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关键词： Computer science   Policy   Technology   Society  

摘要： A guide to how China, the EU and the US are reining in artificial intelligence.

摘要： Objective. To analyze the interaction of positive coping style and quality of life (QoL) between survivors of pancreatic cancer and their family caregivers based on the Conceptual Framework of Caring Experience of Cancer Family and Actor-Partner Interdependence Model (APIM). Methods. This study adopted a cross-sectional design that selected 200 pairs of survivors of pancreatic cancer and their family caregivers hospitalized in the First and Second Afliated Hospitals of Nanjing Medical University from August 2020 to February 2021. Survivors of pancreatic cancer and their family caregivers completed Simple Coping Style Questionnaire and 12-item Short-Form Health Survey, whose results successfully built an APIM model. Results. The spousal relationship was the most common type of dyadic relationship. Family caregivers had higher levels of positive coping and QoL than cancer survivors. In terms of actor effects, the positive coping style of both survivors and caregivers significantly affected individual total scores of QoL and personal physical and psychological dimensions of QoL. As for the partner effects, the positive coping style of both survivors and caregivers significantly impacted their partners' total QoL scores and the positive coping style of survivors significantly impacted their caregivers' psychological level of QoL. Conclusions. The positive coping style of survivors of pancreatic cancer affects both their own QoL and their partner's QoL. Examining mutual effects between coping styles and QoL among survivors and their informal caregivers is an essential first step in providing comprehensive and cooperative care. The study has been reviewed by the Chinese Clinical Trial Registry, and registration information has been sent to the central database of the World Health Organization International Clinical Trial Registration Platform for global retrieval. This trial is registered with ChiCTR2300074087.

关键词： Computational science   Computer science   Nonlinear phenomena   Statistics  

摘要： Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex systems, the corresponding conserved quantities are difficult to identify, making it hard to analyze their dynamics and build stable predictive models. Current approaches for discovering conservation laws often depend on detailed dynamical information or rely on black box parametric deep learning methods. We instead reformulate this task as a manifold learning problem and propose a non-parametric approach for discovering conserved quantities. We test this new approach on a variety of physical systems and demonstrate that our method is able to both identify the number of conserved quantities and extract their values. Using tools from optimal transport theory and manifold learning, our proposed method provides a direct geometric approach to identifying conservation laws that is both robust and interpretable without requiring an explicit model of the system nor accurate time information. Conservation laws are crucial for analyzing and modeling nonlinear dynamical systems;however, identification of conserved quantities is often quite challenging. The authors propose here a geometric approach to discovering conservation laws directly from trajectory data that does not require an explicit dynamical model of the system or detailed time information.

关键词： Computer science   Machine learning   Scientific community   Statistics  

摘要： Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

关键词： Semantics  

摘要： Preprints play an increasingly critical role in academic communities. There are many reasons driving researchers to post their manuscripts to preprint servers before formal submission to journals or conferences, but the use of preprints has also sparked considerable controversy, especially surrounding the claim of priority. In this paper, a case study of computer science preprints submitted to arXiv from 2008 to 2017 is conducted to quantify how many preprints have eventually been printed in peer-reviewed venues. Among those published manuscripts, some are published under different titles and without an update to their preprints on arXiv. In the case of these manuscripts, the traditional fuzzy matching method is incapable of mapping the preprint to the final published version. In view of this issue, we introduce a semantics-based mapping method with the employment of Bidirectional Encoder Representations from Transformers (BERT). With this new mapping method and a plurality of data sources, we find that 66% of all sampled preprints are published under unchanged titles and 11% are published under different titles and with other modifications. A further analysis was then performed to investigate why these preprints but not others were accepted for publication. Our comparison reveals that in the field of computer science, published preprints feature adequate revisions, multiple authorship, detailed abstract and introduction, extensive and authoritative references and available source code. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational science   Computer science  

摘要： Drone swarms can achieve tasks via collaboration that are impossible for single drones alone. Synthetic aperture (SA) sensing is a signal processing technique that takes measurements from limited size sensors and computationally combines the data to mimic sensor apertures of much greater widths. Here we use SA sensing and propose an adaptive real-time particle swarm optimization (PSO) strategy for autonomous drone swarms to detect and track occluded targets in densely forested areas. Simulation results show that our approach achieved a maximum target visibility of 72% within 14 seconds. In comparison, blind sampling strategies resulted in only 51% visibility after 75 seconds and 19% visibility in 3 seconds for sequential brute force sampling and parallel sampling respectively. Our approach provides fast and reliable detection of occluded targets, and demonstrates the feasibility and efficiency of using swarm drones for search and rescue in areas that are not easily accessed by humans, such as forests and disaster sites.

关键词： Black olives   Dry-salted olives   Yeasts   Rep-PCR   RFLP   Amplicon target sequencing  

摘要： In the present study, the physicochemical (pH, water activity, moisture content, salt concentration) classical plate counts (total viable counts, yeasts, lactic acid bacteria, Staphylococcus aureus, Pseudomonas spp., Entero-bacteriaceae) and amplicon sequencing of naturally black dry-salted olives obtained from different retail outlets of the Greek market were investigated. According to the results, the values of the physicochemical characteristics presented great variability among the samples. Specifically, pH and water activity (aw) values ranged between 4.0 and 5.0, as well as between 0.58 and 0.91, respectively. Moisture content varied between 17.3 and 56.7 % (g H2 Omicron/100 g of olive pulp), whereas salt concentration ranged from 5.26 to 9.15 % (g NaCl/100 g of olive pulp). No lactic acid bacteria, S. aureus, Pseudomonas spp. and Enterobacteriaceae were detected. The mycobiota con-sisted of yeasts that were further characterized and identified by culture-dependent (rep-PCR, ITS-PCR, and RFLP) and amplicon target sequencing (ATS). Pichia membranifaciens, Candida sorbosivorans, Citeromyces nyon-sensis, Candida etchelsii, Wickerhamomyces subpelliculosus, Candida apicola, Wickerhamomyces anomalus, Tor-ulaspora delbrueckii and Candida versatilis were the dominant species according to ITS sequencing (culture-dependent), while ATS revealed the dominance of C. etchelsii, Pichia triangularis, P. membranifaciens, and C. versatilis among samples. The results of this study demonstrated considerable variability in quality attributes among the different commercial samples of dry-salted olives, reflecting a lack of standardization in the pro-cessing of this commercial style. However, the majority of the samples were characterized by satisfactory microbiological and hygienic quality and complied with the requirements of the trade standard for table olives of the International Olive Council (IOC) for this processing style in terms of salt concentration. In addition, the diver

关键词： Aircraft engines  

摘要： Remaining useful life (RUL) prediction for aircraft engines plays a critical role in the aviation industry. Although deep learning-based RUL prediction methods have achieved unprecedented success, they still have some limitations on prediction performance in terms of (1) the spatio-temporal dependencies of sensors have not been discovered. (2) the multiscale features constructed for capturing long-term dependencies fail to be treated differently. To break through these limitations, an interactive spatio-temporal feature fusion network (ISTFFN) is proposed for RUL prediction of aircraft engines. In the ISTFFN, multiscale discrete wavelet transformation is first developed for multiscale subseries set construction. Then, a multiscale spatio-temporal feature extraction module is proposed to mine spatio-temporal dependencies from constructed multiscale subseries set, and a versatile fusion strategy is designed to integrate multiscale spatio-temporal features in homo and hetero scale for RUL prediction. The effectiveness of the ISTFFN is validated on a prognostic benchmarking dataset of aircraft engines. Experimental results demonstrate that the ISTFFN can achieve more accurate RUL prediction than existing state-of-the-art algorithms. © 2023, The Authors. All rights reserved.

关键词： 黑水虻   肠道微生物   饲料   养殖   工业化  

摘要： 黑水虻幼虫(black soldier fly larvae)含有粗蛋白、氨基酸、脂肪和微量元素等营养物质,可以在动物饲养中部分甚至全部替代豆粕和鱼粉,是解决动物饲料行业蛋白质短缺最经济安全的措施。为了满足昆虫蛋白饲料的需求,黑水虻工业化养殖已经成为趋势。黑水虻幼虫不同饲养基质和饲养环境是其规模化养殖的限制性因素。文章就黑水虻肠道菌群特点和其对工业化养殖水平的影响因素进行论述,为进一步提高黑水虻工业化养殖效率提供思路和参考。