The Global Research Network for Verified Scholars

Brand slogan as a part of branding plays a defining role for universities to win fierce competition. It creates emotional bonding and memorability in the mind of potential students and stake-holders. This study investigates words choice and word arrangement used in brand slogans of 100 best universities in Asia 2019 by making use of the Systemic Functional Grammar’s Experiential Metafunction. Results indicate that words most preferred for their brand slogans are patterned and are circled around: “Truth” (4/11%), “Integrity, “sincerity” (each 3/8%), and “Act”, “creating”, “creative”, “diligence”, “excellence”, “global”, and “justice” (each 2/6%); Verb (12/48%), Noun (11/44%), Adjective (1/4%), Prepositional phrase (1/4%). They are structured in Structures of Modification (10/40%), Structures of Complementation (9/36%), word (5/20%), and Structures of Predication (1/4%).

This study investigates the discourse strategies of five global social movements#IceBucketChallenge, #ReadyFor100, #FridaysForFuture, #MeToo, and #BlackLivesMatterin order to improve the efficacy of advocacy. It emphasizes three areas: (1) Discourse Practice (production, distribution, consumption), (2) Sociocultural Practice (ideology, hegemony, power), and (3) Text Practice (Interpersonal Metafunction: MOOD and MODALITY in hashtags). Data from official websites and social media were analyzed using Halliday's framework and Faircloughian Critical Discourse Analysis. Findings indicate that the #IceBucketChallenge capitalized on user videos, while #MeToo and #BlackLivesMatter empowered grassroots voices through digital platforms. Sociocultural practices posed a challenge to systemic racism, patriarchy, and fossil fuel dependency. A deontic modality and imperative sentences were identified through hashtag analysis, which underscored the importance of collective participation, moral accountability, and urgency. These movements promote solidarity, challenge power, and accelerate transformative change. In the future, research should investigate the interplay of offline and digital advocacy, diverse movements across regions, and activism under platform restrictions.

This study looked into how Harvard University's EdX Massive Open Online Courses (MOOCs) strategically employed the Aristotelian rhetorical aspects of pathos, ethos, and logos to comprehend their digital persuasion methods. It employed content analysis to examine three MOOCs' "about this course" sections: "Rhetoric," "Ancient Masterpieces," and "Pyramids of Giza." After careful examination, the "Rhetoric" MOOC received an impressive high score of 8, indicating a strong dedication to academic rigor in the digital realm. Deliberately incorporating pathos (7) was found to be an effective strategy for emotionally engrossing learners in virtual environments across MOOCs. In addition, Harvard's excellent display of credibility, authority, and ethical appeal in the digital sphere was underlined by consistently high ethos scores (8 for "Rhetoric," 5 for "Ancient," and 6 for "Giza"). The study's findings highlight Harvard's deft application of digital persuasion techniques in EdX MOOCs, demonstrating the university's smooth transition of its prestigious reputation into the digital learning space while adjusting to the changing needs of education, especially in the AI-driven world of today.

Calamansi (Citrofortunella microcarpa), a vital citrus crop in the Philippines, is increasingly threatened by leaf diseases such as Huanglongbing (HLB), which severely impact yield and quality. While artificial intelligence (AI) and Convolutional Neural Networks (CNNs) have shown potential for automated plant disease detection, most models are trained on ideal, studio-quality images with labor-intensive manual annotation, limiting their applicability in real-world farming conditions. This study investigates how three key factors: image enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE), AI-assisted annotation, and dataset condition (controlled vs. uncontrolled) affect the performance of CNNs in calamansi leaf disease detection. A 2×2×2 factorial experiment was conducted using 3,000 annotated images collected from farms in Oriental Mindoro, Philippines. These images were utilized in both training and evaluation phases of the four deep learning models: YOLOv11, YOLOv12, Faster R-CNN, and RF-DETR. Results show that CLAHE significantly improved model accuracy under uneven lighting by 4.8%, while AI-assisted labeling reduced annotation time by over 70% with minimal loss in precision. Among the models, YOLOv12 achieved the best overall balance of accuracy and speed (mAP@50:95 = 64.2%; Recall = 73.6% with a variation of 0.9), making it most suitable for scenarios prioritizing sensitivity. RF-DETR achieved the highest precision (72.3% with a variation of 0.5), underscoring its strength in minimizing false positives and providing reliable detections. This study underscores the value of preprocessing, semi-automated labeling, and realistic field datasets in building robust, deployable AI systems for plant disease detection, contributing to practical, low-cost solutions for resource-constrained agricultural environments.

Engineering and intelligent systems increasingly require decision-making under heterogeneous evidence. These sources include multi-source data, predictive models, competing objectives, feasibility constraints, and uncertainty. Evolutionary algorithms (EAs) are widely used in such settings, yet the literature is typically organised by algorithmic lineage, which obscures how and where information is fused within the evolutionary process. This survey reframes EAs as adaptive information fusion architectures. We introduce a fusion-centric taxonomy spanning data-level integration of heterogeneous observations, model/feature-level integration through surrogate and learning components, objective-level integration through multi- and many-objective formulations, constraint handling as feasibility-signal integration, and decision-level integration through ensembles, distributed (island) evolution, and multi-run aggregation. Building on this perspective, we synthesise major EA families according to their dominant fusion mechanisms and review fusion-driven applications across structural and mechanical design, energy and smart grids, robotics and control, communications, healthcare engineering, and neural architecture search. To complement this qualitative synthesis, we propose lightweight quantitative indicators—integration depth, integration diversity, and decision outcome entropy—to characterise algorithm–fusion–domain alignment and to identify recurring success and failure modes, including surrogate bias, over-aggregation, and fusion over-complexity. We conclude with practical design guidelines and discuss emerging directions, including federated evolutionary fusion and reliability considerations in learning- and large language model (LLM)-assisted fusion.

Mobile payment apps have transformed daily transactions across urban India, driven by UPI's growth exceeding 15 billion monthly transactions in 2025. This study examines their effects on savings behavior among middle-income households (₹5-15 lakhs annual income) in Indore, a key Tier-2 commercial center. Primary survey data from 200 respondents (aged 25-55) shows weekly app usage negatively predicts savings rates (β = -1.10, p < 0.001), with a mean 3.9 percentage point decline post-adoption. Income and age moderate this relationship, providing partial protection. While digital wallets enhance convenience and inclusion, they increase spending through reduced transaction friction. Recommendations focus on behavioral nudges like automated savings features and financial literacy integration for sustainable adoption.

GRNs represent one of the most significant models to explain biological processes and decipher pathogenicity. However, the current methodologies largely separate network topology or gene expression, thus ignoring the combinative interaction that forms the basis of network structure and regulation processes. We present an in-depth framework in this study whereby network topology analysis is combined with machine learning to detect hub genes and potential biomarkers in lung adenocarcinoma. By using the correlation-based inference algorithms with the data of RNA-sequencing of tumorous and normal tissue samples, we assembled the gene regulatory networks, and then obtain four traditional network centrality measures degree, betweenness, closeness, and eigenvector centrality to be combined with the gene expression properties. Three machine learning models, namely, Random Forest (RF), Support Vector Machine (SVM), and XGBoost, have been trained using stratified five-fold cross-validation with a large-scale hyperparameter evolution. The fifteen hub genes that were found show significant topological changes in cancerous samples. The XGBoost model was shown to be the best in terms of classifying the data as indicated by the area under the ROC curve of 0.912(95%, CI: 0.891 - 0.933), exhibits superiority to expression only models (p < 0.001, DeLong test). The five hub genes namely TP53, MYC, EGFR, CDKN2A and PTEN are characterized by resounding relationships with patient survival. (log-rank p < 0.001) and were statistically significant following multivariate adjustment of the clinically relevant covariates. Combination of network topology and expression profiles boosts the identification efficacy of biomarkers, which results in a 6.2% improvement in the AUC compared to methods that use only expression. Our framework hence provides a generalizable approach that can be used to precision oncology initiatives.

Globally, lung cancer continues to rank among the leading causes of mortality. The lack of sophisticated technology choices for screening and the fact that the sickness is typically discovered at the final stage are the main causes of this. Consequently, this work creates a hybrid AI-based system that combines machine learning (ML) and deep learning (DL) methods to make lung cancer detection quick and accurate. A machine learning classifier such as Random Forest or Support Vector Machine (SVM) is used for the final diagnosis after the created system uses convolutional neural networks (CNN) for automatic feature extraction from computed tomography (CT) images. Such a hybridization allows the model to leverage the deep networks' superior feature extraction ability and also, maintain the interpretability and the efficiency of traditional ML algorithms. The proposed model through rigorous experiments on benchmark datasets is reported to achieve classification accuracy, sensitivity, and specificity of 97.8%, 96.5%, and 98.2%, respectively, thus, outperforming the conventional standalone methods. Besides, the designed scheme is perfect in differentiating malignant and benign lung nodules under various imaging conditions.