Continuous Tracking Research Articles

INTEGRATING SENSOR NETWORKS TO FACILITATE EFFICIENT ENERGY MANAGEMENT FOR SMART GRIDS

Organizations are implementing Smart Energy Management Systems (SEMS) to regulate energy consumption patterns and respond to energy-saving instructions due to rising energy prices and demand. The study proposes an intelligent energy management solution that integrates an IoT middleware module and Energy Controller for effective demand side regulation. The study offers an intelligent energy management solution for smart settings that integrates an IoT middleware module and Energy Controller to regulate the demand side effectively. Every IoT device has an energy controller connected, incorporating sensors and actuators. The proposed solution optimizes air conditioner energy usage in Pakistan by analyzing exterior temperature conditions, building dynamics, and data from smart devices. It uses functional interfaces, robust algorithms, and continuous tracking for assessment. To evaluate the proposed methodology the research highlights the system's key benefits and reveals significant energy savings. The smart energy management system provides better air conditioning system control, real-time monitoring, cost savings, environmental advantages, and longer equipment life.

Serum Potassium Monitoring UsingAI-Enabled Smartwatch Electrocardiograms.

Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laboratory test monitoring is resource-heavy, invasive, and unable to provide continuous tracking. Wearable technologies like smartwatches with electrocardiogram (ECG) capabilities are emerging as valuable tools for remote monitoring, potentially allowing for personalized monitoring with artificial intelligence (AI)-ECG interpretation. The purpose of this study was to develop an AI-ECG algorithm to predict serum potassium level in ESRD patients with smartwatch-generated ECG waveforms. A cohort of 152,508 patients with 293,557 ECGs paired serum potassium levels obtained within 1 hour at Cedars Sinai Medical Center was used to train an AI-ECG model ("Kardio-Net") to predict serum potassium level. The model was further fine-tuned on 4,337 ECGs from 1,463 patients with ESRD using inputs from 12- and single-lead ECGs. Kardio-Net was evaluated in held-out test cohorts from Cedars Sinai Medical Center and Stanford Healthcare (SHC) as well as a prospective international cohort of 40 ESRD patients with smartwatch ECGs at Chang Gung Memorial Hospital. The Kardio-Net, when applied to 12-lead ECGs, identified severe hyperkalemia (>6.5 mEq/L) with an AUC of 0.852 (95%CI: 0.745-0.956) and a mean absolute error (MAE) of 0.527 mEq/L. In external validation at SHC, the model achieved an AUC of 0.849 (95%CI: 0.823-0.875) and an MAE of 0.599 mEq/L. For single-lead ECGs, Kardio-Net detected severe hyperkalemia with an AUC of 0.876 (95%CI: 0.765-0.987) in the primary cohort and had an MAE of 0.575 mEq/L. In the external SHC validation, the AUC was 0.807 (95%CI: 0.778-0.835) with an MAE of 0.740 mEq/L. Using prospectively obtained smartwatch data, the AUC was 0.831 (95%CI: 0.693-0.975), with an MAE of 0.580 mEq/L. We validate a deep learning model to predict serum potassium levels from both 12-lead ECGs and single-lead smartwatch data, demonstrating its utility for remote monitoring of hyperkalemia.

SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone's direction and the user's actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user's walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system's applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Vision‐based monitoring system for in‐plan seismic tests conducted on a shaking table: The case study of prefabricated concrete shells

AbstractComputer vision enables a much more efficient monitoring system of structural behavior, compared to traditional methods. This derives from the fact that it allows the assessment of displacements in a vast number of points that can be processed to the estimate deformations, accelerations, and other key parameters at relevant cross‐sections. This paper proposes a computer vision‐based methodology, specifically designed for monitoring seismic tests conducted on a shaking table with a reduced‐scale model, using a single camera approach. This innovative methodology uses artificial targets with predefined color and geometry to optimize their detection and tracking. These targets are positioned at key points of the reduced model—“Moving Targets”—and at reference points of the seismic table—“Control Targets.” Videos are recorded during the seismic tests and the coordinates (in pixels) of the targets' centers are automatically detected and captured. Then, transformations are applied to each frame to compute the targets coordinates in millimeters and based on the differences between frames, to calculate the displacements of each target. The methodology was first calibrated with dynamic tests performed on a reduced‐scale model (1:33) of a prefabricated concrete shell, printed in acrylonitrile butadiene styrene (ABS), conducted on an educational shaking table. Afterwards, the methodology was validated with seismic tests performed on a 1:3 reduced model of a prefabricated concrete shell, produced according to the similitude theory to best reproduce the behavior of the corresponding prototype. It was demonstrated the ability of the methodology to monitoring seismic tests, enabling continuous tracking of the targets placed on a structure with complex geometry.

Improvement in the Number of Velocity Vector Acquisitions Using an In-Picture Tracking Method for 3D3C Rainbow Particle Tracking Velocimetry

Particle image velocimetry and particle tracking velocimetry (PTV) have developed from two-dimensional two-component (2D2C) velocity vector measurements to 3D3C measurements. Rainbow particle tracking velocimetry is a low-cost 3D3C measurement technique adopting a single color camera. However, the vector acquisition rate is not so high. To increase the number of acquired vectors, this paper proposes a high probability and long-term tracking method. First, particles are tracked in a raw picture instead of in three-dimensional space. The tracking is aided by the color information. Second, a particle that temporarily cannot be tracked due to particle overlap is compensated for using the positional information at times before and after. The proposed method is demonstrated for flow under a rotating disk with different particle densities and velocities. The use of the proposed method improves the tracking rate, number of continuous tracking steps, and number of acquired velocity vectors. The method can be applied under the difficult conditions of high particle density (0.004 particles per pixel) and large particle movement (maximum of 60 pix).

Balancing plant safety and efficiency through innovative engineering practices in oil and gas operations

In the oil and gas industry, balancing plant safety with operational efficiency is a critical challenge that requires innovative engineering practices. This paper explores how advanced engineering techniques can enhance both safety and efficiency in oil and gas operations. By integrating cutting-edge technologies and methodologies, companies can achieve a harmonious balance between protecting personnel, equipment, and the environment, while also optimizing productivity and resource utilization. Key innovative engineering practices discussed include the implementation of real-time monitoring systems and predictive analytics to enhance safety and operational efficiency. Real-time monitoring, powered by Internet of Things (IoT) sensors, allows for continuous tracking of plant conditions, detecting anomalies before they escalate into significant issues. Predictive analytics, utilizing machine learning algorithms, further aids in forecasting potential failures and scheduling maintenance proactively, thereby minimizing downtime and operational disruptions. The paper also examines the role of risk-based approaches and safety management systems in improving plant safety. Techniques such as quantitative risk assessment (QRA) and safety integrity level (SIL) analysis are pivotal in identifying potential hazards and designing mitigation strategies. These practices ensure that safety measures are proportionate to the actual risk, avoiding over-engineering while addressing critical safety concerns. Moreover, the integration of automation and digital twin technology is discussed as a means to enhance both safety and efficiency. Automation reduces human error and improves precision in operations, while digital twins provide a virtual replica of physical assets, enabling simulation and optimization of plant operations in a risk-free environment. Case studies illustrate successful implementations of these practices, highlighting their impact on reducing incidents and improving operational performance. The paper concludes by emphasizing the need for ongoing innovation and collaboration between engineers, safety experts, and operational managers to continuously advance safety standards and operational efficiency in oil and gas operations.

Long-term tracking for high-frequency surface wave radar based on heading constraint filtering combined ELM

Abstract Aiming at the problem of low measurement resolution and poor tracking accuracy in High-frequency surface wave radar (HFSWR) tracking field, a tracking method that combines the heading constraint filter and Extreme Learning Machine (ELM) is proposed. Compared with the existing research, the innovation of this study lies in proposing a two-stage tracking method based on the short-term stable state of the target, and similarity of long-term trajectory. Firstly, the heading constraint information is introduced into the estimator and improve the estimation accuracy. In multi-target tracking scenarios, many tracklets can be obtained. Then, the ELM learns robust features of tracklets to achieve the trajectory segment classification from the same target. This method can be widely applied to long-term tracking of cargo or passenger ships with fixed destinations, significantly improving tracking performance by utilizing implicit domain knowledge without additional information. The actual data of this paper comes from HFSWR on Bohai Bay. Both the simulation and actual measurement experiments show that the proposed cascaded tracking method achieves long-term continuous tracking, and generates more complete trajectories. Moreover, the proposed Extended Kalman filter based on pseudo-measurement and intercept parameters (IPM-EKF) exhibits better tracking stability and accuracy in limited scan steps. This study provides new ideas and methods for improving the tracking performance of special targets in the HFSWR field by utilizing motion features.

Multifunctional Human-Computer Interaction System Based on Deep Learning-Assisted Strain Sensing Array.

Continuous and reliable monitoring of gait is crucial for health monitoring, such as postoperative recovery of bone joint surgery and early diagnosis of disease. However, existing gait analysis systems often suffer from large volumes and the requirement of special space for setting motion capture systems, limiting their application in daily life. Here, we develop an intelligent gait monitoring and analysis prediction system based on flexible piezoelectric sensors and deep learning neural networks with high sensitivity (241.29 mV/N), quick response (66 ms loading, 87 ms recovery), and excellent stability (R2 = 0.9946). The theoretical simulations and experiments confirm that the sensor provides exceptional signal feedback, which can easily acquire accurate gait data when fitted to shoe soles. By integrating high-quality gait data with a custom-built deep learning model, the system can detect and infer human motion states in real time (the recognition accuracy reaches 94.7%). To further validate the sensor's application in real life, we constructed a flexible wearable recognition system with human-computer interaction interface and a simple operation process for long-term and continuous tracking of athletes' gait, potentially aiding personalized health management, early detection of disease, and remote medical care.

CONSTRUCTION OF THE RAILWAY TRACK OF UKRAINE AND PROSPECTS FOR THEIR IMPROVEMENT

The goal. The process of improving the design of the railway track requires a detailed study of the history of the development of its structural elements, methods of combination, interconnections and limits of mutual influence. Modern track designs are the result of many years of hard work by scientists and long operational tests. Some designs were inventions at the time, some were breakthrough technologies, some remained just ideas, and some have existed for more than a century. The study of the history of the development of railway track structures and modern existing structures in operation made it possible to identify a promising direction for improvement and further development, taking into account the existing problems on the mainline transport tracks and industrial enterprise tracks. Reinforced concrete sleepers and intermediate fasteners that allow for track gauge adjustment are the railroad track structure that should be improved in the coming years. Improving railroad track design is a continuous process in the development of rail transport. Recently, new track designs have been introduced on Ukrainian railways. As a rule, the problems of track facilities are first considered and technical tasks are formulated. Then, a public discussion is held in the scientific and practical environment and an appropriate development strategy is born. Such a strategy is reviewed and approved at scientific and technical meetings and specialized commissions. This article aims to identify promising areas for improving the track design and solving the problems of installing and operating a continuous track by analyzing existing designs and world experience of railways. Methods. Methods of scientific analysis and synthesis of track development design, methods of analytical geometry. Results. Based on the analysis of the history of the development of the railroad track structure and modern designs, a promising direction of improvement is proposed. A promising railroad track structure is defined as a reinforced concrete sleeper and fasteners capable of forming an adjustable track gauge of up to 1545 mm. At the same time, it is necessary that the new design has sufficient stability of the jointless track plates. Practical significance. The application of the proposed structure within small radius curves, provided that the results of trial operation and testing of the established procedure are positive, will reduce the operating costs associated with the current maintenance of the tracks, expand the scope of laying of the jointless track and use reinforced concrete sleepers in small radius curves at industrial enterprises.

Investigating NanoLuc-EGFR engineered cell lines for real-time monitoring of EGFR protein dynamics in live cells

Evaluating the steady-state protein level of the EGFR in live cells presents significant challenges compared to measuring its kinase activity. Traditional testing methods, such as immunoblotting, ELISA, and immunofluorescence assays, are generally restricted to fixed cells or cell lysates. Despite their utility, these methods are cumbersome and provide only intermittent snapshots of EGFR levels at specific time points. With emerging trends in drug development shifting toward engineering novel agents that promote protein degradation, rather than simply inhibiting kinase activity, a tool that enables real-time, quantitative detection of drug effects in live cells could catalyze advances in the field. Such an innovation would expedite the drug development process, enhancing the translation of research findings into effective, patient-centered therapies. The NanoLuc-EGFR cell line, created through CRISPR genome editing, allows for the continuous tracking and analysis of EGFR protein levels and their degradation within live cells. This approach provides quantitative monitoring of protein dynamics in real time, offering insights that go beyond absolute protein levels to include aspects such as protein stability and degradation rate. Using this cell line model, we observed that AT13387 and H84T BanLec induce EGFR degradation in A549-HiBiT cells, with the results confirmed by immunoblotting. In contrast, Erlotinib, Osimertinib, and Cetuximab inhibit EGFR phosphorylation without altering total EGFR levels, as validated by the HiBiT luciferase assay. The NanoLuc-EGFR cell line marks a significant advancement in understanding protein regulation and serves as an instrumental platform for investigating targeted therapies that modulate protein kinases, especially those that induce protein degradation.

Fingerprint localisation for fine‐scale wildlife tracking using automated radio telemetry

Abstract Automated radio telemetry systems are one of the most widely applicable methods for tracking wildlife at fine spatiotemporal scales. A growing trend is for these systems to apply large networks of receivers to detect radio signal strength (RSS) from radio‐tagged individuals. Analytical methods to derive position estimates using RSS localisation, however, are relatively underdeveloped in the field of wildlife tracking. Here, we apply approaches from indoor positioning systems to develop a new method, radio fingerprinting, for localising radio‐tagged animals in structurally complex, outdoor environments. This method characterises the RSS patterns at known locations to generate a radio map of the study area, which is then used to predict the location of new RSS patterns. We conducted field tests to evaluate the localisation accuracy of radio fingerprinting relative to multilateration, a commonly used method for RSS localisation. To do so, we established an experimental receiver network covering multiple habitat types and compared the localisation accuracy of radio fingerprinting to multilateration. Additionally, we evaluated how a variety of features characteristic of typical tracking data sets affected the accuracy of both methods. While both methods had a similar median error under ideal conditions (~30 m), radio fingerprint localisation method offered several advantages over multilateration. Multilateration localisation estimates were highly affected by missed detections from the nearest receiver to the test point, which occurred in 30% of cases. In these cases, the median error was 103 m, over a 3.5‐fold increase. Distance to the nearest receiver also biased multilateration estimates with error increasing as the distance increased. Additionally, errors from multilateration estimates were higher in more densely vegetated areas. In contrast, fingerprinting position estimates were largely robust to each of these scenarios. Automated radio telemetry enables the fine‐scale, continuous tracking of range‐resident animals. We present radio fingerprinting as a localisation method in outdoor environments where standard localisation methods are error‐prone due to habitat heterogeneity and missed detections. This approach can be applied to any automated radio telemetry hardware and to any study system where a radio map can be generated.

Application of Linear Algebra in Image Processing for Medical Electronics

A very important part of medical electronics is the use of linear algebra in picture processing to improve the accuracy of diagnosis, treatment plans, and medical study. This abstract talks about the basic ideas and real-world uses of linear algebra in this area, showing how important it is for improving healthcare tools. Linear algebra gives us a strong way to change and understand pictures, which is very important in many types of medical imaging, like MRI, CT scans, ultrasound, and digital pathology. Images and how they are represented and changed are two of the most basic uses of linear algebra. A lot of the time, images are shown as matrices or tensors, where each part is a pixel strength or color value. Linear changes, like translations, rotations, and scaling, are needed to line up pictures, fix errors, and make sure that image data is the same across all modes. In medical image analysis, linear algebra methods like eigenanalysis and matrix decomposition (for example, Singular Value Decomposition) are also used to get information from images and make them simpler. These techniques help doctors and researchers find important patterns, oddities, and structures in pictures, which makes automatic analysis and disease classification easier. Medical image registration is a very important step for lining up pictures from different sources or places in time. Linear algebra makes it easier to figure out transformation matrices that get the best spatial alignment. This is very important for continuous studies and tracking of treatment, where exact comparison and analysis depend on perfectly aligned images. Linear algebra is also very important in methods for improving and fixing images. For example, filtering processes based on convolution matrices are used to get rid of noise, boost contrast, and bring out features in medical pictures, which makes them easier for doctors to see and understand. In computer imaging and tomography, linear algebra makes it possible to rebuild three-dimensional structures from two-dimensional picture slices. This makes it possible to see internal details and diseases more clearly. Overall, combining linear algebra with image processing in medical electronics not only makes medical pictures better and easier to understand, but it also leads to new diagnosis tools and treatment plans. Employing mathematical methods to pull useful data from large sets of images helps healthcare professionals make better choices, which ultimately leads to better patient results and progress in medical study.

A continuous tracking measure of orientation sensitivity and bias in the visual periphery

A continuous tracking measure of orientation sensitivity and bias in the visual periphery

Integration of biofuel-induced electricity generators (BEGs) with wearable biosensing devices

This review article explores the potential revolutionary uses of biofuel-induced electricity generators (BEGs) integrated into portable biosensing devices. It describes the workings, and benefits of BEGs-based biosensors while highlighting the complementary nature of energy production and biosensing. The article delves into the ways BEGs transform biosensing capabilities in wearable technology, highlighting their ability to sustain themselves, compatibility with living organisms, and ongoing power production. BEGs eliminate the need for external power sources by enabling independent and uninterrupted operation through the metabolic activity of microorganisms. Their compatibility with the human body guarantees a secure and pleasant on-body experience. Wearables that incorporate BEGs allow for the continuous tracking of ambient variables, physiological data, and biomarkers. The review highlights the exciting possibilities of BEGs based-biosensors in healthcare, environmental monitoring, and other fields, paving the way for autonomous, continuous monitoring in the future to enhance health management and raise environmental consciousness.

The Impact of Wearable Technology on Health Monitoring

Wearable technology is transforming health monitoring by enabling real-time, continuous tracking of various physiological parameters. This paper examines the evolution, benefits, challenges, and future trends of wearables in healthcare. The integration of sensors and mobile technology allows for more efficient personal and population health management. However, concerns about data accuracy, privacy, and ethical implications persist. As innovations such as smart clothing and AI-driven diagnostics emerge, the role of wearables in healthcare is expected to expand, offering new possibilities for early detection, preventive care, and personalized medicine. Keywords: Wearable technology, Health monitoring, Smart devices, Physiological sensors, Data privacy

Continuous tracking of effort and confidence while listening to speech-in-noise in young and older adults

Reporting discomfort when noise affects listening experience suggests that listeners may be aware, at least to some extent, of adverse environmental conditions and their impact on listening experience. This involves monitoring internal states (effort and confidence). Here we quantified continuous self-report indices that track one’s own internal states and investigated age-related differences in this ability. We instructed two groups of young and older adults to continuously report their confidence and effort while listening to stories in fluctuating noise. Using cross-correlation analyses between the time series of fluctuating noise and those of perceived effort or confidence, we showed that (1) participants modified their assessment of effort and confidence based on variations in the noise, with a 4 s lag; (2) there were no differences between the groups. These findings imply extending this method to other areas, expanding the definition of metacognition, and highlighting the value of this ability for older adults.

Research on Noise Reduction Performance of Wheel–Rail with Amplitude Amplifying Vibration Absorber

This paper introduces a novel rail vibration absorber leveraging the amplitude amplification mechanism. Employing a continuous slab ballastless track as the experimental framework, we develop a predictive model for rail sound radiation considering various absorber configurations. Additionally, we analyze the efficacy of the amplitude-amplifying vibration absorber in mitigating wheel–rail radiated noise. Our findings demonstrate that the amplitude-amplifying vibration absorber significantly reduces rail radiation noise within the frequency band of 800–1[Formula: see text]600[Formula: see text]Hz. Specifically, close to the design frequency (1[Formula: see text]000[Formula: see text]Hz), the sound power level experiences a reduction of 15.34[Formula: see text]dB, leading to a total reduction in rail radiation sound power by 3.21[Formula: see text]dB. Comparative analysis reveals that both the frequency band and noise reduction effect of the amplitude amplifying vibration absorber outperform those of conventional sound radiation absorbers with identical parameters. Moreover, they surpass the performance of conventional vibration absorbers with similar parameters. This study substantiates the superior noise reduction capabilities of the amplitude-amplifying (AA) vibration absorber compared to conventional counterparts.

LDVI-SLAM: A Lightweight Monocular Visual-Inertial SLAM System for Dynamic Environments Based on Motion Constraints

Abstract Traditional Simultaneous Localization and Mapping (SLAM) systems are typically based on the assumption of a static environment. However, in practical applications, the presence of moving objects significantly reduces localization accuracy, limiting the system's versatility. To address the challenges of SLAM systems in dynamic environments, the academic community often employs computationally intensive methods such as deep learning, and some algorithms rely on expensive sensors (e.g., LiDAR or RGBD cameras) to obtain depth information. These factors increase computational complexity or hardware costs, complicating practical deployment. To improve localization accuracy and adaptability of SLAM systems in dynamic scenarios while maintaining low deployment costs, this paper proposes a dynamic environment robust monocular inertial SLAM system named LDVI-SLAM. The system uses more cost-effective sensors—monocular cameras and Inertial Measurement Unit (IMU)—along with lightweight computational methods. In LDVI-SLAM, first, the reliability of IMU data is verified. Then, using the ego-motion information provided by the IMU, along with epipolar constraint and an improved Rotation-aware Flow Vector Bound (R-FVB) constraint, dynamic feature points are eliminated. Additionally, this paper proposes a continuous tracking across interval frames method to enhance the distinction between static and dynamic feature points. Experimental results demonstrate that LDVI-SLAM performs effectively in dynamic environments and is easy to deploy. On the VIODE dataset, experimental results show that compared to the deep learning-based DynaSLAM, this method reduces the RMSE (Root Mean Square Error) of absolute trajectory error by 10.3%. Moreover, in terms of speed, under the same computing power, the single-frame processing speed of this method is comparable to ORB-SLAM3 and is two orders of magnitude faster than DynaSLAM, significantly outperforming deep learning-based SLAM algorithms. Experiments on the OMD dataset further prove that this method effectively avoids the risk of semantic classification errors, demonstrating better robustness and generality.

Designing Novel Physiologic Monitor Displays for Combat Medics.

Combat medics who are responsible for the care of injured warfighters face challenges from their reliance on medical alarms that exceed the noise levels recommended by the WHO. This is because the elevated noise levels in military facilities, particularly from vehicular units and weaponry, compromise the combat medics' effectiveness and attentiveness to medical alarms. We previously designed a graphical ("configural") display to communicate patients' vital signs and found that when the configural display and traditional numerical display were concurrently presented to participants, it produced the fastest identification of patient vital signs and triggered the fewest number of alarms. This study used eye tracking to assess how participants direct visual attention to and engage with concurrently presented numerical and configural vital sign displays. We recruited 30 undergraduate students with normal hearing and vision for this study. Subjects were tasked with monitoring a simulated patient's vital signals using simultaneously presented numerical and configural vital sign displays. Concurrently, they performed an N-back task to simulate the multitasking required in a military environment. We manipulated the eccentricity and display position of the numerical and configural displays through 4 orientations, with each orientation being used in a monitoring block lasting 12 minutes. Continuous eye tracking was utilized to collect physiological data about participant display preference. We used eye tracking to analyze several metrics: Total display viewing time, total viewing time percentage, number of dwells (groups of eye fixations), mean fixations per dwell, and fixation patterns during an emergency event. Participants spent more time looking at the configural display than the numerical display during nominal monitoring and emergency events. During emergencies, the percentage of time individuals spent looking at the configural display increased from 30 to 50%, while there was no corresponding increase in the participants' looking at the numerical display. When there were 2 concurrent emergency events instead of 1, total viewing time did not increase, suggesting that participants did not need to change their viewing strategy when the emergency situation complexity increased. Also, during emergencies, participants directed nearly half of their fixations to the configural display during the first 2 seconds of an emergency, while only directing fewer than 5% of fixations to the numerical display during that same period. The average response time for an emergency event was around 2 seconds, which suggests that participants obtained relevant information from the configural display in this time period. We found that when a patient monitor contains both a configural display and a numerical display, participants look at the configural display. Furthermore, during time-sensitive situations, participants utilize the configural display to provide important information. We suggest this because the configural display integrates the relevant vital signs into one display. These findings provide justification for pursuing integrated vital sign displays to efficiently communicate patient conditions in complex environments. On the battlefield, swift decision-making is essential, as combat medics must minimize the time required to assess and act in critical situations.

Non-uniform variation characteristics of temperature field and corresponding thermal effect of longitudinal continuous slab ballastless track structure

The longitudinal continuous slab ballastless track structure (LCSBTS) easily suffers from interface and joint damage when subjected to extremely high-temperature loads, significantly affecting the running smoothness and safety of high-speed trains. This paper establishes a numerical model of the LCSBTS using the heat transfer principle and real-time shadow technology to replicate the time-varying evolution and spatial distribution of the temperature field. The corresponding thermal effect is obtained based on the sequential thermal-mechanical coupling method. The results show that the temperature variation and the corresponding thermal effect exhibit strong temporal-spatial non-uniformity on the horizontal and vertical planes of the LCSBTS. Solar radiation and heat convection are the dominant factors for the temperature field of the LCSBTS in the daytime and night, respectively, resulting in a higher temperature and greater thermal deformation on the sunny side than on the shady one. The temperature and vertical thermal deformation along the longitudinal direction vary periodically with a specific wavelength between the two adjacent rail support concrete blocks. The thermal deformation increments under the studied non-uniform temperature loads cause an interface damage increment. Finally, the consideration of a non-uniform temperature load produces remarkably different thermal effects from those given by the design temperature load.