Since December 2019, a total of 41 cases of pneumonia of unknown etiology have been confirmed in Wuhan city, Hubei Province, China. Wuhan city is a major transportation hub with a population of more than 11 million people. Most of the patients visited a local fish and wild animal market last month. At a national press conference held today, Dr. Jianguo Xu, an academician of the Chinese Academy of Engineering, who led a scientific team announced that a new-type coronavirus, tentatively named by World Health Organization as the 2019-new coronavirus (2019-nCoV), had caused this outbreak (1).
Corrosion is a ubiquitous and costly problem for a variety of industries. Understanding and reducing the cost of corrosion remain primary interests for corrosion professionals and relevant asset owners. The present study summarises the findings that arose from the landmark “Study of Corrosion Status and Control Strategies in China”, a key consulting project of the Chinese Academy of Engineering in 2015, which sought to determine the national cost of corrosion and costs associated with representative industries in China. The study estimated that the cost of corrosion in China was approximately 2127.8 billion RMB (~ 310 billion USD), representing about 3.34% of the gross domestic product. The transportation and electronics industries were the two that generated the highest costs among all those surveyed. Based on the survey results, corrosion is a major and significant issue, with several key general strategies to reduce the cost of corrosion also outlined.Economics: What corrosion cost China in 2014It is estimated that the effects of corrosion in China cost approximately $310 billion USD in 2014. Corrosion is a costly issue, justifying substantial expenditure into techniques to protect and mitigate susceptible metals from its effects, and research investment. China has seen rapid growth in its economy in recent times, driven in part by investment in industry. In order to understand the monetary impact of corrosion in China, The Chinese Academy of Engineering instigated a nationwide study led by the Institute of Oceanology, Chinese Academy of Sciences. It estimates that approximately $310 billion USD was lost to the consequences of corrosion and money spent addressing it in 2014, accounting for 3.34% of GDP. Transportation and electronics industries generated the highest costs. Several recommendations are made, including the need for a government-coordinated national strategy.
An understanding of the interactions between nanoparticles and biological systems is of significant interest. Studies aimed at correlating the properties of nanomaterials such as size, shape, chemical functionality, surface charge, and composition with biomolecular signaling, biological kinetics, transportation, and toxicity in both cell culture and animal experiments are under way. These fundamental studies will provide a foundation for engineering the next generation of nanoscale devices. Here, we provide rationales for these studies, review the current progress in studies of the interactions of nanomaterials with biological systems, and provide a perspective on the long-term implications of these findings.
Recently, the IMO has completed the guidelines on the life cycle greenhouse gas intensity of marine fuels to accelerate the application of alternative fuels. Low-carbon fuels may persist for decades and have become a key transitional phase in replacing marine fuels. A more comprehensive methodology for evaluating the carbon emission levels of marine fuels was explored, and the carbon emissions and environmental impacts of a 150,000-ton shuttle tanker under 19 dual-fuel power scenarios were evaluated using the Energy Efficiency Design Index (EEDI) and life cycle assessment (LCA) method. The results show that liquefied natural gas (LNG) has a higher carbon control potential level compared to liquefied petroleum gas (LPG) and methanol (MeOH), while LPG is superior to MeOH based on EEDI evaluation. LCA analysis results show that MeOH (biomass) has the best carbon control potential considering the carbon emissions of the well-to-tank phase of the fuel, followed by LNG, LPG, MeOH (natural gas, NG), and MeOH (coal). However, MeOH (NG) and MeOH (coal) had greater negative environmental impacts. This study provides method support and a direction toward improvement for revising related technical specifications and regulations for dual-fuel vessel performance evaluation, considering the limitations of various maritime regulations.
Vo-Nguyen Tuyet-Doan, Young-Woo Youn, Hyun-Soo Choi
et al.
Recently, deep neural networks have shown remarkable success in fault diagnosis in power systems using partial discharges (PDs), thereby enhancing grid asset safety and reliability. However, the prevailing approaches often adopt centralized large-scale datasets for training, without taking into account the impact of noise environments for Intelligent Electronic Devices (IEDs). Noise environments for PD measurements in gas-insulated switchgear (GIS) introduce variations in feature distributions and class representations, challenging the generalization ability of the trained models in new and diverse conditions. In this study, we propose a Shared Knowledge-based Contrastive Federated Learning (SK-CFL) for PD diagnosis in different noise environments for IEDs. The proposed SK-CFL combines federated learning principles with contrastive learning, empowering IEDs to collaboratively learn and share knowledge as regards PD and noise patterns. The proposed framework can learn representations between the same patterns across different IEDs while ensuring data privacy. Experimental results for PD diagnosis in GIS show that the proposed SK-CFL achieves a performance improvement in fault diagnosis, particularly in new and unseen environments. Specifically, the recall for unknown noise in untrained IED 6 demonstrates 92.86% of the proposed SK-CFL, in comparison with 64.29% and 35.71% of the conventional FL and baseline method, respectively. These results suggest that the proposed SK-CFL approach promises more adaptable, and resilient data-driven approaches since it protects data privacy that can operate effectively in challenging real-world environments.
Stem cell factors (SCFs) are pivotal factors existing in both soluble and membrane-bound forms, expressed by endothelial cells (ECs) and fibroblasts throughout the body. These factors enhance cell growth, viability, and migration in multipotent cell lineages. The preferential expression of SCF by arteriolar ECs indicates that arterioles create a unique microenvironment tailored to hematopoietic stem cells (HSCs). Insufficiency of SCF within bone marrow (BM)-derived adipose tissue results in decreased their overall cellularity, affecting HSCs and their immediate progenitors critical for generating diverse blood cells and maintaining the hematopoietic microenvironment. SCF deficiency disrupts BM function, impacting the production and differentiation of HSCs. Additionally, deleting SCF from adipocytes reduces lipogenesis, highlighting the crucial role of SCF/c-kit signaling in controlling lipid accumulation. This review elucidates the sources, roles, mechanisms, and molecular strategies of SCF in bone renewal, offering a comprehensive overview of recent advancements, challenges, and future directions for leveraging SCF as a key agent in regenerative medicine.
Emilia Osmólska, Agnieszka Starek-Wójcicka, Agnieszka Sagan
et al.
The aim of the study was to investigate the effect of cold atmospheric plasma (CAP) and sumac powder (<i>Rhus coriaria</i> L.) on the pH, total soluble solids, color, content of phytochemicals (carotenoids and polyphenols), and microbiological quality of freshly pressed carrot juice. Experiments were carried out with sumac powder concentrations of 0.5 and 3%, which were added before or after 20 min plasma treatment using a gliding arc reactor. The combination of CAP and 3% sumac powder resulted in very effective microbial reduction (to an undetectable level on each day of testing). These juices were characterized by an extended microbiological shelf life of up to 72 h. Additionally, the juice which was first enriched with 3% sumac and then treated with cold plasma, even on the last day of testing, contained 34.36 mg/100 mL of polyphenols and 3.49 mg/100 g more carotenoids than the control samples. The total effect of the application of these method is highly important for the improvement of the quality and safety of carrot juice.
Objective The current secondary positioning system of urban rail transit train beacons has a positioning accuracy of 300 meters, failing to meet the high-precision positioning requirements of wireless environment monitoring grid. Additionally, GPS (global positioning system) and the Beidou Satellite Navigation System are not suitable for underground rail positioning. Therefore, it is necessary to conduct in-depth research on continuous positioning technology for urban rail transit CBTC (communication-based train control) wireless environment monitoring grid. Method Building on the existing urban rail transit train beacon secondary positioning system, an inertial navigation technology enhanced by beacon-assisted training is adopted. By utilizing a multi-sensor fusion positioning algorithm, the sensor data from the inertial navigation module is integrated with the beacon data from the ATS (automatic train supervision) system, thus achieving the continuous positioning of wireless environment monitoring grid in urban rail transit. Additionally, a regression prediction algorithm is used for offline model training to enhance the accuracy of continuous positioning for the urban rail transit wireless environment monitoring grid. Result & Conclusion The proposed methods can achieve continuous and real-time positioning of wireless environment monitoring grid, supporting CBTC wireless environment monitoring grid in tunnels, beneath elevated structures, and in complex environments where metro train passing through areas with tall buildings and tree cover. The positioning accuracy reaches 10 m, meeting the positioning requirements of the CBTC wireless environment monitoring system, and fully ensuring the safety of the urban rail transit CBTC signaling system.
Fiber-reinforced cementitious composite (FRCC) and fiber-reinforced polymer (FRP) have been widely applied in infrastructures. Their mechanical behavior and innovative applications are examined in this article. Firstly, the mechanical behaviors of FRCC and the corresponding improving methods are elaborated. The bond behavior of the FRP–concrete interface, which has a significant effect on the strengthening effect of FRP, is reviewed. A proposed method to enhance the bond behavior is also introduced. In addition, the effectiveness of FRP is demonstrated in terms of improving the load-bearing capacity, stiffness, crack resistance, fatigue resistance, and other behaviors of existing structures. Furthermore, the feasibility of fibers or FRPs in new constructions is also validated. Finally, the future prospects of the research and applications of FRCC and FRP are discussed.
Accurately identifying the high-temperature history experienced by rocks is essential for understanding their behaviour and predicting properties. However, current approaches are limited by the heterogeneity of rocks, test scale and costs. Here, we proposed an economical, efficient and accurate approach to identifying the rocks after high-temperature deterioration via deep learning. This deep learning-based method exhibited superior abilities in distinguishing the heat-treated rock. Using a scanning electron microscopy (SEM) image covering a size of 14.6 μm × 14.6 μm, the high-temperature deterioration history of rocks can be recognized with an accuracy of 80.2%. Features such as cracks, rock patterns, and cleavage steps in SEM images would further improve the recognition accuracy. For example, SEM images with higher fractal box dimensions show a higher recognization accuracy, especially for temperatures under 600 °C. Besides, using the deep Taylor decomposition algorithm, the high-temperature deterioration regions of the rocks in the microscale were successfully located, extracted, and characterized for the first time. This study highlights the vast potential of the deep learning-based approach in damage deterioration identification of rock after high temperature, which significantly extends the application of deep learning in underground projects.