Search Results (12,602)

Pre-sowing tillage under irrigated agriculture is associated with high energy demand and increased risk of soil structural degradation, particularly in heterogeneous loam soils of arid and semi-arid regions. This study presents the engineering optimization and field validation of a combined implement for single-pass rotary strip tillage and precision seeding developed for irrigated sierozem soils of Southern Kazakhstan. The research integrates analytical modeling of soil–blade interaction, optimization of rotary blade geometry, and comparative field experiments using an experimental prototype (FS-2.1). Analytical optimization identified an optimal blade installation angle of 54–56°, resulting in an approximately 22% reduction in specific cutting area. Field results demonstrated that the single-pass system formed a high-quality seedbed, with 85.2% of soil aggregates smaller than 25 mm and a surface leveling deviation below 5 mm. Compared with a conventional multi-pass technology, traction load, fuel consumption, and total energy input were reduced by 38%, 43%, and 54.5%, respectively. The results confirm that combining optimized rotary blade geometry with strip-based soil disturbance enables substantial energy savings without compromising agronomic performance. The proposed engineering solution provides a reproducible framework for low-traction, resource-efficient tillage–seeding systems suitable for irrigated agriculture in Southern Kazakhstan and comparable agroecological regions. Full article

Aiming at the problems of mutual overlapping in high-throughput seed flow and difficulty in accurate detection of seeding rate during high-speed precision wheat seeding, a double-layer orthogonal through-beam photoelectric detection device for high-throughput wheat seed flow was developed in this paper, based on a four-layer staggered hook-type precision wheat seed-metering device. Combined with the least squares method for threshold optimization and an error compensation model, the detection accuracy was effectively improved. Bench test results show that the detection accuracy of the device is stable above 97% at medium and low seeding frequencies of 20–40 Hz, which can meet the requirements of conventional operations. When the seeding frequency increases to 80–120 Hz, the accuracy decreases to 89.05% due to the increase in seed flow density. After introducing the compensation model, the accuracy remains above 95% in the high-frequency range of 90.2–140.2 Hz, which is nearly 10 percentage points higher than that without compensation. The research results can provide effective support and a technical approach for the accurate online detection of high-frequency seed flow in high-speed precision wheat seeding. Full article

This article explores the complex interplay between the process of protein translation and DNA methylation, discussing their combined involvement in brain development. We will emphasize on DNA methylation and related proteins such as DNMTs, TETs, and MeCP2, the latter being the prototype of DNA methyl-binding proteins. Collectively, DNA methylation machinery may be involved in controlling the cell fate commitment of brain cells, as well as their neuronal and glial lineage specification. We aim to summarize current knowledge on the dynamics of protein translation, ribosome biogenesis, and relevant cellular pathways, including the mTOR signaling, in the context of brain development. Special attention is given to MeCP2 because of its unique role as an epigenetic factor that influences the chromatin states with a link to protein translation and its relevance to human disease. We also discuss the impact of DNA methylation-mediated chromatin regulation and protein translation in neurodevelopmental disorders. Our discussions include multi-omics techniques and integrative mechanisms that connect DNA methylation with protein translation. Full article

The assembly interface topography of precision machinery exhibits complex multi-scale geometric features, including roughness, waviness, and form error, which critically influence assembly accuracy and tolerance analysis. To address the lack of adaptivity in existing separation criteria, this paper proposes a multi-scale digital modeling approach oriented toward tolerance analysis of precision assembly interfaces, based on a fractal-wavelet framework. Firstly, multiple Weierstrass–Mandelbrot functions with independent fractal dimensions are superposed to construct a multi-fractal topography model with controllable multi-scale characteristics, grounded in the power spectral density energy additivity property. Subsequently, wavelet functions are employed to hierarchically decompose the topography height field information. The effects of the compact support length and vanishing moments of the wavelet functions on the decomposition performance are analyzed to establish a clear basis for their selection. Finally, an adaptive multi-scale separation criterion based on wavelet energy K-means clustering is then proposed, with the optimal number of scale classes determined by maximizing the silhouette coefficient, eliminating reliance on empirical thresholds. Case study results show that the fused waviness-and-form-error model retains 94.8% of the original energy while reducing convex peak count by over 90%, significantly simplifying the interface microstructure for downstream tolerance computation. The proposed method provides a high-fidelity, adaptive digital foundation for assembly accuracy prediction of precision interfaces. Full article

With the demand for high-voltage electrical insulation systems increasing, the development of environmentally friendly anti-corona materials with reliable nonlinear electrical properties has become essential. In this work, a waterborne polyurethane/epoxy (WPU/EP) composite coating was fabricated using micron-sized SiC (α-SiC), nano-sized SiC (β-SiC), and n-ZnO as multi-scale fillers. Its microstructure, nonlinear conductivity, flashover characteristics, and electro-thermal aging performance were systematically investigated. The results indicate that the incorporation of α-SiC significantly enhances conductivity under high electric fields by forming conductive pathways, while β-SiC further improves nonlinear behavior through interfacial bridging effects. The addition of n-ZnO modifies interfacial characteristics and contributes to improved electrical response. Moreover, the flashover performance is strongly dependent on filler composition, showing a critical role of nano-fillers in charge trapping and transport regulation. Electro-thermal aging tests on simulated stator bars reveal that the developed coating exhibits improved resistance to degradation compared with conventional materials. These findings demonstrate the effectiveness of multi-scale filler design in tailoring the electrical and insulation performance of waterborne anti-corona coatings. Full article

DNA methylation constitutes a primary layer of epigenetic regulation in plants, operating across three sequence contexts (CG, CHG, and CHH) through distinct enzymatic pathways. Over the past fifteen years, accumulating evidence has shown that DNA methylation varies substantially among individuals and populations of wild plants, sometimes independently of underlying genetic polymorphism. This variation can influence gene expression, transposable element activity, and phenotypic traits relevant to ecological adaptation. Population epigenetics, the study of methylation variation at the population scale, has matured from initial surveys using methylation-sensitive amplified fragment length polymorphism (MS-AFLP) into a discipline increasingly reliant on reduced-representation bisulfite sequencing (epiGBS, bsRADseq), whole-genome bisulfite sequencing (WGBS), enzymatic methyl-seq (EM-seq), and direct long-read detection by nanopore sequencing. These methodological advances are opening population epigenetics to non-model organisms across the full breadth of the plant phylogeny, from angiosperms and gymnosperms to ferns and bryophytes. We cover (i) the molecular machinery underlying plant DNA methylation, including the debated status of N6-methyladenine (6mA); (ii) empirical evidence for natural epigenetic variation in plant populations, spanning clonal, invasive, and outcrossing species; (iii) the methodological toolkit available for population-scale methylation profiling, with emphasis on approaches suitable for non-model taxa; and (iv) the ecological and evolutionary significance of population epigenetic variation, including transgenerational inheritance, stress memory, epigenetic clocks, conservation applications, and the emerging integration of epigenetics into the extended evolutionary synthesis. We identify critical knowledge gaps, particularly the near-complete absence of population-level epigenetic data for bryophytes, ferns, and lycophytes, and outline priorities for future research. Full article

Active sonar tracking of multiple underwater targets is frequently challenged by intense reverberation, which leads to sonar returns that are both non-stationary and non-Gaussian. In such scenarios, the generalized labeled multi-Bernoulli (GLMB) filter, which relies on a Gaussian assumption, often experiences a rise in an Optimal Subpattern Assignment (OSPA) distance, along with recurrent label switching. To mitigate this problem, a robust delta-generalized labeled multi-Bernoulli technique (ST-δ-GLMB) is introduced; it characterizes noise using a Student’s t-distribution and employs variational Bayes to estimate the corresponding parameters. More precisely, the Student’s t-distribution is utilized to represent measurement non-stationarity, and an online variational Bayesian estimation of the noise parameters is conducted within a multi-target framework based on the Student’s t-model. Moreover, without altering the GLMB data-association and label-management machinery, we derive closed-form updates and propagation for the Student’s t-parameters, thereby keeping the recursive computational burden and practical implementability under control. Finally, Monte Carlo simulations and lake-trial data demonstrate that, under non-stationary and heavy-clutter conditions, ST-δ-GLMB maintains stable track continuity and accurate target-number (cardinality) estimates in the presence of non-stationary measurements. Full article

The retromer is a highly conserved complex that mediates the trafficking of cargo proteins to the plasma membrane or the trans-Golgi network. In pathogenic microorganisms, retromer-dependent transport contributes to the delivery of virulence factors and promotes infection. The retromer consists of a sorting nexin dimer (SNX) and a cargo-selection complex (CSC), formed by Vps26, Vps35, and Vps29. In Entamoeba histolytica, the parasite that causes human amoebiasis, the retromer functions as a Rab7A GTPase effector and participates in phagocytosis and cytotoxicity. Although we previously characterized the roles of EhVps26 and EhVps35, the function of EhVps29 remained unclear. In this study, we analyzed the subcellular localization and functional role of EhVps29 in adhesion, phagocytosis, and cytopathic effect. EhVps29 localized to the plasma membrane, cytosol, vesicles, tubules, Golgi-like structures, MVBs and, for the first time, the nucleus. Immunofluorescence and Western blot assays demonstrated that EhVps29 modulates the localization of EhVps26, EhADH adhesin, and EhCP112 cysteine protease. Ehvps29 gene silencing and overexpression confirmed its involvement in virulence-associated processes. Immunoprecipitation and confocal microscopy results showed the interaction among EhVps29 and the ESCRT machinery members EhVps36 and EhADH. Our results indicate that EhVps29 is involved in parasite virulence and protein trafficking through recycling or degradation pathways. Full article

Vietnam’s exports expanded dramatically from $14.5 billion in 2000 to $405 billion in 2024, elevating the country to the world’s 22nd largest exporter despite persistent global shocks. This paper introduces the application of the Causal Machine Learning Approach to Resilience Estimation (CLARE) to industry-level trade analysis, utilizing a comprehensive panel of 97 HS2 sectors from 2000 to 2024 (2425 observations) drawn from UN COMTRADE and WITS databases. We implement Double Machine Learning to estimate causal effects of the Global Financial Crisis (2008–2009) and COVID-19 pandemic (2020–2021) on export growth. Results reveal stark industry disparities: electrical machinery (HS85) exhibits exceptional resilience, fueled by 72% high-technology content and low product concentration, while knitted apparel (HS61) proves highly vulnerable. Fixed effect regressions substantiate core hypotheses: a 10-percentage-point increase in high-tech share elevates the resilience index by 0.031 points (approximately 4.1% relative to the sample mean); a one-standard-deviation reduction in product HHI (0.14 units) yields a 0.026-point gain (3.6% relative); and each additional FTA contributes 0.047 points (approximately 6.2% relative), with all estimates significant at conventional levels. Robustness encompassing alternative learners, detrended outcomes, and synthetic controls upholds findings. Policy recommendations center on accelerating high-tech global value chain integration—targeting semiconductors and electric vehicles—while optimizing CPTPP and EVFTA utilization (currently 35%) and mitigating US–China market concentration (45% of exports). These insights chart pathways for Vietnam’s Vision 2045 high-income ambition amid intensifying geopolitical and climate risks, providing a replicable framework for other export-reliant emerging economies. Full article

Modern agriculture depends heavily on machinery to maximize operational efficiency and, consequently, profitability, but the wear-and-tear on the mechanical components of machinery due to ageing can lead to reduced efficiency, more downtime, and higher maintenance expenses, thus raising the operative costs. These problems have been addressed by the use of specific lubricant additives for machinery; however, additives have known disadvantages, such as compatibility restrictions and environmental concerns, which represent critical issues especially in case of possible dispersion in the environment. Modern industry is always looking for techniques and solutions to increase efficiency and productivity, and this study investigates the possible advantages of employing nanotechnology in lubricant formulations. Amongst all possible substances, SiO₂ nanoparticles are increasingly promising as lubricant additives due to their unique properties, which include heat resistance, high levels of stability, and good biocompatibility. Moreover, biolubricants, derived from renewable sources, offer an environmentally friendly alternative to conventional lubricants. This article contributes to the field of agricultural technology by demonstrating the potential of SiO₂ nanoparticles in formulations of biolubricants thought to be used in agricultural machines. Key degradation parameters, including density, viscosity, total acid number (TAN), total base number (TBN), oxidation, and elemental composition, were systematically analysed. The results showed that SiO₂ nanoparticles mitigate viscosity loss and density increase, optimize TAN and TBN, reduce oxidation of the biolubricants by up to 17.7% at 1.00 wt% SiO₂, and stabilize elemental composition during ageing. Nanoparticles remained uniformly dispersed without sedimentation for over 30 days. This provides insights that can prevent machinery performance degradation over time, reduce lubricant changes, and suggest a more sustainable and environmentally friendly lubrication solution, thus promoting more sustainable industry. Full article

Bearing fault diagnosis is an important task for condition monitoring and predictive maintenance of rotating machinery. Nevertheless, many existing deep learning-based methods have difficulty in jointly modeling multi-scale fault characteristics, adaptively highlighting informative features, and maintaining robustness under noisy measurement conditions. To address these issues, this study presents MDCAD-Net, a multi-dilated convolution attention denoising network that integrates multi-scale temporal feature extraction, attention-based feature refinement, and explicit noise suppression within an end-to-end learning framework. Parallel dilated convolutions with different dilation rates are employed to capture short-duration transient impulses as well as long-range periodic patterns in vibration signals. Channel-wise feature recalibration using squeeze-and-excitation networks and spatial-temporal attention via a convolutional block attention module are combined to enhance informative representations. In addition, a denoising block with gated attention and residual connections is introduced to reduce noise interference while retaining fault-related signal components. Experiments conducted on the Case Western Reserve University bearing dataset show that the proposed method achieves a classification accuracy of 98.93% and yields competitive performance compared with several commonly used deep learning models. Ablation studies and feature visualization results further illustrate the contributions of the individual components and the separability of the learned feature representations under noisy conditions. The results indicate the potential of the proposed framework for practical bearing fault diagnosis under noisy operating conditions. Full article

This study proposes a noise-robust unsupervised acoustic anomaly detection framework for early identification of abnormal operating conditions in rotating machinery under submarine-like environments with severe data scarcity. In such environments, underwater background noise and onboard interference sources significantly degrade signal quality, while limited computing resources constrain the deployment of high-complexity deep learning models. To address the lack of labeled fault data, the publicly available MIMII dataset was adopted as a proxy platform, and representative submarine interference sources were physically modeled, including colored background noise, structure-borne resonance, band-limited auxiliary noise, tonal components, and sensor noise. These components were combined and scaled to predefined SNR levels (−6 to 6 dB) to generate realistic noise-augmented data. Three unsupervised approaches were compared under edge deployment constraints: (i) Gaussian Mixture Model (GMM) with statistical MFCC features, (ii) statistical-feature-based Ensemble Autoencoder, and (iii) Conv1D-based Ensemble Autoencoder using 1-s log Mel-spectrogram segments. Performance was evaluated in terms of AUC, F1-score, and computational cost. Results show that GMM provides competitive detection performance with minimal computational burden, whereas Conv1D achieves superior accuracy when temporal fault patterns dominate, at the expense of higher complexity. The study provides practical design guidelines for acoustic anomaly detection under multi-noise and resource-constrained conditions. Full article

With the capacity of generators continuing to increase, higher demands are placed on the heat dissipation of epoxy resin (EP), the main insulation material used in stator bars and windings. To overcome its low thermal conductivity, a multiscale hybrid filler strategy was adopted to investigate the effects of spherical Al₂O₃ (10 and 1 μm), platelet BN (1 μm), and SiO₂ (50 nm) on the thermal and insulating properties of EP composites. Unlike conventional studies focusing on individual fillers, this work highlights the synergistic design of fillers with different sizes and morphologies. The filler ratios were optimized by finite element simulation, and the composites were prepared by melt blending. The results show that, at a total filler loading of 38.5 wt%, the EP composite filled with spherical Al₂O₃ particles of 10 and 1 μm, platelet BN of 1 μm, and nano-SiO₂ of 50 nm achieves a thermal conductivity of 0.5497 W/(m·K), corresponding to an increase of 158.2% compared with pure EP (0.2129 W/(m·K)). This enhancement is attributed to the synergistic effect of multiscale and multishape fillers, where large Al₂O₃ particles form the main thermally conductive framework, small Al₂O₃ particles fill the gaps, platelet BN acts as a bridging filler, and nano-SiO₂ improves the interfacial region. In addition, the composite exhibits low relative permittivity and dissipation factor tanδ in the frequency range of 10⁻²–10⁶ Hz, and its breakdown strength reaches 65.99 kV/mm. These results demonstrate that simulation-guided multiscale hybrid filler design is an effective strategy for improving the thermal conductivity of EP while maintaining acceptable insulating performance. Full article

In modern intelligent food processing, the unpredictable variability in pomelo orientation on high-speed conveyors poses a significant challenge to automated grading and precision peeling operations. To address this, a deep learning-based method is proposed for the real-time detection of pomelo posture. Firstly, a pomelo posture dataset was constructed to support model training and validation. Secondly, to balance the extraction of posture features from uniform fruits with the low-power constraints of edge deployment, a domain-specific architectural optimization is presented. Building on the YOLOv8n framework, the proposed model synergistically integrates specialized modules. A lightweight GhostHGNetV2 foundation is utilized to significantly reduce computational redundancy while maintaining the resolution required to detect key anatomical landmarks. To overcome spatial confusion and capture multi-scale global appearance information, a multi-path coordinate attention (MPCA) module is introduced. Furthermore, the SlimNeck architecture and VoVGSCSP module streamline multi-scale feature fusion via one-time aggregation, effectively preventing computational bottlenecks. This design optimizes the computational efficiency of the model while maintaining detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv8n model, the proposed method increased the mAP50 accuracy by 3.67% while reducing parameter count and computational load by 17.5% and 23.3%, respectively. Additionally, it achieved a processing speed of 19.3 FPS on the Jetson Orin Nano 6G edge platform. This research provides a critical technical foundation for the recognition of pomelo posture, enabling subsequent orientation rectification and fostering the development of streamlined, automated pomelo processing lines. Full article

This research systematically investigates the shape memory properties of re-entrant hexagonal negative Poisson’s ratio (NPR) honeycomb structures fabricated via 4D printing, using polyethylene terephthalate glycol (PETG) and polylactic acid (PLA) as comparative materials. Periodic honeycomb models with varied wall thicknesses and structural unit angles were designed, and their effects on shape recovery time and recovery rate were examined. Response surface methodology (RSM) based on a Box–Behnken design was employed to optimize key process parameters, including the wall thickness, structural unit angle, and mold pressing angle. The results demonstrate that PETG exhibits significantly superior shape memory performance compared to PLA, characterized by a shorter recovery time and higher recovery rate under thermal stimulation. Through RSM optimization, the optimal parameter combination was identified as a wall thickness of 0.5 mm, a structural unit angle of 65°, and a mold pressing angle of 135°, which was subsequently validated experimentally, demonstrating a high degree of consistency between predicted and actual outcomes. This study not only clarifies the influence of the structural parameters on the shape memory behavior of NPR honeycomb systems but also provides parameter guidance and a practical experimental basis for the application of PETG in 4D-printed intelligent structures, with potential implications for soft robotics, aerospace, and biomedical devices. Full article