Past research has suggested that eye movements can be used to uncover perpetrators of a crime to some extent (around 65 % accuracy). We extended this work to examine whether similar or better results could be obtained for eyewitnesses by employing a data-driven eye-tracking approach. We expect that participants who saw the crime before: (1) look more at where the crime happened, (2) differ in the frame-by-frame viewing location, and (3) differ in the frame-by-frame variability in viewing location, compared to non-exposed participants when viewing the now-empty crime scene. Machine learning was used to classify the eye movements of exposed participants (who had seen the knife crime the day before, n = 34) and non-exposed participants (who had not seen the crime before, n = 25) while both groups viewed a video of the now-empty crime scene. Eye-tracking showed that participants who saw the crime previously were more consistent in their viewing patterns and looked more at the perpetrator regions when viewing the same, but empty, crime scene. Fixated regions predicted group membership with moderate accuracy (AUC = 0.758), but the consistency in viewing patterns led to very good classification of observers into exposed and non-exposed participants (AUC = 0.898), although some group differences remained while and after viewing the crime. These results suggest that eye movement patterns can be primed by previous observations, persisting after two days. While currently theoretical, these results may be developed as an implicit measure for detecting previous crime scene exposure through visual attention patterns.
Background Many patients with pelvic floor complaints, including urinary and fecal incontinence, micturition and defecation problems, pelvic organ prolapses, pelvic pain, and painful intercourse, receive pelvic physical therapy treatment. Based on available evidence, pelvic floor complaint profiles, including these complaints, have not been described using real-world pelvic physical therapy records and multi-complaint clustering methods. Pelvic floor complaint profiles may help to enhance therapists’ clinical reasoning and patients’ understanding and self-disclosure. Aim This retrospective file review study explores preliminary complaint profiles and associations between pelvic floor complaints. Method Pelvic physical therapists entered recorded pelvic floor complaints from self-selected pregnant, parous, and nulliparous patients’ files in an online survey. Complaints were extracted from clinical records and coded as binary (recorded/not recorded). Descriptive statistics and correlations were calculated, and latent class analysis was performed to gain insight into pelvic floor complaint profiles and their associations with pregnancy and parity. Model selection was based on BIC/log-likelihood statistics and expert clinical review. Results A model with five profiles was selected based on statistical and theoretical selection criteria. One profile showed the highest probabilities for recorded pelvic pain, one for recorded defecation and micturition problems, one for recorded fecal incontinence and defecation problems, another for recorded pelvic organ prolapses and urinary incontinence, and one for recorded painful intercourse and micturition problems. The first and second profiles appeared most characteristic for pregnant patients, the third and fourth for parous patients, and the fifth for nulliparous patients. Conclusion The identified profiles may facilitate the inclusion and consideration of potential contributing factors for combined pelvic floor complaints in clinical practice and scientific research. Addressing pelvic floor complaints in profiles may help pelvic healthcare providers during their history-taking, enhance multidisciplinary treatment approaches, and help patients understand experienced combinations of pelvic floor complaints. This may ultimately benefit women’s pelvic health.
Rural tourism development presents a significant avenue for economic growth and cultural preservation. However, it negatively impacts the environment through increased human activity, infrastructure development, and heightened waste generation. This study aims to use the concept of environmental sustainability to address negative environmental impact of tourism. Using the Integrated Structural Modeling-Matriced'Impacts Croises Multiplication Appliqu & eacute;e & agrave; un Classement (ISM-MICMAC) analysis technique, we assess and model barriers to sustainable rural tourism development. Key barriers identified include high infrastructure costs, insufficient infrastructure, and water scarcity, each with significant driving power. The ISM-MICMAC analysis reveals interdependencies among these factors, providing insights for policymakers and stakeholders. Our findings underscore the importance of integrating environmental sustainability into rural tourism planning and management, offering strategies to overcome barriers and promote sustainable development.
Background As researchers rush to investigate the potential of AI tools like ChatGPT to enhance learning, well-documented pitfalls threaten the validity of this emerging research. Issues of media comparison research, where the confounding of instructional methods and technological affordances is unrecognised, may render effects uninterpretable. Objectives Using a recent meta-analysis by Deng et al. (Computers & Education, 227, 105224) as an example, we revisit key insights from the media/methods debate to highlight recurring conceptual challenges in ChatGPT efficacy studies. Methods This conceptual article contrasts nascent ChatGPT research with the more established literature on Intelligent Tutoring Systems to identify three non-negotiable considerations for interpretable effects: (1) descriptions of the precise nature of the experimental treatment and (2) the activities of the control group, as well as (3) outcome measures as valid indicators of learning. To provide some initial evidence, we audited a subset of primary experiments included in Deng et al.'s meta-analysis, demonstrating that only a small minority of studies satisfied all three non-negotiable considerations. Results and Conclusions Loosely defined treatments, mismatched or opaque controls, and outcome measures with unclear links to durable learning obscure causal claims of this emerging literature. Observed gains cannot, at this time, be confidently attributed to ChatGPT, and meta-analytics effect sizes may over- or understate its benefits. Progress, we argue, will require rigorous designs, transparent reporting, and a critical stance toward "fast science."
ABSTRACTTo attain global ecological sustainability within the framework of ecological modernization, this study scrutinizes the multifaceted interaction among environmental‐related patent technologies (ERPT), natural resources, energy consumption (both renewable and nonrenewable energy usage) and globalization on the ecological footprint. To assess the novel aspects of the ongoing objective, this study employs Disc/Kraay, fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS) regression to generate comparative results across a panel of technologically advanced countries and resource‐rich least‐developed countries from 1990 to 2022. The empirical outcomes indicate that economies leading in eco‐friendly technologies and renewable sources of energy enhance environmental sustainability by minimizing their ecological footprint over time. In contrast, countries that rely heavily on natural resource extraction to meet energy demands tend to exacerbate ecological degradation. The process of globalization demonstrates the same trend for both developed and developing countries over the long term significantly elevating the level of ecological footprint. The Dumitrescu and Hurlin causality test discloses a reverse causal connection among concerning variables such as natural resources, ERPT, and energy reliance excluding globalization over the extended duration. The recommendations derived from the results suggest that governments in developing countries should strategically promote the adoption and dissemination of innovative, eco‐friendly resource extraction technologies. Meanwhile, governments in developed countries should focus on overhauling their ecological policies to play a crucial role in supporting and advancing ecological sustainability efforts in developing nations.
