573 research outputs found
Pioneers of Library Movement in Pakistan
No full textThe paper aims to describe in brief the contribution of seven leaders of Pakistan librarianship, viz. K.B. Khalifa M. Asadullah, Prof. Dr. Abdul Moid, Dr. Abdus Subuh Qasimi, Muhammad Shafi, Fazal Elahi, Khawaja Nur Elahi and S. V. Hussain. The early library developments are given for better understanding of the role of these leaders
Syed Abul Hasan Ali Hasani an-Nadwi Tentang Keruntuhan Peradaban, Pandangan Hidup, dan Pendidikan Islam
No full textThis article aims to explore Syed Hasan Ali Nadwi’s views on the decline of civilization and the notion of Islamic worldview. First, the author describes about Syed Hasan Ali Nadwi’s life in a short bio. Second, the author explores Syed Hasan Ali Nadwi’s views and thoughts about the essence beyond the civilizations, its glory and decadence. Then I will elaborate Syed Hasan Ali Nadwi’s point of view about the worldview of Islam, what are the substances of an Islamic worldview and how far the worldview could bring civilizations to certain glories and decadence. My point of view on this article is the stronger worldview that Muslims have, the stronger civilization Muslims could establish. It all depend on how Muslims face the crisis of knowledge and the loss of adab by the right ‘knowledge’, right ‘choice’, and right ‘action.â€
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
Estimating Passenger Car Equivalent Factors for Heterogeneous Traffic Using Occupancy-Density Linear Regression Model
No full textA variety of methods have been proposed in the existing literature for the estimation of passenger car equivalent (PCE) factors. These methods are based on the comparison of selected attributes of different vehicles. This research, for the first time, utilizes the basic notion of the linear relationship between road area occupancy and density for the estimation of PCE factors for different vehicle types in heterogeneous traffic. Aerial photographs obtained from an unmanned aerial vehicle (UAV) were analyzed to estimate the road area occupancy and the number of vehicles classified in seven selected groups. A linear least-squares regression model was developed between road area occupancy and classified vehicle count. The coefficients of the occupancy-density linear regression model were used to estimate PCE and motorcycle equivalent (MCE) factors. The comparison of the estimated set of PCE values with the values reported in the literature shows that PCE factors estimated using the proposed method are reasonable and produce a better occupancy-density relationship than the other studies. In comparison with the existing methods that rely on lane-based measurements, the proposed method is well suited for traffic with weak/no lane discipline, as it considers the entire road width and the dynamics of lateral movement of different types of vehicles. The proposed method does not need extensive traffic data of speeds, headways, flow rates, and so forth, and is applicable on aerial photographs obtained from other sources, such as satellites.Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported with funding from Exascale Open Data Analytics Lab, National Center for Big Data and Cloud Computing (NCBC) and the Higher Education Commission of Pakistan.
Acknowledgments
The authors are thankful to research students Syed Hassan Ali, Haseeb Ahmed, Zohaib Ahmed, Aqib Abbasi, Asad Rehan, Mirza Ali Haider, Syed Abbas Hasan Zaidi, and Omema for their help in this research
Life Narratives and the Ten Aspects of the Big Five: Replicating Trait-Narrative Theme Associations in a Georgia Southern student sample.
No full textThis is a pre-registration for secondary data analysis of an existing dataset collected in Georgia Southern university, created before the first author (Chou) obtained access to the data from one of the senior authors (Nicholas S. Holtzman). The other senior authors (Syed, DeYoung) did not have access to the data prior to the pre-registration. Chou is the sole author of this pre-registration, although Holtzman provided the first author with only the necessary information (e.g., sample size, data collection procedures) to prevent factual errors in the pre-registration (nothing that would alter predictions) and Syed provided feedback on the pre-registration without accessing any of the data.
This pre-registration specifies the replication of a set of analyses conducted by the first author on a sample collected from the University of Minnesota, which was posted as a preprint at https://psyarxiv.com/wnqru/ (most recent version dated 25th July, 2022). It also specifies some additional analyses using variables collected from the Georgia Southern sample that were not collected from the Minnesota sample
Folio: The Magazine of Forman Christian College
No full textEditorial. pp. 5; Sheets, S. L.-Poetry-The Departure. pp. 7; Ghaisuddin-Article-Nature in Hardy. pp. 8-10; Bede, Z.-Poetry-Be Worthy of Your Query. pp. 11; Suleman Ghani-Story-At First Sight. pp. 12-16; Fazal, S. K.-Poetry-Oblivision is Red. pp. 17-18; Khawaja Fayyaz Mahmud-Article-Tourism in Kenya. pp. 19-22; Shahid Ikram-Poetry-A Morning Walk. pp. 22; Bede, Z.-Poetry-Dragons. pp. 23; Syed Arif Ahmad-Article-Cultural Awareness and Modern Youth. pp. 24-26; Toheed Ahmad-Poetry-On Meeting a Dead Formanite. pp. 27-29; Muhammad Ramzan Malik-Article-Polygamy. pp. 30-34; Barratt, D. J.-Poetry-Sonnet Sequence. pp. 35-39; Saadat Ali Khan-Article-The Secret of Contentment. pp. 40-41; Shahid Hasan-Poetry-Modern Poetry. pp. 41; Zaki-ud-Din Sheikh-Poetry-HICO. pp. 42; Javed, A. Aziz-Story-I Love You. pp. 43-45; Ghais-ud-Din-Poetry-The Lover. pp. 46; Anon-Poetry-Despair. pp. 47; Mohammad Riaz Shafi-Poetry-A Love Poem. pp. 48; JED-Poetry-The Journey. pp. 49-61; Toheed Ahmad-Kaleidoscope. pp. 62-65; Hamid, S. A.-In Memoriam. pp. 66-69; Folio [Urdu]. 58 p.Professor E. J. Sinclair. before contents page; Dr S. L. Sheets. before Editoria
Deep learning driven radiographic classification of primary bone tumors using attention augmented hybrid models
No full textAccurate classification of primary bone tumors is necessary for timely diagnosis and effective treatment planning, particularly given the complex radiographic heterogeneity exhibited by tumor subtypes. The present study introduces two novel deep learning models, including a Convolutional Neural Network Transformer (CNNT) hybrid and a Residual Network 50 (ResNet50) model, augmented by a Convolutional Block Attention Module (CBAM) to enhance feature discrimination and contextual understanding in radiographic images. The models are trained and validated on the Bone Tumor X-ray Radiograph Dataset (BTXRD) dataset of 3,746 labeled radiographs containing nine tumor subtypes. To counter the effects of noise and class imbalance, advanced preprocessing methods like Block Matching 3D Filtering (BM3D) and data balancing using the Synthetic Minority Over sampling Technique (SMOTE) are employed. Extensive testing demonstrates that our approaches outperform current state of the art models, such as ResNet50, EfficientNet version B3 (EfficientNet-b3), You Only Look Once version 8 classification (YOLOv8s-cls), and Deep Supervision Network (DS-Net). Specifically, the ResNet50-CBAM architecture achieves an F1-score of 0.9759, an AUC-ROC score of 0.984, mean accuracy of CBAM 97.41% and a Cohen's Kappa score of 0.9718, outperforming existing benchmarks for binary tumor classification. The CNNT model also achieves competitive performance, reaching an F1-score of 0.9595 with an accuracy of 92.56%. Incorporating attention mechanisms and dataset guided preprocessing renders this framework appropriate for practical clinical settings. The findings of this research have significant implications for the healthcare sector by introducing a scalable, interpretable, and highly accurate Artificial Intelligence (AI) based diagnostic system that can support radiologists in timely diagnoses and decision making processes, ultimately contributing to better patient outcomes and alleviating the diagnostic burden in musculoskeletal oncology.</p
Pediatric chest X-ray diagnosis using neuromorphic models
No full textThis research presents an innovative neuromorphic method utilizing Spiking Neural Networks (SNNs) to analyze pediatric chest X-rays (PediCXR) to identify prevalent thoracic illnesses. We incorporate spiking-based machine learning models such as Spiking Convolutional Neural Networks (SCNN), Spiking Residual Networks (S-ResNet), and Hierarchical Spiking Neural Networks (HSNN), for pediatric chest radiographic analysis utilizing the publically available benchmark PediCXR dataset. These models employ spatiotemporal feature extraction, residual connections, and event-driven processing to improve diagnostic precision. The HSNN model surpasses benchmark approaches from the literature, with a classification accuracy of 96% across six thoracic illness categories, with an F1-score of 0.95 and a specificity of 1.0 in pneumonia detection. Our research demonstrates that neuromorphic computing is a feasible and biologically inspired approach to real-time medical imaging diagnostics, significantly improving performance
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Corrigendum to “Enhanced lignin extraction and optimisation from oil palm biomass using neural network modelling” [Fuel 293 (2021) 120485] (Fuel (2021) 293, (S0016236121003616), (10.1016/j.fuel.2021.120485))
No full textThe author Syed Ali Ammar Taqvi shows an affiliation with “Neurocomputation Lab, National Centre of Artificial Intelligence, NED University of Engineering and Technology, 75270 Karachi, Pakistan” which has been incorrectly added. Syed Ali Ammar Taqvi only has one affiliation with the Department of Chemical Engineering, NED University of Engineering and Technology Karachi, Pakistan. The authors would like to apologise for any inconvenience caused.</p
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