Advancements in artificial intelligence-powered search engines have enhanced the efficiency of online health information searches by generating direct answers to queries using top-ranked featured snippets (FS). However, such functionalities may contribute to health anxiety, particularly when the displayed results are distressing. This study investigated the effect of algorithmic transparency (AT) explanations (absence vs. presence) on mitigating FS-triggered health anxiety. The results of an online experiment (N = 206) yielded two key findings: First, participants exposed to AT explanations detailing the selection process of FS experienced reduced trust in the search engine and distressing results, which subsequently alleviated health anxiety. Second, the moderating effect of pre-existing cyberchondria on the relationship between AT explanations and trust was observed, but only within a limited threshold. Overall, the findings empirically validate AT explanations as an effective approach to mitigate FS-induced health anxiety. Theoretical and practical implications are discussed.

How do status cues of AI agents alter human users’ interpretations and evaluations of these agents during human-AI interactions? Drawing upon social status theories and the CASA paradigm, this study explores the influences of status cues in AI-powered healthcare chatbots on AI credibility assessment via perceived AI agency and AI anxiety. The moderation effects of message contingency are also examined. A 2 (status cues: high-status vs. low-status) × 2 (message contingency: high vs. low) between-subjects experiment was conducted online (N = 209). Results revealed that healthcare chatbots with high-status (vs. low-status) cues were perceived as having more AI agency, which was further associated with higher perceived AI credibility. Perceived AI agency and AI anxiety sequentially mediated the effect of social status cues on perceived AI credibility. Results also showed that high (vs. low) message contingency significantly ameliorated users’ AI anxiety for high-status healthcare chatbots. Theoretical and practical implications are discussed.

With growing data volume for large-scale virtual screening, the associated data processing and management meet challenges. We have developed UCAPF, A unified platform for large-scale virtual screening. The platform provides a parallel processing framework for large-scale virtual screening data. It also enables scheduling of heterogeneous parallel architectures and hierarchical storage of massive data. The processing framework improves data quality. On the CASF-2016 dataset, the standardized molecules processed by UCAPF showed a 9.5% to 14.62% improvement in scoring performance and a 7.9% to 34.6% improvement in ranking performance compared to the raw molecules. For massive data processing, the framework provides parallel efficiency of 81.20% for molecule standardized processing and 79.51% for docking result processing on a 72-unit Hadoop cluster. In addition, the distributed database for data management improves the ability to retrieve 10,094 molecules from seventy million docking result data by a factor of 2.40 compared to the single-node storage model. Finally, we analyze the variation of input/output (I/O) over time for different phases of virtual screening to reflect the effectiveness of the scheduling strategy and tiered storage for the heterogeneous parallel architecture.

MOTIVATION: In the field of pharmacochemistry, it is a time-consuming and expensive process for the new drug development. The existing drug design methods face a significant challenge in terms of generation efficiency and quality. RESULTS: In this paper, we proposed a novel molecular generation strategy and optimization based on A2C reinforcement learning. In molecular generation strategy, we adopted transformer-DNN to retain the scaffolds advantages, while accounting for the generated molecules' similarity and internal diversity by dynamic parameter adjustment, further improving the overall quality of molecule generation. In molecular optimization, we introduced heterogeneous parallel supercomputing for large-scale molecular docking based on message passing interface communication technology to rapidly obtain bioactive information, thereby enhancing the efficiency of drug design. Experiments show that our model can generate high-quality molecules with multi-objective properties at a high generation efficiency, with effectiveness and novelty close to 100%. Moreover, we used our method to assist shandong university school of pharmacy to find several candidate drugs molecules of anti-PEDV. AVAILABILITY AND IMPLEMENTATION: The datasets involved in this method and the source code are freely available to academic users at https://github.com/wq-sunshine/MomdTDSRL.git.

This paper is motivated by the growing prevalence of obesity, a health problem affecting over 500 million people. Measurements of energy intake are commonly used for the study and treatment of obesity. However, the most widely used tools rely upon self-report and require a considerable manual effort, leading to underreporting of consumption, noncompliance, and discontinued use over the long term. The purpose of this paper is to describe a new method that uses a watch-like configuration of sensors to continuously track wrist motion throughout the day and automatically detect periods of eating. Our method uses the novel idea that meals tend to be preceded and succeeded by the periods of vigorous wrist motion. We describe an algorithm that segments and classifies such periods as eating or noneating activities. We also evaluate our method on a large dataset (43 subjects, 449 total h of data, containing 116 periods of eating) collected during free-living. Our results show an accuracy of 81% for detecting eating at 1-s resolution in comparison to manually marked event logs of periods eating. These results indicate that vigorous wrist motion is a useful indicator for identifying the boundaries of eating activities, and that our method should prove useful in the continued development of body-worn sensor tools for monitoring energy intake.

A wide variety of applications in forensic, government, and commercial fields require reliable personal identification. However, the recognition performance is severely affected when encountering non-ideal images caused by motion blur, poor contrast, various expressions, or illumination artifacts. In this paper, we investigated the use of shape-based eyebrow features under non-ideal imaging conditions for biometric recognition and gender classification. We extracted various shape-based features from the eyebrow images and compared three different classification methods: Minimum Distance Classifier (MD), Linear Discriminant Analysis Classifier (LDA) and Support Vector Machine Classifier (SVM). The methods were tested on images from two publicly available facial image databases: The Multiple Biometric Grand Challenge (MBGC) database and the Face Recognition Grand Challenge (FRGC) database. Obtained recognition rates of 90% using the MBGC database and 75% using the FRGC database as well as gender classification recognition rates of 96% and 97% for each database respectively, suggests the shape-based eyebrow features maybe be used for biometric recognition and soft biometric classification.

We introduce methods for detecting in real-time information concerning bites taken during a meal. Our methods use an orientation sensor placed on the wrist of a user, and analyze the rolling motion of the wrist in order to detect a pattern related to biting behavior. We have built a prototype bite detector device based upon our methods. The device can count the total number of bites the user has taken, and provide the bites-taken rate (bites per minute) of the user. Experiments have been conducted to determine its accuracy. Ten subjects ate a meal of their choice, using utensils (or fingers) of their choice. Video was recorded of subjects eating, and synchronized with our device, in order to evaluate its performance. The sensitivity of the device was found to be 91%. Our methods could find use in a number of applications, including helping a user with obesity, eating disorders or eating rate problems.