1,582 research outputs found
The Folio
No full textEditorial. pp. 6; Mian Khalid Javed-The News. pp. 7-11; Tebbe, R. F.-Article-Education and National Development. pp. 12-22; Karim Nawaz-Article-Muslim Contribution to Knowledge. pp. 23-35; Ratliff, Kathy-Article-Reflections of Henry II on the Eve of his Audience with the Pope. pp. 36-41; Hamid, S. A.-A Man of Intellect. pp. 42-45; Wahid Khan-Article-Islam and Socialism. pp. 46-48; Mian Khalid Javed-Interview-Forty minutes with the Principal. pp. 49-52; Qamar Parvaiz Raj-Article-The Psychological Analysis of Grief and its Remedy. pp. 53-55; Mobashir Salah-ud-Din-Story-A Story with a Moral. pp. 56-57; Travellers. pp. 58; Majid Rafique Mir-Story-An Anti Hero. pp. 59-62; Tariq Baseep Shamsi-Liars the Best Entertainers. pp. 63-65; Omar Yusuf Dar-Story-Travelling Third Class. pp. 66-68; Mian Khalid Javed-The Secondary Union. pp. 69; Muhammad Nisar-A Love Letter in Mathematical Terms. pp. 70; Azam Gill-Memories. pp. 71; Zahid Jamil Khan-The Dreamland. pp. 72; Bashir Mahmud Bajwa-Poetry-And I Long for his Mercy. pp. 73; Omar Yusuf Dar-Poetry-The Soul of Night. pp. 74; Mir, M. Rafiq-Poetry-Tongue in Cheek. pp. 74; Majid Rafique Mir-Poetry-Tempest. pp. 75; Khalid S. Shamas-ud-Din-Poetry-Weekend. pp. 76; Syed Hussain Riaz-Poetry-Of War and Peace. pp. 77; The Folio [Urdu/Punjabi] 135 p.Dr R. F. Tebbe, Principal of F. C. College, Lahore. before contents; Sports Council, F. C. College - 1969-70. after contents; Department of Physical Education, F. C. College - 1969-70. 1 page after contents; The Folio, Board of Editors, F. C. College - 1969-70. 2 pages after contents; Muhammad Akram Sheikh, Chief Student Editor. before editorial; Cabinet Members of the Students Union, F. C. College, 1969-70. after page 68; F. C. College Secondary Union, 1969-70. before page 6
Enhancing Smart City Functions through the Mitigation of Electricity Theft in Smart Grids: A Stacked Ensemble Method
No full textSmart grid is the primary stakeholder in smart cities integrated with modern technologies as the Internet of Things (IoT), smart healthcare systems, industrial IoT, renewable energy, energy communities, and the 6G network. Smart grids provide bidirectional power and information flow by integrating many IoT devices and software. These advanced IOTs and cyber layers introduced new types of vulnerabilities and could compromise the stability of smart grids. Some anomalous consumers leverage these vulnerabilities, launch theft attacks on the power system, and steal electricity to lower their electricity bills. The recent developments in numerous detection methods have been supported by cutting-edge machine learning (ML) approaches. Even so, these recent developments are practically not robust enough because of the limitations of single ML approaches employed. This research introduced a stacked ensemble method for electricity theft detection (ETD) in a smart grid. The framework detects anomalous consumers in two stages; in the first stage, four powerful classifiers are stacked and detect suspicious activity, and the output of these consumers is fed to a single classifier for the second-stage classification to get better results. Furthermore, we incorporate kernel principal component analysis (KPCA) and localized random affine shadow sampling (LoRAS) for feature engineering and data augmentation. We also perform comparative analysis using adaptive synthesis (ADASYN) and independent component analysis (ICA). The simulation findings reveal that the proposed model outperforms with 97% accuracy, 97% AUC score, and 98% precision
Stacked machine learning models for non-technical loss detection in smart grid: A comparative analysis
No full textThe growing prominence and emphasis of renewable energy to decrease carbonization in the power system and reduce the dependability of fossil fuel for energy needs play an important role in the development of smart grids. Many technological advancements are integrated into smart grid to optimize the power system and renewable energy sources. Smart grid leverages electricity and energy consumption data exchange to establish a significantly advanced, automated, and decentralized electricity network. However, this brings numerous vulnerabilities to the power system, including cyber-attacks, grid blackouts, and electricity theft. While the most significant concern is energy theft, where some culprit's consumers manipulate their energy meters to reduce their readings. This destabilizes the country's electricity utility and economic development and causes a high tariff on energy for consumers who pay the bill. Therefore, developing an advanced framework for electricity theft detection is necessary. To address this problem, we propose a machine learning-based stacked framework to detect malicious activity in the smart grid. The proposed data-based stacked ensemble model detects honest and anomalous consumers in two stages. In the first stage, the model employs four individual classifiers at the base level to analyze data and a single classifier at the meta-level to classify the results of the base learners for the second stage classification. Furthermore, the Borderline SMOTE and Principle Component Analysis techniques are employed to address the class imbalance and curse of dimensionality issues respectively. Through experimental analysis, we proved the effectiveness of the proposed framework in detecting suspicious activity in four different experiments, including preprocessed data, feature extracted data, balanced data, and lastly, both feature engineering and data balancing. The simulation outcomes demonstrate that our proposed framework enhanced energy security and overcomes the impact of theft attacks on the smart grid
Intelligent Renewable Energy Agent‐Based Distributed Control Design for Frequency Regulation and Economic Dispatch
No full textThe Distributed Renewable Energy Sources (DRESs) integrate hybrid microgrid and prosumer activities that constitute a dynamic system characterized by unknown network parameters. The dynamic system faces challenges, such as intermittent power supply due to low inertia, renewable intermittence, plug-and-play prosumer activities, network topology variations, and a lack of constraint handling. These complexities pose significant issues in designing effective control for frequency regulation and consensus-based economic load dispatch (ELD) within DRES to meet varying load demands. To address the above challenges, this research employs a machine learning-based distributed multiagent consensus design that offers a rapid and robust approach, mitigating the limitations associated with the Distributed Average Integral (DAI) control design. The proposed multiagent scheme empowers the successful implementation of ELD and frequency regulation, accommodating the intermittent DRES, diverse network topologies, and the dynamic plug-and-play activities of prosumers. Moreover, an optimization-based DAI tuning model is introduced to overcome tuning limitations. Intelligent renewable energy agents are trained through machine learning-based regression models that use root mean square error metrics for performance evaluations. The intelligent agents employ DAI control to overcome inherent limitations. The effectiveness of the machine learning-based DAI is thoroughly evaluated using the DRES-based IEEE 14-bus hybrid microgrid system. The quantitative results prove its efficacy in addressing the complex challenges of integrated microgrid dynamics
Hydraulic simulations to evaluate and predict design and operation of the Chashma Right Bank Canal
No full textIrrigation systems / Irrigation canals / Flow control / Velocity / Canal regulation techniques / Hydraulics / Simulation models / Design / Operations / Crop-based irrigation / Distributary canals / Water delivery / Policy / Protective irrigation / Water allocation / Water requirements / Sedimentation / Water distribution / Equity / Water conveyance / Pakistan / Chashma Right Bank Canal
Katamenes dimidiatus subsp. dimidiatus dimidiatus (Brulle 1932
No full textKatamenes dimidiatus dimidiatus (Brullé, 1932) Material Examined: PAKISTAN: Azad Jummu and Kashmir: Rawalakot, 15.v.05, Leg. Zahid ♀; (Ex. NIM). Remarks: Gusenleitner (2006) reported this species from Baluchistan: Quetta: Hazarganji, Chiltan National Park. Distribution: Cyprus; Greece; Israel; Pakistan; Turkey (Gusenleitner 2006, 2013a).Published as part of Rafi, Muhammad Ather, Carpenter Muhammad Qasim, James M., Shehzad, Anjum, Zia, Ahmed, Khan, Muhammad Rafique, Mastoi, Muhammad Ishaque, Naz, Falak, Ilyas, Muhammad, Shah, Mazafar & Bhatti, Abdul Rauf, 2017, The vespid fauna of Pakistan, pp. 1-28 in Zootaxa 4362 (1) on page 14, DOI: 10.11646/zootaxa.4362.1.1, http://zenodo.org/record/107611
اعجاز رحمانی کی نعتیہ شاعری میں نقوشِ سیرت النبی ﷺ
No full textThe Biographical impression of Hazrat Muhammad (S.A.W) in the naatya poetry of Eijaz Rehmani By Muhammad Zahid Iqbal Khan Afridi, Research Scholar, Department of Urdu, University of Karachi Professor. Doctor. Tanzim-ul-Firdos, Professor. Of Urdu, University of Karachi.
Naat is not any traditional poetry but it is something that have a particular quality and inclination.
To articulate the fundamental characteristics and practices of the beloved of Allah Pak, Hazrat Muhammad (ﷺ) is the real miracle, rather it is better to say that Naat cannot be said, but Allah Pak makes us says.
There are many poets in the world of Naat who have made their different standings by their maners, style and their allusions.
Among the poets migrated to Pakistan from Undivided Sub continent INDIA the prominent name of Natiya Poet is the name of Eijaz Rehmani (1936 to 2019).
He was such a name who kept his poetry life in embrace of Naat happily till the end of his life.
Versified biography is his specialization in Naat poetry. In his Naat, he amusingly conveyed the memoir of Hazrat Muhammad (ﷺ) which is not seen anywhere else
Predicting Risky and Aggressive Driving Behavior among Taxi Drivers: Do Spatio-Temporal Attributes Matter?
No full textRisky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics
Supplemental material for Fabrication, in vitro and in vivo studies of bilayer composite membrane for periodontal guided tissue regeneration
No full textSupplemental Material for Fabrication, in vitro and in vivo studies of bilayer composite membrane for periodontal guided tissue regeneration by Saba Zahid, Abdul Samad Khan, Aqif Anwar Chaudhry, Sarah Ghafoor, Qurat Ul Ain, Ahtasham Raza, Muhammad Imran Rahim, Oliver Goerke, Ihtesham Ur Rehman and Asma Tufail in Journal of Biomaterials Applications</p
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