TikTok video as a health education source of information on heart failure in China: a content analysis

BackgroundHeart failure (HF) is a complex and life-threatening syndrome associated with significant morbidity and mortality. While TikTok has gained popularity as a social media platform for sharing HF-related information, the quality of such content on TikTok remains unexplored.MethodsA cross-sectional analysis was conducted on TikTok videos related to HF in China. The sources of the videos were identified and analyzed. The content comprehensiveness of the videos was evaluated using six questions that covered definition, signs and symptoms, risk factors, evaluation, management, and outcomes. The reliability and quality of the videos were assessed using three standardized evaluation instruments: DISCERN, JAMA benchmarks, and the Global Quality Scale. Additionally, the correlation between video quality and video characteristics was further investigated.ResultsAmong the video sources, 92.2% were attributed to health professionals, while news agencies and non-profit organizations accounted for 5.7% and 2.1%, respectively. The content comprehensiveness score for the videos was 3.36 (SD 3.56), with news agencies receiving the highest scores of 4.06 (SD 3.31). The median DISCERN, JAMA, and GQS scores for all 141 videos were 26.50 (IQR 25.00–28.750), 2.00 (IQR 2.00–2.00), and 2.00 (IQR 2.00–2.00), respectively. Videos from health professionals had significantly higher JAMA scores compared to those from non-profit organizations (P < 0.01). Correlation analysis between video quality and video characteristics showed positive correlations between content comprehensiveness scores and video duration (r = 0.420, P < 0.001), number of comments (r = 0.195, P < 0.05), and number of shares (r = 0.174, P < 0.05). GQS scores were negatively or positively correlated with the number of days since upload (r = −0.212, P < 0.05) and video duration (r = 0.442, P < 0.001).ConclusionThe overall quality of the videos was found to be unsatisfactory, with variations in quality scores observed across different video sources. Content comprehensiveness was inadequate, the reliability and quality of the information presented in the videos was questionable. As TikTok continues to grow as a platform for health information, it is essential to prioritize accuracy and reliability to enhance patients’ self-care abilities and promote public health

Automatic Search of Meet-in-the-Middle Preimage Attacks on AES-like Hashing

The Meet-in-the-Middle (MITM) preimage attack is highly effective in breaking the preimage resistance of many hash functions, including but not limited to the full MD5, HAVAL, and Tiger, and reduced SHA-0/1/2. It was also shown to be a threat to hash functions built on block ciphers like AES by Sasaki in 2011. Recently, such attacks on AES hashing modes evolved from merely using the freedom of choosing the internal state to also exploiting the freedom of choosing the message state. However, detecting such attacks especially those evolved variants is difficult. In previous works, the search space of the configurations of such attacks is limited, such that manual analysis is practical, which results in sub-optimal solutions. In this paper, we remove artificial limitations in previous works, formulate the essential ideas of the construction of the attack in well-defined ways, and translate the problem of searching for the best attacks into optimization problems under constraints in Mixed-Integer-Linear-Programming (MILP) models. The MILP models capture a large solution space of valid attacks; and the objectives of the MILP models are attack configurations with the minimized computational complexity. With such MILP models and using the off-the-shelf solver, it is efficient to search for the best attacks exhaustively. As a result, we obtain the first attacks against the full (5-round) and an extended (5.5-round) version of Haraka-512 v2, and 8-round AES-128 hashing modes, as well as improved attacks covering more rounds of Haraka-256 v2 and other members of AES and Rijndael hashing modes

The Use of Machine Translation among Chinese Students

The study aims at investigating how the use of Machine Translation, especially its latest development ChatGPT hinders or develops students’ English language learning in a prestigious university in Southwest China. A qualitative approach is adopted, involving focus-group interviews, classroom observations and field notes. It is found that the use of Machine Translation is not an issue at all with many supervisors and yet the lack of cognitive engagement with the English language poses a serious threat to the ELT classroom. Implications for teaching practices are explored