A detailed hydrothermal investigation of a helical micro double-tube heat exchanger for a wide range of helix pitch length

The present study was numerically inquired the heat transfer performance and fluid flow characteristic of a helical micro double-tube heat exchanger (HMDTHX) using the finite volume method. The tube length was considered to be constantly equal to 30 mm, and 12 different configurations were modeled by changing in turn number and pitch length (P) for Reynolds numbers of 50, 100, 150, and 200. The findings indicated that the heat transfer would enhance by applying any helix angle in the straight tube. However, it had an optimum point which varied by Reynolds number (Re). Rising Re caused overall heat transfer coefficient (OHTC), pressure drop, and pumping power augment for all cases. Increasing P in overall reduced OHTC, pressure drop, and pumping power which had different maximum points between P = 0.5 to 3. Maximum overall heat transfer coefficient (OHTC) enhancement was equal to 45% for Re = 200 and P = 2. Also, maximum effectiveness was 11.5% for P = 2 and Re = 200. Moreover, a 42% maximum increment was achieved for pressure drop, pumping power, and friction factor at Re = 200 and P = 2. Shear stress for Re = 100 to 200 showed that the values are almost the same for P = 0.5 and 1. Then by increasing P, the shear stress decreases. While, for Re = 50, a maximum is seen at P = 2. The temperature distribution was indicated that the maximum temperature of the straight tube and helical tube are the same, but the difference is in the average temperature, which was 3.2 K between straight and helical tubes. Finally, by investigating the velocity contour, it was determined that a secondary flow through the HMDTHX, affected by centrifugal force, was existed, enhancing the fluid flow turbulency and heat transfer rate

Effect of the COVID-19 Vaccine on the Menstrual Cycle among Females in Saudi Arabia

BACKGROUND: The number of reports of menstrual changes after COVID-19 vaccination in the Saudi population is still unknown. Therefore, this study aimed to assess the effect of the COVID-19 vaccine(Pfizer, AstraZeneca, and Moderna) on the menstrual cycle among females in Saudi Arabia. METHODS: This descriptive cross-sectional study was conducted in Saudi Arabia at Umm Al-Qura University (UQU) from August 2021 to February 2022. Data was collected through a previously validated online questionnaire. RESULTS: A total of 2338 participants who received the first dose of the COVID-19 vaccine participated in this study; 1606 (68.7%) of them received the second dose in addition to the first. The mean age of the study participants was 35.4±9.5 years. No significant associations were found between the type of COVID-19 vaccine and the impact on the menstrual cycle, either for the first or second dose (P-values > 0.05). A significant association was found only between the first dose vaccination day and the impact on the menstrual cycle in the second question of “After receiving the COVID-19 vaccine, your next period was” (P-value ≤ 0.05). Significant associations were found between the second dose vaccination day and the impact on the menstrual cycle in the first and second questions of “After receiving the COVID-19 vaccine, your next period was”, and “After receiving the first dose, your next period was," respectively (P-values ≤ 0.05). CONCLUSION: The study found a potential association between the COVID-19 vaccine and menstrual cycle irregularities, which could impact females' quality of life

FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

BackgroundForests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset.ResultsThe test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with the given data.ConclusionThe integration of the robust feature extraction of FireXnet with the transparency of explainable AI using SHAP enhances the model’s interpretability and allows for the identification of key characteristics triggering wildfire detections. Extensive experimentation reveals that in addition to being accurate, FireXnet has reduced computational complexity due to considerably fewer training and non-training parameters and has significantly fewer training and testing times

Intravenous Formulation of HET0016 Decreased Human Glioblastoma Growth and Implicated Survival Benefit in Rat Xenograft Models

Glioblastoma (GBM) is a hypervascular primary brain tumor with poor prognosis. HET0016 is a selective CYP450 inhibitor, which has been shown to inhibit angiogenesis and tumor growth. Therefore, to explore novel treatments, we have generated an improved intravenous (IV) formulation of HET0016 with HPßCD and tested in animal models of human and syngeneic GBM. Administration of a single IV dose resulted in 7-fold higher levels of HET0016 in plasma and 3.6-fold higher levels in tumor at 60 min than that in IP route. IV treatment with HPßCD-HET0016 decreased tumor growth, and altered vascular kinetics in early and late treatment groups (p \u3c 0.05). Similar growth inhibition was observed in syngeneic GL261 GBM (p \u3c 0.05). Survival studies using patient derived xenografts of GBM811, showed prolonged survival to 26 weeks in animals treated with focal radiation, in combination with HET0016 and TMZ (p \u3c 0.05). We observed reduced expression of markers of cell proliferation (Ki-67), decreased neovascularization (laminin and αSMA), in addition to inflammation and angiogenesis markers in the treatment group (p \u3c 0.05). Our results indicate that HPßCD-HET0016 is effective in inhibiting tumor growth through decreasing proliferation, and neovascularization. Furthermore, HPßCD-HET0016 significantly prolonged survival in PDX GBM811 model

A mid-IR laser diagnostic for HCN detection

Hydrogen cyanide (HCN) is a major source of prompt-NOx formation especially in fuel-bound nitrogen systems. To date, there is still a significant disagreement between experimental data and theoretical predic- tions of the rate coefficients of combustion reactions involving HCN as a prompt-NOx precursor. Accurate modeling of NOx formation would greatly benefit from a diagnostic capable of performing high-fidelity measurements of HCN formation/consumption time-histories. In this study, a laser diagnostic is developed for sensitive and selective HCN sensing by probing its most intense absorption feature in the mid-infrared (MIR). The diagnostic is based on difference-frequency generation (DFG) between a CO2 gas laser and an external-cavity quantum cascade laser in a nonlinear orientation-patterned gallium arsenide crystal which results in a DFG laser tunable over 11.56 − 15 μm. HCN measurements were carried out at the peak of the Q-branch of its strong ν2 vibrational band near 14 μm. Pressure dependence of the absorption cross-section was investigated at room temperature over the pressure range of 0.07 − 1.07 bar. Temperature-dependent absorption cross-section measurements were conducted behind reflected shock waves over the temperature range of 850 − 3000 K. The diagnostic was demonstrated in reactive experiments in a shock tube where HCN mole fraction time-histories were measured during the thermal decomposition of isoxazole (C3H3NO) and the first-order rate coefficients of C3H3NO → HCN + CH2CO reaction were determined

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

In the ever-evolving technological landscape, ensuring high system availability has become a paramount concern. This research paper focuses on cloud computing, a domain witnessing exponential growth and emerging as a critical use case for high-availability systems. To fulfil the criteria, many services in cloud infrastructures should be combined, relying on the user’s demands. Central to this study is load balancing, an integral element in harnessing the full potential of heterogeneous computing systems. In cloud environments, dynamic management of load balancing is crucial. This study explores how virtual machines can effectively remap resources in response to fluctuating loads dynamically, optimizing overall network performance. The core of this research involves an in-depth analysis of several metaheuristic algorithms applied to load balancing in cloud computing. These include Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, and Grey Wolf Optimization. Utilizing CloudAnalyst, the study conducts a comparative analysis of these techniques, focusing on key performance metrics such as Total Response Time (TRT) and Data Center Processing Time (DCPT). The findings of this research offer insights into the varying behaviors of these algorithms under different cloud configurations and user retention levels. The ultimate aim is to pave the way for developing innovative load-balancing strategies in cloud computing. By providing a comprehensive evaluation of existing metaheuristic methods, this paper contributes to advancing high-availability systems, underscoring the importance of tailored solutions in the dynamic realm of cloud technology

A Significant Solar Energy Note on Powell-Eyring Nanofluid with Thermal Jump Conditions: Implementing Cattaneo-Christov Heat Flux Model

PTSCs (parabolic trough solar collectors) are widely employed in solar-thermal applications to attain high temperatures. The purpose of this study is to determine how much entropy is created when Powell-Eyring nanofluid (P-ENF) flows across porous media on a horizontal plane under thermal jump circumstances. The flow in PTSC was generated by nonlinear surface stretching, thermal radiation, and Cattaneo-Christov heat flux, which was utilized to compute heat flux in the thermal boundary layer. Using a similarity transformation approach, partial differential equations were converted into ordinary differential equations with boundary constraints. Then, the boundary restrictions and partial differential equations were merged to form a single set of nonlinear ordinary differential equations. To obtain approximate solutions to ordinary differential equations, the Keller-Box approach is utilized. Nanofluids derived from silver- and copper-based engine oil (EO) has been employed as working fluids. The researchers observed that changing the permeability parameter reduced the Nusselt number while increasing the skin frictional coefficient. Total entropy variation was also calculated using the Brinkman number for flow rates with Reynolds number and viscosity changes. The key result is that thermal efficiency is inversely proportional to particular entropy production. For example, using Cu-EO nanofluid instead of Ag-EO nanofluid increased the heat transport rate efficiency to 15–36%

Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. The exact causes of ASD remain elusive and likely involve a combination of genetic, environmental, and neurobiological factors. Doctors often face challenges in accurately identifying ASD early due to its complex and diverse presentation. Early detection and intervention are crucial for improving outcomes for individuals with ASD. Early diagnosis allows for timely access to appropriate interventions, leading to better social and communication skills development. Artificial intelligence techniques, particularly facial feature extraction using machine learning algorithms, display promise in aiding the early detection of ASD. By analyzing facial expressions and subtle cues, AI models identify patterns associated with ASD features. This study developed various hybrid systems to diagnose facial feature images for an ASD dataset by combining convolutional neural network (CNN) features. The first approach utilized pre-trained VGG16, ResNet101, and MobileNet models. The second approach employed a hybrid technique that combined CNN models (VGG16, ResNet101, and MobileNet) with XGBoost and RF algorithms. The third strategy involved diagnosing ASD using XGBoost and an RF based on features of VGG-16-ResNet101, ResNet101-MobileNet, and VGG16-MobileNet models. Notably, the hybrid RF algorithm that utilized features from the VGG16-MobileNet models demonstrated superior performance, reached an AUC of 99.25%, an accuracy of 98.8%, a precision of 98.9%, a sensitivity of 99%, and a specificity of 99.1%

Optimal Performance of Photovoltaic-Powered Water Pumping System

Photovoltaic (PV) systems are one of the promising renewable energy sources that have many industrial applications; one of them is water pumping systems. This paper proposes a new application of a PV system for water pumping using a three-phase induction motor while maximizing the daily quantity of water pumped while considering maximizing both the efficiency of the three-phase induction motor and the harvested power from the PV system. This harvesting is performed through maximum power point tracking (MPPT) of the PV system. The proposed technique is applied to a PV-powered 3 phase induction motor water pumping system (PV-IMWPS) at any operating point. Firstly, an analytical approach is offered to find the optimal firing pattern of the inverter (V-F) for the motor through optimal flux control. This flux control is presented for maximizing the pump flow rate while achieving MPPT for the PV system and maximum efficiency of the motor at any irradiance and temperature. The provided analytical optimal flux control is compared to a fixed flux one to ascertain its effectiveness. The obtained feature of the suggested optimal flux control validates a significant improvement in the system performances, including the daily pumped quantity, motor power factor, and system efficiency. Then converting the data from the first analytical step into an intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS). This ANFIS is trained offline with the input (irradiance and temperature) while the output is the inverter pattern to enhance the performance of the proposed pumping system, PV-IMWPS

Characterization of Orange Peel Extract and Its Potential Protective Effect against Aluminum Chloride-Induced Alzheimer’s Disease

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder without a cure. Hence, developing an effective treatment or protective agent is crucial for public health. The present study aims to characterize orange peel extract (OPE) through in vitro and in silico studies. Furthermore, it examines the protective effect of OPE against experimentally-induced Alzheimer’s disease in rats. The total phenolic and flavonoid content of OPE was 255.86 ± 1.77 and 52.06 ± 1.74 (mg/100 g), respectively. Gallic acid, the common polyphenol in OPE detected by HPLC was 3388.60 μg/100 g. OPE antioxidant IC50 was 67.90 ± 1.05, 60.48 ± 0.91, and 63.70 ± 0.30 by DPPH, ABTS and Hydroxyl radical scavenging activity methods, respectively. In vitro anti-acetylcholinesterase (AChE) IC50 was 0.87 ± 0.025 mg/mL for OPE and 2.45 ± 0.001 mg/mL for gallic acid. Molecular docking analysis for human AChE (4EY7) with donepezil, gallic acid, and acetylcholine showed binding energy ΔG values of −9.47, −3.72, and −5.69 Kcal/mol, respectively. Aluminum chloride injection (70 mg/Kg/day for 6 weeks) induced Alzheimer’s-like disease in male rats. OPE (100 and 200 mg/kg/d) and gallic acid (50 mg/kg/d) were administered orally to experimental animals for 6 weeks in addition to aluminum chloride injection (as protective). OPE was found to protect against aluminum chloride-induced neuronal damage by decreasing both gene expression and activity of acetylcholinesterase (AChE) and a decrease in amyloid beta (Aβ42) protein level, thiobarbituric acid-reactive substances (TBARS), and nitric oxide (NO), and increased reduced glutathione (GSH) level and activity of the antioxidant enzymes in the brain tissues. Additionally, gene expressions for amyloid precursor protein (APP) and beta secretase enzyme (BACE1) were downregulated, whereas those for presinilin-2 (PSEN2) and beta cell lymphoma-2 (BCL2) were upregulated. Furthermore, the reverse of mitochondrial alternation and restored brain ultrastructure might underlie neuronal dysfunction in AD. In conclusion, our exploration of the neuroprotective effect of OPE in vivo reveals that OPE may be helpful in ameliorating brain oxidative stress, hence protecting from Alzheimer’s disease progression