Search Results (228)

Sensitive Skin Syndrome (SSS) is a worldwide condition characterized by sensory symptoms such as stinging, burning, and itching, often without visible signs. This pilot study investigated individuals with self-reported SSS, focusing on the specific skin conditions, motivations and barriers for seeking medical attention. SSS individuals were divided into two groups: those who consulted a doctor (n = 16) and those who did not (n = 10). While SSS symptom severity was similar in both groups, those with greater severity were five times more likely to seek medical help. Key symptoms prompting consultations included morphological symptoms (papules, macules), sensory symptoms (itch, discomfort), and inflammatory symptoms (redness, rash). Notably, altered sensation and macules/papules showed the strongest trends towards influencing care-seeking behavior. Differences in anatomical sites affected were significant, with the head and face having the highest odds of doctor visits. Barriers to care included high specialist costs, travel distances, and a lack of remote consultation options, particularly for rural residents. Although treatments recommended by healthcare providers often fell short of expectations, partially effective options were more acceptable when endorsed by doctors. Subjects reported improvements within weeks of starting new treatments, though many remained only partially satisfied. This study highlights important aspects of SSS and its entanglement with other skin conditions, as well as how individuals navigate their symptoms and make treatment decisions amidst their sufferings. Full article

Nature-inspired metaheuristic algorithms are commonly applied to complex combinatorial optimization problems where exact methods are computationally impractical. Tourist route optimization is a representative multi-objective problem characterized by realistic constraints such as travel time, cost, opening hours, and transportation modes. Although Mushroom Reproduction Optimization is computationally efficient, it often experiences premature convergence in complex search spaces. This paper proposes a novel hybrid algorithm, Mushroom–Weed Hybrid Reproduction Optimization (MWHRO), which integrates the colony-based local search of the Mushroom Reproduction algorithm with the fitness-proportional reproduction and competitive elimination mechanisms of Invasive Weed Optimization. Hybridization enhances population diversity and global exploration while preserving fast convergence. The proposed algorithm is evaluated based on a realistic tourist route optimization problem using real-world data from Zagreb, Croatia, across multiple transportation modes and objective-weight scenarios. Performance is compared against Ant Colony Optimization, Invasive Weed Optimization, Particle Swarm Optimization, and standard Mushroom Reproduction Optimization under equal evaluation budgets. Experimental results demonstrate that the proposed MWHRO algorithm consistently achieves high-quality solutions with significantly lower execution times, particularly in constrained and multimodal scenarios. Statistical analysis confirms the robustness and practical suitability of the proposed approach for real-world route optimization. Full article

This study examines accessibility barriers experienced by individuals with disabilities in urban public transportation and analyzes how these barriers influence their travel behavior. Survey data were collected from 450 participants with different disability types in Alanya, Turkey, a tourism-oriented city characterized by pronounced seasonal mobility fluctuations. To ensure internal consistency and analytical robustness, the Analytic Hierarchy Process (AHP) was applied to prioritize seven accessibility criteria, and the consistency of pairwise comparisons was verified prior to analysis. Based on the AHP-derived weights, a composite accessibility-based Problem Severity Index (PSI) was constructed and integrated into regression models to quantify behavioral effects. The results show that the Problem Severity Index (PSI) is strongly associated with satisfaction (R² = 0.895), frequency of public transport use (R² = 0.924), and perceived travel difficulty (R² = 0.924), reflecting constrained mobility conditions and limited modal alternatives rather than improved service quality. Deficiencies in bus stop design and vehicle accessibility equipment were identified as the most influential barriers affecting public transport experience. Beyond the case study context, the proposed AHP–regression framework provides a structured analytical approach for evaluating accessibility performance and generating empirical evidence to inform inclusive and sustainable urban mobility planning. The findings offer empirical evidence on the relative importance of accessibility barriers and highlight critical infrastructure and service deficiencies. Rather than constituting a decision-support tool themselves, these results provide structured information that, when appropriately contextualized, can inform and guide transport authorities and urban planners in prioritizing accessibility improvements and enhancing inclusive public transport performance over time. Full article

This study aims to address gaps in understanding healthcare accessibility inequality in rural China, where traditional distance-based assessments and urban-centric biases are insufficient. By integrating real-time travel data from Amap and the two-step floating catchment area (2SFCA) method, we conducted a high-resolution (1 km grid) analysis across transportation modes, administrative scales, and time-sensitive populations. Results reveal that driving enables more stable, equitable access (characterized by higher supply–demand ratios and lower variability) than public transport, which distorts ratios due to limited coverage. Accessibility disparities are most pronounced at the county scale, with eastern rural counties (e.g., Yangtze River Delta) showing far higher accessibility (log₁₀(A-value) > 5.0) than remote western counties (log₁₀(A-value) < 1.5). High time-sensitive populations (urgent care) face extreme accessibility gaps, with only 15% of counties providing optimal access. In contrast, low time-sensitive groups benefit from extended travel time thresholds, achieving 62% coverage of optimal access. Targeted interventions—investing in rural high-tier hospitals, enhancing transit frequency, and county-specific policies—are needed to advance health equity. The findings of this study provide the first nationwide high-resolution healthcare accessibility map for rural China, improve assessment accuracy via real-time data, and identify county-level gaps—offering data-driven insights for targeted policies to advance health equity and support rural revitalization. Full article

This paper addresses the optimization of electric vehicle (EV) charging facility configuration on highways by proposing a collaborative planning method that integrates driver anxiety psychology, mixed traffic flow dynamics, and service area queuing characteristics. By abstracting the road travel and service area replenishment processes into an integrated queuing network, a system analysis framework is constructed to characterize the coupling relationship of “facility supply, traffic assignment, and state feedback.” On this basis, a bi-level optimization model is established with the objective of minimizing the generalized total social cost. The upper level makes decisions on the coordinated quantities of fixed charging piles and mobile charging vehicles, while the lower level describes the stochastic user equilibrium behavior of drivers under the influence of real-time congestion and anxiety. To tackle the high-dimensional nonlinear nature of the model, an efficient solution algorithm based on simultaneous perturbation stochastic approximation (SPSA) is designed. A case study of the Nei-Yi Expressway demonstrates that compared with the traditional peak demand proportional allocation method, the proposed approach can better balance construction costs, operation and dispatching costs, and user travel experience under limited investment, significantly reducing waiting times and psychological anxiety costs. It provides theoretical methods and decision support for planning a resilient energy replenishment network that achieves “fixed facilities ensuring base load and mobile resources responding to peak demands.” Full article

The rapid growth of vehicle ownership has led to increasingly congested road networks, which significantly reduces the energy efficiency of electric vehicles (EVs) and intensifies user range anxiety. To address these challenges, a dynamic EV route planning process is proposed by integrating traffic flow (TF) prediction, charging service modelling, and time-varying path optimization within a unified framework. First, future TF is predicted using a data-driven forecasting module based on the iTransformer model, which captures multivariate temporal dependencies across road links and provides accurate inputs for downstream decision-making. Based on the predicted traffic states, a time-dependent queuing process is formulated to estimate charging station waiting times by modelling the dynamic interaction between vehicle arrivals and service capacity. These components are then embedded into a time-varying shortest path optimization process that explicitly considers mid-journey charging constraints, with the objective of minimizing total travel time and economic cost. The proposed framework establishes a closed-loop decision-making process that couples traffic evolution, charging service dynamics, and routing behaviour. Extensive comparative experiments against classical Time-Dependent Shortest Path (TDSP) methods under different network scales, together with a real-world case study, demonstrate that the proposed approach achieves higher computational efficiency and improved routing performance under dynamic conditions. The results indicate that the proposed process-oriented method provides an effective and practical solution for EV routing in intelligent transportation systems characterized by time-varying traffic and service processes. Full article

The Three Gorges Reservoir (TGR) in China is one of the world’s largest hydropower projects. Interval inflow, originating from ungauged areas between the upstream gauging control stations (Zhutuo, Beibei, Wulong) and the TGR dam site, is a critical component of total reservoir inflow, but its hydrological characteristics have not been fully clarified. The accurate estimation and prediction of interval inflow are essential for reservoir safety and flood control operations. Using daily hydrological data from 2009 to 2017, we propose an integrated analytical framework combining (i) flow travel time estimation using cross-correlation analysis, (ii) multi-scale statistical characterization, and (iii) K-means clustering with bootstrap validation and algorithm comparison. This framework systematically identified hydrological regimes of interval inflow and their associated flood control risks. The key findings are as follows. (1) The optimal flow travel time from the upstream gauging stations to the dam site is 1 day (correlation coefficient $\rho =0.9809,p<0.001$), and it remains stable across different flow regimes. (2) The interval inflow exhibited a highly right-skewed distribution (mean 1279 m³/s, standard deviation 1651 m³/s) and contributed on average 10.1% to the total inflow. The contribution ratio exhibited an inverted U-shaped relationship with increasing total inflow, peaking at 11.4% when the total inflow ($Q$) was 13,014 m³/s. The quartile thresholds were 5788 m³/s, 9575 m³/s, and 16,869 m³/s (corresponding to Q1, Q2, and Q3, respectively), and the 10th and 90th percentiles (P10 and P90) were 4865 m³/s and 24,625 m³/s, respectively. (3) Five distinct hydrological patterns (C1–C5) were successfully identified, among which Cluster C4 (5.7% of days) was defined as the high-impact pattern based on reservoir operational criteria, with a mean $I$ of 6425 m³/s, a mean $R$ of 27.8% (up to 44% in extreme events), a mean flood duration of 5.8 days, a mean flood volume of 36.1 × 10⁸ m³, and a flashiness index of 1.48. (4) C4 is predominantly triggered by localized heavy rainfall, and its flashy nature implies a substantially shorter forecast lead time compared with mainstream-dominated floods, posing major challenges to real-time reservoir operations. This study demonstrates that interval inflow risk is pattern-dependent and that the proposed framework provides a scientific basis for developing pattern-specific reservoir operation strategies. The proposed framework is transferable to other large river-type reservoirs facing similar ungauged interval inflow challenges. Full article

Despite the significant potential of diverse natural, agricultural, cultural, and historical resources, Northern Kazakhstan, as well as the whole country, demonstrates the underdevelopment and unpopularity of agritourism and rural tourism. By Kazakh standards, it is characterized by relatively well-developed agriculture. At the same time, it is characterized by a monopolized rural labor market, lack of a service sector, low incomes, and progressive depopulation of the population. During the implementation of the research project, secondary data analysis (content, historical, statistical) were used. According to the study, the key factors determining the paradoxical underdevelopment of rural tourism and agritourism in a key agricultural region include the state policy of supporting agriculture, the conservatism of farmers, and passive rural stakeholder attitudes, which are influenced by the Soviet past. In addition, the lack of attractiveness of rural tourist and recreational resources in the eyes of travelers plays a significant role. To activate the tangible and intangible assets of rural areas in order to develop the tourism and hospitality industry, both administrative and utilitarian measures are proposed that can diversify the rural economy. This paper is not only a case study of tourism barriers in Kazakhstan, but a theory-informed diagnosis of rural modernization failure. Full article

This paper presents a detailed investigation of the deterministic and stochastic dynamics of a noise-driven forced nonlinear oscillator in a periodically driven framework. An overlap-mapping approach is used to compare multiple traveling-wave solutions and verify the structural consistency among distinct solution families. The qualitative behavior of the system is further characterized through geometric and stability-based analysis, supported by two- and three-dimensional phase portraits, time-series responses, and reconstructed three-dimensional attractors to examine periodic and chaotic regimes under varying parameters and initial conditions. The sensitivity to parameter perturbations is quantified and the distribution of final states is analyzed to identify chaotic regions in the phase space. The high-dimensional chaotic nature of the dynamics is rigorously confirmed through Lyapunov exponent estimation, Poincaré sections, and return-map analysis, collectively demonstrating strong sensitivity to initial conditions and systematic transitions induced by parameter variations. These results provide a comprehensive dynamical description of the nonlinear oscillator and contribute to a deeper understanding of noise-influenced nonlinear driven systems. Full article

The assessment of the degree of accessibility of urban green spaces for the population of the city of Timișoara (Romania) was carried out by taking into account the recommendations of the World Health Organization (WHO). These recommendations address the proximity accessibility of urban green spaces, operationalized through two main indicators: (1) proximity accessibility defined through two metrics–spatial distance and walking time between urban green spaces and residents’ dwellings; and (2) proximity accessibility defined by the area of urban green space available per urban resident capita. Based on the distance and walking time between residential areas and urban green spaces, accessibility classes were established, according to which the city’s green spaces were classified into distinct categories. Even under a simplified Euclidean centroid-to-centroid approach, the measured distances of urban green space accessibility exceed the World Health Organization’s recommended 300 m threshold for optimal access by a factor of 2 to 9 in the city of Timișoara. The measurements showed that none of the 48 studied neighborhoods of the city of Timișoara benefits from access to a public urban green space located at a distance of less than 200 m from the dwelling, according to the classification used in this study, and that only a single neighborhood has access to a public urban green space located at a distance of up to 300 m, as recommended by the WHO. The analysis indicated that for each resident of the city of Timișoara, an area of 8.4 m² of urban green space is allocated, a value below the WHO recommendation of 9 m² and below the legal threshold of 26 m² established by Romanian national legislation. Consequently, the city of Timișoara does not meet either the values established by national legislation or the authoritative international recommendations (WHO) regarding the standard of urban green space per capita, nor the accessibility criteria expressed as distance and walking time from residents’ dwellings to the nearest public urban green space. The results of the study show that, in relation to international standards and national obligations, Timișoara faces a severe deficit of urban green space, which affects the ecological, social, and health functions of the city. The obtained values highlight both a quantitative problem and a structural one, characterized by an uneven distribution and reduced accessibility of green spaces in most neighborhoods, with green spaces concentrated in the central area and limited access for many residents. This situation underscores the need for a strategic reconfiguration of urban policies, oriented toward increasing green capital and ensuring balanced, sustainable urban development aligned with contemporary standards. Urban policy implications include the strategic development of new green spaces in underserved neighborhoods, the establishment of pedestrian and green corridors to reduce travel time, and the redesign of pedestrian connectivity to major parks. These interventions would help reduce territorial inequalities and strengthen the city’s resilience. Full article

Rheumatoid nodules are the most common extra-articular manifestation of rheumatoid arthritis. Long-term immunomodulatory therapies, including corticosteroids, used in the management of rheumatoid arthritis are associated with a higher risk of infections. Leishmaniasis is a neglected protozoal infection that may arise in these patients. Cutaneous presentation is the most common and is characterized by a wide spectrum of clinical manifestations and courses, depending on the interplay between species involved and the host’s immune response. Here, we report the rare and intriguing case of a patient with long-standing rheumatoid arthritis, chronically treated with systemic prednisone, whose rheumatoid nodules were colonized by Leishmania major. In this context, therapeutic strategies must be tailored to species and patient factors. This report expands the differential diagnosis of rheumatoid nodule, highlighting the importance of considering opportunistic infections in exuberant presentations, particularly in immunosuppressed patients coming from or travelling in endemic regions. Intracellular pathogens may exploit the localized immunological niche represented by the rheumatoid nodule of an immunocompromised host to survive and replicate undisturbed. It also underscores the value of the clinico-pathological correlation and the importance of integrating molecular analyses to identify unexpected microorganisms that can be hidden by concomitant disease, avoiding misdiagnosis, ensuring timely treatment, and improving patients outcomes. Full article

This article presents an advanced processing workflow for a Seismic While Drilling (SWD) dataset acquired using Distributed Acoustic Sensing (DAS) in a cross-well setting at the Otway International Test Centre (OITC) in Victoria, Australia, where no pilot signals were recorded. Recording the drill bit signature enables and simplifies the decoding of passive seismic signals emitted by the drill bit while drilling. In conventional SWD, a measured drill bit signature is used to correlate passive seismic recordings and to determine source trigger times, analogous to vibroseis processing. Without this reference, both source timing and signature must be inferred from the recorded wavefield. This can typically be achieved by backpropagating the recorded seismic data over short time windows, estimating the source location and trigger time based on the peak RMS energy in space and time. However, to simplify the processing of SWD data, a data processing workflow is presented, guided by travel time and seismic modelling, which transforms passive SWD data into active equivalents. The transformed data can then be used to characterize the subsurface by implementing travel time tomography and cross-well imaging. The results demonstrate reliable velocity and structural information can be recovered from DAS-based SWD data without pilot measurements, enabling simplified and scalable deployment of passive seismic while-drilling workflows. Full article

As the last critical hydrological control station on the Lancang River before it flows out of China, the daily runoff variations at the Yunjinghong Hydrological Station are directly linked to agricultural irrigation, hydropower development, and ecological security in downstream Mekong River riparian countries such as Laos, Myanmar, and Thailand. Aiming at the core issues of the runoff sequence in the Lancang–Mekong Basin, which is characterized by prominent nonlinearity, non-stationarity, and coupling of multi-scale features, this study proposes a synergistic prediction framework of “multi-scale decomposition-model improvement-parameter optimization”. Firstly, Regenerated Phase-Shifted Sine-Assisted Empirical Mode Decomposition (RPSEMD) is adopted to adaptively decompose the daily runoff data. On this basis, a Convolutional Sparse Attention Transformer (CSAT) model is constructed. A one-dimensional convolutional neural network (1D-CNN) module is embedded in the input layer to enhance local feature perception, making up for the deficiency of traditional Transformers in capturing detailed information. Meanwhile, the sparse attention mechanism replaces the multi-head attention, realizing efficient focusing on key time-step correlations and reducing computational costs. Additionally, an Improved Multi-Verse Optimizer (IMVO) is introduced, which optimizes the hyperparameters of CSAT through a spiral update mechanism, exponential Travel Distance Rate (T_DR), and adaptive compression factor, thereby improving the model’s accuracy in capturing short-term abrupt patterns such as flood peaks and drought transition points. Experiments are conducted using measured daily runoff data from 2010 to 2022, and the proposed model is compared with mainstream models such as LSTM, GRU, and standard Transformer. The results show that the RPSEMD-IMVO-CSAT model reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 15.3–28.7% and 18.6–32.4%, respectively, compared with the comparative models. Full article

During peak hours, urban rail transit systems often face imbalanced spatial–temporal demands. Due to the limited transportation capacity, passengers departing from downstream stations often experience longer waiting times. Mostly traditional timetable and skip-stop strategies overlook passengers’ transfer behavior, which may impact the implementation of optimization strategies. This paper aims to take passengers’ transfer behavior into account and construct a coordinated optimization model of timetable and skip-stop patterns. We regulate passengers’ transfer strategies and design a genetic algorithm for solving the optimization model. In order to characterize feasible passenger travel patterns, strict FCFS rules and capacity constraints are incorporated into the model. Our result demonstrates that considering passengers’ transfer behavior, the coordinated optimization of timetable and skip-stop strategy can not only mitigate the unfairness of acquiring rail service among passengers but also reduce the average waiting time of the entire system. We validate the effectiveness of our algorithm using the dataset from Line 1 of Singapore’s urban rail transit system as a case study. Full article

This paper presents a federated learning (FL) architecture tailored for anomaly detection in wire arc additive manufacturing (WAAM) that preserves data privacy while enabling secure and distributed model training across heterogeneous process units. WAAM’s inherent process complexity, characterized by high-dimensional and asynchronous sensor streams, including current, voltage, travel speed, and visual bead profiles, necessitates a decentralized learning paradigm capable of handling non-identical client distributions without raw data pooling. To this end, the proposed framework integrates reversible data hiding in the encrypted domain (RDHE) for the secure embedding of sensor-derived features into weld images, enabling confidential parameter transmission and tamper-evident federation. Each client node employs a domain-specific long short-term memory (LSTM)-based classifier trained on locally curated time-series or vision-derived features, with model updates embedded and transmitted securely to a central aggregator. Three FL strategies, FedAvg, FedProx, and FedPer, are systematically evaluated against four robust aggregation techniques, including KRUM, Multi KRUM, and Trimmed Mean, across 100 communication rounds using eight non-independent and identically distributed (non-IID) WAAM clients. Experimental results reveal that FedPer coupled with Trimmed Mean delivers the optimal configuration, achieving maximum F1-score (0.912), area under the curve (AUC) (0.939), and client-wise generalization stability under both geometric and temporal noise. The proposed approach demonstrates near-lossless RDHE encoding (PSNR > 90 dB) and robust convergence across adversarial conditions. By embedding encrypted intelligence within weld imagery and tailoring FL to WAAM-specific signal variability, this study introduces a scalable, secure, and generalizable framework for process monitoring. These findings establish a baseline for federated anomaly detection in metal additive manufacturing, with implications for deploying privacy-preserving intelligence across smart manufacturing (SM) networks. The federated pipeline is backbone-agnostic. We instantiate LSTM clients because the sequences are short (five steps) and edge compute is limited in WAAM. The same pipeline can host Transformer/TCN encoders for longer horizons without changing the FL or security flow. Full article