277 research outputs found

    Flexibility from local resources: Congestion management in distribution grids and carbon emission reductions

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    Flexibility from local energy systems has been discussed as a facilitator for the transition towards a more carbon-neutral energy system. Two use cases of this flexibility are congestion management in electricity distribution networks, and an individual-driven reduction of carbon footprints. However, for taping into this flexibility, effective incentive mechanisms and operation planning are essential. This licentiate thesis aims to provide new insights into two areas: 1) the design of market-based incentive mechanisms for congestion management in distribution grids, and 2) the operation planning of local flexible asset owners for reducing their carbon emission footprints.The first area focuses on challenges, design, and evaluation of local flexibility markets (LFMs) for congestion management in distribution grids. The utilized methods include literature review, field studies, scenario planning methods, and demonstration and simulation experiments.Results for identifying the challenges show that the most impactful and uncertain factors are the willingness and ability of end-users to participate in LFMs, and regulatory incentives for distribution system operators (DSOs). Moreover, five challenges are identified for LFM design including low market liquidity, reliability concerns, baselines, forecast errors at low aggregation levels, and the high cost of sub-meter measurements.An LFM design is proposed to address the challenges. The design is a triple horizon market structure including reservation, activation, and adjustment horizons which can support decision making of market participants and improve market liquidity and reliability. Adapted capacity-limitation products are proposed that are calculated based on net-load and subscribed connection capacity of end-users. The products can reduce conflict of interests, and administrative and sub-meter measurement costs related to delivery validation and baselines. Moreover, probabilistic approaches for calculating the cost and value of the products are proposed that can reduce the potential cost of forecast errors for market participants while providing insights on how the utility and cost of the products can be calculated.Evaluating the proposed design is an ongoing work utilizing simulations and real-life demonstrations. The most suitable congestion management solution can vary depending on the context and test-system. Therefore, the evaluation should include comparing the design with other congestion management solutions such as power tariffs. A comparison toolbox is proposed to be used by researchers and DSOs including a qualitative comparison framework and a reusable modeling platform for the quantitative comparison. Four cases are quantitatively compared using the toolbox on a sub-area of Chalmers campus testbed: i) LFM+PT+ET (i.e., considering the LFM, power tariff (PT), and energy cost (ET) simultaneously), ii) LFM+ET, iii) PT+ET, and iv) ET. The most recent results show that case (i), has the lowest number of congested hours. Moreover, congestions due to rebound effects from activating the LFM are observed. The comparison of cases (i) and (ii) suggests that enforcing power tariffs besides the LFM can reduce the rebound effects.The second area utilizes a multi-objective optimization model for identifying CO2 emission abatement strategies and their cost for Chalmers testbed local multi-energy system. The results of the case study show that the carbon emission footprint of the local system can be reduced by 20.8% with a 2.2% increase in the cost. The operation strategies for this purpose include more usage of biomass boilers in heat production, substitution of district heating and absorption chillers with heat pumps, and higher utilization of storage. The cost of the strategies ranged from 36.6-100.2 €/tCO2.This thesis can benefit system operators, flexibility asset owners, policy makers, and researchers dealing with local flexibility resources by offering insights into the challenges and proposing solutions and toolboxes for implementation and evaluation

    Quantum computing for finance

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    Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning. This Review is aimed at physicists, so it outlines the classical techniques used by the financial industry and discusses the potential advantages and limitations of quantum techniques. Finally, we look at the challenges that physicists could help tackle

    A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

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    The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

    Translating Islamic Law: the postcolonial quest for minority representation

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    This research sets out to investigate how culture-specific or signature concepts are rendered in English-language discourse on Islamic, or ‘shariʿa’ law, which has Arabic roots. A large body of literature has investigated Islamic law from a technical perspective. However, from the perspective of linguistics and translation studies, little attention has been paid to the lexicon that makes up this specialised discourse. Much of the commentary has so far been prescriptive, with limited empirical evidence. This thesis aims to bridge this gap by exploring how ‘culturalese’ (i.e., ostensive cultural discourse) travels through language, as evidenced in the self-built Islamic Law Corpus (ILC), a 9-million-word monolingual English corpus, covering diverse genres on Islamic finance and family law. Using a mixed methods design, the study first quantifies the different linguistic strategies used to render shariʿa-based concepts in English, in order to explore ‘translation’ norms based on linguistic frequency in the corpus. This quantitative analysis employs two models: profile-based correspondence analysis, which considers the probability of lexical variation in expressing a conceptual category, and logistic regression (using MATLAB programming software), which measures the influence of the explanatory variables ‘genre’, ‘legal function’ and ‘subject field’ on the choice between an Arabic loanword and an endogenous English lexeme, i.e., a close English equivalent. The findings are then interpreted qualitatively in the light of postcolonial translation agendas, which aim to preserve intangible cultural heritage and promote the representation of minoritised groups. The research finds that the English-language discourse on Islamic law is characterised by linguistic borrowing and glossing, implying an ideologically driven variety of English that can be usefully labelled as a kind of ‘Islamgish’ (blending ‘Islamic’ and ‘English’) aimed at retaining symbols of linguistic hybridity. The regression analysis confirms the influence of the above-mentioned contextual factors on the use of an Arabic loanword versus English alternatives

    Proceedings XXIII Congresso SIAMOC 2023

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    Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto quest’anno alla sua ventitreesima edizione, approda nuovamente a Roma. Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti che operano nell’ambito dell’analisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle più recenti innovazioni riguardanti le procedure e le tecnologie per l’analisi del movimento nella pratica clinica. Il congresso SIAMOC 2023 di Roma si propone l’obiettivo di fornire ulteriore impulso ad una già eccellente attività di ricerca italiana nel settore dell’analisi del movimento e di conferirle ulteriore respiro ed impatto internazionale. Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla società. Tra questi temi anche quello dell’inserimento lavorativo di persone affette da disabilità anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. Verrà infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per l’ottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione

    ForDigitStress: A multi-modal stress dataset employing a digital job interview scenario

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    We present a multi-modal stress dataset that uses digital job interviews to induce stress. The dataset provides multi-modal data of 40 participants including audio, video (motion capturing, facial recognition, eye tracking) as well as physiological information (photoplethysmography, electrodermal activity). In addition to that, the dataset contains time-continuous annotations for stress and occurred emotions (e.g. shame, anger, anxiety, surprise). In order to establish a baseline, five different machine learning classifiers (Support Vector Machine, K-Nearest Neighbors, Random Forest, Long-Short-Term Memory Network) have been trained and evaluated on the proposed dataset for a binary stress classification task. The best-performing classifier achieved an accuracy of 88.3% and an F1-score of 87.5%

    COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory

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    Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination

    Parallel mutation testing for large scale systems

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    Mutation testing is a valuable technique for measuring the quality of test suites in terms of detecting faults. However, one of its main drawbacks is its high computational cost. For this purpose, several approaches have been recently proposed to speed-up the mutation testing process by exploiting computational resources in distributed systems. However, bottlenecks have been detected when those techniques are applied in large-scale systems. This work improves the performance of mutation testing using large-scale systems by proposing a new load distribution algorithm, and parallelising different steps of the process. To demonstrate the benefits of our approach, we report on a thorough empirical evaluation, which analyses and compares our proposal with existing solutions executed in large-scale systems. The results show that our proposal outperforms the state-of-the-art distribution algorithms up to 35% in three different scenarios, reaching a reduction of the execution time of—at best—up to 99.66%This work was supported by the Spanish MINECO/FEDER project under Grants PID2021- 122270OB-I00, TED2021-129381B-C21 and PID2019-108528RBC22, the Comunidad de Madrid project FORTE-CM under Grant S2018/TCS-4314, Project S2018/TCS-4339 (BLOQUES-CM) cofunded by EIE Funds of the European Union and Comunidad de Madrid and the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 Programm
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