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Abstract Foxtail millet is an important minor cereal crop rich in nutrients. Due to the small size of its seeds, there is little information on the diversity of its seed structure among germplasms, limiting the identification of genes controlling seed development and germination. This paper utilized X-ray computed tomography (CT) scanning technology and deep learning models to reveal the microstructure of foxtail millet seeds, gaining insights into their internal features, distribution, and composition. A total of 100 foxtail millet varieties were scanned with X-ray computed tomography to obtain 3D reconstruction images and slices. Pre-processing steps were adopted to improve image segmentation accuracy, including noise reduction, rotation, contrast enhancement, and brightness enhancement. The experiment revealed that traditional OpenCV image processing methods failed to achieve precise segmentation, whereas deep learning models exhibited outstanding performance in segmenting seed CT slice images. We compared UNet, PSPNet, and DeepLabV3 models, selected different backbones and optimizers based on the dataset, and continuously adjusted learning rates and maximum training epochs to train the models. Results demonstrated that VGG16-UNet achieved an accuracy of 99.19% on the foxtail millet seed CT slice image dataset, outperforming PSPNet and DeepLabV3 models. Compared to ResNet-UNet, VGG16-UNet shows an improvement of approximately 3.18% in accuracy, demonstrating superior performance in accurately segmenting the inner glume, outer glume, embryo, and endosperm under various adhesion conditions. Accurate segmentation of foxtail millet CT images enables analysis of embryo size, endosperm size, and glume thickness, which impact germination, growth, and nutrition. This study fills a gap in small grain structure research, offering insights to optimize agriculture and molecular breeding for improved yield and quality.

Abstract Background Foxtail millet [Setaria italica (L.) Beauv] is a C4 graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation. Results This study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH. Conclusion The HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.

IntroductionTomato leaf pests and diseases pose a significant threat to the yield and quality of Q6 tomatoes, highlighting the necessity for comprehensive studies on effective control methods.MethodsCurrent control measures predominantly rely on experience and manual observation, hindering the integration of multi-source data. To address this, we integrated information resources related to tomato leaf pests and diseases from agricultural standards documents, knowledge websites, and relevant literature. Guided by domain experts, we preprocessed this data to construct a sample set.ResultsWe utilized the Named Entity Recognition (NER) model ALBERT-BiLSTM-CRF to conduct end-to-end knowledge extraction experiments, which outperformed traditional models such as 1DCNN-CRF and BiLSTM-CRF, achieving a recall rate of 95.03%. The extracted knowledge was then stored in the Neo4j graph database, effectively visualizing the internal structure of the knowledge graph.DiscussionWe developed a digital diagnostic system for tomato leaf pests and diseases based on the knowledge graph, enabling graphical management and visualization of pest and disease knowledge. The constructed knowledge graph offers insights for controlling tomato leaf pests and diseases and provides new research directions for pest control in other crops.

Abstract Background Although Tai Chi (TC) is an evidence-based fall prevention training for older adults, its effective movements remain unclear, which may limit the practice of TC. The purpose of this study was to compare the effectiveness of TC lower extremity exercise (TC LEE), the 8-form Tai Chi (8-form TC), and a stretching control intervention for improving balance and functional mobility among older adults. Methods This was a randomized controlled trial. A total of 102 participants (79 ± 6 years old) were recruited from assisted living facilities. All participants were randomly assigned to the TC LEE (n = 40), 8-form TC (n = 31), and stretching (n = 31) groups in which they received the respective interventions for 16 weeks. The Berg Balance Scale (BBS), Timed Up and Go (TUG) test, and center of pressure (COP) measurements during quiet stance were collected prior to and following the 16-week interventions. Comparisons on all measurements were conducted among all groups. Results Significant improvements were found in BBS (P = 0.002), TUG test (P = 0.001), root mean square amplitude of COP displacement in the anterior–posterior (P = 0.001) and medial–lateral (P = 0.001) directions, and average COP speed in the anterior–posterior (P = 0.001) and medial–lateral (P = 0.001) directions after training in the TC intervention groups compared with the stretching group. The upper limit of the 95% confidence interval (CI) of differences in change scores on the BBS (-0.8 – 1.3 points) between the TC LEE group and the 8-form TC group was within equivalence margins (1.8 points), while the upper limit of the 95% CI of differences in change scores on the TUG test (0.1 – 2.1 s) exceeded the equivalence margin (0.7 s) with the TC LEE group having the larger change scores. Conclusion TC LEE can improve balance and functional mobility in older adults, and may have greater effect than the 8-form TC on improving functional mobility as measured by the TUG test. Trial registration ChiCTR2300070600 retrospectively registered.

This study demonstrated that wearable devices can distinguish between different levels of horse activity, categorized into three types based on the horse’s gaits: low activity (standing), medium activity (walking), and high activity (trotting, cantering, and galloping). Current research in activity level classification predominantly relies on deep learning techniques, known for their effectiveness but also their demand for substantial data and computational resources. This study introduces a combined acceleration threshold behavior recognition method tailored for wearable hardware devices, enabling these devices to classify the activity levels of horses directly. The approach comprises three sequential phases: first, a combined acceleration interval counting method utilizing a non-linear segmentation strategy for preliminary classification; second, a statistical analysis of the variance among these segments, coupled with multi-level threshold processing; third, a method using variance-based proximity classification for recognition. The experimental results show that the initial stage achieved an accuracy of 87.55% using interval counting, the second stage reached 90.87% with variance analysis, and the third stage achieved 91.27% through variance-based proximity classification. When all three stages are combined, the classification accuracy improves to 92.74%. Extensive testing with the Xinjiang Wild Horse Group validated the feasibility of the proposed solution and demonstrated its practical applicability in real-world scenarios.

As the core technology of precision animal husbandry, efficient and rapid identification of Jinnan cattle individuals can promote the scale, informatization and refinement of breeding, which is very necessary for the development of animal husbandry at this stage. However, the traditional livestock individual recognition method based on ear tag is labour-consuming, time-consuming, inefficient, easy to wear and limited by the recognition distance, and the accuracy is also very low. In order to solve this problem, a new method of Jinnan cattle individual recognition based on deep mutual learning is proposed by using the non-contact image recognition method. Two student networks are designed. They supervise each other and complete the task together. Their efficiency can be higher than that of a strong teacher network. Through this method, the individual recognition performance of Jinnan cattle is also enhanced. The experimental results verify the effectiveness of the method of deep mutual learning. Consult and learn from the peer network are used to improve generalization, so as to improve model recognition performance. Finally, the accuracy is 99.3% on the Jinnan cattle individual dataset established in this paper. The application of the contactless cattle individual recognition method in the farm is of great significance.

In contemporary logistics, the deployment of fixed-wing unmanned aerial vehicles (UAVs) as a transportation platform is experiencing rapid advancements, garnering substantial application within numerous logistic operations with pronounced efficacies. There are notable impediments to the utilization of commercial logistic-oriented fixed-wing UAVs, including elevated procurement and maintenance costs, extensive maintenance intervals, and unsuitability for small-volume, low-altitude transport tasks. These factors collectively exacerbate the risk associated with enterprise procurement and elevate the cost–benefit ratio. This study introduces the design and fabrication of a cost-efficient UAV for logistic delivery purposes, constructed primarily from cost-effective wood materials. This UAV is engineered to ferry payloads of up to 1000 g across a predefined aerial route at an altitude of 40 m. Upon reaching the designated location, the UAV is programmed to initiate the identification of the drop zone, thereafter descending to facilitate the release of the cargo. To mitigate the impact force during the landing phase, the payload was encapsulated within a sponge-damping layer, thereby preserving the integrity of the transported items. The empirical findings from outdoor delivery trials underscore the UAV’s ability to precisely execute payload drops at the targeted locations, confirming its potential to fulfill the logistical requirements for the transportation and delivery of small-volume items in a cost-effective, low-altitude framework. This investigation contributes to the burgeoning discourse on leveraging ultra-low-cost UAVs in logistics, offering a feasible solution to the challenges of cost and efficiency in UAV-operated delivery systems.

The traditional PID controller in quadrotor UAVs has poor performance, a large overshoot, and a long adjustment time, which limit its stability and accuracy in practical applications. In order to solve this problem, an improved whale optimization fuzzy PID control strategy based on CRICLE chaos map initialization is proposed, and a detailed simulation analysis was carried out using MATLAB software (MATLAB R2022B). Firstly, to more realistically reflect quadrotor UAVs’ flight behavior, a dynamic simulation model was established, and the dynamics and kinematic characteristics of the aircraft were considered. Then, CRICLE chaotic mapping initialization was introduced to improve the global search ability of the whale optimization algorithm and to effectively initialize the parameters of the fuzzy PID controller. This improved initialization method helped to speed up the convergence process and improve the stability of the control system. In the simulation experiments, we compared the performance indicators of the improved CRICLE chaotic mapping initialization whale optimization fuzzy PID controller to the traditional PID and fuzzy PID controllers, including overshoot, adjustment time, etc. The results show that the proposed control strategy has better performance than the traditional PID and fuzzy PID controllers, significantly reduces overshoot, and achieves a significant improvement in adjustment time. Therefore, the improved CRICLE chaotic mapping initialization whale optimization fuzzy PID control strategy proposed in this study provides an effective solution for improving the performance of the quadrotor control system and has practical application potential.

Soil moisture critically influences crop growth, especially in dryland environments. Precise agricultural management requires real-time monitoring of stratified soil moisture and assessment of crops’ daily water needs. We aim to provide low-cost, high-throughput information acquisition services for dryland regions with underdeveloped infrastructure and offer scientific support for sustainable water resource management. We used UAVs (Unmanned Aerial Vehicles) with multi-spectral sensors for routine maize monitoring, capturing leaf reflectance. Constructing vegetation indices, we quantified the relationship between leaf water content and surface soil moisture, using the Biswas model to predict deep soil moisture distribution. We used UVAs to monitor crop height and calculated the daily water demand for the entire growth period of corn using the Penman Montes equation. We found an R2 of 0.8603, RMSE of 2.455%, and MAE of 2.099% between NDVI and canopy leaf water content. A strong linear correlation (R2 = 0.7510) between canopy leaf water content and soil moisture was observed in the top 20 cm of soil. Deep soil moisture inversion from the top 20 cm soil moisture showed an R2 of 0.9984, with RE mostly under 10%, but exceeding 20% at 120 cm depth. We also constructed a maize height model aligning with a sigmoidal growth curve (R2 = 0.9724). Maize’s daily water demand varied from 0.7121 to 9.4263 mm, exhibiting a downward-opening parabolic trend. Integration of rainfall and soil water data allowed for dynamic irrigation adjustments, mitigating drought and water stress effects on crops. We highlighted UAV multi-spectral imaging’s effectiveness in monitoring crop water needs, facilitating quick daily water requirement estimations. Our work offers a scientific foundation for managing maize cultivation in drylands, enhancing water resource utilization.

Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content (p < 0.01) and were identified using Pearson correlation analysis, leading to the development of linear and nonlinear regression models for predicting moisture content in canopy leaves and soil. The results show a significant linear correlation between the predicted and actual canopy leaf moisture levels for the four crops, according to the chosen vegetation indices. The use of canopy leaf moisture content to predict surface soil moisture (0–20 cm) demonstrated enhanced accuracy. The models designed for the top 20 cm of soil moisture successfully estimated deep soil moisture levels (up to 200 cm) for all four crops. The 20 cm range soil moisture model showed improvements over the 10 cm range model, with increases in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas.

Accurate identification of plant diseases is a critical task in agricultural production. The existing deep learning crop disease recognition methods require a large number of labeled images for training, limiting the implementation of large-scale detection. To overcome this limitation, this study explores the application of self-supervised learning (SSL) in plant disease recognition. We propose a new model that combines a masked autoencoder (MAE) and a convolutional block attention module (CBAM) to alleviate the harsh requirements of large amounts of labeled data. The performance of the model was validated on the CCMT dataset and our collected dataset. The results show that the improved model achieves an accuracy of 95.35% and 99.61%, recall of 96.2% and 98.51%, and F1 values of 95.52% and 98.62% on the CCMT dataset and our collected dataset, respectively. Compared with ResNet50, ViT, and MAE, the accuracies on the CCMT dataset improved by 1.2%, 0.7%, and 0.8%, respectively, and the accuracy of our collected dataset improved by 1.3%, 1.6%, and 0.6%, respectively. Through experiments on 21 leaf diseases (early blight, late blight, leaf blight, leaf spot, etc.) of five crops, namely, potato, maize, tomato, cashew, and cassava, our model achieved accurate and rapid detection of plant disease categories. This study provides a reference for research work and engineering applications in crop disease detection.

To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster Generalized Iterative Closest Point (Faster_GICP) and Normal Distributions Transform (NDT). First, the algorithm creates a K-Dimensional tree (KD-Tree) structure to eliminate the dynamic obstacle point clouds. Then, the method uses a two-step point filter to reduce the number of feature points of the current frame used for matching and the number of data used for optimization. It also calculates the matching degree of normal distribution probability by meshing the point cloud, and optimizes the precision registration using the Hessian matrix method. In the complex orchard environment with multiple loopback events, the root mean square error and standard deviation of the trajectory of the LeGO-LOAM-FN algorithm are 0.45 m and 0.26 m which are 67% and 73% higher than those of the loopback registration algorithm in the Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain (LeGO-LOAM), respectively. The study proves that this method effectively reduces the influence of the cumulative error, and provides technical support for intelligent operation in the orchard environment.

With the advancement of machine vision technology, pig face recognition has garnered significant attention as a key component in the establishment of precision breeding models. In order to explore non-contact individual pig recognition, this study proposes a lightweight pig face feature learning method based on attention mechanism and two-stage transfer learning. Using a combined approach of online and offline data augmentation, both the self-collected dataset from Shanxi Agricultural University's grazing station and public datasets underwent enhancements in terms of quantity and quality. YOLOv8 was employed for feature extraction and fusion of pig face images. The Coordinate Attention (CA) module was integrated into the YOLOv8 model to enhance the extraction of critical pig face features. Fine-tuning of the feature network was conducted to establish a pig face feature learning model based on two-stage transfer learning. The YOLOv8 model achieved a mean average precision (mAP) of 97.73% for pig face feature learning, surpassing lightweight models such as EfficientDet, SDD, YOLOv5, YOLOv7-tiny, and swin_transformer by 0.32, 1.23, 1.56, 0.43 and 0.14 percentage points, respectively. The YOLOv8-CA model’s mAP reached 98.03%, a 0.3 percentage point improvement from before its addition. Furthermore, the mAP of the two-stage transfer learning-based pig face feature learning model was 95.73%, exceeding the backbone network and pre-trained weight models by 10.92 and 3.13 percentage points, respectively. The lightweight pig face feature learning method, based on attention mechanism and two-stage transfer learning, effectively captures unique pig features. This approach serves as a valuable reference for achieving non-contact individual pig recognition in precision breeding.

To address the current problem of the difficulty of extracting the phenotypic parameters of tomato plants in a non-destructive and accurate way, we proposed a method of stem and leaf segmentation and phenotypic extraction of tomato plants based on skeleton extraction and supervoxel clustering. To carry out growth and cultivation experiments on tomato plants in a solar greenhouse, we obtained multi-view image sequences of the tomato plants to construct three-dimensional models of the plant. We used Laplace’s skeleton extraction algorithm to extract the skeleton of the point cloud after removing the noise points using a multi-filtering algorithm, and, based on the plant skeleton, searched for the highest point path, height constraints, and radius constraints to separate the stem from the leaf. At the same time, a supervoxel segmentation method based on Euclidean distance was used to segment each leaf. We extracted a total of six phenotypic parameters of the plant: height, stem diameter, leaf angle, leaf length, leaf width and leaf area, using the segmented organs, which are important for the phenotype. The results showed that the average accuracy, average recall and average F1 scores of the stem and leaf segmentation were 0.88, 0.80 and 0.84, and the segmentation indexes were better than the other four segmentation algorithms; the coefficients of determination between the measurement values of the phenotypic parameters and the real values were 0.97, 0.84, 0.88, 0.94, 0.92 and 0.93; and the root-mean-square errors were 2.17 cm, 0.346 cm, 5.65°, 3.18 cm, 2.99 cm and 8.79 cm2. The measurement values of the proposed method had a strong correlation with the actual values, which could satisfy the requirements of daily production and provide technical support for the extraction of high-throughput phenotypic parameters of tomato plants in solar greenhouses.

Accurate cattle body detection is crucial for precision livestock farming. However, traditional cattle body detection methods rely on manual observation, which is both time-consuming and labor-intensive. Moreover, computer-vision-based methods suffer prolonged training times and training difficulties. To address these issues, this paper proposes a novel YOLOv5-EMA model for accurate cattle body detection. By incorporating the Efficient Multi-Scale Attention (EMA) module into the backbone of YOLO series detection models, the performance of detecting smaller targets, such as heads and legs, has been significantly improved. The Efficient Multi-Scale Attention (EMA) module utilizes the large receptive fields of parallel sub-networks to gather multi-scale spatial information and establishes mutual dependencies between different spatial positions, enabling cross-spatial learning. This enhancement empowers the model to gather and integrate more comprehensive feature information, thereby improving the effectiveness of cattle body detection. The experimental results confirm the good performance of the YOLOv5-EMA model, showcasing promising results across all quantitative evaluation metrics, qualitative detection findings, and visualized Grad-CAM heatmaps. To be specific, the YOLOv5-EMA model achieves an average precision (mAP@0.5) of 95.1% in cattle body detection, 94.8% in individual cattle detection, 94.8% in leg detection, and 95.5% in head detection. Moreover, this model facilitates the efficient and precise detection of individual cattle and essential body parts in complex scenarios, especially when dealing with small targets and occlusions, significantly advancing the field of precision livestock farming.

The efficient detection and counting of pig populations is critical for the promotion of intelligent breeding. Traditional methods for pig detection and counting mainly rely on manual labor, which is either time-consuming and inefficient or lacks sufficient detection accuracy. To address these issues, a novel model for pig detection and counting based on YOLOv5 enhanced with shuffle attention (SA) and Focal-CIoU (FC) is proposed in this paper, which we call YOLOv5-SA-FC. The SA attention module in this model enables multi-channel information fusion with almost no additional parameters, enhancing the richness and robustness of feature extraction. Furthermore, the Focal-CIoU localization loss helps to reduce the impact of sample imbalance on the detection results, improving the overall performance of the model. From the experimental results, the proposed YOLOv5-SA-FC model achieved a mean average precision (mAP) and count accuracy of 93.8% and 95.6%, outperforming other methods in terms of pig detection and counting by 10.2% and 15.8%, respectively. These findings verify the effectiveness of the proposed YOLOv5-SA-FC model for pig population detection and counting in the context of intelligent pig breeding.

The problem of sparse-view computed tomography (SVCT) reconstruction has become a popular research issue because of its significant capacity for radiation dose reduction. However, the reconstructed images often contain serious artifacts and noise from under-sampled projection data. Although the good results achieved by the prior image constrained compressed sensing (PICCS) method, there may be some unsatisfactory results in the reconstructed images because of the image gradient L1-norm used in the original PICCS model, which leads to the image suffering from step artifacts and over-smoothing of the edge as a result. To address the above-mentioned problem, this paper proposes a novel improved PICCS algorithm (NPICCS) for SVCT reconstruction. The proposed algorithm utilizes the advantages of PICCS, which could recover more details. Moreover, the algorithm introduces the L0-norm of image gradient regularization into the framework, which overcomes the disadvantage of conventional PICCS, and enhances the capability to retain edge and fine image detail. The split Bregman method has been used to resolve the proposed mathematical model. To verify the effectiveness of the proposed method, a large number of experiments with different angles are conducted. Final experimental results show that the proposed algorithm has advantages in edge preservation, noise suppression, and image detail recovery.

xsxsGenomic selection (GS) is an emerging technique for predicting unknown phenotypes using genome-wide marker coverage, allowing the use of efficient computational models to select individuals with high phenotypic values as candidate breeding populations. However, GS remains challenging inefficient crop breeding due to the limited size of training populations, the nature of genotype-environment interactions, and the complex interaction patterns between molecular markers. In this study, we use ensemble learning algorithms to construct gradient boosted decision tree (GBDT) models to achieve the prediction of phenotypic values from genotypic markers. We trained GBDT using the wheat GS dataset and compared the predictive performance with six other widely used GS models. The mean normalized discounted cumulative gain (MNDCG) method was used to evaluate the ability of each model to select individuals with high phenotypic values. The results of the study show that: (1) Bayesian models converge and reach a steady-state only when a sufficient number of iterations are set. As the number of iterations increases, the prediction accuracy of the Bayesian model increases, but the computational efficiency of the model decreases significantly. When 200,000 iterations are performed, the prediction performance of the five Bayesian models is similar and converges to a smooth state, and their prediction accuracy is 7.60% better than the GBDT model overall, and the computational efficiency of the GBDT model is 70 times that of the Bayesian model. (2) Overall, the overall prediction performance of the RRBLUP model was the best, but for some traits, the GBDT model still had a higher ability to select individuals with high phenotypic values than the RRBLUP and Bayesian models. (3) The prediction accuracy of GBDT and RRBLUP models was influenced by the subset of markers, and the higher the number of markers the higher the prediction accuracy of the models, so the reasonable selection of genetic marker data of appropriate size could improve the prediction performance of the models.

Abstract Background This study examines the feasibility, acceptability, and safety of a newly developed cognitive-enhancing Tai Ji Quan training intervention, delivered via remote videoconferencing, for older adults with mild cognitive impairment (MCI). Methods In a three-arm feasibility trial, community-dwelling older adults with MCI (N = 69; mean age = 74.6 years, 57% women) were randomized to a cognitively enhanced Tai Ji Quan (n = 23), standard Tai Ji Quan (n = 22), or stretching group (n = 24) and participated in a 60-minute online exercise session via Zoom, twice weekly for 16 weeks. Participants were recruited primarily in the state of Oregon through mass mailing and word of mouth. The primary outcomes were intervention feasibility (with respect to recruitment, online intervention delivery, fidelity and compliance, and attrition and retention rates), acceptability, and safety. We also assessed feasibility of online data collection and test-retest reliability and explored preliminary trends on secondary outcomes that included global cognitive function, dual-task cost, and domain-specific cognition function. Results The study had an average recruitment rate of 55%. Feasibility was demonstrated by the overall successful online program implementation, with good fidelity, acceptable compliance (76%), and excellent retention (94%). The cognitively enhanced Tai Ji Quan intervention was shown to be acceptable to participants as well as safe, with no major intervention-related moderate/severe events. At week 16, the group receiving cognitively enhanced Tai Ji Quan training showed a positive trend in the cognitive function and dual-task outcome measures whereas the group receiving standard Tai Ji Quan training exhibited positive trends on global and domain-specific cognitive measures. Conclusions Preliminary findings of this pilot study indicate the feasibility, acceptability, and safety of a tailored, cognitively enhanced Tai Ji Quan training intervention delivered remotely to home settings via videoconferencing for community-dwelling older adults with MCI. Trial registration Clinicaltrials.gov identifier NCT04070703

The population of biological species in the ecosystem is known sensitive to the periodic fluctuations of seasonal change, food resources and climatic conditions. Research in the ecological management discipline conventionally models the behavior of such dynamic systems through specific impulsive response functions, but the results of such research are applicable only when the environments conform exactly to the conditions as defined by the specific response functions that have been implemented for specific scenarios. This means that the application of previous work may be somewhat limited. Moreover, the intra and inter competitions among species have been seldom studied for modelling the prey-predator ecosystem. To fill in the gaps this paper models the delicate balance of two-prey and one-predator system by addressing three main areas of: ⅰ) instead of using the specific impulse response this work models the ecosystem through a more general response function; ⅱ) to include the effects due to the competition between species and ⅲ) the system is subjected to the influences of seasonal factors. The seasonal factor has been implemented here in terms of periodic functions to represent the growth rates of predators. The sufficient condition for the local and global asymptotic stability of the prey-free periodic solution and the permanence of the system have been subsequently obtained by using the Comparison techniques and the Floquet theorems. Finally, the correctness of developed theories is verified by numerical simulation, and the corresponding biological explanation is given.

Abstract Background Many prostate cancer survivors are treated with androgen deprivation therapy (ADT), but these therapies may increase frailty, worsen physical functioning, and increase fall risk. While exercise may counter functional declines associated with ADT, no studies have tested whether and which type of exercise may reduce falls and frailty. The purpose of this trial is to compare the relative efficacy of strength training versus tai ji quan training against each other and to a stretching control group on falls, frailty, and physical functioning in men expose to ADT for prostate cancer. Methods Prostate cancer survivors treated with ADT (N = 360) who have fallen in the past year or are at risk of a fall based on validated risk factors will be recruited to participate in this single-blind, parallel group, randomized trial. Participants will be randomized to one of three supervised, group training programs: (i) strength training, (ii) tai ji quan training, or (iii) stretching (control), that train 3×/week for 6 months. Outcomes are assessed at baseline, 3 (mid-intervention), 6 (immediately post-intervention), and 12 (follow-up) months. The primary outcome is falls assessed by monthly self-report. Secondary outcomes include the following: frailty (low lean body mass (by bioelectrical impedance analysis), exhaustion (by SF-36 vitality scale), low activity (by CHAMPS physical activity survey), slowness (by 4 m usual walk speed), and weakness (by chair stand time)); objective and subjective measures of physical function will also be collected. Negative binomial regression models will be used to assess differences in falls between groups, while mixed effects modeling will be used to compare the relative efficacy of training group on secondary outcomes. Discussion Exercise represents a non-pharmacologic approach to mitigate the problem of falls experienced among men treated with ADT. By engaging in appropriate exercise, men may be able to avoid or delay falls, frailty, and disability associated with their cancer treatment. Findings of the trial are expected to inform clinical practice about how exercise could be prescribed as part of cancer care for prostate cancer survivors prescribed ADT. Trial registration ClinicalTrials.gov NCT03741335 . Registered on November 18, 2018.

Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and deep learning. Human observation is often time-consuming and labor-intensive, while deep learning models with a large number of parameters can result in slow training times and low efficiency. To address these issues, this paper proposes a novel deep mutual learning enhanced two-stream pig behavior recognition approach. The proposed model consists of two mutual learning networks, which include the red–green–blue color model (RGB) and flow streams. Additionally, each branch contains two student networks that learn collaboratively to effectively achieve robust and rich appearance or motion features, ultimately leading to improved recognition performance of pig behaviors. Finally, the results of RGB and flow branches are weighted and fused to further improve the performance of pig behavior recognition. Experimental results demonstrate the effectiveness of the proposed model, which achieves state-of-the-art recognition performance with an accuracy of 96.52%, surpassing other models by 2.71%.

Pork is the most widely consumed meat product in the world, and achieving accurate detection of individual pigs is of great significance for intelligent pig breeding and health monitoring. Improved pig detection has important implications for improving pork production and quality, as well as economics. However, most of the current approaches are based on manual labor, resulting in unfeasible performance. In order to improve the efficiency and effectiveness of individual pig detection, this paper describes the development of an attention module enhanced YOLOv3-SC model (YOLOv3-SPP-CBAM. SPP denotes the Spatial Pyramid Pooling module and CBAM indicates the Convolutional Block Attention Module). Specifically, leveraging the attention module, the network will extract much richer feature information, leading the improved performance. Furthermore, by integrating the SPP structured network, multi-scale feature fusion can be achieved, which makes the network more robust. On the constructed dataset of 4019 samples, the experimental results showed that the YOLOv3-SC network achieved 99.24% mAP in identifying individual pigs with a detection time of 16 ms. Compared with the other popular four models, including YOLOv1, YOLOv2, Faster-RCNN, and YOLOv3, the mAP of pig identification was improved by 2.31%, 1.44%, 1.28%, and 0.61%, respectively. The YOLOv3-SC proposed in this paper can achieve accurate individual detection of pigs. Consequently, this novel proposed model can be employed for the rapid detection of individual pigs on farms, and provides new ideas for individual pig detection.

Considering the harvesting of prey and stocking of predator impulsively at different fixed moments of time, this paper studies the dynamics of a seasonally competitive m-prey and n-predator impulsive system, which is focused more specifically in four areas as follows: (i) we emphasize the dynamics of m-prey and n-predator in the ecosystem with a view to understanding how the present work may be able to apply to real environment applications; (ii) this work uses the general functional response instead of using specific impulse responses; (iii) considering the intra- and inter-competitions between species and (iv) the system is subjected to the influences of seasonal factors which imposes direct impacts to the delicate balance of biological systems. By using the comparison techniques and the Floquet theorems, the sufficient conditions for the ecosystem permanence and the asymptotic stabilities of the global and local prey-free periodic solutions have been subsequently obtained. This work is concluded with an in-depth discussion of the biological significance of the results obtained in this research. The obtained results can provide theoretical support for protecting endangered species and to help maintain the ecological balance, especially when it is applied to practical pest management, such as rodent controls in the farmland.

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Copyright of INMATEH - Agricultural Engineering is the property of INMATEH - Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

INTRODUCTION: An improved Yolov7 model. OBJECTIVES: To solve the weed detection and identification in complex field background. METHODS: The dataset was enhanced by online data enhancement, in which the feature extraction, feature fusion and feature point judgment of weed image were carried out by Yolov7 to predict the weed situation corresponding to the prior box. In the enhanced feature extraction part of Yolov7, CBAM, an attention mechanism combining channel and space, is introduced to improve the attention of the algorithm to weeds and strengthen the characteristics of weeds. RESULTS: The mean average precision (mAP) of the improved algorithm reached 91.15%, which was 2.06% higher than that of the original Yolov7 algorithm. Compared with the current mainstream target detection algorithms Yolox, Yolov5l, Fster RCNN, Yolov4-tiny and Yolov3, the mAP value of the improved algorithm increased by 4.35, 4.51, 5.41, 19.77 and 20.65 percentage points. Weed species can be accurately identified when multiple weeds are adjacent. CONCLUSION: This paper provides a detection model based on Yolov7 for weed detection in the field, which has a good detection effect on weed detection, and lays a research foundation for intelligent weeding robot and spraying robot. [ABSTRACT FROM AUTHOR]

Copyright of INMATEH - Agricultural Engineering is the property of INMATEH - Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background Aberrant intracellular or intercellular signaling pathways are important mechanisms that contribute to the development and progression of cancer. However, the intercellular communication associated with the development of ccRCC is currently unknown. The purpose of this study was to examine the aberrant tumor cell-to-cell communication signals during the development of ccRCC. Methods We conducted an analysis on the scRNA-seq data of 6 ccRCC and 6 normal kidney tissues. This analysis included sub clustering, CNV analysis, single-cell trajectory analysis, cell–cell communication analysis, and transcription factor analysis. Moreover, we performed validation tests on clinical samples using multiplex immunofluorescence. Results This study identified eleven aberrantly activated intercellular signaling pathways in tumor clusters from ccRCC samples. Among these, two of the majors signaling molecules, MIF and SPP1, were mainly secreted by a subpopulation of cancer stem cells. This subpopulation demonstrated high expression levels of the cancer stem cell markers POU5F1 and CD44 (POU5F1hiCD44hiE.T), with the transcription factor POU5F1 regulating the expression of SPP1. Further research demonstrated that SPP1 binds to integrin receptors on the surface of target cells and promotes ccRCC development and progression by activating potential signaling mechanisms such as ILK and JAK/STAT. Conclusion Aberrantly activated tumor intercellular signaling pathways promote the development and progression of ccRCC. The cancer stem cell subpopulation (POU5F1hiCD44hiE.T) promotes malignant transformation and the development of a malignant phenotype by releasing aberrant signaling molecules and interacting with other tumor cells.

Abstract Objective To investigate and compare the general information, medication, and the occurrence time of major adverse cardiovascular events (MACE) between patients with myocardial infarction with non‐obstructive coronary artery myocardial infarction (MINOCA) and those with obstructive coronary artery disease (MICAD). Methods A total of 325 acute myocardial infarction (AMI) patients were included (MINOCA: n = 31; MICAD: n = 294). The general information and medication of patients were recorded, including age, gender, prevalence of type 2 diabetes, left ventricular ejection fraction (LVEF), proportion of mitral regurgitation, cTn level, triglyceride level, electrocardiogram (ECG) findings, and drugs used (statins, drugs improving ventricular remodeling, antiplatelet drugs). The above indexes were compared, and statistical analysis was performed at different time points of MACE. Results MACE occurred significantly more in the MICAD group than in the MINOCA group (38.8% vs. 12.9%; p = .004) after 1 month to 1.5 years of treatment. The earlier the period of MACE occurred in patients with high coronary artery stenosis, it was an independent risk factor for the occurrence of MACE from 1 month to 1 year after surgery (p = .002), while the later the occurrence of MACE in patients with LVEF ≥55% (p = .029). It was not related to gender, cTn, and electrocardiography (ECG) indexes. Conclusion A correlation can be established between the risk factors of MINOCA and the occurrence time of MACE. In addition, MICAD is more commonly seen in male patients and patients with a higher cTn level and lower LVEF.

Effective and accurate action recognition is essential to the intelligent breeding of the Jinnan cattle. However, there are still several challenges in the current Jinnan cattle action recognition. Traditional methods are based on manual characteristics and low recognition accuracy. This study is aimed at the efficient and accurate development of Jinnan cattle action recognition methods to overcome existing problems and support intelligent breeding. The acquired data from the previous methods contain a lot of noise, which will cause individual cattle to have excessive behaviors due to unsuitability. Concerning the high labor costs, low efficiency, and low model accuracy of the above approaches, this study developed a bottleneck attention-enhanced two-stream (BATS) Jinnan cattle action recognition method. It primarily comprises a Spatial Stream Subnetwork, a Temporal Stream Subnetwork, and a Bottleneck Attention Module. It can capture the spatial-channel dependencies in RGB and optical flow two branches respectively, so as to extract richer and more robust features. Finally, the decision of the two branches can be fused to gain improved cattle action recognition performance. Compared with the traditional methods, the model proposed in this study has achieved state-of-the-art recognition performance, and the accuracy of motion recognition was 96.53%, which was 4.60% higher than other models. This method significantly improves the efficiency and accuracy of behavior recognition and provides an important research foundation and direction for the development of higher-level behavior analysis models in the future development of smart animal husbandry. [ABSTRACT FROM AUTHOR]