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YouTube/ Bilibili/ TikTok videos as sources of medical information on laryngeal carcinoma: cross-sectional content analysis study

BackgroundYouTube, a widely recognized global video platform, is inaccessible in China, whereas Bilibili and TikTok are popular platforms for long and short videos, respectively. There are many videos related to laryngeal carcinoma on these platforms. This study aims to identify upload sources, contents, and feature information of these videos on YouTube, Bilibili, and TikTok, and further evaluate the video quality.MethodsOn January 1, 2024, we searched the top 100 videos by default sort order (300 videos in total) with the terms “laryngeal carcinoma” and “throat cancer” on YouTube, “喉癌” on Bilibili and TikTok. Videos were screened for relevance and similarity. Video characteristics were documented, and quality was assessed by using the Patient Education Materials Assessment Tool (PEMAT), Video Information and Quality Index (VIQI), Global Quality Score (GQS), and modified DISCERN (mDISCERN).ResultsThe analysis included 99 YouTube videos, 76 from Bilibili, and 73 from TikTok. Median video lengths were 193 s (YouTube), 136 s (Bilibili), and 42 s (TikTok). TikTok videos demonstrated higher audience interaction. Bilibili had the lowest ratio of original contents (69.7%). Treatment was the most popular topic on YouTube and Bilibili, while that was the prognosis on TikTok. Solo narration was the most common video style across all platforms. Video uploaders were predominantly non-profit organizations (YouTube), self-media (Bilibili), and doctors (TikTok), with TikTok authors having the highest certification rate (83.3%). Video quality, assessed using PEMAT, VIQI, GQS, and mDISCERN, varied across platforms, with YouTube generally showing the highest scores. Videos from professional authors performed better than videos from non-professionals based on the GQS and mDISCERN scores. Spearman correlation analysis showed no strong relationships between the video quality and the audience interaction.ConclusionsVideos on social media platforms can help the public learn about the knowledge of laryngeal cancer to some extent. TikTok achieves the best flow, but videos on YouTube are of the best quality. However, the video quality across all platforms still needs enhancement. We need more professional uploaders to ameliorate the video quality related to laryngeal carcinoma. Content creators also should be aware of the certification, the originality, and the style of video shooting. As for the platforms, refining the algorithm will allow users to receive more high-quality videos.

Maintain sort order of grain boundary to investigate the deformation mechanism of CoCuFeNiPd high–entropy alloys

Molecular dynamics simulations of tension tests on CoCuFeNiPd high–entropy alloys (HEAs) are performed to study the effects of grain sizes (d), temperatures, and strain rates on the mechanical properties. After a series of tension simulations with different grain sizes, the Hall – Petch (H–P) and inverse Hall – Petch relationships are discovered. Hall–Petch breakdown is observed at grain size (d) of 10 nm. In the inverse Hall – Petch region, the average flow stress (σ(ε)) decreases from 1.97 to 1.76 (GPa) when grain size is decreased from 10 nm to 4 nm. On the contrary, the average flow stress increased from 1.90 to 1.97 (GPa) as d reduced from 18 nm to 10 nm. The microstructural evolution of the CoCuFeNiPd HEAs model exhibits that the primary deformation process in the H–P relation (d > 10 nm) is dislocation slips. The grain boundaries (GB) prevent dislocation expansion in the H– P relation, causing mechanical strengthening. Meanwhile, the main deformation mechanisms for the inverse H–P (d < 10 nm) are grain rotation and GB migration, decreasing flow stress. In addition, Young's modulus of the HEAs model increases with grain size (d). The yield strength, flow stress, and Young's modulus of HEAs specimens tend to reduce at high temperatures. Additionally, the number dislocation length reduces at high temperatures due to the amorphization phenomenon. The results also showed that Young's modulus and yield strength increase when strain rates rise.

User-oriented metrics for search engine deterministic sort orders

A recent paper proposes the “buying power” (bp) metric for assessing the quality of the product rankings generated by e-commerce sites such as Amazon and eBay. Focusing on the “ordered by price” type of product listing that is often viewed after a keyword search, bp is offered as a way of differentiating between helpful rankings (high bp scores) and unhelpful rankings (low bp scores), with those bp scores intended to reflect both the quality of the product match and also the relative pricing of the items that are listed. In this paper we adopt a user-centric viewpoint from which to evaluate the merits of bp as a scoring mechanism for product rankings, and provide an example that shows bp acting in opposition to likely user reactions. We then describe an alternative product ranking effectiveness metric, price biased gain (PBG), arguing that since it embeds a more plausible user model, it is more likely to reflect the opinions of the user viewing any given product ranking. We give a number of scenarios and motivating examples in support of our alternative proposal, and also discuss its limitations.

The new WHO classification of jaw tumours

Maxillofacial tumours cover abroad spectrum of lesions, including neoplasms, hamartomatous changes and developmental disorders. Since the beginning of 2022, abeta version of the 5th edition of the WHO classification for head and neck tumours has been available online, and aprint version is expected to be published in mid-2023. From aconceptual point of view, little has been changed compared to the 4th edition; the sort order of lesions is more rigorously arranged according to benign and malignant behaviour and identical tumour types are no longer described redundantly in different chapters depending on their location. The diagnostic criteria are now summarized as "essential" and "desirable", and in addition to the clinical features, imaging is now also incorporated, providing an interdisciplinary approach to the classification. Afew new entities are included for the first time. This article gives an overview of the main changes introduced in the new WHO classification with aspecial emphasis on fibro-osseous lesions of the craniofacial skeleton.

Using Q Methodology to Understand Faculty Development Needs to Prepare for Next Generation NCLEX.

Using Q Methodology to Understand Faculty Development Needs to Prepare for Next Generation NCLEX.

Detection of Loss-of-Excitation in Synchronous Machines using Hybrid GMDH Neural Network Intelligent Algorithm in the Presence of UPFC in Power System

Faults in the power system should be detected as soon as possible. Otherwise, some defects in the protection system can result in imposing considerable destructive damage to the system equipment. The excitation system of a synchronous generator plays a significant role in the proper operation and control of the generator. Loss-of-excitation (LOE) may cause harmful effects on both the generator and power system. If LOE is not correctly detected, an unnecessary trip signal may be issued, resulting in the overloading of parallel generators and even a system blackout. Conventional techniques for detecting this fault are based on impedance measurement. Despite the simple structure of these techniques, they are sometimes unable to detect some faults correctly. The presence of flexible alternating current transmission system devices can significantly affect the impedance characteristic of the system and as a result, the operation of impedance relays. In this article, first, LOE and other bugs causing failures and errors in the protection of LOE are simulated in the presence and absence of a unified power flow compensator (UPFC), and parameters with different behavior are determined. Then, the most sensitive parameters are sorted using an intelligent method based on their sensitivity. Once the sort order table is obtained, the first two parameters are applied to a ground data handling neural network method, and LOE is detected. The simulation results show that the proposed algorithm is significantly reliable in detecting LOE faults. Moreover, LOE is detected very quickly in the presence or absence of the UPFC in the system by preventing the tolerance of electrical and mechanical stresses by the machine and system; thus, the maloperation of the protection system is avoided.

A method for assisting the accident consequence prediction and cause investigation in petrochemical industries based on natural language processing technology

Risk analysis for production processes in the petrochemical industry is an important procedure for consequence prediction and investigation of accidents. The analyzer must grasp the correlations between the possible causes and consequences. From the potential cause and effect found in risk analysis reports, complete clarification should be obtained. Therefore, this study presents a method for assisting accident consequence prediction and investigation in the petrochemical industry based on risk analysis reports using natural language processing technology. First, a hazard and operability (HAZOP) historical data table is established by filling over 7200 HAZOP analysis data points. Both the causes and consequences in the table are classified into 20 categories each using the Latent Dirichlet Allocation (LDA) models. The LDA clustering results are assigned classification for the cause and consequence topics to the cause and consequences of the HAZOP analysis data. Based on part-of-speech (POS) tagging, all the words in each cause and consequence record are divided into subject and action words. Next, the word combinations of subject and action words with a higher occurrence are considered the key phrases for describing and representing the corresponding cause and consequence topic classifications. The Apriori algorithm is used to determine the frequent item sets, acquire the association rules, and calculate the association degree to obtain the sort order; it can highlight general trends in relational cause and consequence topics. According to the results, the most likely cause of the consequence and the most likely consequence that the cause may lead to are identified. Finally, a visual interface is developed to present the data for the consequence prediction and cause investigation of accidents. The results reveal that the quantity and quality of historic data are important factors that may influence the results. This method can contribute to predicting the accident evolution trend of an abnormal situation, taking preventive measures in advance, improving the accuracy of early warning, and supporting emergency response measures.

A privacy-preserving trading strategy for blockchain-based P2P electricity transactions

With the rising demand for energy in distribution networks and the growing development of communication technology, peer-to-peer (P2P) electricity trading realized by blockchain technology not only promotes the participation of distributed generation resources in the energy supply but also enhances the fairness of P2P electricity trading through the decentralized and transparent characteristics of the blockchain. However, owing to the openness and transparency of the on-chain data in the blockchain, the order information submitted by P2P market participants to the blockchain may encounter threats of information disclosure. To address this challenge, we have designed a novel three-layer architecture for P2P electricity trading, whose primary objective is to protect the private information submitted to the blockchain through a new privacy-preserving trading strategy. The proposed three-layer architecture is composed of a physical layer, information layer and market layer. An on-chain data protection method with homomorphic encryption and secure multi-party computing (SMPC) is used to sort and clear encrypted orders in the information layer, and the trading checking and safe settlement process of encrypted orders in continuous double auction (CDA) market are realized through grid security-constrained market mechanism in the market layer. In addition, the effectiveness of the proposed method is verified by case studies running on the IBM Hyperledger Fabric.

Efficient Sorting, Duplicate Removal, Grouping, and Aggregation

Database query processing requires algorithms for duplicate removal, grouping, and aggregation. Three algorithms exist: in-stream aggregation is most efficient by far but requires sorted input; sort-based aggregation relies on external merge sort; and hash aggregation relies on an in-memory hash table plus hash partitioning to temporary storage. Cost-based query optimization chooses which algorithm to use based on several factors, including the sort order of the input, input and output sizes, and the need for sorted output. For example, hash-based aggregation is ideal for output smaller than the available memory (e.g., Query 1 of TPC-H), whereas sorting the entire input and aggregating after sorting are preferable when both aggregation input and output are large and the output needs to be sorted for a subsequent operation such as a merge join. Unfortunately, the size information required for a sound choice is often inaccurate or unavailable during query optimization, leading to sub-optimal algorithm choices. In response, this article introduces a new algorithm for sort-based duplicate removal, grouping, and aggregation. The new algorithm always performs at least as well as both traditional hash-based and traditional sort-based algorithms. It can serve as a system’s only aggregation algorithm for unsorted inputs, thus preventing erroneous algorithm choices. Furthermore, the new algorithm produces sorted output that can speed up subsequent operations. Google’s F1 Query uses the new algorithm in production workloads that aggregate petabytes of data every day.

Quality metrics for search engine deterministic sort orders

eCommerce search engines such as eBay and Amazon often allow the user to order their search results on deterministic features such as price, time, or distance from the seller. Using metrics such as precision at 10 documents (P@10), others have already shown that the quality of deterministically sorted results is lower than that of best-match (or relevance) sorted results, and that work is needed in order to improve result quality. But metrics such as P@10 are based purely on relevance, and do not reflect the order-feature: cost (be it price, time, or otherwise) — and it is hard to see how to improve a system without a metric that reflects the quality of the ordering. In this contribution we introduce a set of metrics that, using relevance and cost, measure the quality of deterministically sorted search engine results. We examine metrics from the perspective of the buyer, the seller, and the systems engineer. Using our new metrics (buying power (bp), buying power for K (bp4k), selling power (sp), and cheapest precision (Pc)) we re-evaluate the results of the “eBay SIGIR 2019 eCommerce Search Challenge: High Accuracy Recall Task” and demonstrate how to include cost in a metric designed to evaluate the quality of deterministically ordered lists of results.

The climate cost of saving water by different plastic mulching patterns

Global water shortage and climate change are two of the most significant challenges for our planet, and how to coordinate their relationship is a problem unsolved. In this study, we proposed the synchronization index of water-saving and CO2 mitigation to evaluate the climate cost of saving water by different plastic mulching patterns and explore way to improve the synchronization. The water-saving performance, carbon footprint, and the synchronization index were analyzed for three typical plastic mulching patterns, namely conventional partial plastic mulching, full plastic mulching, and “one film for two years” mulching. The results indicated that all three types of plastic mulching systems were effective in reducing the water footprint of crop production, with full plastic mulching showing the best performance in terms of improving crop yields and water savings. The application of plastic mulching mainly influences the carbon footprint through increasing indirect greenhouse gas emission, accelerating soil organic carbon decline and increasing N2O emission. Area-scale carbon footprint showed a significant positive relationship with the input amounts of plastic film, and the sort order of yield-scale carbon footprint was partial plastic mulching > full plastic mulching > “one film for two years” mulching. Calculated synchronization index was -0.35 kg CO2-eq m−3, -0.10 kg CO2-eq m−3, and 0.50 kg CO2-eq m−3 for partial plastic mulching, full plastic mulching, and “one film for two years” mulching, respectively. Full plastic mulching and partial plastic mulching resulted in an additional 0.35 and 0.10 kg of CO2-eq for saving one cubic meter of water, and only “one film for two years” mulching resulted in simultaneous water savings and greenhouse gas mitigation. This study suggests that water savings achieved using conventional partial plastic mulching have costs in terms of climate change. Faced with the dual pressure of global water shortages and climate change, controlling input amounts of plastic film and prolonging the lifetime of the film are simple and effective way to coordinate water savings and greenhouse gas mitigation. Moreover, the results of this study suggested that water savings and GHG emission mitigation cannot necessarily be achieved simultaneously and that searching for both water-saving performance and its synchronization with GHG mitigation should become a new paradigm for the development of water-saving technologies in the future.

HUBUNGAN MAZHAB (ALIRAN) HUKUM POSITIVISME DENGAN PERKEMBANGANG HUKUM DI INDONESIA

Schools of legal positivism has been associated with the development of law in Indonesia, namely the creation of the sort order for the legislation set out in the Decree of the People's Consultative Assembly (MPR) as well as regulated in Law No. 10 of 2004 which refers to the Stufenbau Theory. Besides, the schools also lead to the formation of the Constitutional Court as an institution that serves as the guardian of the constitution.

ETKDS: An efficient algorithm of Top-K high utility itemsets mining over data streams under sliding window model

The researcher proposed the concept of Top-K high-utility itemsets mining over data streams. Users directly specify the number K of high-utility itemsets they wish to obtain for mining with no need to set a minimum utility threshold. There exist some problems in current Top-K high-utility itemsets mining algorithms over data streams including the complex construction process of the storage structure, the inefficiency of threshold raising strategies and utility pruning strategies, and large scale of the search space, etc., which still can not meet the requirement of real-time processing over data streams with limited time and memory constraints. To solve this problem, this paper proposes an efficient algorithm based on dataset projection for mining Top-K high-utility itemsets from a data stream. A data structure CIUDataListSW is also proposed, which stores the position of the item in the transaction to effectively obtain the initial projected dataset of the item. In order to improve the projection efficiency, this paper innovates a new reorganization technology for projected transactions in common batches to maintain the sort order of transactions in the process of dataset projection. Dual pruning strategy and transaction merging mechanism are also used to further reduce search space and dataset scanning costs. In addition, based on the proposed CUDHSW structure, an efficient threshold raising strategy CUD is used, and a new threshold raising strategy CUDCB is designed to further shorten the mining time. Experimental results show that the algorithm has great advantages in running time and memory consumption, and it is especially suitable for the mining of high-utility itemsets of dense datasets.

Genetically Engineered Cellular Membrane Vesicles as Tailorable Shells for Therapeutics.

Benefiting from the blooming interaction of nanotechnology and biotechnology, biosynthetic cellular membrane vesicles (Bio‐MVs) have shown superior characteristics for therapeutic transportation because of their hydrophilic cavity and hydrophobic bilayer structure, as well as their inherent biocompatibility and negligible immunogenicity. These excellent cell‐like features with specific functional protein expression on the surface can invoke their remarkable ability for Bio‐MVs based recombinant protein therapy to facilitate the advanced synergy in poly‐therapy. To date, various tactics have been developed for Bio‐MVs surface modification with functional proteins through hydrophobic insertion or multivalent electrostatic interactions. While the Bio‐MVs grow through genetically engineering strategies can maintain binding specificity, sort orders, and lead to strict information about artificial proteins in a facile and sustainable way. In this progress report, the most current technology of Bio‐MVs is discussed, with an emphasis on their multi‐functionalities as “tailorable shells” for delivering bio‐functional moieties and therapeutic entities. The most notable success and challenges via genetically engineered tactics to achieve the new generation of Bio‐MVs are highlighted. Besides, future perspectives of Bio‐MVs in novel bio‐nanotherapy are provided.

Tsunami

Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing an index for a particular dataset and workload. However, the performance of that work suffers in the presence of correlated data and skewed query workloads, both of which are common in real applications. In this paper, we introduce Tsunami, which addresses these limitations to achieve up to 6X faster query performance and up to 8X smaller index size than existing learned multi-dimensional indexes, in addition to up to 11X faster query performance and 170X smaller index size than optimally-tuned traditional indexes.

Facet Use in Search Tools is Influenced by the Interface but Remains Difficult to Predict

A Review of: Dahlen, S. P. C., Haeger, H., Hanson, K., &amp; Montellano, M. (2020). Almost in the wild: Student search behaviors when librarians aren’t looking. Journal of Academic Librarianship, 46(1), 102096. https://doi.org/10.1016/j.acalib.2019.102096 Abstract Objective – To examine the relationship between student search behaviours and the quality of scholarly sources chosen from among library search tools. Design – Unmonitored search sessions in a facilitated library setting. Setting – A mid-sized public university in the United States of America. Subjects – 50 upper-level undergraduate students in the social and behavioural sciences. Methods – Recruited participants were given one of two search prompts and asked to use EBSCO’s Social Science Abstracts and two configurations of ProQuest’s Summon, with one being pre-scoped to exclude newspapers and include subject areas within the social sciences. The search tools were assigned in random order. In each case, the participant was asked to find two of the “best quality” articles (p. 3). A librarian was present in the room but did not observe participants; instead, all sessions were recorded using Camtasia Relay. Afterwards, participants were interviewed about the process they used and their impressions of the search tools. They also completed a survey collecting information on their GPA and whether they had previously had library instruction. Main Results – Facet use differed significantly between the EBSCO database and Summon, though not between the two different configurations of Summon. There was a significant relationship between high use of facets in one platform being connected to high use in the other platforms. In contrast to some previous studies, a non-trivial proportion of participants went beyond the first page of search results. In support of most previous studies, participants infrequently searched on the subject field or changed the default sort order. Summon’s article suggestion feature was noted as being especially helpful, and clicking on suggested articles was significantly correlated with the number of article records viewed. Conclusion – The choice of search tool has a large influence on students’ subsequent search behaviour. Many of the advanced features are still missed by students, although in this study the majority of sources picked were of high quality. The authors note the importance of configuring the interface so that facets and other features deemed worthwhile by librarians are higher up on the page. The researchers reason that the prominent display of facets leads to greater uptake. Despite finding no association between library instruction and facet use, teaching students how to use facets remains an advisable strategy.

Novel Regularization for Learning the Fuzzy Choquet Integral With Limited Training Data

Fuzzy integrals (FIs) are powerful aggregation operators that fuse information from multiple sources. The aggregation is parameterized using a fuzzy measure (FM), which encodes the worths of all subsets of sources. Since the FI is defined with respect to an FM, much consideration must be given to defining the FM. However, in practice this is a difficult task—the number of values in an FM scales as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^n$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of input sources, thus manually specifying an FM quickly becomes tedious. In this article, we review an automatic, data-supported method of learning the FM by minimizing a sum-of-squared error objective function in the context of decision-level fusion of classifiers using the Choquet FI. While this solves the specification problem, we illuminate an issue encountered with many real-world data sets; i.e., if the training data do not contain a significant number of all possible sort orders, many of the FM values are not supported by the data. We propose various regularization strategies to alleviate this issue by pushing the learned FM toward a predefined structure; these regularizers allow the user to encode knowledge of the underlying FM to the learning problem. Furthermore, we propose another regularization strategy that constrains the learned FM's structure to be a linear order statistic. Finally, we perform several experiments using synthetic and real-world data sets and show that our proposed extensions can improve the learned FM behavior and classification accuracy. A previously proposed visualization technique is employed to simultaneously quantitatively illustrate the FM as well as the FI.

A ortografia da língua Awetí

Este trabalho descreve e fundamenta a ortografia da língua Awetí (Tupí, Alto Xingu/mt), com base na análise da estrutura fonológica e gramatical do Awetí. A ortografia é resultado de um longo trabalho colaborativo entre os três autores, iniciado em 1998. Ela não define apenas um alfabeto (a representação das vogais e das consoantes da língua), mas também aborda a variação interna, ressilabificação, lenição, palatalização e outros processos (morfo‑)fonológicos. Tanto a representação escrita da oclusiva glotal, quanto as consequências ortográficas da harmonia nasal receberam uma atenção especial. Apesar de o acento lexical não ser ortograficamente marcado em Awetí, a grande maioria dos afixos e partículas é abordada considerando o acento e sua interação com morfemas adjacentes, ao mesmo tempo determinando as palavras ortográficas. Finalmente foi estabelecida a ordem alfabética em que dígrafos são tratados como sequências de letras, já a oclusiva glotal ⟨ʼ⟩ é ignorada, facilitando o aprendizado do Awetí. A ortografia tal como descrita aqui tem sido usada por aproximadamente dez anos na escola para a alfabetização em Awetí, com bons resultados obtidos. Acreditamos que vários dos argumentos aqui levantados podem ser produtivamente transferidos para outras línguas com fenômenos semelhantes (a oclusiva glotal como consoante, harmonia nasal, assimilação morfo-fonológica, etc.).

Arabic terms in embedded book indexes

Texts that include names or terms that use special characters with diacritics can present difficulties for indexers, and more so when the index is embedded in Microsoft Word or another program. Texts about Islam or the Middle East present several specific difficulties arising from the transliteration of Arabic into English. Even when diacritics are not used, many authors will still represent the letters ayn and hamza that, along with the definite article al-, should be ignored in the sort order. Indexing software can handle these tasks. However, when an index is to be embedded, the indexer must also try to get the indexing program to communicate with two or three software programs that code differently: the embedding program, the program in which the text is written, and possibly a layout program that will be used later. This article explains how to use Unicode characters and forced sorting in SKY Index 7 and SKY Index 8 for an index to be embedded in a Microsoft Word document using the DEXembed add-on. It touches on other issues to be considered if the document is to be flowed into InDesign, a layout program. This information is also relevant for other indexing programs and the transliteration of languages other than Arabic.

Deep Feature Ranking for Person Re-Identification

Person re-identification plays a critical part in many surveillance applications. Due to complicated illumination environments and various viewpoints, it is still a challenging problem to extract robust features. To solve this issue, we propose a novel deep feature ranking scheme. Our main contribution is to rank achieved deep features, which are obtained by classic deep learning model, and set the sort order number as our feature vector, named as ordinal deep features (ODFs). Person re-identification results are acquired by ranking person candidates by measuring distance based on ODFs. Since applying for rank orders rather than original feature values, our method achieves robust results, especially under the situation of viewpoints shift. Comprehensive experiments are carried out to demonstrate the significance of the proposed feature. Meanwhile, comparative experiments are applied over the publicly available dataset, our method achieves promising performance and outperforms the state of the art methods. Moreover, we applied the proposed feature in the scenario of image classification and discussed the effectiveness.