The Chinese manufacturing industry faces many challenges to sustainable development. This study examines how transformational leadership, corporate culture, and digital transformation affect Chinese manufacturing organizations’ sustainability. It will also examine the moderating role of environmental dynamism and the mediating effect of innovation capabilities. A self-administered survey was distributed to 350 manufacturing companies’ owners, managers, leaders, and employees, etc. Participants were selected via convenient sampling. This data collection effort validated findings and empirically tested theories. Smart PLS structural equation modelling (PLS-SEM), quantitative research, and cross-research are used in this study. The findings suggest that corporate culture, transformational leadership, and digital transformation significantly affect organizations’ sustainability. Innovation capability does not affect the relationship between corporate culture and sustainability. However, it mediates the relationship between transformational leadership, digital transformation, and business sustainability. Innovation capabilities and business sustainability performance are moderated by environmental dynamism. This study contributes to sustainable corporate performance theory by showing managers how transformational leadership, digital transformation, and corporate culture can help manufacturing companies grow indefinitely. The findings have major implications for China, a highly industrialized nation. This study could benefit regulatory authorities, academic institutions, industry, government agencies, and researchers.

Analysis of hyperspectral imagery (HSI) is a critical aspect of remote sensing in precision agriculture, for which effective dimensionality reduction (DR) strategies for the inherent complexity and uncertainty of the data are highly necessary. The fusion of fuzzy logic with DR techniques offers potential promises to refine enough feature information from the classification system, which may potentially compromise critical information. However, graph-based deep learning, especially the use of graph attention networks (GATs), has advanced the field by ensuring enhanced spatial and spatial-type feature relationships, which alleviates classification errors. Although graph convolution networks and their predecessors have greatly advanced the use of spatial relationship features, deep learning algorithms, such as three-dimensional convolutional neural networks (3D-CNNs), have resorted to reducing the need for and reliance on high samples through spatial feature learning and consideration. Therefore, to advance the field, a novel approach, Enhanced Transformation enabled Fuzzy Graph network (ETFG), was designed, which is a combination of deep fuzzy-based DR, enhanced through the use of 3D-CNN and GAT, with the application of principal component analysis (PCA) for optimized DR. The ETFG model entailed two major processing stages, where the initial stage involved classifying the raw data cube using the 3D-CNN, and then the results processed an algorithm enriched by lightweight GAT-based modules. The ETFG model combines fuzzy C-means clustering and optimized DR using PCA, contributing the best of PCA and GAT capabilities for optimized classification. At high-performance optimal DR, the ETFG model offered optimal multispectral imaging and the analysis and classification of hyperspectral data, which is promising enough to advance the field's needs.

The long-term development goals of most countries face significant challenges in reducing emissions, improving environmental sustainability, and mitigating the negative effects of climate change. This study looks at how the ecological sustainability of BRICS countries is affected by economic growth, financial development, new technologies, and renewable energy consumption with the mediating effect of trade openness. The study covers the years 2004–2023, and it was based on fixed-effect models that use static panel data. Data were collected from the World Development Indicators website. The countries and time frame for this study were selected on the basis of data availability. These findings show that the use of renewable energy sources, technological innovation, and financial development all have a significant and positive impact on environmental sustainability. Nevertheless, environmental sustainability is significantly and negatively impacted by economic growth. Furthermore, trade openness functions as a significant mediator between them. Based on empirical evidence, the paper suggests that the BRICS nations seek sustainable economic development. Moreover, government agencies need to accurately evaluate the connection between financial development and emission reduction when formulating programs to cut emissions.

Artificial intelligence (AI) imitates the human brain’s capacity for problem-solving and making decisions by using computers and other devices. People engage with artificial intelligence-enabled products like virtual agents, social bots, and language-generation software, to name a few. The paradigms of communication theory, which have historically put a significant focus on human-to-human communication, do not easily match these gadgets. AI in multidimensional touch is the subject of this review article, which provides a comprehensive analysis of the most recent research published in the field of AI, specifically related to communication. Additionally, we considered several theories and models (communication theory, AI-based persuasion theory, social exchange theory, Frames of mind, Neural network model, L-LDA model, and Routine model) to explain a complex phenomenon and to create a conceptual framework that is appropriate for this goal and a voluntary relationship between two or more people that lasts for an extended period. Communication and media studies focus on human–machine communication (HMC), a rapidly developing research area. It is our intention to continue investigating the beneficial and detrimental effects of artificial intelligence on human communication as well as to identify novel concepts, theories, and challenges as the research process develops.

The study aims to evaluate and compare the performance of various machine learning (ML) classifiers in the context of detecting cyber-trolling behaviors. With the rising prevalence of online harassment, developing effective automated tools for aggression detection in digital communications has become imperative. This research assesses the efficacy of Random Forest, Light Gradient Boosting Machine (LightGBM), Logistic Regression, Support Vector Machine (SVM), and Naive Bayes classifiers in identifying cyber troll posts within a publicly available dataset. Each ML classifier was trained and tested on a dataset curated for the detection of cyber trolls. The performance of the classifiers was gauged using confusion matrices, which provide detailed counts of true positives, true negatives, false positives, and false negatives. These metrics were then utilized to calculate the accuracy, precision, recall, and F1 scores to better understand each model’s predictive capabilities. The Random Forest classifier outperformed other models, exhibiting the highest accuracy and balanced precision-recall trade-off, as indicated by the highest true positive and true negative rates, alongside the lowest false positive and false negative rates. LightGBM, while effective, showed a tendency towards higher false predictions. Logistic Regression, SVM, and Naive Bayes displayed identical confusion matrix results, an anomaly suggesting potential data handling or model application issues that warrant further investigation. The findings underscore the effectiveness of ensemble methods, with Random Forest leading in the cyber troll detection task. The study highlights the importance of selecting appropriate ML algorithms for text classification tasks in social media contexts and emphasizes the need for further scrutiny into the anomaly observed among the Logistic Regression, SVM, and Naive Bayes results. Future work will focus on exploring the reasons behind this occurrence and the potential of deep learning techniques in enhancing detection performance.

Climate change, caused by the burning of fossil fuels, has emerged as an acute world problem, especially in the Group of Twenty (G20), where CO₂ emissions and other greenhouse gases are accumulating. This research examines how energy consumption, digitization, internationalization, and green finance affect the environmental sustainability of G20 countries in 2004–23. It also discusses how green technology innovation mediates these relationships. The research-based analysis of World Development Indicators and Organization for Economic Cooperation and Development data employed static panel data, fixed-effects models, and STATA software for econometrics. The findings also reveal that the four variables, namely green finance, digitalization, globalization, and energy consumption, significantly and positively correlated with environmental sustainability. It was also revealed that green technology innovation has a significant, although negative, impact on mediating these factors. Logically, these outcomes point to the fact that, as much as green technology innovation is crucial, the integration into the sustainability framework has to improve its synchronization with other variables. The study provides new insights into enhancing green market systems in G20 countries to facilitate low-carbon technology exchange for emissions reduction. The findings of this research could be helpful for policymakers to improve green financing strategies, promote digitalization for environmental upgrading, and support effective energy use.