Journal of Engineering, Science and Technological Trends
Journal of Engineering, Science and Technological Trends (JESTT) ISSN(e): 2959-1937 is a peer-reviewed international journal publishing high-quality articles on engineering, science, and technology. Journal of Engineering, Science and Technological Trends publishes articles (Research Article / Review Article / Perspective ) in fundamental theory, experiments, and simulation, in addition to applications. The scope of JESTT includes, Materials Science and Engineering Nanoscience and Nanotechnology Environmental and Applied Engineering Computational and Data-Driven Approaches Cybersecurity and Smart Systems
Scientific Collaborative Online Publishing Universal Academy
Gas sensors are evolving from passive detectors into intelligent systems that integrate functional materials, device engineering, and data-driven learning to interpret complex chemical environments. This evolution reflects a broader transition in electronics, in which sensing, computation, and decision-making are increasingly co-designed at the device and system levels. Although advances in nanomaterials and microfabrication have substantially improved sensitivity, power consumption, and form factor, most gas sensors continue to operate as reactive components, delivering electrical outputs without contextual interpretation. As applications shift toward distributed, wearable, and autonomous platforms, this limitation has become increasingly restrictive.
Computational and Data-Driven Approaches
Copyright (c) 2026 Naveed Ahmad (Author) https://creativecommons.org/licenses/by/4.0
An Integrated AI Virtual Assistant Platform Featuring Smart Display and Automation Capabilities
The arrival of Artificial Intelligence (AI) and the Internet of Things (IoT) has led to the development of programming-based environments for domestic and commercial applications of automation. This work is distributed into three sections: virtual assistant (VA), domestic robotics, and advanced user interface demonstration. A dissimilar public library is utilised for a virtual assistant and information to identify them, concluding the communication recognition procedure and then replying by viewing all the effects on the display screen. Domestic automation includes monitoring and changing applications. IoT-based methods were used to control the brightness of the bulb, and an IoT-based technique was used to control the speed of the DC fan. Finally, the output was displayed using a Graphical User Interface (GUI) constructed using the QT-designer tool and PyQT library. The primary value of this work is the ability to combine an AI-based virtual assistant, home automation, and a real-time graphical user interface into easy use system. Operator instructions are examined to deliver optimum solutions. The proposed scheme has better functional coverage, real-time visualisation, and better user interaction in comparison with the existing benchmark systems, which mostly concentrate on voice-based helpers and distributed IoT control systems. This scheme is recommended for use as an intellectual virtual assistant, which understands human speech and answers through created voices. This work is new because it is the first work where an AI virtual assistant, IoT based automatization, and real-time smart display are combined to make the interaction and constant situational awareness happen within one platform.
Copyright (c) 2026 Khurram Iqbal, Muniba Lodhi, Syed Saad Ali , Shujaat Ali, Liaquat Ali, Syed Iqbal Hussain (Author) https://creativecommons.org/licenses/by/4.0
Efficient Energy Consumption and Demand Response Using Deep Learning-Based Load Forecasting for Green Grid
This paper presents a deep learning-based load forecasting framework for efficient energy consumption and demand response in green grid computing. We employ Long Short-Term Memory (LSTM) networks with Recurrent Neural Network (RNN) cells to predict energy consumption patterns by analysing historical load data and external factors, including weather conditions, user activity, and temporal patterns. The proposed model achieves high prediction accuracy with Mean Absolute Error (MAE) of 5.2 kW, Root Mean Square Error (RMSE) of 7.1 kW, Mean Absolute Percentage Error (MAPE) of 1.3%, and coefficient of determination (R²) of 0.95, outperforming baseline methods including ARIMA (46% improvement in MAE) and traditional neural networks (29% improvement). Statistical validation using paired t-tests (p < 0.001) and the Diebold-Mariano test confirms significance. These results demonstrate the model’s effectiveness in enabling real-time decision-making for data centres and grid operators, with potential applications in day-ahead energy procurement, demand response optimisation, and renewable energy integration for medium-scale data centre facilities.
Copyright (c) 2026 Zuhaib Nishter, Muhammad Adeel Afzal, Sher Ali , Md Ashraful Islam , Taimoor Ali Khan (Author) https://creativecommons.org/licenses/by/4.0
Green-Synthesized Carbon Quantum Dots for Environmental Monitoring of Heavy Metals: A Critical Review of Mechanisms and Performance
The heavy-metal contamination of water bodies is still a crucial environmental issue and a hazard to the ecosystem as well as human health, and incomplete knowledge of how the synthesis of carbon quantum dots (CQD) can regulate the analytical performance of sensing technologies. Despite the benefits of CQDs reported in many studies, systemic linkages between preparation strategy, surface chemistry, photophysical behaviour, and sensing efficiency are yet to be explained well. This review presents a critical argument of the green bottom-up strategies to fabricate CQDs and compares them to the top-down methods in order to understand the way precursor selection, the introduction of heteroatoms, and the control of defects affect quantum yield, emission mechanisms, and environmental friendliness. The special focus is put on the fluorescence modulation mechanisms such as photoinduced electron transfer, Förster/energy transfer, inner filter effects and static versus dynamic quenching and their application in the detection of priority pollutants, including Pb(II), Hg(II), Cu(II), Cr(V) and Fe(III). In addition to publishing literature research, we compare reported detection limits, linear working ranges and selectivity factors to derive general performance patterns. The analysis has shown that controlled surface passivation and functional groups are the final determinants towards sub-micromolar sensitivity and better anti-interference properties. Other issues raised regarding reproducibility, complexity of real-world samples, long-term stability and scalable green production are also addressed. Combining synthesis-structure-property-performance correlations, this review develops empirical design guidelines of the next-generation CQD sensors and explains feasible ways of developing sustainable monitoring and mitigation of heavy-metal pollution in the environment.
Copyright (c) 2026 Muhammad Arslan Akhtar, Jalwa Anum , Abdul Rehman , Rabia (Author) https://creativecommons.org/licenses/by/4.0
Reactive Multiphase Flows: Interfacial Chemistry in Dynamic Transport Systems
Reactive multiphase flows underpin a wide range of energy, environmental, and chemical technologies, yet their predictive description remains limited. Although interfaces are now widely recognised as chemically active, most theoretical and computational frameworks continue to treat them as quasi-steady boundaries or incorporate their effects through empirical closures. This Perspective argues that such approaches are fundamentally inadequate when reaction, transport, and hydrodynamic timescales become comparable and when reaction-driven interfacial forces actively restructure phase distributions. We critically examine the limitations of prevailing modelling paradigms using dimensionless reasoning and order-of-magnitude estimates, highlighting how coupled Damköhler, Marangoni, and capillary effects undermine model transferability across operating regimes. By synthesising advances in operando diagnostics, interface-resolved simulations, and physics-informed data-driven methods, we propose a unifying framework that treats interfaces as dynamically evolving, rate-determining subsystems. We further articulate a set of concrete, testable research challenges that define a forward-looking agenda for predictive design. This perspective aims to shift reactive multiphase flow research from descriptive integration toward quantitatively grounded, mechanism-based understanding across diverse application domains.
Copyright (c) 2026 Asad Ullah, Samaha , Anus Nadeem , Muhammad Zahid Amin (Author) https://creativecommons.org/licenses/by/4.0
Evaluating Air Quality through Ion Detection: An Application of JEOL Mass Spectrometry in Pollution Studies
Spark source mass spectrometry provides a robust approach for multi-element analysis and controlled ion generation, yet its broader applicability remains insufficiently explored. This study evaluates the analytical performance of a JEOL JMS-01BM-2 spark source mass spectrometer using a controlled aluminium reference system and a heterogeneous airborne particulate matter sample. Stable detection of singly and multiply charged ions with reproducible mass-to-charge (m/z) separation is demonstrated across repeated measurements, confirming reliable ion generation and magnetic field-based ion selection. Aluminium is consistently resolved in multiple charge states, while aerosol analysis reveals a complex elemental composition characteristic of polluted urban–industrial atmospheres, including crustal, combustion-related, and trace metallic species. Differences between reference and aerosol samples highlight the influence of sample heterogeneity and electrode materials on ion formation. Although no direct ion implantation experiments are performed, the results establish the conceptual feasibility of adapting the instrument for ion selection processes relevant to implantation research. Simultaneously, the successful characterisation of airborne particulate matter demonstrates the utility of spark source mass spectrometry for qualitative and semi-quantitative air pollution assessment.
Copyright (c) 2026 Hafiza Rabia Naveed, Ghulam Sarwar Butt, Saira Riaz, Shazad Naseem (Author) https://creativecommons.org/licenses/by/4.0
AI/ML-Driven Design and Optimisation of Quantum Dots: A Perspective Toward Intelligent Materials Discovery
Quantum dots (QDs), nanoscale semiconductors with size-dependent and tunable optoelectronic properties, are central to next-generation technologies spanning displays, photovoltaics, bioimaging, and quantum information systems. However, their synthesis and optimisation remain challenging due to the intricate interplay of reaction parameters and nonlinear physicochemical interactions. The integration of artificial intelligence (AI) and machine learning (ML) is redefining this landscape, enabling predictive design, autonomous synthesis control, and accelerated discovery across the QD domain. This Perspective highlights the conceptual advances and methodological innovations driving AI/ML-assisted QD research, emphasising achievements in data-driven modelling, synthesis optimisation, and materials informatics. Persistent challenges, including data scarcity, model transparency, and limited generalizability, are critically examined, alongside emerging strategies toward physics-informed and autonomous discovery frameworks. We propose that the convergence of intelligent algorithms and human expertise will catalyse a paradigm shift from empirical experimentation toward rational, self-evolving materials design in quantum dot science.
Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
Assessment and Critical Review of PM0.1 Pollution in Pakistan
Particulate matter (PM) is among the most significant air pollutants globally, with severe implications for environmental integrity, human health, and climate stability. Among its various fractions, ultrafine particles (PM0.1, particles with an aerodynamic diameter ≤ 0.1 μm) are gaining increasing attention due to their high surface area-to-mass ratio, deep pulmonary penetration, and potential to translocate into systemic circulation and vital organs. This paper presents a comprehensive critical review of PM0.1 assessment in Pakistan, emphasising its sources, spatiotemporal distribution, measurement limitations, and health consequences. Despite the mounting evidence of air quality degradation in Pakistan, data on PM0.1 remain scarce and fragmented. The few available studies indicate that urban centres such as Lahore, Karachi, Islamabad, and Faisalabad exhibit ultrafine particle concentrations substantially higher than international safety benchmarks. Anthropogenic activities, including vehicular emissions, industrial combustion, biomass burning, and construction dust, are dominant contributors. This review identifies key gaps in current research, highlighting the lack of long-term monitoring, standardised methodologies, and toxicological assessments specific to PM0.1 exposure in local populations. It further stresses the urgent need for policy integration, investment in high-resolution monitoring technologies, and public health interventions. Overall, the assessment underscores that PM0.1 pollution in Pakistan poses an emerging environmental health crisis that remains scientifically underexplored and administratively underprioritized.
Natural Disaster Prediction and Mitigation through Machine Learning
Flooding remains a major natural disaster affecting Pakistan's provinces of Punjab, Sindh, Khyber Pakhtunkhwa, and Balochistan, with increasing severity due to climate change and human activities. This research explores the application of machine learning techniques to enhance flood prediction accuracy for the years 2025 to 2030. The study utilises historical hydro-meteorological data, including rainfall, temperature, and vegetation indices, to train four machine learning models: Decision Tree, Random Forest, Linear Regression, and Support Vector Machine (SVM). Standard evaluation metrics such as precision, recall, F1-score, and mean squared error (MSE) are used to assess model performance. Results show that Random Forest and SVM outperform the other models in terms of both accuracy and generalizability. These models effectively identify high-risk flood zones across the studied provinces. The findings demonstrate the potential of data-driven approaches to support early warning systems, enabling better disaster preparedness, resource allocation, and mitigation planning. This research highlights how machine learning can play a critical role in reducing flood-related risks and enhancing resilience against future natural disasters in Pakistan.
Neutrosophic Logic Reveals Ontological Indeterminacy in Quantum Systems
Quantum mechanics reveals a reality where uncertainty is not merely epistemic but ontological, challenging classical logics that rely on determinacy. We introduce neutrosophic logic as a novel framework to formalise this intrinsic indeterminacy. Unlike probability or fuzzy systems, neutrosophy treats truth (T), falsity (F), and indeterminacy (I) as independent dimensions, enabling a richer representation of quantum superposition, entanglement, and measurement collapse. We show that neutrosophic triplets capture the undefined yet real state of qubits before observation and provide a coherent description of entangled correlations beyond probabilistic models. This approach offers a unifying formalism that accommodates both structural indeterminacy and outcome probabilities, bridging foundational debates in quantum theory with practical advances in quantum computation and information science. By explicitly integrating indeterminacy into mathematical modelling, neutrosophy advances our understanding of quantum reality and suggests new avenues for experimental validation and quantum technology design.
Density Functional Theory: A Quantum Mechanical Framework for Novel Materials Design
Density Functional Theory (DFT) has become a fundamental principle of contemporary materials research, providing a quantum mechanical framework for the examination of matter at the electronic level. By changing the many-body problem into electron density, DFT makes it possible to make precise predictions of structural, electronic, and catalytic properties based on basic principles. Because it can make predictions, it has sped up the discovery of semiconductors, catalysts, and energy storage materials, which means we don't have to rely on expensive experiments as much. At the same time, projects like the Materials Project show how important it is for high-throughput computational design. Even if there are problems with the cost of processing and the accuracy of the results, new developments like hybrid methods, machine learning integration, and new quantum computing technologies keep making it more useful. So, DFT is not only a basic theoretical tool, but it is also a real driver of innovation in the creation of new materials.
AI-Powered Innovation in Flexible and Implantable Electronics: From Sensors to Systems
A significant change in wearable and implantable biomedical technology is being driven by the combination of flexible electronics and artificial intelligence (AI). Real-time, non-invasive monitoring of vital signs, including temperature, heart rate, and mobility, is made possible by flexible devices because of their soft, biocompatible, and conformable architecture. These devices improve wearability and user comfort while providing ongoing healthcare tracking. By facilitating intelligent data processing, adaptable functionality, and expedited material discovery, AI greatly improves gadget performance. AI-driven, context-aware wearable devices support applications such as motion tracking, gesture recognition, and health monitoring. AI enhances quality control in manufacturing by using neural networks and computer vision to find flaws in parts like flexible PCBs. The creation of ecologically friendly devices is aided by AI-assisted predictive modelling and autonomous labs, which also help find high-performance, sustainable materials. The future of self-sustaining biomedical devices is being shaped by significant improvements in energy harvesting, electronic skin (e-skin), and intelligent interfaces, despite persistent constraints such as hardware integration, power management, data privacy, and limited computing capability. In order to create intelligent, sustainable, and high-performing healthcare systems, this paper examines the relationship between AI and flexible electronics, highlighting significant technical developments, existing constraints and new potential.
Twisted Bilayer Graphene at the Magic Angle: A Review of Highly Correlated Physics and Unconventional Superconductivity
A novel platform for researching topological quantum phenomena, unusual superconductivity, and strongly coupled electron physics is Twisted Bilayer Graphene (TBG) at the “Magic Angle” (~1.05°). Two graphene layers are rotated to this exact angle to form a moiré superlattice, which produces flat electronic bands that significantly improve electron-electron interactions. This results in a rich phase diagram with non-Fermi liquid behaviour, correlated insulators, and superconductivity. This review covers the theoretical foundations, experimental results, and unanswered concerns about magic-angle TBG, including significant challenges like twist angle control, disorder effects, and the interaction of strain and electrical characteristics. Beyond basic research, TBG has great technological potential, with potential applications in neuromorphic devices, ultra-sensitive sensors, and quantum computing (e.g., topological qubits). This system provides a novel approach to comprehending high-temperature superconductivity and achieving next-generation quantum technologies by fusing insights from correlated physics with device engineering.
Electrochemical Biosensors for Real-Time Oxidative Stress Monitoring in Saliva
Oxidative stress, resulting from an imbalance between reactive oxygen species and antioxidant defences, is a critical factor in the onset and progression of numerous diseases, including cancer, cardiovascular conditions, and neurological disorders. Early detection of oxidative stress biomarkers is essential for timely diagnosis and effective treatment. Saliva has emerged as a highly attractive biofluid for this purpose due to its non-invasive, easily accessible, and cost-effective collection. Recent advancements in electrochemical biosensors have significantly enhanced the sensitivity, selectivity, and reliability of detecting oxidative stress indicators in saliva. These innovative sensing platforms enable real-time monitoring of key biomarkers at low concentrations, offering great potential for clinical and point-of-care applications. However, challenges such as sensor stability, biofouling, and interference from complex salivary components remain to be addressed to ensure robust performance in practical settings. This review summarises the latest developments in electrochemical biosensing of salivary oxidative stress biomarkers, highlights existing limitations, and discusses prospective strategies to overcome current barriers. The continued evolution of this technology promises to facilitate early disease detection and improve patient outcomes through accessible and precise oxidative stress monitoring.
Critical Review on the Phase Change Properties of VO₂: Influence of Synthesis Methods and Transition Mechanisms
Vanadium dioxide (VO₂) is a strongly correlated material that exhibits a reversible metal-insulator transition (MIT) near room temperature (~68°C), accompanied by a structural phase transition from a monoclinic (M1, insulating) to a tetragonal rutile (R, metallic) phase. This unique property makes VO₂ a promising candidate for applications in smart windows, optoelectronic switches, and thermal sensors. However, the phase transition characteristics, such as transition temperature, hysteresis width, and optical/electrical contrast, are highly dependent on the synthesis method. This review critically examines the impact of different synthesis techniques (e.g., chemical vapour deposition, sol-gel, sputtering, pulsed laser deposition, and hydrothermal synthesis) on the phase transition properties of VO₂. A comparative analysis is presented, highlighting how stoichiometry, strain, defects, and doping influence the MIT. Additionally, the fundamental chemistry governing the phase transition is discussed, with a focus on electron correlation effects and lattice dynamics. A comprehensive comparison table summarises the synthesis methods, their effects on phase transition properties, and key challenges.
First-Principles Study of Cubic CsSrF₃: Effects of Li Doping on Structural, Electronic and Optical Properties
In this work, the structural, electronic, optical, and elastic properties of cubic fluoro-perovskite CsSrF₃ and its lithium-doped derivatives (Cs₁₋ₓLiₓSrF₃, x = 0–1) are systematically investigated using first-principles density functional theory (DFT) calculations utilizing the generalized gradient approximation using the Perdew–Burke–Ernzerhof (GGA-PBE) functional. To evaluate the structural stability, the total energy is fitted to the Birch–Murnaghan equation of state, resulting in equilibrium lattice constants of 5.000 Å for Pristine CsSrF₃ and 5.076 Å for the doped composition (x = 1). The electronic band structure study shows that Pristine CsSrF₃ has an indirect bandgap of 5.543 eV, which gradually narrows to 4.011 eV with full lithium substitution. Li-induced changes in electronic states close to the Fermi level are responsible for this trend. The valence and conduction bands are primarily controlled by the F-p and Sr-s orbitals, respectively, according to Density of States (DOS) studies. Optical properties demonstrate higher tunability with the doping of lithium, the refractive index varies between 1.78 and 1.40 eV, and the reflectivity decreases from 0.27 eV to 0.10 eV. These characteristics indicate considerable amounts of potential for photovoltaic and UV applications. Mechanical investigation shows anisotropic and ductile behaviour, with predicted bulk modulus values ranging from −9.890 GPa (Pristine) to −17.311 GPa (x = 1), demonstrating a softening effect with increased Li concentration. Ultimately, the findings highlight the potential of Pristine CsSrF₃ and its Li-doped concentrations for the implementation into energy-harvesting and next-generation optoelectronic systems.
The Human Rights Implications of Scientific Progress: A Case Study on Gene Editing and Disability Rights
Gene editing tools like CRISPR-Cas9 hold great promise for treating genetic diseases but also raise important ethical concerns, especially regarding disability rights. While gene editing can eliminate inherited diseases, it could also worsen ableism and widen social divides by reinforcing discrimination against people with disabilities. This article will talk about the ethical challenges of gene editing, focusing on the impact on marginalized groups, such as the Deaf community, who see their conditions as part of their identity rather than something to be fixed. The research will focus on practical examples, like gene therapy for sickle cell disease and editing embryos for hearing impairments, and highlight the limited access to these technologies, which could deepen inequality. It calls for stronger global guidelines that include input from disability communities to prevent the technology from reinforcing social divisions. The results show that without clear limits, gene editing may lead to a society that values genetic traits over diversity and human dignity, urging policies that promote fairness and inclusion.
Atomistic DFT simulations are promising techniques for the discovery of hydrogen storage materials
Efficient hydrogen storage for automobiles necessitates materials with high storage capacities, moderate dehydrogenation temperatures, and rapid kinetics for desorption and absorption. However, there are presently no known materials that exhibit all of these qualities and can be reversed. In this presentation, we provide a summary of our recent endeavours focused on creating a fundamental computational method for identifying new hydrogen storage materials. To ensure effectiveness, this technique necessitates the following essential capabilities: (i) precise forecasting of thermodynamics related to decomposition, (ii) anticipation of crystal structures for hydrides that are not yet identified, and (iii) prediction of preferred decomposition and dehydrogenation temperatures. This study demonstrates the capability of atomistic DFT modelling in identifying new materials suitable for hydrogen storage applications.
Improved Solar Power Prediction Using CNN-LSTM Models for Optimized Smart Grid Performance
During the fourth energy revolution, the integration of Artificial Intelligence (AI) across various technological fields is critical to meet rising energy demands and address the depletion of fossil fuel reserves, leading to the adoption of smart grids. This study aims to enhance power generation capacity and minimize losses in smart grids by accurately predicting parameters. Traditional power grid stations transitioning to smart grids require precise parameter predictions. To achieve this, we employed AI-based machine learning models, specifically Random Forest (RF) and Long Short-Term Memory (LSTM), to predict the parameters of a solar power plant. After initial analysis through graphical visualization, we further refined the LSTM model using an advanced technique: Convolutional Neural Network (CNN-LSTM). Comparative results indicate that the CNN-LSTM model outperforms both the LSTM and RF models. For daily power generation, the CNN-LSTM achieved the lowest Mean Absolute Error (MAE) of 0.1335 and Mean Squared Error (MSE) of 0.0497. Consequently, the application of AI in this study significantly improves the accuracy of parameter prediction, enhancing the performance of basic machine learning models. This advancement supports the development of a robust and efficient power system that reduces power losses and boosts production capacity within the framework of smart grids.
Monitoring Flood Waves Due to Overtopping: Case Study of Mosul Dam from Iraq
When highlighting water resource exploitation and environmental management, interest in dam break hydraulics is growing due to the possibility of hydrological events brought on by climate change and the catastrophic dam breaches that often result in severe loss of life. Several areas are exposed to flooding risk due to flood waves resulting from dam failure. The current study aims to apply a numerical model that predicts the flood wave's characteristics due to a hypothetical overtopping failure in two dimensions. A numerical model (hydrologic engineering center river analysis system 2D HEC-RAS) is implemented in this study. Mosul Dam, located on the Tigris River in Iraq, is utilized as a case study in this work. The 2D flow area was delineated on the digital elevation model to determine the inundation region and extracted n manning from a land cover layer connected with RAS MAPPER. The results explained that the maximum values of the depth and velocity in km 5 are 60 m and 10.10 m/sec, respectively. At the same moment, it is shown that maximum flood discharge happened near the dam body and that there is a roughly linear relationship between the flood flow and water surface elevation. The study concluded that the models are successful in analyzing the dam breaches by representing the variation of flood wave depth and velocity in two dimensions. Moreover, the ability of the Model to produce maps monitors the spread of hydrodynamic waves that indicate risk areas that are threatened by inundation, which aids in controlling the risks resulting from them.
Exploring The Influence of Social Network Sites on Students' Academic Achievement: The Moderating Effect of Social Support
The widespread use of social networking sites SNS among university students has raised deep concerns about their impact on the academic performance of the students. This study examines the relationship between SNS usage, academic performance and social support among university students. A quantitative research approach was employed in a self-report questionnaire with data collected from 200 University students in Mardan Pakistan. The results showed a positive correlation between SNS usage and academic performance as well as between social support and academic performance. Regression analysis reveals that SNS usage is a significant predictor of academic performance. Mediation analysis showed that social support mediates the link in between the SNS usage and academic performance. The study reveals that the impact of social networking sites usage on academic performance is dual edged varying according to the manner of usage. The study's results have highlighted important areas of concern for many stakeholders like educator policymakers and researchers highlighting the need to consider the role of social media and academic settings in developing strategies that promote responsible social media use among students.
Cross-Border Data Flows in Pakistan: Legal Challenges and Technological Solutions for Digital Trade
The rapid growth of digital trade has highlighted the importance of cross-border data flow in the global economy. However, emerging economies, such as Pakistan, face unique challenges in managing these flows due to the absence of comprehensive data protection laws and the complexities of international regulations. This study examines the legal and technological landscape of cross-border data flows in Pakistan, identifies key challenges, and proposes solutions to enhance the country's digital trade ecosystem. Through a doctrinal legal approach and comparative analysis, this study reveals that Pakistan's fragmented regulatory environment, potential data localization requirements, and jurisdictional conflicts with extraterritorial data protection laws create significant barriers to efficient data governance. To address these challenges, this study explores the potential of cloud computing, blockchain, and artificial intelligence (AI) technologies to facilitate secure, transparent, and compliant data management. Although these technologies offer promising solutions, their adoption in Pakistan is hindered by limited digital infrastructure and cybersecurity capabilities. The paper concludes that Pakistan must enact a comprehensive data protection law aligned with international standards and invest in capacity-building initiatives to fully leverage these technologies and integrate them into the global digital economy. By doing so, Pakistan can create a secure, efficient, and compliant digital trade environment, attracting foreign investment and fostering innovation.
Overview of Control and Monitoring Specification Study to Help Reach Net Zero in the UK for Hydrogen Flow Measurement Network
Currently, environmental damage appears a worldwide issue that affects everyone on the globe. With the goal of a net-zero carbon footprint by the year 2050, many nations have passed legislation, provided aid, and raised both public and private investment to encourage the use of renewable energy sources instead of traditional pathways like fossil fuels. Many other countries have made similar intentions to initiate their journey to an economy with no emissions at all including the UK. The current objective of this study is to review equipment for monitoring techniques and metering which helps to deal with the modern industry to attain net zero emission through the application of hydrogen. Ultrasonic and Coriolis flowmeters have been compared in terms of measurement techniques for hydrogen in the operative system. It has been identified that the petroleum and natural gas industrial sectors are using mass flow monitoring more frequently. Beyond absolute zero and at atmospheric pressures hydrogen becomes liquified. So, it proved essential to have a huge mass flow measuring apparatus having minimal uncertainty and proper and fixed measurement inaccuracy. Moreover, it has been evaluated that Programmable logic controllers are being used to operate the system to create a trustworthy control framework.
New aspect of Management Engineering: Connotation between FDI Inflow, Gender Gap, Educational Attainment and Skilled Workforce
This research aimed to investigate and explore the empirical relationship between foreign direct investment (FDI) and the workforce at different educational levels. Additionally, it sought to enable national assessments of education statistics and indicators using widely accepted definitions. Pakistan is examined in this analysis. The empirical results imply that inward FDI is positively influenced by a workforce with higher education. Higher education tends to have the biggest impact; nevertheless, educational levels are not equally significant. In terms of gender, it appears that the proportion of women in the workforce is important for drawing foreign direct investment (FDI), consequently the authorities ought to develop measures which promote the equality of women.
A Comprehensive Overview of Photon-Proton Scattering and QCD Methodology
The AdS/CFT correspondence is used as a helpful reference in the light front hyperbolic geometry technique, which solves this issue by mapping a confining gauge theory parameterized on the light front to a greater anti-de Sitter space. Three different interaction processes exist Direct or angular the target photon quark and the photon pair directly. When a lepton-antilepton pair is produced, only quantum electrodynamics (QED) is used; however, when a quark-antiquark pair is produced, both QED and perturbative quantum chromodynamics (QCD) are used. A deep-inelastic electron-photon scattering experiment was used to study the photon structure function, which describes the photon inherent quark composition: Single resolved: the desired spectroscopy quark combination creates the vector meson, one of the constituents of relationships to the investigating photon. The main focus of the current study was on photon-proton and QCD methodology. The main theoretical conclusion resulting from the work carried out can be used in the development of the conceptual concept of further researchers and this work also will be a guideline for future researchers.
Seq2Seq-Based-Day-Ahead Scheduling for SCUC in Islanded Power Systems with Limited Intermittent Generation
Due to their dependence on intermittent renewable energy sources, island power systems, which are generally located in remote places or on islands, offer particular issues for day-ahead scheduling. Using the capabilities of neural networks, we offer a Seq2Seq-based technique for day-ahead scheduling, which increases the precision and flexibility of unit commitment choices. The attention mechanisms in the Seq2Seq model are trained with historical data that includes projections for intermittent generation, demand, and unit commitment choices. The model is tested for its capacity to incorporate dynamic temporal relationships and deal with regenerative uncertainty. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. In comparison to conventional scheduling approaches, the results show potential gains in prediction accuracy and operational efficiency. This study demonstrates how Seq2Seq models may be used to improve the longevity and dependability of isolated electrical grids and the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization.
An Insight into Optical Constants of Iron Phenanthroline Molecules
In this study, we investigate the optical properties of Iron 1,10-phenanthroline molecular nanolayer synthesized were prepared using the complexation reactions method. Fe2+(Phen) molecular thin films were then prepared as samples and subsequently characterized using XRD and UV-Vis spectroscopy. The main objective of this work is to explore the optical constants relevant to the light-induced spin crossover phenomenon in this molecule. UV-Vis study of nanometer thickness layer (100 nm) at wavelength (), the distribution of refractive index is discussed in the framework of the single oscillator Wemple-DiDomenico model and various distribution parameters such as single oscillator energy (Eo), dispersion energy (Ed), refractive index (n(0)), and optical absorption based on absorption edges dielectric constant (e¥) using Miller's rule. The moment of the dielectric constant optical spectrum (M-1, M-3) and energy gap by Wemple-DiDomenico approximation () have been computed, as has non-linear optical susceptibility (), direct energy gap by Tauc relation (), and Urbach energy of the localized states (Eu).
Mega Hydropower Projects and Sustainable Development of Pakistan
This research paper aims to contribute to discussing the impact of mega hydro projects on sustainable development in Pakistan. It is essential to carefully consider the long-term impact of these projects on social, economic, and environmental factors to ensure that they contribute to sustainable development and the population's well-being. Data was collected through a combination of primary and secondary sources; primary sources were field observations, while secondary sources were government reports, academic articles, and other relevant literature. While the project has the potential to generate clean energy and contribute to economic development, it also has negative impacts on the environment and local communities. To contribute to sustainable development of Pakistan, while minimizing its potential negative impact on the environment, social well-being, and economic growth of the region. The study recommendations aim to ensure that the Sukhi Kinari Dam contributes to sustainable development in Pakistan while minimizing its potential negative impact on the environment, social well-being, and economic growth of the region.
Identity Theft in the Digital Age: Legal Gaps, Enforcement Challenges, and the Need for Global Reform
Identity theft has grown into a major international issue that hurts everyone from individual people to banks and government departments. This research examines international legal frameworks designed to combat identity theft. The study examines critical laws such as the Identity Theft and Assumption Deterrence Act, Theft Penalty Enhancement Act, Fair Credit Reporting Act, and Fair and Accurate Credit Transactions (FACT) Act, among others. The study reveals that inconsistencies in enforcement across jurisdictions reduce the effectiveness of identity theft laws. According to analysis, identity theft keeps growing as one of today's most common crimes because people conduct financial transactions online and cyber security poses significant weaknesses. The results call for better consumer security measures plus stronger connections between federal and state agencies along with new anti-fraud technology. The findings recommend that governments create better data security laws while working together across borders and building better support for victims of identity theft. Ongoing improvement of identity theft laws plus education about it helps victims avoid both monetary loss and emotional distress.
Review of Various Aspects of Digital Violence
This study aims to elaborate on different aspects of digital violence; several key trends and challenges are shaping the landscape of digital law. With the rise in data breaches and the misuse of personal information, governments are likely to implement more stringent data protection regulations. Innovations such as blockchain for secure data storage and AI for compliance monitoring and enforcement are expected to play significant roles. As cyber threats become more sophisticated and cross-border in nature, international cooperation and harmonization of cyber security laws will be crucial. Determining liability for cyber incidents will be an expanding area, including the responsibility of companies to protect data and the extent of government oversight.