RNET Technologies (United States)

Missourians are not meeting recommendations for physical activity or nutrition. To address this challenge, the CDC has provided Missouri with state (SPAN), regional (HOP), and community-level (REACH) funding to implement large-scale programs. This study describes the implementation strategies and outcomes of SPAN, HOP, and REACH programs in Missouri. A cross-sectional, online survey was conducted in 2024 of practitioners from SPAN, HOP, and REACH program collaborating organizations, who implemented policy, systems, and environmental change to improve physical activity and nutrition. Implementation strategies were assessed using a comprehensive list of 73 implementation strategies. Implementation, service, and client outcomes were assessed using an 11-item questionnaire. Descriptive statistics were calculated for all study variables. Across programs, most practitioners (n = 44) were female (50.0%-61.5%), White (61.5%-92.3%), and had graduate degrees (62.5%-100%). Overall, the most common strategies used were providing interactive assistance (89.5%-81.0%), adapting and tailoring to context (89.5%-81.0%), and developing stakeholder interrelationships (84.2%-81.0%). Programs were reported to be safe (M = 4.80-4.69), effective (M = 4.47-4.18), timely (M = 4.60-4.35), and likely to improve physical activity and nutrition (M = 4.33-4.06). Missouri's programs employ strategies that reportedly reached priority populations. These programs seem to work upstream of policy, systems, and environment on capacity building. Future longitudinal studies should examine the impact of implementation strategies on built environment changes to ensure lasting health improvements for Missourians.

As Artificial Intelligence (AI) adoption accelerates across industries, the need for robust real-time cybersecurity solutions becomes increasingly critical. Traditional security frameworks that rely on static rules, signature-based detection, and threshold alerts struggle to detect sophisticated identity-based attacks that exploit behavioral anomalies rather than explicit policy violations. These limitations, combined with the growing volume of telemetry data and the latency of conventional detection methods, leave organizations vulnerable to prolonged breaches and data exfiltration. This paper presents a use case with a scalable, AI-driven approach to sensitive data leak detection using NVIDIA Morpheus, a GPU-accelerated cybersecurity framework. We demonstrate how Morpheus, in conjunction with NVIDIA BlueField DPUs and DOCA, enables high-throughput, low-latency inspection of real-time network telemetry without impacting system performance. The solution leverages a pre-trained NLP-based Sensitive Information Detection model capable of identifying ten categories of sensitive data within packet payloads, including credentials, PII, and cryptographic keys. Network-captured PCAP data serialized in JSONlines format passes through a linear, feed-forward Morpheus pipeline, configured via CLI and powered by Triton Inference Server with dynamic batching for optimized inference. The final output provides binary classification results that indicate the presence or absence of sensitive information. This case study illustrates how AI-enhanced telemetry analysis can significantly reduce detection and response times, offering a proactive defense mechanism against modern cyber threats.

Abstract The discovery of novel materials with exceptional nonlinear optical (NLO) properties, especially high second‐harmonic generation (SHG) coefficients, is vital for advancing optical communication technologies and solid‐state laser systems. Computational and machine learning methods open up enormous opportunities for accelerating the identification and characterization of new SHG‐active materials. In this study, OptiXNet, a physics‐informed deep learning model leveraging symmetry‐aware equivariant graph neural networks is introduced, specifically designed to achieve unprecedented accuracy in predicting SHG tensors. Trained and validated on the NOEMD database, OptiXNet significantly outperforms existing state‐of‐the‐art methods in both prediction accuracy and generalization to unexplored chemical compositions. To ensure robust predictions when exploring unknown chemical spaces, an automated clustering algorithm is integrated that quantifies model confidence and flags out‐of‐distribution data points. Using this clustering‐guided approach, MgP 2 O 5 F 2 —a previously unexplored material is successfully identified with promising SHG properties—within the model's high‐confidence predictive domain. Subsequent confirmatory density functional theory (DFT) calculations validated the accuracy and effectiveness of the approach. Combining symmetry‐aware deep learning, rigorous uncertainty quantification, and reliable computational validation, the framework provides a powerful and efficient strategy for the accelerated discovery of next‐generation NLO materials.

BACKGROUND The purpose of the study was to analyse approaches to improving reliability and recovery from failures in the processes of software development and operation. MATERIALS AND METHODS The research utilised a qualitative methodology, conducting semi-structured interviews with industry experts to investigate fault tolerance and disaster recovery solutions within DevOps processes. Grounded theory coding was applied to analyse the data, identifying key themes that were mapped to the research questions and comparison matrix. RESULTS The results showed that the microservices’ architecture is highly flexible and scalable, which allows for faster adaptation to changes in the workload and reduces the likelihood of system failures. However, the complexity of managing dependencies between services can lead to new problems. Serverless computing can be effective in reducing infrastructure costs and simplifying scaling, but there is a need for detailed monitoring of the functioning of functions and management of their resources. Containerisation and container orchestration are suitable for providing a high level of portability and application isolation. At the same time, the growing complexity of orchestration systems can be a challenge for the team working on their administration. Cloud computing and load balancing should be used to allocate resources and reduce the risk of stress on individual components, but they require constant monitoring to ensure optimal performance and identify potential problems. In addition, data replication and backup provide reliable protection against data loss, but the increase in data volume places new demands on the speed and efficiency of backup processes. Monitoring and alerting can improve timely detection and response to problems but require regular updates and adaptation to new threats. Automated testing and disaster recovery planning have proven to be key to reducing system recovery times, but the complexity of setting up automated scripts and tests remains an essential issue. CONCLUSION Overall, the results indicate the need for continuous improvement of existing methods to ensure increased resilience and adaptability of systems to new threats when working with software.

Automated radio telemetry systems (ARTS) consisting of animal-borne radio transmitters and networks of fixed radio receivers are frequently used to continuously track wildlife movements over time. The low weight of available radio transmitters and the ability of these systems to collect data with high temporal resolution make ARTS an attractive alternative to other wildlife tracking technologies. However, the research questions that can be addressed with ARTS are often limited by the spatial accuracy of location data produced by the system. One of the primary methods used to produce location estimates with ARTS involves comparing the received signal strength (RSS) of radio transmissions detected by multiple receivers in a network. The accuracy of the resulting locations is highly dependent on the algorithm used to process the raw RSS data generated by the ARTS into location estimates. In this work, we have developed a grid search algorithm for producing location estimates from RSS data generated by networks of receivers. In an experiment conducted with a radio transmitter and receiver network, we demonstrate that the grid search method produces location estimates that are greater than 2 times more accurate than the commonly used method of multilateration. A simulation was developed to compare the accuracy of the two methods over a large range of receiver spacings and with varying levels of measurement noise in the system. The simulation showed that the grid search method and multilateration perform similarly for receiver networks with relatively close receivers; however, as the distance between receivers in the array increases, the mean error of location estimates increases much more rapidly for multilateration than for grid search. The improvement in spatial accuracy realized through the use of grid search enables the design of wildlife tracking studies utilizing ARTS to address research questions that were previously inaccessible.

Maqolada oliy ta’lim o’quv jarayonlariga zamonaviy veb texnologiyalarning integrasiyasi haqida so‘z boradi. Mavzuning dolzarbligi ta'lim jarayonida innovatsion ta'lim texnologiyalaridan foydalanishdadir. Ta’limda zamonaviy axborot texnologiyalarini rivojlantirishning istiqbolli yo‘nalishlaridan biri bu veb-texnologiyalardan foydalanish ekanligi qayd etilgan.

In order to safely remove fuel debris from the Fukushima Daiichi Nuclear Power Station (1F), it is necessary to quantitatively evaluate the radioactive airborne particulate generated by the cutting of nuclear fuel debris. We fabricated Uranium-bearing simulated fuel debris (SFD) with In/Ex-Vessel compositions and evaluated the physical and chemical properties of aerosols generated by heating the SFDs. Based on these results, we estimated the isotopic composition and radioactivity of aerosols produced when 1F-Unit2 fuel debris are heated (or laser cut). Plutonium, mainly 238Pu, 239Pu, 241Am, and 244Cm were found to be the alpha nuclide, and 241Pu, 137Cs-Ba, and 90Sr-Y were found to be the beta nuclide of interest.

When considering the opening part of 1800 short stories, we find that the first dozen paragraphs of the average narrative follow an action principle as defined in arXiv:2309.06600. When the order of the paragraphs is shuffled, the average no longer exhibits this property. The findings show that there is a preferential direction we take in semantic space when starting a story, possibly related to a common Western storytelling tradition as implied by Aristotle in Poetics.

Accessibility to websites is still a challenge in many countries, including Brazil, despite the existence of eMAG, which is the Brazilian Digital Accessibility Model that provides guidelines for how government websites should ensure accessibility and eliminate access barriers. In parallel, it is also essential to check the accessibility of websites to support the improvement of policies and identify flaws to be addressed. This paper presents a platform for checking the accessibility of websites and their pages. The platform includes the entire verification process, from the collection, processing, and presentation of results to various levels of abstraction, supporting analytics at the page, website or even domain level. The platform enables users and developers to check the accessibility of government websites quickly, flexibly, and objectively. Finally, we present a case study of the proposed platform being applied to the Brazilian governmental Web (gov.br), demonstrating its applicability and effectiveness, in addition to already identifying weak points about accessibility.