Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins

This study addresses the challenge of utilizing incomplete long-term discharge data when using gridded precipitation datasets and data-driven modeling in Iran\u27s Karkheh basin. The Multilayer Perceptron Neural Network (MLPNN), a rainfall-runoff (R-R) model, was applied, leveraging precipitation data from the Asian Precipitation—Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climatic Research Unit (CRU). The MLPNN was trained using the Levenberg–Marquardt algorithm and optimized with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Input data were pre-processed through principal component analysis (PCA) and singular value decomposition (SVD). This study explored two scenarios: Scenario 1 (S1) used in situ data for calibration and gridded dataset data for testing, while Scenario 2 (S2) involved separate calibrations and tests for each dataset. The findings reveal that APHRODITE outperformed in S1, with all datasets showing improved results in S2. The best results were achieved with hybrid applications of the S2-PCA-NSGA-II for APHRODITE and S2-SVD-NSGA-II for GPCC and CRU. This study concludes that gridded precipitation datasets, when properly calibrated, significantly enhance runoff simulation accuracy, highlighting the importance of bias correction in rainfall-runoff modeling. It is important to emphasize that this modeling approach may not be suitable in situations where a catchment is undergoing significant changes, whether due to development interventions or the impacts of anthropogenic climate change. This limitation highlights the need for dynamic modeling approaches that can adapt to changing catchment conditions

Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis

Breast cancer is the second most common cancer among women worldwide. Diagnosis of breast cancer by the pathologists is a time-consuming procedure and subjective. Computer aided diagnosis frameworks are utilized to relieve pathologist workload by classifying the data automatically, in which deep convolutional neural networks (CNNs) are effective solutions. The features extracted from activation layer of pre-trained CNNs are called deep convolutional activation features (DeCAF). In this paper, we have analyzed that all DeCAF features are not necessarily led to a higher accuracy in the classification task and dimension reduction plays an important role. Therefore, different dimension reduction methods are applied to achieve an effective combination of features by capturing the essence of DeCAF features. To this purpose, we have proposed reduced deep convolutional activation features (R-DeCAF). In this framework, pre-trained CNNs such as AlexNet, VGG-16 and VGG-19 are utilized in transfer learning mode as feature extractors. DeCAF features are extracted from the first fully connected layer of the mentioned CNNs and support vector machine has been used for binary classification. Among linear and nonlinear dimensionality reduction algorithms, linear approaches such as principal component analysis (PCA) represent a better combination among deep features and lead to a higher accuracy in the classification task using small number of features considering specific amount of cumulative explained variance (CEV) of features. The proposed method is validated using experimental BreakHis dataset. Comprehensive results show improvement in the classification accuracy up to 4.3% with less computational time. Best achieved accuracy is 91.13% for 400x data with feature vector size (FVS) of 23 and CEV equals to 0.15 using pre-trained AlexNet as feature extractor and PCA as feature reduction algorithm

Evaluating the Role of Biochanin A in Acetic acid-Induced Colitis in Rats: Involvement of Nitric Oxide Pathway

Background and objectives: Inflammatory bowel disease (IBD) refers to idiopathic chronic and inflammatory bowel disorders such as ulcerative colitis. Considering the lack of definitive treatment and the side effects of existing drugs, finding efficient compounds is needed. Biochanin A has attracted the attention of researchers due to its wide range of medicinal activities. Until now, no study was conducted to evaluate its effects on colitis. Therefore, the aim of this study was to determine the effect of biochanin A on the nitrogen pathway in rats with acetic acid-induced colitis. Methods: Male rats were divided into five groups: normal group, negative control group, positive control group, and groups receiving biochanin A (10 and 20 mg/kg). Colitis was induced with 4% acetic acid. Next, the samples were evaluated at different macroscopic and microscopic levels, and biochemical test of superoxide dismutase (SOD) and nitric oxide activity was investigated. Results: Macroscopic and microscopic investigations showed that treatment with biochanin A decreased mucosal damage in rats with acetic acid-induced colitis. Biochanin A reduced neutrophil infiltration in the intestinal tissue. It also led to the reduction in nitric oxide and enhancement of SOD in rats. The optimal dose of biochanin A was 20 mg/kg, which had the best effect on reducing inflammation and mucosal lesions in rats. Conclusion: Biochanin A, due to its anti-inflammatory effects by reducing nitric oxide and enhancement of SOD and reducing mucosal damage in rats with acetic acid-induced colitis, can be a useful alternative drug for the prevention or treatment of IBD patients

Thermodynamic Analysis and Optimization of the Micro-CCHP System with a Biomass Heat Source

In this article, new multiple-production systems based on the micro-combined cooling, heating and power (CCHP) cycle with biomass heat sources are presented. In this proposed system, absorption refrigeration cycle subsystems and a water softener system have been used to increase the efficiency of the basic cycle and reduce waste. Comprehensive thermodynamic modeling was carried out on the proposed system. The validation of subsystems and the optimization of the system via the genetic algorithm method was carried out using Engineering Equation Solver (EES) software. The results show that among the components of the system, the dehumidifier has the highest exergy destruction. The effect of the parameters of evaporator temperature 1, ammonia concentration, absorber temperature, heater temperature difference, generator 1 pressure and heat source temperature on the performance of the system was determined. Based on the parametric study, as the temperature of evaporator 1 increases, the energy efficiency of the system increases. The maximum values of the energy efficiency and exergy of the whole system in the range of heat source temperatures between 740 and 750 K are equal to 74.2% and 47.7%. The energy and exergy efficiencies of the system in the basic mode are equal to 70.68% and 44.32%, respectively, and in the optimization mode with the MOOD mode, they are 87.91 and 49.3, respectively

Examining the role of family factors affecting drug trends (traditional-industrial) among youth of Lorestan province

One of the important issues in today's society is drug addiction and drug abuse and the reasons related to it. This has been associated with a lot of factors that affect it; so, it seems that among these factors, family factors play a crucial role in creating tendency towards drugs. Hence the problem of the present study was to determine the influence of family factors (illiterate parents, divorce, parental addiction, family conflicts, lack of parental supervision, parental inattention to proper and reasonable demands of children, and wealth and prosperity of the family) in creating tendency toward industrial and traditional drugs among young people of Lorestan province. The present study was a retrospective study. The sample of the study were 200 young people, 100 of them chosen from addicted and 100 from normal population, which were recruited after being homogenized with the first group based on age, gender, education, and income. In this study, the causes of drug abuse tendency among young people questionnaire (Mohamadi, 1392) was used. MANOVA was also used in order to analyze the data. The results showed that addicted parents or the presence of addicts in families, family conflicts and lack of love in family relationships, lack of proper parental supervision, use of inappropriate parenting practices, and parental divorce and separation had significant effect on tendency toward drugs. The results also showed that the illiteracy of parents, parental negligenc, and household wealth has no significant effects on propensity to addiction. According to the research findings, it can be stated that family factors associated with child behavior have an important role in youth attitudes toward drugs

Comparison of the effects of fresh leaf and peel extracts of walnut (Juglans regia L.) on blood glucose and β-cells of streptozotocin-induced diabetic rats

There is some report about the hypoglycemic effect of Juglans rejia L. leaf in alloxan induced diabetic rats and hypoglycemic effect of its fruit peel administered intra peritoneally. Thirty male Wistar rats divided into five groups, to evaluate the hypoglycemic and pancreas β-cells regenerative effects of oral methanolic extracts of leaf and fruit peel of walnut. Rats were made diabetic by intravenous (IV) injection of 50 mg kg-1 streptozotocin (STZ). Negative control group did not get STZ and any treatment. Positive control, leaf extract, peel extract and insulin groups were treated orally by extract solvent, 200 mg kg-1 leaf extract, 200 mg kg-1 peel extract and 5 IU kg-1 of subcutaneous neutral protamine Hagedorn (NPH) insulin, respectively. Four weeks later, blood was collected for biochemical analysis and pancreases were removed for β-cells counts in histological sections. Diabetes leads to increase of fast blood sugar (FBS) and HbA1c, and decrease of β-cell number and insulin. FBS decreased only in leaf extract group. HbA1c decreased in leaf extract and insulin groups. The β-cells number increased in leaf and peel extract groups. Insulin increased moderately in all treatment groups. We showed the proliferative properties of leaves and peel of Juglans regia L. methanolic extract in STZ- induced diabetic rats, which was accompanied by hypoglycemic effect of leaf extract

Co-delivery of streptomycin and hydroxychloroquine by labeled solid lipid nanoparticles to treat brucellosis: an animal study

Abstract Can brucellosis-related biochemical and immunological parameters be used as diagnostic and treatment indicators? The goal of this project was to look at biochemical parameters, trace elements, and inflammatory factors in the acute and chronic stages of brucellosis after treatment with streptomycin and hydroxychloroquine-loaded solid lipid nanoparticles (STR-HCQ-SLN). The double emulsion method was used for the synthesis of nanoparticles. Serum levels of trace elements, vitamin D, CRP, and biochemical parameters were measured in rats involved in brucellosis. The therapeutic effect of STR-HCQ-SLN was compared with that of free drugs. In both healthy and infected rats, serum concentrations of copper, zinc, iron, magnesium, potassium, and biochemical parameters of the liver were significantly different. By altering the serum levels of the aforementioned factors, treatment with STR-HCQ-SLN had a positive therapeutic effect on chronic brucellosis. Vitamin D levels declined (46.4%) and CRP levels rose (from 7.5 mg to less than 1 mg) throughout the acute and chronic stages of brucellosis. This study showed that by comparing the biochemical parameters and the levels of trace elements in the serum of healthy and diseased mice in the acute and chronic stages of brucellosis, it is possible to get help from other routine methods for diagnosis

Modeling and optimizing the thermodynamics of a flat plate solar collector in transient mode for economic purposes

This study aims to optimize the economic thermodynamics of a flat plate solar collector and investigate transient heat transfer. This study focuses on modeling and optimization under unfavorable radiation conditions. The method employed here is optimization using a multi-objective genetic algorithm with the assistance of MATLAB software. The key components include objective functions, constraints, and design variables, which are the collector efficiency and the annual total price. The results indicate that increasing the length of the collector has a negative impact on the thermodynamic efficiency and increases the total annual price. Conversely, increasing the width of the collector initially improves the thermodynamic efficiency but then decreases it while also increasing the total annual price. Furthermore, increasing the number of pipes leads to a decrease in the total annual price and an initial increase followed by a decrease in the thermodynamic efficiency. The research was conducted over four different days