Tracking Data Research Articles

EarthRanger: An open‐source platform for ecosystem monitoring, research and management

Abstract Effective approaches are needed to conserve the planet's remaining wildlife and wilderness landscapes, especially concerning global biodiversity conservation targets. Here, we present a new software system called EarthRanger: an open‐source platform built to help monitor, research and manage ecosystems. EarthRanger consists of seven main components (Core Server, API, Storage, Gundi, Web App, Mobile App, Ecoscope) that provide functionality for data (i) aggregation & collection, (ii) storage & management, (iii) real‐time and post hoc analysis, (iv) visualisation and (v) dissemination. The mobile application provides field‐based data recording and visualisation tools. EarthRanger may be deployed for single project use or can aggregate across multiple geographies as a centralised hub. EarthRanger can be used to collect standardised tracking data (e.g. from wildlife collars, vehicles and ranger patrols) and configurable event information (e.g. a singular recording with associated user‐defined attribute information such as a wildlife sighting or encounter with a poacher). Since development began in 2015, the platform has (at the time of writing) been deployed at over 500 sites across 70 countries and with myriad configurations and objectives. EarthRanger has improved the ability to monitor data feeds and manage conservation‐related operations in real time. For instance, the deployment of EarthRanger by African Parks has led to the removal of over 50,000 snares, steady population growth of key species of concern and near cessation of poaching. In Liwonde's protected area, enhanced mitigation efforts supported by EarthRanger reduced the number of deaths from wildlife conflict by more than 91%. EarthRanger is also providing a platform to enhance standardisation, aggregation, transfer and long‐term storage of ecological information and promote collaboration between groups conducting protected area management and ecology and biodiversity research.

Paleozoic-Mesozoic multistage tectonic evolution of the North Qilian Shan revealed by detrital zircon U-Pb and fission-track double dating

Knowledge of the tectonic history of the North Qilian Shan contributes to our understanding of the formation of the Tibetan Plateau. However, when the North Qilian Shan were formed has been controversial. We report new detrital zircon U-Pb (n = 899) and double dating fission track and U-Pb (n = 450) results from nine samples of Lower Cretaceous and Oligocene sandstone units, integrating with published geochronology and thermochronology data in order to reconstruct the evolution history of the North Qilian Shan. Nine samples from this unit show detrital zircon fission track age populations that center at: 636–603, 495, 406–331, 290–266, 247–224, 193–151, and 144–102 Ma. These fission track data are older than their corresponding depositional ages, suggesting that the fission track ages are unreset and record magmatic-related cooling or exhumation stages of the North Qilian Shan since the Late Neoproterozoic. Deconvolved fission track peak ages document major cooling events associated with the opening of the North Qilian Ocean at 636–603 Ma. Late Cambrian peak age (495 Ma) is attributed to the early phase of the subduction of the North Qilian Ocean. The Devonian period exhibited a cooling phase between 406 and 381 Ma, corresponding to the collision event between the Qaidam terrane, Central Qilian Shan, and Alxa Block. The peak age of 331 Ma signifies the cooling event associated with the subduction of the Paleo-Asian Ocean. The peak ages during the Early-Middle Permian period (290–266 Ma) provide evidence of significant exhumation events that occurred during the Late Paleozoic era. These events were linked to the subduction of the Paleo-Tethys Ocean to the south and the Paleo-Asian Ocean to the north. The Triassic peak ages (247–224 Ma) unveil exhumation signals in the North Qilian Shan region that were generated by the far-field effect of the collision between the Qiangtang and Kunlun terranes. During the Jurassic (193–151 Ma) and the Early Cretaceous periods (144–102 Ma), the exhumation of the North Qilian Shan region was a consequence of the subduction of the Neo-Tethys Ocean and the collision between the Lhasa and Qiangtang blocks, respectively. The peak ages of the Xinminpu Formation in the Early Cretaceous (123–121 Ma) are in close proximity to their depositional age (125–105 Ma), indicating that the North Qilian Shan underwent rapid exhumation during this period. The peak ages observed in the Oligocene Baiyanghe Formation (31–24 Ma) from 144 to 102 Ma, indicate that it has been influenced by the recycled sediments originating from the Cretaceous Xinminpu Formation. The North Qilian Shan gradually emerged during the late Paleozoic and Mesozoic eras, providing the foundation for its subsequent reactivation in the Cenozoic era. The multi-stage cooling or exhumation events of North Qilian Shan since Late Neoproterozoic reflect the complex formation process of the northeastern margin of Tibetan Plateau.

Habitat requirements of a critically endangered Ninox hawk‐owl: implications for island‐wide restoration

Small, isolated populations can be especially sensitive to poor habitat conditions due to their limited capacity to adapt or disperse. Management of such populations, with an objective to implement the most effective strategies, is a conservation priority. The Norfolk Island morepork (Ninox novaeseelandiae undulata) is a Critically Endangered hawk‐owl endemic to Norfolk Island with an estimated population of 25 individuals. This species faces compounding threats from habitat degradation and loss, as well as inbreeding depression. It presents an exemplar for the development of habitat management strategies for top‐order predators in small, isolated systems. We paired fine‐scale light detection and ranging‐derived habitat variables with tracking data to quantify habitat suitability and the relative importance of environmental variables. Maximum entropy modeling indicated vegetation patch size and canopy height were important habitat variables, with moreporks more likely to be found in larger patches and canopy heights above 7 m. Morepork presence was not strongly influenced by the type of wooded vegetation; rather, the species appears to be a generalist occupier of forested areas. We demonstrate that suitable habitat during the breeding season is currently constrained, with coverage limited to one‐third (34%) of the island and an estimated carrying capacity of 36 morepork pairs. Restoration of native forest in cleared areas adjacent to existing wooded patches, with a specific goal to increase patch size, was the optimized management strategy to increase in situ carrying capacity. This study has demonstrated how habitat suitability modeling can inform targeted habitat restoration to enhance threatened species recovery.

Deep Learning Detection of Hand Motion During Microvascular Anastomosis Simulations Performed by Expert Cerebrovascular Neurosurgeons

Deep learning enables precise hand tracking without the need for physical sensors, allowing for unsupervised quantitative evaluation of surgical motion and tasks. We quantitatively assessed the hand motions of experienced cerebrovascular neurosurgeons during simulated microvascular anastomosis using deep learning. We explored the extent to which surgical motion data differed among experts. A deep learning detection system tracked 21 landmarks corresponding to digit joints and the wrist on each hand of 5 expert cerebrovascular neurosurgeons. Tracking data for each surgeon were analyzed over long and short time intervals to examine gross movements and micromovements, respectively. Quantitative algorithms assessed the economy and flow of motion by calculating mean movement distances from the baseline median landmark coordinates and median times between sutures, respectively. Tracking data correlated with specific surgical actions observed in microanastomosis video analysis. Economy of motion during suturing was calculated as 19, 26, 29, 27, and 28 pixels for surgeons 1, 2, 3, 4, and 5, respectively. Flow of motion during microanastomosis was 31.96, 29.40, 28.90, 7.37, and 47.21seconds for surgeons 1, 2, 3, 4, and 5, respectively. Hand tracking data showed similarities among experts, with low movements from baseline, minimal excess motion, and rhythmic suturing patterns. The data revealed unique patterns related to each expert's habits and techniques. The results showed that surgical motion can be correlated with hand motion and assessed using mathematical algorithms. We also demonstrated the feasibility and potential of deep learning-based motion detection to enhance surgical training.

Pembuatan Barcode Kemasan Produk Pada CV. Kuliner Makmur Sejahtera Kota Padang

Rendang, a traditional Minangkabau dish from West Sumatra, Indonesia, has gained international recognition and has inspired many entrepreneurs to promote this cuisine globally. One such entrepreneur is CV. Kuliner Makmur Sejahtera (KMS), which produces rendang under the brand Katuju. To enhance operational efficiency and support global market expansion, KMS requires an improved inventory management system. The primary challenge is the difficulty in monitoring inventory and tracking products without a barcode system. This community service program aims to address this issue by implementing barcode technology on Katuju's product packaging. The activities include designing, creating, and printing barcodes, as well as training KMS staff on how to use them. The process starts with observation and interviews to identify needs, followed by barcode design and production using an online barcode generator, and testing the barcode functionality with a scanning system. The results indicate that the community service team successfully trained KMS in barcode creation and printing, and integrated barcodes into product packaging. Evaluation through questionnaires showed participant satisfaction with the program and readiness to apply the barcode system to their products. This program is expected to improve inventory management, data accuracy, and support CV. KMS's global market expansion by providing tools necessary for efficient product tracking and valuable consumer data collection.

Combination of altimetry crossovers and Doppler observables for orbit determination and geodetic parameter recovery: Application to Callisto

An accurate knowledge of the orientation, the tidal deformability, and the gravity field of a celestial body is fundamental to provide constraints on its internal structure. These quantities may be retrieved by processing radiometric tracking and altimetry data from a probe in orbit around such body. This paper presents a method to combine altimetry crossovers with two-way Doppler tracking observations at normal equation level, using the Bernese GNSS Software and the pyXover software library. This method was applied to a proposed 200km altitude orbiter around Callisto, a privileged destination for the upcoming phase of Solar System exploration. Enhancing “standard” Doppler tracking with altimetry generally benefited both orbit determination and a joint estimation of the orientation of the north pole and of planetary librations. The retrieval of low-degree gravity field parameters was also improved by the addition of altimetry data. However, the improvements on the estimated parameters were highly dependent on the characteristics of the simulation, e.g., the underlying topography roughness. Overall, combining radioscience with altimetry data accounted for a visible reduction of correlations among estimated parameters, while also allowing for a consistent estimation of the “vertical” Love number h2 along with gravity.

Evil and allies: Opportunistic gulls as both spreaders and sentinels of antibiotic‐resistant bacteria in human‐transformed landscapes

Abstract Human‐transformed residuals, especially those derived from human waste (dumps), farmland, and livestock are involved in the emergence of antibiotic‐resistant bacteria (ARB) in the environment. Wildlife can act as vectors of ARB dispersal through different environments, but also as sentinels to detect the early spread and determine ARB sources. The development of integrated monitoring programmes focused on wildlife would help to anticipate the risks of ARB to humans and livestock. We used the yellow‐legged gull (Larus michahellis) as a model species to investigate and monitor the spatial patterns of ARB dispersal across an extensive farmland region located in northeastern Spain (Lleida). By integrating GPS tracking data and ARB clinical testing for 26 individuals within a network analysis framework, we modelled the risk of spatial pathogen spread through faeces during the bacteria‐transmission latency period (16 days after sample collection). Additionally, we created a connectivity network to determine the main sources of ARB in the area, focusing on three main habitats of special risk for infection: dumps, livestock facilities, and irrigation ponds. Seven individuals were infected by Escherichia coli, with one also co‐infected with Listeria monocytogenes and Salmonella spp. Potential pathogen dispersal distances ranged from 1.13 km to 23.13 km from the breeding colony. Our network analyses revealed 54 main nodes (i.e. high‐risk habitats recurrently visited by tracked gulls) and 1182 links among them. Our findings revealed a high degree of connectivity between the breeding area, located in a shallow lake, and nearby dumps, highlighting them as significant contributors to ARB dispersal. Synthesis and applications: The integration of GPS data, pathogen testing and network analyses can shed further light on pathogen dynamics by creating spatial risk maps and identifying ARB sources. In combination with complementary molecular epidemiology techniques within a One Health framework, our approach can emerge as an important tool for monitoring ARB dynamics within highly human‐transformed ecosystems. This may empower managers for the development of targeted ARB monitoring programmes and effective mitigation strategies, ultimately improving both animal and public health.

Gaze tracking embedded collaborative robots for automated metrology and reverse engineering

Conventional geometric metrology, or three-dimensional (3D) scanning, and reverse engineering heavily rely on the experience of the operators. With an increasing need for automation, robot arms have been adopted for this task. However, due to the large variety of parts and designs, automated path planning could provide a scanning solution that may overlook the critical area, which could potentially deteriorate the scan results. This article explores the integration of collaborative robotics (cobots) with eye-tracking technology to improve the autonomous 3D scanning process. The primary objective of this study is to enhance the accuracy and efficiency of cobots in 3D scanning, particularly in the capture of functionally critical areas, and to provide a detailed description of regions with complex geometric features. The study develops a framework where the scanning path of the robot-driven scanner is partially guided by eye tracking data, that is, calibrated gaze tracking, to improve the automated 3D scanning process. This framework provides an innovative integration of human gaze movement with automatic robot path planning, providing a new way of human-autonomy teaming. Case studies are presented to present and validate the proposed framework to automatically improve the 3D point cloud collection process, specifically in areas that usually require human manual intervention to capture details.

Automated Identification of Clinically Relevant Regions in Glaucoma OCT Reports Using Expert Eye Tracking Data and Deep Learning.

To propose a deep learning-based approach for predicting the most-fixated regions on optical coherence tomography (OCT) reports using eye tracking data of ophthalmologists, assisting them in finding medically salient image regions. We collected eye tracking data of ophthalmology residents, fellows, and faculty as they viewed OCT reports to detect glaucoma. We used a U-Net model as the deep learning backbone and quantized eye tracking coordinates by dividing the input report into an 11 × 11 grid. The model was trained to predict the grids on which fixations would land in unseen OCT reports. We investigated the contribution of different variables, including the viewer's level of expertise, model architecture, and number of eye gaze patterns included in training. Our approach predicted most-fixated regions in OCT reports with precision of 0.723, recall of 0.562, and f1-score of 0.609. We found that using a grid-based eye tracking structure enabled efficient training and using a U-Net backbone led to the best performance. Our approach has the potential to assist ophthalmologists in diagnosing glaucoma by predicting the most medically salient regions on OCT reports. Our study suggests the value of eye tracking in guiding deep learning algorithms toward informative regions when experts may not be accessible. By suggesting important OCT report regions for a glaucoma diagnosis, our model could aid in medical education and serve as a precursor for self-supervised deep learning approaches to expedite early detection of irreversible vision loss owing to glaucoma.

An integrated framework for eye tracking-assisted task capability recognition of air traffic controllers with machine learning

To effectively address the continuously increasing demands of air transport, air traffic management (ATM) systems are evolving towards a human-artificial intelligence (AI) hybrid automation paradigm. In this paradigm, air traffic controllers (ATCOs) play a crucial role in ensuring safe and efficient operations. Recognizing ATCOs’ task capability is essential for evaluating performance, optimizing task assignments, and personalizing training strategies. Physiological signals, such as eye movements, offer objective insights into human behavior and cognitive processes, making them valuable for identifying the task capability of ATCOs. In this study, an integrated framework leveraging machine learning is proposed to achieve this goal. First, a Transformer-attentional long short-term memory (LSTM) network is developed to analyze eye movement patterns, capturing both global and long-term dependencies for precise skill level detection. Next, manual parameters are extracted from the raw eye tracking data and the SHAP (SHapley Additive exPlanation) method is utilized to determine the importance of these parameters, aiding in the selection of relevant performance metrics. Furthermore, a radar chart is implemented to intuitively visualize and compare performance metrics across different skill levels based on the selected parameters. A case study and extensive experiments are conducted to validate the effectiveness of the proposed framework. This research advances task capability recognition for ATCOs in a human-in-the-loop scenario, with a focus on expertise level detection, parameter importance identification, and performance metric comparison.

Web-платформа для організації хмарних обчислень у нейрофізіологічних дослідженнях на основі даних айтрекінгу

The purpose of the work is to develop the architecture and web version of the software complex based on the proposed new concept of cloud computing organization, which allows to expand the diagnostic capabilities of the tools of model-oriented information technology for assessing the neurophysiological state of a person using methods of nonlinear dynamic identification of the oculomotor system based on eye tracking data. The concept of cloud computing is proposed, which is based on the combination of PaaS and SaaS services as part of the developed software complex, due to which the cross-platform nature of cloud computing is ensured, the productivity and efficiency of scientific research increases. The developed architecture allows you to easily expand the functionality of the software complex and adapt it to different application conditions. The key feature of the complex is its undemanding hardware on the client side thanks to the use of cloud computing and its modular structure, which allows for easy scaling of the complex, as well as the isolation of the script code execution process in the cloud computing environment to increase the level of security when interpreting the script code on the server . Compared to other similar services, the complex has several advantages: it provides effective work in research and educational applications, supports Python and JavaScript programming languages, and also allows the use of software-implemented identification methods through specially developed GUI interfaces. In addition, the complex offers social opportunities and a high level of abstraction, which allows to optimize the research process.

Estimating the Value of Offsite Tracking Data to Advertisers: Evidence from Meta

We run a large-scale field experiment at Meta to estimate how valuable offsite tracking data are for digital ad effectiveness.

MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.

This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the tool addresses key challenges: managing activity signals, accelerating insight generation, and rapidly contextualizing identified patterns. By analyzing sensor measurements, it aims to enhance understanding of MS symptomatology and improve the broader problem of clinical exploratory sensor data analysis. Following a user-centered design approach, we learned that clinicians have 3 priorities for generating insights for the Barka-MS study data: exploration and search for, and contextualization of, sequences and patterns in patient sleep and activity. We compute meaningful sequences for patients using clustering and proximity search, displaying these with an interactive visual interface composed of coordinated views. Our evaluation posed both closed and open-ended tasks to participants, utilizing a scoring system to gauge the tool's usability, and effectiveness in supporting insight generation across 15 clinicians, data scientists, and non-experts. We present MS Pattern Explorer, a visual analytics system that helps clinicians better address complex data-centric challenges by facilitating the understanding of activity patterns. It enables innovative analysis that leads to rapid insight generation and contextualization of temporal activity data, both within and between patients of a cohort. Our evaluation results indicate consistent performance across participant groups and effective support for insight generation in MS patient fitness tracker data. Our implementation offers broad applicability in clinical research, allowing for potential expansion into cohort-wide comparisons or studies of other chronic conditions. MS Pattern Explorer successfully reduces the signal overload clinicians currently experience with activity data, introducing novel opportunities for data exploration, sense-making, and hypothesis generation.

ФОРМИРОВАНИЕ ОБЪЕКТИВНЫХ ПОКАЗАТЕЛЕЙ РАБОТЫ СОРТИРОВОЧНОЙ СТАНЦИИ НА ОСНОВЕ ДАННЫХ «ОТ КОЛЕСА»

The paper presents a new principle for forming freight marshalling yard indicators based on the use of data from various reading devices and sensors located on the station tracks (data “from the wheel”). The paper shows the relevance and objectivity of using the proposed approach, implementing the presented principles for obtaining real freight yard indicators. The implementation is presented on the basis of data generated by the system for monitoring and preparing information on the movements of wagons and locomotives at the station in real time. The difference between the results obtained as a result of implementing the proposed approach and the information generated in real certificates and reporting journals of Russian Railways JSC is shown. Hypotheses-descriptions of the difference between information “from the wheel” and information obtained by manual input of data on the movements of cars and locomotives are proposed. The conclusion describes further prospects for automation of calculation and forecasting of station operation based on data “from the wheel”.

Dodecagonal Parameter-Based Energy-Efficient Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are composed of several sensor nodes (SN) that are dispersed geographically and interact with one another over wireless media in order to track and log physical data from their environment. At the moment, WSNs frequently use routing and clustering strategies to extend the life of their networks. This paper proposes a DODECAGONAL parameter-based Energy-Efficient Routing in WSN (DOPE-WSN) for improvement of network lifetime and saving the energy consumption. Initially the cluster is selected using Agglomerative clustering. In the second phase, Pelican optimization (PELO) based Cluster head selection (CHs) meant for considering Congestion, Node Degree, Node Density, Network area, Network Coverage, Number of clusters, Number of nodes, Communication cost, Distance, Residual Energy, Distance to neighbor, Node Centrality. Moreover, the Sooty Tern Optimization (STO) model are utilized for the determination of the best routing path for the cluster heads. Taking into account node degree, residual energy, and distance, the STO maximizes network performance. The suggested approach has undergone thorough testing for ensuring network durability and energy efficiency. The proposed model achieved a maximum 97% Packet Delivery Ratio (PDR) with 900 nodes in comparison with 91%, 89%, 83%, and 82% for CRPSH, HQCA, EACRLEACH, and BWO-IACO algorithms respectively.

Where to fly? Landscape influences on the movement and spatial ecology of a threatened apex predator

Effectively managing apex predators in human-modified landscapes poses considerable challenges. Habitat fragmentation disperses resources across wider expanses and undermines the ability of apex predators to reach isolated habitat patches as they traverse multi-tenured landscapes comprising unsuitable habitat. Understanding a species response to landscape configuration is key to informing effective management strategies, particularly for threatened, mobile and ecologically important species like apex predators. Here we use GPS tracking data collected from 37 powerful owls (Ninox strenua), an Australian threatened apex predator that occurs across the urban-agricultural-forest gradient, to investigate how landscape characteristics influence their spatial and movement behaviour. We demonstrate that as habitat fragmentation increases, the spatial requirements of powerful owls expand, their home-range shape becomes more intricate and individuals travel further each night, significantly adjusting their movement behaviours and spatial configurations to connect remaining habitat. Landscapes with unified and connected habitats, on the other hand, have smaller spatial requirements, likely due to greater habitat availability that minimises competition for essential resources. This facilitates the establishment of smaller home-ranges and consequently, the occurrence of more territories. Interestingly, landscape characteristics did not influence sequential nightly visitation behaviour, suggesting that powerful owls across all landscape types visit different portions of their home-range each night to exert an unpredictable hunting strategy regardless of local landscape conditions. Our research highlights the importance of integrating the influence of local landscape features and visitation behaviours into decision-making processes. This integration is crucial for informing effective conservation strategies aimed at supporting apex predator survival in disturbed landscapes.

Typhoon Trajectory Prediction by Three CNN+ Deep-Learning Approaches

The accuracy in predicting the typhoon track can be key to minimizing their frequent disastrous effects. This article aims to study the accuracy of typhoon trajectory prediction obtained by combining several algorithms based on current deep-learning techniques. The combination of a Convolutional Neural Network with Long Short-Term Memory (CNN+LSTM), Patch Time-Series Transformer (CNN+PatchTST) and Transformer (CNN+Transformer) were the models chosen for this work. These algorithms were tested on the best typhoon track data from the China Meteorological Administration (CMA), ERA5 data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and structured meteorological data from the Zhuhai Meteorological Bureau (ZMB) as an extension of existing studies that were based only on public data sources. The experimental results were obtained by testing two complete years of data (2021 and 2022), as an alternative to the frequent selection of a small number of typhoons in several years. Using the R-squared metric, results were obtained as significant as CNN+LSTM (0.991), CNN+PatchTST (0.989) and CNN+Transformer (0.969). CNN+LSTM without ZMB data can only obtain 0.987, i.e., 0.004 less than 0.991. Overall, our findings indicate that appropriately augmenting data near land and ocean boundaries around the coast improves typhoon track prediction.

Arrival-breeding interval is flexible in a songbird and is not constrained by migration carry-over effects.

As spring phenology advances with climate change, so too must the timing of life cycle events. Breeding at the right time is critical in many species as it maximizes fitness. For long-distance migratory birds, flexibility in the duration of the arrival-breeding interval (pre-breeding period) may allow populations to adjust their timing of breeding. However, whether first egg-lay dates are flexible to local environmental conditions after arrival, and if they are constrained by the time needed to replenish energy lost during migration, remains unclear. We investigated the regional flexibility of the arrival-breeding interval in an avian migrant, the purple martin, Progne subis, across their breeding range. We evaluated whether the duration of the arrival-breeding interval was flexible to temperature and precipitation at breeding sites, and if timing was limited by migration rate and stopover duration. We also tested if longer interval durations were associated with higher fledging success. To address our hypotheses, we used a combination of migration tracking, weather and breeding data collected from four regions across eastern North America (26.1° N to 52.4° N latitude). We found the arrival-breeding interval to be shortest in the north and longest in the south. Across all regions, warmer temperatures encountered at breeding grounds were associated with shorter intervals, and faster migration rates led to longer intervals. The length of the interval was not influenced by precipitation or stopover duration. Finally, longer intervals were not associated with higher fledge success. Currently, the longer arrival-breeding intervals in this study system, on average 28.3 days, may provide both early and late-arriving birds with ample time for recovery so birds can lay eggs according to temperature. Any negative effects of faster migration may have been buffered by longer arrival-breeding intervals, as interval length did not determine fledge success. With ongoing climate change, further research is needed to examine if arrival-breeding intervals become constrained by migration timing, which may limit opportunities for migrants to match the timing of breeding with key resources.

Multi-agent statistically discriminative sub-trajectory mining and an application to NBA basketball

Abstract Improvements in tracking technology through optical and computer vision systems have enabled a greater understanding of the movement-based behaviour of multiple agents, including in team sports. In this study, a multi-agent statistically discriminative sub-trajectory mining (MA-Stat-DSM) method is proposed that takes a set of binary-labelled agent trajectory matrices as input and incorporates Hausdorff distance to identify sub-matrices that statistically significantly discriminate between the two groups of labelled trajectory matrices. Utilizing 2015/16 SportVU NBA tracking data, agent trajectory matrices representing attacks consisting of the trajectories of five agents (the ball, shooter, last passer, shooter defender, and last passer defender), were truncated to correspond to the time interval following the receipt of the ball by the last passer, and labelled as effective or ineffective based on a definition of attack effectiveness that we devise in the current study. After identifying appropriate parameters for MA-Stat-DSM by iteratively applying it to all matches involving the two top- and two bottom-placed teams from the 2015/16 NBA season, the method was then applied to selected matches and could identify and visualize the portions of plays, e.g., involving passing, on-, and/or off-the-ball movements, which were most relevant in rendering attacks effective or ineffective.

A robust algorithm for authenticated health data access via blockchain and cloud computing.

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting.