A Life Prediction Model of Multilayered PTH Based on Fatigue Mechanism.

Plated through hole (PTH) plays a critical role in printed circuit board (PCB) reliability. Thermal fatigue deformation of the PTH material is regarded as the primary factor affecting the lifetime of electrical devices. Numerous research efforts have focused on the failure mechanism model of PTH. However, most of the existing models were based on the one-dimensional structure hypothesis without taking the multilayered structure and external pad into consideration. In this paper, the constitutive relation of multilayered PTH is developed to establish the stress equation, and finite element analysis (FEA) is performed to locate the maximum stress and simulate the influence of the material properties. Finally, thermal cycle tests are conducted to verify the accuracy of the life prediction results. This model could be used in fatigue failure portable diagnosis and for life prediction of multilayered PCB

Ion channel regulation of gut immunity

Mounting evidence indicates that gastrointestinal (GI) homeostasis hinges on communications among many cellular networks including the intestinal epithelium, the immune system, and both intrinsic and extrinsic nerves innervating the gut. The GI tract, especially the colon, is the home base for gut microbiome which dynamically regulates immune function. The gut\u27s immune system also provides an effective defense against harmful pathogens entering the GI tract while maintaining immune homeostasis to avoid exaggerated immune reaction to innocuous food and commensal antigens which are important causes of inflammatory disorders such as coeliac disease and inflammatory bowel diseases (IBD). Various ion channels have been detected in multiple cell types throughout the GI tract. By regulating membrane properties and intracellular biochemical signaling, ion channels play a critical role in synchronized signaling among diverse cellular components in the gut that orchestrates the GI immune response. This work focuses on the role of ion channels in immune cells, non-immune resident cells, and neuroimmune interactions in the gut at the steady state and pathological conditions. Understanding the cellular and molecular basis of ion channel signaling in these immune-related pathways and initial testing of pharmacological intervention will facilitate the development of ion channel-based therapeutic approaches for the treatment of intestinal inflammation

Renewable energy and economic growth hypothesis: Evidence from N-11 countries

In the recent years, the trend of environmental sustainability is rapidly increasing by adopting renewable energy resources. However, the main concern is that whether renewable energy consumption contributes to economic growth. To investigate the issue, this study analyzes renewable energy led economic growth hypothesis in the Next-11 economies over the period 1990–2020. Also, this study aims to examine the influence of industry value added, gross national expenditure, and trade openness on economic growth of these economies. Along with the second-generation panel unit root test, this study employed the nonparametric panel data approach, i.e., quantile method of moments regression. The estimated results reveal the slopes coefficients are heterogeneous and cross-sectional dependency is present in the panel. The non-parametric approach reveals that validity of renewable energy led growth hypothesis. Also, the industry value added, gross national expenditure, and trade openness are found positively affecting economic growth of these economies. The panel causality test gives indication of the two way causal association between the variables. Based on the obtained results, policy implications are also provided for governors and researchers

How should we define a nociceptor in the gut-brain axis?

In the past few years, there has been extraordinary interest in how the gut communicates with the brain. This is because substantial and gathering data has emerged to suggest that sensory nerve pathways between the gut and brain may contribute much more widely in heath and disease, than was originally presumed. In the skin, the different types of sensory nerve endings have been thoroughly characterized, including the morphology of different nerve endings and the sensory modalities they encode. This knowledge is lacking for most types of visceral afferents, particularly spinal afferents that innervate abdominal organs, like the gut. In fact, only recently have the nerve endings of spinal afferents in any visceral organ been identified. What is clear is that spinal afferents play the major role in pain perception from the gut to the brain. Perhaps surprisingly, the majority of spinal afferent nerve endings in the gut express the ion channel TRPV1, which is often considered to be a marker of nociceptive neurons. And, a majority of gut-projecting spinal afferent neurons expressing TRPV1 are activated at low thresholds, in the normal physiological range, well below the normal threshold for detection of painful sensations. This introduces a major conundrum regarding visceral nociception. How should we define a nociceptor in the gut? We discuss the notion that nociception from the gut wall maybe a process encrypted into multiple different morphological types of spinal afferent nerve ending, rather than a single class of sensory ending, like free-endings, suggested to underlie nociception in skin

PG-VTON: A Novel Image-Based Virtual Try-On Method via Progressive Inference Paradigm

Virtual try-on is a promising computer vision topic with a high commercial value wherein a new garment is visually worn on a person with a photo-realistic effect. Previous studies conduct their shape and content inference at one stage, employing a single-scale warping mechanism and a relatively unsophisticated content inference mechanism. These approaches have led to suboptimal results in terms of garment warping and skin reservation under challenging try-on scenarios. To address these limitations, we propose a novel virtual try-on method via progressive inference paradigm (PGVTON) that leverages a top-down inference pipeline and a general garment try-on strategy. Specifically, we propose a robust try-on parsing inference method by disentangling semantic categories and introducing consistency. Exploiting the try-on parsing as the shape guidance, we implement the garment try-on via warping-mapping-composition. To facilitate adaptation to a wide range of try-on scenarios, we adopt a covering more and selecting one warping strategy and explicitly distinguish tasks based on alignment. Additionally, we regulate StyleGAN2 to implement re-naked skin inpainting, conditioned on the target skin shape and spatial-agnostic skin features. Experiments demonstrate that our method has state-of-the-art performance under two challenging scenarios. The code will be available at https://github.com/NerdFNY/PGVTON

A Cross-Scale Hierarchical Transformer with Correspondence-Augmented Attention for inferring Bird's-Eye-View Semantic Segmentation

As bird's-eye-view (BEV) semantic segmentation is simple-to-visualize and easy-to-handle, it has been applied in autonomous driving to provide the surrounding information to downstream tasks. Inferring BEV semantic segmentation conditioned on multi-camera-view images is a popular scheme in the community as cheap devices and real-time processing. The recent work implemented this task by learning the content and position relationship via the vision Transformer (ViT). However, the quadratic complexity of ViT confines the relationship learning only in the latent layer, leaving the scale gap to impede the representation of fine-grained objects. And their plain fusion method of multi-view features does not conform to the information absorption intention in representing BEV features. To tackle these issues, we propose a novel cross-scale hierarchical Transformer with correspondence-augmented attention for semantic segmentation inferring. Specifically, we devise a hierarchical framework to refine the BEV feature representation, where the last size is only half of the final segmentation. To save the computation increase caused by this hierarchical framework, we exploit the cross-scale Transformer to learn feature relationships in a reversed-aligning way, and leverage the residual connection of BEV features to facilitate information transmission between scales. We propose correspondence-augmented attention to distinguish conducive and inconducive correspondences. It is implemented in a simple yet effective way, amplifying attention scores before the Softmax operation, so that the position-view-related and the position-view-disrelated attention scores are highlighted and suppressed. Extensive experiments demonstrate that our method has state-of-the-art performance in inferring BEV semantic segmentation conditioned on multi-camera-view images

LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement

Recently, researchers have shown an increasing interest in automatically predicting the subjective evaluation for speech synthesis systems. This prediction is a challenging task, especially on the out-of-domain test set. In this paper, we proposed a novel fusion model for MOS prediction that combines supervised and unsupervised approaches. In the supervised aspect, we developed an SSL-based predictor called LE-SSL-MOS. The LE-SSL-MOS utilizes pre-trained self-supervised learning models and further improves prediction accuracy by utilizing the opinion scores of each utterance in the listener enhancement branch. In the unsupervised aspect, two steps are contained: we fine-tuned the unit language model (ULM) using highly intelligible domain data to improve the correlation of an unsupervised metric - SpeechLMScore. Another is that we utilized ASR confidence as a new metric with the help of ensemble learning. To our knowledge, this is the first architecture that fuses supervised and unsupervised methods for MOS prediction. With these approaches, our experimental results on the VoiceMOS Challenge 2023 show that LE-SSL-MOS performs better than the baseline. Our fusion system achieved an absolute improvement of 13% over LE-SSL-MOS on the noisy and enhanced speech track. Our system ranked 1st and 2nd, respectively, in the French speech synthesis track and the challenge's noisy and enhanced speech track.Comment: accepted in IEEE-ASRU202

Balanced Order Batching with Task-Oriented Graph Clustering

Balanced order batching problem (BOBP) arises from the process of warehouse picking in Cainiao, the largest logistics platform in China. Batching orders together in the picking process to form a single picking route, reduces travel distance. The reason for its importance is that order picking is a labor intensive process and, by using good batching methods, substantial savings can be obtained. The BOBP is a NP-hard combinational optimization problem and designing a good problem-specific heuristic under the quasi-real-time system response requirement is non-trivial. In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem. In BTOGCN, a task-oriented estimator network is introduced to guide the type-aware heterogeneous graph clustering networks to find a better clustering result related to the BOBP objective. Through comprehensive experiments on single-graph and multi-graphs, we show: 1) our balanced task-oriented graph clustering network can directly utilize the guidance of target signal and outperforms the two-stage deep embedding and deep clustering method; 2) our method obtains an average 4.57m and 0.13m picking distance ("m" is the abbreviation of the meter (the SI base unit of length)) reduction than the expert-designed algorithm on single and multi-graph set and has a good generalization ability to apply in practical scenario.Comment: 10 pages, 6 figure