Non-invasive, rapid, on-site detection and identification of body fluids is highly desired in forensic investigations. The use of fluorescence-based methods for body fluid identification, have so far remain relatively unexplored. As such, the fluorescent properties of semen, serum, urine, saliva and fingermarks over time were investigated, by means of fluorescence spectroscopy, to identify specific fluorescent signatures for body fluid identification. The samples were excited at 81 different excitation wavelengths ranging from 200 to 600 nm and for each excitation wavelength the emission was recorded between 220 and 700 nm. Subsequently, the total emitted fluorescence intensities of specific fluorescent signatures in the UV–visible range were summed and principal component analysis was performed to cluster the body fluids. Three combinations of four principal components allowed specific clustering of the body fluids, except for fingermarks. Blind testing showed that 71.4% of the unknown samples could be correctly identified. This pilot study shows that the fluorescent behavior of ageing body fluids can be used as a new non-invasive tool for body fluid identification, which can improve the current guidelines for the detection of body fluids in forensic practice and provide the robustness of methods that rely on fluorescence.
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ObjectiveThe Plants for Joints (PFJ) intervention significantly improved pain, stiffness, and physical function, and metabolic outcomes, in people with metabolic syndrome-associated osteoarthritis (MSOA). This secondary analysis investigated its effects on body composition.MethodIn the randomized PFJ study, people with MSOA followed a 16-week intervention based on a whole-food plant-based diet, physical activity, and stress management, or usual care. For this secondary analysis, fat mass, muscle mass, and bone mineral density were measured using dual-energy X-ray absorptiometry (DEXA) for all participants. Additionally, in a subgroup (n = 32), hepatocellular lipid (HCL) content and composition of visceral adipose tissue (VAT) were measured using magnetic resonance spectroscopy (MRS). An intention-to-treat analysis with a linear-mixed model adjusted for baseline values was used to analyse between-group differences.ResultsOf 66 people randomized, 64 (97%) completed the study. The PFJ group experienced significant weight loss (−5.2 kg; 95% CI –6.9, −3.6) compared to controls, primarily from fat mass reduction (−3.9 kg; 95% CI –5.3 to −2.5). No significant differences were found in lean mass, muscle strength, or bone mineral density between groups. In the subgroup who underwent MRI scans, the PFJ group had a greater reduction in HCL (−6.5%; 95% CI –9.9, 3.0) compared to controls, with no observed differences in VAT composition.ConclusionThe PFJ multidisciplinary intervention positively impacted clinical and metabolic outcomes, and appears to significantly reduce body fat, including liver fat, while preserving muscle mass and strength.
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Explainable Artificial Intelligence (XAI) aims to provide insights into the inner workings and the outputs of AI systems. Recently, there’s been growing recognition that explainability is inherently human-centric, tied to how people perceive explanations. Despite this, there is no consensus in the research community on whether user evaluation is crucial in XAI, and if so, what exactly needs to be evaluated and how. This systematic literature review addresses this gap by providing a detailed overview of the current state of affairs in human-centered XAI evaluation. We reviewed 73 papers across various domains where XAI was evaluated with users. These studies assessed what makes an explanation “good” from a user’s perspective, i.e., what makes an explanation meaningful to a user of an AI system. We identified 30 components of meaningful explanations that were evaluated in the reviewed papers and categorized them into a taxonomy of human-centered XAI evaluation, based on: (a) the contextualized quality of the explanation, (b) the contribution of the explanation to human-AI interaction, and (c) the contribution of the explanation to human- AI performance. Our analysis also revealed a lack of standardization in the methodologies applied in XAI user studies, with only 19 of the 73 papers applying an evaluation framework used by at least one other study in the sample. These inconsistencies hinder cross-study comparisons and broader insights. Our findings contribute to understanding what makes explanations meaningful to users and how to measure this, guiding the XAI community toward a more unified approach in human-centered explainability.
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The impacts of tourism on destinations and the perceptions of local communities have been a major concern both for the industry and research in the past decades. However, tourism planning has been mainly focused on traditions that promote the increase of tourism without taking under consideration the wellbeing of both residents and visitors. To develop a more sustainable tourism model, the inclusion of local residents in tourism decision-making is vital. However, this is not always possible due to structural, economic and socio-cultural restrictions that residents face resulting to their disempowerment. This study aims to explore and interpret the formal processes around tourism decision-making and community empowerment in urban settings. The research proposes a comparative study of three urban destinations in Europe (The Hague in the Netherlands, San Sebastian in Spain and, Ioannina in Greece) that experience similar degree of tourism growth. The proposed study will use a design-based approach in order to understand tourism decision-making and what empowers or disempowers community participation within the destinations. Based on the findings of primary and secondary data, a community empowerment model will be applied in one the destinations as a pilot for resident engagement in tourism planning. The evaluation of the pilot will allow for an optimized model to be created with implications for tourism planning at a local level that can contribute to sustainable destinations that safeguard the interests of local residents and tourists.
Within TIND, Christian Roth studies the training of interactive narrative designers with the goal of developing teaching methods and learning tools for artists and designers to enable the creation of more effective artefacts. Interactive Narrative Design (IND) is a complex and challenging interdisciplinary field introducing new affordances in technique and user-experience. This requires practice-based research for further development of the educational format, demonstrating its potential while identifying and overcoming common learners’ challenges. This project aims to develop a framework for the design and evaluation of meaningful interactive narrative experiences that effectively stimulate a variety of cognitive and emotional responses such as reflection, insight, understanding, and potential behavior change. It provides tools, methods and activities to enable aspiring or practicing narrative designers through an interdisciplinary approach, including game design, immersive theatre, behavioral and cognitive psychology, and the learning sciences. HKU education means to prepare students for success in the creative industries and IND plays an important role for current and future jobs in education, arts and entertainment. IND has the potential to create an emotional impact and spark transformative change by offering agency, defined as the ability to influence narrative progression and outcomes in a meaningful way. This enables interactors to feel the weight of their own choices and their consequences, to explore different perspectives, and to more thoroughly understand complex multi-stakeholder issues, which could have significant impact on the success of emerging artistic, and learning applications. The research project is directly embedded in the curriculum of the HKU school Games & Interaction with annual educational offerings such as the Minor Interactive Narrative Design (MIND) and HKU wide broad seminars. Course evaluation and literature research will be used to create new and adjusted training for different HKU schools and the industry. Outcomes will be shared via an interactive website and events.
