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BackgroundActivities directed at attaining health goals are a major part of the daily lives of those fighting chronic diseases. A proliferating population of patients with chronic diseases are participating in web-based patient communities, wherein they can exchange health information and pursue health goals with others virtually. ObjectiveIn this study, we aimed to understand the effect of participation in social media–enabled web-based patient communities on health goal attainment. In particular, we studied the antecedents of health goal attainment in terms of social support and self-reflection in web-based patient communities. MethodsThis data set consists of web-based health management activities of 392 patients across 13 health support groups, that is, groups with medical issues such as high blood pressure, diabetes, and breast cancer; the data of the activities were collected from a leading web-based patient community. Content analysis was used to code the social interactions among the patients on the web-based platform. Cox regression for survival analysis was used to model the hazard ratio of health goal attainment. ResultsOur analysis indicated that emotional support from web-based patient communities can increase patients’ probability of achieving their goals (hazard ratio 1.957, 95% CI 1.416-2.706; P

The main purpose of unbiased cross-domain object detection is to utilize the knowledge of the source domain to the maximum extent through knowledge distillation, and reduce the cross-domain gap of the model through domain adaptation. However, the pseudo labels generated by the mean teacher method commonly used for unbiased cross-domain object detection are not reliable, resulting in significant domain bias issues between teacher and student models. Therefore, inspired by the invariance of phase information in Fourier transform, this paper proposes the Fourier augmentation unbiased mean teacher (FAUMT) model based on the mean teacher. This paper utilizes the invariance of Fourier phase information to design an amplitude mixing data augmentation (AMDA) module, which can effectively mix phase information between the source and target domains to achieve data augmentation. And data augmentation will generate additional noise, thus this paper designs two consistency losses to ensure the consistency of predictions before and after data augmentation. In addition, to balance the cross-domain bias between the source and target domains during model training, this paper also designs a multi-layer adversarial learning (MAL) module, with the aim of domain alignment of pixel level features at different levels. On three benchmark datasets Cilpart1K, Watercolor2K and Comic2K, the mAP of proposed method achieves 47.5%, 58.9% and 46.1%, respectively, outperforming other algorithms.

[Objective] The “3060” dual carbon target introduces new requirements for the low-carbon development of China’s coal-fired power industry. With decreasing carbon quota limits, the focus on low-carbon transformation in this industry has increased among industry and academia. The carbon trading mechanism directs coal-fired power enterprises to reduce emissions and innovate through policy constraints. By enhancing energy efficiency and reducing emissions, it is vital in driving coal-fired power enterprises toward a low-carbon model. However, the micro-level driving mechanism of this transformation process remains underexplored [Methods] This study uses a sample of 2628 coal-fired power enterprises in China from 2022, employing reinforcement learning theory and multi-agent models. Through the Netlogo platform, it simulates and analyzes the interactive and collaborative effects between the carbon market, government, and coal-fired power enterprises with varying carbon reduction tendencies under the carbon trading mechanism. [Results] The research reveals that: (1) The carbon trading mechanism effectively promotes technological carbon reduction in coal-fired power enterprises, achieving both environmental and economic benefits, with significant impacts expected from 2032 onwards. (2) The carbon reduction tendency of these enterprises is influenced by the degree of carbon trading implementation. Before reaching the carbon peak target by 2030, these enterprises did not actively employ carbon reduction technologies as anticipated. (3) A reasonable carbon trading price effectively encourages investment in technology application. Higher carbon prices stimulate greater carbon reduction efforts and increase market activity. The carbon price growth is most significant from 2022 to 2030 and continues to rise steadily from 2032 to 2060. [Conclusion] China’s coal-fired power industry faces challenges in achieving low-carbon transformation. Accelerating the construction of carbon trading market mechanisms, strengthening carbon asset management, improving carbon reduction incentives, and optimizing carbon quota pricing mechanisms are essential for promoting technological emission reduction and the high-quality development of China’s coal-fired power industry.

Abstract Architectural singularity belongs to the Type II singularity, in which a parallel manipulator (PM) gains one or more degrees of freedom and becomes uncontrollable. PMs remaining permanently in a singularity are beneficial for linear-to-rotary motion conversion. Griffis-Duffy (GD) platform is a mobile structure admitting a Bricard motion. In this paper, we present a coordinate-free approach to the design of generalized GD platforms, which consists in determining the shape and attachment of both the moving platform and the fixed base. The generalized GD platform is treated as a combination of six coaxial single-loop mechanisms under the same constraints. Owing to the inversion, hidden in the geometric structure of these single-loop mechanisms, the mapping from a line to a circle establishes the geometric transformation between the fixed base and the moving platform based on the center of inversion, and describes the shape and attachment of the generalized GD platform. Moreover, the center of inversion not only identifies the location of rotation axis, but also affects the shape of the platform mechanism. A graphical construction of generalized GD platforms using inversion, proposed in the paper, provides geometrically feasible solutions of the manipulator design for the requirement of the location of rotation axis.

BackgroundThe Geriatric Nutritional Risk Index (GNRI) is a straightforward and objective tool for nutritional screening in older patients and has been demonstrated to possess prognostic predictive value in several diseases. Nonetheless, there is a lack of research on the nutritional risk associated with brain abscess in the older. This study aimed to evaluate the prevalence of nutritional risk among these patients by GNRI and to investigate its potential prognostic value for clinical outcomes.Materials and methodsFrom August 2019 to April 2023, 100 older patients diagnosed with brain abscess were enrolled in this single-center prospective cohort study, which evaluated the prognostic value of the Geriatric Nutritional Risk Index (GNRI) in elderly brain abscess patients. Data collected included demographic, and clinical characteristics at admission and calculated the GNRI, and the Glasgow Outcome Scale (GOS) score 6 months post-discharge. A GOS score of 5 was considered indicative of a good recovery, whereas scores ranging from 1 to 4 were classified as poor recovery.ResultsThe results revealed that 48% of older brain abscess patients were at risk of malnutrition according to the GNRI. These patients had significantly higher post-admission C-reactive protein (CRP) levels (p = 0.017), more comorbidities (p

This study develops a three-stage adoption framework to measure the impact of selected factors on a student's learning experience and evaluates the effectiveness of service learning in each learning stage. The agribusiness service-learning course utilized in this study matches groups of students with non-profit agribusiness entities to identify current business challenges and propose suitable solutions. A survey instrument was developed to gather student opinion data during the 2017-18 academic year and the multivariate Probit statistical method was used to analyse the data. The results demonstrate that service-learning helps students relate how course subject matter is used in everyday business operations and reveals that students commented positively about experiential learning throughout their projects. The analysis also demonstrates that younger, male, or white students are more likely to obtain a positive service-learning experience versus older, female, or non-white students.