This study involves the adsorption of Crystal Violet (CV) dye adsorbed from solution on the pyrophyllite’s surface. The batch technique was used under a variety of conditions to produce quantitative adsorption, namely amount of adsorbent, dye concentration, contact time, pH solution and temperature. The maximum adsorption capacity of Crystal Violet on pyrophyllite was 9.58mg/g for 10mg/L of CV concentration, pH=6.8 at a temperature 20°C and 1g/L of adsorbent. This study of adsorption kinetics was carried out within framework of three models: intraparticle diffusion, pseudo-first order and pseudo-second order. The experimental isotherm data were analyzed using Langmuir and Freundlich models. Different thermodynamic parameters have shown spontaneous reaction with endothermic nature (The estimated value for ΔG was −7.64kJ/mol at 293K). Various techniques for characterizing the adsorbent were applied including X-ray diffraction (XRD), X-ray fluorescence spectroscopy (XRF), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) coupled by energy dispersive X-ray spectroscopy (EDX). In addition, the regenerated adsorbents technique was reused several times; this demonstrated an economical aspect of using pyrophyllite which underlines the re-use importance considering the material capacity to regenerate

The objective of this study was to investigate the potential of ultra short echo time imaging for the assessment of caries lesions and early demineralization. 12 patients with suspected caries lesions underwent a dental magnetic resonance imaging investigation comprising ultra short echo time imaging (echo time = 50 μs) and spin echo imaging. Before the dental magnetic resonance imaging, all patients underwent a conventional clinical dental investigation including visual assessment of the teeth as well as dental x‐ray imaging. All lesions identifiable in the x‐ray could be clearly identified in the ultra short echo time images, but only about 19% of the lesions were visible in the spin echo images. In 19% of all lesions, the lesions could be more clearly delineated in the ultra short echo time images than in the x‐ray images. This was especially the case for secondary lesions. In direct comparison with the x‐ray images, all lesions appeared substantially larger in the dental magnetic resonance imaging data. The presented data provide evidence that caries lesions can be identified in ultra short echo time magnetic resonance imaging with high sensitivity. The apparent larger volume of the lesions in dental magnetic resonance imaging may be attributed to fluid accumulation in demineralized areas without substantial breakdown of mineral structures. Magn Reson Med, 2011.

Varying public views on cannabis use across countries may explain the variation in the prevalence of use, policies, and research in individual countries, and global regulation of cannabis. This paper aims to describe the current state of cannabis use, policies, and research across sixteen countries. PubMed and Google Scholar were searched for studies published from 2010 to 2020. Searches were conducted using the relevant country of interest as a search term (e.g., “Iran”), as well as relevant predefined keywords such as “cannabis,” “marijuana,” “hashish,” “bhang “dual diagnosis,” “use,” “addiction,” “prevalence,” “co-morbidity,” “substance use disorder,” “legalization” or “policy” (in English and non-English languages). These keywords were used in multiple combinations to create the search string for studies’ titles and abstracts. Official websites of respective governments and international organizations were also searched in English and non-English languages (using countries national languages) to identify the current state of cannabis use, policies, and research in each of those countries. The main findings were inconsistent and heterogeneous reporting of cannabis use, variation in policies (e.g., legalization), and variation in intervention strategies across the countries reviewed. European countries dominate the cannabis research output indexed on PubMed, in contrast to Asian countries (Thailand, Malaysia, India, Iran, and Nepal). Although global cannabis regulation is ongoing, the existing heterogeneities across countries in terms of policies and epidemiology can increase the burden of cannabis use disorders disproportionately and unpredictably. There is an urgent need to develop global strategies to address these cross-country barriers to improve early detection, prevention, and interventions for cannabis use and related disorders.

Heart disease is a leading cause of death worldwide. However, it remains difficult for clinicians to predict heart disease as it is a complex and costly task. Hence, we proposed a clinical support system for predicting heart disease to help clinicians with diagnostic and make better decisions. Machine learning algorithms such as Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Decision Tree are applied in this study for predicting Heart Disease using risk factors data retrieved from medical files. Several experiments have been conducted to predict HD using the UCI data set, and the outcome reveals that Naïve Bayes outperforms using both cross-validation and train-test split techniques with an accuracy of 82.17%, 84.28%, respectively. The second conclusion is that the accuracy of all algorithm decrease after applying the cross-validation technique. Finally, we suggested multi validation techniques in prospectively collected data towards the approval of the proposed approach.