Search

This study evaluated the effects of exercise interventions on sleep quality and weight loss through a systematic literature review and meta-analysis. A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and the Cochrane Library for publications up to December 2022. Only randomized controlled trials (RCTs) were included in the analysis. The Risk of Bias was assessed using the Cochrane Risk of Bias 2 (ROB 2) tool, and disagreements were resolved by consensus. Data synthesis and meta-analysis were performed using Comprehensive Meta-Analysis Version 4 (CMA version 4) software, with outcomes expressed as pooled effect sizes, odds ratios (ORs), and 95% confidence intervals (CIs). Seven RCTs encompassing 908 participants were analyzed. The meta-analysis indicated a significant improvement in sleep outcomes (OR = 3.93, 95% CI [2.04, 7.56], p < 0.001). The combined aerobic and resistance exercise regimen showed the most substantial effects on sleep quality. Additionally, four of the seven RCTs included in the weight loss analysis indicated a significant improvement in weight loss (OR = 2.58, 95% CI [1.79, 3.71], p < 0.001). Exercise interventions have a strong potential for improving sleep quality and weight loss in adults with obesity. Future studies should focus on developing optimized targeted strategies for sleep enhancement.

Blockchain technology has ushered in a transformative paradigm of decentralized and transparent systems, offering innovative solutions across diverse sectors. While these systems strive for unparalleled transparency and trustlessness in a fully distributed framework, permissionless blockchains, such as Bitcoin and Ethereum, encounter vulnerabilities due to their intrinsically public nature. Addressing these vulnerabilities, the emergence of permissioned blockchains presents a fortified alternative, incorporating rigorous access controls and authentication protocols to ensure participation exclusivity and transaction confidentiality. Nevertheless, a keen observation reveals that, despite encryption, the operational traffic within these blockchains manifests distinct time-series patterns and operational relations during sensitive data exchanges. Such patterns hold the potential to inadvertently expose critical details about the network, encompassing its topology and the operational dependencies among nodes. In light of this revelation, we introduce a pioneering blockchain fingerprinting mechanism, denoted as gShock. This system meticulously analyzes periodic patterns and the context of operational relations from the collected blockchain network traffic. It employs a Graph Neural Network (GNN)-based model, adept at capturing the intricate characteristics innate to specialized blockchain operations. Through empirical experiments conducted in a realistic permissioned blockchain environment, comprising various nodes, we ascertain that gShock demonstrates a remarkable proficiency in classifying blockchain operational traffic with an F1-score of $\geq 96$ % and identifying individual dependencies with a macro F1-score of $\geq 93$ %.

Cryptocurrencies have increasingly been used as a medium for illicit financial activities by criminals. Annually, billions of dollars’ worth of Bitcoin penetrate cryptocurrency exchanges. Despite the critical need for advanced Bitcoin financial forensics to investigate these criminal activities, no novel methods have been developed to detect illicit Bitcoin operations. Existing approaches to identifying illegal Bitcoin activity are limited due to their inadequate consideration of graph data. To address these limitations, we present a novel approach, Hyperedge Classification, to detect illegal transactions with greater precision. This approach introduces a novel cluster-based Hyperedge-Node Switching technique, which enables effective hyperedge classification and visualization of hyperedge relationships. Additionally, we propose a framework named CENSor (Cluster-based Edge Node Switching Detector), which offers more powerful and robust detection capabilities compared to traditional techniques for both illegal entity detection and illegal transaction detection. Our cluster-based Hyperedge-Node Switching technique demonstrates its effectiveness with an F1-score of 0.867, outperforming comparative baselines. Moreover, CENSor visualizes the Bitcoin cluster graph and the Hyperedge-Node switched graph, highlighting the importance of utilizing appropriate graph information in Bitcoin analysis. Finally, we demonstrate that CENSor is resilient to an adversarial attack aimed at evading detection.

As Internet of Things (IoT) devices have rooted themselves in the daily life of billions of people, security threats targeting IoT devices are emerging rapidly. Thus, IoT vendors have employed security testing frameworks to examine IoT devices before releasing them. However, existing frameworks have difficulty providing automated testing, as they require a lot of manual effort to support new devices due to the lack of information about the input formats of the new devices. To address this challenge, we introduce FuzzDocs, a document-based black-box IoT testing framework designed to automatically analyze publicly accessible API documents about target IoT devices and extract information, including valid inputs used to call each functionality of the target devices. Based on the extracted information, it generates valid-enough test inputs that are not easily rejected by target devices but can trigger vulnerabilities deep inside them. This document-based input generation allows FuzzDocs to support new devices without manual work, as well as provide effective security testing. To prove its feasibility, we evaluated FuzzDocs in a real-world IoT environment, and the results showed that FuzzDocs extracted input formats with 93% accuracy from hundreds of pages of documents. Also, it outperformed the existing frameworks in testing coverage and found 35 potential vulnerabilities, including two unexpected system failures in five popular IoT devices.

Typically, vaccination is perceived as self-interested act, but it is also a community-oriented action that benefits other members in society, as high vaccine uptake reduces disease transmission. Drawing on the notion of deservingness, the authors ran an online experiment ( n = 516) to assess whose interests motivate people from across the political spectrum to engage in a community-oriented action: the intention to receive a coronavirus disease 2019 vaccine. Results show that liberals and conservatives resonate with self-oriented and community-oriented message frames differently. When a community-oriented message focuses on hard-hit groups such as racial minorities, this increases vaccine intent among liberals but decreases vaccine intent among conservatives. A message focusing on community in a generic sense is the only message frame that increases vaccine intent among moderates and the message that induces the least resistance among conservatives. The findings suggest that members of racial and ethnic minority groups are still excluded from boundaries of moral concern.

Lipids in fish diets provide energy and play important roles in immunity and metabolism. Atlantic salmon, a species that migrates from freshwater to seawater, requires high energy, especially during smoltification. Juvenile teleosts have low lipid requirements, and a high dietary lipid content is known to have negative effects on their growth and digestion. Therefore, this study evaluated the effect of two commercial rainbow trout feeds (low-lipid, 13.41% and 14.6%) on the growth and immune responses of early parr-stage Atlantic salmon compared to commercial salmon feed (high-lipid, 29.52%). Atlantic salmon parr (weight: 14.56 ± 2.1 g; length: 11.23 ± 0.44 cm) were randomly divided into three groups and fed either one of two commercial rainbow trout feeds (RTF1 and RTF2) or the commercial salmon feed (ASF) for 12 weeks. At the end of the feeding trial, growth, haematology, histology and gene expression analyses were performed. There were no significant differences in weight gain rates or feed efficiency between the groups (p > 0.05). Superoxidate dismutase, glutathione peroxidase, lysozyme and immunoglobulin M activities were not different among the experimental groups (p > 0.05). A histological examination of the liver and intestinal tissues showed no pathological symptoms of inflammatory response or lipid accumulation in any of the groups. In an intestinal transcriptome analysis using RNA-seq, the expression levels of several genes linked to lipids, immune-related proteins, cytokines and chemokines did not differ significantly between the groups (p > 0.05). Commercial rainbow trout feed with low lipid content has no clear negative impact on the development of Atlantic salmon during the early parr stage (14.5 to 39.6 g). This study provides basic information for the development of economical feed for early parr-stage Atlantic salmon.

Multi-Target Multi-Camera Tracking (MTMCT), which aims to track multiple targets within a multi-camera network, has recently attracted considerable attention due to its wide range of applications. The main challenge of MTMCT is to match local tracklets (i.e., sub-trajectories) obtained by different cameras and to combine them into global trajectories across the multi-camera network. This paper addresses the cross-camera tracklet matching problem in scenarios with partially overlapping fields of view (FOVs), such as indoor multi-camera environments. We present a new lightweight matching method for the MTMC task that employs similarity analysis for location features. The proposed approach comprises two steps: (i) extracting the motion information of targets based on a ground projection method and (ii) matching the tracklets using similarity analysis based on the Dynamic Time Warping (DTW) algorithm. We use a Kanade–Lucas–Tomasi (KLT) algorithm-based frame-skipping method to reduce the computational overhead in object detection and to produce a smooth estimate of the target’s local tracklets. To improve matching accuracy, we also investigate three different location features to determine the most appropriate feature for similarity analysis. The effectiveness of the proposed method has been evaluated through real experiments, demonstrating its ability to accurately match local tracklets.