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Etodolac-loaded mesoporous silica nanoparticles: A novel nanocarrier approach for anti-inflammatory therapy

Chitosan nanoconjugates loaded with Sophora flavescens phytochemicals for enhanced antimicrobial and antibiofilm activity against nosocomial pathogens

Toward sustainable tribological systems: Copper nanoparticle-enhanced vegetable oil nanolubricants

Calcium copper titanate-modified electrode for selective lead detention in water: Linking structure to sensing

Optimization of mannosylated curcumin-loaded liposomal formulation using Box-Behnken design

Recent advances in metallic nanoparticles: From characterization to toxicity and bioactivity

Optimization of graphene nanoplatelet incorporation in cementitious composites using Taguchi and response surface method

Tailoring structural and optical properties of ZnS nanoparticles via strontium doping

Nanomedicine for neuroinflammation modulation: Next-generation nano-immunotherapies for neurodegenerative disorders

FedMobileNet: A Resource-Conscious Federated Learning Framework for Imbalanced Lung Cancer CT Image Classification

Federated learning enables distributed training without exchanging original images, but non-IID data and class imbalance can degrade model performance. In this study, FedMobileNet is presented as a decentralized machine learning system based on MobileNetV2 for multiclass classification. The training instances were spread among four simulated clients using a Dirichlet-based non-IID partitioning method (with α = 1.0) so that each client had at least five samples per class. The model used a multiclass focal loss with class weights set to α = [2.0, 1.0, 1.0, 1.0] and γ = 2.0; minority oversampling was not applied. The model that achieved the highest validation AUC at the checkpoint was selected and then evaluated once on the separate test dataset. In the test evaluation described here, FedMobileNet reached an accuracy of 89.4% and a macro-AUC of 0.97; the class-wise F1-scores were 0.81 for adenocarcinoma, 0.77 for large cell carcinoma, 0.97 for normal tissue, and 0.83 for squamous cell carcinoma. These results correspond directly to findings from a specific simulated federated configuration. They should not be interpreted as proof of clinical effectiveness or as evidence that the method is ready for deployment. The study uses a single public CT dataset, a single fixed random seed, and a specific client partition. It did not include analysis with repeated seeds and different partitions, external clinical validation, or direct measurements of memory, energy, communication cost, and inference latency. For this reason, the framework is described as resource-conscious rather than having been experimentally validated as resource-efficient for clinical or edge deployment.

Machine Learning-Enabled Quality Analytics for Higher Education: A Multi-Dimensional Classification and Decision-Support Framework

Higher education institutions generate heterogeneous information through curriculum processes, teaching-learning activities, learning resources, student support, research and extension, internal quality assurance, and learner engagement. Converting these heterogeneous signals into actionable quality intelligence is a computationally challenging classification problem because educational quality is multidimensional, noisy, partially observed, and context-dependent. This paper proposes and experimentally evaluates a machine-learning quality analytics pipeline to classify higher-education quality states. We constructed a computational dataset of 500 synthetic student-level observations using seven normalized quality dimensions. Three supervised learning algorithms Logistic Regression, Decision Tree and Random Forest were evaluated using a stratified 70:30 hold-out protocol and five-fold cross-validation. Performance was assessed through accuracy, precision, recall, F1-score and ROC-AUC. Random Forest achieved 69.33% hold-out accuracy and an F1-score of 79.46%, while Logistic Regression achieved 69.33% accuracy and 79.28% F1. We also used five-fold cross-validation to assess performance stability. Feature-importance analysis identified the relative contribution of the quality dimensions, and an ablation experiment examined the effect of removing the most influential predictor. The study also develops a probabilistic feedback-validation formulation and an outcome-oriented Faculty Effectiveness Index architecture for integration into a quality-management pipeline. The results demonstrate the feasibility of treating institutional quality management as a computational decision-support problem rather than as a collection of isolated descriptive indicators. Because the numerical dataset is synthetic, the reported performance values are methodological demonstrations rather than estimates of any real institutional population. The framework provides a technically explicit basis for subsequent validation with ethically collected institutional data.

From Movement to Metaphor: A Review of Junwu Tian’s American Travel Narratives and Cultural Metaphors in Twentieth-Century Fiction

A Critique of Eurocentrism: From the Paradigmatic Perspective of Western Globalization Studies

Other Bodies: An Embodied Perspective on Two Women’s Travel Accounts of Japan (1904)

Anthropology of Travel

From “Travel Writing” to “Travel Literature”: Disciplinary Evolution and Structural Decentring—Serving as an Inaugural Editorial

Human Digital Twins and the Reconfiguration of Social Interaction

Human Digital Twins (HDTs) are a new concept that has become part of the wider picture of Industry 5.0 and Society 5.0, and aim to simulate a representation of a human being that is dynamic and encompasses not only physiological aspects but also cognitive, emotional, and social characteristics. Although much work has been done on technical architectures and biomedical applications, social aspects of HDTs have received relatively little attention. This paper fills this void by systematically reviewing literature from information systems, sociology, ethics and human-computer interaction to analyze the social dimensions of HDTs. It claims that the HDTs are not just technical products, but are social actors that could interact with and change the main dimensions of human relationship: identity, presence, trust, intimacy, and authority. The paper examines application areas in education, healthcare, employment, interpersonal relations, social pairing and the new industry of the digital-afterlife, to pinpoint opportunities for strengthening social connectiveness and threats of social displacement, identity fragmentation and ethical erosion. It suggests a conceptual approach to the understanding of HDTs as socially grounded, structural-functionalist, post humanist and post phenomenological forms and provides recommendations for human-centred development that maintains a sociality of human beings rather than its loss.

The Future of Digital Companions: Intimacy, Attachment, and the Reconfiguration of Human Connection

Digital companions, or conversational agents, have arisen in the wake of generative AI and their ability to support continuous social and emotional engagement. From science fiction emerge AI companions that are now reaching tens of millions of users across the globe, and more specialized platforms and more general-purpose AI assistants are all becoming more and more relational partners. With a sociotechnical approach in mind, this paper explores the potential trajectory of digital companionship, discussing the form of design logic that makes digital companions so attractive, the psychological processes involved in human-AI bonding, and the future of synthetic bonding and human sociality. Informed by recent empirical studies, platform analyses and a critical-theoretical approach, the paper proposes that digital companions are not only a new technology but a profound reconfiguration of intimacy, relational norms and intimacy in the modern society. The future of digital companionship will be defined by the interplay of market forces, regulatory responses and changing cultural attitudes, and will have a significant impact on how humans interact with technology and each other.

Digital Inequality and Digital Oppression: Mapping the Divide, Its Mechanisms, and Pathways to Equity

The gap in access, skills, use, and benefits from digital technologies that were initially identified as “digital inequality” has grown into a “digital oppression” the systematic exclusion, exploitation, and control of existing marginalized groups by digital systems design, deployment, and governance. This paper draws on the most recent scholarship to synthesize the various components of the digital divide and how they intersect with race, gender, class, disability, and geography. It explores how digital technologies replicate and, in some instances, reinforce existing power hierarchies such as algorithmic bias, data extraction through surveillance, exclusionary design, and the offshoring of dangerous forms of digital work to the Global South. The paper offers an overview of the effects of digital inequality in education, employment, health and civic life, based on data from the International Telecommunication Union (ITU), peer-reviewed audits of commercial AI systems, investigative reporting and human rights reports regarding the platform economy, and foundational theoretical work in critical data studies. It argues that the digital divide is not a natural or temporary phenomenon of uneven technological diffusion, but a result of political and economic decisions and choices, and that closing the divide will require conscious investments in infrastructure, skills, inclusive design, and regulatory accountability.

Digital Harassment Against Women: Need for a Gender-Sensitive Legal Framework

Human Digital technological advancement has transformed the way women experience gender-based violence to a larger extent, that it has moved from the physical world of harassment to the almost unsecured area of the cyberspace. It is sad that apart from physical integrity, security, and participation in public, vocational, and economic activities, women's rights also face threats from cyberstalking, consensual dissemination of intimate images without endorsement, doxxing, deepfake pornography, and orchestrated online harassment. The increase in technology-enabled crime has been highlighted by the National Crime Records Bureau in India, a conclusion that has been confirmed by other surveys of which more than 50% of the women have been attacked with some kind of cyber harassment like unsolicited sexual messages, and morphed and deepfake pictures. The existing legal frameworks are however patchy: the Information Technology Act, 2000 and the (Ministry of Home Affairs 2023) which are cyber and criminal statutes respectively do not deal with the harassing act as one entity, while the Sexual Harassment of Women at Workplace which was initially created for a physical workplace is no longer relevant as it is not the real case for professional women, journalists, gig workers, and remote employees who actually work. This paper contends that the digital forms of harassment should not only be through cyber law and criminal law without these two but it must also be proponents of the issue such as occupational health and labor rights that workers suffer from more in the remote working, gig-fitness and online business exchange model where they interact and overlap. By drawing on such materials as the International Labor Organization's Violence and Harassment Convention (No. 190), the CEDAW Committee's General Recommendation No. 35, and the United Kingdom's Online Safety Act, 2023, this paper states that a gender sensitive legal framework must recognize the digital and occupational space of working women as one, uniform space, and it winds up with firm suggestions for reforming the cyber, criminal, and labor law umbrella in India's context to such an extent.