NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Database design.")'

Mastering Dreamweaver MX databases (E-Book)

By Harkins, Susan Sales.. San Francisco : SYBEX, 2003 . xxv, 707 p. : 23 cm. +. 078214148X (pbk.) :

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Computer science : an overview /

By Brookshear, J.Glenn.. [S.l.] : Benjamin-Cummings Publishing Co.,Subs. of Addison Wesley Longman,US, 1985 . 453 p. ; , Paperback. 23 cm.. 0805309004 | 9780805309003

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Introduction to Volume Rendering

By Barthold Lichtenbelt, Randy Crane, . [S.l.] : Prentice Hall, PTR 1998 . 236 p. ; 24 cm.. 0138616833 (hardcover) | 9780138616830 (hardcover)

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Strategic management : creating competitive advantages /

By Dess, Gregory G.. [S.l.] : Irwin/McGraw-Hill, 2004 . 530 p. 007287290X (hardcover) | 9780072872903 (hardcover)

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TH-SOF-1715-A Model-Driven Framework to Generate Data and Presentation Layers' Scaffolding Code for multiplatform Applications

By Mohammad Inayatullah. ISLAMABAD NUST COLLEGE OF EME 2019

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Bioinformatics : a practical guide to the analysis of genes and proteins /

. pages cm 9781119335580

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Life Cycle Impact Assessment of Glass Manufacturing In Pakistan /

By Khan, Muhammad Usman Ali . . 76p. , The production of glass packaging requires a lot of energy and resources, and as the world's population rises, particularly in emerging nations, so does the demand for glass packaging. Although developing nations are also suffering from the effects of climate change, there is relatively little study on environmental impact assessment and analysis, notably the environmental impacts brought on by the activities related to glass manufacturing. This work conducted a thorough life cycle assessment (LCA) for the production of glass packaging in Pakistan in order to close this research gap of quantification of the environmental impacts of glass packaging production. The LCA was carried out for three distinct types of glass packaging solutions (amber, flint, and green), considering 1-ton of glass as the functional unit. The creation of several scenarios allowed for the analysis of the effects and the generation of ideas for ways to lessen the negative effects of glass manufacture on the environment. The study's methodology used a cradle-to-gate approach. From the perspective of the life cycle inventory, the main data were gathered from a renowned glass industry situated in one of Pakistan's designated industrial zones, and the secondary data were obtained from the Ecoinvent database. Environmental impacts were measured at midpoint and endpoint levels utilizing the ReCiPe 2016 approach and SimaPro V 9.2 as the software tool. Results indicated that the extraction and processing of raw material had worse impacts on the environment. At plant level the consumption of natural gas for the melting purposes and electricity consumed from national grid had highest impacts on the environment. The comparison of three packaging solutions indicated that the production of green glass was more harmful to the environment, followed by the production of amber glass and flint glass came out to be more environmentally friendly among the three packaging solutions. The alternative scenarios were generated by changing the energy-mix and the cullet ratios and the results were compared with the baseline scenarios for different midpoint and endpoint impact categories. The alternative scenarios for the energy mix were also compared for different kinds of emissions to air especially the greenhouse gas emissions. The report also identified a number of significant implications. This study may serve as a guide for future research on the environmental impact of the glass packaging industry, particularly in Pakistan and other developing nations. In this sense, developing nations would work toward net-zero emissions and sustainable development goals 30cm.

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Designing data-intensive applications : the big ideas behind reliable, scalable, and maintainable systems /

By Kleppmann, Martin,. Boston : O'Reilly Media, 2017 . xix, 590 pages : , Includes index. 24 cm. 9781449373320

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Direct Messaging eXchange (DMX) /

By Masood, Asphand Yar . . xi, 115 p

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BreachBuster AI Pentesting Assistant /

By Usman, M. Umar . MCS, NUST Rawalpindi 2024 . xi, 79 p

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Automated Karyotyping: Segmentation and Classification /

By Umbreen, Neelam. . 130p. , Karyotyping continues to be the bedrock of cytogenetic diagnosis, providing key information on chromosomal abnormalities causative of a broad range of genetic disorders, developmental abnormalities, and cancers. But standard karyotyping is time-consuming, requires extensive specialist interpretation, and is vulnerable to human mistake and inefficiency, especially in high-volume clinical settings. Despite advances in medical image analysis, the automation of karyotyping faces persistent challenges, including the lack of large-scale annotated datasets, difficulties in segmenting overlapping chromosomes, and variability in chromosome morphology and staining. These challenges define a significant research gap in developing scalable, accurate, and clinically deployable deep learning models for automated chromosome analysis. In order to overcome this gap, we first present a large-scale, clinically annotated cytogenetic database, built from 1,311 patients and consisting of 10,057 karyograms with 514,949 manually annotated chromosome singlets. Furthermore, 3,935 metaphase images are annotated at the instance level in COCO format. This data set reflects true-world diversity, ranging from normal and abnormal karyotypes to different Giemsa (G-banding) staining intensities, structural abnormalities, and overlapping difficult cases, and thus presents a solid basis for the creation and testing of deep learning models within automated cytogenetics. Based on this work, we create two primary methodologies aimed at the fundamental tasks of karyotyping. For segmentation of chromosomes, we introduce a variant Mask R-CNN model involving an Attention-based Feature Pyramid Network (AttFPN), spatial attention, and a LastLevelMaxPool component to improve multi-scale feature representation and contextual perception. It enhances performance in difficult situations, including overlapping chromosomes and weak banding patterns, and gains considerable improvements in mean Average Precision (mAP) compared to standard baselines. For chromosome classification, we present the Dual Attention Multiscale Pyramid Network (DAMP), a specifically designed model that combines channel and spatial attention mechanisms to concentrate on discriminative features, as well as a multiscale pyramid architecture to cope with size, orientation, and quality variation in chromosomes. DAMP's highest classification accuracy is 96.76% on both public and commercial datasets, performingxv better than state-of-the-art models like ResNet-50, Vision Transformers, and Siamese Networks. Overall, this thesis provides interpretable and scalable deep learning models for automating chromosome classification and segmentation. Through the closure of key gaps in dataset quality, model resilience, and clinical utility, the work facilitates the insertion of clever decision-support systems into cytogenetic pipelines, ultimately leading to improved diagnostic reliability and efficiency in the face of chromosomal disorders. 30cm.

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Autonomous Security Services for Organizations Assisted by Blockchain and Cryptographic Protocols /

By Rashid, Aqas. Rawalpindi, MCS (NUST), 2024 . xiv, 119 p

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