Tracking Platform Research Articles

Time-on-task-related decrements in performance in the rodent continuous performance test are not caused by physical disengagement from the task.

Attention deficits, a hallmark of many neuropsychiatric disorders, significantly impair quality of life and functional outcome for patients. Continuous Performance Tests (CPTs) are widely used to assess attentional function in clinical settings and have been adapted for mice as the rodent Continuous Performance Test (rCPT). In this study, we combined traditional analyses of rCPT performance with markerless pose estimation using DeepLabCut and visual field analysis (VFA) to objectively measure the orientation of mice toward stimuli during rCPT sessions. Additionally, we extended session lengths to assess performance decrements over time. Our findings show that extending rCPT sessions from 45 to 90 minutes results in a significant decline in performance in male mice, which aligns with performance decrements observed in clinical research. Importantly, physical engagement with the task remained relatively stable throughout the session, even as performance deteriorated. This suggests that the performance decline specifically reflects a time-on-task (TOT)-dependent vigilance decrement rather than physical disengagement. We also investigated the effects of amphetamine, an FDA-approved treatment for attention-deficit/hyperactivity disorder (ADHD), on rCPT performance. Amphetamine significantly improved rCPT performance in male mice by reducing false alarms without modulating orientation or physical engagement with the task stimuli. Collectively, these findings validate a behavioral tracking platform for objectively measuring physical engagement in the rCPT and a task modification that accentuates TOT-dependent performance decrements, enhancing the translational value of the rCPT for studies related to human neuropsychiatric disorders.

Initial treatment experience obtained with the real-time predictive motion tracking radiotherapy platform Synchrony: A pilot study.

This pilot study presents initial experience obtained with a real-time predictive motion tracking platform called Synchrony mounted on the Radixact radiotherapy device. Synchrony radiotherapy treatments were offered as an alternative to surgical excision for primary pulmonary carcinomas as well as in dogs in a suspected oligometastatic disease state. All dogs were treated with three fractions of 8 Gy. Six dogs with pulmonary targets were successfully treated, while we were unable to treat abdominal targets with implanted fiducials. Cranial targets showed minimal movement, while targets located adjacent to the diaphragm showed a large amplitude of movement. No acute or late clinically apparent side effects were noted in any of the dogs that received radiation therapy. A strong partial response with minimal pneumonitis was seen in follow-up imaging of the one dog where imaging was available. Synchrony motion tracking will continue to be investigated for efficacy.

Bimodal response strategy in Daphnia to ambush predation risk.

Predation's consequences can manifest through either consumptive or nonconsumptive effects, but the prey response may also vary depending on the predator hunting strategy. Considerable attention has been paid to coursing predators, whereas less information is available regarding responses to ambush predators. To remedy this paucity, we utilized a three-dimensional tracking platform to record groups of Daphnia magna under predation risk from the ambush invertebrate predator red-eyed damselfly, Erythromma najas. This design allowed us to test individual antipredator responses in multiple metrics of swimming behaviors. We demonstrate that predation risk was greatest for those that swam at 85% of the available depth and averaged 8.1 mm/s. Examining the swimming behavior of each individual separately showed that predation risk did not affect any of the prey response metrics. Interestingly, however, Daphnia did conform to one of two strategies while under predation risk: either swim fast high up in the water column or swim slowly close to the bottom. Hence, this dichotomous behavior is driven by strategies combining speed and depth in different constellations. In a broader context, our findings highlight the importance of considering both the spatial and temporal dimensions of predation events in order to correctly detect antipredator responses.

Attention and Interoception Alter Perceptual and Neural Pain Signatures-A Case Study.

Fluctuations of chronic pain levels are determined by a complex interplay of cognitive, emotional and perceptual variables. We introduce a pain tracking platform composed of wearable neurotechnology and a smartphone application to measure and predict chronic pain levels and its interplay with other dimensions of experience. Our method measures, dynamically and at home, pain strength, phenomenal and neural time series collected with an online tool and low-density EEG. Here we used data from a single participant who performed an attention task at home for a period of 20 days to investigate the role of attention to different bodily systems in chronic pain. We show a relationship between emotions and pain strength while allocating attention to the heartbeat, the breathing, the affected or the unaffected limb. We found that pain was maximal when attending to the affected limb and decreased when the participant focused on his breathing or his heartbeat. These results provide interesting insights regarding the role of attention to interoceptive signals in chronic pain. We found power changes in the delta, theta, alpha and beta (but not in the gamma) band between the four attention conditions. However, there was no reliable association of these changes to pain intensity ratings. Theta power was higher when attention was directed to the unaffected limb compared to the others. Further, the pain ratings, when attending to unaffected limb, were associated with alpha and theta power band changes. Overall, we demonstrate that our neurophysiology and experience tracking platform can capture how body attention allocation alters the dynamics of subjective measures and its neural correlates. This research approach is proof of concept for the development of personalized clinical assessment tools and a testbed for behavioural, subjective and biomarkers characterization.

Random walk characteristics of particles in slurry pipeline transportation

During the pumping of wet shotcrete in a pipe, the aggregate migrates under various forces. In order to investigate the random walking characteristics of particles in slurry pipeline transportation, particle tracking platform for half-open tubes was constructed. Cement-like slurry colloids were prepared. The particle capture function of high-speed camera was utilized to track the particle movement. Random walk theory was used to analyze the transport trajectories of aggregates with different particle sizes. The transport properties of the particles were quantitatively analyzed. The experimental results showed that cement-like slurry colloids were obtained by using a mixture of powder of xanthan gum, carbomer, polyacrylamide and water mixed in a certain proportion. The trajectory of particle transportation in the slurry pipe had a large correlation with the particle size. In a straight pipe, the smoothest particle trajectories were found in the range of 4.17–6.25 % of the pipe diameter. The spectral dimension was positively correlated with tortuosity and more highly correlated with particle size in pipeline transport. The number of spectral dimensions and tortuosity were negatively correlated with axial walking steps of the particles. This paper provided a new way of thinking to study the particle transportation law in concrete pipeline.

Adaptive Iterative Learning Constrained Control for Linear Motor-Driven Gantry Stage with Fault-Tolerant Non-Repetitive Trajectory Tracking

This article introduces an adaptive fault-tolerant control method for non-repetitive trajectory tracking of linear motor-driven gantry platforms under state constraints. It provides a comprehensive solution to real-world issues involving state constraints and actuator failures in gantry platforms, alleviating the challenges associated with precise modeling. Through the integration of iterative learning and backstepping cooperative design, this method achieves system stability without requiring a priori knowledge of system dynamic models or parameters. Leveraging a barrier composite energy function, the proposed controller can effectively regulate the stability of the controlled system, even when operating under state constraints. Instability issues caused by actuator failures are properly addressed, thereby enhancing controller robustness. The design of a trajectory correction function further extends applicability. Experimental validation on a linear motor-driven gantry platform serves as empirical evidence of the effectiveness of the proposed method.

Determination of OCS Mast Offset in Johore Bahru Sentral for Gemas-Johor Electrified Double Track Project

The Gemas – Johor Bahru Electrified Double Track Project (GJBEDTP) is the most recent railway electrification. GJBEDTP runs from Gemas Railway Station to Johore Bahru (JB) Sentral. Since the JB Sentral station platform track is existing, it cannot achieve the minimal offset in the client's Statement of Needs (SON). The station canopy's acting load capacity is limited, so it cannot support the Overhead Line Equipment (OLE). Malaysia's standard offset value was 3.0 m, while the KTMB guideline's maximum offset value is 3.60 m and the minimum is 2.60 m. Thus, this research investigates the concrete mast offset that should be used when track separation is lower than standard. JB Sentral architecture data was collected to evaluate the mast offset. Sicat Master simulation and a cross-sectional diagram can be used to determine the proper minimum offset from the data collected. The results show that the mast at the centre of the two tracks can achieve clearances with the minimum offset of 1.80 m from the track centreline to the concrete mast face. These results also show that tangent track alignment allows a minimum offset of 1.80 m at a lower train speed.

Optimal trajectory tracking of tractor-trailer wheeled platforms taken into account wheel dynamics

Tractor-trailer wheeled platforms are recognized as articulated robotic systems which consist of a frontrunner active mobile platform (known as tractor) that pulls one or more passive platforms (known as trailers). Regarding to their novel features such as maximum load carrying capacity and minimum energy consumption, this category of mobile platforms is widely employed for conveying purposes and transportation. The presented article deals with path tracking optimization of the tractor-trailer platform using the indirect method of the optimal control. First, nonlinear motion relations of the modular platform are obtained considering kinematic constraints and inertia characteristics of the wheels. Then, the obtained dynamic equations are considered as constraints of the optimal problem, and as a result an appropriate objective criterion is determined for the optimal trajectory tracking. In the following, a proper solution for the optimal problem is developed and accordingly some simulations are presented. In view of the wheels dynamics, the obtained results reveal competency and applicability of the proposed procedure for the optimal tracking of the tractor-trailer wheeled mobile platforms.

Fire and smoke digital twin – A computational framework for modeling fire incident outcomes

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens. CCS conceptsComputer systems organization → Embedded systems; Real- time systems; • Computing methodologies → Modeling and simu- lation; • Applied computing → Physical sciences and engineering.

An augmented reality-guided biopsy system using a high-speed motion tracking and real-time registration platform.

Biopsies play a crucial role in diagnosis of various diseases including cancers. In this study, we developed an augmented reality (AR) system to improve biopsy procedures and increase targeting accuracy. Our AR-guided biopsy system uses a high-speed motion tracking technology and an AR headset to display a holographic representation of the organ, lesions, and other structures of interest superimposed on real physical objects. The first application of our AR system is prostate biopsy. By incorporating preoperative scans, such as computed tomography (CT) or magnetic resonance imaging (MRI), into real-time ultrasound-guided procedures, this innovative AR-guided system enables clinicians to see the lesion as well as the organs in real time. With the enhanced visualization of the prostate, lesion, and surrounding organs, surgeons can perform prostate biopsies with an increased accuracy. Our AR-guided biopsy system yielded an average targeting accuracy of 2.94 ± 1.04 mm and can be applied for real-time guidance of prostate biopsy as well as other biopsy procedures.

Assessment of chitosan-coated zinc cobalt ferrite nanoparticle as a multifunctional theranostic platform facilitating pH-sensitive drug delivery and OCT image contrast enhancement

Colorectal cancer (CC) is one of the most predominant malignancies in the world, with the current treatment regimen consisting of surgery, radiation therapy, and chemotherapy. Chemotherapeutic drugs, such as 5-fluorouracil (5-FU), have gained popularity as first-line antineoplastic agents against CC but have several drawbacks, including variable absorption through the gastrointestinal tract, inconsistent liver metabolism, short half-life, toxicological reactions in several organ systems, and others. Therefore, herein, we develop chitosan-coated zinc-substituted cobalt ferrite nanoparticles (CZCFNPs) for the pH-sensitive (triggered by chitosan degradation within acidic organelles of cells) and sustained delivery of 5-FU in CC cells in vitro. Additionally, the developed nanoplatform served as an excellent exogenous optical coherence tomography (OCT) contrast agent, enabling a significant improvement in the OCT image contrast in a CC tissue phantom model with a biomimetic microvasculature. Further, this study opens up new possibilities for using OCT for the non-invasive monitoring and/or optimization of magnetic targeting capabilities, as well as real-time tracking of magnetic nanoparticle-based therapeutic platforms for biomedical applications. Overall, the current study demonstrates the development of a CZCFNP-based theranostic platform capable of serving as a reliable drug delivery system as well as a superior OCT exogenous contrast agent for tissue imaging.

A roadmap to achieving a healthier information ecosystem through GDPR implementation and privacy compliance technologies

AbstractPrivacy protection has become a central issue in information science, with the General Data Protection Regulation (GDPR) significantly impacting information ecosystems. Research gaps persist in understanding the causal relationship between GDPR implementation and websites' proactive changes, such as adopting privacy compliance technologies. This study aims to examine the influence of GDPR implementation on websites' information ecosystems and identify boundary conditions that may affect this relationship. Utilizing domain‐level data from a professional platform tracking website technologies before and after GDPR implementation, which encompasses a comprehensive longitudinal dataset of over 1.2 million websites, our results indicate that GDPR implementation has increased the decision, breadth, depth, and intensity of adopting related technologies. The variance in these adoptions is significantly shaped by the differing levels of institutional impacts experienced by trailblazing versus laggard industries. Our study contributes to the literature by revealing the importance of policy factors in fostering a healthier information ecosystem. We demonstrate the complex nature of the relationship between policy factors and technology adoption, highlighting the need for researchers to consider institutional context and industry‐specific characteristics. We further discuss valuable insights for relevant stakeholders.

Super SDKs: Tracking personal data and platform monopolies in the mobile

In this article we address the question ‘what is tracking in the mobile ecosystem’ through a comprehensive overview of the Software Development Kit (SDK). Our research reveals a complex infrastructural role for these technical objects connecting end-user data with app developers, third parties and dominant advertising platforms like Google and Facebook. We present an innovative theoretical framework which we call a data monadology to foreground this interrelationship, predicated on an economic model that exchanges personal data for the infrastructural services used to build applications. Our main contribution is an SDK taxonomy, which renders them more transparent and observable. We categorise SDK services into three main categories: (i) Programmatic AdTech for monetisation; (ii) App Development, for building, maintaining and offering additional artificial intelligence features and (iii) App Extensions which more visibly embed third parties into apps like maps, wallets or other payment services. A major finding of our analysis is the special category of the Super SDK, reserved for platforms like Google and Facebook. Not only do they offer a vast array of services across all three categories, making them indispensable to developers, they are super conduits for personal data and the primary technical means for the expansion of platform monopolisation across the mobile ecosystem.

Rail platform allocation for reliable interchanges

Changing trains is a crucial part of many rail journeys, and it is important that arrival and departing trains are allocated close to each other for a swift interchange. Today the platform allocation of trains is often based on local traditions, where trains of the same type to the same destination depart from the same track. In this paper we address the problem of using the platform tracks in the best way, balancing crossing train paths with easy interchanges at the same platform. We use a RailSys model of the station in Norrköping, Sweden, to assess three platform allocation strategies with respect to crossing train paths and train changes at the same platform. Moreover, the capacity utilisation is calculated by a timetable compression-based method. The results show that a platform allocation maximising the changes at the same platform leads to more crossing train paths and higher capacity utilisation. Future work includes more accurate modelling of train connections, assessment of delays through simulation, and a cost-benefit analysis to find the best balance between easy interchanges and conflicting train paths.

Securing Blockchain Enterprises: Legal Due Diligence Amidst Rising Cyber Threats

This study aims to understand the vulnerabilities faced by enterprises operating on token-based blockchain businesses and the role of legal, due diligence procedures in mitigating such risks. It employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method and sourced data from DeFillama, a platform tracking decentralized finance developments, to categorize hacking incidents into five major groups: Ecosystem, Infrastructure, Protocol Logic, Rugpull, and Smart Contract Language. The findings highlight that Infrastructure attacks, mainly through Private Key Compromise, are the most damaging. They cause losses of over 800 million dollars between 2020 and 2023. It necessitates comprehensive and adaptable legal, due diligence strategies focusing on jurisdictional legal frameworks, platform usage terms, regulatory compliance, and potential legal issues. The study underscores the importance of further research to evaluate and enhance the effectiveness of these measures in addressing the unique challenges of blockchain technology, which are crucial for enhancing the resilience and sustainability of blockchain enterprises, thereby promoting global trust in this emerging field.

Stockdiary

STOCKDAIRY, a pioneering stock market tracking platform, represents a novel integration of cutting-edge technologies for financial analysis. This research paper meticulously documents the design, development, and implementation journey of STOCKDAIRY, focusing specifically on its utilization of the React.js framework. Employing a comprehensive approach, STOCKDAIRY integrates diverse data sources, predictive models, and methodologies, providing a holistic solution for stock market predictions. At its core, STOCKDAIRY relies on React.js, renowned for its modular and component-based architecture, ensuring seamless integration and real-time data updates. Trained on a combination of standard patterns and customized models using historical stock market data, STOCKDAIRY delivers a dynamic and user- centric experience, allowing users to tailor predictions and conduct thorough risk analysis, meeting the evolving demands of stock market investors. However, the developmental journey of STOCKDAIRY is not without challenges. Bulk data handling, precise insights filtration, and meticulous verification and validation of each module pose critical obstacles. The research paper transparently details the rigorous testing methodologies employed, highlighting the complexities faced and overcome in the development cycle. Despite these challenges, STOCKDAIRY emerges as a robust and reliable market prediction tool, underscoring its paramount importance in the ever-dynamic financial landscape. The paper emphasizes the need for a standard reference platform like STOCKDAIRY to successfully navigate the intricacies of stock market investments. The platform's guiding motto, "To minimize risk and maximize profit," encapsulates its overarching objective and commitment to delivering valuable insights to users in the realm of stock market predictions.

Design of Optoelectronic Tracking Platform Driven by Ultrasonic Motor with a Novel Limiter.

A high-performance servo control system is the basis for realizing high-precision photoelectric tracking. With high position resolution and power-off self-locking, ultrasonic motors have a wide range of applications for high-precision positioning control. An optoelectronic tracking platform driven by two ultrasonic motors is proposed in this study. The shaft structure of the tracking platform is designed and modeled. The shaft structure is simplified, and a dynamic model is established to analyze the motion characteristics. The parameters of the limit mechanism are optimized based on the analysis. The shaft structure is built to verify the response characteristics of the tracking platform at different velocities. The results show that the proposed design can fully utilize the self-locking of ultrasonic motors for rapid automatic alignment of the axis system. The maximum response time is less than 55 ms. When the operating velocity is less than 70°/s, the positioning error is less than 0.055°, and the lower the speed, the smaller the positioning error.

Deep-learning for automated markerless tracking of infants general movements

The presence of abnormal infant General Movements (GMs) is a strong predictor of progressive neurodevelopmental disorders, including cerebral palsy (CP). Automation of the assessment will overcome scalability barriers that limit its delivery to at-risk individuals. Here, we report a robust markerless pose-estimation scheme, based on advanced deep-learning technology, to track infant movements in consumer mobile device video recordings. Two deep neural network models, namely Efficientnet-b6 and resnet-152, were trained on manually annotated data across twelve anatomical locations (3 per limb) in 12 videos from 6 full-term infants (mean age = 17.33 (SD 2.9) wks, 4 male, 2 female), using the DeepLabCut™ framework. K-fold cross-validation indicates the generalization capability of the deep-nets for GM tracking on out-of-domain data with an overall performance of 95.52% (SD 2.43) from the best performing model (Efficientnet-b6) across all infants (performance range: 84.32–99.24% across all anatomical locations). The paper further introduces an automatic, unsupervised strategy for performance evaluation on extensive out-of-domain recordings through a fusion of likelihoods from a Kalman filter and the deep-net. Findings indicate the possibility of establishing an automated GM tracking platform, as a suitable alternative to, or support for, the current observational protocols for early diagnosis of neurodevelopmental disorders in early infancy.

Improved Genetic Algorithm for Train Platform Rescheduling Under Train Arrival Delays

In this study, the train platform rescheduling problem (TPRP) at a high-speed railway station is analyzed. The adjustments of the train track assignment and train arrival/departure times under train arrival delays are addressed in the TPRP. The problem is formulated as a mixed-integer nonlinear programming model that minimizes the weighted sum of total train delays and rescheduling costs. An improved genetic algorithm (GA) is proposed, and the individual is represented as a platform track assignment and train departure priority, which is a mixed encoding scheme with integers and permutations. The individual is decoded into a feasible schedule comprising the platform track assignment and arrival/departure times of trains using a rule-based method for conflict resolution in the platform tracks and arrival/departure routes. The proposed GA is compared with state-of-the-art evolutionary algorithms. The experimental results confirm the superiority of the GA, which uses the mixed encoding and rule-based decoding, in terms of constraint handling and solution quality.

Extensive Model and Matheuristic Algorithm for the Train Platforming Problem with Two-Train-Capacity Tracks: A Case Study of Prague Central Station

This paper provides a deeper insight into the train platforming problem (TPP). Many studies have focused on different versions of train scheduling and routing problems, and most of them assume that the platform track’s capacity is one train. However, especially in busy and complex railway stations, most platform tracks are divided into two parts, allowing two trains to simultaneously share the same platform track for passenger boarding/alighting. This results in more efficient train assignment to the platform tracks. In addition, consideration of the track capacity makes the problem more difficult because directions of trains are problematic. Motivated by this challenge, we consider the TPP with two-train-capacity tracks. We first describe the problem in detail and then propose a mixed-integer programming model. The objective of the considered problem is to minimize the total weighted train delays, which are defined as the difference between the departure times calculated by the mathematical model (M1) and the scheduled departure times of the trains in the timetable. Because of the NP-hard nature of the problem, the proposed M1 may not find feasible solutions for large-size problems. Thus, a matheuristic algorithm (MA) is developed to solve large-size problems. We used randomly generated test problems to demonstrate the performance of the proposed M1 and MA. Experimental results showed that MA outperforms M1 in both solution quality and solution time. Additionally, a case study was conducted at the central station of Prague, Czechia.