Influence of different metal over-layers on the electrical behaviour of the MIS Schottky diodes

We have employed technology computer-aided design – a Synopsys® tool to carry out a comparative study of the electrical behaviour of the metal–insulator–semiconductor Schottky diodes with different metal contacts (Ag, Au, Pt and Cr), on n-Si(100). We employed physics models to determine the Shockley–Read–Hall (SRH) and Auger recombination rates as a function of electric field profile in the depletion region. An insight was obtained as variation in the electric field at the metal–semiconductor interface due to work function variation affected the current mechanisms and recombination rates. The results were compared with the existing models. On the basis of analysis, merits and demerits of Schottky junctions formed due to these metals are discussed. The ideality factor of Au and Pt was found to be just around 2.0, while it was higher for Cr and Ag. However, the barrier height in the case of Cr was small, thus making it another possible metal layer for the Schottky contact. Similarly, SRH recombination rates were almost negligible for Au and Pt metal layer and became appreciable for Cr and much higher in Ag, making it not a good choice for the Schottky contact.     

Multiobjective Calibration of Reservoir Water Quality Modeling Using Multiobjective Particle Swarm Optimization (MOPSO)

Water resource management encounters large variety of multi objective problems that require powerful optimization tools in order to fully characterize the existing tradeoffs between various objectives that can be minimizing difference between forecasted physical, chemical, and biological behaviors of model and measured data. Calibration of complex water quality models for river and reservoir systems may include conflicting objectives addressed by various combinations of interacting calibration parameters. Calibration of the two dimensional CE-QUAL-W2 water quality and hydrodynamic model is an excellent example where the model must be calibrated for both hydrodynamic and water quality behavior. The aim of the present study is to show how multiobjective particle swarm optimization (MOPSO) can be implemented for automatic calibration of water quality and hydrodynamic parameters of a 2-dimensional, hydrodynamic, and water quality models (CEQUAL-W2) to predict physical, chemical, and biological behaviors of a water body, and then focus on a relevant case study. So MOPSO is utilized to generate Pareto optimal solutions for two conflicting calibration objectives. A combined measure of thermal and reservoir water level is considered as the first calibration objective. The second objective is formulated to forecast the best physical, chemical, and biological behavior of the model. Realizing the strong interactions between water quality and hydrodynamic issues of water bodies and their dependencies on the same set of calibration parameters, the proposed multiobjective approach may provide a wide version of all possible calibration solutions for better decision making to select best solution from pareto front.     

Magnetic properties of maraging steels in relation to nickel concentration

Magnetic properties of maraging steels have been investigated as a function of nickel concentration. The alloys nickel content varied from 12 to 24 wt pct, while other alloying constituents were kept at a level maintained in the 18Ni-2400 MPA-grade maraging steel. The magnetic properties were determined following aging for 1 hour in the temperature range of 450 °C to 750 °C. In every alloy investigated, the coercive field increased with aging temper-ature, reaching a maximum around 670 °C ± 30 °C. The saturation magnetization values were lowest around temperatures where maximum coercive field was observed. The coercive field increased from ∼55 to ∼ 175 Oe (∼4380 to ∼ 13,900 amp/meter) and the corresponding sat-uration magnetization decreased from ∼18,500 to ∼ 4000 G (∼1.85 to ∼0.4 T) in the alloys containing 12 and 24 wt pct Ni, respectively. The reverted austenite increased from 25 vol pct at 12 wt pct Ni to 100 vol pct at 24 wt pct Ni. The hardness and Charpy impact strength of the alloys have also been determined. An attempt has been made to correlate magnetic properties with different phase transformations occurring in maraging steels.     

Mechanical stability and magnetic properties of austenite

Austenitic alloys have been produced by additional alloying in maraging steel grade 18 Ni at 2400 MPa. The concentration of Mo, Ni and Co was increased individually until the martensite start temperature M _s, was suppressed below ambient value. Charpy impact strength, tensile strength and magnetic properties were determined. The impact strength in the annealed condition ranged between 260 to 294 J. In alloys where martensitic transformation occurred following quenching in liquid nitrogen, the impact strength dropped appreciably and was found to be in the range 120–216 J. The tensile strengths of the austenite and martensite phases ranged between 680 to 890 and 1030 to 1100 MPa, respectively. It was observed that the austenite phase transformed to martensite in the region that under went plastic deformation during Charpy and tensile testing. The degree of transformation incorporated, varied as a function of composition. The magnetic properties of the austenite phases were typical of a very weak magnetic material. The coercive field and saturation magnetization values were in the range 1034–2387 Am^–1 and 1.6–2.9 T, respectively. In contrast to the general observation, the austenite phase containing high Co exhibited ferromagnetic behaviour. The coercive field and saturation magnetization of ferromagnetic austenite was 1034 Am^–1 and 11 T, respectively.     

Polyurethane‐incorporated chitosan/alginate core–shell nano‐particles for controlled oral insulin delivery

Chitosan (CS) and polyurethane‐chitosan (PU‐CS) nano‐particles (NPs) were prepared for the core formation by complex coacervation method whereas alginate (ALG) and PU‐ALG were crosslinked by ionic gelation method to form the protective shell‐layer over the core. Effects of PU incorporation either within the core or shell or both were investigated by different in vitro and in vivo parameters. Fourier transform infrared (FTIR) spectroscopy of different compositions of nano‐particles showed distinct characteristic peaks for CS, PU, and ALG, indicating their presence in variable ratios. Significance of polyurethane‐incorporated systems towards insulin encapsulation efficiency, swelling parameters, insulin release, and in vivo pharmacological effect were also studied. Particle sizes, zeta potential, morphological analysis, mucoadhesion study, and in vivo acute toxicity studies of these core–shell nano‐particles were also performed. Bioavailability of insulin ranged from 9.04% to 11.6% for polyurethane‐incorporated chitosan‐alginate core–shell nano‐particle formulations which was significantly higher than the insulin bioavailability of basic CS/ALG core–shell nano‐particle system. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46365.     

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Cloud computing introduced a new paradigm in IT industry by providing on‐demand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state‐of‐the‐art multiobjective VM placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.     

Determination of the leaching rate of Cs from the immobilized low-level radioactive sources in the cement and cement mixed with dolomite grains and with natural pozzolan powder

The leaching rate of ¹³⁴Cs from the immobilized low-level radioactive source in the Portland cement was found to be 4.481 × 10⁻⁴ g/cm² per day. Mixing this cement with dolomite grains and natural pozzolan powder increased and reduced significantly the leaching rate to 7.373 × 10⁻⁴ g/cm² and 3.495 × 10⁻⁴ g/cm² per day for 1 wt% mixing, and to 12.340 × 10⁻⁴ g/cm² and 3.215 × 10⁻⁵ g/cm² per day for 3 wt% mixing, respectively. This increase and reduction of the leaching rate is due to dedolomitization reactions between dolomite grains and cement alkalis, and pozzolanic reaction between pozzolan powder and calcium hydroxide, respectively. It was also found that the application of a latex paint reduced these leaching rates by about 8.8 and 8.2% for cement mixed with dolomite and with pozzolan, respectively. The obtained results help to improve the concrete properties used for the storage and disposal of the low-level radioactive wastes.     

Autophagy,Unfolded Protein Response,and Neuropilin-1 Cross-Talk in SARS-CoV-2 Infection: What Can Be Learned from Other Coronaviruses

The COVID-19 pandemic is caused by the 2019–nCoV/SARS-CoV-2 virus. This severe acute respiratory syndrome is currently a global health emergency and needs much effort to generate an urgent practical treatment to reduce COVID-19 complications and mortality in humans. Viral infection activates various cellular responses in infected cells, including cellular stress responses such as unfolded protein response (UPR) and autophagy, following the inhibition of mTOR. Both UPR and autophagy mechanisms are involved in cellular and tissue homeostasis, apoptosis, innate immunity modulation, and clearance of pathogens such as viral particles. However, during an evolutionary arms race, viruses gain the ability to subvert autophagy and UPR for their benefit. SARS-CoV-2 can enter host cells through binding to cell surface receptors, including angiotensin-converting enzyme 2 (ACE2) and neuropilin-1 (NRP1). ACE2 blockage increases autophagy through mTOR inhibition, leading to gastrointestinal complications during SARS-CoV-2 virus infection. NRP1 is also regulated by the mTOR pathway. An increased NRP1 can enhance the susceptibility of immune system dendritic cells (DCs) to SARS-CoV-2 and induce cytokine storm, which is related to high COVID-19 mortality. Therefore, signaling pathways such as mTOR, UPR, and autophagy may be potential therapeutic targets for COVID-19. Hence, extensive investigations are required to confirm these potentials. Since there is currently no specific treatment for COVID-19 infection, we sought to review and discuss the important roles of autophagy, UPR, and mTOR mechanisms in the regulation of cellular responses to coronavirus infection to help identify new antiviral modalities against SARS-CoV-2 virus.     

Status of biodiesel research and development in Pakistan

Performance of biodiesel in engines is well established and biodiesel is currently adjudged as a low carbon fuel with the most potential of replacing fossil fuels. The fossil fuel sources are dwindling in Pakistan resulting in importation of about 8.1 million tonnes at approximately US$ 9.4 billion per annum. In the ambit of this justification, augmenting the energy scarce resources in Pakistan through intense harnessing of the varied biodiesel sources can adequately address the deficiency and can ensure energy security. Towards this end, the progress attained in biodiesel related researches in Pakistan are evaluated and presented with the view of highlighting ways of achieving the target set by the Government. A feedstock that drew less attention is spent triglycerides, and the little work reported by some organizations appeared promising. Now the onus is upon organizations such as the Alternative Energy Development Board and Pakistan State Oil to harness the research results from several indigenous Universities and develop a full-scale biodiesel economy in Pakistan.     

Data generative machine learning model for the assessment of outdoor thermal and wind comfort in a northern urban environment

Predicting comfort levels in cities is challenging due to the many metric assessment. To overcome these challenges, much research is being done in the computing community to develop methods capable of generating outdoor comfort data. Machine Learning (ML) provides many opportunities to discover patterns in large datasets such as urban data. This paper proposes a data-driven approach to build a predictive and data-generative model to assess outdoor thermal comfort. The model benefits from the results of a study, which analyses Computational Fluid Dynamics (CFD) urban simulation to determine the thermal and wind comfort in Tallinn, Estonia. The ML model was built based on classification, and it uses an opaque ML model. The results were evaluated by applying different metrics and show us that the approach allows the implementation of a data-generative ML model to generate reliable data on outdoor comfort that can be used by urban stakeholders, planners, and researchers.