NIILM University

Global economic policymakers are facing increasing demands from diverse environmental constituents to prioritize sustainable development through the implementation of strict environmental policies, energy transition strategies, and the reduction of fossil fuel consumption by fostering technological advancement. This study aims to assess the moderating role of environmental Policy Stringency (EPS), among Geopolitical Risks (GPR), Energy Transition (ET), Technological Innovation (INV), and environmental degradation (CO2 emission). This study relies on empirical datasets spanning the years 1990 to 2022 in 13 EU countries and employed an extensive range of econometric techniques., Dynamic Ordinary Least Square (DOLS), Fully modified Ordinary Least Square (FMOLS), Canonical Cointegration Regression (CCR), and Method of Moment Quantile Regression (MMQR) to examine the data. The empirical findings demonstrate that interaction of GPR*EPS, INV*EPS, and ET*EPS plays a crucial role in promoting environmental sustainability and lowering CO2 emissions. Whereas, environmental degradation is attributed to higher geopolitical instability and increased economic activities. Further, Quantile Regression (QR) and Feasible Generalized least square (FGLS) methods are employed to check the robustness empirical findings of the primary techniques. Based on our comprehensive empirical research findings, we can put forth constructive policy recommendations aimed at addressing environmental concerns and achievement of SDGs 7 & 13 in EU region.

AI-assisted educational and research tools are bridging the gap between computational and experimental neuroscience, allowing scientists to spend less time coding and greater time validating effects inside the laboratory or clinical environment. AI-incorporated neuroimaging systems such as FSL-MEGNet, DeepBrain AI, and FreeSurfer-AI extensions automate structural and purposeful brain image segmentation, parcellation, and function extraction which require substantial preprocessing and scripting. Numerous hours which were obligated to be invested for computational setup can now be utilised constructively as former can be accomplished in minutes through automated pipelines, allowing researchers to allocate greater time to interpretation, speculation, and validation through wet-lab or behavioral assays. In neuropharmacology, AI-driven predictive modelling tools like DeepChem, ChemBERTa, and Molecule.one rapidly are being extensively employed for ligand-goal interactions and pharmacokinetics for neuroprotective compounds. The workflow updates, lengthy molecular docking or dynamic simulation code are now replaced with AI-generation, significantly cutting preclinical screening time for nanoparticle-drug conjugates. In transcriptomics and proteomics, systems such as Gene Ontology AI help, ChatGPT BioQuery, and OmicVerse can analyze huge omics datasets by means of decoding CSV or FASTA inputs through prompts. Such automation reduces the time for statistical coding and visualization, allowing researchers to at once integrate the computational findings to biological pathways validated experimentally. Further, AI-powered meta-analysis tools like Linked Papers AI, Studies Rabbit, and Elicit synthesize masses of courses into thematic maps in seconds; relieving researchers off rigorous literature evaluation. These rapid automated tools pave way for translational choice-making, such as choosing the optimal nanocarrier for BBB penetration or figuring out molecular goals for antioxidant nanomedicine studies. In wet-lab training and translational practice, AI-augmented lab management structures (e.g., BenchSci, LabTwin) automatically interpret experimental protocols and generate reagent lists, tool settings, and expected records formats. Students and researchers can visualize expected assay effects such as DPPH antioxidant curves, FTIR height overlays, or SEM nanoparticle morphology before attempting actual experiments. Collectively, these advances exhibit how AI accelerates pre-clinical lab processings, predictive modeling, and literature synthesis, shifting the research focus from repetitive coding and data cleaning to innovation, experimentation, and translational application. This paradigm shift not only enhances productiveness and reproducibility but also nurtures a generation of neuroscientists who can critically evaluate both computational logic and biological validation; a core pillar for the future of AI-integrated nano biomedicine and brain research.

This study investigates the root causes of persistent student misconceptions in inorganic chemistry at the undergraduate level. Despite instructional efforts, misconceptions remain widespread, affecting student performance and conceptual understanding. A comparative analysis of chemistry textbooks, structured interviews with teachers, and curriculum mapping were conducted to identify whether textbooks, teaching practices, or curriculum design contribute most significantly to these misconceptions. Findings suggest that curriculum gaps and inconsistent textbook representations are key contributors, while teacher interpretation plays a mediating role. Based on these results, recommendations for teaching practice and curriculum reform are proposed.

Cotton is a vital agricultural crop that significantly contributes to the global textile industry; however, its productivity is severely affected by various leaf diseases caused by insects, bacteria, fungi, and viruses. Early and accurate detection of these diseases is essential to minimize crop losses and ensure sustainable farming practices. In this study, a hybrid framework based on Deep Learning is proposed for multi-level classification and detection of cotton leaf diseases. The framework integrates Convolutional Neural Networks (CNNs) with advanced techniques such as attention mechanisms and hybrid learning models to effectively extract and learn discriminative features from leaf images. The system performs hierarchical classification, including binary classification (healthy vs diseased), multiclass disease identification, and severity level estimation, along with localization of infected regions using object detection methods. The experimental results demonstrate the effectiveness of the proposed model, achieving an overall accuracy of 96.6%, precision of 95.7%, recall of 96.8%, and F1-score of 96.2% across multiple disease classes. The model shows strong performance in identifying diseases such as aphids, bacterial blight, and leaf curl virus under varying conditions. The integration of preprocessing, data augmentation, and hybrid modeling enhances the robustness and generalization capability of the system. The proposed approach provides a reliable and scalable solution for realtime cotton disease detection, supporting precision agriculture and enabling timely decision-making for improved crop health and yield.

This study investigates the systemic impact of prolonged social media consumption on the formulation, maintenance, and stabilization of personal identity among emerging adults (N = 450, ages 18–25). Driven by an integrated framework of classical developmental psychology and contemporary digital commerce dynamics, we analyze the psychological friction between the “engineered digital persona” and the “offline authentic identity.” Utilizing a mixed-methods design, quantitative data evaluates participants across the Self-Concept Clarity Scale (SCCS) and a newly developed Social Media Metric Sensitivity Index (SMMSI). Statistical modeling indicates a profound negative correlation (r = -0.58, p < .001) between intense metric dependency and structured self-concept clarity. Regression mapping reveals that metric sensitivity accounts for 33.6% of the variance in reported personal identity fragmentation. Qualitative thematic parsing further uncovers a systemic reliance on gamified external validation, manifesting in hyper-performative social interactions and severe identity fracturing. The paper concludes with an actionable, interdisciplinary framework for digital consumption literacy to protect identity development within heavily commodified digital networks.