Women’s mental health is significantly impacted by psychological stressors such as stress, anxiety, depression, anger, self-silencing behaviour and fear of sexual assault. These negative emotions not only cause discomfort in life but also limit the freedom of expression and interaction. Self-defence is now considered a means of enhancing one’s mental capability and empowerment. The current study focuses on the effects of self-defence training in lowering negative psychological states related to women’s overall well-being. Seven quantitative studies meeting the inclusion criteria were systematically analysed to assess the effects of self-defence training on women. These studies were sourced from electronic databases, including ResearchGate, PubMed, Google Scholar, Scopus, and ScienceDirect, using keywords such as “self-defence training,” “mental health,” “women,” “psychological states,” “empowerment,” etc. The main psychological factors considered during this study include fear, stress, anxiety, depression, anger, and self-silencing behaviour. Findings indicate that self-defence training is effective in reducing fear, stress, anxiety, depression, anger, and self-silencing behaviour. Moreover, self-defence training also increases a person’s feeling of control and safety. Therefore, this review highlights the benefits of self-defence training in reducing negative psychological states, emphasising its value as a holistic approach to mental health and women’s empowerment in the 21st century. Further research may focus on the long-term impact of martial arts on mental health and improve techniques to ensure maximum psychological benefits.

We investigate the nonequilibrium relaxation dynamics of the one-dimensional (1D) Fermi-Hubbard model subjected to abrupt, global interaction quenches. Specifically, we benchmark the convergence properties, structural accuracy, and algorithmic breakdown of the Magnus expansion against numerically exact results obtained via full Exact Diagonalization (ED) on small, periodic lattice clusters. By tracking the real-time evolution of local observables, double occupancy (doublon density), and many-body state fidelity metrics, we map out the validity bounds of the low-order Magnus series across weak, moderate, and strong interaction regimes. Our findings demonstrate that while the Magnus expansion provides an exceptionally accurate description of short-time coherent dynamics, rapid phase matching, and initial prethermalization tendencies, its convergence is fundamentally bottlenecked at longer timescales. This breakdown is driven by the rapid growth of multi-particle entanglement, non-local operator spreading via nested commutators, and the emergence of severe state-space fragmentation inherent to dense, strongly interacting many-body spectra.

Marriage is an important institution among women folks and marriage bring joy, peace, stability, focus and sense of belonging in womanhood. When delay occur it brings psychological trauma to women. Delayed marriage among women generally refers to women marrying at a later age than what is traditionally expected in each society and this varies by culture. Across many parts of the world, the average age of first marriage has been rising. This shift is linked to social, economic, and cultural changes rather than a single cause. Delayed marriage among women of reproductive age is influenced by several factors including social, economic, cultural, psychological, spiritual and personal factors. This study aimed to Assess the Determinants of Delayed Marriage Among Women of Reproductive Age in South-South Nigeria. This was a cross-sectional study involving 250 women. Participants’ age is between 18 to 47 years. A well-structured questionnaire was administered to participants. The study lasted for a period of 2 months. Statistical analysis was done using SPSS version 25.0 and p < 0.05 was significant. The results revealed that majority (36%) of the participants were between 29-34 years old, 76% had tertiary education, 40% are unemployed, 60% residence in Urban areas, 60% are not in a relationship. Several factors were responsible for the delay in marriage including: financial instability 80%, 80% is delayed due to economic responsibilities, 80% is due to career development, 76% due to cultural influence, 68% is due to psychological influence, 80% influenced by family expectations, 80% due to social pressure, 76% due to personal factor, 68% due to desire for independence, and 68% is due to previous relationship experiences. This delay in marriage is due to social, economic, financial, personal, psychological, cultural, career development, and desire for independence.

Precision oncology increasingly depends on integrating heterogeneous evidence across molecular profiling, medical imaging, and clinical records, yet robust deployment is limited by data fragmentation across hospitals, missing modalities, batch effects, privacy constraints, and weak mechanistic interpretability. We propose a causal and explainable federated multimodal learning framework for cancer prediction and target discovery that fuses multi-omics, radiology or digital pathology imaging, longitudinal EHR features, and CRISPR dependency signals. The system trains across sites without centralizing raw data using federated optimization with secure aggregation and optional differential privacy, and is designed to remain reliable under non-IID site heterogeneity and structured missingness. To move beyond correlational risk scoring, we introduce a causal layer that encodes structural assumptions for treatment response and survival, supports counterfactual prediction, and applies invariant learning style regularization to improve transportability. For clinical safety, the framework outputs calibrated uncertainty and multi-level explanations, including modality contribution reporting, feature attributions over genes, imaging regions, and EHR variables, and causal what-if narratives for treatment changes and gene perturbations. We define a fully public experimental protocol using TCGA and CPTAC for multi-omics and outcomes, TCIA for imaging domain shift evaluation, and DepMap for CRISPR based dependency mapping and pathway level target rationale. This work provides an end-to-end, reproducible blueprint for privacy-preserving, mechanism-aware cancer AI, enabling benchmark driven validation prior to prospective multi-hospital deployment.

Gender-Responsive Budgeting (GRB) represents a substantive reconfiguration of public finance, contesting the long-held assumption of fiscal neutrality and foregrounding equity as a core principle of budgetary governance. This paper optimizes GRB as a paradigm shift that combines gender analysis into the formulation, allocation, execution, and evaluation of public budget, thereby modifying policies into instruments of social justice. It argues that conventional frameworks, aggregate efficiency, and metrics of growth, often creates obstacles in fulfilling gender-differentiated needs and perpetuate structural inequalities that inculcate deviations within labour markets, care economies, and access to public services. Anchored within the normative framework of the Sustainable Development Goals (SDGs), particularly SDG 5 (Gender Equality), SDG 10 (Reduced Inequalities), SDG 8 (Decent Work and Economic Growth), and SDG 16 (Peace, Justice, and Strong Institutions), the study repositions GRB as a pin-point mechanism for translating global promises into actionable fiscal strategies. By drawing insights from public finance theories, the paper gave an edge to how gender-responsive fiscal intervention targeted social expenditure, gender-sensitive taxation, and investments to create impactful spillover effects, i.e., beyond equity to enhance productivity, human capital formation, long-term economic resilience, and social justice. The analysis further underlines GRB’s role in advancing distributive justice for unpaid and underpaid care workers and correcting allocative inefficiencies in disadvantageous women and marginalized groups. By rooting fairness and transparency into budgetary pipeline, GRB arms democratic governance and rebuilds fiscal discipline and coordination between efficiency and equity rather than a trade-off between the two. In a sense, GRB challenges the growth-centric orthodoxy of public finance and advances a welfare-oriented approach. The paper methodically and indispensably restructures public finance as a mutative tool that harmonizes welfare, justice, and growth. In the context of prevailing gender disparities and evolving challenges, GRB emerges not merely as a corrective policy add-on, but a foundational framework for sustainability in economic governance.

The study of time management has gained prominence due to its significant correlation with productivity, academic and professional achievement, psychological well-being, and self-regulation. Multidimensional productivity solutions that incorporate cognitive, behavioral, motivating, and environmental factors have gradually replaced traditional scheduling and prioritization strategies. The field of time management is still conceptually fragmented, with many strategies having a lot of overlap and no comparative synthesis across categories, even though time management techniques are quickly becoming popular in academic literature, professional training, and digital productivity platforms. This review provides a comprehensive narrative synthesis of applications for managing one's own time in a structured functional classification framework. There are many different types of strategies, but some of the most common ones include systems for managing tasks, systems for prioritizing tasks, systems for reducing distractions, systems for managing energy and wellbeing, systems for collaborative time management, and systems for focusing on concentration and deep work. The functional purpose, cognitive and behavioral mechanisms, practical applications, strengths, and limitations of each category were the primary areas of focus in the comparative analysis. Despite differences in terminology and implementation, the review shows that time management systems frequently display similar mechanisms such as attentional control, self-regulation, executive functioning, behavioral automation, and environmental structuring. Because effectiveness depends on contextual needs, cognitive load, personality attributes, and individual goals, the results show that no one method is inherently better. This review provides a structured framework that helps people choose and apply time management strategies based on evidence. It does this by integrating classification, mechanism-based interpretation, and comparative analysis to address conceptual fragmentation in the literature.

The present paper endeavours to assess the fiscal performance of a major States, Uttar Pradesh and a minor state, Uttarakhand separated by Uttar Pradesh in November 2000. The paper highlights that while Uttar Pradesh’s large economy and revenue generation capacity give it an advantage, its high debt burden, reduced social sector spending, and growing central dependence pose fiscal risks. Uttarakhand, despite its smaller size and limited industrial base, exhibits better fiscal discipline, prioritizes human development sectors like education and health, and shows positive signs of debt management. However, its over-reliance on central grants limits its financial independence. The study suggests that both states need to balance infrastructure spending with sustainable social sector investment to ensure long-term fiscal health and economic stability.