Sage University

The increasing availability of multilingual and multimodal data on online platforms has created a need for the development of sophisticated models for cross-lingual sentiment analysis. Although existing sentiment analysis models are largely dependent on monolingual text data, they tend to fail in generalizing across languages and modalities, especially in low-resource and code-switching settings. This work aims to handle the most important task of combining heterogeneous modalities of text and audio data in multiple languages for accurate and interpretable sentiment classification. X-Sent-Fuse, a cross-lingual sentiment fusion model using Transformers, combines text and audio modalities to make accurate, robust, and culturally informed sentiment predictions for a wide range of languages. The architecture incorporates a Multimodal Transformer Cross-Attentive Gated Fusion (MT-CAG2F) module to effectively align semantic and prosodic features, followed by a BiSRO-Net classifier, to ensure stable and high-performing predictions. A lightweight ensemble of multilingual large language models further improves linguistic adaptability. Extensive evaluations on four benchmark datasets, Sentiment analysis in Twitter, Amazon Reviews, mTEDx, and CMU-MOSEAS, demonstrate state-of-the-art performance, achieving up to 98.67

Doxorubicin, a secondary metabolite of Streptomyces peucetius var. caesius and a member of the anthracycline family, exerts anticancer effects via DNA intercalation and topoisomerase II inhibition in tumor cells. However, its clinical application is limited by dose-dependent and cumulative cardiotoxicity. The mechanisms underlying doxorubicin-induced cardiotoxicity (DIC) include oxidative stress, lipid peroxidation, mitochondrial dysfunction, calcium dysregulation, disrupted iron homeostasis, nitric oxide release, and inflammatory mediator production. Emerging evidence highlights autophagy dysregulation, with doxorubicin upregulating cardiac autophagy by suppressing GATA4 and ribosomal protein S6 kinase beta-1(S6K1). Mitochondria-dependent ferroptosis also plays a significant role, driven by downregulation of glutathione peroxidase 4 (GPX4), lipid peroxidation via DOX-Fe2+ complexes, and dysregulated iron metabolism. Additionally, DOX triggers pyroptosis in cardiomyocytes, involving proteins such as NLRP3 (NOD-, LRR-, and pyrin domain-containing protein 3), caspase-3, and gasdermin D (GSDMD). Epigenetic alterations, including DNA hypomethylation (via downregulation of DNMT1 (DNA (cytosine-5)-methyltransferase 1), changes in microRNA levels (e.g., upregulation of miR-520h targeting HDAC19 (histone deacetylase 1), and histone deacetylase inhibition, exacerbate cardiac damage. Recent studies also emphasize the role of gut microbiota in doxorubicin-induced cardiotoxicity. Doxorubicin induces dysbiosis, leading to cardiomyocyte apoptosis and elevated myocardial enzyme levels. Interventions such as dietary modifications, fecal microbiota transplantation, probiotics, and natural compounds like glabridin and emodin show promise. Glabridin reduces inflammation by modulating colonic macrophage polarization, while emodin inhibits ferroptosis via gut microbiota remodeling mediated by Nrf2. This review explores oxidative stress, lipid peroxidation, ferroptosis, apoptosis, inflammation, autophagy, epigenetics, and gut microbiota in DIC, alongside promising pharmacological strategies to mitigate its effects.

PurposeOrganizations often fail to implement strategies internally when they do not effectively orchestrate their resources and capability. Therefore, this study develops a scale for measuring enterprise architecture (EA) capability and establishes a framework for evaluating its components. Properly assessing EA capability enables alignment between business and information technology (IT).Design/methodology/approachA comprehensive literature review was conducted along with expert interviews to determine which systems comprise EA capability. A questionnaire was developed and distributed to 131 firms and convergent and discriminant validity analyses based on partial least squares structural equation modeling were conducted. The proposed model includes 17 characteristics associated with the strategic attributes, the business model that forms the business construct, and the system and technology models that inform the IT construct.FindingsThe results demonstrate that EA capability can orchestrate business and IT, and that IT resources are crucial to building EA capability. Additionally, it was revealed that when aligned with the firm's objectives and business model EA capability can leverage organizational agility, enabling changes through innovation or operational adjustments.Originality/valueThis study addresses the lack of methods for evaluating EA capability within organizations by providing empirical evidence to assess their effectiveness in orchestrating business and IT resources. This study complements the results of previous research by considering IT's influence on resource orchestration theory to enable business.

Optical control of ferroic order offers a transformative route for manipulating magnetic states in multiferroic systems, especially at ferromagnetic/ferroelectric (FM/FE) interfaces. However, achieving stable, reversible, and room-temperature modulation of magnetism via light remains a key challenge, primarily due to limited coupling efficiency and poor structural control at the nanoscale. This limitation can be overcome by utilizing strain-mediated coupling mechanisms that convert photostrictive responses in the FE layer into magnetic reorientation in the FM layer. Here, a reversible, light-induced strain effect is demonstrated in PMN-PT single crystals, where domain variants visualized via Piezoresponse Force Microscopy undergo controlled reconfiguration under visible laser illumination. X-ray diffraction reciprocal space maps confirm lattice deformation, while Raman spectroscopy reveals local structural changes. In Fe/PMN-PT heterostructures, this optically induced strain drives magnetic axis reorientation in the Fe layer, confirming robust magneto-electric coupling. The discovery establishes a non-contact, energy-efficient approach to manipulate magnetism, advancing the field toward ultrafast, reconfigurable magneto-optical devices.

Internet of Things (IoT) intrusion detection is frequently reported as a near-solved classification problem, yet operational deployments remain vulnerable to class imbalance, distribution shift, dataset leakage, poor probability calibration, unstable explanations, and cross-network failure. This review synthesizes 28 verified sources spanning benchmark datasets, adaptive learning, generalization, explainable artificial intelligence, imbalance handling, ensemble models, probability quality, and review methodology. It develops a unified taxonomy in which data integrity, model intelligence, temporal resilience, human trust, and evaluation rigor are treated as interdependent design requirements. Mathematical foundations are provided for class-weighted risk, macro-averaged performance, Matthews correlation coefficient, calibration error, drift detection, soft voting, and additive explanations. Major findings are that random record splits can materially overstate readiness; accuracy alone obscures minority-class failure; oversampling must be confined to training folds; post-hoc explanations need stability tests; and drift adaptation requires governance, not only retraining. A reproducibility case study using an audited RT-IoT2022 copy illustrates how index leakage, duplicates, conflicting labels, and extreme skew can coexist with apparently excellent scores. The review concludes with an evidence-driven architecture and a research agenda for device-aware evaluation, calibrated uncertainty, explanation monitoring, and safe rollback. The article is intended as both a critical survey and a practical blueprint for thesis-level experimental work.