BackgroundTikTok is the world’s fastest-growing video application, with 1.6 billion users in 2021. More and more patients are searching for information on genitourinary cancers via TikTok. We aim to evaluate the functional quality and reliability of genitourinary cancer-related videos on it and share our thoughts based on the results for better public health promotion.Materials and MethodsWe retrieved 167 videos on bladder, prostate, and kidney cancer from TikTok. Only 61 videos (36.53%) met the inclusion criteria and were eventually regarded as sample videos. Each video’s length and descriptions, hashtags, number of views/likes/comments, forms of expression, and the uploader’s profile were included. Three validated assessment instruments: the Hexagonal Radar Schema, the Health on the Net Code scale, and the DISCERN instrument, were used for evaluating the quality and reliability of the information. All misinformation was counted and categorized. Univariate analysis of variance was performed for analyzing the results. The Post-Hoc least significant difference test was conducted to explore further explanation.ResultsAmongst 61 sample videos, healthcare practitioners contributed the most content (n = 29, 47.54%). However, 22 posts (36.07%) were misinformative, and the most common type was using outdated data. More than half of the videos could provide good (> 1 point) content on the diseases’ symptoms and examinations. However, the definition and outcomes were less addressed (tied at 21%). The HONcode scale and the DISCERN instrument revealed a consistent conclusion that most videos (n = 59, 96.72%) on TikTok were of poor to mediocre quality. Videos published by media agencies were statistically better in terms of reliability and overall score (P = 0.003 and 0.008, respectively). Fifty-three videos (86.89%) had at least two unexplained medical terms. Healthcare professionals tend to use professional terms most (mean = 5.28 words).ConclusionsMost videos on genitourinary cancers on TikTok are of poor to medium quality and reliability. However, videos posted by media agencies enjoyed great public attention and interaction. Medical practitioners could improve the video quality by cooperating with media agencies and avoiding unexplained terminologies.

Recently, text-based anomaly detection methods have obtained impressive results in social network services, but their applications are limited to social texts provided by users. To propose a method for generalized evolving social networks that have limited structural information, this study proposes a novel structural evolution-based anomaly detection method ($SeaDM$), which mainly consists of an evolutional state construction algorithm ($ESCA$) and an optimized evolutional observation algorithm ($OEOA$). $ESCA$ characterizes the structural evolution of the evolving social network and constructs the evolutional state to represent the macroscopic evolution of the evolving social network. Subsequently, $OEOA$ reconstructs the quantum-inspired genetic algorithm to discover the optimized observation vector of the evolutional state, which maximally reflects the state change of the evolving social network. Finally, $SeaDM$ combines $ESCA$ and $OEOA$ to evaluate the state change degrees and detect anomalous changes to report anomalies. Experimental results on real-world evolving social networks with artificial and real anomalies show that our proposed $SeaDM$ outperforms the state-of-the-art anomaly detection methods.

In this paper, a novel forgetting factor aided rectangular differential (RD) orthogonal frequency division multiplexing (OFDM) with index modulation (IM) is proposed for dispersive channels, which amalgamates the concept of RD coding for exploiting the benefits of OFDM-IM with the absence of channel state information (CSI). To be more specific, N subcarriers are partitioned into G subblocks for IM mapping. By employing RD coding during two adjacent subblocks, noncoherent detection without CSI can be carried out at the receiver. To further improve the performance, a novel forgetting factor is exploited at the receiver, which can be optimized via a closed form without extra complexity. Based on the derived forgetting factor, the upper bound of the average bit error probability (ABEP) of the proposed RD-OFDM-IM scheme is derived and is validated by the simulation results. Both the theoretical and simulation results indicate that the proposed RD-OFDM-IM scheme is capable of providing a considerable performance gain over its conventional differential OFDM (D-OFDM) counterpart.

A continuous-wave Nd:KGd(WO4)2 single-longitudinal-mode laser is demonstrated with Fabry–Perot etalons in a simple linear cavity. The thermal lens effect is dramatically lowered by propagating the laser beam along the ‘athermal’ direction inside the laser crystal, which is very beneficial to removing the heat generated in the mode selection process. The maximum single-longitudinal-mode output power obtained is 64.8 mW at incident pump power of 4.7 W, corresponding to an optical conversion efficiency of 1.3% and a slope efficiency of 1.7%.