35 10 899 https://linen-dog.lnx.warwick.ac.uk/files/original/1001c7d46b4d4e1e036f38f4c892ca2c.pdf a5d518ac27577b992dc0ab2ae61ca6d9 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Academic Poster An academic poster Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Graphene Oxide Membranes for Microscopy Subject The topic of the resource Physics Creator An entity primarily responsible for making the resource Karishma Gokani Date A point or period of time associated with an event in the lifecycle of the resource 2022 atomic electron lego microscope https://linen-dog.lnx.warwick.ac.uk/files/original/5580d52a95f6a683a72defbb49053d26.pdf aec933674ccf7912145b9dae788ce3e4 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Academic Poster An academic poster Abstract The abstract of the paper This project focuses on developing a graphical model learning method for stochastic Ising networks, specifically using data generated by the Glauber dynamics. Meta Tags Meta tags Ising Model, Stochastic Process, Graphical Model Learning, Networks, Glauber Dynamics Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Akram Bellala Contributor An entity responsible for making contributions to the resource Dr Paul Chleboun Subject The topic of the resource Statistics Title A name given to the resource Graphical model learning of stochastic Ising networks Date A point or period of time associated with an event in the lifecycle of the resource 2024 https://linen-dog.lnx.warwick.ac.uk/files/original/fda6c0888e81d69b51bf6b2195b790e7.pdf e65b6249078ab8af4068cc0127ca04a7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Mathematics Academic Poster An academic poster Meta Tags Meta tags Grassmannian, Catalan numbers, Cohomology Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ned Nicholson Subject The topic of the resource Mathematics Title A name given to the resource Grassmannian Manifolds and Catalan numbers Date A point or period of time associated with an event in the lifecycle of the resource 2025 https://linen-dog.lnx.warwick.ac.uk/files/original/de526c49951a1da4137041b8af08bbb5.pdf 5075d15027f9d147498195087e70312d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Academic Poster An academic poster Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Gravitational Wave Emission from Short Period Binary Stars Subject The topic of the resource Physics Creator An entity primarily responsible for making the resource Stefano Beni Date A point or period of time associated with an event in the lifecycle of the resource 2022 cataclysmic variables gravitational pull stars white dwarf https://linen-dog.lnx.warwick.ac.uk/files/original/92ea65d41daabef114ff8b1d1966bbb4.pdf 493105631245b2b7d284c0c1d4065305 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Social Sciences Subject The topic of the resource Social Sciences Academic Paper An academic paper Abstract The abstract of the paper Purpose – This study examines the suitability of hard versus soft clustering approaches for customer segmentation across distinct business model types. Specifically, it evaluates whether homogeneous marketing treatment implied by K-means is justified in subscription and singular transaction contexts, and whether Fuzzy C-means (FCM) offers a more appropriate representation of behavioural overlap. Methodology – Using the RFM (Recency–Frequency–Monetary) framework, the study applies both K-means and FCM to two datasets: the Online Retail dataset (transaction model) and the Customer Churn dataset (subscription model). RFM metrics were adapted for subscription contexts to reflect product interactions rather than discrete purchases. Clustering performance was assessed through the silhouette score (K-means) and fuzzy partition coefficient (FCM), following a consistent CRISP-DM process for data preparation, modelling, and evaluation. Findings – Results indicate a reversal of theoretical expectations. The subscription dataset exhibited considerable behavioural overlap, with weak cluster separation, challenging the assumption of homogeneous treatment. Conversely, the transaction dataset displayed moderately strong partitions, suggesting that standardisation of product attributes may reduce behavioural heterogeneity. These findings highlight that clustering suitability depends not only on business model type but also on contextual factors such as product standardisation and tier design. Originality – This study is the first to align the selection of hard versus soft clustering methods with business model typologies and to adapt RFM metrics for subscription contexts. By integrating FCM as a diagnostic tool for testing the validity of homogeneous treatment assumptions, it addresses a critical gap in behavioural segmentation research and provides a practical decision framework for guiding clustering choice. Meta Tags Meta tags Customer segmentation, Machine learning, Clustering, Business analytics, Customer behaviour, Business model Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Subject The topic of the resource Warwick Business School Date A point or period of time associated with an event in the lifecycle of the resource 2025 Title A name given to the resource Hard vs. Soft Clustering for RFM Segmentation: A Joint Evaluation of Method Suitability Across Singular Transaction and Subscription Business Models Creator An entity primarily responsible for making the resource Ying Ting Chan https://linen-dog.lnx.warwick.ac.uk/files/original/9cfc38520ececac26c491ed7b3eac094.pdf ea1c11f6668d0fd74ee634f518f8af16 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Academic Paper An academic paper Abstract The abstract of the paper The bio-cementation of sands and other porous media using microbially induced carbonate precipitation (MICP) is of particular interest in various geotechnical applications. The microbial production of carbonates by the breakdown of urea using the urease biochemical pathway results in production of carbonate minerals which means the grains become cemented together giving the material improved compressive strength, shear strength, and stiffness and thus reducing the risk of erosion and failure. The engineering geology and geomicrobiology groups at the British Geological Survey are involved in research into role of bioprecipitation of minerals to mitigate geohazards, which includes work on slope stability, coastal erosion and heritage conservation. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Subject The topic of the resource Engineering Date A point or period of time associated with an event in the lifecycle of the resource 2025 Contributor An entity responsible for making contributions to the resource British Geological Survey Title A name given to the resource Harnessing Natural Microbial Communities for Coastal Protection Description An account of the resource Bio-geoengineering Creator An entity primarily responsible for making the resource Finn Sabin https://linen-dog.lnx.warwick.ac.uk/files/original/efdc33a1346f5388cb4d0d49ba0c5746.pdf b8a348bbc25e4ed73c9d3159948594dc Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Social Sciences Subject The topic of the resource Social Sciences Academic Poster An academic poster Abstract The abstract of the paper Emerging evidence suggests a significant bidirectional interaction between the gastrointestinal system and the central nervous system, known as the gut–brain axis. This systematic review evaluates whether probiotics can serve as an alternative or adjunctive treatment for clinical depression through modulation of the gut microbiota. Current literature indicates that specific probiotic strains may influence neurotransmitter production, reduce systemic inflammation and improve gut barrier integrity. These mechanisms have been found to be implicated in depressive pathology. While several clinical trials demonstrate modest but promising antidepressant effects, heterogeneity in probiotic formulations, study durations and patient populations limit definitive conclusions. Overall, probiotic therapy represents a potential low-risk intervention with biological plausibility, but further high-quality, standardised trials are needed to determine efficacy, optimal strains and dosage for depressive disorders. Meta Tags Meta tags medicine, probiotics, depression, gut-brain axis, antidepressant Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Harnessing The Gut-Brain Axis: Can Probiotics Be Used As An Alternative Treatment In Clinical Depression By Modulating The Gut Microbiota? Creator An entity primarily responsible for making the resource Naeem Ahmed Subject The topic of the resource Warwick Medical School https://linen-dog.lnx.warwick.ac.uk/files/original/da20cc0f2246166684655bd00c89d58a.pdf 868de787436a1c970e6520cc9ff3d94e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Science, Engineering and Medicine Academic Paper An academic paper Abstract The abstract of the paper This work aims to use comparative analysis within a specific academic framework to examine the differences in output between nations of comparative political, economic, and social standing to the UK. Additionally to frame things in an interesting way that is conceptually rich I continually use and reference the myth of Heracles by Apollodorus which has many parallels to this policy debate. Conducting analysis in this manner enables significant differences to be identified and discussed at length. Not only discussing the discrepancy but the reasons for them. By identifying the root causes for many of these issues I am able to suggest remedies and a series of changes to enact within Britain that seeks to tackle such challenges posed. In doing so I challenge traditional notions of underfunding and seek to reason that it is due to centralised control, ineffective doctrines, and poor policy that many of the issues within the NHS exist. I routinely reference the work of intellectuals such as Julian Le Grand, Friedrich Hayek, and Von Neumann to come to these conclusions and to intellectually frame these points. Ultimately I seek to extend the boundaries of debate and offer a coherent explanation for failure and strong rationale for reform. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Date A point or period of time associated with an event in the lifecycle of the resource 2025 Contributor An entity responsible for making contributions to the resource Hugh E Vernon Title A name given to the resource Hauling the Cattle Back to Mycenae Freedom Creator An entity primarily responsible for making the resource Hugh E Vernon Description An account of the resource NHS Performance and Reform: Competition, and Health Policy in Britain - A Comparative Analysis of Healthcare Systems in the Developed https://linen-dog.lnx.warwick.ac.uk/files/original/e39dd700fd0c40f785a9759f7c3bb3ff.png 9f4f9a53e957a810a0980936a3e11578 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Social Sciences Subject The topic of the resource Social Sciences Image A static visual representation. Examples include paintings, drawings, graphic designs, plans and maps. Recommended best practice is to assign the type Text to images of textual materials. Meta Tags Meta tags LGBTQUIA+, Passing, Social Acceptance , Discrimination, Gender , Identity , Sexual Orientation Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Hidden In Plain Sight: An intersectional analysis of the use of passing amongst LGBTQUIA+ populations to mitigate discrimination within peer group settings Creator An entity primarily responsible for making the resource Adam Allen Subject The topic of the resource Sociology Date A point or period of time associated with an event in the lifecycle of the resource 2025 https://linen-dog.lnx.warwick.ac.uk/files/original/1d9bc6ed7a42b2098c402b5e2e1236b9.pdf b2da2219a5e0e09860cef3d310327603 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Social Sciences Subject The topic of the resource Social Sciences Academic Paper An academic paper Abstract The abstract of the paper This paper evaluates the forecasting performance of various econometric and machine learning approaches for predicting UK headline Consumer Price Index (CPI) inflation. Following the methodology of Stock and Watson (2007), I conduct pseudo-out-of-sample forecasts over multiple horizons (1, 2, and 4 quarters) using a comprehensive set of macroeconomic activity variables. My econometric models include Vector Autoregression (VAR), Vector Error Correction Models (VECM), and traditional benchmarks such as rolling IMA(1,1) and autoregressive models. Machine learning methods encompass Random Forest, XGBoost, Support Vector Regression, and Kernel Ridge Regression algorithms, implemented following the nonlinear framework of Coulombe et al. (2020). To address model uncertainty, I im- plement a hierarchical model combination framework using predictive performance- based weighting schemes that adapt through rolling re-estimation. Results indicate that while traditional econometric benchmarks remain competitive, particularly the rolling IMA(1,1) model, Random Forest demonstrates consistent and statistically significant improvements across all forecast horizons. The VECM approach shows horizon-dependent performance, improving from substantial underperformance at short horizons to near-parity at longer horizons. My findings contribute to under- standing inflation dynamics in the UK context and demonstrate the importance of algorithm selection in machine learning applications to macroeconomic forecasting. Meta Tags Meta tags Machine Learning, Data Science, Economics, Econometrics, Artificial Intelligence Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Subject The topic of the resource Economics Date A point or period of time associated with an event in the lifecycle of the resource 2025 Contributor An entity responsible for making contributions to the resource Under the supervision of Professor Mark Steel. Title A name given to the resource Hierarchical Model Combination for UK Inflation Forecasting: Integrating Econometric and Machine Learning Models Description An account of the resource Machine Learning in Forecasting Inflation Creator An entity primarily responsible for making the resource Nitay Carmi