| 1. |
- Ademuyiwa, Adesoji O., et al.
(författare)
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Determinants of morbidity and mortality following emergency abdominal surgery in children in low-income and middle-income countries
- 2016
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Ingår i: BMJ Global Health. - : BMJ Publishing Group Ltd. - 2059-7908. ; 1:4
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Tidskriftsartikel (refereegranskat)abstract
- Background: Child health is a key priority on the global health agenda, yet the provision of essential and emergency surgery in children is patchy in resource-poor regions. This study was aimed to determine the mortality risk for emergency abdominal paediatric surgery in low-income countries globally.Methods: Multicentre, international, prospective, cohort study. Self-selected surgical units performing emergency abdominal surgery submitted prespecified data for consecutive children aged <16 years during a 2-week period between July and December 2014. The United Nation's Human Development Index (HDI) was used to stratify countries. The main outcome measure was 30-day postoperative mortality, analysed by multilevel logistic regression.Results: This study included 1409 patients from 253 centres in 43 countries; 282 children were under 2 years of age. Among them, 265 (18.8%) were from low-HDI, 450 (31.9%) from middle-HDI and 694 (49.3%) from high-HDI countries. The most common operations performed were appendectomy, small bowel resection, pyloromyotomy and correction of intussusception. After adjustment for patient and hospital risk factors, child mortality at 30 days was significantly higher in low-HDI (adjusted OR 7.14 (95% CI 2.52 to 20.23), p<0.001) and middle-HDI (4.42 (1.44 to 13.56), p=0.009) countries compared with high-HDI countries, translating to 40 excess deaths per 1000 procedures performed.Conclusions: Adjusted mortality in children following emergency abdominal surgery may be as high as 7 times greater in low-HDI and middle-HDI countries compared with high-HDI countries. Effective provision of emergency essential surgery should be a key priority for global child health agendas.
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| 2. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 3. |
- Abid, Nosheen, 1993-, et al.
(författare)
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Burnt Forest Estimation from Sentinel-2 Imagery of Australia using Unsupervised Deep Learning
- 2021
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Ingår i: Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA). - : IEEE. ; , s. 74-81
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Konferensbidrag (refereegranskat)abstract
- Massive wildfires not only in Australia, but also worldwide are burning millions of hectares of forests and green land affecting the social, ecological, and economical situation. Widely used indices-based threshold methods like Normalized Burned Ratio (NBR) require a huge amount of data preprocessing and are specific to the data capturing source. State-of-the-art deep learning models, on the other hand, are supervised and require domain experts knowledge for labeling the data in huge quantity. These limitations make the existing models difficult to be adaptable to new variations in the data and capturing sources. In this work, we have proposed an unsupervised deep learning based architecture to map the burnt regions of forests by learning features progressively. The model considers small patches of satellite imagery and classifies them into burnt and not burnt. These small patches are concatenated into binary masks to segment out the burnt region of the forests. The proposed system is composed of two modules: 1) a state-of-the-art deep learning architecture for feature extraction and 2) a clustering algorithm for the generation of pseudo labels to train the deep learning architecture. The proposed method is capable of learning the features progressively in an unsupervised fashion from the data with pseudo labels, reducing the exhausting efforts of data labeling that requires expert knowledge. We have used the realtime data of Sentinel-2 for training the model and mapping the burnt regions. The obtained F1-Score of 0.87 demonstrates the effectiveness of the proposed model.
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| 4. |
- Adnan, Muhammad, et al.
(författare)
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Fine Tuning the Optoelectronic Properties of Triphenylamine Based Donor Molecules for Organic Solar Cells
- 2017
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Ingår i: Zeitschrift fur physikalische Chemie (Munchen. 1991). - : Walter de Gruyter GmbH. - 0942-9352 .- 2196-7156. ; 231:6, s. 1127-1139
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Tidskriftsartikel (refereegranskat)abstract
- Geometrical parameters, electronic structures and photophysical properties of three new triphenylamine (TPA) and diphenylamine (DPA) based electron donor materials M1-M3 (for organic solar cells) have been investigated through density functional theory (DFT) methods at the B3LYP/6-31G(d) level of the theory. TPA and DPA are used as donor moieties due to their electron donating ability while benzothiazole, cyanide and cyanomethylacetate (CMA) moieties have been taken as acceptor moieties. The time dependent-DFT (TD-DFT) method has been employed [TD-B3LYP/6-31G (d)] for the computation of excited state properties in the gas phase and in solvent (chloroform). The polarization continuum model is applied for calculations in the solvent phase. The designed molecules exhibited broad absorption in the visible and near infra-red region of spectrum with respect to a reference molecule "R" of a similar class of compounds. Based on reorganization energies calculations, these materials could act as excellent hole transport materials.
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| 5. |
- 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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| 6. |
- Abid, Nosheen, 1993-, et al.
(författare)
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UCL: Unsupervised Curriculum Learning for Water Body Classification from Remote Sensing Imagery
- 2021
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Ingår i: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier. - 1569-8432 .- 1872-826X. ; 105
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a Convolutional Neural Networks (CNN) based Unsupervised Curriculum Learning approach for the recognition of water bodies to overcome the stated challenges for remote sensing based RGB imagery. The unsupervised nature of the presented algorithm eliminates the need for labelled training data. The problem is cast as a two class clustering problem (water and non-water), while clustering is done on deep features obtained by a pre-trained CNN. After initial clusters have been identified, representative samples from each cluster are chosen by the unsupervised curriculum learning algorithm for fine-tuning the feature extractor. The stated process is repeated iteratively until convergence. Three datasets have been used to evaluate the approach and show its effectiveness on varying scales: (i) SAT-6 dataset comprising high resolution aircraft images, (ii) Sentinel-2 of EuroSAT, comprising remote sensing images with low resolution, and (iii) PakSAT, a new dataset we created for this study. PakSAT is the first Pakistani Sentinel-2 dataset designed to classify water bodies of Pakistan. Extensive experiments on these datasets demonstrate the progressive learning behaviour of UCL and reported promising results of water classification on all three datasets. The obtained accuracies outperform the supervised methods in domain adaptation, demonstrating the effectiveness of the proposed algorithm.
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| 7. |
- Ahsan, Ali, et al.
(författare)
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Formulation and evaluation of miconazole lipogel for enhanced drug permeation
- 2024
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Ingår i: Pakistan Journal of Pharmaceutical Sciences. - : UNIV KARACHI. - 1011-601X. ; 37:1, s. 95-105
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Tidskriftsartikel (refereegranskat)abstract
- Hydrophilic drugs could be incorporated into the skin surface by manes of Lipogel. This study aimed to prepare miconazole lipogel with natural ingredients to enhance drug permeability using dimethyl Sulfoxide (DMSO). The miconazole lipogels, A1 (without DMSO) and A2 (with DMSO) were formulated and evaluated for organoleptic evaluation, pH, viscosity, stability studies, freeze -thawing, drug release profile and drug permeation enhancement. Results had stated that prepared lipogel's pH falls within the acceptable range required for topical delivery (4 to 6) while both formulations show good results in organoleptic evaluation. The A2 formulation containing DMSO shows better permeation of miconazole (84.76%) on the artificial skin membrane as compared to A1 lipogel formulation (50.64%). In in -vitro drug release studies, A2 for-mulation showed 87.48% drug release while A1 showed just 60.1% drug release from lipogel. Stability studies were performed on model formulations under environmental conditions and both showed good spreadibility, stable pH, free of grittiness and good consistency in formulation. The results concluded that A2 formulation containing DMSO shows better results as compared to DMSO-free drug lipogel.
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| 8. |
- Azeem, Muhammad, et al.
(författare)
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Design, synthesis, spectroscopic characterization, in-vitro antibacterial evaluation and in-silico analysis of polycaprolactone containing chitosan-quercetin microspheres
- 2023
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Ingår i: Journal of Biomolecular Structure and Dynamics. - : TAYLOR & FRANCIS INC. - 0739-1102 .- 1538-0254. ; 41:15, s. 7084-7103
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Tidskriftsartikel (refereegranskat)abstract
- Aim of present study was to synthesize a novel chitosan-quercetin (CTS-QT) complex by making a carbodiimide linkage using maleic anhydride as cross-linker and to investigate its enhanced antibacterial and antioxidant activities as compare to pure CTS and QT. Equimolar concentration of QT and maleic anhydride were used to react with 100 mg CTS to form CTS-QT complex. For this purpose, three bacterial strains namely E. Coli, S. Aureus and P. Aeruginosa were used for in-vitro antibacterial analysis (ZOI, MIC, MBC, checker board and time kill assay). Later molecular docking studies were performed on protein structure of E. Coli to assess binding affinity of pure QT and CTS-QT complex. MD simulations with accelerated settings were used to explore the protein-ligand complexs binding interactions and stability. Antioxidant profile was determined by performing DPPH center dot radical scavenging assay, total antioxidant capacity (TAC) and total reducing power (TRP) assays. Delivery mechanism to CTS-QT complex was improved by synthesizing polycaprolactone containing microspheres (CTS-QT-PCL-Levo-Ms) using Levofloxacin as model drug to enhance their antibacterial profile. Resulted microspheres were evaluated by particle size, charge, surface morphology, in-vitro drug release and hemolytic profile and are all were found within limits. Antibacterial assay revealed that CTS-QT-PCL-Levo-Ms showed more than two folds increased bactericidal activity against E. Coli and P. Aeruginosa, while 1.5 folds against S. Aureus. Green colored formation of phosphate molybdate complexes with highest 85 +/- 1.32% TAC confirmed its antioxidant properties. Furthermore, molecular docking and dynamics studies revealed that CTS-QT was embedded nicely within the active pocket of UPPS with binding energy greater than QT with RSMD value of below 1.5. Conclusively, use of maleic acid, in-vitro and in-silico antimicrobial studies confirm the emergence of CTS-QT complex containing microspheres as novel treatment strategy for all types of bacterial infections. Communicated by Ramaswamy H. Sarma
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| 9. |
- Azeem, Muhammad, et al.
(författare)
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Pesticidal potential of some wild plant essential oils against grain pests Tribolium castaneum (Herbst, 1797) and Aspergillus flavus (Link, 1809)
- 2022
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Ingår i: Arabian Journal of Chemistry. - : Elsevier BV. - 1878-5352 .- 1878-5379. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- The red flour beetle, Tribolium castaneum, and the mold Aspergillus flavus are well known threats of stored grain commodities, causing nutritional loss and poisoning of stored products, respectively. T. castaneum has developed resistance against most insecticides, leading to the use of extensive amounts of synthetic insecticides to protect stored grains. Synthetic pesticides not only toxify the environment but also cause serious health issues in humans using pesticide treated grains. This study aimed to identify plant-based natural pesticides to control T. castaneum and A. flavus. Essential oils were extracted from fresh aerial parts of Chenopodium ambrosioides, Conyza sumatrensis, Erigeron canadensis, and Tagetes minuta through steam distillation and investigated for insecticidal and anti-fungal activities against adult T. castaneum and A. flavus, respectively. GC-MS analysis of C. sumatrensis revealed the presence of 37.7% cis-lachnophyllum ester, 13.4% germa-crene D, and 21.6% limonene, whereas in E. canadensis the major compounds were limonene, ger-macrene D, and cis-lachnophyllum ester (43.4%, 12.9% and 5.9%, respectively). In bioassays with treated grain, C. sumatrensis and E. canadensis essential oils exhibited excellent toxicity against adult T. castaneum with LD50 of 3.7 and 5.6 mg per 10 g grains whereas in a fumigation bioassay they showed LD50 of 6.6 and 10.6 mg/L, respectively. The essential oils extracted from C. ambrosioides and E. canadensis exhibited good anti-fungal activity against A. flavus. Our findings suggest that essential oils of C. sumatrensis and E. canadensis can play an important role in protecting stored grains from T. castaneum and A. flavus contamination.
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| 10. |
- Abid, Muhammad Adil, et al.
(författare)
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A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment
- 2023
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Ingår i: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 225, s. 3536-3545
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Tidskriftsartikel (refereegranskat)abstract
- A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.
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