Exposure to climate change information predicts public support for solar geoengineering in Singapore and the United States.

Solar geoengineering is a controversial climate policy measure that could lower global temperature by increasing the amount of light reflected by the Earth. As scientists and policymakers increasingly consider this idea, an understanding of the level and drivers of public support for its research and potential deployment will be key. This study focuses on the role of climate change information in public support for research and deployment of stratospheric aerosol injection (SAI) in Singapore (n = 503) and the United States (n = 505).

Adapting and implementing breast cancer follow-up in primary care: protocol for a mixed methods hybrid type 1 effectiveness-implementation cluster randomized study.

Background: Advances in detection and treatment for breast cancer have led to an increase in the number of individuals managing significant late and long-term treatment effects. Primary care has a role in caring for patients with a history of cancer, yet there is little guidance on how to effectively implement survivorship care evidence into primary care delivery.

Methods: This protocol describes a multi-phase, mixed methods, stakeholder-driven research process that prioritizes actionable, evidence-based primary care improvements to enhance breast cancer survivorship care by integrating implementation and primary care transformation frameworks: the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework and the Practice Change Model (PCM).

Understanding mixed emotions in organized helping through emotionography.

Introduction: Emotionography studies emotion: (a) as it occurs naturally in display, reception, attribution, and avowal; (b) within and across diverse stretches of interaction and varied institutional contexts; (c) grounded purposefully in the perspectives of the interactants as those perspectives are displayed in real-time through unfolding talk; (d) using materials that are recorded and transcribed in sufficient precision to capture the granularity consequential for the interactants. We overview contemporary research on "mixed emotion" highlighting theoretical and methodological issues and explore the potential of emotionography as a generative alternative.

Methods: The analysis will use contemporary conversation analysis and discursive psychology to illuminate the workings of organized helping using a collection of recordings from a child protection helpline all of which include laughter alongside crying.

Multi-UAV Data Collection and Path Planning Method for Large-Scale Terminal Access.

In the context of the relentless evolution of network and communication technologies, the need for enhanced communication content and quality continues to escalate. Addressing the demands of data collection from the abundance of terminals within Internet of Things (IoT) scenarios, this paper presents an advanced approach to multi-Unmanned Aerial Vehicle (UAV) data collection and path planning tailored for extensive terminal accessibility. This paper focuses on optimizing the complex interplay between task completion time and task volume equilibrium.

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook.

Neurological disorders (NDs), such as Alzheimer's disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND.

Enhancing Community Participation in Dengue Control Through Digital Crowdsourcing: An Analysis of a World Mosquito Program Digital Open Call in Sri Lanka.

Background: Two crowdsourcing open calls were created to enhance community engagement in dengue control in Sri Lanka. We analyzed the process and outcomes of these digital crowdsourcing open calls.

Methods: We used standard World Health Organization methods to organize the open calls, which used exclusively digital methods because of coronavirus disease 2019 (COVID-19).

All it takes is empathy: how virtual reality perspective-taking influences intergroup attitudes and stereotypes.

Research in the past decade has demonstrated the potential of virtual reality perspective-taking (VRPT) to reduce bias against salient outgroups. In the perspective-taking literature, both affective and cognitive mechanisms have been theorized and identified as plausible pathways to prejudice reduction. Few studies have systematically compared affective and cognitive mediators, especially in relation to virtual reality, a medium posited to produce visceral, affective experiences.

Misinformation, Trust, and Use of Ivermectin and Hydroxychloroquine for COVID-19.

Importance: The COVID-19 pandemic has been notable for the widespread dissemination of misinformation regarding the virus and appropriate treatment.

Objective: To quantify the prevalence of non-evidence-based treatment for COVID-19 in the US and the association between such treatment and endorsement of misinformation as well as lack of trust in physicians and scientists.

Design, Setting, And Participants: This single-wave, population-based, nonprobability internet survey study was conducted between December 22, 2022, and January 16, 2023, in US residents 18 years or older who reported prior COVID-19 infection.

Spectrum Sensing Method Based on Residual Dense Network and Attention.

To address the problems of gradient vanishing and limited feature extraction capability of traditional CNN spectrum sensing methods in deep network structures and to effectively avoid network degradation issues under deep network structures, this paper proposes a collaborative spectrum sensing method based on Residual Dense Network and attention mechanisms. This method involves stacking and normalizing the time-domain information of the signal, constructing a two-dimensional matrix, and mapping it to a grayscale image. The grayscale images are divided into training and testing sets, and the training set is used to train the neural network to extract deep features.

Improved Breast Cancer Classification through Combining Transfer Learning and Attention Mechanism.

Breast cancer, a leading cause of female mortality worldwide, poses a significant health challenge. Recent advancements in deep learning techniques have revolutionized breast cancer pathology by enabling accurate image classification. Various imaging methods, such as mammography, CT, MRI, ultrasound, and biopsies, aid in breast cancer detection.

Closed-Form Expressions of Upper Bound for Polarization-MDCSK System.

The performance analysis of polarization -ary differential chaos shift keying (P-MDCSK) has been expressed using a tight upper bound with the Q-function. However, evaluating the Q-function directly is not a closed expression and there has been less work on closed expression for the upper bound. In order to solve the problem, this paper presents approximate closed-form expressions on the error probability of P-MDCSK.

A metabolism-functional connectome sparse coupling method to reveal imaging markers for Alzheimer's disease based on simultaneous PET/MRI scans.

Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome-based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage.

Examining the role of moral foundations in promoting intergroup contact willingness.

Improving people's motivation to seek meaningful intergroup contact is considered key to facilitating intergroup harmony. Based on moral foundations theory, this study examines how moral foundations as individual traits predict contact willingness with three minority groups (foreign domestic helpers, LGBT, and Chinese expats) and how moral emotions mediate such associations. We tested our hypotheses based on survey data across Hong Kong and Singapore.

Examining the association between social media fatigue, cognitive ability, narcissism and misinformation sharing: cross-national evidence from eight countries.

Several studies have explored the causes and consequences of public engagement with misinformation and, more recently, COVID-19 misinformation. However, there is still a need to understand the mechanisms that cause misinformation propagation on social media. In addition, evidence from non-Western societies remains rare.

Joint Power Control and Resource Allocation with Rate Fairness Consideration for SWIPT-Based Cognitive Two-Way Relay Networks.

This paper investigates the power control and resource allocation problem in a simultaneously wireless information and power transfer (SWIPT)-based cognitive two-way relay network, in which two secondary users exchange information through a power splitting (PS) energy harvesting (EH) cognitive relay node underlay in a primary network. To enhance the secondary networks's transmission ability without detriment to the primary network, we formulate an optimization to maximize the minimum transmission rates of the cognitive users by jointly optimizing power allocation at the sources, the time allocation of transmission frames and power splitting at the relay, under the constraint that the transmission power of the cognitive network is set not to exceed the primary user interference threshold to ensure primary work performance. To efficiently solve this problem, a sub-optimal algorithm named the joint power control and resource allocation (JPCRA) scheme is proposed, in which we decouple the non-convex problem into convex problems and use alternative steps in the optimization algorithm to get final solutions.

Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data.

The rapid growth of the Internet of Things (IoT) and big data has raised security concerns. Protecting IoT big data from attacks is crucial. Detecting real-time network attacks efficiently is challenging, especially in the resource-limited IoT setting.

A Scalable Video Multicast Scheme Based on User Demand Perception and D2D Communication.

With the widespread application of 5G technology, there has been a significant surge in wireless video service demand and video traffic due to the proliferation of smart terminal devices and multimedia applications. However, the complexity of terminal devices, heterogeneous transmission channels, and the rapid growth of video traffic present new challenges for wireless network-based video applications. Although scalable video coding technology effectively improves video transmission efficiency in complex networks, traditional cellular base stations may struggle to handle video transmissions for all users simultaneously, particularly in large-scale networks.

A trajectory outlier detection method based on variational auto-encoder.

Trajectory outlier detection can identify abnormal phenomena from a large number of trajectory data, which is helpful to discover or predict potential traffic risks. In this work, we proposed a trajectory outlier detection model based on variational auto-encoder. First, the model encodes the trajectory data as parameters of distribution functions based on the statistical characteristics of urban traffic.

Socio-economic disparities in exposure to and endorsement of COVID-19 vaccine misinformation and the associations with vaccine hesitancy and vaccination.

Objectives: We examined disparities in vaccine misinformation exposure and endorsement and the associations with vaccine hesitancy and vaccination uptake.

Study Design: Population-based survey.

Methods: A population-based survey was conducted on 5,002 Hong Kong adults oversampling low socio-economic status (SES, n = 2,200).

Hybrid-supervised bidirectional transfer networks for computer-aided diagnosis.

Medical imaging techniques have been widely used for diagnosis of various diseases. However, the imaging-based diagnosis generally depends on the clinical skill of radiologists. Computer-aided diagnosis (CAD) can help radiologists improve diagnostic accuracy as well as the consistency and reproducibility.

[Electrocardiogram classification algorithm based on CvT-13 and multimodal image fusion].

Electrocardiogram (ECG) signal is an important basis for the diagnosis of arrhythmia and myocardial infarction. In order to further improve the classification effect of arrhythmia and myocardial infarction, an ECG classification algorithm based on Convolutional vision Transformer (CvT) and multimodal image fusion was proposed. Through Gramian summation angular field (GASF), Gramian difference angular field (GADF) and recurrence plot (RP), the one-dimensional ECG signal was converted into three different modes of two-dimensional images, and fused into a multimodal fusion image containing more features.

The nucleus accumbens functional connectivity in patients with insomnia using resting-state fMRI.

Background: The aim of this study was to investigate the functional abnormalities between the nucleus accumbens (NAc) and the whole brain in individuals with Insomnia Disorder (ID) using resting-state functional magnetic resonance imaging (fMRI). Additionally, the study aimed to explore the underlying neural mechanisms of ID.

Methods: We enrolled 18 participants with ID and 16 normal controls (NC).

Smartphone ownership, digital literacy, and the mediating role of social connectedness and loneliness in improving the wellbeing of community-dwelling older adults of low socio-economic status in Singapore.

Introduction: During the COVID-19 pandemic, safe-distancing measures resulted in many community-dwelling older adults being socially isolated and lonely, with its attending negative impact on wellbeing and quality of life. While digital technology may have mitigated this, older adults of low socioeconomic status (SES) are more likely to be digitally excluded and hence susceptible to the adverse effects of social isolation and loneliness. This study aims to understand the factors that affect digital literacy, smartphone ownership, and willingness to participate in a digital literacy program (DLP), and to test the hypothesized relations between digital literacy, social connectedness, loneliness, wellbeing, and quality of life amongst community dwelling older adults of low SES.

Association of Hospice Agency Location and Neighborhood Socioeconomic Disadvantage in the U.S.

Background: Despite the growing increase in the utilization of hospice in the U.S, disparities exist in the utilization of hospice. Accumulating evidence has shown that neighborhood characteristics have an impact on availability of hospice agencies.