Search Results (688)

In modern large-scale pig farming, accurately identifying sow estrus and ensuring timely breeding are crucial for maximizing economic benefits. However, the short duration of estrus and the reliance on subjective human judgment pose significant challenges for precise insemination timing. To enable non-contact, automated estrus detection, this study proposes an improved algorithm, Enhanced Context-Attention YOLO (ECA-YOLO), based on YOLOv11. The model utilizes ocular appearance features—eye’s spirit, color, shape, and morphology—across different estrus stages as key indicators. The MSCA module enhances small-object detection efficiency, while the PPA and GAM modules improve feature extraction capabilities. Additionally, the Adaptive Threshold Focal Loss (ATFL) function increases the model’s sensitivity to hard-to-classify samples, enabling accurate estrus stage classification. The model was trained and validated on a dataset comprising 4461 images of sow eyes during estrus and was benchmarked against YOLOv5n, YOLOv7tiny, YOLOv8n, YOLOv10n, YOLOv11n, and Faster R-CNN. Experimental results demonstrate that ECA-YOLO achieves a mean average precision (mAP) of 93.2%, an F1-score of 88.0%, with 5.31M parameters, and FPS reaches 75.53 frames per second, exhibiting superior overall performance. The findings confirm the feasibility of using ocular features for estrus detection and highlight the potential of ECA-YOLO for real-time, accurate monitoring of sow estrus under complex farming conditions. This study lays the groundwork for automated estrus detection in intensive pig farming. Full article

Objectives: Carotid geometry and cardiovascular risk factors play a significant role in the development of carotid atherosclerotic plaques. This study aimed to investigate the correlation between carotid plaque formation and carotid artery geometry characteristics. Methods: A retrospective cross-sectional analysis was performed on 1227 patients, categorized into a normal group (n = 685) and carotid plaque groups causing either mild stenosis (<50% stenosis based on NASCET criteria, n = 385) or moderate-to-severe stenosis (>50%, n = 232). The left and right carotid were evaluated individually for each group. Patient data, including cardiovascular risk factors and laboratory test results, were collected. Carotid geometric measurements were obtained from 3D models reconstructed from cranio-cervical computed tomography angiography (CTA) using semi-automated software (MIMICS). The geometric variables analyzed included the vascular diameter and sectional area of the common carotid artery (CCA), internal carotid artery (ICA), external carotid artery (ECA), and carotid artery bifurcation (CAB), as well as the carotid bifurcation angles and carotid tortuosity. Results: Compared to the normal group, in both the right and left carotid arteries, patients with carotid plaques exhibited a significantly higher age (p < 0.001) and a greater prevalence of hypertension (p < 0.001) and diabetes mellitus (p < 0.001). Additionally, they demonstrated a larger CCA and a smaller carotid bifurcation dimension (p < 0.05). In the analysis of the left carotid artery, patients with carotid plaques also had a significantly smaller ICA dimension (p < 0.05) than the normal group. Conclusions: This study found that patients with carotid plaques were older and had a higher prevalence of hypertension and diabetes, larger CCAs, and smaller carotid bifurcations. The plaque-positive left ICA was significantly smaller than that of the plaque-negative group, suggesting a side-specific vulnerability. These findings highlight the role of carotid geometry in plaque formation and its potential clinical implications for personalized risk assessment and targeted interventions. Full article

Embodied conversational agents (ECAs) are autonomous interaction interfaces designed to communicate with humans. This study investigates the impact of response delays and emotional facial expressions of ECAs on user perception and engagement. The motivation for this study stems from the growing integration of ECAs in various sectors, where their ability to mimic human-like interactions significantly enhances user experience. To this end, we developed an ECA with multimodal emotion recognition, both with voice and facial feature recognition and emotional facial expressions of the agent avatar. The system generates answers in real time based on media content. The development was supported by a case study of artwork images with the agent playing the role of a museum curator, where the user asks the agent for information on the artwork. We evaluated the developed system in two aspects. First, we investigated how the delay in an agent’s responses influences user satisfaction and perception. Secondly, we explored the role of emotion in an ECA’s face in shaping the user’s perception of responsiveness. The results showed that the longer response delay negatively impacted the user’s perception of responsiveness when the ECA did not express emotion, while the emotional expression improved the responsiveness perception. Full article

Accurate and real-time ship detection is crucial for intelligent waterborne transportation systems. However, detecting ships across various scales remains challenging due to category diversity, shape similarity, and complex environmental interference. In this work, we propose EGM-YOLOv8, a lightweight and enhanced model for real-time ship detection. We integrate the Efficient Channel Attention (ECA) module to improve feature extraction and employ a lightweight Generalized Efficient Layer Aggregation Network (GELAN) combined with Path Aggregation Network (PANet) for efficient multi-scale feature fusion. Additionally, we introduce MPDIoU, a minimum-distance-based loss function, to enhance localization accuracy. Compared to YOLOv8, EGM-YOLOv8 reduces the number of parameters by 13.57%, reduces the computational complexity by 11.05%, and improves the recall rate by 1.13%, demonstrating its effectiveness in maritime environments. The model is well-suited for deployment on resource-constrained devices, balancing precision and efficiency for real-time applications. Full article

Face anti-spoofing detection is crucial for identity verification and security monitoring. However, existing single-modal models struggle with feature extraction under complex lighting conditions and background variations. Moreover, the feature distributions of live and spoofed samples often overlap, resulting in suboptimal classification performance. To address these issues, we propose a jointly optimized framework integrating the Enhanced Channel Attention (ECA) mechanism and the Intra-Class Differentiator (ICD). The ECA module extracts features through deep convolution, while the Bottleneck Reconstruction Module (BRM) employs a channel compression–expansion mechanism to refine spatial feature selection. Furthermore, the channel attention mechanism enhances key channel representation. Meanwhile, the ICD mechanism enforces intra-class compactness and inter-class separability, optimizing feature distribution both within and across classes, thereby improving feature learning and generalization performance. Experimental results show that our framework achieves average classification error rates (ACERs) of 2.45%, 1.16%, 1.74%, and 2.17% on the CASIA-SURF, CASIA-SURF CeFA, CASIA-FASD, and OULU-NPU datasets, outperforming existing methods. Full article

Accurate fault diagnosis in analog circuits faces significant challenges owing to the inherent complexity of fault data patterns and the limited feature representation capabilities of conventional methodologies. Addressing the limitations of current convolutional neural networks (CNN) in handling heterogeneous fault characteristics, this study presents an efficient channel attention-enhanced multi-input CNN framework (ECA-MI-CNN) with dual-domain feature fusion, demonstrating three key innovations. First, the proposed framework addresses multi-domain feature extraction through parallel CNN branches specifically designed for processing time-domain and frequency-domain features, effectively preserving their distinct characteristic information. Second, the incorporation of an efficient channel attention (ECA) module between convolutional layers enables adaptive feature response recalibration, significantly enhancing discriminative feature learning while maintaining computational efficiency. Third, a hierarchical fusion strategy systematically integrates time-frequency domain features through concatenation and fully connected layer transformations prior to classification. Comprehensive simulation experiments conducted on Butterworth low-pass filters and two-stage quad op-amp dual second-order low-pass filters demonstrate the framework’s superior diagnostic capabilities. Real-world validation on Butterworth low-pass filters further reveals substantial performance advantages over existing methods, establishing an effective solution for complex fault pattern recognition in electronic systems. Full article

With the rapid development and widespread application of drones across various fields, drone recognition and classification at medium and long distances have become increasingly important yet challenging tasks. This paper proposes a novel network architecture called AECM-Net, which integrates an adaptive feature fusion (AF) module, an efficient channel attention (ECA), and a criss-cross attention (CCA) mechanism-enhanced multi-scale feature extraction module (MSC). The network employs both Mel-frequency cepstral coefficients (MFCCs) and Gammatone cepstral coefficients (GFCC) as input features, utilizing the AF module to adaptively adjust fusion weights of different feature maps while incorporating ECA channel attention to emphasize key channel features and CCA mechanism to capture long-range dependencies. To validate our approach, we construct a comprehensive dataset containing various drone models within a 50-m range and conduct extensive experiments. The experimental results demonstrate that our proposed AECM-Net achieves superior classification performance with an average accuracy of 95.2% within the 50-m range. These findings suggest that our proposed architecture effectively addresses the challenges of medium and long-range drone acoustic signal recognition through its innovative feature fusion and enhancement mechanisms. Full article

Background: Diabetes mellitus (DM) is a major determinant of aging-related vascular diseases. The arterial pulsatility index (PI) and resistance index (RI) are biomarkers of vascular aging. The available data regarding DM with arterial PI and RI are limited. The specific aim of this study was to explore the relationships between DM and the segment-specific PI and RI of the extracranial carotid arteries. Methods: We enrolled 402 DM cases and 3416 non-DM controls from a community-based cohort. Each subject’s blood flow velocities in the extracranial common (CCA), internal (ICA), and external (ECA) carotid arteries were measured by color Doppler ultrasonography and used to calculate PIs and RIs. Results: The DM cases had significantly higher age–sex-adjusted means of carotid RIs and PIs than the non-DM controls (all p-values < 0.005). After controlling for the effects of conventional cardio-metabolic risk factors, all carotid RIs and PIs remained significantly correlated with higher odds ratios (ORs) of having DM. The relationships with DM were stronger and more significant for the ECA RI and PI. The multivariable-adjusted ORs were 1.36 (95% confidence interval [CI], 1.21~1.54, p = 3.9 × 10⁻⁷) and 1.30 (95% CI, 1.17~1.45, p = 8.7 × 10⁻⁷) for 1.0 SD increases in the ECA RI and PI, respectively. Compared to the best fit model of conventional cardio-metabolic risk factors, the additions of the ECA RI and PI significantly increased the area under the receiver operating characteristic curve by 0.85% (95% CI, 0.11~1.59%; p = 0.023) and 0.69% (95% CI, 0.01~1.37%; p = 0.046), respectively. Conclusions: This study shows significantly positive associations between DM and carotid RIs and PIs. Carotid RIs and PIs are potential biomarkers for DM-related vascular diseases. Full article

Our understanding of water and salt changes in the context of declining groundwater levels in the Tarim Basin remains limited, largely due to the scarcity of hydrological monitoring stations and field observation data. This study utilizes water and salt monitoring data from 474 apparent electromagnetic induction (ECa, measured by EM38-MK2 device) sites across seven oases, combined with groundwater level observation data from representative areas, to analyze the spatiotemporal changes in ECa within the oases of the Tarim Basin from 2000 to 2022. Specific results are shown below: Numerous algorithmic predictions show the ensemble learning algorithm with the smallest error explained 71% of the ECa spatial variability. The ECa was particularly effective at identifying areas where groundwater extends beyond a depth of 5 m, demonstrating increased efficacy when ECa readings exceed the threshold of 1100 mS/m. Our spatiotemporal analysis spanning the years 2000 to 2022 has revealed a significant decline in ECa values within the artificially irrigated zones of the oasis clusters. In contrast, the transitional ecotone between the desert and the oases in Atux, Aksu, Kuqa, and Luntai have experienced a significant increase in ECa value. The variations observed within the defined Zone B, where ECa values ranged from 800 mS/m to 1100 mS/m, and Zone A, characterized by ECa values exceeding 1100 mS/m, aligned with the periodic fluctuations in the groundwater drought index (GDI), indicating a clear pattern of correlation. This study demonstrated that ECa can serve as a valuable tool for revealing the spatial and temporal variations of water resources in arid zones. The results obtained through this approach provided essential references for the local scientific management of soil and water resources. Full article

Background: Antimicrobial resistance is a problem that threatens the entire world population. Blue light irradiation (BLI) is a novel technology with a bactericidal effect. However, it has only been employed in experimental and preclinical trials. Methods: We employed BLI on four kinds of bacteria (Staphylococcus aureus, Pseudomonas aeruginosa, Proteus mirabilis, Klebsiella pneumoniae, and Escherichia coli) and six kinds of artificial implants utilizing a BioLight LED lamp and MEDILIGHT at a 453 nm wavelength. Results: The results showed that the antibacterial effect of BLI enhanced with time and dosage. Irradiation of 165.6 J/cm² corresponding to 120 min of constant mode irradiation, resulted in a significant reduction in the CFU for all four strains. Moreover, the cycling mode (30 s on/30 s off) of the MEDILIGHT prototype showed a more effective microbial effect compared to the constant mode using the BioLight LED lamp. Pseudomonas aeruginosa was the most sensitive strain to BLI, and Staphylococcus aureus showed relatively greater resistance to BLI. BLI showed different antibacterial effects on various types of implants, indicating that different physical properties of artificial implants were more likely to influence the bactericidal effect of BLI. Decreased ATP highlighted energy deprivation after BLI. Genechip analysis of Escherichia coli under constant mode and cycling mode of BLI revealed the downregulation of metabolism-related pathways, and most genes involved in the TCA cycle were downregulated. Conclusions: Our results showed that cycling mode BLI has great potential for use in future disinfection applications. We also proposed a new viewpoint that energy deprivation might be another possible mechanism underlying the antibacterial effect of BLI. Full article

This study describes a novel technique to analyze the extracellular vesicle (EV)-derived microRNA (miRNA) crosstalk between equine chondrocytes and synoviocytes. Donor cells (chondrocytes, n = 8; synoviocytes, n = 9) were labelled with 5-ethynyl uridine (5-EU); EVs were isolated from culture media and incubated with recipient cells (chondrocytes [n = 5] were incubated with synoviocyte-derived EVs, and synoviocytes [n = 4] were incubated with chondrocyte-derived EVs). Total RNA was extracted from recipient cells; the 5-EU-labelled RNA was recovered and sequenced. Differential expression analysis, pathway analysis, and miRNA target prediction were performed. Overall, 198 and 213 miRNAs were identified in recipient synoviocytes and chondrocytes, respectively. The top five most abundant miRNAs were similar for synoviocytes and chondrocytes (eca-miR-21, eca-miR-221, eca-miR-222, eca-miR-100, eca-miR-26a), and appeared to be linked to joint homeostasis. There were nine differentially expressed (p < 0.05) miRNAs (eca-miR-27b, eca-miR-23b, eca-miR-31, eca-miR-191a, eca-miR-199a-5p, eca-miR-143, eca-miR-21, eca-miR-181a, and eca-miR-181b) between chondrocytes and synoviocytes, which appeared to be linked to migration of cells, apoptosis, cell viability of connective tissue cell, and inflammation. In conclusion, the reported technique was effective in recovering and characterizing the EV-derived miRNA crosstalk between equine chondrocytes and synoviocytes and allowed for the identification of EV-communicated miRNA patterns potentially related to cell viability, inflammation, and joint homeostasis. Full article

Driving behavior recognition based on Frequency-Modulated Continuous-Wave (FMCW) radar systems has become a widely adopted paradigm. Numerous methods have been developed to accurately identify driving behaviors. Recently, deep learning has gained significant attention in radar signal processing due to its ability to eliminate the need for intricate signal preprocessing and its automatic feature extraction capabilities. In this article, we present a network that incorporates multi-scale and channel-time attention modules, referred to as MCT-CNN-LSTM. Initially, a multi-channel convolutional neural network (CNN) combined with a Long Short-Term Memory Network (LSTM) is employed. This model captures both the spatial features and the temporal dependencies from the input radar signal. Subsequently, an Efficient Channel Attention (ECA) module is utilized to allocate adaptive weights to the feature channels that carry the most relevant information. In the final step, domain-adversarial training is applied to extract common features from both the source and target domains, which helps reduce the domain shift. This approach enables the accurate classification of driving behaviors by effectively bridging the gap between domains. Evaluation results show that our method reached an accuracy of 97.3% in a real measured dataset. Full article

Apical periodontitis is a common inflammatory condition associated with root canal treatment (RCT) failure. The quality of the three-dimensional root canal seal is critical to the success of the treatment. Bioceramic sealants, such as Neosealer Flo, offer biological advantages such as osteoconduction, biocompatibility and sustained calcium ion release, which may improve apical healing. The aim of this study was to compare AH Plus and Neosealer Flo in terms of postoperative pain, extrusion and periapical healing. A single-blind, randomised clinical trial was conducted with 60 patients divided into AH Plus and Neosealer Flo groups. Post-operative pain was assessed using a visual analogue scale (VAS) at 24 and 48 h and at 7 days. Seal quality and periapical healing were assessed at 6 months using the AAE success criteria by clinical and radiographic evaluation. Neosealer Flo resulted in less postoperative pain at 24 h and 7 days compared to AH Plus. Extrusion did not significantly affect pain or correlate with the type of sealer used. Both materials achieved similar periapical healing rates. Neosealer Flo demonstrated advantages in pain reduction, while both sealants showed comparable efficacy. Full article

Wheat is a crucial grain crop in China, yet differentiating different wheat varieties at the mature stage solely through visual observation remains challenging. However, the automatic identification of wheat varieties at the mature stage is very important for field management, planting area, and yield prediction. In order to achieve accurate and efficient recognition of wheat varieties planted in wheat fields, a recognition method based on an enhanced DenseNet network model is proposed in this study. The incorporation of SE and ECA attention mechanisms enhances the feature representation capability, leading to improved model performance and the development of the SECA-L-DenseNet model for wheat variety recognition. The experimental results show that the SECA-L-DenseNet model achieves a classification accuracy of 97.15% on the custom dataset, surpassing the original DenseNet model by 2.13%, which demonstrates a significant improvement. The model enables the accurate identification of wheat varieties in the field and can be integrated into applications for the automated identification of varieties, planting area estimation, and yield prediction in harvester equipment. Full article

Agriculture is central to Nepal’s economy but faces growing challenges such as environmental degradation, labor shortages, and the increasing feminization of farming due to male outmigration. Environmental Conservation Agriculture (ECA) offers a sustainable solution, yet adoption remains inconsistent due to knowledge gaps and resource constraints. This study examines the socio-demographic, economic, and environmental factors influencing the farmers’ willingness to learn about ECA and its relationship with women’s empowerment. A cross-sectional survey of 383 ECA farmers across the Kavre, Dhading, and Chitwan districts in Bagmati Province reveals that 72.6% are willing to learn about ECA, driven by climate change concerns, economic incentives, and market access. Farmers who have experienced climate-related crop losses (64%) and those engaged in consumer-driven markets (59%) show a greater inclination to learn ECA. Spearman correlation analysis highlights key factors influencing willingness to learn, including perceptions of ECA as a climate-resilient practice, interest in ECA, and awareness of FAO’s promotion of ECA. Farmers who believe that ECA enhances sustainability, resilience, and income are also more likely to engage, while market dissatisfaction presents a challenge. Receiving ECA subsidies is positively associated with willingness to learn, highlighting the role of financial support in adoption. Women play a crucial role in agriculture but face barriers such as household responsibilities (22%), lack of education and training (18%), and limited financial access (12%). Key motivators for their participation include capacity-building initiatives (48%), financial support (16%), and empowerment programs (5%). Notably, households where women participate in early decision-making are 19% more likely to express willingness to learn about ECA, and perceptions of ECA as empowering women are positively linked to willingness to learn. Addressing these barriers through targeted policies, institutional support, and market-based incentives is essential for fostering inclusive and sustainable agricultural development. This study provides actionable insights for strengthening ECA adoption, promoting gender equity, and enhancing Nepal’s climate resilience. Full article