Search Results (198,932)

The rapid development of Connected and Autonomous Vehicles (CAVs) presents challenges in managing mixed traffic flows. Previous studies have primarily focused on mixed traffic flow involving CAVs and Human-Driven Vehicles (HDVs), or on the combination of trucks and cars. However, these studies have not fully addressed the heterogeneous mixed traffic flow consisting of CAVs and HDVs, including trucks and cars, influenced by varying human driving styles. Therefore, this study investigates the influences of the market penetration rate (MPR) of CAVs, truck proportion, and driving style on operational characteristics in heterogeneous mixed traffic flow. A total of 1105 events were extracted from highD dataset to analyze four car-following types: car-following-car (CC), car-following-truck (CT), truck-following-car (TC), and truck-following-truck (TT). Principal Component Analysis (PCA) and clustering techniques were employed to categorize distinct driving styles, while the Intelligent Driver Model (IDM) was calibrated to represent the various car-following behaviors. Subsequently, microscopic simulations were conducted using the Simulation of Urban Mobility (SUMO) platform to evaluate the impact of CAVs on sustainable traffic operations, including road capacity, stability, safety, traffic oscillations, fuel consumption, and emissions under various traffic conditions. The results demonstrate that CAVs can significantly enhance road capacity, improve emissions, and stabilize traffic flow at high MPRs. For instance, when the MPR increases from 40% to 80%, the road capacity improves by approximately 25%, while stability enhances by approximately 33%. In contrast, higher truck proportions lead to reduced capacity, increased emissions, and decreased traffic flow stability. In addition, an increased proportion of mild drivers reduces capacity, raises emissions per kilometer, and improves stability and safety. However, a high proportion of mild human drivers (e.g., 100% mild drivers) may negatively impact traffic safety when CAVs are present. This study provides valuable insights into evaluating heterogeneous traffic flows and supports the development of future traffic management strategies for more sustainable transportation systems. Full article

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. By analogizing terrain profiles to non-stationary spectral signals, LIHA applies locally estimated scatterplot smoothing (Loess smoothing), wavelet decomposition, and high-frequency component amplification to emphasize subtle features such as landslide boundaries, cracks, and gullies. The algorithm was validated using the Mengu landslide case study, where edge detection analysis revealed a 20-fold increase in identified micro-topographical features (from 1907 to 37,452) after enhancement. Quantitative evaluation demonstrated LIHA’s effectiveness in improving both human interpretation and automated detection accuracy. The results highlight LIHA’s potential to advance early geological hazard identification and mitigation, particularly when integrated with machine learning for future applications. This work bridges signal processing and geospatial analysis, offering a reproducible framework for high-precision terrain feature extraction in complex environments. Full article

In-space manufacturing represents a transformative frontier in space exploration and industrial production, offering the potential to revolutionize how goods are produced and resources are utilized beyond Earth. This paper explores the multifaceted aspects of in-space manufacturing, including its evolution, technologies, challenges, and future prospects, while also addressing ethical and legal dimensions critical to its development. Beginning with an overview of its significance and historical context, this paper underscores key concepts such as resource optimization and the reduction of launch costs. It examines terrestrial and space-based manufacturing processes, emphasizing additive manufacturing, advanced materials processing, autonomous robotic systems, and biomanufacturing for pharmaceuticals. Unique challenges posed by the space environment, such as microgravity, vacuum conditions, and radiation exposure, are analyzed alongside issues related to supply chains, quality assurance, and energy management. Drawing from case studies, including missions aboard the International Space Station, this paper evaluates the lessons learned over six decades of innovation in in-space manufacturing. It further explores the potential for large-scale production to support deep-space missions and assesses the commercial and economic feasibility of these technologies. This paper also delves into the policy, legal, and ethical considerations to address as space-based manufacturing becomes integral to future space exploration and the global space economy. Ultimately, this work provides a comprehensive roadmap for advancing in-space manufacturing technologies and integrating them into humanity’s pursuit of sustainable and scalable space exploration. Full article

Soil biodiversity is profoundly affected by variations in climate conditions and land use practices. As one of the major grain-producing areas in China, the belowground biodiversity of the black soil region of the Northeast is also affected by the variations in climate conditions and land use types. However, most of the previous studies have focused on aboveground biodiversity, and the research of soil biodiversity is limited. The main aim of this study was to investigate the effects of variations in climate conditions and land use practices on Collembola communities of different life forms in the black soil region of Northeast China. Here, we selected three climatic areas from high to low latitudes in the black soil region of the Northeast, with three variations in land use practices (soybean, maize, and rice) sampled in each area. We found that higher temperatures and higher humidity and land use practices from rice to soybean and maize are associated with a higher Collembola density and species richness. Specifically, the density and species richness of euedaphic Colmbola are higher in climate conditions with higher temperatures and humidity, while the density and species richness of all three life forms of Collembola are higher in land use practices from rice to soybean and maize. Additionally, we discovered that environmental factors and feeding resources (soil microorganisms) both have significant effects on Collembola communities, with environmental factors exerting a more substantial influence. Our results suggest that euedaphic Collembola are more vulnerable to climate differences than epedaphic and hemiedaphic Collembola. Consequently, this may alter the vertical distribution characteristics of soil fauna (e.g., increasing soil-dwelling fauna) as well as the ecological processes associated with soil fauna in different agricultural environments. Full article

The development of modern society puts a serious strain on the environment. To protect the future of our planet, it is necessary to develop smarter and more sustainable ways in all industrial sectors for humanity to grow while reducing its impact on nature. Graphic designers can also contribute to a smarter future by designing an environmentally friendly typography. The use of ecofonts is an innovative approach that could potentially have great economic and environmental benefits. However, it also leads to a reduction in the print quality of text compared to standard fonts. This research aims to investigate the efficiency of ecofonts by determining the level of toner savings in laser-printed documents, as well as evaluating the extent to which individuals perceive differences between text printed with ecofonts and their regular font counterparts. This research found that the application of ecofonts can lead to significant toner savings, while visual tests further revealed that the overall visual quality of text printed with ecofonts remains adequate. Full article

Ecological carrying capacity (ECC) is a crucial indicator for assessing sustainable development capabilities. However, mountain ecosystems possess unique complexities due to their diverse topography, high biodiversity, and fragile ecological environments. Addressing the current shortcomings in mountain ECC assessments, this paper proposes a novel hybrid evaluation framework that integrates improved ecological footprint (EF) and ecosystem service value (ESV) approaches with spatial econometric models. This framework allows for a more comprehensive understanding of the dynamic changes and driving factors of the mountain ecological carrying capacity index (ECCI), using Pingbian County as a case study. The results indicate the following: (1) Land use changes and biodiversity exert varying impacts on the ECCI across different regions. The ECCI decreased by 42% from 2003 to 2021 (from 4.41 to 2.54), exhibiting significant spatial autocorrelation and heterogeneity. (2) The ecological service value coefficient is the main factor increasing the ECCI, while the energy consumption value and per capita consumption value inhibited the increase in the ECCI. For every 1% increase in the ecosystem service value coefficient, the ECCI increased by 0.66%, whereas every 1% increase in energy consumption value and per capita consumption value reduced the ECCI by 0.18% and 0.28%, respectively. (3) The overall spatial distribution pattern of the ECCI is primarily “southwest to northeast”, with the distance of centroid migration expanding over time. Based on these key findings, implementing differentiated land use practices and ecological restoration measures can effectively enhance the mountain ECCI, providing scientific support for the sustainable management of mountain areas. Full article

Complementors are the source of complementary goods. Increased participation by complementors fosters innovation in complementary goods, contributing to the sustainability of the ecosystem. This study examines how complementors’ experiential knowledge and their engagement with the community as an external knowledge source are correlated with the degree of innovation in complementary goods. A multiple regression analysis was conducted using data from the game mod platform Nexus Mods. Prior evidence indicates an inverted U-shaped relationship between experiential knowledge and the degree of innovation. It is suggested that when experiential knowledge accumulation exceeds an optimal level, further accumulation may lead to a decline in the degree of innovation. This study reveals that when complementors possess a high level of experiential knowledge, the positive relationship between their engagement with the community and the degree of innovation in complementary goods is strengthened. Complementors with abundant experience, who actively engage with the community as an external knowledge source, are more likely to drive innovation. Consequently, they play a crucial role in supporting the sustainable development of the ecosystem. Full article

An air handling unit utilizes economizer control to reduce cooling energy consumption by intaking outdoor air (OA) at lower temperatures. This control modulates OA intake rates based on the OA temperature, adjusting to maximum and partial rates when the OA temperature is below the maximum limit set-point (MLSP), and to minimum rates when it exceeds the MLSP. The MLSP acts as a baseline for determining OA intake rates. However, current MLSPs do not account for the specific OA conditions in South Korea, leading to the intake of unnecessarily warm OA or underutilization of available cooler OA, both of which negatively impact cooling energy performance. Therefore, this study aims to identify the optimal MLSP for OA conditions in South Korea. Through evaluation of cooling energy performance and the indoor thermal environment at various MLSP, it was determined that an MLSP of 22 °C facilitates the lowest cooling energy consumption without adversely affecting the indoor thermal environment. Implementing this MLSP resulted in 5.9% energy savings compared to Case #1 (baseline). The findings indicate that setting an MLSP according to local OA conditions is crucial for maximizing energy savings through economizer control. Full article

Low Overall Equipment Effectiveness (OEE) remains a critical challenge in manufacturing, affecting productivity and operational efficiency. This study investigates the persistent issue of low OEE in the kit packing department of a South African Original Equipment Manufacturer, where frequent downtime (DT) has resulted in OEE that is consistently below 60%. Using the Define–Measure–Analyze–Improve–Control (DMAIC) methodology, this research identifies the root causes of inefficiencies before implementing corrective actions. Data analysis revealed that material-related issues (84%) and manpower issues (15%) were the primary contributors to downtime. These inefficiencies led to equipment underutilization and financial losses due to production delays and overproduction of unnecessary kits. This study significantly enhances manufacturing efficiency by addressing these root causes, leading to reduced downtime and optimized machine usage. The financial benefits include substantial cost savings and improved resource utilization. The methodology and findings are applicable across various industries, contributing to the broader field of industrial engineering. The research highlights how misalignment between production planning and execution exacerbates inefficiencies. While this paper presents findings from the Define, Measure, and Analyze phases, the Improve and Control phases will follow in future work. The results provide a foundation for developing targeted interventions to enhance OEE and manufacturing performance. Full article

Due to the complex operating environment of valves, when a fault occurs inside a valve, the vibration signal generated by the fault is easily affected by the environmental noise, making the extraction of fault features difficult. To address this problem, this paper proposes a feature extraction method based on the combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Fuzzy Entropy (FN). Due to the slow convergence speed and the tendency to fall into local optimal solutions of the Hippopotamus Optimization Algorithm (HO), an improved Hippopotamus Optimization (IHO) algorithm-optimized Support Vector Machine (SVM) model for valve leakage diagnosis is introduced to further enhance the accuracy of valve leakage diagnosis. The improved Hippopotamus Optimization algorithm initializes the hippopotamus population with Tent chaotic mapping, designs an adaptive weight factor, and incorporates adaptive variation perturbation. Moreover, the performance of IHO was proven to be optimal compared to HO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Sparrow Search Algorithm (SSA) by calculating twelve test functions. Subsequently, the IHO-SVM classification model was established and applied to valve leakage diagnosis. The prediction effects of the seven models, IHO-SVM. HO-SVM, PSO-SVM, GWO-SVM, WOA-SVM, SSA-SVM, and SVM were compared and analyzed with actual data. As a result, the comparison indicated that IHO-SVM has desirable robustness and generalization, which successfully improves the classification efficiency and the recognition rate in fault diagnosis. Full article

The aim of this study is to examine the impact of key competitiveness factors on sustainable business performance in the hospitality sector through the application of an integrated approach, from the perspective of hotel service users. The research was conducted on a sample of 1640 hotel guests who stayed in hotels operating in the Republic of Serbia, Croatia, and Slovenia. Utilizing a structural equation modeling (SEM) framework, the study meticulously analyzed various competitiveness factors: service quality, service, service recovery, hotel user satisfaction, loyalty and discretionary behavior and dysfunctional consumer behavior. The results of the research reveal that all identified key factors significantly impact the sustainable performance of hotel operations. The findings suggest that hotels must prioritize these elements to enhance their competitiveness and ensure ongoing success in a challenging market environment. Notably, one intriguing finding is that loyalty does not serve as a buffer in the relationship between customer dissatisfaction and dysfunctional behavior, indicating that even loyal customers can exhibit negative behaviors when their expectations are not met. This underscores the importance of addressing guest satisfaction proactively to mitigate potential adverse outcomes and retain a loyal customer base. Moreover, this study provides valuable insights for hotel management, highlighting the necessity for holistic strategies that not only aim to improve guest experiences but also consider the intricate dynamics between various competitiveness factors that ultimately contribute to the sustainability and profitability of the hospitality industry. Rejecting the sub-hypothesis that loyalty among hotel service users moderates the impact of dissatisfaction on the expression of dysfunctional consumer behavior indicates the need to review certain theories that comprise the dominant theoretical framework in the field of hospitality. This implies the need for further analysis of the validity of the dominant theories in the hospitality industry, especially in defining the conditions under which their postulates hold indisputably. Second, further examination of the role of loyalty is needed, since there are different types of loyalty. Full article

Bedding is an important component of equine accommodation management. Choosing the right bedding is important for stable management and its selection may include considerations such as the sourcing of the material, the capital investment and ongoing costs, delivery, storage, installation, ongoing labour and maintenance, removal and disposal. Furthermore, it is crucial that the consequences for the health and welfare of horses and humans and the impact on the environment should also be considered. This review aimed to outline the advantages and disadvantages of different horse bedding types, focusing on their effects on the well-being of horses, humans, and the environment. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique was used as the methodology for this review. The search was performed in Scopus and Web of Science bibliometric databases and a total of 176 records were screened reading the title and the abstract. After screening, 58 records were retained and another 19 records were identified using their reference lists (i.e., snowballing). Therefore, a total of 77 records were considered. Straw and wood shavings were the most commonly used and studied bedding materials, while research on alternative options remains limited. Straw is identified as horses’ preferred option, while shavings appear to be the easiest to clean, making them the preferred choice for stable workers. The parameters to consider when choosing the bedding most fit for purpose are many and their attributes differ across the various bedding types. This review has compared all the bedding types within the research literature to determine the best overall option using the research-based evidence. Each bedding type offers unique benefits and drawbacks summarised in a user-friendly table. Stable managers must consider and evaluate them to suit their specific needs, including the health and welfare of each horse and the husbandry system involved. Our findings may, therefore, be useful in the decision-making process of equine industry members. Full article

The rapid advancement of digital technologies in education is revolutionizing learning environments and influencing the future of educational methodologies. This study seeks to determine the parameters affecting students’ satisfaction with e-learning platforms utilized during the COVID-19 pandemic, within a multimodal educational framework. A Structural Equation Modeling (SEM) approach was used to examine the contributions of different components to students’ views of e-learning tools and the inter-relationships between them. Data were gathered from 314 students via a questionnaire, with the dependent variable being student satisfaction with e-learning platforms and the independent variables comprising the perceived benefits and disadvantages, ease of use, prior experience, perceptions of the platforms, perceived risks, and communication efficiency between students and professors. The results indicated that 78% of the variance in student satisfaction was explained by these parameters (R-squared = 0.78), underscoring the substantial impact of these features on the digital learning experience. This study enhances the comprehension of the influence of e-learning platforms within a multimodal educational framework on students’ learning experiences, especially with the challenges presented by the pandemic. The collected insights can guide the development of more effective, accessible, and user-focused educational tools. Full article

Weeds are a significant adverse factor affecting rice growth, and their efficient removal necessitates an accurate, efficient, and well-generalizing weed detection method. However, weed detection faces challenges such as a complex vegetation environment, the similar morphology and color of weeds, and crops and varying lighting conditions. The current research has yet to address these issues adequately. Therefore, we propose GE-YOLO to identify three common types of weeds in rice fields in the Hunan province of China and to validate its generalization performance. GE-YOLO is an improvement based on the YOLOv8 baseline model. It introduces the Neck network with the Gold-YOLO feature aggregation and distribution network to enhance the network’s ability to fuse multi-scale features and detect weeds of different sizes. Additionally, an EMA attention mechanism is used to better learn weed feature representations, while a GIOU loss function provides smoother gradients and reduces computational complexity. Multiple experiments demonstrate that GE-YOLO achieves 93.1% mAP, 90.3% F1 Score, and 85.9 FPS, surpassing almost all mainstream object detection algorithms such as YOLOv8, YOLOv10, and YOLOv11 in terms of detection accuracy and overall performance. Furthermore, the detection results under different lighting conditions consistently maintained a high level above 90% mAP, and under conditions of heavy occlusion, the average mAP for all weed types reached 88.7%. These results indicate that GE-YOLO has excellent detection accuracy and generalization performance, highlighting the potential of GE-YOLO as a valuable tool for enhancing weed management practices in rice cultivation. Full article

The state monitoring of power load equipment plays a crucial role in ensuring its normal operation. However, in densely deployed environments, the target equipment often exhibits low clarity, making real-time warnings challenging. In this study, a load equipment segmentation and assessment method based on multi-source tensor feature fusion (LSA-MT) is proposed. First, a lightweight residual block based on the attention mechanism is introduced into the backbone network to emphasize key features of load devices and enhance target segmentation efficiency. Second, a 3D edge detail feature perception module is designed to facilitate multi-scale feature fusion while preserving boundary detail features of different devices, thereby improving local recognition accuracy. Finally, tensor decomposition and reorganization are employed to guide visual feature reconstruction in conjunction with equipment monitoring images, while tensor mapping of equipment monitoring data is utilized for automated fault classification. The experimental results demonstrate that LSE-MT produces visually clearer segmentations compared to models such as the classic UNet++ and the more recent EGE-UNet when segmenting multiple load devices, achieving Dice and mIoU scores of 92.48 and 92.90, respectively. Regarding classification across the four datasets, the average accuracy can reach 92.92%. These findings fully demonstrate the effectiveness of the LSA-MT method in load equipment fault alarms and grid operation and maintenance. Full article