Search Results (19,739)

Crop root development, and in turn crop growth, is strongly influenced by soil strength and the mechanical impedance of compacted layers, which restrict root elongation and exploration. Because the depth and thickness of compacted layers vary across a field, their identification is essential for site-specific tillage and sustainable root-zone management. A sensing approach that can support future real-time identification of compacted layers after soil-specific calibration, which would enable variable-depth tillage, reducing mechanical impedance and improving energy-use efficiency while maintaining crop yields. This study aimed to develop and evaluate prediction models that can support future real-time identification of compacted soil layers using soil electrical conductivity (EC) and moisture content as non-destructive indicators. A sandy clay soil (48.6% sand, 29.3% clay, 22.1% silt) was tested in a soil-bin laboratory under controlled conditions at three moisture levels (13, 18, and 22% db.) and six depth layers (C1–C6, 0–30 cm) identified from the penetration-resistance profile to measure penetration resistance, shear resistance, and EC. Penetration and shear resistance increased toward the most resistant depth layer and decreased with increasing moisture content, whereas EC generally increased with both depth layer and moisture content. Linear regression models relating penetration resistance ($R^2=0.893$) and shear resistance ($R^2=0.782$) to EC and moisture content were developed and evaluated. Field validation in a paddy field of similar texture showed that predicted penetration resistance differed from measured values by 3–6% across the three compaction treatments evaluated. Root length density and root volume decreased with increasing machine-induced compaction, confirming the agronomic relevance of the modeled patterns and supporting the suitability of the proposed indicators. Together, these results demonstrate that EC and moisture content can potentially be used as non-destructive proxies for compacted-layer identification and provide a calibration basis for future on-the-go sensing systems to support site-specific, variable-depth tillage in agricultural fields. Full article

Pest detection in the field is crucial for realizing smart agriculture. Deep learning-based target detection algorithms have become an important pest identification method due to their high detection accuracy, but the existing methods still suffer from misdetection and omission when detecting small-targeted pests and small-targeted pests in more complex backgrounds. For this reason, this study improves on YOLO11 and proposes a new model called MSDS-YOLO for enhanced detection of small-target pests. First, a new dynamic multi-scale feature extraction module (C3k2_DMSFE) is introduced, which can be adaptively adjusted according to different input features and thus effectively capture multi-scale and diverse feature information. Next, a novel Dimensional Selective Feature Pyramid Network (DSFPN) is proposed, which employs adaptive feature selection and multi-dimensional fusion mechanisms to enhance small-target saliency. Finally, the ability to fit small targets was enhanced by adding 160 × 160 detection heads removing 20 × 20 detection heads and using Normalized Gaussian Wasserstein Distance (NWD) combined with CIoU as a position loss function to measure the prediction error. In addition, a real small-target pest dataset, Cottonpest2, is constructed for validating the proposed model. The experimental results showed that a mAP50 of 86.7% was achieved on the self-constructed dataset Cottonpest2, which was improved by 3.0% compared to the baseline. At the same time, MSDS-YOLO has achieved better detection accuracy than other YOLO models on public datasets. Model evaluation on these three datasets shows that the MSDS-YOLO model has excellent robustness and model generalization ability. Full article

Fragaria vesca L., a widely distributed model species, serves as a key resource for studying the evolution and genetics of the Fragaria genus. Research has shown that R2R3-MYB transcription factors are crucial for plant growth and development. However, their specific role in cold resistance in F. vesca is not well understood. In this study, we used the latest genome data for the strawberry (F. vesca v6.0). We performed a genome-wide identification of the R2R3-MYB gene family in F. vesca. We identified a total of 106 R2R3-FvMYBs. Based on their predicted functions in plants, we classified these genes into 25 distinct subfamilies. We then conducted a comprehensive bioinformatics analysis of this family. We performed a detailed examination of the R2R3-FvMYBs structures and physicochemical properties. This analysis provided five key parameters for each protein: molecular weight, the number of amino acids, theoretical isoelectric point, grand average of hydropathicity (GRAVY), and instability index. Gene duplication analysis suggested that segmental duplications were a primary driver of the proliferation of this gene family. Promoter cis-acting element prediction revealed that a large proportion of R2R3-FvMYBs possess elements predominantly associated with phytohormone responsiveness and biotic/abiotic stress responses. Quantitative real-time reverse transcription PCR (qRT-PCR) results confirmed that the expression levels of several R2R3-FvMYBs were upregulated under cold stress. Furthermore, compared to wild-type controls, the overexpression of FvMYB103 in Arabidopsis thaliana enhanced cold tolerance, accompanied by increases in the relevant physiological indices. Collectively, these findings support further investigation into R2R3-MYB gene family to directly assess their contribution to cold resistance. Full article

Arboviral diseases are emerging public health challenges in Burkina Faso, largely driven by the proliferation of Aedes aegypti mosquitoes in the environment. Effective surveillance of arbovirus circulation is critical to inform interventions. From August 2022 to June 2023, we implemented a comprehensive entomological surveillance platform in five sectors of Bobo-Dioulasso. Surveillance methods included oviposition traps to collect eggs, larval surveys in some concessions per sector conducted bimonthly, and adult mosquito collections using BG-Sentinel traps and Prokopack aspirators. Mosquito samples colonized by Ae. aegypti were identified morphologically, confirmed by conventional PCR, and screened by RT-PCR for dengue (DENV), chikungunya (CHIKV), yellow fever (YFV), and Zika (ZIKV) viruses. Molecular analysis detected dengue virus and yellow fever virus in mosquito pools from sector 22 and chikungunya virus in sectors 9 and 26; no Zika virus was found. This study demonstrates the successful establishment of an integrated entomological surveillance platform capable of capturing the spatial and temporal dynamics of arboviral vectors and virus circulation in Bobo-Dioulasso. The identification of active dengue and chikungunya transmission underlines the urgent need for sustained vector monitoring and targeted control strategies. Our approach provides a scalable model for arboviral disease surveillance and epidemic preparedness in West African urban settings. Full article

Accurate detection of premature atrial contractions (PACs) and premature ventricular contractions (PVCs) in single-lead electrocardiograms (ECGs) is crucial for early identification of patients at risk for atrial fibrillation, cardiomyopathy, and other adverse outcomes. In this work, we present a fully convolutional one-dimensional U-Net that reframes beat classification as a segmentation task and directly detects normal beats, PACs, and PVCs from raw ECG signals. The architecture employs a ConvNeXt V2 encoder with simple decoder blocks and does not rely on explicit R-peak detection, handcrafted features, or fixed-length input windows. The model is trained on the Icentia11k database and an in-house single-lead ECG dataset that emphasizes challenging, noisy recordings, and is validated on the CPSC2020 database. Generalization is assessed across several benchmark and clinical datasets, including MIT-BIH Arrhythmia (ADB), MIT 11, AHA, NST, SVDB, CST STRIPS, and CPSC2020. The proposed method achieves near-perfect QRS detection (sensitivity and precision up to 0.999) and competitive PVC performance, with sensitivity ranging from 0.820 (AHA) to 0.986 (MIT 11) and precision up to 0.993 (MIT 11). PAC detection is more variable, with sensitivities between 0.539 and 0.797 and precisions between 0.751 and 0.910, yet the resulting F1-score of 0.72 on SVDB exceeds that of previously published approaches. Model interpretability is addressed using Layer-wise Gradient-weighted Class Activation Mapping (LayerGradCAM), which confirms physiologically plausible attention to QRS complexes for PVCs and to P-waves for PACs. Overall, the proposed framework provides a robust, interpretable, and hardware-efficient solution for joint PAC and PVC detection in noisy, single-lead ECG recordings, suitable for integration into Holter and wearable monitoring systems. Full article

The green-making stage is crucial for forming the distinctive aroma and flavor of Tieguanyin tea. Current green-making stage recognition relies on tea makers’ sensory experience, which is labor-intensive and time-consuming. To address these issues, this paper proposes a lightweight automatic recognition model named T-GSR for the accurate and objective identification of Tieguanyin tea green-making stages. First, an extensive set of Tieguanyin tea images at different green-making stages was collected. Subsequently, preprocessing techniques, i.e., multi-color-space fusion and morphological filtering, were applied to enhance the representation of target tea features. Furthermore, three targeted improvements were implemented based on the MobileNet V3 backbone network: (1) an adaptive residual branch was introduced to strengthen feature propagation; (2) the Rectified Linear Unit (ReLU) activation function was replaced with the Gaussian Error Linear Unit (GELU) to improve gradient propagation efficiency; and (3) an Improved Coordinate Attention (ICA) mechanism was adopted to replace the original Squeeze-and-Excitation (SE) module, enabling more accurate capture of complex tea features. Experimental results demonstrate that the T-GSR model outperforms the original MobileNet V3 in both classification performance and model complexity, achieving a recognition accuracy of 93.38%, an F1-score of 93.33%, with only 3.025 M parameters and 0.242 G FLOPs. The proposed model offers an effective solution for the intelligent recognition of Tieguanyin tea green-making stages, facilitating online monitoring and supporting automated tea production. Full article

Robotic peach harvesting represents a pivotal strategy for reducing labor costs and improving production efficiency. The fundamental prerequisite for a harvesting robot to successfully complete picking tasks is the accurate recognition of fruit growth posture subsequent to target identification. This study proposes a novel methodology for peach growth posture recognition by integrating an enhanced YOLOv8 algorithm with the RTMpose keypoint detection framework. Specifically, the conventional Neck network in YOLOv8 was replaced by an Atrous Feature Pyramid Network (AFPN) to bolster multi-scale feature representation. Additionally, the Soft Non-Maximum Suppression (Soft-NMS) algorithm was implemented to suppress redundant detections. The RTMpose model was further employed to locate critical morphological landmarks, including the stem and apex, to facilitate precise growth posture recognition. Experimental results indicated that the refined YOLOv8 model attained precision, recall, and mean average precision (mAP) of 98.62%, 96.3%, and 98.01%, respectively, surpassing the baseline model by 8.5%, 6.2%, and 3.0%. The overall accuracy for growth posture recognition achieved 89.60%. This integrated approach enables robust peach detection and reliable posture recognition, thereby providing actionable guidance for the end-effector of an autonomous harvesting robot. Full article

Background/Objectives: Schizophrenia (SCH) and bipolar disorder (BD) share overlapping symptoms and genetic factors, making differential diagnosis challenging and often leading to misdiagnosis. This study aimed to identify potential lipid biomarkers of serum capable of distinguishing BD from SCH. Methods: Lipid profiles of serum from 30 SCH and 31 BD patients were analyzed in triplicates using liquid chromatography–high-resolution mass spectrometry (LC-HRMS). Chemometric analysis was applied, including class and gender identifiers. Orthogonal partial least squares (OPLS) models with 1000 cross-validations were used to validate feature subsets. Results: The chemometric analysis included the most relevant metabolites in the comparison between all samples of SCH and BD patients, identifying five key biomarkers (LPC 16:0, SM 33:1, SM 32:1, compound C30H58O3, and PC 30:0) with VIP scores > 1 for distinguishing BD from SCH. Gender-specific models revealed five biomarkers in males (SM 32:1, SM 33:1, PC 32:1, PC 30:0, and FA 16:1) and two in females (LPC 16:0 and C30H58O3). These biomarkers primarily belonged to glycerophospholipids (GPs) and sphingophospholipids (SPs). Conclusions: Comparative lipid profiling between SCH and BD, including gender-specific subgroups, enabled identification of potential diagnosis-specific biomarkers. Elevated levels of GPs and SPs in SCH patients suggest lipid metabolism differences that may support improved diagnostic accuracy and personalized treatment strategies. Full article

This study systematically investigates the kinematic characteristics and static stability of a spatial under-constrained four-cable-driven parallel mechanism, specifically designed for supporting aircraft models in wind tunnel tests. Addressing the inherent strong coupling between kinematics and statics in such systems, an integrated solution framework is proposed. Firstly, a hybrid intelligent algorithm integrating genetic algorithm, chaos optimization, and particle swarm optimization is introduced to efficiently solve the direct and inverse geometric-statics problems, ensuring the identification of physically feasible equilibrium configurations under constraints such as cable tension limits and mechanical interference. Subsequently, a stability evaluation method based on the eigenvalue analysis of the system’s total stiffness matrix is employed, establishing a criterion (minimum eigenvalue λ_min > 0) to identify statically stable equilibrium points. Finally, the static feasible workspace and the static stable workspace are systematically analyzed and quantified, providing practical operational limits for mechanism design and trajectory planning. The effectiveness of the proposed solution framework is validated through numerical computations, simulations, and experimental tests, demonstrating its superiority over benchmark methods. This study provides theoretical support for the design, analysis, and control of under-constrained four-cable-driven parallel mechanisms. Full article

The article addresses the problem of the insufficient empirical recognition of the determinants of trust in artificial intelligence (AI) algorithms in organizations operating under conditions of sustainable development. The aim of the study was to identify the factors shaping organizational trust in AI and to examine how perceived trustworthiness, transparency, and effectiveness of algorithms influence their acceptance in the work environment. The research was conducted using a quantitative survey-based approach among organizational employees, which enabled the analysis of relationships between key variables and the identification of factors that strengthen or limit trust. The results indicate that algorithmic transparency, the reliability of generated outcomes, and the perceived effectiveness of AI applications significantly foster trust, whereas concerns related to errors and the decision-making autonomy of systems constitute important barriers to acceptance. Based on the findings, a conceptual and exploratory model of trust in AI was proposed, which may be used to diagnose the level of technology acceptance and to support the responsible implementation of artificial intelligence-based solutions in organizations. The contribution of the article lies in integrating organizational and technological perspectives and in providing an empirical approach to trust in AI within the context of sustainable development. Full article

Reliable operation of rolling bearings is essential for mechanical systems. Acoustic emission (AE) offers a promising approach for bearing fault detection because of its high-frequency response and strong noise-suppression capability. This study proposes an intelligent diagnostic method that combines an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and a convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture. The method first applies wavelet denoising to AE signals, then uses ICEEMDAN decomposition followed by kurtosis-based screening to extract key fault components and construct feature vectors. Subsequently, a CNN automatically learns deep time–frequency features, and a BiLSTM captures temporal dependencies among these features, enabling end-to-end fault identification. Experiments were conducted on a bearing acoustic emission dataset comprising 15 operating conditions, five fault types, and three rotational speeds; comparative model tests were also performed. Results indicate that ICEEMDAN effectively suppresses mode mixing (average mixing rate 6.08%), and the proposed model attained an average test-set recognition accuracy of 98.00%, significantly outperforming comparative models. Moreover, the model maintained 96.67% accuracy on an independent validation set, demonstrating strong generalization and practical application potential. Full article

Face recognition has become a major research area due to the rapid growth of intelligent software applications. However, reliable face identification remains challenging because human facial features vary significantly under different conditions. Originating from pattern recognition, image processing, and computer vision, modern face recognition continues to advance through new algorithms and learning-based approaches. This paper describes and analyzes the existing literature regarding facial recognition and surveillance systems. It describes and explains the principles underlying facial recognition and surveillance in a general sense and analyzes the most significant application domains. Furthermore, it describes and analyzes the most relevant and widely used benchmark datasets that can be used to measure the recognition and surveillance performance of such systems. We also discuss and analyze the most relevant and significant issues related to existing systems and datasets. Two primary feature extraction categories are discussed in detail, followed by a comparison of appearance-based, model-based, and hybrid methods. Important components such as feature selection, distance measures, classification techniques, and evaluation protocols are also reviewed. Finally, the review summarizes current challenges and emerging research trends, offering insights into future directions for developing more accurate, robust, and practical face recognition systems. Full article

Optical Fiber Composite Overhead Ground Wire (OPGW) cables serve dual functions in power systems, lightning protection and critical communication infrastructure for real-time grid monitoring. Accurate OPGW identification during UAV inspections is essential to prevent miscuts and maintain power-communication functionality. However, detecting small, twisted OPGW segments among visually similar ground wires is challenging, particularly given the computational and energy constraints of edge-based UAV platforms. We propose OPGW-DETR, a lightweight detector based on the D-FINE framework, optimized for low-power operation to enable reliable detection. The model incorporates two key innovations: multi-scale convolutional global average pooling (MC-GAP), which fuses spatial features across multiple receptive fields and integrates spectrally motivated features for enhanced fine-grained representation, and a hybrid gating mechanism that dynamically balances global and spatial features while preserving original information through residual connections. By enabling real-time inference with minimal energy consumption, OPGW-DETR addresses UAV battery and bandwidth limitations while ensuring continuous detection capability. Evaluated on a custom OPGW dataset, the S-scale model achieves 3.9% improvement in average precision (AP) and 2.5% improvement in AP${}_{50}$ over the baseline. By mitigating misidentification risks, these gains improve communication reliability. As a result, uninterrupted grid monitoring becomes feasible in low-power UAV inspection scenarios, where accurate detection is essential to ensure communication integrity and safeguard the power grid. Full article

Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provided basic insights, they often struggle to capture the nonlinear features of complex operational conditions. To address the challenge of data scarcity commonly found in experimental settings, this study employs a data augmentation strategy that combines the Synthetic Minority Over-sampling Technique (SMOTE) with Gaussian noise injection, effectively expanding the feature space from 60 original experimental nodes. Next, a physics-constrained attention mechanism model was developed that incorporates a physical constraint matrix to effectively mask irrelevant feature interactions. Experimental results show that while the standard attention model (83.88%) and the baseline BP neural network (84.25%) have limitations in generalizing to complex regimes, the proposed physics-constrained model achieves a peak test accuracy of 96.62%. Importantly, the model demonstrates exceptional robustness in identifying complex transition regions—specifically Dispersed Oil-in-Water (DO/W) flows—where it improved recall rates by about 24.6% compared to baselines. Additionally, visualization of attention scores confirms that the distribution of attention weights aligns closely with fluid-dynamic mechanisms—favoring inclination for stratified flows and flow rate for turbulence-dominated dispersions—thus validating the model’s interpretability. This research offers a novel, interpretable approach for modeling dynamic feature interactions in multiphase flows and provides valuable insights for intelligent oilfield development. Full article

To address gaps in the characterization of Roman chamomile (Anthemis nobilis L., Asteraceae)—an ethnobotanically and commercially important species—we profiled its essential oil (EO), focusing on esters that are incompletely characterized or unreported. Comprehensive GC-MS of two commercial EOs and their chromatographic fractions, combined with synthesis and co-injection of reference compounds, enabled the identification of 190 constituents. We uncovered a homologous series of angelates, tiglates, and senecioates by partial-ion-current (PIC) screening (m/z 55, 83, 100, 101), augmented by co-injection and NMR confirmation. Among these EO constituents, four esters, methallyl 3-methylbutanoate (6h), methallyl senecioate (3h), 3-methylpentyl 2-methylbutanoate (5c), and 5-methylhexyl angelate (2g) are reported here as new natural products and previously unreported compounds in the literature. Selected methacrylates and related α,β-unsaturated esters underwent model Michael additions to methanethiol (generated in situ from dimethyl disulfide and NaBH₄), confirming their thiol-acceptor reactivity. In an Artemia salina assay, the EO and most esters were non-toxic; methacrylates showed only low toxicity at the highest concentrations. These results refine the chemical map of A. nobilis EO and highlight specific ester families for future mechanistic and biological evaluation. Full article