This study aims to employ supervised Advanced machine learning for the classification of lithological facies from geophysical log data in wells without drilling core samples. For this purpose, a dataset from seven wells in a training set from one of the oil fields in southern Iran has been utilized. This dataset includes natural gamma ray (SGR), corrected gamma ray (CGR), bulk density (RHOB), neutron porosity (NPHI), compressional wave slowness (DTSM), and shear wave slowness (DTCO), which directly influence the classification of geomechanical facies. These parameters are employed as independent variables, while lithological facies serve as the dependent variable for classification. This dataset pertains to depths ranging from 3000 to 4000 meters in the Ilam and Sarvak fractured limestone formations (Bangestan Limestone) of the subsurface. As the title suggests in this article, Initially, through artificial intelligence clustering methods and laboratory studies, these formations were categorized into five distinct lithological facies After this stage, eight supervised machine learning methods were employed, including Regression Logistic, K Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gaussian NB, Gradient Boosting Classifier, Extra Trees Classifier, and Support Vector Machine (SVM), to predict lithological facies in wells without existing classifications. The dataset of these wells underwent training and testing stages with each of these algorithms to construct an appropriate model. As a result, facies labels were predicted. The performance of the models was evaluated using multiple metrics including Accuracy, Precision, F1-Score, and Recall through confusion matrices and ROC curves. The Extra Trees Classifier, Gradient Boosting Classifier, and K Neighbors Classifier showed superior results among these methods. Finally, the model's performance in predicting lithological facies of unseen or out-of-sample wells was presented.

A Permian residual horizon is located in ~30 km northeast of Malekan, which was developed as stratiform layer in Ruteh carbonate rocks.Mineralogically, this horizon includes minerals such as boehmite, diaspore, hematite, kaolinite, rutile, anatase, montmorillonite, muscovite, calcite, and chlorite. Calculations of normative values of minerals in a selective profile show that this horizon consists of five distinct lithological facies which are, from bottom to the top, (1) kaolinitic ferrite, (2) ferritic kaolin, (3) ferritic bauxite, (4) kaolin, and (5) bauxitic kaolin. Based on obtained data, it seems that the distribution of REEs in the studied profile was principally controlled by factors such as (1) Eh variations of the environment due to decomposition of organic matters, (2) the pH increase of weathering solutions by carbonate bedrocks, (3) scavenging and fixation processes, and (4) fluctuations of underground waters table. Further geochemical evidence indicates that the concentration of LREEs were occurred by muscovite, Mn-oxides, and secondary phosphates (e.g., monazite, gorceixite, rhabdophane) and that of HREEs by rutile, anatase, and zircon in the studied profile.

Background and Objective: Warts are benign proliferations or mucosal tumors due to infection with various types of human papillomavirus that can affect the skin and mucous membranes. Some warts recover on their own without any effective treatments, but sometimes they need treatment because of pain and other problems, especially beauty issues. We aimed to compare the therapeutic effect of salicylic acid solution (40%) and cryotherapy for the treatment of skin warts.Materials and Methods: This randomized clinical trial was carried out on patients presenting to clinics and Dermatology Department of Farshchian Hospital in Hamadan, Iran. All the patients were diagnosed with wart. Patients who met the inclusion criteria provided written informed consent. In each patient, one to three warts were randomly treated with cryotherapy and one to three others with salicylic acid solution (40%). After completion of the treatment period, we evaluated improvements and complications in the subjects. Then, the collected data were analyzed using SPSS software.Results: Overall, we studied 160 warts, which were randomly assigned to cryotherapy and salicylic acid solution (40%) groups (n=80 per group). Regarding the efficacy of treatment, the rates of non-healing, normal skin color, normal lines and both in the cryotherapy group were 6.2%, 27.6%, and 66.2%, respectively, and in the acid salicylic 40% group, these rates were 16.2%, 38.8%, and 42.0%, respectively (P=0.016). The incidence rates of pain, and blistering complications were significantly higher in the cryotherapy group than in the salicylic acid solution (40%) group (P<0.001). There was no significant difference in the effectiveness of treatment with respect to age, gender, and type of wart.Conclusion: The treatment of Extra-genital warts with cryotherapy was more successful than treatment with salicylic acid solution (40%), and there were fewer cases of itching and hyperpigmentation in this group. However, pain and blister complications following cryotherapy were significantly higher than those in the salicylic acid solution (40%) group.

The Shemshak Formation with the age of the Jurassic is a thick clastic unit which was deposited in Alborz Zone. This article is going to research facies and sedimentary environment of this formation in eastern Alborz-Tazareh (1028 /Lias) lower Jurassic. Researches in this Zone have revealed the sequences and repetition of thick layers of sand stone – shale – shaly sand – coaly shale – silt – coaly sand- and coal. From a microscopic point of view 50 samples have been taken from this area, which resulted in the determination of six groups of facies and subfacies in 2 environments, namely river meandering and deltaic environments. Also, vertical change of micro facies and related diagrams show the existence of 18 progressive and 19 regressive cycles by effect of atuocycliy in the region. So the sedimentary environment of the Shemshak Formation is a step by step progressive area to the sea.

Accurate predictions of air pollutant PM10 concentrations are essential for crafting effective air quality management strategies. This study compares three decision tree ensemble models—Random Forest (RF), Extra Trees, and Extreme Gradient Boosting (XGBoost)—to forecast daily PM10 levels in Thiruvananthapuram, India. By integrating meteorological data and air pollutant variables, this study aims to enhance both the accuracy and interpretability of urban air pollution dynamics. Spearman correlation analysis is employed to analyse the relationships between PM10 and the various input features. The predictive performance of the ensemble models is evaluated using Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). The Extra Trees model demonstrates superior predictive performance, achieving an R² of 0.945 and an RMSE of 8.174 μg/m³. The model-agnostic interpretability method SHapley Additive exPlanations (SHAP) demonstrates that PM2.5, NH3, NO2, and O3 have a major impact on PM10 forecasts. Additionally, it reveals that meteorological conditions, particularly rainfall and relative humidity, play a crucial role in determining PM10 concentrations. This research highlights the potential of machine learning techniques, especially when combining the Extra Trees model with SHAP, to assist local governments in strategic planning and air quality management efforts. Although temporal coverage limits are acknowledged, this study offers useful information to environmental agencies and policymakers looking for data-driven strategies to reduce air pollution.

An Intelligent Particle Swarm Classifier (IPSClassifier) is proposed in this paper. This Classifier is described for finding the decision hyperplanes to classify patterns of different classes in the feature space using particle swarm optimization (PSO) algorithm. An intelligent fuzzy controller is designed to improve the performance and efficiency of proposed swarm intelligence based Classifier by adapting three important parameters of PSO (i.e., swarm size, neighborhood size, and constriction coefficient). Three pattern recognition problems with different feature vector dimensions were used to demonstrate the effectiveness of the proposed Classifier. They are the Iris data classification, the Wine data classification, and radar targets classification from backscattered signals. The experimental results show that the performance of the IPS-Classifier is comparable to or better than the k-nearest neighbor (k-NN) and multi-layer perceptron (MLP) Classifiers, which are two conventional Classifiers.

The inefficiency of some medications to cross the blood-brain barrier (BBB) is often attributed to their poor physicochemical or pharmacokinetic properties. Recent studies have demonstrated promising outcomes using machine learning algorithms to predict drug permeability across the BBB. In light of these findings, our study was conducted to explore the potential of machine learning in predicting the permeability of drugs across the BBB.We utilized the B3DB dataset, a comprehensive BBB permeability molecular database, to build machine learning models. The dataset comprises 7,807 molecules, including information on their permeability, stereochemistry, and physicochemical properties. After preprocessing and cleaning, various machine learning algorithms were implemented using the Python library Pycaret to predict permeability.The Extra Trees Classifier model outperformed others when using Morgan fingerprints and Mordred chemical descriptors (MCDs), achieving an area under the curve (AUC) of 0.93 and 0.95 on the test dataset. Additionally, we conducted an experiment to train a voting Classifier combining the top three performing models. The best-blended model, trained on MCDs, achieved an AUC of 0.96. Furthermore, Shapley additive exPlanations (SHAP) analysis was applied to our best-performing single model, the Extra Trees Classifier trained on MCDs, identifying the Lipinski rule of five as the most significant feature in predicting BBB permeability.In conclusion, our combined model trained on MCDs achieved an AUC of 0.96, an F1 Score of 0.91, and an MCC of 0.74. These results are consistent with prior studies on CNS drug permeability, highlighting the potential of machine learning in this domain.

This paper presents a Transimpedance Amplifier (TIA) for high sensitivity Near Infrared Spectroscopy (NIRS). The proposed TIA is based on the Regulated Cascode (RGC) structure with an Extra transistor employed to implement additional feed-forward path and achieve higher gain values. The Extra transistor senses a partially amplified input signal, available in the conventional circuit, and conveys an additional ac current into the load, which provides a higher gain. In addition, a bandwidth extension method is introduced using a capacitor and resistor, which can improve amplifier’s bandwidth by 40%. The proposed TIA is designed in 0.18µm CMOS technology and achieves a transimpedance gain of 101.9dB with a -3dB bandwidth of 91.2 MHz considering 2pF of photodiode capacitance at the TIA input. The input referred noise is 4.4pA/√Hz while dissipating 151µW power.