Novel Information About The Kinetic Effects Of Equine Shoe Modifications And Kinematic Effects Of Human Digital Devices For Improved Performance In Both Species

Equine shoes are frequently modified to enhance traction for horses that travel on paved surfaces for work, pleasure, or entertainment. Little is known about other common shoe modifications used to enhance traction like calks, tungsten carbide granules, or plastic composition. This information is vital to shoe design to protect the safety and welfare of all service, working, and leisure horses. The objective of the first part of this thesis was to quantify the effect of shoes with and without traction adaptions on kinetic measures in non-lame, light breed horses at a trot. Kinetic data was collected with a force platform from horses while unshod (U) and subsequently shod in random order with five distinct shoes: standard (S), high profile-low surface area calk (HC), low profile-high surface area calk (LC), thin layer tungsten carbide (TLC), and plastic-steel composite (C). Results indicate that in the forelimbs, peak vertical force increased with C versus S (P=0.0001), HC (P=0.0049), LC (P= 0.0110), and TLC (P=0.0246) shoes. In the hind limbs, peak braking force increased with C versus S (PPPP=0.0041). It increased with TLC versus HC (PPP=0.0079) and S shoe (P=0.0474). The human wrist (radiocarpal joints) has complex anatomy and motion that likely contributes to overuse injuries. Digital device use requires distinct wrist motions that may contribute to tissue damage with frequent, prolonged use and static loading. The second part of the thesis aimed to quantify wrist motion in radial-ulnar deviation and flexion-extension planes for use of digital devices and their manual counterparts in dominant and non-dominant hands of male and female professionals. Twelve subjects completed 4 paired daily living activities using digital and manual devices. Left and right wrist 3D motion was recorded with eight markers of a wireless, active motion detection system. This study established baseline values for medial and lateral radiocarpal extension and radial-ulnar deviation angles and ROM using digital devices. Both sex, handedness, and device size influence wrist motion

AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets

This paper studies the Binary Neural Networks (BNNs) in which weights and activations are both binarized into 1-bit values, thus greatly reducing the memory usage and computational complexity. Since the modern deep neural networks are of sophisticated design with complex architecture for the accuracy reason, the diversity on distributions of weights and activations is very high. Therefore, the conventional sign function cannot be well used for effectively binarizing full-precision values in BNNs. To this end, we present a simple yet effective approach called AdaBin to adaptively obtain the optimal binary sets $\{b_1, b_2\}$ ($b_1, b_2\in \mathbb{R}$) of weights and activations for each layer instead of a fixed set (\textit{i.e.}, $\{-1, +1\}$). In this way, the proposed method can better fit different distributions and increase the representation ability of binarized features. In practice, we use the center position and distance of 1-bit values to define a new binary quantization function. For the weights, we propose an equalization method to align the symmetrical center of binary distribution to real-valued distribution, and minimize the Kullback-Leibler divergence of them. Meanwhile, we introduce a gradient-based optimization method to get these two parameters for activations, which are jointly trained in an end-to-end manner. Experimental results on benchmark models and datasets demonstrate that the proposed AdaBin is able to achieve state-of-the-art performance. For instance, we obtain a 66.4% Top-1 accuracy on the ImageNet using ResNet-18 architecture, and a 69.4 mAP on PASCAL VOC using SSD300. The PyTorch code is available at \url{https://github.com/huawei-noah/Efficient-Computing/tree/master/BinaryNetworks/AdaBin} and the MindSpore code is available at \url{https://gitee.com/mindspore/models/tree/master/research/cv/AdaBin}.Comment: ECCV 202

Exploring the relationship between failure-learning-based entrepreneurship education and youth entrepreneurial resilience: A mediated moderation model

Entrepreneurial failure exists objectively in the process of entrepreneurship, and the fear of entrepreneurship failure inhibits youth entrepreneurship activities to a certain extent. Thus, failure-learning-based entrepreneurship education is critical to cultivating youth entrepreneurial literacy. However, previous research on this topic has not provided a clear answer to how to improve youth entrepreneurial resilience. To explore the relationship between failure-learning-based entreprene urship education and youth entrepreneurial resilience, using the questionnaire data of 399 youth recruited from China in October 2021 via the Credamo platform, the multiple regression analysis, and the Bootstrap method, we empirically analyzed the impact of failure-learning-based entrepreneurship education on youth entrepreneurial resilience, as well as the mediating effect of entrepreneurial cognition and the moderating effect of the fault-tolerant environment on the above relationship. The results show that failure-learning-based entrepreneurship education has a significant positive impact on youth entrepreneurial resilience. The two dimensions of willingness cognition and ability cognition in entrepreneurial cognition have a complete mediating effect on the impact of failure-learning-based entrepreneurship education on youth entrepreneurial resilience while the mediating effect of arrangements cognition is not significant. The fault-tolerant environment positively moderates the impact of failure-learning-based entrepreneurship education on entrepreneurial resilience, and its moderating effect is transmitted through the mediating effect of willingness cognition and ability cognition. A strong fault-tolerant environment enhances the impact of failure-learning-based entrepreneurship education on the formation of youth rational cognition, through the mediating effect of willing cognition and ability cognition, which further strengthens the positive impact on youth entrepreneurial

Characterization of protein-protein interactions between the nucleocapsid protein and membrane protein of the avian infectious bronchitis virus

Avian infectious bronchitis virus (IBV) is one of the major viral respiratory diseases of chickens. Better understanding of the molecular mechanism of viral pathogenesis may contribute significantly to the development of prophylactic, therapeutic and diagnostic reagents as well as help in infection control. Avian IBV belongs to the Coronaviridaes and is similar to the other known coronaviruses. Previous studies have indicated that protein–protein interactions between nucleocapsid (N) and the membrane (M) proteins in coronavirus are related to coronavirus viral assembly. However, cases of IBV are seldom reported. In this study, yeast two-hybrid and  co-immunoprecipitation techniques were applied to investigate possible interactions between IBV N and M proteins. We found that interaction of the N and M proteins took place in vivo and the residues 168 – 225 of the M protein and the residues 150 - 210 of the N protein were determined to be involved in their interaction. These results may provide some useful information on the molecular mechanism of IBV’s N and M proteins, which will facilitate therapeutic strategies aiming at the disruption of the association between membrane and nucleocapsid proteins and indicate a new drug target for IBV.Key words: Co-immunoprecipitation, membrane protein, nucleocapsid protein, protein-protein interaction, yeast two-hybrid