Search Results (21,793)

Flexible polymer–ferrite composites provide a route for lightweight functional materials with tunable surface and structural properties. Here, poly(ε-caprolactone) (PCL)/Ti⁴⁺-doped barium hexaferrite (BaMTi⁴⁺) films containing 10–50 wt.% ferrite were prepared by solvent casting and characterized by XRD, FTIR, SEM, AFM, and fractal analysis. XRD confirmed the coexistence of semicrystalline PCL and magnetoplumbite-type BaMTi⁴⁺, whereas FTIR indicated preservation of the polymer backbone and non-covalent interfacial interactions. Morphological and topographical analyses revealed a composition-dependent transition from compact polymer-rich surfaces to rougher ferrite-rich architectures. Surface roughness decreased at 10 wt.% BaMTi⁴⁺ and increased at higher loadings, reaching Sa = 33.10 nm and Sq = 42.54 nm at 50 wt.%. Power spectral density and fractal analyses showed enhanced spatial correlation, with the Hurst exponent increasing from 0.51 to 0.81 and the fractal dimension decreasing from 2.49 to 2.19. These results demonstrate that ferrite loading effectively controls the multiscale surface organization of magnetically active PCL films. Full article

Acute pancreatitis is a common and heterogeneous inflammatory disorder whose clinical course ranges from a self-limited illness to persistent organ failure, infected pancreatic necrosis, and prolonged critical illness. Contemporary management has moved away from protocolised aggressive fluid loading, prolonged fasting, prophylactic antibiotics, and early open necrosectomy. Instead, current care emphasises repeated physiological assessment, moderate goal-directed resuscitation, early enteral or oral nutrition, organ-specific support, antimicrobial stewardship, and delayed minimally invasive intervention within a multidisciplinary step-up strategy. This narrative review examines acute pancreatitis from an intensive care perspective. Particular attention is given to early risk stratification, intensive care unit triage, haemodynamic and respiratory support, acute kidney injury, intra-abdominal hypertension, nutrition, biliary source control, diagnosis and treatment of infected necrosis, and the timing and selection of endoscopic, radiological, and surgical interventions. The implications of obesity, pregnancy, advanced age, and multimorbidity are also discussed. Recent randomised trials have clarified several clinically important questions: aggressive hydration increases fluid overload without improving outcomes; routine urgent endoscopic retrograde cholangiopancreatography is not beneficial in predicted severe biliary pancreatitis without cholangitis; postponed drainage may avoid invasive intervention in a substantial proportion of patients with infected necrosis; and endoscopic or minimally invasive approaches reduce treatment burden compared with primary open surgery. Persistent organ failure remains the principal determinant of mortality, while infected necrosis further increases risk and complexity. Future progress will depend on dynamic prediction models, biomarker-guided antimicrobial decisions, personalised haemodynamic strategies, phenotype-directed immunomodulation, and regionalised multidisciplinary care. Full article

High-throughput microfluidic deformation assays enable label-free single-cell mechanophenotyping by quantifying how cells deform under controlled hydrodynamic loading. These approaches commonly extract deformation-related observables, such as projected area, axis ratio, and deformation index, and use them as indicators for cellular mechanical properties. However, deformation is not solely determined by stiffness; it is a coupled outcome of cell size, local hydrodynamic stress, and intrinsic mechanical response. Therefore, we present a simulation-based microfluidic framework for estimating region-dependent apparent Young’s modulus (E, a quantitative indicator characterizing cellular mechanical stiffness) from diameter–deformation measurements at the single-cell level. A three-region microfluidic channel is designed to impose distinct hydrodynamic loading conditions, while numerical simulations establish quantitative maps linking cell diameter, deformation, and E. Based on these results, region-specific nonlinear surface models are constructed to invert experimental diameter–deformation measurements into E values. Finally, application to primary T cells and K562 cells demonstrates clear region-dependent differences in E, highlighting the influence of local loading conditions on inferred mechanical properties. Overall, this work provides a simplified but practical route for transforming deformation-based phenotypes into quantitative, loading-aware mechanical parameters for single-cell analysis. Full article

The wake effect inside wind farms reduces the inflow wind speed and increases the turbulence intensity of downstream turbines, resulting in power loss and increased fatigue loads. Active yaw control can mitigate wake interference through collaborative optimization of turbine yaw angles. However, most existing methods rely on steady-state wake models, which fail to capture the dynamic delay characteristics of wakes and usually lead to excessive yaw actuation losses. To address these issues, this paper constructs a dynamic wake model suitable for real-time control based on the OFF dynamic wake framework (OnWARDS, FLORIDyn, and FLORIS), adopting an improved three-dimensional analytical wake model at the lowest level. On this basis, a finite control set model predictive control (MPC) active yaw controller is designed. Aiming to maximize power generation and minimize yaw loss, the controller realizes rolling optimization of yaw actions combined with ARIMA-based wind direction prediction and particle swarm optimization. Simulations on the 4 × 4 turbine array of the Horns Rev I wind farm show that the proposed method increases the total power by 2.25%, which is 0.79% higher than that obtained by the deadband controller. It results in lower power loss for upstream turbines and higher power gain for downstream turbines, reduces the total yaw travel by nearly 1000° compared with the deadband controller, and produces smaller power fluctuations under sharply changing wind directions. Full article

The combined use of recycled aggregate concrete (RAC) and basalt fiber-reinforced polymer (BFRP) bars offers a promising sustainable and corrosion-resistant solution for reinforced concrete structures. However, the inferior quality of recycled aggregates and the relatively low elastic modulus of BFRP bars can compromise the mechanical and flexural performance of RAC members. To address these issues, hybrid fiber-reinforced recycled aggregate concrete (HFRAC) incorporating polyvinyl alcohol (PVA) and steel fibers was developed, and its mechanical and flexural performances were experimentally investigated. The basic mechanical properties of conventional Portland cement concrete (PC), fiber-free RAC, and RAC with hybrid fiber (HF) contents of 0.6%, 0.9%, 1.2%, and 1.5% were first evaluated. A total of nine beams were subsequently tested under four-point bending to investigate the effects of HF content (0–1.5%) and BFRP reinforcement ratio (0.48–1.98%) on flexural behavior. The results showed that an HF content of 1.2% provided the best performance among the investigated fiber contents at both the material and structural levels. At the material level, compared with RAC, 1.2% HF increased the cube compressive strength, axial compressive strength, elastic modulus and splitting tensile strength by 19.44%, 23.08%, 11.39% and 32.55%, respectively. The incorporation of HF effectively mitigated the mechanical deterioration caused by recycled aggregates, allowing HFRAC to achieve comparable or improved basic mechanical properties relative to RAC. At the structural level, compared with the fiber-free RAC beam, the beam with 1.2% HF exhibited increases of 132.51% and 11.92% in cracking and ultimate loads, respectively, and a 47.9% reduction in crack width, while also demonstrating improved flexural performance compared with the PC beam under the investigated conditions. Three failure modes were observed, namely BFRP bar rupture, balanced failure, and concrete crushing, with balanced failure occurring at a reinforcement ratio of approximately 1.0–1.1%. The hybrid fibers effectively refined cracks through a bridging effect, demonstrating superior crack control compared to increasing the reinforcement ratio alone. This study offers valuable insights into improving the performance of RAC and facilitating the wider adoption of BFRP bars in structural applications. Full article

The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain. Full article

Increasing frequency and intensity of heat events pose significant challenges for the production and early establishment of urban trees. This study evaluated whether horticultural interventions could mitigate heat stress and improve growth of young red maple (Acer rubrum ‘FranksRed’) under full-sun conditions representative of urban planting environments. Six treatments (control, canopy misting, paclobutrazol, propiconazole, kaolin clay, and potassium phosphite) were evaluated over two growing seasons in the Willamette Valley, Oregon, which were characterized by hot, dry summers and episodic heat waves. Canopy temperature, soil volumetric water content, and growth were monitored using high-resolution sensor networks and analyzed using mixed-effects modeling to account for repeated measures and environmental covariates. Across both years, mean canopy temperature largely tracked ambient conditions, and treatment effects on absolute temperature were modest. However, canopy misting reduced daily canopy temperature amplitude (ΔT) and maintained the highest soil volumetric water content, while both misting and kaolin consistently reduced exposure to the highest canopy temperature thresholds. Although these reductions in cumulative thermal exposure were not statistically significant, they coincided with improved tree growth. The chemical treatments produced smaller, context-dependent effects. Despite modest temperature differences, stem caliper increased by 10–20% under misting relative to the control (p < 0.05). Growth responses indicate that small changes in canopy thermal exposure and soil water availability can translate into meaningful differences in early tree performance. These results demonstrate that the absence of strong treatment effects on mean canopy temperature does not preclude biologically relevant outcomes. Management strategies that modify canopy thermal dynamics or plant water relations may improve growth and establishment potential of young trees under increasingly extreme thermal conditions, even when ambient heat loads cannot be fully mitigated. Full article

Octet lattice cylindrical shell combines the stretching-dominated load transfer of Octet lattices with the geometric characteristics of the cylindrical shell, but the effects of different density gradients under different compression directions remain unclear. Uniform and three-layer graded 316L Octet LCSs were evaluated using quasi-static compression tests and validated finite element simulations. The results demonstrate that relative density is the primary factor controlling the overall stiffness, strength, and energy-absorption capacity of Octet LCSs. Under vertical compression, rearranging the density layers at a fixed average relative density regulates the yielding sequence and collapse path, enabling more controllable multistage energy absorption but with reduced stiffness and absolute SEA compared with uniform structures. Under transverse compression, the response is governed mainly by cross-sectional flattening, strut bending and local contact, and thus, the influence of layer arrangement on global load-bearing capacity is limited. The validated numerical model agrees well with the experiments and provides insights into the layer-sequence design of lightweight lattice cylindrical shells for protective and energy-absorbing applications. Full article

Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic pathogen that causes substantial livestock losses and a range of severe human illnesses, including hemorrhagic disease, hepatitis, retinitis, vision loss, and delayed encephalitis. Despite its public health and One Health importance, no approved RVFV-specific antiviral therapy or licensed human vaccine is available. Therapeutic development is challenged by the progression of RVFV disease from acute viremia and hepatic injury to delayed neurologic and ocular complications, highlighting the need for countermeasures that protect both systemic organs and CNS tissues. This review examines RVFV antiviral development in the context of neuroinvasive disease. We summarize evidence on RVFV neuroinvasion and central nervous system injury, including route-dependent entry, blood–brain barrier interactions, immune responses, neuroinflammation, and neuronal damage. We then evaluate major antiviral strategies by mechanism, treatment timing, tissue exposure, and central nervous system relevance. Finally, we propose that future RVFV therapeutics are assessed not only by survival, viremia, and hepatic viral-load endpoints, but also by blood–brain barrier penetration, brain pharmacokinetics, efficacy in neuroinvasive models, delayed-treatment activity, and protection against encephalitis-associated injury. This framework may help prioritize antivirals that control both acute systemic disease and delayed neurological complications. Full article

Periprosthetic joint infection (PJI) remains one of the most serious complications of arthroplasty, largely due to the formation of microbial biofilms on implant surfaces. Biofilm-associated infections exhibit increased tolerance to antimicrobial therapy and host immune responses, making eradication difficult and often requiring repeated surgical interventions. Consequently, there is a growing need for effective local therapeutic strategies capable of delivering high concentrations of antimicrobial agents directly to the site of infection while minimizing systemic toxicity. Hydrogels have emerged as promising drug delivery platforms for the management of biofilm-associated PJI. Their biocompatibility, injectability, high water content, and tunable physicochemical properties enable controlled and localized release of therapeutic agents within the infected peri-implant environment. This narrative review summarizes recent advances in hydrogel-based approaches, including antibiotic-loaded hydrogels, systems incorporating anti-biofilm enzymes, bacteriophage-loaded formulations, and nanoparticle-enhanced platforms. It also highlights future research directions, with particular emphasis on the need for expanded clinical studies to facilitate the translation of emerging hydrogel-based therapies into clinical practice. Further development of these systems should focus on the incorporation of novel therapeutic agents into hydrogel platforms, aiming to enhance biofilm eradication and improve treatment outcomes in patients with PJI. Particular attention is given to stimuli-responsive (“smart”) hydrogels that release therapeutic payloads in response to infection-related triggers such as pH changes, with emphasis on the need for expanded clinical studies to facilitate the translation of emerging hydrogel-based therapies into clinical practice. Further development of these systems should focus on the incorporation of novel therapeutic agents into hydrogel platforms, aiming to enhance biofilm eradication and improve treatment outcomes in patients with PJI. Full article

This study investigated the self-healing behavior of Engineered Cementitious Composites (ECCs), with a focus on the influence of fiber content. ECC mixtures have shown a distinctive response under tensile loading, particularly their high tensile strain capacity, which leads to the formation of microcracks and the redistribution of stress. Three-point bending tests (at 80–90% of the maximum load) were performed to induce controlled cracking in beam specimens, followed by weekly wet–dry curing. Crack closure was monitored at the specimen surface using microscopic image acquisition and within the specimens by means of ultrasonic monitoring. The results indicated that higher fiber dosage generally promoted microcracking and reduced crack widths, thereby creating favorable conditions for self-healing. Specimens with crack widths not exceeding 0.10 mm exhibited complete or nearly complete crack closure after approximately 60 days of weekly wet–dry curing, whereas specimens with wider cracks showed only partial healing. These findings highlight the beneficial role of fibers in controlling crack development, limiting crack widths, and providing favorable sites for the formation of apparent healing products, thereby enhancing the self-healing capacity of ECCs. Full article

To develop an analysis-oriented stress–strain model for prestressed fiber-reinforced polymer (FRP)-confined circular concrete, the path-dependent responses of prestressed FRP-confined concrete and actively confined concrete were systematically investigated. Existing experimental data were used to examine the applicability of the stress-path independence and strain-path independence assumptions under different prestressing methods. The filament winding method generally satisfies the stress-path independence assumption, with relative errors in axial stress mostly within 10%, whereas direct application of the actively confined concrete model to expansive-concrete method specimens results in errors close to 20%. After accounting for the initial confinement effect, these errors are generally reduced to within 10%. Strain-path comparisons further show that the prestress-induced initial lateral strain should be considered; after removing this initial strain component, the lateral strain–axial strain relationship shows strong consistency with that of actively confined concrete. Accordingly, the peak stress, peak strain, and lateral strain–axial strain relationship were modified, and a strain-controlled incremental calculation procedure was established to generate the complete stress–strain response. Validation against compiled published experimental data yielded an R² of 0.931 and a MAPE of 8.88% for compressive-strength prediction, while the predicted axial stress–strain and lateral dilation responses also showed reasonable agreement with the experimental results. The proposed model can therefore support nonlinear analysis over the complete loading range and quantitative assessment of strength and deformation for different prestress levels and FRP confinement parameters within the validated range. Full article

Smart lighting is becoming a strategic component of intelligent and low-carbon urban infrastructure because it combines efficient illumination with connectivity, remote management, and continuous operational monitoring. This study proposes a reproducible unit-level methodological framework that transforms field records from a functional smart-lighting installation into traceable indicators of electrical performance, energy efficiency, avoided emissions, preliminary economic benefit, sensitivity, and conditional scalability. The approach treats the luminaire not only as an electrical load, but as a monitored urban energy node whose operation can be validated, characterized, and compared under planning-oriented control scenarios. The methodology integrates data preprocessing, electrical consistency assessment, representative baseline definition, scenario-based energy modeling, explicit environmental conversion, and conditional scaling to homogeneous lighting assets. The results reveal a stable electrical operating regime and show that managed operating conditions can generate sustained reductions in energy use and associated environmental impacts while preserving analytical transparency between measured variables and scenario-derived indicators. Sensitivity and multivariable analyses further support the robustness of the unit-level interpretation and highlight the value of monitored lighting data for comparative decision-making. The framework therefore provides a technically grounded reference for smart-city lighting management, energy planning, and scalable infrastructure assessment, with relevance to the objectives of SDG 7, SDG 11, and SDG 13. Overall, the study contributes an original data-driven perspective for integrating IoT-enabled lighting, remote supervision, and sustainability-oriented urban management within a common analytical structure. Full article

Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications. Full article

Wireless Sensor Networks (WSNs) are fundamental to the expansion of the Internet of Things (IoT), yet severe node energy constraints remain the primary bottleneck for remote environmental monitoring where power infrastructure is unavailable. Because these devices rely on finite battery capacities, optimizing energy usage is critical for maximizing network longevity and architectural sustainability. Sourcing literature across the Scopus, IEEE Xplore, and Elsevier digital databases, this study executes a systematic review evaluating a final cohort of $n=86$ contemporary energy management frameworks published between 2020 and 2026. The analysis synthesizes advanced multi-tier optimization techniques, specifically focusing on hierarchical clustering methodologies, metaheuristic routing protocols, and advanced scheduling algorithms. Beyond traditional approaches, the technical findings investigate the cross-layer impacts of duty cycle scheduling, transmission power control, and sleep protocols on maintaining rigid network coverage and connectivity. Ultimately, this review identifies significant research gaps regarding topological fault tolerance and localized load imbalances near base stations. The findings highlight how the strategic integration of cohesive, cross-layer hybrid optimization strategies can mitigate active energy dissipation, providing actionable technical recommendations for future IoT-based WSN architectures. Full article