Journal of Engineering, Science and Technological Trends
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29 research outputs found
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Seq2Seq-Based-Day-Ahead Scheduling for SCUC in Islanded Power Systems with Limited Intermittent Generation
Get PDFDue 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
Cross-Border Data Flows in Pakistan: Legal Challenges and Technological Solutions for Digital Trade
Get PDFThe 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\u27s digital trade ecosystem. Through a doctrinal legal approach and comparative analysis, this study reveals that Pakistan\u27s 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
New aspect of Management Engineering: Connotation between FDI Inflow, Gender Gap, Educational Attainment and Skilled Workforce
Get PDFThis 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 Short Review Note on Finite Element Method for Hydraulic Structural Engineering
Get PDFDams play a critical role in water resources engineering, providing irrigation and drinking water through the creation of reservoirs. However, geotechnical and hydraulic engineers face challenges such as the possibility of piping events and collapse due to water leaking under the dam body, as well as variation in bearing capacity, void ratio, and water content in different regions of the ground where the dam body consolidates. The paper also highlights the advances in hydraulic structural engineering through the Finite Element Method (FEM) and other numerical modelling techniques, which have enabled more accurate design and analysis of hydraulic structures such as dams, spillways, weirs, and sluice gates. The maintenance and rehabilitation of these structures are also discussed, with a focus on developing non-destructive testing methods and innovative repair and retrofitting techniques to improve their structural integrity and hydraulic efficiency. The challenges and opportunities in hydraulic structural engineering research are explored, including the impact of climate change, sustainable design, and integration of new technologies like artificial intelligence and the Internet of Things. As hydraulic structures become more resilient and adaptive to withstand extreme events and support sustainable development, there will be an increased need for continued research and innovation in FEM and other advanced numerical modelling techniques to support hydraulic structural engineering advancements
Improved Solar Power Prediction Using CNN-LSTM Models for Optimized Smart Grid Performance
Get PDFDuring 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
Atomistic DFT simulations are promising techniques for the discovery of hydrogen storage materials
Get PDFEfficient 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
Review of Various Aspects of Digital Violence
Get PDFThis 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
A Comprehensive Overview of Photon-Proton Scattering and QCD Methodology
Get PDFThe 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
Mega Hydropower Projects and Sustainable Development of Pakistan
Get PDFThis 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\u27s 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