The effectiveness of a novel parental training program in reducing problematic internet use of adolescents.

Problematic internet use and unsafe internet use are the two main potential negative consequences of children's online activities. Parents play a vital role in mitigating these consequences and creating a safe digital environment. Parental Vigilant Care (PVC) is a systematic approach that integrates active and restrictive mediation practices, allowing parents to regulate their involvement according to the alarm signs they detect.

Multi-label speech feature selection for Parkinson's Disease subtype recognition using graph model.

Parkinson's Disease (PD) is the second-most common neurodegenerative disorder. There is a certain pathological connection between PD and dysphonia. Speech signals have been successfully used to identify PD and predict its severity.

Decreased Cortical Sulcus Depth in Parkinson's Disease with Excessive Daytime Sleepiness.

Introduction: Excessive daytime sleepiness (EDS), a prevalent non-motor symptom in Parkinson's disease (PD), significantly impacts the quality of life for PD patients and elevates the risks of injury. Our study is to investigate the altered cortical surface morphology characteristics in PD patients with EDS (PD-EDS).

Methods: Clinical data and magnetic resonance imaging were obtained from the Parkinson's Progression Marker Initiative database, comprising 36 PD-EDS and 98 PD patients without EDS (PD-nEDS).

Non-Contact Cross-Person Activity Recognition by Deep Metric Ensemble Learning.

In elderly monitoring or indoor intrusion detection, the recognition of human activity is a key task. Owing to several strengths of Wi-Fi-based devices, including their non-contact and privacy protection, these devices have been widely applied in the area of smart homes. By the deep learning technique, numerous Wi-Fi-based activity recognition methods can realize satisfied recognitions, however, these methods may fail to recognize the activities of an unknown person without the learning process.

Model-free distributed state estimation with local measurements.

In this paper, the state estimation problem of physical plants with unknown system dynamic is revisited from the perspective of limited output information measurement, which corresponds to those with characteristics of high-dimensional, wide-area coverage and scatter. Given this fact, a network of sensors are used to carry out the measurement with each one accessing only partial outputs of the targeted systems and a novel model-free state estimation approach, named distributed stochastic variational inference state estimation, is proposed. The key idea of this method is to compensate for the impacts of local output measurements by adding nearest-neighbor rule-based information interaction among estimators to complete the state estimation.

Spectral network based on lattice convolution and adversarial training for noise-robust speech super-resolution.

Speech super-resolution aims to predict a high-resolution speech signal from its low-resolution counterpart. The previous models usually perform this task at a fixed sampling rate, reconstructing only high-frequency spectrogram components and merging them with low-frequency ones in noise-free cases. These methods achieve high accuracy, but they are less effective in real-world settings, where ambient noise and flexible sampling rates are presented.

Behavioral and Psychosocial Dynamics of Engagement: The Digital Divide in Artificial Intelligence [AI]-Driven Sports Podcasts.

The digital divide, particularly within the context of Artificial Intelligence (AI) sport podcasts, presents significant behavioral and psychosocial challenges for student engagement. This study examines the disparities in access to and proficiency with Information Communication Technologies (ICTs) across different demographic groups, focusing on gender, age, and religious level. The advent of the commercial web has heightened the significance of these divides, as the first-level digital divide concerns access to the internet, while the second-level digital divide pertains to the ability to use technology proficiently.

A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans.

Background: Liver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals.

Psychosocial determinants of handwashing and physical distancing behaviour during the COVID-19 pandemic in the Netherlands: A longitudinal analysis.

Objectives: Physical distancing and handwashing can be important infection prevention measures during an infectious disease outbreak such as the COVID-19 pandemic. To stimulate these behaviours, knowledge of psychosocial determinants as well as contextual factors is vital. We present longitudinal, within-person analyses of the impact of contextual and psychosocial factors on handwashing and distancing behaviour.

Altmetric coverage of health research in Ireland 2017-2023: a protocol for a cross-sectional analysis.

Background: Scientific publications have been growing exponentially, contributing to an oversaturated information environment. Quantifying a research output's impact and reach cannot be solely measured by traditional metrics like citation counts as these have a lag time and are largely focused on an academic audience. There is increasing recognition to consider 'alternative metrics' or altmetrics to measure more immediate and broader impacts of research.

Telerobotic Intergroup Contact: Acceptance and Preferences in Israel and Palestine.

We explore telerobotics as a novel form of intergroup communication. In this form, remotely operated robots facilitate embodied and situated intergroup contact between groups in conflict over long distances, potentially reducing prejudice and promoting positive social change. Based on previous conceptual frameworks and design hypotheses, we conducted a survey on the acceptance and preferences of the telerobotic medium in Israel and Palestine.

Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation.

This paper explores physical layer group key generation in wireless relay networks with a star topology. In this setup, the relay node plays the role of either a trusted or untrusted central node, while one legitimate node (Alice) acts as the reference node. The channel between the relay and Alice serves as the reference channel.

Explainable Deep Learning-Based Feature Selection and Intrusion Detection Method on the Internet of Things.

With the rapid advancement of the Internet of Things, network security has garnered increasing attention from researchers. Applying deep learning (DL) has significantly enhanced the performance of Network Intrusion Detection Systems (NIDSs). However, due to its complexity and "black box" problem, deploying DL-based NIDS models in practical scenarios poses several challenges, including model interpretability and being lightweight.

Coverage Analysis for High-Speed Railway Communications with Narrow-Strip-Shaped Cells over Suzuki Fading Channels.

Unlike circular cell coverage in public land mobile communications, narrow-strip-shaped cell coverage should be considered in high-speed railway (HSR) communications. Moreover, for the coverage analysis in HSR communications, most works ignore the effect of small-scale fading, which results in an inaccurate coverage performance evaluation. In this paper, we focus on the coverage analysis for HSR communications with narrow-strip-shaped cells over the Suzuki fading channel, where the composite channel fading includes path loss, lognormal shadowing, and Rayleigh-distributed small-scale fading.

When Your Partner is Being Flirted With: The Impact of Unsolicited Attention on Perceived Partner Desirability and Mate Retention Efforts.

When searching for a partner, people often rely on social cues to determine partners' suitability, finding those who attract attention from others particularly appealing. While people continue to evaluate their partners beyond relationship initiation, existing research has predominantly concentrated on the effects of observing others' choices during the stage of partner selection, neglecting to consider whether viewing others' attention toward current partners yields similar effects or instead elicits defensive devaluation. In three experiments, we exposed Israeli participants to situations where their partners received unsolicited flirtatious advances, utilizing visualization, virtual reality, and recall techniques.

Passive Vision Detection of Torch Pose in Swing Arc Narrow Gap Welding.

To enhance the synchronous detection of the horizontal and vertical positions of the torch in swing arc narrow gap welding, a torch pose detection (TPD) method is proposed. This approach utilizes passive visual sensing to capture images of the arc on the groove sidewall, using advanced image processing methods to extract and fit the arc contour. The coordinates of the arc contour center point and the highest point are determined through the arc contour fitting line.

Retinex theory-based nonlinear luminance enhancement and denoising for low-light endoscopic images.

Background: The quality of low-light endoscopic images involves applications in medical disciplines such as physiology and anatomy for the identification and judgement of tissue structures. Due to the use of point light sources and the constraints of narrow physiological structures, medical endoscopic images display uneven brightness, low contrast, and a lack of texture information, presenting diagnostic challenges for physicians.

Methods: In this paper, a nonlinear brightness enhancement and denoising network based on Retinex theory is designed to improve the brightness and details of low-light endoscopic images.

Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction.

Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied physical acquisition conditions, constrain their generalizability across new imaging contexts and settings.To overcome these limitations, we propose an unsupervised approach grounded in the deep image prior framework.

The Therapeutic Power of Cute: Effects of Media Sub-Forms and Individual Differences in Media Prescription.

Prior research showed that prescribing positively-valenced media can reduce people's perceived stress. This study explored the potential of cute media by further considering media sub-forms and individual differences in stress responses. We conducted a between-subjects experiment ( = 436) to assess how small doses of various cute media (none vs.

Multi-modal evaluation of respiratory diaphragm motion in chronic obstructive pulmonary disease using MRI series and CT images.

Purpose: Chronic obstructive pulmonary disease (COPD), characterized by airflow limitation and breathing difficulty, is usually caused by prolonged inhalation of toxic substances or long-term smoking habits. Some abnormal features of COPD can be observed using medical imaging methods, such as magnetic resonance imaging (MRI) and computed tomography (CT). This study aimed to conduct a multi-modal analysis of COPD, focusing on assessing respiratory diaphragm motion using MRI series in conjunction with low attenuation volume (LAV) data derived from CT images.

Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System.

This paper presents an innovative approach towards space-ground integrated communication systems by combining terrestrial cellular networks, UAV networks, and satellite networks, leveraging advanced slicing technology. The proposed architecture addresses the challenges posed by future user surges and aims to reduce network overhead effectively. Central to our approach is the introduction of a marginal mobile station (MS)-assisted network resource allocation decision architecture.