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This review proposes a concise literature review aimed at identifying the current body of knowledge on the adoption of Social Networks in crisis management. The major input is a structured research question based on the initial reading about the topic. Before the recent pandemic, most literature focused on local crises, with relatively few exceptions. Additionally, self-organising systems are spontaneously established between people who are affected by a crisis. The fundamental assumption underlying this study is the huge potential of Social Networks in the field of crisis management. That is supported, directly or indirectly, by a number of previous studies, which emphasise how effective adoption leads to better decision-making for crisis managers and local communities. Among the identified challenges is the need to integrate official communication by emergency agencies with citizen-generated content in a contest for credibility and trustworthiness. In certain cases, it has been reported that there is a lack of specific competence, knowledge, and expertise, as well as a lack of sufficient policies and guidelines for the use of Social Networks. Those challenges need to be framed by considering the classic difficulties of providing timely and accurate information to deal with fake news, unverified or misleading information, and information overload. Bridging major gaps through advanced analytics and AI-based technology is expected to provide a key contribution to establishing and safely enabling the practice of effective and efficient communication. This technology can help contrast dissonant mental models, which are often fostered by Social Networks, and enable shared situational awareness. Future research may take a closer look at AI technology and its impact on the role of Social Networks in managing crises.

Given that the implementation of renewable technologies has some key bottlenecks in adoption, this topic has been explored. Particularly, we are reviewing existing theories and models to understand their fit for changing social structures and evolving world contexts. This review begins with an introduction followed by a background study on renewable energy technology (RET). We have employed a mixed-approach methodology to synthesize the relevant literature. The review comprises a summary and comparison of some existing theories and models such as TAM, TRA, and UTAUT, elucidating factors influencing technology adoption processes. Additionally, the review discusses the scope for future research, emphasizing the need for more nuanced frameworks that account for contextual intricacies and emerging trends in renewable energy adoption. Ultimately, the review concludes with insights into the ongoing discourse surrounding energy technology acceptance and recommendations on the inclusion of current world views in the scope for future study.

This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis.

Occupant behaviors are one of the most dominant factors that influence building energy use. Understanding the influences from building occupants can promote the development of energy–efficient buildings. This paper quantifies the impact of different occupant behavior information on building energy model (BEM) from multiple perspectives. For this purpose, an occupant behavior model that uses agent–based modeling (ABM) approach is implemented via co-simulation with a BEM of an existing commercial building. Then, actual occupant behavior data in correspondence to ABM output, including operations on window, door, and blinds in selected thermal zones of the building are recorded using survey logs. A simulation experiment is conducted by creating three BEMs with constant, actual, and modeled occupant behavioral inputs. The analysis of the simulation results among these scenarios helps us gain an in–depth understanding of how occupant behaviors influence building performance. This study aims to facilitate robust building design and operation with human–in–the–loop system optimization.