Search Results (89,185)

Fire represents a relatively infrequent but potentially severe hazard for bridges, with collapse rates comparable to or exceeding those caused by seismic events. Despite this, fire risk is often neglected in bridge design and assessment, particularly for existing infrastructures in urban contexts. Beyond collapse, fire can induce significant post-event consequences, including material degradation, serviceability loss, traffic disruption, and economic and social impacts. Existing studies highlight the influence of bridge material, fire scenario, and traffic characteristics—especially the presence of fuel tankers—on damage severity. In this context, this paper proposes a rapid fire-risk assessment methodology applicable to large bridge stocks. The approach adapts and modifies existing methods from the literature, integrating them into the multi-risk framework defined by the Italian Guidelines for existing bridges, where fire is not explicitly addressed. The methodology is specifically adapted to urban and suburban bridges and European roadways, validated through its application to a stock of 30 bridges along the Palermo ring road. The results enable the classification of bridges by fire risk, supporting infrastructure Authorities in prioritizing detailed assessments and intervention strategies on the most vulnerable bridges. Multi-risk assessment considering the fire–seismic risk is also addressed, by adopting a simplified seismic risk approach consistent with the Italian Guidelines for existing bridges and comparing it with internationally accepted methods, particularly the North American HAZUS system. Results show that accounting for the actual condition and deterioration of bridges leads to higher seismic risk classes, more consistent with the fire risk assessment procedure proposed. In contrast, expedited methods such as HAZUS, which neglect maintenance conditions, may underestimate seismic risk. Full article

As a critical hydraulic component of pumped storage power stations, the side inlet/outlet directly affects unit efficiency, flow stability, and system safety. This study investigates the side inlet/outlet of a pumped storage power station using three-dimensional numerical simulations, focusing on the influence of the diffuser length L on hydraulic performance, and further analyzes the underlying mechanisms of energy loss based on entropy production theory. The results indicate that, with increasing diffuser length L, the flow rates in individual channels gradually deviate from the design values, leading to an aggravated imbalance in flow distribution. In contrast, the velocity non-uniformity coefficient C_V at the trash rack decreases, accompanied by a pronounced attenuation of recirculation and local flow separation, resulting in a more uniform and stable flow field. Moreover, increasing L improves the streamwise velocity uniformity within each channel, while the extent and intensity of the top recirculation zone are reduced, suppressing local flow separation. Quantitative analysis shows that when L increases from 65 m to 85 m, the total turbulent dissipation entropy production rate in the diffuser section increases linearly from 2732.32 W/K to 2842.32 W/K, whereas the direct dissipation entropy production rate increases from 0.41 W/K to 0.59 W/K. This indicates that turbulent dissipation entropy production plays a dominant role in the overall energy loss. Shorter diffusers tend to induce high-intensity local dissipation, whereas longer diffusers reduce local peak dissipation but increase the overall entropy production within the diffuser, reflecting a trade-off between local optimization and global energy loss. This study reveals the sensitivity and governing effects of diffuser length on the hydraulic characteristics of side inlet/outlets, providing a reference for geometry optimization and engineering design of similar hydraulic components. Full article

Objective: This study aimed to investigate the association between the neutrophil-to-albumin ratio (NAR) and coronary atherosclerotic burden, assessed using the Gensini score, in patients referred for coronary angiography (CAG). Methods: A total of 987 patients who underwent CAG at Sanliurfa Mehmet Akif Inan Training and Research Hospital between January 2020 and June 2023 were retrospectively analyzed. Demographic, clinical, and laboratory data, including complete blood count, albumin, lipid profile, and creatinine, were collected prior to angiography. The NAR was calculated as the ratio of absolute neutrophil count to serum albumin. Coronary atherosclerotic burden was assessed using the Gensini scoring system and analyzed across predefined score categories representing increasing anatomical disease extent: normal (score 0), mild (1–24), and severe (≥25). Statistical analyses included group comparisons and multivariable regression analyses appropriate to the study design. Results: Higher NAR values were associated with increased angiographic coronary atherosclerosis severity at the group level across Gensini score categories. This association remained statistically significant after prespecified multivariable adjustment for established cardiovascular risk factors. Conclusions: Higher NAR values were associated with greater angiographic coronary atherosclerotic burden, as quantified by the Gensini score, in an angiography-referred population. Full article

Implantable artificial kidneys represent a promising alternative for patients with end-stage renal disease (ESRD), aiming to overcome the limitations of conventional dialysis through the integration of microfluidic and electrokinetic technologies. In this study, we present a sawtooth electrode microfluidic chamber that achieves blood cell separation via negative dielectrophoresis at a record-low operating voltage of 1.4 V, representing a fivefold reduction compared with rectangular electrode designs and supporting potential integration into implantable artificial kidney systems. A microfluidic chip incorporating an asymmetric sawtooth electrode geometry was developed to enhance local electric field gradients while reducing power consumption. Device performance was investigated using COMSOL Multiphysics simulations. Response Surface Methodology (RSM) based on a Box–Behnken design was employed to optimize the number of teeth per unit length (N), sawtooth height (H), and applied voltage (V), while excitation frequency was fixed at 1 MHz and flow velocity was maintained constant at 0.1 µL·min⁻¹. Statistical analysis was conducted using analysis of variance (ANOVA) in Minitab (Version 27; Minitab, LLC, State College, PA, USA, 2024) . The optimization model showed strong predictive capability (R² = 95.8%) and identified applied voltage (59.45% contribution) and sawtooth height (33%) as the dominant factors affecting separation efficiency, with a significant H × V interaction (p = 0.023). Comprehensive voltage-response mapping over the range of 0.8–4.0 V revealed four operational regimes, including a previously unreported high-voltage failure zone above 2.8 V, where electrothermal flow and electroporation degrade performance. Under physiological conductivity conditions, the optimized design maintained a separation efficiency of 78.3% at 1.4 V with a tip temperature rise of only 1.2 °C, while full recovery of performance was achieved at 2.2 V. Cell-specific separation efficiencies reached 97.3% for white blood cells, 95.8% for red blood cells, and 84.7% for platelets, reducing the downstream cellular load by 92.6%. These findings demonstrate that the proposed low-voltage, high-efficiency separation platform has strong potential as a cellular pre-filtration module in implantable artificial kidney systems and other lab-on-chip biomedical devices. Full article

Metallic contaminants in powdered foods represent a serious safety concern. Therefore, effective detection is crucial for food safety. This study aimed to develop an electric-field-based detection system and quantitatively evaluate its performance. An alternating (+/−) electrode array (gap 1–2 mm) was designed, and resonance analysis identified 15 kHz with a 2 mm gap as the optimal operating condition. Using an IGBT-based high-voltage source, 1.35 kV was selected to ensure stable operation without partial discharge. A real-time algorithm based on a minimum current-change threshold was implemented, and detection responses to stainless steel (SUS), aluminum (Al), and copper (Cu) particles in three size classes (<0.5, 0.5–1.0, and 1.0–2.0 mm) were evaluated using hit/miss modeling and logistic regression to obtain probability-of-detection (POD) curves and limits of detection (LOD). The system achieved POD ≥ 0.9 for 1.0–2.0 mm particles; in the 0.5–1.0 mm range, observed POD values were 84%, 90%, and 68% for SUS, Al, and Cu, respectively. Safety was assessed by COMSOL-based localized heating simulation validated by infrared thermography and by ozone monitoring for real-time operation. Compared with conventional inspection approaches, the proposed system provides a compact, cost-effective architecture while reporting inspection-oriented reliability metrics (POD/LOD) for process-line deployment. Full article

This paper presents a comprehensive study on constructing exact and approximate solutions to Cauchy problems for time-fractional systems with variable coefficients. An innovative iterative approach is developed for solving functional equations with initial conditions, combining rigorous mathematical foundations with practical computational efficiency. The proposed technique effectively handles the nonlocal nature of fractional operators through a carefully designed iterative scheme that maintains simplicity while achieving high accuracy. It demonstrates particular strength in solving nonlinear systems with well-defined conditions and variable coefficients, where traditional methods often fail. Through systematic theoretical analysis and numerical validation, we establish the method’s convergence properties and computational advantages, showing its capability to generate both exact closed-form solutions, when available, and high-precision approximations otherwise. The approach remains computationally tractable even for complex cases where variable coefficients and memory effects of fractional systems present significant challenges to conventional solution approaches. Full article

Field-Programmable Gate Arrays (FPGAs) have become an important platform for accelerating real-time communication systems, and System-on-Chip (SoC) devices provide the flexibility to design and optimize architectures that support high data rates, different modulation formats, and channel equalization schemes. Selecting the appropriate architecture can be guided through Design Space Exploration (DSE) using high-level synthesis tools, which enables the identification of numerical representations that balance performance with reduced hardware resource consumption. Despite their relevance, recent developments in communication systems often overlook the impact of numerical precision in Digital Signal Processing algorithms, particularly the trade-offs between floating- and fixed-point arithmetic when targeting hardware implementations. In this work, two widely used blind equalization algorithms, the Constant Modulus Algorithm (CMA) and the Multi-Modulus Algorithm (MMA), were implemented on a low-cost Ultra96 SoC-FPGA to analyze the effect of a fixed-point representation. A multi-objective Design Space Exploration methodology was applied to minimize hardware utilization while maintaining reliable transmission performance. Resource consumption, latency, and throughput were measured across different binary formats using the Minimum Mean Square Error (MMSE) criterion. Parallelization techniques were incorporated to improve throughput. The DSE generated comprehensive performance surfaces quantifying latency, MMSE convergence, and FPGA resource utilization (DSP48E/FF/LUT/BRAM) across fixed-point formats, achieving optimal 4 MS/s throughput configurations. Although this throughput is naturally lower than the Gigabit speeds required in backbone optical networks, the results demonstrate the effectiveness of numerical representation optimization in resource-constrained SoC-FPGA devices, offering a practical approach for real-time Edge and IoT implementations where cost and hardware limitations are critical. Full article

This paper presents the design and analysis of solar systems for agricultural applications and the sustainable energy supply of villages, based on a case study of a rural settlement comprising 30 households. The village energy demand is quantified through a detailed assessment of hourly load profiles for daytime and nighttime operation, identifying peak loads and total daily energy consumption. Energy usage patterns are established for residential buildings, agricultural water pumping, public lighting, healthcare facilities, and commercial services. To meet these energy requirements sustainably, a 60 kW photovoltaic (PV) system is proposed in combination with a solar thermal water heating system designed to supply domestic and agricultural hot water. This study details the design methodology and simulation of the solar thermal system, including heat transfer modeling and system dimensioning. MATLAB (V.22b) simulations are conducted to evaluate system performance, covering PV energy generation, battery charge–discharge cycles, and thermal behavior over a 24 h period. Comparative analyses of standalone PV, hybrid PV/T, and combined PV and solar thermal configurations demonstrate that separate PV and thermal systems provide superior cost-effectiveness, operational reliability, and reduced maintenance requirements. The results confirm the technical feasibility, economic viability, and environmental benefits of solar-based solutions for rural electrification and agricultural applications. The results indicate that the analyzed rural settlement has an estimated daily electricity demand of approximately 590 kWh. Based on this demand, a 60 kW photovoltaic system was selected to ensure sufficient daytime electricity production while also allowing battery charging for nighttime consumption. In addition, the solar thermal system can increase the water temperature from approximately 10 °C to 55–80 °C, depending on solar irradiance conditions. The combined PV and solar thermal configuration demonstrates the potential to provide a reliable and sustainable energy solution for rural off-grid communities. Full article

The energy transition represents a complex, multi-level system subject to profound uncertainty and recurrent shocks. Current policy design approaches predominantly rely on static optimization frameworks (centralized, calculative models that presume stable conditions and predictable technological trajectories). Yet evidence from the 2021–2023 energy crisis in Europe, coupled with structural challenges in market liberalization and renewable integration, demonstrates persistent challenges in policy implementation. Price interventions affect competitive dynamics; subsidies influence technology selection; capacity mechanisms create coordination tensions; and rigid tariff structures create misalignments with evolving grid needs. This paper argues that these recurrent policy tensions stem not from implementation gaps, but from an inadequate theoretical foundation: the treatment of energy systems as optimizable rather than as complex, adaptive systems operating under Knight–Mises uncertainty and Huerta de Soto dynamic efficiency. This work explores an alternative framework grounded in dynamic efficiency, complex–uncertain systems, decentralized incentives, and adaptive governance (international–domestic, public–private, etc.). This review uses the theoretical and methodological framework of the Heterodox Synthesis, an alternative to the Neoclassical Synthesis. There is a reinterpretation of some insights from Knight and Mises (uncertainty), Hayek (distributed knowledge), Huerta de Soto (dynamic efficiency) and contemporary complexity economics into operational criteria applicable to energy policy design: (1) robustness to deep uncertainty; (2) preservation of price signals and risk-bearing mechanisms; (3) alignment of incentives across distributed actors; (4) institutional adaptability; and (5) minimization of ex post policy corrections. Through illustrative application to four critical policy instruments (price caps, renewable subsidies, capacity mechanisms, and network tariff design), it is shown how this framework identifies systematic tensions and consequences that conventional analysis overlooks. The contribution is exploratory in a bootstrap way: theoretical, by integrating classical and contemporary economics into energy governance; methodological, by operationalizing dynamic efficiency into evaluable criteria distinct from existing adaptive governance frameworks; and sectorial, by providing policymakers and regulators with diagnostic tools for assessing design robustness in conditions of deep uncertainty and rapid transition. According to this review, improved energy policy design under uncertainty is not achieved through more sophisticated optimization (in a calculative way), but through institutional architectures that preserve creative and adaptive learning, maintain distributed decision-making capacity, and remain functional when assumptions prove incorrect or not well-known. Full article

Urban geophysical exploration faces significant hurdles due to strong electromagnetic interference and limited operational space, which restrict the efficiency and depth of traditional Electrical Resistivity Tomography (ERT). To overcome these limitations, this paper presents a novel ERT measurement and control system based on the Frequency Division Multiplexing (FDM) principle. Unlike conventional time-domain methods, this instrument synchronously transmits three independent AC signals at distinct frequencies. The acquisition station utilizes Fast Fourier Transform (FFT) to isolate specific frequency responses, enabling the simultaneous retrieval of apparent resistivity data for three different electrode spacings from a single transmission. The system architecture integrates low-power STM32 microcontrollers with an Android-based control terminal via Bluetooth, Wi-Fi, and NB-IoT technologies. This wireless design supports real-time current monitoring and cloud-based data synchronization. Experimental results demonstrate that the FDM operating mode significantly enhances data acquisition efficiency and anti-interference capability through frequency-domain separation. Controlled indoor and preliminary field tests indicate that FDM mode substantially improves acquisition efficiency through concurrent multi-channel measurement while effectively resolving target signals from noise. This study demonstrates the system’s technical feasibility and provides a practical foundation for future geophysical detection in time-constrained urban environments. Full article

Efficient aero-acoustic regression is critical for unmanned aerial vehicle (UAV) design and urban air mobility operations, where noise mitigation is essential for regulatory compliance and public acceptance. While data-driven fuzzy Takagi–Sugeno–Kang (TSK) systems have shown potential for modeling complex aero-acoustic behaviors in UAV applications, their performance is strongly affected by input dimensionality and rule-base complexity. This work extends previous research on dimensionality reduction for genetic algorithm-optimized fuzzy systems by conducting a comparative benchmark on an aero-acoustic database regression task relevant to drone propulsion noise prediction. Several TSK architectures are evaluated, including zero- and first-order models, different membership function granularities, and clustering-based rule-generation strategies. In addition, a physics-based heuristic TSK rule system incorporating aero-acoustic knowledge is introduced and compared against data-driven fuzzy configurations. Model performance is primarily assessed through graphical regression analysis and optimization convergence behavior, with a focus on computational efficiency, structural complexity, and qualitative prediction trends—critical considerations for onboard UAV systems and real-time acoustic monitoring. The results highlight the trade-offs between data-driven learning and physics-informed rule construction, demonstrating that physics-based heuristics can reduce optimization complexity while preserving physically consistent behavior. This study provides practical insights into the design of interpretable and efficient fuzzy regression models for UAV aero-acoustic applications, supporting next-generation drone acoustic signature management. Full article

High-precision trajectory tracking of robotic manipulators is fundamentally challenged by strong nonlinear dynamics, unmodeled uncertainties, and external disturbances. This paper proposes a Reciprocal Neural State–Disturbance Observer (RNSDO) featuring a neural activation mechanism for adaptive gain modulation and a reciprocally coupled state–disturbance estimation architecture. By reshaping the observer error dynamics through mutual feedback between state and disturbance estimation, the proposed structure alleviates the conflict between fast transient disturbance reconstruction and steady-state noise suppression, while requiring only position measurements. A decentralized position controller is designed based on RNSDO. The global asymptotic stability of the resulting closed-loop system is rigorously established via Lyapunov analysis. Extensive simulations on a PUMA 560 and experiments on a 7-DOF Franka FR3 robotic manipulator demonstrate highly consistent performance trends. The proposed method achieves improved state and disturbance estimation accuracy and enhanced robustness against unmodeled dynamics and payload variations compared with a linear Improved Extended State Observer (IESO), a classical Nonlinear Extended State Observer (NLESO), and a model-based Nonlinear Disturbance Observer-based Adaptive Robust Controller (NDO-ARC). Furthermore, the algorithm exhibits excellent real-time feasibility with a minimal computational footprint. Full article

Ride comfort is a critical issue in vehicle dynamics, as excessive vibrations adversely affect passenger comfort and human health. This paper presents a comparative performance analysis of a passive suspension system, fuzzy logic control (FLC), and a newly designed adaptive robust control (ARC) strategy applied to a nonlinear quarter-car suspension–seat–driver model. The primary objective is to improve ride comfort while maintaining vibration levels within accepted health criteria. First, the nonlinear dynamic model of the suspension–seat–driver system is established. The FLC structure and rule base are determined based on heuristic knowledge. Passive and FLC-based systems, while effective to some extent, suffer from limited adaptability to external disturbances and modeling uncertainties, slower convergence, and suboptimal vibration attenuation. The main contribution of this study is the design and implementation of a novel adaptive robust controller that effectively handles modeling uncertainties, external disturbances, and parameter variations. Different controller placement approaches within the system are also investigated. Numerical simulations are conducted under identical operating conditions for the uncontrolled system and all control strategies. The results demonstrate that although the FLC improves ride comfort compared to the passive system, the proposed ARC achieves the best overall performance, providing superior vibration attenuation, faster convergence, and enhanced robustness for nonlinear vehicle suspension systems. Quantitatively, the ARC reduces head acceleration RMS from 0.1693 m/s² (passive) and 0.1422 m/s² (FLC) to 0.0705 m/s², and upper torso RMS from 0.1689 m/s² (passive) and 0.1417 m/s² (FLC) to 0.0703 m/s², corresponding to approximately 58% reduction relative to passive and 50% improvement over FLC. Full article

This review comprehensively examines the influence of clay plasticity on thermally induced volume changes in saturated clays, which is a critical factor in the design and performance of energy geostructures, nuclear waste repositories, and thermal ground improvement systems. This study synthesises experimental and theoretical findings, demonstrating that the plasticity index and mineralogical composition significantly govern the magnitude and nature of volume change during heating and cooling cycles, with stress history playing a pivotal role. Unlike previous review papers that primarily discuss general thermo-mechanical behaviour or constitutive modelling frameworks, this review explicitly focuses on plasticity as the central unifying parameter influencing thermally induced volume change. It further provides a structured synthesis that integrates plasticity, stress history, and microstructural mechanisms. Normally consolidated clays exhibit irreversible thermal contraction, which intensifies with plasticity, whereas highly overconsolidated clays typically exhibit reversible expansion. Lightly overconsolidated clays exhibit transitional behaviour characterised by initial expansion followed by collapse. This review links these macroscopic responses to microstructural mechanisms, including interparticle physicochemical forces, diffuse double-layer dynamics, and bound water behaviour, highlighting the limitations of idealised electrochemical models and emphasising the importance of micromechanical processes. It further explores how plasticity modulates temperature-dependent reductions in preconsolidation pressure, thermal softening, cyclic thermal deformation, and time-dependent thermal creep, with higher plasticity clays showing greater sensitivity and longer stabilisation periods. The findings underscore the necessity of incorporating plasticity and stress history into constitutive models to accurately predict the thermo-mechanical behaviour of clays under service conditions, with significant implications for the long-term reliability of thermal geotechnical applications. Full article

The purpose of this study is to examine whether analyst-level credit can be assigned quantitatively in a lightweight human-feedback decision-support pipeline. In intelligence and national security workflows, analysts often provide edits, comments, and evaluative feedback during the production of analytic products, yet these intermediate contributions are usually discarded, leaving no auditable record of how individual feedback shaped the final output. To address this problem, this study proposes a proof-of-concept Analyst-of-Record framework that combines synthetic analyst feedback, a linear ridge reward model, first-order influence functions, and additive Shapley aggregation to estimate both feedback-item and analyst-level contribution scores. The research design uses the Fact Extraction and VERification (FEVER) fact-verification dataset under controlled experimental settings. The pipeline retrieves evidence with Best Matching 25 (BM25), generates a grounded template-based response, derives three synthetic analyst feedback channels from FEVER annotations, trains a reward model on simple claim–answer and analyst-identity features, and aggregates per-feedback influence scores into an Analyst Contribution Index (ACI). The main experiments are conducted on a 500-claim subset across five random seeds, with additional ablation and bootstrap analyses used to assess sensitivity and stability. The findings show that the reward model achieves a mean validation $R^2$ of $0.801\pm 0.037$, indicating that the synthetic feedback signals are learnable under the selected featureization. The analyst-level contribution scores remain stable across random seeds, with approximately half of the total influence magnitude attributed to the explanation-quality channel and the remainder split across the other two channels. Ablation results further show that removing the explanation-quality channel collapses validation fit, while bootstrap resampling demonstrates tight concentration of absolute ACI magnitudes. Theoretically, this study extends attribution research beyond document-only grounding by showing how analyst feedback itself can be modeled as an object of contribution analysis. It also demonstrates that influence functions and Shapley-style aggregation can be adapted into a tractable framework for estimating interpretable analyst-level credit in a reproducible experimental setting. Practically, the proposed framework offers an initial foundation for more traceable and accountable decision-support workflows in which intermediate analyst contributions can be preserved rather than lost. The results also provide a feasible implementation path for future systems that incorporate stronger generators, richer evidence representations, and real analyst annotations. Full article