SFC active reconfiguration based on user mobility and resource demand prediction in dynamic IoT-MEC networks.

To achieve secure, reliable, and scalable traffic delivery, request streams in mobile Internet of Things (IoT) networks supporting Multi-access Edge Computing (MEC) typically need to pass through a service function chain (SFC) consisting of an ordered series of Virtual Network Functions (VNFs), and then arrive at the target application in the MEC for processing. The high mobility of users and the real-time variability of network traffic in IoT-MEC networks lead to constant changes in the network state, which results in a mismatch between the performance requirements of the currently deployed SFCs and the allocated resources. Meanwhile, there are usually multiple instances of the same VNF in the network, and proactively reconfiguring the deployed SFCs based on the network state changes to ensure high quality of service in the network is a great challenge.

Trust in Physicians and Hospitals During the COVID-19 Pandemic in a 50-State Survey of US Adults.

Importance: Trust in physicians and hospitals has been associated with achieving public health goals, but the increasing politicization of public health policies during the COVID-19 pandemic may have adversely affected such trust.

Objective: To characterize changes in US adults' trust in physicians and hospitals over the course of the COVID-19 pandemic and the association between this trust and health-related behaviors.

Design, Setting, And Participants: This survey study uses data from 24 waves of a nonprobability internet survey conducted between April 1, 2020, and January 31, 2024, among 443 455 unique respondents aged 18 years or older residing in the US, with state-level representative quotas for race and ethnicity, age, and gender.

UIDF-Net: Unsupervised Image Dehazing and Fusion Utilizing GAN and Encoder-Decoder.

Haze weather deteriorates image quality, causing images to become blurry with reduced contrast. This makes object edges and features unclear, leading to lower detection accuracy and reliability. To enhance haze removal effectiveness, we propose an image dehazing and fusion network based on the encoder-decoder paradigm (UIDF-Net).

"I Will Exercise to Look Great!": The Impact of Message Frame, Message Focus, and Age Label on Enhancing Older Adults' Physical Activity Intentions.

Promoting physical activity (PA) in older adults is a long-standing and crucial aspect of public health. It is essential for improving quality of life and maintaining overall health as people age. This study aims to identify an effective message strategy for enhancing PA intentions in aging population.

Co-Creating a Synchronous Tele-Education Program With Community-Dwelling Older Adults Using a Participatory Approach: A Mixed-Methods Study.

There is a pressing need to include older individuals in health education and uncover their specific needs. Leveraging the advantages of digitized health education, this study employed a participatory approach to engage community-dwelling older adults in co-creating a synchronous tele-education program, with dementia as the focus due to its rising prevalence and associated stigma in Singapore. Our findings demonstrate the preliminary effectiveness and feasibility of tele-education.

A Hybrid Handover Scheme for Vehicular VLC/RF Communication Networks.

Visible light communication (VLC) is a promising complementary technology to its radio frequency (RF) counterpart to satisfy the high quality-of-service (QoS) requirements of intelligent vehicular communications by reusing LED street lights. In this paper, a hybrid handover scheme for vehicular VLC/RF communication networks is proposed to balance QoS and handover costs by considering the vertical handover and horizontal handover together judging from the mobile state of the vehicle. A Markov decision process (MDP) is formulated to describe this hybrid handover problem, with a cost function balancing the handover consumption, delay, and reliability.

ChatGPT: perspectives from human-computer interaction and psychology.

The release of GPT-4 has garnered widespread attention across various fields, signaling the impending widespread adoption and application of Large Language Models (LLMs). However, previous research has predominantly focused on the technical principles of ChatGPT and its social impact, overlooking its effects on human-computer interaction and user psychology. This paper explores the multifaceted impacts of ChatGPT on human-computer interaction, psychology, and society through a literature review.

Filter bank temporally local multivariate synchronization index for SSVEP-based BCI.

Background: Multivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy.

Abnormal Scanning Patterns Based on Eye Movement Entropy in Early Psychosis.

Background: Restricted scan path mode is hypothesized to explain abnormal scanning patterns in patients with schizophrenia. Here, we calculated entropy scores (drawing on gaze data to measure the statistical randomness of eye movements) to quantify how strategical and random participants were when processing image stimuli.

Methods: Eighty-six patients with first-episode schizophrenia (FES), 124 individuals at clinical high risk (CHR) for psychosis, and 115 healthy control participants (HCs) completed an eye-tracking examination while freely viewing 35 static images (each presented for 10 seconds) and cognitive assessments.

RB-GAT: A Text Classification Model Based on RoBERTa-BiGRU with Graph ATtention Network.

With the development of deep learning, several graph neural network (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges when capturing contextual text information within a document sequence. To address this, a novel text classification model, RB-GAT, is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT).

Physician Perspectives on Internet-Informed Patients: Systematic Review.

Background: The internet has become a prevalent source of health information for patients. However, its accuracy and relevance are often questionable. While patients seek physicians' expertise in interpreting internet health information, physicians' perspectives on patients' information-seeking behavior are less explored.

Load Prediction in Double-Channel Residual Self-Attention Temporal Convolutional Network with Weight Adaptive Updating in Cloud Computing.

When resource demand increases and decreases rapidly, container clusters in the cloud environment need to respond to the number of containers in a timely manner to ensure service quality. Resource load prediction is a prominent challenge issue with the widespread adoption of cloud computing. A novel cloud computing load prediction method has been proposed, the Double-channel residual Self-attention Temporal convolutional Network with Weight adaptive updating (DSTNW), in order to make the response of the container cluster more rapid and accurate.

TSANN-TG: Temporal-Spatial Attention Neural Networks with Task-Specific Graph for EEG Emotion Recognition.

Electroencephalography (EEG)-based emotion recognition is increasingly pivotal in the realm of affective brain-computer interfaces. In this paper, we propose TSANN-TG (temporal-spatial attention neural network with a task-specific graph), a novel neural network architecture tailored for enhancing feature extraction and effectively integrating temporal-spatial features. TSANN-TG comprises three primary components: a node-feature-encoding-and-adjacency-matrices-construction block, a graph-aggregation block, and a graph-feature-fusion-and-classification block.

EWT: Efficient Wavelet-Transformer for single image denoising.

Transformer-based image denoising methods have shown remarkable potential but suffer from high computational cost and large memory footprint due to their linear operations for capturing long-range dependencies. In this work, we aim to develop a more resource-efficient Transformer-based image denoising method that maintains high performance. To this end, we propose an Efficient Wavelet Transformer (EWT), which incorporates a Frequency-domain Conversion Pipeline (FCP) to reduce image resolution without losing critical features, and a Multi-level Feature Aggregation Module (MFAM) with a Dual-stream Feature Extraction Block (DFEB) to harness hierarchical features effectively.

A novel approach for melanoma detection utilizing GAN synthesis and vision transformer.

Background And Objective: Melanoma, a malignant form of skin cancer, is a critical health concern worldwide. Early and accurate detection plays a pivotal role in improving patient's conditions. Current diagnosis of skin cancer largely relies on visual inspections such as dermoscopy examinations, clinical screening and histopathological examinations.

Examining health apps and wearable use in improving physical and mental well-being across U.S., China, and Singapore.

Health apps and wearables are touted to improve physical health and mental well-being. However, it is unclear from existing research the extent to which these health technologies are efficacious in improving physical and mental well-being at a population level, particularly for the underserved groups from the perspective of health equity and social determinants. Also, it is unclear if the relationship between health apps and wearables use and physical and mental well-being differs across individualistic, collectivistic, and a mix of individual-collectivistic cultures.

Unravelling moderated mediating effects of loss aversion, information avoidance and self-efficacy on cancer fear and cancer screening.

Background: Cancer health campaigns provide information to drive early detection.

Objective: To examine the effect of cancer fear on cancer screening focusing on the mediating role of loss aversion, a concept derived from prospect theory. We hypothesize that fear initiates negative beliefs that cancer can cause the loss of way of life leading to information avoidance, and indirectly influences cancer screening intentions.

Healing Health Care Disparities: Development and Pilot Testing of a Virtual Reality Implicit Bias Training Module for Physicians in the Context of Black Maternal Health.

Grounded in communication models of cultural competence, this study reports on the development and testing of the first module in a larger virtual reality (VR) implicit bias training for physicians to help them better: (a) recognize implicit bias and its effects on communication, patients, and patient care; (b) identify their own implicit biases and exercise strategies for managing them; and (c) learn and practice communicating with BIPOC patients in a culture-centered manner that demonstrates respect and builds trust. Led by communication faculty, a large, interdisciplinary team of researchers, clinicians, and engineers developed the first module tested herein focused on training goal (a). Within the module, participants observe five scenes between patient Marilyn Hayes (a Black woman) and Dr.

Identifying priority areas to support primary care engagement in breast cancer survivorship care: A Delphi study.

Introduction: Existing approaches in cancer survivorship care delivery have proven to be insufficient to engage primary care. This study aimed to identify stakeholder-informed priorities to improve primary care engagement in breast cancer survivorship care.

Methods: Experts in U.

Adaptive UAV Navigation Method Based on AHRS.

To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm.

A 2 μm Wavelength Band Low-Loss Spot Size Converter Based on Trident Structure on the SOI Platform.

A 2 μm wavelength band spot size converter (SSC) based on a trident structure is proposed, which is coupled to a lensed fiber with a mode field diameter of 5 μm. The cross-section of the first segment of the tapered waveguide structure in the trident structure is designed as a right-angled trapezoidal shape, which can further improve the performance of the SSC. The coupling loss of the SSC is less than 0.