A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.

Helical deep hole drilling is a process frequently used in industrial applications to produce bores with a large length to diameter ratio. For better cooling and lubrication, the deep drilling oil is fed directly into the bore hole via two internal cooling channels. Due to the inaccessibility of the cutting area, experimental investigations that provide information on the actual machining and cooling behavior are difficult to carry out. In this paper, the distribution of the deep drilling oil is investigated both experimentally and simulatively and the results are evaluated. For the Computational Fluid Dynamics (CFD) simulation, two different turbulence models, i.e. the RANS k-ω-SST and hybrid SAS-SST model, are used and compared. Thereby, the actual used deep drilling oil is modelled instead of using fluid dynamic parameters of water, as is often the case. With the hybrid SAS-SST model, the flow could be analyzed much better than with the RANS k-ω-SST model and thus the processes that take place during helical deep drilling could be  simulated with realistic details. Both the experimental and the simulative results show that the deep drilling oil movement is almost exclusively generated by the tool rotation. At the tool’s cutting edges and in the flute, the flow velocity drops to zero for the most part, so that no efficient cooling and lubrication could take place there. In addition, cavitation bubbles form and implode, concluding in the assumption that the process heat is not adequately dissipated and the removal of chips is adversely affected, which in turn can affect the service life of the tool and the bore quality. The carried out investigations show that the application of CFD simulation is an important research instrument in machining technology and that there is still great potential in the area of tool and process optimization.

Recent times have seen significant advancements in AI and NLP technologies, poised to revolutionize logistical decision-making across industries. This study investigates integrating ChatGPT, an advanced AI language model, into strategic, tactical, and operational logistics. Examining its applicability, benefits, and limitations, the study delves into ChatGPT's capacity for strategic logistics planning, facilitating nuanced decision-making through natural language interactions. At the tactical level, it explores ChatGPT's role in optimizing route planning and enhancing real-time decision support. The operational aspect scrutinizes ChatGPT's capabilities in micro-level logistics and emergency response. Ethical implications, encompassing data security and human-AI trust dynamics, are also analyzed. This report furnishes valuable insights for the logistics sector, emphasizing AI's potential in reshaping decision-making while underscoring the necessity for foresight, evaluation, and ethical considerations in AI integration. In this publication, it is assumed that ChatGPT is not entirely reliable for decision-making in the logistics field: at the strategic level, it can be effectively used for "brainstormin" in preparing decisions, but at the tactical and operational level, the depth of the knowledge is not sufficient to make appropriate decisions. Therefore, the answers provided by ChatGPT to the defined logistic tasks are compared with real logistic solutions. The article highlights ChatGPT's effectiveness at different levels of logistics and clarifies its potential and limitations in the logistics field.

Given the growing significance of the metaverse in research, it is crucial to understand its scope, relevance in the tourism industry, and the human-computer interaction it involves. The emerging field of metaverse tourism has a noticeable research gap, limiting a comprehensive understanding of the concept. This article addresses this gap by conducting a hybrid systematic review, including a variable-oriented literature review, to assess the extent and scope of metaverse tourism. A scrutiny on Scopus identified a reduced number of relevant documents. The analysis exposes theoretical and empirical gaps, along with promising opportunities in the metaverse and tourism intersection. These insights contribute to shaping a contemporary research agenda, emphasizing metaverse tourism. While this study offers an overview of current research in metaverse tourism, it is essential to recognize that this field is still in its early stages, marked by the convergence of technology and transformations in tourism. This exploration underscores the challenges and opportunities arising from the evolving narrative of metaverse tourism.

Surface-enhanced Raman scattering (SERS) spectrum has the characteristics of fast-detection, high-sensitivity and low-requirements for sample pretreatment. It plays a more and more important role in the detection of organic pollutants. In this study, MIL-101 and Au nanoparticles were prepared by hydrothermal method and aqueous solution reduction method respectively, and MIL-101/Au composite nanoparticles were prepared by electrostatic interaction. The SERS properties of the composite substrate were optimized by adjusting the size of Au nanoparticles and the surface distribution density of MIL-101 nanoparticles. The detection limit of Rhodamine 6G (R6G) for the composite substrate with the optimal ratio was investigated, which was as low as 10^–11 M. It is proved that MIL-101/Au composite nanoparticles have high sensitivity to probe molecules. When they are applied to the detection of persistent organic pollutants, the detection limit for fluoranthene can reach 10^–9 M and for 3,3’,4,4’-tetrachlorobiphenyl (PCB-77) can reach 10^–5 M.

Japan’s investment in the domestic construction industry has fallen to less than half its peak in 1992. Given the country’s declining population, Japanese construction companies must go global to remain profitable. To what extent the Japanese government and Japanese companies can contribute to meeting the growing infrastructure needs in the region is unclear as Japanese companies have long been operating primarily in Japan. The Japanese government has in recent years passed a series of new laws that encourage private sector participation in financing, building and operating public infrastructure. Through involvement in such public projects, Japanese companies have developed the skills and technologies to build a variety of infrastructures that are resilient to natural disasters and adaptable to various geographical conditions and social and economic development. But the major challenge for Japanese companies is to transform their business model drastically from one that relies on the domestic market to one that contributes to the social and economic development of third countries.