Video Information Research Articles

Genetic therapies for cardiomyopathy: survey of attitudes of the patient community for the CureHeart project

Cardiomyopathies are a group of inherited heart muscle disorders. Expressivity is variable and while sometimes mild, complications can result in sudden cardiac death (SCD) at any age, heart failure and stroke. In around a third of patients a monogenic cause is identifiable, and development of genetic therapies that aim to correct the underlying genetic defect is underway. Here we describe results of a survey designed to understand preliminary views of the patient community about genetic therapies in the context of disease burden. The internet survey was publicized with a bespoke information video via patient support groups in the UK and USA; 634 people responded of whom 96% had a personal and/or family history of cardiomyopathy. Findings show that concern about cardiomyopathy-related issues with a future dimension, such as disease progression, is significantly greater than concern about current issues. A total of 93.6% thought that genetic therapies should be developed for cardiomyopathy. A majority would consider participation in a genetic therapy trial in six scenarios varying by age and clinical situation significantly more in the scenario of an adult with symptomatic disease and evident progression than an asymptomatic adult with SCD risk, or a child. In all scenarios, a majority said that the chance genetic therapy would stop or slow progression, and risk of serious adverse and unintended effects, were important considerations. Qualitative analysis of free-text responses found that concern was often informed by family experience. Patient consideration of genetic therapy is likely to require individualized assessment of the benefits and risks.

Exoscopic Clipping of a Ruptured Posterior Communicating Artery Aneurysm Through a Minipterional Approach: 2-Dimensional Operative Video

Posterior communicating artery (PCoA) aneurysms represent approximately 15% to 25% of intracranial aneurysms and carry an estimated risk of rupture of 44%. 1-4 Surgical clipping of PCoA aneurysms bears notable challenges associated with operating in a confined space with limited visualization of deep structures. 1,5 We illustrate the case of a 54-year-old woman who presented with a seven-hour course of intense headaches, vomiting and loss of consciousness. Her neurological examination revealed she had a 4/5 left-sided hemiparesis, a Glasgow coma scale score of 12, and a Hunt and Hess score of 3. A head computed tomography scan without contrast showed subarachnoid hemorrhage in the basal cisterns and Sylvian fissures (Fisher grade 3). Digital subtraction angiography and three-dimensional reconstruction imaging revealed a right bilobed saccular PCoA aneurysm of 4.8 × 5.9 mm projecting anterior-inferiorly. After consideration of her clinical course and aneurysmal features, she underwent a right minipterional craniotomy for exoscopic clipping of the aneurysm neck. The patient tolerated the procedure well, her hemiparesis improved, and she was discharged with a modified Rankin scale score of 1 on postoperative day 3. Exoscopy offered high image quality and expanded 3-dimensional view with digital zoom during the aneurysm repair. 6,7 Although evidence suggests exoscopy is non-inferior to microscopy regarding surgery duration, safety, and outcomes, the capability of providing operator ergonomy during surgery is a worth-noting advantage. 8 IRB and patient consent declaration: This study was exempt from the Institution Review Board because patient consent was granted for the use of clinical information, images, and operative videos. The patient consented to the procedure, and the participants and any identifiable individuals consented to publication of his/her image.

An effective video inpainting technique using morphological Haar wavelet transform with krill herd based criminisi algorithm

In recent times, video inpainting techniques have intended to fill the missing areas or gaps in a video by utilizing known pixels. The variety in brightness or difference of the patches causes the state-of-the-art video inpainting techniques to exhibit high computation complexity and create seams in the target areas. To resolve these issues, this paper introduces a novel video inpainting technique that employs the Morphological Haar Wavelet Transform combined with the Krill Herd based Criminisi algorithm (MHWT-KHCA) to address the challenges of high computational demand and visible seam artifacts in current inpainting practices. The proposed MHWT-KHCA algorithm strategically reduces computation times and enhances the seamlessness of the inpainting process in videos. Through a series of experiments, the technique is validated against standard metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), where it demonstrates superior performance compared to existing methods. Additionally, the paper outlines potential real-world applications ranging from video restoration to real-time surveillance enhancement, highlighting the technique’s versatility and effectiveness. Future research directions include optimizing the algorithm for diverse video formats and integrating machine learning models to advance its capabilities further.

YouTube as a possible learning platform for patients and their family caregivers for gastrostomy tube feeding: A cross-sectional study.

Many patients and family caregivers have informational needs, especially regarding gastrostomy care and home gastrostomy tube feeding. YouTube is a potential accessible option for educational resources concerning these topics. This study aimed to explore the educational quality and content of informational YouTube videos. We used "gastrostomy," "G-tube," "enteral feeding," and "enteral nutrition," as search keywords on YouTube on October 3, 2021. A total of 229 videos were evaluated using the global quality scale (GQS) and modified DISCERN scoring system. Variables extracted from the videos included general features, video parameters, and content themes. The GQS and modified DISCERN scores were 3.31 ± 0.90 and 2.63 ± 1.23, respectively. There were educational quality and differences among videos uploaded by various agencies. Frequent video content themes included "cleaning and dressing a gastrostomy tube," "bolus method," and "replacing a balloon-type of gastrostomy tube." Results showed that YouTube can be a supplemental educational resource for people requiring gastrostomy care and for their caregivers. However, given the open-access nature of YouTube, healthcare professionals' guidance is needed for video selection. Healthcare professionals should know and use specific, reliable resources to effectively guide and educate patients with gastrostomy and their caregivers, enhancing their self-management skills and knowledge.

‘Are ad endorsements really annoying?’ The impact of advertising in short-form tourism videos on tourists’ information processing

ABSTRACT Short-form tourism videos show high effectiveness in online destination marketing. However, some disruptive factors (i.e. embedded endorsement advertisements) diminish their marketing effectiveness by burdening the viewer’s cognitive processing and breaking one’s online interactive experience with the original tourism-related content in the video. Using a between-subjects online experimental design and 354 valid data from short-form video users, this study explored the effect of interruption by advertisements on the cognitive experience evoked by information stimuli from the perspective of information processing. This study also assessed the difference in short-form tourism video information processing between disturbed and undisturbed situations. In general, results revealed that the formation of destination-related mental imagery in short-form tourism videos is influenced by information processing fluency, and co-predicted both the attitudes and behavioural intentions of travel consumers. Meanwhile, the presence of advertising information in short-form videos notably obstructed users’ information processing fluency as well as the formation of mental imagery, and it also attenuated their destination visit intentions. This study provides a theoretical basis for the moderate interpretation of endorsement advertisements in short-form tourism videos and sheds light on insights related to tourism marketing strategies for short-form tourism videos for relevant practising practitioners.

Improving young patient- practitioner communication about sexual health during medical visits.

Young people face barriers that lead to gaps in sexual and reproductive health care communications. Issues such as discomfort discussing sexual health lead to inadequate delivery of services resulting in unintended pregnancies and STIs. Closing this communication gap between patients and health care practitioners would improve communication and health outcomes. The objective of this study was to gain feedback from focus groups about: (a)barriers and facilitators to communication surrounding sexual health and (b)the feasibility and acceptability of a question prompt list (QPL) and informational video emphasizing asking questions about sexual health during medical visits as tools young people could use to be more involved during visits. Three focus groups were conducted: two with young adults (n=14) and one with practitioners (n=5) of sexual/reproductive health care services. Practitioners were recruited from healthcare clinics. Young adults were aged 18-22 years old. Participants identified barriers to communication such as embarrassment over sexual health topics and practitioner assumptions about patients' base of knowledge. A facilitator to communication was patient-friendly language. Focus group participants offered suggestions on how to improve the QPL as well as themes that should be covered in an educational video. Participants viewed the QPL and educational video as useful for encouraging conversations between patient and practitioner. Many barriers obstruct communication between young adults and practitioners on topics relating to sexual health. Both the QPL and an educational videocould be used to enhance patient-practitioner communication.

Адаптивная интеллектуальная обучающая система

<p>The goal of the work is to create a modern adaptive intelligent system using current machine learning technologies to automate a significant part of the teacher’s work. Existing intelligent systems, the purpose of which is to train students to work in various subject areas, currently have a set of various disadvantages, for example, the need to prepare educational material in a given format, which is sometimes a very labor-intensive task. In addition, in such systems there is a need to assess knowledge to correct the training plan for students, which requires various practical tasks for their formal presentation. In this case, practical assignments must be compiled by the course author, which can also be very labor-intensive. The novelty of the adaptive intelligent system presented in the work lies in the improvement of learning approaches using the latest machine learning methods. To help the teacher prepare educational material provides the ability to create video material automatically. This approach provides an opportunity for students to receive material not only in text form, but also in video format, without increasing the labor intensity on the part of the teacher. In addition, the teacher will be given the opportunity to manipulate versions of educational materials in accordance with the statistics provided by the system on student performance.</p>

Playful Trauma: TikTok Creators and the Use of the Platformed Body in Times of War

Amid the Russian invasion of Ukraine, TikTok has emerged as a pivotal platform, where creators utilize its compressed video formats to mediate the harsh realities of war zones. In this article, we examine 97 videos produced by 12 Ukrainian and Russian TikTok creators in response to the 2022 war in Ukraine. We focus on the playful embodiment of trauma using digital ethnography, analyzing creators’ practices and their interconnected memetic communication on TikTok. We identified the use of play through three dynamic practices where platforms, bodies, and trauma converge: (1) the utilization of POV (point-of-view) aesthetic in shareable templates to convey the realities of war from engaging first-person perspectives; (2) the incorporation of dance as a means of embodied creative expression and amplification of trauma; and (3) the harnessing of platform features to facilitate whimsical dialogues with followers about life under war. We argue that playfulness is entrenched in and enacted through platform vernacular modes, driving creators to forge communal ties during adversities and shape the ongoing representation of trauma and its accelerated visibility in digital spaces. We present the concept of playful trauma as a framework for understanding the structural dissonance that arises when creators utilize their bodies alongside platform-specific humorous, ironic, or subversive dialects to perform and amplify the gravity of trauma. This tension provides a unique space for creators to convert their shared experiences of grief and resilience into participatory coping mechanisms. In doing so, they subject and harness their playful platformed body as both the medium and the message, documenting injustices, bearing witness, and galvanizing crowds into action during times of war.

Analysis of banana plant health using machine learning techniques

The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.

Chinese Title Generation for Short Videos: Dataset, Metric and Algorithm.

Previous work for video captioning aims to objectively describe the video content but the captions lack human interest and attractiveness, limiting its practical application scenarios. The intention of video title generation (video titling) is to produce attractive titles, but there is a lack of benchmarks. This work offers CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title gEneration dataset, to assist research and applications in video titling, video captioning, and video retrieval in Chinese. CREATE comprises a high-quality labeled 210 K dataset and two web-scale 3 M and 10 M pre-training datasets, covering 51 categories, 50K+ tags, 537K+ manually annotated titles and captions, and 10M+ short videos with original video information. This work presents ACTEr, a unique Attractiveness-Consensus-based Title Evaluation, to objectively evaluate the quality of video title generation. This metric measures the semantic correlation between the candidate (model-generated title) and references (manual-labeled titles) and introduces attractive consensus weights to assess the attractiveness and relevance of the video title. Accordingly, this work proposes a novel multi-modal ALignment WIth Generation model, ALWIG, as one strong baseline to aid future model development. With the help of a tag-driven video-text alignment module and a GPT-based generation module, this model achieves video titling, captioning, and retrieval simultaneously. We believe that the release of the CREATE dataset, ACTEr metric, and ALWIG model will encourage in-depth research on the analysis and creation of Chinese short videos.

Editorial Comment: Artificial Intelligence—Generated Patient Information Videos for Partial Nephrectomy by Medical Students With Expert Validation: A Pilot Study

Editorial Comment: Artificial Intelligence—Generated Patient Information Videos for Partial Nephrectomy by Medical Students With Expert Validation: A Pilot Study

Artificial Intelligence–Generated Patient Information Videos for Partial Nephrectomy by Medical Students With Expert Validation: A Pilot Study

Artificial Intelligence–Generated Patient Information Videos for Partial Nephrectomy by Medical Students With Expert Validation: A Pilot Study

N-DEPTH: Neural Depth Encoding for Compression-Resilient 3D Video Streaming

Recent advancements in 3D data capture have enabled the real-time acquisition of high-resolution 3D range data, even in mobile devices. However, this type of high bit-depth data remains difficult to efficiently transmit over a standard broadband connection. The most successful techniques for tackling this data problem thus far have been image-based depth encoding schemes that leverage modern image and video codecs. To our knowledge, no published work has directly optimized the end-to-end losses of a depth encoding scheme sandwiched around a lossy image compression codec. We present N-DEPTH, a compression-resilient neural depth encoding method that leverages deep learning to efficiently encode depth maps into 24-bit RGB representations that minimize end-to-end depth reconstruction errors when compressed with JPEG. N-DEPTH’s learned robustness to lossy compression expands to video codecs as well. Compared to an existing state-of-the-art encoding method, N-DEPTH achieves smaller file sizes and lower errors across a large range of compression qualities, in both image (JPEG) and video (H.264) formats. For example, reconstructions from N-DEPTH encodings stored with JPEG had dramatically lower error while still offering 29.8%-smaller file sizes. When H.264 video was used to target a 10 Mbps bit rate, N-DEPTH reconstructions had 85.1%-lower root mean square error (RMSE) and 15.3%-lower mean absolute error (MAE). Overall, our method offers an efficient and robust solution for emerging 3D streaming and 3D telepresence applications, enabling high-quality 3D depth data storage and transmission.

Effect of Informative Cesarean Delivery Operative Steps Video on Maternal Anxiety Level: A Randomized Controlled Trial

Background Cesarean section is the most common obstetric procedure performed. This can lead to maternal anxiety, which is a significant contributor to postpartum depression. This can adversely affect pregnant women both mentally and emotionally, negatively impacting their well-being and family bonding. The aim of this study was to evaluate the effect of the addition of an informative cesarean section operative steps video on the maternal anxiety score compared with standard pre-cesarean section care. Methods This randomized controlled trial was conducted at Thammasat University Hospital, Thailand, between April and September 2023. Pregnant women who underwent their first cesarean section were allocated to two groups: intervention and control groups. Participants in the intervention group were required to watch a 5-minute informative video that elaborately described the process from pre-operative steps until post-operative care on the day scheduled for cesarean delivery. All participants received the same routine pre-operative and post-operative care. The State-Trait Anxiety Inventory (STAI) was used to measure both populations on two occasions: the day of scheduled cesarean delivery and postpartum day 1. Results A total of 178 women were recruited. The demographic and obstetric characteristics were similar between the two groups. The pre-operative STAI scores of the intervention and control groups were 42.9 and 44.1 points, respectively, with no significant difference. However, the post-operative anxiety score showed a significant decline in the intervention group compared to that in the control group (p = 0.002). Moreover, most of the participants in the intervention group showed a low level of anxiety after the operation, while half of the control group remained at a moderate to high level score. Conclusions The provision of an informative educational video before cesarean delivery is a powerful tool that significantly reduces cesarean operative anxiety and improves health outcomes. Thai Clinical Trials Registry on the 28 March 2023 (TCTR20230328001).

Assessment of flight range and transmission of video information when monitoring emergency situations from an unmanned aircraft in difficult meteorological conditions

The purpose of research. Modern unmanned aerial vehicles of various types provide the ability to perform various tasks of operational information collection, monitoring the state of the environment, technological facilities and territories, updating data on these facilities, as well as for reconnaissance and monitoring their condition. The purpose of the study is to establish the maximum flight range and distance necessary for the organization of a communication channel through which the video stream and flight control commands using the KAM-16 Turbo ¾ method will be transmitted between the ground control complex and the unmanned aerial vehicle in a difficult meteorological situation.Methods are based on the basics of radio electronics, diagnostics and forecasting of the technical condition of aircraft. The methods of multicriteria analysis, parametric and structural synthesis were used. The principles of transmitting video images from drones used to monitor emergency situations were studied. A critical assessment of the maximum flight range of an unmanned aerial vehicle in a difficult meteorological situation was carried out.Results. Analytical formulas for determining the energy potential of a data transmission channel in line of sight are presented. Graphs of the dependence of the energy reserve in the data transmission channel between the unmanned aerial vehicle and the ground control system are presented, which make it possible to determine the maximum distance for transmitting video images in Full HD quality in the 2.4 GHz band. Calculations were carried out on the basis of complex analytical expressions and graphical dependences of the flight range of a micro UAV on the speed of tailwind and headwind were constructed. The maximum headwind speed is calculated, which does not allow the use of micro UAVs in blizzard conditions.Conclusion. A promising area of study of the use of UAV in emergency monitoring are micro-class copters capable of detecting victims by transmitting video information from a thermal imager during search and rescue operations in difficult weather conditions, such as snowstorms.

A Retina-inspired Sampling Method for Coding and Compression for Enhancement Image Based on Discrete Double Density Wavelet Transformer

The retina, a layer of light-sensitive tissue on the inner surface of the eye, is what allows for vision. That propose to design and analytically examine an image quantizer coding that is inspired by the way the retina manages and compresses visual features based on past studies. Retina-inspired coding is a new cipher algorithm which is very promising. Comparing the Double-Density Discrete Wavelet Transform (DDDWT) to earlier traditional approaches, it is more difficult but also produces excellent results. This efficient coding is credited to a streamlined account that, by the way, deals with 2-D and 3-D pictures, transformation matrices, and the conversion of the number DDWT as if via matrix multiplication. Naturally, this code makes a lot of interesting points of view available. In this proposal, we'll try to show how various theoretical model extensions can be applied to applications in video surveillance, information technology, and neuroscience. Therefore, that believe that using a multidisciplinary approach will help system achieve objectives. This strategy would apply signal processing methods with data from neurophysiology. There are hoping that our efforts will result in some innovative new coding algorithms. The proposed codes will be implemented in MATLAB for ease of experimentation. To implement this code more successfully, a low-level programming language is required. Index Terms— DDDWT, Retina-inspired, Coding.

An Overview of Behavioral Recognition

Human behavior recognition has become a popular research topic in the field of computer vision. With the introduction of deep learning and attention mechanisms, this field has been further promoted. However, issues such as dataset acquisition and preprocessing operations on multimodal datasets, modeling of long time information in videos, and fusion of temporal and spatial information still exist. In this paper, we first outline some video action recognition datasets and related preprocessing techniques, including frame extraction, optical flow extraction, and skeletal feature acquisition. Then, the relevant models are classified and parsed according to their characteristics and the types of input data modalities. In addition, we evaluate the performance of the models on several benchmark datasets to gain a deeper understanding of the model development process. Finally, we summarized the current challenges faced in the field of video behavior recognition, including model timeliness, data set subjectivity and effective fusion of multi-modal features, and proposed possible future improvement directions in order to provide more ideas and methods for subsequent research.

Instant TV. The Forgotten History of Video Tape Recording (and the Coverage of the Eichmann Trial)

This article adopts a media archaeological approach to the Eichmann trial (1961) to explore the technology and media constellations that enabled its global television coverage. Drawing on extensive archival research, it offers insights into the technological setup and institutional cooperation crucial for the broadcast. In this context, video tape recording played a pivotal role, facilitating instantaneous reporting from around the world. Informed by actor-network theory, the article highlights the interplay of different (non)human actors who were interested and enrolled in a short-lived actor-network that soon became obsolete with the advent of communication satellites. Through recounting the story of the trial coverage, it not only recalls the forgotten use of a technology but also sheds light on emerging television formats and infrastructures that persisted far beyond the brief prominence of Instant TV.

The benefits of socioemotional learning strategies and video formats for older digital immigrants learning a novel smartphone application.

The need to continually learn and adjust to new technology can be an arduous demand, particularly for older adults who did not grow up with digital technology ("older digital immigrants" or ODIs). This study tests the efficacy of socioemotional learning strategies (i.e., encoding information in a socially- or emotionally-meaningful way) for ODIs learning a new software application from an instructional video (Experiment 1) or a written manual (Experiment 2). An experiment-by-condition effect was identified, where memory was greatest for participants engaging socioemotional learning strategies while learning from a video, suggesting a synergistic effect of these manipulations. These findings serve as a first step toward identifying and implementing an optimal learning context for ODIs to learn new technologies in everyday life.

Model of Interest in Buying Fashion Products on Tiktok Shop Which is Influenced by Influencer Marketing and Price Perception

In the rapidly developing digital era, competition between companies in the e-commerce sector is increasingly competitive. Social media is a supporting tool for consumers in sharing text, video, image and audio information, and currently technology is developing more quickly, because of the large number of internet users, therefore all activities can be done online, one of which is online shopping. one example is TikTok Shop. TikTok Shop is a social commerce feature that advertises and sells their products via TikTok. This shopping feature is present on TikTok as a response to increasing sales of various products after brands carried out promotions via social media platforms. Influencers are netizen actors who have social media accounts and continue to spread messages or campaign content to other netizens to strengthen the message or content. Influencer marketing is a marketing strategy in product sales by promoting a product by someone who is considered to have influence. Price is one component of a company's marketing strategy, with the aim of providing added value to consumers so that they continue to consume the products and services being sold. Consumers will tend to prefer a more affordable price to obtain an item. Consumers tend to prefer more affordable prices to get the goods they want. The aim of this research is to determine the formation of the Purchase Interest model which is influenced by Influencer Marketing and Price Perception. This research uses a side purposive method which involves selecting samples from the population studied based on certain criteria. Structural Equation Modeling (SEM) analysis was used to analyze the data. The results of the validity test output show that this research data is declared valid and the construct reliability test shows that each variable in this research is declared consistent. The statistical assumption test shows that it has a positive and significant effect on influencer marketing which can provide a stimulus to prices and purchasing interest which encourages customers to make purchases. The path analysis model fit test shows the similarity of several samples which can substitute for the population as a whole