Rough-set based learning: Assessing patterns and predictability of anxiety, depression, and sleep scores associated with the use of cannabinoid-based medicine during COVID-19.

Recently, research is emerging highlighting the potential of cannabinoids' beneficial effects related to anxiety, mood, and sleep disorders as well as pointing to an increased use of cannabinoid-based medicines since COVID-19 was declared a pandemic. The objective of this research is 3 fold: i) to evaluate the relationship of the clinical delivery of cannabinoid-based medicine for anxiety, depression and sleep scores by utilizing machine learning specifically rough set methods; ii) to discover patterns based on patient features such as specific cannabinoid recommendations, diagnosis information, decreasing/increasing levels of clinical assessment tools (CAT) scores over a period of time; and iii) to predict whether new patients could potentially experience either an increase or decrease in CAT scores. The dataset for this study was derived from patient visits to Ekosi Health Centres, Canada over a 2 year period including the COVID timeline. Extensive pre-processing and feature engineering was performed. A class feature indicative of their progress or lack thereof due to the treatment received was introduced. Six Rough/Fuzzy-Rough classifiers as well as Random Forest and RIPPER classifiers were trained on the patient dataset using a 10-fold stratified CV method. The highest overall accuracy, sensitivity and specificity measures of over 99% was obtained using the rule-based rough-set learning model. In this study, we have identified rough-set based machine learning model with high accuracy that could be utilized for future studies regarding cannabinoids and precision medicine.

Magnetic, Electrical, and Physical Properties Evolution in Fe3O4 Nanofiller Reinforced Aluminium Matrix Composite Produced by Powder Metallurgy Method.

An investigation into the addition of different weight percentages of Fe3O4 nanoparticles to find the optimum wt.% and its effect on the microstructure, thermal, magnetic, and electrical properties of aluminum matrix composite was conducted using the powder metallurgy method. The purpose of this research was to develop magnetic properties in aluminum. Based on the obtained results, the value of density, hardness, and saturation magnetization (Ms) from 2.33 g/cm3, 43 HV and 2.49 emu/g for Al-10 Fe3O4 reached a maximum value of 3.29 g/cm3, 47 HV and 13.06 emu/g for the Al-35 Fe3O4 which showed an improvement of 41.2%, 9.3%, and 424.5%, respectively. The maximum and minimum coercivity (Hc) was 231.87 G for Al-10 Fe3O4 and 142.34 G for Al-35 Fe3O4. Moreover, the thermal conductivity and electrical resistivity at a high weight percentage (35wt.%) were 159 w/mK, 9.9 × 10-4 Ω·m, and the highest compressive strength was 133 Mpa.

Microstructural, Tribology and Corrosion Properties of Optimized Fe3O4-SiC Reinforced Aluminum Matrix Hybrid Nano Filler Composite Fabricated through Powder Metallurgy Method.

Hybrid reinforcement's novel composite (Al-Fe3O4-SiC) via powder metallurgy method was successfully fabricated. In this study, the aim was to define the influence of SiC-Fe3O4 nanoparticles on microstructure, mechanical, tribology, and corrosion properties of the composite. Various researchers confirmed that aluminum matrix composite (AMC) is an excellent multifunctional lightweight material with remarkable properties. However, to improve the wear resistance in high-performance tribological application, hardening and developing corrosion resistance was needed; thus, an optimized hybrid reinforcement of particulates (SiC-Fe3O4) into an aluminum matrix was explored. Based on obtained results, the density and hardness were 2.69 g/cm3, 91 HV for Al-30Fe3O4-20SiC, after the sintering process. Coefficient of friction (COF) was decreased after adding Fe3O4 and SiC hybrid composite in tribology behaviors, and the lowest COF was 0.412 for Al-30Fe3O4-20SiC. The corrosion protection efficiency increased from 88.07%, 90.91%, and 99.83% for Al-30Fe3O4, Al-15Fe3O4-30SiC, and Al-30Fe3O4-20SiC samples, respectively. Hence, the addition of this reinforcement (Al-Fe3O4-SiC) to the composite shows a positive outcome toward corrosion resistance (lower corrosion rate), in order to increase the durability and life span of material during operation. The accomplished results indicated that, by increasing the weight percentage of SiC-Fe3O4, it had improved the mechanical properties, tribology, and corrosion resistance in aluminum matrix. After comparing all samples, we then selected Al-30Fe3O4-20SiC as an optimized composite.