bluuebunny/crossref_metadata_2025 huggingface dataset:
Metallic Nanoparticles for Surface-Enhanced Raman Scattering Based Biosensing Applications
Authors: Karlo Jiro , Razi Syed S. , Phaneeswar Mahamkali Sri , Vijay Arunsree , Singh Surya Pratap
Published: September 2024

Surface-enhanced Raman scattering (SERS) is a powerful tool for biosensing with high sensitivity, selectivity, and capability of multiplex monitoring for both in vivo and in vitro studies. This has been applied for the identification and detection of different biological metabolites such as lipids, nucleic acids, and proteins. The present review article explores the vast applications of metallic nanoparticles for SERS-based biosensing. We have summarized and discussed the fundamental principles, theories, developments, challenges, and perspectives in the field of SERS-based biosensing using different metal nanoparticle substrates namely gold, silver, copper, and bimetallic nanoparticles.
Delineating Regional BES–ELM Neural Networks for Studying Indoor Visible Light Positioning
Authors: Zhang Jiaming , Ke Xizheng
Published: September 2024

This paper introduces a single LED and four photodetectors (PDs) as a visible light system structure and collects the received signal strength values and corresponding physical coordinates at the PD receiving end, establishing a comprehensive dataset. The K-means clustering algorithm is employed to separate the room into center and boundary areas through the fingerprint database. The bald eagle search (BES) algorithm is employed to optimize the initial parameters, specifically the weights and thresholds, in the extreme learning machine (ELM) neural network, and the BES–ELM indoor positioning model is established by region to improve positioning accuracy. Due to the impact exerted by the ambient environment, there are fluctuations in the positioning accuracy of the center and edge regions, and the positioning of the edge region needs to be further improved. To address this, it is proposed to use the enhanced weighted K-nearest neighbor (EWKNN) algorithm based on the BES–ELM neural network to correct the prediction points with higher-than-average positioning errors, achieving precise edge positioning. The simulation demonstrates that within an indoor space measuring 5 m × 5 m × 3 m, the algorithm achieves an average positioning error of 2.93 cm, and the positioning accuracy is improved by 86.07% relative to conventional BP neural networks.
Tight Focusing of Light
Authors: Sheppard Colin J. R.
Published: September 2024

The effects of various properties on the tight focusing of light are considered. In particular, polarization of the incident field is an important consideration. Plots are presented for the variations in the focal intensity, and the area and volume of the focal spot, with numerical aperture. We consider Bessel beams, focusing with a system of circular pupil, and 4Pi focusing by a pair of opposing high-numerical-aperture lenses or a single paraboloidal mirror.
Scaling of Average Power in All-Fiber Side-Pumped Sub-MW Peak Power ps-Pulses Yb-Doped Tapered Amplifier
Authors: Mikhailov Egor K. , Levchenko Andrey E. , Velmiskin Vladimir V. , Zaushitsyna Tatiana S. , Bubnov Mikhail M. , Lipatov Denis S. , Shirmankin Andrey V. , Kamynin Vladimir A. , Likhachev Mikhail E.
Published: September 2024

In this study, we explored the potential for average power scaling in a monolithic side-counter-pumped combiner based on Yb-doped tapered fibers. The optimal configuration of the pump-feeding fibers was determined through experiments with passive signal fibers. It is shown that pump coupling efficiencies higher than 83% can be achieved for fibers coated with low-index polymer with a numerical aperture (NA) around 0.45 and more than 74% for fibers with second cladding made of F-doped silica (NA ~ 0.26) for pump power up to 100 W. It was shown that the main factor significantly reducing the pump-to-signal conversion efficiency in the developed monolithic Yb-doped tapered fiber amplifiers is the pump leakage due to the decrease of the first cladding diameter along the tapered fiber and the corresponding increase of the pump NA (which becomes higher than the NA of the first cladding). A solution to this problem based on a narrowing diameter at the output end of the tapered fiber was proposed and realized. The record-high average power of 41 W, with a coupling efficiency of 77.7%, was demonstrated in a monolithic amplifier with a threshold of nonlinear effects of more than 600 kW (for ps pulses). Prospects for further power scaling in all-fiber sub-MW peak power amplifiers are discussed.
Copper Nanoparticle Loaded Electrospun Patches for Infected Wound Treatment: From Development to In-Vivo Application
Authors: Butsyk Anna , Varava Yulia , Moskalenko Roman , Husak Yevheniia , Piddubnyi Artem , Denysenko Anastasiia , Korniienko Valeriia , Ramanaviciute Agne , Banasiuk Rafal , Pogorielov Maksym , Ramanavicius Arunas , Korniienko Viktoriia
Published: September 2024

This study investigates the development and application of electrospun wound dressings based on polylactic acid (PLA) nanofibers, chitosan, and copper nanoparticles (CuNPs) for the treatment of purulent skin wounds. The materials were evaluated for their structural, antibacterial, and wound healing properties using an animal model. PLA/Ch-CuNPs demonstrated the most significant antibacterial activity against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa, surpassing the other tested materials. The integration of CuNPs into the nanofiber matrices not only enhanced the antimicrobial efficacy but also maintained the structural integrity and biocompatibility of the dressings. In vivo experiments using a rat model showed that PLA/Ch-CuNPs facilitated faster wound healing with reduced exudative and inflammatory responses compared to PLA alone or PLA-CuNPs. Histological and immunohistochemical assessments revealed that the combination of PLA, chitosan, and CuNPs mitigated the inflammatory processes and promoted tissue regeneration more effectively. However, this study identified potential toxicity related to copper ions, emphasizing the need for careful optimization of CuNP concentrations. These findings suggest that PLA/Ch-CuNPs could serve as a potent, cost-effective wound dressing with broad-spectrum antibacterial properties, addressing the challenge of antibiotic-resistant infections and enhancing wound healing outcomes.
Mechanical and Biological Characterization of Ionic and Photo-Crosslinking Effects on Gelatin-Based Hydrogel for Cartilage Tissue Engineering Applications
Authors: Boretti Gabriele , Baldursson Hafsteinn Esjar , Buonarrivo Luca , Simonsson Stina , Brynjólfsson Sigurður , Gargiulo Paolo , Sigurjónsson Ólafur Eysteinn
Published: September 2024

Articular cartilage degeneration poses a significant public health challenge; techniques such as 3D bioprinting are being explored for its regeneration in vitro. Gelatin-based hydrogels represent one of the most promising biopolymers used in cartilage tissue engineering, especially for its collagen composition and tunable mechanical properties. However, there are no standard protocols that define process parameters such as the crosslinking method to apply. To this aim, a reproducible study was conducted for exploring the influence of different crosslinking methods on 3D bioprinted gelatin structures. This study assessed mechanical properties and cell viability in relation to various crosslinking techniques, revealing promising results particularly for dual (photo + ionic) crosslinking methods, which achieved high cell viability and tunable stiffness. These findings offer new insights into the effects of crosslinking methods on 3D bioprinted gelatin for cartilage applications. For example, ionic and photo-crosslinking methods provide softer materials, with photo-crosslinking supporting cell stretching and diffusion, while ionic crosslinking preserves a spherical stem cell morphology. On the other hand, dual crosslinking provides a stiffer, optimized solution for creating stable cartilage-like constructs. The results of this study offer a new perspective on the standardization of gelatin for cartilage bioprinting, bridging the gap between research and clinical applications.
An Eight-Coil Wireless Power Transfer Method for Improving the Coupling Tolerance Based on Uniform Magnetic Field
Authors: Liu Suqi , Yan Xueying , Xu Guiqiang , Wang Gang , Liu Yuping
Published: September 2024

In wireless power transfers (WPTs), it is challenging to obtain a constant output of power (COP) and constant transmission efficiency (CTE) in large coupling variation ranges. In this study, the eight-coil WPT system achieves a uniform magnetic field (UMF) in the transmitter and receiver sides using two transmitting (Tx) coils and two receiving (Rx) coils, respectively. COP and CTE are then achieved with large coupling variation ranges. The circuit model and equations of the transmission characteristics are first obtained based on the structure and working principle of the Helmholtz coil. The model of the mutual inductance and equation of the impedance coupled factor are then developed. The laws of the transmission characteristic are also determined by adopting a simulation tool and equations of the transmission characteristics. Finally, the eight-coil WPT experimental system is designed. In a fixed-frequency mode, the COP and CTE are achieved when the coupling and misalignment distances are changed within a quarter or one-fifth of the relay coil diameter, respectively. This topology provides an efficient solution for problems faced in practical applications, such as wireless chargers of kitchen appliances and automatic mobile robots of small size.
Wulfila, the Gothic Bible, and the Mission to the Goths: Rethinking the ‘Apostle to the Goths’ in Terms of Homoian Theology, Conversion as a Strategy of Empire, and Fourth Century Social and Cultural Transformations
Authors: Cusack Carole Mary
Published: September 2024

Wulfila (c. 311–c. 383) translated the Bible into Gothic, creating the first literary text in a Germanic language. His biography is contested; his parentage, place of birth, episcopal consecration, and theological position are all disputed. The fourth century saw heated debates about the Trinity, and the Goths were often termed ‘Arians’, despite the fact that the teachings of the African presbyter Arius (c. 256–336) were not directly transmitted to them. This article notes a rebirth of interest in Wulfila, his mission, and the Gothic Bible, employing the notion that ‘Homoian’ (a more neutral term than ‘Arian’) theology was a possible bridge between Catholic monotheism and Gothic polytheism as the starting point for a re-examination of Wulfila’s evangelism as both an imperially mandated strategy and the creation of a route into civilization and modernity for the Goths. Christianity was modern and fashionable in the fourth century; Germanic tribes wishing to abandon their status as pagani (rustics) or heathens (heath-dwellers, not civilized city dwellers), viewed conversion as a move ‘up’. The Gothic Bible played a role in developing Gothic literacy, but was also a magical object, the first of its kind, a book/roadmap for a people undergoing a great cultural transformation.
PESTEL Analysis of the Photovoltaic Market in Poland—A Systematic Review of Opportunities and Threats
Authors: Hysa Beata , Mularczyk Anna
Published: September 2024

In recent years, Poland has implemented substantial changes to its energy mix, resulting in an increased proportion of energy production from photovoltaics (PV). However, the photovoltaic energy market’s development is determined by several factors, and still requires further analysis. Therefore, the study’s main objective was to comprehensively understand the PV phenomenon and its development in Poland. Furthermore, a PESTEL analysis was undertaken to assess the macroeconomic context of the photovoltaic industry in Poland. A systematic literature review methodology was employed to achieve this. The study’s principal findings identified a number of pivotal opportunities and barriers to PV development. The environmental benefits of CO2 reduction and the economic advantages, including cost savings and subsidies, were identified as significant opportunities, as were social acceptance and enhanced energy security. However, obstacles to progress include outdated grid infrastructure, high investment costs, environmental concerns during the PV lifecycle, and political uncertainties. Technical challenges like grid stability and high battery costs also impede growth. Potential strategies for improvement involve better public awareness campaigns, enhanced self-consumption through storage systems, and optimised system placement. Addressing these factors could transform current neutral aspects into either opportunities or threats for PV deployment.
Application of Remote Sensing and Explainable Artificial Intelligence (XAI) for Wildfire Occurrence Mapping in the Mountainous Region of Southwest China
Authors: Liu Jia , Wang Yukuan , Lu Yafeng , Zhao Pengguo , Wang Shunjiu , Sun Yu , Luo Yu
Published: September 2024

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region.
Carbon and Energy Balance in a Primary Amazonian Forest and Its Relationship with Remote Sensing Estimates
Authors: Alves Mailson P. , da Silva Rommel B. C. , Silva Cláudio M. Santos e , Bezerra Bergson G. , Rêgo Mendes Keila , Marinho Larice A. , Barbosa Melahel L. , Nunes Hildo Giuseppe Garcia Caldas , Dos Santos José Guilherme Martins , Neves Theomar Trindade de Araújo Tiburtino , Santana Raoni A. , Peres Lucas Vaz , da Silva Alex Santos , Oliveira Petia , Moutinho Victor Hugo Pereira , Machado Wilderclay B. , Reis Iolanda M. S. , Seruffo Marcos Cesar da Rocha , Gaspar Avner Brasileiro dos Santos , Pereira Waldeir , Brito-Costa Gabriel
Published: September 2024

With few measurement sites and a great need to validate satellite data to characterize the exchange of energy and carbon fluxes in tropical forest areas, quantified by the Net Ecosystem Exchange (NEE) and associated with phenological measurements, there is an increasing need for studies aimed at characterizing the Amazonian environment in its biosphere–atmosphere interaction, considering the accelerated deforestation in recent years. Using data from a flux measurement tower in the Caxiuanã-PA forest (2005–2008), climatic data, CO2 exchange estimated by eddy covariance, as well as Gross Primary Productivity (GPP) data and satellite vegetation indices (from MODIS), this work aimed to describe the site’s energy, climatic and carbon cycle flux patterns, correlating its gross primary productivity with satellite vegetation indices. The results found were: (1) marked seasonality of climatic variables and energy flows, with evapotranspiration and air temperature on the site following the annual march of solar radiation and precipitation; (2) energy fluxes in phase and dependent on available energy; (3) the site as a carbon sink (−569.7 ± 444.9 gC m−2 year−1), with intensity varying according to the site’s annual water availability; (4) low correlation between productivity data and vegetation indices, corroborating data in the literature on these variables in this type of ecosystem. The results show the importance of preserving this type of environment for the mitigation of global warming and the need to improve satellite estimates for this region. NDVI and EVI patterns follow radiative availability, as does LAI, but without direct capture related to GPP data, which correlates better with satellite data only in the months with the highest LAI. The results show the significant difference at a point measurement to a satellite interpolation, presenting how important preserving any type of environment is, even related to its size, for the global climate balance, and also the need to improve satellite estimates for smaller areas.
Tidal Flat Extraction and Analysis in China Based on Multi-Source Remote Sensing Image Collection and MSIC-OA Algorithm
Authors: Sun Jixiang , Tang Cheng , Mu Ke , Li Yanfang , Zheng Xiangyang , Zou Tao
Published: September 2024

Tidal flats, a critical part of coastal wetlands, offer unique ecosystem services and functions. However, in China, these areas are under significant threat from industrialization, urbanization, aquaculture expansion, and coastline reconstruction. There is an urgent need for macroscopic, accurate and periodic tidal flat resource data to support the scientific management and development of coastal resources. At present, the lack of macroscopic, accurate and periodic high-resolution tidal flat maps in China greatly limits the spatio-temporal analysis of the dynamic changes of tidal flats in China, and is insufficient to support practical management efforts. In this study, we used the Google Earth Engine (GEE) platform to construct multi-source intensive time series remote sensing image collection from Sentinel-2 (MSI), Landsat 8 (OLI) and Landsat 9 (OLI-2) images, and then automated the execution of improved MSIC-OA (Maximum Spectral Index Composite and Otsu Algorithm) to process the collection, and then extracted and analyzed the tidal flat data of China in 2018 and 2023. The results are as follows: (1) the overall classification accuracy of the tidal flat in 2023 is 95.19%, with an F1 score of 0.92. In 2018, these values are 92.77% and 0.88, respectively. (2) The total tidal flat area in 2018 and 2023 is 8300.34 km2 and 8151.54 km2, respectively, showing a decrease of 148.80 km2. (3) In 2023, estuarine and bay tidal flats account for 54.88% of the total area, with most tidal flats distribute near river inlets and bays. (4) In 2023, the total length of the coastline adjacent to the tidal flat is 10,196.17 km, of which the artificial shoreline accounts for 67.06%. The development degree of the tidal flat is 2.04, indicating that the majority of tidal flats have been developed and utilized. The results can provide a valuable data reference for the protection and scientific planning of tidal flat resources in China.
KSL-POSE: A Real-Time 2D Human Pose Estimation Method Based on Modified YOLOv8-Pose Framework
Authors: Lu Tianyi , Cheng Ke , Hua Xuecheng , Qin Suning
Published: September 2024

Two-dimensional human pose estimation aims to equip computers with the ability to accurately recognize human keypoints and comprehend their spatial contexts within media content. However, the accuracy of real-time human pose estimation diminishes when processing images with occluded body parts or overlapped individuals. To address these issues, we propose a method based on the YOLO framework. We integrate the convolutional concepts of Kolmogorov–Arnold Networks (KANs) through introducing non-linear activation functions to enhance the feature extraction capabilities of the convolutional kernels. Moreover, to improve the detection of small target keypoints, we integrate the cross-stage partial (CSP) approach and utilize the small object enhance pyramid (SOEP) module for feature integration. We also innovatively incorporate a layered shared convolution with batch normalization detection head (LSCB), consisting of multiple shared convolutional layers and batch normalization layers, to enable cross-stage feature fusion and address the low utilization of model parameters. Given the structure and purpose of the proposed model, we name it KSL-POSE. Compared to the baseline model YOLOv8l-POSE, KSL-POSE achieves significant improvements, increasing the average detection accuracy by 1.5% on the public MS COCO 2017 data set. Furthermore, the model also demonstrates competitive performance on the CrowdPOSE data set, thus validating its generalization ability.
Research on a Metal Surface Defect Detection Algorithm Based on DSL-YOLO
Authors: Wang Zhiwen , Zhao Lei , Li Heng , Xue Xiaojun , Liu Hui
Published: September 2024

In industrial manufacturing, metal surface defect detection often suffers from low detection accuracy, high leakage rates, and false detection rates. To address these issues, this paper proposes a novel model named DSL-YOLO for metal surface defect detection. First, we introduce the C2f_DWRB structure by integrating the DWRB module with C2f, enhancing the model’s ability to detect small and occluded targets and effectively extract sparse spatial features. Second, we design the SADown module to improve feature extraction in challenging tasks involving blurred images or very small objects. Finally, to further enhance the model’s capacity to extract multi-scale features and capture critical image information (such as edges, textures, and shapes) without significantly increasing memory usage and computational cost, we propose the LASPPF structure. Experimental results demonstrate that the improved model achieves significant performance gains on both the GC10-DET and NEU-DET datasets, with a mAP@0.5 increase of 4.2% and 2.6%, respectively. The improvements in detection accuracy highlight the model’s ability to address common challenges while maintaining efficiency and feasibility in metal surface defect detection, providing a valuable solution for industrial applications.
Efficacy of Sensor-Based Training Using Exergaming or Virtual Reality in Patients with Chronic Low Back Pain: A Systematic Review
Authors: Morone Giovanni , Papaioannou Foivos , Alberti Alberto , Ciancarelli Irene , Bonanno Mirjam , Calabrò Rocco Salvatore
Published: September 2024

In its chronic and non-specific form, low back pain is experienced by a large percentage of the population; its persistence impacts the quality of life and increases costs to the health care system. In recent years, the scientific literature highlights how treatment based on assessment and functional recovery is effective through IMU technology with biofeedback or exergaming as part of the tools available to assist the evaluation and treatment of these patients, who present not only with symptoms affecting the lumbar spine but often also incorrect postural attitudes. Aim: Evaluate the impact of technology, based on inertial sensors with biofeedback or exergaming, in patients with chronic non-specific low back pain. A systematic review of clinical studies obtained from PubMed, Scopus, Science Direct, and Web of Science databases from 1 January 2016 to 1 July 2024 was conducted, developing the search string based on keywords and combinations of terms with Boolean AND/OR operators; on the retrieved articles were applied inclusion and exclusion criteria. The procedure of publication selection will be represented with the PRISMA diagram, the risk of bias through the RoB scale 2, and methodological validity with the PEDro scale. Eleven articles were included, all RCTs, and most of the publications use technology with exergaming within about 1–2 months. Of the outcomes measured, improvements were reported in pain, disability, and increased function; the neuropsychological sphere related to experiencing the pathology underwent improvements. From the results obtained, the efficacy of using technology based on exergames and inertial sensors, in patients with chronic non-specific low back pain, was increased. Further clinical studies are required to achieve more uniformity in the proposed treatment to create a common guideline for health care providers.
An Integrated Approach to Remediate Saline Soils and Mining Waste Using Technosols and Pasture Development
Authors: Aguilar-Garrido Antonio , Vidigal Patrícia , Caperta Ana Delaunay , Abreu Maria Manuela
Published: September 2024

Reclaiming marginal lands such as saline soils or mining waste for livestock grazing through Technosols and phytostabilisation can provide a solution to the growing food demand. This study evaluated the enhancement of soil properties by two Technosol constructions, along with pasture development. The experimental set-up consisted of gossan waste (G), Fluvisol (VF), Technosol/gossan (TG), and Technosol/Fluvisol (TVF), both Technosols consisting of G and VF, respectively, mixed with organic and inorganic amendments. These substrates were sown in pasture in pots (1.5 dm3) that was cut one and two months after sowing to simulate grazing. Both Technosols improved soils properties, with the acidity of G neutralising in TG. Yet, in TVF, a 65% reduction in salinity and a 60% drop in exchangeable Na occurred compared with VF. Nutrient pool, aggregate stability, and microbiological activity were also improved. Dehydrogenase activity was practically 0 in G, while in TG it was 15 times higher, and with pasture it increased 6-fold. In FV, some activity was already present, but in TVF it was six times higher and even increased with pasture. Finally, these improvements allowed the establishment of a healthy pasture, with twice the biomass and less accumulation of potentially hazardous elements in TG, and considerable growth in TVF. Thus, the co-application of Technosols and pasture may be effective in converting marginal lands into productive areas (grazing, foraging, biomass energy).
Design of a Novel Hybrid Concentrated Photovoltaic–Thermal System Equipped with Energy Storages, Optimized for Use in Residential Contexts
Authors: Cancro Carmine , Palladino Valeria , Borriello Aniello , Romano Antonio , Mongibello Luigi
Published: September 2024

Concentrated photovoltaic (CPV) technology is based on the principle of concentrating direct sunlight onto small but very efficient photovoltaic (PV) cells. This approach allows the realization of PV modules with conversion efficiencies exceeding 30%, which is significantly higher than that of the flat panels. However, to achieve optimal performance, these modules must always be perpendicular to solar radiation; hence, they are mounted on high-precision solar trackers. This requirement has led to the predominant use of CPV technology in the construction of solar power plants in open and large fields for utility scale applications. In this paper, the authors present a novel approach allowing the use of this technology for residential installations, mounting the system both on flat and sloped roofs. Therefore, the main components of cell and primary lens have been chosen to contain the dimensions and, in particular, the thickness of the module. This paper describes the main design steps: thermal analysis allowed the housing construction material to be defined to contain cell working temperature, while with deep optical studies, experimentally validated main geometrical and functional characteristics of the CPV have been identified. The design of a whole CPV system includes thermal storage for domestic hot water and a 1 kWh electrical battery. The main design results indicate an estimated electrical conversion efficiency of 30%, based on a cell efficiency of approximately 42% under operational conditions and a measured optical efficiency of 74%. The CPV system has a nominal electric output of 550 Wp and can simultaneously generate 630 W of thermal power, resulting in an overall system efficiency of 65.5%. The system also boasts high optical acceptance angles (±0.6°) and broad assembly tolerances (±1 mm). Cost analysis reveals higher unit costs compared to conventional PV and CPV systems, but these become competitive when considering the benefit of excess thermal energy recovery and use by the end user.
Exploring Engagement, Performance, and Satisfaction in Online Self-Directed Professional Learning Using LMS Logs
Authors: Hu Juan , Xiao Wen
Published: September 2024

Online self-directed professional learning plays a crucial role in sustainable career development. This study leverages a high-quality log dataset to thoroughly analyze the learning features of online self-directed professional learners, focusing on their engagement, performance, and satisfaction. The study reveals that the engagement levels among learners are predominantly low, with 56% categorized as low, 33% as medium, and 11% as high. The performance is generally strong, with 47% of learners achieving excellent results, although 4% fall into the poor category. The satisfaction levels are largely neutral (76%), with only 17% of learners expressing satisfaction and 7% feeling delighted. Despite high course ratings, the number of courses purchased remains minimal. The analysis found no significant correlations between engagement, performance, and satisfaction, but noted that purchasing additional courses can enhance engagement. Furthermore, lesson learning shows significant day-to-day fluctuations and minimal linear autocorrelation. The most significant predictor of course performance is the number of questions answered in quizzes. These findings help us to understand the patterns and relationships among these variables to inform future improvements in online learning platforms. Future research should expand LMS log collection to encompass a wider array of learning features for a more thorough analysis, and empirical research should be conducted to investigate potential underlying causes.
Sustainable Tourism Dynamics: Understanding the Impact of Tourist Stays on Regional Revenue and Development
Authors: Santos Eleonora
Published: September 2024

Utilizing a dataset covering four Portuguese NUTS II regions from 2009 to 2022 and employing multiple regression analyses, this research challenges traditional tourism theories by providing empirical evidence on how various factors—average stay, overnight stays, and foreign guests—affect regional revenue in tourist accommodations. In the context of sustainable tourism development, these findings have revealed a significant negative relationship between average stay duration and revenue, indicating that longer stays may be associated with lower daily revenues. This necessitates a re-evaluation of tourist spending behavior and accommodation of pricing strategies to promote economic sustainability. This study underscores the consistently positive impact of overnight stays on revenue, reinforcing the importance of maximizing overnight stays as a strategy for sustainable economic growth within the tourism sector. Additionally, the mixed results regarding the proportion of foreign guests reveal varying regional impacts, suggesting that region-specific strategies are essential for optimizing revenue from international tourists in a sustainable manner. This research integrates these factors to offer practical insights for refining revenue strategies in tourism management, emphasizing the need for multifaceted theoretical models that account for the complexity of sustainable tourism dynamics. Future research should focus on these nuanced approaches to better understand and manage the interplay between tourism dynamics and sustainable development.
Sustainable Hygiene Solutions: Developing a Foot-Operated Door Mechanism for Communal Spaces Using TRIZ and Universal Design Principles
Authors: Yao Kai-Chao , Cheng Chun-Nu , Li Kuo-Yi , Xu Jing-Ran , Huang Wei-Lun , Ho Wei-Sho , Liao Chin-Wen , Yang Shu-Chen , Hsiao Hui-Ling , Lin Yin-Chi , Lai Ching-Yi
Published: September 2024

Traditional door mechanisms in public spaces, such as knob locks and standard handles, require manual contact, making them prone to contamination and posing significant health risks. To address the critical need for a safer and more hygienic solution, this study aimed to develop an innovative foot-operated door mechanism that is accessible and intuitive for all users. The study applies the Theory of Inventive Problem Solving (TRIZ), ergonomic principles, and universal design to develop the foot-operated mechanism, while using Importance–Performance Analysis (IPA) and the Kano model to evaluate user satisfaction and identify design improvements. The foot-operated mechanism developed in this study features internal and external pedals for seamless door operation, a secure locking system, and color-coded indicators for clear occupancy status communication, ensuring both ease of use and privacy. The design significantly enhances hygiene by minimizing manual contact and improves user convenience, as confirmed through the IPA-Kano analysis. This mechanism not only provides a practical and effective solution to contamination risks but also demonstrates versatility, making it suitable for various public spaces and accessible to a wide range of users. This study represents a significant contribution to public infrastructure by providing a safer, more hygienic, and sustainable solution for door operation in public spaces.
Study on Transportation Carbon Emissions in Tibet: Measurement, Prediction Model Development, and Analysis
Authors: Bo Wu , Zhao Kunming , Cheng Gang , Wang Yaping , Zhang Jiazhe , Cheng Mingkai , Yang Can , Da Wa
Published: September 2024

In recent years, the socio-economic development in the Tibet region of China has experienced substantial growth. However, transportation increasingly strains the region’s fragile ecological environment. Most studies overlook the accurate measurement and analysis of factors influencing traffic carbon emissions in Tibet due to data scarcity. To address this, this paper applies an improved traffic carbon emissions model, using transportation turnover data to estimate emissions in Tibet from 2008 to 2020. Simultaneously, the estimated traffic carbon emissions in Tibet served as the predicted variable, and various machine learning algorithms, including Radial Basis Function Support Vector Machine (RBF-SVM), eXtreme Gradient Boosting (XGBoost), Random Forest, and Gradient Boosting Decision Tree (GBDT) are employed to conduct an initial comparison of the constructed prediction models using three-fold cross-validation and multiple evaluation metrics. The best-performing model undergoes further optimization using Grid Search (GS) and Real-coded Genetic Algorithm (RGA). Finally, the central difference method and Local Interpretable Model-Agnostic Explanation (LIME) algorithm are used for local sensitivity and interpretability analyses on twelve core variables. The results assess each variable’s contribution to the model’s output, enabling a comprehensive analysis of their impact on Tibet’s traffic carbon emissions. The findings demonstrate a significant upward trend in Tibet’s traffic carbon emissions, with road transportation and civil aviation being the main contributors. The RBF-SVM algorithm is most suitable for predicting traffic carbon emissions in this region. After GS optimization, the model’s R2 value exceeded 0.99, indicating high predictive accuracy and stability. Key factors influencing traffic carbon emissions in Tibet include civilian vehicle numbers, transportation land-use area, transportation output value, urban green coverage areas, per capita GDP, and built-up area. This paper provides a systematic framework and empirical support for measuring, predicting, and analyzing factors influencing traffic carbon emissions in Tibet. It employs innovative measurement methods, optimized machine learning models, and detailed sensitivity and interpretability analyses. The results can guide regional carbon reduction targets and promote green sustainable development.
Analyzing the Impact of Internet Use on Peer Effects in Farmers’ Adoption of Clean Energy: Strengthening or Weakening?
Authors: Hu Zeping , Zhang Tianshu , Zhang Kaiyue , Li Xinran
Published: September 2024

Enhancing farmers’ adoption of clean energy is crucial for promoting sustainable rural development and ecological environmental protection. It not only reduces the consumption of traditional fossil fuels, greenhouse gas emissions, and environmental pollution but also optimizes the structure of rural energy consumption, improves farmers’ quality of life, and supports the goal of building a green countryside. This paper investigates the impact of internet use on farmers’ adoption of clean energy and the associated peer effects, further exploring how internet use influences these peer effects. The analysis is based on data from the 2018 and 2020 waves of the China Family Panel Studies (CFPS). The study’s findings reveal that (1) farmers’ adoption of clean energy exhibits a significant peer effect, and internet use also has a significant positive impact on this adoption. Both the peer effect and internet use effectively enhance farmers’ clean energy utilization, a conclusion that holds even after robustness checks. (2) Internet use significantly strengthens the peer effect, particularly when it is used for social and entertainment purposes, where this reinforcing effect is most pronounced. (3) The peer effect, the impact of internet use on clean energy adoption, and the strengthening of the peer effect by internet use vary according to farmers’ geographical location and household income. These findings provide valuable insights and recommendations for improving policies aimed at promoting clean energy adoption among farmers, ultimately fostering its broader diffusion and application in rural areas.
Characterizing Inter-Seasonal Meteorological Drought Using Random Effect Logistic Regression
Authors: Hussain Anwar , Reihanifar Masoud , Niaz Rizwan , Albalawi Olayan , Maghrebi Mohsen , Ahmed Abdelkader T. , Danandeh Mehr Ali
Published: September 2024

Sustainable watershed development focuses on building resilience to drought through better water resource management, ecosystem protection, and adaptation strategies. In this study, the spatiotemporal dynamics and inter-seasonal characteristics of meteorological drought across Ankara Province, Turkey, were investigated and compared using a conditional fixed effect logistic regression model (CFELogRM) and a random effect logistic regression model (RELogRM). To assess the statistical validity and effectiveness of these models, we conducted significance tests, including the log-likelihood ratio chi-square, and Wald chi-square tests. The obtained p-values associated with both the RELogRM and CFELogRM models for the selected seasons demonstrate their statistical significance. Additionally, we conducted the Hausman test (HT) to compare the efficiency of the RELogRM and CFELogRM models. Remarkably, the results of the HT suggest that RELogRM is the optimal model for modeling fall-to-winter season drought dynamics across the study area. Notably, the significant coefficient derived from RELogRM indicates a statistically significant negative correlation between spring moisture conditions and the probability of summer droughts. Specifically, the odds ratio of 0.2416 reflects a 24.16% reduction in the likelihood of transitioning to a higher drought category, emphasizing the crucial role of antecedent moisture conditions in influencing drought propensity.
Disaggregated Impact of Non-Renewable Energy Consumption on the Environmental Sustainability of the United States: A Novel Dynamic ARDL Approach
Authors: Ghose Tanmoy Kumar , Islam Md Rezanual , Aruga Kentaka , Jannat Arifa , Islam Md. Monirul
Published: September 2024

While there is a vast body of literature on environmental sustainability, the disaggregated impact of major non-renewable energy (NRE) consumption on the environmental sustainability of the United States (U.S.) is understudied, particularly in terms of using a load capacity factor (LCF) perspective. In this study, the above research gap is addressed using a dynamic autoregressive distributed lag (DYNARDL) model to analyze the heterogeneous impact of NRE consumption on the environmental sustainability of the U.S. from 1961 to 2022. Given the U.S.’s heavy reliance on energy consumption from NRE sources, this analysis provides an in-depth examination of the long-term effects of this energy consumption on the environment. Based on the analysis of the DYNARDL model, it is found that an increase of one unit of coal, natural gas, and petroleum energy consumption reduces environmental sustainability by 0.007, 0.006, and 0.008 units in the short-run and 0.006, 0.004, and 0.005 units in the long-run, respectively. However, one unit of nuclear energy consumption increases environmental sustainability by 0.007 units in the long-run. The kernel-based regularized system (KRLS) result reveals that coal and petroleum energy consumption have a significantly negative causal link with environmental sustainability, while nuclear energy consumption demonstrates a significant positive causal relationship. The research suggests the expansion of the use of nuclear energy by gradually reducing the utilization of coal and petroleum-based forms of energy, then natural gas, to improve environmental sustainability in the U.S., while considering the social and economic implications of efforts aimed at shifting away from the use of fossil fuels.
Quantifying Water Storage Changes and Groundwater Drought in the Huaihe River Basin of China Based on GRACE Data
Authors: Zhou Zunguang , Lu Baohong , Jiang Zhengfang , Zhao Yirui
Published: September 2024

The Huaihe River Basin is an important ecological function conservation area in China, and it is also an important production area for national food, energy, minerals, and manufacturing. The groundwater storage and groundwater drought in this region are of great significance for ecological maintenance and water resources management. In this study, based on GRACE data and GLDAS data, a dynamic calculation method for groundwater storage in the Huaihe River Basin was developed, and a groundwater drought index (GRACE-GDI) was derived. By coupling GRACE-GDI with run theory, the quantitative identification of groundwater drought events, as well as their duration, intensity, and other characteristics within the basin, was achieved. The spatiotemporal changes in groundwater storage and groundwater drought in the Huaihe River Basin were analyzed using the developed method. The results showed that GRACE data are highly applicable in the Huaihe River Basin and is capable of capturing the spatiotemporal variations in groundwater storage in this region. Over the study period, mainly affected by rainfall, the terrestrial water storage and surface water storage in the Huaihe River Basin showed a decreasing trend, while groundwater storage showed a slight increasing trend. The duration of groundwater drought events in the basin ranged from 78 to 152 months, with an intensity of 82.77 to 104.4. The duration of drought gradually increased from north to south, while the intensity increased from south to north.
A Study on the Cognitive, Normative, and Affective Factors That Enhance the Pro-Environmental Behaviors of Consumers in the Context of Drone Food Delivery Services
Authors: Joo Kyuhyeon , Kim Heather Markham , Hwang Jinsoo
Published: September 2024

The current study examined consumers’ sustainable behavior in the context of eco-friendly drone food delivery services. It thus focused on the cognitive triggers, normative factors, and affective triggers in forming pro-environmental intentions. This study more specifically developed a comprehensive framework that included cognitive triggers, which included environmental awareness, ascribed responsibility, biospheric value, environmental concern, and perceived effectiveness, personal norm, and affective triggers, which included positive anticipated emotion and negative anticipated emotion, subjective norm, and pro-environmental intentions. The 312 samples were randomly extracted through an online survey company in Korea, which targeted individuals who have dined out within the last six months. A theoretical model that included mediation and causal hypotheses was proposed and tested by conducting structural equation modeling. The data analysis results indicated that the four dimensions of the cognitive triggers, such as ascribed responsibility, biospheric value, environmental concern, and perceived effectiveness, positively affect personal norm, and consequently have a positive effect on positive anticipated emotion. In addition, subjective norm aids in regards to enhancing personal norm and pro-environmental intentions. The study consequently contributes knowledge that addresses the acknowledged research gap and provides practical suggestions for green marketing strategies in regards to promoting the services.
The Impact of Sustainability Considerations on Investment Intentions—The Case of Generation Y
Authors: Hinrichs Keno , Sobol Iwona
Published: September 2024

When investing, the investment motives of return, liquidity, and risk play a role in decision-making. However, due to the increasing relevance of environmental and social issues and the higher availability of sustainable investments, sustainability is an additional investment motive. The attitude of an individual toward sustainability has implications for other investment motives. This paper examines the interplay between the established financial investment motives of return, liquidity, and risk on the one hand and sustainability considerations on the other hand, with a view to the Generation Y cohort. A questionnaire approach was used to collect data from randomly selected Generation Y retail banking customers from Germany. The data were analyzed using correlation and regression methods. The findings of the paper confirm that there is a negatively directed relationship between the profit maximization motive and the green tradeoff intention. Furthermore, education moderates the relationship between the risk minimization motive and the green tradeoff intention. The paper contributes to different stakeholders. Practical implications result for retail banks and investment firms, which could continue to motivate Generation Y customers for sustainable investments and generate stronger financial education through targeted marketing and information campaigns.