Lahore Garrison University

This work aims to investigate and differentiate skin cancers as Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC) using a combination of Fourier Transform Infrared (FTIR) spectroscopy, Mueller Matrix Polarimetry (MMP), and Support Vector Machine (SVM) classification. Ex vivo characterization was performed on SCC (malignant) and BCC (malignant and more aggressive) tissue samples collected from 100 different patients. Each sample was analyzed using microscopy for morphological insights, FTIR spectroscopy for biochemical composition across the wavenumber range of 400–4000 cm⁻1, and MMP in the visible range (400–800 nm) for polarimetric behavior. A total of 13 polarimetric parameters were extracted and statistically analyzed. Furthermore, a Machine learning SVM model was trained using 1150 observations and 34 predictors to differentiate between the tissue types. FTIR analysis revealed significant spectral differences between SCC and BCC. Notably, SCC exhibited peaks related to Aldehyde (lipids), Amide I (proteins), Z-DNA (nucleic acids), and Glycosylation, whereas BCC showed prominent signatures of Amide B (proteins), CH₂ stretching (lipids), Aldehyde (lipids), Amide I, B-DNA (nucleic acids), and Glycosylation. Polarimetric analysis through MMP demonstrated statistically significant (p < 0.05) differences in all parameters except latitude, orientation, and ellipticity, which were higher in BCC samples. The SVM classifier achieved 90% accuracy, sensitivity, and specificity, demonstrating strong potential for automated cancer classification. The integrated approach of FTIR spectroscopy and polarimetric analysis, coupled with machine learning via SVM, provides a powerful diagnostic tool for distinguishing between SCC and BCC. This novel methodology supports the potential for developing automated, non-invasive, and accurate systems for early cancer diagnosis.

In this paper, we present novel exact traveling wave solutions for the coupled Whitham–Broer–Kaup equation, which includes both odd- and even-order partial derivative terms. These solutions are obtained using the extended generalized Riccati equation mapping method. The resulting traveling wave solutions exhibit diverse forms, including bright solitons, periodic waves, breather solitons, lump solitons, and others. Furthermore, the stability and conservation laws associated with these solutions are analyzed in detail. These findings have significant implications in physics and various applied sciences. In addition, the proposed method proves effective for solving a broader class of coupled nonlinear partial differential equations.

The rapid growth in adoption of artificial intelligence (AI) for decision-making processes, administrative functions and academic services has created an environment of rapid change throughout the university setting. As technology advances, concerns surrounding transparency, fairness and accountability of algorithms also arise; these issues affect how employees of universities perceive and trust AI systems. Research on AI governance and trust in organisations will be utilised in this study to explore how transparency and communication among leaders impact employee trust in AI as a governance tool within universities. Data from academic and administrative staff from universities that are currently undergoing transformation through digitalisation influenced by AI will be analysed using a structural equation modelling (SEM) method. The study will propose that algorithm transparency has both a direct and indirect positive correlation to employee trust through perceived organisational fairness. Additionally, the relationship between algorithm transparency and employee trust will be moderated by leader communication through reducing uncertainty and creating understanding of AI as a decision tool. Overall, this research is intended to advance the body of literature related to human-centred governance of AI by identifying socio-organisational mechanisms through which employee trust in AI systems develops. Furthermore, it will provide university leaders and policymakers with actionable recommendations for creating responsible governance structures for AI that prioritise transparency, communication and employee participation. By evaluating individual level trust related to the ethical implementation of AI at universities, our research will assist universities in understanding how they can balance their drive towards innovation through technology and caring for people.

Tuberculosis is one of the most dangerous health conditions on the globe. As it affects thehuman body, tuberculosis is an infectious illness. According to the World Health Organization, roughly1.7 million individuals get TB throughout their lifetimes. Pakistan ranks fifth among high-burdennations and is responsible for 61% of the TB burden within the WHO Eastern Mediterranean Region.Various methods and procedures exist for the early identification of TB. However, all methods andtechniques have their limits. Most currently known approaches for detecting TB rely on model-basedlung segmentation. The primary purpose of the proposed study is to identify pulmonary TB utilisingchest X-ray (Poster Anterior) lung pictures processed using image processing and machine learningmethods. The recommended study introduces a unique model segmentation strategy for TB identification.For classification, CNN and Google Net. Other systems based on deep learning are used. Thebest accuracy attained by the suggested method utilising Google Net on merged datasets was 89.58percent. The recommended study will aid in the detection and accurate diagnosis of TB.