Palynostratigraphical studies on some subsurface middle Albian–early Cenomanian sediments from North Western Desert,Egypt

The Kharita and Bahariya formations are of great importance for Egyptian economy. They are the target of the oil and gas exploration in the Egyptian Western Desert. The present study investigated the palynostratigraphy, palynofacies and spore coloration of samples from these two formations in Negelah-1 Borehole. The investigations were used to evaluate their thermal maturity and potentiality to generate the hydrocarbons. The identified palynozones are Afropollis jardinus Acme Zone (late Albian–early Cenomanian) and Araucariacites australis Acme Zone (middle Albian). The first zone is differentiated into two subzones, which are Elaterosporites klaszii Interval Subzone and Cretacaeiporites densimurus Interval Subzone. The palynofacies analysis revealed that the Kharita Formation was deposited in proximal near shore marine environment under oxic conditions. Whereas the lower part of the Bahariya Formation was initially deposited in proximal near shore dysoxic marine conditions and the environment of deposition getting more distal to inner shelf dysoxic to anoxic marine conditions in the younger upper part. The samples from both formations include gas prone type III and IV. The spore color observations indicated that the samples from both formations are immature to yield gas and are not effective hydrocarbon source.     

Development of Isatin-Based Schiff Bases Targeting VEGFR-2 Inhibition: Synthesis,Characterization, Antiproliferative Properties,and QSAR Studies

Three sets of isatin-based Schiff bases were synthesized utilizing the molecular hybridization approach. Some of the synthesized Schiff bases show significant to moderate antiproliferative properties against MCF7 (breast), HCT116 (colon), and PaCa2 (pancreatic) cancer cell lines with potency compared to reference drugs 5-fluorouracil (5-FU) and Sunitinib. Among all, compound 17 f (3-((1,5-dimethyl-3-oxo-2-phenyl-2,3-dihydro-1H-pyrazol-4-yl)imino)-1-((1-(2-methoxyphenyl)-1H-1,2,3-triazol-4-yl)methyl)-5-methylindolin-2-one) exhibits promising antiproliferative properties against the MCF7 cancer cell line with 2.1-fold more potency than Sunitinib. However, among all the synthesized compounds, three (5-methylisatin derivatives) were the most effective against HCT116 in comparison to 5-FU. Compound 17 f exhibited the highest anti-angiogenic effect on the vasculature as it significantly reduced BV from 43 mm to 2 mm in comparison to 5.7 mm for Sunitinib and flow cytometry supports the arrest of the cell cycle at G1/S phases. In addition, compound 17 f also showed high VEGFR-2 inhibition properties against breast cancer cell lines. Robust 2D-QSAR studies supported the biological data.     

Combined advanced oxidation and biological treatment processes for the removal of pesticides from aqueous solutions

Advanced oxidation processes were combined with biological treatment processes in this study to remove both pesticides and then the COD load from aqueous solutions. It was found that O(3) and O(3)/UV oxidation systems were able to reach 90 and 100%, removal of the pesticide Deltamethrin, respectively, in a period of 210 min. The use of O(3) combined with UV radiation enhances pesticides degradation and the residual pesticide reaches zero in the case of Deltamethrin. The combined O(3)/UV system can reduce COD up to 20% if the pH of the solution is above 4. Both pesticide degradation and COD removal in the combined O(3)/UV system follow the pseudo-first-order kinetics and the parameters of this model were evaluated. The application of the biological treatment to remove the bulk COD from different types of feed solution was investigated. More than 95% COD removal was achieved when treated wastewater by the O(3)/UV system was fed to the bioreactor. The parameters of the proposed Grau model were estimated.     

New Magnetic Phase Transitions in the Tetragonal Compounds PrBa2Cu3O6+x at Low Temperature

The anomalous magnetic properties of Pr ions in the PrBa₂Cu₃O_6+x system are investigated at low temperature. Measurements of the specific heat C _P(T) and the magnetic susceptibility χ(T) are performed on ceramic samples in the tetragonal structure with x=0.44 and x=0. Two new magnetic transitions are observed below the Néel temperature of the Pr antiferromagnetic ordering $T_{\mathrm{N}}^{\mathrm{Pr}} \sim 9\mbox{--}10~\mathrm{K}$ . The first one is observed at the low-critical temperature T _cr～4–5?K and the second one is observed at $T_{2}^{\mathrm{Pr}\text{--}\mathrm{Cu}} \sim 6\mbox{--}7~\mathrm{K}$ , respectively. Assuming that ΔC _P(T) can be used to represent the Pr contribution to the specific heat C _P(T), the data are well fitted for T<T _cr by using the development of ΔC _P(T)/T=ΔA(T ²)^?3/2 +Δγ+M(T ²) ¹ +m(T ²) ² . The values of the electronic coefficient Δγ are found much lower than all previous results obtained in compounds of the orthorhombic structure, and this is, in good agreement with the insulating character of our non-superconducting samples. The high values obtained for the coefficient M, permits us to confirm the existence of strong Pr–Pr exchange interactions. Some non-linear effects attributed to the values of the coefficient m are revealed and discussed in terms of the previous Pr–Cu coupling with a spin reorientation phase transition of both spin sublattices around $T_{2}^{\mathrm{Pr}\text{--}\mathrm{Cu}}$ . The appearance of a weak ferromagnetic tendency in the magnetic susceptibility analysis below T _cr, could be associated with the reordering of the Pr subsystem.     

Food consumption frequency and perceived stress and depressive symptoms among students in three European countries

Background  

Certain foods might be more frequently eaten under stress or when higher levels of depressive symptoms are experienced. We examined whether poor nutritional habits are associated with stress and depressive symptoms and whether the relationships differ by country and gender in a sample from three European countries collected as part of a Cross National Student Health Survey.     

A new reactive,distortion and non-active power measurement method for nonstationary waveforms using wavelet packet transform

The definitions of power components that are contained in the IEEE Standard 1459-2000 [IEEE Std. 1459-2000, Definitions for the measurement of electric quantities under sinusoidal, non-sinusoidal, balanced or unbalanced conditions, 2000] are based on the Fourier transform (FT) which is suitable only for the case of stationary waveforms. However, for nonstationary waveforms, the FT produces large errors. Therefore, the power components based on this transform become inaccurate. A new approach based on the wavelet packet transform (WPT) for defining these power components is developed in this paper. The advantages of using the wavelet transform are that it can accurately represent and measure nonstationary waveforms, providing uniform frequency bands while preserving both time and frequency information. In addition, this paper addresses the problem of choosing the most appropriate mother wavelet for power components measurements. The results of applying both approaches to stationary and nonstationary waveforms show that the currently used definitions according to the IEEE Standard 1459-2000 result in large errors for the case of nonstationary waveforms while the proposed approach (WPT based) gives more accurate results in this situation.     

A Stretchable Solid Ionic Electrode-Based Triboelectric Nanogenerator for Biomechanical Energy Harvesting and Self-Powered Sensors

Stretchable power devices and self-powered sensors have become increasingly desired for wearable electronics and artificial intelligence. In this study, an all-solid-state triboelectric nanogenerator (TENG) is reported, whose one solid-state structure prevents delamination during stretch and release cycles and increasing the patch adhesive force (3.5 N) and strain (586% elongation at break). Through the synergetic virtues of stretchability, ionic conductivity, and excellent adhesion to the tribo-layer, reproducible open-circuit voltage (V_OC) of 84 V, charge (Q_SC) of 27.5 nC, and short-circuit current (I_SC) of 3.1 µA after drying at 60°C or 20,000 contact-separation cycles are obtained. Apart from contact-separation, this device shows unprecedented electricity generation through stretch–release of solid materials leading to a linear relationship between V_OC and strain. For the first time, this work provides a clear explanation of the working mechanism of contact-free stretching–releasing and investigates the relationships of exerted force, strain, thickness of the device, and electric output. Benefitting from the one solid-state structure, this contact-free device remains stable even after repeated stretch–release cycling, maintaining 100% of its V_OC after 2500 stretch–release cycles. These findings provide a strategy toward highly conductive and stretchable electrodes for harvesting mechanical energy and health monitoring.     

Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

In recent years, Digital Twin (DT) has gained significant interest from academia and industry due to the advanced in information technology, communication systems, Artificial Intelligence (AI), Cloud Computing (CC), and Industrial Internet of Things (IIoT). The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complexity during the life cycle that produces a massive amount of engendered data and information. Likewise, with the development of AI, digital twins can be redefined and could be a crucial approach to aid the Internet of Things (IoT)-based DT applications for transferring the data and value onto the Internet with better decision-making. Therefore, this paper introduces an efficient DT-based fault diagnosis model based on machine learning (ML) tools. In this framework, the DT model of the machine is constructed by creating the simulation model. In the proposed framework, the Genetic algorithm (GA) is used for the optimization task to improve the classification accuracy. Furthermore, we evaluate the proposed fault diagnosis framework using performance metrics such as precision, accuracy, F-measure, and recall. The proposed framework is comprehensively examined using the triplex pump fault diagnosis. The experimental results demonstrated that the hybrid GA-ML method gives outstanding results compared to ML methods like Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machine (SVM). The suggested framework achieves the highest accuracy of 95% for the employed hybrid GA-SVM. The proposed framework will effectively help industrial operators make an appropriate decision concerning the fault analysis for IIoT applications in the context of Industry 4.0.     

Efficient Deep CNN Model for COVID-19 Classification

Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate the diagnosis process. Deep Learning (DL) is an effective tool that can be utilized for detection and classification this type of medical images. The deep Convolutional Neural Networks (CNNs) can learn and extract essential features from different medical image datasets. In this paper, a CNN architecture for automated COVID-19 detection from CXR and CT images is offered. Three activation functions as well as three optimizers are tested and compared for this task. The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it. The performance is tested and investigated on the CT and CXR datasets. Three activation functions: Tanh, Sigmoid, and ReLU are compared using a constant learning rate and different batch sizes. Different optimizers are studied with different batch sizes and a constant learning rate. Finally, a comparison between different combinations of activation functions and optimizers is presented, and the optimal configuration is determined. Hence, the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage. The proposed model achieves a classification accuracy of 91.67% on CXR image dataset, and a classification accuracy of 100% on CT dataset with training times of 58 min and 46 min on CXR and CT datasets, respectively. The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10⁻⁵ and a minibatch size of 16.