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A New Reliable & Parsimonious Learning Strategy Comprising Two Layers of Gaussian Processes, to Address Inhomogeneous Empirical Correlation Structures
Get PDFMSC classes: Probability theory and stochastic processes : 60-XX, Stochastic Processes : 60Gxx, Gaussian Processes : 60G15, Generalised stochastic processes : 60G20This is a preprint version of the article. It has not been certified by peer review.We present a new strategy for learning the functional relation between a pair of variables, while addressing inhomogeneities in the correlation structure of the available data, by modelling the sought function as a sample function of a non-stationary Gaussian Process (GP), that nests within itself multiple other GPs, each of which we prove can be stationary, thereby establishing sufficiency of two GP layers. In fact, a non-stationary kernel is envisaged, with each hyperparameter set as dependent on the sample function drawn from the outer non-stationary GP, such that a new sample function is drawn at every pair of input values at which the kernel is computed. However, such a model cannot be implemented, and we substitute this by recalling that the average effect of drawing different sample functions from a given GP is equivalent to that of drawing a sample function from each of a set of GPs that are rendered different, as updated during the equilibrium stage of the undertaken inference (via MCMC). The kernel is fully non-parametric, and it suffices to learn one hyperparameter per layer of GP, for each dimension of the input variable. We illustrate this new learning strategy on a real dataset.GR is funded by an EPSRC DTP studentship
Investigation into the rockfall impact process of a quarry landfill slope under highway expansion
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The data underpinning this publication can be accessed from Brunel University London's data repository, Brunelfigshare, under a CCBY licence with the DOI of https://doi.org/10.17633/rd.brunel.27652422.A quarry landfill slope is commonly partially or entirely filled with quarry waste. On the surface, a substantial amount of rough stone waste accumulates. This study specifically investigated the hazards posed by individual rockfalls and cluster rockfalls induced by landslides in such slopes, using an engineering slope as an illustrative example. The discontinuous deformation and displacement analysis method was employed to analyze the individual and cluster rockfall motion characteristics, as well as the dynamic response of protection structures. The results indicate that: (1) The impact of individual falling rocks on structures results in deformation and damage that far surpasses that caused by a flat plane impact. Interestingly, the stress generated upon rockfall contact with the structure is not initially at its maximum; it gradually increases to a peak as deformation occurs. When the structure is damaged or rebounds, the impact stress significantly diminishes. For wedge-shaped falling rocks impacting the upper part of the structure, bending tilting failure tends to occur. Conversely, irregular blocks with larger volumes impacting the lower part of the structure often lead to direct toppling failure; (2) Clusters falling rocks impede the movement of the sliding body. As the front and rear sliding bodies fracture along the middle, the rear sliding body tilts. Consequently, accumulated blocks are struck by the sliding body, initiating oblique throwing movements. There is a high likelihood of these rocks crossing protective structures; (3) The protection rate of the protective structure against single block stone impact stands at 86.7%. However, when subjected to the impact of a group of rockfalls, the protective structure completely fails. Overall, although the current protective measures are relatively cost-effective, the extremely high probability of casualties makes them unacceptable.The present work was financially supported by the UK Research and Innovation (UKRI), UK (Grant No. EP/Y02754X/1) and the Collaborative Innovation Center for Prevention and Control of Mountain Geological Hazards of Zhejiang Province, China (Grant No. IBGDP-2023–05)
The CMS Statistical Analysis and Combination Tool: Combine
Get PDFMetrics: https://link.springer.com/article/10.1007/s41781-024-00121-4/metricsThis paper describes the Combine software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. It is not specific to the CMS experiment, and this paper is intended to serve as a reference for users outside of the CMS Collaboration, providing an outline of the most salient features and capabilities. Readers are provided with the possibility to run Combine and reproduce examples provided in this paper using a publicly available container image. Since the package is constantly evolving to meet the demands of ever-increasing data sets and analysis sophistication, this paper cannot cover all details of Combine. However, the online documentation referenced within this paper provides an up-to-date and complete user guide.CERN (European Organization for Nuclear Research)STFC (United Kingdom)Marie-Curie programme and the European Research Council and Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207, and COST Action CA16108 (European Union); the Leventis Foundation; the Alfred P. Sloan Foundatio
Improving household waste management in Indonesia: a mixed-methods approach for waste sorting
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No data was used for the research described in the article.This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.This study explores effective interventions that improve household waste sorting and tests how labels, captions, and intervention campaigns enhance waste-sorting knowledge and improve waste-sorting practices. This study used a mixed–method approach using data collected from 29 households. The results from the survey were tested using a t-test comparison, the data obtained from the observation during the lab experiment were described, and the information from the interview was interpreted. The results show that the two interventions used in the study improved knowledge about waste and sorting practices. The results show significant differences between the groups before and after intervention exposure. Moreover, the findings highlighted that labels and captions do not help to reduce household waste generation. However, an intervention campaign was crucial in reducing the amount of waste produced by households.The research is part of the Partnership for Plastics in Indonesian Society (PISCES) project funded by UK Research and Innovation, Global Challenges Research Fund (GCRF), project reference: NE/V006428/1
Stock market returns and climate risk in the U.S.
Get PDFJEL classification: D24; O13; O47; Q40.Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S1042444X24000525?via%3Dihub#appSB .Using a data set for all companies forming the S&P 500 index, we investigate the stock price responses to acute physical risks, chronic physical risks, and transition risks. Our findings reveal that certain sectors are more vulnerable to climate risks, whereas others appear to be relatively unaffected. In addition, our results show that listed firms with poor environmental performance scores are more exposed to climate risk, as indicated by their stock returns being negatively affected, compared to firms with higher environmental performance scores. This suggests that improving environmental performance may help companies to better cope with climate risks and improve their financial performances. Our analysis provides evidence that the short-term systematic risk is more vulnerable to the climate risk events, whereas effects on long-term systematic risk do not appear to be statistically significant. These findings indicate that investors and firms should pay a particular attention to short-term systematic risk when considering the potential impact of climate risk on stock market performances.Fabio Spagnolo acknowledges the financial support provided for the project “ESCAPE - Economic and Social Consequences of Altered Planet Environment,” part of the GRINS project (PE00000018). Yiyang Chen and Rogemar Mamon acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) through a Discovery Grant (RGPIN-2017-04235)
Short-term gain, long-term loss: Exploring the effects of Covid-19 survival strategies on rural livelihoods and the agrarian economy
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Data will be made available on request.In this paper, we explore how the practices of agricultural chain actors within the contingencies of the Covid-19 crisis, may have contributed to precarious rural livelihoods and the agrarian economy. Developing our contribution in the context of Ghana's agricultural sector, which is grappling with socio-economic and sustainability challenges such as land degradation, climate change, and biodiversity loss, we identified salient survival practices in the actions adopted during the Covid-19 pandemic which resulted in short-term gain, but also accounted for the long-term intractable decline in production and for producers' wellbeing. Explicating a fine analysis of how individual practices induced by the pandemic may have contributed to foster a decline in the agrarian economy, our study goes on to shed light on the devastating outcomes of the pandemic on rural livelihoods and the agrarian economies often marked by weak institutions and underdeveloped markets
Machines vs. humans: The evolving role of artificial intelligence in livestreaming e-commerce
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Data will be made available on request.As the capability of artificial intelligence (AI) improves, online retailers are exploring AI-based agents to communicate with viewers in live streaming, which is referred to as AI stremer. However, it is unclear where, what, and when the implementation of AI stremer is more effective than human beings in live-streaming e-commerce. To explore the dynamic interrelationships and temporal evolution between AI and human streamers and viewer engagement, this study examined the evolving role of AI streamers in live-streaming e-commerce. We utilised the linear mixed model (LMM) and the time-varying effect model (TVEM) to examine whether AI and human streamers differ in both monetary and non-monetary engagement activities. Additionally, we investigated how these differences change over time and whether such changes are consistent across different consumption contexts. The dataset consists of 924,036 products from 21,190 live streaming shows in 123 live broadcasting rooms over a period of four months was used in this study. The results suggest that AI streamers can substitute for humans in monetary activities in the context of utilitarian consumption but not in hedonic consumption. However, the substitute effect of AI may gradually diminish over time. In addition, in a hedonic context, AI exhibits an increasing effect on viewer engagement over time.This work was supported by the Anhui Provincial Natural Science Foundation [grant number 2308085MG231] and the National Nature Science Foundation of China [grant number 71972001, 72302001]
Dietary interventions for the management of chronic constipation in children
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This is a protocol for a Cochrane Review (intervention). The objectives are as follows:
To evaluate the efficacy and safety of dietary interventions for the management of chronic childhood functional constipation.None
Fragility Modeling of Power Grid Infrastructure for Addressing Climate Change Risks and Adaptation
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Data sharing is not applicable to this article as no new data were created or analyzed in this study.The resilience of electric power grids is threatened by natural hazards. Climate-related hazards are becoming more frequent and intense due to climate change. Statistical analyses clearly demonstrate a rise in the number of incidents (power failures) and their consequences in recent years. Therefore, it is of utmost importance to understand and quantify the resilience of the infrastructure to external stressors, which is essential for developing efficient climate change adaptation strategies. To accomplish this, robust fragility and other vulnerability models are necessary. These models are employed to assess the level of asset damage and to quantify losses for given hazard intensity measures. In this context, a comprehensive literature review is carried out to shed light on existing fragility models specific to the transmission network, distribution network, and substations. The review is organized into three main sections: damage assessment, fragility curves, and recommendations for climate change adaptation. The first section provides a comprehensive review of past incidents, their causes, and failure modes. The second section reviews analytical and empirical fragility models, emphasizing the need for further research on compound and non-compound hazards, especially windstorms, floods, lightning, and wildfires. Finally, the third section examines risk mitigation and adaptation strategies in the context of climate change. This review aims to improve the understanding of approaches to enhance the resilience of power grid assets in the face of climate change. These insights are valuable to various stakeholders, including risk analysts and policymakers, who are involved in risk modeling and developing adaptation strategies.The second author received funding from the European Union HORIZON-MSCA-2021- SE- 01 (grant agreement no. 101086413) ReCharged—Climate-aware Resilience for Sustainable Critical and interdependent Infrastructure Systems enhanced by emerging Digital Technologies. The third authorreceived funding from the UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee (Ref. EP/X037665/1). Thisis the funding guarantee for the European Union HORIZON-MSCA-2021- SE- 01 (grant agreement no. 101086413) ReCharged project
Exploring student consensus about module-level ethnicity awarding gaps: a Delphi approach
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The excel sheets of the survey are available upon request from the first author ([email protected]).Attention paid to awarding gaps in higher education linked to ethnicity tends to focus on outcomes at the final award stage. Our project sought to scrutinise awarding gaps at module level where these gaps may emerge. Our aim was twofold: to identify the most important barriers to student success and determine strategies to reduce awarding gaps at module level, as perceived by students from various ethnic backgrounds and to investigate to what extent there is consensus amongst students of various ethnic backgrounds regarding these barriers and strategies. We employed a two-phase Delphi approach. The first phase involved data analytics to identify modules with awarding gaps in health and life sciences undergraduate degree programmes. The second phase employed a Delphi approach to collect student feedback on barriers to success and strategies to overcome them, focusing on culture, curriculum, and assessment. The study engaged 36 students in the first round and 53 in the second round. Our research confirmed the existence of awarding gaps at the module level. Students reached consensus on 55 out of 79 factors affecting their academic performance, with notable differences between White and racially minoritised student groups. This study suggests that, to close awarding gaps, both a module-level approach and a deep commitment to listening to our students is needed. Our study is the first to use a consensus-driven Delphi approach to identify key barriers and strategies at the module level, offering a framework for addressing awarding gaps and fostering inclusive, equitable education within and beyond the UK.Access and Participation Research and Evaluation funding was received from Brunel University London
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