Biomarkers.

Background: Previous studies have found that AD-related plasma markers are associated with Aβ deposition, but the specific effects remain unclear.

Method: Data were obtained from the Longitudinal Study of Cognitive Decline in China (SILCODE). Comprehensive neuropsychological assessments, MRI, plasma samples, and amyloid positron-emission tomography (Aβ-PET) data were collected.

PTSD and challenges among older Chinese in Shenzhen during COVID-19 pandemic: Trust in authority and medical professionals as moderators.

Aim: This research aimed to comprehensively explore the impact of diverse challenges encountered by older adults on the development of post-traumatic stress disorder (PTSD). It delved into how these effects vary depending on individuals' levels of trust in authority and medical professionals, providing a nuanced understanding of the interplay between external challenges, personal trust, and mental health outcomes in the older population.

Background: The COVID-19 pandemic has imposed significant hardships, particularly on the ageing population, with potential psychological repercussions such as PTSD.

Pilot Testing Digital Stories to Influence College Men's Intentions to Vaccinate Against HPV.

College men are among those least likely to be vaccinated against the human papillomavirus (HPV). Viewing digital stories from other college men who were vaccinated against HPV as young adults may help influence them to seek the vaccine. Guided by the Theory of Planned Behavior, this research reports on the creation and pilot testing of digital stories to increase college men's intentions to vaccinate against HPV.

Improving communication to increase uptake of high-risk breast cancer prevention appointments: Building a better letter.

Objective: Mailed letters to women identified as being at high-risk for developing breast cancer were not having the desired effect for encouraging appointments with prevention-focused providers at a large Midwest healthcare system. A partnership with communication scholars sought to revise the letter to increase awareness, intentions, and appointments.

Methods: Guided by the Extended Parallel Process Model, survey responses were collected from letter recipients over the course of two years, both pre and post letter revision.

Irritability and Social Media Use in US Adults.

Importance: Efforts to understand the complex association between social media use and mental health have focused on depression, with little investigation of other forms of negative affect, such as irritability and anxiety.

Objective: To characterize the association between self-reported use of individual social media platforms and irritability among US adults.

Design, Setting, And Participants: This survey study analyzed data from 2 waves of the COVID States Project, a nonprobability web-based survey conducted between November 2, 2023, and January 8, 2024, and applied multiple linear regression models to estimate associations with irritability.

Ultrasonic detection of wrinkles in composites with gradual phase shift migration.

Fiber reinforced polymer composites (FRPs) are essential for various industrial fields, but wrinkles inside will greatly reduce their mechanical properties. Full-matrix capture (FMC) is a popular data structure for ultrasonic phased array imaging in composites. However, such structure may lead to data redundancy and noise interference.

Implementing an inclusive digital health ecosystem for healthy aging: a case study on project SingaporeWALK.

Objective: To pilot a digital health technologies ecosystem known as project SingaporeWALK (earables and pps for ommunity iving and nowledge) that build capacity in older adults, senior center managers, health coaches, and caregivers in using health technologies (eg, wearables, apps, exergames) collaboratively in a gamified way for active aging.

Materials And Methods: The SingaporeWALK ecosystem was set up through 3 initiatives: (1) co-developing technologies with stakeholders; (2) raising digital literacy and capacity building; and (3) cultivating community and intergenerational bonding for active aging through gamified technology use.

Results: Significant improvements in older adults' self-reported physical and mental health post-intervention were observed.

A client-server based recognition system: Non-contact single/multiple emotional and behavioral state assessment methods.

Background And Objectives: In the current global health landscape, there is an increasing demand for rapid and accurate assessment of mental states. Traditional assessment methods typically rely on face-to-face interactions, which are not only time-consuming but also highly subjective. Addressing this issue, this study aims to develop a client-server-based, non-contact multimodal emotion and behavior recognition system to enhance the efficiency and accuracy of mental state assessments.

Trust in government vaccine recommendations during the Covid-19 pandemic in Singapore: A longitudinal survey study.

Background: Trust in governments has been decreasing in recent years, especially during the Covid-19 pandemic, where low-trust societies showed reduced compliance with disease control measures. Few studies have examined how trust in authorities changed over the pandemic. This study investigated the trajectory of public trust in the Singapore government's vaccine recommendations during this period.

The multilayer semantic network structure of community tensions.

Introduction: Semantic network analysis is an important tool researchers can use to untangle the knots of tension that arise as communities debate and discuss complex issues. Yet words connect not only to each other in community discourse but to larger themes or issues.

Methods: In this paper, we demonstrate the use of multilayer analysis for the study of semantic networks, helping to unravel connections within and between community tensions.

Explainable AI and trust: How news media shapes public support for AI-powered autonomous passenger drones.

This study delves into the intricate relationships between attention to AI in news media, perceived AI explainability, trust in AI, and public support for autonomous passenger drones. Using structural equation modelling ( = 1,002), we found significant associations between perceived AI explainability and all trust dimensions (i.e.

Too much to process? Exploring the relationships between communication and information overload and videoconference fatigue.

The adoption of videoconferencing has brought significant convenience to people's lives. However, as videoconferencing usage has skyrocketed, it has unveiled a range of side effects, most notably videoconference fatigue (VF). In response, this paper employed the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP) as a theoretical framework to conduct two comprehensive investigations, centering on the impact of verbal communication overload on users' information overload and VF.

Secure and Lightweight Cluster-Based User Authentication Protocol for IoMT Deployment.

Authentication is considered one of the most critical technologies for the next generation of the Internet of Medical Things (IoMT) due to its ability to significantly improve the security of sensors. However, higher frequency cyber-attacks and more intrusion methods significantly increase the security risks of IoMT sensor devices, resulting in more and more patients' privacy being threatened. Different from traditional IoT devices, sensors are generally considered to be based on low-cost hardware designs with limited storage resources; thus, authentication techniques for IoMT scenarios might not be applicable anymore.

New Families of Frequency-Hopping Sequence Sets with a Low-Hit-Zone.

As a means of spread spectrum communication, frequency-hopping technology has good performance in anti-jamming, multiple-access, security, covert communications, and so on. In order to meet the needs of different frequency-hopping multiple-access (FHMA) communication scenarios, the research on frequency-hopping sequence (FHS) sets with a low-hit-zone (LHZ) is now becoming more and more crucial. In this paper, a general construction to obtain new families of LHZ-FHS sets is achieved via interleaving technique.

A survey on representation learning for multi-view data.

Multi-view clustering has become a rapidly growing field in machine learning and data mining areas by combining useful information from different views for last decades. Although there have been some surveys based on multi-view clustering, most of these works ignore simultaneously taking the self-supervised and non-self supervised multi-view clustering into consideration. We give a novel survey for sorting out the existing algorithms of multi-view clustering in this work, which can be classified into two different categories, i.

Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [F]FDG PET study.

Objectives: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [F]FDG PET images to diagnose the clinical spectrum of Alzheimer's disease (AD) and predict the progression from mild cognitive impairment (MCI) to AD.

Methods: This multicentre study included 1962 subjects from two ethnically diverse, independent cohorts (a Caucasian cohort from ADNI and an Asian cohort merged from two hospitals in China). The IDLR model involved feature extraction, feature selection, and classification/prediction.

Rate Optimization of Intelligent Reflecting Surface-Assisted Coal Mine Wireless Communication Systems.

This paper proposes a three-step joint rate optimization method for intelligent reflecting surface (IRS)-assisted coal mine wireless communication systems. Different from terrestrial IRS-assisted communication scenarios, in coal mines, IRSs can be installed flexibly on the tops of rectangular tunnels to address the issues of signals being blocked and interfered with by mining equipment. Therefore, it is necessary to optimize the IRS deployment position, the transmit power and IRS phase shifts to achieve the maximum effective achievable rate at user stations equipped with the proposed system.

Intelligent-Reflecting-Surface-Assisted Single-Input Single-Output Secure Transmission: A Joint Multiplicative Perturbation and Constructive Reflection Perspective.

Due to the inherent broadcasting nature and openness of wireless transmission channels, wireless communication systems are vulnerable to the eavesdropping of malicious attackers and usually encounter undesirable situations of information leakage. The problem may be more serious when a passive eavesdropping device is directly connected to the transmitter of a single-input single-output (SISO) system. To deal with this urgent situation, a novel IRS-assisted physical-layer secure transmission scheme based on joint transmitter perturbation and IRS reflection (JPR) is proposed, such that the secrecy of wireless SISO systems can be comprehensively guaranteed regardless of whether the reflection-based jamming from the IRS to the eavesdropper is blocked or not.

Knowledge-Assisted Actor Critic Proximal Policy Optimization-Based Service Function Chain Reconfiguration Algorithm for 6G IoT Scenario.

Future 6G networks will inherit and develop Network Function Virtualization (NFV) architecture. With the NFV-enabled network architecture, it becomes possible to establish different virtual networks within the same infrastructure, create different Virtual Network Functions (VNFs) in different virtual networks, and form Service Function Chains (SFCs) that meet different service requirements through the orderly combination of VNFs. These SFCs can be deployed to physical entities as needed to provide network functions that support different services.

Cross-institutional evaluation of deep learning and radiomics models in predicting microvascular invasion in hepatocellular carcinoma: validity, robustness, and ultrasound modality efficacy comparison.

Purpose: To conduct a head-to-head comparison between deep learning (DL) and radiomics models across institutions for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and to investigate the model robustness and generalizability through rigorous internal and external validation.

Methods: This retrospective study included 2304 preoperative images of 576 HCC lesions from two centers, with MVI status determined by postoperative histopathology. We developed DL and radiomics models for predicting the presence of MVI using B-mode ultrasound, contrast-enhanced ultrasound (CEUS) at the arterial, portal, and delayed phases, and a combined modality (B + CEUS).

Disentangle the group and individual components of functional connectome with autoencoders.

One of the central goals of neuroscience is to understand the group commonality and individual variability in functional connectome. However, the entangled nature of the group and individual components in functional connectome poses challenges. Some methods have attempted to disentangle these group and individual components, typically using functional connectivity (FC).