Journal of Engineering, Science and Technological Trends
type: channel
description: 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
publisher: Scientific Collaborative Online Publishing Universal Academy
language: en
publication name: Journal of Engineering, Science and Technological Trends
issn: 2959-1937
items: Role of Pectinases and Carbohydrate-Degrading Enzymes in Biosensing Applications
items: Mitigating the Grey Sheep Problem in Collaborative Filtering via Hybrid Machine Learning Architectures
items: Neutrosophic Modelling of Qubit Uncertainty in Noisy Quantum Computing Systems
items: Nature-Derived Polymers for Next-Generation Sustainable Electronics
items: Toward Intelligent Gas Sensors
items: Stimuli-Response in Organic Cocrystals with Different Donor-Acceptors
items: An Integrated AI Virtual Assistant Platform Featuring Smart Display and Automation Capabilities
items: Efficient Energy Consumption and Demand Response Using Deep Learning-Based Load Forecasting for Green Grid
items: Green-Synthesized Carbon Quantum Dots for Environmental Monitoring of Heavy Metals: A Critical Review of Mechanisms and Performance
items: Reactive Multiphase Flows: Interfacial Chemistry in Dynamic Transport Systems
items: Evaluating Air Quality through Ion Detection: An Application of JEOL Mass Spectrometry in Pollution Studies
items: AI/ML-Driven Design and Optimisation of Quantum Dots: A Perspective Toward Intelligent Materials Discovery
items: Assessment and Critical Review of PM0.1 Pollution in Pakistan
items: Natural Disaster Prediction and Mitigation through Machine Learning
items: Neutrosophic Logic Reveals Ontological Indeterminacy in Quantum Systems
items: Density Functional Theory: A Quantum Mechanical Framework for Novel Materials Design
items: AI-Powered Innovation in Flexible and Implantable Electronics: From Sensors to Systems
items: Twisted Bilayer Graphene at the Magic Angle: A Review of Highly Correlated Physics and Unconventional Superconductivity
items: Electrochemical Biosensors for Real-Time Oxidative Stress Monitoring in Saliva
items: Critical Review on the Phase Change Properties of VO₂: Influence of Synthesis Methods and Transition Mechanisms
items: First-Principles Study of Cubic CsSrF₃: Effects of Li Doping on Structural, Electronic and Optical Properties
items: Identity Theft in the Digital Age: Legal Gaps, Enforcement Challenges, and the Need for Global Reform
items: The Human Rights Implications of Scientific Progress: A Case Study on Gene Editing and Disability Rights
items: Overview of Control and Monitoring Specification Study to Help Reach Net Zero in the UK for Hydrogen Flow Measurement Network
items: New aspect of Management Engineering: Connotation between FDI Inflow, Gender Gap, Educational Attainment and Skilled Workforce
items: A Comprehensive Overview of Photon-Proton Scattering and QCD Methodology
items: Seq2Seq-Based-Day-Ahead Scheduling for SCUC in Islanded Power Systems with Limited Intermittent Generation
items: An Insight into Optical Constants of Iron Phenanthroline Molecules
items: Mega Hydropower Projects and Sustainable Development of Pakistan
items: Exploring The Influence of Social Network Sites on Students' Academic Achievement: The Moderating Effect of Social Support

Role of Pectinases and Carbohydrate-Degrading Enzymes in Biosensing Applications
type: item
description: Enzyme-based biosensors have emerged as powerful analytical platforms for transforming specific biochemical interactions into measurable signals, enabling rapid, sensitive, and selective monitoring across healthcare, food safety, environmental analysis, and industrial biotechnology. However, current enzyme biosensing research has predominantly focused on oxidoreductases, while carbohydrate-active enzymes (CAZymes) remain comparatively underexplored despite their exceptional substrate specificity and catalytic diversity. This review provides a comprehensive overview of carbohydrate-degrading enzyme-based biosensors, highlighting their fundamental mechanisms, sensing strategies, and future technological opportunities. Particular emphasis is placed on pectinases, including pectin methylesterase, polygalacturonase, and pectin lyase, which offer unique capabilities for detecting pectin degradation, plant physiological changes, food quality deterioration, and environmental processes. The catalytic principles of these enzymes, their production approaches, and their integration with electrochemical, optical, piezoelectric, and colourimetric transduction systems are systematically discussed. Furthermore, advanced immobilisation approaches involving nanomaterials, microfluidic architectures, and hybrid enzyme–nanozyme systems are examined to address challenges associated with enzyme instability, sensitivity, matrix interference, and reproducibility. Emerging concepts including artificial intelligence-assisted biosensing, wearable sensing platforms, multiplex detection, and sustainable biodegradable sensor designs are also explored as pathways toward next-generation intelligent biosensors. Despite significant progress, practical translation remains limited by substrate variability, large-scale manufacturing challenges, and insufficient standardisation. Future advances in enzyme engineering, smart data analysis, and integrated sensing technologies are expected to establish robu…
subject: Articles
  topic: section
subject: Materials Science and Engineering
  topic: category
subject: Nanoscience and Nanotechnology
  topic: category
subject: Biosensing
  topic: keywords
subject: Carbohydrate-Active Enzymes
  topic: keywords
subject: Enzyme Engineering
  topic: keywords
subject: Nanomaterials
  topic: keywords
subject: Pectinase
  topic: keywords
creator: Rahmat Ullah
rights: Copyright (c) 2026 Rahmat Ullah (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-07-10
publication date: 2026-07-10
starting page: 33
ending page: 46
doi: 10.64060/JESTT3i25

Mitigating the Grey Sheep Problem in Collaborative Filtering via Hybrid Machine Learning Architectures
type: item
description: In every nation, the major source of entertainment is movies. Worldwide, thousands of people watch movies to feel relaxed and escape from depression. The huge amount of data is an issue that is getting worse in online media. A System is created by using data mining techniques that helps to anticipate future trends, known as a prediction system or filtering system. In this system, the term grey sheep problem refers to users with unique preferences and interests that make it difficult to predict movies. In this research, Machine Learning models are used to identify the grey sheep problem and enhance movie search. A dataset of 15,538 movies was used and different attributes were selected that are publicly available on MovieLens. Then five main data mining techniques were used: Logistic Regression, Support Vector Machine, K-Nearest Neighbour, Naïve Bayes, and Decision Tree. All of the techniques gave their accuracy values, but out of all, Decision Tree gave the highest accuracy of 0.99% for movie prediction for grey sheep users.
subject: Articles
  topic: section
subject: Computational and Data-Driven Approaches
  topic: category
subject: Machine Learning Algorithms
  topic: keywords
subject: Movies
  topic: keywords
subject: Algorithm
  topic: keywords
subject: Movie Lens
  topic: keywords
subject: Grey Sheep Users
  topic: keywords
subject: Hybrid Learning
  topic: keywords
creator: Fiza Noreen
creator: Qurat Ul Aen Naqvi
creator: Awais Rasool
creator: Nimra Razzaq
creator: Fatima Abbas
creator: Bira Alam
rights: Copyright (c) 2026 Fiza Noreen, Qurat Ul Aen Naqvi, Awais Rasool , Nimra Razzaq , Fatima Abbas, Bira Alam (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-07-10
publication date: 2026-07-10
starting page: 24
ending page: 32
doi: 10.64060/JESTT3i24

Neutrosophic Modelling of Qubit Uncertainty in Noisy Quantum Computing Systems
type: item
description: Quantum computing exploits superposition and entanglement to perform computations beyond the capabilities of classical machines. However, practical quantum systems operate under substantial environmental noise, measurement uncertainty, and decoherence, leading to ambiguous or partially determined quantum states. Conventional probabilistic descriptions capture stochastic behaviour but do not explicitly represent indeterminacy arising from incomplete information or measurement ambiguity. In this work, we introduce a neutrosophic representation of qubit states that integrates principles of Neutrosophic Logic with the mathematical framework of Quantum Computing. In the proposed model, a qubit is characterised by three independent parameters representing truth, indeterminacy, and falsity. This formulation enables explicit representation of ambiguous quantum states and uncertain measurement outcomes. We extend the neutrosophic formalism to quantum gate operations and analyse how the model captures uncertainty propagation in quantum circuits. Through theoretical examples and graphical analysis, we demonstrate that neutrosophic representation provides a richer description of uncertainty compared with conventional probabilistic approaches. The proposed framework offers a conceptual pathway for improved modelling of noisy intermediate-scale quantum devices and may contribute to the development of uncertainty-aware quantum computing architectures.
subject: Articles
  topic: section
subject: Nanoscience and Nanotechnology
  topic: category
subject: Computational and Data-Driven Approaches
  topic: category
subject: Ambiguous Quantum States
  topic: keywords
subject: Neutrosophic Logic
  topic: keywords
subject: Noisy Quantum Systems
  topic: keywords
subject: Quantum Circuits
  topic: keywords
subject: Quantum Computing
  topic: keywords
subject: Uncertainty Modeling
  topic: keywords
creator: Muhammad Raza Sheikh
rights: Copyright (c) 2026 Muhammad Raza Sheikh (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-11
publication date: 2026-05-11
starting page: 7
ending page: 13
doi: 10.64060/JESTT3i22

Nature-Derived Polymers for Next-Generation Sustainable Electronics
type: item
description: The rapid growth of modern electronic technologies has led to a significant increase in electronic waste, posing major environmental and sustainability challenges. Most conventional electronic devices rely on petroleum-based polymers that are non-biodegradable and environmentally persistent, creating an urgent need for sustainable material alternatives. Natural polymers derived from renewable biomass have emerged as promising candidates for next-generation sustainable electronics due to their biodegradability, biocompatibility, abundance, and low environmental impact. This perspective highlights recent progress in natural polymer-based electronic materials, including polysaccharide-based polymers, protein-based polymers, and natural polymer nanocomposites. Their applications in flexible and wearable electronics, transient electronics, and sustainable sensing devices are discussed. Key strategies such as molecular engineering, nanocomposite design, and green processing methods are explored to improve electrical performance and stability. Major challenges, including limited conductivity, moisture sensitivity, thermal stability, and device integration, are also addressed, along with future opportunities for developing fully biodegradable and environmentally friendly electronic systems.
subject: Articles
  topic: section
subject: Materials Science and Engineering
  topic: category
subject: Nanoscience and Nanotechnology
  topic: category
subject: Biodegradable electronics
  topic: keywords
subject: Biopolymer nanocomposites
  topic: keywords
subject: Flexible electronics
  topic: keywords
subject: Green electronics
  topic: keywords
subject: Natural polymers
  topic: keywords
subject: Sustainable electronics
  topic: keywords
subject: Transient electronics
  topic: keywords
subject: Wearable sensors
  topic: keywords
creator: Aqsa Nadeem
creator: Asim Ullah
rights: Copyright (c) 2026 Aqsa Nadeem, Asim Ullah (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-16
publication date: 2026-06-16
starting page: 14
ending page: 23
doi: 10.64060/JESTT3i23

Toward Intelligent Gas Sensors
type: item
description: 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.
subject: Editorial
  topic: section
subject: Materials Science and Engineering
  topic: category
subject: Nanoscience and Nanotechnology
  topic: category
subject: Environmental and Applied Engineering
  topic: category
subject: Computational and Data-Driven Approaches
  topic: category
subject: Cybersecurity and Smart Systems
  topic: category
subject: Intelligent Gas Sensors
  topic: keywords
subject: Editorial
  topic: keywords
creator: Naveed Ahmad
rights: Copyright (c) 2026 Naveed Ahmad (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 1
ending page: 2
doi: 10.64060/jestt.v3i1.1

Stimuli-Response in Organic Cocrystals with Different Donor-Acceptors
type: item
description: Organic cocrystal engineering refers to the stoichiometric combination of two or more organic molecules through non-covalent intermolecular interactions, but achieving excellent electrical and optical properties remains a challenge due to the lack of clear directions for the rational design of co-crystals. In addition, herein we compare and report these three new cocrystals' chemical and physical properties, crystalline structures and determined response to acid stimuli. This investigation of organic cocrystals contains intermolecular interactions and growth methods, especially the solvent evaporation approach. As a result, we synthesised three new organic cocrystals; among these cocrystals, 3-ATC has the highest response to acid stimuli (different acid fuming). The cocrystal’s response time is also in the order of seconds, which is much faster than that of most other stimuli-responsive cocrystals previously described. 3-ATC is a highly sensitive fluorescence switching exhibited under various acid stimuli. But, the other two cocrystals (AETC and DTTC) are strong enough to resist acid stimuli; this is due to the close face-to-face and mixed stacking patterns with CT and π-π interactions. These three cocrystals exhibited different colours and low-dimensional (1D, 2D) morphology structures.
subject: Research Article
  topic: section
subject: Materials Science and Engineering
  topic: category
subject: Acid stimuli
  topic: keywords
subject: Cocrystal growth
  topic: keywords
subject: CT interactions
  topic: keywords
subject: Organic cocrystals
  topic: keywords
subject: Solvent evaporation
  topic: keywords
creator: Darya khan Hassand
creator: Nawrooz Ali Zahedi
creator: Abdul Samad Hamid
creator: Mohammad Sharif Stanikzai
rights: Copyright (c) 2026 Darya khan Hassand, Nawrooz Ali Zahedi, Abdul Samad Hamid, Mohammad Sharif Stanikzai (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-11
publication date: 2026-05-11
starting page: 1
ending page: 6
doi: 10.64060/JESTT3i21

An Integrated AI Virtual Assistant Platform Featuring Smart Display and Automation Capabilities
type: item
description: 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.
subject: Articles
  topic: section
subject: Computational and Data-Driven Approaches
  topic: category
subject: Cybersecurity and Smart Systems
  topic: category
subject: Internet of Things
  topic: keywords
subject: Virtual Assistant
  topic: keywords
subject: Advance Automation
  topic: keywords
subject: Natural language processing
  topic: keywords
creator: Khurram Iqbal
creator: Muniba Lodhi
creator: Syed Saad Ali
creator: Shujaat Ali
creator: Liaquat Ali
creator: Syed Iqbal Hussain
rights: 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
date: 2026-03-01
publication date: 2026-03-01
starting page: 26
ending page: 32
doi: 10.64060/jestt.v3i1.5

Efficient Energy Consumption and Demand Response Using Deep Learning-Based Load Forecasting for Green Grid
type: item
description: 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.
subject: Articles
  topic: section
subject: Materials Science and Engineering
  topic: category
subject: Environmental and Applied Engineering
  topic: category
subject: Data Centers
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Green Grid Computing
  topic: keywords
subject: Load Forecasting
  topic: keywords
subject: Long Short-Term Memory (LSTM)
  topic: keywords
subject: Sustainable Energy Management
  topic: keywords
subject: Recurrent Neural Networks (RNN)
  topic: keywords
creator: Zuhaib Nishter
creator: Muhammad Adeel Afzal
creator: Sher Ali
creator: Md Ashraful Islam
creator: Taimoor Ali Khan
rights: Copyright (c) 2026 Zuhaib Nishter, Muhammad Adeel Afzal, Sher Ali , Md Ashraful Islam , Taimoor Ali Khan (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 16
ending page: 25
doi: 10.64060/jestt.v3i1.4

Green-Synthesized Carbon Quantum Dots for Environmental Monitoring of Heavy Metals: A Critical Review of Mechanisms and Performance
type: item
description: 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.
subject: Articles
  topic: section
subject: Materials Science and Engineering
  topic: category
subject: Nanoscience and Nanotechnology
  topic: category
subject: Environmental and Applied Engineering
  topic: category
subject: CQD
  topic: keywords
subject: Carbon Quantum Dots
  topic: keywords
subject: Environmental Monitoring
  topic: keywords
subject: Fluorescence Quenching
  topic: keywords
subject: Green Synthesis
  topic: keywords
subject: Heavy Metal Detection
  topic: keywords
creator: Muhammad Arslan Akhtar
creator: Jalwa Anum
creator: Abdul Rehman
creator: Rabia
rights: Copyright (c) 2026 Muhammad Arslan Akhtar, Jalwa Anum , Abdul Rehman , Rabia (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 33
ending page: 51
doi: 10.64060/jestt.v3i1.6

Reactive Multiphase Flows: Interfacial Chemistry in Dynamic Transport Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Materials Science and Engineering
  topic: category
subject: Nanoscience and Nanotechnology
  topic: category
subject: Carbon capture
  topic: keywords
subject: Electrochemical systems
  topic: keywords
subject: Interfacial phenomena
  topic: keywords
subject: Multiphase flow
  topic: keywords
subject: Reactive transport
  topic: keywords
subject: Sustainable chemical engineering
  topic: keywords
creator: Asad Ullah
creator: Samaha
creator: Anus Nadeem
creator: Muhammad Zahid Amin
rights: Copyright (c) 2026 Asad Ullah, Samaha , Anus Nadeem , Muhammad Zahid Amin (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 11
ending page: 15
doi: 10.64060/jestt.v3i1.3

Evaluating Air Quality through Ion Detection: An Application of JEOL Mass Spectrometry in Pollution Studies
type: item
description: 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.
subject: Articles
  topic: section
subject: Environmental and Applied Engineering
  topic: category
subject: Aluminum
  topic: keywords
subject: air particles
  topic: keywords
subject: electrical detection system
  topic: keywords
subject: ion implanter
  topic: keywords
subject: spark source mass spectrometer
  topic: keywords
creator: Hafiza Rabia Naveed
creator: Ghulam Sarwar Butt
creator: Saira Riaz
creator: Shazad Naseem
rights: Copyright (c) 2026 Hafiza Rabia Naveed, Ghulam Sarwar Butt, Saira Riaz, Shazad Naseem (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 3
ending page: 10
doi: 10.64060/jestt.v3i1.2

AI/ML-Driven Design and Optimisation of Quantum Dots: A Perspective Toward Intelligent Materials Discovery
type: item
description: 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.
subject: Research Article
  topic: section
subject: Materials Science and Engineering
  topic: category
subject: Nanoscience and Nanotechnology
  topic: category
subject: Computational and Data-Driven Approaches
  topic: category
subject: Artificial intelligence (AI)
  topic: keywords
subject: Autonomous synthesis
  topic: keywords
subject: Data-driven modelling
  topic: keywords
subject: Intelligent materials discovery
  topic: keywords
subject: Machine learning (ML)
  topic: keywords
subject: Materials informatics
  topic: keywords
subject: Optoelectronic properties
  topic: keywords
subject: Physics-informed learning
  topic: keywords
subject: Predictive design
  topic: keywords
subject: Quantum dots (QDs)
  topic: keywords
subject: Synthesis optimization
  topic: keywords
creator: Asma Batool
creator: Tanzeela Urooj
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-11-15
publication date: 2025-11-15
doi: 10.64060/jestt.v2i3.5

Assessment and Critical Review of PM0.1 Pollution in Pakistan
type: item
description: 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.
subject: Research Article
  topic: section
subject: Environmental and Applied Engineering
  topic: category
subject: Air pollution
  topic: keywords
subject: Air quality monitoring
  topic: keywords
subject: Combustion emissions
  topic: keywords
subject: Environmental policy
  topic: keywords
subject: Health effects
  topic: keywords
subject: Ultrafine particles
  topic: keywords
creator: Faiza Irfan
creator: Rida Munir
creator: Sadique Akbar
creator: Ramna Zafar
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-11-15
publication date: 2025-11-15
doi: 10.64060/jestt.v2i3.4

Natural Disaster Prediction and Mitigation through Machine Learning
type: item
description: 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.
subject: Research Article
  topic: section
subject: Environmental and Applied Engineering
  topic: category
subject: Climate Change
  topic: keywords
subject: Disaster management
  topic: keywords
subject: Flood Forecasting
  topic: keywords
subject: Machine learning
  topic: keywords
creator: Fatima Abbas
creator: Dr. Jamil Afzal
creator: Sidra Shahid
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-11-15
publication date: 2025-11-15
doi: 10.64060/jestt.v2i3.2

Neutrosophic Logic Reveals Ontological Indeterminacy in Quantum Systems
type: item
description: 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.
subject: Research Article
  topic: section
subject: Materials Science and Engineering
  topic: category
subject: Computational and Data-Driven Approaches
  topic: category
subject: Entanglement
  topic: keywords
subject: Indeterminacy
  topic: keywords
subject: Neutrosophic logic
  topic: keywords
subject: Superposition
  topic: keywords
subject: Quantum information science
  topic: keywords
subject: Quantum uncertainty
  topic: keywords
creator: Ansa Firdous
creator: Hafiza Kanza Maryam
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-11-15
publication date: 2025-11-15
doi: 10.64060/jestt.v2i3.3

Density Functional Theory: A Quantum Mechanical Framework for Novel Materials Design
type: item
description: 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.
subject: Research Article
  topic: section
subject: Materials Science and Engineering
  topic: category
subject: Computational and Data-Driven Approaches
  topic: category
subject: DFT
  topic: keywords
subject: Kohn-Sham equation
  topic: keywords
subject: Application of DFT
  topic: keywords
subject: DFT Methods
  topic: keywords
subject: Exchange-Correlation function
  topic: keywords
subject: GGA and LDA
  topic: keywords
subject: Hybrid Functional
  topic: keywords
creator: Mubashr Ali
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-11-15
publication date: 2025-11-15
doi: 10.64060/jestt.v2i3.1

AI-Powered Innovation in Flexible and Implantable Electronics: From Sensors to Systems
type: item
description: 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.
subject: Research Article
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Biomedical Sensors
  topic: keywords
subject: Flexible Electronics
  topic: keywords
subject: Intelligent Healthcare Systems
  topic: keywords
subject: Wearable and Implantable Devices
  topic: keywords
creator: Abdullah
creator: Hafiza Kanza Maryam
creator: Laraib
creator: Hafiza Fatima
creator: Muhammad Abubakar Rafi
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-08-25
publication date: 2025-08-25
doi: 10.64060/jestt.v2i2.1

Twisted Bilayer Graphene at the Magic Angle: A Review of Highly Correlated Physics and Unconventional Superconductivity
type: item
description: 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.
subject: Research Article
  topic: section
subject: Twisted Bilayer Graphene
  topic: keywords
subject: Magic Angle
  topic: keywords
subject: Superconductivity
  topic: keywords
subject: Correlated Insulators
  topic: keywords
subject: Moire Superlattice
  topic: keywords
creator: Adeena Fatima
creator: Husnain Ahmed
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-07-10
publication date: 2025-07-10
doi: 10.64060/jestt.v2i2.4

Electrochemical Biosensors for Real-Time Oxidative Stress Monitoring in Saliva
type: item
description: 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.
subject: Research Article
  topic: section
subject: Slaviary biomarkers ofoxidative stress
  topic: keywords
subject: Electrochemcal Biosensors
  topic: keywords
subject: wearable sensing
  topic: keywords
creator: Hafiza Kanza Maryam
creator: Abdullah
creator: Hafiza Fatima
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-08-25
publication date: 2025-08-25
doi: 10.64060/jestt.v2i2.3

Critical Review on the Phase Change Properties of VO₂: Influence of Synthesis Methods and Transition Mechanisms
type: item
description: 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.
subject: Research Article
  topic: section
subject: Defect chemistry
  topic: keywords
subject: Mott transition
  topic: keywords
subject: Phase-change materials
  topic: keywords
subject: Thermochromic materials
  topic: keywords
creator: Ansa Firdous
creator: Abrar Ashraf
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-07-10
publication date: 2025-07-10
doi: 10.64060/jestt.v2i2.2

First-Principles Study of Cubic CsSrF₃: Effects of Li Doping on Structural, Electronic and Optical Properties
type: item
description: 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.
subject: Research Article
  topic: section
subject: Bandgap Engineering
  topic: keywords
subject: CASTEP
  topic: keywords
subject: DFT
  topic: keywords
subject: Elastic Properties
  topic: keywords
subject: First principle
  topic: keywords
subject: Fluoro-Perovskite
  topic: keywords
subject: Li-doped
  topic: keywords
creator: Fozia Hafeez
creator: Adeena Fatima
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-07-10
publication date: 2025-07-10
doi: 10.64060/jestt.v2i2.5

Identity Theft in the Digital Age: Legal Gaps, Enforcement Challenges, and the Need for Global Reform
type: item
description: 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.
subject: Research Article
  topic: section
subject: Identity Theft
  topic: keywords
subject: Cyber Fraud
  topic: keywords
subject: International Law
  topic: keywords
subject: Consumer Protection
  topic: keywords
subject: Financial Crime
  topic: keywords
subject: Legal Framework
  topic: keywords
subject: Data Security
  topic: keywords
subject: Fraud Prevention
  topic: keywords
subject: Digital Crime
  topic: keywords
subject: Cybersecurity Policy
  topic: keywords
creator: Sidra Raza
creator: Shaista Naznin
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-02-28
publication date: 2025-02-28
doi: 10.48112/jestt.v2i1.10

The Human Rights Implications of Scientific Progress: A Case Study on Gene Editing and Disability Rights
type: item
description: 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.
subject: Research Article
  topic: section
subject: Gene Editing
  topic: keywords
subject: Human Rights
  topic: keywords
subject: Disability Ethics
  topic: keywords
subject: Genetic Discrimination
  topic: keywords
subject: Bioethics and Regulation
  topic: keywords
creator: Anza Fatima
creator: Aftab Haider
creator: Asma Batool
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-02-28
publication date: 2025-02-28
doi: 10.48112/jestt.v2i1.13

Overview of Control and Monitoring Specification Study to Help Reach Net Zero in the UK for Hydrogen Flow Measurement Network
type: item
description: 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.
subject: Research Article
  topic: section
subject: Environmental Damage
  topic: keywords
subject: Net Zero Emission
  topic: keywords
subject: Renewable Energy
  topic: keywords
subject: Flowmeters
  topic: keywords
subject: Programmable Logic Controllers
  topic: keywords
creator: Adeena Fatima
creator: Fozia Hafeez
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2024-02-29
publication date: 2024-02-29
doi: 10.48112/jestt.v1i1c.2

New aspect of Management Engineering: Connotation between FDI Inflow, Gender Gap, Educational Attainment and Skilled Workforce
type: item
description: 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.
subject: Research Article
  topic: section
subject: Skilled Workforce
  topic: keywords
subject: Foreign Direct Investment
  topic: keywords
subject: Gender Gap
  topic: keywords
subject: Higher Education
  topic: keywords
creator: Maria Qayum
creator: Zhao Xu
creator: Shanza Qayyum
creator: Asma Batool
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2024-02-29
publication date: 2024-02-29
doi: 10.48112/jestt.v1i1c.3

A Comprehensive Overview of Photon-Proton Scattering and QCD Methodology
type: item
description: 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.
subject: Research Article
  topic: section
subject: QCD
  topic: keywords
subject: QED
  topic: keywords
subject: AdS
  topic: keywords
subject: Holographic QCD
  topic: keywords
subject: Photon-Proton Scattering
  topic: keywords
subject: Photon-Photon Scattering
  topic: keywords
creator: Jamil Afzal
creator: Ayesha Rani
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2024-02-29
publication date: 2024-02-29
doi: 10.48112/jestt.v1i1c.4

Seq2Seq-Based-Day-Ahead Scheduling for SCUC in Islanded Power Systems with Limited Intermittent Generation
type: item
description: 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.
subject: Research Article
  topic: section
subject: Renewable Energy
  topic: keywords
subject: Power Systems
  topic: keywords
subject: Seq2Seq-based Technique
  topic: keywords
subject: Power Grid
  topic: keywords
creator: Zuhaib Nishtar
creator: Jamil Afzal
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2024-02-29
publication date: 2024-02-29
doi: 10.48112/jestt.v1i1c.5

An Insight into Optical Constants of Iron Phenanthroline Molecules
type: item
description: 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).
subject: Research Article
  topic: section
subject: Fe2+(Phen) Molecular
  topic: keywords
subject: Iron 1
  topic: keywords
subject: 10-phenanthroline
  topic: keywords
subject: Ligand Exchange Synthesis Method
  topic: keywords
subject: Spin Crossover Phenomenon
  topic: keywords
creator: Hayder Fahim
creator: Hashim Jabbar
creator: Adil A. Al-Fregi
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2024-08-31
publication date: 2024-08-31
doi: 10.48112/jestt.v1i2c.1

Mega Hydropower Projects and Sustainable Development of Pakistan
type: item
description: 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.
subject: Research Article
  topic: section
subject: Sukhi Kinari Dam
  topic: keywords
subject: Mega Hydropower Project
  topic: keywords
subject: Policy Framework
  topic: keywords
subject: Sustainable Development
  topic: keywords
creator: Muhammad Adeel Afzal
creator: Jamil Afzal
creator: Zuhaib Nishtar
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2024-08-31
publication date: 2024-08-31
doi: 10.48112/jestt.v1i2c.2

Exploring The Influence of Social Network Sites on Students' Academic Achievement: The Moderating Effect of Social Support
type: item
description: 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.
subject: Research Article
  topic: section
subject: Social Networking Sites
  topic: keywords
subject: Academic Performance
  topic: keywords
subject: Social Support University Students
  topic: keywords
subject: Quantitative Research
  topic: keywords
creator: Layiba Bibi
creator: Jalwa Sufyan Hussain
creator: Sanaullah
creator: Saddam ur Rahman
rights: Copyright (c) 2025 Journal of Engineering, Science and Technological Trends https://creativecommons.org/licenses/by/4.0
date: 2025-02-28
publication date: 2025-02-28
doi: 10.48112/jestt.v2i1.12