculations.
Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning
Authors: Qiu Zhichao , Tian Ye , Luo Yanhong , Gu Taiyu , Liu Hengyu
Published: December 2024

Virtual power plants (VPPs) have emerged as an innovative solution for modern power systems, particularly for integrating renewable energy sources. This study proposes a novel prediction approach combining improved K-means clustering with Time Convolutional Networks (TCNs), a Bi-directional Gated Recurrent Unit (BiGRU), and an attention mechanism to enhance the forecasting accuracy of wind and photovoltaic power generation in VPPs. The proposed TCN-BiGRU-Attention model demonstrates superior predictive performance compared to traditional models, achieving high accuracy and robustness. These results provide a reliable basis for optimizing VPP operations and integrating renewable energy sources effectively.
Impacts of Air Pollution on Community Well-Being in the Tourism Sector: A Comprehensive Literature Review
Authors: Bacos Ioan Bogdan , Veres Cristina , Curta Petru Alexandru , Gabor Manuela Rozalia , Oltean Flavia Dana
Published: December 2024

This critical review examines the interplay between air pollution and community well-being within the tourism industry. Utilizing a systematic literature review methodology, the study focuses on three domains: air quality, tourism, and sustainability. Employing VOSviewer for bibliometric analysis, the research identifies key themes and trends. The findings reveal that air pollution, particularly in urban tourist destinations, negatively impacts tourist behavior, reduces visitor numbers, and threatens the economic sustainability of tourism. Additionally, it highlights health risks to both tourists and local communities, underscoring the need for adaptive strategies and sustainable practices. The study prioritizes articles employing statistical methods and incorporates geographic visualizations using GeoPandas and Matplotlib to illustrate research distribution. By synthesizing the existing literature, this review emphasizes the importance of effective policies that balance tourism growth with environmental protection amidst escalating ecological challenges.
Enhancing Transformer Protection: A Machine Learning Framework for Early Fault Detection
Authors: Alenezi Mohammed , Anayi Fatih , Packianather Michael , Shouran Mokhtar
Published: December 2024

The reliable operation of power transformers is essential for grid stability, yet existing fault detection methods often suffer from inaccuracies and high false alarm rates. This study introduces a machine learning framework leveraging voltage signals for early fault detection. Simulating diverse fault conditions—including single line-to-ground, line-to-line, turn-to-ground, and turn-to-turn faults—on a laboratory-scale three-phase transformer, we evaluated decision trees, support vector machines, and logistic regression models on a dataset of 6000 samples. Decision trees emerged as the most effective, achieving 99.90% accuracy during 5-fold cross-validation and 95% accuracy on a separate test set of 400 unseen samples. Notably, the framework achieved a low false alarm rate of 0.47% on a separate 6000-sample healthy condition dataset. These results highlight the proposed method’s potential to provide a cost-effective, robust, and scalable solution for enhancing transformer fault detection and advancing grid reliability. This demonstrates the efficacy of voltage-based machine learning for transformer diagnostics, offering a practical and resource-efficient alternative to traditional methods.
Carbon Neutrality Pathways for Industrial Parks and Reduction Potential Based on Text Analysis and Empirical Research
Authors: Sun Yuheng , Feng Zhengyuan , Liu Fangtao , Li Yuanhang , Ning Jun , Fu Hang , Tang Shoujuan , Liu Guangxin , Li Yang , Shi Lei
Published: December 2024

Climate change is seriously threatening ecological environments essential for human survival. Achieving the carbon neutrality goals of industrial parks (IPs), the gathering places of industrial activity, plays a crucial role in climate change mitigation and in promoting sustainable industrial development. In this study, we use an AI-facilitated literature review and empirical research to analyze and classify the 12 pathways for carbon neutrality for IPs based on the development nature and carbon flow framework. In addition, the carbon emission reduction potentials of various pathways are quantified using a case study. The results of the literature analysis show that, in terms of IP carbon emission reductions, most research has focused on the energy infrastructure related to energy management and utilization, as well as the service infrastructure related to the carbon footprint and industrial symbiosis. Among the 148 studies, “industrial symbiosis” and “carbon footprint” appeared with frequencies of 1278 and 474, respectively. Additionally, 102 of these studies mentioned decarbonization of energy infrastructure. In total, 11 of the carbon neutrality pathways existed in the empirical research, with the exception of carbon storage, and among them, 7 pathways are applied across all 104 IPs. Case studies indicate that the pathways with greater reduction potentials were the energy infrastructure, carbon capture, and the product and waste infrastructure, with potential reductions of 250,000 tons, 200,000 tons, and 145,080 tons of CO2 emissions, respectively. This study provides valuable references for IPs to achieve their carbon neutrality goals.
Research on Function Optimization of Electric Vehicle Charging Stations Based on User Demand Analysis: An Empirical Study Using the Kano Model and AHP Method
Authors: Liu Xiaoxue
Published: December 2024

With the widespread adoption of electric vehicles (EVs), the demand for reliable and user-friendly charging infrastructure has increased significantly. However, user dissatisfaction with public EV charging stations has also intensified, and the level of satisfaction with charging stations directly influences the development of the EV market. This study aimed to identify and prioritize user needs for EV charging stations to improve their design and functionality, ultimately enhancing user satisfaction and effectively promoting the sustainable development of the EV market. Using the KJ method, this study identified 23 key user needs and categorized them into must-be, one-dimensional, attractive, and indifferent requirements using the Kano model. The analytic hierarchy process (AHP) was subsequently applied to rank these requirements by their importance. The results indicate that, in the optimization of charging station functionality, the most critical user requirements include C1 charging gun stability (0.3176), C2 system stability (0.2822), C7 safety performance (0.0885), C15 payment convenience (0.0648), and C8 accurate feedback on charging station status (0.0501). This study provides valuable insights for designers and developers, offering a user-centered approach to optimizing public EV charging stations and improving the overall charging experience.
The Influence of Social Networks on Tourism Support Behaviors Among Ethnic Village Residents
Authors: Jiang Yajun , Wu Wei , Guo Yu , Zhou Huiling , Wu Ke
Published: December 2024

In the realm of tourism intervention and the reconfiguration of social networks among residents in ethnic villages, research on tourism support behavior is pivotal for comprehending the motivations behind resident participation and fostering sustainable tourism development. This study carried out a survey of 440 residents from Huangluo Yao Village and Jinkeng Dazhai Village in Longsheng County, Guilin City, Guangxi Zhuang Autonomous Region, China, using a questionnaire and explored the influencing mechanism of social networks on tourism support behavior based on the cognitive–affective processing system (CAPS) theory using structural equation modeling (SEM). The findings reveal that both strong and weak ties exert direct impacts on tourism support behavior while also indirectly influencing it via cultural identity. Furthermore, government governance capacity serves as a positive moderating factor in the relationships between ethnic village residents’ social networks—both strong and weak—and cultural identity, as well as between weak ties and tourism support behavior. The conclusions drawn from this study not only enhance the understanding of the formation mechanisms underlying tourism support behavior but also offer valuable insights for local governments aiming to implement effective strategies to promote tourism development.
Eco Breakthroughs: Sustainable Materials Transforming the Future of Our Planet
Authors: Popescu Catalin , Dissanayake Hiranya , Mansi Egla , Stancu Adrian
Published: December 2024

Interest in the sustainable materials sector is growing and accelerated. These materials are designed to reduce the use of non-renewable resources, limit greenhouse gas emissions, and be recyclable or biodegradable, making them highly attractive to both academia and industry. Constantly updating on innovations in this field is essential to speed up the transition to a circular economy and significantly reduce environmental impact. The paper analyzes the current status and future trends of the scientific literature for seven sustainability-related materials categories, such as sustainable materials, green materials, biomaterials, eco-friendly materials, alternative materials, material recycling and material recovery from complex products, and sustainable applied materials. Next, it assesses the impacts, benefits, and challenges associated with sustainable materials from the scientific literature according to six research fields (impact on the environment, performance and durability, economic efficiency, health and safety, social sustainability, and implementation and use). Furthermore, the paper outlines recent advances in sustainable material design, including biomimicry, nanotechnology, additive manufacturing, 3D printing, and sustainable composite materials. Additionally, a bibliometric analysis of 545 studies on sustainable materials published between 1999 and 2023 was conducted based on eight criteria, namely trend, source, author, country, keywords, thematic, co-citation, and content. The findings show that the sustainability-related materials categories have a particular distribution among the domains. Also, the thematic map analysis outlines that biopolymers, nanocellulose, and biocomposites are critical research areas for developing sustainable materials.
Differences in the Lateralization of Theta and Alpha Power During n-Back Task Performance Between Older and Young Adults in the Context of the Hemispheric Asymmetry Reduction in Older Adults (HAROLD) Model
Authors: Zając-Lamparska Ludmiła , Zabielska-Mendyk Emilia , Zapała Dariusz , Augustynowicz Paweł
Published: December 2024

Hemispheric Asymmetry Reduction in Old Adults (HAROLD) is one of the most well-known models of compensatory brain involvement in older adults. Most evidence supports its occurrence from the perspective of PET and fMRI studies, with a deficiency in electroencephalographic research in this domain. Therefore, we aimed to investigate the possibility of identifying the HAROLD pattern in older adults’ power of theta and alpha. The study sample comprised 50 older adults and 60 young adults performing n-back tasks while recording EEG signals. The level of cognitive performance and the theta and alpha power for pairs of symmetrical electrodes in the prefrontal, frontal, and parietal areas were analyzed. Older adults exhibited inferior cognitive performance compared to young adults and heightened theta power in the right hemisphere within the prefrontal and parietal areas. However, they also demonstrated increased alpha power in the right frontal pole, which contradicts the compensatory effects of theta power. Moreover, the two indicated phenomena of lateralization of theta and alpha power in older adults were unrelated to individual cognitive performance. The results make it challenging to discern whether the revealed age-related differences in theta and alpha power lateralization denote compensation, dedifferentiation, or nonselective recruitment as neutral features of brain activity in old adults.
Robustness of the Proxy-SU(3) Symmetry in Atomic Nuclei and the Role of the Next-Highest-Weight Irreducible Representation
Authors: Bonatsos Dennis , Martinou Andriana , Peroulis Spyridon K. , Petrellis Dimitrios , Vasileiou Polytimos , Mertzimekis Theodoros J. , Minkov Nikolay
Published: December 2024

The proxy-SU(3) symmetry predicts, in a parameter-free way, the collective deformation variables β and γ in even–even atomic nuclei away from closed shells based on the highest weight irreducible representations (irreps) of SU(3) in the relevant proton and neutron shells, which are the most symmetric irreps allowed by the Pauli principle and the short-range nature of the nucleon–nucleon interactions. The special cases in which the use of the next-highest-weight irrep of SU(3) becomes necessary are pointed out, and numerical results are given for several regions of the nuclear chart, which can be used as input for irrep-mixing calculations.
Modeling a Green and Reliable Intermodal Routing Problem for Food Grain Transportation Under Carbon Tax and Trading Regulations and Multi-Source Uncertainty
Authors: Sun Yan , Zhang Chen , Chen Ailing , Sun Guohua
Published: December 2024

This study addresses an intermodal routing problem encountered by an intermodal transportation operator fulfilling the food grain transportation order of an agri-food company. To enhance the environmental sustainability of food logistics, carbon tax and trading regulations have been employed to reduce the carbon emissions associated with transportation. Multi-source uncertainties, including the company’s demand for food grains and various parameters related to the intermodal transportation activities, are modeled via trapezoidal fuzzy numbers to optimize the comprehensive reliability of the solution. This work incorporates wastage reduction by lowering the wastage costs and formulating a wastage threshold constraint in intermodal routing. Accordingly, a fuzzy mixed-integer nonlinear programming model for a green and reliable intermodal routing problem for food grain transportation is proposed. To overcome the model’s insolvability and the difficulty in finding the global optimum solution to a nonlinear optimization model, a two-stage solution method is developed, employing chance-constrained programming and linearization technique to reformulate the initial model. A numerical case study is given to verify the feasibility of the proposed methods. Sensitivity analysis reveals the influence of confidence levels and wastage threshold, providing insights for the agri-food company to balance economics, reliability, and wastage reduction in food grain transportation. The numerical case study also analyzes the feasibility of carbon tax and trading regulations in reducing carbon emissions, concluding that carbon tax regulations consistently achieve greater reductions and are universally feasible. In contrast, the feasibility of carbon trading regulations depends on confidence levels and wastage threshold. The findings of this work could provide strong quantitative support for intermodal transportation operators and agri-food companies seeking to implement sustainable food grain transportation.
Cell-Cycle-Related and Expression Elevated Protein in Tumor Upregulates the Antioxidant Genes via Activation of NF-κB/Nrf2 in Acute Liver Injury
Authors: Wang Minghan , Wu Bin , Tang Kaiyang , Wang Xuexin , Liu Xinyan , Duan Yinan , Wang Jiayu , Wang Xiaoguang , Wang Yinyin , Li Jun , Cao Chenxi , Ren Fangli , Chang Zhijie
Published: December 2024

Background and aims: Cell-cycle-related and expression elevated protein in tumor (CREPT, also named RPRD1B) is highly expressed in tumors and functions to promote tumorigenesis. However, the role of CREPT in the pathophysiology of acute liver injury is limited. Here, we demonstrate that CREPT plays an essential role during acute liver injury. Approach and results: Hepatocyte-specific CREPT knockout (CREPThep−/−) and CREPTflox/flox mice were generated and subjected to the CCl4 challenge for the acute (24 h) liver injury. The acute CCl4 challenge triggered increased inflammation as well as liver injury, associated with stronger apoptotic and necroptotic cell death in CREPThep−/− mice. CREPT knockout down-regulated the expression of different genes involved in cell survival, inflammation and fibrosis under acute CCl4 challenge conditions. Antioxidant enzymes such as superoxide dismutase 2 (Sod2) and ferritin heavy chain 1 (Fth1) are dramatically induced at 24 h post-CCl4 treatment, but this induction is blocked by transcriptional inactivation of NF-κB/Nrf2, indicating that CREPT might promote hepatocyte survival in acute liver injury by participating in the transactivation of antioxidant genes. Conclusions: These results elucidate the role of CREPT in acute liver injury and provide hints for future research on how CREPT might function in hepatocyte renewal.
Acceptance and Completion Rates of 3-Month Isoniazid-Rifampicin (3HR) Tuberculosis Preventive Treatment (TPT) Among Contacts of Bacteriologically Confirmed TB Patients—Patients’ and Healthcare Workers’ Perspectives
Authors: Ihesie Austin , Chukwuogo Ogoamaka , Eneogu Rupert , Daniel Olugbenga Kayode , Agbaje Aderonke , Odume Bethrand , Nongo Debby , Ohikhuai Charles , Kadiri-Eneh Nera , Oyelaran Omosalewa , Obianeri Victor , Van Gemert Wayne , Masini Enos Okumu , D’auvergne Cleophas , Ochuko Urhioke , Anyaike Chukwuma , Olarewaju Sunday Olakunle
Published: December 2024

Providing tuberculosis preventive treatment (TPT) to close contacts of persons with TB is a core strategy recommended by WHO for the prevention and control of TB. Nigeria rolled out the 3-month Isoniazid-Rifampicin (3HR) shorter regimen TPT as a pilot for use among eligible adult and child contacts. This study assesses acceptance and completion rates of 3HR TPT among contacts and determines the perspectives of healthcare workers (HCWs) and contacts on acceptance and completion of 3HR TPT in Nigeria. In this cross-sectional descriptive study using mixed methods, records of TPT-eligible clients were retrospectively reviewed, while 18 purposely selected HCWs and 18 contacts on 3HR were interviewed. Of the 30,012 eligible contacts, 12,040 (40.1%) were initiated on TPT. Among these, 8213 (68%) were enrolled on 3HR, and 6972 (84.7%) of them completed treatment. Perceived facilitators include belief in its effectiveness, training among HCWs, and a good understanding of TPT from HCW counseling sessions. Barriers reported were linked to stockouts, misconceptions about side effects, non-disclosures, and disincentive follow-up strategies. The acceptance and completion rate for 3HR TPT was good. Scaling up 3HR TPT will require redesigning policies towards addressing identified barriers and utilizing interventions linked to capabilities, opportunities, and motivations among contacts of TB patients and HCWs.
Analyzing Urban Crime Through Street View Imagery: Insights from Urban Micro Built Environment and Perceptions
Authors: Wu Devin Yongzhao , Wang Jue
Published: December 2024

Understanding the relationship between urban crime and the built environment is crucial for developing effective crime prevention strategies, particularly in the context of rapid urban development and city planning. As cities grow, urbanization leads to environments that either promote or inhibit criminal activity, making it essential to explore the interactions between urban design and crime. This study investigates the impact of micro built environment (MBE) elements and place perceptions on crime occurrences in Toronto using street view imagery (SVI) data and machine learning models. We used logistic regression models and an XGBoost (Version 1.7.5) classifier to assess the significance of MBE and perception variables in classifying crime and non-crime intersections. Our findings reveal that intersections with criminal activity tend to be related to more mobility-related features, such as roads and vehicles, and fewer natural elements, such as vegetation. The “beautiful” and “depressing” perceptions emerged as the most significant variables in explaining crime events, surpassing the commonly studied “safety” perception. The XGBoost model achieved 86% accuracy, indicating that MBE and perception variables are strong predictors of crime risk. These findings suggest that enhancing vegetation and improving street aesthetics could serve as effective crime prevention measures in urban environments. However, limitations include the general nature of the perception model and the reliance on aggregated crime data. Future research should incorporate local perceptions and fine-scale crime data to provide more tailored insights for urban planning and crime prevention
Mapping the HPV Landscape in South African Women: A Systematic Review and Meta-Analysis of Viral Genotypes, Microbiota, and Immune Signals
Authors: Maswanganye Carol K. , Mkhize Pamela P. , Matume Nontokozo D.
Published: December 2024

This systematic review and meta-analysis evaluate human papillomavirus (HPV) prevalence, genotype distribution, and associations with cervicovaginal microbiota and cytokine profiles among South African women, where cervical cancer ranks as the second most common cancer. PubMed, SCOPUS, and Web of Science were searched for studies on HPV infection up to 21 September 2024. The pooled prevalence was estimated using a random-effects model, with subgroup analyses by province, sample type, and HIV status. Publication bias was evaluated using funnel plots and Egger’s test. Of the 19,765 studies screened, 120 met the inclusion criteria, comprising 83,266 participants. Results indicate a high HPV burden, with a pooled prevalence of 58% (95% CI: 52–64%), varying regionally from 53% (95% CI: 41–65%) to 64% (95% CI: 55–73%), with some regions under-researched. Cervical samples had the highest HPV prevalence (60% (95% CI: 54–66%)), while non-genital samples were less studied. High-risk (HR) HPV types, notably HPV 16 (7.5%), HPV 35 (4.1%), and HPV 18 (3.9%), were prominent, with HPV 35 emphasizing the need for expanded vaccine coverage. HIV-positive women had a higher pooled HPV prevalence (63% (95% CI: 55–71%)). Funnel plot analysis and Egger’s test suggested a potential publication bias (p = 0.047). HPV-positive women exhibited lower Lactobacillus levels and an increase in Bacterial Vaginosis (BV)-associated species like Gardnerella, potentially supporting HPV persistence. Cytokine analysis showed elevated MIP-1α and MIP-1β in HPV infections, though cytokine profiles may depend on HPV genotypes. These findings underscore the need for research on HPV–microbiome-immune interactions and call for comprehensive HPV-prevention strategies, including vaccines targeting regional HPV types and tailored interventions for HIV-positive populations.