Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy.

The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids.

Usage of double injector for efficient mixing of the fuel behind the ramp injector at supersonic combustion chamber.

This investigation presents extensive computational analyses of the compressible flow near ramp injector with double circular injectors at supersonic combustor of scramjet engine. Comparison of the fuel mixing and fuel jet penetration of hydrogen jet are done for two injector configurations at free stream Mach number of 2. The simulation of the supersonic flow near ramp injector is done via solving RANS equations with computational fluid dynamic technique.

Eco-reliable operation based on clean environmental condition for the grid-connected renewable energy hubs with heat pump and hydrogen, thermal and compressed air storage systems.

Energy hubs, with their diverse regeneration and storage sources, can engage concurrently in energy transfer and storage. It is anticipated that managing the energy of these hubs within energy networks could enhance economic, environmental, and technical metrics. This article explains how electrical and thermal network hubs manage their energy consumption in the context of the multi-criteria objectives of efficiency, sustainability, reliability of the network operator, and operation.

An information gap decision theory and improved gradient-based optimizer for robust optimization of renewable energy systems in distribution network.

In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock's direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs.

A comprehensive analysis of advanced solar panel productivity and efficiency through numerical models and emotional neural networks.

This study presents an in-depth analysis and evaluation of the performance of a standard 200 W solar cell, focusing on the energy and exergy aspects. A significant research gap exists in the comprehensive integration of numerical models with advanced machine-learning approaches, specifically emotional artificial neural networks (EANN), to simulate and optimize the electrical characteristics and efficiency of solar panels. To address this gap, a numerical model alongside a novel EANN was employed to simulate the system's electrical characteristics, including open-circuit voltage, short-circuit current, system resistances, maximum power point characteristics, and characteristic curves.

Mimicking myocardial infarction: a subarachnoid haemorrhage case report.

We present a case detailing the diagnostic challenges of a 23-year-old male presenting with a sudden severe headache, nausea, vomiting, and chest heaviness. Initial evaluation showed elevated blood pressure and respiratory rate. An emergency electrocardiogram (ECG) indicated ST-segment elevation myocardial infarction (STEMI), leading to immediate referral for percutaneous coronary intervention, which revealed normal coronary arteries.

Innovative MIM diplexer with neural network enhanced refractive index detection for advanced photonic applications.

This study introduces a high-performance 4-channel Metal-Insulator-Metal (MIM) diplexer, employing silver and Teflon, optimized for advanced photonic applications. The proposed diplexer, configured with two novel band-pass filters (BPFs), operates across four distinct wavelength bands (843 nm, 1090 nm, 1452 nm, 1675 nm) by precisely manipulating the passband dimensions. Utilizing Finite-Difference Time-Domain (FDTD) simulations, the designed diplexer achieves exceptional sensitivity values of 3500 nm/RIU, 4250 nm/RIU, 3375 nm/RIU, and 4003 nm/RIU, along with high figures of merit (FOM) ranging from 113.

Chitosan and its derivatives: A novel approach to gut microbiota modulation and immune system enhancement.

Chitosan, a biopolymer derived from the deacetylation of chitin found in crustacean shells and certain fungi, has attracted considerable attention for its promising health benefits, particularly in gut microbiota maintenance and immune system modulation. This review critically examines chitosan's multifaceted role in supporting gut health and enhancing immunity, beginning with a comprehensive overview of its sources, chemical structure, and its dual function as a dietary supplement and biomaterial. Chitosan's prebiotic effects are highlighted, with a focus on its ability to selectively stimulate beneficial gut bacteria, such as Bifidobacteria and Lactobacillus, while enhancing gut barrier integrity and inhibiting the growth of pathogenic microorganisms.

Exponential stability analysis of delayed partial differential equation systems: Applying the Lyapunov method and delay-dependent techniques.

This paper presents an investigation into the stability and control aspects of delayed partial differential equation (PDE) systems utilizing the Lyapunov method. PDEs serve as powerful mathematical tools for modeling diverse and intricate systems such as heat transfer processes, chemical reactors, flexible arms, and population dynamics. However, the presence of delays within the feedback loop of such systems can introduce significant challenges, as even minor delays can potentially trigger system instability.

Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T solar systems.

Optimization of thermophysical properties (TPPs) of MXene-based nanofluids is essential to increase the performance of hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a hybrid approach to optimize the TPPs of MXene-based Ionanofluids. The input variables are the MXene mass fraction (MF) and temperature.

A multi-objective improved horse herd optimizer based on convex lens imaging for stochastic optimization of wind energy resources in distribution networks considering reliability and uncertainty.

In this study, stochastic multi-objective allocation of wind turbines (WTs) in radial distribution networks is performed using a new multi-objective improved horse herd optimizer (MOIHHO) and an unscented transformation (UT) method for modeling the uncertainties of WTs power and network load. The objective function aims to minimize power loss, improve reliability, and reduce the costs associated with wind turbines (WTs), presenting these goals as a three-dimensional function. The Multi-Objective Improved Horse Herd Optimizer (MOIHHO) is derived from an enhanced version of the traditional horse herd optimizer.

Ag@WO core-shell nanocomposite for wide range photo detection.

This study successfully synthesized high-performance photodetectors based on Ag-WO3 core-shell heterostructures using a simple and economical two-step pulsed laser ablation in water method and has investigated the electrical characteristics of the Ag@WO3 nanocomposite heterojunction. The Hall effect tests indicate that the synthesized Ag@WO exhibits n-type conduction with a Hall mobility of 1.25 × 10 cmVS.

Bi-level expansion planning of power system considering wind farms based on economic and technical objectives of transmission system operator.

Planning for the expansion of generation and transmission infrastructure, with a focus on integrating wind farms is presented in this study according to the goals of economic efficiency, operational performance, security, and reliability of the transmission system operator. The approach incorporates a bi-level optimization framework, with the upper level outlining the formulation of power system expansion planning to reduce construction and operational costs while adhering to the investment budget limits. In the lower-level model, which economic reliable-secure functioning is formulated for the transmission network.

Evaluating the Therapeutic Efficacy of Lactobacillus Strains in the Management of Ulcerative Colitis: An Overview of Recent Advances.

Ulcerative Colitis (UC) known as a sub-category of Inflammatory Bowel Diseases (IBD) is a longterm condition that causes inflammation, irritation, and ulcers in the colon and rectum. Though the precise pathogenesis of UC is not fully understood yet, impaired immune responses and imbalanced intestinal microbiome composition have been regarded as two main key players in colitis pathobiology. As conventional treatments are challenged with limitations and side effects, finding a new therapeutic approach has gained increasing attention.

Are hybrid approaches combining EKF-UKF and artificial neural networks key to unlocking the full potential of sustainable energy technologies and reducing environmental footprint?

The integration of traditional state estimation techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) with modern artificial neural networks (ANNs) presents a promising avenue for advancing state estimation in sustainable energy systems. This study explores the potential of hybridizing EKF-UKF with ANNs to optimize renewable energy integration and mitigate environmental impact. Through comprehensive experimentation and analysis, significant improvements in state estimation accuracy and sustainability metrics are revealed.

Exploring Hepatocellular Carcinoma Pathogenesis: The Influence of Genetic Polymorphisms.

Hepatocellular carcinoma (HCC) is influenced by several factors, among which genetic polymorphisms play a key role. Polymorphisms in various genes affect key pathways involved in HCC development, including metabolism, expression of inflammatory cytokines, cell proliferation, and apoptosis regulation. These polymorphisms induce differential effects on susceptibility to HCC, disease progression, and treatment outcomes.

Experimental study on the various varieties of photovoltaic panels (PVs) cooling systems to increase their electrical efficiency.

This study investigates the impact of cooling methods on the electrical efficiency of photovoltaic panels (PVs). The efficiency of four cooling techniques is experimentally analyzed. The most effective approach is identified as water-spray cooling on the front surface of PVs, which increases efficiency by 3.

Exergy, exergoeconomic optimization and exergoenvironmental analysis of a hybrid solar, wind, and marine energy power system: A strategy for carbon-free electrical production.

In this research, aligned with global policies aimed at reducing CO2 emissions from traditional power plants, we developed a holistic energy system utilizing solar, wind, and ocean thermal energy sources, tailored to regions optimal for ocean thermal energy conversion (OTEC). The selected site, characterized by favorable wind and solar conditions close to areas with high OTEC potential, is designed to meet the electricity needs of a coastal community. The system's core components include an Organic Rankine Cycle, turbines, thermoelectric elements, pumps, a heat exchanger, a wind turbine, and a solar collector.

A new multi-objective-stochastic framework for reconfiguration and wind energy resource allocation in distribution network incorporating improved dandelion optimizer and uncertainty.

Improving the reliability and power quality of unbalanced distribution networks is crucial for ensuring consistent and reliable electricity supply. In this research, multi-objective optimization of unbalanced distribution networks reconfiguration integrated with wind turbine allocation (MORWTA) is implemented considering uncertainties of networks load, and also wind power incorporating a stochastic framework. The multi-objective function is defined by the minimization of power loss, voltage sag (VS), total harmonic distortion (THD), voltage unbalance (VU), energy not-supplied (ENS), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and momentary average interruption frequency (MAIFI).

UAV propeller fault diagnosis using deep learning of non-traditional χ-selected Taguchi method-tested Lempel-Ziv complexity and Teager-Kaiser energy features.

Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for operational safety and efficiency. Most existing fault diagnosis techniques rely basically on traditional statistical-based methods that necessitate better approaches. This study explores the application of untraditional feature extraction methodologies, namely Permutation Entropy (PE), Lempel-Ziv Complexity (LZC), and Teager-Kaiser Energy Operator (TKEO), on the PADRE dataset, which encapsulates various rotor fault configurations.

The effect of sumac on cardiovascular risk factors in adults: A systematic review and meta-analysis of randomized controlled trials.

Clinical evidence suggests the beneficial effects of sumac on cardiovascular risk factors. However, these results are controversial. This systematic review and meta-analysis of randomized controlled trials (RCTs) was performed to determine the effect of sumac supplementation on cardiovascular risk factors in adults.

Seronegative celiac disease with transient protein-losing enteropathy and vitamin B12 deficiency in a pediatric patient: Case report.

Key Clinical Message: Early recognition and management of seronegative celiac disease, even in the absence of typical serological markers, can prevent complications and ensure better health outcomes in pediatric patients. Consideration of a gluten-free diet in similar cases can lead to significant clinical improvement.

Abstract: Celiac disease, characterized by its diverse clinical manifestations, often necessitates adherence to a gluten-free diet, particularly in pediatric patients for optimal growth and development.