147 research outputs found
Biography of Dr Ishrat Hussain: IBA organises book launch ceremony
No full textThis article is about the book look launch ceremony that was held for Dr. Ishrat Husain for his biography authored by Dr. Sibtain Naqvi, Unravelling Gordian Knots – The Work and Worlds of Dr Ishrat Hussain, at The IBA City Campus. Esteemed speakers including, Dr Hussain, Dr S Akbar Zaidi, Executive Director, IBA, Dr Syed Noman ul Haq, Dean, UMT, Lahore and Sibtain Naqvi, book author, were invited to the stage
Incidence of New-Onset Cardiac Complications among Requiring Invasive Mechanical Ventilation: Findings from a Retrospective Observational Study
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Ups and downs of COVID-19: can we predict the future? Local analysis with Google Trends for forecasting the burden of COVID-19 in Pakistan
No full textBackground. We aim to study the utility of Google Trends search history data for demonstrating if a correlation may exist between web-based information and actual coronavirus disease 2019 (COVID-19) cases, as well as if such data can be used to forecast patterns of disease spikes. Patients & methods. Weekly data of COVID-19 cases in Pakistan was re¬trieved from online COVID-19 data banks for a peri¬od of 60 weeks. Search history related to COVID-19, coronavirus and the most common symptoms of dis¬ease was retrieved from Google Trends during the to analyze the correlation between the two data sets. Search terms were adjusted for time-lag over weeks, to find the highest cross-correlation for each of the search terms. Results. Search terms of ‘fever’ and ‘cough’ were the most commonly searched online, followed by corona¬virus and COVID. The highest peak correlations with the weekly case series, with a 1-week back¬log, was noted for loss of smell and loss of taste. The combined model yielded a modest perfor¬mance for forecasting positive cases. The linear regression model revealed loss of smell (adjust¬ed R2 of 0.7) with significant 1-week, 2-week and 3-week lagged time series, as the best predictor of weekly positive case counts. Conclusions. Our local analysis of Pakistan-based data seem¬ingly confirms that Google trends can be used as an important tool for anticipating and predict¬ing pandemic patterns and pre-hand prepared¬ness in such unprecedented pandemic crisis
Dr Ishrat Husain calls for empowerment of local govts
No full textThis news piece is about the launch and subsequent panel discussion on ‘Unravelling Gordian Knots: The Works and Worlds of Dr. Ishrat Husain’ held at the Institute of Business Management (IoBM) attended by IoBM President Talib Karim, Javed jabbar, Dr. Mehtab Karim, the author Sibtain Naqvi and Dr. Ishrat Husain himself
Exploiting statistical side information to optimize secondary spectrum access
No full textUnlicensed access of bands in the wireless spectra, that have been left under-utilized by the primary (or licensed) users, is the subject that addressed throughout this dissertation. Unlicensed users (or commonly known as secondary users) attempt to efficiently utilize these bands by exploiting the underlying spatial, spectral and temporal opportunities. We demonstrate that partial or complete knowledge of primary activity, transceiver locations and channel gains can be incorporated into the design of secondary access strategies to improve their throughput performance. Surveys have shown that most of the bands auctioned to primary networks are under-utilized. To resolve the issue of spectrum scarcity, the idea of secondary access to the under-utilized bands has gained popularity over the years. Secondary networks are opportunistic in their attempts of acquiring resources, i.e. they search and acquire resources that are not in use by the primary network, and are expected to release them as primary transmissions resume. The quality-of-service of the primary system, and its throughput requirements supersede those of a secondary system. A logical consequence of a secondary system design is the efficiency of grasping and using under-utilized spectral opportunities.
Optimizing channel acquisition procedures of SUs for opportunistic access, by incorporating side information, is the essence of the contributions presented throughout this dissertation. Conventional sensing systems prove to be inadequate when it comes to streamlined secondary procedures. Firstly, we optimize a single SU's decision making process that directly impacts the achievable capacity of secondary and primary systems. This decision is based on a threshold, and by incorporating spatial (geographical) side information, the capacity maximization objective is achieved. This dissertation further shows that partial or complete side information allows systems to break away from the conventional methods of channel selection. Typically, channels are defined by the primary activity on them, and are pursued by SUs in the same discrete manner. We show that if side information regarding interference is made available, this discrete method of channel selections becomes suboptimal. By relaxing the discrete constraints of the channel selections, and allowing users to select any band of frequencies, the contention and thus the interference among multiple SUs can be manipulated in a continuous manner. Moving on to a multi-SU system, we shift our focus on the contentions among SUs that play a pivotal role in adversely affecting the transmission efficiency. These contentions result when multiple SUs, in a non-cooperative manner try to access premium frequency resources. Cooperative spectrum access methods minimize contention among SUs by performing orthogonal channel selections, for SUs. The extent of cooperation among SUs may be bounded by system constraints, but the operational conflict-minimization of these SUs can be improved. It has been shown that the thresholds, if carefully selected before sensing begins, can strike an optimal trade-off between premium-channel acquisition and contention minimization. We achieve this goal by modelling the throughput maximization problem to incorporate spatial and channel side information, if made available. Furthermore, a distributed sensing order selection approach is also proposed. Finally, this dissertation also considers the secondary systems that allow SUs to sense multiple channels within a frame, before they can acquire any one of them for transmissions. The order or sequence in which the SUs perform sensing affects the contention among them. We show that if location side information is utilized in the design of sensing orders, the contentions among the SUs is minimized. Without side information, the SU system design has to rely on assumptions regarding the detection outcomes that result in a waste of valuable spatial opportunity. Numerical results and analytical proofs presented throughout this dissertation advocate our thesis of incorporating partial or complete side information for maximal exploitation of the spatial and spectral opportunities left unused by the primary networks.DOCTOR OF PHILOSOPHY (EEE
Optimal and Economic Programming in a Reconfigured Competitive Electricity Market Considering Dispersed Generation Sources with the Firefly Algorithm
No full textNowadays, the majority of electric energy demand in different countries is supplied by using fossil fuels. As fossil energies are non-renewable and cause environmental pollution, the use of renewable energy sources (RES) is essential for supplying electric energy. Due to the fluctuations in RES, dispersed generation (DG) units are used as microgrids (MGs) to prevent problems in supplying the energy demanded by customers. Here, a novel two-phase method is proposed to simultaneously find the optimal location and operation of DGs. In phase 1, the DG location problem is formulated as a multi-objective problem, aiming to reduce active power losses, improve voltage profile, and increase voltage. This multi-objective problem is solved by using the firefly optimization algorithm, and the optimal DG location is determined. In phase 2, the revenues of DG owners and the total payment of the distribution network (DN) are calculated. The optimal sales prices of the units are also calculated by the game theory. The proposed method is implemented on a 33-bus system in MATLAB, and its results are compared with PSO and GA results to demonstrate the efficiency
Foregrounding through Lexical Deviation: A Corpus-Based Analysis of Yousafi’s Aab-e-Gum
No full textThis paper provides a corpus-based analysis of lexical deviation as a foregrounding technique in Yousafi’s prose fiction Aab-e-Gum. The use of unusual and uncommon language imparts a strong impression on the readers’ minds. This distinguished use of language diverges from the literary conventions maintaining a dominant structure in a text (Leech, 1969) and is known as deviation. The theoretical and conceptual grounds for this work are Ross' (1998) structural incongruity theory and Leech and Short's (2007) conceptions of deviation in literary texts. The corpus for this research is the Urdu text Aab-e-Gum. The data is tagged using the UAM Corpus tool (UAMCT3). The system is used to manually tag all occurrences of deviation across various linguistic levels and sub-levels in the first stage. The more frequent lexical divergence is investigated in the second stage. It is discovered that the author used lexical variation as a foregrounding strategy to create novel Urdu terms. His use of lexical variation serves to both heighten the event being portrayed and to entertain the audience. Future work on different linguistic levels can be done in depth. This study is a first step in assisting linguistic researchers working on the Urdu language
Data mining for prothrombin time and international normalized ratio reference intervals in children
No full textReference intervals (RIs) help physicians in differentiating healthy from sick individuals. The prothrombin time (PT) and International normalized ratio (INR) fluctuate in coagulation pathway defects and have interlaboratory variability due to the instrument/reagent used. As direct method is difficult in children, we chose an indirect data mining method for the determining PT/INR RIs. The indirect method overcomes the substantial financial and logistic challenges, and ethical restrictions in children, moreover, allows partitioning in more fine-grained age groups. Prothrombin Time/INR measurements performed in patients aged birth-18 years between January 2013 and December 2020, were retrieved from laboratory management system of the Aga Khan Hospital. Reference intervals were computed using an indirect KOSMIC algorithm. The KOSMIC package function on the assumption that the non-pathologic samples follow a Gaussian distribution (after Box-Cox transformation of the data), following an elaborate statistical process to isolate distribution of physiological samples from mixed dataset. A total of 56,712 and 52,245 values were retrieved for PT and INR respectively. After the exclusion of patients with multiple specimens obtained during the study period, RIs were calculated for 37,356 (PT) and 37,192 (INR) children with stratification into 9 age groups. A comparison of 2.5th and 97.5th percentile results with those of established RIs from SickKids Handbook of Pediatric Thrombosis and Hemostasis demonstrated good agreement in between different age groups. This study supports data mining as an alternate approach for establishing PT/INR RIs, specifically in resource-limited settings. The results obtained are specific to studied population and instrument/reagent used. The study also allows understanding of fluctuations in coagulation pathways with increasing age and hence better clinical decision-making based on PT and INR results
Understanding purchase intention towards eco-friendly clothing for generation Y & Z
No full textGreen consumption and pro-environmental behaviour have attracted considerable attention from academic marketing scholars. From the South Asian perspective, investigation on environmental awareness, social recognition, and self-image building through green consumption is very limited, particularly for young generations. Therefore, this study seeks to comprehend the motives of consumers that belong to generations Y & Z towards green apparel purchases. The theoretical base integrates the (TBP) Theory of Planned Behaviour with three additional constructs of Environmental Apparel knowledge (EAK), Social Status (SS), Green self-concept (GSC). An online survey of 347 consumers belonging to generation Y & Z was conducted. For determining measurement and structural models, Structural Equation Modelling (SEM-PLS) was employed. The results indicated that Environmental Apparel Knowledge (EAK) and Green Self-concept (GSC) positively impact attitude towards green apparel, subjective norms (SN), perceived behavioural control (PBC), and purchase intention towards green apparel. In contrast, Social Status (SS) only impacts subjective norms. Moreover, mediation analysis showed that attitude mediated all relationships between Environmental Apparel knowledge (EAK), Social Status (SS), Green self-concept (GSC), and purchase intention towards green apparel. However, perceived behavioural control only mediated Green self-concept (GSC) and purchase intention, while subjective norm did not mediate any relationship. The study contributes to the existing literature by examining young green consumers' specific personal and social values. It highlighted the role of knowledge about environmental concerns in designing purchase intention strategies for emerging countries. Practical implications for marketers and policymakers were presented
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