The teaching and learning in physics have undergone complete transformation through AI-driven int... more The teaching and learning in physics have undergone complete transformation through AI-driven intelligent tutoring systems adaptive learning stages and virtual labs that produce enhanced student involvement, interactive simulations and immediate feedback. The AI-powered Physics Education Technology (PhET) Interactive Simulations together with ChatGPT enables students to experiment with concepts by applying detailed explanations on complex physics theories to help abstract knowledge become more understandable. The deployment of AI resolves key educational obstacles since it delivers virtual labs alongside chatbots to make quality training more accessible for differential learners and resource-challenged students. Education integration of AI in physics faces major hurdles because of equity problems together with its expense burden and human resistance to adaptation and worries related to privacy of data and algorithmic discrimination. While AI instruments deliver better solutions for complex problems and enhanced understanding of concepts they do not reach scientific problem complexity levels which require human-like reasoning and heavy reliance on AI tools could hinder students' critical thinking abilities. Organizations receiving government financing along with training programs for teachers and ethical guidance must identify these challenges to establish high-quality physics education instruments. The supportive combination of AI with augmented reality and quantum computing enables educators and policymakers and researchers to collaborate for new educational breakthroughs. Responsible AI implementation enables physics education to achieve its goal of accessibility along with producing intriguing learning experiences which deliver effective 21st-century skill acquisition. The evolution emphasizes future progress in making traditional learning practices better through AI while developing careful implementation methods to help teacher reforms.
Polymer matrix composites (PMCs) have gained prominence for their light weight, corrosion resista... more Polymer matrix composites (PMCs) have gained prominence for their light weight, corrosion resistance, and low cost; however, their poor mechanical strength limits their use in high-performance applications. This review focuses on the role of iron oxide nanoparticles (Fe₃O₄ and γ-Fe₂O₃) in enhancing the mechanical properties of PMCs through mechanisms such as improved stress transfer, crack resistance, and interface strength. This study includes a detailed analysis of the effect of nanoparticle parameters; such as size, surface functionalization, dispersion quality, and orientation on mechanical behavior. It also explores recent developments in hybrid nanofillers, magnetic alignment techniques, and smart functionalities like shape-memory effects and electromagnetic responsiveness. This study further reviews advanced processing methods such as in-situ polymerization and green synthesis, highlighting their advantages in achieving uniform dispersion and environmental compatibility. A special focus is placed on interface engineering and stress transfer mechanisms, supported by modern characterization techniques like Raman spectroscopy and electron microscopy. Additionally, the review compiles key challenges in nanoparticle dispersion, alignment, and scale-up, along with their contributing causes. To offer practical insights, categorized tables summarize performance outcomes, processing conditions, and recurring fabrication issues. Hence, this comprehensive study aims to provide researchers with both foundational understanding and advanced insights into the development of high-performance, multifunctional Fe₃O₄-reinforced polymer nanocomposites.
Laser-Induced Breakdown Spectroscopy (LIBS) is a new source of distinct role which is now being w... more Laser-Induced Breakdown Spectroscopy (LIBS) is a new source of distinct role which is now being widely used as an efficient, fast and more versatile method of analysis in soil analysis which has contributed to the best sustainable practice of agriculture. The objective of this review is to provide a comprehensive overview of recent advancements in the application of LIBS for soil analysis, with a particular focus on its role in the detection and quantification of soil nutrients and contaminants. This review aims to highlight how LIBS contributes to improving analytical accuracy, enhancing real-time monitoring capabilities, and supporting sustainable agricultural practices through precise soil characterization. More recent developments have centered on defeating some of the critical drawbacks of LIBS accuracy, including matrix effects, moisture content, and variability of particle size. Optimized experimental procedures, such as spatial confinement, addition of a conductive material and laser-induced fluorescence (LIF) support have shown significant increases in detection limit and precision of the analytical method. The combination of machine learning, deep learning, and chemometric processes continue to optimize LIBS applications by allowing predictive models that can withstand the balkier soil matrices. Moreover, portable and handheld LIBS have contributed to its use in field based real time soil monitoring. Reproducibility is being promised by efforts of standardization through certified reference materials and interlaboratory protocols into increasing acceptance by the scientific community. All these innovations make LIBS one of the most promising instruments in terms of accurate soil nutrient management and contamination testing, with valuable security providing strategic resources, resource-efficient and environmentally sustainable agricultural systems.
Large-scale neural networks have recently transformed medical diagnosis with exceptional accuracy... more Large-scale neural networks have recently transformed medical diagnosis with exceptional accuracy across various imaging tasks with high accuracy and efficiency. It is true that as one relies on artificial intelligence (AI) for clinical settings, the necessity of interpretability and transparency becomes more and more critical. In this review, we focus on Layer-wise Relevance Propagation (LRP), a technique that enables us to enhanced interpretability of neural networks by identifying on what regions the model is relying the most to its decision. Additionally, it demonstrates neural networks in the medical field of radiology, pathology, cardiology, neurology, stating where advanced learning algorithms are utilized for such tasks as tumor detection, image segmentation, and disease classification, and also outlines their findings. LRP creates clinical trust and genuine collaboration between health care team and machine systems to resolve key transparency issues. Relevant current challenges, including scalability and computational demands, that must be addressed via further research are discussed, in order to further refine LRP for complex models and to integrate it into clinical workflows. Despite these challenges, LRP shows great promise in generating robust chains of divisions as clinical applications across all of these imaging modalities (X-ray, MRI, CT scan).
Raman Spectroscopy (RS) has become a key diagnostic instrument for cancer detection along with bi... more Raman Spectroscopy (RS) has become a key diagnostic instrument for cancer detection along with bio-imaging as it offers non-contact sample analysis through label-less approaches which provides detailed chemical signatures in biological specimens. The fundamental aspects of RS and its versions like Surface-Enhanced Raman Spectroscopy (SERS), Raman imaging (RI) and Tip-Enhanced Raman Spectroscopy (TERS) are emphasized for their functions in enhancing detection accuracy and resolution performance. RS finds clinical implementation throughout cancer types including lung tissue and breast tissue as well as thyroid tissue, liver tissue and colorectal tissue where it helps identify early conditions while the surgeon operates under real-time and provide accurate margin assessments for tumor removal. Bridging RS with advanced Machine Learning (ML) approaches using convolutional neural networks (CNNs) along with Raman Net models delivers better spectral identification performance while overcoming noise issues. The field of RS progressed from examination of tissues to study of individual cells which permits the examination of tumor variations and metastatic properties. Raman-based optical probes and hybrid systems allow researchers to use the technology for in vivo imaging while monitoring therapy using these systems. Integration of Artificial Intelligence (AI) and ML with Raman has proven beneficial in terms of fast tracking the results and better accuracy. This review reveals that RS keeps expanding its role in precise cancer care while promising diagnostic advancements as well as individualized medical treatments leading to better result outcomes.
Purpose: The objective of this study is to examine the post-COVID impacts on Pakistan's education... more Purpose: The objective of this study is to examine the post-COVID impacts on Pakistan's education system and offer recommendations to overcome these issues. Design/Methodology/Approach: A well-structured questionnaire was provided to education’s intellectuals, including teachers and principals. A total of 1164 responses were recorded, quantitatively examined, and statistically analyzed. The significance of the results was compared by p-value (p = 0.05) with a 95% confidence interval. Findings: Intellectuals’ responses comprised on 653(56.1%) male and 511(43.9%) female from government 784(67.4%) and private 380(32.6%) educational institutions. Research results indicate that a significant number of intellectuals have expressed serious concerns regarding the decline in education during this pandemic that include the promotion of students without proper evaluation (28.7%), uncertainty about the reopening of institutions (29.7%) with limited classes (30.8%), and lack of technology (39.9%) and face-to-face classes (31.7%). The non-serious attitudes of students and teachers (35%), and adoption of smart syllabus (33.8%) also posed challenges. Additionally, the dropout rate (36.4%), unemployment (42.3%), and financial decline (31.4%) contributed to the overall downfall in educational quality. The erosion of education persist post-COVID-19, with strong agreement on key factors contributing to the decline which include low teaching quality (37.2%), poor reading and writing skills (40.2%), the diversity of private schooling (43%), flaws in the current education system (44.2%), students' socioeconomic backgrounds (41.5%) and interest in social media (40.5%), a large number of vacations (41.8%), and additional duties assigned to teachers (42.9%). Implications/Originality/Value: The study concludes that integrating social media and technology into the education system can mitigate the decline in education during future disruptions, such as those caused by COVID-19. Moreover, steady educational policies and infrastructure development are essential for long-term improvements.
The primary objective of this study is to seek the underlying causes of low enrollment and analyz... more The primary objective of this study is to seek the underlying causes of low enrollment and analyze the key factors contributing to the low enrollment rates in Public institutions as compared to private colleges in the Punjab, Pakistan. This research work adopts the quantitative research design under the cross-sectional descriptive research model. Data was collected from 28th July, 2024 to 7th November, 2024 by employing a structured questionnaire from educational professionals that included Subject Specialists and Principals of the Faisalabad division. Quantitative data analysis was done, and statistical description was performed using SPSS. The level of significance was determined at p-value (p = 0.05) with a test of confidence level of 95%. The study reveals that teachers’ shortage (91.8%), parents’ lack of education and awareness (85.8%), nonexistence of parents’ involvement (85.2%), and insufficient facilities (84.3%) in government institutions are the main reasons for the decline of enrollment in public type of educational institutions. In addition, lack of individual attention (73.6 %), and poor discipline and safety measures (72.3 %) in the public sector’s institutions reduce enrollment in these institutions. This paper discusses the main issues of concern in relation to low enrollment in government institutions, focusing on infrastructure, teachers and parents. The findings provide useful recommendations for policymakers and educators to increase demand for public education. The study's uniqueness lies in its comparison of government and private schooling, providing insights that can drive reforms for more equitable educational admittance.
Specific responses and environmental factors trigger a cascade of genetic alternations which pass... more Specific responses and environmental factors trigger a cascade of genetic alternations which pass on to future generations. The Coronavirus disease 2019 (COVID-19), the pandemic has created a huge disaster and environmental effect around the globe which can cause an enormous change in the forthcoming generation. This study is aimed to assess the posttraumatic effects of this pandemic on Pakistan's residents. Hence, we have investigated the distressing effects of COVID-19 on Pakistan's general population through an online survey to envisage the upcoming belongings. Over 1.1k respondents recorded their responses. The data reflect the impact of the COVID-19 outbreak on change in physical and mental health, financial conditions, anxiety/depression, and immunity, which can revolutionize the new generation's dynamics.
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Papers by MR PK
Design/Methodology/Approach: A well-structured questionnaire was provided to education’s intellectuals, including teachers and principals. A total of 1164 responses were recorded, quantitatively examined, and statistically analyzed. The significance of the results was compared by p-value (p = 0.05) with a 95% confidence interval.
Findings: Intellectuals’ responses comprised on 653(56.1%) male and 511(43.9%) female from government 784(67.4%) and private 380(32.6%) educational institutions. Research results indicate that a significant number of intellectuals have expressed serious concerns regarding the decline in education during this pandemic that include the promotion of students without proper evaluation (28.7%), uncertainty about the reopening of institutions (29.7%) with limited classes (30.8%), and lack of technology (39.9%) and face-to-face classes (31.7%). The non-serious attitudes of students and teachers (35%), and adoption of smart syllabus (33.8%) also posed challenges. Additionally, the dropout rate (36.4%), unemployment (42.3%), and financial decline (31.4%) contributed to the overall downfall in educational quality. The erosion of education persist post-COVID-19, with strong agreement on key factors contributing to the decline which include low teaching quality (37.2%), poor reading and writing skills (40.2%), the diversity of private schooling (43%), flaws in the current education system (44.2%), students' socioeconomic backgrounds (41.5%) and interest in social media (40.5%), a large number of vacations (41.8%), and additional duties assigned to teachers (42.9%).
Implications/Originality/Value: The study concludes that integrating social media and technology into the education system can mitigate the decline in education during future disruptions, such as those caused by COVID-19. Moreover, steady educational policies and infrastructure development are essential for long-term improvements.