Enhancing brain tumor diagnosis: an optimized CNN hyperparameter model for improved accuracy and reliability.

Hyperparameter tuning plays a pivotal role in the accuracy and reliability of convolutional neural network (CNN) models used in brain tumor diagnosis. These hyperparameters exert control over various aspects of the neural network, encompassing feature extraction, spatial resolution, non-linear mapping, convergence speed, and model complexity. We propose a meticulously refined CNN hyperparameter model designed to optimize critical parameters, including filter number and size, stride padding, pooling techniques, activation functions, learning rate, batch size, and the number of layers. Our approach leverages two publicly available brain tumor MRI datasets for research purposes. The first dataset comprises a total of 7,023 human brain images, categorized into four classes: glioma, meningioma, no tumor, and pituitary. The second dataset contains 253 images classified as "yes" and "no." Our approach delivers exceptional results, demonstrating an average 94.25% precision, recall, and F1-score with 96% accuracy for dataset 1, while an average 87.5% precision, recall, and F1-score, with accuracy of 88% for dataset 2. To affirm the robustness of our findings, we perform a comprehensive comparison with existing techniques, revealing that our method consistently outperforms these approaches. By systematically fine-tuning these critical hyperparameters, our model not only enhances its performance but also bolsters its generalization capabilities. This optimized CNN model provides medical experts with a more precise and efficient tool for supporting their decision-making processes in brain tumor diagnosis.

An improved class of estimators for estimation of population distribution functions under stratified random sampling.

The main objective of the current study is to suggest an enhanced family of log ratio-exponential type estimators for population distribution function (DF) using auxiliary information under stratified random sampling. Putting different choices in our suggested generalized class of estimators, we found some Specific estimators. The bias and MSE expressions of the estimators have been approximated up to the first order. By using the actual and simulated data sets, we measured the performance of estimators. Based on the results, the suggested estimators for DF show better performance as compared to the preliminary estimators considered here. The suggested estimators have a advanced efficiency than the other estimators examined with the estimators F‾ˆlogPR(st)2, and F‾ˆlogPR(st)4 for both the actual and simulated data sets. The magnitude of the improvement in efficiency is noteworthy, indicating the superiority of the proposed estimators in terms of MSE.

Exposure to Short- and Medium-Chain Chlorinated Paraffins and the Risk of Gestational Diabetes Mellitus: A Nested Case-Control Study in Eastern China.

Toxicological studies have indicated that exposure to chlorinated paraffins (CPs) may disrupt intracellular glucose and energy metabolism. However, limited information exists regarding the impact of human CP exposure on glucose homeostasis and its potential association with an increased risk of developing gestational diabetes mellitus (GDM). Here, we conducted a prospective study with a nested case-control design to evaluate the link between short- and medium-chain CP (SCCPs and MCCPs) exposures during pregnancy and the risk of GDM. Serum samples from 102 GDM-diagnosed pregnant women and 204 healthy controls were collected in Hangzhou, Eastern China. The median (interquartile range, IQR) concentration of SCCPs was 161 (127, 236) ng/mL in the GDM group compared to 127 (96.9, 176) ng/mL in the non-GDM group (p < 0.01). For MCCPs, the GDM group had a median concentration of 144 (117, 174) ng/mL, while the control group was 114 (78.1, 162) ng/mL (p < 0.01). Compared to the lowest quartile as the reference, the adjusted odds ratios (ORs) of GDM were 7.07 (95% CI: 2.87, 17.40) and 3.34 (95% CI: 1.48, 7.53) in the highest quartile of ∑SCCP and ∑MCCP levels, respectively, with MCCPs demonstrating an inverted U-shaped association with GDM. Weighted quantile sum regression evaluated the joint effects of all CPs on GDM and glucose homeostasis. Among all CP congeners, C13H23Cl5 and C10H16Cl6 were the crucial variables driving the positive association with the GDM risk. Our results demonstrated a significant positive association between CP concentration in maternal serum and GDM risk, and exposure to SCCPs and MCCPs may disturb maternal glucose homeostasis. These findings contribute to a better understanding of the health risks of CP exposure and the role of environmental contaminants in the pathogenesis of GDM.

Left bundle branch pacing vs biventricular pacing in heart failure patients with left bundle branch block: A systematic review and meta-analysis.

BACKGROUND: Left bundle branch pacing (LBBP) is a novel pacing modality of cardiac resynchronization therapy (CRT) that achieves more physiologic native ventricular activation than biventricular pacing (BiVP). AIM: To explore the validity of electromechanical resynchronization, clinical and echocardiographic response of LBBP-CRT. METHODS: Systematic review and Meta-analysis were conducted in accordance with the standard guidelines as mentioned in detail in the methodology section. RESULTS: In our analysis, the success rate of LBBP-CRT was determined to be 91.1%. LBBP-CRT significantly shortened QRS duration, with significant improvement in echocardiographic parameters, including left ventricular ejection fraction, left ventricular end-diastolic diameter and left ventricular end-systolic diameter in comparison with BiVP-CRT. CONCLUSION: A significant reduction in New York Heart Association class and B-type natriuretic peptide levels was also observed in the LBBP-CRT group vs BiVP-CRT group. Lastly, the LBBP-CRT cohort had a reduced pacing threshold at follow-up as compared to BiVP-CRT.

Impacts of crude oil market on global economy: Evidence from the Ukraine-Russia conflict via fuzzy models.

The increasing Russia-Ukraine crisis is without a doubt Europe's most prominent conflict since World War II, changing the dynamics of the oil and other key markets. Because the oil market has traditionally interacted with other financial and commodity markets, it will be intriguing to examine how it interacts with substantial financial assets amid market volatility induced by a conflict. The goal of this study is to propose a fuzzy time series (FTS) model and to compare its competitiveness to existing fuzzy time series (FTS) models, Autoregressive Integrated Moving Average (ARIMA) model and some machine learning methods i.e. Artificial Neural Networks (ANN), Support Vector Machine (SVM) and XGBoost models. We considered changes in the partitioning universe of discourse, optimization of parameters method(s), and interval estimation to make the forecast accuracy more precise forecasting than traditional methods via MAPE. The event-based data results show the proposed fuzzy time series model is outperforming all the competitive methods in the study. Furthermore, the proposed model forecasting shows a future decline tendency in WTi market crude oil prices (US$/BBL) after being at the record highest level, which is good news for the worldwide economy.

AMDDLmodel: Android smartphones malware detection using deep learning model.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.

Color-tunable stable quasi-2D hybrid metal halide perovskites: synthesis, characterization, and optical analysis.

Metal halide perovskites show remarkable optical properties and useful applications in optoelectronic devices. However, the instability of three-dimensional (3D) metal halide perovskites limits their applications, leading to the emergence of more stable two-dimensional (2D) metal halide perovskites. Herein, we present a facile synthesis of the 2D hybrid metal halide perovskite (EDA)(MA)n-1PbnBr3n+1 (EDA: ethylene diammonium, MA: methylammonium), where n = 1-6, and MAPbBr3 perovskite layers using an anti-solvent co-precipitation technique. The synthesized materials exhibited tunable optical properties, and the color emissions of pure EDAPbBr4 and (EDA)(MA)2Pb3Br10 perovskites were successfully tailored by altering halide anion layers. The band gap decreases as the value of n in the (EDA)(MA)n-1PbnBr3n+1 compound increases from 1 to 6. The as-prepared materials were characterized using X-ray diffraction (XRD) technique, Fourier transform infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-Vis), photoluminescence spectroscopy (PL), scanning electron microscopy (SEM), and energy dispersive X-ray analysis (EDX). Finally, the stability of the 2D hybrid metal halide perovskite structures was evaluated under ambient conditions over different periods. Their tunable color emission was investigated and robust fluorescence was observed after 55 days. Thus, this study provides valuable insights into the synthesis and characterization of 2D hybrid metal halide perovskites for tunable color emission, highlighting their potential for use in various optoelectronic applications.

Next-Gen brain tumor classification: pioneering with deep learning and fine-tuned conditional generative adversarial networks.

Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2.

An enhanced estimator of finite population variance using two auxiliary variables under simple random sampling.

In this article, we have suggested a new improved estimator for estimation of finite population variance under simple random sampling. We use two auxiliary variables to improve the efficiency of estimator. The numerical expressions for the bias and mean square error are derived up to the first order approximation. To evaluate the efficiency of the new estimator, we conduct a numerical study using four real data sets and a simulation study. The result shows that the suggested estimator has a minimum mean square error and higher percentage relative efficiency as compared to all the existing estimators. These findings demonstrate the significance of our suggested estimator and highlight its potential applications in various fields. Theoretical and numerical analyses show that our suggested estimator outperforms all existing estimators in terms of efficiency. This demonstrates the practical value of incorporating auxiliary variables into the estimation process and the potential for future research in this area.

Comparative Analysis of Female Leadership Styles in Public and Private Sector Universities: A Study in Pakistan.

This study aimed to compare the styles of leadership practices of female leaders in public and private Universities in Pakistan. In this study, both quantitative and qualitative data were collected using a mixed-method approach. An adapted and developed questionnaire was used for quantitative data collection, whereas qualitative data were collected through a semi-structured interview schedule. A sample of 200 female leaders was selected for quantitative data collection, while 10 females from the said sample were selected for qualitative data collection through a simple random method. Quantitative data were analyzed using SPSS, whereas qualitative data were analyzed using thematic analysis. Based on the statistical results, this study discovered statistically significant differences in female transformational and transactional leadership styles and significant differences in job performance based on the university sector. This study discovered statistically insignificant differences based on the different positions of female leaders regarding transformational and transactional leadership, and job performance. Moreover, qualitative data revealed that female leaders clearly understood both leadership styles and how to improve job performance by practicing them. The originality of this study concerns the identification of the differences between leadership styles (transformational and transactional) practiced by female leaders of public and private Universities in Pakistan and explains female leaders' perceptions of the role of leadership styles in their job performance.

New generalized class of estimators for estimation of finite population mean based on probability proportional to size sampling using two auxiliary variables: A simulation study.

This article aims to suggest a new generalized class of estimators based on probability proportional to size sampling using two auxiliary variables. The numerical expressions for the bias and mean squared error (MSE) are derived up to the first order of approximation. Four actual data sets are used to examine the performances of a new improved generalized class of estimators. From the results of real data sets, it is examined that the suggested estimator gives the minimum MSE and the percentage relative efficiency is higher than all existing estimators, which shows the importance of the new generalized class of estimators. To check the strength and generalizability of our proposed class of estimators, a simulation study is also accompanied. The consequence of the simulation study shows the worth of newly found proposed class estimators. Overall, we get to the conclusion that the proposed estimator outperforms as compared to all other estimators taken into account in this study.

Efficacy and safety of finerenone in chronic kidney disease and type 2 diabetes patients: a systematic review and meta-analysis.

Background and objectives: The incidence of morbidity and mortality in patients with type 2 diabetes mellitus is substantially correlated with cardiovascular disease and chronic kidney disease. The current guidelines recommend the use of renin-angiotensin system blockers, but recent studies probed into the effects of finerenone to mitigate the risk of cardiorenal events. This meta-analysis was performed to demonstrate the effects of finerenone on cardiorenal events, comprising cardiovascular mortality, heart failure, change in estimated glomerular filtration rate, and serum potassium levels. Methods: After screening with our eligibility criteria, 350 articles were identified with an initial literature search on multiple databases, including PubMed, Science Direct, and Cochrane Central. Seven randomized controlled trials with a total of 15 462 patients (n=8487 in the finerenone group; n=6975 in the control group) were included. Results: Patients receiving finerenone were at a reduced risk for cardiovascular mortality [HR: 0.84 (0.74, 0.95)], heart failure [OR: 0.79 (0.68, 0.92)], decrease in estimated glomerular filtration rate by 40% [OR: 0.82 (0.74, 0.91)] and by 57% [OR: 0.70 (0.59, 0.82)]; and a higher incidence of moderate hyperkalemia [OR: 2.25 (1.78, 2.84)]. Conclusion: Finerenone, owing to its better mineralocorticoid affinity, and a much lower risk of adverse effects, promises to be a much better alternative than other renin-angiotensin system blockers available for the treatment of chronic kidney disease patients with type 2 diabetes. Further trials should be conducted to provide more definitive evidence to assess the safety and efficacy of finerenone compared to spironolactone and eplerenone.

Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models.

This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.

Integrating compound-specific stable isotope and enantiomer-specific analysis to characterize the isomeric and enantiomeric signatures of hexachlorocyclohexanes (HCHs) in paddy soils.

Organic pollutants in paddy fields may undergo different processes from those in dryland due to the anaerobic environment. The integrated use of compound-specific stable isotope analysis (CSIA) and enantiomer-specific analysis is a promising technique for understanding the behavior and fate of organic pollutants in soils. In this study, soil samples were collected from paddy fields in three major rice cultivation regions of China, spanning a transect of 4000 km. The mean concentrations of Æ©HCHs in paddy soils from the Taihu Plain were the highest (1.44 ng/g). The ratios of α-HCH/ß-HCH (all below 11.8) and α-HCH/γ-HCH (92% below 4.64), as well as the enantiomeric fractions (EFs) of chiral α-HCH (mean of 0.81), reflected that the distribution of HCHs was affected by the use of both technical HCHs and lindane. The preferential depletion of (-)-α-HCH and pronounced carbon isotope fractionation of α-HCH (δ13C of -28.22 ± 0.92‰ -23.63 ± 1.89‰) demonstrated its effective transformation. Factors such as altitude, soil temperature, soil pH, soil conductivity and soil organic matter significantly influenced the fate and transformation of HCHs. The current study highlights the integrated application of CSIA and enantiomer-specific analysis to provide multiple lines of evidence for the transformation of HCHs in soils.

Exploring the Therapeutic Properties of Alga-Based Silver Nanoparticles: Anticancer, Antibacterial, and Free Radical Scavenging Capabilities.

The current study was designed to evaluate the antioxidant, anticancer and antimicrobial activities of silver nanoparticles (AgNPs) biosynthesized by Spirulina platensis extract. The biosynthesized silver nanoparticles were characterized using Fourier transform infrared (FT-IR) analysis, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD) analysis. The antioxidant activity of the biosynthesized AgNPs were determined via DPPH radical scavenging assay while its anticancer activity was determined using the MTT assay. The antimicrobial activity of the biosynthesized AgNPs were analyzed by disc diffusion method. Spirulina platensis acts as a reducing and capping agent. The efficacy of silver nanoparticles (AgNPs) in inhibiting the growth of Gram-negative bacteria, specifically Acetobacter, Klebsiella, Proteus vulgaris, and Pseudomonas aeruginosa, was assessed by the utilisation of the diffusion method. The study aimed to evaluate the efficacy of biosynthesized silver nanoparticles (AgNPs) against many strains of Pseudomonas aeruginosa bacteria. The findings of the study revealed that when administered in doses of 50 µl, 75 µl, and 100 µl, the largest observed zone of inhibition corresponded to measurements of 10.5 mm, 14 mm, and 16 mm, respectively. A zone of inhibition with dimensions of 8 mm, 10.5 mm, and 12 mm was detected during testing against Acetobacter at concentrations of 50 µl, 75 µl, and 100 µl, respectively. The findings also indicate that there is a positive correlation between the concentration of AgNP and the DPPH scavenging ability of silver nanoparticles. The percentage of inhibition observed at concentrations of 500 µg/ml, 400 µg/ml, 300 µg/ml, 200 µg/ml, and 100 µg/ml were recorded as 80±1.98, 61±1.98, 52±1.5, 42±1.99, and 36±1.97, respectively. In addition, it was observed that the silver nanoparticles exhibited the greatest antioxidant activity at a concentration of 500 g/ml, with a measured value of 80.89±1.99. The IC-50 values, representing the inhibitory concentration required to achieve 50 % inhibition, were found to be 8.16, 19.15, 30.14, 41.13, and 63.11 at inhibition levels of 36±1.97, 42±1.99, 52±1.5, 61±1.98, and 80±1.98, respectively.

Microwave-vacuum extraction cum drying of tomato slices: Optimization and functional characterization.

Fruits and vegetables have shorter shelf life due to their perishable nature. Tomato, being a nutritionally rich fruit needs to be preserved for a longer period. In this context, this study was designed to dry the tomato slices through microwave-vacuum drying. This process was optimized for moisture ratio and drying rate using response surface methodology (RSM). The process was investigated at different power levels (30, 50, 80, and 100 W), pressure (0, 15, 20, and 25 inHg), and time (0, 4, 6, and 10 min) using Box-Behnken design. Results indicated that color, energy efficiency, and drying characteristics were significantly affected by changing power, vacuum levels, and processing time. Besides, nine mathematical models were applied on experimental data to deeply understand the moisture ratio of tomato slices. Amongst, Midilli model was found best to describe the drying process at 100 W and 25 inHg supported by R 2 (0.9989), RMSE (0.001), and X 2 (1.34e-4). This study was focused on finding the optimal combinations of power, vacuum pressure, and time for better drying and reduced wastage of the fruit owing to its perishable nature. From all the microwave powers, higher microwave power and vacuum level showed better energy consumption, energy efficiencies, color retention, and rehydration capacity.

Green synthesis of Cu-Mn co-incorporated ZnO nanoparticles for antibacterial and photocatalytic applications.

The synergistic effect of bimetallic co-incorporated metal oxides have gained enormous attention due to their unique optoelectronic properties. Herein, we present the green synthesis of ZnO, Cu-incorporated ZnO, Mn-incorporated ZnO, and Cu-Mn co-incorporated nanoparticles (ZnO NPs, CuZnO NPs, MnZnO NPs, MnCuZnO NPs) for antimicrobial and photocatalytic reduction applications using corn silk extract and industrial metal wastes. The as-synthesized NPs were characterized by using UV-visible absorption spectroscopy (UV-Vis), photoluminescence (PL) spectroscopy, Fourier-transformed infrared spectroscopy (FT-IR), powdered x-ray diffraction (XRD), and scanning electron microscopy (SEM). CuZnO, MnZnO, and MnCuZnO NPs efficiently inhibited bacterial culture growth. The photocatalytic reduction activity of as-synthesized NPs against the different concentrations of 4-nitrophenol (4-NP) in water was also investigated. CuZnO and MnCuZnO nanoparticles were to be efficient photocatalysts for reducing 4-NP into 4-aminophenol (4-AP). RESEARCH HIGHLIGHTS: Green synthesis of nanomaterials by agricultural and industrial wastes Cu and Mn co-incorporated ZnO NPs have shown good photocatalysis and antimicrobial activities Green approach for waste conversion to value-added products.

Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications.

Nowadays, brain tumors have become a leading cause of mortality worldwide. The brain cells in the tumor grow abnormally and badly affect the surrounding brain cells. These cells could be either cancerous or non-cancerous types, and their symptoms can vary depending on their location, size, and type. Due to its complex and varying structure, detecting and classifying the brain tumor accurately at the initial stages to avoid maximum death loss is challenging. This research proposes an improved fine-tuned model based on CNN with ResNet50 and U-Net to solve this problem. This model works on the publicly available dataset known as TCGA-LGG and TCIA. The dataset consists of 120 patients. The proposed CNN and fine-tuned ResNet50 model are used to detect and classify the tumor or no-tumor images. Furthermore, the U-Net model is integrated for the segmentation of the tumor regions correctly. The model performance evaluation metrics are accuracy, intersection over union, dice similarity coefficient, and similarity index. The results from fine-tuned ResNet50 model are IoU: 0.91, DSC: 0.95, SI: 0.95. In contrast, U-Net with ResNet50 outperforms all other models and correctly classified and segmented the tumor region.