1,720,958 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Human-Centric HVAC Control: Balancing Comfort and Energy Efficiency
Get PDFIn the dynamic landscape of modern building automation, this research paper intricately delves into the paradigm shift represented by human-centric HVAC control systems. The central focus of this exploration is the pursuit of an optimal equilibrium between occupant comfort and energy efficiency, marking a significant departure from conventional approaches. As contemporary buildings evolve into smarter, more responsive entities, the study investigates the innovative methodologies, cutting-edge technological advancements, and the profound psychological dimensions involved in tailoring HVAC systems to prioritize human well-being. This research undertakes a comprehensive examination of the multifaceted dimensions inherent in human-centric HVAC control, dissecting the intricate interplay between comfort-centric design principles and sustainable energy practices. By shedding light on the latest developments and emerging trends, the paper aims to contribute valuable insights into the transformative potential of human-centric HVAC control systems. Beyond merely adapting to occupants\u27 needs, the envisioned environments strive to elevate the human experience while upholding the fundamental principles of sustainable and efficient energy consumption. Through a synthesis of research findings, technological advancements, and psychological considerations, this paper endeavors to present a holistic view of the complex relationship between occupants and their built environments. The ultimate goal is to guide future HVAC system designs toward a harmonious integration of comfort, well-being, and energy efficiency, thereby setting a new standard for the ever-evolving field of building automation
Automated Fault Detection and Diagnostics in HVAC Systems
Get PDFIn this comprehensive research paper, we delve into the transformative realm of Automated Fault Detection and Diagnostics (AFDD) within Heating, Ventilation, and Air Conditioning (HVAC) systems. As the intricacies of HVAC systems continue to evolve, AFDD emerges as a pivotal and proactive solution for the identification and diagnosis of faults, thereby bolstering system reliability, optimizing energy efficiency, and elevating overall performance. The exploration within this study encompasses a multifaceted analysis of AFDD, encompassing its fundamental principles, cutting-edge technologies, diverse applications, and the extensive benefits it brings to the domain of HVAC systems. The research sheds light on the underlying mechanisms and methodologies that enable AFDD to operate seamlessly, ensuring timely detection and diagnosis of faults that may impede system functionality. By investigating the applications of AFDD in HVAC systems, the research paper aims to elucidate its role in mitigating potential issues and improving the operational efficiency of these complex systems. The study emphasizes the practical implications of AFDD across various HVAC contexts, offering insights into its adaptability and effectiveness in diverse environments, from commercial buildings to residential structures. Furthermore, the paper discusses the technological advancements driving AFDD, including sophisticated sensor technologies, machine learning algorithms, and data analytics. It explores how these innovations collectively contribute to the real-time monitoring and diagnostic capabilities of AFDD, enabling HVAC systems to operate at peak performance levels while minimizing energy consumption and environmental impact. Ultimately, this research paper aims to provide a comprehensive understanding of AFDD, shedding light on its transformative potential within HVAC systems. By outlining the principles, technologies, applications, and benefits associated with AFDD, the study contributes valuable insights to the ongoing discourse on enhancing the reliability, efficiency, and sustainability of HVAC systems in the face of evolving technological landscapes
HVAC Load Prediction and Energy Saving Strategies in Building Automation
Get PDFIn the ever-evolving landscape of building automation, the effective management of Heating, Ventilation, and Air Conditioning (HVAC) systems is integral to achieving optimal energy efficiency and overall sustainability. This research paper endeavors to meticulously explore the profound significance of HVAC load prediction and delineate innovative energy-saving strategies within the intricate framework of building automation systems. The study embarks on a comprehensive analysis of the predictive capabilities that underpin the proactive management of HVAC loads. By scrutinizing cutting-edge technologies and methodologies, the research aims to unravel the intricate intricacies involved in anticipating HVAC load variations with precision. Understanding and harnessing the predictive potential in building automation systems form the cornerstone of this investigation. Furthermore, the paper delves into a multifaceted exploration of energy-saving strategies within the purview of HVAC load management. By examining real-world applications and success stories, the study seeks to distill the most effective and scalable approaches to curbing energy consumption without compromising the comfort and well-being of building occupants. These strategies encompass adaptive control mechanisms, advanced sensor technologies, and integration with emerging smart grid solutions, fostering a holistic approach towards sustainable building operations. The research also addresses the symbiotic relationship between predictive HVAC load management and the broader objectives of building automation systems. In doing so, it sheds light on the seamless integration of predictive analytics, machine learning algorithms, and data-driven decision-making processes that culminate in an intelligent, responsive, and energy-efficient HVAC infrastructure. The significance of this research extends beyond theoretical frameworks, aiming to provide actionable insights for industry practitioners, building managers, and policymakers alike. By synthesizing the latest advancements in HVAC load prediction and energy-saving strategies, this paper aspires to be a valuable resource for shaping the future trajectory of smart and sustainable buildings. In conclusion, this research paper emerges as a comprehensive exploration into the convergence of HVAC load prediction and energy-saving strategies within building automation systems. Through a meticulous examination of predictive technologies and a nuanced analysis of sustainable practices, the study seeks to illuminate the path towards more energy-efficient, resilient, and intelligent building operations
HVAC Zoning Control Systems and Building Energy Management
Get PDFThis research paper delves into the intricate integration of HVAC Zoning Control Systems with Building Energy Management (BEM), unveiling a symbiotic relationship poised to revolutionize the dynamics of modern building environments. HVAC zoning, an innovative approach to climate control, permits precise regulation of temperatures in distinct zones within a structure, offering unprecedented customization. Simultaneously, BEM, a strategic framework, is dedicated to orchestrating holistic energy consumption optimization. This study navigates the intersection of HVAC zoning and BEM, meticulously exploring their synergies, applications, and the potential transformative impacts on energy efficiency, occupant comfort, and operational cost savings within the built environment. The exploration begins with an in-depth analysis of HVAC zoning, elucidating its principles and mechanisms that empower tailored temperature control across diverse spatial regions within a building. The paper then shifts focus to BEM, unraveling its multifaceted strategies for comprehensive energy management. As the research unfolds, it investigates the convergence of these two systems, emphasizing their collaborative potential in achieving a harmonious balance between localized comfort and global energy efficiency. The synergies between HVAC zoning and BEM are examined through various lenses, including the adaptability of HVAC zoning strategies to BEM-driven optimization algorithms. The study explores how real-time data from HVAC zoning systems can inform BEM algorithms, contributing to dynamic and responsive energy management. Additionally, the research delves into the potential benefits arising from this integration, such as enhanced energy efficiency through targeted climate control, improved occupant comfort, and substantial operational cost savings. Case studies and practical applications are scrutinized to provide concrete examples of successful implementations, illustrating how the integration of HVAC zoning and BEM has manifested in diverse building environments. The paper concludes with insights into the future trajectory of this integration, shedding light on potential advancements, challenges, and avenues for further research and innovation. Through this comprehensive exploration, the research paper not only contributes to the current discourse on sustainable building practices but also provides valuable insights for professionals, researchers, and stakeholders aiming to optimize energy performance, occupant satisfaction, and operational efficiency in the ever-evolving landscape of modern construction and building management
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Machine Learning Algorithms for Predictive Maintenance in HVAC Systems
Get PDFThis comprehensive research endeavors to explore the integration of machine learning algorithms as a transformative solution for predictive maintenance in Heating, Ventilation, and Air Conditioning (HVAC) systems. The escalating demand for efficiency and sustainability in building practices has necessitated innovative approaches, and this study focuses on the proactive utilization of machine learning in HVAC system management. The investigation delves into the latest advancements in machine learning, offering a nuanced examination of its applications within HVAC systems. By predicting maintenance needs, these algorithms play a pivotal role in ensuring system reliability, optimizing energy efficiency, and contributing to substantial cost savings. The research not only scrutinizes the technical aspects of machine learning integration but also emphasizes its practical implications for HVAC systems. Real-world applications and case studies will be explored to illustrate the efficacy of machine learning algorithms in pre-emptive maintenance. The potential benefits identified in this study extend beyond immediate problem mitigation. Proactive maintenance, enabled by machine learning, promises to revolutionize the HVAC landscape by minimizing downtime, enhancing overall system performance, and significantly reducing operational costs. As the findings unfold, it becomes evident that the incorporation of machine learning algorithms in HVAC systems represents a crucial step towards the future of sustainable and resilient building management. The insights gleaned from this research are poised to guide industry professionals, researchers, and policymakers in embracing innovative strategies for predictive maintenance and, consequently, steering HVAC systems towards unparalleled efficiency and reliability
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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