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A new breed of notebooks is taking data visualization and collaborative functionality to the next level, with spreadsheet simplicity. A new breed of notebooks is taking data visualization and collaborative functionality to the next level, with spreadsheet simplicity.

Supercomputers play a significant and growing role in a variety of areas important to the nation. They are used to address challenging science and technology problems. In recent years, however, progress in supercomputing in the United States has slowed. The development of the Earth Simulator supercomputer by Japan that the United States could lose its competitive advantage and, more importantly, the national competence needed to achieve national goals. In the wake of this development, the Department of Energy asked the NRC to assess the state of U.S. supercomputing capabilities and relevant R&D. Subsequently, the Senate directed DOE in S. Rpt. 107-220 to ask the NRC to evaluate the Advanced Simulation and Computing program of the National Nuclear Security Administration at DOE in light of the development of the Earth Simulator. This report provides an assessment of the current status of supercomputing in the United States including a review of current demand and technology, infrastructure and institutions, and international activities. The report also presents a number of recommendations to enable the United States to meet current and future needs for capability supercomputers.

The monitoring of large infrastructures such as bridges, dams, and Tailings Storage Facilities (TSFs) is critical for ensuring structural safety and preventing catastrophic failures. Traditional geodetic monitoring approaches, while accurate, are often labour-intensive, expensive, and impractical for large-scale or remote deployments. This study evaluates the capability of dual-frequency low-cost GNSS receivers (ublox ZED-F9R) integrated with a minicomputer to measure millimeter-scale movements over extended monitoring periods. Two measurement campaigns are conducted: a 16-hour short-term test and a 60-day long-term deployment. A rigid aluminium beam with photogrammetrically measured baseline served as ground truth for assessing positioning accuracy. Short-term experiments demonstrated sub-millimeter accuracy while the 60-day campaign achieved 3D baseline measurement accuracy and precision below 2 mm despite significant environmental variations. The results confirm that low-cost dual-frequency GNSS systems can reliably detect centimeter/year-level deformations, making them suitable for monitoring slow-moving processes in critical infrastructure. The collected data, including raw GNSS observations, processed coordinates, and meteorological data, is publicly available for research purposes at https://doi.org/10.5281/zenodo.17378723.

Differentiating between shockable and non-shockable Electrocardiogram (ECG) signals would increase the success of resuscitation by the Automated External Defibrillators (AED). In this study, a Deep Neural Network (DNN) algorithm is used to distinguish 1.4-second segment shockable signals from non-shockable signals promptly. The proposed technique is frequency-independent and is trained with signals from diverse patients extracted from MIT-BIH, MIT-BIH Malignant Ventricular Ectopy Database (VFDB), and a database for ventricular tachyarrhythmia signals from Creighton University (CUDB) resulting, in an accuracy of 99.1%. Finally, the raspberry pi minicomputer is used to load the optimized version of the model on it. Testing the implemented model on the processor by unseen ECG signals resulted in an average latency of 0.845 seconds meeting the IEC 60601-2-4 requirements. According to the evaluated results, the proposed technique could be used by AED’s.

Objective The objective of this study was to develop a portable and modular brain–computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A). Background BCIs can restore independence for individuals with paralysis by using brain signals to control prosthetics or trigger functional electrical stimulation. Though several studies have successfully implemented this technology in the laboratory and the home, portability, device configuration, and caregiver setup remain challenges that limit deployment to the home environment. Portability is essential for transitioning BCI from the laboratory to the home. Methods The BCI platform implementation consisted of an Activa PC + S generator with two subdural four-contact electrodes implanted over the dominant left hand-arm region of the sensorimotor cortex, a minicomputer fixed to the back of the subject’s wheelchair, a custom mobile phone application, and a mechanical glove as the end effector. To quantify the performance for this at-home implementation of the BCI, we quantified system setup time at home, chronic (14-month) decoding accuracy, hardware and software profiling, and Bluetooth communication latency between the App and the minicomputer. We created a dataset of motor-imagery labeled signals to train a binary motor imagery classifier on a remote computer for online, at-home use. Results Average bluetooth data transmission delay between the minicomputer and mobile App was 23 ± 0.014 ms. The average setup time for the subject’s caregiver was 5.6 ± 0.83 min. The average times to acquire and decode neural signals and to send those decoded signals to the end-effector were respectively 404.1 ms and 1.02 ms. The 14-month median accuracy of the trained motor imagery classifier was 87.5 ± 4.71% without retraining. Conclusions The study presents the feasibility of an at-home BCI system that subjects can seamlessly operate using a friendly mobile user interface, which does not require daily calibration nor the presence of a technical person for at-home setup. The study also describes the portability of the BCI system and the ability to plug-and-play multiple end effectors, providing the end-user the flexibility to choose the end effector to accomplish specific motor tasks for daily needs. Trial registration ClinicalTrials.gov: NCT02564419. First posted on 9/30/2015

Does frequent head-to-ball contact cause cognitive dysfunctions and brain injury to soccer players? An iPad-based experiment was designed to examine the impact of ball-heading among high school female soccer players. We examined both direct, stimulus-driven, or reflexive point responses (Pro-Point) as well as indirect, goal-driven, or voluntary point responses (Anti-Point), thought to require cognitive functions in the frontal lobe. The results show that soccer players were significantly slower than controls in the Anti-Point task but displayed no difference in Pro-Point latencies, indicating a disruption specific to voluntary responses. These findings suggest that even subconcussive blows in soccer can result in cognitive function changes that are consistent with mild traumatic brain injury of the frontal lobes. There is great clinical and practical potential of a tablet-based application for quick detection and monitoring of cognitive dysfunction.

Raspberry Pi is a mini-computer that is provided to carry out activities quickly and precisely, but Raspberry Pi was created to not be able to do the real-time system with the support of Windows 10 IoT operating system, so the real-time system can be done on Raspberry Pi. The real-time applied in the application needs to be tested with the Nyquist theory. The purpose of this study was to get real-time system measurements available on Windows 10 IoT. This test is done using the Nyquist theory by calculating the results of measurements on mp3 streaming performed on Windows 10 IoT.

With the advent of physiological computing systems, new avenues are emerging for the field of learning analytics related to the potential integration of physiological data. To this end, we developed a physiological computing infrastructure to collect physiological data, surveys, and browsing behavior data to capture students’ learning journey in remote learning. Specifically, our solution is based on the Raspberry Pi minicomputer and Polar H10 chest belt. In this work-in-progress paper, we present preliminary results and experiences we collected from a field study with medical students using our developed infrastructure. Our results do not only provide a new direction for more effectively capturing different types of data in remote learning by addressing the underlying challenges of remote setups, but also serve as a foundation for future work on developing a less obtrusive, (near) real-time measurement method based on the classification of cognitive-affective states such as flow or other learning-relevant constructs with the captured data using supervised machine learning.

Raspberry Pi 3 is a tiny affordable chip used to learn and program through interactive projects. It has gained a lot of traction as the first choice in single-board computers due to its versatility. It extends a wide range of support to Python programming. This recipe-based guide will allow you to showcase the capabilities of Raspberry Pi 3.

Most educators consider note-taking a critical component of formal classroom learning. Advancements in technology such as tablet computers, mobile applications, and recorded lectures are altering classroom dynamics and affecting the way students compose and review class notes. These tools may improve a student’s ability to take notes, but they also may hinder learning. In an era of dynamic technology developments, it is important for educators to routinely examine and evaluate influences on formal and informal learning environments. This paper discusses key background literature on student note-taking, identifies recent trends and potential implications of mobile technologies on classroom note-taking and student learning, and discusses future directions for note-taking in the context of digitally enabled lifelong learning.