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Existing methods estimate treatment effects from observational data and assume that covariates are all confounders. However, observed covariates may not directly represent confounding variables that influence both treatment and outcome. They always include variables that only affect the treatment or the outcome. In addition, for multi-dimensional binary treatments, disentangled methods are mainly designed for binary variables and ignore the impact of multi-cause treatment variables on the inference of latent factors. To address these two issues, based on variable decomposition and proxy inference, we propose the Disentangled Latent Factors for Multi-cause Treatment Estimation (DEMTE) algorithm. It utilizes an identifiable autoencoder to infer and disentangle latent factors based on the joint distribution of variables in observational data. DEMTE evaluates the treatment effect on the disentangled factors. Synthetic experiments and semi-synthetic experiments demonstrate the effectiveness of the inference and disentanglement techniques and our method achieves more accurate treatment effect estimation.

ABSTRACT Background and Aims The COVID‐19 pandemic restriction impacted physical or face‐to‐face interactions, leading to an upsurge in the use of information technology (IT). This necessitated the adoption of various remote healthcare services including telehealth. This study aimed to examine the role of technological and socio‐demographic factors in enhancing telemedicine literacy. Methods This cross‐sectional study was conducted in Trinidad and Tobago (T&T) in 2022 involving 528 participants. The study employed the Alkire‐Foster multidimensional method to measure the telemedicine literacy of participants. The multidimensional telemedicine literacy index was constructed using nine indicators spread across three dimensions (i.e., knowledge, attitude/perception, and practice dimensions), where a threshold of 0.5 was employed to identify those with adequate knowledge to be considered literate in telemedicine. The technological component was captured using IT ability. Participants completed a 31‐item questionnaire administered electronically via iPads. A “Yes” response was coded as 1 and “No” as 0. Results Most respondents (62%) were aged 21–40, with 60% identifying as female. Most were Afro‐Trinidadian (54.46%), urban residents (84%), employed (80%), and earned a high income (87%). Overall, participants demonstrated a high perceived IT ability, with a mean score of 0.918 (SD = 0.27). Urban residents exhibited IT skills that were 10% superior to those of rural residents; however, this did not necessarily translate into higher telemedicine literacy. Gender differences were observed, with males reporting IT skills 3% higher than females. Notably, IT ability was a significant predictor of telemedicine literacy, particularly among females and urban residents. Additionally, individuals with postgraduate qualifications, Indo‐Trinidadians, and Christians exhibited significantly higher telemedicine literacy. Conclusions This study emphasizes the pivotal role of IT ability in telemedicine literacy across varied socio‐demographic groups in T&T. To promote healthcare for all, interventions targeting digital literacy are crucial to ensure equitable access and enhance the reach of telemedicine in T&T.

Standard MCMC methods can scale poorly to big data settings due to the need to evaluate the likelihood at each iteration. There have been a number of approximate MCMC algorithms that use sub-sampling ideas to reduce this computational burden, but with the drawback that these algorithms no longer target the true posterior distribution. We introduce a new family of Monte Carlo methods based upon a multidimensional version of the Zig-Zag process of [Ann. Appl. Probab. 27 (2017) 846–882], a continuous-time piecewise deterministic Markov process. While traditional MCMC methods are reversible by construction (a property which is known to inhibit rapid convergence) the Zig-Zag process offers a flexible nonreversible alternative which we observe to often have favourable convergence properties. We show how the Zig-Zag process can be simulated without discretisation error, and give conditions for the process to be ergodic. Most importantly, we introduce a sub-sampling version of the Zig-Zag process that is an example of an exact approximate scheme, that is, the resulting approximate process still has the posterior as its stationary distribution. Furthermore, if we use a control-variate idea to reduce the variance of our unbiased estimator, then the Zig-Zag process can be super-efficient: after an initial preprocessing step, essentially independent samples from the posterior distribution are obtained at a computational cost which does not depend on the size of the data.

Post-acute infection syndromes may develop after acute viral disease 1 . Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions 2 – 4 . However, the biological processes that are associated with the development and persistence of these symptoms are unclear. Here 275 individuals with or without long COVID were enrolled in a cross-sectional study that included multidimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with long COVID. Marked differences were noted in circulating myeloid and lymphocyte populations relative to the matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with long COVID. Furthermore, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with long COVID, particularly Epstein–Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with long COVID. Integration of immune phenotyping data into unbiased machine learning models identified the key features that are most strongly associated with long COVID status. Collectively, these findings may help to guide future studies into the pathobiology of long COVID and help with developing relevant biomarkers. Individuals with long COVID show marked biological changes in cortisol and immune factors relative to convalescent populations.

Background Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP). Methods To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB). Results The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions. Conclusions We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being.

With the rapid development of intelligent and electronic social development, people have proposed new and higher requirements for the multi-dimensional and comprehensive measurement of carrier acceleration. Based on the emergence of a new multi-dimensional sensor structure, this paper proposes a six-dimensional accelerometer measurement verification device and a traceability method for its value. Using the six-dimensional characteristics of the standard shaker, multiple accelerometers collect the multi-dimensional acceleration signal, and the six-dimensional acceleration of the standard shaker is obtained by solving the matrix and processing the acceleration signal. This method provides a direct verification method with strong compatibility and high feasibility, which can solve the problems caused by the current verification methods.

In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW’s efficiency, together with multiple empirical studies showing that DTW-based classifiers at least equal (and generally surpass) the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways, dependent or independent warping. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant. In this work, we show that this is not the case. The two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we should trust at the time of classification. Our method allows us to ensure that classification results are at least as accurate as the better of the two rival methods, and, in many cases, our method is significantly more accurate. We demonstrate our ideas with the most extensive set of multi-dimensional time series classification experiments ever attempted.

Aiming at the harbor environment, the target detection accuracy of fishing vessels is low, and it is prone to the problems of fishing vessel misdetection and omission detection. In this paper, we propose a fishing vessel target detection algorithm called ASDNet based on YOLOX. Firstly, an Adaptive Spatial Attention Module (ASAM) was designed and used to improve the detection of fishing vessel targets; secondly, a two-branch backbone network was designed for multidimensional fishing vessel feature extraction. Meanwhile, a bilateral enhanced fusion strategy (BFFS) is designed to fuse the branch features to improve the characterization ability of the network; finally, the loss function is improved by introducing the Focal-CIOU loss bounding box loss function to reduce the effects of the detection position deviation of the fishing vessel target and the overlap of the vessel hull to improve the detection performance. The above methods are validated using the homemade fishing vessel dataset, and the results show that the precision rate (P) and recall rate (R) are greatly improved. The average precision rate (mAP@50-95) value reaches 80.25%, which is 2.39% higher than that of the 77.86% of the YOLOX. It significantly improves the precision of the detection, meets the requirements of the performance of the target detection of the fishing vessel, and has certain practical significance in engineering.

ChatGPT, an AI-based chatbot with automatic code generation abilities, has shown its promise in improving the quality of programming education by providing learners with opportunities to better understand the principles of programming. However, limited empirical studies have explored the impact of ChatGPT on learners’ programming processes. This study employed a quasi-experimental design to explore the possible impact of ChatGPT-facilitated programming mode on college students’ programming behaviors, performances, and perceptions. 82 college students were randomly divided into two classes. One class employed ChatGPT-facilitated programming (CFP) practice and the other class utilized self-directed programming (SDP) mode. Mixed methods were utilized to collect multidimensional data. Data analysis uncovered some intriguing results. Firstly, students in the CFP mode had more frequent behaviors of debugging and receiving error messages, as well as pasting console messages on the website and reading feedback. At the same time, students in the CFP mode had more frequent behaviors of copying and pasting codes from ChatGPT and debugging, as well as pasting codes to ChatGPT and reading feedback from ChatGPT. Secondly, CFP practice would improve college students’ programming performance, while the results indicated that there was no statistically significant difference between the students in CFP mode and the SDP mode. Thirdly, student interviews revealed three highly concerned themes from students' user experience about ChatGPT: the services offered by ChatGPT, the stages of ChatGPT usage, and experience with ChatGPT. Finally, college students’ perceptions toward ChatGPT significantly changed after CFP practice, including its perceived usefulness, perceived ease of use, and intention to use. Based on these findings, the study proposes implications for future instructional design and the development of AI-powered tools like ChatGPT.

This paper describes the development of a multidimensional self-report measure of interoceptive body awareness. The systematic mixed-methods process involved reviewing the current literature, specifying a multidimensional conceptual framework, evaluating prior instruments, developing items, and analyzing focus group responses to scale items by instructors and patients of body awareness-enhancing therapies. Following refinement by cognitive testing, items were field-tested in students and instructors of mind-body approaches. Final item selection was achieved by submitting the field test data to an iterative process using multiple validation methods, including exploratory cluster and confirmatory factor analyses, comparison between known groups, and correlations with established measures of related constructs. The resulting 32-item multidimensional instrument assesses eight concepts. The psychometric properties of these final scales suggest that the Multidimensional Assessment of Interoceptive Awareness (MAIA) may serve as a starting point for research and further collaborative refinement.