Rincon Research (United States)

The rapid recovery of reliable communication networks in disaster areas is crucial for effective emergency response. Uncrewed aerial vehicles (UAVs) equipped with free-space optical (FSO) communication systems offer a flexible and rapid solution to restore connectivity when traditional infrastructure is compromised. However, the performance of FSO systems is highly susceptible to environmental factors such as fog, rain, and turbulence, which can degrade signal quality. This paper proposes a machine learning-based system to improve the deployment and operation of UAV-based FSO networks in disaster scenarios. The method uses predictive modeling to anticipate atmospheric conditions, optimizes UAV positioning to maximize communication reliability, and integrates anomaly detection to maintain network resilience. The proposed ML-FSO model simultaneously addresses regression (signal prediction using RMSE and $\mathbf{R}^{\mathbf{2}}$) and classification (signal quality assessment using Precision, Recall, F1-score, and Accuracy). Despite the relatively small dataset (100 samples), extensive cross-validation, simulations, and proposed real-world tests demonstrate the system’s ability to improve communication reliability and resilience in dynamic and challenging environments. The results show that the ensemble model (MLFSO) significantly outperforms conventional approaches, making it a promising tool for enhancing communication networks in disaster situations. To our knowledge, this is the first integrated approach combining predictive ML, trajectory optimization, and anomaly detection for UAV-FSO disaster communication.

The transition toward Industry 4.0 and the emerging concept of Industry 5.0 demand intelligent tools that integrate efficiency, adaptability, and human-centered design. This paper presents a Computer Vision-based framework for automated motion classification in Methods-Time Measurement 2 (MTM-2), with the aim of supporting industrial time studies and ergonomic risk assessment. The system uses a Convolutional Neural Network (CNN) for pose estimation and derives angular kinematic features of key joints to characterize upper limb movements. A two-stage experimental design was conducted: first, three lightweight classifiers—K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), and a Shallow Neural Network (SNN)—were compared, with KNN demonstrating the best trade-off between accuracy and efficiency; second, KNN was tested under noisy conditions to assess robustness. The results show near-perfect accuracy (≈100%) on 8919 motion instances, with an average inference time of 1 microsecond per sample, reducing the analysis time compared to manual transcription. Beyond efficiency, the framework addresses ergonomic risks such as wrist hyperextension, offering a scalable and cost-effective solution for Small and Medium-sized Enterprises. It also facilitates integration with Manufacturing Execution Systems and Digital Twins, and is therefore aligned with Industry 5.0 goals.

The Kepler Mission was designed to identify and characterize transiting planets in the Kepler Field of View and to determine their occurrence rates. Emphasis was placed on identification of Earth-size planets orbiting in the Habitable Zone of their host stars. Science data were acquired for a period of four years. Long-cadence data with 29.4 min sampling were obtained for ∼200,000 individual stellar targets in at least one observing quarter in the primary Kepler Mission. Light curves for target stars are extracted in the Kepler Science Data Processing Pipeline, and are searched for transiting planet signatures. A Threshold Crossing Event is generated in the transit search for targets where the transit detection threshold is exceeded and transit consistency checks are satisfied. These targets are subjected to further scrutiny in the Data Validation (DV) component of the Pipeline. Transiting planet candidates are characterized in DV, and light curves are searched for additional planets after transit signatures are modeled and removed. A suite of diagnostic tests is performed on all candidates to aid in discrimination between genuine transiting planets and instrumental or astrophysical false positives. Data products are generated per target and planet candidate to document and display transiting planet model fit and diagnostic test results. These products are exported to the Exoplanet Archive at the NASA Exoplanet Science Institute, and are available to the community. We describe the DV architecture and diagnostic tests, and provide a brief overview of the data products. Transiting planet modeling and the search for multiple planets on individual targets are described in a companion paper. The final revision of the Kepler Pipeline code base is available to the general public through GitHub. The Kepler Pipeline has also been modified to support the Transiting Exoplanet Survey Satellite (TESS) Mission which is expected to commence in 2018.

This paper describes a new method for radar pulse association that enables frequency-of-arrival (FOA) geolocation to function in dense radar environments. The technique associates coherent radar pulses based on the pulse-to-pulse phase linearity for a specific Doppler frequency from a unique radar location. The technique is shown to also provide superior frequency resolution to FFT approaches in cases where the Doppler frequency separation from different radars is less than or close to the conventional resolution limit of the available data. The proposed pulse association approach does not rely on a library of known radar pulse train specifications as is common for many de-interleaving techniques nor does it rely on often unreliable features used for specific emitter identification (SEI) but rather exploits the underlying physics of a unique radar location observed through its Doppler. This makes this FOA based association approach well suited to future de-interleaving techniques that must account for modern radars becoming more agile and unpredictable.

<div class="page" title="Page 2"><div class="section"><div class="layoutArea"><div class="column"><p><span>Este artículo propone la problematización del aula tradicional occidental y su reflejo en América Latina y para ello, aunque a la primera aciertos, propende por la construcción de un aula / vital / ampliada, aula habitada por sujetos políticos que intercalan el rol de maestro – alumno. Aula / vital / ampliada que ve en Oriente una opción para crear un espacio que procure que la conciencia esté aquí y ahora; la posibilidad de que exista el lugar, el sentir y las posibilidades en el aquí y el ahora </span></p></div></div></div></div>

Introduction. Hyperthermia is an independent predictor of poor outcome after stroke. We sought to determine early predictors of hyperthermia after spontaneous ICH. Methods. Data of patients from acute ICH trials registered in the Virtual International Stroke Trials Archive (VISTA-ICH) were analyzed. Admission variables were assessed in relation to the recorded temperatures at baseline, 24hr, 48hr, 72hr and 7-days, using linear regression based on the normal distribution of the dependent variables. Models were fitted and odds ratios (OR) and 95% confidence intervals (CI) were calculated. Results. In total, 303 patients were included in the analysis. The average age of the cohort was 66 ± 12 years, 200 (66%) were males, the median admission NIHSS was 13 (Interquartile range [IQR], 9-18), median GCS was 15 (IQR, 14-15), mean temperature was 36.4 ± 0.5 °C at baseline; 36.7 ± 0.6 °C at 24hr; 37.1 ± 0.7 °C at 48hr; 36.9 ± 0.6 °C at 72hr, and 36.8 ± 0.6 °C at 7-days. The median hematoma volume at baseline CT scan was 15cc (IQR, 8-33), intraventricular hemorrhage (IVH) was present in 104 (34%) of patients, and hematoma growth >33% occurred in 50 patients (34%, n=185). Variables associated with higher temperature were: admission NIHSS >13, IVH, infratentorial origin or location, volume of hematoma growth, hematoma growth >33%, and the following additional components of ICH score: base volume >30ml, and Glasgow Coma Scale (GCS) <13. After adjustment, ICH baseline volume >30cc (OR, 1.2; 95%CI, 1.1-1.3) and hematoma growth >33% (OR, 1.1; 95%CI, 1.01-1.2) (Baseline intercept = 37.2 C) were associated with hyperthermia at 24hrs; and hematoma growth >33% (OR, 1.1; 95%CI, 1.02-1.2) and IVH (OR, 1.1; 95%CI, 1.02-1.2) (Baseline intercept = 37.1 C) were associated with higher temperature at 7-days. Conclusions. These data suggests that in ICH patients, hematoma baseline volume >30cc and hematoma growth are independently associated with higher temperature at 24 hrs and hematoma growth and IVH with higher temperature at 7 days. Prevention of hematoma growth and treatment of IVH may decrease the prevalence of hyperthermia in these patients.

Most complex software systems are written in many languages and utilize multiple frameworks. The PicklingTools is an open-source collection of libraries (see the website at http://www.picklingtools.com) allowing multiple systems (both modern and legacy) to communicate. The original purpose of the PicklingTools was to allow users to communicate with a legacy product (written with a monolithic legacy framework called Midas 2k) without needing the legacy framework. Since then, the toolset has evolved to become a standalone framework for building applications in the C++ and Python programming languages. This case-study explores how the PicklingTools has been used to evolve existing legacy applications, exploit existing legacy installations, and author new modern applications.

This is a report on the third in a series of petascale workshops co-sponsored by Blue Waters and TeraGrid to address challenges and opportunities for making effective use of emerging extreme-scale computing. This workshop was held to discuss fault tolerance on large systems for running large, possibly long-running applications. The main point of the workshop was to have systems people, middleware people (including fault-tolerance experts), and applications people talk about the issues and figure out what needs to be done, mostly at the middleware and application levels, to run such applications on the emerging petascale systems, without having faults cause large numbers of application failures. The workshop found that there is considerable interest in fault tolerance, resilience, and reliability of high-performance computing (HPC) systems in general, at all levels of HPC. The only way to recover from faults is through the use of some redundancy, either in space or in time. Redundancy in time, in the form of writing checkpoints to disk and restarting at the most recent checkpoint after a fault that cause an application to crash/halt, is the most common tool used in applications today, but there are questions about how long this can continue to be a good solution as systems and memories grow faster than I/O bandwidth to disk. There is interest in both modifications to this, such as checkpoints to memory, partial checkpoints, and message logging, and alternative ideas, such as in-memory recovery using residues. We believe that systematic exploration of these ideas holds the most promise for the scientific applications community. Fault tolerance has been an issue of discussion in the HPC community for at least the past 10 years; but much like other issues, the community has managed to put off addressing it during this period. There is a growing recognition that as systems continue to grow to petascale and beyond, the field is approaching the point where we don't have any choice but to address this through R&D efforts.

In the present article the management of an academic scientific publication in a digital atmosphere against the traditional printed way will be considered, like thus also the costs associated to the digital model of production, from the reception and treatment of original to the publication of the article. Finally one analyzes the features of the financing of the scientific publishing industry in Argentina, and its different types and economic models of recovery from oriented costs from the present electronic scientific publication (ESP).