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Presents the state of the technology, from fundamentals to new materials and applications Today's electronic devices, computers, solar cells, printing, imaging, copying, and recording technology, to name a few, all owe a debt to our growing understanding of the photophysics and photochemistry of polymeric materials. This book draws together, analyzes, and presents our current understanding of polymer photochemistry and photophysics. In addition to exploring materials, mechanisms, processes, and properties, the handbook also highlights the latest applications in the field and points to new developments on the horizon. Photochemistry and Photophysics of Polymer Materials is divided into seventeen chapters, including: Optical and luminescent properties and applications of metal complex-based polymers Photoinitiators for free radical polymerization reactions Photovoltaic polymer materials Photoimaging and lithographic processes in polymers Photostabilization of polymer materials Photodegradation processes in polymeric materials Each chapter, written by one or more leading experts and pioneers in the field, incorporates all the latest findings and developments as well as the authors' own personal insights and perspectives. References guide readers to the literature for further investigation of individual topics. Together, the contributions represent a series of major developments in the polymer world in which light and its energy have been put to valuable use. Not only does this reference capture our current state of knowledge, but it also provides the foundation for new research and the development of new materials and new applications.

\"New York Times Silicon Valley beat reporter Cade Metz has an insider's perspective on the greatest tech story of our time--a story that no one else has been in a position to tell\"-- Provided by publisher.

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice. We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review. Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering. Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Background Billions of dollars are lost annually in health research that fails to create meaningful benefits for patients. Engaging in research co-design – the meaningful involvement of end-users in research – may help address this research waste. This rapid overview of reviews addressed three related questions, namely (1) what approaches to research co-design exist in health settings? (2) What activities do these research co-design approaches involve? (3) What do we know about the effectiveness of existing research co-design approaches? The review focused on the study planning phase of research, defined as the point up to which the research question and study design are finalised. Methods Reviews of research co-design were systematically identified using a rapid overview of reviews approach (PROSPERO: CRD42019123034). The search strategy encompassed three academic databases, three grey literature databases, and a hand-search of the journal Research Involvement and Engagement . Two reviewers independently conducted the screening and data extraction and resolved disagreements through discussion. Disputes were resolved through discussion with a senior author (PB). One reviewer performed quality assessment. The results were narratively synthesised. Results A total of 26 records (reporting on 23 reviews) met the inclusion criteria. Reviews varied widely in their application of ‘research co-design’ and their application contexts, scope and theoretical foci. The research co-design approaches identified involved interactions with end-users outside of study planning, such as recruitment and dissemination. Activities involved in research co-design included focus groups, interviews and surveys. The effectiveness of research co-design has rarely been evaluated empirically or experimentally; however, qualitative exploration has described the positive and negative outcomes associated with co-design. The research provided many recommendations for conducting research co-design, including training participating end-users in research skills, having regular communication between researchers and end-users, setting clear end-user expectations, and assigning set roles to all parties involved in co-design. Conclusions Research co-design appears to be widely used but seldom described or evaluated in detail. Though it has rarely been tested empirically or experimentally, existing research suggests that it can benefit researchers, practitioners, research processes and research outcomes. Realising the potential of research co-design may require the development of clearer and more consistent terminology, better reporting of the activities involved and better evaluation.

We propose and explore the possibility that language models can be studied as effective proxies for specific human subpopulations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are often treated as uniform properties of the models. We show that the “algorithmic bias” within one such tool—the GPT-3 language model—is instead both fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups. We term this property algorithmic fidelity and explore its extent in GPT-3. We create “silicon samples” by conditioning the model on thousands of sociodemographic backstories from real human participants in multiple large surveys conducted in the United States. We then compare the silicon and human samples to demonstrate that the information contained in GPT-3 goes far beyond surface similarity. It is nuanced, multifaceted, and reflects the complex interplay between ideas, attitudes, and sociocultural context that characterize human attitudes. We suggest that language models with sufficient algorithmic fidelity thus constitute a novel and powerful tool to advance understanding of humans and society across a variety of disciplines.

Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care. This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application. We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms \"conversational agents,\" \"conversational AI,\" \"chatbots,\" and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis. The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education. The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence-driven, and smartphone app-delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents' formats, focusing on their acceptability, safety, and effectiveness.

This study recommends a definition of \"decision support\" that emphasizes communication rather than translation and a strategy by which the small NOAA Sectoral Applications Research program can advance decision support. The book emphasizes that seasonal climate forecasts provide fundamentally new kinds of information and that integrating this information into real-world decisions will require social innovations that are not easily accomplished. It recommends that the program invest in (a) research to identify and foster the innovations needed to make information about climate variability and change more usable in specific sectors, including research on the processes that influence success or failure in the creation of knowledge-action networks for making climate information; (b) workshops to identify, catalyze, and assess the potential of knowledge-action networks in particular resource areas or decision domains; and (c) pilot projects to create or enhance these networks for supporting decisions in climate-affected sectors. It recommends that evaluation of the program be addressed with a monitoring approach.

The relevance and prominence of the partial least squares structural equation modeling (PLS-SEM) method has recently increased in higher education research, especially in explanatory and predictive studies. We therefore first aim to assess previous PLS-SEM applications by providing a systematic review; second, we aim to highlight and summarize important guidelines for conducting a rigorous PLS-SEM analysis of the current state of results reporting in higher education journals. Specifically, this study focuses on empirical PLS-SEM applications in 14 major higher education journals indexed in the Thomson Reuters Web of Science and in the Elsevier-Scopus databases between 1999 and 2018. We initially identified 49 relevant papers published in 10 higher education journals. Based on these papers’ generally followed guidelines, we thereafter identified various issues related to data screening, model characteristics, measurement model evaluation, structural model evaluation, and the application of state-of-the-art PLS-SEM advanced methods requiring particular attention. Furthermore, we recommend recent guidelines to improve PLS-SEM applications and practices, besides providing specific suggestions regarding utilizing the method’s strength in terms of relevant higher education research questions. Our findings remind researchers, reviewers, and journal editors to remain vigilant, should help them avoid inaccuracies in future publications, and ensure rigor.