Search Results (2,532)

Non-attributed graph sequence offers a powerful formalism for modeling the structural dynamics of complex systems—such as social networks, urban infrastructures, and document transmission pathways—where vertex interactions evolve over time without explicit attribute information. Mining association rules from such sequences to uncover recurring topological patterns have attracted growing interest. Yet two fundamental challenges remain: (1) how to effectively encode edge-level temporal dynamics in non-attributed settings, and (2) how to perform efficient and semantically meaningful temporal association rule mining under structural uncertainty. To address these within a systems-oriented framework, we propose two novel algorithms: the weighted temporal association rule mining algorithm and the fuzzy weighted temporal association rule mining algorithm. The first algorithm introduces time-dependent numerical weights to quantify the strength and persistence of vertex connectivity, integrating them into support and confidence measures to capture both the intensity and evolution of interactions. The second algorithm extends this by incorporating fuzzy set theory, modeling ambiguous or context-sensitive relationships (e.g., indistinct links or weakly correlated vertices) and generating fuzzy-weighted rules that enhance interpretability for real-world system analysis. Evaluated through five comprehensive experiments across diverse datasets and scales using standard metrics (support, confidence, rule count, running time), our methods produce more selective rule sets and achieve lower computational times compared to the classical Apriori algorithm. The proposed approaches thus establish a robust, data-driven foundation for analyzing temporal evolution and structural uncertainty in dynamic complex systems—providing a generalizable methodology applicable beyond domain-specific constraints. Full article

Contemporary buildings and urban spaces are increasingly expected to support psychological well-being—a quality often termed “restorativeness.” Conventional approaches to quantifying restorativeness rely on subjective surveys or coarse green metrics, failing to capture how specific building morphologies and street-level visual configurations shape restorative experiences, particularly for stress-prone groups such as young adults. This study develops a deep-learning-driven framework linking building visual elements to youth-specific perceived restorativeness, using Changsha, China, as a testbed. The framework comprises three AI-powered modules: the TrueSkill algorithm trains a deep learning model to predict six dimensions of youth perception (e.g., beautiful, clean, safe) from pairwise comparisons of street view images; the Mask2Former architecture segments street-level imagery into 18 building and street attributes; and the XGBoost-SHAP pipeline uncovers nonlinear associations and threshold-like patterns between these attributes and the composite Built Environment Restorativeness Index (BERI). Results reveal three key insights: tree coverage shows a sustained positive association without saturation; building density exhibits a weakening association at high levels, suggesting possible saturation; and road proportion follows a bidirectional pattern, shifting from negative to positive beyond a certain range. Spatially, high BERI zones concentrate where ecological assets and diverse building functions co-occur, while youth perception exhibits systematic mismatches (e.g., “beautiful but not clean,” “safe but not lively”), traceable to imbalances in building form, street furniture, and commercial mix. These findings advance AI-assisted evaluation of built environments by shifting from one-dimensional metrics to interpretable, design-relevant diagnostics, offering a replicable evidence base for crafting youth-responsive buildings and streets. Full article

Background/Objectives: Tumor-infiltrating lymphocyte (TIL) therapy is an important option for patients with metastatic melanoma progressing after standard systemic therapy, but real-world data on treatment delivery, toxicity monitoring, and immune recovery remain limited. We evaluated clinical outcomes, treatment tolerance, immune reconstitution, and cardiac biomarker dynamics across three Mayo Clinic sites. Methods: We retrospectively analyzed adults with metastatic melanoma who received lymphodepleting chemotherapy followed by TIL infusion and high-dose interleukin-2 (IL-2) between April 2024 and December 2025. Clinical outcomes, treatment delivery, and adverse events were assessed. Longitudinal immune monitoring included CD4 and CD8 T-cell counts, CD4:CD8 ratio, and immunoglobulin G (IgG) at baseline and follow-up. In a prespecified cardiac sub-cohort, high-sensitivity troponin (hs-Tn) was measured during IL-2 administration to evaluate associations with cardiac events and IL-2 interruption. Results: Thirty-six patients underwent TIL infusion. The objective response rate was 50.0%, including complete responses in 13.9%, and the disease control rate was 72.2%. Median progression-free survival was 3.61 months, and median overall survival was 12.94 months. M1d disease was associated with inferior overall survival on univariable analysis (HR 6.55, 95% CI 2.03–21.17; p = 0.002), with attenuation after multivariable adjustment. Receipt of ≥3 IL-2 doses was associated with longer overall survival on univariable analysis (HR 0.20, 95% CI 0.06–0.64; p = 0.007), but this association also attenuated after adjustment. Longitudinal immune monitoring demonstrated persistent CD4 lymphopenia through 6 months, sustained inversion of the CD4:CD8 ratio, and declining IgG at months 3 and 6. In the cardiac sub-cohort (24 patients; 87 IL-2 doses), post-dose hs-Tn ≥15 ng/L was associated with clinically significant cardiac events (OR 9.6, 95% CI 1.5–60.6; p = 0.016) and IL-2 interruption (OR 3.4, 95% CI 1.1–10.7; p = 0.036). For cardiac events, hs-Tn ≥15 ng/L had 100% sensitivity and 100% negative predictive value. Conclusions: In routine practice, TIL therapy was feasible and active in metastatic melanoma. M1d disease identified a subgroup with poor survival, peri-dose hs-Tn showed promise as a tool to support safer IL-2 delivery, and prolonged CD4 suppression with IgG decline suggests that recovery after TIL therapy extends beyond initial hematologic reconstitution. These findings support prospective validation of biomarker-guided IL-2 monitoring and extended post-treatment immune surveillance. Full article

The sustainable transformation of idle built environments represents a critical pathway for advancing low-carbon development and achieving carbon neutrality targets. This study examines how idle rural building stocks may contribute to sustainable built environment systems through rural building repurposing and regeneration strategies. It introduces the concept of Green Rural Centers (GRCs), multifunctional facilities formed through the adaptive reuse of idle buildings that integrate low-carbon design, community services, and local economic functions. Within the proposed framework, GRCs are conceptually characterized as facilities that may: (1) achieve 50–70% reductions in operational energy demand through passive and renewable measures, (2) incorporate two or more community-oriented functions (e.g., education, governance, cultural services), and (3) demonstrate embodied carbon savings of ≥40% compared to demolition-and-rebuild scenarios. Grounded in fieldwork from Fujian Province, China, and aligned with national policies, the study evaluates spatial transformation, carbon mitigation, and institutional integration. Using a mixed-methods approach that combines scenario-based carbon-reduction estimation and appraisal, spatial analysis, comparative case studies, and policy evaluation, the findings indicate that retrofitting 30% of approximately 68,000 idle rural schools could achieve approximately 734,400 metric tons of cumulative CO₂ reduction by 2060 under the baseline scenario. Under conservative and ambitious implementation conditions, the estimated cumulative reductions are approximately 408,000 and 1,224,000 metric tons of CO₂, respectively. Sensitivity analysis shows that moderate improvements in retrofit quality or implementation rates significantly amplify emissions reduction outcomes. Beyond environmental performance, the proposed framework may also support community resilience, decentralized service provision, and socio-economic revitalization. This research reframes idle building stock as a strategic asset within sustainable built environment systems, policy-relevant exploratory framework potentially adaptable to comparable rural contexts. This study contributes to the sustainable built environment discourse by demonstrating how underutilized rural building stocks can function as broader low-carbon rural regeneration systems. Full article

Background/Objectives: Given the rising prevalence of overweight and obesity in pediatric populations, identifying effective nutritional interventions for metabolic management is crucial. Beyond their nutritional value, soy and lupin proteins are recognized for their bioactive properties. We formulated two protein-enriched functional beverages and evaluated their impact on the metabolic profile and gut microbiota of adolescent boys with overweight or obesity. Methods: A randomized, double-blind clinical trial was conducted with 30 Mexican male adolescents (12–16 years old). Participants were randomly assigned to consume a functional beverage providing a daily 10 g portion of either soy or lupin protein for 5 weeks. Results: Following the intervention, both groups exhibited significantly attenuated fasting glucose (soy: 93.1 vs. 99.5 mg/dL; lupin: 92.3 vs. 97.9 mg/dL) and C-peptide levels. Consequently, insulin sensitivity, assessed via the HOMA2 index, improved significantly in both cohorts. The soy protein group showed a marked reduction in total cholesterol (–10.4%) and triglycerides (–17.1%). Furthermore, serum levels of plasminogen activator inhibitor-1 (PAI-1) and visfatin were decreased after both interventions. A post-treatment reduction in glucose-dependent insulinotropic polypeptide (GIP) was specifically observed in the lupin group. Regarding the gut microbiota, both protein-based beverage interventions correlated with enhanced 16S rDNA diversity and increased the abundance of the Bacillota phylum and butyryl-CoA transferase-positive bacteria. Conclusions: Our data suggests that the daily consumption of soy or lupin protein-based beverages could exert beneficial metabolic and endocrine effects in adolescent boys with overweight and obesity, potentially mediated by the modulation of the gut microbiome. Full article

Fatty acid-binding protein 7 (FABP7) is a multifunctional lipid chaperone that is enriched in radial glia and astrocytes within the central nervous system (CNS) and is frequently upregulated in glioma. Beyond its established roles in glial development, lipid homeostasis, and circadian regulation, growing evidence positions FABP7 at the intersection of tumor metabolism, neuronal activity, and immune modulation in the brain. In this review, we integrate the physiological functions of FABP7 in glial cells with its tumor-intrinsic and microenvironmental roles in glioma. We summarize how gliomas co-opt FABP7-dependent metabolic, transcriptional, and post-transcriptional programs to promote stemness, lipid remodeling (e.g., altered fatty acid composition, lipid droplet formation, and lipid peroxidation resistance), inflammatory signaling, and invasive growth, including nuclear FABP7-mediated transcriptional activation linked to oncogene status. Furthermore, we discuss the role of FABP7 in shaping the tumor–neuro–immune interface, including regulating immunosuppressive gene networks, pro-tumoral macrophage polarization, resistance to T-cell-induced ferroptosis and immunotherapy, and tumor microtube-mediated integration into neuronal circuits to support glioma progression. Finally, we highlight therapeutic opportunities and challenges, including small-molecule FABP7 inhibitors, brain-directed delivery strategies, chronotherapeutic considerations, and combination approaches with immunotherapy. Collectively, this work positions FABP7-centered metabolic, circadian, and neuro-immune networks as potential vulnerabilities in glioma, linking fundamental glial biology to glioma therapeutics. Full article

Two-dimensional black phosphorus (2D BP) has emerged as one of the most promising two-dimensional semiconductors for next-generation micro and nanoelectronics beyond Moore’s Law. It is distinguished by its unique combination of a layer dependent direct bandgap, broadband photoresponse, and pronounced in-plane anisotropy, addressing key intrinsic limitations that have hindered the widespread application of graphene and conventional transition metal dichalcogenides (TMDCs). This review provides a systematic and comprehensive overview of recent advances in the controllable fabrication of 2D BP and its applications in transistors and photodetectors. We first elucidate its crystal lattice structure and fundamental physical properties, then categorize and summarize synthesis strategies based on production scale ranging from small scale methods (e.g., mechanical exfoliation and solution based exfoliation) to large scale methods (e.g., Chemical Vapor Deposition (CVD) and Pulsed Laser Deposition (PLD)), with a particular focus on recent advances in high-speed field-effect transistors and broadband photodetectors. In summary, the key to achieving large-scale controllable synthesis lies in addressing the challenges of high-temperature oxidation of black phosphorus and the uncontrollable diffusion of phosphorus sources. In the future, industrial applications are expected to be realized through CVD based regulation of phosphorus sources, low-temperature growth by PLD, and deep integration with silicon-based processes. Full article

At present, treated wastewater may still contain residual nutrients and micropollutants, including heavy metals, pharmaceuticals, and dyes, which can negatively affect receiving water bodies. Increasingly stringent environmental regulations, including Directive (EU) 2024/3019, require both enhanced removal of these contaminants and greater integration of renewable energy sources in wastewater treatment plants. This paper presents a review of biomass-based wastewater polishing technologies employing biological agents such as microalgae, fungi, bacteria, co-cultures and duckweed for the removal of residual contaminants from treated effluents. The compiled data indicate that while optimal conditions can drive pollutant removal efficiencies beyond 90%, system performance varies widely depending on species selection, wastewater characteristics, and operational conditions (e.g., pH, temperature, salinity, nutrient availability, and light intensity). In addition to effluent polishing, the produced biomass can be valorized for bioenergy generation, contributing to renewable energy production and supporting circular economy principles in wastewater treatment plants. Despite these benefits, biomass harvesting remains a major technical and economic bottleneck, often representing a significant share of operational costs and limiting large-scale implementation. Overall, biomass-based treatment technologies are a promising approach for improving effluent quality and supporting renewable energy objectives; however, further advances in biomass recovery are required for broader application. Full article

Photovoltaic (PV) power forecasting plays a central role in power system operation, electricity markets, and the integration of high shares of renewable energy. Over the past decade, forecasting approaches have evolved from classical statistical time-series models to advanced machine learning and deep learning architectures. This review analyses 119 studies published between 2016 and 2026, providing a structured assessment of PV forecasting methodologies, including model types, data requirements, validation strategies, and performance evaluation practices. Beyond summarizing existing approaches, the paper identifies three major methodological gaps in the literature: (i) fragmentation of evaluation metrics, which limits cross-study comparability; (ii) insufficient reporting of data preprocessing procedures and temporal leakage prevention; and (iii) limited integration of forecasting accuracy with economic and operational performance metrics. A systematic comparison of representative studies is conducted to highlight dominant modelling trends and persistent limitations. Beyond a descriptive summary, this review highlights significant limitations in methodological reporting across the 119 studies analysed, particularly regarding temporal leakage prevention in Deep Learning-based forecasting. To address these issues, we introduce a reproducibility checklist and propose a strategic roadmap aimed at strengthening the link between statistical accuracy (e.g., RMSE/MAE) and operational relevance in electricity markets. Full article

Controversy persists on a global scale regarding the trade-offs between greenhouse gas (GHG) emissions, yield, the global warming potential (GWP), and GHG intensity (GHGI) following organic fertilizer substitution within vegetable cropping systems. This study aimed to quantify these effects under diverse conditions and elucidate the direct and indirect drivers governing these outcomes through a meta-analysis and structural equation modeling (SEM). We synthesized 655 paired observations from 69 published studies using random-effects meta-analysis, finding that organic fertilizer substitution significantly increased CH₄ emissions and GWP compared to inorganic fertilizer controls. Although this was the general trend, organic fertilizer could reduce GWP under specific climatic and soil conditions by reducing N₂O emissions, such as mean annual precipitation <400 mm or soil total nitrogen ≥3 g kg⁻¹. These conditions were also associated with substantially higher yield and lower GHGI. Furthermore, SEM demonstrated that field management practices exerted significant direct effects on N₂O emissions, GWP, and GHGI. Reductions in N₂O emissions, GWP, and GHGI could be achieved with fertilizer application duration ≥10 years, total N application rate ≥300 kg ha⁻¹, and field cultivation or plowing. GHGI was also reduced through yield enhancement under a moderate organic substitution rate (33–66%) or irrigation ≥300 mm. Our study provides a scientific basis for moving beyond universal recommendations towards precision organic management, which is essential for optimizing fertilization strategies to mitigate agricultural GHG emissions. Full article

The subcellular localization of long noncoding RNAs (lncRNAs) is a central determinant of their function, yet its molecular determinants remain incompletely defined, and most existing predictors rely on the primary sequence. Because RNA-binding proteins (RBPs) are the proximal effectors of RNA compartmentalization, this study tested whether the composition of RBPs bound to a lncRNA is predictive of its nuclear or cytoplasmic localization. Enhanced crosslinking and immunoprecipitation (eCLIP) occupancy for 139 RBPs in K562 cells was integrated with the cytoplasmic–nuclear relative concentration indices (CN-RCIs) derived from matched subcellular fractionation, and localization was modeled under chromosome-grouped cross-validation with nested regularization. RBP-occupancy composition predicted localization beyond the transcript size and total binding amount (incremental cross-validated coefficient of determination, delta-R-squared = 0.17; receiver-operating-characteristic area under the curve, AUC = 0.73, a moderate-strength association; Freedman–Lane permutation, p = 0.005). This increment persisted (delta-R-squared = 0.12; p = 0.005) against an expanded baseline that additionally absorbed the transcript abundance, intron content and exon number, indicating predictive information that is not reducible to these transcript features, and the classifier was well calibrated (Brier score = 0.10; expected calibration error = 0.02). The signed coefficient profile separated RBP function systematically: factors acting in nuclear processes (splicing, 3′-end processing, and nuclear-matrix association) carried negative, nuclear-direction weights, whereas factors acting in cytoplasmic processes (translation and messenger RNA stability) carried positive, cytoplasmic-direction weights (Mann–Whitney p = 0.013). The profile generalized across cell lines: a K562-trained model predicted HepG2 localization (transfer AUC = 0.71 using 76 shared RBPs), and HepG2 reproduced the association independently (AUC = 0.77). The association is correlational and of moderate strength; it is presented as an interpretable, RBP-occupancy-based complement to sequence-based predictors of lncRNA localization. Full article

Accurate road-event detection and timely alert message dissemination are essential for the safety of connected and automated vehicles. In many scenarios, alert messages must reach not only nearby vehicles but also remote stakeholders, such as traffic management centers, cloud services, and infrastructure operators. This requirement motivates the adoption of cellular-based communication technologies in addition to short-range vehicle-to-everything (V2X) communications for data dissemination. In this work, we investigate vehicle-to-network-to-everything (V2N2X) communications for the dissemination of alert messages generated after the on-board detection of hazardous road events through machine learning (ML) algorithms. Although V2N2X connectivity is well suited for extending data dissemination beyond the local vehicular environment, its capability to guarantee prompt message delivery under strict latency constraints remains an open challenge, particularly when ML inference is integrated into the end-to-end processing pipeline. To address this issue, we develop and experimentally evaluate a proof-of-concept (PoC) platform that combines real-time road-event detection with relevant message dissemination towards both nearby and remote recipients. The proposed framework leverages 5G connectivity and publish/subscribe messaging protocols. The experimental results showcase that dissemination latency is highly influenced by both the adopted type of 5G deployment (private versus commercial networks) and the load conditions at the message broker. Full article

High-entropy alloys (HEAs), composed of five or more principal elements in near-equimolar ratios, have emerged as a groundbreaking class of materials for next-generation flexible electronics. This review systematically examines the unique potential of HEAs as sensing materials, moving beyond their traditional role as structural components. We first elucidate the fundamental mechanisms—core effects including lattice distortion, sluggish diffusion, and the cocktail effect—that endow HEAs with an exceptional synergy of high strength, good ductility, tunable electrical resistivity, and superior electrocatalytic activity. Subsequently, we critically analyze the state-of-the-art strategies for processing HEA-based micro/nano structures, including mechanical alloying, wet-chemical synthesis, and non-equilibrium deposition techniques, with an emphasis on their compatibility with flexible substrates. The core of the review categorizes and discusses the latest advances in HEA-based flexible sensors for strain/stress, gas, and electrochemical (e.g., glucose, biomarkers, heavy metals) detection, highlighting the structure–property–performance relationships. Representative studies have demonstrated that HEA flexible strain sensors achieve a temperature coefficient of resistance as low as 45.59 ppm/K with no signal drift over 6000 stretching cycles; room-temperature hydrogen sensors reach a detection limit down to 31 ppb with a response time of 19 s; and non-enzymatic glucose sensors deliver a sensitivity up to 3043 μA·mM⁻¹·cm⁻². Finally, we summarize the key challenges—such as manufacturing scalability, long-term stability under dynamic deformation, and cost-effectiveness—and provide a forward-looking perspective on promising research directions, including high-throughput compositional screening, multi-functional sensor arrays, and the integration of machine learning for rational material design. Full article

Background: The triglyceride–glucose (TyG) index has emerged as a simple surrogate marker of insulin resistance and metabolic disruption. In the context of ST-elevation myocardial infarction (STEMI), such disturbances have been associated with adverse cardiovascular outcomes, more complex angiographic profiles, and microvascular complications. However, data on the association between TyG and intracoronary thrombus burden (TB) in STEMI remain limited. Methods: In this prospective observational study, we included consecutive STEMI patients treated with primary percutaneous coronary intervention (pPCI). The TyG index was calculated using the following formula: ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. TB was graded according to the modified thrombolysis in myocardial infarction (mTIMI) thrombus classification score after restoration of antegrade flow with a wire or small balloon when the culprit vessel was initially totally occluded. Patients were categorized as low-TB (LTB; mTIMI grades 1–3) and high-TB (HTB; mTIMI grade 4). The primary outcome was HTB; secondary outcomes were distal embolization and no-reflow. Associations between TyG and outcomes were assessed using univariable and multivariable logistic regression, restricted cubic spline analysis, and receiver operating characteristic (ROC) curves to evaluate incremental predictive value. Results: A total of 309 patients were analyzed. The TyG index was significantly higher in the HTB group compared with the LTB group (9.12 ± 0.62 vs. 8.92 ± 0.64, p = 0.004). In a stepwise multivariable model, TyG remained independently associated with HTB (adjusted odds ratio = 1.61; 95% confidence interval: 1.11–2.37; p = 0.014). Adding TyG to a baseline clinical model only numerically improved discrimination for HTB, as reflected by a small increase in ROC area under the curve. Restricted cubic spline analysis demonstrated a monotonic rise in the probability of HTB with higher TyG values. Higher TyG also showed non-significant trends toward increased odds of distal embolization and no-reflow. Conclusions: The TyG index was independently associated with HTB in STEMI patients undergoing pPCI and may serve as an accessible adjunctive marker for incremental risk stratification beyond conventional clinical and angiographic factors. Full article

The present integrative umbrella review aims to provide a comprehensive overview of the evidence and practices related to mental health and career transitions in elite sport toward the implementation of service provision through digital interventions. Following PRIO guidelines, an extensive search across five databases (2015–2025) identified 52 eligible manuscripts (e.g., conceptual, review, and position studies). Data extraction focused on mental health, dual-career pathways, career transition challenges and needs, and identity-related issues among high-performance athletes. The findings revealed a strong consensus that athlete well-being is shaped by the dynamic interaction of mental health symptoms, sport-specific stressors, identity processes, and structural conditions across the athletic lifespan. Mental health vulnerabilities (e.g., anxiety, depression, disordered eating, and distress) were consistently reported, particularly during injury, deselection, and retirement. Dual-career engagement, diversified identities, and proactive career planning emerged as key protective factors, while stigma, limited literacy, and uneven access to psychological services remained persistent barriers. Five main thematic areas (Matrix 1) operationalized in ten higher-order intervention domains (e.g., Matrix 2, screening, monitoring, literacy, and others) and 14 potential online implementation strategies (Matrix 3) were identified. However, the evidence highlights fragmented implementation and a lack of scalable, cross-national tools to support athletes during and beyond their competitive careers. Therefore, a harmonized, evidence-based, multidimensional framework for the development and implementation of digital support resources has been proposed. This integrative review underscores the need for integrated, culturally sensitive, and digitally enabled support systems to promote sustainable transitions and long-term athlete well-being. Full article