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Purposes In order to improve the detection accuracy of electromagnetic abrasive particles on small particles, the influencing factors of the induced signal of one-excitation, two-in-ductive three-coil electromagnetic abrasive sensor are studied, and structural optimization and signal enhancement measures are proposed. Methods First, the mathematical model of one-excita tion, two-inductance three-coil electromagnetic abrasive sensor is established, and the model is simulated, and verified. Second, different radial positions and different sizes of abrasive grains are experimentally studied, and the relationship between the size and radial position of abrasive grains and the output signal is explored. Finally, the change law of output signal under different structural parameters is studied through finite element analysis, the parameter optimization scheme of the sensor is proposed and verified, and the shielding structure of the sensor is designed. Findings The results show that the strength of the output signal of the optimized sensor is increased by 13.75 times compared with that of the optimization period, and the minimum diameter of the ferromagnetic abrasive particles that can be detected is 82.26 μm, and the minimum diameter of non-ferromagnetic abrasive particles is 294.38 μm. The results are helpful to improve the detection accuracy on electromagnetic abrasive particles of the electromagnetic abrasive sensor.

The severe nuclear accident at the Fukushima Daiichi Nuclear Power Plant (NPP) in Japan on March 11, 2011 had a huge impact on the world’s nuclear regulatory bodies and industry. This paper summarizes the lessons learned from the Fukushima Daiichi Nuclear Accident, which include inadequacy of sitespecific extreme external natural hazards evaluation, prolonged loss of power and ultimate heat sink, insufficiency of response and management of severe accident monitoring and mitigation means and etc. It is also important to aware of that the accident can occur simultaneously on several units at a multiunit plant. In the decade after the Fukushima nuclear accident, nuclear safety improvement actions and nuclear safety regulatory standard revisions were implemented by major nuclear power countries such as the United States, Japan, France, and China. In United States, the new regulation 10CFR 50.155 Mitigation of BeyondDesignBasis Events (MBDBE) was come into forces to further enhance the capability of NPP to address the BDBE. In Japan, a new independent regulatory body, Nuclear Regulatory Authority (NRA), was established and new safety standards were released in three aspects, including design basis safety standards, severe accident measures, and safety standards relative to earthquakes/tsunamis. In France, complementary safety assessments (CSAs) were performed on all nuclear facilities, the concept hardened safety core was prepared and longterm improvement measures are still undergoing. In China, comprehensive safety inspection and external hazard safety margin assessment on NPPs were carried out, the “Code on the Safety of Nuclear Power Plant Design” (HAF102) was revised in 2016, which is a highlevel requirement on design and new nuclear safety goal, safety philosophy, and safety requirements were established in general. In this paper, the nuclear safety philosophy and requirements were elaborated in detail in the fields of practical elimination of early or large radioactive release, category of accident conditions, defense-in-depth concept, mobile equipment configuration and etc. some of which are explicit in nuclear safety review practice while others may be needed to be further clarified. In summary, certain specific suggestions of NPP safety in the future were given with international requirements and practices, as well as the domestic NPP characteristics being considered. It is also recommended that the relevant safety guides and technical positions on specific requirements should be improved constantly, a complete and logical nuclear safety regulation regime should be established, a balanced combination of deterministic and probabilistic approaches should be adapted, the specific nuclear safety and assessment criteria should be consolidated in areas such as practical elimination of early or large radioactive release, the independence between all defenseindepth levels, design extension conditions with core melting, as well as the number and configuration of mobile equipment on the multipleunit plant site.

Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation and efficiency. Addressing these challenges is imperative for advancing sustainable development within the geological environment. This paper aims to address issues related to the generation of geological environment remote sensing thematic products, sorting through the overall process of remote sensing thematic product generation, exploring algorithm encapsulation, combination, and execution under technical methods for container and workflow, and relies on the Spark distributed processing architecture to achieve efficient thematic product generation supported by multiple geological environment data processing models. Finally, taking the three SDGs of SDG6, SDG11, and SDG15 as examples, we achieved the generation of a variety of thematic products such as the interpretation of water body distribution, extraction of urban informal settlements and distribution of water and soil erosion. Meanwhile, we comparatively analyzed the efficiency of thematic product generation on different processing architectures, and the experimental results further verified the feasibility and effectiveness of our proposed solution. This research provides a programme for the automated and intelligent generation of geological environment remote sensing thematic products and effectively assists the construction of sustainable development in the geological environment. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) techniques leverage the justice system in terms of effectiveness and efficiency. AI-empowered multi-label crime classification can facilitate the precise and expedient categorization of various legal documents. Multi-label classification within the justice system has an indispensable role in achieving accurate legal categorization that invigorates case analysis, optimizes resource distribution and refines the contours of legal processes. To support this critical function, this paper proposes a temporal convolutional network (TCN)-bidirectional encoder representations from transformers (BERT)-coupled model for multi-label crime classification. The proposed method fuses the temporal formation and semantic information in the model to obtain a high-quality result. The experimental results show that the proposed method achieved the best accuracy in comparison to existing methods on a public dataset. [ABSTRACT FROM AUTHOR]

Coastal cities are increasingly vulnerable to urban storm surge hazards and the secondary hazards they cause (e.g., coastal flooding). Accurate representation of the spatio-temporal process of hazard event development is essential for effective emergency response. However, current knowledge graph representations face the challenge of integrating multi-source information with various spatial and temporal scales. To address this challenge, we propose a new information model for storm surge hazard events, involving a two-step process. First, a hazard event ontology is designed to model the components and hierarchical relationships of hazard event information. Second, we utilize multi-scale time segment integer coding and geographical coordinate subdividing grid coding to create a spatio-temporal framework, for modeling spatio-temporal features and spatio-temporal relationships. Using the 2018 typhoon Mangkhut storm surge event in Shenzhen as a case study and the hazard event information model as a schema layer, a storm surge event knowledge graph is constructed, demonstrating the integration and formal representation of heterogeneous hazard event information and enabling the fast retrieval of disasters in a given spatial or temporal range. [ABSTRACT FROM AUTHOR]

A test fleet including 47 gasoline vehicles with 6 different technical types was organized, and the adaptability of these vehicles under China 6 standard was studied. By analyzing the performance of the vehicles tested in laboratory and in real-world, technical types that could simultaneously reconcile emission and fuel consumption performance were proposed. The results show that all vehicles were well performed under laboratory conditions, but in real-world conditions, 50% direct injection vehicles without GPF would fail the RDE test. On the other hand, vehicles with GPF had better performance, all of which passed the RDE tests even if the conformity factor decreased from 2.1 to 1. In addition, it is found that the fuel consumption of the DI vehicles were lower than that of the PFI vehicles. As the DI vehicles with GPF showed better performance on the balance of emission and fuel consumption, indicating that they may have better potentials to deal with the increasing tightened emission and fuel consumption standards at the same time.