Real-time User Research Articles

Rapid Earthquake Rupture Characterization for New Zealand Using the FinDer Algorithm

ABSTRACT Immediately after a significant earthquake, rapid scientific information is critical for response decision-making and estimating secondary hazards, and is a key component of advisories and public communication. Characterization of the fault rupture extent is especially valuable because it strongly controls ground-motion estimates, or tsunami forecasts in offshore settings. The Finite-fault rupture Detector (FinDer) is designed to rapidly estimate location, extent, and orientation of earthquake fault rupture by matching spatial distributions of high-frequency seismic amplitudes with precomputed templates. Under a large public initiative to better prepare for and respond to natural disasters, FinDer is being implemented in New Zealand for rapid source characterization. Here, we report on implementation and performance, including offline and real-time testing using configurations modified for the New Zealand setting. Systematic testing is used to inform guidelines for real-time usage and interpretation. Analysis of rupture parameter recovery when using national network GeoNet stations demonstrates that for moderate (M 6+) onshore earthquakes FinDer can resolve magnitude and location well, and the rupture strike is also well determined for large (M 7+) onshore earthquakes. For near-offshore earthquakes (within 100 km), FinDer can provide reasonable magnitude estimates but cannot determine the location or strike. Real-time testing shows reliable detection for onshore earthquakes of M 4.5+, with reasonable location and magnitude accuracy. First detection times range between 7 and 65 s of earthquake origin, and stable solutions even for large (M 7+) magnitude events are delivered within 2 min. Although the GeoNet seismic network is not optimized for earthquake early warning, this provides a first exploration of network-based capability for New Zealand. Offline testing of significant M 7+ historic earthquakes demonstrates that FinDer’s rupture solutions can be used to improve rapid shaking predictions, and may be used to infer additional directivity and tsunami hazard even for complex events like the 2016 M 7.8 Kaikōura earthquake.

IoT based Siddha diagnosis for human health monitoring

In Siddha, the process of diagnosis is completely related to physical and clinical metrics of the human body. The art of diagnosing and the prognosis are conveyed by the siddhars by using silk or twine thread, which is an ancient technique followed till now to diagnose diseases in human. This paper proposes an IoT based automated device to detect the disease using siddha methodologies. The aim is to design a novel non-invasive device to identify human health problems using the carpus (wrist) circummetric sign. This device is developed and integrated on the basis of IoT platform for real time usage. It determines the wrist circummetric sign, condition of person health to be measured automatically using IR sensor, color sensor, actuators. Here, we inbuilt the embedded IoT system for validating the accurate results for better performance and display the output results in mobile through MQTT dashboard. This developed human health monitoring device can be used for diagnosing human physiological based parameters related to siddha which is termed as Manikadainool. Results proved that this device can predict the wrist parameters for Manikadainool in accurate and efficient manner.

Performance of single axis tracker technology and automatic battery monitoring in solar hybrid systems

Utilization of elevation angles and azimuth angles is a very important part in maximizing solar energy into electrical energy in photovoltaic (PV). One way to maximize PV power output is to design a single axis tracker system and take into account the azimuth and elevation angles of the sun using the sun position calculator application. The single axis tracker system is set based on the position of the angle of inclination of the surface of the PV 45°, then the angle of 90° and the angle of inclination of 135°. The test results show that the single axis tracker PV system design can work based on the angle settings that have been programmed. Then the use of a battery control system to support the PV reliability system automatically cuts off electricity when the battery voltage drops below 12 V during cloudy weather conditions and excessive battery usage. The integration of the PZEM-017 module with the battery will support monitoring of battery power usage in real time. PV energy data conversion performance uses single axis tracker technology for maximum power reaching 631.72 Watt DC at 12.00 pm and the lowest power reaching 56.02 Watt DC at 6.00 pm.

The Influence of JavaScript, on Website Development and User Experience

Abstract: JavaScript, a programming language has brought about a revolution in the construction of websites and web applications. Its capability to generate interactive web pages while enabling real time user interaction has made it an indispensable tool for developers. This article delves into the impact of JavaScript on website design and development highlighting its contributions to user experience. Additionally it emphasizes the importance of research and development in this field.

Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method

Time-sync comment (TSC) has emerged as a new type of textual comment for real-time user interactions on online video platforms. The sentiment classification of TSCs provides considerable potential for platforms to optimize operation strategies but inevitably faces great challenges due to the TSCs’ often uninformative and informal text. Considering the contextual dependency among TSCs posted within the same video clip, this study posits that contextual TSCs may benefit the sentiment classification of a target TSC. To address the challenges of leveraging contextual TSCs, such as their semantic representation and fusion, we propose a semi-supervised hierarchical deep learning method for the sentiment classification of TSCs. We design a hierarchical architecture to capture the semantics of TSCs at the word, comment, and context levels. Considering the varying importance of words and comments, we also design attention mechanisms to focus on important sentiment information and fuse semantic representations. Empirical evaluation shows that the proposed method outperforms benchmarked sentiment classification methods. This study advances our knowledge of contextual information indicative of TSC sentiment, and contributes to improving the service operation of online video platforms.

Enhancing the Performance of XR Environments Using Fog and Cloud Computing

The extended reality (XR) environment demands high-performance computing and data processing capabilities, while requiring continuous technological development to enable a real-time integration between the physical and virtual worlds for user interactions. XR systems have traditionally been deployed in local environments primarily because of the need for the real-time collection of user behavioral patterns. On the other hand, these XR systems face limitations in local deployments, such as latency issues arising from factors, such as network bandwidth and GPU performance. Consequently, several studies have examined cloud-based XR solutions. While offering centralized management advantages, these solutions present bandwidth, data transmission, and real-time processing challenges. Addressing these challenges necessitates reconfiguring the XR environment and adopting new approaches and strategies focusing on network bandwidth and real-time processing optimization. This paper examines the computational complexities, latency issues, and real-time user interaction challenges of XR. A system architecture that leverages edge and fog computing is proposed to overcome these challenges and enhance the XR experience by efficiently processing input data, rendering output content, and minimizing latency for real-time user interactions.

An Animated Visualization Method for Large-Scale Unstructured Unsteady Flow

Animation visualization is one of the primary methods for analyzing unsteady flow fields. In this paper, we addressed the issue of data visualization for large-scale unsteady flow fields using animation. Loading and rendering individual time steps sequentially can result in substantial frame delay, whereas loading and rendering all time steps simultaneously can result in excessive memory usage. To address these issues, the proposed method analyzes the variable description information in the data files to bypass redundant variables and read the flow field data as required. Second, a hash table is constructed to derive the two-dimensional surface mesh of the flow field and complex mesh cells are simplified into simple linear cells to reduce the mesh’s complexity. This paper presents a method for reducing the memory usage of complex data sets by more than 90%, compared with the ParaView data reading method. The proposed method is tested on four sets of unstructured unsteady flow field data with different data structures. The animation visualization method based on simplified data can achieve an average frame rate of less than 100ms and supports real-time user interaction on personal computers. It extends the ability of personal computers to analyze large-scale unstructured unsteady flow fields.

Multisensor Agile Adaptive Sampling (MAAS): A Methodology to Collect Radar Observations of Convective Cell Life Cycle

Abstract Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at subkilometer scale), and temporal evolution (at ∼2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect three sector plan position indicator (PPI) scans toward the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of three to six range–height indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a predetermined set of criteria. Between 1 June and 30 September 2022 over 315 000 vertical cross-section observations were collected by the C-band radars through ∼1300 unique isolated convective cells, most of which were observed for over 15 min of their life cycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.

Streaming process based on Spark and Kafka and its case application

E-commerce platforms gradually integrate into our lives. Online shopping has become a daily habit for people. People who shop online will generate online shopping records in real time. These online shopping records are often massive. E-commerce platforms use relevant big data technology to conduct statistics on real-time user data. In response to the current demand for e-commerce platforms for big data real-time computing technology, this article provides an idea of big data streaming computing, a framework for combining two big data technologies, Spark and Kafka, to process streaming data. This framework uses Kafka to send real-time data to SparkStreaming for calculation. Then the obtained data is visualized. This article applies these two big data technologies to simulate and implement an application case of an e-commerce platform. This case design and implementation enables real-time statistics of the click volume of a mobile phone brand on an e-commerce platform and dynamic display of streaming data on the Web.

Virtual, augmented, and mixed reality as a versatile tool in food consumer behavior evaluation: Recent advances in aroma, taste, and texture incorporation.

The perceptual behavior of consumers on a product displayed in the market has a vital role in analyzing the importance given to that product. Therefore, various strategies have been developed to understand this consumer behavior in the selection of products. Immersive technologies like virtual, augmented, and mixed reality are among them. With the foremost feature of immersion in the virtual world and interaction of users with virtual objects, virtual reality, and augmented reality have unlocked their potential in research and a user-friendly tool for analyzing consumer behavior. In addition to these technologies, mixed reality also has a significant role in investigating consumer behavior. Studies on immersive technologies in food applications are vast, hence this review focuses on the applications of virtual, augmented, and mixed reality in the food selection behavior of consumers. The behavioral studies are elicited to develop new products based on consumer needs, to understand the shopping behavior in supermarkets for real-time usage, and to know the influence of emotions in a selection of products. The findings suggest that virtual, augmented, and mixed reality induce immersion of the users in food selection behavioral studies. Information on the technological advancements in the tools used for bringing immersion and interaction are discussed for its futuristic applications in food. Though immersive technology gives users a realistic virtual environment experience, its application in food systems is in the budding stage. More research on human response studies would contribute to its innovative and inevitable application in the future.

Low-cost system for real-time verification of personal protective equipment in industrial facilities using edge computing devices

Ensure worker safety in the industry is crucial. Despite efforts to improve safety, statistics show a plateau in the reduction of these accidents in recent years. To decrease the number of accidents, compliance with established industrial safety standards and regulations by competent authorities must be ensured, including the use of Personal Protective Equipment (PPE). PPE usage is of paramount importance, as it is essential to prevent accidents from occurring. This work aims to improve worker safety by verifying PPE usage. Technology plays a key role here. A cost-effective solution is proposed to monitor PPE usage in real time. Most existing safety control systems are costly and require considerable maintenance. A low-cost computer vision system is proposed to supervise safety in industrial facilities. This system uses object detection and tracking technology in low-cost embedded devices and can generate alarms in real time if PPE is not used. Unlike other works, temporal information is used to generate the alarms. Safety managers receive this information to take necessary actions. Emphasis has been placed on cost, scalability, and ease of use to facilitate system implementation in industrial plants. The result is an effective system that improves worker safety by verifying established safety measures at a reduced cost. The methodology used improves the Average Precision of PPE detection by 6%. In addition, unlike other studies, the problem of application deployment is addressed, which has an impact on its cost.

A wearable assistive system for the visually impaired using object detection, distance measurement and tactile presentation

With the current development of society, ensuring traffic and walking safety for the visually impaired is becoming increasingly important. We propose a wearable system based on a system previously developed by us that uses object recognition, a distance measurement function, and the corresponding vibration pattern presentation to support the mobility of the visually impaired. The system recognizes obstacles in front of a user in real time, measures their distances, processes the information, and then presents safety actions through vibration patterns from a tactile glove woven with shape memory alloy (SMA) actuators. The deep learning model is compressed to achieve real-time recognition using a microcomputer while maintaining recognition accuracy. Measurements of the distances to multiple objects are realized using a stereo camera, and vibration patterns are presented through a tactile glove in response to these distances. Experiments are conducted to verify the system performance to provide safe navigation depending on the positions and the distances of multiple obstacles in front of the user.

Geolocated social media data for measuring park visitation in Shenzhen, China

Understanding park visitation patterns and factors that correlate with park use is conducive to urban green space management and planning. Although a growing number of studies have indicated geolocated social media can be used as a proxy for recreational use analysis, most of them relied on a single or similar social media platform(s). In this study, we used geolocated social media data among four popular platforms in China to estimate park visitation and explore the influence of 13 potential factors on park usage in Shenzhen by the geographical detector model. A park visitation index was introduced to estimate park usage, and it indicated large parks, such as natural and city parks, have a higher park visitation index than community parks in Shenzhen. In contrast to the check-in data, the real-time user data has a higher potential to describe park visitation citywide. Our findings demonstrate park size, sports and recreation amenities, the length of trails, and the nearby building density of a given park are likely to influence park visitation. The enhanced interactive relationship of 13 potential determinants of park usage can provide implications for urban park management and planning.

Piloting a digital campaign to promote awareness of the Louisiana TelePrEP program among sexual and gender minority young adults.

Despite the proven efficacy of pre-exposure prophylaxis (PrEP) in preventing HIV acquisition, PrEP is underutilized by sexual and gender minority young adults in the 13 states in the United States "South." In this paper, we describe the process we used to develop a digital campaign to promote awareness and engagement of sexual and gender minority young adults in the Louisiana Department of Health's TelePrEP Program and provide campaign performance metrics. In Phase 1, we conducted formative research that informed campaign development and strategy. In total, 109 sexual and gender minority young adults completed a survey of PrEP constructs (e.g., facilitators, barriers). We also conducted three, sequential focus groups to iteratively generate, revise and refine the digital material. In collaboration with our strategic marketing partner (SMP), we developed 3 different ads and 1 video ad to promote on web and mobile-in app display, as well as Facebook, Instagram, and YouTube. Phase 2 focused on campaign implementation and evaluation (e.g., number of impressions, user activities on LA TelePrEP landing page). In the first few weeks of the campaign, data from tracking pixels indicated minimal activities on the landing page. We paused to revamp the campaign. Our SMP determined that a more young adult-focused landing page could bolster engagement. We created a new landing page and reran the campaign for 33 days. We saw substantially more user activities on the new landing page (n = 382) compared to the LA TelePrEP landing page (n = 185). Overall, we had 730,665 impressions and 475 link clicks. By collaborating with our SMP, we effectively and efficiently translated our community-engaged formative research into relevant and engaging digital content. This pilot study is one of the first to demonstrate the importance of using tracking pixels to monitor real-time user data to optimize performance of a digital PrEP campaign.

Delineating the Dichotomy and Synergistic Dynamics of Environmental Determinants on Temporally Responsive Park Vitality

Promoting park vitality is fundamental for advancing both residents’ well-being and sustainable urban development. Current research often sidesteps the temporal fluctuations and combined effects of environmental factors on park vitality. Drawing on real-time user density data from Tencent, this investigation analyzed park vitality across 64 urban parks in Fuzhou, China, divided into five specific temporal periods on weekdays and weekends. Through the application of geographic detector models, this study examined the impact and interplay of both intrinsic and extrinsic environmental characteristics on park vitality over these different times. Our primary findings include: (1) environmental attributes affecting park vitality vary temporally, with aspects like commercial density, leisure facility density, and park size consistently influencing vitality; (2) on weekdays, external attributes linked to convenience are predominant, while on weekends, internal attributes connected to recreation take precedence; and (3) there is a synergetic interaction between environmental determinants, often leading to either additive or more intricate effects on park vitality. Based on these insights, we propose recommendations for spatial planning and time-based policies to enhance the alignment between urban settings and park quality. This research provides actionable strategies for enhancing park vitality, both within China and internationally.

Multi-Party Secured Collaboration Architecture from Cloud to Edge

ABSTRACT Distributed Federated Collaboration Secured Services is a proposed novel secure distributed Unified Communication and Collaboration (UCC) reference architecture planned for multi-environment ecosystems (including hybrid cloud, edge computing for IoT and real-time usages, and portable scenarios). DFCS-RA encourages new businesses and models, connecting people, “things,” and processes in secured and sensitive workloads, supporting efficient and cost-effective enhancements to traditional UCC (mostly centralized/monolithic services) tailored for different vertical markets having varying requirements. It provides a novel secure multi-environment communication and collaboration technology and services between large numbers of players. Key security concepts developed are secure separation, federation, and collaboration. The cryptographic architecture extends the Signal messenger security and encryption architecture. DFCS-RA comprises a toolbox for security modeling, a set of security design principles, and a set of security functions and mechanisms to implement controls to achieve stated security objectives. It was tested in diverse use cases; and found suitable, secure, and reliable.

Development of a hybrid word recognition system and dataset for the Azerbaijani Sign Language dactyl alphabet

The paper introduces a real-time fingerspelling-to-text translation system for the Azerbaijani Sign Language (AzSL), targeted to the clarification of the words with no available or ambiguous signs. The system consists of both statistical and probabilistic models, used in the sign recognition and sequence generation phases. Linguistic, technical, and human–computer interaction-related challenges, which are usually not considered in publicly available sign-based recognition application programming interfaces and tools, are addressed in this study. The specifics of the AzSL are reviewed, feature selection strategies are evaluated, and a robust model for the translation of hand signs is suggested. The two-stage recognition model exhibits high accuracy during real-time inference. Considering the lack of a publicly available dataset with the benchmark, a new, comprehensive AzSL dataset consisting of 13,444 samples collected by 221 volunteers is described and made publicly available for the sign language recognition community. To extend the dataset and make the model robust to changes, augmentation methods and their effect on the performance are analyzed. A lexicon-based validation method used for the probabilistic analysis and candidate word selection enhances the probability of the recognized phrases. Experiments delivered 94% accuracy on the test dataset, which was close to the real-time user experience. The dataset and implemented software are shared in a public repository for review and further research (CeDAR, 2021; Alishzade et al., 2022). The work has been presented at TeknoFest 2022 and ranked as the first in the category of social-oriented technologies.

Towards adaptive and transparent tourism recommendations: A survey

AbstractCrowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non‐curated nature of the crowd‐originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state‐of‐art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream‐based scenario, and identifies future research opportunities.

Real-Time Wi-Fi Signal Monitoring from a User Perspective in a Wireless Environment Using the Internet of Things

Mobility issues, such as handover, should be considered in a wireless environment. Real-time Wi-Fi monitoring from a user perspective is important because it is used to keep track of the Wi-Fi performance and status. Thus, improving network efficiency allows users to work more efficiently. The monitoring currently being held on the Wi-Fi is not in a real-time perspective. The monitoring is only focused on the connection between the controller and the Access Point (AP) and the AP to the user devices. We proposed a way to monitor the Wi-Fi from a real-time user perspective in a wireless environment. This project will use the Raspberry Pi as a device (RP). This is because RP has an operating system that can replace personal computers in terms of monitoring the access point from the user’s point of view. This device will make monitoring tasks more efficient and faster for the user to identify the problems occurring at the Wi-Fi network. This research will also enable the usage of the existing Internet of Things (IoT) to develop new things. To conclude, monitoring using an IoT device can project the view of the Wi-Fi performance from a user perspective.

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images.

Ultrasound imaging is a critical tool for triaging and diagnosing subjects but only if images can be properly interpreted. Unfortunately, in remote or military medicine situations, the expertise to interpret images can be lacking. Machine-learning image interpretation models that are explainable to the end user and deployable in real time with ultrasound equipment have the potential to solve this problem. We have previously shown how a YOLOv3 (You Only Look Once) object detection algorithm can be used for tracking shrapnel, artery, vein, and nerve fiber bundle features in a tissue phantom. However, real-time implementation of an object detection model requires optimizing model inference time. Here, we compare the performance of five different object detection deep-learning models with varying architectures and trainable parameters to determine which model is most suitable for this shrapnel-tracking ultrasound image application. We used a dataset of more than 16,000 ultrasound images from gelatin tissue phantoms containing artery, vein, nerve fiber, and shrapnel features for training and evaluating each model. Every object detection model surpassed 0.85 mean average precision except for the detection transformer model. Overall, the YOLOv7tiny model had the higher mean average precision and quickest inference time, making it the obvious model choice for this ultrasound imaging application. Other object detection models were overfitting the data as was determined by lower testing performance compared with higher training performance. In summary, the YOLOv7tiny object detection model had the best mean average precision and inference time and was selected as optimal for this application. Next steps will implement this object detection algorithm for real-time applications, an important next step in translating AI models for emergency and military medicine.