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Pork belly, prized for its unique flavor and texture, is often overlooked in breeding programs that prioritize lean meat production. The quality of pork belly is determined by the number and distribution of muscle and fat layers. This study aimed to assess the number of pork belly layers using deep learning techniques. Initially, semantic segmentation was considered, but the intersection over union (IoU) scores for the segmented parts were below 70%, which is insufficient for practical application. Consequently, the focus shifted to image classification methods. Based on the number of fat and muscle layers, a dataset was categorized into three groups: three layers (n = 1811), five layers (n = 1294), and seven layers (n = 879). Drawing upon established model architectures, the initial model was refined for the task of learning and predicting layer traits from B-ultrasound images of pork belly. After a thorough evaluation of various performance metrics, the ResNet18 model emerged as the most effective, achieving a remarkable training set accuracy of 99.99% and a validation set accuracy of 96.22%, with corresponding loss values of 0.1478 and 0.1976. The robustness of the model was confirmed through three interpretable analysis methods, including grad-CAM, ensuring its reliability. Furthermore, the model was successfully deployed in a local setting to process B-ultrasound video frames in real time, consistently identifying the pork belly layer count with a confidence level exceeding 70%. By employing a scoring system with 100 points as the threshold, the number of pork belly layers in vivo was categorized into superior and inferior grades. This innovative system offers immediate decision-making support for breeding determinations and presents a highly efficient and precise method for assessment of pork belly layers.

Sugar derived from crops is a crucial organic energy source studied in the Earth sciences, serving as a renewable and clean energy alternative. Biofuels produced from crop sugars are more environmentally friendly than traditional fossil fuel sources and contribute to solar energy storage and conversion within the Earth’s cycle. Using mangoes as a case study, this research employs near-infrared spectral analysis technology to develop an algorithm for a mango brix detection device. The study investigates the relationship between brix and absorbance, as well as changes in brix levels, and their application for on-site mango brix detection. Near-infrared spectral data in the range of 1300 nm to 2300 nm were collected during the mango ripening season in summer and preprocessed using various techniques. A neural network-based least squares modeling approach was utilized to develop a mango sugar content detection model, resulting in a correlation coefficient of 0.9055 and a root-mean-square error of 0.2192. To enhance model accuracy and avoid local optimization issues, this study incorporated the simulated annealing algorithm for model optimization, leading to a correlation coefficient of 0.9854 and a root-mean-square error of 0.0431. The findings demonstrate that the non-destructive testing model of mangoes based on near-infrared spectroscopy effectively detects brix changes and storage potential post-harvest, offering valuable insights for mango quality assessment, optimal picking and selling times, and market selection.

Article highlights A Markov model of multi-modal hierarchical spatial states is created. The analytical expression of comprehensive availability for the reliability index of the prediction device is derived. The CPU module failure rate threshold for intelligent production testing is amended. A theoretical basis for determining the type of key modules to be employed in field service installations is provided. The rationality and accuracy of the prediction model are verified by comparing the actual working conditions.

In pig production, pigs often show more aggressive behavior after mixing, which adversely affects animal welfare and growth performance. The Jumonji and structural domain-rich AT interaction domain 2 (JARID2) gene plays an important role in neurodevelopment in mice and various psychiatric disorders in humans. The JARID2 gene may impact the aggressive behavior of pigs. By observing the behavior of 500 weaned pigs during the first 72 h after mixing, the ear tissue samples of the 12 most aggressive and 12 least aggressive pigs were selected for DNA resequencing based on the intensity of their aggressive behavior. Large group correlation analysis indicated that the rs3262221458 site located in the 3′-UTR region of the porcine JARID2 gene has a strong relationship with the aggressive behavior of weaned pigs. Pigs with the mutant TT genotype of rs3262221458 have more aggressive behavior than those pigs with the GG and GT genotypes. The dual luciferase assay indicated that the luciferase activity of the plasmids containing the G allele of rs326221458 was significantly less than that of plasmids containing the T allele of rs326221458 and control groups. The binding ability of miR-9828-3p to sequences containing the T allele was less than that of sequences containing the G allele. The overexpression of miR-9828-3p in porcine neuroglial cells (PNGCs) and PK15 cells significantly decreased the mRNA and protein levels of the JARID2 gene. In addition, miR-9828-3p inhibited the proliferation of PNGCs. After inhibiting miR-9828-3p, the mRNA and protein expression levels of JARID2 increased, and the proliferation of PNGCs showed an opposite trend to the cells that forced the expression of miR-9828-3p. In addition, interference with the JARID2 gene by siRNA can effectively inhibit the proliferation of PNGCs. In summary, we found that the rs326221458 locus regulates the expression of the JARID2 gene by affecting the binding of miR-9828-3p and the JARID2 gene, thereby affecting the aggressive behavior of weaned pigs after mixing.

The exoskeleton robots to assist load-carrying have received much attention in recent years. However, their load capacity and comfort are still insufficient. In this paper, we have developed a novel parallel actuated lower limb exoskeleton(PALExo), that addresses these problems. The novelty is that each limb of the exoskeleton is a 2-SPU underactuated parallel mechanism. Owing to the innovative series elastic module (SEM) with multi-segment stiffness, PALExo is compliant and comfortable for the wearer. The high load characteristic of the parallel structure increases the exoskeleton load capacity, and it can accommodate wearers of different heights without manual adjustment. A dynamic model of the exoskeleton was established, and in order to increase the robustness of the system, we designed a force control method based on sliding mode control and disturbance observer. Finally, the rationality and load-bearing effect of the exoskeleton were verified through a series of experiments including swing following, squatting and walking with loads.

Background: Circular non-coding RNA (circRNA) has a variety of biological functions. However, the expression profile and potential effects of circRNA on atherosclerosis (AS) and vascular endothelial injury have not been fully elucidated. This study aims to identify the differentially expressed circRNAs in atherosclerotic aortic vessels and predict their potential functions in endothelial injury.Method: ApoE-/- mice were fed with high-fat diet for 12 weeks to induce AS. Atherosclerotic plaques were evaluated by H&E and Masson staining and immunohistochemistry; differentially expressed circRNAs were detected by Arraystar Circular RNA Microarray and verified by RT-PCR; the potential target mircoRNAs of circRNAs were predicted by miRanda, Tarbase, Targetscan and their expression changes were verified by RT-PCR; the potential target genes of mircoRNAs were predicted by Targetscan and verified by Western blot; the signaling pathways that they might annotate or regulate and their potential functions in vascular endothelial injury were predicted by gene enrichment analysis.Results: Fifty two circRNAs were up-regulated more than twice and 47 circRNAs were down-regulated more than 1.5 times in AS aortic vessels. Mmmu_circRNA_36781 and 37699 were up-regulated both in AS aortic vessels and H2O2-treated mouse aortic endothelial cells (MAECs). The expression of miR-30d-3p and miR-140-3p, the target microRNA of circRNA_37699 and circRNA_36781, were downregulated both in AS vessels and H2O2-treated MAECs. On the contrary, MKK6 and TP53RK, the potential target gene of miR-140-3p and miR-30d-3p, were upregulated both in AS aortic roots and H2O2-treated MAECs. Besides, gene enrichment analysis showed that MAPK and PI3K-AKT signaling pathway were the most potential signaling pathways regulated by the differentially expressed circRNAs in atherosclerosis.Conclusions: Mmu_circRNA_36781 (circRNA ABCA1) and 37699 (circRNA KHDRBS1) were significantly up-regulated in AS aortic vessels and H2O2-treated MAECs. They have potential regulatory effects on atherosclerosis and vascular endothelial injury by targeting miR-30d-3p-TP53RK and miR-140-3p-MKK6 axis and their downstream signaling pathways.

Weight-bearing exoskeletons are robots that need to carry loads and interact with humans frequently. Therefore, the actuators of these exoskeletons are supposed to be capable of outputting sufficient force with high compliance and little weight. A series–parallel elastic actuator (SPEA) is designed, in this work, to meet the demanding requirements of an exoskeleton robot called PALExo. A gas spring is installed in parallel with an electric cylinder to adjust the force output range of the actuator according to the needs of the exoskeleton. A series elastic module (SEM) is installed in series with the electric cylinder and gas spring to improve the compliance of the actuator, the stiffness of which is variable to adapt to the different stiffness requirements of the exoskeleton’s legs in the standing phase and swinging phase. A force controller combining dynamic compensation and a cascade control with an inner velocity loop and a disturbance observer is designed for the SPEA. The performance of the force controller is verified by experiments and the results demonstrate that the controller has good adaptability to the stiffness of the SEM.

In order to meet the assist requirements of extravehicular activity (EVA) for astronauts, such as moving outside the international space station (ISS) or performing on-orbit tasks by a single astronaut, this paper proposes an astronaut robotic limbs system (AstroLimbs) for extravehicular activities assistance. This system has two robotic limbs that can be fixed on the backpack of the astronaut. Each limb is composed of several basic module units with identical structure and function, which makes it modularized and reconfigurable. The robotic limbs can work as extra arms of the astronaut to assist them outside the space station cabin. In this paper, the robotic limbs are designed and developed. The reinforcement learning method is introduced to achieve autonomous motion planning capacity for the robot, which makes the robot intelligent enough to assist the astronaut in unstructured environment. In the meantime, the movement of the robot is also planned to make it move smoothly. The structure scene of the ISS for extravehicular activities is modeled in a simulation environment, which verified the effectiveness of the proposed method.

In this paper, an obstacle-surmounting-enabled lower limb exoskeleton with novel linkage joints that perfectly mimicked human motions was proposed. Currently, most lower exoskeletons that use linear actuators have a direct connection between the wearer and the controlled part. Compared to the existing joints, the novel linkage joint not only fitted better into compact chasis, but also provided greater torque when the joint was at a large bend angle. As a result, it extended the angle range of joint peak torque output. With any given power, torque was prioritized over rotational speed, because instead of rotational speed, sufficiency of torque is the premise for most joint actions. With insufficient torque, the exoskeleton will be a burden instead of enhancement to its wearer. With optimized distribution of torque among the joints, the novel linkage method may contribute to easier exoskeleton movements. [ABSTRACT FROM AUTHOR]