7,268 research outputs found
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021
É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio
Redefining Community in the Age of the Internet: Will the Internet of Things (IoT) generate sustainable and equitable community development?
There is a problem so immense in our built world that it is often not fully realized. This problem is the disconnection between humanity and the physical world. In an era of limitless data and information at our fingertips, buildings, public spaces, and landscapes are divided from us due to their physical nature. Compared with the intense flow of information from our online world driven by the beating engine of the internet, our physical world is silent. This lack of connection not only has consequences for sustainability but also for how we perceive and communicate with our built environment in the modern age. A possible solution to bridge the gap between our physical and online worlds is a technology known as the Internet of Things (IoT). What is IoT? How does it work? Will IoT change the concept of the built environment for a participant within it, and in doing so enhance the dynamic link between humans and place? And what are the implications of IoT for privacy, security, and data for the public good? Lastly, we will identify the most pressing issues existing in the built environment by conducting and analyzing case studies from Pomona College and California State University, Northridge. By analyzing IoT in the context of case studies we can assess its viability and value as a tool for sustainability and equality in communities across the world
Stakeholder Governance: Empirical and Theoretical Developments
Stakeholder governance receives attention across many disciplines, resulting in fragmented knowledge. The inherent complexity of stakeholder governance requires the integration of this knowledge to develop comprehensive and inclusive theories to better conceptualize this phenomenon. In this research, we develop stakeholder governance through empirical and theoretical approaches. In the first essay, we use multiple case comparisons to empirically examine how and why organizations manage food waste to develop grounded theory through contextualized explanations. We contribute grounded theoretical and empirical evidence to show that food waste represents a significant business problem. Our data suggests that dimensions of logistics and stakeholder governance dictate how and why organizations manage food waste. These findings stimulate a deeper dive into stakeholder governance, revealing fragmentations in knowledge that require holistic, interdisciplinary review and synthesis. In the second essay, we identify definitions and terminologies, review the evolution of theories and orientations, organize mechanisms and conceptualizations, synthesize key theoretical tensions, and offer suggestions for future research to contribute theoretical developments for stakeholder governance. We contribute pluralist conceptual frameworks that integrate knowledge across disciplines to provide a comprehensive overview and recommendations. Overall, we contribute empirical and theoretical research to advance theory development for stakeholder governance
Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning
Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project “HOLOTRAIN” (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project “AWAKEN: content-Aware and netWork-Aware faKE News mitigation” (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project “Deep-Learning Anomaly Detection for Human and Automated Users Behavior” (grant no. 91809358)
Full stack development toward a trapped ion logical qubit
Quantum error correction is a key step toward the construction of a large-scale quantum computer, by preventing small infidelities in quantum gates from accumulating over the course of an algorithm. Detecting and correcting errors is achieved by using multiple physical qubits to form a smaller number of robust logical
qubits. The physical implementation of a logical qubit requires multiple qubits, on which high fidelity gates
can be performed.
The project aims to realize a logical qubit based on ions confined on a microfabricated surface trap. Each
physical qubit will be a microwave dressed state qubit based on 171Yb+ ions. Gates are intended to be realized through RF and microwave radiation in combination with magnetic field gradients. The project vertically integrates software down to hardware compilation layers in order to deliver, in the near future, a fully functional small device demonstrator.
This thesis presents novel results on multiple layers of a full stack quantum computer model. On the hardware level a robust quantum gate is studied and ion displacement over the X-junction geometry is demonstrated.
The experimental organization is optimized through automation and compressed waveform data transmission. A new quantum assembly language purely dedicated to trapped ion quantum computers is introduced. The demonstrator is aimed at testing implementation of quantum error correction codes while preparing for larger
scale iterations.Open Acces
The interaction of risk and protective factors for mental disorders on psychopathology and brain morphometry
As per the diathesis-stress model, combined early risk factors (diathesis) and current risk factors (stress) determine an individual’s likelihood for the development of psychopathology. If the combined impact of diathesis and stress surpasses a certain threshold, individuals will develop psychopathology. At the same time, such threshold could be raised in the presence of protective factors, as they buffer the negative impact of risk factors, and lead to a reduced likelihood of developing psychopathology.
Early risk factors for mental disorders include trait anxiety, childhood maltreatment and familial risk, and have been associated with specific brain morphometric alterations. Stressful life events, including the Covid-19 pandemic as a global example of that, constitute current risk factors. On the other hand, current literature suggests social support and conscientiousness as exemplary protective factors. These may increase resilience, a concept describing an individual’s ability to adaptively cope in the face of adversity and maintain mental health. However, contrary to risk factors, neural correlates of resilience are only sparsely known and hardly understood.
Thus, to make precise predictions about the emergence of psychopathology in certain circumstances and understand possible neurobiological pathways, it is essential to jointly consider both risk and protective factors in mental health research.
The aim of this dissertation was to investigate the interaction of risk and protective factors in three different but complementary contexts to gain a deeper understanding of these factors and their impact on brain morphometry and psychopathology.
In STUDY I, morphometric correlates (specifically grey matter volume) of resilience were investigated. In this study, resilience was conceptualized as the maintenance of mental health despite a high risk (i.e., childhood maltreatment and familial risk). A key finding is that healthy high-risk individuals demonstrated larger grey matter volume in the left dorsolateral prefrontal cortex, an area associated with cognitive flexibility and emotional regulation skills, compared to the other groups. It seems plausible that an increased volume in this area is a neural correlate of resilience to high risk and may represent compensatory processes aiding high-risk individuals in maintaining mental health.
STUDY II approached the subject in the opposite way, with transdiagnostic grey matter volume alterations in psychiatric patients compared to healthy subjects being associated with risk and protective factors. This study identified reduced volume in the left hippocampus as a transdiagnostic vulnerability marker in patients with major depression, bipolar disorder, and schizophrenia spectrum disorder. Volume in this area was further negatively associated with stressful life events, and executive and global functioning in both patients and healthy subjects. We conclude that stressful life events likely constitute a dimensional risk factor for reduced hippocampal volume and, therefore, are independent of diagnosis.
STUDY III investigated the impact of a unique, acute global stressor, the Covid-19 pandemic, on healthy subjects and transdiagnostic patients. Multiple trait risk and protective factors were tested for their explanatory value of current Covid-19-related fear and isolation. This study identified trait anxiety and conscientiousness as risk factors for increased Covid-19-related fear, and social support as a protective factor against increased Covid-19-isolation. Again, the respective effect (harmful or protective) of all these factors was dimensional, i.e., relevant in both psychiatric patients and healthy subjects. STUDY III also highlighted the context-dependency of risk and protective factors: although generally considered a protective trait, increased conscientiousness was harmful in the context of a global pandemic due to the immense level of uncertainty and unpredictability.
In conclusion, this dissertation identified brain correlates as potential biomarkers of psychopathology and resilience, and procedural contributors to adaptive and maladaptive responses to acute stressors. It highlighted the importance of taking protective factors, in addition to risk factors, into account in research. A major strength is the integration of multiple risk and protective factors, as such integrative approaches are crucial to advance the understanding of their complex interplay. By identifying dimensionality and context-dependency as important modulatory influences in the risk and protective factor interplay, it provided a framework for a more comprehensive understanding of the development of psychopathology, and the concept of resilience as a dynamic, continuous process of adaptation to changing environments, which enables individuals to maintain mental health even in the face of adversity
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Reliable Decision-Making with Imprecise Models
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if the system has access to a comprehensive decision-making model that accounts for all the details in the environment and all possible scenarios the agent may encounter, it may be intractable to solve this complex model optimally. Consequently, this complex, high fidelity model may be simplified to accelerate planning, introducing imprecision. Reasoning with such imprecise models affects the reliability of autonomous systems. A system\u27s actions may sometimes produce unexpected, undesirable consequences, which are often identified after deployment. How can we design autonomous systems that can operate reliably in the presence of uncertainty and model imprecision?
This dissertation presents solutions to address three classes of model imprecision in a Markov decision process, along with an analysis of the conditions under which bounded-performance can be guaranteed. First, an adaptive outcome selection approach is introduced to devise risk-aware reduced models of the environment that efficiently balance the trade-off between model simplicity and fidelity, to accelerate planning in resource-constrained settings. Second, a framework that extends stochastic shortest path framework to problems with imperfect information about the goal state during planning is introduced, along with two solution approaches to solve this problem. Finally, two complementary solution approaches are presented to minimize the negative side effects of agent actions. The techniques presented in this dissertation enable an autonomous system to detect and mitigate undesirable behavior, without redesigning the model entirely
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Drivers and Direct Impacts of Lean Mass Dynamics on the Stopover Ecology and Migratory Pace of Nearctic-Neotropical Migrant Songbirds in Spring
Annual migration in songbirds is one of the most demanding life-history stages. It represents a period of high mortality, yet there is still much unknown about the ecological correlates that influence its successful completion. After long non-stop migratory flights, birds require a stopover period to rest and replenish depleted energy reserves. Birds use fat as the primary fuel to power long-distance flights. However, birds also burn lean tissue, which results in significant reductions in muscle and organ masses. The discovery and quantification of lean mass catabolism represented a paradigm shift in migration ecology because non-fat components were thought to remain homeostatic. Because rebuilding protein is slow, muscle and organ breakdown during migration may dramatically prolong stopover periods and delay overall migration time, which in turn dramatically reduces breeding success. Therefore, the breakdown of lean tissue, the conditions that lead to it, and its consequences are important considerations in understanding the migration strategies of birds.
Through this dissertation research, I aim to understand the impact of weather on body condition and how physiological condition impacts subsequent migratory performance. I investigate (1) how weather impacts the lean mass of songbirds after crossing an ecological barrier, and (2) how body condition after crossing an ecological barrier affects stopover duration, refueling rate, and habitat use. My predictions are that higher nightly temperatures or drier conditions experienced during migratory flight will correspond with lower lean body mass on arrival; and that birds with lower lean body mass will require longer stopovers, different habitat, or higher foraging effort to continue migration.
I used an integrative approach, combining the field and lab, to better understand how weather experienced during flight can impact the body condition of migratory birds and how this can influence the entire migratory cycle. By using Quantitative Magnetic Resonance (QMR) technology in combination with a novel automated radio-telemetry system, my research provides unprecedented access to detailed physiological and movement data for small migratory songbirds. This research underlines that successfully crossing the Gulf of Mexico may be a key driver of physiological and morphological adaptations. My findings challenge the current paradigm that birds with low lean mass require longer stopover and demonstrates that species under time constraints may shorten stopover even when in poor condition, departing in sub-optimal body condition
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