Search Results (2,017)

Travel–time-based thermal tracer tomography (TTT) has emerged as a promising technique for characterizing aquifer heterogeneity. However, the influence of travel–time definitions and data density on inversion performance is not well understood. In this study, we present a controlled two-dimensional sandbox experiment designed to systematically investigate three travel–time definitions (early-time t₁₀, intermediate t₅₀, and peak-time t_peak) under data-rich (32 travel times) and data-sparse (10 travel times) conditions. The obtained hydraulic conductivity (K) fields are benchmarked against permeameter measurements and a geostatistical inversion that assimilates dense steady-state head observations. The results demonstrate that all three travel–time definitions satisfactorily reproduce the primary layered heterogeneity when abundant travel–time data are available, with t₅₀ and t_peak providing marginally better structural fidelity under data-rich conditions. However, only the early-time t₁₀ definition preserves the spatial continuity of dominant geological structures under data-sparse conditions, exhibiting superior robustness. All TTT inversions systematically underestimate the K ranges and exhibit pronounced range compression, whereas the geostatistical inversion overestimates K and introduces spurious high-value extremes. Forward thermal transport simulations reveal that TTT-derived K fields yield systematically delayed thermal breakthroughs, while the geostatistical inversion yields more accurate predictions. These findings highlight the critical interplay between travel–time diagnostics and observation density. They also underscore the necessity of jointly inverting hydraulic and thermal data to overcome the limitations of single-dataset approaches for reliable aquifer characterization and transport prediction. Full article

The genus Aegilops comprises important wild relatives of cultivated wheat and represents a valuable genetic resource for wheat improvement. In this study, the complete chloroplast genomes of five Aegilops species (Ae. crassa, Ae. cylindrica, Ae. juvenalis, Ae. tauschii, and Ae. triuncialis) collected from Kazakhstan and Uzbekistan were sequenced, assembled, and comparatively analyzed. The chloroplast genomes exhibited a conserved quadripartite structure consisting of a large single-copy (LSC), a small single-copy (SSC), and two inverted repeat (IR) regions. Genome sizes ranged from 135,612 to 136,840 bp, with an identical GC content of 38% across all species. Comparative analyses revealed high structural conservation among chloroplast genomes, particularly within IR regions, whereas greater sequence divergence was observed in the non-coding regions of the LSC and SSC. Sliding-window analysis identified several highly polymorphic regions, including rpl32-trnL(UAG), ndhF-rpl32, trnC(GCA)-rpoA, psbA, and ndhD, which may serve as potential DNA barcodes and informative markers for phylogenetic studies. A total of 850 chloroplast simple sequence repeats (SSRs) were detected, predominantly A/T-rich mononucleotide repeats. Codon usage analysis demonstrated a conserved preference for A/U-ending codons across all species. Ka/Ks analysis indicated that most chloroplast protein-coding genes are under strong purifying selection, although relatively elevated evolutionary rates were detected in rpoA and ycf4. Phylogenetic analyses based on complete chloroplast genomes strongly supported sectional relationships within Aegilops and confirmed close maternal relationships among several species. Overall, this study provides chloroplast genome resources for Aegilops and contributes to understanding chloroplast genome evolution, phylogeny, and molecular marker development. Full article

This work proposes an Adaptive Neural Control for the asymptotic stabilization of a chain of integrators at the origin. The proposed approach addresses the stabilization of the integrator chain by means of a control law whose applied signal is structurally bounded to $(-1,1)$ by the hyperbolic tangent architecture, i.e., $u\left(t\right)=tanh\left(z\right)$, where z represents a weighted linear combination of the system states and a bias term. Furthermore, an adaptation law for the weights is proposed, based on the classical backpropagation algorithm for neural networks. The stability analysis is conducted using singular perturbation theory, demonstrating that, under a sufficiently high learning rate, the closed-loop system exhibits a Standard Singular Perturbation Form. This formulation allows for the analysis of the system across two distinct time scales: the adaptation dynamics (fast subsystem) and the state dynamics (slow subsystem). Based on this formulation, explicit conditions on the learning rate and the initial conditions are derived to guarantee local asymptotic stability using Tikhonov’s theorem. These conditions characterize the region of attraction and ensure that the adaptive neural controller stabilizes the system. Numerical simulations were carried out to evaluate the controller’s performance under three different scenarios: ideal conditions, initialization outside the region of attraction, and a low learning rate. These scenarios illustrate the closed-loop system behavior and validate the theoretical conditions required for asymptotic stability. Furthermore, comparative numerical simulations were conducted on an Inverted Pendulum on a Cart system to benchmark the proposed Adaptive Neural Control against Linear Quadratic Regulator, Sliding Mode Control, and Nested Saturation Function controllers. Based on the Integral of Time-weighted Squared Error performance index, the Adaptive Neural Control demonstrated a significant reduction in control effort, achieving performance improvements of up to 95.02% compared to the aforementioned strategies. Full article

Accurate and rapid detection of Volvariella volvacea (straw mushroom) fruiting bodies at harvestable maturity is a critical prerequisite for automated industrial cultivation. However, existing detection methods often yield high false-negative and false-positive rates when processing a small-scale, densely distributed, and heavily occluded targets against complex straw substrate backgrounds. Furthermore, these methods frequently struggle to balance the competing requirements of architectural efficiency (such as parameter volume and computational complexity) and real-time performance for edge computing. To address these challenges, this study proposes a YOLO11n-CPDM, a lightweight detection model based on an improved YOLO11n architecture. The model incorporates synergistic optimizations across feature extraction, fusion, and reconstruction. First, a Dual Coordinate Attention Feature Extraction mechanism is integrated into the C3k2 bottleneck blocks of the backbone network. This enhances target perception in complex, occluded environments by concurrently modeling global context and local salient features. Second, within the neck network, the standard attention module is replaced with the PnPNystraAttention module, coupled with the DySample dynamic upsampling operator. This modification strengthens contextual relationships among multi-scale features and improves spatial consistency during reconstruction while preserving linear computational complexity. Finally, the detection head is optimized using MBConv blocks based on an inverted residual structure to minimize parameter volume. Experimental results on a custom V. volvacea dataset demonstrate that the proposed YOLO11n-CPDM model achieves significant performance gains, with Precision (P), Recall (R), and Mean Average Precision (mAP50) reaching 86.8%, 87.5%, and 88.4%, respectively. These figures represent improvements of 2.7, 3.0, and 3.2 percentage points over the baseline YOLO11n model. Additionally, the model size is reduced to 4.8 MB (a 12.7% decrease), while achieving inference speeds of 42.7 FPS on Jetson AGX Orin and 21.2 FPS on Jetson Nano, outperforming the baseline model on both embedded platforms. Consequently, the proposed model effectively enhances detection performance in complex environments while maintaining excellent lightweight characteristics and deployment flexibility, providing a solid technical foundation for intelligent perception and automated harvesting of V. volvacea. Full article

Against the dual objectives of food security and sustainable agriculture, this study examines how agricultural machinery services—China’s primary organized mode of agricultural production—affect agricultural carbon emissions. Using panel data covering 30 provinces in China from 2010 to 2022, this study applies two-way fixed effects, mediation, and moderation models to investigate the effects of these services on carbon emissions as well as the mechanisms involved. The results show: (1) Both carbon emissions and the level of machinery services in China differ by region and over time. Carbon emissions are stabilizing, while machinery services are steadily improving. Both variables cluster in certain areas. (2) Machinery services exhibit a significant inverted U-shaped impact on carbon emissions. As the level of machinery services grows, emissions first rise, then fall. (3) The emission reduction impact of machinery services varies widely. It differs across topographic relief, farmland types, and grain crop types, but the inverted U-shaped relationship remains in most cases. (4) The efficiency of the division of labor and agricultural chemical input intensity partly explain the effect. They help reduce emissions by enabling labor substitution and lower input levels. (5) Large-scale agricultural operations strongly influence how machinery services affect carbon emissions. To accelerate the low-carbon sustainable transformation of Chinese agriculture, efforts should prioritize establishing a differentiated, regionally tailored agricultural machinery socialized service system, improving service efficiency and green development capacity, and optimizing large-scale land management structures. Full article

Accurate plant-level photovoltaic (PV) power estimation is important for performance monitoring, model benchmarking, and grid-integration studies. In bifacial horizontal single-axis tracking (HSAT) systems, this task is complicated by the coupled effects of front-side irradiance, rear-side irradiance, tracker position, and module temperature. This study proposes an algorithmic framework for same-time-step AC power estimation in a bifacial HSAT PV plant using field measurements of irradiance, tracker angle, module temperature, and inverter active power. The framework is not intended as an operational forecasting model because future irradiance and weather conditions are not predicted; instead, it evaluates how compact physics-based structure, interpretable nonlinear learning, and ensemble learning estimate measured AC power under nominal operating conditions. An empirical rear-to-front irradiance relationship was derived using solar-elevation bins and incorporated into a compact physics-based benchmark. This benchmark was compared with an additive Explainable Boosting Machine (EBM) and a Random Forest (RF) on a common test subset of 3916 observations. The physics-based model achieved an RMSE of 19.6 kW, an R² of 0.72, and an NRMSE of 0.38. The EBM improved these values to 17.09 kW, 0.786, and 0.334, respectively, while the RF achieved 15.96 kW, 0.814, and 0.312. Chronological validation showed weaker and more variable performance than randomized validation, indicating that temporal generalization remains challenging. Overall, the results support the use of interpretable PV-domain-guided learning as a transparent intermediate approach between compact physics-based modeling and more flexible ensemble regression. Full article

Following Yangtze River protection policies, many small dams have been removed. Using the Heishui River as an example, we compared food web structures among areas affected by dams (DAs), areas from which dams were removed (DRAs), and natural reference areas (NRAs) using stable isotopes and a Bayesian mixing model. NRAs had balanced basal carbon sources; DAs relied on periphytic algae (43.6%) and POM (23.5%); and DRAs were dominated by periphytic algae (50.8%), with terrestrial inputs lower in both DAs and DRAs. NRAs showed a compressed food chain (1.89–2.23). DAs exhibited an extended but inverted structure, with benthivorous fish (3.00) surpassing carnivores, perhaps reflecting the accumulation of sedimentary organic matter under the lentic conditions created by damming. DRAs expanded the vertical dimension, with carnivorous fish reaching the highest level (3.70). DAs had the fewest nodes (16) but the highest connectance (0.492). DRAs showed the most nodes (20), highest linkage density (4.00), lowest connectance (0.421), and lowest trophic redundancy, indicating functional differentiation. NRAs had an intermediate number of nodes (17) and the lowest linkage density (3.41). These findings indicate that small dam removal altered basal resource use, triggered food web reorganisation that increased the trophic position of carnivorous fish, and enhanced topological complexity, although terrestrial energy inputs in DRA remained below natural levels. Full article

Background/Objectives: A macular hole represents a significant surgical condition in an increasingly aging population. Advances in surgical techniques, particularly pars plana vitrectomy with inverted internal limiting membrane (ILM) flap, have established high anatomical closure rates exceeding 90%. The prognostic factors influencing visual recovery remain incompletely understood, and it is unclear which patients can be expected to achieve optimal functional outcomes. Methods: This retrospective longitudinal study included 35 eyes of 32 patients followed for 3–12 months. Preoperative OCT parameters (minimum linear diameter, basal diameter, and hole height) and derived indices were correlated with functional outcomes, including best-corrected visual acuity (BCVA) and microperimetry, stratified as central macular sensitivity (CMS) and sensitivity at 4° and 20°. Postoperative ellipsoid zone (EZ) and external limiting membrane (ELM) integrity were also analyzed. Predictive performance was assessed using root mean square error (RMSE) and coefficient of determination (R²). A linear regression model based on BCVA served as baseline, while Extreme Gradient Boosting (XGBoost) models incorporating OCT features were developed. Feature importance was evaluated using Shapley Additive Explanations (SHAP). Results: Overall closure rate was 100%, including 91.4% Type 1 and 8.6% Type 2 closure. Models incorporating OCT parameters outperformed BCVA-based models (lower RMSE, and higher R²). Minimum linear diameter and hole height were the strongest predictors of postoperative outcomes. Microperimetry detected functional improvement beyond BCVA and correlated with EZ and ELM restoration. Conclusions: Preoperative macular hole morphology represents a key determinant of postoperative functional recovery. These structural parameters provide meaningful prognostic value beyond visual acuity alone, supporting the role of combined OCT and microperimetric assessment in predicting surgical outcomes. Full article

There is a growing trend towards vehicles powered by alternative energy sources due to the environmental pollution caused by fossil fuel vehicles. Electric vehicles (EVs) are thought to make a significant contribution to reducing environmental pollution. This study presents a performance comparison of field-oriented control (FOC) and finite control set-based model predictive current control (FCS-MPCC) methods for controlling PMSM motors, which are commonly preferred for EV applications. A multilevel ANPC inverter topology, which has a higher-quality power flow than classical two-level inverters, was preferred to power the PMSM. While the classical FOC method has a fixed switching frequency by including cascaded PI controllers and a pulse width modulation (PWM) modulator, the FCS-MPCC method determines a variable frequency-switching signal that minimizes the cost function by predicting the future current behavior of the PMSM using the mathematical model of the system. The performance comparison of FOC and FCS-MPCC methods was carried out by conducting real-time experimental studies. Both control algorithms were analyzed under variable speed and load conditions using the same motor and drive structure. Performance analysis of FOC and FCS-MPCC control algorithms was carried out in terms of speed tracking, torque, current, and harmonics. According to the results obtained, the total harmonic distortion (THD) value of the stator current was 7.03% in the FOC method, while it was 22.19% in the FCS-MPCC method. Furthermore, a comparative analysis was conducted on the dynamic performance of the two methods in different scenarios using the mean absolute error (MAE), root mean square error (RMSE), integral absolute error (IAE), integrated time absolute error (ITAE), and integral squared error (ISE) criteria. The FCS-MPCC method was observed to be superior in different speed scenarios according to these criteria. In terms of processor load, it was calculated as 17.09% in the FOC method and 63.75% in the FCS-MPCC method. This study is important for determining the control strategy of PMSMs used in EV drives. Full article

Face super-resolution (FSR) aims to reconstruct high-quality high-resolution face images from low-resolution inputs. Although CNN–Transformer hybrid models have shown promising performance by jointly modeling local textures and global dependencies, their large parameter sizes and high computational costs hinder practical deployment in resource-constrained scenarios such as mobile devices and embedded systems. Meanwhile, existing lightweight SR models usually reduce complexity by simplifying network depth, channel dimensions, or convolutional operations, which may weaken feature representation capability and lead to insufficient recovery of fine facial structures. To address these issues, this paper proposes HCTIUNet, a lightweight CNN–Transformer hybrid network based on an inverted U-shaped architecture. Specifically, the proposed network integrates lightweight CNN branches for local facial texture extraction and Transformer branches for global dependency modeling, while introducing a multi-scale feature interaction strategy and a global feature refinement module to enhance facial structural details. Experimental results on the FFHQ, CelebA, and Helen datasets demonstrate that HCTIUNet achieves competitive performance under the ×8 face super-resolution setting, obtaining PSNR/SSIM/LPIPS values of 27.55 dB/0.765/0.225, 27.63 dB/0.761/0.212, and 27.53 dB/0.777/0.213, respectively. Moreover, HCTIUNet contains 10.5 M parameters, requires 9.9 G FLOPs, and achieves an inference time of 0.021 s. These results indicate that the proposed method achieves a favorable trade-off between reconstruction accuracy, perceptual quality, and computational efficiency, making it suitable for efficient face super-resolution applications. Full article

Wireless power transfer (WPT) systems for electric vehicles (EVs) are increasingly being studied in the MHz range to increase power density and reduce the size of passive components. However, operation at higher frequencies significantly changes electromagnetic interference (EMI) behaavior. Fast switching in SiC- and GaN-based inverters, high-Q resonant operation, and frequency-dependent parasitic capacitances create conductive, capacitive, and magnetic interference mechanisms that are less significant in conventional kHz-range systems. Although many existing studies focus on power-transfer efficiency and converter optimization, EMI mechanisms in MHz-range EV WPT systems remain insufficiently systematized from a system-level electromagnetic perspective. This paper presents a state-of-the-art review of EMI generation mechanisms and system-level effects in high-frequency WPT systems for electric vehicles. The review considers the main interference sources and coupling paths, including switching-induced common-mode currents, resonant amplification of current and voltage stress, capacitive coupling between the coupler and nearby conductive structures, and magnetic-field redistribution caused by coil misalignment. Special attention is given to the transition from lumped-element assumptions to more distributed electromagnetic behavior at higher frequencies. The review also discusses the possible impact of these mechanisms on vehicle electronic subsystems and highlights the need for frequency-aware electromagnetic design, integrated modeling, and more rigorous EMC assessment for reliable MHz-range wireless EV charging systems. Full article

To address the bottlenecks of conventional valve-controlled marine steering systems—characterized by high throttling losses, low efficiency, and high leakage risk—as well as the insufficient power density and impact resistance of electro-mechanical actuators (EMAs) for high-load steering of large vessels, this paper proposes and validates a high-performance integrated solution for an electro-hydrostatic actuator (EHA) for ship steering. First, a fifth-order electro–hydraulic–mechanical coupled dynamic model comprising a permanent magnet synchronous motor, hydraulic pump, hydraulic cylinder, and load is established. The validity and applicability boundaries of three simplifying assumptions—neglecting leakage, pipeline pressure losses, and steady-state fluid compressibility effects—are quantitatively analysed, with a total introduced error ≤3%. These assumptions are justified under medium-pressure, short-pipeline, and well-sealed conditions typical of marine EHA systems. Second, a composite control architecture combining outer-loop sliding mode control with inner-loop motor PID dual-loop control is proposed. Parameter tuning is performed using pole placement for the sliding surface and the Ziegler–Nichols critical ratio method for the inner loops, effectively suppressing hydraulic system parameter perturbations and random wave-induced load disturbances. Quantitative comparisons show that the proposed method reduces overshoot by 11.63% and improves sinusoidal tracking accuracy by 90.13% compared to conventional single-loop PID control. An integrated drive-control structure is designed, and a three-phase full-bridge inverter main circuit with wide-voltage input capability—including EMI filtering, soft-start, and LC filtering—is developed to accommodate the ±20% voltage fluctuations typical of ship power grids, thereby enhancing system integration and grid adaptability. Phased bench tests demonstrate that the settling time from no-load start-up to 200 r/min is only 0.01 s. When a sudden 20 N·m load is applied, the speed drop is less than 3%, and the recovery time is less than 0.025 s. The steady-state steering angle error does not exceed 0.12°, the maximum average steering rate reaches 3.33°/s, and the steering response time is within 0.3 s. All core performance indicators exceed the general technical standards for marine steering systems, with a 65.7% improvement in steady-state accuracy and a 62.5% improvement in response speed over conventional PID control. The research findings provide an effective general technical solution and experimental data support for the performance optimization and engineering application of marine EHA systems. Full article

Purpose: This study investigates the multi-dimensional nature of corporate risk-taking by examining how governance mechanisms exert differing pressures on accounting-based stability versus market-perceived volatility in the Saudi context, as the biggest emerging market in the Middle East. Moreover, the research uses accounting conservatism as a critical moderating variable and the sample is partitioned into high-conservative and low-conservative groups. Design/methodology/approach: The research analyzed data from 69 non-financial listed firms from 2017 to 2024 using four statistical models. Corporate risk-taking values have been captured from both accounting-based and market-based perspectives. Moreover, managerial, institutional, and concentration ownership have been used to capture ownership structure. However, board size, independence, and CEO power have been used to capture board structure. Findings: The research findings reported three main results: (1) Ownership structures have an asymmetric impact on accounting-based corporate risk-taking, as managerial and institutional ownership take a U-shaped curve, but ownership concentration has a positive impact. Moreover, from market-based corporate risk-taking, managerial and institutional ownership have a negative impact, but ownership concentration has a positive impact. (2) Board structures have an asymmetric impact on accounting-based corporate risk-taking, as managerial and institutional ownership have a negative impact, but ownership concentration has an inverted U-shaped impact. Moreover, from market-based corporate risk-taking, managerial and institutional ownership have no significant impact, but ownership concentration has a negative impact. (3) Accounting conservatism can change the nexus between ownership structure, board structure, and corporate risk behavior. Research limitations/implications: The research has many implications. For policymakers, the results discovered the role of ownership and board structures in shaping corporate risk-taking behavior in the Saudi context. Moreover, we have provided evidence-based guidance for governance reforms and firm-level decision-making. Moreover, the results can be incorporated by investors and creditors into their risk assessment frameworks, improving portfolio allocation and credit evaluation. Originality/value: The research captured corporate risk-taking behavior in the Saudi context from two perspectives at the same time. Likewise, it provides new empirical evidence that accounting conservatism can have a role in risky behavior. Full article

Monitoring the activity of the autonomic nervous system, including the sympathetic and parasympathetic divisions, plays a crucial role in studying emotional processing. However, few methods allow the dynamic tracking of parasympathetic activity. Here, we propose a testable hypothesis that ear thermography may serve as a dynamic index of sympathetic and parasympathetic activity, with a time resolution of seconds. Anatomical and physiological evidence suggests that the vascular structures of the ear may be innervated in a region-specific manner by the autonomic nervous system, with the posterior regions (e.g., the helix) predominantly influenced by sympathetic activity and the anterior regions (e.g., the tragus) potentially affected by parasympathetic mechanisms. Recent thermographic studies during emotional film viewing have demonstrated distinct spatial and functional patterns: posterior regions showed a linear negative association between temperature and emotional arousal, consistent with sympathetic vasoconstriction, whereas anterior regions exhibited a non-linear (inverted-U-shaped) relationship, resembling the known non-monotonic characteristics of parasympathetic activity. These findings suggest that ear thermography may be used to assess sympathetic- and parasympathetic-related dynamic processes, although direct evidence remains to be established. Full article

Multimodal image fusion aims to integrate complementary information from heterogeneous imaging modalities into a single informative image. However, many deep learning-based fusion methods rely on complex feature extractors, leading to high computational cost and limited suitability for real-time deployment on resource-constrained devices. To address this issue, this paper proposes a lightweight Mamba-INN dual-branch network for efficient multimodal image fusion. The proposed model decouples global structure modeling from local detail preservation. A simplified Mamba-inspired branch is designed to capture long-range contextual dependencies, while a lightweight invertible neural network branch preserves high-frequency textures and edge information through information-preserving transformations. The lightweight INN branch preserves high-frequency texture and edge information during the forward feature transformation process through reversible feature partitioning, coupled transformations, and exponential scale modulation, thereby reducing the loss of detail caused by feature compression. Compact shallow feature refinement, module reuse, low-dimensional channel design, and a streamlined decoder are further introduced to reduce redundant computation. Experiments on infrared-visible and medical image fusion benchmarks, including MSRS, TNO, RoadScene, MRI-CT, MRI-PET, and MRI-SPECT datasets, demonstrate that the proposed method achieves competitive fusion quality with low model complexity. The proposed method achieves performance comparable to or better than that of methods such as CDDFuse, U2Fusion, CNN and SDNet on metrics including MI, VIF, Qabf, and SSIM for infrared-visible and medical image fusion tasks, while containing only 0.24 million parameters and requiring 24.04 GFLOPs of computational power at an input resolution of 256 × 256. Compared to CDDFuse, our method significantly reduces model complexity, enhancing the potential for lightweight deployment while maintaining fusion quality. Full article