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Fulltext Patients' involvement in the care process: time to value nontraditional indicators in HIV care

Ahmed SI, Hasan SS, Babar MG

HIV Med, 2018 May 10.
PMID: 29745469 DOI: 10.1111/hiv.12620
Fulltext Technique for Early Reliability Prediction of Software Components Using Behaviour Models

Ali A, N A Jawawi D, Adham Isa M, Imran Babar M

PLoS One, 2016 Sep 26;11(9):e0163346.
PMID: 27668748 DOI: 10.1371/journal.pone.0163346

Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction.
Fulltext Ensemble learning for multi-class COVID-19 detection from big data

Kaleem S, Sohail A, Tariq MU, Babar M, Qureshi B

PLoS One, 2023;18(10):e0292587.
PMID: 37819992 DOI: 10.1371/journal.pone.0292587

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model's efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.
Fulltext Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting

Tariq MU, Ismail SB, Babar M, Ahmad A

PLoS One, 2023;18(7):e0287755.
PMID: 37471397 DOI: 10.1371/journal.pone.0287755

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population.
Sustainable functionalized metal-organic framework NH2-MIL-101(Al) for CO2 separation under cryogenic conditions

Babar M, Mubashir M, Mukhtar A, Saqib S, Ullah S, Bustam MA, et al.

Environ Pollut, 2021 Jun 15;279:116924.
PMID: 33751951 DOI: 10.1016/j.envpol.2021.116924

In this study, a sustainable NH2-MIL-101(Al) is synthesized and subjected to characterization for cryogenic CO2 adsorption, isotherms, and thermodynamic study. The morphology revealed a highly porous surface. The XRD showed that NH2-MIL-101(Al) was crystalline. The NH2-MIL-101(Al) decomposes at a temperature (>500 °C) indicating excellent thermal stability. The BET investigation revealed the specific surface area of 2530 m2/g and the pore volume of 1.32 cm3/g. The CO2 adsorption capacity was found to be 9.55 wt% to 2.31 wt% within the investigated temperature range. The isotherms revealed the availability of adsorption sites with favorable adsorption at lower temperatures indicating the thermodynamically controlled process. The thermodynamics showed that the process is non-spontaneous, endothermic, with fewer disorders, chemisorption. Finally, the breakthrough time of NH2-MIL-101(Al) is 31.25% more than spherical glass beads. The CO2 captured by the particles was 2.29 kg m-3. The CO2 capture using glass packing was 121% less than NH2-MIL-101(Al) under similar conditions of temperature and pressure.
Microsatellite based Genetic Variation among the Buffalo Breed Populations in Pakistan

Hussain T, Ellahi Babar M, Ali A, Nadeem A, Rehman ZU, Musthafa MM, et al.

J Vet Res, 2017 Dec;61(4):535-542.
PMID: 29978120 DOI: 10.1515/jvetres-2017-0057

Introduction: Eight microsatellite loci were used to define genetic diversity among five native water buffalo breeds in Pakistan.
Material and Methods: Blood samples (10 mL) from 25 buffaloes of each of the Nili, Ravi, Nili-Ravi, Kundhi, and Azi-Kheli breeds were collected aseptically from the jugular vein into 50 ml Falcon tubes containing 200 µl of 0.5 M EDTA. The phenol-chloroform method was used to extract DNA and the regions were amplified for microsatellite analysis. The eight microsatellite markers ETH10, INRA005, ILSTS029, ILSTS033, ILSTS049, ILSTS052, ETH225, and CSSM66 were analysed.
Results: The effective number of alleles across all loci was as usual lower than the observed values with a mean value of 2.52 alleles per locus. The overall allele frequency varied from 0.0041 for alleles B, I, and J over respective loci ILSTS052, INRA005, and ILSTS029 to 0.80 for allele H over locus ILSTS029. The average observed and expected heterozygosity values across all polymorphic loci in all studied buffalo breeds were 0.43 and 0.53, respectively. The overall value for polymorphic information content of considered microsatellite markers was 0.53, suggesting their appropriateness for genetic diversity analysis in buffalo. The mean Fis value was 0.13 and all loci except ILSTS049 were found significantly deviated from HWE, most likely due to non-random breeding. The five buffalo populations were genetically less diverse as indicated by a small mean Fst value (0.07). The average gene flow (Nm) indicative for population migration was calculated as 3.31. Nei's original measures of genetic distance (Ds) revealed ancient divergence of the Nili and Azi-Kheli breeds (Ds = 0.1747) and recent divergence of the Nili and Ravi breeds (Ds = 0.0374).
Conclusion: These estimates of genetic diversity were seen to coincide with phenotypic differentiation among the studied buffalo breeds. The present study reports the first microsatellite marker-based genetic diversity analysis in Pakistani buffalo breeds, and might facilitate similar studies in other livestock breeds of Pakistan.
High pressure CO2 adsorption onto Malaysian Mukah-Balingian coals: Adsorption isotherms, thermodynamic and kinetic investigations

Abunowara M, Bustam MA, Sufian S, Babar M, Eldemerdash U, Mukhtar A, et al.

Environ Res, 2023 Feb 01;218:114905.
PMID: 36442522 DOI: 10.1016/j.envres.2022.114905

CO2 sequestration into coalbed seams is one of the practical routes for mitigating CO2 emissions. The adsorption mechanisms of CO2 onto Malaysian coals, however, are not yet investigated. In this research CO2 adsorption isotherms were first performed on dry and wet Mukah-Balingian coal samples at temperatures ranging from 300 to 348 K and pressures up to 6 MPa using volumetric technique. The dry S1 coal showed the highest CO2 adsorption capacity of 1.3 mmol g-1, at 300 K and 6 MPa among the other coal samples. The experimental results of CO2 adsorption were investigated using adsorption isotherms, thermodynamics, and kinetic models. Nonlinear analysis has been employed to investigate the data of CO2 adsorption onto coal samples via three parameter isotherm equilibrium models, namely Redlich Peterson, Koble Corrigan, Toth, Sips, and Hill, and four parameter equilibrium model, namely Jensen Seaton. The results of adsorption isotherm suggested that the Jensen Seaton model described the experimental data well. Gibb's free energy change values are negative, suggesting that CO2 adsorption onto the coal occurred randomly. Enthalpy change values in the negative range established that CO2 adsorption onto coal is an exothermic mechanism. Webber's pore-diffusion model, in particular, demonstrated that pore-diffusion was the main controlling stage in CO2 adsorption onto coal matrix. The activation energy of the coals was calculated to be below -13 kJ mol-1, indicating that adsorption of CO2 onto coals occurred through physisorption. The results demonstrate that CO2 adsorption onto coal matrix is favorable, spontaneous, and the adsorbed CO2 molecules accumulate more onto coal matrix. The observations of this investigation have significant implications for a more accurate measurement of CO2 injection into Malaysian coalbed seams.
Performance evaluation of phosphonium based deep eutectic solvents coated cerium oxide nanoparticles for CO2 capture

Ahmad T, Iqbal J, Bustam MA, Babar M, Tahir MB, Sagir M, et al.

Environ Res, 2023 Apr 01;222:115314.
PMID: 36738770 DOI: 10.1016/j.envres.2023.115314

The critical challenge being faced by our current modern society on a global scale is to reduce the surging effects of climate change and global warming, being caused by anthropogenic emissions of CO2 in the environment. Present study reports the surface driven adsorption potential of deep eutectic solvents (DESs) surface functionalized cerium oxide nanoparticles (CeNPs) for low pressure CO2 separation. The phosphonium based DESs were prepared using tetra butyl phosphoniumbromide as hydrogen bond acceptor (HBA) and 6 acids as hydrogen bond donors (HBDs). The as-developed DESs were characterized and employed for the surface functionalization of CeNPs with their subsequent utilization in adsorption-based CO2 adsorption. The synthesis of as-prepared DESs was confirmed through FTIR measurements and absence of precipitates, revealed through visual observations. It was found that DES6 surface functionalized CeNPs demonstrated 27% higher adsorption performance for CO2 capturing. On the contrary, DES3 coated CeNPs exhibited the least adsorption progress for CO2 separation. The higher adsorption performance associated with DES6 coated CeNPs was due to enhanced surface affinity with CO2 molecules that must have facilitated the mass transport characteristics and resulted an enhancement in CO2 adsorption performance. Carboxylic groups could have generated an electric field inside the pores to attract more polarizable adsorbates including CO2, are responsible for the relatively high values of CO2 adsorption. The quadruple movement of the CO2 molecules with the electron-deficient and pluralizable nature led to the enhancement of the interactive forces between the CO2 molecules and the CeNPs decorated with the carboxylic group hydrogen bond donor rich DES. The current findings may disclose the new research horizons and theoretical guidance for reduction in the environmental effects associated with uncontrolled CO2 emission via employing DES surface coated potential CeNPs.