Digital supply chain: literature review of seven related technologies

This paper systematically reviews literature related with digital supply chains (DSC) and investigates the application status and development trend of different digital technologies in supply chain management. The review is conducted from the perspective of seven key digital supply chain technologies, i.e. Internet of Things (IoT) & Radio Frequency Identification (RFID), 5th Generation Mobile Communication Technology (5G), 3D Printing, Big data (BD), Blockchain, Digital Twins (DT), and Intelligent autonomous vehicles (IAVs). It highlights the main limitations and opportunities of the various DSC technologies, provides an overview of prior studies, and identifies knowledge gaps by outlining the advantages, weaknesses and restrictions of individual technology. The paper also aims at providing a development framework as a roadmap for the match of different digital technologies with different strategic goals

Evidence for the protein leverage hypothesis in preschool children prone to obesity.

BACKGROUND & AIMS The protein leverage hypothesis (PLH) proposed that strict regulation of protein intake drives energy overconsumption and obesity when diets are diluted by fat and/or carbohydrates. Evidence about the PLH has been found in adults, while studies in children are limited. Thus, we aimed to test the PLH by assessing the role of dietary protein on macronutrients, energy intake, and obesity risk using data from preschool children followed for 1.3 years. METHODS 553 preschool children aged 2-6 years from the 'Healthy Start' project were included. EXPOSURES The proportion of energy intake from protein, fat, and carbohydrates collected from a 4-day dietary record. OUTCOMES Energy intake, BMI z-score, fat mass (FM) %, waist- (WHtR) and hip-height ratio (HHtR). Power function analysis was used to test the leverage of protein on energy intake. Mixture models were used to explore interactive associations of macronutrient composition on all these outcomes, with results visualized as response surfaces on the nutritional geometry. RESULTS Evidence for the PLH was confirmed in preschool children. The distribution of protein intake (% of MJ, IQR: 3.2) varied substantially less than for carbohydrate (IQR: 5.7) or fat (IQR: 6.3) intakes, suggesting protein intake is most tightly regulated. Absolute energy intake varied inversely with dietary percentage energy from protein (L = -0.14, 95% CI: -0.25, -0.04). Compared to children with high fat or carbohydrate intakes, children with high dietary protein intake (>20% of MJ) had a greater decrease in WHtR and HHtR over the 1.3-year follow-up, offering evidence for the PLH in prospective analysis. But no association was observed between macronutrient distribution and changes in BMI z-score or FM%. CONCLUSIONS In this study in preschool children, protein intake was the most tightly regulated macronutrient, and energy intake was an inverse function of dietary protein concentration, indicating the evidence for protein leverage. Increases in WHtR and HHtR were principally associated with the dietary protein dilution, supporting the PLH. These findings highlight the importance of protein in children's diets, which seems to have significant implications for childhood obesity risk and overall health

Towards Exascale Computation for Turbomachinery Flows

A state-of-the-art large eddy simulation code has been developed to solve compressible flows in turbomachinery. The code has been engineered with a high degree of scalability, enabling it to effectively leverage the many-core architecture of the new Sunway system. A consistent performance of 115.8 DP-PFLOPs has been achieved on a high-pressure turbine cascade consisting of over 1.69 billion mesh elements and 865 billion Degree of Freedoms (DOFs). By leveraging a high-order unstructured solver and its portability to large heterogeneous parallel systems, we have progressed towards solving the grand challenge problem outlined by NASA, which involves a time-dependent simulation of a complete engine, incorporating all the aerodynamic and heat transfer components.Comment: SC23, November, 2023, Denver, CO., US

A unique subseafloor microbiosphere in the Mariana Trench driven by episodic sedimentation

Hadal trenches are characterized by enhanced and infrequent high-rate episodic sedimentation events that likely introduce not only labile organic carbon and key nutrients but also new microbes that significantly alter the subseafloor microbiosphere. Currently, the role of high-rate episodic sedimentation in controlling the composition of the hadal subseafloor microbiosphere is unknown. Here, analyses of carbon isotope composition in a ~ 750 cm long sediment core from the Challenger Deep revealed noncontinuous deposition, with anomalous 14C ages likely caused by seismically driven mass transport and the funneling effect of trench geomorphology. Microbial community composition and diverse enzyme activities in the upper ~ 27 cm differed from those at lower depths, probably due to sudden sediment deposition and differences in redox condition and organic matter availability. At lower depths, microbial population numbers, and composition remained relatively constant, except at some discrete depths with altered enzyme activity and microbial phyla abundance, possibly due to additional sudden sedimentation events of different magnitude. Evidence is provided of a unique role for high-rate episodic sedimentation events in controlling the subsurface microbiosphere in Earth’s deepest ocean floor and highlight the need to perform thorough analysis over a large depth range to characterize hadal benthic populations. Such depositional processes are likely crucial in shaping deep-water geochemical environments and thereby the deep subseafloor biosphere

Smart QR code generation and applications

This project had developed a web application that provide a one-stop solution for designing and generating the conference materials. The conference material generated includes almost all the essential administrative materials needed for a conference, which are attendees’ name badges, receipts and conference events’ tickets. The main objective of this project is to improve the efficiency of organizing committee in preparing conference materials, and enhance contacts exchanging among attendees as well. This web application was designed and developed to serve an academic conference Photonics @ 2017. Photonics @ 2017 is an international conference that combined from 3 international photonics and optics academic conferences. The expected number of conference attendees will be 1000 - 2000. This application mainly serves two types of users, administrator user and end-user. The administrator user of this web application is Photonics @ 2017 committee, while end-users are the conference attendees. For conference administrator users, the available functions are 1) interactive QR code design UI, 2) interactive conference material A4 page design, 3) configuration management, 4) files uploading and management, 5) conference material saving as PDF/printing, 6) private site that given access only to authorized personnel. The available functions for end-user are 1) scanning the QR code generated, 2) saving the QR code owner’s contact to local mobile device contact book, 3) make a phone call to the QR code owner, 4) send an email to the QR code owner.Bachelor of Engineerin

Does Smart City Construction Promote New Urbanization and Market Integration: Can We Have Both?

Smart city construction will have a profound impact on urban development and regional pattern, and it is necessary to explore its impact on new urbanization and regional market integration, so as to provide empirical evidence for accelerating the construction of smart cities. Based on the panel data of 238 prefecture-level cities from 2005 to 2019, this study considers the establishment of the national smart city pilot as a quasi-natural experiment and includes it in the analysis of the factors influencing market integration. The multi-period difference method was used to empirically investigate the influence of smart city pilot construction on market integration at the prefecture-level city level. The conclusions are as follows. According to the calculation results, the degree of inter-city market segmentation in the whole sample period from 2005 to 2019 showed a trend of shock decline, except that the degree of inter-city market segmentation increased in 2008 and 2009. The benchmark regression results show that although smart city pilot construction can promote economic urbanization, social urbanization, and environmental urbanization of the pilot cities, it is not conducive to market integration among cities. The conclusion that smart city construction will intensify market segmentation remains valid even after a series of robustness tests. The influence of smart city construction on inter-city market integration varies according to city level, city size, and city location, which is mainly manifested in the fact that it significantly inhibits the market integration of low-level cities, small and medium-sized cities, and inland cities, and significantly promotes the market integration of big cities and coastal cities. The influence mechanism shows that narrowing the information infrastructure gap not only helps in playing the role of smart city construction in promoting the development of new urbanization but also promotes market integration among cities. Narrowing the gap in public expenditure is conducive to the promotion of smart city construction for social urbanization and alleviates market segmentation among cities. Promoting the development of secondary industries can enhance environmental urbanization and ease market segmentation. Therefore, in the context of building a national unified large market, the pilot construction of smart cities should pay full attention to the negative effects of the "digital divide" between regions, encourage some localities with conditions to strengthen the construction of smart cities independently, and prevent the "digital divide" between regions from widening. This study further enriches the research on the factors influencing market integration at the city level and simultaneously provides some references for urban digital transformation and the development of new urbanization

Research on the Development Status and Countermeasures of Enterprise Earnings Management from the Perspective of Big Data

Earnings management plays a leading role in the development of enterprises. With the popularity of big data, when enterprises implement earnings management, analyzing the big data model from the computer can improve the development speed of enterprises. It is not only the key of accounting theory and corporate governance research, but also the top priority in practice. With the continuous development of economy and society in recent years, the problems of interrelated financial have frequently appeared, which leads investors to query the earning ability of related companies. Therefore, this paper studies the current situation of enterprise earnings management from the perspective of big data, and further analyzes the existing problems, and puts forward the countermeasures and suggestions for the relevant enterprise earnings management from the perspective of big data, hoping to standardize the enterprise earnings management behavior and maintain the order of the investment market

PGLR: Pseudo Graph and Label Reuse for Entity Relation Extraction

Named entity recognition and relation extraction are essential for structured information construction. However, existing methods, particularly in the field of relation prediction, struggled with information redundancy. In addition, conventional classification models only focus on predicting labels and comparing them to the ground truth, without considering the values and semantics of the labels. In this paper, we propose a novel end-to-end joint entity recognition and relation extraction model named PGLR (Pseudo Graph and Label Reuse for Entity Relation Extraction), which mainly decomposes the task into three hierarchical components to cope with the above issues. Firstly, the model generates a fully connected pseudo-graph that connects all possible entities and relations, followed by a confidence-based module to remove nodes and edges associated with non-relational facts. Secondly, a label reuse approach is employed to aggregate predicted labels and external knowledge, and the representations are reconstructed using an optimal re-parameterization technique. Thirdly, a gating mechanism is utilized to gauge the confidence of the reconstructed representations. The experimental results from three benchmark datasets (ACE04, ACE05, and SciERC) indicate that the proposed model achieves greater accuracy, faster inference speed, and requires fewer parameters

Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network

Vibration signals of gearbox under different loads are sensitive to the existence of the fault and composite fault vibration signals are complex. Traditional fault diagnosis methods mostly rely on signal processing methods. It is difficult for signal processing methods to separate effective information from those fault signals. Therefore, traditional fault diagnosis methods are difficult to accurately identify those faults. In this paper, a one-dimensional convolutional neural network (1-D CNN) intelligent diagnosis method with improved SoftMax function is proposed. Local mean decomposition (LMD) decomposes the signals into different physical fictions (PF). PFs are input into the matrix sample entropy based on Euclidean distance (MESE), and the PFs which best reflect fault characteristics are selected. Finally, the PFs by MESE are used to train the CNN to identify the faults of parallel-shaft gearbox. Experiment shows that MESE can quickly and accurately select the PFs with the most significant fault features. 1-D CNN can get nearly 100% recognition rate with less time and the CNN of SoftMax improved can effectively eliminate LMD endpoint effect. This method can successfully identify single faults, combination faults, and faults under different loads of the gearbox. Compared with other methods, this method has the characteristics of high efficiency, accuracy, and strong anti-interference. Therefore, it can effectively solve the problem of complex fault signal decomposition of gearbox and can diagnose the gearbox fault under different load operation. It has great significance for gearbox fault diagnosis in actual production