Towards the Fabrication Strategies for Intelligent Wire Arc Additive Manufacturing of Wire Structures from CAD Input to Finished Product

With the increasing demand for freedom of part design in the industry, additive manufacturing (AM) has become a vital fabrication process for manufacturing metallic workpieces with high geometrical complexity. Among all metal additive manufacturing technologies, wire arc additive manufacturing (WAAM), which uses gas metal arc welding (GMAW), is gaining popularity for rapid prototyping of sizeable metallic workpieces due to its high deposition rate, low processing conditions limit, and environmental friendliness. In recent years, WAAM has been developed synergistically with industrial robotic systems or CNC machining centers, enabling multi-axis free-form deposition in 3D space. On this basis, the current research of WAAM has gradually focused on fabricating strut-based wire structures to enhance its capability of producing low-fidelity workpieces with high spatial complexity. As a typical wire structure, the large-size free-form lattice structure, featuring lightweight, superior energy absorption, and a high strength-weight ratio, has received extensive attention in developing its WAAM fabrication process. However, there is currently no sophisticated WAAM system commercially available in the industry to implement an automated fabrication process of wire or lattice structures. The challenges faced in depositing wire structures include the lack of methods to effectively identify individual struts in wire structures, 3D slicing algorithms for the whole wire structures, and path planning algorithms to establish reasonable deposition paths for these generated discrete sliced layers. Moreover, the welded area of the struts within the wire structure is relatively small, so the strut forming is more sensitive and more easily affected by the interlayer temperature. Therefore, the control and prediction of strut formation during the fabricating process is still another industry challenge. Simultaneously, there is also an urgent need to improve the processing efficiency of these structures while ensuring the reliability of their forming result

YOLO-FaceV2: A Scale and Occlusion Aware Face Detector

In recent years, face detection algorithms based on deep learning have made great progress. These algorithms can be generally divided into two categories, i.e. two-stage detector like Faster R-CNN and one-stage detector like YOLO. Because of the better balance between accuracy and speed, one-stage detectors have been widely used in many applications. In this paper, we propose a real-time face detector based on the one-stage detector YOLOv5, named YOLO-FaceV2. We design a Receptive Field Enhancement module called RFE to enhance receptive field of small face, and use NWD Loss to make up for the sensitivity of IoU to the location deviation of tiny objects. For face occlusion, we present an attention module named SEAM and introduce Repulsion Loss to solve it. Moreover, we use a weight function Slide to solve the imbalance between easy and hard samples and use the information of the effective receptive field to design the anchor. The experimental results on WiderFace dataset show that our face detector outperforms YOLO and its variants can be find in all easy, medium and hard subsets. Source code in https://github.com/Krasjet-Yu/YOLO-FaceV

Exploring Spatio-Temporal Representations by Integrating Attention-based Bidirectional-LSTM-RNNs and FCNs for Speech Emotion Recognition

Automatic emotion recognition from speech, which is an important and challenging task in the field of affective computing, heavily relies on the effectiveness of the speech features for classification. Previous approaches to emotion recognition have mostly focused on the extraction of carefully hand-crafted features. How to model spatio-temporal dynamics for speech emotion recognition effectively is still under active investigation. In this paper, we propose a method to tackle the problem of emotional relevant feature extraction from speech by leveraging Attention-based Bidirectional Long Short-Term Memory Recurrent Neural Networks with fully convolutional networks in order to automatically learn the best spatio-temporal representations of speech signals. The learned high-level features are then fed into a deep neural network (DNN) to predict the final emotion. The experimental results on the Chinese Natural Audio-Visual Emotion Database (CHEAVD) and the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpora show that our method provides more accurate predictions compared with other existing emotion recognition algorithms

Investigation of thermal management for lithium-ion pouch battery module based on phase change slurry and mini channel cooling plate

In this paper, the thermal management based on phase change slurry (PCS) and mini channel cooling plate for the lithium-ion pouch battery module was proposed. The three-dimensional thermal model was established and the optimum structure of the cooling plate with mini channel was designed with the orthogonal matrix experimental method to balance the cooling performance and energy consumption. The simulation results showed that the cooling performance of PCS consisting of 20% n-octadecane microcapsules and 80% water was better than that of pure water, glycol solution and mineral oil, when the mass flow rate was less than 3 x 10(-4) kg s(-1). For different concentrations of PCS, if the mass flow rate exceeded the critical value, its cooling performance was worse than that of pure water. When the cooling target for battery maximum temperature was higher than 309 K, the PCS cooling with appropriate microcapsule concentration had the edge over in energy consumption compared with water cooling. At last, the dimensionless empirical formula was obtained to predict the effect of the PCS's physical parameters and flow characteristics on the heat transfer and cooling performance. The simulation results will be useful for the design of PCS based battery thermal management systems. (C) 2018 Elsevier Ltd. All rights reserved

The sea lamprey Petromyzon marinus genome reveals the early origin of several chemosensory receptor families in the vertebrate lineage

<p>Abstract</p> <p>Background</p> <p>In gnathostomes, chemosensory receptors (CR) expressed in olfactory epithelia are encoded by evolutionarily dynamic gene families encoding odorant receptors (OR), trace amine-associated receptors (TAAR), V1Rs and V2Rs. A limited number of OR-like sequences have been found in invertebrate chordate genomes. Whether these gene families arose in basal or advanced vertebrates has not been resolved because these families have not been examined systematically in agnathan genomes.</p> <p>Results</p> <p><it>Petromyzon </it>is the only extant jawless vertebrate whose genome has been sequenced. Known to be exquisitely sensitive to several classes of odorants, lampreys detect fewer amino acids and steroids than teleosts. This reduced number of detectable odorants is indicative of reduced numbers of CR gene families or a reduced number of genes within CR families, or both, in the sea lamprey. In the lamprey genome we identified a repertoire of 59 intact single-exon CR genes, including 27 OR, 28 TAAR, and four V1R-like genes. These three CR families were expressed in the olfactory organ of both parasitic and adult life stages.</p> <p>Conclusion</p> <p>An extensive search in the lamprey genome failed to identify potential orthologs or pseudogenes of the multi-exon V2R family that is greatly expanded in teleost genomes, but did find intact calcium-sensing receptors (CASR) and intact metabotropic glutamate receptors (MGR). We conclude that OR and V1R arose in chordates after the cephalochordate-urochordate split, but before the diversification of jawed and jawless vertebrates. The advent and diversification of V2R genes from glutamate receptor-family G protein-coupled receptors, most likely the CASR, occurred after the agnathan-gnathostome divergence.</p

Anthropogenic activities dominated the spatial and temporal changes of normalized difference vegetation index (NDVI) in the Hehuang valley in the northeastern Qinghai Province between 2000 and 2020

The Hehuang Valley (HV) is a key development area in the Qinghai Province; understanding changes in the vegetation within this area is of great significance if we are to maintain the ecological quality of this regional environment. Based on the 30 m spatial resolution Normalized difference vegetation index (NDVI) time series dataset, this paper analyzes the spatial and temporal characteristics and evolutionary trends of NDVI in the HV from 2001 to 2020 under the influences of climate change and human activities, by applying Mann-Kendall trend analysis, the Hurst index, and residual analysis. Analysis showed that firstly, high NDVI values (&gt;0.5) were distributed in the low elevation areas of the HV except for towns and cropland, while the low NDVI values (&lt;0.5) were mainly distributed in the high elevation regions; the NDVI exhibited an increasing trend over the study period. Second, human activities promoted NDVI growth in the HV by changing land-use types, although there is a risk of vegetation degradation in the future. Third, the proportion of NDVI changes affected by climate change and human activities was determined to be 87.24% of the HV; furthermore, the contribution of human activities was three-fold higher than that of climate change. Fourth, managers should scientifically manage grasslands and forests and implement specific anthropogenic interventions based on the characteristics of regional NDVI degradation, to improve ecosystem resilience. These results can be used to quantitatively analyze the relative contributions of natural and anthropogenic factors to the ecological changes in the HV, and provide reference guidelines for the management of ecological environments

Machine learning-enabled globally guaranteed evolutionary computation

Evolutionary computation, for example, particle swarm optimization, has impressive achievements in solving complex problems in science and industry; however, an important open problem in evolutionary computation is that there is no theoretical guarantee of reaching the global optimum and general reliability; this is due to the lack of a unified representation of diverse problem structures and a generic mechanism by which to avoid local optima. This unresolved challenge impairs trust in the applicability of evolutionary computation to a variety of problems. Here we report an evolutionary computation framework aided by machine learning, named EVOLER, which enables the theoretically guaranteed global optimization of a range of complex non-convex problems. This is achieved by: (1) learning a low-rank representation of a problem with limited samples, which helps to identify an attention subspace; and (2) exploring this small attention subspace via the evolutionary computation method, which helps to reliably avoid local optima. As validated on 20 challenging benchmarks, this method finds the global optimum with a probability approaching 1. We use EVOLER to tackle two important problems: power grid dispatch and the inverse design of nanophotonics devices. The method consistently reached optimal results that were challenging to achieve with previous state-of-the-art methods. EVOLER takes a leap forwards in globally guaranteed evolutionary computation, overcoming the uncertainty of data-driven black-box methods, and offering broad prospects for tackling complex real-world problems

MiR-29a Knockout Aggravates Neurological Damage by Pre-polarizing M1 Microglia in Experimental Rat Models of Acute Stroke

ObjectiveBy exploring the effects of miR-29a-5p knockout on neurological damage after acute ischemic stroke, we aim to deepen understanding of the molecular mechanisms of post-ischemic injury and thus provide new ideas for the treatment of ischemic brain injury.MethodsmiR-29a-5p knockout rats and wild-type SD rats were subjected to transient middle cerebral artery occlusion (MCAO). miR-29a levels in plasma, cortex, and basal ganglia of ischemic rats, and in plasma and neutrophils of ischemic stroke patients, as well as hypoxic glial cells were detected by real-time PCR. The infarct volume was detected by TTC staining and the activation of astrocytes and microglia was detected by western blotting.ResultsThe expression of miR-29a-5p was decreased in parallel in blood and brain tissue of rat MCAO models. Besides, miR-29a-5p levels were reduced in the peripheral blood of acute stroke patients. Knockout of miR-29a enhanced infarct volume of the MCAO rat model, and miR-29a knockout showed M1 polarization of microglia in the MCAO rat brain. miR-29a knockout in rats after MCAO promoted astrocyte proliferation and increased glutamate release.ConclusionKnockout of miR-29a in rats promoted M1 microglial polarization and increased glutamate release, thereby aggravating neurological damage in experimental stroke rat models

100 essential questions for the future of agriculture

Publication history: Accepted - 8 March 2023; Published online - 11 April 2023.The world is at a crossroad when it comes to agriculture. The global population is growing, and the demand for food is increasing, putting a strain on our agricultural resources and practices. To address this challenge, innovative, sustainable, and inclusive approaches to agriculture are urgently required. In this paper, we launched a call for Essential Questions for the Future of Agriculture and identified a priority list of 100 questions. We focus on 10 primary themes: transforming agri-food systems, enhancing resilience of agriculture to climate change, mitigating climate change through agriculture, exploring resources and technologies for breeding, advancing cultivation methods, sustaining healthy agroecosystems, enabling smart and controlled-environment agriculture for food security, promoting health and nutrition-driven agriculture, exploring economic opportunities and addressing social challenges, and integrating one health and modern agriculture. We emphasise the critical importance of interdisciplinary and multidisciplinary research that integrates both basic and applied sciences and bridges the gaps among various stakeholders for achieving sustainable agriculture. Key points Growing demand and resource limitations pose a critical challenge for agriculture, necessitating innovative and sustainable approaches. The paper identifies 100 priority questions for the future of agriculture, indicating current and future research directions. Sustainable agriculture depends on interdisciplinary and multidisciplinary research that harmonises basic and applied sciences and fosters collaboration among different stakeholders