NUST Institutions Library Catalogue Search for 'kw,wrdl: su-br:an:"6148"'

Ethical Innovation in Digital Influencer Marketing: How Can Social Media Influencers Maintain Ethical Conduct When Collaborating with Brands?/

By Asif, Duaa.

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Long-Term Temperature Forecasting using Deep Learning and Extreme Indices Computation /

By Munir, Safdar . . 105p. , Climate change has intensified the demand for accurate long-term temperature forecasting, particularly in highly vulnerable regions such as Pakistan, where rising temperatures and extreme events threaten agriculture, water security, public health, and infrastructure. Conventional climate models, including General Circulation Models (GCMs) and Regional Climate Models (RCMs), provide essential large-scale projections but face constraints in spatial resolution, computational cost, and applicability in data-scarce contexts. Recent advances in deep learning offer powerful alternatives, capable of capturing complex nonlinear relationships and producing high-resolution forecasts that can also inform the computation of extreme climate indices. This study integrates DL based forecasting with extreme temperature index computation to generate actionable insights for adaptation planning in Major Cities of Pakistan. Daily temperature data from the EC-EARTH3-VEG climate model (converted to °C) were bias-corrected using quantile mapping under two Shared Socioeconomic Pathways (SSP4.8 and SSP5.85). After data preprocessing multiple deep learning models LSTM, CNN-LSTM, Transformer, Transformer XL and Deep Ensemble with Monte Carlo (MC) Dropout, were trained on 1980–2040 data and evaluated for 2040–2050 and then forecasted till 2100. Model was assessed using MAE, RMSE, R² with MC dropout. Deep Ensemble outperformed all the single model (MAE = 0.4588; RMSE = 0.751; R² = 0.992). Projections indicate a Tmax rise of ~2–4 °C and Tmin rise of ~1.5–3 °C by 2100. According to extreme climates indices calculation there continue to be increases in warming, especially with regards to more heat waves that last longer in addition to increases in the number of hot days and warm nights as well as fewer frost days. The warming of nights more than the warming of days, closing the diurnal temperature range. There is also more total precipitation which is distributed in a growing number of days, and longer periods of rain which means that heavier rains are becoming less common, which indicates the beginnings of a warmer, more humid, but drier and stressed climate. The study highlights that DL Ensemble frameworks can deliver robust, long term temperature projections and calculation of extreme indices for 25 major cities of Pakistan and for sake of brevity only data of Lahore, Karachi, Quetta and Gilgit city shown in paper reset are in appendix. 30cm.

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Investigation of Impact Properties of Wave Springs Designed for Additive Manufacturing /

By Ahmed, Faizan . . 119p. , Innovation continues to transform various fields, including the design of springs. Initially helical springs were used as compression springs for most of the applications. However, the design innovation has led to the introduction of a new type of compression spring which is the wave spring. At the same time the advancements in manufacturing technologies are reshaping the production methods. Traditional manufacturing methods are gradually being replaced with additive manufacturing. Because AM has the ability to fabricate complex geometries with high precision and minimal material waste. These advantages make AM a key driver of innovation in modern design and engineering. Wave springs possess better mechanical properties as compared to helical springs, as highlighted by previous studies. But previous studies are limited to only compression analysis of wave springs at slow speeds. The behavior of this newly developed spring is unknown for sudden high speed impact loadings. This research involves the experimental and computational analysis of six different geometries of wave spring under the high speed loading conditions of 17mm/sec. The six geometries of wave springs are fabricated using FDM technology. PLA material was considered for the fabrication due to its availability and compatibility with the FDM. The other 2 materials including spring steel, and TPU (Thermoplastic Polyurethane) were used in computational modelling only. The results mainly showed that the material properties had a greater influence over the geometric parameters. PLA due to its brittle nature resulted in formation of local stresses that minimized the performance parameters of all geometries of wave springs. Spring steel having high elasticity and compressive strength showed better impact properties unlike PLA. TPU although elastic but moderate compressive strength was not able to show impact properties like spring steel, but due to its elasticity, it was a better choice over PLA. Each of the 6 geometries had different configurations that resulted in different local stress formations and thus different energy absorption, stiffness, and load-bearing capacity. To apply the concept of wave spring to real-engineering world, multiple wave spring designs were integrated in the car suspension system and analyzed on MSC Adams (Automated Dynamic Analysis of Mechanical Systems) for their energy absorption, stiffness, and maximum load bearing capacity. This analysis further validated the initial results and provided a gateway to the innovation in the car suspension system design and analysis. 30cm.

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Improving Frequency-Aware Image Restoration Techniques for Real-World Degradations /

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.

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