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There are many ships and ports in Shanghai coastal zones, where disastrous weather occurs frequently. Meteorological disasters often threaten the safety of people’s lives and properties along the coast and in the ports. In the past, the meteorological warnings for Shanghai coastal zones are mainly based on those issued for Yangshan Port by Shanghai Marine Meteorological Centre (SMMC), which are called “unified warnings”. However, there are obvious differences in the time and intensity of meteorological disasters in each region, and the unified warnings cannot meet the needs of the production and operation of the shipping and ports. In 2020, Shanghai coastal zones were divided into five sub-zones, where the meteorological forecast and warnings were carried out separately from July 2020. Based on hourly observational data of the representative stations in Shanghai coastal zones and warning signal data from 2016 to 2022, the gale events are selected to analyse the statistical characteristics of temporal and spatial distribution and evaluate the forecast quality and the economic benefits of the zonal warnings. The results show that: (1) The farther away from the coastline, the more gale days, the higher the wind speed and the longer the duration; the higher the wind speed during the process, the more obvious the difference of wind scale. In particular, the wind scale caused by typhoons can range up to 5 levels. (2) Compared with the “unified warning”, the missing alarm rate (MAR) of gale warnings in each sea area has been reduced significantly, by up to 5%, the false alarm rate (FAR) for the western part of Yangtze River estuary is reduced by more than 8% and the TS score is significantly improved by more than 10%. (3) The advance time of gale warnings has been reduced by more than 3 hours, the maintaining duration has been shortened by more than 16 hours at most, which can reduce the loss of nearly 18 million RMB and improve the production efficiency of the coastal zones of Shanghai greatly. The results of this paper show that refined marine meteorological forecasts and early warnings provide a safety guarantee for marine transportation and port production operations, resulting in significant social and economic benefits and the enhancement of the comprehensive guarantee level of marine meteorological services in Shanghai. In the next step, we will continue to research and develop more refined objective forecast methods adapted to this business, then build a regional shared operational system platform and extend it to the Yangtze River Delta region, so as to promote the high-quality development of shipping meteorological integration in the Yangtze River Delta region.

We developed an accurate and efficient machine learning potential with DFT accuracy and applied it to the silicon dry/wet oxidation process to investigate the underlying physics of thermal oxidation of silicon (001) surfaces. The accuracy of the potential was verified by comparing the melting point and structural properties of silicon, the structural properties of a-SiO2, and the adsorption properties on the silicon surface with experiment and DFT data. In subsequent thermal oxidation simulations, we successfully reproduced the accelerated growth phenomenon of the wet oxidation in the experiment, discussed the oxide growth process in detail, and elucidated that the accelerated growth is due to hydrogen in the system that both enhances the adsorption of oxygen on the silicon surface and promotes the migration of oxygen atoms. Finally, we annealed the oxidized structure, counted the defect information in the structure before and after annealing, and analyzed the defect evolution behavior during the annealing process. [ABSTRACT FROM AUTHOR]

Somatic design is crucial for soft grippers to emulate the embodied intelligence of their biological counterparts, due to the blurry boundaries among material, structure, and function. There are five critical parts in somatic design, including morphology, material, fabrication, actuation, and variable stiffness. The strong nonlinear coupling among these factors often leads to mutual influence and functional compromise after integration. Herein, methods and strategies harnessed in these parts for performance improvement of soft grippers are systematically reviewed, particularly clarifying how to exert the coupling enhancement effects of the somatic parts for functional synergy. Finally, the remaining challenges and the future development directions of soft grippers for organism‐like intelligence and wide‐range applications are discussed. [ABSTRACT FROM AUTHOR]

Cloud manufacturing is one of the modern manufacturing systems that aim to control and manage production processes through a cloud platform to optimize the usage of resources and achieve customer objectives. Scheduling large-scale problems with diversity in the required tasks and the available services is a significant challenge for any researcher, especially with the possibility of recurring dynamic events, such as the arrival of new tasks. Thus, our first objective is to present a mixed-integer mathematical model that demonstrates the complexity of scheduling problems in a dynamic CMfg environment. We developed a parallel distributed genetic algorithm (PDGA) for optimizing large-scale scheduling problems on Apache Spark. The PDGA adopts a resilient distributed dataset (RDD) to improve the decomposition of the population and also enhance both the performance and the speed of the evolution process. The PDGA then updates the schedule by using a greedy strategy to improve the overall performance. To verify the effectiveness of the PDGA, we evaluated the algorithm on a benchmark and generated 8 large-scale problems based on this benchmark. The experiments show that the PDGA provides better performance and lower computational time compared to the traditional genetic algorithm and particle swarm optimization algorithm. [ABSTRACT FROM AUTHOR]

In an era where countries worldwide are emphasizing the process of educational informatization, traditional experimental teaching methods encounter inherent limitations, such as constraints related to the availability of experimental sites and the amount of experimental equipment. Consequently, it becomes a challenge to provide students with an unconstrained, open, and collaborative experimental environment. The digital twin, as a pivotal technology for achieving interactive mapping of the physical world and the information world, possesses attributes characterized by real time interactivity and the seamless fusion of virtual and tangible elements. As a result, various virtual simulation experimental teaching platforms grounded in digital twin technology are now extensively integrated into talent development initiatives. In the context of the "Production Planning and Control" course at Harbin Institute of Technology (HIT), the virtual simulation experimental teaching method is examined, leading to the creation of a virtual simulation instructional platform that blends the virtual and tangible realms, thanks to the foundation of digital twin technology. The virtual simulation experimental teaching platform detailed in this paper, specifically designed for optimizing scheduling within assembly line workshops, represents a pioneering model practice in the integration of digital twin technology into workshop-based experimental instruction and assumes an influential role in enhancing students' grasp of theoretical knowledge and their capacity for practical innovation. [ABSTRACT FROM AUTHOR]

Through the advancement of Data Science methodologies, a new era in output-only identification techniques has been inaugurated, driven by the integration of data-driven methodologies within the realm of Structural Health Monitoring (SHM). This study endeavors to introduce a simplified data-driven approach catering to System Identification (SI) and Response Estimation (RE). This is realized through the utilization of a summation of sine functions, fashioned as a model to harmonize with time domain vibration and acoustic responses. The fidelity of the findings is subsequently authenticated through the application of the Frequency Domain Decomposition (FDD) technique. In addition to the identification process, the proposed approach extends its applicability to predicting time domain responses at novel locations. This augmentation is achieved by harnessing an enhanced methodology founded on the principles of the Dynamic Mode Decomposition (DMD) technique. The veracity of these predicted outcomes is underscored through a comparison with measurements recorded at the same locations, alongside concurrent analysis of DMD-derived results. In order to affirm the efficacy of the proposed methodology, a case study involving a building grappling with enigmatic vibration issues is meticulously selected. The findings underscore that the proposed technique not only adeptly discerns unidentified vibrations without resorting to frequency domain transformation techniques, but also facilitates precise estimation of time domain responses. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine STC-1's structure, function, and differential expression in large and miniature pigs. We cloned the Hezuo pig's coding sequence, compared its homology, and used bioinformatics to assess the structure. RT-qPCR and Western blot were used to detect the expression in ten tissues of Hezuo pig and Landrace pig. The results showed that Hezuo pig was most closely related to Capra hircus and most distantly related to Danio rerio. The protein STC-1 has a signal peptide and its secondary structure is dominated by the alpha helix. The mRNA expression in the spleen, duodenum, jejunum, and stomach of Hezuo pigs was higher than that of Landrace pigs. And except for heart and duodenum, expression of the protein in Hezuo pig was higher than in another. In conclusion, STC-1 is highly conserved among different breeds of pigs, and the expression and distribution of its mRNA and protein are different in large and miniature pigs. This work can lay the foundation for future study into the mechanism of action of STC-1 in Hezuo pigs and the enhancement of breeding in miniature pigs. [ABSTRACT FROM AUTHOR]

The assembly process of large-scale and non-standard industrial equipment poses significant challenges due to its inherent scale-related complexity and proneness to errors, making it difficult to ensure process cost, production cycle, and assembly accuracy. In response to the limitations of traditional ineffective production models, this paper aims to explore and propose a digital twin (DT)-based technology paradigm for the intelligent assembly of large-scale and non-standard industrial equipment, focusing on both the equipment structure and assembly process levels. The paradigm incorporates key technologies that facilitate the integration of virtual and physical information, including the establishment and updating of DT models for assembly structures using actual data, the assessment of structural assemblability based on DT models, the planning and simulation of assembly processes, and the implementation of virtual commissioning technology tailored to the actual assembly process. The effectiveness of the proposed paradigm is demonstrated through a case study involving the actual assembly of a large-scale aerodynamic experimental equipment. The results confirm its ability to provide valuable technical support for the design, evaluation, and optimization of industrial equipment assembly processes. By leveraging the DT-based methodological system proposed in this paper, significant improvements in the transparency and intelligence of industrial equipment production processes can be achieved. [ABSTRACT FROM AUTHOR]

The welding process is characterized by its high energy density, making it imperative to optimize the energy consumption of welding robots without compromising the quality and efficiency of the welding process for their sustainable development. The above evaluation objectives in a particular welding situation are mostly influenced by the welding process parameters. Although numerical analysis and simulation methods have demonstrated their viability in optimizing process parameters, there are still limitations in terms of modeling accuracy and efficiency. This paper presented a framework for optimizing process parameters of welding robots in industry settings, where data augmentation was applied to expand sample size, auto machine learning theory was incorporated to quantify reflections from process parameters to evaluation objectives, and the enhanced non-dominated sorting algorithm was employed to identify an optimal solution by balancing these objectives. Additionally, an experiment using Q235 as welding plates was designed and conducted on a welding platform, and the findings indicated that the prediction accuracy on different objectives obtained by the enlarged dataset through ensembled models all exceeded 95%. It is proven that the proposed methods enabled the efficient and optimal determination of parameter instructions for welding scenarios and exhibited superior performance compared with other optimization methods in terms of model correctness, modeling efficiency, and method applicability. [ABSTRACT FROM AUTHOR]

Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots' process parameters in practical manufacturing applications. [ABSTRACT FROM AUTHOR]