Search Results (11,341)

This study investigates the substitution of calcium carbonate (CaCO₃) with ground tire rubber (GTR) in EPDM-based elastomer formulations as a strategy for sustainable material development. Unlike conventional approaches, this work employs GTR as a direct filler replacement. Temperature scanning stress relaxation (TSSR) analyses confirm that GTR participates in vulcanization. Initial incorporation of GTR reduces crosslink density (CLD) and mechanical performance due to structural defects, while accelerators present in the recycled phase promote faster curing. This study focuses on the aging behavior of the compounds to evaluate possible long-term effects on the material. The thermo-oxidative stress leads to further crosslinking, resulting in higher CLD, increased stiffness and reduced elongation at break. Overall, partial replacement of CaCO₃ by GTR proves feasible, offering a balanced compromise between sustainability and performance, whereas high GTR contents significantly impair mechanical properties. Full article

Objectives: The present study aimed to explore psychiatrists’ perceptions of duloxetine in routine clinical practice, focusing on its efficacy, tolerability, and treatment adherence in major depressive disorder (MDD) and generalized anxiety disorder (GAD). Methods: A structured questionnaire was administered to 97 psychiatrists from different regions of Spain. The survey covered demographic and professional data, prescription frequency, perceived clinical efficacy, tolerability, dosing patterns, and factors influencing adherence. Results: Overall, duloxetine was perceived as an effective treatment for both MDD and GAD, particularly in patients with somatic symptoms or comorbid anxiety. Tolerability was also positively rated, with nausea and fatigue identified as the adverse effects most commonly associated with reduced adherence. In addition, patient education and close follow-up were identified as the most effective strategies to improve adherence, whereas digital tools were considered promising but underused. Compared with other antidepressants, duloxetine was viewed as having a favorable balance between efficacy and tolerability, with similar or slightly higher adherence rates. Conclusions: These findings reflect a positive clinical appraisal of duloxetine among psychiatrists, highlighting its role as a versatile therapeutic option for affective and anxiety disorders, within the context of routine clinical practice in Spain, provided that appropriate adherence-support strategies are implemented. Full article

The purpose of this paper is to analyze recent publications in scientific journals on the circular economy and energy through a systematic review using the PRISMA method and to propose a framework. In recent years, the circular economy has been widely recognized as a viable solution to address environmental and economic challenges. The transition to renewable sources, such as solar, wind, and biomass, is essential for a clean and balanced energy market. The methodology adopted was a systematic review of the scientific literature using the PRISMA method, which aims to categorize published research, evaluating it in terms of its objectives, methodologies, results, and conclusions. To this end, full articles published in scientific journals between 2021 and 2025 on the subject were identified. The analysis of the selected studies reveals an intrinsic relationship between the circular economy and sustainable energy, particularly in the context of Sustainable Development Goals (SDGs) 7 and 12. The results highlight that circular economy practices, such as waste recovery, bioenergy generation, and gasification, not only demonstrate their ability to create sustainable value chains but also contribute to reducing environmental impacts, promoting energy efficiency, and present a proposed framework for analysis and proposition. Full article

The integration of a user-side energy storage system (ESS) faces notable economic challenges, including high upfront investment, uncertainty in quantifying battery degradation, and fragmented ancillary service revenue streams, which hinder large-scale deployment. Conventional configuration studies often handle capacity planning and operational scheduling at different stages, complicating consistent life-cycle valuation under degradation and multi-service participation. This paper proposes a life-cycle multi-service co-optimization model (LC-MSCOM) to jointly determine ESS power–energy ratings and operating strategies. A unified revenue framework quantifies stacked revenues from time-of-use arbitrage, demand charge management, demand response, and renewable energy accommodation, while depth of discharge (DoD)-related lifetime loss is converted into an equivalent degradation cost and embedded in the optimization. The model is validated on a modified IEEE benchmark system using real generation and load data. Results show that LC-MSCOM increases net present value (NPV) by 26.8% and reduces discounted payback period (DPP) by 12.7% relative to conventional benchmarks, and sensitivity analyses confirm robustness under discount-rate, inflation-rate, and tariff uncertainties. By coordinating ESS dispatch with distribution network operating limits (nodal power balance, voltage bounds, and branch ampacity constraints), the framework provides practical, investment-oriented decision support for user-side ESS deployment. Full article

This investigation designs grilles of two configurations inside an S-shaped inlet for UAVs. The present work numerically investigates the effects of the configurations, numbers, diameters, and lengths of the grilles on the inlet aerodynamic performance under different flight conditions, such as airflow Mach number, angle of attack, and sideslip angle. The influences of the baseline configuration, Configuration 1, and Configuration 2 on the aerodynamic performance of the inlet are systematically compared. The numerical results show that after installing the grilles, the total pressure recovery decreases by an average of 5.42% for Configuration 1 and 3.46% for Configuration 2. In terms of the absolute circumferential total pressure distortion, which decreases by 1.26% for Configuration 1 and 2.34% for Configuration 2, the swirl distortion index of Configuration 2 approaches zero. It is found that a large sideslip angle significantly degrades the inlet performance, and Configuration 1 experiences the maximum decline of approximately 0.0124 in the total pressure recovery. Based on the optimized design of Configuration 1, the optimal parameters are determined as 5 grille rows, a grille diameter of 4 mm, and a grille length of 6 mm. This configuration achieves an optimal balance between flow regulation and resistance suppression, with a maximum total pressure recovery of 0.9884 and the absolute circumferential total pressure distortion controlled below 0.015. This study clarifies the optimization direction of key parameters for grilles and provides a theoretical basis and technical reference for the design of UAV S-shaped inlet and grille integrations. Full article

This study presents an integrated decision support system (DSS) designed to optimize real-time reservoir operation during typhoons by balancing flood control and water supply. The system combines ensemble quantitative precipitation forecasts (QPF) from WRF/MM5 models, a physically based rainfall–runoff model (KW-GIUH), and a three-stage optimization algorithm for reservoir release decisions. Eighteen ensemble rainfall members are processed to generate 6 h inflow forecasts, which serve as inputs for determining adaptive outflow strategies that consider both storage requirements and downstream flood risks. The DSS was tested using historical typhoon events—Talim, Saola, Trami, and Kong-rey—at the Tseng-Wen Reservoir in Taiwan. Results show that the KW-GIUH model effectively reproduces hydrograph characteristics, with a coefficient of efficiency around 0.80, while the optimization algorithm successfully maintains reservoir levels near target storage, even under imperfect rainfall forecasts. The mean deviation of reservoir water levels from the recorded to the target values is less than 0.18 m. The system enhances operational flexibility by adjusting release rates according to the proposed outflow index and flood-stage classification. During major storms, the DSS effectively allocates storage space for incoming floods while maximizing water retention during recession periods. Overall, the integrated framework demonstrates strong potential to support real-time reservoir management during extreme weather conditions, thereby improving both flood mitigation and water-supply reliability. Full article

Natural products have emerged as promising multi-target agents for addressing the complex biology of gastric cancer, a malignancy characterized by marked molecular heterogeneity, late clinical presentation, and frequent resistance to systemic therapies. This narrative synthesis integrates primarily preclinical evidence, with emerging clinical data, on how naturally derived compounds modulate three central molecular processes that drive gastric tumor progression and therapeutic failure: evasion of programmed cell death, persistent tumor-promoting inflammation, and chemoresistance. Compounds such as curcumin, resveratrol, berberine, ginsenosides, quercetin, and epigallocatechin gallate restore apoptotic competence by shifting the balance between pro-survival and pro-death proteins, destabilizing mitochondrial membranes, promoting cytochrome c release, and activating caspase-dependent pathways. These agents also exert potent anti-inflammatory effects by inhibiting nuclear factor kappa B and signal transducer and activator of transcription signaling, suppressing pro-inflammatory cytokine production, reducing cyclooxygenase activity, and modulating the tumor microenvironment through changes in immune cell behavior. In parallel, multiple natural compounds function as chemo-sensitizers by inhibiting drug efflux transporters, reversing epithelial–mesenchymal transition, attenuating cancer stem cell-associated traits, and suppressing pro-survival signaling pathways that sustain resistance. Collectively, these mechanistic actions highlight the capacity of natural products to simultaneously target interconnected hallmarks of gastric cancer biology. Ongoing advances in formulation strategies may help overcome pharmacokinetic limitations; however, rigorous biomarker-guided studies and well-designed clinical trials remain essential to define the translational relevance of these compounds. Full article

Deep learning-based methods have achieved promising performance in image watermarking tasks due to their powerful capability to fully exploit the rich information present in images, which is crucial for ensuring watermark robustness. Although existing methods have improved robustness against various distortions, directly using deep neural networks for feature extraction and watermark expansion often introduces irrelevant and redundant features, thereby limiting the watermarking model’s imperceptibility and robustness. To address these limitations, in this paper, we introduce a robust image watermarking framework (GCMark) based on Gated Feature Selection and Cover-Guided Expansion, which consists of two key components: (1) the Dual-Stage Gated Modulation Block (DSGMB) that serves as the core backbone of both the encoder and decoder, adaptively suppressing irrelevant or redundant activations to enable more precise watermark embedding and extraction; (2) the Cover-Guided Message Expansion Block (CGMEB), which exploits the cover image’s structural features to guide watermark message expansion. By promoting a structurally consistent and well-balanced fusion between the watermark and host image, this implicit symmetry facilitates stable watermark propagation and enhances resilience against various distortions without introducing noticeable visual artifacts. Extensive experimental results demonstrate that the proposed GCMark framework outperforms existing methods with respect to both robustness and imperceptibility. Full article

This paper presents a robust multistage optimization framework for the integration of composite laminates into the car body shell of a low-floor light rail vehicle (LRV). While structural design in low-floor vehicles is typically complex, this methodology successfully balances both static and dynamic requirements through a sequential optimization process. Developed in strict accordance with reference European standards, the methodology addresses the structural challenges inherent in low-floor architectures, where complex load paths and redistributed equipment masses require targeted reinforcement. The proposed approach sequentially addresses dynamic and static requirements through a structural optimization process. Two distinct 10-ply laminate configurations, one symmetric and one asymmetric, were investigated. The results demonstrate that the multistage optimization successfully converged to a highly mass-efficient solution, achieving a 66% reduction in laminate thickness compared to the baseline design. This significant result was accomplished while maintaining full regulatory compliance; the failure index increased by approximately 22.5% and 23.3% for the two composite laminate configurations, respectively, effectively maximizing material utilization. A key finding of this study is the preservation of structural dynamic integrity; the fundamental natural frequency was maintained at approximately 16 Hz, with a high correlation across the first ten vibration modes, confirming that the global dynamic behaviour remains unaffected. These observations provide critical insights into the synergy between hybridization and structural constraints, suggesting a systematic pathway for designers to achieve an optimal trade-off between manufacturing costs, weight reduction, and performance in advanced urban transit platforms. Full article

This paper presents a simulation framework for analysing race car overtaking manoeuvres under aerodynamic wake effects using optimal control theory. The proposed formulation integrates wake-dependent aerodynamic disturbances into a spatial-domain optimal control problem, enabling simultaneous optimisation of racing line and control inputs. A planar vehicle model representative of a modern FIA Formula 3 car is employed and calibrated using real telemetry data obtained from Campos Racing. Wake effects are modelled as distance- and offset-dependent aerodynamic loss factors that influence drag, downforce, and aerodynamic balance of the following vehicle. The framework is implemented using the Dymos optimal control library and applied to single-car and two-car overtaking scenarios on a closed circuit. Simulation results demonstrate that wake effects significantly modify optimal braking points, corner entry trajectories, and corner-exit strategies. Moreover, we show that optimal overtaking requires deliberate lateral deviations from the wake core to recover downforce and traction. The study highlights the importance of incorporating aerodynamic interaction effects into trajectory optimisation when analysing performance-critical motorsport manoeuvres. Full article

The intestine–liver–muscle axis plays an essential role in drug and nutrient absorption, metabolism, and energy balance. Yet in vitro models capable of recapitulating this inter-organ communication remain limited. Here, we present a pump-free, 3D-printed multi-organ-on-a-chip device that enables dynamic co-culture of Caco-2 intestinal epithelial cells, HepG2 hepatocytes, and primary human skeletal myoblasts (HSkMs) under gravity-driven oscillatory flow. The device consists of five interconnected chambers designed to accommodate Transwell cell culture inserts for intestine and muscle compartments and hydrogel-embedded hepatocyte spheroids in the central hepatic compartment. The device was fabricated by low-cost fused deposition modeling (FDM) using acrylonitrile butadiene styrene (ABS) polymers. Under dynamic rocking, oscillatory perfusion promoted inter-organ communication without the need for external pumps or complex tubing. Biological assessments revealed that dynamic co-culture significantly enhanced the characteristics of skeletal muscle, as indicated by increased myosin heavy chain expression and elevated lactate production, while HepG2 spheroids exhibited improved hepatic function with higher albumin expression compared with monoculture. Additionally, Caco-2 cells maintained stable tight junctions and transepithelial electrical resistance, demonstrating preserved intestinal barrier integrity under dynamic flow. These results establish the device as a versatile, accessible 3D-printed platform for modeling the intestine–liver–muscle axis and investigating metabolic cross-talk in drug discovery and disease modeling. Full article

Achieving Net-Zero Energy Building (NZEB) performance through retrofitting requires identifying optimal measures that effectively enhance energy efficiency. Determining these optimal retrofit strategies typically involves running thousands of building energy simulations, which imposes a substantial computational burden. To address this challenge, a novel machine learning-based framework is proposed to optimize retrofit strategies for NZEBs under future climate change scenarios. A Non-Dominated Sorting Genetic Algorithm (NSGA-III) is employed to minimize both annual energy consumption and the Predicted Percentage of Dissatisfied (PPD), while simultaneously ensuring net-zero energy balance, thereby generating a Pareto front of optimal solutions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is then applied to rank the Pareto-front solutions and identify the most favorable retrofit scenario. The results show that the proposed framework reduces optimization time by at least a factor of two compared with simulation-only optimization. Leveraging these computational savings, the framework evaluates a suite of passive and renewable measures across multiple future timeframes to capture the influence of climate change on retrofit performance. The findings indicate that achieving NZEB under future climate conditions requires higher levels of thermal insulation and greater renewable integration than under present-day conditions. Under the Shared Socioeconomic Pathways (SSP) framework, optimal insulation levels in the fossil fuel-dependent scenario are lower than in the sustainable scenario by up to 18% in C-type (warm temperate), 12% in D-type (snow), and 13% in E-type (polar) climates. The combined retrofit measures can reduce annual energy consumption by up to 80% and lower PPD by as much as 67% compared to the base case. Full article

The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities. Full article

This article presents an ontology-driven Digital Twin (DT) for hotel front-desk operations that fuses two real-time data streams: (i) physiological and activity signals from wrist-worn wearables assigned to staff, and (ii) 3D people-positioning and occupancy events captured by reception-area cameras using a proprietary implementation of Optical Camera Communication (OCC). Building on a previously proposed front-desk ontology, the semantic model is extended with positional events, zone semantics, and wearable-derived workload indices to estimate queue state, staff workload, and service demand in real time. A vendor-agnostic, property-based REST API specifies the DT interface in terms of observable properties, including authentication and authorization, idempotent ingestion, timestamp conventions, version negotiation, integrity protection for signed webhooks, rate limiting and backoff, pagination and filtering, and privacy-preserving identifiers, enabling any compliant backend to implement the specification. The proposed layered architecture connects ingestion, spatial reasoning, and decision services to dashboards and key performance indicators (KPIs). This article details the positioning pipeline (calibration, normalized 3D coordinates, zone mapping, and confidence handling), the wearable workload pipeline, and an evaluation protocol covering localization error, zone classification, queue-length estimation, and workload accuracy. The results indicate that a spatially aware, ontology-based DT can support more balanced staff allocation and improved guest experience while remaining technology-agnostic and privacy-conscious. Full article

Recent studies have achieved significant advances in all-in-one adverse weather image restoration, primarily driven by the development of sophisticated model architectures. In this work, we find that effectively coordinating the complex interactions and potential optimization conflicts among different restoration tasks is also a critical factor determining the overall performance of all-in-one adverse weather image restoration models. To this end, we propose an effective all-in-one adverse weather image restoration framework, named MOE-WIRNet, designed to harmonize the learning process across various degradation types and ensure well-balanced performance among different restoration tasks. To enhance training equilibrium, we integrate a multi-task collaboration optimization strategy into the framework, coordinating the convergence dynamics of distinct restoration objectives. Furthermore, we incorporate an asymmetric mixture-of-experts (MoE) architecture into the framework to effectively address the distinct degradation patterns and varying severity levels presented by different tasks. Extensive experiments demonstrate that our framework consistently outperforms current state-of-the-art models on multiple real-world adverse weather benchmark datasets. Full article