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Malaria, a parasite based infectious disease spread by anopheles mosquitos, is widespread, affecting people of all ages. Malaria blood-borne pathogens cause approximately 110 million clinical cases of malaria and between one and two million deaths associated with Plasmodium falciparum each year worldwide, including Bangladesh. In this paper, we develop a human–mosquito transmission dynamics malaria model and analyse of the system properties and solutions. Both analytical and numerical results suggest that if the basic reproduction number R 0 1 , the disease-free equilibrium is asymptotically stable, meaning malaria naturally dies out. Further, if R 0 > 1 , the malaria disease persists in the population. We also provide the model calibration to estimate parameters with year-wise malaria incidence data from 2001 to 2014 in Bangladesh. Sensitivity analysis also performs to identify the most critical parameters through the partial rank correlation coefficient method. We found that the contact rate of both humans and mosquitoes had the most extensive influence on malaria prevalence. Finally, the impacts of progression rate, disease-related death rate, recovery rate and the rate of losing immunity are examined through numerical simulations and graphical analysis.

The structure, surface topography and magnetic properties of LaCoO3 (LCO) thin films deposited epitaxially on the SrTiO3 substrates have been investigated in detail. The LCO thin films show a typical ferromagnetic-paramagnetic (FM-PM) phase transition at 78 K. Based on the measurements of isothermal magnetization around the Curie point, the magnetic entropy change (ΔSM) of LCO thin films under various applied magnetic fields were obtained. By utilizing scaling theory, all of the ΔSM curves can be re-scaled, confirming that the FM-PM phase transition is second-order magnetic phase transition. Moreover, the magnetic entropy change -ΔSM features a maximum around TC, whereas, power law fitting of - Δ S M max with H gives n = 0.9704, which is obviously deviating from the standard value n = 2/3 for the Landau mean field model. This indicates that the mean field theory cannot be used to explain the critical behavior of LCO thin films which implies that the critical behavior of LCO thin films may involve complex magnetic interactions. These interactions are correlated with a long-term puzzling finding in this system why its Curie point is always around 80 K regardless of the different fabrication methods and conditions used in the film growth.

With the significant increase in WQI applications worldwide and lack of specific application guidelines, accuracy and reliability of WQI models is a major issue. It has been reported that WQI models produce significant uncertainties during the various stages of their application including: (i) water quality indicator selection, (ii) sub-index (SI) calculation, (iii) water quality indicator weighting and (iv) aggregation of sub-indices to calculate the overall index. This research provides a robust statistically sound methodology for assessment of WQI model uncertainties. Eight WQI models are considered. The Monte Carlo simulation (MCS) technique was applied to estimate model uncertainty, while the Gaussian Process Regression (GPR) algorithm was utilised to predict uncertainties in the WQI models at each sampling site. The sub-index functions were found to contribute to considerable uncertainty and hence affect the model reliability – they contributed 12.86% and 10.27% of uncertainty for summer and winter applications, respectively. Therefore, the selection of sub-index function needs to be made with care. A low uncertainty of less than 1% was produced by the water quality indicator selection and weighting processes. Significant statistical differences were found between various aggregation functions. The weighted quadratic mean (WQM) function was found to provide a plausible assessment of water quality of coastal waters at reduced uncertainty levels. The findings of this study also suggest that the unweighted root means squared (RMS) aggregation function could be potentially also used for assessment of coastal water quality. Findings from this research could inform a range of stakeholders including decision-makers, researchers, and agencies responsible for water quality monitoring, assessment and management.

Background The accurate estimation of temporal patterns of influenza may help in utilizing hospital resources and guiding influenza surveillance. This paper proposes functional data analysis (FDA) to improve the prediction of temporal patterns of influenza. Methods We illustrate FDA methods using the weekly Influenza-like Illness (ILI) activity level data from the U.S. We propose to use the Fourier basis function for transforming discrete weekly data to the smoothed functional ILI activities. Functional analysis of variance (FANOVA) is used to examine the regional differences in temporal patterns and the impact of state's political orientation. Results The ILI activity has a very distinct peak at the beginning and end of the year. There are significant differences in average level of ILI activities among geographic regions. However, the temporal patterns in terms of the peak and flat time are quite consistent across regions. The geographic and temporal patterns of ILI activities also depend on the political make-up of states. The states affiliated with Republicans had higher ILI activities than those affiliated with Democrats across the whole year. The influence of political party affiliation on temporal pattern is quite different among geographic regions. Conclusions Functional data analysis can help us to reveal the temporal variability in average ILI levels, rate of change in ILI levels, and the effect of geographical regions. Consideration should be given to wider application of FDA to generate more accurate estimates in public health and biomedical research.

Background The plant Cajanus cajan had earlier shown protective effect against hypoxic-ischemic brain damage in rats. Objective Hence, hydroalcoholic extract of C. cajan Linn leaves (HECC) was evaluated for its protective role against memory impairment in sleep-deprived Sprague Dawley rats. Materials and methods Adult rats were divided into five groups each consisting of 5 rats (n = 5). Groups I, II, III, IV and V received 1 mL/kg 1% CMC, 1 mL/kg 1% CMC, 200 mg/kg HECC, 400 mg/kg HECC and 200 mg/kg piracetam respectively as per b.wt. orally everyday for 14 days. Animals of every groups except group-I were subjected to sleep-deprivation from 15th to 19th day for induction of memory impairment. Behavioral activities i.e., elevated plus maze test and locomotor activity were evaluated. Afterwards, brain was isolated from the sacrificed animals for biochemical investigation of acetylcholinesterase (AChE); antioxidant activities i.e., catalase (CAT), superoxide dismutase (SOD), lipid peroxide; and histopathological changes. Results The percent number of entries, number of entries in open arm, AChE activity, lipid peroxide activity of HECC-treated group-III and group-IV were significantly (p, Graphical abstract Image 1

Additive manufacturing (AM) is a rapidly developing technology in biomedical, aerospace and automobile industries. However, adoption of this technology on a larger production scale remains limited. This is primarily due to the drawbacks of present AM processes associated with achievable dimensional accuracy and surface integrity of the fabricated component. The average surface roughness (Ra) of the component ranges from 3 μm to 10 μm with stair-stepping effects, balling on surfaces resulting in poor dimensional accuracy. Therefore, post-processing methods like abrasive flow finishing, laser polishing, chemical polishing and traditional finish machining is often used to meet the desired surface integrity and accuracy. However, some of these post-processing methods are quite expensive leading to overall increase in the production cost of the component. On the other hand, methods like etching and sand blasting are time consuming and not suitable for component with intricate geometries. In this paper, low energy wire electrical discharge polishing (WEDP) has been employed to achieve the desired surface integrity and finish. Initially, experiments were conducted to analyse the finishing achieved on planer additive manufactured stainless steel (SS316L) specimens. A significant reduction in roughness of maximum 80 % was obtained at various settings for pulse on time and servo voltage. In addition, SEM and EDS analysis were also carried out to study the microstructure and composition after WEDP. From the study, it was found that the WEDP process is a promising method to finish metallic AMed components.

Coastal water quality assessment is an essential task to keep “good water quality” status for living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool to assess water quality but this technique has received much criticism due to the model's reliability and inconsistence. The present study used a recently developed improved WQI model for calculating coastal WQIs in Cork Harbour. The aim of the research is to determine the most reliable and robust machine learning (ML) algorithm(s) to anticipate WQIs at each monitoring point instead of repeatedly employing SI and weight values in order to reduce model uncertainty. In this study, we compared eight commonly used algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra Tree (ExT), Support Vector Machine (SVM), Linear Regression (LR), and Gaussian Naïve Bayes (GNB). For the purposes of developing the prediction models, the dataset was divided into two groups: training (70%) and testing (30%), whereas the models were validated using the 10-fold cross-validation method. In order to evaluate the models' performance, the RMSE, MSE, MAE, R2, and PREI metrics were used in this study. The tree-based DT (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R2 = 1.0 and PERI = 0.0) and the ExT (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R2 = 1.0 and PERI = 0.0) and ensemble tree-based XGB (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R2 = 1.0 and PERI = +0.16 to −0.17) and RF (RMSE = 2.0, MSE = 3.80, MAE = 1.10, R2 = 0.98, PERI = +3.52 to −25.38) models outperformed other models. The results of model performance and PREI indicate that the DT, ExT, and GXB models could be effective, robust and significantly reduce model uncertainty in predicting WQIs. The findings of this study are also useful for reducing model uncertainty and optimizing the WQM-WQI model architecture for predicting WQI values.

In order to keep the "good" status of coastal water quality, it is essential to monitor and assess frequently. The Water quality index (WQI) model is one of the most widely used techniques for the assessment of water quality. It consists of five components, with the indicator selection technique being one of the more crucial components. Several studies conducted recently have shown that the use of the existing techniques results in a significant amount of uncertainty being produced in the final assessment due to the inappropriate indicator selection. The present study carried out a comprehensive assessment of various features selection (FS) techniques for selecting crucial coastal water quality indicators in order to develop an efficient WQI model. This study aims to analyse the effects of eighteen different FS techniques, including (i) nine filter methods, (ii) two wrapper methods, and (iii) seven embedded methods for the comparison of model performance of the WQI. In total, fifteen combinations (subsets) of water quality indicators were constructed, and WQI values were calculated for each combination using the improvement methodology for coastal water quality. The WQI model's performance was tested using nine machine-learning algorithms, which validated the model's performance using various metrics. The results indicated that the tree-based random forest algorithm could be effective for selecting crucial water quality indicators in terms of assessing coastal water. Deep neural network algorithm showed better performance for predicting coastal water quality more accurately incorporating the subset of the random forest.

The ongoing COVID-19 pandemic, caused by a novel coronavirus SARS-CoV-2, has created a severe threat to global human health. We are extremely lucky because within the first year of the COVID-19 pandemic, scientists developed a number of vaccines against COVID-19. In this paper, the authors discuss the difficulties and challenges faced in different low-and middle-income countries due to the ongoing pandemic.This research is primarily based on secondary data and existing literature reviews. The authors use maps and graphical representations to show information about vaccination coverage.The lacking vaccination coverage and insufficient supply of oxygen tanks in hospitals of low- and middle-income countries (LMICs) raise the likelihood of death of the critical COVID-19 patients. Developed countries vaccinate their citizens more quickly than LMICs. In comparison to wealthy countries, LMICs usually lack the resources and capacity to obtain the required vaccination doses.It is frequently observed that hospitals in low- and middle-income nations with a dearth of oxygen tanks result in increased suffering and mortality. To avoid a worldwide disaster, LMICs urgently require COVID-19 vaccinations since viruses have no borders, and no one is safe until every one is protected in our interconnected world. Therefore, more national and international collaborative supports are urgently necessary for LMICs in this regard.

Nursing is a rapidly growing profession that has traditionally been dominated by women. Research has highlighted various challenges associated with increasing the number of male nurses, such as cultural challenges and stigma related to nursing. The United Arab Emirates (UAE) has experienced an increase in male students enrolling in nursing programs. To ensure this is sustainable, it is important to understand how male students perceive nursing and clarify factors that influence their decision to choose nursing as a career.To explore male nursing students' perspectives of the nursing profession and understand why they chose nursing as a career.Participants in this qualitative analysis were 30 male students that were selected using purposive sampling. Semi-structured interviews were used to collect data about participants' experiences. We used a qualitative content analysis to guide the data analysis.Three main themes were identified that described male students' perspectives of and reasons for choosing nursing: (1) perceptions of nurses and nursing profession; (2) individuals as facilitators or role models in choosing the nursing profession; and (3) barriers to choosing the nursing profession.Our results regarding barriers and facilitators for male students in choosing to study nursing may be useful to help improve the recruitment of and learning opportunities for male nursing students. Highlighting the male presence in the nursing profession and positive male role models may encourage males to enter the profession. Therefore, understanding the experiences of male nursing students in choosing nursing as a career has important implications for nursing practice and education.

In this study, we investigate the feasibility of foam enhanced oil recovery (EOR) for a dual porosity and heterogeneous carbonate reservoir in the Middle East with medium temperature (55 °C) and high formation salinity (16% TDS). An Alkyl Poly-Glycoside (APG) surfactant was firstly selected based on the solubility tests and bulk foam tests. Afterwards, a series of core flooding experiments both in the absence and in the presence of crude oil were performed on Estaillades limestone, a dual-porosity and heterogeneous carbonate presenting reasonable similarities with the actual formation. In these foam tests, the influence of surfactant concentration, foam quality, injection velocity, brine composition, injection mode and permeability on foam strength and incremental oil recovery were systematically investigated. The optimal foam quality is found to be around 60–70% from foam quality scan tests in the absence of crude oil. Moreover, foam can still be generated in Estaillades under strongly oil-wet conditions, and the foam strength in the high-quality regime is largely dependent on surfactant concentration. More than 10% original oil in place (OOIP) of the water flooded residual oil was recovered after co-injecting 2.0 total pore volume (TPV) of nitrogen and 0.5 wt% APG surfactant (in synthetic seawater brine) at 65% foam quality and 4 ft./d. Interestingly, it was observed that the presence of lauryl betaine (LB) can significantly enhance the stability of APG foam in the presence of crude oil, though LB surfactant itself is not a good foamer. At last, the three phase co-injection tests proved the presence of foam at approximately 70% oil saturation. The results of this study may provide insights into the foam transport behavior in a dual porosity and heterogeneous porous media under strongly oil-wet condition.

Directional drilling has become popular in recent decades in both onshore and offshore operations due to reduced drilling costs and improved recovery. In a directional well drilling operation, drill cuttings tend to settle down at the lower side of the inclined annular section. If the generated cuttings are not removed from the hole section properly, it causes a cuttings bed formation in the annular section. Different drilling related problem such as poor rate of penetration, excessive torque and drag, increase differential sticking often associated with a poor hole cleaning which eventually lead to increase in non-productive time (NPT). Therefore, the success of inclined well drilling operation largely depends on effective cleaning of drill cuttings from the annular section. A variety of parameters, including the fluid rheology, mud velocity, cuttings size, drill pipe rotation and drill pipe inclination generally influence the cuttings transport performance. Optimization of these drilling parameters is crucial to ensure proper hole cleaning. In this study, a Computational Fluid Dynamics (CFD) model for the inclined well section is used to investigate the cuttings transport efficiency (CTE). An Eulerian-Eulerian multiphase flow model is proposed and validated with lab scale experimental data. The experiments were performed in a 6.16-m-long annular test section having an outer pipe diameter of 4.5-inch and an inner pipe diameter of 2.5-inch. The setup is equipped with Electrical Resistance Tomography (ERT) system to measure the instantaneous solid volume fraction and a visualization section. A non-Newtonian Heschel-Bulkley (HB) fluid was used as drilling mud and solid glass beads of 2.50 mm–3.00 mm were used as cuttings in the experiment. This study shows a good agreement in visualization of mechanistic three-layer model of cuttings transport in terms of ERT data from experiment and CFD simulation. The validated CFD model is used to perform 5- Factors factorial design and analysis of variance (ANOVA) study. ANOVA shows that the interaction effects of mud velocity-cuttings size, mud velocity-inclination are statistically significant. Finally, this study proposed a statistical model to estimate the CTE of an inclined well considering the two factor and three factor interactions among the variables. Also, the model shows that drill pipe rotation has negligible effect in improving cuttings transport efficiency in the inclined well. The proposed model also reveals that cuttings size and fluid velocity account for 78% contribution in the transport efficiency for an inclined well. Furthermore, an Artificial Neural Network (ANN) method is used to verify the contribution of lower order interaction in the statistical model. Though empirical model shows few lower order two factor interactions (fluid rheology -cuttings size, fluid rheology-inclination), and three factor interactions (velocity-cuttings size-inclination, fluid rheology-cuttings size-inclination); ANN model shows that lower order interaction are significant in model prediction and should not be ignored. The findings of this study can help in better understanding the interaction behaviour among drilling parameters and optimized cuttings transport efficiency in the inclined drilling operation for a wide variety drilling parameters.

In this work Sr doped BiFeO3 (BSFO) and CoFe2O4 (CFO), (BSFO-CFO) composite polycrystalline samples are prepared via simple solid state route. We report the existence of an irreversible room temperature field-induced a ferromagnetic (FM) transition that does not quench even after removing the magnetic field in BSFO and CFO composite system. The field induced FM order transition occurs over a long range of temperature from 330 K to 5 K and remain stable for at least 45 min after the field quenching. The fraction of field induced FM phase increases and its decay rate decreases at lower temperatures. The strong temperature dependence of the magnetic field induced FM phase is also observed. These findings will lead the researcher to design new composite materials system for desired applications.

The reservoir productivity index depends on the performance of fluid flow through the perforation tunnels. Experimentally, it is observed that higher fluid flow rate occurs in perforation by drilling (PD) technique than the traditional shooting technique. This behavior is favorable for the increased hydrocarbon production from a formation. The better understanding of formation damage mechanisms for various reservoir conditions can be optimized for the economic benefits and managerial decision. The perforation by drilling technique is proposed as an alternative perforation technique since this technique induces less formation damage. Experimental and numerical investigations are ongoing research in this regards. The primary results of the two-phase fluid flow through a perforation tunnel of porous media are modeled using ANSYS CFX-15.07 platform. The numerical data are validated with the experimental data. The effects of different petro-physical properties such as permeability, porosity, fluid viscosity, flow rates, and injection pressure are analyzed in the simulations.

Arsenic contamination of drinking water, which can occur naturally or because of human activities such as mining, is the single most important public health issue in Bangladesh. Fifty out of the 64 districts in the country have arsenic concentration of groundwater exceeding 50µgL-1, the Bangladeshi threshold, affecting 35-77 million people or 21-48% of the total population. Chronic arsenic exposure through drinking water and other dietary sources is an important public health issue worldwide affecting hundreds of millions of people. Consequently, arsenic poisoning has attracted the attention of researchers and has been profiled extensively in the literature. Most of the literature has focused on characterising arsenic poisoning and factors associated with it. However, studies examining the socio-economic aspects of chronic exposure of arsenic through either drinking water or foods remain underexplored. The objectives of this paper are (i) to review arsenic exposure pathways to humans; (ii) to summarise public health impacts of chronic arsenic exposure; and (iii) to examine socio-economic implications and consequences of arsenicosis with a focus on Bangladesh. This scoping review evaluates the contributions of different exposure pathways by analysing arsenic concentrations in dietary and non-dietary sources. The socio-economic consequences of arsenicosis disease in Bangladesh are discussed in this review by considering food habits, nutritional status, socio-economic conditions, and socio-cultural behaviours of the people of the country. The pathways of arsenic exposure in Bangladesh include drinking water, various plant foods and non-dietary sources such as soil. Arsenic affected people are often abandoned by the society, lose their jobs and get divorced and are forced to live a sub-standard life. The fragile public health system in Bangladesh has been burdened by the management of thousands of arsenicosis victims in Bangladesh.

Single-phase multiferroic Bi1−xLaxFeO3 (x = 0.00, 0.10, 0.20, 0.30, and 0.40) ceramics were studied by positron annihilation lifetime spectroscopy and the effect of the cation vacancies on the magnetism were investigated. Positron lifetime measurements and density-functional theory calculation of positron lifetimes identified the presence of Bi and Fe vacancies. It was observed that when x > 0.20 the Bi monovacancies disappeared gradually and some larger vacancy clusters begin to appear. Magnetic property measurements showed that the remnant magnetization increases greatly when x > 0.20. It is found that the enhanced magnetization is not only due to the suppressed spin cycloid structure, but the variation of cation defects also contributes.

In the past, although a significant level of understanding of the chip formation mechanism has been reached, the knowledge of complex tool-work interaction at small scale material removal is still lacking. This is critical when the undeformed chip thickness (h) falls below the size cutting tool edge radius (rβ). Thus, the quest for the understanding of the physical mechanisms of material removal in ultra-precision machining has built the foundation of this study. In an ultraprecision set up with a highly stiff machine tool and rigid cutting tool, the dominant factors considered for analysing the cutting performance are machined surface finishing, material flow stress and specific cutting energy. In this respect, this paper aims to provide a holistic approach of predicting and optimizing of machined surface quality by considering the material property and cutting tool edge radius effect. More detailed attention is paid to the phenomenology of the surface generation mechanisms at small material removal scales. This could be a promising research to improve the surface finishing of metallic parts. Therefore, ‘material perspective’ and ‘cutting tool edge radius effect’ are of significant important in ultra-precision machining for optimising machining parameters and improving machined surface quality with nanoscale finishing.

For reliable gas kick detection modeling and simulation, the PVT properties of the gas must be predicted accurately. The property correlations available in open literature are developed mostly for certain regions and conditions, which usually over-predict or under-predict when applied to different regions and conditions. To assess these correlations and to determine their applicability and accuracy, a comprehensive evaluation is performed for 63 empirical correlations of four gas properties; gas-oil ratio GOR, oil formation volume factor OFVF, gas viscosity, and gas density based on published laboratory measurements. The GOR and OFVF correlations were evaluated on a regional basis and three best-fit correlations are recommended for each selected region including the Middle East, Central & South America, North America, Africa, and Asia. A universal new correlation for the GOR is developed in this study that can be used for any region in the world with better accuracy and wider range than all available correlations. Furthermore, based on the evaluation results, the most accurate correlations for gas viscosity and density at high-temperature and high-pressure (HTHP) conditions are recommended. The density-based models of the gas viscosity show close results within a minimum average absolute relative error (AARE) of 3.50% to a maximum of 4.45%. Further assessment for the equations of state based on real compositions of the gas kick is recommended for future work. The present work provides a comprehensive and one-stop source database for property correlations and measured data related to gas kick detection.

The aim of this paper is to present a new numerical model to study the drilling fluid flow through eccentric annulus during tripping operations and to investigate the effect of the eccentricity on the annular velocity and apparent viscosity profiles. Many published works studied surge and swab phenomenon using simplified numerical models that do not consider the azimuthal variation of the shear stress in the eccentric annuli. In this paper, the developed numerical model takes into consideration this variation. Non-orthogonal, curvilinear coordinates were used to generate a body-fitted elliptic mesh that maps the irregular complicated eccentric annulus into a simple rectangle where flow equations can be discretized using the finite difference method then solved numerically. Besides, a commercial software (ANSYS Fluent 19R3) was used to support the findings of the numerical model. Results of these models were validated against the experimental data from literature where good agreement was observed with an average relative error of 2.6%, 3.8%, and 6.8% for the three Herschel-Bulkley fluids studied in the eccentric case. The profiles of velocity and viscosity were plotted, the contours showed that we cannot use an average velocity or a single value for the apparent viscosity to describe the drilling fluid flowing through an eccentric annulus, but, the whole profile should be used, instead. The developed numerical model was used in a parametric study to investigate the effect of eccentricity on the relationship between surge pressure and the relevant drilling parameters namely tripping velocity, annular geometry, and fluid rheological properties. The results showed that the eccentricity decreases the surge pressure independently of the previous parameters and that the rate of decrease varies from one parameter to another. The outcome of this parametric study was used to construct a surrogate model using Random Forest Regressor. Predictions from the surrogate model fit the numerical data very well with R-squared of 0.99 and 0.97 for training and test data, respectively.

Poor cuttings transport in deviated wells limit drill rate, induce excessive torque and drag, or in severe cases result in a stuck pipe. This paper presents a generalized data-driven model that utilizes statistical techniques for optimizing hole cleaning efficiency under different drilling conditions in deviated and extended reach wells. For this purpose, the model is constructed based on three approaches including extensive experiments conducted in our flow loop of 5-m horizontal length (4.5in. × 2in.), a validated Computational Fluid Dynamics (CFD) model was developed, and experimental data were collected from the literature to develop a reliable predictive tool that can estimate cuttings concentration in deviated wells. The developed model utilized a non-linear regression method, and was trained with 75% of the gathered data and validated with the remaining 25% to ensure the capability of the proposed model for accurate estimation of cuttings accumulation under different conditions. Unique dimensionless parameters were developed to shift the model results from lab-scale to field-scale applications. Findings revealed that the developed model provides promising results in estimating cuttings accumulation in deviated wells (20–90° from vertical). Predicted points lay in between 30% error margin in most cases, and the relation between estimated and measured cuttings accumulation has an adjusted R2 = 0.9. The proposed model outperforms the Duan, and Song models and introduces new dimensionless parameters to characterize hole cleaning efficiency during daily operations. The developed model proves to be a robust tool for simulating cuttings transport in real-time, monitoring cuttings accumulation, improving drilling efficiency, and avoiding Non-Productive Time (NPT) related to hole cleaning issues.

Spatial domains defining either geological models or mineral estimation envelopes are among the few components of the mining life cycle that are not quantitatively assessed to communicate uncertainty or error in mineral resource projects. Recent work has investigated the use of Bayesian approximation methods to assess interpretation uncertainty of the classification of drill hole intercepts to spatial domain categories. A fundamental assumption is that the spatial domains being tested are used for geostatistics and that the spatial domains contain data that is homogenous to satisfy an assumption of stationarity. A binomial or multinomial model of data used to generate the spatial domains can be trained and simulated to assess the model's ability to predict categories. Categorical subjective geological logging and multielement data have shown suitable data inputs to assess interpretation uncertainty at two case studies. In early-stage projects laboratory multielement data may not be available or economically feasible. Portable X-Ray Fluorescence (pXRF) is a cost and time-effective method of providing multielement data at lower precision and accuracy. A case study had both Inductively coupled plasma mass spectrometry (ICP) and pXRF data. Bayesian approximation models were created from both data sets to assess interpretation uncertainty. Visual assessment of uncertainty band graphs and statistical testing using the Bayesian correlated t-test shows that the two data sets' interpretation uncertainty models are practically equivalent and that pXRF data can be used in early stage projects to assess interpretation uncertainty.

Inducing compressive stress on the machined surface is a desirable practice in ultra-precision metal cutting to improve the part quality. An important parameter to analyse the machining performance is the material flow stress, which plays a crucial role in the material deformation characteristics in machining. As the dimensions of the workpiece descend from macro to micro scale in metal forming, the flow stress reduces accordingly. Hence, based on the ‘surface model’ analogy developed for metal forming coupled with the crystal plasticity effect, an attempt has been made in this study to construct a flow stress model for ultra-precision machining process. While developing the model, for the first time, the size factor (η, d/ho) is introduced to incorporate the material ‘grain size effect’ in ultra-precision machining. The proposed model is validated with experimental results of Al6082 alloy of different grain sizes. Orthogonal turning experiments were conducted on an ultra-precision machine by utilizing the concept of ‘cutting edge radius effect’, which is identified as the relative tool sharpness (RTS) and quantified as the ratio of undeformed chip thickness (hc) to edge radius (rn). In this paper, the investigation on the material flow stress is carried out by considering the phenomenon of the shifting material flow separation (cutting) from primary deformation zone to material deformation (ploughing and rubbing) at tertiary deformation zone. The distribution of contact stresses along the tool rake and flank faces at the minimum value of RTS (hc/rn) of 0.01 substantiates the ploughing effect (compressive stresses are induced into the machined layer) rather than chip separation. Moreover, the distinct variation of the machined surface quality and μ-chip morphology at the extreme low and high RTS conditions distinguishes the material ploughing effect from the cutting effect. Additionally, for the same RTS value, it is found that different grain materials (Cu and Mg alloy) exhibited variations in flow stress, chip morphology and surface quality. Therefore, material grain size is an influential factor for analysing machining performance with material flow stress at ultra-precision level.

An understanding of the transport and dynamics of two fluids in porous media, as well as the bubbly flow regime, is important for many engineering applications, such as enhanced oil recovery (EOR) method, drilling technology, multiphase production system, etc. In this respect, the dependence of capillary stresses on the excess free-energy of a thin interfacial layer formed by two immiscible fluids is not fully clear, particularly in porous media. Of particular interests are the closure models for interphase forces which often hinder the reliable prediction of the homogeneous flow regime. This article presents a multiphase Computational Fluid Dynamics (CFD) study of bubbles in homogeneous porous media to model the flow of oil and gas, and investigates a closure model that is based on the Allen-Cahn phase-field method, where the capillary stress is derived from the excess free-energy. The governing dynamics is simulated with the volume averaged Navier-Stokes equations extended for multiphase flow in porous media. The equations have been discretized by a wavelet transform method to accurately capture the topological change of the fluid-fluid interface. To validate the closure model for interphase forces, the results of the present phase-field method have been compared with that from experiments, as well as from reference numerical models. An excellent agreement among the results from present phase-field simulations, experiments, and some reference numerical simulations has been observed. The terminal velocity of the rising gas bubble in a liquid saturated porous medium, as well as in a pure liquid has been investigated. The bubble rising velocity in both cases have been compared with respect to the theoretical and experimental results. The study illustrates how the bubble dynamics in porous media depend on the excess free energy of a thin interfacial layer formed by two immiscible fluids.