The concept of brand activism has recently emerged as a distinct phenomenon, garnering increasing scholarly interest. However, research in this area is still in its infancy, characterized by fragmented studies and a lack of cohesive theoretical frameworks. This review aims to analyze the prevailing research patterns in brand activism by classifying the essential attributes of publications. Using specified search criteria, the Scopus database was used to find, extract, select, and review relevant publications from 1984 to 2025. In the search, 259 publications were selected for this study after application of the exclusion and inclusion criteria. This bibliometric analysis is based on performance analysis and science mapping. The results reveal the most influential papers, authors, journals, and nations in this study area. The findings suggest that brand activism research is multidisciplinary, encompassing business ethics, psychology, public policy, management, and marketing. The study identified six thematic clusters: Corporate Political Advocacy and Institutional Change; Consumer Ethical Responses and Brand-Directed Activism; Societal Impact of Brand Activism; Market–Society Tensions in Brand Activism; Moral Emotional Responses to Brand Activism; Authenticity, Credibility, and Competitive Outcomes of Brand Advocacy. These themes highlight their applications, ranging from directing corporate political advocacy and stakeholder alignment on socio-political issues to enhancing authenticity, credibility, and the competitive outcomes of brand activism. The implications of this research emphasize brand activism as a strategic approach that links corporate values to societal requirements to create social change in an increasingly polarized marketplace. This study is novel in presenting a comprehensive bibliometric and science-mapping of brand activism research to date, providing key takeaways, outlining future research directions, and motivating scholars to explore this phenomenon further.
This study examines the corrosion and the tribological performance of a thin-walled structure fabricated using Rotational Arc Dual Wire (RADW) Wire Arc Additive Manufacturing (WAAM) with interlayer hammering. ER70S-6 low-alloy steel was used as the primary wire and Nichrome as the secondary wire for the build. Microstructural characterization was done by using Optical microscopy, Scanning electron microscopy and with Electron backscatter diffraction techniques. The hardness of the build was measure throughout the height of the build. Corrosion studies on the build were done by using potentiodynamic polarisation and Electrochemical impedance spectroscopy. A detailed tribological study was conducted by varying load, sliding velocity, and sliding distance, and the wear rate was optimized using two approaches: Exhaustive Search and Genetic Algorithm (GA)-based optimization. Additionally, a machine learning model based on Multiple Linear Regression (MLR) with interaction terms was employed to predict wear behaviour. The addition of nichrome and interlayer hammering was found to modify the corrosion response and wear behaviour of the RADW-WAAM builds, while the optimization techniques and the MLR model helped in identifying and predicting the tribological behaviour for a given set of parameters.
The persistent presence of malachite green oxalate (MGO) in industrial effluents poses a significant threat to environment, aquatic life and human health. This study reports the synthesis of ZnO nanorods via a magnetic stirred mechanically assisted thermal decomposition method, with systematic variation in magnetic stirring durations (0, 1, and 4 h) post heat treatment to enhance photocatalytic performance. Comprehensive characterization using XRD, FE-SEM, UV-Vis diffuse reflectance, and photoluminescence spectroscopy confirmed the formation of wurtzite-phase ZnO nanorods. Photocatalytic degradation experiments, conducted under 80 W Hg bulb irradiation, revealed that increasing magnetic stirring duration led to enhanced dye degradation efficiency (4-hour stirred sample heat treated at 300 °C). Magnetic stirring post heat treatment has tremendously increased the rate constant by 64
This study presents a data-driven investigation of energy and exergy performance of parabolic trough solar collectors (PTSCs) using ensemble machine learning regression models. To predict the energy and exergy efficiencies, a comprehensive dataset comprising thermo-fluid and solar operating parameters such as Reynolds number, nanoparticles volume fraction, inlet fluid temperature, and direct solar irradiance is used. The dataset considered three heat transfer fluids, “Dowtherm Q”, “Syltherm 800”, and “Therminol VP-1”, each blended with nanoparticles “Al₂O₃”, “CuO”, and “SiO₂”. Three ensemble algorithms namely AdaBoost, Gradient Boosting, and XGBoost, were trained and optimised through random search hyperparameter tuning and validated using five-fold cross-validation. The model’s performance was evaluated using mean squared error ( MSE ), mean absolute error ( MAE ), and coefficient of determination ( R^2 ). The results show that Gradient Boosting had the highest accuracy ( R^2>0.99 for energy and up to 0.9999 for some cases) but AdaBoost also performed well ( R^2>0.97 for exergy). And XGBoost underperformed ( R^2<0.53 ), confirming boosting-based ensembles are the most reliable models for PTSC efficiency prediction. This work provided a novel framework for integrating data-driven models into performance analysis and optimization of solar thermal systems.
The increasing concerns about the environmental impact of fossil fuels have emphasized the importance of clean solar energy, which offers a pollution-free alternative for meeting growing energy needs. However, the accurate prediction of solar photovoltaic (SPV) based power generation is a very challenging task because of its inherent variability and uncertainty. To address this challenging problem, this paper applies several machine-learning, deep-learning, and their hybrid models such as: One-Dimensional Convolutional Neural Network (1D CNN), Bi-Directional Long Short-Term Memory (Bi-LSTM), Stacked LSTM, Artificial Neural Network (ANN), Linear Regression (LR), Support Vector Regression (SVR), XGBoost, and a hybrid CNN-LSTM model. These models are examined and compared on four different data sequences of DKASC Alice Springs dataset. The prediction performances of all these models are evaluated based on various error metrics: MAE (mean absolute error), explained variance, RMSE (root mean square error), R², and sMAPE (symmetric mean absolute percentage error). The simulation results demonstrates that Stacked LSTM model outperforms all other benchmark forecasting models and able to obtains average values of performance metrics i.e. MAE of 1.1157, RMSE of 2.3408, an Explained Variance of 0.8998, R² of 0.9004, and sMAPE of 1.1795 as evaluated across all four different data sequences. Moreover, a comprehensive statistical analysis, using Diebold Mariano Test and boxplots, confirms the further superiority of Stacked-LSTM model to efficiently address inherent uncertainty of solar power generation.
