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     <title><![CDATA[NUST Institutions Library Catalogue Search for 'kw,wrdl: su-br:au:&quot;Khan, A. Q.&quot;']]></title>
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     <description><![CDATA[ Search results for 'kw,wrdl: su-br:au:&quot;Khan, A. Q.&quot;' at NUST Institutions Library Catalogue]]></description>
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       <title>
    Medicinal plant biotechnology /






</title>
       <dc:identifier>ISBN:81-239-1097-5</dc:identifier>
        
        <link>http://catalogue.nust.edu.pk:8081/cgi-bin/koha/opac-detail.pl?biblionumber=480229</link>
        
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	   <p>
	   
                        . xv,506 p. :
                        
                         26 cm.. 
                         81-239-1097-5
       </p>

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       <title>
    Hacking h(app)iness :


    why your personal data counts and how tracking it can change the world /





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       <dc:identifier>ISBN:9780399165313 (hardback)</dc:identifier>
        
        <link>http://catalogue.nust.edu.pk:8081/cgi-bin/koha/opac-detail.pl?biblionumber=587817</link>
        
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	   <p>By Havens, John C.. 
	   
                        . xxxvi, 268 pages ;
                        
                         24 cm. 
                         9780399165313 (hardback)
       </p>

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						]]></description>
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       <title>
    Improving Frequency-Aware Image Restoration Techniques for Real-World Degradations /






</title>
       <dc:identifier>ISBN:</dc:identifier>
        
        <link>http://catalogue.nust.edu.pk:8081/cgi-bin/koha/opac-detail.pl?biblionumber=614895</link>
        
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	   <p>By Ayub, Nooh . 
	   
                        . 74p.
                        , Image quality often deteriorates under challenging weather conditions such as haze
and snow, which create significant barriers for vision-driven intelligent systems, including autonomous vehicles, real-time surveillance, and remote sensing applications.
To address this pressing challenge, we propose a comprehensive deep learning framework capable of simultaneously handling both image dehazing and desnowing in a
unified manner. At the core of our design lies the NAFXT block—an advanced extension of NAFNet—that integrates Dynamic Tanh (DyT) normalization in place of
the traditional layer normalization. Notably, DyT normalization is introduced for the
first time in this context as a more effective alternative, offering superior adaptability compared to standard layer normalization within convolutional neural network
(CNN) architectures, particularly the NAFNet framework. While layer normalization primarily ensures stable training and faster convergence, DyT normalization
enhances the model’s ability to capture richer and more expressive feature representations. As a result, the proposed framework achieves better restoration quality
while requiring fewer training epochs than several cutting-edge methods. In addition, we incorporate the Multi-Branch Dynamic Selective Frequency (MDSF) and
Multi-Branch Compact Selective Frequency (MCSF) modules, originally introduced
in FSNet, to promote frequency-aware global feature learning across multiple scales.
The network further adopts a multi-input and multi-output encoder–decoder structure, where both NAFXT and residual blocks are systematically integrated at every stage to progressively refine the restored outputs. Extensive experiments performed on three widely recognized benchmark datasets confirm that our method consistently surpasses state-of-the-art approaches, achieving higher quantitative scores
and delivering visually sharper, clearer, and more natural results in both dehazing
and desnowing tasks.
                         30cm. 
                        
       </p>

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     Advanced nanocarriers for skin diseases /






</title>
       <dc:identifier>ISBN:</dc:identifier>
        
        <link>http://catalogue.nust.edu.pk:8081/cgi-bin/koha/opac-detail.pl?biblionumber=615165</link>
        
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	   <p>By Khan, Rida Khalid. 
	   
                        . 68p.
                        , The development and evaluation of an advanced nanocarrier hydrogel system designed to address
the major barriers in diabetic wound healing, specifically targeting chronic inflammation,
oxidative stress, bacterial infection, and impaired angiogenesis. This therapeutic system integrates
cerium oxide nanoparticles (CeO₂), metal-organic frameworks (MOFs), and a chitosan-gelatin
hydrogel platform to create a multifunctional wound dressing. CeO₂ nanoparticles provide strong
antioxidant and antibacterial activity, MOFs enable controlled drug release and add antimicrobial
properties, while the chitosan-gelatin hydrogel ensures biocompatibility, moisture retention, and
accelerated tissue repair. Comprehensive characterization, including Raman spectroscopy, FTIR,
UV-Vis, EDX analysis, and optical microscopy, confirmed successful synthesis, structural
integrity, and the presence of desired functional groups. Biological assays demonstrated superior
antibacterial and antibiofilm performance of the CeZn-Gel composite against E. coli and S. aureus,
surpassing individual system components and controls. The integrated approach not only enhances
wound healing outcomes but also reduces infection and inflammation, with significant potential
for improving patient quality of life in chronic wound care. Future work will focus on optimizing
formulation and advancing toward clinical testing for broader therapeutic impact. 
                         30cm. 
                        
       </p>

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						]]></description>
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