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2,159 results on '"Khan, Mansoor A."'

107. Outcomes of laparoscopic fundoplication

Author

Khan, Mansoor Ali

Subjects
616.3, RC Internal medicine
Abstract

Gastro-esophageal reflux disease (GORD) is common and a variety of surgical repair techniques have been shown to be effective. This thesis contains two randomised controlled trials and a combined data analysis of both studies to establish which techniques appear the most effective in controlling reflux. It also contains a pilot study to determine whether intraoperative manometry can predict which patients are likely to suffer from postoperative dysphagia. One hundred and three underwent partial fundoplication (Anterior or Posterior) and one hundred and twenty one patients underwent total/subtotal (Nissen or Lind) in the randomised controlled trials and 40 patients were recruited into the intraoperative manometry study. Patients were followed up for 12 months and their change in symptoms recorded. In the partial fundoplication trial, patients who underwent posterior fundoplication had better control of symptoms compared to those who underwent anterior fundoplication at the 12 month follow up point. There was no difference between the groups who underwent Nissen and Lind fundoplication. When the studies were collated, the laparoscopic total/subtotal fundoplication appears to be superior in the control of reflux when compared to the laparoscopic partial fundoplication. Intraoperative manometry may be advantageous as the study does suggest that this investigation may be useful in predicting post-operative dysphagia.

Published
2016

110. A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability inWind Power Forecasting.

Author

Ullah, Farhan, Xuexia Zhang, Khan, Mansoor, Abid, Muhammad, and Mohamed, Abdullah

Abstract

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows. Traditional approaches frequently struggle with complex data and non-linear connections. This article presents a novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts. The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Era Retrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms using in-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model, while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporal neural network is enhanced by providing different input parameters including training layers, hidden and dropout layers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof- the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), respectively. The energy efficiency performance indicators showed that the proposed model demonstrates error reduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78% for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitative results show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSE values ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in wind power forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for more sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Published
2024
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112. Spatio-temporal prediction of photovoltaic power based on a broad learning system and an improved backtracking search optimization algorithm.

Author

Tang, Wenhu, Huang, Kecan, Qian, Tong, Li, Weiwei, Xie, Xuehua, Yin, Linfei, and Khan, Mansoor

Subjects
OPTIMIZATION algorithms, SEARCH algorithms, INSTRUCTIONAL systems, SPRING, ARTIFICIAL neural networks
Abstract

The accuracy of photovoltaic (PV) power forecasting techniques relies not only on high-quality spatiotemporal data but also on an efficient feature-mining methodology. In this study, a spatiotemporal power forecasting model based on the broad learning system (BLS) and the improved backtracking search optimization algorithm (IBSOA) is proposed. The objective is to enhance the accuracy of PV power predictions while reducing the time-intensive training process associated with an extensive set of broad learning system parameters. The spatiotemporal attributes of historical data from multiple PV sites are clustered using a self-organizing map. The clustering analysis explores the spatiotemporal correlation among five photovoltaic (PV) power stations for each season between 2017 and 2018. Subsequently, the IBSOA is employed to optimize the hyperparameters of the BLS model, particularly the mapping and enhancement nodes. By utilizing hyperparameter optimization, a BSOA-based broad learning model is introduced to achieve superior accuracy. The results are assessed using the proposed method in comparison with three popular optimization algorithms: 1) genetic algorithm (GA), 2) bird swarm algorithm (BSA), and 3) backtracking search optimization algorithm (BSOA). All scenarios are validated and compared using PV plant data from the DKA center in Australia. The root-mean-square error (RMSE) indicators of the proposed prediction method are consistently lower than the worst-case scenario in each season, decreasing by 3.2283 kW in spring, 3.9159 kW in summer, 1.3425 kW in autumn, and 1.4058 kW in winter. Similarly, the mean absolute percentage error (MAPE) exhibits a reduction compared to the worst case, with a decreases of 0.882% in spring, 1.2399% in summer, 1.803% in autumn, and 1.087% in winter. The comprehensive results affirm that the proposed method surpasses alternative optimization techniques, delivering high-quality power forecasts for the given case study. [ABSTRACT FROM AUTHOR]

Published
2024
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117. Patterns of Injury

Author

Magnus, Danyal, primary, Brown, Katherine A., additional, Khan, Mansoor, additional, and Proud, William G., additional

Published
2021
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141. Maximizing detection sensitivity of levofloxacin and tryptophan in dairy products: a carbon-based electrochemical sensor incorporating Ti3AlC2MAX phase and activated nanodiamonds

Author

Kholafazadehastamal, Ghazaleh, Khan, Mansoor, Soylak, Mustafa, and Erk, Nevin

Abstract

Accurate and rapid detection of antibiotics is critical for protecting human health and the environment. To this end, we report a novel electrochemical sensor for the simultaneous detection of Levofloxacin (LFX) and Tryptophan (TRP) in dairy samples. Outstanding electrocatalytic activity for the oxidation of LFX and TRP is exhibited by the Activated Nanodiamond (AND) and Ti3AlC2max phase (Ti3AlC2max) nanocomposite-modified glassy carbon electrode (Ti3AlC2maxAND/GCE) featured in our sensor. High selectivity and sensitivity are achieved by the sensor, with limits of detection (LOD) of 20.47 nM and 0.309 μM for LFX and TRP, respectively. Moreover, strong anti-parasite capacity is demonstrated by the developed sensor, making it an excellent candidate for the establishment of a reliable sensing platform for antibiotic detection. Findings suggest that this novel sensor could serve as a valuable tool for monitoring the content of LFX and TRP in dairy samples and enhancing the safety of these products.

Published
2024
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