Context
This study concerns the adoption of the IoT technology for the home monitoring of the health status of fragile patients. IoT adoption in healthcare is one of the most promising business domains today; according to the current trend, the market will increase in the near future.
Problem
The prevalent deployment model of IoT systems is the Cloud which offers powerful services and unlimited storage/computing capacity on-demand; unfortunately, connecting smart devices to the Cloud poses severe issues. First of all, connected devices create large volumes of data, which will drive inevitably to performance and network congestion challenges. Secondly, there are performance, security, bandwidth, and reliability concerns that make the Cloud-only solution not suitable for all the potential real-world applications. The Fog computing paradigm has been introduced to bridge the gap between the Cloud and IoT devices.
Results
This paper gives a twofold contribution: (a) a Fog-based architecture is proposed using 3-tiers solution where the Fog computing layer constitutes the middle tier; (b) simulations have been carried out in order to compare Cloud-Fog computing as an alternative to the Cloud-only solution. The experimental results demonstrate a remarkable reduction of the values of several parameters in the first solution with respect to the second one. The measured benefits are important in the target application domain. The iFogSim2 open-source toolkit has been used to carry out the experiments.

Emerging contaminants pose great health risks to humans and living organisms, even when released at minute concentrations over prolonged exposure times. In this work, we fabricate nanocomposites based on activated charcoal coated magnetite by incorporating the biopolymers of xylan or pectin into their structure. Two synthesis routes which differ in their sequential steps were investigated. It was demonstrated that the synthesis route affects the morphology and chemical structure of the nanocomposites, as confirmed by SEM imaging and FTIR measurements, respectively. Hence, in turn, it influenced the performance of the nanocomposites in their adsorptive removal for the emerging contaminants of Fluoxetine and Famotidine whose presence in wastewater have been confirmed in several studies.

Hydroxycinnamic acids are one of the most widely distributed class of natural phenolics in plants. Their coexistence requires selective methods for quantification. Voltammetry on chemically modified electrodes is one of the approaches to solve this problem. Electrodes based on the electropolymerized triphenylmethane dyes have shown sensitive and selective response to natural phenolic antioxidants of different classes. In this work, combination of functionalized single-walled carbon nanotubes and poly(phenol red) has been used as electrode surface modifier. The polymeric coverage has been obtained by potentiodynamic electropolymerization which conditions have been optimized on the basis of voltammetric response of hydroxycinnamic acids mixture. Poly(phenol red)-based electrode provides well-resolved peaks of caffeic, ferulic, and p-coumaric acids and statistically significant increase of the oxidation currents in comparison to bare glassy carbon and polyaminobenzene sulfonic acid functionalized single-walled carbon nanotubes modified electrodes. Electrooxidation of hydroxycinnamic acids is diffusion controlled process and involves proton transfer. Simultaneous voltammetric quantification of caffeic, ferulic, and p-coumaric acids has been performed for the first time. Two linear dynamic ranges of 0.10-2.5 µM for all acids and 2.5-100 µM for caffeic acid and 2.5-50 µM for ferulic and p-coumaric acids have been achieved using differential pulse voltammetry in acidic medium (Britton-Robinson buffer pH 2.0). The limits of detection are 47.6, 22.4, and 38.0 nM for the caffeic, ferulic, and p-coumaric acids, respectively. Then method developed has been successfully applied for the quantification of hydroxycinnamic acids in coffee.

Organic-inorganic hybrid perovskite solar cells are expected to be alternative photovoltaic devices of solar cells, because of their high conversion efficiency, easy fabrication process and low cost. On the other hand, the problems are reduced durability due to the volatility of organic cations and toxic lead. One of the effective methods is to introduce additives into the perovskite photoactive layer. By adding copper (Cu) to the perovskite, new orbitals are generated at the upper end of the valence band due to Cu-iodine bonding, resulting in narrower energy gaps. Short-circuit current density is expected to be improved as the concentration of excited carriers increases.

Ca/P-doped TiO₂ nanofibers were produced via sol-gel electrospinning technique and used for the electrochemical detection of guanine and oxidaiton of ssDNA molecules. The nanofibers first morphologically analysed with scanning electrone microscopy (SEM). Fiber diameters decreased after the calcination process at both neat and Ca/P-doped samples. Fiber uniformatey was too much deterioarted after the calcination process at Ca/P-doped sample. Elementel analysis were carried out with EDX measurement and crystal structure of the nanofiber were investigated via X-ray diffraction method. XRD results revealted that evet though just anatase and rutile TiO₂ cryatslas were detected at neat TiO₂ nanofibers, Ca/P related crystalline peaks were also appeared after Ca/P doping. The crystallinity of TiO₂ decreased with Ca/P doping. EDX spectra also confiremed the existence of Ti, O and Ca/P in Ca/P-doped TiO₂ nanofibers. Ca/P-doped TiO₂ nanofibers were immobilized on SPE with single strand DNA molecules (ssDNA), and the DPV measurement were performed. The measurement were carried out under dark condition, solar and UV light irridations. Guanine and adenin oxidaitons were observed by immobilizing ssDNA molecules on SPEs with the prepared Ca/P-doped TiO₂ nanofibers. The results revealed that the intensities of guanine and adenine oxidaiton peaks were increased when Ca/P-doped TiO₂ nanofibers were used.

A number of free applications for predicting and designing the performance of solar power systems exist. Among these tools, three reputable ones were used recently while assessing different solar energy technologies, which belong to the concentrated solar power (CSP) type and the photovoltaic (PV) type.

The data represent computer modeling files as well as tabulated values and digital images for simulations conducted by the desktop software program Energy3D (by the Concord Consortium) for solar systems analysis. Thus, the interested user can reproduce the simulations and customize them for their particular needs. The variety of modeled solar power systems include solar farms with fixed or moving array of panels, linear Fresnel reflectors, parabolic troughs, parabolic dish-engines, and solar towers.

Supporting benchmarking data are also included, which are prediction reports for three PV systems using the cloud-based application PVGIS (Photovoltaic Geographical Information System), developed by the European Commission Joint Research Center (JRC). These PV systems are related to three systems modeled via the program Energy3D, and thus help in validation.

Another set of benchmarking data comes from another cloud-based application for modeling PV systems, which is PVWatts by the National Renewable Energy Laboratory (NREL) of the United States Department of Energy (DoE).

This paper focuses on describing data used in the analysis. It gives an opportunity for learning, gaining skills, and practicing in the area of solar energy science and technology. It also briefly discussed a fourth free solar-energy tool, which is 'Aladdin' (by the Institute for Future Intelligence), possessing artificial-intelligence capabilities.

The high industrial demand generates increased consumption and high waste of materials that impact the environment in different spheres, one of the most affected environments are aquatic systems. Moreover, one of the most common forms of water contamination is the improper disposal of dyes by industries such as textiles, cosmetics, and pharmaceuticals. These dyes are organic substances that can give color to a substrate through chemical affinity. The most commonly used synthetic dyes are the ones containing the azo group, which have been reported as carcinogenic, mutagenic, and genotoxic, causing harm to the environment and living beings. Therefore, the study of methods that contribute to the degradation of these species will contribute to better treatment of polluted aquatic environments. Thus, the main objective of this work was to promote the catalytic degradation of organic dyes, such as Methyl Orange and Congo Red, through silver nanoparticles (AgNPs). For this, AgNPs were synthesized with spherical shapes using two stabilizers (polyvinylalcohol - PVA, and polyvinylpyrrolidone - PVP). Subsequently, the AgNPs were applied for the degradation of organic dyes, with the catalysis analyzed via UV-Vis absorption spectrometry in a maximum time of 40 minutes. Finally, it was observed that these nanocatalysts were successful in degrading the organic dyes. Thus, AgNPs have the potential to be used as a catalyst for wastewater treatment.

Methanotrophs are bacteria that can consume methane as their sole carbon and energy source to produce a wide variety of bio-products such as lipids. The lipids derived from methanotrophs are vital precursors for the production of green fuels and oleochemical feedstocks. Thus, this research aimed to establish the suitable growth conditions for methanotrophs consortium present in wastewater sludge that maximizes methane consumption, biomass concentration, yield coefficient and lipid content. Growth parameters were varied to study the effects of two media types [nitrate mineral salts (NMS) and synthetic wastewater (SW)], initial pH (4.0 and 6.8) and CH₄ to air ratio (1:1 and 1:4 vol.) on the bioconversion of CH₄ to lipids. After 18 days of incubation, the consortium that was cultivated in SW medium, at pH=4.0, and CH₄ to air volume ratio of 1:1 showed the highest CH₄ consumption of 43.24 ml/L which also corresponds to the highest lipid content of 459.33 mg/L. These results provide the basis of conditions for further optimization. Additionally, the findings highlight the potential of natural microbial consortium to convert methane into lipids using wastewater as medium that could address concerns on GHG emissions as well as for value-added resources recovery

Coronavirus disease (Covid-19) is the latest pandemic in the world. One of the ways to diagnose an individual with Covid-19 is the use of the Polymerase Chain Reaction (PCR) test. However, this test is rather invasive. An alternative would be to use chest images of the patients to diagnose if the patient has Covid-19. These Chest X-Ray images have to be manually annotated by a medical professional such as a radiologist, and due to privacy concerns, getting access to readily available and annotated Covid-19 Chest X-Ray images is difficult.

In order to train a deep learning model to perform image classification tasks, it is prudent to train the deep learning model on a large enough dataset to avoid the problem of overfitting. In this paper, we explored using Generative Adversarial Networks (GANs) as a form of data augmentation technique to enlarge the training data for deep learning models. We first explored how the synthetic data generated by GANs are affected by its training size. Following which, we compared the performance of the two different GANs architecture, namely the Deep Convolutional Generative Adversarial Networks (DCGAN) and the Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP). We successfully used GANs to generated synthetic Covid-19 Chest X-Ray images with a Fréchet Inception Distance (FID) score that was below 2.

Nuclear Power Plant (NPP) of Dabaa region -Egypt is considered as one of the new energy sources (for energy conversion from nuclear to electric type). However, emission of radioactive nuclide must be quantitatively assessed especially in case of nuclear accidents. Therefore, the present study uses the lagrangian flexible particle dispersion model (FLEXPART) version 10.4) to calculate the concentration and total deposition of Cs-137. FLEXPART is driven by the NCEP FNL (Final) Operational Global Analysis and the model simulation is integrated over the period 2008-2018. Possible risk is assessed assuming a hypothetical accident scenario based on probabilistic risk assessment method. Moreover, average wind speed and direction as well as the total surface precipitation are also considered to examine the possible influence of these weather factors on dispersion of the emitted radionuclide, its concentration and total Deposition. The results show that high concentration and total deposition are observed particularly during the summer season. Also, considering different emission scenarios indicate that Egypt is expected to be strongly affected. In conclusion, FLEXPART is considered as a promising tool to explore the possible nuclear hazards under a variety of meteorological conditions. Further, a future study will consider the influence of the horizontal grid spacing and lateral boundary condition using the coupled Weather Research and Forecasting (WRF)-FLEXPART system.