This study adopts a qualitative lens and explores the mechanisms through which women negotiate internalized sociocultural norms to shape their career aspirations in engineering within the Indian context. Drawing on Pierre Bourdieu’s concept of habitus and symbolic violence, this work investigates the depth of assimilation of gendered beliefs and social conditioning that colour women’s perceptions of their academic potential, leadership capabilities and life trajectories. Content analysis of the narratives from 12 female doctoral students from three premier Indian universities revealed three interconnected themes: “Internalized devaluation of ambition”, “Self-policing and avoidance of high-visibility roles”, and “Perceived incompatibility with gendered life paths”. Findings reveal that these internal mechanisms operate beneath formal institutional structures and perpetuate gender disparity in chemical engineering academia. The study proposes various interventions for mitigating and disrupting this subtle socio-cultural conditioning and advances the theoretical understanding of the unconscious, internalized barriers that impede women’s career progression.
Nomophobia is emerging as a developing psychological condition characterized by fear and anxiety are caused from the lack of access to smartphones. Furthermore, the high dependence on smartphone use for interactions, academic production, and fun has worsened the problem, especially among teens and young adults. Still, despite being widespread, existing studies lack significant automated approaches that can be implemented for early nomophobia detection. This research paper attempts to bridge the gap between traditional methods and design an effective and potentially scalable framework for the early detection of nomophobia by adopting advanced machine learning techniques. Thus, an effective and domain-adapted approach using the TabTransformer model was proposed, which is a robust Deep Learning model developed to handle tabular data with categorical and numerical features in which a self-attention mechanism can capture the more complex inter-feature relationships. A newly designed dataset, surveyed from 2013 respondents with structured surveys, consisted of smartphone usage patterns, psychological indicators, and behavioral metrics like insomnia, late-night usage, and loneliness scores. The preprocessing steps applied include feature scaling, encoding, and class balancing through SVM_SMOTE to prepare the data for analysis. It indicates that the proposed TabTransformer model was superior and achieved a greater accuracy of 91
The combination of Generative Artificial Intelligence (GenAI) and Internet of Things (IoT) opens unexplored opportunities concerning the potential technological revolution and creates critical security concerns that requires systematic study. Based on refined 72 high-quality peer-reviewed studies published from 2017 to 2025, this systematic review adopts the rigorous Scientific Procedures and Rationales of Systematic Literature Reviews (SPAR-4-SLR) methodology to critically review these articles and provide two critical research questions: how GenAI can transform the capabilities, efficiency and robustness of IoT systems in diverse areas and what are the security concerns in the intersection of these two technologies. Through databases search in Scopus, Web of Science (WoS), and IEEE Xplore and further refinement of the search results with advanced bibliometric analysis software, including Visualisation of Similarities (VOS) viewer and Biblioshiny, this literature review shall be used to provide a compilation of the existing evidence-based research. The thematic analysis has found four thematic research clusters as Artificial Intelligence (AI) and Deep Learning (40
Glioblastoma Multiforme (GBM) is an aggressive and highly heterogeneous brain tumor with poor survival outcomes. While conventional radiomic analyses focus on tumor-centric regions, emerging surgical strategies, such as GTR and supratotal resection (SupTR), highlight the importance of the peritumoral zone. Moreover, due to intratumoral heterogeneity, MGMTpm, a key molecular biomarker guiding chemotherapeutic decisions, is assessed invasively and remains prone to sampling bias. Therefore, this study examined the prognostic and predictive utility of deep radiomic features from tumor and peritumoral regions in preoperative MRI to improve OS prediction and enable non-invasive MGMTpm classification. Multi-parametric structural MRI scans (T1, T1-Gd, T2, and T2-FLAIR) from 520 to 200 GBM patients were analyzed for OS and MGMTpm prediction, respectively. Tumor and peritumoral masks were segmented and expanded using morphological dilation from 2 to 12 mm. 11,000 deep features per patient were extracted using ResNet50 and ViT-B16 models, capturing patterns that may reflect tumor infiltration and microenvironmental changes. Hybrid feature selection using variance thresholding and Recursive Feature Elimination (RFE) was applied, including age and gender. Support Vector Machine (SVM) classifiers were trained using 10-fold cross-validation. In OS prediction, the inclusion of an 8 mm peritumoral margin improved AUC from 0.74 (95
Predator–prey interactions are central to maintaining the stability and biodiversity of natural ecosystems. In this study, we develop and analyze a Leslie–Gower-type predator–prey model, incorporating hunting cooperation and nonlinear harvesting of predators to better capture the complex dynamics of ecological systems. Our theoretical analysis establishes the positivity of solutions as well as feasibility and stability of biologically feasible equilibria, revealing that while species-free equilibrium is inherently unstable prey-free equilibria may exhibit conditional stability. Through numerical simulations and sensitivity analysis, we show that both prey and predator populations are highly sensitive to prey growth rate, minimal reproductive capacity under fear, and intraspecific competition. One and two-parameter bifurcation analyses uncover rich dynamical patterns driven by variations in prey and predator reproduction rates. The system undergoes saddle-node, Hopf, and transcritical bifurcations, as well as codimension-two bifurcations such as cusp and Bogdanov–Takens. These bifurcations give rise to diverse dynamical regimes—mono-, bi- and tri-stability-indicating that small changes in the parameter can shift the system among coexistence, oscillatory, and extinction states. To further explore ecosystem resilience under uncertainty, a stochastic extension of the model is introduced by incorporating environmental noise. Using a framework based on transition probability densities and the stochastic sensitivity function technique, we determine the most probable transition pathway, tipping time, and critical noise intensity associated with population collapse. Our stochastic analysis demonstrates that strong environmental perturbations can induce regime shifts between the states of high and low population densities, destabilize coexistence equilibria, and lead to predator extinction. Overall, our findings highlight that both intrinsic biological interactions and extrinsic environmental fluctuations play decisive roles in shaping the long-term stability, resilience, and adaptability of predator–prey systems.
