For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

The transition to smart grids is revolutionizing the management and distribution of electrical energy. Nowadays, power systems must precisely estimate real-time loads and use adaptive regulation to operate in the era of sustainable energy. To address these issues, this paper presents a new approach—a hybrid neuro-fuzzy system—that combines neural networks with fuzzy logic. We use neural networks’ adaptability to describe complex load patterns and fuzzy logic’s interpretability to fine-tune control techniques in our approach. Our improved load forecasting system can now respond to changes in real-time due to the combination of these two powerful methodologies. Developing, training, and implementing the forecasting and control system are detailed in this article, which also explores the theoretical underpinnings of our hybrid neuro-fuzzy approach. We demonstrate how the technology improves grid stability and the accuracy of load forecasts by using adaptive control methods. Furthermore, comprehensive simulations confirm the proposed technology, showcasing its smooth integration with smart grid infrastructure. Better energy management is just the beginning of what our method can accomplish; it also paves the way for a more sustainable energy future that is easier on the planet and its inhabitants. In conclusion, this study’s innovative approach to adaptive control and real-time load forecasting advances smart grid technology, which, in turn, improves sustainability and energy efficiency.

Public-Private Partnerships (PPPs) have emerged as a vital strategy for addressing infrastructure development challenges worldwide, including in Pakistan. This study explores the challenges and effectiveness of PPP models in the construction sector in Pakistan, focusing on critical aspects of regulatory complexity, changing jurisdiction, coordination issues, financial constraints, and the absence of standardized resources. To gauge the effectiveness of different PPP models, the study examines the Build-Operate-Transfer (BOT), Design-Build-Operate-Maintain (DBOM), and Concession Agreement models. The BOT model has proven effective for large-scale infrastructure projects but remains vulnerable to political instability. The DBOM model faces financial and regulatory challenges, while concession agreements may encounter public accountability and stakeholder conflict issues. The study highlights that the choice of an appropriate PPP model in Pakistan should align with project-specific requirements and challenges. It underscores the significance of addressing political risks, regulatory issues, and ensuring transparent communication with stakeholders. In conclusion, the study offers insights into the challenges and effectiveness of PPP models in Pakistan's construction sector. It recommends the need for a coordinated approach among government entities, robust project screening and evaluation, transparent and competitive tendering processes, and thorough project monitoring. Additionally, it calls for the development of comprehensive guidelines and model documents. Future research should consider the contextual variations in different settings to enhance generalizability and employ a combination of data sources to mitigate interview bias. This study contributes to the ongoing discourse on improving PPP implementation in Pakistan, with the potential for broader applicability in similar contexts.

This research aims to address the challenges of fault detection and isolation (FDI) in digital grids, focusing on improving the reliability and stability of power systems. Traditional fault detection techniques, such as rule-... | Find, read and cite all the research you need on Tech Science Press

Dynamic load forecasting is essential for effective energy management and grid operation. The use of GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) networks for precise load prediction is investigated in this paper. This research examines dynamic load patterns by innovatively integrating heterogeneous information from several datasets. The results show that the LSTM and GRU models are equally good at making predictions and that this holds true across a variety of datasets. Furthermore, the models’ ability to accurately capture the temporal relationships in the load data is demonstrated by their low Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) values. Additionally, the comparative analysis results, which highlight flexibility in model selection, can aid energy sector decision makers. The significance of precise load projections for maintaining grid dependability and optimizing resources is further highlighted by this work, which also elucidates the effects of forecast inaccuracies on decision-making procedures. Our research study provides important information for power system management strategy planning, which in turn promotes the continuous innovation of smart grids in dynamic load forecasting to keep up with changing energy consumption patterns.

Fishery development refers to interrelated measures made up of the management of resources, industry, and market that serve as the basic pillars for maintaining the growth of the sector. In the Pakistani fishery industry, we identified the ecological setting, wherein Pakistan's fishery operates and explained how recent technological advancement affects fish catches. Government initiatives, fleet extension, and the creation of export markets are the contributing factors to the growth of the fisheries in Pakistan. For the broader goal of national economic development, development strategies of marine resources have great significance. Current policies and methods for managing fishery industry development have proved less effective in achieving coastal communities' needs. Pakistan should require a balanced resource organization in order to develop its fishery sector. This report focuses on the shortcomings, significance, and guidelines of upholding effective development policy to improve Pakistan’s fishery industry.