Search Results (201)

Dockless bike-sharing systems are increasingly important for sustainable urban mobility, yet they frequently suffer from spatial misallocation between supply and demand. Conventional demand forecasting relies primarily on historical trip records and therefore systematically underestimates true demand when users abandon bike-finding attempts. To address this limitation, we propose a user-behaviour-based dynamic clustering optimisation algorithm that integrates observed riding behaviour with latent unmet demand through the concept of Golden Distance—a district-adaptive service-radius threshold estimated heuristically from operational logs of successful rides and inferred unmet-demand events, serving as a behaviourally motivated proxy for aggregate walking tolerance. Building on a prior AIoT-enabled demand-prediction framework, the method first applies HDBSCAN density-based clustering to discover intrinsic demand topology, then selectively refines only those clusters that violate Golden Distance coverage constraints via an elongation-aware adaptive k-medoids formulation. District-level true demand is predicted using an XGBoost regression model and subsequently downscaled to the cluster level based on historical activity shares. Experiments on one full year of operational data from three Hong Kong districts (Tseung Kwan O, Sha Tin, and Tuen Mun) show that the proposed pipeline produces Golden-Distance-compliant service zones at a substantially finer operational resolution than the DBSCAN baseline: 86.5% to 91.6% of clustered demand points lie within the Golden Distance district of their assigned medoid, at cluster coverages of 68.0% to 78.5%, against 60.0% to 71.1% at coverages of 49.7% to 88.1% for the baseline. A resolution-fair evaluation shows that the cluster-level RMSE advantage reported previously largely reflects the finer reporting unit rather than better prediction: the same fixed district-level forecast spans a five- to seven-fold range of RMSE when scored on progressively coarser spatial units, and at a matched clustering resolution the DBSCAN baseline equals or exceeds the proposed pipeline on cluster-level metrics. Accuracy is therefore evaluated with scale-free metrics, on which the proposed method is not the more accurate of the two, and the contribution of this work is positioned on operationally deployable, behaviourally constrained spatial zoning rather than on per-cluster forecasting accuracy. Full article

After infrastructure disruption, an emergency wireless controller must meet urgent communication demand and preserve resources for later periods. We propose Scarcity-Coefficient Gated Projection Reinforcement Learning (SCGP-RL). It jointly selects total planning-layer activation and a regional capacity upper-bound vector. Scarcity and urgent-demand evidence shape the activation intent. Scalar and capped-simplex projections enforce the coupled action constraints. A planning capacity unit (PCU) is defined as a calibratable service-capacity quantum. In the common constrained evaluation, SCGP-RL reduced the unmet urgent-demand score from 0.6643 for Projected CPO to 0.4919. It also satisfied all the executed hard constraints. Component tests show that the urgent-demand gate drives rapid service response and that the marginal demand-relief estimate provides a smaller benefit. Binding-condition tests show that the power, backhaul, and node-health mechanisms protect the resources they represent. Full article

Advances in healthcare have led to substantial growth in the global population of cancer survivors, yet young adult cancer survivors (YACS) aged 18–39 years remain an underrepresented population in cancer survivorship research. This review examines the health challenges and psychosocial factors that shape nutritional status in YACS, with the aim of informing age-appropriate nutritional care. YACS face significant financial and psychosocial demands unique to this life stage, balancing education, early careers and family responsibilities. They report greater unmet nutritional and health needs than other age cohorts, in addition to concerns regarding long-term side effects, financial strain, fear of cancer recurrence and increased responsibility for managing their own care. Cancer treatment can cause nutrition impact symptoms (e.g., nausea, taste changes, fatigue, dysphagia and gastrointestinal (GI) disturbances) that may persist for years, and YACS are vulnerable to late effects such as endocrine dysfunction, cardiometabolic disease, chronic pain and osteoporosis, all of which can be influenced by diet. Poor dietary patterns have been observed in YACS, indicating low intakes of fruits, vegetables, fibre and dairy, and higher consumption of saturated fat, sodium and processed foods. This narrative review evaluated fourteen nutritional intervention studies targeting YACS aged 18–39. Most existing interventions demonstrate minimal recruitment and retention rates, small sample sizes, and a reliance on self-report methods rather than objective nutritional measures. These limitations highlight the need for a deeper understanding of YACS’ specific nutritional needs and more effective strategies to engage this cohort. Future research should prioritise larger, more representative samples, incorporate objective nutritional and clinical measures, and explicitly address psychosocial and age-related barriers to healthy eating in young adulthood. Full article

Post-disaster repair priorities can change when building demand and available supply recover at different rates. This study models a power-grid-building system, defines demand loss as cumulative unmet demand divided by cumulative demand, and uses a genetic algorithm (GA) with deterministic feasibility rules to select repair task order and repair mode. The two GA searches used the same settings, 20 runs for each objective, and 36,200 schedules evaluated per run. In the baseline case, the lowest demand loss found was 0.3925 for the demand-targeted search and 0.3995 for the supply-targeted search. The demand-targeted result was 1.7464% lower and reduced cumulative unmet demand by 238 kW-day. Across the same 20 random seeds, the demand-targeted search produced lower demand loss in 16 runs and the supply-targeted search produced lower demand loss in four runs. In a separate comparison with different computational effort, the demand-targeted GA result had 20.3% lower demand loss than one deterministic greedy schedule. Additional five-run analyses show that the observed results depend on GA settings, crew availability, repair duration, and demand timing. The findings apply to the tested deterministic case study and support demand-aware repair scheduling when demand and supply recover at different rates. Full article

Background/Objectives: In Italy, Ministerial Decree 77/2022 provides the regulatory framework for reorganizing community-based care around community health centers and hub-and-spoke territorial networks. However, translating territorial allocation into specialist working-hour requirements remains a key operational challenge, particularly in low-density and mountainous areas. This study developed an activity-based workload model to translate observed outpatient cardiology activity into estimated specialist working-hour requirements within the hub-and-spoke community health center network of local health authority (ASL 1) Avezzano-Sulmona-L’Aquila, Abruzzo, Italy. Methods: A second-stage workforce planning analysis was conducted using the baseline 30 min territorial configuration from a previously defined allocation framework. Municipal-level outpatient cardiology administrative data for 2019 were linked to standardized procedure scheduling durations and aggregated across the catchments of three hub centers and eight spoke centers according to predefined municipality-to-node assignments. Procedure volumes were converted into annual and weekly workload hours and then into nominal 36 h weekly specialist-schedule equivalents. The resulting estimates represent a pre-pandemic, utilization-based workload scenario derived from observed activity; they should not be interpreted as direct measures of underlying population demand, epidemiological need, or unmet need, nor as validated staffing requirements. Results: The reference network covered 286,832 residents and included 44,039 outpatient cardiology procedures, corresponding to 12,719.8 annual workload hours. These volumes generated 244.6 weekly workload hours, equivalent to 6.79 nominal 36 h weekly specialist-schedule equivalents. Workload was unevenly distributed across the network: the L’Aquila and Sulmona hubs accounted for the largest annual workloads, with 3014.9 and 2894.3 h, respectively, whereas Trasacco was the spoke center with the highest observed workload, with 1775.5 h. Population-standardized indicators showed substantial variation in workload intensity across the catchments, indicating that catchment population size alone did not adequately capture the observed workload differences. Conclusions: Activity-based workload estimation adds an operational workforce layer to hub-and-spoke territorial planning. By translating observed outpatient service-use volumes into specialist working-hour requirements, the model provides a transparent and potentially transferable framework to support the operational planning of community health centers in geographically complex settings. The approach is adaptable to other specialties, territories, and service configurations where comparable activity data, standardized scheduling durations, and territorial assignment inputs are available. Full article

Background: Manufacturing supply chains must increasingly coordinate cost, environmental impact, and service continuity under demand uncertainty and limited capacity. Methods: This study develops a stochastic mixed-integer linear programming framework for carbon-aware production–distribution planning in an automotive supply chain. The model jointly optimizes production quantities, inventory levels, shipments, truck usage, and lost sales over a multi-period horizon. Demand uncertainty is represented through scenarios, while production- and transportation-related emissions are monetized using an internal carbon price. Lost-sales penalties capture service degradation when demand cannot be fulfilled by the focal plant, and a rolling-horizon analysis evaluates planning responsiveness as demand information is updated. The framework is applied to an industrially inspired, capacity-constrained automotive case with multiple products, production lines, destinations, and demand scenarios. Computational experiments assess carbon pricing, lost-sales penalties, demand volatility, deterministic versus stochastic planning, and rolling-horizon replanning. Results: Results show that carbon pricing mainly acts as an economic valuation mechanism under the studied fixed-structure configuration, whereas lost-sales penalties strongly influence service performance. Demand volatility increases unmet demand, and lower emissions may reflect lower fulfilled demand rather than improved efficiency. Conclusions: The study provides a decision-support framework for evaluating cost–carbon–service trade-offs under stochastic demand while acknowledging single-plant and fixed-routing limitations. Full article

The repair and functional restoration of skeletal muscle tissue following trauma, degenerative disease, or volumetric muscle loss remains a significant unmet clinical challenge in tissue engineering, where the need to recapitulate the anisotropic architecture, mechanical compliance, and high metabolic demands of native muscle imposes stringent requirements on biomaterial design. Traditional cell culturing and scaffold fabrication strategies have proven insufficient to address these demands in isolation, particularly in integrating mechanical integrity, biochemical functionality, and biological activity within a single biomaterial system. Recent advances in material science have accelerated the evolution of skeletal muscle tissue engineering toward a more precise and technologically sophisticated discipline. In this context, surface-engineered magnetic nanoparticle (MNP) hybrids have emerged as a promising multifunctional platform, owing to their intrinsic biocompatibility, tunable physicochemical properties, and rapid, non-invasive responsiveness to external magnetic fields. These unique characteristics have enabled the development of magnetic force-based tissue engineering strategies, facilitating controlled myogenic cell organization, magnetically guided delivery of therapeutic agents and stem cells, enhanced muscle construct formation within responsive scaffolds, and real-time non-invasive monitoring of engineered systems via MRI. This review systematically synthesizes the recent advances in surface-engineered MNP platforms for skeletal muscle tissue engineering, covering organic and inorganic coating strategies, magnetically responsive scaffold integration, guided cell and drug delivery, and construct monitoring, whilst critically appraising the biocompatibility, biodistribution, and regulatory challenges that currently define the translational pathway for MNP-augmented skeletal muscle constructs. Full article

Fresh-food retail demand is only partially observed when products stock out: point-of-sale records report sales, whereas the unmet part of demand is censored. This study develops a probabilistic decision-support framework, termed CADRE, that treats stockout-flagged observations as lower bounds, separates recurring diurnal structure from censoring effects, and shapes the replenishment decision with an asymmetric shortage–spoilage loss. The evaluation uses FreshRetailNet-50K, an hourly Chinese fresh-retail benchmark with observed stockout labels, and a complementary daily robustness experiment on the Ecuadorian Favorita panel with a constructed high-demand censoring mask. On FreshRetailNet-50K, CADRE reduces weighted absolute percentage error (WAPE) from 39.42% for the TimeXer backbone to 36.71% and reduces re-censored demand bias from $-8.1\%$ to $-1.3\%$. In a controlled replenishment simulation on known re-censored demand, modelled waste decreases from 9.8% under a censored-sales policy to 6.4% under CADRE while service level increases from 92.9% to 94.7%. These results indicate potential improvements in replenishment decision support under the evaluated assumptions, rather than measured operational food-waste or emissions reductions. Operational food-waste and emission effects remain to be validated through field deployment. Full article

Off-grid residential microgrids in tropical regions require storage architectures capable of maintaining renewable electricity supply under variable solar resources, evening demand peaks, and diverse household consumption levels. In PV–battery–hydrogen systems, however, economic indicators can be difficult to interpret when software-reported costs are compared directly with externally calculated LCOE values based on different accounting conventions. This study presents a metric-reconciled techno-economic reconstruction approach for retained PV–battery–hydrogen microgrid configurations serving off-grid residential demand in Chetumal, Mexico. The objective is not to introduce a new global optimization or to claim the universal superiority of a specific architecture, but to separate archived HOMER Pro benchmark outputs from an external techno-economic model (TEM). The TEM reconstructs net present cost, scheduled replacements, salvage treatment, discounted delivered electricity, HOMER-derived LCOE, TEM-derived LCOE, sensitivity indicators, and storage role metrics using declared accounting assumptions. The approach is applied to two representative residential demand scenarios of 16.67 and 53.42 kWh/day. Both retained configurations achieved a 100% renewable fraction with negligible unmet load. Battery discharge increased from 827.12 kWh/year in the low-demand case to 6125.52 kWh/year in the high-demand case, highlighting the increasing role of the battery in short-duration balancing. In contrast, the hydrogen pathway acted as a delayed-backup layer by converting surplus PV electricity into hydrogen and later recovering it through PEM fuel cell generation. The TEM closely matched the HOMER-derived LCOE benchmark, with deviations below 4%, yielding TEM-derived LCOE values of 0.3320 and 0.3571 USD/kWh for the low- and high-demand cases, respectively. Sensitivity analysis showed that delivered electricity, discount rate, PV cost, and battery cost were the main LCOE drivers, while deterministic multi-parameter scenarios confirmed the combined influence of financing, component costs, O&M, PV degradation, and electricity delivered. Overall, the proposed approach provides an auditable basis for metric reconciliation, early-stage technology assessment, and storage role interpretation in tropical off-grid microgrids. Future extensions should include architecture-level re-optimization, flexible loads, degradation-aware modeling, and part-load component behavior. Full article

Background/objectives: As Guangzhou’s population ages rapidly, the supply-and-demand gap in elderly care facilities has become severe. We propose a ‘4A’ model that draws on accessibility, affordability, acceptability, and availability from the classic ‘5A’ access framework to optimize nursing home bed allocation from existing facilities. Methods: We applied the ‘4A’ model to 2630 communities/villages and 243 nursing homes in Guangzhou, using linear programming to maximize a matching score under capacity, demand, and occupancy constraints. Inequality was assessed using the Gini coefficient, Theil index, Moran’s I, and hot spot analysis. Four policy scenarios were simulated under resource-scarce and resource-abundant conditions. Results: A total of 753 (28.6%) communities/villages had no access to nursing homes. The unmet demand rates followed a ‘dual-center’ clustering pattern, and hot spots were not only in the urban core, where ‘scale disadvantage’ and ‘matching disadvantage’ coexisted, but also in outer suburban districts to form a ‘supply vacuum.’ Inequity was moderate, and it originated from between-district disparities. Compared with a ‘distance-only variant’ model, our ‘4A’ model allocated 3813 more beds. Policy simulations showed that minimum service ($\alpha =0.1$) eliminated all unserved communities/villages; targeted bed expansion increased satisfaction rates to 74.8% and reduced the Gini coefficient to 0.280; and subsidies and quality upgrades became effective only when bed supply was abundant. The four simulations were phased into a three-phase policy roadmap. Conclusions: The ‘4A’ model transforms the qualitative ‘5A’ framework into a computable allocation matrix, offering actionable recommendations for equitable access to elderly care resources. Full article

Background: Perishable agri-food supply chains require logistics networks that remain economically viable despite fluctuations in demand, transportation costs, and market prices. This study develops and evaluates a two-echelon logistics network for the banana supply chain in Magdalena, Colombia. Methods: A mixed-integer linear programming model was formulated to maximise daily profit by jointly determining collection-centre activation and product flows among 14 producers, five candidate collection centres, and two commercial buyers. The deterministic solution was complemented by sensitivity analysis and 1000 Monte Carlo optimisation scenarios incorporating variability in demand, transportation costs, and selling prices. Results: Under nominal conditions, all five collection centres were activated, the full demand of 42,000 kg/day was served, and the optimal profit was USD 3093/day. Centres C1–C4 operated at full capacity, whereas C5 reached 42.9% utilization. Under uncertainty, the mean profit decreased to USD 1955.70/day, the mean unmet demand was 954.03 kg/day, and shortages occurred in 72.3% of scenarios. C1–C4 remained the network core, while C5 acted as a flexible contingency facility. Conclusions: The proposed framework reveals an efficiency–resilience trade-off overlooked by deterministic optimisation. Demand growth and capacity reductions are the principal operational risks, supporting investment in collection capacity and proactive demand management. Full article

Global population aging has emerged as a major driver of the growing burden of neurological diseases, highlighting the urgent demand for advances in early diagnosis, prevention, and rehabilitation. These conditions are typically characterized by insidious onset and irreversible progression, yet their clinical management remains critically compromised by substantial diagnostic delays, representing an intractable bottleneck for existing detection technologies. Therefore, the development of precise, early-stage detection technologies is crucial for expanding the therapeutic window and improving long-term clinical outcomes, addressing a critical unmet clinical need. Herein, we review and compare precision detection strategies for neurological diseases, focusing on the types and mechanisms of mainstream biosensing platforms. Based on the classification of detection substrates and signal transduction mechanisms, four major bio-detection branches are analyzed, including liquid, exosomal, imaging, and digital biomarker detection, with representative studies demonstrating detection limits reaching femtomolar concentrations, clinical diagnostic sensitivities exceeding 90%, and classification accuracies comparable to or surpassing conventional imaging modalities. The inherent advantages and limitations of each biosensing technology are also comprehensively discussed. This review underscores that future research on neurological biomarker sensing is trending toward multimodal integration, which enables the construction of more robust early warning and prognostic assessment systems. This work aims to provide valuable theoretical insights for clinical translation of relevant sensing technologies and integrated diagnostic and treatment strategies, thereby facilitating the progress of early intervention and rehabilitation for common neurological diseases. Full article

With increasing residential electricity demand, hybrid energy systems capable of simultaneously improving affordability, reliability, and environmental performance have become increasingly important. This paper develops an integrated techno-economic and environmental assessment framework for grid-connected residential energy systems under unreliable grid conditions and applies it to a real-world residential case study in Fayoum, Egypt. In the proposed framework, the utility grid is treated as the primary electricity source, while PV, diesel generation, and battery storage are evaluated as backup/support options. Six grid-connected hybrid configurations, namely Grid/Diesel, Grid/PV/Diesel, Grid/PV/Diesel/Battery, Grid/Diesel/Battery, Grid/PV/Battery, and Grid/Battery, were evaluated under identical load, solar resource, and economic conditions to identify the minimum net present cost (NPC)configuration capable of satisfying a specified service level, expressed in terms of the maximum allowable unmet load ratio. The optimization problem was formulated as a single-objective model that minimizes NPC, subject to technical constraints and a service level constraint represented by a zero unmet load requirement in this study. Additional indicators, including levelized cost of energy (LCOE), renewable fraction, CO₂ emissions, and electricity purchased from the grid, were used for comparative performance evaluation. The candidate systems were simulated and optimized under frequent grid outage conditions using HOMER Pro. The results identify the Grid/PV/Battery configuration as the preferred base case backup/support configuration among the evaluated alternatives, achieving the lowest NPC of USD 8949, the lowest LCOE of USD 0.135/kWh, the highest renewable fraction of 55.1%, and the lowest annual CO₂ emissions of 2333 kg/yr, while satisfying the zero unmet load requirement. Compared with the base Grid/Diesel system, the optimal configuration reduces annual operating cost from USD 1204/yr to USD 648.19/yr and lowers emissions by approximately 50%, despite requiring a higher initial capital investment. Sensitivity analysis shows that the preferred solution remains robust across most of the examined financing parameter space. The PV derating factor analysis further indicates that the Grid/PV/Battery configuration remains optimal at higher PV derating levels of 70–80%, whereas the preferred solution shifts toward Grid/Diesel at lower derating levels of 50–60%. Overall, the results demonstrate that combining service-level-constrained NPC minimization with comparative techno-economic and environmental evaluation provides a robust basis for identifying suitable backup-supported grid-connected residential energy solutions under unreliable grid conditions. Full article

Environmental policies and intermittent renewable energy (RE) drive large-scale hydrogen production towards hybrid supply configurations, combining collocated RE units and the electricity market (EM). This links the power and hydrogen sectors through EM/hydrogen prices, dispatch, and hydrogen demand profiles. In a hybrid configuration, the strategic role of RE in the EM enhances these links by creating profit opportunities. This work develops a bi-level model, optimizing electrolyzer size and location, operational decisions and RES bidding strategies, while explicitly modeling EM clearing. In the upper-level, an EM player, owning strategically bidding RE assets, evaluates expanding into the use of electrolyzers that act as price-takers. The lower-level problem clears the EM. The proposed framework is applied to an IEEE 24-node test system. The results show how EM conditions determine investments for different hydrogen price cases. It is revealed that differentiated electricity sourcing across electrolyzers and efficiency-preserving dispatch impact operational decisions, leading to revenue improvements. Moreover, renewable capacity withholding is used to avoid zero EM prices and mitigate the economic impact of unmet hydrogen demand when RE availability is limited and electrolyzer participation in the EM is restricted. Time-window-constrained hydrogen demand mitigates unutilized RE by 39% compared to that for hourly demand. Full article

Objective: To evaluate the impact of urban–rural health insurance integration on the health outcomes and financial burden of rural older adults. Methods: Utilizing panel data from the China Health and Retirement Longitudinal Study (CHARLS) spanning 2013 to 2018, we employed a staggered difference-in-differences model coupled with propensity score matching (PSM-DID) for rigorous causal identification. Results: The policy significantly reduced out-of-pocket medical expenditures for rural households by approximately 5.6% (p = 0.034). Concurrently, significant improvements were observed in both physical health (a 0.092-point reduction in ADL impairment scores) and mental health (a 0.725-point reduction in CES-D depression scores). Mechanism analyses revealed that the integration did not significantly increase the probability of outpatient or inpatient visits—thereby ruling out supplier-induced demand and moral hazard—while effectively reducing the incidence of catastrophic health expenditure by 1.9% (p = 0.004). Heterogeneity analyses indicated that while the financial relief was universally distributed across varying educational levels, the policy dividends were predominantly captured by the younger-old demographic. Notably, the reduction in financial burden was not statistically significant for the oldest-old cohort (aged 75 and older). Conclusions: The urban–rural health insurance integration has achieved a dual dividend of financial protection and health enhancement without triggering the overutilization of medical services. Nevertheless, the unmet care expenses for older adults with severe disabilities underscore the urgent necessity for a secondary safety net, such as long-term care insurance. Full article