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Digital twin technology is considered as a key technology to realize cyber-physical systems (CPS). However, due to the complexity of building a digital equivalent in virtual space to its physical counterpart, very little progress has been achieved in digital twin application, especially in the complex product assembly shop-floor. In this paper, we propose a framework of digital twin-based smart production management and control approach for complex product assembly shop-floors. Four core techniques embodied in the framework are illustrated in detail as follows: (1) real-time acquisition, organization, and management of the physical assembly shop-floor data, (2) construction of the assembly shop-floor digital twin, (3) digital twin and big data-driven prediction of the assembly shop-floor, and (4) digital twin-based assembly shop-floor production management and control service. To elaborate how to apply the proposed approach to reality, we present detailed implementation process of the proposed digital twin-based smart production management and control approach in a satellite assembly shop-floor scenario. Meanwhile, the future work to completely fulfill digital twin-based smart production management and control concept for complex product assembly shop-floors are discussed.

Similarly to China, Japan has a long history of worshiping fortune gods. The act of making offerings and praying to these deities has been practiced since ancient times. Fortune gods are figures in Japanese folk religion that are believed to bring happiness, hope, and good luck. When speaking of fortune gods in Japan, people will first think of the Seven Lucky Gods. Apart from them, there are also some local fortune gods such as Fukusuke and Sendai Shiro. These gods share some common traits and also have connections with the Japanese folk belief in Fukuko (fortune child). This study adopts a comparative methodology to compare Japan’s Seven Lucky Gods with the local Japanese fortune gods as well as Fukuko, and then analyze their similarities. This article argues that the Japanese fortune gods have two major common characteristics: the super power to bring good fortune, and their distinctive appearance. By systematically analyzing the common features of Japanese fortune gods, this study will clarify the mechanism behind their deification as fortune deities and also help us to gain a better insight into the Japanese conceptions of deities and spirits.

Achieving Sustainable Development Goals (SDGs) in an era of rapid economic growth poses a significant challenge for policymakers and researchers. This study investigates the interconnected relationships between economic growth, innovation promoters, and sustainable development using data from 102 countries between 2016 and 2022. Employing multiple linear regression (MLR) models, we identify crucial factors influencing sustainable development. Our findings reveal a multifaceted relationship between economic growth and sustainable development, emphasizing the need for tailored development strategies. We highlight the diverse roles played by innovation promoters across various country clusters. We discern patterns of change in sustainable development and innovation performance through a comparative analysis of country clusters over time. These insights carry significant implications for policymakers, underscoring the importance of context-specific approaches to achieve SDGs.

Atherosclerosis is a complex inflammatory disease that affects the arteries and can lead to severe complications such as heart attack and stroke. Macrophages, a type of immune cell, play a crucial role in atherosclerosis initiation and progression. Emerging studies revealed that ion channels regulate macrophage activation, polarization, phagocytosis, and cytokine secretion. Moreover, macrophage ion channel dysfunction is implicated in macrophage-derived foam cell formation and atherogenesis. In this context, exploring the regulatory role of ion channels in macrophage function and their impacts on the progression of atherosclerosis emerges as a promising avenue for research. Studies in the field will provide insights into novel therapeutic targets for the treatment of atherosclerosis.

Breast cancer heterogeneity determines cancer progression, treatment effects, and prognosis. However, the precise mechanism for this heterogeneity remains unknown owing to its complexity. Here, we summarize the origins of breast cancer heterogeneity and its influence on disease progression, recurrence, and therapeutic resistance. We review the possible mechanisms of heterogeneity and the research methods used to analyze it. We also highlight the importance of cell interactions for the origins of breast cancer heterogeneity, which can be further categorized into cooperative and competitive interactions. Finally, we provide new insights into precise individual treatments based on heterogeneity.

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

A gram-negative aliphatic hydrocarbon-degrading bacterium SJTD-1 isolated from oil-contaminated soil was identified as Pseudomonas aeruginosa by comparative analyses of the 16S rRNA sequence, phenotype, and physiological features. SJTD-1 could efficiently mineralize medium- and long-chain n-alkanes (C12-C30) as its sole carbon source within seven days, showing the most optimal growth on n-hexadecane, followed by n-octadecane, and n-eicosane. In 36 h, 500 mg/L of tetradecane, hexadecane, and octadecane were transformed completely; and 2 g/L n-hexadecane was degraded to undetectable levels within 72 h. Two putative alkane-degrading genes (gene 3623 and gene 4712) were characterized and our results indicated that their gene products were rate-limiting enzymes involved in the synergetic catabolism of C12-C16 alkanes. On the basis of bioinformatics and transcriptional analysis, two P450 monooxygenases, along with a putative AlmA-like oxygenase, were examined. Genetically defective mutants lacking the characteristic alkane hydroxylase failed to degrade n-octadecane, thereby suggesting a different catalytic mechanism for the microbial transformation of alkanes with chain lengths over C18.

Positioning information greatly enhances the convenience of people’s lives and the efficiency of societal operations. However, due to the impact of complex indoor environments, GNSS signals suffer from multipath effects, blockages, and attenuation, making it difficult to provide reliable positioning services indoors. Smartphone indoor positioning and navigation is a crucial technology for enabling indoor location services. Nevertheless, relying solely on a single positioning technique can hardly achieve accurate indoor localization. We reviewed several main methods for indoor positioning using smartphone sensors, including Wi-Fi, Bluetooth, cameras, microphones, inertial sensors, and others. Among these, wireless medium-based positioning methods are prone to interference from signals and obstacles in the indoor environment, while inertial sensors are limited by error accumulation. The fusion of multi-source sensors in complex indoor scenarios benefits from the complementary advantages of various sensors and has become a research hotspot in the field of pervasive indoor localization applications for smartphones. In this paper, we extensively review the current mainstream sensors and indoor positioning methods for smartphone multi-source sensor fusion. We summarize the recent research progress in this domain along with the characteristics of the relevant techniques and applicable scenarios. Finally, we collate and organize the key issues and technological outlooks of this field.

Molecule-targeted therapy, such as sorafenib, is one of the effectively therapeutic options for advanced hepatocellular carcinoma (HCC). However, acquired resistance to sorafenib has been found in some HCC patients, resulting in poor prognosis. It is reported that PD-L1 and DNA methyltransferases (DNMTs) contribute to drug resistance. In this study, by inducing sorafenib-resistant HCC cell lines, we investigated their molecular and functional characteristics. Our data indicated that highly upregulated DNMT1 was positively correlated with PD-L1 overexpression in sorafenib-resistant HCC cells. We demonstrate that PD-L1 regulate DNMT1 through STAT3 signaling pathway. Knockdown of PD-L1 induced DNMT1-dependent DNA hypomethylation and restored the expression of methylation-silenced CDH1. Moreover, inactivation of NFκB blocked PD-L1/STAT3/DNMT1 pathway in sorafenib-resistant HCC cells. Functionally, genetic or pharmacological disruption of PD-L1 or/and DNMT1 sensitize HCC resistance to sorafenib. Importantly, dual inactivation of PD-L1 and DNMT1 by their inhibitor synergistically disrupts the colony formation of sorafenib-resistant HCC cells. These results demonstrate that targeting NFκB/PDL1/STAT3/DNMT1 axis is a new therapeutic strategy for preventing or overcoming the acquired resistance to sorafenib in HCC patients.

Carbon capture, utilization, and storage (CCUS) technology is considered an effective way to reduce greenhouse gases, such as carbon dioxide (CO2), which is significant for achieving carbon neutrality. Based on Derwent patent data, this paper explored the technology topics in CCUS patents by using the latent Dirichlet allocation (LDA) topic model to analyze technology’s hot topics and content evolution. Furthermore, the logistic model was used to fit the patent volume of the key CCUS technologies and predict the maturity and development trends of the key CCUS technologies to provide a reference for the future development of CCUS technology. We found that CCUS technology patents are gradually transforming to the application level, with increases in emerging fields, such as computer science. The main R&D institutes in the United States, Europe, Japan, Korea, and other countries are enterprises, while in China they are universities and research institutes. Hydride production, biological carbon sequestration, dynamic monitoring, geological utilization, geological storage, and CO2 mineralization are the six key technologies of CCUS. In addition, technologies such as hydride production, biological carbon sequestration, and dynamic monitoring have good development prospects, such as CCUS being coupled with hydrogen production to regenerate synthetic methane and CCUS being coupled with biomass to build a dynamic monitoring and safety system.