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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.

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]

Chiral inversions of enantiomers have significantly different biological activities, so it is important to develop simple and effective methods to efficiently identify optically pure compounds. Inspired by enzyme catalysis, the construction of chiral microenvironments resembling enzyme pockets in the pore space structure of metal-organic frameworks (MOFs) to achieve asymmetric enantioselective recognition and catalysis has become a new research hotspot. Here, a super-stable porphyrin-containing material PCN-224 is constructed by solvothermal method and a chiral microenvironment around the existing catalytic site of the material is created by post-synthesis modifications of the histidine (His) enantiomers. Experimental and theoretical calculations results show that the modulation of chiral ligands around Zr oxide clusters produces different spatial site resistances, which can greatly affect the adsorption and catalytic level of the enantiomeric molecules of tryptophan guests, resulting in a good enantioselective property of the material. It provides new ideas and possibilities for future chiral recognition and asymmetric catalysis., (© 2024 Wiley‐VCH GmbH.)