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London Vignettes [Screendance]
No full textAn embodied site exploration, engaging with the dynamic flow and the urban textures of the city. Filmed on locations across London.Direction: Ana Baer & Heike SalzerCinematography and Editing: Ana BaerChoreography: Ana Baer & Heike Salzer in collaboration with the dancersDance: Chen Siqi, Chen Daini, Du Yu, Hu Fang, , Hu Zhouran, Jiang Yunxian, Li Dailin, Li Jiaying, Lin Lisha, Liu Yuhan, Pan Ziyu, Song Jianing, Song Ke’Yan, Yu Xizi,Yuan Rong and Zhang XinyuMusic Composition and Mixing: Richard HallProduction: WECreate Production & Lalitaraja ChandlerLength: 5.22minAn embodied site exploration, engaging with the dynamic flow and the urban textures of the city. Filmed on locations across London. All filming on location in London, United Kingdom:Leake Street Arches, Barbican Centre, Charterhouse Square, Canary Wharf, Tate Modern Garden, Bankside, Millennium Bridge and Bear Gardens Supported by: University of Roehampton, School of Arts, Humanities, and Social Sciences. London, MA Dance Practice and Performance, and BA Dance, London, UKTexas State Department of Theatre and Dance, San Marcos, TX, US<br/
“Be open to all those ways that people can live their lives”: LGBTQ+ client recommendations for adapting Emotionally Focused Couple Therapy
No full textEmotionally focused couple therapy (EFCT) is an empirically supported treatment for relationship distress. Yet, despite EFCT's substantial evidence base, to date, there have been no studies that have integrated LGBTQ+ clients' experiences and therapeutic needs into the EFCT process. Thirty-five EFCT clients participated in theater testing focus groups to generate client recommendations for the use of EFCT for LGBTQ+ relationships. Data were analyzed using thematic analysis. Participants drew on their own lived experiences, their experiences receiving EFCT, and EFCT video observation to make recommendations about the use of EFCT for LGBTQ+ relationships
Evidence on antidepressant withdrawal: an appraisal and reanalysis of a recent systematic review
No full textThere has been debate about the frequency and severity of antidepressant withdrawal effects. We set out to appraise and reanalyze an influential systematic review by Henssler and colleagues that concluded that withdrawal effects are not particularly common and rarely severe. We repeated the meta-analysis, including only studies where data were derived from systematic measures of withdrawal symptoms. Most data in the Henssler review are derived from pharmaceutical industry-sponsored efficacy studies in which withdrawal was a minor consideration. Shortcomings of the review include the use of spontaneously reported adverse events to estimate withdrawal symptoms, potential misclassification of withdrawal symptoms as relapse, inclusion of data from retrospective case-note studies, short duration of prior antidepressant use, short observation periods, the overlooking of differences between placebo and drug withdrawal effects, and the use of questionable proxies for severe withdrawal. There were also discrepancies and uncertainties in some figures used. In our reanalysis, we included only the five studies that used a systematic and relevant method to assess the incidence of any withdrawal symptom. Prior treatment was short-term (12 weeks or less) in all but one of these. The pooled percentage was 55% (95% confidence interval, CI, 31% to 81%; = 601) without subtracting nocebo effects, with high heterogeneity. Henssler's review is based on unreliable data and does not provide an adequate basis for the evaluation of antidepressant withdrawal effects. Further good-quality research on antidepressant withdrawal is required
Research directions for using LLM in software requirement engineering: a systematic review
Get PDFNatural Language Processing (NLP) and Large Language Models (LLMs) are transforming the landscape of software engineering, especially in the domain of requirement engineering. Despite significant advancements, there is a notable lack of comprehensive survey papers that provide a holistic view of the impact of these technologies on requirement engineering. This paper addresses this gap by reviewing the current state of NLP and LLMs in requirement engineering, highlighting their effects on improving requirement extraction, analysis, and specification. We analyze trends in software requirement engineering papers, noting an upward trajectory in the application of LLMs in software engineering tasks. This review underscores the critical role of requirement engineering in the software development lifecycle and emphasizes the transformative potential of LLMs in enhancing precision and reducing ambiguities in requirement specifications. Our findings indicate a growing interest and significant progress in leveraging LLMs for various software engineering tasks, particularly in requirement engineering. This paper aims to provide a foundation for future research and identify key challenges and opportunities in this evolving field
Advancing subterranean conservation through Global Research on eDNA in Groundwaters (GReG).
No full textReal-time e-health framework for efficient AI-driven disability monitoring using secured Internet of Medical Things:Real-time e-health framework for efficient AI-driven..
No full textThe integration of Internet of Medical Things (IoMT) technology is revolutionizing patient monitoring by enabling real-time and remote assessment. It utilized various health sensors and wireless technologies to communicate with patients and transmit health records to cloud systems for analysis and disease identification. This study proposes an AI-driven framework for disability detection using secured IoMT, leveraging motion analysis, efficient data routing, and secure cloud storage. The framework captures motion data through simulated IoT-enabled wearable devices, represented by open-source datasets such as publicly available PAMAP2 and MHEALTH. To identify movement patterns connected to disability and train the model, the motion data is first preprocessed using noise reduction and normalization techniques. The proposed framework utilizes a Support Vector Machine to classify the patterns due to its lightweight features, ultimately providing a rapid, real-time analysis in crucial health circumstances. It processes the extracted features and predicts whether a movement pattern indicates normal or disability related human behavior. Moreover, health records are transmitted from IoMT devices to the cloud using network optimization by exploring LPWAN/LoRaWAN protocols, ensuring energy-efficient, low-latency communication. By combining intelligent learning with optimized network protocols and secure cloud integration, the proposed framework gives a practical approach to the healthcare domain to access timely insights into patient health. The proposed framework is simulated in NS3 for performance evaluation and provides significant outcomes as compared to existing approaches in terms of energy consumption by an average of 47%, attack detection rate by an average of 35%, packet drop ratio by an average of 48% and false positive rate by an average of 42%
