Volume 14 | Issue 3 |

 Abstract

Dissociative experiences are states of the human mind wherein one's thoughts, feelings, behaviors and memories can become split-off from one another. This may enable individuals to temporarily avoid distressing stimuli. Dissociation, thus, relies upon a person's primary response to stress or acts as a distorted coping strategy to demanding circumstances. This investigation aims to explore the relationship of Dissociative Experiences with Subjective Sleep Quality, Internet Addiction and Cyberbullying Victimization. A total of 205 female young adults completed the Dissociative Experiences Scale - II (Carlson & Putnam, 1993); Pittsburgh Sleep Quality Index (PSQI) (Buysse et al., 1989); Internet Addiction Test (IAT) (Young, 1998) and Cyberbullying Victimization (CBV) Scale (Lee et al., 2017). Only those qualifying the cut-off for DES-II scale were included into the sample for the correlational analysis (N=122). The demographic data, academic information and online habits were taken a note of. t-test and Pearson correlational analysis were applied to identify the differences and associations between the psychological variables. The results showed significant differences as well as associations were found between the Dissociative experiences on subjective sleep quality, internet addiction and cyberbullying victimization. These conclusions could help forth in clinical assessment, prevention and awareness efforts, while ameliorating the under-recognition of dissociation among Indian youth.

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 Keywords

Cyberbullying victimization, Dissociative experiences, Internet addiction and Subjective sleep quality.

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 Abstract

The art of storytelling is as old as the time of evolved human species from the ruins of extinct ones. Story encapsulates a life time or a moment of life in a narrative web of thoughts and impressions. Storytelling inspires generations of people to think and ponder over the issues related to existence and essence. It is one of the most suitable medium though which thoughts and philosophies can easily be conveyed. Many related genres like exemplums, anecdotes, parables carry an iota of story in them to get their purpose fulfilled. When it comes to feministic thought, Indian feminism carries a different identity and modality than the western feministic thought in its relation to society and gender disparities where women are give identities which are not actually suitable for them. Indian feministic thought is a rich and evolving intellectual tradition that reflects the complex social, cultural, and historical realities of the country. Rooted in both indigenous philosophies and global feminist movements, Indian feminism addresses issues unique to the subcontinent--such as caste, class, religion, colonial history, and rural-urban disparities--while also advocating universal principles of gender equality, justice, and dignity. Indian feminism not just limited to psycho feminism or socio feminism but it incorporates various premises on which society stands itself. In order to understand the scenario in which Indian women exist, one needs to understand entire fabric of socio political and economic structure, otherwise, the understand remains to a limits sphere of narrowed biased vision. Among the multitude of Indian women thinkers, Bilkees L Latif stands apart when it comes to understand the community in which she was born. She represents the society in which women have been given little freedom to express and their identities are entwined with the identities of men counterparts. She resurfaces the women who defined their own times and carved out a niche in annuls of power realms. She founds her interest in understanding the challenges those women who faced in totally male dominated spheres of politics and administration.

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 Keywords

Key terms: Indian Feminism, Power structures, Gender roles, Identities, Male politics, Conformity.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Keywords : Mercury toxicity, environmental contamination,interns, amalgam separator,high volume suction,safe exposure limit, awareness, knowledge, attitude

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This academic article examines how adaptive technologies improve learner engagement in higher education by personalising learning, providing immediate feedback, and using mastery-based pathways. A scoping review of 69 studies found that 59% reported better academic performance and 36% reported increased engagement. Key mechanisms such as gamification and personalisation are used in programs like Connect LearnSmart, which can raise grades by 25%. Despite these promising results, challenges like technological barriers, costs, digital divide and equity gaps, data privacy concerns, and faculty resistance persist. The paper stresses that hybrid models combining adaptive digital preparation with synchronous instruction, along with faculty training, equity assessments, and ethical governance, are essential for scalable, inclusive adoption. Future directions include innovations such as generative AI tutors, immersive VR/AR simulations, affective computing, and long-term research to verify sustained benefits and equity outcomes. Overall, adaptive learning technologies are shifting education from traditional lectures to a more student-centred approach, with great potential to enhance behavioural, emotional, and cognitive engagement when implemented thoughtfully and ethically. When used carefully, they can increase students' participation, interest, and deeper understanding of the learning process.

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 Keywords

Adaptive learning, student engagement, Higher education, AI personalisation, personalised pathways, learning analytics, educational technology, retention rates.

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Architecture has evolved significantly in response to cultural, environmental, and technological changes. This research examines architectural transition focusing on history, sustainability, and digital innovation in the built environment. Traditional architecture emphasized cultural identity and craftsmanship, while modern architecture focuses on functionality and efficiency. Sustainable architecture reduces environmental impact through energy-efficient designs and eco-friendly materials. Digital technologies such as Building Information Modeling and computer-aided design improve architectural accuracy and performance. The study uses literature review and comparative analysis. Results show that integration of sustainability and digital innovation improves building efficiency and environmental performance. Future architecture will depend on the integration of historical knowledge, sustainable practices, and digital innovation.

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 Keywords

Architecture evolution, Sustainable architecture, Digital architecture, Built environment, Smart buildings, Green buildings, BIM, Digital innovation

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Triple-negative breast cancer (TNBC) is an aggressive subtype lacking established therapeutic targets, making an accurate molecular classification a key research interest. Although computational methods of classification using molecular data are increasingly adopted, limited multi-omics availability remains a major analytical challenge. This study aims for a flexible machine-learning framework to classify TNBC using genomic, transcriptomic, and multi-omics data, enabling independent analysis of incomplete data. Public TCGA-BRCA resources were utilized, including genomic, transcriptomic, and clinical metadata for TNBC status annotation and model development. Individual genomic and transcriptomic data were independently processed and subsequently integrated using matched sample identifiers. ML models were trained on genomic, transcriptomic, and integrated datasets using consistent train-test splits. Logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost were evaluated using ROC-AUC, recall, F1-score, accuracy, and runtime, with recall emphasized to improve TNBC detection sensitivity in this imbalanced dataset. Multi-omics integration provided the most desirable discriminative ability, the ROC-AUC of which was 0.95. In all modalities of data, the logistic regression provided the most reliable balance between TNBC recall, discrimination, computational efficiency, and model stability. Optimization of the probability decision threshold further improved TNBC sensitivity with insignificant effects on overall classification. This study indicates that the TNBC classification may be effectively aided by machine-learning models capable of operating under partial or complete multi-omics availability. The framework was deployed through a Streamlit-based web application for demonstrating research purposes. Overall, this study introduces scalable and interpretable TNBC classification and strong translational relevance using multi-omics machine learning.

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 Keywords

triple-negative breast cancer, multi-omics, machine learning, genomics, transcriptomics.

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Creative Commons Attribution 4.0 and The Open Definition

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India, the world's largest democracy, functions through a well-established electoral system that ensures representation of the people. Free and fair elections are the foundation of democratic governance. However, challenges such as criminalization of politics, the influence of money power, voter apathy, and misuse of media have affected the quality of elections. Electoral reforms have been introduced from time to time to strengthen democratic values, improve transparency, and ensure accountability in the electoral process. This paper examines the concept of electoral reforms, major reforms introduced in India, and their impact on strengthening Indian democracy. The study is based on secondary sources such as books, journals, constitutional provisions, and reports of the Election Commission of India.

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 Keywords

Electoral Reforms, Democracy, Indian Constitution, Governance, Elections

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The proliferation of misinformation across social media platforms has necessitated the development of advanced automated detection systems capable of handling multimodal content. While early fake news detection approaches relied on handcrafted linguistic features and unimodal deep learning models, recent research has shifted toward multimodal transformer-based architectures that enable joint reasoning over textual and visual information. This survey provides a comprehensive review of fake news detection methods, with a particular emphasis on multimodal transformer frameworks developed recently. We present a structured taxonomy that categorizes detection approaches into traditional machine learning, deep neural text models, CNN-based multimodal systems, unimodal transformers, and state-of-the-art multimodal transformer architectures. The survey further examines the transition from binary classification to fine-grained misinformation detection, highlighting the importance of cross-modal alignment and semantic consistency modelling. Comparative analysis demonstrates that multimodal transformers consistently outperform earlier paradigms due to their cross-modal attention mechanisms and shared representation learning capabilities. In addition, we identify key research challenges, including dataset bias, annotation subjectivity, adversarial robustness, computational scalability, and explainability. Emerging directions such as multimodal large language models, causal reasoning frameworks, graph-enhanced architectures, and lightweight deployment strategies are discussed to outline future research opportunities. This survey synthesizes recent advances and provides a unified analytical framework and research roadmap for advancing multimodal transformer-based fake news detection toward robust, interpretable, and real-world deployment-ready systems.

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 Keywords

Multimodal Fake News Detection, Multimodal Transformers, Fine-Grained Classification, Vision-Language Models, Misinformation Analysis.

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Schizophrenia is a severe psychiatric disorder characterized by disturbances in perception, cognition, emotional regulation, and behaviour. Increasing scientific evidence suggests that structural and functional abnormalities in the brain play a crucial role in the development and manifestation of the disorder. The present study examines the role of neuroimaging techniques in understanding schizophrenia and explores the relationship between neuroimaging findings and clinical symptom severity. A quantitative, descriptive, and correlational research design was adopted. The study included 17 individuals diagnosed with schizophrenia, comprising 9 males and 8 females. Symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), while neuroimaging data were obtained from clinical records including Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and Functional Magnetic Resonance Imaging (fMRI). Descriptive statistics were used to summarize the distribution of symptom severity across positive, negative, and general psychopathology domains. The Mann-Whitney U test was applied to examine gender differences, and Spearman's rank correlation was used to analyze relationships among symptom variables. The findings indicated that both male and female patients demonstrated comparable levels of symptom severity. Although females showed slightly higher mean scores across PANSS domains, these differences were not statistically significant. Correlation analysis revealed strong relationships among symptom domains but no significant association between gender and neuroimaging outcomes. The study highlights the importance of integrating neuroimaging findings with standardized clinical assessments to improve understanding of schizophrenia. Neuroimaging provides valuable insights into the biological basis of the disorder and supports the development of more comprehensive diagnostic and treatment approaches.

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 Keywords

Keywords: Schizophrenia, Neuroimaging, PANSS, MRI, CT Scan, PET Scan, fMRI, Symptom Severity, Mann-Whitney U Test, Spearman Correlation.

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This study examined the reintegration experiences of former members of the Communist Party of the Philippines-New People's Army (CPP-NPA) who availed themselves of the Philippine government's Enhanced Comprehensive Local Integration Program (E-CLIP) in the Province of Cagayan. The research aimed to determine the current state of life of Balik-Loob beneficiaries in terms of self-adjustment, community intervention, and government assistance. Using a qualitative case study design, data were collected through semi-structured interviews with twenty (20) identified Balik-Loob recipients. Thematic analysis was employed to interpret participants' lived experiences. Findings revealed significant life changes among participants following their surrender, primarily motivated by their desire to reunite with their families and pursue a peaceful life. Government assistance through E-CLIP provided financial support and livelihood opportunities that facilitated reintegration. Community acceptance also played an important role in their adjustment. The study concludes that E-CLIP has contributed positively to the reintegration of former rebels in Cagayan, although sustained institutional and community support remains essential to ensure long-term stability.

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 Keywords

Balik-Loob, E-CLIP, reintegration, former rebels, CPP-NPA

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Creative Commons Attribution 4.0 and The Open Definition