Search Results (468)

Blood transfusion near the end of life for patients with cancer may relieve symptoms attributed to anaemia or control bleeding, but its palliative value depends on whether benefit occurs within the patient’s expected survival and outweighs the clinical and practical burden of treatment. This structured narrative review examines evidence concerning red-cell and platelet transfusion, transfusion-sparing patient blood management, hospice access and alternative delivery models in adults receiving palliative or end-of-life care. Structured searches of MEDLINE, Embase, Scopus and the Cochrane Library identified original studies, audits, qualitative research and relevant evidence syntheses. The evidence was predominantly observational and methodologically heterogeneous. Reported symptomatic response rates after red-cell transfusion ranged from 31% to 70%, most commonly involving short-term improvement in fatigue, dyspnoea or general well-being. Benefit often diminished within 14 days, while 23–35% of participants in historical cohorts died within two weeks, limiting the opportunity to experience benefit. Response was not reliably predicted by haemoglobin concentration. Red-cell transfusions were frequently guided by laboratory values, administered late in the disease course and not followed by systematic reassessment. Evidence concerning platelet transfusion remains largely descriptive and does not establish a prophylactic threshold for end-of-life care. Intravenous iron and haemostatic radiotherapy may reduce transfusion requirements in selected patients, although their applicability depends on clinical stability, the source of bleeding and sufficient time to benefit. In haematological malignancies, transfusion dependence has been associated with lower hospice enrolment and shorter hospice stays, whereas home- and hospice-based transfusion may reduce travel and waiting burdens for appropriately selected patients. Transfusion near the end of life should therefore be considered a goal-concordant, time-limited trial. A patient-valued outcome should be defined beforehand, the fewest units likely to achieve that outcome should be administered, and benefit should be reassessed using the same measure. Further transfusion should be offered only when documented symptomatic or functional benefit outweighs adverse effects and treatment burden for the individual patient. Full article

The implementation of the Air Quality Health Index (AQHI) is a regional climate action intended to protect population health, but its empirical linkage to the sustainability goal of good health and well-being remains untested. Using mortality data from Hong Kong (2013–2022), we employed a two-month-lag fixed-effects model to estimate the change in years of life lost (YLL) associated with monthly AQHI warning frequency. We further assessed heterogeneity across demographic and geographic subgroups, and explored the underlying mechanisms. The findings show that monthly AQHI warning frequency is negatively associated with YLL two months later. Each additional warning per month is associated with a reduction of 0.0032 units (SE = 0.0011) in per capita YLL across districts in Hong Kong. The baseline result was further supported by extended survival analyses. Heterogeneity analyses showed that each additional AQHI warning was associated with a significant reduction in YLL among males (coef. = −0.0066, SE = 0.0001) and those with spouses (coef. = −0.0065, SE = 0.0083), but not among females or those without spouses. Mechanism analyses suggested that the effect of AQHI warning frequency on YLL reduction was significantly moderated by individual behavioral responses, including increased face mask usage (int. coef. = −0.0047, SE = 0.0016) and reduced short-term travel (int. coef.= −0.0069, SE = 0.0004), and through socio-environmental pathways such as reduced traffic accidents (int. coef. = −0.0134, SE = 0.0034) and lower carbon emissions (int. coef. = −0.0367, SE = 0.0112). Hong Kong’s AQHI forecasting experience demonstrates the viability of health risk warnings as a climate adaptation strategy in sustainable urban governance, while its observed equity gaps offer critical lessons for refining inclusive environmental health policies. Full article

Urban regeneration is an important policy tool for restructuring urban space, but its implications for operational transport-related carbon emissions remain unclear. Using the City Smart Planning System (CitySPS), this study simulates emissions in Shenzhen from 2020 to 2035 under a baseline and five policy scenarios representing temporal modification, land-use type modification and spatial replacement. The analysis uses legally designated regeneration parcels and multi-source spatial data. The accounting boundary covers CitySPS-represented operational transport-related carbon emissions from urban travel and excludes demolition, construction, building operation, embodied emissions, and other life-cycle sources. The baseline emissions rose from 1.840 × 10⁷ t CO₂ in 2020 to 2.269 × 10⁷ t CO₂ in 2035 (approximately 23%). Relative to the same-year baseline, all the policy scenarios produce higher simulated emissions in 2030 (+0.11% to +1.26%) but lower simulated emissions in 2035 (−0.34% to −1.97%). Under the evaluated configurations and the shared CitySPS assumptions, spatial replacement produces the largest simulated reduction in 2035 (−1.97%) despite an increase in 2030 (+0.41%). The results indicate time-dependent and heterogeneous outcomes across the scenario configurations. Full article

Smart charging stations require mobile charging robots to respond to dynamically arriving charging requests with heterogeneous priorities, varying travel costs, and uneven workloads while maintaining online scheduling feasibility. Conventional single-layer approaches often optimize task assignment or route ordering separately, which limits their ability to coordinate allocation quality, route efficiency, and workload regulation under real-time constraints. This study proposes a hierarchical improved consensus-based bundle algorithm–ant colony optimization (ICBBA-ACO) framework for dynamic multi-robot task allocation. The upper ICBBA layer combines deterministic task clustering, intra-cluster greedy bundling, conflict resolution, and feedback-guided workload-aware reassignment, while the lower ACO layer refines the visiting order of unstarted tasks under fixed ownership using the same normalized four-objective scheduling cost. Complete decision time is evaluated separately against a $200\mathrm{ms}$ online requirement, and estimated motion energy is retained only as a distance-derived auxiliary indicator. In a five-method comparison over 100 paired scenarios, ICBBA-ACO achieves a mean composite objective of $J=0.663052$, a mean decision time of $33.07\mathrm{ms}$, and 100% deadline compliance. GA-MRTA obtains a lower unconstrained mean objective of $J=0.615790$, but requires approximately $2199.30\mathrm{ms}$ on average and satisfies the $200\mathrm{ms}$ requirement in only $8.89\%$ of the evaluated updates. Thus, ICBBA-ACO provides the lowest mean objective among the compared methods that maintain full deadline compliance, demonstrating a favorable quality–runtime trade-off within the tested operating range. ROS-based engineering verification further completes all 15 repeated trials and all 48 verification tasks with no recorded invariant violations. Full article

Urban transportation systems face increasing sustainability and resilience challenges due to the growing frequency and intensity of weather extremes. Weather-related congestion may increase travel delays, fuel consumption, and unequal economic costs, thereby undermining urban sustainability. Although previous studies have examined the relationship between weather conditions and traffic congestion, limited attention has been paid to whether street-network design can enhance transportation resilience under extreme weather conditions. This study investigates the relationships among extreme weather, traffic congestion, and street density using daily congestion and meteorological data from 35 major Chinese cities between 2018 and 2024. Fixed-effects regressions estimate the associations between multiple weather extremes and congestion and examine the moderating role of street density. Heavy rainfall, extreme cold, and low visibility are associated with increased congestion, whereas extreme heat is associated with reduced congestion. Street density could buffer congestion under extreme cold and heavy snow cover, suggesting that denser networks may improve resilience to localized road-surface disruptions. Heterogeneity analyses reveal weaker weather-related congestion responses in megacities and clustered cities, and during the COVID-19 period. These findings highlight the potential role of street-network design in supporting sustainable and climate-resilient transportation by reducing vulnerability to weather-related congestion. Full article

To address the need for low-power, real-time underwater acoustic signal processing and autonomous target detection on deep-sea unmanned mobile platforms, such as profiling acoustic buoys and underwater gliders, this study developed an embedded Linux-based signal processing system for buoy platforms. Conventional digital signal processing hardware platforms are often constrained by large size, high power consumption, and limited data communication capability. The proposed system adopts a compact, low-power architecture and a multithreaded processing framework based on the AM6254 heterogeneous multicore processor. It acquires four-channel vector-hydrophone signals together with attitude data from an inertial navigation module and performs band-pass filtering, fast Fourier transform (FFT), direction-of-arrival (DOA) estimation, and constant false alarm rate (CFAR) detection for autonomous target detection. The measured typical power consumption was approximately 2.3 W. Anechoic-tank and sea-trial results showed the lowest tested spectral level at which autonomous detection was achieved was 54 dB at 1 kHz, corresponding to an average in-band level of 46 dB. Under sea state 3, the system maintained continuous bearing tracking after target acquisition for a surface target traveling at 7 kn, up to a range of approximately 7 km, and provided unambiguous bearing estimation. These results demonstrate the target-detection capability and practical applicability of the system under representative operating conditions and indicate its potential for marine environmental monitoring and unmanned-platform observation and detection. Full article

To address the issue of unbalanced path allocation in multi-UUV cooperative operations under inhomogeneous ocean environments during full-coverage search missions, this paper proposes a heterogeneous UUV path planning method that integrates a hybrid waypoint deployment strategy with a dual-chromosome co-evolutionary genetic algorithm. First, heterogeneous UUVs are adaptively assigned to sub-regions according to the search value of the sea area, and a combination of Poisson sampling and Voronoi iterative refinement is adopted to complete the layout of detection points. Subsequently, connectivity-constrained K-means clustering is introduced to decompose the multi-traveling salesman problem (MTSP) into several independent TSP sub-problems. Finally, a dual-chromosome encoding scheme for task sequences and split points is designed, and a penalty matrix is incorporated into the fitness function to account for obstacle avoidance constraints, thereby establishing an integrated genetic-algorithm-based solution framework that incorporates both decomposition and obstacle avoidance. Simulation results demonstrate that the proposed method reduces the number of planned detection points by 12.4%, 12.8%, and 9.3% compared with baseline methods in circular, rectangular, and irregular sea areas, respectively, while the optimal path lengths are shortened by 5.8%, 4.5%, and 5.9%. Moreover, the cooperative mission time with four UUVs is reduced by 73.9%, 72.7%, and 71.2% relative to a single UUV, demonstrating an approximately linear speedup relative to the number of UUVs. Convergence analysis and extended experiments on 15 instances further confirm the algorithm’s solution stability and robustness under varying regional scales, shapes, and obstacle configurations. These results validate that the proposed approach not only reduces the number of deployment points and path cost, but also effectively balances obstacle avoidance and multi-robot load distribution. Full article

High-voltage vacuum circuit breakers are critical switching devices in power systems, and their reliable condition assessment is essential for safe operation and maintenance decision-making. However, breaker abnormalities are often reflected by heterogeneous operation-related evidence, and existing methods based on single-source measurements or generic feature fusion may weaken source-specific diagnostic roles and limit the recognition of compound abnormal conditions. To address this problem, this paper proposes the Physics-Guided Multi-Source State Assessment Network (PMSA-Net), a reliability-aware framework that integrates mechanical motion, opening- and closing-position limit events, and infrared thermography. Source-specific encoders first extract dynamic, end-position, and thermal representations. Reliability-aware Asymmetric Selective Interaction (RASI) then calibrates primary and auxiliary evidence, using mechanical motion as the operational context and the other sources as complementary constraints. Thermal Frequency-aware Selective Modulation (TFSM) stabilizes the low-frequency thermal field and enhances high-frequency hotspot responses. Task-conditioned evidence allocation jointly predicts state category, travel anomaly, limit-event consistency, thermal risk, and overall risk. On a laboratory-simulated benchmark covering 15 operating conditions, PMSA-Net achieved $90.18\pm 0.36\%$ state-category accuracy and an $80.00\pm 0.50\%$ overall-risk macro-F1 score over five independent runs. Under cross-source abnormalities, it exceeded the variational-fusion baseline by 2.59 and 3.05 percentage points on these metrics, respectively. These results indicate that reliability-aware calibration improves multi-task assessment of compound breaker abnormalities. Full article

The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends. Full article

Tourism expansion can stimulate economic activity that can potentially increase energy consumption and environmental pressures. The tourism-induced Environmental Kuznets Curve (T-EKC) literature has focused mainly on CO₂ emissions and has greatly conceptualized economic growth on aggregate indicators, such as GDP. In this way, model specifications might not adequately capture the environmental effects directly associated with tourism activity. Addressing this gap, the present study examines the nonlinear relationship between tourism development and industrial-combustion CO₂ and power-industry methane emissions across Eurozone countries during 1996–2019. Unlike conventional T-EKC studies, internal travel and tourism consumption is employed as a sector-specific measure. Furthermore, primary energy consumption, an energy efficiency measure, and renewables are included in the model specifications to capture structural energy-related dynamics. The empirical analysis discloses heterogeneous relationships across pollutants. An inverted U-shaped relationship is found between tourism consumption and industrial-combustion CO₂ emissions. Methane emissions exhibit a U-shaped relationship. Causality analysis indicates a feedback relationship between tourism consumption and industrial-combustion CO₂ emissions. A unidirectional relationship running from methane emissions to tourism consumption is also justified. The study contributes to the literature by demonstrating that tourism-environment relationships depend on both the pollutant examined and the measurement of tourism-related economic activity. The findings highlight the need for differentiated environmental strategies that combine decoupling-oriented measures for industrial-combustion CO₂ emissions with targeted energy-sector and methane-mitigation policies to support more sustainable tourism growth. Full article

In high-density urban areas, pedestrian route choice behavior is influenced differently by the attributes of the objective environment, depending on the purpose of the trip. To explore this issue, this paper first constructs the traditional Path Size Logit (PSL) model to perform baseline estimation of the effects of objective attributes such as path distance, intersections, number of lanes, greenery, and commercial facilities. It then introduces a hierarchical Bayesian framework to build the Hierarchical Bayesian Path Size Logit (HB-PSL) model, using the No-U-Turn Sampler (NUTS) for posterior sampling to quantify parameter uncertainty and capture inter-group heterogeneity. The model achieves inter-group information sharing through a hierarchical prior structure and uses Automatic Differentiation Variational Inference (ADVI) to provide rapid approximate estimation. An empirical analysis shows that different importance is given to objective environmental attributes for different travel purposes. Furthermore, this paper proposes the “Equivalent Distance (ED)” index, which transforms the preference of different groups for different environmental attributes into an actionable spatial length. For shoppers, the greening level increases by one level for every 100 m, which is equivalent to shortening the path by about 31.31 m; commercial facilities increase by one for every 100 m, which is equivalent to shortening the path by about 76.63 m. The results provide behavioral support for the formulation of differentiated walking strategies in high-density urban areas: priority should be given to strengthening the green coverage and commercial facility layout in commercial blocks to synergistically improve walking efficiency, safety, and comfort. Full article

Accurate short-term travel time prediction is foundational for sustainable urban mobility and intelligent transportation systems on urban arterial corridors, where travel conditions are shaped by interacting traffic, weather, and public transport factors. This study proposes a multi-source machine learning framework for segment-direction-level prediction in the Denizli city center. Floating car data (FCD), Traffic Control Center (TCC) inductive loop detector measurements, historical weather, and public transport indicators were integrated into a 15 min time-segment structure. The final dataset includes 60 segment-direction targets. Performance was evaluated using Linear Regression, Random Forest, LightGBM, and LSTM under a chronological train-validation-test design. Tree-based ensemble models produced the most stable overall performance, with LightGBM and Random Forest yielding similarly low pooled test errors. Segment-level analyses revealed clear spatial and temporal heterogeneity, showing no single model is universally superior across all links. By providing reliable traffic-state information, the framework enables efficient traffic management and may indirectly reduce delay, fuel use, and emissions; these environmental effects were not quantified. SHAP-based interpretation showed that temporal and traffic-state variables dominate predictions, while weather and public transport provide complementary value. Full article

Airport apron buses, which shuttle passengers between terminal gates and remotely parked aircraft, are vital for efficient ground operations. However, flight delays and disruptions make demand for apron-bus services uncertain and time-varying, creating substantial challenges for planning driver capacity and work schedules. To tackle this challenge, we develop an Integrated Stochastic-Flexible Planning Model (ISFPM), formulated as a mixed-integer linear program (MILP), that simultaneously optimizes workforce sizing, duty scheduling, and roster assignment for apron-bus drivers. The objective is to minimize the sum of labor costs and the expected penalty for understaffing. This penalty is evaluated under the assumption that driver demand in each period follows a Poisson-binomial distribution, which is derived from probabilistic models of flight delays. For computational efficiency, we reformulate the expected penalty term using continuity-corrected normal approximations based on the cumulative distribution function (CDF). Furthermore, we incorporate practical workforce flexibility features, including hourly-granularity duty start times and heterogeneous workday patterns across roster groups. The computational study is based on Beijing Capital International Airport and accompanied by deidentified replication materials. Across 100 materialized baseline scenarios, the ISFPM uses 148 drivers instead of the 175-driver deterministic fixed-shift benchmark, reducing average management cost by 21.9% and passenger waiting time by 85.6%. Across 18 representative-day scenarios with correlated and severe disruptions, it reduces mean management cost by 15.8% and passenger waiting time by 53.6%. The disruption-scenario analysis also indicates that deterministic flexible staffing attains the lowest waiting time at a higher cost, while common apron travel-time shocks reduce the service advantage of the ISFPM. Full article

Demand-Responsive Transport (DRT) has emerged as a flexible public transport strategy to improve accessibility, service coverage, and operational efficiency in contexts where conventional fixed-route services are inefficient, insufficient, or difficult to operate. In parallel, Artificial Intelligence (AI), and particularly Machine Learning (ML), has increasingly been investigated for predictive tasks relevant to DRT planning and operations, including demand forecasting, travel-time estimation, service reliability assessment, and decision support. This study presents a systematic literature review of ML-based predictive analytics in public-transport-oriented DRT services. After the screening and eligibility process, the included studies were analyzed according to predictive task, data source, modelling technique, validation strategy, performance metrics, and implementation-related challenges; methodological quality, risk of bias (RoB), and applicability were assessed using a PROBAST+AI-based framework. The results show that research in this field has expanded rapidly in recent years and is moving from isolated demand prediction models towards more integrated frameworks linking prediction, optimization, and service planning. However, the evidence remains fragmented, with substantial heterogeneity in data sources, spatial and temporal scales, modelling approaches, and evaluation procedures. The quality assessment showed generally favorable predictor quality and low outcome-related RoB, but analysis-related RoB was high in just over half of the studies, mainly because independent evaluation and validation accounting for temporal, spatial, or simulation-induced dependence were often lacking. Most studies provided retrospective, offline, simulation-based, or conceptual decision-support evidence, whereas prospective field deployment, external validation, and post-implementation monitoring were rarely or insufficiently documented. This review therefore provides a structured synthesis of current ML research in DRT and identifies priorities for future work, including improved reproducibility, stronger validation, robust baseline comparisons, multiple evaluation metrics, greater interpretability, and prospective assessment under real-world operational conditions. Full article

We formulate and study a predator–prey model with pursuit-evasion spatial behaviour, incorporating the fear effect in a sexually-reproducing prey population. The pursuit-evasion movements are described as indirect taxis: populations respond to diffusively dispersed and decaying kairomonal cues of their antagonists. The prey-emitted kairomone attracts predators, while the predator-emitted kairomone repels prey and locally reduces prey reproduction rate, mimicking a physiological fear response. To isolate the net effect of predator’s prey-taxis, we assume predator birth/death rates are negligible, implying a constant predator abundance. Linear stability analysis yields a condition for taxis-driven oscillatory instability of the homogeneous steady state. Numerical simulations reveal spatially heterogeneous dynamics, coexistence of periodic travelling waves, and transitions to spatiotemporal chaos. The results highlight interrelations between fear responses, spatial movements, spatiotemporal heterogeneity, and viability of the trophic system. Full article