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

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

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

Effects of nanoparticle volume fraction in hydrodynamic and thermal characteristics of forced plane jet

The effects of nanoparticle volume fraction in hydrodynamic and thermal characteristics of an incompressible forced 2-D plane jet flow are investigated. Direct Numerical Simulation (DNS) of a two dimensional incompressible plane forced jet flow for two nanofluids has been performed. The base fluid is water and the nanoparticles are Al O ,CuO 2 3 . The numerical simulation is carried out for the solid volume fraction between 0 to 4%. The results for both nanofluids indicate that any increase in the solid volume fraction decreases the amplitude of temperature, velocity time histories, the turbulent intensities and that of the Reynolds stresses. The results for both two nanoparticles also indicate that with any increase in nanoparticle volume fraction, the velocity amplitude of velocity time history, the turbulent intensities and Reynolds stress in 2 3 Al O -water are greater than that ofCuO-water nanofluid

Entropy generation and nanofluid mixed convection in a C-shaped cavity with heat corner and inclined magnetic field

A numerical simulation of entropy generation due to MHD mixed convective of water-copper nanofluid in a C-shaped cavity with a heated corner is performed. The cavity is subjected to an inclined uniform magnetic field, and the top wall of the cavity is adiabatic and moves with a constant velocity. The governing equations are formulated by employing the single-phase nanofluid approach and the resulting equations have been solved by the Finite Volume Method (FVM). Impacts of the pertinent parameters on the fluid flow and heat transfer inside the cavity have been presented graphically. The numerical data have been plotted in the portrait of streamlines, isotherms and the average Nusselt numbers. The effects of volume fraction, Hartmann number, and the aspect ratio on the entropy generation and mixed convection of the C-shaped cavity are investigated. It is found that for an aspect ratio = 0.1, increasing the nanofluid volume fraction causes an increment in the heat transfer. At low volume fractions and Hartmann numbers, the outcomes manifest a better thermal performance. Generally, the rate of entropy generation increases by increasing both the Hartmann number and the volume fraction

Drilling and blasting improvement in aggregate quarry at Thailand - a case study

World class infrastructure around Bangkok is developed due to medium to large aggregate quarries situated up to 200 km from Bangkok utilizing granite, limestone and basalt as resource. Aggregate consumption will be exceeding 350 MTPA by 2019. Aggregate quarry under study has proposed increased production from 2.5 MTPA to 5 MTPA. Drilling and blasting accounts for 28% of total quarrying cost based on the study of various aggregate quarries in Thailand. Powder factor values (P.F.) is 0.40 - 0.66 kg/m3 at 80% passing over 0.5m. Considering drilling and blasting as major cost, existing practices of drilling and blasting are reviewed. Top hammer (TH) 102mm diameter drill is selected as compared to existing down the hole (DTH) 76mm diameter drill. For large quarries 6m × 7m is blasting pattern as optimized with 150mm diameter drill. Projected blast pattern is 4m × 4.75m. TH, DTH, rotary cutting and rotary crushing drills are evaluated. Some of the best practices at an aggregate quarry in Thailand for drilling include drilling accuracy, bench’s surface flattening by auto leveling. Blast performance is monitored for blast fragmentation, back break. Nonelectric detonators instead of electrical detonators and bulk emulsion instead of ANFO shall be utilized for future blasting operation