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Measurement of the primary Lund jet plane density in proton-proton collisions at = 13 TeV
Get PDFA preprint version of the article is available at arXiv:2312.16343v1 [hep-ex], https://arxiv.org/abs/2312.16343v1 [it will be replaced with v2 in due course). Comments: Submitted to the Journal of High Energy Physics. All figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/SMP-22-007 (CMS Public Pages). Report number: CMS-SMP-22-007, CERN-EP-2023-282.A measurement is presented of the primary Lund jet plane (LJP) density in inclusive jet production in proton-proton collisions. The analysis uses 138 fb−1 of data collected by the CMS experiment at √s = 13 TeV. The LJP, a representation of the phase space of emissions inside jets, is constructed using iterative jet declustering. The transverse momentum kT and the splitting angle ΔR of an emission relative to its emitter are measured at each step of the jet declustering process. The average density of emissions as function of ln(kT/GeV) and ln(R/ΔR) is measured for jets with distance parameters R = 0.4 or 0.8, transverse momentum pT> 700 GeV, and rapidity |y|< 1.7. The jet substructure is measured using the charged-particle tracks of the jet. The measured distributions, unfolded to the level of stable particles, are compared with theoretical predictions from simulations and with perturbative quantum chromodynamics calculations. Due to the ability of the LJP to factorize physical effects, these measurements can be used to improve different aspects of the physics modeling in event generators.SCOAP3
Development of a probabilistic agricultural drought forecasting (PADF) framework under climate change
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Data will be made available on request.Supplementary materials are available online at: https://www.sciencedirect.com/science/article/pii/S0168192324000807?via%3Dihub#sec0022 .Drought has significant impacts on human survival and social development, particularly on crop production. Agricultural drought is the most direct consequence of drought on crops. In this study, a Probabilistic Agricultural Drought Forecasting (PADF) framework was developed to employ the Ensemble Bayesian Least Square Support Vector Machine (EBLSSVM) method for bias correction in precipitation and temperature projections from multiple Regional Climate Models (RCMs). Vine Copula-Based Projection Model (VCPM) was then developed for accurate agricultural drought projections, providing deterministic results and valuable 90 % predictive intervals. The results indicate that the EBLSSVM method can generate better climate projections than the original outputs from RCMs and bias-corrected results from other bias-correction techniques. Based on the projection results from VCPM, the study found that drought will be a significant concern in Fujian province, especially in the southeast coastal region. Drought conditions are projected to be more severe in the 2050s than in the 2080s, under both RCP4.5 and RCP8.5. The average SSI values during months with a wet trend ranged from 0.1 to 0.3, whereas months with a drought trend predominantly exhibited average SSI values exceeding -0.5. Notably, SSI values as low as -2.0 were observed during wet trend months, underscoring the urgency of addressing future drought, particularly in coastal regions. However, even during wet periods, at least one extreme drought month is expected, suggesting that extreme drought conditions will become more severe in the future. CMIP5 and CMIP6 predictions showed good consistency in temporal and spatial dimensions, with CMIP6 indicating more significant and consistent future drought changes compared to CMIP5.Natural Science Foundation of Fujian Province, China (2021J011180)
Author Correction: Climate threats to coastal infrastructure and sustainable development outcomes (Nature Climate Change, (2024), 10.1038/s41558-024-01950-2)
No full textCorrection to: Nature Climate Change https://doi.org/10.1038/s41558-024-01950-2, published online 1 March 2024.
In the version of the article initially published, the bottom section of Fig. 3 was inadvertently omitted. This has been corrected and the full figure can now be seen in the HTML and PDF versions of the article
Reclassifying historical disasters: from single to multi-hazards
Get PDFEGU General Assembly 2024, Session NH10.1.Multi-hazard events, characterized by the simultaneous, cascading, or cumulative occurrence of multiple natural hazards, pose a significant threat to human lives and assets. This is primarily due to the cumulative and cascading effects arising from the interplay of various natural hazards across space and time. However, their identification is challenging, which is attributable to the complex nature of natural hazard interactions and the limited availability of multi-hazard observations. This presentation, focused on a recently published article in Science of the Total Environment (https://doi.org/10.1016/j.scitotenv.2023.169120), presents an approach for identifying multi-hazard events during the past 123 years (1900-2023) using the EM-DAT global disaster database. Leveraging the ‘associated hazard’ information in EM-DAT, multi-hazard events are detected and assessed in relation to their frequency, impact on human lives and assets, and reporting trends. The interactions between various combinations of natural hazard pairs are explored, reclassifying them into four categories: preconditioned/triggering, multivariate, temporally compounding, and spatially compounding multi-hazard events. The results show, globally, approximately 19% of the 16,535 disasters recorded in EM-DAT can be classified as multi-hazard events. However, the multi-hazard events recorded in EM-DAT are disproportionately responsible for nearly 59% of the estimated global economic losses. Conversely, single hazard events resulted in higher fatalities compared to multi-hazard events. The largest proportion of multi-hazard events are associated with floods, storms, and earthquakes. Landslides emerge as the predominant secondary hazards within multi-hazard pairs, primarily triggered by floods, storms, and earthquakes, with the majority of multi-hazard events exhibiting preconditioned/triggering and multivariate characteristics. There is a higher prevalence of multi-hazard events in Asia and North America, whilst temporal overlaps of multiple hazards predominate in Europe. These results can be used to increase the integration of multi-hazard thinking in risk assessments, emergency management response plans and mitigation policies at both national and international levels
Improving classifier-based effort-aware software defect prediction by reducing ranking errors
Get PDFContext: Software defect prediction utilizes historical data to direct software quality assurance resources to potentially problematic components. Effort-aware (EA) defect prediction prioritizes more bug-like components by taking cost-effectiveness into account. In other words, it is a ranking problem, however, existing ranking strategies based on classification, give limited consideration to ranking errors. Objective: Improve the performance of classifier-based EA ranking methods by focusing on ranking errors. Method: We propose a ranking score calculation strategy called EA-Z which sets a lower bound to avoid near-zero ranking errors. We investigate four primary EA ranking strategies with 16 classification learners, and conduct the experiments for EA-Z and the other four existing strategies. Results: Experimental results from 72 data sets show EA-Z is the best ranking score calculation strategy in terms of Recall@20% and Popt when considering all 16 learners. For particular learners, imbalanced ensemble learner UBag-svm and UBst-rf achieve top performance with EA-Z. Conclusion: Our study indicates the effectiveness of reducing ranking errors for classifier-based effort-aware defect prediction. We recommend using EA-Z with imbalanced ensemble learning...
Bayesian fractional polynomial approach to quantile regression and variable selection with application in the analysis of blood pressure among US adults
Get PDFAlthough the fractional polynomials (FPs) can act as a concise and accurate formula for examining smooth relationships between response and predictors, modelling conditional mean functions observes the partial view of a distribution of response variable, as distributions of many response variables such as blood pressure (BP) measures are typically skew. Conditional quantile functions with FPs provide a comprehensive relationship between the response variable and its predictors, such as median and extremely high-BP measures that may be often required in practical data analysis generally. To the best of our knowledge, this is new in the literature. Therefore, in this article, we develop and employ Bayesian variable selection with quantile-dependent prior for the FP model to propose a Bayesian variable selection with parametric non-linear quantile regression model. The objective is to examine a non-linear relationship between BP measures and their risk factors across median and upper quantile levels using data extracted from the 2007 to 2008 National Health and Nutrition Examination Survey (NHANES). The variable selection in the model analysis identified that the non-linear terms of continuous variables (body mass index, age), and categorical variables (ethnicity, gender, and marital status) were selected as important predictors in the model across all quantile levels.UK Engineering and Physical Sciences Research Council (EPSRC) grant 2295266 for the Brunel University London for Doctoral Training
A novel Multiple-Expert Protocol to manage uncertainty and subjective choices in probabilistic single and multi-hazard risk analyses
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No data was used for the research described in the article.Integrating diverse expert opinions in hazard and risk projects is essential to managing subjective decisions and quantifying uncertainty to produce stable and trustworthy results. A structured procedure is necessary to organize the gathering of experts' opinions while ensuring transparency, accountability, and independence in judgements. We propose a novel Multiple-Expert management Protocol (MEP) to address this challenge, providing procedural guidelines for conducting single to multi-hazard risk analyses. MEP establishes a workflow to manage subjectivity rooted in (i) moderated and staged group interactions, (ii) trackable blind advice through written elicitations with mathematical aggregation, (iii) participatory independent review, (iv) close cooperation between scientific and managerial coordination, and (v) proper and comprehensive documentation. Originally developed for stress testing critical infrastructure, MEP is designed as a single, flexible, technology-neutral procedural workflow applicable to various sectors. Moreover, its scalability allows it to adapt from high to low-budget projects and from complex probabilistic multi-hazard risk assessments to standard single-hazard analyses, with different experts' degree and type of involvement depending on available funding and emerging controversies. We present two compelling case studies to showcase MEP's practical applicability: a multi-hazard risk analysis for a port infrastructure and a single-hazard regional tsunami hazard assessment.European Community's Seventh Framework Programme under Grant Agreement No. 603389 & Mechanism of the European Civil Protection and Humanitarian Aid Operations with grant no. ECHO/SUB/2015/718568/PREV26 (https://ec.europa.eu/echo/funding-evaluations/financing-civil-protection-europe/selected-projects/probabilistic-tsunami-hazard_en)
A novel efficient energy optimization in smart urban buildings based on optimal demand side management
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The data used for this research and prepatation of this article can be accessed from Brunel University of London repository at: https://doi.org/10.17633/rd.brunel.26049436.v1.Increasing electrical energy consumption during peak hours leads to increased electrical energy losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced to reduce consumption during peak hours. This study proposes an efficient energy optimization in Smart Urban Buildings (SUBs) based on Improved Sine Cosine Algorithm (ISCA) that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a SUBs. The proposed system's goal is to optimize the energy of SUBs appliances in order to effectively regulate load demand, with the end result being a reduction in the peak to average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. The proposed system is evaluated by comparing it with the Grasshopper Optimization Algorithm (GOA) and unscheduled cases. Without applying an optimization algorithm, the total electricity cost, carbon emission, PAR and waiting time are equal to 1703.576 ID, 34.16664 (kW), and 413.5864s respectively for RTP. While, after applying GOA, the total electricity cost, carbon emission, PAR and waiting time are improved to 1469.72 ID, 21.17 (kW), and 355.772s respectively for RTP. While, after applying the ISCA Improves the total electricity cost, PAR, and waiting time by 1206.748 ID, 16.5648 (kW), and 268.525384s respectively. Where after applying GOA, the total electricity cost, PAR, and waiting time are improved to 13.72 %, 38.00 %, and 13.97 % respectively. And after applying proposed method, the total electricity cost, PAR, and waiting time are improved to 29.16 %, 51.51 %, and 35.07 % respectively. According to the results, the created ISCA algorithm performed better than the unscheduled case and GOA scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers. Furthermore, this study has presented a novel two-stage stochastic model based on Moth-Flame Optimization Algorithm (MFOA) for the co-optimization of energy scheduling and capacity planning for systems of energy storage that would be incorporated to grid connected smart urban buildings.The research has been partially supported by the Faculty of Informatics and Management UHK excellence project “Methodological perspectives on modeling and simulation of hard and soft systems”
‘Mining women’ and livelihoods: Examining the dominant and emerging issues in the ASM gendered economic space
Get PDFCopyright © The Author(s) 2024. The intractable challenges faced by female mine workers have come to dominate the discourse and scholarship on artisanal and small-scale mining (ASM) operations. However, the extensive focus on the informal and labour-intensive segments has engendered a failure to capture the nuances in the duality of ASM operations and how it impacts female outcomes. Drawing on intersectionality as a lens, in this article the authors map the dynamics on how issues related to the gender, situatedness and positionality of female mine workers interact to shape their situated labour outcomes. Highlighting the differentiated outcomes for female mine workers within the contingencies of the broader socio-cultural context in which ASM work is organised, the article sheds light on how the social identity structures such as gender, sexuality and class interact to give form to the marginalisation, occupational roles, the ‘boom town’ narrative and occupational and health challenges that characterise the ASM gendered economic space.The authors received no financial support for the research, authorship, and/or publication of this article
Adaptive Numeral System (ANS) and its applications in data compression
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis research explores innovative methodologies for advancing lossless data compression by developing adaptive numeral systems to calculate and reduce binary data representations effectively. Building on the foundational theories of Shannon and Kolmogorov, the study introduces the Adaptive Numeral System (ANS), Improved Adaptive Numeral System (IANS), and Modified Adaptive Numeral System (MANS), novel approaches for adaptively calculating binary values. These systems can be shown to exhibit the unique capability of compressing each segment iteratively, thereby reducing the overall data size progressively and form the basis for an iterative and progressive approach to data compression.
To leverage these adaptive numeral systems, the study presents the Data Extraction (DE) technique, a compression framework that uses MANS to perform conversions from binary values into more compact representations. DE achieves significant compression rates, demonstrating competitive performance compared to traditional methods like Huffman coding, particularly in its fully decodable state before binary conversion. Furthermore, the research addresses challenges in identifying flag locations within segmented data, proposing a range of solutions to enhance compression efficiency and reliability.
The combined contributions of ANS, IANS, MANS, and DE represent a significant advancement in the field of lossless compression, particularly in their ability to process already compressed data and transform non-prefix codes into prefix codes. These advancements hold substantial promise for applications in areas such as medical imaging, digital media, machine learning, artificial intelligence, embedded systems, and the Internet of Things
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