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.

Tracheal tube infections in critical care: A narrative review of influencing factors, microbial agents, and mitigation strategies in intensive care unit settings.

Tracheal tube infections pose significant challenges in the management of mechanically ventilated patients in intensive care units. These infections contribute to prolonged intensive care unit stays, increased healthcare costs, the spread of antibiotic resistance, and poor patient outcomes. This study aims to elucidate the complex relationship between environmental factors, hospital practices, and the incidence of tracheal tube infections.

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.

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.

Antiproliferative Impact of Linagliptin on the Cervical Cancer Cell Line.

Objective: This study aimed to assess linagliptin's inhibitory effects on the proliferation of cervical cancer cell lines and investigate its potential for targeting human heat shock protein 90.

Methods: Linagliptin's cytotoxicity was assessed on a cervical cancer cell line (Hela cancer cell line) at two different incubation periods, 24 and 72 hours. The molecular docking between linagliptin and the receptor protein human Hsp 90 (PDB code: 5XRE) was performed using the Biovia Discovery Studio and AutoDock tool software.

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.

Evaluation and selection of the best artificial lift method for optimal production using PIPESIM software.

A well can be produced and exploited when it has production power, in other words, if the well does not have enough pressure, it will not be able to flow. Artificial production is a method to increase the lifespan of well production. The well studied in this article has a significant annual lower pressure drop; So now with the current pressure of the existing well, it is not possible to send the oil of this well to the separator of the first stage of the exploitation unit.

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).

An improved meta-heuristic method for optimal optimization of electric parking lots in distribution network.

In this study, a stochastic multi-objective structure for optimization of the intelligent electric parking lots (EPLs) is implemented in the distribution network for minimizing the power losses annual costs, power purchased from the main grid, unsupplied energy of subscribers, cost of vehicles to the grid as well as minimizing the network voltage deviations considering battery degradation cost (BDC) and network load uncertainty (NLUn). In this research, the unscented transformation method (UTM) is used for NLUn modeling and this method is easily applicable and has a low computational cost. An improved meta-heuristic algorithm named improved fire hawks optimization (IFHO) is utilized for decision variables finding defined as the site and size of the EPLs in the distribution network.

The effect of constant electric field on the crack growth process of aluminum nanosheet using molecular dynamics simulation.

Aluminum nanosheets are a form of Al nanoparticle that have been recently manufactured on an industrial scale and have a variety of uses. Al nanoparticles are extensively used in a variety of sectors, including aerospace, construction, medical, chemistry, and marine industries. Crack propagation in various constructions must be investigated thoroughly for structural design purposes.

BCNA monolayer as an ideal anode material for high-performance sodium-ion batteries.

Selecting an appropriate anode material (AM) has been considered to be a crucial initial step in advancing high-performance batteries. Within this piece of research, we examine the suitability of the BCNA monolayer (referred to as BCNAML) as an AM by first-principles calculations. The BCNAML exhibits metallic behavior consistently, even with varying concentrations of Na atoms, making it an ideal choice for battery usages.

Day-ahead resilience-economic energy management and feeder reconfiguration of a CCHP-based microgrid, considering flexibility of supply.

Many challenges have emerged due to the intense integration of renewables in the distribution system and the associated uncertainties in power generation. Consequently, local management strategies are developed at the distribution level, leading to the emergence of concepts such as microgrids. Microgrids include a variety of heating, cooling, and electrical resources and loads, and the operators' aim is to minimize operation and outage costs.

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms.

In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler's laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data.

Synthesis of 3D SbO-based heterojunction reinforced by SPR effect and photo-Fenton mechanism for upgraded oxidation of metronidazole in water environments.

The traditional homogenous and heterogenous Fenton reactions have frequently been restrained by the lower production of Fe ions, which significantly obstructs the generation of hydroxyl radicals from the decomposition of HO. Thus, we introduce novel photo-Fenton-assisted plasmonic heterojunctions by immobilizing FeO and Bi nanoparticles onto 3D SbO via co-precipitation and solvothermal approaches. The ternary SbO/FeO/Bi composites offered boosted photo-Fenton behavior with a metronidazole (MNZ) oxidation efficiency of 92% within 60 min.

Investigating the effect of welding tool length on mechanical strength of welded metallic matrix by molecular dynamics simulation.

The welding process and the properties of welding instruments may improve the mechanical performance of an item. One of these properties is the length of the welding tool. This approach has a substantial effect on the mechanical strength of the metallic matrix.

Technical and financial feasibility of a chemicals recovery and energy and water production from a dairy wastewater treatment plant.

Due to the high volume of wastewater produced from dairy factories, it is necessary to integrate a water recovery process with the treatment plant. Today, bipolar membrane electrodialysis units (BMEUs) are increasingly developed for wastewater treatment and reutilizing. This article aims to develop and evaluate (technical and cost analyses) a combined BMEU/batch reverse osmosis unit (BROU) process for the recovery of chemicals and water from the dairy wastewater plant.