Search Results (5,774)

Identifying urban functional areas increasingly relies on data-driven approaches that utilize multimodal spatial information. There is a growing focus on purpose-oriented functional area identification with greater policy relevance. This paper proposes a data-driven methodology to identify functional areas from the perspective of bicycle users. To achieve this, line-based road network units were defined around bicycle stations, and spatial–temporal data such as Origin–Destination flows and Point of Interest information were semantically integrated to delineate functional areas. An experiment was conducted on 2628 public bicycle stations in Seoul, Republic of Korea, for May 2022, and a total of five functional areas were identified via a Co-Matrix Factorization-based fusion approach. Additionally, the proposed method was validated through visual evaluation and comparison with actual bicycle usage data. The results demonstrate that by simultaneously incorporating spatial–temporal information and latent connectivity, this approach identifies bicycle-friendly areas, even with low observed usage, highlighting its potential for policy applications. Full article

Background/Objectives: The diagnosis of acute compartment syndrome (ACS) of the extremities is typically based on subjective clinical signs and symptoms, highlighting the need for user-friendly diagnostic tools to improve accuracy and reliability. This study evaluates the performance of two commercial devices, the MY01^® continuous pressure monitoring system and the Moxy Monitor near-infrared spectroscopy-based system, against a reference standard of continuous intracompartmental pressure (ICP) monitoring in a preclinical ACS model. Methods: ACS was induced in the anterior compartment of the distal hind limb in eight Yorkshire pigs using a balloon displacement model. ICP was incrementally elevated and maintained for four hours at >30 mmHg above mean arterial pressure. This was followed by balloon deflation and reperfusion. Final assessments were performed at 24 h post-injury. ICP measurements from the MY01^® and muscle oxygen saturation (SmO₂) data from the Moxy Monitor were compared to reference ICP measurements. Histologic analysis of muscle tissue was performed to assess the severity of necrosis. Results: The MY01^® provided accurate ICP measurements, with a mean bias of 2.21 ± 18.77 mmHg during pre-ischemia, 4.86 ± 10.43 mmHg during reperfusion, and 4.69 ± 3.28 mmHg 24 h post-injury, compared to reference probes. Correlation at 24 h post-injury was (r = 0.86, R² = 0.73, p < 0.0001). In contrast, the Moxy Monitor failed to detect significant differences in SmO₂ between injured and control limbs at 24 h post-injury, despite pronounced ICP differences. Our volumetric displacement ACS model demonstrated its efficacy as a testing platform by allowing for controlled, incremental elevation in ICP and sustaining elevated ICP levels after 24 h. Histologic evaluation confirmed extensive muscle damage, including edema and necrosis. Conclusions: The MY01^® provides accurate, continuous ICP monitoring, supporting its clinical utility in ACS diagnosis. However, the use of near-infrared spectroscopy-based systems such as the Moxy Monitor for ACS diagnosis and management should continue to be critically scrutinized. Full article

Electrospun fibers serve as a medium for the targeted release of active compounds, facilitating the desired therapeutic effects in drug administration. The point of this study was to find the best conditions for making electrospun fibers from cellulose acetate (CA) and poly(ε-caprolactone) (PCL), mixed with pure oxyresveratrol extract from Artrocarpus lakoocha Roxberg (Moraceae). Additionally, the study focused on evaluating the antioxidant properties, antityrosinase activity, and freeze–thaw stability of the resulting fibers. We incorporated a concentration of oxyresveratrol at 0.1% w/w into various mass ratios of CA/PCL blended fiber sheets (1:0, 3:1, 1:1, 1:3), utilizing mixed solvents of acetone/DMF (2:1% v/v) and chloroform/DMF (9:1% v/v) for preparation. The fiber sheets displayed a continuous and uniform structure, with fiber diameters ranging from 300 to 1000 nanometers. We investigated the release kinetics of oxyresveratrol from the fibrous substrates using the total immersion technique, specifically in phosphate-buffered saline at a pH of 7.4. The results showed that the fiber sheet with a 3:1 w/w ratio of CA to PCL and a 0.1 w/w loading of oxyresveratrol showed the most significant release of oxyresveratrol at the 2 h mark, and it continued to release consistently at this peak value for up to 24 h. The antioxidant and anti-tyrosinase properties of oxyresveratrol in fiber sheets were more stable than those of free oxyresveratrol at the same concentrations. The fiber sheet presents a promising avenue for a user-friendly transdermal patch application. Full article

Hydrogen-based technologies, prominently fuel cells, are emerging as strategic solutions for decarbonization. They offer an efficient and clean alternative to fossil fuels for electricity generation, making a tangible contribution to the European Green Deal climate objectives. The primary issue is the production and transportation of hydrogen. An on-site hydrogen production system that includes CO₂ capture could be a viable solution. The proposed power system integrates an internal combustion engine (ICE) with a steam methane reformer (SMR) equipped with a CO₂ capture and energy storage system to produce “blue hydrogen”. The hydrogen fuels a high-temperature polymer electrolyte membrane (HT-PEM) fuel cell. A battery pack, incorporated into the system, manages rapid fluctuations in electrical load, ensuring stability and continuity of supply and enabling the fuel cell to operate at a fixed point under nominal conditions. This hybrid system utilizes natural gas as its primary source, reducing climate-altering emissions and representing an efficient and sustainable solution. The simulation was conducted in two distinct environments: Thermoflex code for the integration of the engine, reformer, and CO₂ capture system; and Matlab/Simulink for fuel cell and battery pack sizing and dynamic system behavior analysis in response to user-demanded load variations, with particular attention to energy flow management within the simulated electrical grid. The main results show an overall efficiency of the power system of 39.9% with a 33.5% reduction in CO₂ emissions compared to traditional systems based solely on internal combustion engines. Full article

This paper analyses the operation of a water supply system located in the Silesian Voivodeship, serving six small localities covering a rural area of about 50 km² with a total population of 6130. The region’s varied elevation presents challenges to system performance. A hydraulic model was developed in EPANET, then validated and calibrated based on selected measurement points. Previous studies revealed that the network operates under unstable conditions. Although all users receive water under average demand, some areas experience excessive pressure. During peak demand, water shortages occur due to limited inflow to the main reservoir, which the existing pumping system cannot compensate for. In response, the validated model was used in this study to propose a reconstruction strategy to ensure a reliable water supply under all demand conditions. The analysis focused on the introduction of new water intakes, identifying their required capacity and optimal locations. It has been demonstrated that the inclusion of a new water intake positively impacts the stability of the water distribution network, and that a hydraulic model is a valuable tool for supporting the selection of its location. Hydraulic convergence of the model was necessary to optimize and evaluate the proposed solutions. As part of the selection criteria, two parameters were analysed: the percentage of nodes with pressure below 20 m H₂O, and the percentage of nodes where the pressure exceeds 55 m H₂O. Among the evaluated options, the most effective solution was intake no. 1, directly connected to a DN 160 transmission pipe supplying the area with the lowest recorded pressure. The upgraded system operates stably and meets the demands of all users. The obtained results provide valuable support for water utility management in making decisions on the development and operational optimization of water supply networks. Full article

This study introduces the Humanizing Sustainable Corridors Framework (HSCF), developed to guide the transformation of Car-Oriented corridors into Human-centered, sustainable spaces. Rooted in a human-centered approach, the framework emphasizes enhancing social interaction, addressing environmental needs, and supporting local economies through urban design. The framework was applied to the Al-Hada Ring Road in Taif, Saudi Arabia, as a case study. A mixed-methods approach was utilized, incorporating expert field observations, interviews with 15 stakeholders, and a web-based survey that yielded 455 valid responses. The findings revealed that 78% of respondents prioritized natural landscapes, 72% highlighted the importance of walkability, and 69% emphasized the need for shaded areas and culturally rooted design elements that enhance comfort and safety. These results demonstrate that planning strategies reflecting local climate conditions, user behavior, and cultural identity can increase corridor sustainability and resilience by over 65% in terms of perceived user satisfaction and safety. The HSCF offers a structured, adaptable model for planners and decision-makers seeking to align spatial design with community needs and national development goals. Full article

As the influence of indoor environments on human emotional regulation and cognitive function becomes increasingly critical in modern society, there is a growing need for intelligent lighting systems that dynamically respond to users’ emotional states. While previous studies have investigated either illuminance or color in isolation, this study concentrates on quantitatively analyzing the interaction of these two key elements on human emotion and cognitive control capabilities. Utilizing electroencephalography (EEG) and electrocardiography (ECG) signals, we measured participants’ physiological responses and subjective emotional assessments in 18 unique lighting conditions, combining six colors and three levels of illuminance. The results confirmed that the interaction between light color and illuminance significantly affects physiological indicators related to emotion regulation. Notably, low-illuminance purple lighting was found to promote positive emotions and inhibit negative ones by increasing frontal alpha asymmetry (FAA) and gamma wave activity. Conversely, low-illuminance environments generally diminished cognitive reappraisal and negative emotion inhibition capabilities. Furthermore, a random forest model integrating time-series data from EEG and ECG predicted emotional valence and arousal with accuracies of 87% and 79%, respectively, demonstrating the validity of multi-modal physiological signal-based emotion prediction. This study provides empirical data and a theoretical foundation for the development of human-centered, emotion-adaptive lighting systems by presenting a quantitative causal model linking lighting, physiological responses, and emotion. These findings also provide a biomimetic perspective by linking lighting-induced physiological responses with emotion regulation, offering a foundation for the development of adaptive lighting systems that emulate natural light–human interactions. Full article

Value-added services (VASs) are widely used to incentivize user adoption in the platform economy. While considering the symmetry of cross-network externalities of a platform, i.e., suppliers and manufacturers exert balanced and mutually reinforcing influences on each other’s participation, this study develops a stylized game model to investigate platforms’ optimal bilateral user pricing decisions and VAS provision strategies, such as outsourcing to a third-party service provider (Model OS) or in-house provision (Model PS). Then, the platform’s and the third-party service provider’s optimal pricing decisions are derived, and the equilibrium results are compared. The findings demonstrate that a platform should implement Model PS when the outsourced VAS cost coefficient is sufficiently high or the outsourced VAS quality and cost coefficient are low concurrently. Only when the outsourced VAS quality is relatively high and cost coefficient is in a low range should a platform choose Model OS. Additionally, to address the problem of declines in supply chain members’ profits caused by investment in low-quality outsourced VASs (VAS utility provided by a third party exceeds the specific value 1.38), this study also proposes a feasible VAS cost-sharing contract (Model CS) to incentivize the third-party provider to provide investment in high-quality VASs. The contract design can achieve a “win-win” outcome when the sharing ratio is at a moderate rate (especially a range from 0.291 to 0.5) and the outsourced VAS cost coefficient meets suitable thresholds. Full article

Given the environmental threats, the adoption of green and clean mobility is crucial for decarbonizing the mobility sector. Green mobility will bring a mass integration of electric vehicle charging stations (EVCSs) to ensure sufficiency for electric vehicle (EVs) users. To achieve this, intelligently distributing the charging load of EVs is essential to prevent stress on local electrical grids. The uneven distribution of EV charging at specific EVCSs leads to load imbalances compared to underutilized stations, necessitating dynamic load-balancing (in real time) mechanisms to optimize grid demands and prevent overloading. To address this challenge, the authors propose an algorithm for balancing EV loads at EVCSs using dynamic charging prices. This algorithm is intended to be integrated into the OCPP. Simulation results indicate that lower pricing at Station A (0.22 $/kWh) attracts more users, reducing congestion at higher-priced Stations B (0.31 $/kWh) and E (0.29 $/kWh). The proposed model encourages users to utilize less crowded stations, achieving a fairer distribution of EV charging demand while providing cost benefits to users selecting those stations. Full article

The growing diversity of cloud services has made evaluating their relative merits in terms of price, functionality, and availability increasingly complex, particularly given the wide range of deployment alternatives and service capabilities. Cloud manufacturing often requires the integration of multiple services to accomplish user tasks, where the effectiveness of resource utilization and capacity sharing is closely tied to the adopted service composition strategy. This complexity, intensified by competition among providers, renders cloud service selection and composition an NP-hard problem involving multiple challenges, such as identifying suitable services from large pools, handling composition constraints, assessing the importance of quality-of-service (QoS) parameters, adapting to dynamic conditions, and managing abrupt changes in service and network characteristics. To address these issues, this study applies the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) in conjunction with Multi-Criteria Decision Making (MCDM) to evaluate and rank cloud services, while the Analytic Hierarchy Process (AHP) combined with the entropy weight method is employed to mitigate subjective bias and improve evaluation accuracy. Building on these techniques, a novel Adaptive Multi-Level Linked-Priority-based Best Method Selection with Multistage User-Feedback-driven Cloud Service Composition (MLLP-BMS-MUFCSC) framework is proposed, demonstrating enhanced service selection efficiency and superior quality of service compared to existing approaches. Full article

This paper introduces a gesture-controlled conversational interface driven by a local AI model, aimed at improving accessibility and facilitating hands-free interaction within digital environments. The technology utilizes real-time hand gesture recognition via a typical laptop camera and connects with a local AI engine to produce customized learning materials. Users can peruse educational documents, obtain topic summaries, and generate automated quizzes with intuitive gestures, including lateral finger movements, a two-finger gesture, or an open palm, without the need for conventional input devices. Upon selection of a file, the AI model analyzes its whole content, producing a structured summary and a multiple-choice assessment, both of which are immediately saved for subsequent inspection. A unified set of gestures facilitates seamless navigating within the user interface and the opened documents. The system underwent testing with university students and faculty (n = 31), utilizing assessment measures such as gesture detection accuracy, command-response latency, and user satisfaction. The findings demonstrate that the system offers a seamless, hands-free user experience with significant potential for usage in accessibility, human–computer interaction, and intelligent interface design. This work advances the creation of multimodal AI-driven educational aids, providing a pragmatic framework for gesture-based document navigation and intelligent content enhancement. Full article

This study presents a data-driven framework for optimizing parking space allocation and pricing in large commercial complexes, addressing persistent spatial imbalances in occupancy between high- and low-demand zones. A mixed Logit (ML) model with interaction terms is estimated from stated preference survey data to capture heterogeneous user preferences across trip purposes. A dual clustering algorithm is then applied to generate spatially coherent pricing zones, integrating geometric, functional, and occupancy-based attributes. Two differential pricing strategies are formulated: an administered model with regulatory price bounds and a market-based model without such constraints. Both pricing models are solved using an improved multi-objective Particle Swarm Optimization–Grey Wolf Optimizer (PSO–GWO) algorithm that jointly optimizes spatial zoning and zone–time pricing schedules. Using data from the Kingmo Complex in Nanjing, China, the results show that both strategies significantly reduce spatio-temporal occupancy variance and improve utilization balance. The administered strategy reduces variance by up to 67% on weekdays, with only a 1% increase in revenue, making it suitable for contexts prioritizing regulatory compliance and price stability. In contrast, the market-based strategy reduces variance by over 40% while generating substantially higher revenue, particularly during periods of high and uneven demand. The proposed framework demonstrates the potential of integrating behavioral modeling, spatial clustering, and multi-objective optimization to improve parking efficiency. The findings provide practical guidance for operators and policymakers seeking to implement adaptive pricing strategies in large-scale parking facilities. Full article

In the operational management of e-commerce platforms, online reviews and user feedback render the issue of anticipated product failure more transparent. The anticipated product failures are often negatively correlated with product quality, while related service guarantees can help customers avoid utility losses caused by such failures. Additionally, the network effect characteristics of products significantly influence customer purchasing behavior and firms’ pricing strategies. This paper employs the multinomial logit (MNL) model to establish an optimization framework for product line and value-added services pricing that accounts for the anticipated failure and associated services. It analyses three scenarios: developing a single product, homogeneous products, and heterogeneous products, deriving optimal price, market share, and maximum profit. Theoretical analysis focuses on how the optimal solutions for single and homogeneous products vary with changes in anticipated failure-induced utility losses, negative network effects, product quality, and service quality. In the numerical experiment, the study explores the effects of variations in utility losses from anticipated failure, network effects, and product and service quality on optimal solutions for heterogeneous products. Finally, the importance of incorporating anticipated failure-induced utility losses into product line and service pricing decisions is emphasized. Full article

River–lake systems are essential for sustaining ecosystems and human livelihoods. However, the complexity and variability of large river–lake systems, coupled with characteristic differences in water bodies across regions, have made quantifying their extent and changes inherently challenging. This study implements a robust water extraction method based on the multidimensional X-means clustering algorithm. This method leverages the advantages of Sentinel-2 imagery for water detection. Utilizing the X-means algorithm, it generates a new seasonal surface water area (SWA) product for the mid-lower reaches of the Yangtze River (MLRYR). The implemented method achieved an overall accuracy of 97.98%, a producer’s accuracy of 98.02%, a user’s accuracy of 96.01%, a Matthews correlation coefficient of 0.954, and a Kappa coefficient of 0.954. Analysis of water body dynamics reveals that over the past six years, the overall trend of SWA in the MLRYR has remained stable. However, within a broad range including multiple sub-basins, a decline in SWA has been observed on an inter-annual scale. Among the large lakes and reservoirs in the MLRYR, the water areas of Poyang Lake, Dongting Lake and Shijiu Lake all showed a marked decline. Among all water bodies with a significant increase in area, the Danjiangkou Reservoir is the largest. Further correlation analysis indicates that SWA exhibited the strongest correlations with precipitation and drought index in most sub-basins. In sub-basins where large lakes and reservoirs exist, the presence of river networks played a buffering role by regulating and storing water, thereby reducing the direct influence of climatic factors on lake and reservoir water extent. These findings highlight the complex interplay of climatic and hydrological factors. By integrating satellite imagery and Earth observation, this study advances understanding of MLRYR surface water dynamics, providing a robust framework for monitoring in other regions. It offers critical insights into drought impacts and informs effective water resource management and conservation strategies. Full article

The deterioration of the global climate and accelerated urbanization have led to intense pressure on surface space resources. As a strategic development field, deep underground space has become a crucial direction for alleviating human habitation pressure. However, current research on deep underground space mostly focuses on fields such as geology and medicine, while the design of habitable environments lacks interdisciplinary integration and systematic approaches. Taking deep underground space as the research object, this study first clarifies the interdisciplinary research context through bibliometric analysis. Then, combined with geological data (ground temperature, groundwater, and ground stress, etc.) from major cities in China, it defines the characteristics of the in situ environment and the characteristics of the development and utilization of deep underground space. By comparing the habitable design experiences of extreme environments, such as space stations, Moon habitats, and desert survival modules, the study extracts five categories of design elements: natural conditions, construction status, social economy, users, and existing resources. Ultimately, it establishes a demand-oriented, five-dimensional habitable design methodology covering in situ environment adaptation, living support, medical and health services, resilience and flexibility, and existing space renovation. This research clarifies the differentiated design strategies for hundred-meter-level and kilometer-level deep underground spaces, providing theoretical support for the scientific development of deep underground space and serving as a reference for habitable design in other extreme environments. Full article