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The limitations of exploiting renewable energy within a relatively limited surface area have hindered the use of off-grid residential equipment (i.e., refrigerators). The implementation of feasible PV systems is crucial for the energy management of these appliances. Therefore, the current investigation is motivated by promoting heat emissions from refrigerator condensers throughout the day. Ten condenser cooling case studies that incorporated both partially and fully loaded refrigerator storage capacities were examined experimentally. The results of the modified condenser cooling systems, powered by either an electrical grid or a limited (PV) system, were compared to those of the original refrigerator. The experiments show that the blast cooling and freezing efficiency of chilled and frozen products increased by 32 %, 39.4 %, 39.3 %, and 36 %, respectively, when the refrigerator was partially and fully loaded. Nevertheless, the electrical grid showcased the highest level of savings in cases (3) and (9), with a daily energy saving ratio (DESR) of 9.3 % and 5.4 %, respectively. In the same cases, the integration of a hybrid power source that includes a PV panel resulted in the most significant savings of 17.5 % and 11.4 %, respectively. Finally, the novel refrigerator cost analysis indicates that the extra life cycle payback ratio peaked at 12.48 %.

A designer's work is based on creativity, innovation, and fresh ideas therefore, they are constantly searching for creative designs, especially when it comes to furniture and internal design. Meanwhile, innovative materials and ideas are actively sought after by designers for usage in furniture and interior design. In this scope, our study explores the practical implications of using date palm fronds and plastic waste from drinking water bottle to create a composite with varying loadings of date palm frond nanoparticles (DPFNP) at 10, 20, and 30 wt % and its applications on furniture design. A desktop extruder was used to form the composites into filaments, which were subsequently used to create 3D-printed specimens. The impact of adding (DPFNP) on the composite's mechanical, thermal, and chemical properties was assessed. Polyethylene terephthalate (PET) drinking water bottle waste, and filament composites presented similar FTIR spectra; however, filament composites presented increased thermal stability and mechanical strength. The filament with 10 wt% DPFNP has been effectively printed with a 3D printer, demonstrating the practicality of employing the developed filaments and perhaps encouraging new uses in furniture and interior design. As a result, employing DPFNP in the recycled PET (RPET) matrix offers a cheap substitute for making filaments. It could increase the use of 3D printing in furniture and interior design by using more environmentally friendly materials. This result will be considered the first phase of future research investigation on a full-scale furniture prototype, examining its physical, thermal, and mechanical properties.

Thermal energy storage is critically necessary for most solar energy applications. The phase change energy storage systems are the most promising; however, the poor thermal conductivity of the material is a major disadvantage. This work aims to simulate the melting process and investigate the optimum design parameters for a stable and high melting rate of phase-change energy storage systems. A 2-D axisymmetric numerical model has been developed using ANSYS 17. The model was validated by published experimental data. A new elliptical design of the inner tube was investigated; and the main parameters considered were eccentricity (0, 0.1, 0.2, 0.3, 0.4, and 0.5), aspect ratio (1, 0.9, 0.8, 0.7, 0.6, and angle of 0.5), and inclination (0°, 30°, 60°, and 90°) of the inner tube. The outcome of this investigation revealed that the maximum enhancement of the average melting rate was 19.14%, 42.65%, 71.3%, 105.51%, and 150.1%, for eccentricity of 0.1,0.2,0.3,0.4, and 0.5, respectively, for an aspect ratio of 1. The maximum and minimum average melting rate were 0.00516 and 0.00417 kg/min for the vertical inner elliptical tube (θ = 90°) and the horizontal inner elliptic (θ = 0°) respectively for an aspect ratio of 0.5.

Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly in the use of artificial neural networks (ANNs) as the most widely used technique. To address the shortage of solar measurement in various places worldwide, several solar radiation methods have been developed to forecast global solar radiation (GSR). With this consideration, this study aims to develop temperature-based GSR models using a commonly utilized approach in machine learning techniques, ANNs, to predict GSR using just temperature data. It also compares the performance of these models to the commonly used empirical technique. Additionally, it develops precise GSR models for five new sites and the entire region, which currently lacks AI-based models despite the presence of proposed solar energy plants in the area. The study also examines the impact of varying lengths of validation datasets on solar radiation models’ prediction and accuracy, which has received little attention. Furthermore, it investigates different ANN architectures for GSR estimation and introduces a comprehensive comparative study. The findings indicate that the most advanced models of both methods accurately predict GSR, with coefficient of determination, R2, values ranging from 96% to 98%. Moreover, the local and general formulas of the empirical model exhibit comparable performance at non-coastal sites. Conversely, the local and general ANN-based models perform almost identically, with a high ability to forecast GSR in any location, even during the winter months. Additionally, ANN architectures with fewer neurons in their single hidden layer generally outperform those with more. Furthermore, the efficacy and precision of the models, particularly ANN-based ones, are minimally impacted by the size of the validation data sets. This study also reveals that the performance of the empirical models was significantly influenced by weather conditions such as clouds and rain, especially at coastal sites. In contrast, the ANN-based models were less impacted by such weather variations, with a performance that was approximately 7% better than the empirical ones at coastal sites. The best-developed models, particularly the ANN-based models, are thus highly recommended. They enable the precise and rapid forecast of GSR, which is useful in the design and performance evaluation of various solar applications, with the temperature data continuously and easily recorded for various purposes.

The present study examines experimentally the effect of pairing phase change material (PCM) with frozen items (ice/fish) contained in the freezer cabin of an energy-efficient refrigerator. The significance of (PCM) presence was underscored throughout extended periods of electrical grid failure in diverse environmental circumstances. Tests are started by power absence (after cyclic operation) until reaching a temperature of (−2 °C) to avoid ice melting or fish spoilage.The optimal PMR (product to PCM mass ratio) for frozen ice and fish is recommended via the extended under-thawing duration. RSER (Recovered and Saved Energy Ratio) was coined to describe the relationship between the amount of energy saved by utilizing the device during a power outage and the amount of energy the device is designed to consume on a normal day. The highest acquired RSER for ice products is 8.14% at PMR equal to 4.0 under summer conditions, while during winter conditions the highest value of 20.44% is demonstrated at PMR equal to 1.0. On the contrary, fish products reveal the highest RSER values at the same PMR of 5.0 under different ambient conditions. The ratio reached 12.5% during summer conditions, while the RSER acquired 36.5% under winter conditions.

Fuel cells (FCs) are one of the most promising forms of energy production. The huge challenge of fuel cell devices is the development of bi-functional, non-precious and highly active catalysts. In this study, nickel oxide supported on graphene (NiO/G) was synthesized by one pot thermal decomposition process of mineral water waste bottles with nickel oxide. Under alkaline conditions, the composite was employed as an electrode catalyst, replacing platinum (Pt) as a catalyst for the oxygen reduction reaction (ORR). The electrochemical behavior of synthesized materials exhibited an increase in oxygen reduction reaction using NiO/G with respect to using graphene only as a catalyst. All steps of the reaction at the efficient four-electron reduction path were spontaneous. The half-wave potential (E1/2) of the (ORR) equals −0.1 V and −0.18 V for NiO/G and graphene, respectively. The oxygen evaluation reaction (OER) was also investigated. The results showed that the OER current and the current density of NiO/G were significantly higher than those of graphene, which revealed the best OER performance among the graphene samples. Using the NiO/G composite in FCs is anticipated to lower the cost and boost the use of electrochemical energy devices, and it could be advantageous in other catalytic systems.

Introduction The syndrome of inappropriate antidiuretic hormone secretion (SIADH) is a serious condition associated with persistently high ADH with water retention despite sufficient vascular volume. Sodium valproate (VPA), an antiepileptic indicated to treat bipolar disorder, blocks sodium (Na) and calcium ions. Few studies have examined the association between VPA and SIADH. Objectives This abstract has two interrelated objectives: (1) to describe a VPA-associated SIADH case study we encountered in our clinical setting; and (2) to review literature for other VPA-associated SIADH cases to illuminate associations and possible risk factors. Methods After recording a case from clinical experiences, we completed a literature review of other cases of hyponatremia associated with VPA. We reviewed resulting artticles from searches in PubMed and in the aggregate Dartmouth Biomedical Library indices with no date or language parameters. We then searched those articles’ bibliographies. Results Ms. A is a 63-year-old woman with schizoaffective disorder, bipolar type, hospitalized for the resurgence of visual hallucinations (VH) of “monsters” asking her to hurt herself and others. She had been adherent to VPA (500mg twice daily) and non-adherent to prescribed Olanzapine (25mg). On Day 1 (D1), her labs were concerning for serum Na 119mEq/L (n=135-145), serum osmolality (SOsm) 264mEq/L (n=275-295), and inappropriately high urine osmolality 111mOsm/kg (n

Obtaining precise solar radiation data is the first stage in determining the availability of solar energy. It is also regarded as one of the major inputs for a variety of solar applications. Due to the scarcity of solar radiation measurement data for many locations throughout the world, many solar radiation models are utilized to predict global solar radiation. Indeed, the most widely used AI technique is artificial neural networks (ANNs). Hitherto, while ANNs have been utilized in various studies to estimate global solar radiation (GSR), limited attention has been given to the architecture of ANN. Thus, this study aimed to: first, optimize the design of one of the faster and most used machine-learning (ML) algorithms, the ANN, to forecast GSR more accurately while saving computation power; second, optimize the number of neurons in the hidden layer to obtain the most significant ANN model for accurate GSR estimation, since it is still lacking; in addition to investigating the impact of varying the number of neurons in the hidden layer on the proficiency of the ANN-based model to predict GSR with high accuracy; and, finally, conduct a comparative study between the ANN and empirical techniques for estimating GSR. The results showed that the best ANN model and the empirical model provided an excellent estimation for the GSR, with a Coefficient of Determination R2 greater than 0.98%. Additionally, ANN architectures with a smaller number of neurons in the single hidden layer (1–3 neurons) provided the best performance, with R2 > 0.98%. Furthermore, the performance of the developed ANN models remained approximately stable and excellent when the number of hidden layer’s neurons was less than ten neurons (R2 > 0.97%), as their performance was very close to each other. However, the ANN models experienced performance instability when the number of hidden layer’s neurons exceeded nine neurons. Furthermore, the performance comparison between the best ANN-based model and the empirical one revealed that both models performed well (R2 > 0.98%). Moreover, while the relative error for the best ANN model slightly exceeded the range, ±10% in November and December, it remained within the range for the empirical model even in the winter months. Additionally, the obtained results of the best ANN model in this work were compared with the recent related work. While it had a good RMSE value of 0.8361 MJ/m2 day−1 within the ranges of previous work, its correlation coefficient (r) was the best one. Therefore, the developed models in this study can be utilized for accurate GSR forecasting. The accurate and efficient estimation of global solar radiation using both models can be valuable in designing and performance evaluation for different solar applications.

To improve the mechanical properties of proton exchange membranes, consequently improving the performance of direct borohydride fuel cells, the present study prepared sulfonated chitosan-g-sulfonated polyvinyl alcohol/polyethylene oxide doped with sulfated zirconia composite (SCS-g-SPVA/PEO/SZrO2) polyelectrolyte membranes. Two fabrication techniques were followed, solution casting and electrospinning, to have the membranes in film and fiber forms and study the effect of the different forms on the membrane’s physicochemical properties. For the casting technique, different concentrations of SZrO2 (1-3 wt%) were used, while the optimum concentration of SZrO2 (3 wt%) was used in the electrospun one (SCS-g-SPVA/PEO/SZrO2-CF). SCS-g-SPVA/PEO/SZrO2-C membranes were prepared in a single step. The grafting and the crosslinking were carried out using glutaraldehyde and sulfosuccinic acid as sulfonating agents for chitosan and PVA and coupling agents simultaneously using click chemistry. On the other hand, SCS-g-SPVA/PEO/SZrO2-CF membranes were prepared in two steps. They were fabricated with electrospinning and then dipped into the coupling and crosslinking solutions. The casting membranes’ physicochemical properties were improved by increasing the SZrO2 content. The experimental results further show that the fabrication procedure significantly influences the physicochemical properties of the membranes. For instance, the composite fiber membrane demonstrated higher selectivity and higher ion exchange capacity (IEC) than the casting membrane. Furthermore, by using the response surface methodology model, the effects of ion exchange capacity, water uptake, and oxidative stability were optimized as three independent variables that affected the ionic conductivity of SCS-g-SPVA/PEO/SZrO2-3C. The optimized ionic conductivity of the SCS-g-SPVA/PEO/SZrO2-3C membrane was 13.6 mS cm−1, achieved at the maximum point of the polynomial model, with an IEC of 0.74 meq g−1, ∼92% water uptake, and about 93% oxidative stability.

In this work, for the first time, a simple casting process is used to create an efficient and highly stable cellulose acetate (CA) based membrane with dispersive graphene oxide nanosheets (GO). The successful preparation of GO and its integration into the polymer matrix was verified by structural and morphological characterization using FTIR, TEM, SEM, and XRD. Furthermore, the impact of GO nanosheets and their content on the composite membranes' physicochemical properties is investigated. The water uptake increased up to 24% as the concentration of GO increased, while the ion exchange capacity increased threefold compared to the blank CA membrane. Additionally, increasing GO loading also enhanced the proton conductivity and the tensile strength of the developed membranes. The homogeneous CA/GO nanocomposite membranes with GO filler amounts ranging from 0.3 to 0.8 wt% were found to have excellent proton conductivity varying from 9.2 to 15.5 mS/cm compared to 6.94 mS/cm for Nafion 212. Further, as systematically studied and compared in membrane performance, the overall power density of the membrane electrode assembly (MEA) with GO content was increased up to 519 mW/cm2 compared to 401 mW/cm2 for Nafion 212 with significantly lower cost. The encouraging outcomes of this study pave the way for a simple, environmentally friendly, and cost-effective approach for developing nanocomposite membranes for application in PEMFCs.

Abstract Nanoparticles are considered viable options in the treatment of cancer. This study was conducted to investigate the effect of magnetite nanoparticles (MNPs) and magnetite folate core shell (MFCS) on leukemic and hepatocarcinoma cell cultures as well as their effect on the animal model of acute myelocytic leukemia (AML). Through current study nanoparticles were synthesized, characterized by various techniques, and their properties were studied to confirm their nanostructure. Invivo study, nanoparticles were evaluated to inspect their cytotoxic activity against SNU-182 (human hepatocellular carcinoma), K562 (human leukemia), and THLE2 (human normal epithelial liver) cells via MTT test. Apoptotic signaling proteins Bcl-2 and Caspase-3 expression were inspected through RT-PCR method. A cytotoxic effect of MNPs and MFCS was detected in previous cell cultures. Moreover, the apoptosis was identified through significant up-regulation of caspase-3, with Bcl-2 down-regulation. Invitro study, AML was induced in rats by N-methyl-N-nitrosourea followed by oral treatment with MNPS and MFCS. Biochemical indices such as aspartate and alanine amino transferases, and lactate dehydrogenase activities, uric acid, complete blood count, and Beta -2-microglubulin were assessed in serum. Immunophenotyping for CD34 and CD38 detection was performed. Liver, kidney, and bone marrow were microscopically examined. Bcl-2 promoter methylation, and mRNA levels were examined. Although, both MNPs and MFCS depict amelioration in biochemical parameters, MFCS alleviated them toward normal control. Anticancer activity of MNPs and MFCS was approved especially for AML. Whenever, administration of MFCS was more effective than MNPs. The present work is one of few studies used MFCS as anticancer agent.

Underwater photogrammetry in archaeology in Egypt is a completely new experience applied for the first time on the submerged archaeological site of the lighthouse of Alexandria situated on the eastern extremity of the ancient island of Pharos at the foot of Qaitbay Fort at a depth of 2 to 9 metres. In 2009/2010, the CEAlex launched a 3D photogrammetry data-gathering programme for the virtual reassembly of broken artefacts. In 2013 and the beginning of 2014, with the support of the Honor Frost Foundation, methods were developed and refined to acquire manual photographic data of the entire underwater site of Qaitbay using a DSLR camera, simple and low cost materials to obtain a digital surface model (DSM) of the submerged site of the lighthouse, and also to create 3D models of the objects themselves, such as statues, bases of statues and architectural elements. In this paper we present the methodology used for underwater data acquisition, data processing and modelling in order to generate a DSM of the submerged site of Alexandria’s ancient lighthouse. Until 2016, only about 7200 m2 of the submerged site, which exceeds more than 13000 m2, was covered. One of our main objectives in this project is to georeference the site since this would allow for a very precise 3D model and for correcting the orientation of the site as regards the real-world space.

Background: COVID-19 has superseded all medical scientific challenges in 2020. It presented with a range of respiratory involvement from the mild upper respiratory tract to severe pneumonia with ARDS. A percentage of these patients manifested variable neurological presentations expanding the challenges of patients' assessment, care, and management. Objective: To discuss the neurological manifestations and the possible immunopathology of COVID-19. Methods: A literature search was performed in the PubMed database for the relevant articles published in English language between April 9 th , 2020 and June 1, 2020. Few papers were extracted from Google Scholar and pre-print material as well. The keywords used to perform search included “COVID-19,” “SARS Cov2,” “neurological manifestations,” and “immunology and pathology of COVID-19”. However, animal studies were excluded in the neurological manifestations, and the final number of literature search outcome was 27 articles. Results: The immunopathology involves angiotensin-converting enzyme (ACE) receptor 2 and spike protein S1. COVID-19 has a tremendous affinity to the ACE2 receptor. The status of secondary hemophagocytic lymphohistiocytosis (sHLH) and the cytokine storm lead to different organ damage and the corresponding clinical manifestations. The documented neurological manifestations mainly include acute ischemic stroke, hemorrhagic stroke, temporal lobe, thalamic encephalitis, and one case of Guillain-Barre Syndrome, in addition to agitation, headache, and seizures. Conclusion: Although the intimate relationship between the neurons and the immune activation is the basic concept of COVID-19 immunopathology, the definite routes of entry to the neuronal cells are yet to be disclosed. Acute stroke, myositis, headache, and meningoencephalitis are the most common types of complications of COVID-19 so far.

A low-temperature proton exchange membrane fuel cell (LT-PEMFC) is a promising clean and effective technology for power generation because of its simplified water and heat management. Due to the non-uniform of H2 and air distributions within fuel cells, the stack design is one of the key factors to enhance the performance and efficiency of LT-PEMFC. In this study, a single, two cells, 6 cells and 11 cells LT-PEMFC stack was investigated with cell active area 114 cm2, Nafion membrane 112 and catalyst loading 0.4 mg/cm2 working at 25 °C and atmospheric pressure using hydrogen and air as a fuel and oxidant, respectively. The power output that is obtained from each stack is presented and the overall power output is compared with single cell stack. The stack prototype has been fabricated, constructed and tested producing a maximum value of 70 W electrical power using 11 cells stack. Keywords: PEMFC, Stack, Bipolar plate, Performance analysis

Wireless Sensor Networks (WSN) consist of low power devices that are distributed in geographically isolated areas. Sensors are arranged in clusters. Each cluster defines a vital node which is known as a cluster head (CH). Each CH collects the sensed data from its sensor nodes to be transmitted to a base station (BS). Sensors have deployed with batteries that cannot be replaced. The energy consumption is an important concern for WSN. We propose an enhancement approach to reduce the energy consumption and extend the network lifetime. It has been accomplished by augmenting the energy balancing in clusters among all sensor nodes to minimize the energy dissipation during network communications. The improved method is based on a cluster head selection method. In addition, an enhanced schedule of the TDMA has been implemented. Finally, the development approach indicates the progress in terms of network lifetime, Number of cluster head, energy consumption and number of packets transferred to BS compared to LEACH and other related protocols. Mathematical analysis and MATLAB 2015a simulation results show the effectiveness of the proposed approach. The energy consumption of WSN has been reduced up to about 60% and prolong the network life cycle by 73% than LEACH. Keywords: Cluster, Wireless, Sensor, Network, LEACH, Threshold

The success of fuel cells depends on the proper design of the electrodes and membrane allowing easy access of oxygen and protons. Using non-precious catalyst electrodes based on recyclable carbon nanostructures is most important to produce clean energy and increase the ability to commercialize the fuel cells. Herein, reduced graphene oxide (rGO) and graphene/magnetic iron oxide nanocomposite (rGO/MIO) are successfully synthesized as anode and cathode, respectively, from polyethylene terephthalate (PET) waste bottles using easy steps in order to simplify the method and reducing the production cost. While, the membrane is prepared from low cost and eco-friendly ternary polymers blend which are polyvinyl alcohol (PVA), polyethylene oxide (PEO) and polyvinyl pyrrolidone (PVP) then doped with sulfonated graphene oxide. The prepared electrodes have characteristic high porosity and their electrocatalytic performances are evaluated using three-electrode cell electrochemical studies as cyclic voltammetry and linear scan voltammetry combined with rotating disk electrode. A new assembly of the membrane between two non-precious catalyst electrodes as a single polymer electrolyte membrane fuel cell (PEMFC) was developed using a catalyst-coated membrane technique. The membrane electrode assembly (MEA) design parameters which affect its performance in hydrogen fuel cells as number of used catalyst layers (CL) or using gas diffusion layer (GDL) were evaluated in a single cell set-up with H2/O2 operation and the results revealed that the performance MEA was enhanced with using GDL more than that of MEA without GDL by 66% at a current density of 0.8 A cm−2 while the performance with double CL was better than that of the conventional single CL by 30% at a current density of 0.98 mA cm−2.