| 1. |
- Qureshi, Tayyab, et al.
(författare)
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Structural and thermal investigation of lignocellulosic biomass conversion for enhancing sustainable imperative in progressive organic refinery paradigm for waste-to-energy applications
- 2024
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Ingår i: Environmental Research. - : Elsevier. - 0013-9351 .- 1096-0953. ; 246
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Tidskriftsartikel (refereegranskat)abstract
- The depletion of finite fossil fuel reserves and the severe environmental degradation resulting from human activities have compelled the expeditious development and application of sustainable waste to energy technologies. To encapsulate energy and environment in sustainability paradigm, bio waste based energy production is need to be forged in organic bio refinery setup. According to world bioenergy association, biomass can cover 50 % of the primary energy demand of the world. Therefore, the present study focuses on reforming the energy mix for a clean energy generation, where, sample composition of cotton stalk was acidified in dilute (5% wt.) hydrochloric acid (HCL) for analyzing material burnout patterns in biomass conversion systems utilized in organic bio refinery sector. Advanced thermochemical burning technique, which includes pyrolysis and combustion was applied at four different leaching times from 0 to 180 min under nitrogen environment from 0 degrees C to 500 degrees C and air from 500 degrees C to 900 degrees C, respectively. Different analyses including proximate, ultimate, gross calorific value (GCV), thermos-gravimetric, kinetic, XRD, FTIR, SEM-EDS were used for analyzing the degradation of demineralized cotton stalk at different treatment rates. Proximate study demonstrated that cotton stalk leaching for 180 min has efficiently infused HCL, leading in a significant increase in fixed carbon and higher heating value of 20.23 % and 12.48%, respectively, as well as a reduction in carbon footprint of around 54.80%. The findings of proximate was validated by GCV analysis and CHNS analysis as value of carbon and hydrogen has shown increasing behavior with the time delay in demineralization Thermo-gravimetric and derivative thermo-gravimetric data analyses shows an increasing trend of conversion efficiency, with the maximum increase of 98 % reported for sample 3H. TT.DEM. XRD characterization has reported 23 degrees to 25 degrees angle for all the observed peaks. Sample 3H.TT.DEM has shown maximum angle inclination along with matured crystalline peak. The latter observations has been validated by FTIR spectroscopy as sample 3H.TT.DEM has reported maximum O-H group formation. Sample 3H.TT. DEM has reported lowest activation energy of 139.51 kJ*mole-1 and lowest reactivity of 0.000293649%*min 0C, due to moderate and stable reactiveness. In SEM examination, increment in pore size and number of pores within the structural matrix of cotton stalk was observed with the enhancement in acidulation process. Furthermore, in EDS analysis, 3H.TT.DEM has shown most balanced distribution of the elements. In this research, sustainable transformation of biomass is envisioned to improve the waste bio refinery system, significantly contributing to the achievement of Sustainable Development Goals 7, 12 and 13.
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| 2. |
- Mubeen, Iqra, et al.
(författare)
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Formulation of Modified-Release Bilayer Tablets of Atorvastatin and Ezetimibe : An In-Vitro and In-Vivo Analysis
- 2022
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Ingår i: Polymers. - : MDPI AG. - 2073-4360. ; 14:18
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Tidskriftsartikel (refereegranskat)abstract
- The objective of this work was to formulate co-loaded bilayer tablets containing ezetimibe (EZB) and atorvastatin (ATC). ATC loaded in the immediate-release (IR) layer is an HMG CoA reductase inhibitor, while EZB, added in the sustained-release (SR) layer, is a lipid-lowering agent. This study was conducted to evaluate the effects of polymer on the formulation and characterization of bilayer tablets, as well as the therapeutic impact of the concurrent use of both drugs having a sequential release pattern. To obtain the optimized results, four different formulations with variable compositions were developed and evaluated for different parameters. The drug release studies were carried out using a type II dissolution apparatus, using phosphate buffer solution (PBS) of 1.2 pH for IR of EZB for an initial 2 h, followed by 24 h studies for ATC in PBS 6.8 pH. The IR layer showed rapid drug release (96%) in 2 h, while 80% of the ATC was released in 24 h from the SR layer. Locally obtained, 6-week-old female albino rats were selected for in vivo studies. Both preventive and curative models were applied to check the effects of the drug combination on the lipid profile, atherosclerosis and physiology of different organs. Studies have shown that the administration of both drugs with different release patterns has a better therapeutic effect (p < 0.05), both in preventing and in curing hyperlipidemia. Conclusively, through the sequential release of ATC and EZB, a better therapeutic response could be obtained.
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| 3. |
- Salman, Muhammad, et al.
(författare)
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Trajectory of COVID-19 vaccine hesitancy post-vaccination and public's intention to take booster vaccines : A cross-sectional analysis
- 2023
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Ingår i: Human Vaccines & Immunotherapeutics. - : Taylor & Francis. - 2164-5515 .- 2164-554X. ; 19:2
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Tidskriftsartikel (refereegranskat)abstract
- Vaccine hesitancy (VH) is not a new phenomenon in Pakistan and is regarded as one of the primary causes of unsatisfactory vaccination campaigns. This study determined post-vaccination COVID-19 VH, factors influencing COVID-19 vaccine uptake, and public's intent to receive booster vaccinations. A cross-sectional study was conducted among adult population of Lahore, Pakistan. Participants were recruited via convenience sampling between March and May 2022. SPSS version 22 was used for the data analysis. A total of 650 participants were included in the study (age = 28.1 & PLUSMN; 9.7 years; male-to-female ratio nearly 1: 1). The majority of participants received Sinopharm followed by Sinovac vaccine. The top three reasons of vaccine uptake were "only vaccinated individuals are allowed at the workplace, and educational institutes" (Relative importance index (RII) = 0.749), "only vaccinated people are allowed to go to markets, malls and other public places" (RII = 0.746), and "protect myself from the infection" (RII = 0.742). The mean COVID-19 VH score was 24.5 & PLUSMN; 6.2 (95% CI 23.9-24.9), with not being pro-vaccines and poor economic status were the significant predictors of COVID-19 vaccine hesitancy among immunized individuals (p < .05). Acceptance of booster vaccines was negatively associated with younger age and a lower level of education. Furthermore, being pro-vaccine was associated with a greater likelihood of accepting booster vaccines (p = .001). The Pakistani public continues to express VH toward COVID-19 vaccines. Therefore, aggressive measures must be taken to combat the community factors that contribute to it.
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| 4. |
- Ashraf, Waqar Muhammad, et al.
(författare)
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Artificial intelligence based operational strategy development and implementation for vibration reduction of a supercritical steam turbine shaft bearing
- 2022
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Ingår i: Alexandria Engineering Journal. - 1110-0168 .- 2090-2670. ; 61:3, s. 1864-1880
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Tidskriftsartikel (refereegranskat)abstract
- The vibrations of bearings holding the high-speed shaft of a steam turbine are critically controlled for the safe and reliable power generation at the power plants. In this paper, two artificial intelligence (AI) process models, i.e., artificial neural network (ANN) and support vector machine (SVM) based relative vibration modeling of a steam turbine shaft bearing of a 660 MW supercritical steam turbine system is presented. After extensive data processing and machine learning based visualization tests performed on the raw operational data, ANN and SVM models are trained, validated and compared by external validation tests. ANN has outperformed SVM in terms of better prediction capability and is, therefore, deployed for simulating the constructed operating scenarios. ANN process model is tested for the complete load range of power plant, i.e., from 353 MW to 662 MW and 4.07% reduction in the relative vibration of the bearing is predicted by the network. Further, various vibration reduction operating strategies are developed and tested on the validated and robust ANN process model. A selected operating strategy which has predicted a promising reduction in the relative vibration of bearing is selected. In order to confirm the effectiveness of the prediction of the ANN process model, the selected operating strategy is implemented on the actual operation of the power plant. The resulting reduction in the relative vibrations of the turbine's bearing, which is less than the alarm limit, are confirmed. This cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment. (c) 2021 THE AUTHORS. Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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| 5. |
- Asim, Muhammad Nabeel, et al.
(författare)
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EL-RMLocNet : An explainable LSTM network for RNA-associated multi-compartment localization prediction
- 2022
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Ingår i: Computational and Structural Biotechnology Journal. - : Elsevier. - 2001-0370. ; 20, s. 3986-4002
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Tidskriftsartikel (refereegranskat)abstract
- Subcellular localization of Ribonucleic Acid (RNA) molecules provide significant insights into the functionality of RNAs and helps to explore their association with various diseases. Predominantly developed single-compartment localization predictors (SCLPs) lack to demystify RNA association with diverse biochemical and pathological processes mainly happen through RNA co-localization in multiple compartments. Limited multi-compartment localization predictors (MCLPs) manage to produce decent performance only for target RNA class of particular sub-type. Further, existing computational approaches have limited practical significance and potential to optimize therapeutics due to the poor degree of model explainability. The paper in hand presents an explainable Long Short-Term Memory (LSTM) network “EL-RMLocNet”, predictive performance and interpretability of which are optimized using a novel GeneticSeq2Vec statistical representation learning scheme and attention mechanism for accurate multi-compartment localization prediction of different RNAs solely using raw RNA sequences. GeneticSeq2Vec generates optimized statistical vectors of raw RNA sequences by capturing short and long range relations of nucleotide k-mers. Using sequence vectors generated by GeneticSeq2Vec scheme, Long Short Term Memory layers extract most informative features, weighting of which on the basis of discriminative potential for accurate multi-compartment localization prediction is performed using attention layer. Through reverse engineering, weights of statistical feature space are mapped to nucleotide k-mers patterns to make multi-compartment localization prediction decision making transparent and explainable for different RNA classes and species. Empirical evaluation indicates that EL-RMLocNet outperforms state-of-the-art predictor for subcellular localization prediction of 4 different RNA classes by an average accuracy figure of 8% for Homo Sapiens species and 6% for Mus Musculus species. EL-RMLocNet is freely available as a web server at (https://sds_genetic_analysis.opendfki.de/subcellular_loc/).
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| 6. |
- Fatima, Iza, et al.
(författare)
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Individual and synergistic effects of different fertilizers and gibberellin on growth and morphology of chili seedlings
- 2024
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Ingår i: Acta Ecologica Sinica. - 1872-2032. ; 44:2, s. 275-281
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Tidskriftsartikel (refereegranskat)abstract
- Gibberellins (GA3), as well as the basic elements phosphorus (P), nitrogen (N), and potassium (K), are crucial to the growth of chili. This study investigates the effect of different fertilizers and plant growth regulator on the growth and morphology of chili seedlings. Soil application of NPK, urea, SOP, and DAP (2.5 g/L) was applied during sowing, and N in two splits at sowing and after twenty days of sowing while foliar application of GA3 (50 mg/L) was applied after fifteen days of germination. The result of five seedlings' traits plant height (PH), plant girth (PG), plant spread (PS), number of leaves (NOF), and root length (RL) demonstrated a significant difference among growth-related traits in chili seedlings owing to the use of fertilizers, GA3, and their combinations. An optimum level of K and P alone or in combination with GA3 had a significant effect on all traits. PH was particularly influenced by the combination of GA3 with K and P whereas other traits like PG, NOF, PS, and RL are greatly influenced by the application of NPK, urea, SOP, DAP, and their combination with GA3. The study results showed an increase in chili seedlings' growth and morphology in response to various fertilizers and GA3.
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| 7. |
- Mehmood, Muhammad Asim, et al.
(författare)
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CFD study of pressure loss characteristics of multi-holed orifice plates using central composite design
- 2019
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Ingår i: Flow Measurement and Instrumentation. - : Elsevier. - 0955-5986 .- 1873-6998. ; 70, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- The study on the use of multi-holed orifice plate for measuring flow rate is a growing area of research. As compared to standard orifice plate, multi-holed orifice plates (MO) have number of advantages, such as, these plates require minimum straight piping at the upstream without compromising the pressure losses and provide better accuracy in the measurement of flow rates. In this study, a systematic methodology is adopted for investigating the effect of different geometrical parameters on pressure loss coefficient and values of parameters under investigation varied using central composite design. The geometrical parameters chosen for the study are: (a) Number of holes; (b) Multi-hole Diameter ratio and (c) Compactness of holes. Commercial computational fluid dynamics code (ANSYS Fluent) is employed to perform simulations for 15 different settings of these parameters to analyze their effect on pressure loss coefficient and flow development length at downstream of multi-holed orifice plates. It is found that values of pressure loss coefficient is a strong function of multi-hole diameter ratio, whereas, the flow conditioning properties are strongly affected by the number of holes.
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| 8. |
- Asim, Muhammad, et al.
(författare)
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Experimental analysis of solar thermal integrated MD system for cogeneration of drinking water and hot water for single family villa in Dubai using flat plate and evacuated tube solar collectors
- 2017
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Ingår i: Desalination and Water Treatment. - : DESALINATION PUBL. - 1944-3994 .- 1944-3986. ; 92, s. 46-59
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents the experimental analysis performed on solar thermal integrated membrane distillation (MD) system using flat plate and evacuated tube collectors. The system will be utilized for cogeneration of drinking water and domestic hot water for single family in Dubai comprising of four to five members. Experiments have been performed in Ras Al Khaimah Research and Innovation Centre (RAKRIC) facility. The experimental setup has been installed to achieve the required production of 15-25 L/d of drinking water and 250 L/d of hot water for domestic purposes. Experiments have been performed on MD setup at optimized flow rates of 6 L/min on hot side and 3 L/min on cold side for producing the desired distillate. The hot side and cold side MD temperature has been maintained between 60 degrees C and 70 degrees C, and 20 degrees C and 30 degrees C. The total annual energy demand comes out to be 8,223 kWh (6,000 kWh is for pure water and 2,223 kWh for hot water). The optimum aperture areas for flat plate and evacuated tube collector field have been identified as 8.5 and 7.5 m(2), respectively. Annual energy consumption per liter for pure water production is 1, 0.85 and 0.7 kWh/L for different MD hot and cold inlet temperatures.
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| 9. |
- Asim, Muhammad Nabeel, et al.
(författare)
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A robust and precise convnet for small non-coding rna classification (rpc-snrc)
- 2021
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Ingår i: IEEE Access. - 2169-3536. ; 9, s. 19379-19390
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Tidskriftsartikel (refereegranskat)abstract
- Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures. To reproduce the results Source code and data set is available at https://github.com/muas16/small-non-coding-RNA-classification.
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| 10. |
- Asim, Muhammad Nabeel, et al.
(författare)
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BoT-Net : a lightweight bag of tricks-based neural network for efficient LncRNA–miRNA interaction prediction
- 2022
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Ingår i: Interdisciplinary Sciences: Computational Life Sciences. - : Springer. - 1913-2751 .- 1867-1462. ; 14:4, s. 841-862
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Tidskriftsartikel (refereegranskat)abstract
- Background and objective: Interactions of long non-coding ribonucleic acids (lncRNAs) with micro-ribonucleic acids (miRNAs) play an essential role in gene regulation, cellular metabolic, and pathological processes. Existing purely sequence based computational approaches lack robustness and efficiency mainly due to the high length variability of lncRNA sequences. Hence, the prime focus of the current study is to find optimal length trade-offs between highly flexible length lncRNA sequences.Method: The paper at hand performs in-depth exploration of diverse copy padding, sequence truncation approaches, and presents a novel idea of utilizing only subregions of lncRNA sequences to generate fixed-length lncRNA sequences. Furthermore, it presents a novel bag of tricks-based deep learning approach “Bot-Net” which leverages a single layer long-short-term memory network regularized through DropConnect to capture higher order residue dependencies, pooling to retain most salient features, normalization to prevent exploding and vanishing gradient issues, learning rate decay, and dropout to regularize precise neural network for lncRNA–miRNA interaction prediction.Results: BoT-Net outperforms the state-of-the-art lncRNA–miRNA interaction prediction approach by 2%, 8%, and 4% in terms of accuracy, specificity, and matthews correlation coefficient. Furthermore, a case study analysis indicates that BoT-Net also outperforms state-of-the-art lncRNA–protein interaction predictor on a benchmark dataset by accuracy of 10%, sensitivity of 19%, specificity of 6%, precision of 14%, and matthews correlation coefficient of 26%.Conclusion: In the benchmark lncRNA–miRNA interaction prediction dataset, the length of the lncRNA sequence varies from 213 residues to 22,743 residues and in the benchmark lncRNA–protein interaction prediction dataset, lncRNA sequences vary from 15 residues to 1504 residues. For such highly flexible length sequences, fixed length generation using copy padding introduces a significant level of bias which makes a large number of lncRNA sequences very much identical to each other and eventually derail classifier generalizeability. Empirical evaluation reveals that within 50 residues of only the starting region of long lncRNA sequences, a highly informative distribution for lncRNA–miRNA interaction prediction is contained, a crucial finding exploited by the proposed BoT-Net approach to optimize the lncRNA fixed length generation process.
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