Temperature governs the motion of molecules at the nanoscale and thus should play an essential role in determining the transport of water and ions through a nanochannel, which is still poorly understood. This work devotes to revealing the temperature effect on the coupling transport of water and ions through a carbon nanotube by molecular dynamics simulations. A fascinating finding is that the ion flux order changes from cation > anion to anion > cation with the increase in field strength, leading to the same direction change of water flux. The competition between ion hydration strength and mobility should be a partial reason for this ion flux order transition. High temperatures significantly promote the transport of water and ions, stabilize the water flux direction, and enhance the critical field strength. The ion translocation time exhibits an excellent Arrhenius relation with the temperature and a power law relation with the field strength, yielding to the Langevin dynamics. However, because of self-diffusion, the water translocation time displays different behaviors without following the ions. The high temperature also leads to an abnormal maximum behavior of the ion flux, deciphered by the massive increase in water flow that inversely hinders the ion flux, suggesting the coexistence of water–ion coupling transport and competition. Our results shed deep light on the temperature dependence of coupling transport of water and ions, answering a fundamental question on the water flux direction during the ionic transport, and thus should have great implications in the design of high flux nanofluidic devices.

The development of microelectronics is always driven by reducing transistor size and increasing integration, from the initial micron-scale to the current few nanometers. The photolithography technique for manufacturing the transistor needs to reduce the wavelength of the optical wave, from ultraviolet to the extreme ultraviolet radiation. One approach toward decreasing the working wavelength is using lithography based on beyond extreme ultraviolet radiation (BEUV) with a wavelength around 7 nm. The BEUV lithography relies on advanced reflective optics such as periodic multilayer film X-ray mirrors (PMMs). PMMs are artificial Bragg crystals having alternate layers of “light” and “heavy” materials. The periodicity of such a structure is relatively half of the working wavelength. Because a BEUV lithographical system contains at least 10 mirrors, the optics’ reflectivity becomes a crucial point. The increasing of a single mirror’s reflectivity by 10% will increase the system’s overall throughput six-fold. In this work, the properties and development status of PMMs, particularly for BEUV lithography, were reviewed to gain a better understanding of their advantages and limitations. Emphasis was given to materials, design concepts, structure, deposition method, and optical characteristics of these coatings.

Aims We aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern. Methods A mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve(AUC), time series analysis and spatial cross-test were conducted. Results The transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal. Conclusions We recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications.

Virus spreading and its mitigation is an important safety issue that has drawn wide attention of many countries and people. For researchers in this area, it is an interesting work to study virus spreading with safety theories and methods. In this paper, we worked on the spatial extent of SIR model, which considers the known facts of Covid-19 behavior i.e. its spreading extent with time, the total population of area concerned and dedicated health facilities. Also, a special relationship between Covid-19 cases and NLDI data driven by night-time satellite imagery is being discussed. Results predicted a huge gap between predicted and presently available facilities for number of hospitals, beds, and ventilators. Findings suggest that developing countries like our study area Lahore District, Pakistan needs to follow social distancing at immense level, which not only helps in reducing the numbers of infections and fatalities but also the time duration of the whole epidemic. Maps based on NLDI vales, predicted cases, hospitals and ventilators needs could be greatly helpful for policymakers to analyze situation and concentrate on areas which needs immediate attention. Dealing with the pandemic requires a pre-planned command and control structure that could make quick and informed decisions in the whole city. We recommend that the use of proper model prediction at Union Council level can help local government in policymaking related social distancing and healthcare systems. The decision of social distancing should be on time and like what percent of social distancing is needed, which tackle with the already available health care structure.

In recent times, with the increasing population in urban areas, the adverse events happening due to explosion and dispersion of toxic chemicals has increased. Events like toxic gas dispersion can cause severe environmental issues that endanger human safety and health. Consequence hazard modeling and vulnerable population assessment are critical to predict and minimize the losses. Chlorine is not only utilized in water treatment plants as a disinfectant, but it ca n also lead to some serious concerns to human health. In this paper, the modeling of chlorine release has been investigated for chlorine storage plant by using Heavy gas dispersion model. The modeling results showed that the cloud of chlorine is about 1.9 miles across just downwind of the release for accidental release of chlorine in the summer; however, the probability of fatality is 100 % in a whole year in a distance of 0.36 miles from the Storage Place. As a significant result, the land around the chlorination unit covering a range of approximately 1.9 miles is vulnerable in all wind directions and in the case of South-West direction of the wind, vulnerable population is highly dense, risk prevention in that region should be accounted for. Affected Population and areas at risk are calculated, which illustrates the toxically impacted areas and the population in need of immediate help and evacuation. Such studies can serve as a useful tool for decision-makers to prepare an emergency plan in case of accidental releases.