2D-SnS-Embedded Schottky Device with Neurotransmitter-Like Functionality Produced Using Proximity Vapor Transfer Method for Photonic Neurocomputing.

Van der Waals (vdW) materials consisting of two-dimensional (2D) building blocks have strong in-plane covalent bonding and weak interlayer interactions. While monolayer 2D materials exhibit impressive fracture resistance, as demonstrated in hexagonal boron nitride (h-BN), preserving these remarkable properties in vdW materials remains a challenge. Here we reveal an anomalous mechanical interlayer coupling that involves interlayer-friction toughening and edge-reconstruction embrittlement during the fracture of multilayer h-BN. Both asynchronous and synchronous fracture modes and their flaw-size dependence are identified. Edge reconstruction in the synchronous fracture mode can eliminate a toughening mechanism induced by lattice asymmetry in monolayer h-BN, leading to embrittlement of the multilayer h-BN, while the asynchronous fracture mode results in greater fracture resistance. Such findings will provide fundamental guidelines for engineering interlayer interactions in vdW materials including heterostructures and layered architectures for better mechanical and functional performances.

Germanene, a 2D honeycomb germanium crystal, is grown at graphene/Ag(111) and hexagonal boron nitride (h‐BN)/Ag(111) interfaces by segregating germanium atoms. A simple annealing process in N2 or H2/Ar at ambient pressure leads to the formation of germanene, indicating that an ultrahigh‐vacuum condition is not necessary. The grown germanene is stable in air and uniform over the entire area covered with a van der Waals (vdW) material. As an important finding, it is necessary to use a vdW material as a cap layer for the present germanene growth method since the use of an Al2O3 cap layer results in no germanene formation. The present study also proves that Raman spectroscopy in air is a powerful tool for characterizing germanene at the interfaces, which is concluded by multiple analyses including first‐principles density functional theory calculations. The direct growth of h‐BN‐capped germanene on Ag(111), which is demonstrated in the present study, is considered to be a promising technique for the fabrication of future germanene‐based electronic devices.

The presented article discusses the social impact of ‎innovations in the field of ICT and artificial ‎intelligence on society. The article examines the ‎possibilities of artificial intelligence's impact on the ‎social character of society, analyzes artificial ‎intelligence from its positive and negative aspects, and ‎notes the possibilities of pragmatic use of artificial ‎intelligence against the background of new changes, as ‎well as its real and probable consequences. At the same ‎time, it discusses global and regional challenges related ‎to artificial intelligence and analyzes the main goals of ‎national strategies related to artificial intelligence. ‎Preventive security measures and ethical aspects during ‎the use of artificial intelligence against the background ‎of changes in the field of artificial intelligence in ‎Azerbaijan are also among the main research topics.

Origins of heat transport anisotropy in MoTe2 and other bulk van der Waals materials

The rapid development in the field of artificial intelligence is bringing advances in many research fields including the renewable electricity‐driven CO 2 and CO reduction, which features as a promising approach to mitigate carbon emission and electrically synthesize value‐added chemicals. This review focuses on the recent progresses on artificial intelligence and machine learning in the field of CO (2) reduction based on the three aspects: the catalyst screening and design, reaction mechanism investigation, and the knowledge graph construction. For the catalyst development, this work discusses the acceleration of catalyst optimization and the generative artificial intelligence frameworks based on machine learning model predictions, which represent an emerging paradigm for designing efficient catalysts. For the investigation of reaction mechanisms, the combination of machine learning and in situ spectroscopies can present detailed information on the structure evolution and the electric field effects, enabling to bridge experiments and theories. For constructing the knowledge graph in the CO (2) electroreduction, the natural language processing technologies are demonstrating strong capabilities. Finally, the current challenges and potential perspectives on the interdisciplinary fields of CO (2) RR and artificial intelligence are proposed to provide inspiration for the continuous developments of this field.

The exploration of van der Waals (vdW) materials, renowned for their unique optical properties, is pivotal for advanced photonics. These materials exhibit exceptional optical anisotropy, both in-plane and out-of-plane, making them an ideal platform for novel photonic applications. However, the manual search for vdW materials with giant optical anisotropy is a labor-intensive process unsuitable for the fast screening of materials with unique properties. Here, we leverage geometrical and machine learning (ML) approaches to streamline this search, employing deep learning architectures, including the recently developed Atomistic Line Graph Neural Network. Within the geometrical approach, we clustered vdW materials based on in-plane and out-of-plane birefringence values and correlated optical anisotropy with crystallographic parameters. The more accurate ML model demonstrates high predictive capability, validated through density functional theory and ellipsometry measurements. Experimental verification with 2H-MoTe2 and CdPS3 confirms the theoretical predictions, underscoring the potential of ML in discovering and optimizing vdW materials with unprecedented optical performance.

The long-standing von Neumann architecture, while foundational to modern computing, intrinsically suffers from data-transfer inefficiency, which imposes severe limits on speed and energy efficiency in artificial intelligence, machine learning, and real-time data processing. Inspired by the remarkable energy efficiency of the human brain, neuromorphic computing seeks to emulate neural architectures through hardware capable of adaptive learning. Although early complementary metal-oxide-semiconductor (CMOS)-based neuromorphic systems captured basic synaptic behaviors, their narrow dynamic ranges and high operating voltages impede their application prospects. Van der Waals (vdW) materials, particularly tellurium (Te) and Selenium (Se), have recently emerged as promising platforms for next-generation neuromorphic devices due to their intriguing electronic and optoelectronic properties including considerable carrier mobilities, broadband photoresponse, and strong coupling between electrical, optical, and mechanical stimuli. These unique properties offer a direct physical analogy to biological synapses, thereby enabling neuromorphic computing. This Perspective introduces the fundamentals of synaptic behavior and neuromorphic computing and highlights the distinctive properties of Te/Se nanomaterials for synaptic devices. Then we discuss critical advances in Te/Se-based memristors, heterostructures, and electronic/optoelectronic synaptic transistors. In the end, we conclude this perspective with a discussion on the remaining challenges and future opportunities in this evolving field.

Biochemical origin of the schizophrenia and of the behavioral pathologies of schizoid type

The current stage of integration of artificial intelligence (AI) technologies into Educa- tion is characterized by a gradual transition to the triad “teacher – student – artificial intelligence”. AI is gradually beginning to take on many functions previously associated with the teacher, and this brings changes to the traditional learning process, transferring it to a new, more complex level in terms of solving cognitive problems. In turn, it creates a need for teachers and lecturers to solve new didactic objectives, which requires a revision of some of the teacher’s functions and requirements for his competence in the field of AI. The purpose of the study is to develop the structure and content of a teacher’s competence in the field of AI and to determine which of the structural components of this type of competence higher education teachers are able to implement at the present stage. Based on the analysis of academic literature, the following structural components of a teacher’s competence in the field of AI were proposed: 1) motivational; 2) normative and legal; 3) information security; 4) ethical; 5) prompt engineering; 6) teaching and assessment; 7) management of the edu- cational process; 8) professional development. As part of the empirical component of the study, an online survey was conducted to determine the structural components of competence in the field of AI of higher education teachers, which they are able to implement. The respondents were 219 teach- ers of specialized disciplines from 17 universities of the Russian Federation. The results of the study showed that among the substantive components of competence in the field of AI, teachers are more proficient in such aspects as teaching and assessment (x̄ = 3,35–3,71, Мо = 4), information security (x̄ = 3,56–3,88, Мо = 4), management of the educational process (x̄ = 3,41–3,84, Мо = 4). The most difficulties for teachers at the present stage are caused by the normative and legal component (x̄ = 3,35–3,47, Мо = 3) and prompt engineering (x̄ = 2,97–3,21, Мо = 3). The structure and content of the teacher’s competence in the field of AI proposed in the paper are of a recommendatory and framework nature. Based on them, depending on the specifics of the subject area and the availability of AI technical solutions, it is possible to develop the content of the competence in the field of using AI by teachers of specific academic disciplines or specialties.

This study is devoted to the consideration of the peculiarities of the policy of the EAEU states in the field of artificial intelligence (AI) in the context of global technological confrontation.Aim. To identify the key directions of the strategy of the EAEU member states in the field of AI, as well as to identify priority policy directions in this area at the union level.Tasks. To analyze the results of the development of the AI industry in Armenia, Belarus, Kazakhstan, Kyrgyzstan and Russia, to consider the main economic indicators, achievements in the creation of computing infrastructure, the development of their own research and development in the field of AI, to identify the main provisions of the key official documents of the EAEU member states in the field of AI. To identify the main directions of AI policy at the Union level.Methods. Research of official documents and on the basis of information and analytical technologies, comparative analysis, content analysis, inductive method using approaches of critical geopolitics.Results. The study showed that the policy of the EAEU countries in the field of AI is characterized by a focus on external technologies and investments, the creation of hotbeds of technological growth, largely due to the efforts of large commercial organizations around which the AI ecosystem is being built, including infrastructure, education and R&D. In conditions of relatively small domestic investments, the implementation of research and development results is largely associated with participation in international outsourcing projects. Against the background of increasingly escalating global technological competition, it is of paramount importance for the EAEU to strengthen its own technological sovereignty and to develop as soon as possible a unified union policy in the field of the most advanced technologies, including in the field of AI, as a conceptual basis for the development of bilateral and multilateral cooperation formats both within the EAEU itself and with other countries.Conclusions. International cooperation in high-tech spheres is becoming particularly relevant not only in the context of the accelerating transition to a new technological order, but also against the background of escalating trends of decoupling, concentration of technologies, tightening of sanctions policy. For the EAEU, the development of a unified AI policy is of priority importance, which will contribute to the intensification of scientific and technical cooperation, strengthening integration potential and technological sovereignty.

A comprehensive analysis of the Pan-Canadian Artificial Intelligence Strategy and its implementation measures aimed at the growth of Canada’s scientific and research potential in the field of artificial intelligence forms the foundation of this study. Canada’s selection as the subject of study is attributed to its distinction as a pioneering country to adopt a strategy of this nature, and proving its status through drafting the Artificial Intelligence and Data Act, known as AIDA. The authors have discerned and deliberated on the main organizational factors that have positioned Canada as one of the leading nations in artificial intelligence in accordance with AI country rankings. This article presents the components of the Pan-Canadian Strategy, encompassing principal tasks and areas, including the practical introduction of novel technologies due to second-phase commercialization. It outlines the key focus areas of Canada’s public policy, including research, development and retention of skilled professionals, and the creation of essential infrastructure. The article also consolidates some significant societal outcomes realized during its implementation while identifying current trends. The foundation and activities of national institutions are underscored as pivotal in fostering scientific and research potential, with special emphasis on the initiative to establish a new institute dedicated to the safety of artificial intelligence under the strong influence of AI Safety Summit at Bletchley Park. The authors identify the key participants in the artificial intelligence ecosystem who have the most influence on implementing the Strategy. The conclusions drawn from the article aid in fostering a deeper comprehension of the role played by organizational and administrative processes in propelling advancements in the field of artificial intelligence. The favorable impact on societal development is highlighted, provided risks are mitigated. Given Ukraine’s historical association with high intellectual potential, the findings of this study can be instrumental in honing the national policy pertaining to artificial intelligence in Ukraine.

The properties of van der Waals (vdW) materials often vary dramatically with the atomic stacking order between layers, but this order can be difficult to control. Trilayer graphene (TLG) stacks in either a semimetallic ABA or a semiconducting ABC configuration with a gate-tunable band gap, but the latter has only been produced by exfoliation. Here we present a chemical vapor deposition approach to TLG growth that yields greatly enhanced fraction and size of ABC domains. The key insight is that substrate curvature can stabilize ABC domains. Controllable ABC yields ~59% were achieved by tailoring substrate curvature levels. ABC fractions remained high after transfer to device substrates, as confirmed by transport measurements revealing the expected tunable ABC band gap. Substrate topography engineering provides a path to large-scale synthesis of epitaxial ABC-TLG and other vdW materials.

In this era of rapid development of global science and technology, artificial intelligence and industrial technology are converging at an unprecedented speed, driving social progress and industrial upgrading. In order to deeply discuss the latest development in these two and relevant fields, share research results, and promote academic exchanges and cooperation, we are honored to have organized the 2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA 2024). This proceedings is the crystallization of this event, which brings together the wisdom and insights of experts, scholars, engineers and technicians from all over the world. AIITA 2024 was grandly held in Guangzhou, China from April 12th to 14th, 2024 via hybrid form, attracting over 200 participants from all over the world. The conference aimed to provide a platform for experts and scholars, engineers and technicians, researchers and developers in the fields of artificial intelligence and industrial engineering to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, and strengthen academic research and discussion. During the conference, the participants had an in-depth discussion on a number of topics related to the application of artificial intelligence and industrial technology, and jointly explored the new direction of future development. In the keynote speech part, latest research results and cutting-edge technologies in related fields including Graph Neural Networks: Models and Applications in Pattern Recognition (by Prof. Bo Jiang, Anhui University, China), Distributed GPU-accelerated Evolutionary Computation (by Dr. Ran Cheng, Southern University of Science and Technology, China), Quantum Model Learning (by Prof. Daowen Qiu, Sun Yat-sen University, China), Face Image Editing from the Perspective of Conditional Generative (by Prof. Si Wu, South China University of Technology, China), Cooperative Global Robust Output Regulation for Nonlinear Multi-Agent Systems with Switching Networks (by Prof. Wei Liu, South China University of Technology, China) were discussed. At the same time, we have set up a number of presentations and panel discussion sessions, in which participants gained more opportunities to have in-depth discussions and exchanges on specific issues. This conference calls for papers on a wide range of topics, covering all aspects of artificial intelligence and industrial technology applications. We received hundreds of submissions from all over the world, and after strict evaluation and screening, we finally included some excellent papers in this proceedings. These papers not only have high academic value and practical significance, but also represent the latest research dynamics and trends in the fields of artificial intelligence and industrial technology applications. Finally, we would like to thank all the experts, scholars, engineers and staff who participated in the AIITA 2024 for their support and contribution. We believe that with the joint efforts of all of us, the research and application in the fields of artificial intelligence and industrial technology applications will achieve more brilliant achievements. The Committee of AIITA 2024 List of Committee Member is available in this Pdf.

Training personnel for the development of artificial intelligence technologies is one of the strategic tasks facing Russian universities. This article aims to analyze the employment of graduates from Russian universities in educational programs related to artificial intelligence. The study tests the hypothesis about the influence of university status on the Alliance ranking in the field of AI and the presence of specialized educational programs in AI at the university on the effectiveness of graduate employment in these programs. The stud y is based on the monitoring of universities implementing educational programs in the field of artificial intelligence (n = 191). As part of the analysis of the employment of graduates with competencies in the field of artificial intelligence, the specifics of their distribution among employment channels were determined. The authors identified key universities in terms of the effectiveness of training AI personnel, the amount of wages, and the most popular positions in employment. Based on the comparison of the employment rates of university graduates, as well as alternative sources of AI personnel training (self-education and professional retraining) with the indicators of personnel needs in the field of artificial intelligence, a conclusion was made about the provision of this need by 43.9 %. The obtained data allow us to conclude that universities have successfully implemented the Russian Government’s order to increase the volume of training of AI specialists. The value of the article lies in the presentation of unique factual materials that, for the first time, describe the employment of university graduates in the field of AI. In particular, the article details the losses of human resources in the AI field on the way from the admission of applicants to universities to the direct employment of graduates. The target audience of the article is researchers, experts, analysts, employees, and managers of universities, as well as government representatives involved in the process of developing artificial intelligence in Russia.

Many fascinating properties and applications of van der Waals (vdW) materials arise from how their layers slide against each other. Quantifying friction at the nanoscale is essential for further advancing the assembling of sophisticated heterostructures and their applications. A unified model and experimental validation for such friction forces remain elusive. A simple linear relationship between friction force and the energy barrier encountered by an atomic force microscope (AFM) tip interacting with crystal atoms has been proposed but not experimentally validated. Here, we present a unified understanding of nanoscale friction in vdW materials through lateral force microscopy measurements using a single AFM tip on four commonly studied vdW materials: MoS2, MoSe2, WS2, and graphene. Remarkably, the measured friction not only follows a proportional relationship with respect to the energy barriers calculated using the Lennard-Jones potential but agrees with predicted values within 5% across all examined materials. This agreement is achieved by accounting for variations in the number of contact-edge tip atoms that likely settle simultaneously into hollow sites in each material. Our findings provide critical insights to support a wide range of research efforts that rely on an advanced understanding of nanotribology in vdW materials.