Non-magnetic hexagonal nanocrystalline ni films grown by radio frequency magnetron sputtering

Nanoscale Ni films in the thickness range 15-500 nm were grown on various substrates, such as amorphous glass, single crystalline silicon and sapphire, and polycrystalline alumina, at a temperature of about 350 K by radio frequency magnetron sputtering. It is demonstrated, via X-ray diffraction and high-resolution transmission electron microscopy, that there is an Ar-gas pressure window that favors the growth of stable single-phase hexagonal nanocrystalline Ni films regardless of the film thickness and the kind of the substrate. At lower or higher Ar pressures the films grow in the regular face centered cubic phase of Ni. The structural habits are attributed to differences in the kinetic energy of the Ni atoms impinging on the substrates. Superconducting quantum interference device magnetometry measurements reveal that the hexagonal films show zero magnetic response down to liquid Helium temperature. This result is discussed with respect to earlier first principle calculations and to experimental results on Ni nanoparticles.     

Evaluation of monobasic calcium phosphate for the immobilization of heavy metals in contaminated soils from Lavrion

The objective of this work was to evaluate the efficiency of monobasic calcium phosphate for the stabilization of heavy metals in contaminated soils. The treatment was applied on a soil sample from the Lavrion mining area, Greece, heavily contaminated with Pb, Zn, Cd and As and characterized as toxic in respect to Pb according to the US EPA toxicity characteristics leaching procedure (TCLP). The efficiency of stabilization was evaluated based on two criteria: (a) the reduction of metals mobility below the TCLP regulatory limits; (b) the reduction of phytoaccumulation. Phytoaccumulation was evaluated both indirectly by applying leaching tests using EDTA, DTPA and NaHCO(3) solutions and directly by carrying out pot experiments with Phaseolus vulgaris as plant indicator. This treatment was found to immobilize Pb and Cd, whereas As and Zn were slightly mobilized. No effect on phytoaccumulation was observed. Moreover, the treatment had a negative effect on plants growth, which was combined with a strong deficiency of Ca in the tissue of leaves.     

Developing New Treatments for COVID-19 through Dual-Action Antiviral/Anti-Inflammatory Small Molecules and Physiologically Based Pharmacokinetic Modeling

Broad-spectrum antiviral agents that are effective against many viruses are difficult to develop, as the key molecules, as well as the biochemical pathways by which they cause infection, differ largely from one virus to another. This was more strongly highlighted by the COVID-19 pandemic, which found health systems all over the world largely unprepared and proved that the existing armamentarium of antiviral agents is not sufficient to address viral threats with pandemic potential. The clinical protocols for the treatment of COVID-19 are currently based on the use of inhibitors of the inflammatory cascade (dexamethasone, baricitinib), or inhibitors of the cytopathic effect of the virus (monoclonal antibodies, molnupiravir or nirmatrelvir/ritonavir), using different agents. There is a critical need for an expanded armamentarium of orally bioavailable small-molecular medicinal agents, including those that possess dual antiviral and anti-inflammatory (AAI) activity that would be readily available for the early treatment of mild to moderate COVID-19 in high-risk patients. A multidisciplinary approach that involves the use of in silico screening tools to identify potential drug targets of an emerging pathogen, as well as in vitro and in vivo models for the determination of a candidate drug’s efficacy and safety, are necessary for the rapid and successful development of antiviral agents with potentially dual AAI activity. Characterization of candidate AAI molecules with physiologically based pharmacokinetics (PBPK) modeling would provide critical data for the accurate dosing of new therapeutic agents against COVID-19. This review analyzes the dual mechanisms of AAI agents with potential anti-SARS-CoV-2 activity and discusses the principles of PBPK modeling as a conceptual guide to develop new pharmacological modalities for the treatment of COVID-19.     

Anonymity and everlasting privacy in electronic voting

International Journal of Information Security - Everlasting privacy protects cryptographic voting systems against the weakening of intractability assumptions on which they may be based. We find...     

Efficient searchable symmetric encryption supporting range queries

International Journal of Information Security - The demand for cloud storage services continuously increases putting at risk the privacy of the outsourced data. Data encryption is the obvious...     

SRv6-based Time-Sensitive Networks (TSN) with low-overhead rerouting

Time-Sensitive Networks (TSN) aims at providing a solid underpinning for the support of application connectivity demands across a wide spectrum of use cases and operational environments, such as industrial automation and automotive networks. However, handling network updates in TSN entails additional challenges, stemming from the need to perform both flow rerouting and TSN schedule reconfiguration. To address this issue, we propose a software-defined network (SDN)-based approach for low-overhead TSN network updates, exploiting segment routing over IPv6 (SRv6) for path control. To this end, we introduce the concept of TSN subgraphs in order to quickly reschedule the flows traversing the problematic area and propose a TSN-aware routing heuristic to minimize the convergence time. We further describe the control plane implementation and its integration into Mininet, which empowers us to conduct a wide range of performance tests. Our evaluation results indicate that our approach yields faster recovery and reduces significantly the number of required reconfigurations upon failures, at the expense of a small SRv6 encoding/decoding overhead.     

Study of the dyeing properties of saffron and ultrafiltrated saffron powders,as colourants for natural and synthetic fibres

There is a noticeable growing interest in the use of natural dyes in contemporary textile dyeing, motivated by the requirements imposed, in favour of environmental prosperity. The use of natural dyes reduces significantly both the energy requirements and the environmental impact of the process. In the present work natural and synthetic fibres were dyed with the dyestuff isolated from Crocus sativus L., after aqueous extraction of the dried stigmas of the plant. Additionally, part of the powder was purified by using ultrafiltration technology. The saffron extract and the ultrafiltrated saffron retentate were used to dye cotton, wool, nylon and polyester, in various depths of shade and temperatures. Both saffron and ultrafiltrated saffron successfully dyed not only the natural substrates, but also the synthetic ones, while higher dyeing temperatures produced level dyeing with all substrates used. Ultrafiltrated saffron powder produced brighter and much stronger dyeing to the original saffron powder, due to the elimination of extraction by-products. Isothermal adsorptions for both colourants were performed on all substrates in order to investigate their adsorption mechanism. It was found that saffron and ultrafiltrated saffron follow a Freundlich-type adsorption isotherm on cotton, wool and nylon which is a typical mechanism for a planar-directed dye of big molecular weight. Nernst-type adsorption was found to occur on polyester which again is typical for the adsorption of disperse dyes on polyester. Thus, saffron can be claimed as a universal dye, able to successfully dye natural and synthetic substrates.     

Data-driven coordination of subproblems in enterprise-wide optimization under organizational considerations

While decomposition techniques in mathematical programming are usually designed for numerical efficiency, coordination problems within enterprise-wide optimization are often limited by organizational rather than numerical considerations. We propose a “data-driven” coordination framework which manages to recover the same optimum as the equivalent centralized formulation while allowing coordinating agents to retain autonomy, privacy, and flexibility over their own objectives, constraints, and variables. This approach updates the coordinated, or shared, variables based on derivative-free optimization (DFO) using only coordinated variables to agent-level optimal subproblem evaluation “data.” We compare the performance of our framework using different DFO solvers (CUATRO, Py-BOBYQA, DIRECT-L, GPyOpt) against conventional distributed optimization (ADMM) on three case studies: collaborative learning, facility location, and multiobjective blending. We show that in low-dimensional and nonconvex subproblems, the exploration-exploitation trade-offs of DFO solvers can be leveraged to converge faster and to a better solution than in distributed optimization.