Search Results (7,459)

Aluminum alloy 2024 (AA2024) is widely used in the aerospace sector, where a fine, uniform, and equiaxed grain structure is crucial for achieving enhanced mechanical properties. This study examines the effect of dynamic solidification, assisted by mechanical vibrations and conformal cooling, on the microstructural evolution and mechanical properties of permanent mold-cast AA2024. Mechanical vibrations were applied during solidification in the frequency range of 15–45 Hz and acceleration of 0.5–1.5 g. Process parameters, including pouring temperature, die temperature, vibration frequency, and acceleration, were optimized using an L9 orthogonal array based on the Taguchi method. Analysis of variance (ANOVA) was performed to determine the significance of the aforementioned process parameters. In addition, the alloy’s microstructure was observed through a microscope, which revealed a transition from dendritic to non-dendritic microstructure due to dynamic solidification. The average grain size of the alloy was significantly reduced by 40.9%. Moreover, the values of hardness and Ultimate Tensile Strength (UTS) of the alloy were improved by 13.5% and 10.6%, respectively. Optimal results were obtained at a pouring temperature of 750 °C, die temperature of 150 °C, frequency of 45 Hz, and acceleration of 1.0 g. Moreover, uncertainty analysis for all three responses was also performed. Full article

Natural disasters pose significant risks to engineering projects, necessitating a systematic analysis of their risk factors. This study focuses on identifying and mapping these factors using a mixed-methods approach that integrates a qualitative literature review with scientometric analysis via Social Network Analysis (SNA). Through a meta-analysis of 81 peer-reviewed articles from Web of Science, Scopus, and ScienceDirect, the qualitative review establishes a comprehensive list and classification of 48 natural disaster risk factors, categorized into geological, climatic, hydrological, topographic, and biological groups, while providing a theoretical foundation. SNA complements this by quantifying co-occurrence frequencies, centrality metrics (degree, betweenness, and eigenvector), and network structures, revealing dynamic interactions, key influential factors, and research gaps—particularly in under-explored areas like hydrological hazards, extreme temperatures, lightning storms, and temperature variations—that qualitative methods alone might miss. This multi-perspective integration highlights discrepancies between theoretical discussions and practical applications, underscoring overlooked cascading effects. Findings emphasize the absence of an integrated model for all 48 factors, urging the development of a holistic predictive framework to bolster disaster resilience. Theoretically, the study offers a novel SNA-based quantification of factor importance and interrelations, addressing literature fragmentation. Practically, it guides project managers in prioritizing risks for optimized design, resource allocation, and prevention strategies. Future research should incorporate real-time data sources to refine this framework for enhanced risk management in engineering projects. Full article

With the increasing volatility and extremity of global climate change, the frequency, intensity, and associated impacts of compound extreme climate events have escalated substantially. To investigate the temporal trends and characteristics of such events, we identified compound extreme climate events in the Huangshui River Basin, located in the northeastern Qinghai–Tibet Plateau, using daily mean temperature and precipitation records from eight meteorological stations. Compound warm–wet, warm–dry, cold–wet, and cold–dry events from 1960 to 2022 were detected based on cumulative distribution functions, and their long-term trends and intensity structures were examined. The results show that: (1) Warm–dry events dominate the basin, with an average annual frequency of 32.84 days per year, occurring frequently across all seasons; cold–dry events rank second (22.38 days per year) and are particularly frequent in winter. (2) Warm–dry events are highly concentrated in the river valley region (e.g., Minhe station), whereas cold–dry and warm–wet events mainly occur in the low-mountain areas (e.g., Huangyuan and Datong). (3) From 1960 to 2022, warm–dry and warm–wet events exhibit a highly significant increasing trend (p < 0.001), cold–dry events show a significant decreasing trend, and cold–wet events display no statistically significant trend. (4) In terms of intensity, all four types of compound events—warm–wet, warm–dry, cold–wet, and cold–dry—are dominated by weak to moderate grades. Overall, the basin is undergoing a compound-risk transition from historically “cold–dry dominated” conditions toward a regime characterized by “warm–dry predominance with emerging warm–wet events.” By identifying compound extreme climate events and analyzing their spatiotemporal variability and intensity characteristics, this study provides scientific support for disaster prevention, daily management, and risk mitigation in climate-sensitive regions. It also offers a useful reference for developing strategies to address compound extreme events induced by climate change and for implementing regional risk-prevention measures. Full article

(1) Foods with attractive shapes have been receiving increasing interest from researchers, particularly for foods for children. The ability to particularize foods by imparting attractive aspects to nutritious and less attractive food ingredients, such as vegetables or proteins, is an interesting challenge for the food industry. In this context, the rheological characteristics of food doughs are fundamental for obtaining form-forming foods that are able to maintain a shape of their own. (2) Broccoli, pumpkin, carrot and zucchini wastes (stems, leaves, and off-gauge veggies), which are still rich in nutrients, from the food industry were used in this work to enrich burgers with vegetable proteins. The doughs were characterized by rheological analysis using a frequency sweep test and a temperature ramp test. They were also shaped with attractive molds and baked. (3) From the frequency sweep test, the formulation with brown rice proteins resulted in better consistency; all samples showed a solid-like behavior. (4) Workable doughs were formulated using vegetal wastes from the food industry. Among the proteins used, those from brown rice were found to be the most suitable for the preparation of a vegetable burger. Full article

This work introduces a tunable technique to push the low-frequency corner ($f_L$) of capacitively coupled instrumentation amplifiers (CCIAs) to the sub-mHz range for emerging biosensing applications. The proposed approach combines Complementary Transimpedance Boosting (CTB) to limit the DC feedback current and segmented duty-cycled resistors (SDR) for tunable resistance. The CTB-SDR technique achieves a stable effective post-layout pseudo-resistance of $535.8 \mathrm{T}\Omega$, equivalent to $f_L=660 \mu \mathrm{Hz}$ while occupying $0.062 {\mathrm{mm}}^2$ in a 180 nm process. According to JESD91 standards, it shows a standard deviation of $0.19 \mathrm{mHz}$ under post-layout Monte Carlo + process analysis, $1.1\%$ spread under voltage variations $(\pm 5.56\% V_{DD})$ and $6.2\%$ under temperature variations $(-20 °\mathrm{C}, 27 °\mathrm{C},$ and $60 °\mathrm{C})$. In addition, duty-cycling calibration can compensate for worst-case process corner variations and mismatch-induced feedback instability. Full article

In this work, we produced a new polyurea (PU)-based thickener based on serine derivatives (ethanolamine or L-Serine ethyl ester) and 1,5 pentamethylene diisocyanate (PDI), using castor oil as base oil and methylene diphenyl diisocyanate (MDI) as a reference. Polymerization was carried out in a planetary ball mill at room temperature for 75 min. The polymerization degree of the PU thickener was examined via ¹H NMR, which ranged between 1.8 and 14.6 repeating units after the extraction of the base oil. Rheological analysis showed gel formation for ten out of twelve samples, which was strongly dependent on the polymerization degree and thickener amount. The decomposition temperature of the MDI-based PU greases was consistently roughly 20 °C higher than that of PDI-based systems. The lubricants were further evaluated through rheology experiments before and after the gels underwent an annealing process at 100 °C for 1 h (amplitude and frequency test), indicating a strong increase in the storage modulus G’, whereas the yield point γ_F remained constant or decreased. Full article

WS₂ is a promising material for applications in wearable devices, field-effect transistors, and high-performance heterojunctions. However, significant challenges remain regarding effective regulation and temperature stability. This study investigates the temperature-dependent electrical properties of WS₂ heterojunctions prepared by electrophoretic deposition on boron-doped diamond films. The results reveal that the rectification ratio of lightly doped boron heterojunctions at room temperature is 9.1, indicating thermal excitation behavior at temperatures above 100 °C. In contrast, heavily doped boron heterojunctions maintain a rectification ratio consistently below 1 over a temperature range from room temperature to 180 °C, indicating reverse rectification. The lowest rectification ratio observed at 140 °C is 0.17. Density functional theory (DFT) calculations suggest that hydrogen (H) termination generates an internal electric field in the opposite direction, causing a reversal of the rectification polarity, while oxygen (O) termination favors forward rectification. Additionally, due to vacancy defects in WS₂, the heterojunction exhibits negative differential resistance at 120 °C, with a peak-to-valley ratio of 2.4. Higher doping levels, in comparison to lower concentrations, offer a more stable rectification ratio at elevated temperatures, making the material more suitable for high-temperature, high-frequency, and high-power applications. Full article

Unpredictable climate fluctuations are a major constraint for chickpea production in the Mediterranean region, increasing the frequency of drought and temperature extremes. Landraces consist of locally adapted genotypes, offering valuable genetic variability. In this context, 12 chickpea landraces and 2 commercial varieties were tested. The breeding scheme consisted of two cycles of single-plant selection for high yield at nil-competition, followed by a 2-year evaluation under farming density in replicated trials. Selection cycles and evaluation were conducted under two different sowing dates, one normal and one nearly 30 days later (off-season), to implement the breeding method under extreme drought and heat stress conditions during yield’s critical stages. Among Improved Lines (ILs) developed under normal conditions, those from landraces 7 and 14 yielded 34% and 31% higher than the controls’ mean, while ILs from landraces 7, 9, and 12 developed under stress showed 11%, 8%, and 11% higher yield than the controls. Furthermore, ILs 7, 9, and 12 expressed the highest tolerance based on drought and heat stress indices and are considered as promising genetic material. Overall, the breeding scheme is suggested as effective for exploiting the natural genetic diversity of chickpea landraces towards the development of high-yielding and tolerant lines. Full article

A cascade deep learning approach is proposed for optimizing the design and control of a dual-frequency induction heating system used in semiconductor manufacturing. The system is composed of two independent power inductors, fed at different frequencies, to achieve a homogeneous temperature profile along a graphite susceptor surface, crucial for enhancing layer quality and integrity. The optimization process considers both electrical (current magnitudes and frequencies) and geometrical parameters of the coils, which influence the power penetration and subsequent temperature distribution within the graphite disk. A two-step procedure based on deep neural networks (DNNs) is employed. The first step, namely optimal design, identifies the optimal operating frequencies and geometrical parameters of the two coils. The second step, namely optimal control, determines the optimal current magnitudes. The DNNs are trained using a database generated through finite element (FE) analysis. This deep learning-based cascade approach reduces computational time and multiphysics simulations compared to classical methods by reducing the dimensionality of parameter mapping. Therefore, the proposed method proves to be effective in solving high-dimensional multiphysics inverse problems. From the application point of view, achieving thermal uniformity (±7% fluctuation at 1100 °C) improves layer quality, increases efficiency, and reduces operating costs of epitaxy reactors. Full article

A combined flow control method, integrating nanosecond pulsed surface dielectric barrier discharge (NS-SDBD) with pulsed jets, is proposed to address the challenge of low mixing efficiency in supersonic combustion. Numerical validation and mechanism analysis were conducted by solving the three-dimensional unsteady Reynolds-averaged Navier–Stokes (RANS) equations, coupled with the shear stress transport (SST) k–ω turbulence model. The simulations were carried out under a Mach 2.8 inflow condition with a 50 kHz pulsed frequency for H₂ jets. The results demonstrate that, compared to the steady jet case, the combined control scheme increases the combustion product mass flow rate by 27.1% and enhances combustion efficiency by 26.8%. The average temperature in the wake region increases by 65 K, while the total pressure recovery coefficient shows only a marginal change. The pressure disturbance center evolves along the outer edge of the counter-rotating vortex pair (CVP) and is eventually absorbed by the vortex core. This process generates favorable velocity and vorticity perturbations, which enhance O₂ entrainment into the CVP and increase the average wake temperature. Meanwhile, the strengthened reflected shock induces favorable velocity perturbations in the upper shear layer of the wake and further elevates the local temperature. Full article

Addressing the limitations of existing power amplifiers, particularly in terms of accuracy and noise performance, a high-voltage and high-current power amplifier has been developed. The input stage utilizes a rail-to-rail circuit structure, allowing the amplifier to deal with the full swing of input signals from the negative to the positive power supply. The output stage features an innovative class AB configuration with a bias structure, effectively reducing the crossover distortion typically associated with traditional circuits. This design improves linearity, achieving an output range that extends to the rails, while also enhancing the power supply rejection ratio and optimizing noise performance. Furthermore, over-temperature protection and current limiting circuits have been integrated to safeguard the system against permanent damage under extreme conditions. The power amplifier circuit was simulated and validated using Cadence 61 Spectre software. With a power supply of ±30 V, the amplifier achieved an output current of 560 mA, a low-frequency gain of 138 dB, a bandwidth of 24 MHz, and a noise level of 4.8 nV/$\sqrt{\mathrm{H}\mathrm{z}}$. The slew rate was measured at 14.2 V/μs. Compared to existing literature, significant advancements have been achieved in terms of gain, bandwidth, and noise performance. Full article

Ester-based lubricants have been widely used owing to their excellent overall performance. In this study, the quantitative structure–property relationship (QSPR) approach was combined with molecular descriptors, a genetic algorithm (GA), and an artificial neural network (ANN) to systematically predict the key properties—kinematic viscosity at 40 °C and 100 °C, viscosity index, pour point, and flash point—of 64 diester-based lubricants. Quantum chemical calculations were first performed to obtain the equilibrium geometries and electronic information of the molecules. Geometry optimizations and frequency analyses were carried out using the Gaussian 16 software at the B3LYP/6-31G (d, p) level, providing a reliable foundation for molecular descriptor computation. Subsequently, topological, geometrical, and electronic descriptors were calculated using the RDKit toolkit, and the optimal feature subsets were selected by GA and used as ANN inputs for property prediction. The results showed that the ANN models exhibited good performance in predicting viscosity and flash point, with R² values of 0.9455 and 0.8835, respectively, indicating that the ANN effectively captured the nonlinear relationships between molecular structure and physicochemical properties. In contrast, the prediction accuracy for pour point was relatively lower (R² = 0.6155), suggesting that it is influenced by complex molecular packing and crystallization behaviors at low temperatures. Overall, the study demonstrates the feasibility of integrating quantum chemical calculations with the QSPR–ANN framework for lubricant property prediction, providing a theoretical basis and data-driven tool for molecular design and performance optimization of ester-based lubricants. Full article

Modeling elementary diffusion processes in nanostructured materials is essential for developing platforms capable of interacting with high-speed physical signals. In this work, the photothermal response of a nano-hydroxyapatite/carbon nanotube (nHAp/CNT) composite was experimentally characterized and modeled through a cellular automaton (CA) framework designed to capture the thermal propagation of the hybrid system. Synthesizing nHAp/CNT composites enables the combination of the biocompatible and piezoelectric nature of nHAp with the enhanced photothermal response introduced by CNTs. UV–Vis reflectance measurements confirmed that CNT incorporation increases the optical absorption of the ceramic matrix, resulting in more efficient photothermal conversion. The composite was irradiated with a nanosecond pulsed laser, and the resulting thermal transients were compared with CA simulations based on a D2Q9 lattice configuration. The model accurately reproduces experiments, achieving R² > 0.991 and NRMSE below 2.4% for all tested laser powers. This strong correspondence validates the CA approach for predicting spatiotemporal heat diffusion in heterogeneous nanostructured composites. Furthermore, the model revealed a sensitive thermal coupling when two heat sources were considered, indicating synergistic enhancement of local temperature fields. These findings demonstrate both the effective integration of CNTs within the nHAp matrix and the capability of CA-based modeling to describe their photothermal behavior. Overall, this study establishes a computational–experimental basis for designing controlled thermal-wave propagation and guiding future multi-frequency or multi-source photothermal mixing experiments. Full article

Lightning is the primary natural cause of wildfires in mid- to high-latitude forests, and it is increasing in frequency under climate change. Traditional fire danger forecasts, reliant on standard meteorological data, often fail to capture extreme events and future risk. To address this issue, we integrate extreme climate indices with meteorological, vegetation, soil, and topographic data, and apply four machine learning methods to build probabilistic models for lightning fire occurrence. The results show that incorporating extreme climate indices significantly improves model performance. Among the models, XGBoost achieved the highest accuracy (87.4%) and AUC (0.903), clearly outperforming traditional fire weather indices (accuracy 60%–71%). Model interpretation with SHapley Additive exPlanations (SHAP) further revealed the driving mechanisms and interaction effects of extreme factors. Extreme temperature and precipitation indices contributed nearly 60% to fire occurrence, with growing season length (GSL), minimum of daily maximum temperature (TXn), diurnal temperature range (DTR), and warm spell duration index (WSDI) identified as key drivers. In contrast, heavy precipitation indices exerted a suppressing effect. Compound hot and dry conditions amplified fuel aridity and markedly increased ignition probability. This interpretable framework improves short-term lightning fire prediction and offers quantitative support for risk warning and resource allocation in a warming climate. Full article

Soil Health (SH) is a key concept in discussions on sustainable land use, with implications that extend beyond agriculture. To address the need for integrated assessments, this study developed a Sustainable Agricultural Development Index (SADI) by combining the Soil Health Index (SHI) with socioeconomic and management indicators. The analysis was conducted across Germany using 3300 soil analysis sites and environmental covariates, including climate, topography, vegetation indices, and bare soil reflectance. From this foundation, SADI was designed to evaluate agricultural sustainability across German states based on three dimensions: Management (Bare Soil Frequency), Environment (SHI Maps), and Economy (Profit per Hectare). Results revealed that SHI correlated significantly with land surface temperature (R = −0.47), bare soil frequency (R = −0.40), and vegetation indices (R = 0.43). Soil organic carbon also played a key role in explaining degradation patterns. While economically stronger states tended to achieve higher SH scores, environmentally sound and well-managed regions also performed well despite lower economic returns. These findings emphasize that sustainable agriculture depends on balancing economic growth, environmental integrity, and management efficiency. The SADI provides a comprehensive framework for policymakers and land managers to evaluate and guide sustainable agricultural development. Full article