12560 research outputs found
Sort by
Embracing multimodality to overhaul assessment of English for Academic Purposes: task design and validation
No full textIn response to the increasing importance of multimodal communication in the academic target language use (TLU) domain and the expanding construct of English for academic purposes (EAP) skills, the current study proposes a multimodal EAP test. The study consists of three stages: (i) construct definition, (ii) design of a digital multimodal test, and (iii) empirical validation. The research questions address the extent to which the new test design reflects the construct of multimodal EAP skills in relation to two aspects: language functions (as evidenced in test takers’ performance) and the perspectives of test takers and examiners. The findings showed that 79% of the target multimodal EAP language functions were observed in the dataset. This indicates that multimodality can be successfully operationalised in EAP assessment with a cumulative, thematically-linked multimodal test design. The new design goes beyond what is typically operationalised by traditional independent skill-based tests and/or linear integrated language tests. The observation checklist developed can be used to help indicate the construct elicited by multimodal EAP assessments. Implications of the findings for the operationalisation of multimodal EAP constructs in large-scale assessment are discussed
Gendered food insecurity:achieving SDG 2 for climate-affected women in rural economies
Full text linkThis article examines systemic and structural governance barriers to achieving Sustainable Development Goal 2 (SDG 2) for rural women in climate-affected regions of the Global South, using Ghana and Bangladesh as focal countries. While the centrality of women's roles in food systems is acknowledged in literature, intersecting gender inequalities, climate vulnerability, and institutional blind spots continue to marginalise rural women within food security and adaptation policies. The study employs an integrative literature review and interpretive qualitative content analysis, grounded in an intersectional and rights-based analytical framework, to synthesise how gender, land tenure, climate exposure, economic informality, and policy recognition are addressed within SDG 2–related scholarship and policy documents. The focal countries provide illustrative reference contexts that help the analysis identify recurring patterns and omissions that constrain gender-responsive food systems governance. The review indicates that the absence of appropriate policy frameworks is not the major impediment to achieving SDG 2 goals. Still, rather fragmented institutional framing, including gender-blind adaptation strategies, insecure land governance, undervaluation of informal labour, and weak participatory accountability, are the greater impediments to success. The study concludes by outlining governance-oriented policy implications for aligning SDG 2 implementation with gender equity and climate resilience.</p
PyBayesDM: Hybrid Framework for Bayesian Adaptive Management under Deep Uncertainty
No full textDecision-making under deep uncertainty presents a persistent challenge for adaptive management in complex systems. Standard Bayesian Decision Theory performs well for prescriptive optimization under confident beliefs but is less robust when uncertainty is profound. Philosophical frameworks, which emphasize resilience and learning, typically lack a formal integration with Bayesian rigor. This paper develops a Hybrid Bayesian Adaptive Management Framework that unifies these prescriptive and philosophical paradigms within a single Bayesian formulation. The framework formally unifies both decision rules as parametric instances of a Bayesian adaptive management problem. It employs an entropy-driven dynamic weighting mechanism to continuously adjust the balance between efficiency and resilience based on posterior uncertainty, and introduces a suite of performance metrics designed to evaluate multi-dimensional outcomes beyond conventional measures. Experiments conducted on simulations inspired by climate-vulnerable agriculture in Vietnam show that the hybrid approach achieves statistically significant improvements (p < 0.05) in resilience and risk-adjusted performance under high uncertainty. The prescriptive framework excels in short-term reward but degrades under increased noise, while the philosophical framework maintains robustness, sometimes at a cost to efficiency. The hybrid strategy delivers balanced performance across uncertainty levels, confirming the utility of entropy-based arbitration. All experiments are reproducible using the developed Python implementations. This work offers a principled approach to adaptive decision-making under deep uncertainty, with implications for climate adaptation, resource management, and policy desig
Enhanced pathological tissue image categorization using a bag-of-features approach with roulette wheel whale optimization
No full textPathological tissue image categorization is essential in medical diagnostics, offering insights into disease types, progression, and treatment alternatives. The significant variability in tissue morphology and the overlapping visual patterns across different classes complicate accurate categorization. This study introduces an improved categorization model utilizing a bag-of-features (BoF) methodology integrated with the Roulette Wheel Whale Optimization Algorithm (RWWOA) to enhance classification accuracy and optimize feature selection efficiency. The proposed model utilizes the Bag-of-Features (BoF) technique to extract discriminative features from tissue images, thereby generating a feature-rich dictionary that represents various pathological structures. The RWWOA is employed to optimize feature selection, thereby reducing dimensionality and concentrating on the most pertinent features for precise categorization. Our method integrates the exploration capabilities of the Whale Optimization Algorithm (WOA) with the probabilistic selection mechanism of the roulette wheel, thereby dynamically balancing exploitation and exploration, which enhances convergence speed and categorization accuracy. Experimental results indicate that the RWWOA-BoF method outperforms traditional methods across various datasets, showing enhancements in classification precision, recall, and F1-score. This method offers a reliable resource for aiding pathologists in diagnostic imaging, which may expedite diagnostic processes and improve consistency in clinical practice.</p
Critique as a means of Jiaohua (Cultivation): insights from Confucianism
No full textFrom a Confucian perspective, critique serves as a tool for jiaohua (cultivation), encompassing not only the transmission of knowledge but also the cultivation of morality. This article adopts theoretical and empirical approaches to explore the Confucian understanding of critique. Theoretically, critique in Confucianism is not merely a challenge directed at external individuals or society; rather, it is viewed as a personal moral and social responsibility. Empirically, this article draws on fieldwork conducted in Confucian schools to demonstrate how students, teachers, and parents employ critique as a corrective tool in educational practice. Confucian critique challenges the monolithic framework of Euro-American critical traditions, offering a pathway of ‘multiple modernities’ to global higher education while addressing the pressing need for a more equitable and diverse knowledge production system
ViMIC 2.0: an updated database of human disease-related viral mutations, integration sites, and multi-omics data
Full text linkViMIC 2.0 is an updated database that provides comprehensively curated data on virus mutations (VMs), viral integration sites (VISs), and multi-omics datasets related to human diseases. Leveraging expanding public data, ViMIC 2.0 significantly enhanced data scale, diversity, and analytical capabilities compared to the previous version. In terms of data volume, the number of virus types has increased from 8 to 28, VM entries have grown from 31 712 to 64 168, virus-related diseases expanded from 77 to 177, literature rose from 2539 to 6433, and omics datasets have substantially increased from 28 sets of single expression profile data to 255 sets of multi-omics data. In addition, ViMIC 2.0 has updated 9409 VISs, 173 048 sequences, newly incorporated sequencing types such as single-cell transcriptomic sequencing (scRNA-seq), and genome binding/occupancy profiling. Regarding the visualization module, ViMIC 2.0 now provides results of differential gene expression analysis for bulk RNA-seq or array, cell type annotation and gene feature plot for scRNA-seq data, and differential methylation analysis for methylation profiling, as well as peak annotation for ChIP-seq/ChIP-on-chip/ATAC-seq data. In summary, ViMIC 2.0 serves as a user-friendly, up-to-date, and well-maintained resource for the virology research community. ViMIC 2.0 is freely accessible at http://www.biomedinfo.cn/ViMIC2.0/index.php.<br/
Multi-agent deep reinforcement learning for resource allocation in beyond 5G network slicing: solutions, challenges and future research directions
No full textNetwork Slicing (NS) technology in Beyond Fifth Generation (B5G) mobile networks enables the personalized needs of different services by logically dividing the physical network into logical segments (namely slices). However, the resource competition between slices, dynamic traffic changes, and global optimization requirements of data-intensive applications make it difficult for traditional Resource Allocation (RA) methods to satisfy the network requirements of B5G. Deep Reinforcement Learning (DRL) offers an intelligent approach to RA of NS, leveraging its autonomous learning and adaptive capabilities. This study focused on the multi-agent approach of DRL for RA of NS optimization in B5G. It introduced the process of RA in a multi-slice environment, then summarized the key challenges of RA in B5G scenarios, including multi-domain resource coordination, adaptive resource orchestration, and joint optimization of computation and communication resources. At the same time, this study summarized the training process of Multi-Agent DRL (MADRL), then classified the recent RA methods based on DRL into value-based, policy-based and hybrid methods. Additionally, the challenges faced in deploying B5G environments by current optimization methods are highlighted, and future research directions are discussed. By analyzing the practical challenges between advanced DRL algorithms and RA optimization of NS in B5G, this study lays a theoretical foundation for designing scalable and adaptive multi-agent resource allocation optimization schemes in future communication systems
Intervention strategies for healthcare workers to promote vaccine uptake in ethnic minority populations: a systematic review of behaviour change techniques
Full text linkBackground/Objectives: Healthcare workers (HCWs) have a crucial role in addressing vaccine hesitancy in ethnic minority populations as they are a trusted source of information. The aim of this systematic review is to synthesise and evaluate behaviour change techniques (BCTs) and strategies in interventions aimed at HCWs to promote vaccine uptake among ethnic minority populations. Methods: The literature was systematically searched in peer-reviewed databases and the grey literature. Studies were included if they reported interventions for respiratory and routinely recommended vaccine-preventable diseases which were delivered by HCWs to increase vaccine uptake in ethnic minority groups. Interventions were coded using the Behaviour Change Wheel (BCW) and BCT Taxonomy. Results: From 7250 records identified, 14 studies were included in the review. Vaccines targeted by interventions included influenza, pneumococcal disease, pertussis, tetanus, diphtheria, meningitis and hepatitis B. Seven BCW intervention types, six policy options and 22 BCTs were identified. Main intervention types used were persuasion, enablement and education. Effective interventions had multi-components and were tailored to specific populations. Staff training to improve vaccine recommendation and dialogue with patients, and prompts/cues were associated with positive effects, but there was no strong evidence to recommend one specific intervention strategy over another as effectiveness was linked to a multitude of BCTs and intervention types. Conclusions: Several strategies aimed at HCWs can be used and tailored to increase vaccine uptake among ethnic minority communities; however, this does not address all issues related to low vaccine uptake. While HCWs are necessary, without system-level enablement, they cannot fully address barriers to vaccine uptake
How will smart technology support SDG 12? an empirical study on sustainability in Indian agricultural operations
Full text linkIndia is one of the fastest growing economies with significant potential for the use of smart farming operations. Although agriculture is a major sector, implementation of smart technologies in the agriculture sector has not progressed in India. We use a mixed-methods approach to develop knowledge on the factors determining this slow adoption of smart technology and develop strategies for large-scale adoption in the Indian agriculture sector. First, qualitative interviews are used to understand the factors behind the slow diffusion of smart technology in the agriculture sector. Based on the responses, we link the results of the qualitative study from the agri-sector to the well-known Diffusion of Innovations (DoI) theory. We then develop a framework for applying Fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the impact of multiple causal factors. We apply our research findings to help achieve SDG 12 in the agriculture sector. Our findings indicate individual factors on their own may influence adoption, but some reasonable combinations of factors (e.g., a combination of technology, knowhow, experience, benefits-operation, and finance and reliability) could also result in the large-scale adoption of smart technologies in improving Indian agricultural operations. By doing so, we provide a contextual empirical configurational test of the DoI theory in the Indian smart agricultural context.</p
The road to understanding in lecture listening: cognitive processes engaged in the integration of auditory and textual information
No full textRecent technological advances offer test developers the opportunity to develop new assessment tasks which closely replicate behaviour in real-world domains such as academic lectures, thus achieving greater ecological and cognitive validity. However, developing new task types that demand the integration of auditory and textual information relies on understanding the relationship between what is said and what is written on the slides, and how students integrate these two streams of information.With a dual focus, this study first examined the relationship between the lecturer's speech and the textual information on the lecture slides before investigating the processes students use to integrate these two streams of information to develop an understanding of the lecture content.In Phase 1, five lecture recordings were collected from universities across the UK. Following Hallewell and Crook (2019), the lecturer’s speech was segmented and mapped to slide-text units to form discourse units. The discourse units were coded according to the role that the lecturer's speech performed in relation to the slide text.In Phase 2, the most obvious speech/slide variable—the extent to which the information delivered verbally also appears in the slide text—was manipulated to assess the effect on participants’ processes. Two lecture excerpts were selected from Phase 1 and two versions of the slides were created for each lecture clip: 1) topic headings plus a short text outlining the main teaching points, and 2) topic headings only. Using a counter-balanced presentation, eye-tracking and stimulated recall were used to investigate participants' perceptual processes as they read the slides and listened to the lecture excerpts.Phase 1 resulted in a taxonomy of discourse relations and Phase 2 reported the factors which impacted participants’ ability to develop an understanding of the lecture content. The implications for teaching and assessing academic listening are discussed
Data sources:
Access Repository Dashboard
Do you manage University of Bedfordshire Repository? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!