Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage and Selection Operator (Lasso), and Tikhonov Regularization (Ridge). The simulation studiesshow that the performance of our method is better than the othersaccording to the error and the time complexity. Thesemethodsare applied to a real dataset, which is called Rock StrengthDataset.The new approach implemented using the Gibbs sampler is more powerful and effective than other approaches.All the statistical computations conducted for this paper are done using R version 4.0.3 on a single processor computer.
The present work is concerned with the investigation of the behavior and ultimate capacity of axially loaded reinforced concrete columns in presence of transverse openings under axial load plus uniaxial bending. The experimental program includes testing of twenty reinforced concrete columns (150 × 150 × 700 mm) under concentric and eccentric load. Parameters considered include opening size, load eccentricity and influence of the direction of load eccentricity with respect to the longitudinal axis of the opening. Experimental results are discussed based on load – lateral mid height deflection curves, load – longitudinal shortening behavior, ultimate load and failure modes. It is found that when the direction of load
... Show MoreThe Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats. This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat
... Show MoreMicroalgae have been used widely in bioremediation processes to degrade or adsorb toxic dyes. Here, we evaluated the decolorization efficiency of Chlorella vulgaris and Nostoc paludosum against two toxic dyes, crystal violet (CV) and malachite green (MG). Furthermore, the effect of CV and MG dyes on the metabolic profiling of the studied algae has been investigated. The data showed that C. vulgaris was most efficient in decolorization of CV and MG: the highest percentage of decolorization was 93.55% in case of MG, while CV decolorization percentage was 62.98%. N. paludosum decolorized MG dye by 77.6%, and the decolorization percentage of CV was 35.1%. Metabolic profiling of
... Show MoreThis research introduce a study with application on Principal Component Regression obtained from some of the explainatory variables to limitate Multicollinearity problem among these variables and gain staibilty in their estimations more than those which yield from Ordinary Least Squares. But the cost that we pay in the other hand losing a little power of the estimation of the predictive regression function in explaining the essential variations. A suggested numerical formula has been proposed and applied by the researchers as optimal solution, and vererifing the its efficiency by a program written by the researchers themselves for this porpuse through some creterions: Cumulative Percentage Variance, Coefficient of Determination, Variance
... Show MoreThis research foxed on the effect of fire flame of different burning temperatures (300, 400 and 500)oC on the compressive strength of reactive powder concrete (RPC).The steady state duration of the burning test was (60)min. Local consuming material were used to mixed a RPC of compressive strength around (100) MPa. The tested specimens were reinforced by (3.0) cm hooked end steel fiber of (1100) MPa yield strength. Three steel fiber volume fraction were adopted in this study (0, 1.0and 1.5)% and two cooling process were included, gradual and sudden. It was concluding that increasing burning temperature decreases the residual compressive strength for RPC specimens of(0%) steel fiber volume fraction by (12.16, 19.46&24.49) and (18.20, 27.77 &3
... Show MoreThe research aims to delve into the nature of international financial reporting standards and the unified accounting system adopted in the Iraqi environment and financial ratios in the theoretical side. In the practical aspect, the results of some financial ratios of Basrah Gas Company were compared with the adoption of the financial statements prepared on the basis of the consolidated accounting system with the prepared financial statements Based on the International Financial Reporting Standards (IFRS). The research has reached a number of conclusions, including a difference in the accounting rules and practices between IFRS and the consolidated accounting system in force in Iraq. The adoption of IFRS has led to a decrease in profitabi
... Show MoreIn this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A
... Show MoreNaturally carbonate reservoirs have very low oil recovery efficiency owing to their wettability and tightness of matrix. However, adding surfactant to brine solution can enhance oil recovery by changing the wettability of the carbonate rock surface from oil-wet to water-wet. In the present study, the effects of different brines in the presence of surfactant hexadecyltrimethylammonium bromide (CTAB), on the wettability of carbonate rock were investigated through different characteristics such as surface tension interfacial tension and contact angle. The best concentration of CTAB was investigated at 500 ppm in brine (NaCl) reduced surface tension and interfacial tensions to 42 mN/m and 15 mN/m respectively and prevented reservoir fro
... Show MoreIn this research a new system identification algorithm is presented for obtaining an optimal set of mathematical models for system with perturbed coefficients, then this algorithm is applied practically by an “On Line System Identification Circuit”, based on real time speed response data of a permanent magnet DC motor. Such set of mathematical models represents the physical plant against all variation which may exist in its parameters, and forms a strong mathematical foundation for stability and performance analysis in control theory problems.