ATDM:An Anthropomorphic Aerial Tendon-driven Manipulator with Low-Inertia and High-Stiffness

Aerial Manipulator Systems (AMS) have garnered significant interest for their utility in aerial operations. Nonetheless, challenges related to the manipulator's limited stiffness and the coupling disturbance with manipulator movement persist. This paper introduces the Aerial Tendon-Driven Manipulator (ATDM), an innovative AMS that integrates a hexrotor Unmanned Aerial Vehicle (UAV) with a 4-degree-of-freedom (4-DOF) anthropomorphic tendon-driven manipulator. The design of the manipulator is anatomically inspired, emulating the human arm anatomy from the shoulder joint downward. To enhance the structural integrity and performance, finite element topology optimization and lattice optimization are employed on the links to replicate the radially graded structure characteristic of bone, this approach effectively reduces weight and inertia while simultaneously maximizing stiffness. A novel tensioning mechanism with adjustable tension is introduced to address cable relaxation, and a Tension-amplification tendon mechanism is implemented to increase the manipulator's overall stiffness and output. The paper presents a kinematic model based on virtual coupled joints, a comprehensive workspace analysis, and detailed calculations of output torques and stiffness for individual arm joints. The prototype arm has a total weight of 2.7 kg, with the end effector contributing only 0.818 kg. By positioning all actuators at the base, coupling disturbance are minimized. The paper includes a detailed mechanical design and validates the system's performance through semi-physical multi-body dynamics simulations, confirming the efficacy of the proposed design

Representation Separation for Semantic Segmentation with Vision Transformers

Vision transformers (ViTs) encoding an image as a sequence of patches bring new paradigms for semantic segmentation.We present an efficient framework of representation separation in local-patch level and global-region level for semantic segmentation with ViTs. It is targeted for the peculiar over-smoothness of ViTs in semantic segmentation, and therefore differs from current popular paradigms of context modeling and most existing related methods reinforcing the advantage of attention. We first deliver the decoupled two-pathway network in which another pathway enhances and passes down local-patch discrepancy complementary to global representations of transformers. We then propose the spatially adaptive separation module to obtain more separate deep representations and the discriminative cross-attention which yields more discriminative region representations through novel auxiliary supervisions. The proposed methods achieve some impressive results: 1) incorporated with large-scale plain ViTs, our methods achieve new state-of-the-art performances on five widely used benchmarks; 2) using masked pre-trained plain ViTs, we achieve 68.9% mIoU on Pascal Context, setting a new record; 3) pyramid ViTs integrated with the decoupled two-pathway network even surpass the well-designed high-resolution ViTs on Cityscapes; 4) the improved representations by our framework have favorable transferability in images with natural corruptions. The codes will be released publicly.Comment: 17 pages, 13 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Presence of CP4-EPSPS Component in Roundup Ready Soybean-Derived Food Products

With the widespread use of Roundup Ready soya (event 40-3-2) (RRS), the traceability of transgenic components, especially protein residues, in different soya-related foodstuffs has become an important issue. In this report, transgenic components in commercial soya (including RRS) protein concentrates were firstly detected by using polymerase chain reaction (PCR) and western blot. The results illustrated the different degradation patterns of the cp4-epsps gene and corresponding protein in RRS-derived protein concentrates. Furthermore, western blot was applied to investigate the single factor of food processing and the matrix on the disintegration of CP4-EPSPS protein in RRS powder and soya-derived foodstuffs, and trace the degradation patterns during the food production chain. Our results suggested that the exogenous full length of CP4-EPSPS protein in RRS powder was distinctively sensitive to various heat treatments, including heat, microwave and autoclave (especially), and only one degradation fragment (23.4 kD) of CP4-EPSPS protein was apparently observed when autoclaving was applied. By tracing the protein degradation during RRS-related products, including tofu, tou-kan, and bean curd sheets, however, four degradation fragments (42.9, 38.2, 32.2 and 23.4 kD) were displayed, suggesting that both boiling and bittern adding procedures might have extensive effects on CP4-EPSPS protein degradation. Our results thus confirmed that the distinctive residues of the CP4-EPSPS component could be traced in RRS-related foodstuffs

Characterising User Transfer Amid Industrial Resource Variation: A Bayesian Nonparametric Approach

In a multitude of industrial fields, a key objective entails optimising resource management whilst satisfying user requirements. Resource management by industrial practitioners can result in a passive transfer of user loads across resource providers, a phenomenon whose accurate characterisation is both challenging and crucial. This research reveals the existence of user clusters, which capture macro-level user transfer patterns amid resource variation. We then propose CLUSTER, an interpretable hierarchical Bayesian nonparametric model capable of automating cluster identification, and thereby predicting user transfer in response to resource variation. Furthermore, CLUSTER facilitates uncertainty quantification for further reliable decision-making. Our method enables privacy protection by functioning independently of personally identifiable information. Experiments with simulated and real-world data from the communications industry reveal a pronounced alignment between prediction results and empirical observations across a spectrum of resource management scenarios. This research establishes a solid groundwork for advancing resource management strategy development

Classifying Cervical Spondylosis Based on Fuzzy Calculation

Conventional evaluation of X-ray radiographs aiming at diagnosing cervical spondylosis (CS) often depends on the clinic experiences, visual reading of radiography, and analysis of certain regions of interest (ROIs) about clinician himself or herself. These steps are not only time consuming and subjective, but also prone to error for inexperienced clinicians due to low resolution of X-ray. This paper proposed an approach based on fuzzy calculation to classify CS. From the X-ray of CS manifestations, we extracted 10 effective ROIs to establish X-ray symptom-disease table of CS. Fuzzy calculation model based on the table can be carried out to classify CS and improve the diagnosis accuracy. The proposed model yields approximately 80.33% accuracy in classifying CS

The Study of Crash-Tolerant, Multi-Agent Offensive and Defensive Games Using Deep Reinforcement Learning

In the multi-agent offensive and defensive game (ODG), each agent achieves its goal by cooperating or competing with other agents. The multi-agent deep reinforcement learning (MADRL) method is applied in similar scenarios to help agents make decisions. In various situations, the agents of both sides may crash due to collisions. However, the existing algorithms cannot deal with the situation where the number of agents reduces. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we study a method to deal with a reduction in the number of agents in the training process without changing the structure of the neural network (NN), which is called the frozen agent method for the MADDPG (FA-MADDPG) algorithm. In addition, we design a distance–collision reward function to help agents learn strategies better. Through the experiments in four scenarios with different numbers of agents, it is verified that the algorithm we proposed can not only successfully deal with the problem of agent number reduction in the training stage but also show better performance and higher efficiency than the MADDPG algorithm in simulation

Iterative Learning Observer-Based High-Precision Motion Control for Repetitive Motion Tasks of Linear Motor-Driven Systems

Repetitive motion is one of the most common motion tasks in linear motor (LM)-driven system. The LM performs repetitive motion based on a periodic target trajectory under control, thus leading to periodic characteristics in certain system uncertainties. For this type of task, this article proposes an iterative learning observer-based high-precision motion control scheme that comprehensively considers high-accuracy model compensation and periodic uncertainties estimation. A recursive least squares (RLS) algorithm-based indirect adaptation strategy is used to achieve high-accuracy parameter estimation and model compensation. A saturated constrained-type iterative learning observer is designed to effectively estimate and compensate for periodic uncertainties. The closed-loop stability of the system is guaranteed in the presence of both periodic and nonperiodic uncertainties due to the composite adaptive robust control design. Comparative experiments are conducted on an LM-driven motion platform to verify the effectiveness and advantages of the proposed control scheme. Furthermore, the experimental results confirm the enhancement of both the transient and steady-state performance of the system

Potential to Reduce Chemical Fertilizer Application in Tea Plantations at Various Spatial Scales

Tea is the main commercial crop grown in China, and excessive application of chemical fertilizers in tea plantations is common. However, the potential to reduce chemical fertilizer use in tea plantations is unclear. In this study, Zhejiang Province was selected as the research object to systematically analyze the potential for tea plantation chemical-fertilizer reduction at different spatial scales. The geographic information system-based analytic hierarchy process method and Soil and Water Assessment Tool model were used to determine the chemical fertilizer reduction potential at the province scale and watershed scale, respectively. At the field scale, two consecutive years of field experiments were conducted on a tea plantation. Province-level analysis showed that 51.7% of the area had an average total fertilization intensity greater than 350 kg/hm2 and a high reduction potential. Watershed analysis revealed that chemical fertilizer reduction had better potential in reducing total nitrogen and total phosphorus inputs to runoff in the short term, whereas 50% organic fertilizer substitution was the best strategy to achieve long-term effects. The field experiments further proved that organic fertilizer substitution balanced tea growth and environmental protection. This study provides a useful method to investigate strategies to reduce chemical fertilizer use in tea-growing areas

Polymorphism of CONNEXIN37 gene is a risk factor for ischemic stroke in Han Chinese population

Abstract Background Stroke has a high fatality and disability rate, and is one of the main burdens to human health. It is thus very important to identify biomarkers for the development of effective approaches for the prevention and treatment of stroke. Connexin37 is an anti-inflammatory cytokine and is involved in chronic inflammation and atherosclerosis. Recent studies have found that CONNEXIN37 gene variations are associated with atherosclerosis diseases, such as coronary heart disease and stroke, but its association with stroke in distinct human populations remains to be determined. We report here the analysis of the association of the single nucleotide polymorphisms (SNPs) of CONNEXIN37 with ischemic stroke in Han Chinese population. Methods Two SNPs of CONNEXIN37 gene were analyzed in 385 ischemic stroke patients and 362 hypertension control patients using ligase detection reaction (LDR) method. Results Logistic regression analysis demonstrated that, AG and GG genotypes of SNP rs1764390 and CC genotype of rs1764391 of CONNEXIN37 were associated with an increased risk of ischemic stroke, and that G allele of rs1764390 is a risk factor for ischemic stroke. Further, we found that SNP rs1764390 and SNP rs1764391 in CONNEXIN37 were associated with ischemic stroke under additive/dominant model, and recessive/dominant model, respectively. Conclusion Our results indicate that CONNEXIN37 gene polymorphism is an ischemic stroke risk factor in Northern Han Chinese