Search Results (2,697)

We present FilterForge, a chat-driven VS Code environment that pulls the synthesis, analysis, simulation, and optimization stages of microwave bandpass filter design, normally coordinated by hand across tools written in different languages, into one workflow. A deployed Model Context Protocol (MCP) server exposes deterministic Python implementations of coupling-matrix synthesis, uniform predistortion, topology reconfiguration, a genetic-algorithm transmission-zero selector, a mode-matching engine for H-plane iris-coupled rectangular waveguide geometries, and a skill that generates PyAEDT/HFSS notebooks for various dimensioning design-curves. A language-model orchestrator turns natural-language requests into typed tool calls, while every reported quantity stays inside the deterministic kernels, so the numerics remain reproducible and model-agnostic. We evaluate the call layer on a 45-task benchmark across the five tool categories: gemini-3-flash reaches $96.3\%$ tool-selection and $94.8\%$ full-call accuracy with an $88.9\%$ pass${}^3$ rate, which an ablation traces to the curated tool-selection prompt rather than to raw model capability. The mode-matching engine is validated against full-wave HFSS on a six-pole 4 GHz Chebyshev filter tuned from the chat panel, and on an 8 GHz WR-112 counterpart taken end-to-end with no engineer in the loop, where a deterministic critique gates each round until a manufacturable geometry is reached. We then exercise the full workflow on two folded six-pole WR-90 cross-coupled filters at $10\mathrm{GHz}$, a high-selectivity design synthesized against a stop-band mask and a group-delay-equalized variant whose positive cross-coupling uses a pair of side-wall irises, the latter settling to a peak-to-peak in-band group-delay ripple below $1.5\mathrm{ns}$ while recovering the synthesized return loss. Full article

Road construction contributes to embodied carbon in infrastructure, with asphalt-bound layers often dominating construction-stage greenhouse gas emissions in flexible pavements. Reclaimed asphalt pavement (RAP) and high-modulus asphalt concrete can reduce virgin material demand and improve structural efficiency, but their sustainability benefit depends on maintaining equivalent pavement performance. This study develops a climate-informed, mechanistic, environmental, and economic Pareto optimization framework for RAP-modified high-performance asphalt concrete (RAP-HPAC) pavement sections in Alberta. The framework couples fitted dynamic modulus master curves, monthly pavement temperature inputs, ALVA layered elastic analysis, Asphalt Institute fatigue and rutting criteria, A1–A5 global warming potential (GWP), and Alberta 2026 installed unit-price cost data. The RAP-HPAC mixture contains 50% RAP and was designed through a balanced mix design to target approximately 80% effective RAP binder activation. Three traffic classes were evaluated: 731, 1300, and 5426 ESAL/day/direction, each with 2% annual compound growth over a 20-year design period. Relative to independently optimized conventional HMA controls, Pareto-selected RAP-HPAC sections reduced P50 construction-stage GWP by approximately 19–30% and first cost by approximately 6–11% at a conservative 0.90× RAP-HPAC cost multiplier. The results show that RAP-HPAC is most beneficial when used as a structural-bound base that replaces conventional asphalt-bound capacity while preserving sufficient granular support. The framework provides a reproducible design-stage approach for comparing recycled high-modulus asphalt mixtures using performance, carbon, and cost criteria simultaneously. Full article

The MASW (Multichannel Analysis of Surface Waves) method oriented toward seismic microzoning has been evolving consistently and steadily for several decades, providing increasingly reliable solutions that are consistent with field and laboratory data typical of classical geotechnics. This study evaluates the improvement achieved when using a sequence of inversion algorithms on MASW test results: first with a global algorithm—specifically Differential Evolution (DE)—and subsequently, using the best model obtained from the global search, a second local algorithm—Trust Region Reflective (TRF). This second stage refines the previous model, further adjusting it to the borehole model used as the starting point of the sequence. The procedure has been automated using a Python script that incorporates two innovations compared to traditional inversion approaches. These consist of parameterising two variables: (i) an adaptive expansion factor for the $V_s$ limits establisheda priori in the borehole model, and (ii) a subdivision into thinner layers for borehole models with excessively thick strata. This provides the algorithms with greater flexibility, particularly in scenarios with complex stratification. Additionally, to better define the deeper layers, the passive ESAC method in an “L-shape” configuration was also employed. The parameterised sequential hybrid inversion process was validated using synthetic data from two curves (Curve #1 and Curve #2), obtained by adding 5% Gaussian noise to the forward modelling results of the same initial synthetic model. The TRF refinement stage in the sequential hybrid inversion succeeded in reducing the error obtained by the global algorithm by percentages ranging from 59.7% to 5.8% across all conducted tests, confirming the stability of the methodology used. Full article

Fluidized solidified soil piles combine slurry-like constructability with post-hardening strength development and provide a potential approach for soft ground improvement. This study investigated the vertical bearing behavior and load-transfer mechanism of fluidized solidified soil piles in layered soft ground through field single-pile vertical static load tests, core drilling, and three-dimensional numerical simulation. The field tests and core drilling provided experimental evidence for evaluating load–settlement behavior, pile integrity, and material strength, while the internal load-transfer mechanism and geometric parameters were mainly interpreted using the numerical model. The field results showed that the Q-s curves exhibited staged deformation characteristics, with relatively stable settlement development during the main loading stage and more pronounced nonlinearity under high load levels. The ultimate vertical bearing capacities of the 10 m and 20 m test piles were 1050 kN and 950 kN, respectively. Core drilling indicated that the two pile groups had similar material strength, suggesting that the bearing capacity difference was mainly associated with the pile toe bearing stratum rather than pile material strength. After comparison with the measured Q-s curves, the numerical analysis showed that the 20 m pile mobilized a longer shaft resistance range and a higher shaft resistance contribution, but its pile toe extended into the lower mucky soil layer, resulting in reduced pile toe resistance. Parametric analysis indicated that increasing pile length does not necessarily improve bearing performance when the pile toe bearing stratum is unfavorable, whereas increasing pile diameter more directly reduces pile head settlement under the same pile toe bearing condition. These findings highlight the need to consider both shaft resistance mobilization and pile toe bearing stratum in the design of fluidized solidified soil piles in layered soft ground. Full article

In this study, free vibration analysis of three-layer sandwich panels with cores based on a triply periodic minimum surface (TPMS) and graphene platelet-reinforced composite (GPLRC) faces is performed. Four different geometries including cylindrical, spherical, saddle and flat panels were investigated and the governing equations were solved using higher-order shear deformation theory (HSDT) extracted from Hamilton’s principle. The accuracy and precision of the presented analytical method is verified by comparing the dimensionless natural frequencies with reference studies. Then, the effect of various parameters including panel geometry, core topology type and graphene weight percentage on the vibration response was investigated. The results show that adding graphene to the face layers significantly increases the natural frequencies and improves the overall stiffness of the structure. In addition, the frequencies of the panel may be controlled through different patterns and topologies. Also, double-curved panels, especially spherical geometries, present the highest fundamental natural frequency. The findings of this research could play an important role in the design and performance evaluation of advanced structures with TPMS cores and nanoscale reinforcement. Full article

Apnea of prematurity (AOP) remains a critical challenge in neonatal care, with caffeine citrate serving as the cornerstone of pharmacological intervention. However, the current standardized dosing schedule fails to account for significant inter-individual variability in caffeine pharmacokinetics and clinical response. This narrative review explores the transformative potential of integrating wearable sensor technologies and multi-modal data analytics into a closed-loop framework for personalized caffeine therapy. Based on a synthesis of current monitoring literature, we propose a theoretical, comprehensive monitoring system utilizing the area under the respiratory curve (rAUC) as a continuous proxy metric, alongside waveform amplitude analysis aligned with pediatric polysomnography standards. By incorporating emerging metrics such as respiratory rate variability (RRV) and hypoxic burden, the framework enables the objective quantification of respiratory stability. Furthermore, the integration of established neonatal intensive care unit (NICU) parameters for bradycardia and oxygen saturation detection provides a critical cross-validation layer to minimize artifact-induced false alarms. This conceptual model bridges the gap between advanced signal processing and clinical oversight, offering a scalable pathway toward precision dosing. By shifting from reactive to predictive neonatology, sensor-driven optimization can enhance therapeutic efficacy, reduce alarm fatigue, and ultimately improve developmental outcomes for preterm infants. Full article

To address the insufficient accuracy of short-term load forecasting caused by the strong randomness of distributed energy output, variable electricity consumption patterns, and complex meteorological factors, this study proposes a load forecasting method that integrates K-means scene clustering and a Transformer–BiGRU–Attention (CTBA) hybrid deep learning architecture. Different from conventional Transformer–BiGRU hybrid forecasters that train a single global predictor across all operating conditions, the proposed CTBA framework first partitions daily load curves into representative scenes and then routes each sample to a scene-specific Transformer–BiGRU–Attention predictor, thereby reducing distributional heterogeneity before temporal modeling. First, the K-means algorithm is used to perform scene clustering on historical daily load curves, and the optimal number of clusters is selected according to the silhouette coefficient and downstream prediction performance. Subsequently, the CTBA model is trained separately for each clustering subset. The Transformer encoder captures the long-range global dependencies of load sequences through the self-attention mechanism, the BiGRU module extracts local bidirectional temporal fluctuation features, and the Attention mechanism further focuses on key time nodes such as morning and evening peaks while fusing multi-source data including historical load, day-ahead electricity price, and multi-dimensional meteorological factors. Experimental results based on the German ENTSO-E power dataset show that the coefficient of determination R² of the proposed model reaches 0.9893, with MAE, RMSE, and MAPE as low as 0.0141, 0.0187, and 3.92%, respectively, which are significantly improved compared to benchmark models such as SVR, LSTM, CNN, and TCN-BiGRU. Ablation experiments further demonstrate that removing the clustering, Transformer, BiGRU, or attention layer will degrade performance, thus verifying the effectiveness and superiority of the method in short-term load forecasting and providing an accurate solution for the short-term load forecasting of power systems. Full article

Flexible polyurethane (PUR) foams and latex rubber foams are widely used in furniture and mattress cushioning, yet conventional standardized mechanical tests only partially capture comfort-relevant behavior, particularly in layered constructions where material interactions and sequencing can alter elastic response. This study aimed to compare the mechanical (elastic) properties of selected three-layer composites of approximately 60 mm thickness (composed of conventional PUR, high-resilience PUR, low-resilience PUR, and latex foam) and to preliminarily assess whether combining foam types improves support of such setup and whether changing layer order modifies elasticity and support. Indentation hardness testing of multilayer cushions was conducted by ISO 2439:2008 Method E. Six three-layer systems (Alpha–Zeta) were assembled in two groups. Group X showed nearly identical support factors (2.6–2.7), high recovery (64.3–66.2%), low hysteresis loss (24.3–24.5%), and overlapping force–indentation (IFD) curves, indicating minimal effect of layer order and dominance of the PUR layers. Group Y exhibited higher but more sequence-dependent support (3.1–3.7), markedly reduced, wider range recovery (30.0–45.9%), increased hysteresis (33.0–34.7%), and more dispersed IFD curves. Placing high-resilience foam at the top partially improve recovery, whereas locating low-resilience foam at the surface increase energy loss. The research contributes in part to the body of knowledge about the behavior of the tested materials according to standardized rules. These preliminary results can be compared with other research findings and used in the preparation of testing models for multilayer foam composites, thereby generating new knowledge to improve the design of future experiments, which will result in increased sitting and lying comfort. Full article

To investigate the dynamic impact performance of carbon fiber reinforced polymer (CFRP)-confined recycled concrete, this study designed four series comprising 80 specimens with parameters including strain rate, recycled coarse aggregate replacement ratio, and number of CFRP confinement layers. Split Hopkinson Pressure Bar (SHPB) impact tests were conducted to analyze the dynamic failure mode, stress–strain responses under dynamic loading, and variation in compressive strength of the CFRP-confined concrete specimens. Additionally, a modified Weibull statistical model and fractal theory were employed to analyze the dispersion characteristics of dynamic compressive strength. The results show that the dynamic compressive strength exhibits clear strain-rate sensitivity. The presence of CFRP confinement does not alter the fundamental shape of the stress–strain curves under different strain rates. The proposed modified Weibull statistical model accurately predicts the distribution of dynamic compressive strength at varying strain rates, with an average prediction error of 3.4% and a maximum error of 5.3%. Fractal dimension can quantitatively characterize the evolution trend and degree of crack-induced damage. Within the strain rate range of 52.85–138.42 s⁻¹, the fractal dimension of unconfined ordinary concrete specimens increases from 1.647 to 2.138; for unconfined recycled concrete, it increases from 1.612 to 2.158. The fractal dimension for CFRP-confined ordinary concrete specimens increases from 1.524 to 1.938, and for CFRP-confined recycled concrete specimens, from 1.503 to 2.019. The fractal dimension increases with the increase of strain rate, reflecting a typical strain rate effect. Full article

Low-frequency sound insulation remains a major challenge for conventional passive materials, as improved attenuation is usually achieved at the expense of increased thickness and mass. In this work, a smooth fixed third-order ring-curve fractal-maze acoustic metamaterial is proposed for compact low-frequency sound insulation, and a physics-guided long short-term memory–physics prediction surrogate network (LSTM–PPS-Net) tandem framework is developed for its full-spectrum inverse design. Different from conventional Hilbert-type, right-angled, or sharply folded labyrinthine structures, the proposed topology uses recursively arranged curved channels to extend the effective acoustic propagation path and enhance phase accumulation within a limited space. Based on this mechanism, four physically meaningful parameters, namely slit width d, characteristic radius R₃, wall thickness t_w, and inter-column spacing l_E, are selected to construct a low-dimensional design space. A COMSOL–MATLAB automated finite-element method (FEM) workflow is established to generate 1000 valid transmission-loss (TL) spectra over 100–1700 Hz with a 5 Hz interval. For forward prediction, PPS-Net is developed by integrating geometry encoding, frequency-conditioned spectral decoding, and peak-weighted learning. The proposed PPS-Net achieves the best prediction accuracy among the tested models, with a mean absolute error (MAE) of 0.75 dB, a root mean square error (RMSE) of 1.88 dB, and a coefficient of determination (R²) of 0.96, outperforming multi-layer perceptron (MLP), convolutional neural network (CNN) and Transformer models under the same dataset and training protocol. For inverse design, the LSTM encoder extracts frequency-ordered spectral features from the target TL curve, while the frozen PPS-Net decoder provides differentiable acoustic-response feedback, thereby addressing the non-unique mapping from acoustic response to structural parameters. Furthermore, a compactness-oriented optimization strategy is introduced to balance spectral consistency, peak alignment, bandwidth preservation, and occupied-area reduction. In two representative cases, the optimized designs reduce the occupied area by approximately 21% in both representative cases, while maintaining the target attenuation characteristics after FEM verification. These results demonstrate that the proposed framework provides an efficient and physically interpretable route for the full-spectrum inverse design and compact optimization of low-frequency acoustic metamaterials. Full article

Accurate, uncertainty-aware estimation of instantaneous wind turbine output is a prerequisite for integrating onshore assets into low-emission energy systems, where operational monitoring, energy-performance verification, and cooperative asset management depend on auditable digital representations of turbine behaviour. This study develops a Digital Shadow-based power-curve modelling framework on fourteen years of Supervisory Control and Data Acquisition records from an operational Vestas V52 onshore turbine (850 kW, Dundalk Institute of Technology, Ireland; 457,429 ten-minute records spanning 2006–2020) and benchmarks seven methods under identical preprocessing on a strict chronological hold-out (training 2006–2017; testing 2018–2020; n = 52,388). A parallel random 75/25 split is reported only as a within-distribution diagnostic; it quantifies an optimistic R² inflation of 0.003–0.027 depending on architecture. The Artificial Neural Network attains the best chronological performance (R² = 0.9924, BCa 95% confidence interval 0.9910–0.9931, RMSE = 19.79 kW); only the ANN and a one-dimensional Convolutional Neural Network with twenty-four-step wind-speed lags (R² = 0.9921) deliver clear positive skill against the IEC-style manufacturer power curve. Split-conformal calibration of a Quantile Regression Forest raises empirical 90% prediction-interval coverage from 0.534 to 0.904 at a width inflation from 30 to 51 kW. The framework qualifies as a Digital Shadow and is positioned, through a Horizon Europe Technology Readiness Level audit and an explicit mapping to ISO 50001:2018 Plan–Do–Check–Act energy management and Renewable Energy Community governance under Directive (EU) 2018/2001, as an auditable monitoring layer for cooperative onshore wind operations. The empirical evidence base is a single turbine; multi-turbine, multi-site replication is the natural follow-on validation. Full article

Nature provides efficient strategies for fluid transport and thermal regulation through evolved structural features. This review summarizes recent progress in biomimetic thermal–fluid structures for enhancing fluid flow and heat transfer, with emphasis on the links among biological inspiration, engineering geometry, transport mechanisms, and application performance. Representative designs are classified into tree-like branching and fractal networks, compact hexagonal layouts, and bio-inspired curved morphologies, including riblets, grooves, fins, fluctuating channels, and TPMS structures. Their enhancement mechanisms involve flow redistribution, boundary-layer disturbance, secondary-flow and vortex generation, local acceleration, enlarged heat-transfer area, drag reduction, and compact flow organization. Applications using biomimetic structures are assessed in detail, such as in battery thermal management, electronic cooling, etc. The reviewed studies indicate that biomimetic structures can improve temperature uniformity, suppress hotspots, and enhance thermohydraulic performance, but the gains may be accompanied by pressure-drop or pumping-power penalties. Therefore, coupled thermal–hydraulic evaluation is essential for objective comparison. Key challenges of practical usage are identified in mechanism-based design, manufacturability, reliability, etc. This work establishes the guidance for translating biological forms into practical thermal–fluid structures with balanced efficacy. Full article

Reliable temperature monitoring is essential for the thermal management and safe operation of modern telecommunication equipment. However, conventional temperature sensors are often relatively large and rigid, which limits their applicability for localized temperature measurement on compact electronic components. In this study, a flexible thin-film thermocouple based on NiCr–NiSi thermoelectric materials was developed for temperature monitoring of telecommunication equipment. The sensor adopts a multilayer structure consisting of a polyimide (PI) flexible substrate, an Al₂O₃ insulating layer, NiCr and NiSi thermoelectric films, and a SiO protective layer and was fabricated using magnetron sputtering. Static calibration experiments show that the fabricated sensor exhibits a thermoelectric sensitivity of approximately 40.45 µV/°C, which is close to the reference value of conventional K-type thermocouples, with a relative error of about 1.34%. Repeated heating–cooling cycles demonstrate good repeatability and stable thermoelectric characteristics. Dynamic tests under representative transient thermal conditions showed that the sensor could continuously capture temperature variations without signal interruption or abnormal fluctuations. To further quantify its dynamic behavior, a numerical step-response simulation was performed for the PI/Al₂O₃/NiCr–NiSi/SiO multilayer structure. The simulated thermal time constant and curve-extracted 90% response time were 0.0343 s and 0.0803 s, respectively, under the specified boundary conditions. Owing to its small thickness, low thermal mass, and good mechanical flexibility, the proposed thin-film thermocouple can be conformally attached to compact and curved electronic surfaces, indicating promising potential for real-time localized temperature monitoring of telecommunication equipment and other compact electronic systems. Full article

Although foamed lightweight soil is widely used for its light weight and high strength, its insufficient dynamic performance under cyclic loading and the poorly understood reinforcement mechanism have become key bottlenecks restricting its optimized application. To investigate the dynamic characteristics and influencing factors of geogrid-reinforced foamed lightweight soil (GRFLS), laboratory dynamic triaxial tests were conducted using a DJSZ-100D dynamic–static triaxial testing system. The effects of the number of geogrid layers and wet density on the dynamic mechanical properties were examined, with analysis focused on failure patterns, backbone curves, dynamic strength, dynamic shear modulus, and damping ratio. The results indicate that the inclusion of geogrids effectively restrained the propagation of longitudinal cracks in the foamed lightweight soil. The hyperbolic backbone curves were well characterized by the Hardin–Drnevich model. An increase in wet density significantly enhanced the dynamic strength, and an optimal number of two reinforcement layers was identified based on the reinforced strength–stress ratio. The dynamic elastic modulus and damping ratio of GRFLS increased with growing dynamic strain. Compared with the unreinforced condition, the initial dynamic elastic modulus of the specimens with two geogrid layers increased by an average of 15.6%, and the maximum damping ratio increased by an average of 12.9%. While both geogrid reinforcement and higher wet density effectively increased the dynamic elastic modulus, only an increase in wet density notably improved the damping ratio. Finally, predictive models for the enhanced dynamic elastic modulus and damping ratio, which incorporate wet density and the number of reinforcement layers, were established. These models indirectly reflect the dynamic deviator stress–strain relationship of GRFLS. This study provides a theoretical basis for engineering construction. Full article

Neutral salt spray tests were conducted on assemblies comprising H59 brass and either electrogalvanized or hot-dip galvanized bolts. The polarization curves, electrochemical impedance spectroscopy (EIS), corrosion morphology, elemental distribution, and corrosion product composition of the H59 brass were systematically characterized. The results demonstrated that upon coupling with galvanized bolts, the formation of a protective Cu₂O film on the H59 brass is significantly weakened, leading to accelerated corrosion. After coupling with electrogalvanized bolts, the i_corr reached a maximum value of 0.21 mA/cm². A corrosion layer predominantly composed of ZnO formed on the sample surface with a thickness of approximately 13 μm, and no penetration or enrichment of Cl was observed in the matrix. More seriously, when the brass was assembled with hot-dip galvanized bolts, the i_corr never dropped below 0.2 mA/cm². A porous and complex Zn-Cu-O-Cl mixed corrosion layer developed on its surface. This loose structure allows Cl⁻ to reach a depth of 55 μm into the matrix and continue causing corrosion. The mechanisms underlying the different corrosion behaviors of H59 brass caused by different galvanizing bolt processes require further investigation. Full article