736 research outputs found

    Sustainable Development Goals, Circularity and the Data Centre Industry: a Review of Real-world Challenges in a Rapidly Expanding Sector

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    The last three decades have seen rapid growth in the Data Centre Industry (DCI), which has significantly affected the world we live in today. With the supposedly positive impact of digital technologies, nobody questioned the sustainability of the industry for many years. Only recently, research has started to identify the trade-offs of information and communication technology, particularly for data centres. The increasing environmental concerns sparked discussions about sustainability in many industries, governments and communities, including the DCI. Although the relationship between business and the goal of pursuing sustainability remains complicated and has not been fully explored through research, various studies have emphasised the need to move beyond business as usual. Therefore, businesses within the DCI need to contribute to achieving the Sustainable Development Goals (SDGs) and offset the significant impacts of this sector on the environment, including resource depletion, critical raw materials’ extraction and unethical labour practices. This chapter presents an overview of this unique sector in the context of the impacts across three pillars of sustainability and summaries circular economy-inspired initiatives. Furthermore, it reviews opportunities for the sector to contribute to the SDGs and presents research gaps in present awareness and approaches to tackling the SDGs

    Preferences and perceptions of pharmacy students on the sectoral development of community pharmacy in Belgium

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    peer reviewedIntroduction:Building the future of the pharmacist profession today must be done by listening to the actors of tomorrow. Their wishes and main motivations must be integrated into reflections. The university needs to understand how students plan for their future professions. Consistency between teaching and sectoral development is at the heart of university concerns: anticipating professional changes can help the academic body build flexible programmes to align with professional development and best prepare actors of tomorrow.Objectives:To assess the preferences and perception of Master's students in pharmaceutical sciences among various potential sectoral evolution in the field of pharmacies open to the public. This researchquestions how future pharmacists rank in order of importance and preference for the potential sectoral developments in their profession.Methods: An online questionnaire was sent to Belgian student in pharmaceutical sciences to understand their preferences concerning the various missions expected to be part of the role of pharmacists in the years to come. Some of these missions already exist in Belgium, others already exist abroad, and others still need to be the responsibility of the pharmacist at present. The questionnaire used a best-worst scaling (BWS) approach to determine a hierarchy of preferences on a set of attributes describing the potential sectoral developments in community 389pharmacists. The BWS then makes it possible to classify preferences based on choices and to compare preferences among all the attributes considered. Respondents do not only express their preferences among the proposed attributes but also provide information through their responses as to the most preferable and least preferable attributes in their eyes. The research team agreed on a list of 18 attributes to characterize the profession of community pharmacists and its potential sectoral developments. The 18 attributes were: preparation and dispensing of medication, pharmaceutical care, adjustment/substitution, continuity of treatment, care monitoring/risk prevention, medication review, self-medication, prescription, adherence support, health prevention and promotion, drug analysis, inter-professional collaboration, pharmaceutical care, vaccination, screening, withdrawal/deprescription, return home after hospitalization and home care.Results: The topics for which students showed the greatest interest were delivery of medication with advice on the proper use, prevention, identifying and resolving potential drug-related problems or even assisting the patient in a self-medication situation.The themes with the lowest interest were Greenpharmacy, the collection of used products and sustainable practices.Conclusion: Future pharmacists do not wish to replace medical doctorsand have little interest in diagnosis, prescription and laboratory analysis. Moreover, the lack of interest of future pharmacists in Greenpharmacy raises questions. Making students aware of this significant environmental challenge should be encouraged

    Summer 2023 Full Issue

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    Limit order books in statistical arbitrage and anomaly detection

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    Cette thèse propose des méthodes exploitant la vaste information contenue dans les carnets d’ordres (LOBs). La première partie de cette thèse découvre des inefficacités dans les LOBs qui sont source d’arbitrage statistique pour les traders haute fréquence. Le chapitre 1 développe de nouvelles relations théoriques entre les actions intercotées afin que leurs prix soient exempts d’arbitrage. Toute déviation de prix est capturée par une stratégie novatrice qui est ensuite évaluée dans un nouvel environnement de backtesting permettant l’étude de la latence et de son importance pour les traders haute fréquence. Le chapitre 2 démontre empiriquement l’existence d’arbitrage lead-lag à haute fréquence. Les relations dites lead-lag ont été bien documentées par le passé, mais aucune étude n’a montré leur véritable potentiel économique. Un modèle économétrique original est proposé pour prédire les rendements de l’actif en retard, ce qu’il réalise de manière précise hors échantillon, conduisant à des opportunités d’arbitrage de courte durée. Dans ces deux chapitres, les inefficacités des LOBs découvertes sont démontrées comme étant rentables, fournissant ainsi une meilleure compréhension des activités des traders haute fréquence. La deuxième partie de cette thèse investigue les séquences anormales dans les LOBs. Le chapitre 3 évalue la performance de méthodes d’apprentissage automatique dans la détection d’ordres frauduleux. En raison de la grande quantité de données, les fraudes sont difficilement détectables et peu de cas sont disponibles pour ajuster les modèles de détection. Un nouveau cadre d’apprentissage profond non supervisé est proposé afin de discerner les comportements anormaux du LOB dans ce contexte ardu. Celui-ci est indépendant de l’actif et peut évoluer avec les marchés, offrant alors de meilleures capacités de détection pour les régulateurs financiers.This thesis proposes methods exploiting the vast informational content of limit order books (LOBs). The first part of this thesis discovers LOB inefficiencies that are sources of statistical arbitrage for high-frequency traders. Chapter 1 develops new theoretical relationships between cross-listed stocks, so their prices are arbitrage free. Price deviations are captured by a novel strategy that is then evaluated in a new backtesting environment enabling the study of latency and its importance for high-frequency traders. Chapter 2 empirically demonstrates the existence of lead-lag arbitrage at high-frequency. Lead-lag relationships have been well documented in the past, but no study has shown their true economic potential. An original econometric model is proposed to forecast returns on the lagging asset, and does so accurately out-of-sample, resulting in short-lived arbitrage opportunities. In both chapters, the discovered LOB inefficiencies are shown to be profitable, thus providing a better understanding of high-frequency traders’ activities. The second part of this thesis investigates anomalous patterns in LOBs. Chapter 3 studies the performance of machine learning methods in the detection of fraudulent orders. Because of the large amount of LOB data generated daily, trade frauds are challenging to catch, and very few cases are available to fit detection models. A novel unsupervised deep learning–based framework is proposed to discern abnormal LOB behavior in this difficult context. It is asset independent and can evolve alongside markets, providing better fraud detection capabilities to market regulators

    Essays on Labour and Regional Economics

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    This thesis consists of three chapters in empirical labour and regional economics. They generally analyze how local labour market performance varies across different times and spaces. The first chapter provides a comprehensive analysis of labour market evolutions in rural areas in four most populous European countries since 1970. We document large differences in employment growth and changes in the industry structure are fast. Furthermore, industry turnover is positively associated with employment growth. Finally, our evidence indicates that successful rural areas experience stronger employment growth in manufacturing of food and beverages. In the second chapter, I investigate the employment consequences of deindustrialization between 2010 and 2020 for cities in seven Chinese provinces, which could be viewed as China's Rust Belt, and explore the role of local multipliers. Cities within this Rust Belt reacted very differently to the aggregate decreasing trend of manufacturing employment. I document a high level of spatial heterogeneity across the local labour markets. I then study the role of local multiplier effects exploiting a shift-share approach. My estimates indicate that for every job created (lost) in the tradable sector in a given city, between 1.6 and 1.9 additional jobs are created (lost) in the non-tradable sector in the same city. The third chapter presents direct evidence on the extent to which firms’ innovation is affected by access to knowledgeable labor through co-worker network connections. Displacements of inventors because of plant closures generate labor supply shocks to firms that employ their previous co-workers. We estimate (a) event-study models where the treatment is the displacement of a connected inventor and (b) IV specifications where we use such a displacement as an instrument for the hire of a connected inventor. Estimates indicate that firms take advantage of displacements to recruit connected inventors and that the improved capacity increases innovation

    Emerging Technology’s Language Wars: AI and Criminal Justice

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    Business Sustainability Among Women Entrepreneurs in Ghana

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    Women entrepreneurs in Ghana are not prepared for entrepreneurship and face individual and contextual barriers that include social, cultural, economic, political, demographic, institutional, and technological perceived support. Guided by the institutional theory, the purpose of this qualitative exploratory multiple case study was to explore business strategic information that women entrepreneurs in Ghana need to learn to make their business sustainable beyond 5 years. Ten successful women entrepreneurs from 5 industries in Ghana, who had the training, experience, and information on the causes of business failure and had applied that information to gain business sustainability beyond 5 years, were recruited. Data analysis involved methodological triangulation, member checking, and Yin’s 5 steps. Key findings were change management and adaptation, agility and flexibility in operations, comprehensive analysis of stakeholders, creation of business policies and objectives, designing and executing digital implementation plan, developing, and supporting corporate culture, developing, and measuring performance standards, and enhancing internal control and processes. Findings may be used to reduce poverty and increase women’s employment, sustainability in women-owned businesses, community development, and the standard of living. The implication to social change includes increase in women employment, poverty reduction among women and community, community development, and improvement of living standard for women population

    Machine-Learning-Powered Cyber-Physical Systems

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    In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety of services and applications across multiple domains. Machine learning processes and analyses such data to generate a feedback, which represents a status the environment is in. A feedback given to the user in order to make an informed decision is called an open-loop feedback. Thus, an open-loop CPS is characterized by the lack of an actuation directed at improving the system itself. A feedback used by the system itself to actuate a change aimed at optimizing the system itself is called a closed-loop feedback. Thus, a closed-loop CPS pairs feedback based on sensing data with an actuation that impacts the system directly. In this dissertation, we propose several applications in the context of CPS. We propose open-loop CPSs designed for the early prediction, diagnosis, and persistency detection of Bovine Respiratory Disease (BRD) in dairy calves, and for gait activity recognition in horses.These works use sensor data, such as pedometers and automated feeders, to perform valuable real-field data collection. Data are then processed by a mix of state-of-the-art approaches as well as novel techniques, before being fed to machine learning algorithms for classification, which informs the user on the status of their animals. Our work further evaluates a variety of trade-offs. In the context of BRD, we adopt optimization techniques to explore the trade-offs of using sensor data as opposed to manual examination performed by domain experts. Similarly, we carry out an extensive analysis on the cost-accuracy trade-offs, which farmers can adopt to make informed decisions on their barn investments. In the context of horse gait recognition we evaluate the benefits of lighter classifications algorithms to improve energy and storage usage, and their impact on classification accuracy. With respect to closed-loop CPS we proposes an incentive-based demand response approach for Heating Ventilation and Air Conditioning (HVAC) designed for peak load reduction in the context of smart grids. Specifically, our approach uses machine learning to process power data from smart thermostats deployed in user homes, along with their personal temperature preferences. Our machine learning models predict power savings due to thermostat changes, which are then plugged into our optimization problem that uses auction theory coupled with behavioral science. This framework selects the set of users who fulfill the power saving requirement, while minimizing financial incentives paid to the users, and, as a consequence, their discomfort. Our work on BRD has been published on IEEE DCOSS 2022 and Frontiers in Animal Science. Our work on gait recognition has been published on IEEE SMARTCOMP 2019 and Elsevier PMC 2020, and our work on energy management and energy prediction has been published on IEEE PerCom 2022 and IEEE SMARTCOMP 2022. Several other works are under submission when this thesis was written, and are included in this document as well

    Disadvantages Ashore—Constraints on Achieving Integrated All-Domain Naval Power

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    Strategists and analysts should be aware that the recently issued triservices strategy suffers from at least six political-bureaucratic-doctrinal disadvantages that very easily could turn into material disadvantages if the U.S. Navy were to face a competent enemy on the actual oceans

    Barriers and Challenges to the Adoption of Collaborative Project Delivery Methods: A Multiple-Case Study Analysis

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    The last 30 years have seen considerable advantages afforded to those who chose to adopt more collaborative project delivery methods (IPD, Progressive Design Build) in the Construction Industry; however, despite these benefits, construction projects utilizing collaborative delivery methods still only account for a small fraction of overall construction deliveries. This research focused on probing leading industry professionals to better identify the barriers and challenges which are currently preventing project stakeholders across the United States from adopting these more collaborative project delivery methods, particularly first-time adopters. This study collected data from semi-structured interviews with 13 professionals in the Construction Industry who had experience with collaborative project delivery methods. Detailed analysis of stakeholder responses using NVivo software led to key insights associated with the implementation of IPD and Progressive Design Build projects including those related to teaming, learning, and administration. A new class of challenges is proposed related to managerial obstacles. The implication of this research is that appropriately recognizing and categorizing the pitfalls associated with the implementation of collaborative project delivery methods can provide Construction Industry professionals with a valuable framework for formulating effective solutions to overcome them
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