Upload Time Research Articles

Do Hospitals Get EMS Patient Care Reports?

Background: EMS patient care reports (PCRs) are an important component of the EMS system and a foundational element of quality improvement. In many cases, PCRs provide hand-offs between providers, and may be the only context for patient presentation. Little data exists regarding the frequency and timeliness of PCR provision to Emergency Departments (ED). Objective: To describe the frequency of missing PCRs following EMS transport and timing of receipt by EDs within a regional healthcare system. Methods: We performed a retrospective study of EMS PCRs for patients transported to a single health system from 1/1/21 – 7/1/22. We stratified agencies by 911 response or interfacility transport. PCR receipt and time of EPIC upload were obtained from EPIC Crystal Report® query. Primary outcome was frequency of PCR receipt by EDs (in aggregate and by agency). Secondary outcome was interval from EMS ED arrival to EMR upload (greater or less than 120 minutes of arrival). We provide descriptive statistics reporting proportion of receipt of PCR (mean, range) by hospital and agency Results: There were 106,496 patients transported by 62 agencies, 87.3% were 911 responders. Overall, receipt of PCRs varied substantially by hospital – 54.2% (13.5% - 80.1%). 30.7% (2.4% - 63.4%) were uploaded within 120 minutes of ED arrival. PCR submission rate varied substantially by agency, 45.8% (2.5% - 96.8%). Interfacility EMS services were far less likely to submit a PCR after ED transport (35.9% vs. 56.9%). Conclusions: PCR’s are frequently missing after EMS transport within this large hospital system, and infrequently available for timely review. Interfacility transport agencies are less likely to submit compared to 911 responders. Although hospital manual upload of PCRs may impact time of their receipt, this does not explain the overall rate of missing PCRs. Further work is needed to identify obstacles to PCR delivery and assure their availability.

Analysis of image compression methods for storage in decentralized blockchain repositories

The methods of image compression for storage in decentralized blockchain repositories, specifically in the IPFS system, are examined. A methodology for evaluating the efficiency of different image compression algorithms is proposed, incorporating both lossless compression (PNG, TIFF, GIF) and lossy compression (JPEG, WEBP). The comparison includes metrics such as file size, image quality (measured by PSNR), upload time to IPFS, and bandwidth for each compression method. Each image is hashed, and the hash is stored in a smart contract within a local blockchain network deployed using Ganache. Such methods enable the optimization of image storage and transfer within blockchain repositories, ensuring a balance between image quality, file size, and access speed.

Construction of English corpus oral instant translation model based on internet of things and deep learning of information security

In order to improve the security and performance of the oral English instant translation model, this paper optimizes the instant translation model through the Internet of Things (IoT) security technology and deep learning technology. In this paper, the real-time translation model based on deep learning and IoT technology is analyzed in detail to show the application of these two technologies in the real-time translation model, and the related information security issues are discussed. Meanwhile, this paper proposes a method combining deep learning network and IoT technology to further improve the security of instant translation model. The experimental results show that under the optimized model, the parameter upload time is 60 seconds, the aggregation calculation time is 6.5 seconds, and the authentication time is 7.5 seconds. Moreover, the average recognition accuracy of the optimized model reaches 93.1%, and it is superior to the traditional machine translation method in accuracy and real-time, which has wide practical value and application prospects. Therefore, the research has certain reference significance for improving the security of the English corpus oral instant translation model.

Secure pharmaceutical supply chain using blockchain in IoT cloud systems

Supply Chain Management (SCM) systems require time sequencing, coordination and tracking the movement of goods and processes. Internet of Things (IoT) and Blockchain technologies are useful to develop a secure automated SCM. IoT devices with in-built sensors and actuators help to keep track of the state, location, temperature or other parameters of an entity, and control the automation of routine as well as hazardous tasks. Blockchain technology supports time-stamping, authentication, process coordination, non-repudiation, commercial transactions, and also provides security for transactions and storage. The pharmaceutical SCM demands accurate, immediate and secure control system. Additionally, the supply chain process data from IoTs is stored and processed in Cloud by Analytics applications, for business planning and improvement. An efficient and secure IoT-Cloud-Blockchain based system for both SCM automation and analytics has been proposed in this work. It leverages a hierarchical IoT, Mist, Edge, Fog, Cloud computing (IMEFC) architecture to enhance Communication-Response-Compute-Security-Storage (CRCSS) in the system. Blockchain technology provides security for the SCM transactions and data. The efficiency of the Blockchain is measured in terms of upload time, download time and transaction fees for Bitcoin, Ethereum and Filecoin platforms. The Filecoin blockchain platform is quicker and cost-effective for larger file sizes, compared to Ethereum and Bitcoin, making it suitable for Pharmaceutical SCM systems.

Improving Security on Election Results Data Transmission via Cloud Using Hybrid Homomorphic Encryption

Elections in recent years have become topical issues and characterized by violence and conflicts leading to loss of lives and properties. The integrity and confidentiality of the declared collated results have been questioned by individuals and organisations with keen interest in the outcomes of every election. Different countries have adopted different Election Results Management Systems (RMS) to help present a credible, fair and transparent election results. These systems adopted are not without criticisms and suspicions. This research paper has presented various factors that need to be considered when selecting a Results Management System (RMS) for elections. A cloud-based using a Hybrid homomorphic encryption approach is proposed in managing the election results data security and transmission. The proposed scheme has demonstrated effectiveness in handling data integrity, data confidentiality, data privacy and access control. The proposed scheme presented has enhanced the security of election results data against Chosen Ciphetext Attacks (CCA) and Denial of Service (DoS) as well as other cyber related attacks. The time required for the entire election data encryption, transmission, decryption, upload time and download time has been greatly enhanced with the proposed system. The proposed system workflow algorithm, key generation algorithm, encryption algorithm and decryption have been presented in this research paper. The outcome of the research work indicates that only 0.00078 seconds is required to generate keys for about 100 users. About 0.705 seconds and 0.863 seconds is required for the encryption and decryption of a 500MB election results data. It was observed that the overall election results data transmission time is about 51.779 seconds which is less than one minute (60 seconds) for about 500MB data size of the election results data. This paper makes a case for the adoption and implementation of the proposed system since it performs better in terms of securing the election results data and transmission time in the cloud environment.

Myopia information on TikTok: analysis factors that impact video quality and audience engagement

BackgroundTikTok is emerging as a vital platform for health information dissemination. Despite myopia being a global public health issue, the high-quality myopia information shared by health educators often fails to go viral. It is imperative to analyze the factors influencing video quality and popularity, especially from diverse perspectives of researchers, health educators, and audiences.MethodsTikTok myopia-related videos were retrieved using TikTok’s default comprehensive search (DCS) and most liked search (MLS) strategies. Venn diagrams were employed to illustrate the relationships and commonalities between the two strategies across four sample sizes (top 200, 150, 100, and 50). Video metadata, including details such as creator information, production properties, upload time, video duration, and viewer engagement, were collected. Video quality was assessed using the DISCERN tool. Video content covering six aspects of myopia were evaluated. The impact of search strategies, video sample sizes, production properties, and myopia content on video quality and audience engagement was analyzed through single-factor or multi-factor analysis.ResultsDCS and MLS retrieval strategies, as well as varying sample sizes, resulted in differences in audience engagement for myopia videos (P < 0.039), while The DISCERN quality scores remained comparable (P > 0.221). Videos published by healthcare professionals (HCPs) and non-profit organizations (NPOs) were associated with high-quality (P ≤ 0.014) but comparatively lower popularity (P < 0.033). Videos that reported contents of risk factors, management, and outcomes showed high popularity (P < 0.018), while longer video duration (> 60s) exhibited the opposite trend (P < 0.032). Content on myopia evaluation (P ≤ 0.001) and management (P ≤ 0.022) and video duration were positively correlated with higher DISCERN quality.ConclusionVideos created by HCPs and NPOs deserve greater attention. Rather than pursuing entertaining effects, professional educators should emphasize producing concise, and high-quality myopia content that readily resonates with the audience and has the potential to go viral on the platform.

Assessment of the Quality and Reliability of Content Available on YouTube About Palpitations.

Palpitations refer to the sensation of rapid, fluttering, or pounding heartbeats in the chest, the determinants of which may range from hormonal changes to anxiety or arrhythmias. YouTube is one of the most prevailing and accepted web-based platforms people trust to help them understand more about their health conditions. Thus, this study aims to assess whether the quality of content about palpitations on this platform is reliable and sufficient. Seventy-one YouTube videos were analyzed using criteria such as date and time of upload, type of uploader, and type of content. The Global Quality Score (GQS) and modified DISCERN score were used to analyze the quality and reliability of the information provided. Microsoft Excel (Microsoft Corporation, Redmond, WA, US) was used for data analysis, and StataCorp's 2023 Stata Statistical Software (College Station, TX, US) was used for statistical analysis and visualization. Of the 71 videos analyzed, 90.14% were uploaded more than a year ago, 80.28% described the symptomatology in detail, and 81.69% accurately described the etiological factors. Hospitals and doctors were the most common uploaders, constituting 23% and 19% of the uploaded videos, respectively, and had high GQSs (Median GQS = 4). The highest scores also belonged to videos uploaded by patients suffering from the disease (Median GQS = 5). Hospitals and news channels ranked highest on the reliability score (Median DISCERN = 4, respectively). It was determined that despite varied sources, the nature of content provided by the platform contains promotional material and content gaps; YouTube should, therefore, be used critically and as per professional sources.

Text-Based Content Analysis on Social Media Using Topic Modelling to Support Digital Marketing

This study aims to create Social Media Analytics (SMA) tools to help Digital Marketers or Content Creators create content topics for creating text-based Instagram content and support digital marketing strategy. Since no SMA tools can provide topic discovery for text-based Instagram content, this research aims to make an SMA tool. The data requirements to make an SMA tool include content text, content caption text, likes, comments, upload time, and content category obtained through the Instascrapper. The method used in this study is the Topic Modelling method using the Latent Dirichlet Allocation (LDA) approach to find the most dominant topic in the content. Optical Character Recognition (OCR) performs an image transformation process to extract text from text-based Instagram content images. The results of SMA tool creation are tested on three expert users, which shows that 93% of test participants could use the SMA to find topic references, and 85% can still be used by users even though they find it difficult. Since the test result shows that SMA tools still need development, for further research, SMA tools can focus on developing the user experience to increase the value of user acceptance by paying attention to the ease of the SMA tools. Also, SMA tools can focus on target users such as Data Analysts, Business Intelligence Analysts, or others within a company to support decision-making for the marketing department.

An Efficient Secure Sharing of Electronic Health Records Using IoT-Based Hyperledger Blockchain

Electronic Health Record (EHR) systems are a valuable and effective tool for exchanging medical information about patients between hospitals and other significant healthcare sector stakeholders in order to improve patient diagnosis and treatment around the world. Nevertheless, the majority of the hospital infrastructures that are now in place lack the proper security, trusted access control, and management of privacy and confidentiality concerns that the current EHR systems are supposed to provide. Goal. For various EHR systems, this research proposes a Blockchain-enabled Hyperledger Fabric Architecture as a solution to this delicate issue. The three steps of the suggested system are the secure upload phase, the secure download phase, and authentication. Patient registration, login, and verification make up the authentication step. The administrator grants authorization to read, edit, delete, or revoke the files following user details verification. In the secure upload phase, feature extraction is carried out first, and then a hashed access policy is created from the extracted feature. Next, the hash value is stored in an IoT-based Hyperledger blockchain. The uploaded EHR files are additionally encrypted before being stored on the cloud server. In the secure download step, the physician uses a hashed access policy to send the request to the cloud and decrypts the corresponding files. The experimental findings demonstrate that the system outperformed cutting-edge techniques. The proposed Modified Key Policy Attribute-Based Encryption performs better for the remaining 10 to 25 mb file sizes. This IoT framework compares MKP-ABE with certain efficiency indicators, such as encryption, decryption period, protection level analysis and encrypted memory use, resource use on decryption, upload time, and transfer time, which are present in the KP-ABE, the ECC, RSA, and AES. Here, the IoT device suggested requires 4008 ms for data encryption and 4138 ms for the data decryption.

Is it safe to learn about vital pulp capping from YouTube™ videos? A content and quality analysis

AimTo evaluate YouTube videos on vital pulp capping (VPC) for content, quality, source, usefulness, and reliability. Materials and MethodsThis study assessed 249 English-language videos on vital pulp therapy using the Total Content Score (TCS), Video Information and Quality Index (VIQI), Global Quality Scale (GQS), Journal of the American Medical Association (JAMA) score and modified DISCERN score. Videos were categorized based on communication quality, duration, likes and dislikes, views, source, and video type. Of the videos analyzed, 22.1% met the inclusion criteria. Data were analyzed using Shapiro-Wilk, Kruskal-Wallis, and post hoc Bonferroni tests. Sperman's correlation, Kendal tau tests for correlations, and Fisher's exact test were used to assess associations between categorical variables. ResultsThe study identified significant correlations between various parameters. A higher TCS was correlated with increased VIQI (p = 0.005) and GQS scores (r = 0.685, p < 0.05). A moderate positive correlation was found between GQS scores and TCSs (r = 0.577, p < 0.05). VIQI scores were significantly and positively correlated with TCS (r = 0.573, p < 0.05) and comment count (r = 0.306, p < 0.05). JAMA scores were positively correlated with upload time (r = 0.304, p < 0.05), comment count (r = 0.337, p < 0.05), and likes (r = 0.301, p = 0.05). ConclusionsYouTube videos provided low-to-average quality VPC information and tended to be inaccurate. Therefore, public videos may be incorrect, incomplete, and low-quality. Clinicians and patients should seek reliable information from specialists.

Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing

Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources. However, the overwhelming upload traffic may lead to unacceptable uploading time. To tackle this issue, for tasks taking environmental data as input, the data perceived by roadside units (RSU) equipped with several sensors can be directly exploited for computation, resulting in a novel task offloading paradigm with integrated communications, sensing and computing (I-CSC). With this paradigm, vehicles can select to upload their sensed data to RSUs or transmit computing instructions to RSUs during the offloading. By optimizing the computation mode and network resources, in this paper, we investigate an I-CSC-based task offloading problem to reduce the cost caused by resource consumption while guaranteeing the latency of each task. Although this non-convex problem can be handled by the alternating minimization (AM) algorithm that alternatively minimizes the divided four sub-problems, it leads to high computational complexity and local optimal solution. To tackle this challenge, we propose a creative structural knowledge-driven meta-learning (SKDML) method, involving both the model-based AM algorithm and neural networks. Specifically, borrowing the iterative structure of the AM algorithm, also referred to as structural knowledge, the proposed SKDML adopts long short-term memory (LSTM) network-based meta-learning to learn an adaptive optimizer for updating variables in each sub-problem, instead of the handcrafted counterpart in the AM algorithm. Furthermore, to pull out the solution from the local optimum, our proposed SKDML updates parameters in LSTM with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the meta-learning without structural knowledge in terms of both the online processing time and the network performance.

The Marketing Communication Strategy of RSIA Bunda Jakarta Through Instagram Social Media to Enhance Brand Awareness

The development of the health sector in Indonesia has made significant progress, reflected in the increase in the number of hospitals in Indonesia. As the sector grows, people are increasingly realizing the importance of quality healthcare. This phenomenon is the basis for strategic steps of hospitals, such as RSIA Bunda Jakarta which utilizes Social Media, especially Instagram, as the main means of carrying out marketing activities. This research uses the basis of marketing communication theory, especially in the context of message media strategy. This research adopts qualitative methodology with a descriptive approach. Data are obtained by data collection techniques through observation, interviews and documentation. The results showed that RSIA Bunda Jakarta has set their marketing goals on Instagram as the main means of maintaining existence and increasing brand awareness. The right segmentation of the female and Gen Z audience confirms RSIA Bunda Jakarta's understanding of the preferences and needs of their target market. The messaging strategy is supported by accurate medical information, engaging educational content, and active interaction with the audience thus strengthening credibility. As well as at the media strategy stage, adjusting Instagram content and features to audience needs, choosing the right upload time, and utilizing influencer moments and engagement. Overall, this study concluded that the marketing team of RSIA Bunda Jakarta itself has succeeded in carrying out marketing communication strategies through Instagram to increase brand awareness.

Edge Computing in Power IoT Security and Intelligent Monitoring

Abstract In the context of the rapid development of power IoT, the application of edge computing technology has become the key to improving the level of grid intelligence and enhancing the data processing capability. This paper initially designs the edge computing system for electric power IoT based on the edge computing model. Key-edge computing technologies are combined to process and analyze power IoT data in real-time. Simulation experiments have formed and verified an intelligent security monitoring system for electric power IoT using the LightGBM algorithm. The training convergence speed and effectiveness of this paper’s scheme are better than Stroj’s scheme, and this paper’s scheme can increase the security of power IoT data through key generation and filter de-duplication. This paper’s nodes have an average synchronization time of 9.25 ms. The 128MB data node has an upload time of 57143ms. The data sharing time is about 292~7489 ms faster than the comparison scheme, and in the data search phase, the time overhead of this paper’s scheme is less than the comparison scheme. In summary, this paper’s constructed security monitoring system can offer robust technical support for the advancement of intelligent, efficient, and omnipotent power in the Internet of Things.

Development of an augmented reality guidance system for head and neck cancer resection.

The use of head-mounted augmented reality (AR) for surgeries has grown rapidly in recent years. AR aids in intraoperative surgical navigation through overlaying three-dimensional (3D) holographic reconstructions of medical data. However, performing AR surgeries on complex areas such as the head and neck region poses challenges in terms of accuracy and speed. This study explores the feasibility of an AR guidance system for resections of positive tumour margins in a cadaveric specimen. The authors present an intraoperative solution that enables surgeons to upload and visualize holographic reconstructions of resected cadaver tissues. The solution involves using a 3D scanner to capture detailed scans of the resected tissue, which are subsequently uploaded into our software. The software converts the scans of resected tissues into specimen holograms that are viewable through a head-mounted AR display. By re-aligning these holograms with cadavers with gestures or voice commands, surgeons can navigate the head and neck tumour site. This workflow can run concurrently with frozen section analysis. On average, the authors achieve an uploading time of 2.98min, visualization time of 1.05min, and re-alignment time of 4.39min, compared to the 20 to 30min typical for frozen section analysis. The authors achieve a mean re-alignment error of 3.1mm. The authors' software provides a foundation for new research and product development for using AR to navigate complex 3D anatomy in surgery.

Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models

As cloud computing rises in popularity across diverse industries, the necessity to compare and select the most appropriate cloud provider for specific use cases becomes imperative. This research conducts an in-depth comparative analysis of two prominent cloud platforms, Microsoft Azure and Amazon Web Services (AWS), with a specific focus on their suitability for deploying object-detection algorithms. The analysis covers both quantitative metrics—encompassing upload and download times, throughput, and inference time—and qualitative assessments like cost effectiveness, machine learning resource availability, deployment ease, and service-level agreement (SLA). Through the deployment of the YOLOv8 object-detection model, this study measures these metrics on both platforms, providing empirical evidence for platform evaluation. Furthermore, this research examines general platform availability and information accessibility to highlight differences in qualitative aspects. This paper concludes that Azure excels in download time (average 0.49 s/MB), inference time (average 0.60 s/MB), and throughput (1145.78 MB/s), and AWS excels in upload time (average 1.84 s/MB), cost effectiveness, ease of deployment, a wider ML service catalog, and superior SLA. However, the decision between either platform is based on the importance of their performance based on business-specific requirements. Hence, this paper ends by presenting a comprehensive comparison based on business-specific requirements, aiding stakeholders in making informed decisions when selecting a cloud platform for their machine learning projects.

Exploring YouTube Videos About Anorexia Nervosa on the Basis of Reliability, Popularity, and Contributions of Healthcare Professionals: A Cross-Sectional Study.

Anorexia nervosa, an eating disorder, is characterized by a distorted body image, intense fear of gaining weight, and self-imposed starvation. The aim of this study is to analyze type of information as well as its quality and reliability on YouTube about anorexia nervosa. Methodology: A cross-sectional observational study was conducted on 59 Youtube videos using anorexia nervosa-related keywords in June 2023. The characteristics of the YouTube videos, such as the language of the information, the time of upload, and the qualifications of the uploaders, were recorded. The content and quality of 59 videos were assessed using the Global Quality Score (GQS) and reliability grading systems. The videos accumulated a total of 256,602 likes, 5,644 dislikes, and 17,761 comments. Treatment-related content accounted for 81.36% of the videos, while descriptions of symptoms comprised 79.66%. Doctors contributed to 18.6% of the total uploads, making them the second-largest group of uploaders after the 'Other' category. The median reliability score for doctors is 4, the same as the hospital healthcare organization. This indicates that the perceived reliability of doctors as a source of information is as high as that of hospital healthcare organizations. In conclusion, this study highlights the importance of critically evaluating information on anorexia nervosa videos on YouTube. Despite variations in popularity, the overall quality and reliability remained consistent. Doctors were perceived as reliable sources of information, comparable to hospital healthcare organizations. Ensuring accurate and trustworthy content is crucial for supporting those affected by anorexia nervosa and promoting reliable information to the public.

Remote Bridge Inspection and Actual Bridge Verification Based on 4G/5G Communication Environments

The close-up visual inspection of bridges faces several problems, including a lack of financial resources and human personnel. Hence, there has been increasing use of artificial intelligence (AI) and information and communications technology (ICT) to solve them. We previously investigated remote inspection—in which skilled engineers provided on-site support from a remote location—with the aim of reducing the labor required for on-site work and addressing the lack of personnel through the use of AI and ICT. Sharing images of bridges from inspection sites to remote locations via the Internet enables remote assessment of the sites and the ability to consider and diagnose damage. Mobile communications can be used to upload images, although the volume of image data required for inspection can be enormous and take considerable time to upload. Consequently, in this study, we investigated image uploads using 5G communication—that is, the fifth-generation technology standard for broadband cellular networks. Moreover, we measured the upload times when using 4G and 5G services and examined their operation based on differences in the communication environments. We concluded that the simulated remote inspection can be efficiently performed by adjusting the inspection method to the communication environment.

Quality and Reliability Analysis of YouTube Videos on Magnetic Resonance Imaging Claustrophobia.

Aim Magnetic resonance imaging (MRI)-induced anxiety is not infrequent with a reported incidence as high as 37% and the rate of failed MRI imaging due to claustrophobia ranges between 0.5% and 14.5%. The objective of this study was to evaluate the quality and reliability of YouTubeTM videos on MRI claustrophobia. Methods Sixty-five videos were included in the final analysis. Video information analyzed included video length (minutes), video content, qualification of the video uploaders, time of upload, time since upload, the number of total views and the mean daily views, and like counts. We divided the videos according to theuploaders into professional and non-professionalgroups and further grouped the videos as useful and misleading. Data obtained from the videos were evaluated with three tools including subjective evaluation, Quality Criteria for Consumer Health Information (DISCERN), and Global Quality Scale (GQS) tools. Results The mean video duration was found as 4.14±4.45 minutes. The mean view count was 104.59±408,788.68 and the mean like count was found as 272.55±1096.25. Seventeen (26.15%) videos were uploaded by professionals and 48 (73.85%) by non-professionals. Twenty-eight (43.08%) of the videos were useful and 37 (56.92%) were useless. The mean DISCERN and GQS scores were statistically significantly higher in the professional videos compared to the non-professional videos and in useful videos compared to non-useful videos (for all, p<0.001). Conclusion A majority of the YouTubeTM videos concerning MRI claustrophobia were uploaded by non-professionals. Physicians and other healthcare personnel should be encouraged to provide useful and accurate videos and to direct patients appropriately.

Resource Allocation for Integrated Sensing and Communication in Digital Twin Enabled Internet of Vehicles

With the development of the sixth-generation (6 G) network, virtualization remains critical. The key to future virtualization lies in the service provisioning capability of the network and the service requirements of end users, which will lead to virtualization of the network and end users. Therefore, this paper proposes a holistic network virtualization architecture that integrates digital twin (DT) and network slicing to achieve the network management of service-centric and user-centric. With the explosive growth of latency-sensitive and computing-intensive in-vehicle applications, limited in-vehicle computing resources are difficult to meet diverse network requirements, and vehicle edge computing (VEC) has become a potential solution. However, computation offloading may face the dilemma of excessive upload traffic and unbearable upload time. Therefore, in order to minimize the overall response time (ORT) of the system, this paper proposes a new environment aware offloading mechanism (EAOM) based on the integrated sensing and communication system (ISAC) to solve the joint optimization problem of task scheduling and resource allocation. Considering the mobility of vehicles and the time-varying of environment, the optimization problem is modeled as a Markov decision process, and an improved algorithm combining Shapley-Q value and deep deterministic policy gradient (DDPG) is used to solve it. The simulation results indicate the effectiveness and superiority of the scheme proposed in our work.

The Evaluation of YouTube™ Videos Pertaining to Intraoperative Anaesthesia Awareness: A Reliability and Quality Analysis.

Aim The objective of this study was to evaluate the reliability and quality of YouTube™ (Google, Inc., Mountain View, CA) videos pertaining to anaesthesia awareness. Methods We evaluated the most commonly viewed 100 videos pertaining to anaesthesia awareness. The YouTube™videos' image type, qualification of the uploaders, video content, video length in minutes, upload time, time since upload, total view count, daily view count and comment and like counts were recorded. The quality of the YouTube™videos was evaluated using the Global Quality Scale (GQS), and the reliability was determined using the modified DISCERN scale. Results Of all videos, 34 (34%) were uploaded directly by physicians, 16 (16%) by patients, 14 (14%) by health channels, 13 (13%) by TV shows and 23 (23%) by others.The mean video length was 11.48±11.96 minutes. The average DISCERN score was 4.47±0.58 in the professional and 3.28±0.65 in the non-professional video group (p<0.001). The mean GQS score was 4.47±0.52 in the professionaland 3.35±0.67 in the non-professional video group (p<0.001). Conclusion The results of this study indicate that a significant portion of the YouTube™videos pertaining to anaesthesia awareness were uploaded directly by physicians or by health channels. Physicians and professional health institutions should be promoted to provide accurate andmore reliable videos to direct patients to the right solutions for their problems. YouTube™videos should be subjected to supervision before they can be publicly viewed.