Introduction to the Special Issue on Soft Computing Techniques in Materials Science and Engineering

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Geological study and mining plan importance for mitigating alkali silica reaction in aggregate quarry operation

More than 80 million tonnes of construction aggregate are produced in Peninsular Malaysia. Majority of construction aggregate are produced from granite. Developing regions of Johor Bahru, Kuala Lumpur, Penang and Selangar utilize granite aggregates. Normally it is considered aggregates as non-alkali reactive. Geological study can identify various rock types, geological structures, and reactive minerals which contribute to Alkali Silica Reaction (ASR). Deformed granites formed through faulting results in reduction of quartz grain size. Microcrystalline quartz and phyllosilicates are found in granites in contact with country rocks. Secondary reactive minerals such as chalcedony and opal may be found in granite. Alkali Silica reaction is slow chemical reaction in concrete due to reactive silica minerals in aggregates, alkalis in cement and moisture. For long term durable concrete, it is essential to identify potential alkali silica reactive aggregates. Lack of identifying reactive aggregates may result spalling, cracking in concrete and ultimately ASR can result in hazard to concrete structure. This paper deals with geological study of any aggregate quarry to identify rock type and geological structures with laboratory test –petrographic analysis and bar mortar test can identify type of aggregates being produced. Mine plan with Surpac software can be developed for systematic working for aggregate quarry to meet construction aggregate demand

Testosterone therapy in men with Parkinson disease : results of the TEST-PD study

Background: Testosterone deficiency has been reported in patients with Parkinson disease (PD), Alzheimer disease, and Huntington disease. It is not known whether testosterone therapy (TT) in men with borderline hypogonadism and neurodegenerative diseases will be of substantial benefit. Previously, we reported that testosterone deficiency is more common in patients with PD compared with age-matched control subjects, and we also reported in 2 small open-label studies that some nonmotor symptoms responded favorably to TT. Objective To define the effects of TT on nonmotor and motor symptoms in men with PD and probable testosterone deficiency. Design: Double-masked, placebo-controlled, parallel-group, single-center trial. Patients: Two experimental groups: patients with PD who were receiving either TT or placebo. Interventions: Participants received either the study drug by intramuscular injection (200 mg/mL of testosterone enanthate every 2 weeks for 8 weeks) or placebo (isotonic sodium chloride solution injections). In patients in each group, the testosterone serum concentration was obtained at each study visit. During 2 study visits, testosterone levels were blindly evaluated and the intramuscular testosterone dose was increased by 200 mg/mL if the free testosterone value failed to double from the baseline value. Main Outcome Measures: The primary outcome variable was the St Louis Testosterone Deficiency Questionnaire, and secondary outcome measures included measures of mood, cognition, fatigue, motor function, and frequency of adverse events. At the end of the double-blind phase, all patients were offered open-label TT and were followed up after 3 and 6 months. Results: Fifteen patients in the placebo group (mean age, 69.9 years), receiving a mean total levodopa equivalent dose of 924 mg/d, had a baseline free testosterone level of 47.91 pg/mL, compared with 15 patients in the TT group (mean age, 66.7 years), receiving an average total levodopa equivalent dose of 734 mg/d, who had a baseline free testosterone level of 63.49 pg/mL. Testosterone was generally well tolerated. More subjects in the TT group experienced lower extremity edema (40% vs 20%). In 2 patients, 1 in each group, prostate-specific antigen levels were elevated from baseline. The improvement in the TT group compared with the placebo group (1.7 vs 1.1) on the St Louis Testosterone Deficiency Scale was not statistically significant. In addition, there were no significant differences in motor and nonmotor features of PD between the 2 groups, although a few subscales showed improvements (Hopkins Verbal Learning Test, P<.04; and Backward Visual Span subtrial, P<.03). However, long-term open-label TT resulted in delayed but sustained improvement in subjects in the TT group who continued to receive treatment (n = 6) compared with subjects in the placebo group who elected not to receive TT (n = 3). Conclusions: Testosterone therapy was generally well tolerated in elderly men with PD and probable testosterone deficiency. While there was no significant difference in the motor and nonmotor scales between the TT and placebo groups at the end of 8 weeks compared with baseline, this may be due to several study limitations, including small sample size, a strong placebo effect with intramuscular therapy, and short follow-up that did not allow measurement of delayed effects of TT in some subjects. Until more definitive studies are reported, practitioners should be particularly cautious in treatment of low testosterone concentrations in men with PD and borderline testosterone deficiency, and careful consideration should be given to the risks vs the benefits of TT

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices

We aimed to develop a prediction model for intensive care unit (ICU) hospitalization of Coronavirus disease-19 (COVID-19) patients using artificial neural networks (ANN). We assessed 25 laboratory parameters at first from 248 consecutive adult COVID-19 patients for database creation, training, and development of ANN models. We developed a new alpha-index to assess association of each parameter with outcome. We used 166 records for training of computational simulations (training), 41 for documentation of computational simulations (validation), and 41 for reliability check of computational simulations (testing). The first five laboratory indices ranked by importance were Neutrophil-to-lymphocyte ratio, Lactate Dehydrogenase, Fibrinogen, Albumin, and D-Dimers. The best ANN based on these indices achieved accuracy 95.97%, precision 90.63%, sensitivity 93.55%. and F1-score 92.06%, verified in the validation cohort. Our preliminary findings reveal for the first time an ANN to predict ICU hospitalization accurately and early, using only 5 easily accessible laboratory indices

A novel heuristic algorithm for the modeling and risk assessment of the covid-19 pandemic phenomenon

The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the quality of the health system in each area, the age distribution of population, geographical and environmental factors as well as other conditions. Based on derived predicted epidemic curves, a new 3D-epidemic surface is proposed to assess the epidemic phenomenon at any time of its evolution. This research highlights the potential of the proposed model as a tool which can assist in the risk assessment of the COVID-19. Mapping its development through 3D-epidemic surface can assist in revealing its dynamic nature as well as differences and similarities among different districts

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype

Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique

The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN

A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

The aim of this research is to develop three soft-computing techniques, including adaptive-neuro-fuzzy inference system (ANFIS), genetic-programming (GP) tree-based, and simulated annealing–GP or SA–GP for prediction of the ultimate-bearing capacity (Qult) of the pile. The collected database consists of 50 driven piles properties with pile length, pile cross-sectional area, hammer weight, pile set and drop height as model inputs and Qult as model output. Many GP and SA–GP models were constructed for estimating pile bearing capacity and the best models were selected using some performance indices. For comparison purposes, the ANFIS model was also applied to predict Qult of the pile. It was observed that the developed models are able to provide higher prediction performance in the design of Qult of the pile. Concerning the coefficient of correlation, and mean square error, the SA–GP model had the best values for both training and testing data sets, followed by the GP and ANFIS models, respectively. It implies that the neural-based predictive machine learning techniques like ANFIS are not as powerful as evolutionary predictive machine learning techniques like GP and SA–GP in estimating the ultimate-bearing capacity of the pile. Besides, GP and SA–GP can propose a formula for Qult prediction which is a privilege of these models over the ANFIS predictive model. The sensitivity analysis also showed that the Qult of pile looks to be more affected by pile cross-sectional area and pile set