4208 research outputs found
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Matrescence Performance Repetitions: towards “letting go”
Full text linkIn this article we explore the work of artists engaging with matrescence to consider the (im)possibility of repetition in maternal art making and performance. We are keen to think through matrescence and artmaking processes together towards an eventual ‘letting go’ as both the mothers and the children move into a new life stage. We draw on the work of Young, Iris Marion [2005. On Female Body Experience: Throwing Like a Girl and Other Essays. USA: Oxford University Press] to introduce concepts around the home, Halberstam, J. [2011. The Queer Art of Failure. London: Duke University Press] to examine the notion of futurity, repetition and return, and apply a psychoanalytical framing, in particular, the work of Ferenczi, Sándor [1988. “Confusion of Tongues between Adults and the Child.” Contemporary Psychoanalysis 24 (2): 196–206. doi:10.1080/00107530.1988.10746234]. We consider two contemporary shows – GOO:GA (Ballou, Hannah. 2021. GOO:GA (film)) by Hannah Ballou and the 2021 video re-working of Tender (Long, Josie. 2019. Tender (performance)) by Josie Long. In order to aid our engagement with the matrescence in various media representations,we also re-think certain historical examples of maternal creativity and artmaking including both our own and those of renowned women artists Susan Hiller, Mary Kelly and Bobby Bake
Lithological and geochemical characterization of ‘adinole’ artefacts from cave deposits in southwest Wales: a material of choice during the late Middle to Upper Palaeolithic
Full text linkTwenty-three artefacts previously identified as being manufactured from adinole, a fine-grained metasomatic rock, from late Middle to Upper Palaeolithic cave sites in southwest Wales have been re-examined in terms of their petrology and geochemistry. Standard petrography has been combined with automated SEM-EDS analysis for a single artefact to determine the mineralogy and textures of that artefact, while portable XRF and μXRF have been combined to establish the geochemical characteristics of all twenty-three artefacts analysed. These investigations have shown that the artefacts were manufactured from rhyolite rather than adinole, a misidentification that has been in the literature for over 100 years. Some artefacts appear to cluster on geochemical plots, such as a group of eight artefacts from Hoyle’s Mouth Cave which share petrological characteristics and appear to have come from a common source. In other cases, however, certain artefacts with similar chemistries have dissimilar petrological characteristics and are not from a common source. This highlights the need to consider both petrological and geochemical characteristics when classifying rhyolitic artefacts. The artefacts studied show that this spotted variety of rhyolite was a preferred source of raw material throughout the late Middle and Upper Palaeolithic, despite having no obvious physical or practical advantages. Identifying rhyolite rather than adinole as the raw material used in the manufacture of the studied artefacts negates the need to consider long distance transport of either raw materials or finished artefacts. It strongly suggests that people in southwest Wales, where raw materials were scarce, were using materials that were local to them. Further, there is evidence that people were effectively planning for future use or reuse of artefacts, involving curation of tools. The next phase of work will use the lithological characteristics identified here to explore potential sources for the raw material used in the manufacture of these artefacts
A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles
Full text linkThe rapid advancement of communication is introducing a new era for the Internet of Vehicles (IoV) in the context of Smart Cities. Although these technologies provide unparalleled connectivity and communication capabilities, they also introduce new security challenges, particularly in terms of Intrusion Detection. This paper presents a robust machine learning (ML) technique to enhance the security of IoV networks by developing an efficient intrusion detection system (IDS). In this paper, we proposed a fine tree-based model to study the complex behavior of network traffic inside the IoV to detect and classify anomalies for securing the IoV. The proposed fine tree-based model can be validated by conducting extensive experiments with benchmark real-world datasets which can simulate emerging IoV scenarios. The proposed Fine Tree-based IDS model, along with other models, has been evaluated using metrics such as mean accuracy, precision, recall, F1-score, specificity and error rate. The proposed model outperformed the others across each metric, achieving near-perfect results with a mean accuracy, precision, recall, F1-score, and specificity of 0.99999. However, the other models achieved mean values ranging from 0.90 to 0.98 across these metrics. Additionally, the proposed model achieved an exceptionally low mean error rate of 0.00001, while the error rates of the other models ranged from 0.02 to 0.05. The experimental findings demonstrate the superior performance of the proposed model in detecting and classifying intrusions within IoV
Determinants and Prevalence of Hepatitis B Among Multigravidae Pregnant Women in Bengkulu Province, Indonesia
Full text linkBackground: Morbidity and mortality caused by Hepatitis B Virus (HBV) infection continue to pose a serious global public health concern. Globally, an estimated three million new cases of chronic HBV infection are reported annually, contributing to approximately 51,000 deaths. In Indonesia, HBV is recognized as the fourth leading cause of mortality.Objectives: This study aimed to investigate the factors associated with HBV infection among multigravida pregnant women in Kota Bengkulu, Indonesia, based on an evidence-based approach.Methods: A quantitative case-control study was conducted involving 148 pregnant women, comprising 74 women who tested positive for hepatitis B and 74 who tested negative. The study employed total sampling as the sampling technique. Data were analyzed using chi-square tests and multiple logistic regression to identify significant associations.Results: The analysis revealed significant associations between HBV infection and several variables: age (p = 0.004; OR = 2.867), educational level (p = 0.004; OR = 2.889), type of previous delivery (p = 0.003; OR = 3,753.9), history of blood transfusion (p = 0.002; OR = 2.887), and low level of knowledge (p = 0.004; OR = 2.935). Among these, the most dominant factor associated with HBV infection was a history of blood transfusion (p = 0.002; OR = 3.767). The overall prevalence of hepatitis B in the study population was 0.40%.Conclusion: This study concludes that a history of blood transfusion is the most significant factor associated with HBV infection among multigravida pregnant women in Bengkulu Province, Indonesia. These findings highlight the need for enhanced screening and preventive measures, particularly in maternal healthcare settings
FAIRSpec-Ready Spectroscopic Data Collections – Advice for Researchers, Authors, and Data Managers (IUPAC Technical Report)
Full text linkIn this Technical Report, we introduce the application of FAIR (findable, accessible, interoperable, and reusable) data management in the form of a “FAIRSpec-ready spectroscopic data collection” – that is, a collection of instrument data, chemical structure representations, and related digital items that is ready to be automatically or semi-automatically extracted for metadata that will allow the production of an IUPAC FAIRSpec Finding Aid. Associating this finding aid with the collection produces an IUPAC FAIRSpec Data Collection. The challenge we set for researchers is relatively simple: to maintain their data in a form that allows critical metadata to be extracted in a discipline-specific way, increasing the probability that the data will be findable and reusable both during the research process and after publication. We focus on a few specific suggestions that researchers can use to maximize the “fairness” of their spectroscopic data collection. Most importantly, following these guidelines ensures that instrument datasets are unambiguously associated with the chemical structure. The guidelines promote the inclusion of the instrument dataset itself in the collection and describe ways of organizing the collection such that automated metadata creation is possible. In these guidelines, we emphasize the importance of systematically organizing data throughout the entire research process, not just at the time of publication
Ontop-driven Federated Virtual Knowledge Graphs: A Robust Framework to Revolutionizing Fragmented Battery Data Integration
Full text linkOver the past decade, the exponential growth of the Electric Vehicle(EV) industry has experienced unprecedented surge and diversification. Thismultifaceted field results in building comprehensive, cross-domain, extensible, sophisticated knowledge management systems that incorporate future needs and address battery-related information integration challenges. It needs sophisticated knowledge representation techniques such as ontologies and knowledge graphs (KGs) leveraging federated approaches to integrate diverse, disparate data from distributed sources, resolve data interoperability challenges, and also present in a standardized format to build various battery-related services on top of virtual data integration layer, without the need for data materialization. We propose the state-of-the-art federated virtual knowledge graph (FVKG) framework embedded with the virtualized knowledge graph (VKG) methodology to handle the auspicious challenges effectively across distributed environments.The suggested FVKG framework offers a unified view of scattered data sources and different models to create a virtual data federation leveraging Ontop, resolving data bottlenecks efficiently. The FVKG assists in automated data mapping from diverse, relational sources, enabling intuitive queries based on domain-centric federated ontology and loads into the VKG intelligently. The FVKG utilizes a virtualized technique to reduce data migration, guarantees low latency and freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. The FVKG incorporates ontology-based data access (OBDA) to build a monolithic ontological model, integrating ontology-driven artifacts and ensuring semantic alignment using schema mapping techniques. As a result, the FVKG targets enabling more efficient battery performance analysis, predictive maintenance, and strategic decision-making in the EV ecosystem
Leveraging AI and data science across the cervical cancer care continuum in developing economies
Full text linkCervical cancer remains a significant public health challenge, particularly in developing economies, where late diagnosis and limited access to advanced medical care contribute to high mortality rates. Early detection, accurate diagnosis, and effective management are crucial for improving patient outcomes. In recent years, Artificial Intelligence (AI) and Data Science (DS) have emerged as transformative tools in cervical cancer care, with applications in screening, diagnosis, treatment planning, patient management, and drug discovery. However, these technologies have not been fully leveraged in resource-limited settings. This review systematically analysed 40 peer-reviewed studies, digital tools, and mobile applications published between 2010 and 2025 to assess how AI is being applied across various stages of cervical cancer management. Studies were identified through structured searches in PubMed, Google Scholar, IEEE, Scopus, and ScienceDirect, and data were extracted on use cases, model types, datasets, and performance metrics. The findings reveal that Convolutional Neural Networks (CNNs) dominate image-based diagnostic tasks, while Support Vector Machines (SVMs), Decision Trees, and Random Forests are frequently applied in structured data analysis. NLP techniques are emerging for public engagement and symptom surveillance. Most models demonstrated strong performance, with CNN-based tools achieving up to 98% accuracy in Pap smear classification. However, disparities in AI adoption persist, with high-income countries leading in precision diagnostics and low-resource regions lagging due to infrastructural, regulatory, and data limitations. Notably, few studies have addressed real-world deployment challenges, and recent advances, such as explainable AI (XAI), federated learning, and multimodal approaches, remain underrepresented in this context. Our review recommends a shift toward equitable AI development, utilising open-access datasets, investing in digital infrastructure, providing interdisciplinary training, and establishing ethical frameworks. In conclusion, while AI offers immense potential to revolutionise cervical cancer care, realising this promise requires inclusive, context-aware innovation that addresses both technological and systemic barriers. Bridging these gaps is essential to ensure that advancements in AI benefit underserved populations and contribute meaningfully to global cervical cancer control efforts
