Papers

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

Researchers developed an electromagnetic field optimization-based neural network to predict solar irradiance from environmental conditions, demonstrating improved accuracy over standard neural network approaches for solar energy management applications.

Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Researchers developed a hybrid model combining artificial neural networks with a stochastic fractal search algorithm (SFS-ANN) for early prediction of building cooling loads, finding it outperformed benchmark optimization algorithms and offering a practical tool for energy-efficient residential building design.

Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers

Researchers developed a water cycle algorithm-optimized neural network to predict electrical power output from combined cycle power plants, demonstrating improved prediction accuracy compared to standard optimization algorithms on a publicly available dataset.

An Innovative Metaheuristic Strategy for Solar Energy Management Through a Neural Framework

Researchers used an optimization algorithm to tune a neural network for predicting solar energy availability from environmental conditions. This renewable energy modeling paper is unrelated to microplastic research.

Some Unmodified Household Adsorbents for the Adsorption of Benzalkonium Chloride—A Kinetic and Thermodynamic Case Study for Commercially Available Paper

Researchers investigated the adsorption of benzalkonium chloride (BAC), a biocide surfactant that accumulated in water systems during the SARS-CoV-2 pandemic, using unmodified household paper-based adsorbents. The study conducted kinetic and thermodynamic analyses to characterise the adsorption capacity and mechanism of commercially available paper materials for removing this emerging contaminant.

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Researchers developed a machine learning method combining neural networks and stochastic optimization to predict cooling energy loads in residential buildings. This engineering modeling paper is unrelated to microplastic research.

Hybridizing Neural Network with Multi-Verse, Black Hole, and Shuffled Complex Evolution Optimizer Algorithms Predicting the Dissolved Oxygen

Researchers developed and compared neural network models for predicting dissolved oxygen concentrations in water using machine learning metaheuristic algorithms. Dissolved oxygen is a key indicator of aquatic ecosystem health, and accurate prediction tools support monitoring of water bodies affected by plastic and other pollutants.

Synthesizing Multi-Layer Perceptron Network with Ant Lion, Biogeography-Based, Dragonfly Algorithm, Evolutionary Strategy, Invasive Weed, and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

This study evaluated neural network models trained with metaheuristic algorithms for predicting building heating load, comparing several optimization approaches. While focused on energy efficiency modeling, similar machine learning techniques are used to predict environmental pollutant distributions, including microplastics.

Utilization of Household Sewage Sludge as Resource: The Effect of the Temperature of Pyrolysis on the Chemical Properties

Developing Invasive Weed, Social Spider, Shuffled Frog Leaping, Biogeography-Based, and Harmony Search Optimization Algorithms for the Early Prediction of Residential Building’s Cooling Load Simulation

This paper is not related to microplastics — it reviews and compares several nature-inspired computational algorithms including Invasive Weed Optimization, Social Spider Optimization, Shuffled Frog Leaping, and Biogeography-Based Optimization. The work belongs to the field of computational intelligence and metaheuristic optimization.

Fashion Design in ROWE Fashion PLMs

This paper examines how fashion designers working in a Results Only Work Environment (ROWE) are adapting to new professional methods within product lifecycle management (PLM) systems. It discusses how contemporary design practice is changing traditional methods and the aesthetics and quality of fashion market outputs.