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Droughts have an adverse influence on agriculture, the environment, water supplies, and the global economy. The drought risk was computed using an integrated prospective approach: drought hazard, exposure, and vulnerability based on biophysical and socio-economic conditions over Karachi, Pakistan during 2000–2019. Drought hazard map (DHM) was created using annual Palmer drought severity Index (PDSI). Drought exposure map (DEM) was derived using population density and gross domestic product (GDP), as well as land surface temperature (LST), Normal difference vegetation index (NDVI), Night light images (NTL), land use land cover (LULC), and Distance to water were used for drought vulnerability map (DVM). An estimation of drought Risk (EDR) was derived by integrating layers of DHM, DEM, and DVM. Results showed that Central, South, and East regions of Karachi were at high risk, whereas the North East and North were less affected by the drought. The estimated average drought hazard (EDH) was 0.84, with minimum (maximum) value of 0.68 (1). Similarly, the average estimated drought exposure (estimated drought vulnerability) for EDE (EDV) was 0.27 (0.42), with the maximum value of 0.55 (0.84) and the minimum value of 0 (0). The drought risk assessment map (DRAM) shows that the average risk values is 0.18 while highest value is 0.36.

With the advent of new generation information technologies in industry and product design, the big data-driven product design era has arrived. However, the big data-driven product design mainly places emphasis on the analysis of physical data rather than the virtual models, in other words, the convergence between product physical and virtual space is usually absent. Digital twin, a new emerging and fast growing technology which connects the physical and virtual world, has attracted much attention worldwide recently. This paper presents a new method for product design based on the digital twin approach. The development of product design is briefly introduced first. The framework of digital twin-driven product design (DTPD) is then proposed and analysed. A case is presented to illustrate the application of the proposed DTPD method. [ABSTRACT FROM AUTHOR]

Zangsiwei(ZSW) is a traditional Tibetan medicine from China consisting of extracts of Rhododendron anthopogonoides Maxim, Gentiana Tourn, Corydalis hendersonii Hemsl and Berberis kansuensis C.K.Schneid. Traditionally, ZSW has been used by Tibetan physicians to treat chronic respiratory diseases. The role of ZSW in particulate matter-induced lung inflammation and fibrosis remains unclear. Combining non-targeted metabolomics, network pharmacology, and molecular docking to explore the mechanism of ZSW in the treatment of particulate matter-induced lung inflammation and fibrosis, and validated by in vivo and in vitro experiments. The serum metabolite profile post-ZSW administration was first identified utilizing non-targeted metabolomics. Network pharmacology and molecular docking were employed to predict potential bioactive components and their corresponding targets. The in silico predictions were subsequently validated through in vivo studies in mice exposed to PM2.5 and silica dust, as well as in vitro studies utilizing human lung epithelial cells (A549) and lung fibroblasts (MRC5). Metabolomic analysis identified specific serum metabolites that were associated with ZSW treatment. Network pharmacology and molecular docking identified key targets involved in the Transforming growth factor-β (TGF-β)/SMAD pathway, which were subsequently validated through in vivo experiments demonstrating a reduction in lung inflammation and fibrosis in ZSW-treated mice. In vitro studies demonstrated that ZSW exerts protective effects against PM2.5-induced cytotoxicity and modulates fibrotic markers in a dose-dependent manner. This is consistent with the inhibition of the TGF-β/SMAD pathway. Our integrated approach, which combines non-targeted metabolomics, network pharmacology, and molecular docking, followed by rigorous in vivo and in vitro validation, establishes ZSW as a potential therapeutic agent for particulate matter-induced lung inflammation and fibrosis. [Display omitted] • Identification of serum components post Zangsiwei administration using non-targeted metabolomics. • Prediction of possible targets of action through network pharmacology and molecular docking. • Zangsiwei has a therapeutic role in lung inflammation and fibrosis. • Zangsiwei inhibits lung inflammation and fibrosis through the Tgf-β/Smad pathway. [ABSTRACT FROM AUTHOR]

Automation, driven by informatics, enables manufacturing companies to increase productivity and meet market demands for cost-effective and high-quality products. However, many manufacturing operations across industry verticals continue to be manual even today. One such example is the manual assembly of the final trim and wheels in an automotive production line where there is heavy reliance on human decision-making pertaining to when, where and how to install components on and inside a constantly moving vehicle body. The main aim of this work is to develop a rule-based decision support system that will enable an automation solution to make human-like decisions in moving assembly operations. The wheel loading operation is chosen as a case study and a decision support framework and tool is developed and successfully tested using multiple assembly scenarios generated from experimental data provided by gaming interface sensors installed on the laboratory-based shopfloor. The resulting decision support system has the potential to enable the automation of moving assembly operations in various sectors of the manufacturing industry. [ABSTRACT FROM AUTHOR]

Several manufacturing operations continue to be manual even in today's highly automated industry because the complexity of such operations makes them heavily reliant on human skills, intellect and experience. This work aims to aid the automation of one such operation, the wheel loading operation on the trim and final moving assembly line in automotive production. It proposes a new method that uses multiple low-cost depth imaging sensors, commonly used in gaming, to acquire and digitise key shopfloor data associated with the operation, such as motion characteristics of the vehicle body on the moving conveyor line and the angular positions of alignment features of the parts to be assembled, in order to inform an intelligent automation solution. Experiments are conducted to test the performance of the proposed method across various assembly conditions, and the results are validated against an industry standard method using laser tracking. Some disadvantages of the method are discussed, and suggestions for improvements are suggested. The proposed method has the potential to be adopted to enable the automation of a wide range of moving assembly operations in multiple sectors of the manufacturing industry. [ABSTRACT FROM AUTHOR]