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"Shen, Yang"
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The impact of artificial intelligence on employment: the role of virtual agglomeration
Sustainable Development Goal 8 proposes the promotion of full and productive employment for all. Intelligent production factors, such as robots, the Internet of Things, and extensive data analysis, are reshaping the dynamics of labour supply and demand. In China, which is a developing country with a large population and labour force, analysing the impact of artificial intelligence technology on the labour market is of particular importance. Based on panel data from 30 provinces in China from 2006 to 2020, a two-way fixed-effect model and the two-stage least squares method are used to analyse the impact of AI on employment and to assess its heterogeneity. The introduction and installation of artificial intelligence technology as represented by industrial robots in Chinese enterprises has increased the number of jobs. The results of some mechanism studies show that the increase of labour productivity, the deepening of capital and the refinement of the division of labour that has been introduced into industrial enterprises through the introduction of robotics have successfully mitigated the damaging impact of the adoption of robot technology on employment. Rather than the traditional perceptions of robotics crowding out labour jobs, the overall impact on the labour market has exerted a promotional effect. The positive effect of artificial intelligence on employment exhibits an inevitable heterogeneity, and it serves to relatively improves the job share of women and workers in labour-intensive industries. Mechanism research has shown that virtual agglomeration, which evolved from traditional industrial agglomeration in the era of the digital economy, is an important channel for increasing employment. The findings of this study contribute to the understanding of the impact of modern digital technologies on the well-being of people in developing countries. To give full play to the positive role of artificial intelligence technology in employment, we should improve the social security system, accelerate the process of developing high-end domestic robots and deepen the reform of the education and training system.
Journal Article
Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks
A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed (
ϕ
,
ψ
) torsion angles of ca 12º. TALOS-N also reports sidechain χ
1
rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.
Journal Article
Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression
Rumination is strongly and consistently correlated with depression. Although multiple studies have explored the neural correlates of rumination, findings have been inconsistent and the mechanisms underlying rumination remain elusive. Functional brain imaging studies have identified areas in the default mode network (DMN) that appear to be critically involved in ruminative processes. However, a meta-analysis to synthesize the findings of brain regions underlying rumination is currently lacking. Here, we conducted a meta-analysis consisting of experimental tasks that investigate rumination by using Signed Differential Mapping of 14 fMRI studies comprising 286 healthy participants. Furthermore, rather than treat the DMN as a unitary network, we examined the contribution of three DMN subsystems to rumination. Results confirm the suspected association between rumination and DMN activation, specifically implicating the DMN core regions and the dorsal medial prefrontal cortex subsystem. Based on these findings, we suggest a hypothesis of how DMN regions support rumination and present the implications of this model for treating major depressive disorder characterized by rumination.
•Rumination is strongly and consistently correlated with depression.•Meta-analyze the findings of brain regions regarding to rumination.•Specifically examined the contribution of three DMN subsystems to rumination.•Rumination is specifically correlated with the DMN core regions and the dorsal medial prefrontal cortex subsystem.
Journal Article
أفكار حول تعميق الإصلاح
يناقش الكتاب سلسلة من الإيضاحات الهامة قدمها الرئيس الصيني والأمين العام للجنة المركزية للحزب الشيوعي الصيني، شي جين بينغ، وتدور حول أفكار الإصلاح وتوسيع الانفتاح على نحو شامل في الصين. يضم الكتاب أكثر من 70 وثيقة هامة على صورة كلمات شي جين بينغ وخطاباته وتعليقاته وتوجيهاته وينقسم الكتاب إلى 12 موضوعا خاصا تتضمن 274 قطعة من مقتطفات الأقوال، نشر بعضها لأول مرة.
Book
Progress and perspectives in dielectric energy storage ceramics
Dielectric ceramic capacitors, with the advantages of high power density, fast charge-discharge capability, excellent fatigue endurance, and good high temperature stability, have been acknowledged to be promising candidates for solid-state pulse power systems. This review investigates the energy storage performances of linear dielectric, relaxor ferroelectric, and antiferroelectric from the viewpoint of chemical modification, macro/microstructural design, and electrical property optimization. Research progress of ceramic bulks and films for Pb-based and/or Pb-free systems is summarized. Finally, we propose the perspectives on the development of energy storage ceramics for pulse power capacitors in the future.
Journal Article
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics
Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.
Journal Article
Enhanced high-temperature energy storage performances in polymer dielectrics by synergistically optimizing band-gap and polarization of dipolar glass
Polymer dielectrics play an irreplaceable role in electrostatic capacitors in modern electrical systems, and have been intensively studied with their polarization and breakdown strength (
E
b
) optimized for high discharged energy density (
U
d
) at elevated temperatures. Small molecules have been explored as fillers, yet they deteriorate thermal stability of matrix which limits their optimal loading to ~1 wt%. Herein, we develop a polymer blend dielectric consisting of common polyimide and a bifunctional dipolar glass polymer which are synthesized from two small molecule components with wide band-gap and large dipole moment. The bifunctional dipolar glass with large molecular weight not only maintains thermal stability of polymer blends even at a high loading of 10 wt%, but also induces substantial enhancement in polarization and
E
b
than any of individual components does, achieving an ultrahigh
U
d
of 8.34 J cm
−3
(150 °C) and 6.21 J cm
−3
(200 °C) with a charge-discharge efficiency of 90%.
The authors develop a polymer blend dielectric consisting of common polyimide and a bifunctional dipolar glass polymer which are synthesized through condensation polymerization of two small molecule components with wide band-gap and large dipole moment.
Journal Article