Search Results (1,833)

Mobility hubs promise to reduce car dependence and make multimodal travel work in practice, yet behavioural evidence remains limited when hub improvements coexist with easier car access. This article examines the tension at Rome Trastevere, an urban rail node that gradually acquires mobility-hub functions while facing improved parking access near Piazza della Radio. The empirical analysis combines a pilot survey of 83 users with an on-site stated preference survey of 204 valid respondents. The stated preference instrument uses a route-based feasible-choice design with nine choice sets per experiment: respondents evaluate alternatives among bikes, walking, e-scooters, e-mopeds, public transport, private cars, and shared cars under variations in travel time, travel cost, and search time. The paper estimates a multinomial logit model in Apollo and uses sample enumeration, supported by Monte Carlo simulation, to assess four parking and shared-mobility scenarios and produce confidence intervals around predicted probabilities. Results show that users respond to time, monetary cost, and search friction in coherent and policy-relevant ways. Setting the car parking search time to zero increases predicted car probability only marginally, by about 0.9% relative to the baseline. By contrast, a EUR 1/h increase in parking cost reduces predicted car probability by about 14.7%, while a EUR 1.5/h increase reduces it by about 22.4%. A coordinated scenario combining higher parking cost and lower shared-mode search time produces the lowest predicted car probability and strengthens e-scooter and e-moped alternatives, while public transport remains the dominant option. Findings indicate that parking pricing steers behaviour more clearly than parking convenience destabilizes it in the tested range. The paper shows that mobility-hub performance depends on coordinated access management, including parking regulation, shared-service reliability, and legible multimodal transfer. Full article

The increasing diversity and complexity of modern mission scenarios have led to growing heterogeneity among nodes in mobile ad hoc networks: node functions, onboard devices, and operational parameters are becoming more diverse, and inter-node links are correspondingly no longer homogeneous. Such networks, termed heterogeneous functional chain networks, orchestrate nodes with distinct functions into multiple functional chains that cooperate to accomplish the overall mission. Accordingly, the evaluation of connectivity reliability in these networks has shifted from a topology-oriented paradigm to a functional structure-oriented one. This paper investigates the impact of dynamic reconfiguration mechanisms on the connectivity reliability of heterogeneous functional chain networks, accounting for node failures, node mobility, and link reliability. A Dynamic Reconfiguration Scheme (DRS) is designed based on the principles of minimum movement and minimum-ordinal decision node, and a suite of evaluation metrics—including normalized connectivity reliability, network quality, and connectivity reliability—is proposed together with a Monte Carlo simulation algorithm. The proposed approach is validated via MATLAB simulations involving 210 heterogeneous nodes organized into 70 functional chains. Results demonstrate that dynamic reconfiguration increases the terminal number of functional chains by 170.83% (from 12.10 ± 0.673 to 32.77 ± 2.241), improves normalized connectivity reliability by 170.73% (from 0.1729 ± 0.010 to 0.4681 ± 0.032), and enhances network quality by 82.96%. The connectivity reliability is further shown to evolve through three distinct temporal stages: an initial stable period where functional chains remain largely intact, a mid-stage fluctuation period characterized by iterative destruction–reconfiguration dynamics, and a late-stage degradation period triggered by candidate node pool depletion. Full article

The Santiago River is a highly anthropogenically impaired lotic system globally, yet the mechanisms governing its contaminant transport remain poorly understood under static monitoring paradigms. This study evaluates how hydrological forcing dictates the mobilization and bioavailability of trace metals by integrating a 15-year public hydrochemical database from 10 monitoring nodes with SAR-derived discharge estimates and thermodynamic metal modeling (PHREEQC). To validate the structural integrity of the mass load estimates against hydrometric uncertainties, a deterministic boundary-sensitivity analysis was conducted. Results empirically refute the classical dilution paradigm, introducing the “Anthropogenic Pulse” to describe the non-linear acceleration of pollutant export during high-flow events (discharge Q surging from 36.62 to 286.13 m³/s). While climate-driven parameters follow seasonal cycles, industrial stressors (COD, Pb, Cd) remain in a chronic steady state, decoupling from volumetric dilution. Based on coupled × CQ × C (discharge × concentration) estimates, this dynamic induces a synchronized flushing of toxic burdens, exporting monthly peak loads exceeding 51,000 kg of Zinc, 6500 kg of Lead, and 3100 kg of Cadmium. Thermodynamic simulations reveal that this hydrological flushing functions as a chemical activator; the seasonal dilution of natural Alkalinity and Hardness suppresses the river’s theoretical buffered pH (from 8.5 to 7.0), maintaining metals in their uncomplexed free-ion states (Me²⁺). Modeling indicates that nearly 90% of the exported Cadmium remains in this highly labile, toxic form due to a dual coupling with both river Discharge (r_s = 0.87) and pH (r_s = 0.79). The identification of stochastic arsenic peaks 100 times above regulatory limits at Paso de Guadalupe (RS-08) underscores the failure of concentration-based monitoring. Our findings suggest that restoration strategies should shift toward mass-loading-based regulatory frameworks and targeted sediment management at critical nodes to mitigate the chronic export of bioavailable industrial waste. Full article

This study proposes a multi-criteria decision-making framework for determining optimal locations for shared micromobility stations in Kars, Türkiye. The approach integrates spatial data with structured expert evaluation and applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank candidate locations. Eight representative locations were evaluated based on five criteria: points of interest (POId), public transport distance, activity level, accessibility, and installation suitability. Spatial indicators were obtained through map-based measurements, while qualitative criteria were assessed using expert-based scoring by 11 experts. The results indicate that locations with high activity density, strong accessibility, and a high concentration of POIs achieve the highest suitability scores. The city center (L2) and Kafkas University (L1) were identified as the most suitable locations, with closeness coefficients of 0.862 and 0.783, respectively. In contrast, the train station (L5) showed the lowest suitability, with a closeness coefficient of 0.326. A sensitivity analysis confirmed that the ranking structure remained unchanged under moderate variations in criteria weights, indicating the robustness of the proposed model. The findings suggest that micromobility systems are primarily driven by intra-urban mobility demand rather than by long-distance transportation nodes. From a sustainability perspective, the proposed framework supports evidence-based planning of shared micromobility infrastructure, which can contribute to reducing dependence on private automobiles, improving urban accessibility, and promoting low-carbon transportation. The findings provide practical guidance for municipalities seeking to develop environmentally sustainable, socially accessible, and resource-efficient urban mobility systems in medium-sized cities. The framework can also support broader sustainable urban development strategies and contribute to the achievement of sustainable mobility objectives. Full article

Facility agricultural robots operating in greenhouse environments often encounter narrow passages, dense obstacle distributions, and frequent path-direction changes, which increase the difficulty of achieving efficient and smooth autonomous navigation. Conventional A* algorithms usually suffer from redundant node expansion, dense turning-point distributions, and insufficient path continuity under such constrained conditions. To address these issues, this study proposes an improved A*-based path-planning framework that integrates adaptive heuristic weighting, dynamic corner correction, and Bézier-curve-based path smoothing. Rather than introducing an entirely new planning paradigm, the proposed method coordinates several existing optimization strategies within a unified framework to improve search efficiency, path regularity, and path continuity for facility agricultural scenarios. The adaptive heuristic weighting strategy dynamically adjusts the contribution of the heuristic term according to the relative distance between the current node and the target node, thereby improving global search guidance while reducing unnecessary exploration. Dynamic corner correction is introduced to suppress zigzag path structures and reduce redundant turning nodes in obstacle-dense regions, while Bézier-curve-based smoothing is employed to improve path continuity and compatibility with the kinematic characteristics of agricultural mobile robots. Simulation experiments were conducted on grid maps and greenhouse-like environments with different obstacle distributions, and comparative evaluations were performed against Dijkstra, RRT, and conventional A* algorithms. Under representative simulation scenarios, the proposed framework reduced the number of turning points by up to 53.7% and decreased computation time by approximately 19.4% compared with the conventional A* algorithm, based on the average results of repeated trials under identical conditions. In addition, physical platform experiments on a ROS2-based agricultural robot demonstrated that the planned trajectories maintained relatively stable navigation performance and smoother directional transitions in constrained greenhouse-like environments. The results indicate that the proposed framework achieves a more balanced trade-off between computational efficiency, path compactness, and path smoothness than the benchmark methods considered in this study. Nevertheless, the current validation remains limited to structured or semi-structured greenhouse environments under static obstacle conditions. Future work will focus on improving adaptability to dynamic agricultural scenarios and integrating the framework with real-time perception and motion-control systems for practical greenhouse deployment. Full article

The rapid expansion of wireless connectivity has led to vast amounts of radio-frequency (RF) energy being continuously radiated into the environment, much of which is dissipated due to severe propagation losses. Recycling this otherwise wasted RF energy is, therefore, a critical enabler for energy-efficient and sustainable wireless systems. RF energy harvesting nodes and passive backscatter communication devices provide promising solutions by enabling battery-less or low-maintenance operation for future green networks. However, both paradigms suffer from fundamental limitations, including restricted communication range, near–far effects, and insufficient harvested energy at extended distances. This review examines how cooperative relays can address these challenges by harvesting ambient RF energy and assisting both information transfer and power delivery. From a communication perspective, we review cooperative backscatter communication and harvest-then-transmit (HTT) protocols, highlighting how multi-hop relaying significantly extends coverage and improves throughput for energy-constrained devices. Particular emphasis is placed on tag-to-tag (T2T) backscatter systems, relay-assisted architectures, decode-and-forward and amplify-and-forward protocols, and optimal multi-access time allocation strategies that mitigate the doubly near–far problem in passive networks. From an energy-transfer perspective, the review is structured around three pillars: wireless power transfer (WPT), multi-hop energy transfer (MET), and integrated charging-and-sensing frameworks. We discuss relay deployment and placement optimisation, UAV-enabled mobile energy relays, waveform and beam-forming design, and the transition from idealised linear harvesting models to practical nonlinear rectification models. Key practical constraints, such as regulatory limits, safety compliance, self-interference, protocol overhead, synchronisation, and imperfect channel knowledge, are systematically reviewed. The paper concludes by identifying the scalability limits of multi-hop cooperative systems, outlining how the joint optimisation of energy relaying and cooperative communication enables RF energy recycling for sustainable, low-carbon wireless networks and highlighting open challenges and future research directions. Full article

In the 1930s and 1940s, Kumbum Monastery (Tibetan: sku’ bum byams pa gling) emerged as a significant spatial node in visual culture during the period of war and modern nation-building in the Republic of China (1912–1949). Through photography, painting, and film, a diverse range of visual media depicted the monastery’s architectural layout, inscribed plaques and steles, Cham dance (Tibetan: འཆམ་, Wylie: ’cham) rituals, lamaic prayers, and scenes of temple fairs and marketplaces. These visual representations not only documented historical detail but also constructed a composite space in which sacredness, transcendence, and secularity intersected. Due to its unique geographical location, religious doctrines, historical narratives, and political entanglements, Kumbum functioned as both a spiritual center and a politically charged symbol. Within this visual discourse, cham rituals and collective prayers were imbued with wartime ideological meanings, aligning religious transcendence with the national aspiration for resistance and victory. The inscribed plaques by state officials visually asserted political authority over sacred religious spaces, while the depiction of temple fairs foregrounded the entanglement of religious practices with everyday secular life, becoming key arenas for ethnic integration and political mobilization. Artists and photographers actively engaged with and reproduced both the symbolic and the quotidian landscapes of the monastery. These visual materials contributed to the broader project of narrating the Republic’s frontier and constructing the nation’s image. By examining how both monastic actors and external observers visually constructed Kumbum Monastery’s political and spiritual space, this study illuminates the complex interplay between religion and state power, and shows how visual media articulated ideological meanings and negotiated spatial relationships as collective responses to the site within the conditions of modernity. Full article

Wireless sensor networks supported by mobile edge computing are increasingly required to process heterogeneous sensing data under stringent latency, reliability, and energy constraints. However, most existing task-offloading studies are still formulated for generic user equipment and primarily focus on uplink transmission, which is insufficient for practical sensing systems where sensor nodes continuously upload measurements while simultaneously receiving control commands, model updates, and feedback from the edge. To address this gap, this paper reformulates joint computation offloading and power control as a sensor-aware optimization problem in an edge-assisted wireless sensor network. We propose a three-layer architecture consisting of sensor nodes, access points with lightweight edge servers, and a cloud coordination layer. Each sensing task is characterized by data size, computation density, latency deadline, and sensing priority, while the optimization objective jointly minimizes long-term task delay, communication and computation energy, and packet-loss penalty under transmission power, edge resource, and residual-energy constraints. To solve the resulting mixed discrete–continuous problem, we develop a multi-agent reinforcement learning framework in which each sensor node acts as an autonomous agent and learns offloading and transmission policies with clipped proximal policy optimization, while the cloud layer performs coordinated edge-resource allocation through the alternating direction method of multipliers. In addition to delay and energy, network lifetime and sensing delivery performance are incorporated into the evaluation. Simulation results in a sensor-network monitoring scenario demonstrate that the proposed framework consistently reduces latency, lowers energy consumption, and prolongs network lifetime compared with representative baselines, highlighting its effectiveness and practical potential for intelligent sensing applications that require integrated sensing, communication, and edge computing. Full article

Environmental monitoring in hazardous indoor zones conventionally relies on fixed-sensor networks or manual inspections, both of which suffer from spatial blind spots and increased human exposure risks. This paper addresses the problem of transforming sparse, mobile sensor measurements into spatially resolved risk assessments in GPS-denied environments. We propose a Hazard Index (HI) framework that normalizes environmental parameters against established safety thresholds into a unified, graduated risk metric with $O\left(N\right)$ computational complexity, where N is the number of monitored parameters. The framework is designed for multi-parameter hazard assessment; the present work validates the computational pipeline, spatial mapping methodology, and classification logic through single-parameter CO₂ detection ($N=1$) deployed on a LiDAR-guided robotic platform integrating an MQ-135 gas sensor interfaced via a NodeMCU ESP8266 microcontroller. Experimental validation across a 144 sq ft indoor area achieved a trajectory-following RMSE of 0.54 ft relative to planned waypoints using Hector SLAM without odometry, detected CO₂ concentrations ranging from 0.02% to 0.25%, and identified a hazardous region encompassing eight measurement points ($\mathrm{HI}\ge 1.0$) using a three-tier classification scheme (Safe, Elevated, Hazardous) within 225 s of active mapping. The framework provides a lightweight computational footprint suitable for real-time evaluation on an NVIDIA Jetson Nano. The proposed approach establishes a cost-effective, reproducible methodology for autonomous indoor environmental monitoring, with the modular architecture designed for future expansion to multi-parameter sensing. Full article

Efficient data processing and signal acquisition are becoming increasingly critical. Pipeline networks present unique topological constraints that complicate the balance between signal sampling efficiency and data-transmission reliability. In this paper, we propose a bi-objective optimization model for the urban pipeline network (UPN). The model optimizes autonomous mobile sensor (AMS) path planning using an Euler path scheme and communication node (CN) deployment using a deterministic deployment scheme. The model aims to minimize both monitoring time (MMT) and data delay (MDD). These two indicators are used as quality of service (QoS) metrics for communication and sensing. By representing the UPN as a graph structure, we establish two mathematical models for the MMT and MDD problems. Then, we introduce a topology-guided heuristic virtual-edge strategy to construct an Euler traversal for the MMT problem. An adaptive simulated annealing (ASA) algorithm is designed to solve the MMT problem. On this basis, the MDD problem is solved using an enhanced ant colony optimization (EACO) algorithm. Simulation results show that the proposed scheme achieves shorter monitoring times and lower data delays. Specifically, the Euler path scheme for the AMS reduces MMT by more than 43.26%, and the deterministic CN-deployment scheme reduces MDD by more than 44.10%. Full article

Improving land green utilization efficiency (LGUE) is essential for achieving sustainable development in China. This study investigates the spatiotemporal evolution and localized driving mechanisms of land green utilization efficiency across 127 cities in six major Chinese urban agglomerations from 2011 to 2023. Previous research frequently overlooks the spatial non-stationarity and structural interactions within regional land governance. To address this theoretical gap, a comprehensive multiscale framework is employed. This framework integrates the Super-SBM model, Dagum Gini decomposition, Spatial Markov chains, and Multiscale Geographically Weighted Regression. The empirical results reveal an overall upward efficiency trajectory alongside persistent spatial inequalities. A pronounced scale-efficiency inversion is observed between developed eastern coastal and developing central-western inland regions. Furthermore, spatial interaction analysis identifies a significant backwash effect. This mechanism constrains the upward mobility of peripheral cities adjacent to high-efficiency core nodes. The multiscale regression demonstrates substantial spatial heterogeneity in the effects of key driving factors. Elements such as industrial structure and financial development exhibit highly localized associations dependent on regional institutional contexts. These findings bridge macroeconomic growth models with micro-environmental governance. The study provides critical empirical evidence for shifting from uniform administrative management to spatially targeted regional policy frameworks. Full article

Head and neck lymphedema (HNL) is a common complication of head and neck cancer (HNC) treatment. Surgery and radiation, the backbones of HNC treatment, disrupt lymphatic networks through direct injury and fibrosis, leading to accumulation of lymphatic fluid in interstitial spaces. This causes swelling of external and internal structures, leading to decreased quality of life, cosmetic distress, social withdrawal, and functional deficits such as dysphagia, dysphonia, and reduced cervical mobility. In this narrative review, we provide a broad overview of the pathophysiology, assessment, and prevention of HNL. Key surgical factors include the extent of neck dissection, including specific levels removed. Radiation compounds surgical injury through lymphatic fibrosis in a dose-dependent manner. Emerging radiation de-escalation strategies may reduce HNL, though lymphedema is rarely studied as a trial endpoint. Moreover, assessment of HNL remains challenging due to the absence of a gold standard—patient-reported outcome measures, clinician-reported scales, and instrumental tests each capture distinct components of external and internal HNL. Currently, the cornerstone of HNL treatment is conservative management with complete decongestive therapy, which shows mixed efficacy and does not address internal HNL. Surgical options including lymphovenous anastomosis and vascularized lymph node transfer show early promise but remain limited to case reports and small series. Lymphatic imaging, particularly indocyanine green lymphography, represents a promising emerging modality for guiding personalized treatment planning, though application to the head and neck remains challenging. Ultimately, current management of HNL remains largely reactive, with a noticeable lack of preventative therapies. Future research may benefit from better defining surgical options, including HNL as an endpoint in radiation de-escalation trials, and validate emerging lymphatic imaging techniques in order to improve outcomes for HNC survivors. Full article

Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node for future crop-monitoring applications, rather than as a fully validated autonomous field robot. An open-source tracked chassis was extended with Raspberry Pi edge computing, a Cube Orange autopilot, RTK-capable GNSS, 5G/VPN/MAVLink communication, and BME280, BH1750, MLX90614, RGB camera, and LiDAR-ready sensing. The platform measured 35 × 25 × 40 cm, weighed 6.4 kg, operated from a 12 V supply, and provided about 4 h of runtime under favorable conditions. Sensor data were logged locally and could be transmitted remotely, while telemetry was visualized in QGroundControl. The environmental sensing layer was compared with a calibrated Libelium Smart Agriculture Pro station in a greenhouse using 70 synchronized samples per variable across three sessions. Because the two nodes were placed close to one another but were not strictly co-located, the comparison quantifies operational sensing differences under greenhouse microclimatic gradients rather than pure laboratory sensor error. Regression was retained only as a trend-tracking metric, while method-comparison interpretation was added using bias and Bland–Altman limits of agreement. The pressure channel showed strong trend tracking (R² = 0.992, RMSE = 0.024 hPa), whereas air temperature (R² = 0.756, RMSE = 2.537 °C) and relative humidity (R² = 0.817, RMSE = 5.024%) were suitable mainly for exploratory microclimate mapping and relative trend monitoring unless local calibration is applied. The title, claims and conclusions were therefore narrowed to greenhouse sensing-layer validation and future crop-monitoring deployment. Full article

Mobile edge computing (MEC) enables computation-intensive and latency-sensitive tasks to be offloaded from mobile devices to nearby edge servers. Most existing MEC task offloading studies formulate offloading as a selection problem over a fixed or fully available set of candidate servers, which is restrictive in heterogeneous MEC scenarios with task-node eligibility constraints. Under such constraints, a task can be processed by an edge server only when task attributes, service requirements, link conditions, and node states jointly satisfy the corresponding eligibility conditions. The feasible action set therefore varies over time, while offloading decisions are further coupled with edge-node-side queue competition and long-term load evolution. To address this problem, this paper proposes Resource-oriented Scheduling Coordination (RoSCo), a load-aware task offloading method with scheduling-level constraint handling for eligibility-constrained MEC systems. In this paper, scheduling coordination refers to the joint use of feasible-action control, priority-aware edge-node service-order modeling, and load-responsive feedback within the task offloading decision process; it does not denote inter-server communication, task aggregation, federated model aggregation, or a distributed coordination protocol. RoSCo constructs a dynamic feasible action set, applies eligibility-aware action masking to exclude infeasible offloading actions, incorporates priority-aware edge-node service-order information to characterize queueing competition among heterogeneous tasks, and designs a load-responsive reward to guide congestion mitigation and load balancing. A dueling double deep Q-network (D3QN) is adopted as the value-learning backbone, while the main methodological contribution lies in embedding task-specific feasible-action control, priority-aware node-side queue information, and load-responsive feedback into the constrained offloading process. Simulation results show that RoSCo reduces the task drop rate and edge-node load imbalance while maintaining competitive task completion delay and energy consumption, especially under high-load and sparse-eligibility conditions. Full article

A 23-year-old man reported a 3-year history of slowly growing, slightly itchy, flesh-colored papules on the distal glans penis. He denied any history of trauma or previous treatment. Additionally, he experienced no difficulties with urination, discharge, or erectile function. Upon examination, three firm, dome-shaped papules were found to be attached to the skin but mobile over the underlying tissues. There was no regional lymph node enlargement, hardening, or fluctuation observed. Histopathological analysis revealed a well-defined, encapsulated tumor in the dermis, consisting of spindle cells interspersed with varying numbers of axons. Full article