Nanoparticles typically have dimensions of less than 100 nm. Scientists around the world have recently become interested in nanotechnology because of its potential applications in a wide range of fields, including catalysis, gas sensing, renewable energy, electronics, medicine, diagnostics, medication delivery, cosmetics, the construction industry, and the food industry. The sizes and forms of nanoparticles (NPs) are the primary determinants of their properties. Nanoparticles’ unique characteristics may be explored for use in electronics (transistors, LEDs, reusable catalysts), energy (oil recovery), medicine (imaging, tumor detection, drug administration), and more. For the aforementioned applications, the synthesis of nanoparticles with an appropriate size, structure, monodispersity, and morphology is essential. New procedures have been developed in nanotechnology that are safe for the environment and can be used to reliably create nanoparticles and nanomaterials. This research aims to illustrate top-down and bottom-up strategies for nanomaterial production, and numerous characterization methodologies, nanoparticle features, and sector-specific applications of nanotechnology.

Juettner et al. describe a novel phosphatase-activity–independent mechanism by which the phosphatase VE-PTP restricts endothelial permeability. VE-PTP functions as a scaffold that binds and inhibits the RhoGEF GEF-H1, limiting RhoA-dependent tension across VE-cadherin junctions and decreasing VE-cadherin internalization to stabilize adherens junctions and reduce endothelial permeability.

Multivariate control charts are usually implemented in statistical process control to monitor several correlated quality characteristics. Process dispersion charts are used to determine the stability of process variation (which is typically done before monitoring the process location/mean). A Phase-I study is generally used when population parameters are unknown. This article develops Phase-I |S| and |G| control charts, to monitor the dispersion of a bivariate normal process. The charting constants are determined to achieve the required nominal false alarm probability (FAP 0 ). The performance of the proposed charts is evaluated in terms of (i) the attained false rate and (ii) the probability of signaling for out-of-control situations. The analysis shows that the proposed Phase-I bivariate charts correctly control the FAP (the false alarm probability) and detect a shift occurring in the bivariate dispersion matrix with adequate probability. An example is given to explain the practical implementation of these charts.

No abstract

Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non‐linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non‐linear as well as non‐stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non‐linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA‐ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA‐SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5 years to overcome the impact of COVID‐19 in these countries.

Energy is considered the oxygen of an economy fueling all economic activities. Energy utilization and its type have an intertemporal and size-based effect on economic development. Therefore, this study empirically analyzes the relationship of fossil energy consumption with economic development in the case of BRICS countries between 1990 and 2019. Fully modified ordinary least squares is used with the quadratic function of coal, oil, and gas consumption to assess the size-based effect across time. This study shows that coal and natural gas consumption follows the inverted U-shaped relationship with HDI, while coal consumption shows a negative relationship with HDI. Hence, coal and gas energy assists in development when its share is small, while over-consumption hampers development. The BRICS countries should optimize coal and gas consumption with respect to economic development. Reducing fossil energy should be substituted with alternative clean energy resources by using advanced technology such as the gasification process.

No abstract

Anthropogenic activities are responsible for greenhouse gas emissions, causing extreme events like soil erosion, droughts, floods, forest fires and tornadoes. Fossil fuel consumption produces CO2, and trapping heat is the major reason for a rapid increase in global temperature, and electricity generation is responsible for 25% of greenhouse gas emissions. Fossil fuel consumption, CO2 emissions and their adverse impact have become the focus of efforts to mitigate climate change vulnerability. This study explores empirical determinants of vulnerability to climate change such as ecosystem, food, health and infrastructure. The sustainable use of energy is necessary for development, and a source of response to climate change. The present study focuses on renewable energy consumption to determine climate vulnerability in G7 countries between 1995 and 2019. The panel ARDL approach showed that the renewable to non-renewable energy mix showed a quadratic effect on vulnerability, whereby a minimum threshold of renewable energy is required to witness a reduction in food, health and infrastructure vulnerability. Other results indicate that trade openness and development expenditures reduce health vulnerability. Development expenditures also decrease ecosystem vulnerability, while trade openness increases it. However, both of these variables increase infrastructure vulnerability. Avoiding severe food and water crises requires investment to tackle climate change, conserve energy and water resources, reform global trade and food markets, and adapting and adopting climate-resilient responses to change.