Browse Articles

Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm

Abstract This study presents a multi-objective optimization of a hybrid microgrid (HMG) targeting the energy trilemma goals—energy security, affordability, and sustainability—using the Slime Mould Algorithm (SMA). The proposed HMG integrates renewable energy sources, diesel generators, and electric vehicle (EV) batteries as distributed energy resources (DERs) with bidirectional vehicle-to-grid (V2G) capabilities. Compared to conventional metaheuristic such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), the SMA achieves a power loss reduction of 12.3% and a levelized cost of energy (LCOE) improvement of 9.8%. The loss of power supply probability (LPSP) is reduced to 0.012, outperforming benchmark results from HOMER and Salp Swarm Algorithm (SSA), which reported LPSP values of 0.021 and 0.017, respectively. The superior performance of SMA is attributed to its dynamic balance between exploration and exploitation, leading to faster convergence and enhanced computational efficiency. The novel integration of EV batteries as DERs, with explicit modeling of bidirectional V2G operations, distinguishes this work from previous studies that considered only unidirectional or static EV participation. While the proposed approach demonstrates significant improvements, scalability to larger microgrid networks and the computational demands of SMA in real-time applications remain challenges for future research.

Roadway roof stability grading method based on multi-index optimization

An ensemble of deep representation learning with metaheuristic optimisation algorithm for critical health monitoring using internet of medical things

Flow and heat transfer characteristics of 3D printed sheet and solid triply periodic minimal surfaces porous structures

Harnessing electroacupuncture: a promising strategy against sleep deprivation-exacerbated post-cardiac arrest brain injury

AI simulation models for diagnosing disabilities in smart electrical prosthetics using bipolar fuzzy decision making based on choquet integral

Pulmonary diseases accurate recognition using adaptive multiscale feature fusion in chest radiography

Feature fusion and selection using handcrafted vs. deep learning methods for multimodal hand biometric recognition

Genome-wide CRISPR screen for human factors involved in alternative polyadenylation based on differential localization of CD47

Optical multi-wavelength selectors based on distributed feedback chirped grating in arrays of graphene

Design of mTCN framework for disaster prediction a fusion of massive machine type communications and temporal convolutional networks

Facilitators and barriers for use of digital technology in chronic disease management

DSRR model for safety vulnerability assessment of power grid infrastructure in Henan Province China

Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm

Efficacy of remimazolam in preventing postoperative nausea and vomiting: a systematic review and meta-analysis

Shielding failure analysis of extra high voltage unconventional transmission lines with increased power delivery capability

Formulation of a dynamic convective adjustment time-scale in the CESM1.2 and its influence on the Indian summer monsoon simulations

Explainable ML modeling of saltwater intrusion control with underground barriers in coastal sloping aquifers

Abstract Reliable modeling of saltwater intrusion (SWI) into freshwater aquifers is essential for the sustainable management of coastal groundwater resources and the protection of water quality. This study evaluates the performance of four Bayesian-optimized gradient boosting models in predicting the SWI wedge length ratio (L/L a ) in coastal sloping aquifers with underground barriers. A dataset of 456 samples was generated through numerical simulations using SEAWAT, incorporating key variables such as bed slope, hydraulic gradient, relative density, relative hydraulic conductivity, barrier wall depth ratio, and distance ratio. The dataset was divided into 70% for training and 30% for testing. Model performance was assessed using both visual and quantitative metrics. Among the models, Light Gradient Boosting (LGB) achieved the highest predictive accuracy, with RMSE values of 0.016 and 0.037 for the training and testing sets, respectively, and the highest coefficient of determination (R²). Stochastic Gradient Boosting (SGB) followed closely, while Categorical Gradient Boosting (CGB) and eXtreme Gradient Boosting (XGB) showed slightly higher error rates. SHapley Additive exPlanations (SHAP) analysis identified relative barrier wall distance and bed slope as the most influential features affecting model predictions. To support practical application, an interactive graphical user interface (GUI) was developed, allowing users to input key variables and easily estimate L/L a values. Finally, the best-performing model was validated against the Akrotiri coastal aquifer in Cyprus, a realistic benchmark case derived from numerical simulations. The model’s predictions showed strong agreement with reference results, achieving an RMSE of 0.04, thereby confirming its practical applicability. This study highlights the potential of interpretable, optimized ML models to enhance SWI prediction and support informed decision-making in coastal aquifer management.

Contrasting niche dynamics in the invasion processes of two congeneric dinoflagellates

Abstract Niche-based models are essential for predicting invasion risks. Although most invasive species tend to conserve their ecological niches after introduction, some challenge this assumption by expanding or contracting their niches, yet such patterns remain underexplored in microorganisms. Since larger niche shifts can reduce the predictive performance of these models, this study examines whether the climatic niches of the invasive dinoflagellates Ceratium hirundinella (Müller, 1841) and C. furcoides (Langhans, 1925) have shifted following their invasion from native European to non-native American ranges, where they have caused significant impacts on biodiversity and water quality. Though both species are native to temperate European lakes, their colonization patterns in the Americas differ, and the drivers of their spread remain unclear. In this study, niche conservatism was analyzed using five niche dynamic metrics for both species. The current distribution of C. hirundinella primarily in subtropical and temperate non-native areas aligns with its native climate (i.e., higher niche stability), suggesting preadaptation. Meanwhile, for C. furcoides, a niche shift—indicated by maximum expansion and unfilling—suggests a much higher potential for rapid spread across both tropical and subtropical climates. These findings show distinct climatic responses of congeneric species in non-native ranges, emphasizing the need to move beyond native environmental predictors when assessing invasion risk. Future research should explore niche shifts over time and whether invasions begin in ecologically matched habitats (as expected for C. hirundinella) or are driven by propagule pressure and human activity despite niche mismatches (as in C. furcoides).

SARS-CoV-2 seroprevalence and COVID-19 vaccination coverage in two states of Nigeria from a population based household survey