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2. Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification

Author

Hussain, Sadam, Ali, Mansoor, Naseem, Usman, Palomo, Beatriz Alejandra Bosques, Molina, Mario Alexis Monsivais, Abdala, Jorge Alberto Garza, Avalos, Daly Betzabeth Avendano, Cardona-Huerta, Servando, Gulliver, T. Aaron, and Pena, Jose Gerardo Tamez

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Rising breast cancer (BC) occurrence and mortality are major global concerns for women. Deep learning (DL) has demonstrated superior diagnostic performance in BC classification compared to human expert readers. However, the predominant use of unimodal (digital mammography) features may limit the current performance of diagnostic models. To address this, we collected a novel multimodal dataset comprising both imaging and textual data. This study proposes a multimodal DL architecture for BC classification, utilising images (mammograms; four views) and textual data (radiological reports) from our new in-house dataset. Various augmentation techniques were applied to enhance the training data size for both imaging and textual data. We explored the performance of eleven SOTA DL architectures (VGG16, VGG19, ResNet34, ResNet50, MobileNet-v3, EffNet-b0, EffNet-b1, EffNet-b2, EffNet-b3, EffNet-b7, and Vision Transformer (ViT)) as imaging feature extractors. For textual feature extraction, we utilised either artificial neural networks (ANNs) or long short-term memory (LSTM) networks. The combined imaging and textual features were then inputted into an ANN classifier for BC classification, using the late fusion technique. We evaluated different feature extractor and classifier arrangements. The VGG19 and ANN combinations achieved the highest accuracy of 0.951. For precision, the VGG19 and ANN combination again surpassed other CNN and LSTM, ANN based architectures by achieving a score of 0.95. The best sensitivity score of 0.903 was achieved by the VGG16+LSTM. The highest F1 score of 0.931 was achieved by VGG19+LSTM. Only the VGG16+LSTM achieved the best area under the curve (AUC) of 0.937, with VGG16+LSTM closely following with a 0.929 AUC score., Comment: The paper was accepted and presented in 3rd Workshop on Cancer Prevention, detection, and intervenTion (CaPTion @ MICCAI 2024)

Published
2024
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3. Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification

Author

Hussain, Sadam, Ali, Mansoor, Naseem, Usman, Bosques Palomo, Beatriz Alejandra, Monsivais Molina, Mario Alexis, Garza Abdala, Jorge Alberto, Avendano Avalos, Daly Betzabeth, Cardona-Huerta, Servando, Aaron Gulliver, T., Tamez Pena, Jose Gerardo, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ali, Sharib, editor, van der Sommen, Fons, editor, Papież, Bartłomiej Władysław, editor, Ghatwary, Noha, editor, Jin, Yueming, editor, and Kolenbrander, Iris, editor

Published
2025
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7. Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review

Author

Ali, Mansoor, Naeem, Faisal, Adam, Nadir, Kaddoum, Georges, Huda, Noor ul, Adnan, Muhammad, and Tariq, Muhammad

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

A modern-day society demands resilient, reliable, and smart urban infrastructure for effective and in telligent operations and deployment. However, unexpected, high-impact, and low-probability events such as earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust infrastructure more complex. As a result of such events, a power system infrastructure can be severely affected, leading to unprecedented events, such as blackouts. Nevertheless, the integration of smart grids into the existing framework of smart cities adds to their resilience. Therefore, designing a resilient and reliable power system network is an inevitable requirement of modern smart city infras tructure. With the deployment of the Internet of Things (IoT), smart cities infrastructures have taken a transformational turn towards introducing technologies that do not only provide ease and comfort to the citizens but are also feasible in terms of sustainability and dependability. This paper presents a holistic view of a resilient and sustainable smart city architecture that utilizes IoT, big data analytics, unmanned aerial vehicles, and smart grids through intelligent integration of renew able energy resources. In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed. Furthermore, a comparative review analysis of different data-driven machine learning techniques for sustainable smart cities is performed along with the discussion on open research issues and challenges.

Published
2023

8. Metaverse Communications, Networking, Security, and Applications: Research Issues, State-of-the-Art, and Future Directions

Author

Ali, Mansoor, Naeem, Faisal, Kaddoum, Georges, and Hossain, Ekram

Subjects
Computer Science - Cryptography and Security, Electrical Engineering and Systems Science - Systems and Control
Abstract

Metaverse is an evolving orchestrator of the next-generation Internet architecture that produces an immersive and self-adapting virtual world in which humans perform activities similar to those in the real world, such as playing sports, doing work, and socializing. It is becoming a reality and is driven by ever-evolving advanced technologies such as extended reality, artificial intelligence, and blockchain. In this context, Metaverse will play an essential role in developing smart cities, which becomes more evident in the post COVID 19 pandemic metropolitan setting. However, the new paradigm imposes new challenges, such as developing novel privacy and security threats that can emerge in the digital Metaverse ecosystem. Moreover, it requires the convergence of several media types with the capability to quickly process massive amounts of data to keep the residents safe and well-informed, which can raise issues related to scalability and interoperability. In light of this, this research study aims to review the literature on the state of the art of integrating the Metaverse architecture concepts in smart cities. First, this paper presents the theoretical architecture of Metaverse and discusses international companies interest in this emerging technology. It also examines the notion of Metaverse relevant to virtual reality, identifies the prevalent threats, and determines the importance of communication infrastructure in information gathering for efficient Metaverse operation. Next, the notion of blockchain technologies is discussed regarding privacy preservation and how it can provide tamper-proof content sharing among Metaverse users. Finally, the application of distributed Metaverse for social good is highlighted.

Published
2022

9. A comprehensive survey on recent deep learning-based methods applied to surgical data

Author

Ali, Mansoor, Pena, Rafael Martinez Garcia, Ruiz, Gilberto Ochoa, and Ali, Sharib

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Minimally invasive surgery is highly operator dependant with a lengthy procedural time causing fatigue to surgeon and risks to patients such as injury to organs, infection, bleeding, and complications of anesthesia. To mitigate such risks, real-time systems are desired to be developed that can provide intra-operative guidance to surgeons. For example, an automated system for tool localization, tool (or tissue) tracking, and depth estimation can enable a clear understanding of surgical scenes preventing miscalculations during surgical procedures. In this work, we present a systematic review of recent machine learning-based approaches including surgical tool localization, segmentation, tracking, and 3D scene perception. Furthermore, we provide a detailed overview of publicly available benchmark datasets widely used for surgical navigation tasks. While recent deep learning architectures have shown promising results, there are still several open research problems such as a lack of annotated datasets, the presence of artifacts in surgical scenes, and non-textured surfaces that hinder 3D reconstruction of the anatomical structures. Based on our comprehensive review, we present a discussion on current gaps and needed steps to improve the adaptation of technology in surgery., Comment: This paper is to be submitted to International journal of computer vision

Published
2022

10. A semi-supervised Teacher-Student framework for surgical tool detection and localization

Author

Ali, Mansoor, Ochoa-Ruiz, Gilberto, and Ali, Sharib

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Surgical tool detection in minimally invasive surgery is an essential part of computer-assisted interventions. Current approaches are mostly based on supervised methods which require large fully labeled data to train supervised models and suffer from pseudo label bias because of class imbalance issues. However large image datasets with bounding box annotations are often scarcely available. Semi-supervised learning (SSL) has recently emerged as a means for training large models using only a modest amount of annotated data; apart from reducing the annotation cost. SSL has also shown promise to produce models that are more robust and generalizable. Therefore, in this paper we introduce a semi-supervised learning (SSL) framework in surgical tool detection paradigm which aims to mitigate the scarcity of training data and the data imbalance through a knowledge distillation approach. In the proposed work, we train a model with labeled data which initialises the Teacher-Student joint learning, where the Student is trained on Teacher-generated pseudo labels from unlabeled data. We propose a multi-class distance with a margin based classification loss function in the region-of-interest head of the detector to effectively segregate foreground classes from background region. Our results on m2cai16-tool-locations dataset indicate the superiority of our approach on different supervised data settings (1%, 2%, 5%, 10% of annotated data) where our model achieves overall improvements of 8%, 12% and 27% in mAP (on 1% labeled data) over the state-of-the-art SSL methods and a fully supervised baseline, respectively. The code is available at https://github.com/Mansoor-at/Semi-supervised-surgical-tool-det, Comment: Paper accepted at Augmented Reality, Augmented Environments for Computer Assisted Interventions (AE-CAI), Computer Assisted and Robotic Endoscopy (CARE) and Context-Aware Operating Theaters (OR 2.0) at MICCAI 2022

Published
2022
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11. Out-of-hospital cardiac arrest in Bahrain: National retrospective cohort study

Author

Feras Husain Abuzeyad, Yasser Chomayil, Moonis Farooq, Hamid Zafar, Ghada Al Qassim, Emad Minwer Saad Albashtawi, Leena Alqasem, Naser Mohammed Ali Mansoor, Danya Adel AlAseeri, Ahmed Zuhair Salman, Muhammad Murad Ashraf, Maryam Ahmed Shams, Faisal Sami Alserdieh, Mustafa Ali AlShaaban, and Abdulla Fuad Mubarak

Subjects
Out of hospital cardiac arrest, Emergency medical services, Cardiopulmonary resuscitation, Bystander CPR, OHCA registry, Specialties of internal medicine, RC581-951
Abstract

Aim: There is limited research on Out-of-hospital cardiac arrest (OHCA) in the Gulf Cooperation Council (GCC) and especially in Bahrain. This is the first study to describe the incidence, characteristics, and outcomes of OHCA in Bahrain. Methods: This was a retrospective national observational study on OHCA patients in Bahrain using the Utstein framework for resuscitation. Data was collected between 1st July 2022 to 30th June 2023 from the electronic medical records of the only three governmental hospitals emergency departments (EDs) and National Ambulance (NA). Results: The annual incidence of OHCA attended by (Emergency Medical Services) EMS was nearly 21 per 100,000 population. The majority were males (n = 228, 68.8 %) with median age of 65 years (IQR=49–78). Most OHCA cases were witnessed (n = 265, 81 %), with (n = 247, 76 %) happened at home/residence. Rates for bystander CPR was low (n = 122, 36.8 %) and bystander automated external defibrillator (AED) was not performed in any of the cases. The OHCA cases transported by the NA was (n = 314, 94.8 %), with median response time of 9 min (IQR=7–12). However, only (n = 20, 6.0 %) were witnessed by EMS, and (n = 7, 2.1 %) received EMS defibrillation for shockable rhythms. First monitored rhythms included shockable rhythm in (n = 28, 8.5 %) versus non-shockable rhythm in (n = 303, 91.5 %). In the EDs, return of spontaneous circulation was achieved in (n = 60, 18.1 %) cases. But survival rate to hospital discharge at 30-day was (n = 4, 1.2 %) and survival rate to hospital discharge with good neurological outcomes was (n = 0, 0 %). Conclusion: In Bahrain the estimated annual incidence of OHCA is 21 individuals per 100,000 population, with a very low survival rate. Solutions should focus on community-level CPR and AED training, evaluating OHCA care provided by EMS, and establishing OHCA registry.

Published
2024
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13. Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

Author

Ali, Mansoor, Naeem, Faisal, Tariq, Muhammad, and Kaddoum, Geroges

Subjects
Electrical Engineering and Systems Science - Systems and Control, Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centralized training approach of artificial intelligence (AI), the use of mobile and wearable IoMT devices raises privacy concerns with respect to the information that has been communicated between hospitals and end users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to end users as only gradients are shared during training. For these specific properties of FL, in this paper we present privacy related issues in IoMT. Afterwards, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Subsequently, we present some practical opportunities of FL in smart healthcare systems. At the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems

Published
2022

16. English Language Teaching through a Short Story: A Technique for Improving Students' Vocabulary Retrieving

Author

Ali Mansoor, Ali Ahm, Mohammed, Othman Saleh Mahdy, Ahmed, Hazhar Ramadhan, Munasser Awadh, Awadh Nasser, Abdulfatah, Hameed Mohamm, and Sheikh, Ebrahim Yahay

Abstract

Vocabulary is the kernel for all language skills; it helps learners to interact with the speakers of that language. This study aims to scrutinize the effect of a short story on developing students' vocabulary. It is an out-product of fieldwork to figure out the useful points for teaching and learning vocabulary through short stories. The test was used to identify the effect of utilising the short story on vocabulary retention. The test was distributed to forty (40) students of Jalna College. The data of the study were collected from students after teaching them the short story for one month--three hours per week. The data was analyzed by using the SPSS software package. The overall results from the test indicated that the participants were very much impressed with the short story. The students successfully recalled most of the words used in the short story context. Some recommendations were suggested for the teachers to include short stories in their lessons and curriculum.

Published
2023
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22. A Survey of Saudi General Practitioners on the Use of Thromboprophylaxis Guidelines and Risk Assessment Tools in Atrial Fibrillation

Author

Mohammed Ibrahim Alnami, Ali Mansoor Alsalim, Ruwaida Faisal Alhakeem, Bushra Abdulrahman Al-Somali, Haitham Ali Bahkali, Hanaa Ali Alhabshi, Hailah Talaq Alotaibi, Rahma Abdulrazzaq Alqallaf, and Sheraz Ali

Subjects
thromboprophylaxis, atrial fibrillation, oral anticoagulants, stroke, Medicine (General), R5-920
Abstract

Clinical practice guidelines advise patients with atrial fibrillation who are at risk for stroke to undergo thromboprophylaxis with oral anticoagulants. However, it is noted that guidelines are not always followed. We sought to learn how Saudi Arabian general practitioners (GPs) self-reported using risk assessment tools and atrial fibrillation clinical practice guidelines created by cardiology associations, as well as how GPs felt about the resources that were available. Through the use of a self-administered questionnaire, we carried out a cross-sectional survey. A total of two-hundred GPs participated in the study. The guidelines were frequently used when a clinical decision regarding anticoagulation therapy appeared difficult (n = 57, 28.4%). The most predominant strengths of participants’ chosen clinical guidelines were clear recommendations (n = 56, 27.9%), easy-to-follow algorithms (n = 39, 16.9%), detailed recommendations supported by evidence (n = 34, 16.9%), and online availability (n = 27, 13.4%). Many respondents said they used a formal stroke risk assessment tool in addition to their clinical judgment as a GP for most decisions (60%). Most respondents preferred using the CHA2DS2-VASc (n = 106, 52.7%), CHA2DS2-VA (n = 45, 22.4%), CHADS2 (n = 35, 17.4%), and GARFIELD (n = 14, 7.0%). HAS-BLED (n = 100, 49.8%) and HEMORR2HAGES (n = 50, 24.9%) were the most frequently utilized formal tools for assessing the risk of bleeding among GPs. Over half of the participants referred to guidelines when deciding thromboprophylaxis in patients with atrial fibrillation. Additionally, many respondents used formal procedures for assessing the risks of bleeding and stroke in addition to their clinical judgement in their roles as GPs. The guideline was assessed as being extremely helpful overall by GPs who used it to make thromboprophylaxis decisions.

Published
2023
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23. Secure Key-Based Substitution-Boxes Design Using Systematic Search for High Nonlinearity

Author

Amjad Hussain Zahid, Hafiz Ali Mansoor Elahi, Musheer Ahmad, Louai A. Maghrabi, and Ramy Said Agieb Said

Subjects
Key-based substitution-box, chaotic map, cryptosystem, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The significance of data security has become more critical due to the ever-changing goals and capabilities of attackers. As a result, many cryptosystems employing diverse approaches are being developed to safeguard sensitive data. A Substitution box (S-box) plays a vigorous role in modern cryptosystems because of its credence for inducing confusion during the encryption process and ultimately protecting the data. Currently, chaotic maps are being developed and widely employed to yield S-Boxes as the use of chaotic maps aids in the randomness and resistance to mitigate many cryptanalytic attacks. In this paper, a novel chaotic map and an inventive systematic search method are proposed for the generation of key-dependent dynamic and highly nonlinear S-boxes. A variety of standard cryptographic tests such as fixed-point analysis, nonlinearity (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), linear probability (LP), differential probability (DP), etc.) are applied to assess and analyze the cryptographic strengths of S-boxes generated using the proposed method. The findings from experimental and comparative analyses show that the proposed S-box provides stronger and better cryptographic features (no fixed point, no opposite fixed point, average NL = 111.75, SAC offset = 0.0000, BIC-NL = 103.9, LP = 0.125, and DP = 0.039) than many of the existing S-boxes studies presented in recent years. Hence, the proposed S-box construction technique has a lot of potential and genuine prospects for its utilization in cryptographic applications to protect sensitive data from attackers.

Published
2023
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26. Assessment of stress and physical activity in male medical students.

Author

Kaur, Harminder, Mann, Navpreet, Ali, Mansoor, and Chaudhary, Pragyashaa

Subjects
STUDENT health, PERCEIVED Stress Scale, COLLEGE student adjustment, HIGH-salt diet, INTERPERSONAL relations
Abstract

Background: Transition to university, academic performance pressure, lack of sleep, personal relationships, and poor coping skills are the common sources of stress for undergraduate students. The health of students can be negatively impacted by high-energy, high-salt diets, irregular diets, and insufficient physical activity (PA), which can result in overweight and obesity. Aims and Objectives: To find out the relationship between PA and stress among male medical students. Materials and Methods: The study was conducted on 60 male undergraduate students in the age group 18-21 years of 1st professional MBBS. After taking a detailed history body mass index (kg/m2) was calculated. Then subjects were asked to fill out the perceived stress scale and global PA questionnaire to assess their stress and PA status. Results: In the present study there is a statistically significant relationship between the stress score and the PA. This shows that those students who were leading a more active daily or weekly schedule were exhibiting lower scores on the stress scale tool employed. Conclusion: The study revealed a highly significant (P < 0.001) correlation between the stress level and PA status of the students. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Heterogeneous Dynamic Hybrid Algorithm in Cloud Computing to Load Balance to Improve Cloud Server Speed Efficiency

Author

Al-Khulaidi, Abdualmajed A. G., Ali, Mansoor N., Hazaa, Muneer A. S., Mohammed, A. A., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Silhavy, Radek, editor, Silhavy, Petr, editor, and Prokopova, Zdenka, editor

Published
2021
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32. The predictive power of stock micro-blogging sentiment in forecasting stock market behaviour

Author

Al Nasseri, Alya Ali Mansoor and De Cesare, S.

Subjects
332.64, Sentiment, Data mining, Classification algorithms, Financial market, Behavioral finance
Abstract

Online stock forums have become a vital investing platform on which to publish relevant and valuable user-generated content (UGC) data such as investment recommendations and other stock-related information that allow investors to view the opinions of a large number of users and share-trading ideas. This thesis applies methods from computational linguistics and text-mining techniques to analyse and extract, on a daily basis, sentiments from stock-related micro-blogging messages called “StockTwits”. The primary aim of this research is to provide an understanding of the predictive ability of stock micro-blogging sentiments to forecast future stock price behavioural movements by investigating the various roles played by investor sentiments in determining asset pricing on the stock market. The empirical analysis in this thesis consists of four main parts based on the predictive power and the role of investor sentiment in the stock market. The first part discusses the findings of the text-mining procedure for extracting and predicting sentiments from stock-related micro-blogging data. The purpose is to provide a comparative textual analysis of different machine learning algorithms for the purpose of selecting the most accurate text-mining techniques for predicting sentiment analysis on StockTwits through the provision of two different applications of feature selection, namely filter and wrapper approaches. The second part of the analysis focuses on investigating the predictive correlations between StockTwits features and the stock market indicators. It aims to examine the explanatory power of StockTwits variables in explaining the dynamic nature of different financial market indicators. The third part of the analysis investigates the role played by noise traders in determining asset prices. The aim is to show that stock returns, volatility and trading volumes are affected by investor sentiment; it also seeks to investigate whether changes in sentiment (bullish or bearish) will have different effects on stock market prices. The fourth part offers an in-depth analysis of some tweet-market relationships which represent an open problem in the empirical literature (e.g. sentiment-return relations and volume-disagreement relations). The results suggest that StockTwits sentiments exhibit explanatory power in explaining the dynamics of stock prices in the U.S. market. Taking different approaches by combining text-mining techniques with feature selection methods has proved successful in predicting StockTwits sentiments. The applications of the approach presented in this thesis offer real-time investment ideas that may provide investors and their peers with a decision support mechanism. Investor sentiment plays a critical role in determining asset prices in capital markets. Overall, the findings suggest that investor sentiment among noise traders is a priced factor. The findings confirm the existence of asymmetric spillover effects of bullish and bearish sentiments on the stock market. They also suggest that sentiment is a significant factor in explaining stock price behaviour in the capital market and imply the positive role of the stock market in the formation of investor sentiment in stock markets. Furthermore, the research findings demonstrate that disagreement is not only an important factor in determining trading volumes but it is also considered a very significant factor in influencing asset prices and returns in capital markets. Overall, the findings of the thesis provide empirical evidence that failure to consider the role of investor sentiment in traditional finance theory could lead to an imperfect picture when explaining the behaviour of stock prices in stock markets.

Published
2016

33. Motivations and attitudes towards learning English in Pakistan : a mixed-methods study of urban-rural postgraduate learners' motivations and attitudes towards studying English at a public university in the Khyber Pakhtunkhwa province

Author

Ali, Mansoor, Saraceni, Mario, Wyatt, Mark, and Van Laar, Darren

Subjects
428.0071
Abstract

Postgraduate learners are not only important for contributing to the development of Pakistan, but their age and exposure to English has the potential to reveal interesting motivations and attitudes towards learning English when compared to lower-level learners. This thesis investigates the motivations and attitudes toward learning English in rural-urban contexts of postgraduate (MA/MSc) non-major English as Second Language (ESL) students in Pakistan. This thesis addresses three main objectives. First, to identify postgraduate learners' motivational orientations toward learning English and their attitudes toward English as a World Language (EWL); second to determine whether the learners' gender has an effect on their attitudes toward EWL; third to identify learners' perceptions about the de-motivational factors related to impeding their successful English learning in the classroom. This study employed a concurrent mixed-methods design. The data collected from a remote public university in the developing province of Khyber Pakhtunkhwa in Pakistan, with the survey completed by 500 students, and the semi-structured interviews were conducted on 26 postgraduate students. The quantitative data were analysed using factor, correlation, and regression analyses and t-tests. The qualitative data were analysed using thematic analysis. In contrast to earlier studies, this study identified that learners exhibit a wide range of L2 motivational orientations. The findings confirmed the newly-discovered ‘National Interest’ orientation in its extended form categorised as 'National-Islamic Interests, and confirmed Dörnyei's recent Ideal L2 self in this context, and also points toward the emergence of the rarely noted 'L2 Indigenous Integrative’ orientation, related to using English for local purposes. Similarly, the other rarely noted orientations of 'EWL as a L2 motivation', 'Family Interests', and 'Use of English for Voicing Females' Rights' emerged in the study. The traditional concept of L2 Integrative did not emerge, which confirms findings from studies such those by Lamb (2004) suggesting that L2 Integrative may be unattainable in a globalizing world. The findings also highlighted four aspects of the learners' positive attitudes towards EWL: 'attitudes toward the non-native varieties of English', ‘attitudes toward English as a main source of global communication', ‘attitudes toward the use of English as a tool of cross-cultural communication', and ‘attitudes toward the supremacy of native speakers and their Englishes'. The study revealed that learners' attitudes toward EWL had a positive correlation with learners’ motivational orientations. In general, learners' gender did not have an impact on their attitudes toward EWL. However, females appeared to hold more positive beliefs in the supremacy of native speakers and their use of English. Finally, the three main sources of learners' de-motivation: teacher-related factors (methods of teaching, behaviour, etc.), learner-related factors (feelings of L2 anxiety and reduced confidence), and factors related to the classroom environment and facilities are highlighted. On the basis of all findings, theoretical implications, suggestions for language policymakers and recommendations for further research are provided in Pakistan.

Published
2016

34. INTERDISCIPLINARY PERSPECTIVES ON CULTURAL IDENTITY AND ACCULTURATION IN THE CONTEXT OF GLOBAL MIGRATION.

Author

Tullah, Masoud Bashar, Ali, Mansoor, and Surin, Sanjay

Subjects
CULTURAL identity, CULTURAL pluralism, ACCULTURATION, IDENTITY (Psychology), EMIGRATION & immigration, SOCIAL support, DISCRIMINATION (Sociology), CONSCIOUSNESS raising, SOCIAL cohesion, SOCIAL justice
Abstract

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2024
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39. Healthcare providers’ perspectives on family presence during resuscitation in the emergency departments of the Kingdom of Bahrain

Author

Feras H. Abuzeyad, Ahmed Elhobi, Wael Kamkoum, Luma Bashmi, Ghada Al-Qasim, Leena Alqasem, Naser Mohamed Ali Mansoor, Stephanie Hsu, and Priya Das

Subjects
Resuscitation, Health policy, Emergency medicine, Family presence, FPDR, Middle East, Special situations and conditions, RC952-1245, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

Abstract Background Worldwide, policies exist on family presence during resuscitation (FPDR), however, this is still lacking in the Gulf Corporation Countries (GCC) in general and in the Kingdom of Bahrain in particular. The aim of this study is to assess the perspectives of healthcare providers (HP) on FPDR among those working in the emergency departments (EDs) in the Kingdom. Methods A self-administered anonymous electronic survey was collected from 146 HPs (emergency physicians and nurses) working in the three major EDs in the Kingdom of Bahrain. Besides demographic data, 18 items measuring HPs’ perceptions of FPDR were generated using the 5-point Likert scale. Results Surveys (n = 146) from physicians and nurses were analysed (45.9% vs. 54.1%, respectively). There were significant differences between physicians and nurses in terms of personal beliefs, FPDR enhancing professional satisfaction and behaviour, and the importance of a support person and saying goodbye (p

Published
2020
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42. Frequency Of Functional Outcome In Management Of Post Burn Neck Contractures.

Author

Memon, Sidra, Ali, Mansoor, Rasheed, Sadia, Bahatti, Aisha, and Naz, Samina

Subjects
*FUNCTIONAL status, *PATIENT satisfaction, *NECK, *MEDICAL sciences, *PLASTIC surgery
Abstract

Objective: To determine the frequency of functional outcomes in managing post-burn neck contractures. Descriptive study. At the department of plastic surgery. Methods: A total of 150 cases presenting from Liaquat University of Medical and Health Sciences with any degree of post-burn neck contracture admitted to the plastic surgery unit and fulfilling the inclusion criteria, were included in the study. A detailed clinical and radiological examination was carried out. Before the interventional procedure was instituted, the cause of burn injury and functional grade of neck contracture was documented. A follow-up of 6 months was done to evaluate the range of neck motion and postoperative improvement in neck extension to document patient satisfaction. All data was recorded on a predesigned proforma. Results: The average age of the patient was 29.67±7.67 years. Out of 150, 96 (64%) patients showed satisfactory outcomes, 52 (34.67%) had good outcomes and 2 (1.33%) patients were poor outcomes. Conclusion: After oral intubation and SSG resurfacing of the raw region, it can be stated that contracture release under tumescent anaesthesia is a successful treatment for individuals with post-burn contractures of the neck. [ABSTRACT FROM AUTHOR]

Published
2024
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46. DIAGNOSTIC ACCURACY OF MAGNETIC RESONANCE SPECTROSCOPY AND DIFFUSION WEIGHTED IMAGING IN GRADING OF INTRACRANIAL GLIOMAS TAKING HISTOPATHOLOGY AS GOLD STANDARD

Author

Nawaz Rashid, Iqra Sarwar, Ali Mansoor, Iqbal Hussain Dogar, Amina Hameed, and Maria Tanveer

Subjects
General Economics, Econometrics and Finance
Abstract

Objectives: To find diagnostic accuracy of magnetic resonance spectroscopy and magnetic resonance diffusion in grading of gliomas with histopathology as gold standard. Methods: It is a Cross Sectional Study conducted at Department of Radiology, Mayo Hospital, Lahore. Duration was 06 months i.e Sep, 2018 to Mar, 2019. One hundred patients fulfilling selection criteria were enrolled in the study. Then all patients underwent MRI including conventional sequences as well as DWI and MRS using a spin echo sequence (SE) with short TE (35 mm/s) and long TE (144 mm/s). Patients were labeled as positive or negative (as per operational definition). Subsequently patients underwent biopsy under general anesthesia. Reports were assessed and patients were confirmed as positive or negative according to operational definition. All of this information was recorded in a predesigned Performa. Results: Total number of patients in the study were one hundred. Mean age of the patients were 38.61(years) +11.40 whereas there were 43 (43.0) male and 57 (57.0) female patients included in the study. The diagnostic accuracy, sensitivity, specificity, PPV and NPV of magnetic resonance spectroscopy was 84.00%, 89.86%, 70.97%, 87.32% and 75.86% respectively whereas diagnostic accuracy, sensitivity, specificity, PPV and NPV of diffusion weighted imaging was 75.00%, 78.26%, 67.74%, 84.38% and 58.33% respectively. Conclusion: The study concluded that diagnostic accuracy of MRS is more helpful and reliable than DWI in grading of gliomas taking histopathology as gold standard. Future studies at multiple setups must be conducted which could help in deciding whether to rely on MRS or DWI for determination of gliomas grading.

Published
2023

47. Toward 5G High Utilizations: A survey on OFDMA-based Resource Allocation Techniques in Next-Generation Broadband Wireless Access Networks

Author

Ibrahim Al-Surmi, Ali Mansoor, and Aqeel Ahmed

Subjects
wireless communication, packet scheduling, resource allocation, lte, qos, Telecommunication, TK5101-6720
Abstract

LTE is marketed as 4G standard for wireless communication services. LTE network technologies provide a high speed triple play services like data, voice, and video with QoS provision. The resource allocation techniques primary concerns are the QoS support that aims to satisfy the diverse service requirements as well as to guarantee the required data rates from the available resources. In this context different network architecture for broadband wireless data network like LTE made OFDMA a practical access technology for wireless multimedia services. Therefore, this survey investigates the techniques that aim to support QoS for real-time and non real-time services in OFDMA-based packet scheduling and adaptive multiuser frequency-time domain resource allocation. Moreover, this survey addressed the different aspects of resource allocation algorithms design to be contributed to the emerging 5G next generation networks.

Published
2021
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49. EFFICIENT in vitro PROPAGATION OF Amaranthus viridis L. USING NODE EXPLANTS

Author

Tour Jan, Ikram Ullah, Bilal Muhammad, _ Tariq, Ali Mansoor, Zaheer Ullah, and Muhammad Asif Nawaz

Subjects
agar, Amarinthus viridis, HgCl2, hyperhydricity, pH, subcultures, Biochemistry, QD415-436, Plant culture, SB1-1110, Science
Abstract

Hyperhydricity is a frequently problem in plants during in vitro culture and affected micropropagation of plants. To develop an efficient in vitro regenerated system without hyperdydricity, we demonstrated the effect of different disinfected agents (mercuric chlorite and hypochlorite), growth regulators, their concentrations and combinations, Agar, pH, ammonium nitrate (NH4NO3) and number of subcultures. Mercuric chlorite at 0.07% and exposing time (9–10 min) was appropriate for hygienic culture. The shoots induced by Benzyladnine (BA) alone or in combination with α-Naphthaleneacetic acid (NAA) exhibited maximum multiplication with symptoms of hyperhydricity than those induced by Kinetin alone or in combination with NAA. Hyperhydricity was also reduced by increasing the concentration of agar, pH and elimination of NH4NO3 from the macroelements of Murashig and Skoog (MS) medium. Repeated subcultures affected both multiplication and hyperhydricity. The multiplication of shoots increased from parental culture up to 5th subculture and thereafter declined in 6th subculture. Although shoot hyperhydricity were observed from 1st subculture (19%) and then increased up to 85% in 6th subculture. This increased in hyperhydricity could be due to the remaining influence of hormones. In shoots of 5th subculture the content of chlorophyll (dark green) were higher than shoots of 6th subculture.

Published
2020
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50. Adiponectin may be used as a marker in prediction of diabetic retinopathy patients

Author

Zahra Saad Hatef, Shaymaa Zahraw Nada, and Ali Mansoor Jasim

Subjects
Medicine
Abstract

Objective To investigate the validity of serum adiponectin to differentiate diabetic retinopathy and to evaluate the relation between the tumor necrosis factor-alpha (TNFα) and adiponectin in this disease. Methods A total of 120 persons were included in the study, 80 diabetic patients, which divided into two groups, 40 patients were with diabetic retinopathy and 40 patients without diabetic retinopathy and 40 healthy control subjects. Results There was no significant difference in the age, BMI and gender between the studied groups. Serum levels of TNFα pg/ml was significantly higher in diabetic patients with retinopathy compared to without retinopathy and both groups were significantly highercompared to control, adiponectin ng/ml was significantly lower in diabetic patients with retinopathy compared to without retinopathy and both groups were significantly lower compared to control. In the control group, adiponectin inversely and significantly correlated with age, waist circumference, triglyceride, cholesterol and LDL. Both adiponectin and TNFα have excellent ability to discriminate between diabetic patients from normal to control. But only adiponectin found to have the ability to discriminate diabetic retinopathy from diabetic without retinopathy. Conclusion We found decrease serum level of adiponectin in diabetic retinopathy patients and increase level of TNFα in this patients in compared with diabetic only and control subjects.

Published
2018

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