Search Results (384)

The public deployment of electric vehicle charging stations must simultaneously balance construction economics, user accessibility, queueing pressure, feeder security, tail risk under demand uncertainty, and spatial fairness. These criteria are strongly coupled, yet most existing studies either rely on static optimization with limited behavioral realism or use multi-agent reinforcement learning for short-term charging operation rather than for long-term siting. This paper proposes a preference-conditioned multi-agent deep deterministic policy gradient (PC-MADDPG) framework for the urban charging station siting problem in a coupled traffic–distribution environment. Candidate charging sites are modeled as cooperative agents under centralized training and decentralized execution. Each agent outputs a continuous pile-allocation action, which is repaired into an integer expansion plan under a budget constraint. The environment evaluates each plan through attraction-based demand assignment, queue approximation, LinDistFlow-style feeder analysis, and a six-objective performance vector, including annual net cost, travel burden, service inconvenience, grid penalty, CVaR of unmet charging demand, and equity loss. On a reproducible benchmark with 12 demand zones, 10 candidate sites, an 11-bus radial feeder, and 16 stochastic daily scenarios, the proposed framework generates a non-dominated archive with 42 unique feasible plans. A representative PC-MADDPG solution opens 5 of 10 candidate sites and installs 20 fast-charging piles, achieving 99.88% mean demand coverage with an annual profit of 2.083 M$ and a maximum line utilization of 0.999. Relative to the NoGrid ablation, the selected full model reduces grid penalty by 23.87% and equity Gini by 51.08%, with only a 0.35% profit concession. Relative to the NoRisk ablation, the CVaR of unmet demand is lowered by 69.70%. Compared with a demand-greedy baseline, the proposed method reduces grid penalty by 11.72% and equity Gini by 25.19% while preserving similar demand coverage. These results provide proof-of-concept evidence, on a reproducible coupled benchmark, that preference-conditioned multi-agent learning can serve as a practical many-objective siting engine for charging-infrastructure planning when coupled traffic and feeder constraints are explicitly modeled. Full article

Aging populations have intensified the demand for thermally comfortable and energy-efficient housing, particularly for elderly residents whose diminished thermoregulatory capacity renders them disproportionately vulnerable to indoor temperature fluctuations. Existing senior apartments in cold-climate regions frequently fail to meet age-specific thermal comfort standards, yet systematic retrofit optimization frameworks explicitly tailored to elderly occupants remain scarce. This study presents a data-driven multi-objective optimization framework for building envelope retrofitting, which is validated using on-site temperature measurements from a representative 1980s brick–concrete senior apartment building in Beijing. The framework integrates Latin Hypercube Sampling (LHS) for design space exploration, a Long Short-Term Memory (LSTM) surrogate model for simultaneous prediction of three performance objectives, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto-optimal solution generation, with final selection performed via a weighted Mahalanobis distance-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Optimization targets—annual energy consumption, indoor thermal discomfort hours, and retrofit cost—are parameterized using the age-sensitive comfort thresholds specified in GB 50340-2016. The LSTM surrogate achieved R² values of 0.91–0.93 across all objectives with training–testing differences below 0.02. The optimal retrofit package—Polyvinyl Chloride (PVC) Low Emissivity (Low-E) double-glazed windows (5 + 6A + 5), glass fiber roof insulation (65.25 mm), and Extruded Polystyrene (XPS) external wall insulation (65.39 mm)—reduces annual energy consumption by 47.1% (from 40,867 to 21,626 kWh) and annual thermal discomfort hours by 62.4% (from 2454 °C·h to 923 °C·h). SHapley Additive exPlanations (SHAP)-based sensitivity analysis further identifies wall U-value and roof thickness as the dominant performance drivers. A reproducible and computationally efficient pathway is provided by the proposed framework for evidence-based envelope retrofit decision-making in existing senior residential buildings. Full article

Outbreaks of crown-of-thorns sea stars (CoTS) threaten coral reef integrity and biodiversity, yet local dynamics and short-term responses to control remain insufficiently described. This study characterised an outbreaking Acanthaster population in two specific sites of the coast of Khor Fakkan (Gulf of Oman, United Arab Emirates) to resolve species identity, population composition, prey selection and the effects of targeted removals. All sequenced individuals clustered in two related haplotypes belonging to the species Acanthaster planci. Benthic surveys showed moderate live-coral cover, dominated by massive Porites sp. colonies. Moreover, the observations of 139 preyed colonies revealed pronounced genus-level selectivity, with branching and complex morphologies suffering disproportionately and massive forms largely avoided. However, the selection of massive Plesiastrea and Favites genera as preferred coral prey might suggest a shift towards less preferred coral in the CoTS diet, posing a severe threat to coral reefs’ integrity. Intensive removal reduced the local density, up to 86%, and provided substantial short-term relief, but continued monitoring is required to secure long-term reef resilience. Full article

Drone logistics is emerging as a key trend in future delivery systems due to its efficiency. However, current benefit assessments are often one-dimensional, focusing on single-node modes and overlooking load variations and charging processes in continuous multi-node delivery. To address this gap, this paper develops an integrated assessment framework across three dimensions: environment, economy, and time. Based on lifecycle emissions and total cost of ownership, a structured time-performance indicator, time value, is introduced. By incorporating an energy consumption model that accounts for dynamic loads and a charging model that considers charging behavior, an improved genetic algorithm is designed to optimize large-scale urban drone dispatch. Furthermore, a comparative sensitivity analysis with electric trucks quantifies the effects of market demand, charging strategy and technological progress. Results show that, under the modeled scenarios and parameter assumptions, electric trucks remain preferable in the short term, while drones demonstrate stronger long-term potential. Enterprises should align drone and truck deployment with demand and manage charging dynamically, while governments should combine initial subsidies with long-term guidance and systemic support to enable large-scale drone logistics adoption. Full article

The rapid expansion of the tourism industry underscores the necessity for sustainable hotel recommendation systems that guide user choices while safeguarding the long-term viability of the tourism ecosystem. However, existing methods often struggle to reconcile individual user preferences with sustainable consumption objectives, frequently encountering the “information cocoon” effect and lacking interpretability in their decision-making processes. To address these issues, this study proposes a multi-objective, context-aware hotel recommendation framework that integrates text mining, sequential behavior modeling, and reinforcement learning. The framework begins by employing unsupervised learning to extract multidimensional hotel features from online reviews, with an explicit emphasis on comprehensive sustainability metrics. It subsequently applies a dynamic state representation approach that merges long-term and short-term interests with real-time contextual information to accurately reflect evolving consumer needs. Furthermore, a dynamic feature weighting module is incorporated to enhance interpretability and enable context-adaptive evaluation of both commercial and sustainable attributes. The recommendation process is structured as a Markov Decision Process, leveraging a composite reward function comprising diversity penalties and sustainability incentives. Empirical analysis using real-world data validates the framework, demonstrating its contribution to sustainable tourism and achieving recommendation accuracy that surpasses existing benchmark models. Full article

Breast-conserving therapy (BCT), consisting of lumpectomy followed by adjuvant radiation, provides oncologic outcomes equivalent to mastectomy for many patients with breast cancer. As survivorship increases, the demand for aesthetic restoration after BCT has grown; however, reconstructive strategies in this setting remain less standardized than those following mastectomy. Reconstruction after BCT presents distinct challenges due to partial tissue loss, nonuniform radiation injury, progressive fibrosis, and wide variability in patient expectations and tolerance for revision surgery. Consequently, mastectomy-based reconstructive algorithms are often insufficient for guiding care in this population. This review synthesizes contemporary reconstructive options following BCT through a personalized medicine framework, emphasizing patient-specific risk factors that influence technique selection, timing, and long-term outcomes. Key determinants include radiation exposure, breast morphology, comorbid conditions, prior breast surgery, and psychosocial preferences. Oncoplastic volume displacement, implant-based augmentation, fat grafting, and autologous reconstruction each demonstrate distinct risk profiles in the post-BCT tissue environment and require individualized application. Timing of reconstruction and willingness to undergo staged procedures play a central role in outcome durability and patient satisfaction. Across reconstructive strategies, revision burden emerges as a clinically meaningful, patient-centered outcome that is not adequately captured by traditional short-term complication metrics. A risk-informed approach that integrates individualized risk assessment with transparent counseling and shared decision-making may improve alignment between reconstructive planning and patient goals. Personalized reconstruction after BCT requires moving beyond technique-driven paradigms toward flexible, longitudinal care pathways. Future efforts should focus on developing BCT-specific predictive models and incorporating patient-reported outcomes to advance personalized reconstructive care. Full article

Non-motor symptoms of PD impair quality of life and remain challenging to treat. Here, we examined the effects of short- (38 days) and long-term (178 days) supplementation with the natural polyamine spermidine on anhedonia and anxiety-like behaviours in a 6-hydroxydopamine-induced rat model of PD and linked them with spermidine’s anti-inflammatory properties. Behavioural assessments (cylinder, sucrose preference, elevated plus-maze tests) were conducted during progressive neurodegeneration and after oral treatment. Under the same conditions, peripheral inflammation was evaluated by the total leukocytes and their subpopulation numbers (hematological analysis) and by CD4⁺ and CD8⁺ T lymphocyte percentages (imaging flow cytometry); the plasma levels of interleukins 4 and 10 and corticosterone (enzyme-linked immunosorbent assay) were also evaluated. The safety of long-term supplementation was assessed using standard biochemical markers (chemistry analyser). Both treatment regimens reversed 6-hydroxydopamine-induced lymphopenia. Long-term spermidine treatment increased the number of TCD4⁺ lymphocytes and monocytes and elevated the plasma concentrations of IL-4 and IL-10, while reducing corticosterone levels. These immunomodulatory effects were associated with reduced anhedonia and anxiety. All of the biochemical safety parameters remained within normal ranges. Spermidine alleviates neuropsychiatric symptoms in a rat model of progressive neurodegeneration in the nigrostriatal system through its regulatory influence on peripheral immune responses. Exploring the systemic mechanisms underlying spermidine’s effects could unveil innovative supplementation strategies and expand treatment options for managing symptoms in PD. Full article

This study presents a systematic comparative benchmark between two distinct paradigms for solving nonlinear partial differential equations (PDEs): the data-driven Physics-Informed Neural Networks (PINNs) and the analytical Homotopy Analysis Method (HAM). We apply both methods to a unified family of canonical PDEs, the Burgers, Huxley, Fisher, Burgers–Huxley, and Burgers–Fisher equations, under identical problem setups, domain discretization, and validation metrics. PINNs incorporate physical laws directly into neural network training by minimizing a loss function that enforces PDE residuals, yielding physically consistent solutions even for strongly nonlinear problems. HAM provides approximate analytical solutions using a unified framework, and the same initial guess, auxiliary linear operator, and auxiliary function across all equations despite their distinct nonlinearities. The controlled, consistent application of both methods enables a fair, reproducible comparison across this equation family. The results provide a quantitative performance map under identical conditions, delineating when PINNs (high accuracy, long-term stability, and generalization capability) are preferable, versus when HAM (computational speed, short-term analytic approximation, and lower memory footprint) offers advantages. While the finite radius of convergence of the truncated HAM series is theoretically expected, our controlled comparison quantifies for the first time how this degradation varies across equation types, revealing that the choice between methods depends on specific problem requirements including error tolerance, available computational resources, and temporal horizon. The novelty lies not in solving each equation individually, but in deriving a performance taxonomy that systematically connects equation features (shocks, stiffness, and reaction–diffusion coupling) to optimal solver choice—providing previously unavailable, evidence-based guidance for the scientific computing community. This study establishes the first rigorous, controlled comparative benchmark between analytic and data-driven PDE solvers across a spectrum of nonlinearities, providing a reproducible baseline for future hybrid scientific machine learning solvers. Full article

Background: Preventing diabetic foot ulcer (DFU) recurrence after healing is a major challenge in the remission phase. In this context, remission is not synonymous with healed; it refers to a confirmed post-healing state in which the ulcer is closed, but the individual remains at high risk of recurrence and requires ongoing preventive care. Armstrong, Boulton, and Bus suggested that DFU recurrence is about 40% at 1 year, 60% at 3 years, and 65% at 5 years and argued that limb preservation should follow a long-term “survivorship” model similar to cancer care. However, these estimates combine heterogeneous follow-up intervals and definitions, and there is limited work focusing specifically on the first 12 months after confirmed remission. Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Searches of PubMed/MEDLINE, Scopus, ScienceDirect, and the Cochrane Library were performed on 16 December 2025. Eligible studies enrolled adults with diabetes in confirmed remission after a healed DFU and reported an exact 12-month recurrence outcome (n/N or Kaplan–Meier estimate). Risk of bias was assessed using the Critical Appraisal Skills Program and Joanna Briggs Institute tools, and certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. Twelve-month recurrence proportions were pooled using a random-effects model on the logit scale, and results were interpreted cautiously due to the limited number of eligible cohorts. Results: Across three cohorts with confirmed remission at baseline (total n = 469) and an exact 12-month outcome, the pooled 12-month recurrence proportion was 29.3% (random-effects; 95% CI 24.9–34.1), i.e., about one in three. Although this estimate is lower than the widely cited ~40% 1-year recurrence benchmark, it reflects a strictly defined remission population and a fixed 12-month timepoint, rather than mixed follow-up intervals or less precise definitions. Conclusions: Approximately one in three adults in remission after a healed DFU develop a recurrent ulcer within 12 months. Because this estimate is based on a small number of cohorts and on strictly confirmed remission, it should be interpreted cautiously and should not be generalized to all individuals with a healed DFU. These findings support prevention-focused surveillance and ongoing risk management during remission. Larger, preregistered multicenter cohorts with standardized remission and recurrence definitions are needed to refine short-term recurrence estimates and inform survivorship-style models of care. Full article

Accurate river discharge estimation is fundamental for water resource management under increasingly variable hydrological conditions. While conventional in situ techniques remain hydrometric reference standards, their operational deployment is constrained by cost, accessibility, and limited spatial coverage. Advances in remote sensing and artificial intelligence (AI) have introduced non-contact discharge estimation frameworks based on image-derived observations. This systematic review, conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines, examines the evolution of river discharge measurement methods between 2004 and 2024 through a structured two-stage design. An initial search in Web of Science and Scopus identified 2809 records, of which 249 were retained for first-stage synthesis. A focused second-stage screening isolated seven studies that directly integrate image-based data with machine learning or deep learning architectures for discharge estimation. The analysis reveals a methodological transition from instrument-based hydrometry toward computationally assisted, image-driven approaches. The retained studies employ close-range and satellite imagery combined with Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and related models. Although reported validation metrics indicate strong predictive capability under specific conditions, performance remains dependent on site-specific calibration and reference discharge records. Broader operational deployment requires improved transferability, uncertainty integration, and cross-basin validation. Full article

This systematic review summarizes the application of artificial intelligence (AI) in classifying upper-limb motion using Electroencephalogram (EEG) and Electromyogram (EMG) signals, focusing on the field’s progression from Traditional Machine Learning (TML) to Deep Learning (DL) architectures. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a search of PubMed, IEEEXplore, and Web of Science yielded 301 eligible studies published up to June 2025. The results indicate a change from classical classifiers like Linear Discriminant Analysis (LDA) and Support Vector Machines (SVMs) toward DL approaches. While Convolutional Neural Networks (CNNs) remain the most frequently implemented, emerging architectures, including Long Short-Term Memory (LSTM) networks and Transformers, have demonstrated remarkable performance. Despite the rise of DL, classical models remain highly relevant due to their robustness and efficiency. This review also identifies a heavy reliance on EEG-only modalities (60%), with only 7% of studies utilizing hybrid EEG-EMG systems, representing a potential missed opportunity for signal fusion. Full article

Background/Objectives: Stevens–Johnson Syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare, potentially fatal immunological conditions that affect cutaneous and mucosal surfaces and have the potential to involve the genitourinary tract. While genital involvement is common, urological manifestations are under-recognised clinically and there is a paucity of clear, evidence-based management pathways specific to urological manifestations of SJS/TEN. To map the spectrum of urological manifestations of SJS/TEN, to describe the short- and long-term outcomes of these manifestations, and to synthesise management and prevention strategies to inform clinical practice. Methods: This was a scoping review conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guideline. Data sources: Medline and PubMed articles published in English with publication date up to December 2025. Study selection: Eligible studies included case reports, case series, observational studies, clinical guidelines, and review articles describing urological manifestations, outcomes, management, or prevention strategies for patients with SJS/TEN. Articles limited to renal or isolated gynaecological involvement were excluded. Data extraction and synthesis: Articles were screened independently by two reviewers using a pre-defined data extraction template covering four domains: urological manifestations, outcomes and sequelae, management strategies, and prevention strategies. This criterion was refined after a pilot of 20 studies. Discrepancies were resolved by consensus with a third reviewer. Formal risk-of-bias assessment was not performed, consistent with scoping review methodology. Results: One hundred and four studies published between 1987 and 2025 were included in this review. Selected articles included case reports (n = 63), retrospective cohort studies (n = 23), prospective studies (n = 2), guidelines (n = 5), and summary articles (n = 11). Reported urological involvement ranged from genital cutaneous and mucosal disease including erosions, adhesions, and balanitis to urethral manifestations such as urethritis, stenosis, and strictures, as well as scarce upper urinary tract involvement including ureteric stricture and ureteric mucosal sloughing. While some manifestations resolved with supportive care, others progressed to chronic sequelae including persistent urethral strictures, voiding dysfunction, sexual dysfunction, recurrent infection, and in rare cases, obstructive uropathy. A multidisciplinary approach was recommended for all patients with SJS/TEN. Urological management centred around early and repeated urogenital examination, manual lysis of adhesions, urinary catheterisation, and timely intervention for urethral or ureteric obstruction. Long-term urological follow-up of 12 months was recommended for patients with significant urogenital involvement. Conclusions: Urological manifestations of SJS/TEN are diverse, clinically significant, and frequently under-recognised. Early urological involvement, systematic genital and urinary tract assessment, and proactive preventative measures may reduce long-term morbidity. This review provides a comprehensive synthesis of knowledge and recommendations to support urologists’ role in multidisciplinary care of patients with this pathology. This review also highlights the need for prospective research to guide further evidence-based management of urological complications of SJS/TEN. Full article

Objectives: This study aimed to compare laparoscopic deroofing and laparoscopic partial splenectomy regarding recurrence, perioperative safety, and short-term postoperative outcomes in pediatric patients. Methods: This retrospective single-center study included pediatric patients who underwent laparoscopic partial splenectomy or laparoscopic deroofing for benign splenic cysts between January 2012 and August 2025. Demographics, cyst characteristics, operative variables, postoperative complications, and recurrence were analyzed. The primary outcome was cyst recurrence; secondary outcomes included duration of surgery and length of hospitalization. Results: Twenty-three patients met the inclusion criteria: 10 underwent laparoscopic partial splenectomy and 13 laparoscopic deroofing. Groups were comparable in age, sex distribution, cyst diameter, body mass index, and American Society of Anesthesiologists classification (all p > 0.3). No conversions to open surgery occurred. Operative time was significantly shorter for partial splenectomy (37.8 ± 7.1 min) compared with deroofing (77.3 ± 33.6 min; p = 0.001). Length of hospitalization did not differ significantly between groups (median 2 days; p = 0.596). Two minor postoperative complications occurred in the deroofing group, without a significant difference between techniques (p = 0.486). A striking difference in recurrence was observed: no recurrences occurred after partial splenectomy (0%), whereas recurrence was documented in 8/13 patients (61.5%) after deroofing (p = 0.003). Conclusions: Laparoscopic partial splenectomy offers superior long-term efficacy in treating benign splenic cysts in children, with significantly lower recurrence rates and shorter operative times than laparoscopic deroofing. While deroofing remains a technically accessible option, its high recurrence rate limits its role as a definitive treatment. Partial splenectomy appears to be the preferred spleen-preserving technique. Future prospective, multicenter, and ideally randomized studies are warranted to confirm these findings. Full article

Personalized recommender systems play an essential role in enhancing user experience by accurately predicting user preferences. Previous approaches mainly focus on modeling long-term preferences or capturing short-term dynamics through sequential patterns, while few achieve an effective balance between the two. This study proposes Rec-SSP, a novel review-aware recommendation model that integrates long-term and short-term preferences through a gated fusion mechanism. Long-term preferences are extracted from aggregated user reviews, whereas short-term preferences are modeled by identifying sequential patterns from recent interactions at both the review and category levels. This multilevel design captures fine-grained opinions across items, ensuring a more accurate understanding of the evolving user intent. This study conducted various experiments on real-world datasets, showing that Rec-SSP outperforms baseline models. These findings demonstrate that balancing long-term and short-term preferences with multilevel sequence modeling can significantly improve recommendation accuracy across diverse domains. Full article

Background and Objectives: Partial nephrectomy is the preferred intervention for many localized renal tumors; but intraoperative tumor localization, real-time perfusion evaluation, and vascular control optimization can be technically demanding, especially in endophytic or complex lesions. Near-infrared fluorescence (NIRF) imaging with indocyanine green (ICG) has been adopted as an intraoperative adjunct to improve visualization, support selective or super-selective clamping strategies and assist tumor to parenchyma contrast and selective vascular control. However, current evidence regarding the benefit of ICG-NIRF is often inconsistent, and a significant gap exists due to the lack of standardized intraoperative protocols, which limits the reproducibility of clinical results. This review aims to synthesize existing comparative evidence, identify the sources of methodological heterogeneity, and propose minimum criteria for the standardization of ICG use in renal surgery. Materials and Methods: A narrative review was conducted using PubMed with the terms near-infrared fluorescence, indocyanine green, and partial nephrectomy, focusing on comparative clinical studies published since 2012. Key endpoints included warm ischemia time (WIT), positive surgical margins (PSMs), perioperative outcomes, short-term renal functional measures (eGFR and or split renal function), and available oncologic follow-up. Results: ICG-NIRF enables real-time visualization of renal perfusion and vascular anatomy and may improve tumor parenchyma contrast in superficial or partially exophytic tumors, facilitating selective clamping in selected cases. Comparative cohorts and meta-analyses report small reductions in WIT (approximately 1 to 3 min) in some series, modest short-term superiority in eGFR (e.g., 4.62 mL/min at discharge or 9.26 mL/min at 1 to 3 months), no consistent differences in PSM rates (reported ranges of 0 to 11 percent across studies), major complications, or recurrence outcomes. Durable improvements in long-term renal function and consistent benefits in split renal function have not been demonstrated. Interpretation is limited by heterogeneity in ICG dosing, timing, imaging platforms, and acquisition. Conclusions: ICG-NIRF is a useful adjunct for intraoperative perfusion assessment and selective vascular control during partial nephrectomy, but current evidence does not demonstrate long-term functional or oncologic benefit over standard approaches. Further progress requires protocol standardization, quantitative fluorescence metrics, and adequately powered trials with long-term functional and oncologic endpoints, together with the development of deeper-penetrating and more tumor-specific fluorophores. Full article