bluuebunny/crossref_metadata_2025 huggingface dataset:
Low-Tech and Low-Cost System for High-Resolution Underwater RTK Photogrammetry in Coastal Shallow Waters
Authors: Jaud Marion , Delsol Simon , Urbina-Barreto Isabel , Augereau Emmanuel , Cordier Emmanuel , Guilhaumon François , Le Dantec Nicolas , Floc’h France , Delacourt Christophe
Published: December 2023

Monitoring coastal seabed in very shallow waters (0–5 m) is a challenging methodological issue, even though such data is of major importance to many scientific and technical communities. Over the years, Structure-from-Motion (SfM) photogrammetry has emerged as a flexible and inexpensive method able to provide both a 3D model and high-resolution imagery of the seabed (~cm level). In this study, we propose a low-cost (about USD 1500), adaptable, lightweight and easily dismantled system called POSEIDON (for Platform Operating in Shallow-water Environment for Imaging and 3D reconstructiON). This prototype combines a floating support (typically a bodyboard), two imagery sensors (here, GoPro® cameras) and an accurate positioning system using Real Time Kinematic GNSS. Validation of this method was deployed in a macrotidal zone, comparing on the foreshore the point cloud provided by POSEIDON “SfM bathymetry” and by classical terrestrial SfM survey. Mean deviation was 5.2 cm and standard deviation was 4.6 cm. Such high-resolution SfM bathymetric surveys have a great potential for a wide range of applications: micro-bathymetry, hydrodynamics (bottom roughness), benthic habitats, ecological inventories, archaeology, etc.
A Hybrid-Scale Feature Enhancement Network for Hyperspectral Image Classification
Authors: Liu Dongxu , Shao Tao , Qi Guanglin , Li Meihui , Zhang Jianlin
Published: December 2023

Due to their devastating ability to extract features, convolutional neural network (CNN)-based approaches have achieved tremendous success in hyperspectral image (HSI) classification. However, previous works have been dedicated to constructing deeper or wider deep learning networks to obtain exceptional classification performance, but as the layers get deeper, the gradient disappearance problem impedes the convergence stability of network models. Additionally, previous works usually focused on utilizing fixed-scale convolutional kernels or multiple available, receptive fields with varying scales to capture features, which leads to the underutilization of information and is vulnerable to feature learning. To remedy the above issues, we propose an innovative hybrid-scale feature enhancement network (HFENet) for HSI classification. Specifically, HFENet contains two key modules: a hybrid-scale feature extraction block (HFEB) and a shuffle attention enhancement block (SAEB). HFEB is designed to excavate spectral–spatial structure information of distinct scales, types, and branches, which can augment the multiplicity of spectral–spatial features while modeling the global long-range dependencies of spectral–spatial informative features. SAEB is devised to adaptively recalibrate spectral-wise and spatial-wise feature responses to generate the purified spectral–spatial information, which effectively filters redundant information and noisy pixels and is conducive to enhancing classification performance. Compared with several sophisticated baselines, a series of experiments conducted on three public hyperspectral datasets showed that the accuracies of OA, AA, and Kappa all exceed 99%, demonstrating that the presented HFENet achieves state-of-the-art performance.
Studying the Impact of the Geospace Environment on Solar Lithosphere Coupling and Earthquake Activity
Authors: Ouzounov Dimitar , Khachikyan Galina
Published: December 2023

In solar–terrestrial physics, there is an open question: does a geomagnetic storm affect earthquakes? We expand research in this direction, analyzing the seismic situation after geomagnetic storms (GMs) accompanied by the precipitation of relativistic electrons from the outer radiation belt to form an additional radiation belt (RB) around lower geomagnetic lines. We consider four widely discussed cases in the literature for long-lived (weeks, months) RBs due to GMs and revealed that the 1/GMs 24 March 1991 with a new RB at L~2.6 was followed by an M7.0 earthquake in Alaska, 30 May 1991, near footprint L = 2.69; the 2/GMs 29 October 2003 (Ap = 204) with new RB first in the slot region at L = 2–2.5 cases followed by an M7.8 earthquake on 17 November 2003 at the Aleutian Islands near footprint L = 2.1, and after forming an RB at L~1.5 which lasted for ~26 months, two mega quakes, M9.1 in 2004 and M8.6 in 2005, occurred at the globe; the 3/GMs 3 September 2012 with a new RB at L= 3.0–3.5 was followed by an M7.8 earthquake in Canada near footprint L = 3.2; and the 4/GMs 21 June 2015 with a new RB at L = 1.5–1.8 was followed by an M6.3 earthquake on 7 September 2015 in New Zealand, near footprint L = 1.58. The obtained results suggest that (1) major earthquakes occur near the footprints of geomagnetic lines filled with relativistic electrons precipitating from the outer radiation belt due to geomagnetic storms, and (2) the time delay between geomagnetic storm onset and earthquake occurrence may vary from several weeks to several months. The results may expand the framework for developing mathematical magnetosphere–ionosphere coupling models.
Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model
Authors: Li Haixing , Liu Jinrong , Xiao Mengge , Bao Xiaolong
Published: December 2023

It is essential to investigate the influences of environmental elements on snow cover to understand the mechanism of the snowmelt process. These elements, as influencing factors, have spatial heterogeneity, which results in significant differences and uncertainties in the extent and range of their effects at different scales. However, little research has been conducted on the spatial interaction and mechanisms of these factors at multiple scales. This study selected the Manas River basin in the Tianshan Mountains as the study area. The study period is 2015–2020. The snow cover status index is calculated based on available Landsat8-OLS/TIRS data; influencing factors are collected from multiple datasets. Their relationships are explored using a novel zonal spatial panel model, fully considering the spatial, proximity, and scale effects. The findings are as follows: (1) There is a robust spatial interaction and proximity effect between snowmelt and various factors, and such effects display distinct spatial heterogeneity. The elevation (ELE), slope (SLP), land surface temperature (LST), and normalized digital vegetation index (NDVI) showed significant overall dominant effects on the snow melting process. The influencing factors with apparent proximity effects are LST, ELE, SLP, NDVI, and air temperature (TEMP), and their influence ranges are different. (2) The relative importance and significance rank of dominant influencing factors vary under different partition schemes and scales. As the scale decreases, the significance of terrain- and vegetation-related factors increases, whereas the significance of temperature- and elevation-related factors decreases, and the number of dominant factors also decreases. (3) The influencing factors represent distinct characteristics among each zone at the optimally partitioned scale we defined. The overall influencing pattern demonstrates a characteristic of being globally dictated by elevation and temperature, with local terrain factors, vegetation, and wind speed modifying this pattern. Our study provides practical data support and a theoretical basis for deepening our understanding of the influence mechanism of the snow melting process.
Development of Intensity–Duration–Frequency (IDF) Curves over the United Arab Emirates (UAE) Using CHIRPS Satellite-Based Precipitation Products
Authors: Alsumaiti Tareefa S. , Hussein Khalid A. , Ghebreyesus Dawit T. , Petchprayoon Pakorn , Sharif Hatim O. , Abdalati Waleed
Published: December 2023

The recent flooding events in the UAE have emphasized the need for a reassessment of flood frequencies to mitigate risks. The exponential urbanization and climatic changes in the UAE require a reform for developing and updating intensity–duration–frequency (IDF) curves. This study introduces a methodology to develop and update IDF curves for the UAE at a high spatial resolution using CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data. A bias correction was applied to the CHIRPS data, resulting in an improved capture of extreme events across the country. The Gumbel distribution was the most suitable theoretical distribution for the UAE, exhibiting a strong fit to the observed data. The study also revealed that the CHIRPS-derived IDF curves matched the shape of IDF curves generated using rain gauges. Due to orographic rainfall in the northeastern region, the IDF intensities were at their highest there, while the aridity of inland regions resulted in the lowest intensities. These findings enhance our understanding of rainfall patterns in the UAE and support effective water resource management and infrastructure planning. This study demonstrates the potential of the CHIRPS dataset for IDF curve development, emphasizes the importance of performing bias corrections, and recommends tailoring adjustments to the intended application.
Wildfire Burnt Area Severity Classification from UAV-Based RGB and Multispectral Imagery
Authors: Simes Tomás , Pádua Luís , Moutinho Alexandra
Published: December 2023

Wildfires present a significant threat to ecosystems and human life, requiring effective prevention and response strategies. Equally important is the study of post-fire damages, specifically burnt areas, which can provide valuable insights. This research focuses on the detection and classification of burnt areas and their severity using RGB and multispectral aerial imagery captured by an unmanned aerial vehicle. Datasets containing features computed from multispectral and/or RGB imagery were generated and used to train and optimize support vector machine (SVM) and random forest (RF) models. Hyperparameter tuning was performed to identify the best parameters for a pixel-based classification. The findings demonstrate the superiority of multispectral data for burnt area and burn severity classification with both RF and SVM models. While the RF model achieved a 95.5% overall accuracy for the burnt area classification using RGB data, the RGB models encountered challenges in distinguishing between mildly and severely burnt classes in the burn severity classification. However, the RF model incorporating mixed data (RGB and multispectral) achieved the highest accuracy of 96.59%. The outcomes of this study contribute to the understanding and practical implementation of machine learning techniques for assessing and managing burnt areas.
Extended Reality Telemedicine Collaboration System Using Patient Avatar Based on 3D Body Pose Estimation
Authors: Šarić Matko , Russo Mladen , Kraljević Luka , Meter Davor
Published: December 2023

Recent advances in extended reality (XR) technology have opened the possibility of significantly improving telemedicine systems. This is primarily achieved by transferring 3D information about patient state, which is utilized to create more immersive experiences on VR/AR headsets. In this paper, we propose an XR-based telemedicine collaboration system in which the patient is represented as a 3D avatar in an XR space shared by local and remote clinicians. The proposed system consists of an AR client application running on Microsoft HoloLens 2 used by a local clinician, a VR client application running on the HTC vive Pro used by a remote clinician, and a backend part running on the server. The patient is captured by a camera on the AR side, and the 3D body pose estimation is performed on frames from this camera stream to form a 3D patient avatar. Additionally, the AR and VR sides can interact with the patient avatar via virtual hands, and annotations can be performed on a 3D model. The main contribution of our work is the use of 3D body pose estimation for the creation of a 3D patient avatar. In this way, 3D body reconstruction using depth cameras is avoided, which reduces system complexity and hardware and network resources. Another contribution is the novel architecture of the proposed system, where audio and video streaming are realized using WebRTC protocol. The performance evaluation showed that the proposed system ensures high frame rates for both AR and VR client applications, while the processing latency remains at an acceptable level.
Design and Verification of Integrated Circuitry for Real-Time Frailty Monitoring
Authors: Rodriguez-Cobo Luis , Diaz-SanMartin Guillermo , Algorri Jose Francisco , Fernandez-Viadero Carlos , Lopez-Higuera Jose Miguel , Cobo Adolfo
Published: December 2023

In this study, a new wireless electronic circuitry to analyze weight distribution was designed and incorporated into a chair to gather data related to common human postures (sitting and standing up). These common actions have a significant impact on various motor capabilities, including gait parameters, fall risk, and information on sarcopenia. The quality of these actions lacks an absolute measurement, and currently, there is no qualitative and objective metric for it. To address this, the designed analyzer introduces variables like Smoothness and Percussion to provide more information and objectify measurements in the assessment of stand-up/sit-down actions. Both the analyzer and the proposed variables offer additional information that can objectify assessments depending on the clinical eye of the physicians.
Blast-Assisted Subsurface Characterisation Using a Novel Distributed Acoustic Sensing Setup Based on Geometric Phases
Authors: Shaheen Sabahat , Hicke Konstantin , Krebber Katerina
Published: December 2023

A novel DAS setup based on geometric phases in coherent heterodyne detection is applied for the first time to the characterisation of the Earth’s subsurface. In addition, an optimisation of the proposed setup in terms of its spatial resolution is also presented for the first time. The surface waves are generated by strong blasts of 25 kg of explosives at a dedicated test site. A 10 km dark fiber link in the vicinity of the test site connected to the test setup records the resulting strain signals. The spike-free and low-noise strain data thus obtained minimize post-processing requirements, making the setup a candidate for real-time seismic monitoring. An analysis of the dispersion characteristics of the generated surface waves is performed using a recently reported optimised seismic interferometric technique. Based on the dispersion characteristics, the shear wave velocities of the surface waves as a function of the depth profile of the Earth’s crust are determined using an optimised evolutionary algorithm.
Improving the Physical Properties and Water Retention of Sandy Soils by the Synergistic Utilization of Natural Clay Deposits and Wheat Straw
Authors: Alghamdi Abdulaziz G. , Majrashi Mosaed A. , Ibrahim Hesham M.
Published: December 2023

Improving the physical properties and water retention of sandy soils is of critical importance in arid and water-scarce regions such as Saudi Arabia. The impacts of organic amendments of different particle sizes coupled with natural clay deposits on improving the soil physio-chemical characteristics, nutrient availability, and growth of Sudan grass were investigated in this study. A loamy sand soil was amended with natural clay deposits at 2.5%, 5.0%, and 10% (w/w) application rates, and in combination with 1.0% (w/w) wheat straw of different particle sizes. The water infiltration, evaporation, and retention characteristics of the amended soil were studied for 9 weeks, and then Sudan grass was grown for 7 weeks. The impacts of the particle size of wheat straw on soil properties and nutrient availability were significant (p < 0.05) when combined with clay deposits. The highest application rate of clay (10%) demonstrated the highest water content (20.63–21.73%), and increased P and K availability to 35.54 and 6980 mg kg−1, respectively, in soil, which were 33% and 88% higher, respectively, compared to the control. Plant N, P, and K concentrations were increased to 0.95%, 0.26%, and 4.33%, respectively, which were 2–3.5-fold higher than the control. Therefore, the integrated application of natural clay deposits and wheat straw of fine particle size could be an effective strategy in improving plant production in water-scarce regions.
Understanding Active Transportation to School Behavior in Socioeconomically Disadvantaged Communities: A Machine Learning and SHAP Analysis Approach
Authors: Etaati Bita , Jahangiri Arash , Fernandez Gabriela , Tsou Ming-Hsiang , Ghanipoor Machiani Sahar
Published: December 2023

Active Transportation to School (ATS) offers numerous health benefits and is considered an affordable option, especially in disadvantaged neighborhoods. The US Centers for Disease Control and Prevention (CDC) advises 60 min of daily physical exercise for children aged 6 to 17, making ATS a compelling approach to promote a healthier lifestyle among students. Initiated in 2005 by the US Department of Transportation (DOT), the Safe Routes to School (SRTS) program aims to foster safe and regular walking and biking to school for students. This paper examines students’ travel behavior using SRTS survey data and assesses the program’s effectiveness in promoting ATS in Chula Vista, California. Employing machine learning algorithms (random forest, logistic regression, and support vector machines) to predict students’ likelihood to walk to school, it utilizes SHAP (SHapley Additive exPlanations) to pinpoint significant variables influencing ATS across all models. SHAP underscores critical factors affecting transportation choices to school, highlighting the importance of home-to-school distance, with shorter distances positively impacting active transportation. However, only half of students within schools’ walking distance opted to walk to school, underscoring the necessity of addressing parental safety concerns, including factors such as crime rates and traffic speed along the route.
Psychological and Educational Factors of Digital Competence Optimization Interventions Pre- and Post-COVID-19 Lockdown: A Systematic Review
Authors: Díaz-Burgos Alberto , García-Sánchez Jesús-Nicasio , Álvarez-Fernández M. Lourdes , de Brito-Costa Sonia M.
Published: December 2023

The rapid development of the ever-changing information and communication society demands skills from its members that allow access to and adapt to the various situations that they may face. To achieve this, it is essential to acquire a set of key competencies throughout different stages of life, among which we find digital competence. This systematic review aims to analyse, through a series of focal points and indicators, the internationally published interventions in the last ten years aimed at improving digital literacy and the acquisition of this competence by students in early childhood education, primary education, and higher education, as well as professionals from various fields. The procedure followed for the selection of the interventions has been documented and graphically represented according to the PRISMA statement, with searches conducted across various databases and journals. In total, 26 studies were selected, covering the period before, during, and after the COVID-19 health lockdown, and the influence of the lockdown on the development of digital competence was examined. The obtained results show the evolution of the selected interventions in terms of general aspects, instructional and evaluative procedures, fidelity, and encountered limitations. The results demonstrate a growing concern for the development of digital competence, amplified by the needs arising during the COVID-19 lockdown and evidenced by an increase in interventions aimed at this goal. It also showcases the relationship between adequate acquisition and the nurturing of other psychoeducational variables like motivation or satisfaction.
Can a Water Bank Improve Groundwater Rights Market Efficiency?—A Study Based on Monte Carlo Simulations
Authors: Chen Yiying , Luo Taolu , Tian Guiliang , Zhao Qiuya
Published: December 2023

Groundwater possesses a natural advantage as a reservoir, making it well-suited for storage. Introducing a water bank to the groundwater market not only reduces storage costs but also revitalizes the groundwater rights market, achieving synergy between the economic and ecological values of regional water resources. This study combines the core functions of a water bank to establish a theoretical framework for mechanisms concerning value appreciation and risk prevention. Approaching the topic from the perspectives of transaction costs and market price mechanisms, we employ the Monte Carlo algorithm for simulation to study the mechanistic impact of a water bank on the efficiency of the water market. It reveals that a water bank, compared to direct transactions between parties, can effectively reduce the number of transactions in the fragmented water rights market. Additionally, it effectively utilizes market transaction information to provide accurate pricing. Furthermore, we alter model parameters to explore the relationship between the average number of transactions in the market and supply–demand equilibrium. We also investigate how changes in the acceptable price range in the supply–demand market affect the pricing strategies of a water bank and open market pricing. These findings substantiate the necessity of developing a water bank in practice.
Leveraging Information and Communication Technologies in Forest Ecotourism: A Case Study from Poland
Authors: Kozłowska-Adamczak Monika , Essing-Jelonkiewicz Patrycja , Jezierska-Thöle Aleksandra
Published: December 2023

Ecotourism encompasses various forms of tourism which are implemented in accordance with the principles of sustainable development of the EU. They maintain a balance between the three pillars of sustainable development: economic development, sustainable use of natural resources, and respect for social and cultural values. Ecotourism in forests holds particular significance due to its ability to sustain the ecological, social, and economic integrity of these areas. Activities promoting access to forests focus on ensuring a safe and enriching experience for forest users while simultaneously conserving nature. In response to the needs of forest tourism and recreation enthusiasts, innovative developments and new information and communications technologies (ICTs) such as websites, web platforms, mobile apps, QR codes, and others are being introduced with increasing frequency. These technologies already have a wide range of applications in providing access to tourism information and services in other sectors of tourism management and are now supplanting conventional sightseeing information systems. This paper aims to present the activities currently underway in connection with the development of a new tourism and recreation offering in the forests owned by the State Treasury and managed by the State Forests National Forest Holding in Poland. The research involved conducting an environmental survey among representatives of all forest districts in Poland (n = 425). A questionnaire survey was created using the Microsoft Forms web-based programme and made available online to representatives of all State Forests’ forest districts in Poland participating in the Stay Overnight in the Forest programme. The scope of this paper covers the present day, with a primary focus on desk research and analysis of statistics and source materials pertaining to the period from 2007 to 2022, retrieved from internal documents and reports prepared by the General Directorate of the State Forests. The research demonstrated that technological innovations such as mobile apps, social media, websites, web portals, and YouTube TV, when employed for developing forest tourism and forest recreation in Poland, can contribute to the advancement of the forest’s non-productive role, namely, a social role and in particular, the tourism, leisure, and recreational function of the State Forests.
Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization
Authors: Hu Lingling , Zhou Junming , Jiang Feng , Xie Guangming , Hu Jie , Mo Qinglie
Published: December 2023

Many electric vehicles connected to the grid will lead to problems such as poor stability of power grid generation. The key to solving these problems is to propose an efficient, stable, and economical valley-filling charging scheme for electric vehicles and grid users in the vehicle network system. Firstly, the convex optimization theory is used to make the grid achieve the optimization effect of valley filling. On this basis, the electricity price scheme with a time-varying coefficient as the variable is proposed to meet the single objective optimization of EV charging cost optimization, and its degree of influence on the grid valley-filling effect is analyzed. Secondly, based on the competitive relationship between EV charging cost and battery life, the P2D model is simplified and analyzed, and the attenuation law of battery capacity is quantitatively described. The multi-objective optimization problem is established to express in a Pareto matrix. Finally, the compatibility between the multi-objective optimization and grid valley charging is analyzed. The simulation results show that: (1) The convexity electricity price scheme can satisfy the requirements of various retention rates to achieve the valley-filling effect; (2) The filling effect is satisfied with the electricity price scheme that minimizes the charging cost, and the key factors affecting the filling effect are analyzed; (3) The multi-objective optimization scheme with charging cost and battery life is compatible with the valley-filling effect.
Coordination Relationship of Carbon Emissions and Air Pollutants under Governance Measures in a Typical Industrial City in China
Authors: Wang Junjie , Ma Juntao , Wang Sihui , Shu Zhuozhi , Feng Xiaoqiong , Xu Xuemei , Yin Hanmei , Zhang Yi , Jiang Tao
Published: December 2023

Coordinating and controlling carbon and atmospheric pollutant emissions in industrial cities poses challenges, making it difficult to formulate effective environmental governance strategies in China. This study used the Community Multiscale Air Quality (CMAQ) and Long-range Energy Alternatives Planning (LEAP) models, with a typical industrial city in the Sichuan Basin as the case study. Five emission reduction scenarios, one integration scenario, and one baseline scenario were set to quantitatively analyze the synergistic effect between carbon emissions and atmospheric pollutant emissions. The results indicate a high synergy between sulfur dioxide and greenhouse gases. For every one-point decrease in the Air Quality Composite Index (AQCI), the Industrial Restructuring Scenario (IR), Other Source Management Scenario (OSM), Transportation Energy Efficiency Improvement Scenario (TEEI), Industrial Energy Efficiency Improvement Scenario (IEEI), and Transportation Restructuring (TR) scenarios would require a reduction in carbon emissions by 56,492.79 kilotons, 39,850.45 kilotons, 34,027.5 kilotons, 22,356.58 kilotons, and 3243.33 kilotons, respectively. The results indicate that governance measures, such as improving transportation structure and upgrading industrial technologies, provide stronger support for simultaneous carbon emissions reductions and air quality improvement.
Comprehensive Review of Crystalline Silicon Solar Panel Recycling: From Historical Context to Advanced Techniques
Authors: Chen Pin-Han , Chen Wei-Sheng , Lee Cheng-Han , Wu Jun-Yi
Published: December 2023

This review addresses the growing need for the efficient recycling of crystalline silicon photovoltaic modules (PVMs), in the context of global solar energy adoption and the impending surge in end-of-life (EoL) panel waste. It examines current recycling methodologies and associated challenges, given PVMs’ finite lifespan and the anticipated rise in solar panel waste. The study explores various recycling methods—mechanical, thermal, and chemical—each with unique advantages and limitations. Mechanical recycling, while efficient, faces economic and environmental constraints. Thermal methods, particularly pyrolysis, effectively break down organic materials but are energy-intensive. Chemical processes are adept at recovering high-purity materials but struggle with ecological and cost considerations. The review also highlights multifaceted challenges in recycling, including hazardous by-product generation, environmental impact, and the economic feasibility of recycling infrastructures. The conclusion emphasizes the need for innovative, sustainable, and economically viable recycling technologies. Such advancements, alongside global standards and policy development, are crucial for the long-term sustainability of solar energy and effective management of PVM waste.
A Multi-Objective Optimization Method of Sustainable Wind–Photovoltaic–Hydro Systems Considering Source–Grid Coordination
Authors: Shen Qin , Mo Li , Liu Zixuan , Sun Xutong , Liu Guanjun , Zhang Yongchuan
Published: December 2023

Hydropower compensating for wind and solar power is an efficient approach to overcoming challenges in the integration of sustainable energy. Our study proposes a multi-objective scheduling model for the complementary operation of wind–photovoltaic–hydro systems. The model aims to maximize the total generation while minimizing the mean square deviation of the system output and grid load. Taking wind and solar bases and key peak-shaving cascade hydropower stations in Hubei Province as a case study, various multi-objective Pareto solution sets were obtained for different scheduling periods. The analysis dissects the relationship between total generation and the stability of residual load after adjustment by the wind–photovoltaic–hydro systems. Furthermore, the study analyzes the role that a complementary system should play in the power grid and discusses the effect of cascade hydropower scheduling methods on the operational characteristics of multi-energy complementary systems.
Analysing the Effects of Scenario-Based Explanations on Automated Vehicle HMIs from Objective and Subjective Perspectives
Authors: Ma Jun , Feng Xuejing
Published: December 2023

Automated vehicles (AVs) are recognized as one of the most effective measures to realize sustainable transport. These vehicles can reduce emissions and environmental pollution, enhance accessibility, improve safety, and produce economic benefits through congestion reduction and cost savings. However, the consumer acceptance of and trust in these vehicles are not ideal, which affects the diffusion speed of AVs on the market. Providing transparent explanations of AV behaviour is a method for building confidence and trust in AV technologies. In this study, we investigated the explainability of user interface information in an Automated Valet Parking (AVP) system—one of the first L4 automated driving systems with a large commercial landing. Specifically, we proposed a scenario-based explanation framework based on explainable AI and examined the effects of these explanations on drivers’ objective and subjective performance. The results of Experiment 1 indicated that the scenario-based explanations effectively improved drivers’ situational trust and user experience (UX), thereby enhancing the perception and understanding that drivers had of the system’s intelligence capabilities. These explanations significantly reduced the mental workload and elevated the user performance in objective evaluations. In Experiment 2, we uncovered distinct explainability preferences among new and frequent users. New users sought increased trust and transparency, benefiting from guided explanations. In contrast, frequent users emphasised efficiency and driving safety. The final experimental results confirmed that solutions customised for different segments of the population are significantly more effective, satisfying, and trustworthy than generic solutions. These findings demonstrate that the explanations for individual differences, based on our proposed scenario-based framework, have significant implications for the adoption and sustainability of AVs.
A Novel Approach for the Reuse of Waste from the Extractive and Processing Industry of Natural Stone Binders: Development of Stone Composites
Authors: Afonso Paula , Pires Vera , Faria Paula , Azzalini Antônio , Lopes Luis , Mourão Paulo , Martins Ruben
Published: December 2023

This paper discusses the historical use of natural stone for various purposes and highlights the substantial waste generated by quarries and stone processing plants in modern industrialized production. These waste materials are typically categorized into unused rock and sludges from processing. This accumulation of waste presents a global environmental challenge and a financial burden for the stone industry. In-depth investigation into the development of a binder incorporating carbonated sludge from marble and limestone industries, combined with polyester resin, for building stone composites was performed. This research involved chemical and microstructural characterization of the sludges, preparation of mixtures with polyester resin, stone composites manufacturing, and subsequent testing to determine and validate the optimal binder composition. Given the achieved results, and the demonstrated feasibility of using a binder composed of polyester resin and carbonated sludge for stone composite production, it was concluded that employing carbonated sludge as an economic resource is indeed viable.
From Image to Imagination: Exploring the Impact of Generative AI on Cultural Translation in Jewelry Design
Authors: Lyu Yanru , Shi Minghong , Zhang Yanbo , Lin Rungtai
Published: December 2023

The current proliferation of artificial intelligence (AI) is prominently shaping the design industry. Generative AI, such as text-to-image and image-to-image models, has gained widespread use, notably for its efficiency and quality improvements. However, their potential to aid in cultural translation within creative design is underexplored. To address the existing gap, this study aims to assess the impact of generative AI on cultural translation within jewelry design. Specifically, a comprehensive study was conducted through a design-action experiment, collecting 46 student designers’ design-action data and self-reports, and enlisting the evaluation from 30 design experts. The findings highlight the substantial influence of generative AI on the ideation phase of jewelry design, especially in depth rather than breadth, and in the shape factor at the technical level such as detailization and unexpected composition. Leveraging AI image generators has shifted the designer’s focus from technical tasks to strategic decisions related to visual appeal, cognitive engagement, and emotional resonance. Furthermore, the challenges inherent in human–AI collaboration have been revealed, stemming from communication difficulties and the risk of fixating on specific details to stylistic constraints. Based on data analysis, a novel hybrid model regarding human–AI co-creation on cultural translation in jewelry design is proposed. Overall, this current study offers a valuable reference point to future research in terms of examining the effect of emerging technologies on cultural translation in creative fields.
A Study on Korean Customers’ Intentions to Repurchase for the Sustainable Growth of the Athleisure Market
Authors: Lee Hong Joo
Published: December 2023

The athleisure market has experienced significant growth in recent years, establishing itself as a mainstream trend in the fashion industry. Given the escalating demand for athleisure wear, businesses must secure sustainable growth by comprehending customer intentions to repurchase. This study conducted a survey to collect customer data in order to study customers’ intentions to repurchase athleisure wear. Additionally, survey questions were developed through a literature review and incorporated into the survey. To determine the suitable sample size for the analysis while considering statistical significance, the study took into account the current total population of South Korea, a confidence level of 95%, and a margin of error of 5%. This calculation determined that a sample size of 400 in this study was well-suited to the data analysis. In a study investigating customer intentions to repurchase athleisure wear, several influential factors were identified. Firstly, the purpose of product use (motivation for wearing) and functionality emerged as critical determinants affecting customer intentions to repurchase. Customers who perceived a clear purpose of use and functional excellence were more inclined to repurchase, underscoring the paramount importance of designing athleisure products with a strong focus on functionality. Nevertheless, it was observed that male and female customers exhibited distinctive levels of satisfaction with the product. Male customers placed greater emphasis on the product’s essential attributes, whereas female customers highlighted the importance of how athleisure wear fits into their everyday lives and its aesthetics. This divergence indicates that male and female customers possess varying preferences when it comes to product attributes. Consequently, it is imperative to devise tailored marketing strategies that align with the distinct priorities and interests of male and female consumers during the sale of athleisure wear products. In summary, this study underscores the significance of comprehending customer behavior and intentions to repurchase within the athleisure market. By prioritizing the articulation of the product’s purpose and enhancing its functionality, businesses operating in the athleisure sector can secure enduring growth and success.
Analyzing EU’s Agricultural Sector and Public Spending under Climate Change
Authors: Dincă Gheorghița , Netcu Ioana-Cătălina , El-Naser Asmaa
Published: December 2023

Climate change not only affects weather conditions, patterns, and the frequency and severity of extreme weather events but also changes the structure of government spending. Agriculture is an important sector of the European Union (EU). However, by 2050, the industry will most likely decrease by 16%. One-third of the EU’s budget has been spent on agricultural funding, adaptation, and climate action. The effect of climate change on agriculture is mixed and dependent on the location of the region. The southern EU is adversely affected, while the northern EU is positively affected by the changes in weather patterns. The main goal of this paper is to gain insight regarding the effect climate change has on public spending in relation to the agricultural sector of the EU, using the pooled Ordinary Least Squares (OLS) and Generalized Method of Moments (GMM) methodology. The study concludes that public spending is influenced by government expenditure and government support in agricultural research and development in the EU region. In the southern EU region, the variables impacting public spending are greenhouse gases from the agricultural sector, temperature, and GDP, while in the northern region, no variable has a significant impact on public spending proxied by agricultural subsidies. The policy recommendations include a better allocation of agricultural subsidies, reconsideration of the efficiency of Common Agricultural Policy (CAP), and a focus on expanding investment in research and development in the agricultural sector.
Bacillus thuringiensis Bt_UNVM-84, a Novel Strain Showing Insecticidal Activity against Anthonomus grandis Boheman (Coleoptera: Curculionidae)
Authors: Sauka Diego Herman , Peralta Cecilia , Pérez Melisa Paula , Molla Antonella , Fernandez-Göbel Tadeo , Ocampo Federico , Palma Leopoldo
Published: December 2023

Bacillus thuringiensis is a Gram-positive bacterium known for its insecticidal proteins effective against various insect pests. However, limited strains and proteins target coleopteran pests like Anthonomous grandis Boheman, causing substantial economic losses in the cotton industry. This study focuses on characterizing a Bacillus sp. strain, isolated from Oncativo (Argentina), which exhibits ovoid to amorphous parasporal crystals and was designated Bt_UNVM-84. Its genome encodes genes for the production of two pairs of binary Vpb1/Vpa2 proteins and three Cry-like proteins showing similarity with different Cry8 proteins. Interestingly, this gene content was found to be conserved in a previously characterized Argentine isolate of B. thuringiensis designated INTA Fr7-4. SDS-PAGE analysis revealed a major band of 130 kDa that is proteolytically processed to an approximately 66-kDa protein fragment by trypsin. Bioassays performed with spore-crystal mixtures demonstrated an interesting insecticidal activity against the cotton boll weevil A. grandis neonate larvae, resulting in 91% mortality. Strain Bt_UNVM-84 is, therefore, an interesting candidate for the efficient biological control of this species, causing significant economic losses in the cotton industry in the Americas.
The Effect of Botanical Pesticides Azadirachtin, Celangulin, and Veratramine Exposure on an Invertebrate Species Solenopsis invicta (Hymenoptera: Formicidae)
Authors: Liang Yuling , Liang Mingrong , Chen Huimei , Hong Jingxin , Song Yunbo , Yue Kuo , Lu Yongyue
Published: December 2023

The injudicious and excessive use of synthetic pesticides has deleterious effects on humans, ecosystems, and biodiversity. As an alternative to traditional crop-protection methods, botanical pesticides are gaining importance. In this research endeavor, we examined the contact toxicity, knockdown time, lethal time, and toxicity horizontal transmission of three natural pesticides from plants (azadirachtin, celangulin, and veratramine) on red imported fire ants (RIFA; Solenopsis invicta). Our research findings indicated that azadirachtin and celangulin exhibited relatively high toxicity, with median lethal dose (LD50) values of 0.200 and 0.046 ng/ant, respectively, whereas veratramine exhibited an LD50 value of 544.610 ng/ant for large workers of S. invicta at 24 h post-treatment. Upon treatment with 0.125 mg/L, the (median lethal time) LT50 values of azadirachtin and celangulin were determined to be 60.410 and 9.905 h, respectively. For veratramine, an LT50 value of 46.967 h was achieved after being tested with 200 mg/L. Remarkably, azadirachtin and celangulin were found to exhibit high horizontal transfer among RIFA, with high secondary mortality (100%) and tertiary mortalities (>61%) after 48 h of treatment with 250 mg/L, as well as with their dust formulations for 72 h. However, veratramine did not exhibit significant toxicity or horizontal transfer effects on RIFA, even at high concentrations. These findings suggest that azadirachtin and celangulin are likely to have a highly prominent potential in the management of S. invicta.
Cellular Processes Induced by HSV-1 Infections in Vestibular Neuritis
Authors: Zhao Zhengdong , Liu Xiaozhou , Zong Yanjun , Shi Xinyu , Sun Yu
Published: December 2023

Herpesvirus is a prevalent pathogen that primarily infects human epithelial cells and has the ability to reside in neurons. In the field of otolaryngology, herpesvirus infection primarily leads to hearing loss and vestibular neuritis and is considered the primary hypothesis regarding the pathogenesis of vestibular neuritis. In this review, we provide a summary of the effects of the herpes virus on cellular processes in both host cells and immune cells, with a focus on HSV-1 as illustrative examples.
Reference Evapotranspiration Estimation Using Genetic Algorithm-Optimized Machine Learning Models and Standardized Penman–Monteith Equation in a Highly Advective Environment
Authors: Kiraga Shafik , Peters R. Troy , Molaei Behnaz , Evett Steven R. , Marek Gary
Published: December 2023

Accurate estimation of reference evapotranspiration (ETr) is important for irrigation planning, water resource management, and preserving agricultural and forest habitats. The widely used Penman–Monteith equation (ASCE-PM) estimates ETr across various timescales using ground weather station data. However, discrepancies persist between estimated ETr and measured ETr obtained from weighing lysimeters (ETr-lys), particularly in advective environments. This study assessed different machine learning (ML) models in comparison to ASCE-PM for ETr estimation in highly advective conditions. Various variable combinations, representing both radiation and aerodynamic components, were organized for evaluation. Eleven datasets (DT) were created for the daily timescale, while seven were established for hourly and quarter-hourly timescales. ML models were optimized by a genetic algorithm (GA) and included support vector regression (GA-SVR), random forest (GA-RF), artificial neural networks (GA-ANN), and extreme learning machines (GA-ELM). Meteorological data and direct measurements of well-watered alfalfa grown under reference ET conditions obtained from weighing lysimeters and a nearby weather station in Bushland, Texas (1996–1998), were used for training and testing. Model performance was assessed using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2). ASCE-PM consistently underestimated alfalfa ET across all timescales (above 7.5 mm/day, 0.6 mm/h, and 0.2 mm/h daily, hourly, and quarter-hourly, respectively). On hourly and quarter-hourly timescales, datasets predominantly composed of radiation components or a blend of radiation and aerodynamic components demonstrated superior performance. Conversely, datasets primarily composed of aerodynamic components exhibited enhanced performance on a daily timescale. Overall, GA-ELM outperformed the other models and was thus recommended for ETr estimation at all timescales. The findings emphasize the significance of ML models in accurately estimating ETr across varying temporal resolutions, crucial for effective water management, water resources, and agricultural planning.
Secchi Disk Depth or Turbidity, Which Is Better for Assessing Environmental Quality in Eutrophic Waters? A Case Study in a Shallow Hypereutrophic Reservoir
Authors: Golubkov Mikhail S. , Golubkov Sergey M.
Published: December 2023

Water transparency is widely used in environmental monitoring programs and in assessing water quality in aquatic environments. The purpose of this study was to determine which of two water transparency-measuring tools, a Secchi disk or a water turbidity meter, is better to assess environments in shallow eutrophic waters. Measurements of the Secchi disk depth (Dsd) and water turbidity (Turb) were carried out simultaneously at eight stations of the hypereutrophic Sestroretsky Razliv reservoir in 2015–2018. In May, October, and December, Dsd varied around 0.6 m but was significantly lower in August during algal blooms. Turbidity ranged from 10 to 20 nephelometric turbidity units (NTU) in different seasons but increased to almost 70 NTU in August. Principal component analysis revealed that Dsd was inversely proportional to Turb, total suspended solids, and chlorophyll concentrations. The data showed that at turbidities below 20 NTU, the Secchi disk clearly distinguishes differences in water transparency, but when Turb exceeds 40 NTU, measuring transparency using the Secchi disk no longer allows for water differentiation. In this case, it is preferable to use water turbidity measurements, which remain an effective indicator even in highly turbid waters. This should be taken into account when assessing the environment in eutrophic waters.