Search Results (15,294)

To address the limited constraints of ground-based lidar with few channels in retrieving aerosol microphysical parameters in urban atmospheres, this study developed a method to retrieve aerosol volume size distribution and effective radius from a 355/532/1064 nm triple-wavelength elastic-scattering, single-polarization lidar system. The method uses 3β + 2α optical quantities as input constraints, applies Mie scattering theory as the forward model, parameterizes the volume size distribution with B-spline functions, and achieves stable solutions through Tikhonov regularization and cross-validation. To reduce uncertainties in prior parameters, including the complex refractive index, particle size range, and lidar ratio, an optimization strategy based on parameter search, retrieval reconstruction, and error minimization was introduced. Numerical simulations showed that the method reproduced the main features of a bimodal lognormal aerosol volume size distribution with good feasibility and stability. Two case studies further showed fine-mode dominance and decreasing extinction coefficient, depolarization ratio, and effective radius with height under good air quality conditions, but enhanced coarse-mode contribution and effective radius in the upper cloud-influenced layer under lightly polluted conditions, as inferred from the combined variations in RSCS, extinction coefficient, depolarization ratio, and effective radius. Full article

The increasing computational demands of deep neural network inference drive the need for energy-efficient hardware accelerators that minimize data movement between memory and processing units. Compute-in-memory (CIM) architectures address this bottleneck by embedding computation directly within memory arrays, reducing the overhead of repeated weight transfers in conventional von Neumann systems. Conventional 6T SRAM-based digital CIM bitcells incur significant transistor overhead as arrays scale, motivating exploration of reduced-transistor bitcell alternatives. We propose a compact 4T+2T SRAM-based digital CIM bitcell implemented in 45 nm CMOS, combining a 4T SRAM storage cell with a 2T multiplier for bitwise multiply-and-accumulate (MAC) operations. The proposed design reduces transistor count from 8 to 6 compared to the 6T+2T reference, lowering parasitic capacitance and hardware overhead without compromising memory or computation functionality. Transient simulations confirm correct write, read, and CIM operations. The bitcell achieves a read delay of 26.91 ps, read power of 1.351 nW, and read energy of 0.005403 fJ—reductions of 98.7%, 86.5%, and 73.1% over the 6T+2T reference, respectively. For CIM operation, bitwise multiplication power decreases from 1.772 µW to 0.8014 µW and energy from 10.63 fJ to 4.808 fJ, representing a 54.8% reduction in both metrics, with only a marginal CIM delay increase of 3.13 ps. Monte Carlo analysis across 100 samples confirms robust write behavior under process variation, with write delay ranging from 55.02 to 69.59 ps and write energy from 0.05870 to 0.06557 fJ. Static noise margin analysis yields an SNM of 83.7 mV under nominal conditions, confirming stable data retention. These results demonstrate that the proposed 4T+2T bitcell offers strong transistor efficiency, energy savings, and computational correctness, making it a promising candidate for area-efficient digital CIM architectures targeting edge AI inference. Full article

Internet of things long range (IoT/LoRa) devices emit radiofrequency electromagnetic fields (RF-EMF), ensuring long-range, low-power communication, and their use in precision agriculture continuously expands. Thus, the interest in the impact of low-intensity but long-term EMF exposure on plants has increased. In this study, maize plants were exposed to 868 MHz, 10 mW EMF for the first 28 days of their development with soil-buried antennas. Plants were divided into three groups: Control, Sham-exposed, and EMF-exposed. Biological effects were followed on morphological, physiological, and biochemical levels every week. The plant height values were fitted to a Gompertz function modeling the growth. The results showed slightly faster early development of EMF-exposed plants in about 21 days. The relative dry-leaf biomass from EMF-affected plants was a bit higher than in the Control and Sham groups until day 21. Chlorophyll fluorescence analysis (JIP-test) indicated photosynthetic stability. Antioxidant enzyme activity, antioxidant capacity, content of malondialdehyde, hydrogen peroxide, and reducing sugars were measured, and principal component analysis was done for all parameters. Overall, the developmental stage accounts for most of the observed variations in the data rather than EMF exposure. The results suggest that under the tested conditions, IoT/LoRa-emitted EMF did not provoke adverse effects in maize and acted as a modest modulator of physiological functions. Full article

Triethylamine (TEA), a widely used volatile organic compound (VOC), poses severe threats to environmental safety and human health upon accidental leakage, making the development of high-performance TEA detection techniques urgently needed. Herein, we report a Sn-based metal–organic framework (Sn-MOF) constructed from 4,5-dichloroimidazole ligands synthesized via a solvothermal approach. The resulting MOF-derived SnO₂ materials were obtained by calcination at 400–600 °C, yielding SnO₂ with tunable specific surface area and surface defect-site density. Structural and surface characterizations revealed that the materials consist of primary nanoparticles in the range of 10–50 nm, forming aggregated particles of 1–2 µm. The gas sensing performance toward TEA was systematically evaluated. The SnO₂-400 °C sensor exhibited the highest response (S = 85.0) to 100 ppm TEA at 190 °C, with a low detection limit of 1 ppm, superior selectivity, good repeatability, and excellent long-term stability. The observed performance variation was attributed to the combined effects of specific surface area, abundant defect-associated surface sites, and suitable mesoporous structure. This work not only provides a high-performance TEA sensor for industrial and food safety monitoring but also offers a rational strategy for designing MOF-derived metal oxide gas sensors with tailored microstructures and surface defect chemistry. Full article

Milk titratable acidity is a key indicator of raw milk freshness and quality, but its variation across different dairy animal species remains incompletely characterized. Based on 16,984 raw milk samples from eight dairy animal species (Holstein cow, goat, buffalo, camel, sheep, yak, donkey, and horse) collected within a retrospective raw milk quality monitoring framework in China from 2016 to 2024, this study provides a large-scale descriptive comparison of milk titratable acidity across species. Distinct titratable acidity profiles were observed among species, with camel and yak milk showing relatively high values, sheep, Holstein, and buffalo milk exhibiting intermediate values, and donkey and horse milk presenting markedly low values. Calendar-season-associated patterns also differed among species. Correlations between titratable acidity and milk components varied by species, with relatively stronger positive associations with protein and solids-not-fat (SNF) in several ruminant milks, suggesting that milk composition may contribute to differences in titratable acidity. However, because this study was based on an unbalanced observational dataset with limited animal-level, farm-level, feeding, management, physiological, and environmental metadata, these observations should be interpreted as descriptive and exploratory patterns rather than causal biological mechanisms. This dataset provides preliminary reference information for future studies on species-associated variation in raw milk titratable acidity and for discussions on species-specific raw milk quality evaluation. Full article

This paper studies multiplication operators and their associated strongly continuous semigroups acting on variable exponent Lebesgue spaces. We study the abstract Cauchy problem $\dot{u}\left(t\right)=Au\left(t\right)$, $u\left(0\right)=u_0$, in the space $L^{p\left(x\right)}(0,\ell )$ with $\ell >0$, where the generator A is given by the multiplication operator $A=M_q$. Using the modular ${\rho }_{p(·)}\left(u\right)={\int }_0^{\ell }{\left|u\left(x\right)\right|}^{p\left(x\right)}dx$, we establish the fundamental properties of $M_q$, including ${\rho }_{p(·)}$-closedness, density of its domain, and boundedness criteria in terms of the essential range of q.We show that $M_q$ generates a strongly continuous semigroup ${\left(S\left(t\right)\right)}_{t\ge 0}$ given explicitly by $S\left(t\right)=e^{tA}=M_{e^{tq}}$, and we derive modular growth estimates for the semigroup. We also obtain a complete characterization of the spectrum and resolvent of A, showing that $\sigma \left(A\right)=q_{\mathrm{ess}}(0,\ell )$ and $R(\lambda ,A)={(\lambda I-A)}^{-1}=M_{1/(\lambda -q)}$ for $\lambda \notin \sigma \left(A\right)$. The spectral mapping behavior of the associated semigroup is also analyzed, highlighting the validity of the weak spectral mapping theorem and the possible failure of the full spectral identity. As an application, we present a concrete example on $(0,4)$ involving a singular initial datum that does not belong to $L^2(0,4)$ but lies in $L^{p\left(x\right)}(0,4)$ due to a suitable spatial variation of the exponent. The corresponding evolution is explicitly given by $u(t,x)=e^{tq\left(x\right)}f\left(x\right)$ and remains well posed in $L^{p\left(x\right)}(0,4)$ for all $t\ge 0$. This shows that the variable exponent framework can accommodate singular behavior while preserving semigroup dynamics. These results show that multiplication operators provide an explicit model for semigroup theory in variable exponent spaces, connecting modular analysis with pointwise evolution equations. Full article

High sound pressure level (SPL) acoustic sensing requires miniaturized microphones that can operate under large acoustic loading while maintaining mechanical linearity, sufficient sensing response, and broadband audio frequency behavior. This work targets high-SPL operation and numerically investigates a graphene piezoresistive MEMS microphone based on a membrane-supported beam–island diaphragm. The proposed structure retains a continuous membrane for acoustic load bearing, while the upper beam–island topology redirects deformation-induced strain toward beam root regions where graphene piezoresistors are placed. This design is intended to increase the local strain available for piezoresistive readout without simply relying on larger global diaphragm deflection. Finite-element analysis was used to optimize the diaphragm geometry and evaluate strain enhancement, pressure response linearity, modal behavior, and harmonic response. Under the 170 dB SPL reference condition, the optimized structure increases the peak structural strain from 47.83 με in a thickness-equivalent solid diaphragm to 562.53 με, achieving an approximately 11.8-fold enhancement in local sensing strain while maintaining a highly linear pressure response (R2 > 0.9999). Additionally, the results also show that the sensor exhibits a high first natural frequency of 64.07 kHz and a small response variation of approximately 0.94 dB within the 0–20 kHz target frequency range, indicating excellent dynamic stability and high-fidelity signal transduction characteristics. To connect the structural response with piezoresistive readout, first-order electromechanical output estimation was further performed using representative graphene gauge factors, quarter-bridge readout assumptions, contact resistance correction, and Johnson-noise-limited signal-to-noise ratio estimation. A ±5% geometric tolerance check further indicates that the membrane side length is the most fabrication-sensitive parameter, while the selected design remains generally robust except for reduced linearity margin under positive membrane side-length deviation. These results demonstrate the potential of the proposed graphene-based MEMS microphone for high-SPL broadband acoustic sensing applications in harsh and high-intensity acoustic environments. Full article

Accurate multi-horizon Significant Wave Height (SWH) forecasting is vital for offshore safety and efficiency. Beyond scheduling maintenance windows, reliable lead-time predictions provide critical early warnings to protect personnel and high-value assets from hazardous high-wave conditions. However, the non-stationary and multi-scale nature of sea states poses challenges for consistent long-term accuracy. To address this challenge, we propose a robust three-stage framework for decomposition, feature selection, and multi-horizon forecasting. Specifically, Optimal Variational Mode Decomposition (OVMD) is adopted to construct multiscale and multi-view representations of nonlinear SWH sequences, while a Triangulated Maximally Filtered Graph (TMFG) constructs a sparse dependency network to select informative and non-redundant predictors from decomposed components and environmental variables. A hybrid prediction model then combines a Temporal Convolutional Network (TCN) for local multi-scale patterns with a Bidirectional Gated Recurrent Unit (BiGRU) for long-range dependencies. Experiments on real-world buoy observations show that the proposed approach improves accuracy and robustness over commonly used statistical and deep-learning baselines across short-, medium-, and long-term horizons. Ablation studies confirm that integrating modal decomposition with sparse feature selection enhances model robustness, offering reliable decision support for offshore window planning and high-wave condition monitoring. Full article

Numerous factors influence the formation of blasting craters in engineering blasting. Based on the actual parameters of the Daye Iron Mine, this study established six sets of single-hole blasting crater numerical models with different borehole diameters using ANSYS(19.0)/LS-DYNA(R13) software. The variation in blasting crater volume with the scaled depth was analyzed to determine the optimum scaled depth for each borehole diameter, and a functional relationship between the optimum scaled depth and borehole diameter was derived through curve fitting. Furthermore, using a borehole diameter of 0.076 m as a case study, a double-hole blasting crater was developed to investigate the effect of varying hole spacing on blasting crater volume and to determine the optimal hole spacing. The blasting parameters were optimized based on the numerical simulation results. The results show that within the range of borehole diameters considered, the blasting crater volume initially increases and then decreases with increasing scaled depth of the explosive charge. The fitted relationship between the optimum scaled depth and borehole diameter is y = −180.7197x³ + 86.3754x² − 9.5504x + 1.0782. For a borehole diameter of 0.076 m, the optimum scaled depth is 0.7278 m/kg^1/3, and the optimal hole spacing is 0.52 m. Based on blasting similarity theory, the calculated optimum burial depth of the explosive charge is 0.59 m, the critical burial depth is 1.1 m, and the recommended row spacing ranges from 0.95 m to 1.18 m. The findings of this study provide a theoretical basis for optimizing blasting parameters at the Daye Iron Mine and similar mining operations. Full article

Shallow eutrophic lakes recover from nutrient loading on time scales ranging from less than one year to many decades, yet whether this range is set by the lake or by the biological response group has rarely been quantified within a single monitoring framework. We assembled a five-year (2020–2025) quarterly monitoring panel from three shallow Yangtze-floodplain lakes (Lake Changhu, Lake Liangzihu, and Lake Honghu; 15 stations, 21 quarters) and applied a panel mixed-effect distributed lag model (PME-DLM) to estimate the lag-response windows of phytoplankton and benthic macroinvertebrate densities against five water-quality drivers. Cross-lake consistency was tested with a station-resampled bootstrap, and the contributions of water quality, season, and lake identity to community variation were resolved by three-table variation partitioning. The PME-DLM resolved a 3-month temperature window for phytoplankton and 9–15 month chlorophyll a and temperature windows for benthic communities, while total nitrogen and total phosphorus were non-significant in either group. Cross-lake bootstrap intervals on window width overlapped substantially across the three lakes, whereas cross-group differences in window centre and shape were an order of magnitude greater. Variation partitioning further showed a mirror-image structure in which phytoplankton variation was dominated by the pure water-quality fraction (12.2%) and benthic variation by the water-quality × season joint fraction (5.8%). Within the resolution of this five-year, three-lake panel, group-level differences in lag-response time scale were more apparent than lake-level differences and provide a quantitative basis for matching restoration assessment cadence to pelagic versus benthic recovery. Full article

In this study, Fe₇₀Ni₃₀ metal was deposited onto continuous glass fiber composites via magnetron sputtering, followed by surface coating with SiO₂. The effects of key process parameters-including Fe₇₀Ni₃₀ sputtering duration (2, 5, 10, 20, and 30 min) and SiO₂ surface coating-on the electromagnetic properties and microwave absorption performance of the materials were systematically investigated. Scanning electron microscopy (SEM) characterization revealed that as sputtering time increased, the metal coating evolved from discrete small particles into a continuous film. Cross-sectional SEM analysis further demonstrated the formation of a bilayer structure after SiO₂ introduction. X-ray diffraction (XRD) patterns confirmed the presence of diffraction peaks corresponding to the Fe₇₀Ni₃₀ alloy solid solution. Electromagnetic parameter measurements indicated that the influence of sputtering time on electromagnetic properties was primarily pronounced during the metal layer growth stage; once a continuous film was formed, the variation in electromagnetic parameters diminished. Concurrently, the SiO₂ coating exhibited a significant regulatory effect on dielectric parameters. Reflection coefficient calculations showed that the optimal absorption thickness for the single-layer material ranged from 2.5 to 3.0 mm, with the absorption peak shifting toward lower frequencies as thickness increased. However, the effective absorption bandwidth (EAB) was only 3–5 GHz, failing to meet wideband requirements. In contrast, the three-layer composite structure (total thickness: 3.8 mm) optimized via genetic algorithm achieved impedance gradient and loss synergy, expanding the EBW (R < −10 dB) from 4.8 GHz (single layer) to 10 GHz (8–18.0 GHz)-a substantial improvement over the single-layer configuration. This work provides experimental evidence and technical support for the structural design and process optimization of lightweight, high-efficiency, wideband microwave-absorbing materials. Full article

This work presents a case study of sensitivity analysis applied to the modeling of ethanol steam reforming (SRE) in a catalytic porous medium, with a focus on hydrogen production. Considering the high variability of parameters reported in the literature, the objective is not to propose a universal model, but rather to assess the impact of uncertainties associated with input parameters on the model outcomes. The model was developed under steady-state conditions, coupling flow in porous media, species transport, and heat transfer, with kinetics described as a function of partial pressures. The sensitivity analysis was conducted through the systematic variation of kinetic and physicochemical parameters within ranges associated with their uncertainties. The results indicate that activation energy is the parameter most sensitive to uncertainty variation, exhibiting the greatest impact on hydrogen production. The thermal properties of the medium, particularly thermal conductivity and solid density, also stand out, highlighting the role of thermo-kinetic coupling. In contrast, parameters such as porosity, water reaction order, and particle diameter exhibited low sensitivity under the analyzed conditions. As a main contribution, this work establishes a sensitivity hierarchy associated with parameter uncertainties and provides guidance for other researchers regarding the prioritization of their determination and calibration in hydrogen production models. Full article

Maintaining stable operation and maximum power extraction in wind turbines under significant wind speed variations remains a key challenge in wind energy systems. This study aims to analyze the stability and maximum power point operation of a wind turbine equipped with a squirrel-cage induction generator using stator-side frequency control. This study examines the operational performance of medium-power wind turbines in the kilowatt range under significant wind speed variability. The analysis focuses on a turbine equipped with a squirrel-cage induction generator and a control architecture that incorporates a power converter integrated into the stator circuit. The findings show that adjusting the stator frequency through the converter allows the generator to track the optimal rotational speed, ensuring operation at the maximum power point across a wide range of wind conditions. Based on these results, the study defines the stable operating region of the turbine under time-varying wind speeds, making it suitable for distributed energy projects in coastal regions where wind can be highly variable. It also shows that, for a given electrical load, the generator must be calibrated to an appropriate maximum stator frequency to maintain stable and efficient energy conversion. Full article

This study presents numerical and experimental investigations of a voltage-controlled active preload adjustment system for an ultrasonic traveling wave piezoelectric motor intended for potential use in space-related systems. The proposed preload system consists of two ring-shaped piezoceramic elements driven by a DC voltage of up to 300 V_DC. The passive conical spring provides the nominal rotor preload, while the piezoelectric ring stack enables open-loop remote fine adjustment of the stator–rotor contact force by modifying the axial compression of the spring. Finite element simulations were performed over a temperature range from −25 °C to 55 °C to evaluate the electromechanical response and thermal sensitivity of the preload system. The numerical results indicated that the active preload system can generate a simulated preload force variation of approximately 0.47 N at 300 V_DC, corresponding to approximately 21.4% of the nominal initial preload force of 2.2 N. Experimental tests were conducted in a thermal vacuum chamber at a pressure of 5.6 × 10⁻⁶ mbar. The measured displacement of the piezoceramic preload stack ranged from 0.33 µm to 2.36 µm and showed good agreement with the numerical displacement results. Motor speed measurements demonstrated that increasing the preload-control voltage from 0 to 300 V_DC resulted in an average angular speed increase of approximately 17–20 RPM, depending on temperature. The results demonstrate that the proposed system can provide compact open-loop preload fine adjustment under thermal vacuum conditions, with preload force variation supported by FEM estimation and experimentally validated displacement response. Full article

This study evaluates the vulnerability of Siberian larch (Larix sibirica L.) populations to future climate change using a genomic offset (GO) framework that integrates genome-wide SNP data with environmental variables. We analyzed 488 individuals from 37 populations across climatically diverse regions of Russia, genotyped by sequencing at over 20,000 SNP loci using the ddRADseq method. Gene–environment association (GEA) analyses (BayeScEnv, LFMM2, and RDA) identified candidate adaptive loci linked to six key bioclimatic variables. Based on these loci, GO was estimated using three approaches implemented in RONA–RDA, RDA, and Gradient Forest frameworks under multiple climate models (MIROC6, BCC-CSM2-MR, MRI-ESM2-0), scenarios (SSP2-4.5, SSP3-7.0, SSP5-8.5), and time periods (2041–2060, 2061–2080, and 2081–2100). Results revealed consistent spatial patterns of vulnerability, with northern and high-altitude populations, as well as populations from more continental and moisture-limited regions, exhibiting the highest GO and thus the greatest risk of maladaptation. In contrast, several central and southern populations showed relatively low vulnerability. The importance of temperature stability (isothermality) and precipitation of the driest month as key drivers of adaptive variation was highlighted. Despite differences in SNP datasets, population rankings remained highly consistent, supporting the robustness of predictions. Overall, our findings demonstrate substantial heterogeneity in climate vulnerability across the species range and provide a genomic basis for conservation strategies, including assisted gene exchange and climate-adaptive forest management. Full article