ABSTRACT This paper reframes the identity and historical positioning of the Śeṣa lineage within the socio-political and intellectual milieu of early modern Benares. Focusing on the celebrated grammarian Śeṣakṛṣṇa, who migrated from Mahārāṣṭra to Benares in a movement emblematic of wider patterns of scholarly mobility in early modern North India, it re-examines the long-standing scholarly debate concerning his relationship to Rāmacandra, the author of the pioneering Prakriyākaumudī, which reorganised the Pāṇinian rules of Sanskrit grammar. Drawing on original Sanskrit sources and recent scholarly approaches to the identity of the Śeṣa clan, the study reassesses the extant textual, genealogical, and historical evidence while reconstructing Śeṣakṛṣṇa’s immediate familial networks to clarify his chronological placement and social standing within Benares’s learned circles. By integrating intellectual history with social and institutional contexts, this paper contributes to a more nuanced understanding of lineage, authorship, and scholarly authority in early modern Sanskrit intellectual culture.

Modern question answering systems rely on advanced neural architectures to bridge the semantic gap between natural language queries and relevant textual evidence. However, single-model approaches often struggle to capture diverse linguistic variations, while traditional ensemble methods suffer from high computational complexity and static model combination strategies. Existing answer reranking systems lack adaptive mechanisms to dynamically adjust model contributions based on question characteristics, limiting their effectiveness across knowledge-intensive tasks. This study proposes an adaptive confidence ensemble reranking framework designed to improve reliability and efficiency in knowledge-intensive question answering. The proposed approach integrates dual-encoder retrieval with a cross-encoder ensemble of BERT, RoBERTa, and DeBERTa models, combined through an instance-aware adaptive confidence weighting mechanism. The framework dynamically adjusts model contributions using cross-entropy-based evaluation to optimize answer ranking performance while maintaining computational feasibility. Experimental results demonstrate significant performance improvements across multiple benchmark datasets, including a 9.3% increase in mean average precision on MS MARCO, a 2.1% gain on WikiQA, and a 1.2% improvement on TREC-QA. These findings highlight the effectiveness of the proposed method in enhancing reliability and accuracy across diverse knowledge-intensive question answering scenarios.

Health polices in residential care settings for older adults are essential to align housing and care provision with the needs of ageing populations, ensuring safety, participation, and quality of life. Despite their importance, evidence regarding policy implementation in long-term care (LTC) facilities remains fragmented and inconsistent. This protocol describes an original method for synthesizing health policy documents using the Joanna Briggs Institute (JBI) framework for textual evidence. It aims to identify, analyze, and integrate policy evidence related to LTC facilities for older adults. Following JBI methodological guidance for systematic reviews of text and opinion, this protocol employs the PICo framework to define inclusion criteria and a three-step search strategy. Searches will be conducted in MEDLINE, CINAHL Complete®, and the Virtual Health Library (BVS). Eligible documents include laws, regulations, policy frameworks, and technical guidelines addressing long-term care within publicly regulated systems. Data extraction and quality appraisal will be independently performed by two reviewers using JBI instruments. The review will synthesize existing polices, highlighting their characteristics, implementation strategies, and outcomes. By applying a transparent and replicable JBI-based method, this protocol supports the production of high-quality evidence to inform equitable and effective governance in LTC facilities.

Financial analysis increasingly depends on the ability to transform heterogeneous textual evidence into reliable, verifiable, and actionable knowledge. However, adoption in finance requires generated outputs to be not only accurate, but also traceable and auditable. This work presents an audit-oriented LLM-RAG architecture for financial document intelligence. Rather than proposing a new foundation model, the contribution is a reproducible pipeline that integrates financial document processing, hybrid retrieval, evidence-grounded generation, structured validation, and persistent audit artifacts within a state-machine-based workflow. Designed for analyst-facing use, the system produces structured answers linked to explicit evidence while preserving the intermediate artifacts needed to inspect, reproduce, and validate each result. Experiments on AI-FinanceQA, a benchmark of heterogeneous financial documents and analyst-style questions, show that hybrid retrieval with reranking improves evidence selection over single-signal baselines and that the selected LLM backend achieves a compliance-oriented score of Scomp=0.9527. Additional experiments on FinQA confirm that targeted evidence selection improves numerical robustness and semantic alignment compared with uncontrolled context expansion. Overall, the proposed architecture provides an evidence-grounded and audit-oriented framework that supports human review rather than replacing expert financial judgment.

Constructing a cultivation conceptual model for interdisciplinary talents in nursing and engineering: A systematic review and thematic synthesis informed by grounded theory principles.

The term ultra-rare diseases was first introduced in 2004 by the National Institute for Health and Care Excellence during the evaluation of drugs indicated for conditions with a prevalence of fewer than 1 case per 50,000 people. This review aims to systematically examine the clinical evidence synthesis practices used worldwide to support decision-making on the incorporation of new drugs for treating ultra-rare diseases and to identify the definitions of ultra-rare diseases adopted internationally. This review follows the JBI methodology for reviews of textual evidence, including narrative reports, expert opinions, and policy documents. Eligible sources will include texts and studies addressing patients with ultra-rare diseases. The phenomenon of interest is the set of methods and practices used for synthesizing clinical evidence to inform the incorporation of health technologies for ultra-rare genetic diseases. This review focuses on the context of global health technology assessment (HTA) agencies. Searches will be conducted in the PubMed, Embase, CENTRAL, and Virtual Health Library databases, as well as on the websites of major international HTA agencies. Study selection, data extraction, and risk-of-bias assessment will be performed independently and in duplicate. Proportional meta-analyses will be conducted to summarize the frequency of study designs, outcomes, and recommendations according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach applied by HTA agencies when assessing drugs for ultra-rare diseases. The findings of this review are expected to inform the development of methodological guidelines for evidence synthesis in ultra-rare diseases-from defining ultra-rare conditions to establishing best practices for evidence generation and evaluation. In the long term, the results may guide strategies to address methodological challenges in primary studies on the efficacy and safety of health technologies for ultra-rare diseases, such as strengthening national patient registries and promoting multicenter collaborative research. PROSPERO CRD420250652106.

Singular Design Foresight (SDF) is proposed as a foundational methodological framework for advancing Design Foresight (DF) toward a more explicit, traceable, and evaluable scientific discipline. The framework formalizes DF as a structured cycle in which qualitative foresight inputs—such as signals, trends, and expert interpretations—are progressively transformed into analyzable representations that support decision closure under conditions of structural uncertainty. SDF combines an expert-defined conceptual universe with semantic projections to relate textual and contextual evidence to anticipatory constructs, enabling the generation of traceable indicators and structured configurations of viable futures. Within this architecture, the Stakeholder Viability Principle (SVP) functions as a filtering mechanism that delimits relevant futures according to continuity, agency, and axiological coherence, while Social Singularity captures context-specific critical transitions that shape when and why decision closure becomes necessary. The framework is organized in alignment with Design Science Research (DSR), adopting an evaluation logic centered on validity, utility, and attribution. Rather than presenting conclusive system-level validation, the article synthesizes summative evidence from previously published studies on semantic projections, singularity detection, and mixed expert–corpus foresight applications to support the plausibility, internal coherence, and operational feasibility of the proposed framework, while delimiting full integrated validation as a future research objective. SDF does not aim to provide deterministic prediction; instead, it enables auditable anticipatory representations and justified closure under uncertainty. In this sense, the framework is compatible with forecasting understood as the production of evaluable anticipations under explicit assumptions, while preserving the interpretive and situated character of strategic decision-making.

ABSTRACT Since the days of Henri Pirenne, much energy has been invested into measuring connectivity in the post-Roman Mediterranean. Although research on this topic has considered archaeological material and textual evidence in several languages, including Arabic, it has neglected the many references to transmediterranean mobility in Islamic law. This article analyses how legal texts from the Mālikī school, written between the second/eighth and fourth/tenth centuries, describe and evaluate movements and exchange between the Islamic and the non-Muslim sphere, in particular maritime trade. According to Pirenne, the Arabic–Islamic expansion divided the Mediterranean and obstructed the flow of persons, objects and ideas. An analysis of Mālikī law, in turn, proves that the conceptual division between dār al-islām and dār al-ḥarb introduced a mobility regime that facilitated transmediterranean relations under new conditions. Although theoretical, the legal deliberations of Mālikī jurists provide additional evidence for ongoing trade relations in the post-Roman Mediterranean.

Accounting students have historically had their cognitive ability examined through the lens of Bloom’s taxonomy. However, does a relationship exist between Bloom’s continuum of categories of cognitive processes and a student’s cognitive ability? This study aims to answer this question by investigating whether Bloom’s taxonomy aligns with an accounting student’s measured level of cognitive ability. The analytical focus of this study is to extract qualitative information from accounting students’ critical thinking exercises. This information provides the basis for analyzing whether textual evidence from accounting students based on Bloom’s taxonomy significantly relates to an assessment of cognitive ability. The implications of this research are both theoretical and practical in nature. From a theoretical perspective, this study provides evidence of the overlap between Bloom’s taxonomy and measurements of critical thinking and reflective judgment. In this understanding, accounting educators can refine their use of Bloom’s taxonomy when educating the next generation of accounting students. By analyzing each category and its relationship to cognitive ability, accounting educators can more effectively apply each level of Bloom’s framework to enhance critical thinking while also focusing on areas where there appears to be minimal overlap to maximize the development of cognitive abilities in students.

To identify and describe the current literature on neurodivergence in nursing and to identify existing gaps in knowledge. Scoping review guided by Joanna Briggs Institute scoping review guidelines and PRISMA-Scoping Review framework. Comprehensive search with inclusion following Participants, Concept, and Context framework: nurses or nursing workforce; neurodiversity or neurodivergence defined as autism, attention deficit hyperactivity disorder, dyslexia; workplace environment. Articles were screened and data extracted by independent reviewers. Dates of inclusion were 1999-2025. Data analysed through descriptive categorisation. MEDLINE (PubMed), CINAHL (EBSCOhost), and PsycINFO (Ovid) were searched in April 2025, followed by reference mining and citation cross-referencing. Inclusion criteria were set for empirical studies, reviews, or textual evidence (expert opinions or narratives). Twenty-two sources met inclusion criteria. Most addressed dyslexia and neurodiversity. Sources described strengths of neurodivergent nurses (e.g., deep focus, relationality, novel perspectives), workable challenges (e.g., documentation and multitasking). Studies reported adaptations at multiple levels: individual, interpersonal, and intraprofessional, noting system influences of medical model framing and gender bias in diagnosis. Literature on neurodivergent nurses in the workplace is scarce. Neurodivergence offers strengths and challenges, yet ableism and limited research restrict well-being and professional advancement. Greater recognition of neurodivergence in nursing could enhance workforce retention, innovation, and inclusivity. Neurodivergent nurses' perspectives should inform research, workplace design, and professional development. What problem did the study address?: Limited research on neurodivergence in nursing despite growing awareness of neurodivergence generally and overall challenges in nursing retention and well-being. What were the main findings?: Literature encompasses empirical studies and textual evidence presenting strengths and challenges for nurses. Adaptation centered on the individual. Where and on whom will the research have an impact?: Researchers to increase empirical studies on neurodivergent nurses, including participatory methods; Leaders interested in workplace inclusion, sustainable workplaces, and professional innovation; Neurodivergent nurses and policy-makers concerned with workforce rights and healthcare resilience. No Patient or Public Contribution.

ABSTRACT This article reconstructs and explains the introduction and spread of plague in the Great Lakes region of East Africa in the pre- and early colonial period (c.1700–1900). It argues that plague arrived and focalised in the region, which was marked by isolation from the outside world, towards the end of the seventeenth century and in unique circumstances of human migration, triggered by climatic anomaly and environmental factors. The most likely route of introduction was along the Nile, from Egypt and Sudan. Subsequent spread waves, each characterised by different spatio-temporal contours, were triggered largely by anthropogenic and demographic factors (including conflicts and human migration), but it was not until the colonial period that the disease left the region and spread farther east to the Swahili Coast and beyond. The article conjoins both textual and non-textual evidence, including recent palaeogenetic work (the study of ancient DNA), to demonstrate the value of an inherently interdisciplinary approach to plague history.

Green credit as ex ante climate-risk mitigation: Financing constraints, fintech amplification, and textual evidence from Chinese banks

Urban governance is hindered by the disconnect between qualitative knowledge embedded in textual sources and the predictive capabilities of quantitative models, making proactive policymaking challenging. This study proposes and validates a two-phase framework that automates the 'dialogue' between the evidence paradigms for urban air quality management. In the first phase, a Retrieval-Augmented Generation (RAG) pipeline synthesises extensive literature into a structured knowledge graph, generating evidence-based policy hypotheses. This approach navigates the challenge of evaluating overwhelming potential policy combinations. Following this, these qualitative hypotheses are automatically translated into simulation scenarios and subjected to rigorous, ex-ante quantitative testing, providing the rapid feedback necessary to overcome slow policy evaluation cycles. A case study in London, addressing post-2023 ULEZ expansion air quality challenges, demonstrates the framework's real-world utility. The RAG identified a portfolio of next-generation interventions targeting heating systems, construction machinery, and solvent emissions. Subsequent simulation with the SHERPA model produced spatial impact maps of pollution-level reductions and, critically, uncovered complex system dynamics. The framework's ability to efficiently screen a broad portfolio of options while revealing such complex consequences underscores its value for proactive governance. By integrating textual and simulation evidence into a scalable methodology, this research provides a robust tool to enable more effective, evidence-informed urban environmental policy.

Graph-based Retrieval-Augmented Generation (GraphRAG) improves multi-hop reasoning but remains limited when required relationships are absent from the knowledge graph despite being supported by textual evidence—a problem we term topological incompleteness. We introduce TSAAT (Topological Synthesis via Asynchronous Agentic Traversal), a query-time framework that synthesizes missing edges during live reasoning. TSAAT deploys asynchronous scout agents that traverse the knowledge graph concurrently and trigger relational hypothesis generation upon frustration. Candidate relations undergo dual-evidence validation: (1) a learned topological plausibility score and (2) textual entailment verification over retrieved corpus evidence. Validated edges are injected into a query-local subgraph, enabling multi-hop reasoning to proceed without offline graph enrichment. On TI-Bench, TSAAT achieves a 91.2% path completion rate (p < 0.001) compared to 42.1% for GraphRAG, with 89.5% human-verified synthesis precision at 4.2s average latency

Abstract This study presents a comparative multimodal action research examining the effects of films and podcasts on students’ depth of literary interpretation in a BlendFlex literature classroom. Grounded in multimodal and reader-response perspectives, the study explored how different instructional media such as film, podcast, and their combination affect students’ ability to interpret classic literary texts beyond surface-level comprehension. The participants were grouped into four instructional conditions: text-only, text with film, text with podcast, and text with both film and podcast. Data were gathered from students’ outputs submitted during onsite classes and through Canvas LMS and analyzed using a rubric assessing depth of literary interpretation, including textual evidence, thematic depth, and critical insight. Findings revealed that multimedia integration significantly enhanced students’ interpretive skills, with the combined use of films and podcasts yielding the highest level of performance. The study highlights the effectiveness of comparative multimodal pedagogy in promoting deeper engagement with literary texts in a BlendFlex learning environment.

Abstract This article focuses on the placement of ruins in the Mixtec landscape and in painted screen-fold manuscripts or codices during the Late Postclassic period, with an eye toward shedding light on broader Mesoamerican dynamics. I argue that while ruins of previous ages constituted meaningful links to the past in and of themselves, much of their significance, or even “vibrancy,” in the Postclassic inhered from the processes of persons journeying to and from them across the landscape. In the highly mountainous terrain of the Mixtec highlands, this movement frequently involved dramatic vertical ascents and descents, a phenomenon accentuated in the surviving codices from the region. Drawing from archaeological, textual, and iconographic evidence, I argue that this vertical movement to and from ruins of the past was closely intertwined with Mesoamerican understandings of temporality, and that traversing up and down the landscape could effectively constitute a kind of movement through time. Consistent with our grasp of Mesoamerican temporalities more generally, these spatiotemporal movements should not be seen as linear or teleological but instead as largely cyclical and bound up with concerns surrounding cosmic renewal.

Abstract Data-driven materials design and discovery require high-quality, machine-readable data. However, curating data from unstructured text is a bottleneck. Advances in natural language processing–especially attention-based large language models–enable automated information extraction. Here, we present a zero-shot extraction–synthensis framework that coordinates a two-stage ensemble of smaller, open-weight models ( ⩽ 27B parameters) to extract blood–brain barrier (BBB) nanoparticle design parameters from the literature. The Stage-2 synthesis step enforces fidelity by requiring textual evidence for every extracted value. On a 50-article gold-standard set, the framework outperforms Claude Sonnet 4 on key parameters by F 1 -score: Zeta potential (96.2% vs 87.1%), Particle number (73.7% vs 54.3%), and Particle concentration (40.0% vs 18.6%). Applied at scale, we extract 28–32 BBB-relevant parameters per article across 526 articles. We publicly release the machine-readable dataset to support downstream modeling and benchmarking. Our approach demonstrates an accessible, reproducible path to accelerated data curation for AI-driven materials design.

Consumer-generated narratives contain rich experiential knowledge for product design, yet their perceptual and weakly structured nature limits direct translation into actionable engineering insight. This challenge is particularly evident in EV design, where user insight plays a critical role but remains weakly grounded in formal engineering representations. To address this gap, this study proposes a Structured consumer semantic insight small language model (SCSI-SLM). First, Structured Semantic Encoding (SSE) maps weakly structured user narratives into a multi-level engineering semantic space, transforming perceptual expressions into comparable and reusable semantic tokens. Second, Product-User Dynamic Mapping (PUDM) decouples product-centric performance assessment from user-centric preference representation, enabling importance-performance analysis and preference heterogeneity to be modelled from the same corpus. Third, an Engineering Design Knowledge Graph (EDKG) integrates user profiles, product attributes, perceptual features, and textual evidence into an interpretable reasoning space. Building on this representation, a hybrid retrieval-reasoning engine combines semantic similarity search with graph-constrained inference to generate evidence-backed and engineer-readable insight chains. A case study in the EV domain demonstrates that the proposed framework can generate traceable design insights from large-scale consumer feedback, suggesting structured language modelling as a viable paradigm for data-driven engineering design.

ABSTRACT This paper considers the value of writing in human geography. It comments on forms of writing that reflect on personal experience to inform our understanding of questions of power and social difference. We offer a methodological reflection for connecting writing as practice with understanding social difference, as well as applying these ideas to our study of youth identities. Through close engagements with writing, as both method and textual evidence, we reveal how it provides access to more intimate experiences of social processes. Crucially, we suggest that this potential has not yet been fully developed, particularly in terms of how such writings can be better connected collectively through a framing that allows for a deeper understanding of social (in)justice and difference. This also allows us to reflect on the distinctive nature of writing as a praxis through which social identity can be critically engaged with. Through a ‘reflection-upon-reflection’ of past student submissions to undergraduate modules, we work with students involved as co-authors to draw out three key aspects that are enabled through reflexive writing-as-method: first the importance of writing as storytelling, second writing and its relationship with self and identity, and third, the temporality of writing as an emergent practice.

This article examines potential approaches to the Commandment of Love, a central ethical principle in Christianity, assessing how plausibly these approaches explain its adoption and enactment as the patient-universal foundation for moral attitudes. Three approaches are distinguished: a top-down approach and two varieties of bottom-up approaches: logical and psychological. It is argued that the psychological bottom-up approach finds support in the textual evidence from Scripture and aligns more closely with the natural mechanisms underlying moral judgments, as identified by social psychologists and evolutionary biologists. This approach suggests that the natural moral sense serves as a prerequisite for recognizing and internalizing the Commandment of Love—an instinct that is refined and elevated through a person's love for God, rather than being suppressed or abandoned. In this context, disciplines such as psychology, biology, anthropology, etc., are rendered as auxiliary branches of moral theology.