Search Results (58)

Timely, objective pain recognition in dairy cattle is essential for welfare assurance, productivity, and ethical husbandry yet remains elusive because evolutionary pressure renders bovine distress signals brief and inconspicuous. Without verbal self-reporting, cows suppress overt cues, so automated vision is indispensable for on-farm triage. Although earlier systems tracked whole-body posture or static grimace scales, frame-level detection of facial micro-expressions has not been explored fully in livestock. We translate micro-expression analytics from automotive driver monitoring to the barn, linking modern computer vision with veterinary ethology. Our two-stage pipeline first detects faces and 30 landmarks using a custom You Only Look Once (YOLO) version 8-Pose network, achieving a 96.9% mean average precision ($mAP$) at an Intersection over the Union (IoU) threshold of 0.50 for detection and 83.8% Object Keypoint Similarity (OKS) for keypoint placement. Cropped eye, ear, and muzzle patches are encoded using a pretrained MobileNetV2, generating 3840-dimensional descriptors that capture millisecond muscle twitches. Sequences of five consecutive frames are fed into a 128-unit Long Short-Term Memory (LSTM) classifier that outputs pain probabilities. On a held-out validation set of 1700 frames, the system records 99.65% accuracy and an F1-score of 0.997, with only three false positives and three false negatives. Tested on 14 unseen barn videos, it attains 64.3% clip-level accuracy (i.e., overall accuracy for the whole video clip) and 83% precision for the pain class, using a hybrid aggregation rule that combines a 30% mean probability threshold with micro-burst counting to temper false alarms. As an early exploration from our proof-of-concept study on a subset of our custom dairy farm datasets, these results show that micro-expression mining can deliver scalable, non-invasive pain surveillance across variations in illumination, camera angle, background, and individual morphology. Future work will explore attention-based temporal pooling, curriculum learning for variable window lengths, domain-adaptive fine-tuning, and multimodal fusion with accelerometry on the complete datasets to elevate the performance toward clinical deployment. Full article

Precise wheat head detection is essential for plant counting and yield estimation in precision agriculture. To tackle the difficulties arising from densely packed wheat heads with diverse scales and intricate occlusions in real-world field conditions, this research introduces YOLO v11n-GRN, an improved wheat head detection model founded on the streamlined YOLO v11n framework. The model optimizes performance through three key innovations: This study introduces a Global Edge Information Transfer (GEIT) module architecture that incorporates a Multi-Scale Edge Information Generator (MSEIG) to enhance the perception of wheat head contours through effective modeling of edge features and deep semantic fusion. Additionally, a C3k2_RFCAConv module is developed to improve spatial awareness and multi-scale feature representation by integrating receptive field augmentation and a coordinate attention mechanism. The utilization of the Normalized Gaussian Wasserstein Distance (NWD) as the localization loss function enhances regression stability for distant small targets. Experiments were, respectively, validated on the self-built multi-temporal wheat field image dataset and the GWHD2021 public dataset. Results showed that, while maintaining a lightweight design (3.6 MB, 10.3 GFLOPs), the YOLOv11n-GRN model achieved a precision, recall, and mAP@0.5 of 92.5%, 91.1%, and 95.7%, respectively, on the self-built dataset, and 91.6%, 89.7%, and 94.4%, respectively, on the GWHD2021 dataset. This fully demonstrates that the improvements can effectively enhance the model’s comprehensive detection performance for wheat ear targets in complex backgrounds. Meanwhile, this study offers an effective technical approach for wheat head detection and yield estimation in challenging field conditions, showcasing promising practical implications. Full article

Background and Objectives: Chronic rhinosinusitis with nasal polyps (CRSwNP) and eosinophilic otitis media (EOM) are frequently co-existing eosinophilic disorders related to type 2 inflammation, which significantly impair the quality of life of patients. Dupilumab, a monoclonal antibody targeting the IL-4 receptor alpha and anti-IL-13, has demonstrated a promising profile of efficacy and safety in the treatment of CRSwNP; however, evidence on its role in concomitant EOM and CRSwNP remains limited in the literature. This study aims to evaluate the clinical efficacy of dupilumab in patients with concomitant CRSwNP and EOM over a six-month observational period. Materials and Methods: A retrospective observational cohort study was conducted on twenty-two patients (aged 18–75 years) over six months with severe uncontrolled CRSwNP and confirmed refractory EOM who were treated with dupilumab (300 mg every two weeks). Demographic data are collected, and outcome measures included Nasal Polyp Score (NPS), Sino-Nasal Outcome Test (SNOT-22), Visual Analog Scale for nasal congestion (VAS), tympanogram classification, and Chronic Otitis Media Outcome Test (COMOT-15), evaluated at baseline and 6 months. Results: Over the six-month treatment period, patients with coexisting CRSwNP and eosinophilic otitis media experienced significant improvements across the multiple validated clinical and patient-reported outcome measures. The Nasal Polyp Score (NPS) significantly decreased from a median of 5.7 (IQR: 1.2) at baseline to 1.5 (IQR: 1.3) at six months (p < 0.0001). The SNOT-22 showed a substantial decline from a median of 77.6 (IQR: 19.0) to 21.5 (IQR: 13.4), p < 0.0001. Visual Analog Scale (VAS) scores for nasal congestion improved significantly from 8.4 (IQR: 1.1) to 1.7 (IQR: 1.2), p < 0.0001. Tympanogram scores improved from Tympanogram type “B” to Tympanogram type “A” (p = 0.018). COMOT-15 scale decreased from a median of 51.3 (IQR: 8.4) to 19.2 (IQR: 5.0) (p < 0.0001). Peripheral eosinophil counts remained unchanged or increased (baseline 0.80 vs. 0.84 cells/μL at six months, (p = 0.834)). Conclusions: Dupilumab treatment in patients with CRSwNP and EOM led to significant clinical improvements in sinonasal symptoms, middle ear function, and quality of life over six months, with no significant change in peripheral eosinophilia. Full article

Background/Objectives: Dermatitis is frequently treated with dexamethasone cutaneous application, which causes adverse effects mainly when it is chronically administered. Sesamol is a phytochemical compound known for its anti-inflammatory activity and low toxicity. Therefore, this study reports the optimization of a guar gum hydrogel with enhanced physicochemical and microbiological stability, providing an effective dosage form for topical application of sesamol nanocapsules to treat irritant contact dermatitis. Methods: Nano-based hydrogel containing 1 mg/g sesamol was prepared by adding the nanocapsule suspension to form a 2.5% (w/v) guar gum dispersion. Dynamic rheological analysis indicates that the formulations exhibit a non-Newtonian flow with pseudoplastic behavior. Hydrogels were evaluated by Fourier-transformed infrared (FTIR) spectroscopy, and, following spectrum acquisition, an unsupervised chemometrics model was developed to identify crucial variables. Additionally, the physicochemical and microbiological stability of the hydrogel was evaluated over a 60-day period. Results: ATR-FTIR spectra of all hydrogels evaluated are very similar after preparation and 60 days of storage. However, it showed a slight increase in average diameter and PDI and decreased pH values after 60 days. Microbiological assessment demonstrated that the hydrogel met the requirements for the microbial count over 60 days. The dermatitis model was induced by repeated applications of croton oil in the right ears of mice. The effectiveness of the hydrogels was evaluated by assessing ear edema and migration of polymorphonuclear cells. The nano-based hydrogel exhibited anti-inflammatory properties similar to those of dexamethasone. Conclusions: Therefore, the nano-based hydrogel containing sesamol exhibits therapeutic potential for treating cutaneous inflammatory diseases. Full article

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the ${\mathrm{AP}}_{50}$ of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article

This study investigates the performance of B-series marine propellers enhanced through geometric modifications, namely face camber ratio (FCR) and cupping percentage modifications, using a machine learning (ML)-driven optimization framework. A large dataset of over 7000 open-water propeller configurations is curated, incorporating variations in blade number, expanded area ratio (EAR), pitch-to-diameter ratio (P/D), FCR, and cupping percentage. A multi-layer artificial neural network (ANN) is trained to predict thrust, torque, and open-water efficiency (η_o) with a high coefficient of determination (R²), greater than 0.9999. The ANN is integrated into an optimization algorithm to identify optimal propeller designs for the KRISO Container Ship (KCS) using empirical constraints for cavitation and tip speed. Unlike prior studies that rely on boundary element method (BEM)-ML hybrids or multi-fidelity simulations, this study introduces a geometry-coupled analysis of FCR and cupping—parameters often treated independently—and applies empirical cavitation and acoustic (tip speed) limits to guide the design process. The results indicate that incorporating 1.0–1.5% cupping leads to a significant improvement in efficiency, up to 9.3% above the reference propeller, while maintaining cavitation safety margins and acoustic limits. Conversely, designs with non-zero FCR values (0.5–1.5%) show a modest efficiency penalty (up to 4.3%), although some configurations remain competitive when compensated by higher EAR, P/D, or blade count. The study confirms that the combination of cupping with optimized geometric parameters yields high-efficiency, cavitation-safe propellers. Furthermore, the ML-based framework demonstrates excellent potential for rapid, accurate, and scalable propeller design optimization that meets both performance and regulatory constraints. Full article

A single intratympanic application of the small-molecule drug AC102 was previously shown to promote significant recovery of hearing thresholds in a noise-induced hearing loss model in guinea pigs. Here, we report the effects of AC102 to revert synaptopathy of inner hair cells (IHCs) and behavioral signs of tinnitus in Mongolian gerbils following mild noise trauma. This experimental protocol led to minor hearing threshold shifts with no loss of auditory hair cells (HCs) but induced synaptopathy and a sustained and significant tinnitus percept. Treatment by intratympanic application of AC102 was evaluated in two protocols: 1. three weekly injections or 2. a single application. We evaluated hearing threshold changes using the auditory brainstem response (ABR) and the development of a tinnitus percept using the gap prepulse inhibition of acoustic startle (GPIAS) behavioral response. The number of IHC ribbon synapses along the cochlear frequency map were counted by immunostaining for the synaptic ribbon protein carboxy-terminal binding protein 2 (CTBP2). AC102 strongly and significantly reduced behavioral signs of tinnitus, as reflected by altered GPIAS. Noise-induced loss of IHC ribbon synapses was significantly reduced by AC102 compared to vehicle-treated ears. These results demonstrate that a single application of AC102 restores ribbon synapses following mild noise trauma thereby promoting recovery from tinnitus-related behavioral responses in vivo. Full article

Low-Earth-Orbit (LEO) satellite networks offer a promising avenue for achieving global connectivity, despite certain technical and economic challenges such as high implementation costs and the complexity of network management. Nonetheless, real-time routing remains challenging because of rapid topology changes and strict energy constraints. This paper proposes a GPU-accelerated Eclipse-Aware Routing (EAR) method that simultaneously minimizes hop count and balances energy consumption for real-time routing on an onboard computer (OBC). The approach first employs a Breadth-First Search (BFS)–based K-Shortest Paths (KSP) algorithm to generate candidate routes and then evaluates battery usage to select the most efficient path. In large-scale networks, the computational load of the KSP search increases substantially. Therefore, CUDA-based parallel processing was integrated to enhance performance, resulting in a speedup of approximately 3.081 times over the conventional CPU-based method. The practical applicability of the proposed method is further validated by successfully updating routing tables in a SpaceWire network. Full article

Gentamicin-induced ototoxicity leads to irreversible sensorineural hearing loss due to structural and functional damage to inner ear hair cells. In this study, we identified (-)-butaclamol as a potent protective agent against gentamicin-induced cytotoxicity through high-content screening (HCS) of a natural compound library. (-)-Butaclamol significantly enhanced cell viability in both HEI-OC1 cells and zebrafish neuromasts, demonstrating robust protection against gentamicin toxicity. Mechanistically, (-)-butaclamol inhibited intrinsic apoptosis, as evidenced by reduced TUNEL-positive cell counts and the downregulation of BAX and caspase-3, alongside the upregulation of BCL-2. Moreover, (-)-butaclamol activated key survival signaling pathways, including AKT/mTOR and ERK, while suppressing the inflammatory regulator NF-κB. Additional analyses revealed that (-)-butaclamol effectively mitigated oxidative stress and restored autophagic activity, as confirmed by CellROX and LysoTracker assays. Notably, TMRE staining showed that (-)-butaclamol preserved mitochondrial membrane potential in zebrafish hair cells, indicating mitochondrial protection. Collectively, these findings suggest that (-)-butaclamol exerts comprehensive cytoprotective effects against gentamicin-induced ototoxicity by modulating apoptosis, enhancing survival signaling, and restoring mitochondrial and cellular homeostasis. These results highlight the therapeutic potential of (-)-butaclamol and provide a foundation for future studies aimed at its clinical application. Full article

In order to improve the efficiency of the deep network model in processing the radiated noise signals of underwater acoustic targets, this paper introduces a Singular Spectrum Analysis and Channel Attention Convolutional Neural Network (SSA-CACNN) model. The front end of the model is designed as an SSA filter, and its input is the time-domain signal that has undergone simple preprocessing. The SSA method is utilized to separate the noise efficiently and reliably from useful signals. The first three orders of useful signals are then fed into the CACNN model, which has a convolutional layer set up at the beginning of the model to further remove noise from the signal. Then, the attention of the model to the feature signal channels is enhanced through the combination of multiple groups of convolutional operations and the channel attention mechanism, which facilitates the model’s ability to discern the essential characteristics of the underwater acoustic signals and improve the target recognition rate. Experimental Results: The signal reconstructed by the first three-order waveforms at the front end of the SSA-CACNN model proposed in this paper can retain most of the features of the target. In the experimental verification using the ShipsEar dataset, the model achieved a recognition accuracy of 98.64%. The model’s parameter count of 0.26 M was notably lower than that of other comparable deep models, indicating a more efficient use of resources. Additionally, the SSA-CACNN model had a certain degree of robustness to noise, with a correct recognition rate of 84.61% maintained when the signal-to-noise ratio (SNR) was −10 dB. Finally, the pre-trained SSA-CACNN model on the ShipsEar dataset was transferred to the DeepShip dataset with a recognition accuracy of 94.98%. Full article

People are naturally drawn to symmetrical faces, as symmetry is often associated with attractiveness. In contrast to human faces, animated characters often emphasize certain geometric features, exaggerating them while maintaining symmetry and enhancing their visual appeal. This study investigated the impact of geometric parameters of facial features on fixation duration and explored 60 facial samples across two races (American, Japanese) and two conditions (animated, real). Relevant length, angle, and area parameters were extracted from the eyebrows, eyes, ears, nose, and chin regions of the facial samples. Using an eye-tracking experiment design, fixation duration (FD) and fixation count (FC) were extracted from 10 s gaze stimuli. Sixty participants (32 males and 28 females) took part. The results showed that, compared to Japanese animation, American animation typically induced a longer FD and higher FC on features like the eyes (p < 0.001), nose (p < 0.001), ears (p < 0.01), and chin (p < 0.01). Compared to real faces, animated characters typically attracted a longer FD and higher FC on areas such as the eyebrows (p < 0.001), eyes (p < 0.001), and ears (p < 0.001), while the nose (p < 0.001) and chin (p < 0.001) attracted a shorter FD and lower FC. Additionally, a correlation analysis between FD and geometric features showed a high positive correlation in the geometric features of the eyes, nose, and chin for both American and Japanese animated faces. The geometric features of the nose in real American and Japanese faces showed a high negative correlation coefficient. These findings highlight notable differences in FD and FC across different races and facial conditions, suggesting that facial geometric features may play a role in shaping gaze patterns and contributing to the objective quantitative assessment of FD. These insights are critical for optimizing animated character design and enhancing engagement in cross-cultural media and digital interfaces. Full article

Enhancing chlorophyll retention in maize leaves and prolonging the grain-filling duration constitute critical strategies for yield improvement in agricultural production systems. This study investigated the mechanistic relationship between yield enhancement pathways and the leaf senescence process induced by high-input straw pellets amendment. We analyzed the impact mechanisms of green leaf area dynamics and dry matter redistribution on yield during late reproductive stages, establishing theoretical foundations for yield optimization through intensive straw pellets incorporation. The study used the maize variety Jingnongke 728 as the experimental material. Based on previous research, four treatments were set up, including no straw returning (CK), chopped straw (15 t/ha) returning to the field (FS1), a large amount of chopped straw (75 t/ha) returning to the field (FS5), and a large amount of pelletized straw (75 t/ha) returning to the field (KL5), with four replicates. A two-year experimental design systematically assessed green leaf area index (GLAI), dry matter accumulation, distribution, translocation, yield components, and grain yield to explore the differences among various treatments under different straw returning amounts and returning forms. The study detected no significant differences between FS1 and CK. Although KL5 and FS5 delayed leaf senescence, FS5 significantly depressed green leaf area index (GLAI) at the R1 stage (silking), which results in it not having more effective photosynthetic area during late phenological phases. In dry matter dynamics, KL5 exhibited 5.52–25.71% greater pre-anthesis accumulation, 2.73–60.74% higher post-anthesis accumulation, and 9.48–25.76% elevated ear dry matter allocation relative to other treatments. KL5’s post-anthesis assimilates contributed 2.43–17.02% more to grain development, concurrently increasing ear-to-total biomass ratio. Yield analysis ranked KL5 as the superior treatment with 0.68–25.15% yield advantage, driven by significantly enhanced kernel number per ear and 100-kernel mass, whereas FS5 displayed the lowest kernel count among all treatments. Returning 75 t/ha of straw pellets to the black soil area in Northeast China can significantly delay the senescence of maize leaves and increase the accumulation of dry matter after anthesis by maintaining the effective photosynthetic area of leaves in the later stage of growth, thereby achieving the goal of increasing yield. The research can offer a practical and novel approach for straw return in the black soil region of Northeast China and provide a new technological pathway for enhancing crop productivity. Full article

Background: Cannabis sativa L. and its products are becoming popular for the treatment of inflammatory diseases. One of the main phytocannabinoids contained in cannabis is cannabidiol (CBD), which is a component of numerous cosmetic preparations used to treat inflammatory skin diseases such as atopic dermatitis (AD) and psoriasis. However, current data regarding the efficacy and safety of CBD for dermatological indications are limited. Therefore, the aim of the present study was to evaluate the anti-inflammatory effect of high-CBD Cannabis sativa L. extract (eCBD) in a model of AD. Methods: Dermatitis was induced by repeated application of 2,4-dinitrochlorobenzene (DNCB) to the skin of the rats’ ears. The therapeutic effect of eCBD was evaluated in behavioral, histopathological, and hematological studies following topical application as an ointment containing 2% CBD. Results: Application of the ointment containing eCBD resulted in attenuation of DNCB-induced inflammation. Interestingly, an anti-edematous effect was more pronounced in rats treated with the eCBD than in rats treated with 1% hydrocortisone ointment. However, eCBD did not reduce the frequency of DNCB-induced scratching, while there was a visible antipruritic effect of 1% hydrocortisone application. Histopathological analysis revealed that both eCBD and 1% hydrocortisone ointments significantly decreased mast cell count compared with the Vaseline control group. Furthermore, treatment with an ointment containing eCBD resulted in a decrease in the number of leukocytes in the blood. Conclusions: Topically administered eCBD had a stronger anti-edematous effect than glucocorticosteroid and differently affected hematological parameters. It is suggested that eCBD has therapeutic potential for the treatment of AD. Full article

In order to enable oat ears to be quickly and accurately identified in the natural environment, this paper proposes an oat ears detection and counting model based on an improved Faster R-CNN. In the backbone network, the commonly used single convolutional neural network is replaced by a parallel convolutional neural network to realize the feature extraction of oat ears, and a feature pyramid network (FPN) is incorporated to improve the small target-missed detection problem and the multi-scale problem of oat ears. Then, the anchor box configuration is optimized according to the size and distribution of the labeled boxes in the dataset, which improves the efficiency of the model to detect oat ears. Finally, progressive non-maximum suppression (Progressive-NMS) was used to replace non-maximum suppression (NMS) to optimize the screening process of prediction boxes. According to the data from different experiments designed, the optimized model can effectively detect oat ears in the natural environment and complete the counting of oat ears per unit area. Compared with the traditional Faster R-CNN detection model, the mean average precision (mAP) of the improved model is increased by 13.01%, which could provide reference for oat yield prediction and intelligent operation. Full article

Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles (UAVs). Drone-based images were captured during the late growth stage of wheat across 15 fields in Bosnia and Herzegovina. The images, processed to a resolution of 1024 × 1024 pixels, were manually annotated with regions of interest (ROIs) containing wheat ears. A dataset consisting of 556 high-resolution images was compiled, and advanced models including Faster R-CNN, YOLOv8, and RT-DETR were utilised for ear detection. The study found that although lower-quality images had a minor effect on detection accuracy, they did not significantly hinder the overall performance of the models. This research demonstrates the potential of digital technologies, particularly machine learning and UAVs, in transforming traditional agricultural practices. The novel application of automated ear counting via machine learning provides a scalable, efficient solution for yield prediction, enhancing sustainability and competitiveness in agriculture. Full article