Background: Inflammatory diseases represent a major global health burden, and the search for novel, effective, and safe anti-inflammatory agents from natural sources remains a critical area of research. Lablab purpureus, a plant traditionally used in folk medicine, is a promising candidate due to its reported pharmacological properties. This study aimed to investigate the anti-inflammatory potential of the methanolic extract of L. purpureus leaves.

Methods: The anti-inflammatory activity of the L. purpureus methanolic leaf extract was evaluated in Wistar albino rats using two established in vivo models: the carrageenan-induced paw edema model for acute inflammation and the formalin-induced paw edema model for chronic inflammation. Doses of 100 mg/kg and 200 mg/kg of the extract were administered orally to the experimental groups, while control groups received a standard anti-inflammatory drug (Indomethacin) or a vehicle. The anti-inflammatory effect was determined by measuring changes in paw volume at specific time intervals.

Results: The results demonstrated that the methanolic leaf extract of L. purpureus exhibited significant anti-inflammatory activity in a dose-dependent manner. In the carrageenan-induced model, the extract significantly reduced paw edema compared to the control group, with efficacy comparable to the standard drug. Similarly, in the formalin-induced model, the extract showed a marked reduction in paw swelling, indicating its potential to manage both acute and chronic inflammatory responses.

Conclusion: This study confirms the significant anti-inflammatory activity of Lablab purpureus leaf extract in Wistar albino rats. The findings suggest that the plant possesses therapeutic value and could serve as a potential source for developing new anti-inflammatory agents. Further research is warranted to isolate and characterize the specific bioactive compounds responsible for this observed activity.

Large‑scale language models (LLMs) built on the Transformer architecture have demonstrated extraordinary capabilities but impose heavy computational and latency burdens, especially during inference. This paper investigates a dual‑pronged approach to mitigating such burdens: first, firmware‑level optimization techniques that reduce latency and enhance inference throughput, and second, integrating architectural modifications—specifically sparse attention mechanisms—to reduce redundant computation without degrading model performance. We develop a conceptual framework that unifies hardware‑level and algorithmic‑level improvements; then we simulate (in thought‑experiment form) how a Transformer‑derived LLM would perform under such optimizations, drawing on empirical evidence from prior work on attention sparsity and head pruning. We find that by pruning redundant attention heads (as in head‑importance analyses) and replacing conventional softmax attention with sparse activation mechanisms, it is theoretically possible to greatly reduce both memory–compute load and inference latency while preserving semantic fidelity and downstream task performance. We discuss implications for deploying LLMs in resource‑constrained environments (e.g., edge devices), potential trade‑offs (coverage, hallucination risk), and directions for future empirical validation, especially in the context of firmware‑level optimizations for inference engines.

Software supply chain security has emerged as a critical domain within cybersecurity research and enterprise practices due to the increasing complexity of interconnected software ecosystems and the proliferation of dependency networks. This research article provides an in-depth theoretical and empirical synthesis of supply chain risk vectors, with an emphasis on third‑party component vulnerabilities, dependency freshness, automated malicious package detection, and holistic risk management frameworks. Drawing on prolific work in software supply chain risk assessment (Croll et al.), open source trust paradoxes (Silic & Back), and empirical analyses of ecosystem vulnerabilities (Delamore & Ko; Decan et al.), this research scrutinizes both systemic and component‑level risk dynamics that threaten software integrity. We explore supply chain risk management foundations, interrogate real‑world attack case studies such as malicious npm packages and backdoored Docker images, and articulate the role of software bill of materials (SBOM) in supply chain transparency. Furthermore, we revisit dependency freshness and outdated workflows as persistent challenges in ecosystem hygiene, integrating perspectives on vulnerability datasets and advanced detection techniques. Our contribution contextualizes existing research within a unified narrative that elucidates theoretical underpinnings, identifies persistent gaps, and proposes an integrated, resilient software supply chain security model, emphasizing rigorous monitoring, automated detection, governance frameworks, and future research trajectories.

The accelerated adoption of artificial intelligence in socially sensitive and high-risk domains has intensified scholarly and institutional concern regarding the opacity, accountability, and ethical legitimacy of algorithmic decision-making. While modern machine learning systems, particularly deep learning architectures, demonstrate remarkable predictive and classificatory performance, they often do so at the cost of human comprehensibility. This growing disconnect between algorithmic capability and human understanding has positioned Explainable Artificial Intelligence as a central research paradigm aimed at restoring transparency, interpretability, and trust in AI-driven systems. This article presents a comprehensive and original scholarly examination of Explainable AI grounded exclusively in established academic literature, with particular emphasis on healthcare, financial risk assessment, and broader socio-technical systems. Rather than offering a superficial overview, the study engages in extensive theoretical elaboration, exploring how explainability functions as a cognitive, ethical, and institutional requirement rather than a purely technical feature. Through a qualitative synthesis of interdisciplinary research, the article examines the evolution of explainability from early interpretable models to contemporary post-hoc explanation frameworks, analyzing their philosophical assumptions, practical benefits, and inherent limitations. The findings reveal that explainability is deeply contextual, shaped by human expectations, regulatory environments, and domain-specific epistemologies. The discussion critically addresses counterarguments that question the feasibility and desirability of explainability, including concerns about oversimplification, false reassurance, and strategic misuse. By situating Explainable AI within a broader framework of trustworthy and human-centered artificial intelligence, the article argues that meaningful explainability requires not only algorithmic techniques but also institutional governance, user education, and ethical reflexivity. The paper concludes by outlining future research trajectories necessary to advance Explainable AI as a mature scientific discipline capable of supporting responsible AI deployment in complex real-world environments.

General Ledger (GL) reconciliation has long been recognized as a foundational accounting control that ensures the accuracy, completeness, and reliability of financial statements. Traditionally viewed as a routine back-office activity, GL reconciliation has gained renewed strategic importance in the contemporary financial environment characterized by high transaction volumes, multi-GAAP reporting obligations, digital payment systems, and increasing regulatory scrutiny. This study undertakes an extensive theoretical and practice-oriented examination of GL reconciliation by synthesizing insights from professional guidance, industry case studies, and interdisciplinary academic literature spanning accounting, auditing, information systems, governance, and financial technology. Drawing strictly on the provided references, the article explores how reconciliation practices are evolving from manual, retrospective controls into intelligent, automated, and governance-integrated mechanisms that support real-time financial accuracy and organizational transparency. The methodology is qualitative and conceptual, relying on systematic literature analysis, cross-domain synthesis, and interpretive reasoning to derive patterns, implications, and future trajectories. The results highlight that automation, artificial intelligence, and emerging technologies such as blockchain are reshaping reconciliation by enhancing timeliness, reducing human error, strengthening compliance, and supporting audit judgment. At the same time, the discussion reveals persistent challenges related to data quality, control design, ethical oversight, and organizational readiness. The article contributes to the literature by positioning GL reconciliation as a strategic assurance function rather than a clerical task, emphasizing its role in mitigating financial restatements, supporting fair value reporting, and enabling trustworthy digital transformation. The study concludes that robust, technology-enabled reconciliation frameworks are indispensable for sustainable financial reporting quality in complex and high-risk environments.