Deep Learning-Based Spectrum Sensing for Cognitive Radio Applications.

In order for cognitive radios to identify and take advantage of unused frequency bands, spectrum sensing is essential. Conventional techniques for spectrum sensing rely on extracting features from received signals at specific locations. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have recently demonstrated promise in improving the precision and efficacy of spectrum sensing.

Reinforcement learning algorithm for improving speed response of a five-phase permanent magnet synchronous motor based model predictive control.

Enhancing the performance of 5ph-IPMSM control plays a crucial role in advancing various innovative applications such as electric vehicles. This paper proposes a new reinforcement learning (RL) control algorithm based twin-delayed deep deterministic policy gradient (TD3) algorithm to tune two cascaded PI controllers in a five-phase interior permanent magnet synchronous motor (5ph-IPMSM) drive system based model predictive control (MPC). The main purpose of the control methodology is to optimize the 5ph-IPMSM speed response either in constant torque region or constant power region.

Ultra-sensitive detection of 4-chloro-2-methylphenoxyacetic acid herbicide using a porous Co-1,4-benzenedicarboxylate /montmorillonite nanocomposite sensor.

The detection of 4-chloro-2-methylphenoxyacetic acid (CMPA) herbicide is crucial due to the potential health risks linked to exposure through drinking water, air, and food, which may adversely affect liver and kidney functions. To address this environmental concern and promote sustainable agriculture, a sensitive carbon paste sensor incorporating a composite material was developed. The composite sensor is based on porous cobalt-1,4-benzenedicarboxylate metal-organic framework and exfoliated montmorillonite nanolayers (Co-OF/MMt).

High early strength concrete incorporating waste derived nanomaterials for sustainable construction.

This paper contributes to the expanding knowledge base on nanomaterial-enhanced cementitious composites, offering valuable insights for developing high-performance, sustainable concrete solutions. The study assessed the effects of three different types of nanomaterials-nano clay (NC), nano silica (NS), and nano cellulose (NCel)-on the compressive strength of high-early-strength concrete (HESC) through both experimental studies and a 2 factorial design. Incorporating nanomaterials into the HESC matrix led to a decrease in workability, with NCel demonstrating the least impact on this property across all studied replacement percentages.

Shear Strengthening of RC Beams Using Prestressed Near-Surface Mounted Bars Reducing the Probability of Construction Failure Risk.

In this study, shear-critical reinforced concrete (RC) beams were strengthened by combining the prestressing and near-surface mounted (NSM) rods approaches. The potential danger of failure in such RC beams is a substantial concern as it is considered a potential threat. This study addresses its careful mitigation through experimental identification and numerical analysis to enhance the safety and sustainability of buildings by reducing the probability of failure risk for these RC beams.

Structural, Spectroscopic, and Docking Analysis of N,O-Donor Ligand Metal Complex Nanoparticles With Hypolipidemic Effects via Lipoprotein Lipase Activation in High-Fat Diet Mice.

New Cd(II), Zn(II), and Cu(II) chelates with cetirizine.2HCl (CETZ.2HCl) in incidence of 1,10 phenanthroline monohydrate (Phen.

Experimental and numerical investigation of a cold-formed steel system used to restore old buildings floor.

This paper presents a novel configuration of built-up cold-formed steel (CFS) flooring system in the shape of a box section. A new technique is applied to produce the components of the flooring system, which are fastened by self-drilling screws. This box section consists of a cast-in-situ concrete slab, trapezoidal steel decking, two sigma section, steel plate and stiffening equal angles.

Coordinated distributed model predictive control for multi energy carrier systems.

Introducing Energy hubs (EHs) is a beneficial strategy for incorporating quickly expanding renewable energies. However, the stochastic nature of renewable energy sources (RESs) and fluctuating energy demand have produced a number of difficulties, including unstable voltage/frequency, challenging energy management, and difficult network interaction. Additionally, the changing in response time of electrical and heat demands will make control challenging.

Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques.

This paper presents a non-linear finite element model (FEM) to predict the load-carrying capacity of three different configurations of elliptical concrete-filled steel tubular (CFST) short columns: double steel tubes with sandwich concrete (CFDST), double steel tubes with sandwich concrete and concrete inside the inner steel tube, and a single outer steel tube with sandwich concrete. Then, a parametric and analytical study was performed to explore the influence of geometric and material parameters on the load-carrying capacity of elliptical CFST short columns. Furthermore, the current study investigates the effectiveness of machine learning (ML) techniques in predicting the load-carrying capacity of elliptical CFST short columns.

EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm.

This article introduces the Modified Al-Biruni Earth Radius (MBER) algorithm, which seeks to improve the precision of categorizing eye states as either open (0) or closed (1). The evaluation of the proposed algorithm was assessed using an available EEG dataset that applied preprocessing techniques, including scaling, normalization, and elimination of null values. The MBER algorithm's binary format is specifically designed to select features that can significantly enhance the accuracy of classification.

Compressive strength of nano concrete materials under elevated temperatures using machine learning.

In this study, four Artificial intelligence (AI) - based machine learning models were developed to estimate the Residual compressive strength (RCS) value of concrete supported with nano additives of Nanocarbon tubes (NCTs) and Nano alumina (NAl), after exposure to elevated temperatures ranging from 200 to 800 degrees. These models were developed via adapting meta- heuristic models including the Water cycle algorithm (WCA), Genetic algorithm (GA), and classical AI models of Artificial neural networks (ANNs), Fuzzy logic models (FLM), in addition to the statistical method of Multiple linear regression (MLR). 156 post heating experimental results available as a literature data (represents four input parameters of temperature change, heat exposure duration, nanomaterial type, and replacement proportion) are used to achieve the study's objective.

AHP VIKOR framework for selecting wind turbine materials with a focus on corrosion and efficiency.

The research objective in the context of the study relates to the major concern of corrosion affecting the wind turbines in operation to find materials with high durability in relation to environmental conditions of operation, strength, and cost. A method is an integration of the Analytical Hierarchy Process (AHP) and VIKOR Multi-Criteria Decision Making (MCDM) techniques that will assess seven different material options on sixteen criteria that comprise corrosion resistance, mechanical properties, cost, and a negative environmental impact. From this result, the AHP method calculated the weights for the indicators and chose potential materials, and finally, the VIKOR method used these materials and compared and ranked them to obtain a compromise solution.

Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification.

Lung cancer is an important global health problem, and it is defined by abnormal growth of the cells in the tissues of the lung, mostly leading to significant morbidity and mortality. Its timely identification and correct staging are very important for proper therapy and prognosis. Different computational methods have been used to enhance the precision of lung cancer classification, among which optimization algorithms such as Greylag Goose Optimization (GGO) are employed.

Biochemical Characterization and Disease Control Efficacy of -Derived Chitosan-An In Vivo Study against , the Causal Agent of Plum Brown Rot.

Chitin (Ct) is a crucial biopolymer present in fungi, algae, arthropods, and is usually obtained from crustacean shells. Chitosan (Cs) is a derivative from Ct deacetylation, and possesses numerous uses in various agro-industrial fields. Research on fungal-derived Ct and Cs is mostly focused on pharmaceutical uses, however their uses for plant disease control remain less explored.

Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection.

The evaluation of slope stability is of crucial importance in geotechnical engineering and has significant implications for infrastructure safety, natural hazard mitigation, and environmental protection. This study aimed to identify the most influential factors affecting slope stability and evaluate the performance of various machine learning models for classifying slope stability. Through correlation analysis and feature importance evaluation using a random forest regressor, cohesion, unit weight, slope height, and friction angle were identified as the most critical parameters influencing slope stability.

Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions.

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed.

Bioreceptor modified electrochemical biosensors for the detection of life threating pathogenic bacteria: a review.

The lack of reliable and efficient techniques for early monitoring to stop long-term effects on human health is an increasing problem as the pathogenesis effect of infectious bacteria is growing continuously. Therefore, developing an effective early detection technique coupled with efficient and continuous monitoring of pathogenic bacteria is increasingly becoming a global public health prime target. Electrochemical biosensors are among the strategies that can be utilized for accomplishing that goal with promising potential.

Variance in multi-blade induced lightning overvoltages among different wind farm topologies.

The impact of the topological formation of wind farms upon the lightning induced overvoltages injected into the grid was not covered earlier in literature. However, this topic is highly important to be investigated to allow the usage of the most reliable topology against lightning strikes. For such reason, the paper investigates this point with consideration of most damaging cases as lightning strikes to multi-blades.

Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability.

A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building's life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely.

Conceptional design for a universal HVDC-HVAC vacuum-interrupter-based circuit breaker.

Modern power systems high voltage transmission systems either HVDC or HVAC has mandated the presence of two types of circuit breakers (CB), HVAC-CB and HVDC-CB. That required two different production lines, higher costs and more complicated manufacturing process. A solution is proposed in this paper which is a concept design for a universal HVCB (UHVCB) that is applicable to both HVDC and HVAC system.

Active learning-based machine learning approach for enhancing environmental sustainability in green building energy consumption.

Green building (GB) techniques are essential for reducing energy waste in the construction sector, which accounts for almost 40% of global energy consumption. Despite their importance, challenges such as occupant behavior and energy management gaps often result in GBs consuming up to 2.5 times more energy than intended.

Exploring bee venom and silver nanoparticles for controlling foulbrood pathogen and enhancing lifespan of honeybees.

The beekeeping industry plays a crucial role in local economies, contributing significantly to their growth. However, bee colonies often face the threat of American foulbrood (AFB), a dangerous disease caused by the Gram-positive bacterium Paenibacillus larvae (P. l.