Land Sea Air Autonomy (United States)

Cooperative spectrum sensing (CSS) plays a vital role in cognitive radio networks (CRNs). CSS enables efficient spectrum utilization and improves communication reliability through dynamic detection of underutilized frequency bands. However, the methods which evolved earlier face limitations like reduced detection accuracy and high false alarm rates in low Signal-to-Noise Ratio (SNR) conditions. Efficient allocation of spectrum resources in cognitive radio networks is affected by unreliable sensing at low signal-to-noise ratios, which leads to missed detections and false alarms. To overcome these challenges a novel optimized deep learning model is presented in this research work which provides reliable spectrum detection under noisy environments. The proposed Optimized Multi-Scale Graph Neural Network with Attention Mechanism (OMSGNNA) utilizes graph-based data representation and provides multi-scale feature extraction. The attention mechanism effectively fuses spatial and temporal information which enhances the performances. Additionally, an Adaptive Butterfly Optimization with Lévy Flights (ABO-LF) is incorporated to fine tune the parameters of proposed model. The proposed model experiments which are conducted using benchmark RadioML2016.10b dataset includes I/Q samples from 11 modulation schemes across SNR levels ranging from − 20 dB to 18 dB. The performance evaluation exhibits better performance of proposed OMSGNNA in terms of 98% accuracy at high SNR with better precision, recall, and F1-score. Comparative analysis reveals that the proposed OMSGNNA outperforms traditional deep learning models even at low SNR conditions.

• Evaluated ChatGPT and Gemini in stand-alone STPA and FRAM safety assessments. • The study used LH2 aircraft refuelling as a novel, complex case study. • ChatGPT outperformed Gemini in structured STPA analysis tasks. • Both LLMs struggled with systems thinking and output consistency. • Findings offer lessons for AI integration in system safety assessments. The advancement of large language models (LLMs) shows immense promise in many domains. However, their reliability is still questionable. This study aims to comparatively examine the performance of ChatGPT and Gemini in conducting a stand-alone systems-based risk assessment using System-Theoretic Process Analysis (STPA) and the Functional Resonance Analysis Method (FRAM). Our findings revealed that both LLMs demonstrated weaknesses in their analyses, with ChatGPT generally outperforming Gemini regarding response comprehensiveness and adhering to the prompted format. Specifically, LLMs failed to use systems thinking in their stand-alone applications and failed to follow up on previous prompt outputs. While LLMs can provide substantial amounts of information quickly, the effectiveness of LLMs in system safety assessment is contingent on addressing their limitations and implementing strategies to improve their capabilities.

<div class="section abstract"><div class="htmlview paragraph">Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors. This evaluation is performed using ground truth data, obtained by measuring the lane positions and transforming them into pixel coordinates. The performance of each method is assessed using the statistical <i>R</i><sup>2</sup> score, indicating the correlation between the detected lane lines and the ground truth. The results show that the U-Net camera-based method exhibits limitations in accurately detecting and aligning the lane lines, particularly in challenging scenarios. However, the radar-based lane detection method demonstrates a strong correlation with the ground truth which implies that the use of this sensor may improve current reliability issues from conventional camera lane detection approach. Furthermore, the study highlights the limitations of the U-Net model for camera lane detection, especially in scenarios with sun glare. This study shows that infrastructure-based radar retro-reflectors can improve autonomous vehicle lane detection reliability. The integration of different sensor modalities and the development of advanced computer vision algorithms are crucial for improving the accuracy, reliability, and energy efficiency of lane detection systems. Addressing these challenges contributes to the advancement of autonomous vehicles and the realization of safer and more efficient transportation systems.</div></div>

There is no standard way in which physicians teach or evaluate surgical residents intraoperatively, and residents are proving to not be fully competent at core surgical procedures upon graduating. The Surgical Autonomy Program (SAP) is a novel educational model that combines a modified version of the Zwisch scale with Vygotsky's social learning theory. The objective of this study was to establish preliminary validity evidence that SAP is a reliable measure of autonomy and a useful tool for tracking competency over time. The SAP breaks each surgical case into 4 parts, or zones of proximal development (ZPDs). Residents are evaluated on a 4-tier autonomy scale (TAGS scale) for each ZPD in every surgical case. Attendings were provided with a teaching session about SAP and identified appropriate ZPDs for surgical cases under their area of expertise. All neurosurgery residents at Duke University Hospital from July 2017 to July 2021 participated in this study. Chi-square tests and ordinal logistic regression were used for the analyses. Between 2017 and 2021, there were 4885 cases logged by 27 residents. There were 30 attendings who evaluated residents using SAP. Faculty completed evaluations on 91% of cases. The ZPD of focus directly correlated with year of residency (postgraduate year) (χ2 = 1221.1, df = 15, p < 0.001). The autonomy level increased with year of residency (χ2 = 3553.5, df = 15, p < 0.001). An ordinal regression analysis showed that for every year increase in postgraduate year, the odds of operating at a higher level of independence was 2.16 times greater (95% CI 2.11-2.21, p < 0.001). The odds of residents performing with greater autonomy was lowest for the most complex portion of the case (ZPD3) (OR 0.18, 95% CI 0.17-0.20, p < 0.001). Residents have less autonomy with increased case complexity (χ2 = 160.28, df = 6, p < 0.001). Compared with average cases, residents were more likely to operate with greater autonomy on easy cases (OR 1.44, 95% CI 1.29-1.61, p < 0.001) and less likely to do so on difficult cases (OR 0.72, 95% CI 0.67-0.77, p < 0.001). This study demonstrates preliminary evidence supporting the construct validity of the SAP. This tool successfully tracks resident autonomy and progress over time. The authors' smartphone application was widely used among surgical faculty and residents, supporting integration into the perioperative workflow. Wide implementation of SAP across multiple surgical centers will aid in the movement toward a competency-based residency education system.

A significant challenge in estimating operational feasibility of Urban Air Mobility (UAM) missions lies in understanding how choices in design impact the performance of a complex system-of-systems. This work examines the ability of the UAM ecosystem and the operations within it to meet a variety of demand profiles that may emerge in the coming years. We perform a set of simulation driven feasibility and scalability analyses based on UAM operational models with the goal of estimating capacity and throughput for a given set of parameters that represent an operational UAM ecosystem. UAM ecosystem design guidelines, vehicle constraints, and effective operational policies can be drawn from our analysis. Results show that, while critical for enabling UAM, the performance of the UAM ecosystem is robust to variations in ground infrastructure and fleet design decisions, while being sensitive to decisions for fleet and traffic management policies. We show that so long as the ecosystem design parameters for ground infrastructure and fleet design fall within a sensible range, the performance of the UAM ecosystem is affected by the policies used to manage the UAM traffic.

A small, consumable-free, low-power, ultra-high-speed comprehensive GC×GC system consisting of microfabricated columns, nanoelectromechanical system (NEMS) cantilever resonators for detection, and a valve-based stop-flow modulator is demonstrated. The separation of a highly polar 29-component mixture covering a boiling point range of 46 to 253 °C on a pair of microfabricated columns using a Staiger valve manifold in less than 7 seconds, and just over 4 seconds after the ensemble holdup time is demonstrated with a downstream FID. The analysis time of the second dimension was 160 ms, and peak widths in the second dimension range from 10-60 ms. A peak capacity of just over 300 was calculated for a separation of just over 6 s. Data from a continuous operation testing over 40 days and 20 000 runs of the GC×GC columns with the NEMS resonators using a 4-component test set is presented. The GC×GC-NEMS resonator system generated second-dimension peak widths as narrow as 8 ms with no discernable peak distortion due to under-sampling from the detector.

We try to present in this paper some questions related to the phonology of the Terena Sign Language. Recently recognized as an autonomous language (SOARES, 2018), it presents clear differences with LIBRAS, as will be pointed out. Used by deaf people in the Terena community of MatoGrosso do Sul, as well as any sign language, it does not constitute a transposition of the oral language terena for signs. We will also present some of the guidelines that we follow in our fieldwork methodology, which we have built.From this methodology we have been able to find minimal pairs and phonemes in the sign language. With this, we intend to contribute to the knowledge and recognition of sign languages, especially the indigenous sign languages of Brazil.

Il est des temps qui sont charnieres. Ainsi les historiens avec une analyse a posteriori decrivent les faits, les idees et les personnes qui a une periode precise, vont se conjuguer, presque malgre eux, quoi qu’ils en disent, pour changer l’epoque et rendre « toutes choses nouvelles ».Pour l’illustrer, on peut se demander si les philosophes des lumieres auraient eu, quoi qu’ils aient dit, une posterite revolutionnaire aussi majeure, s’il n’y avait eu les rigueurs du climat [...]

Enterprise information management (EIM) deals with the demands upon enterprise unstructured information placed by applications such as eDiscovery, compliance, information lifecycle management, etc. Each of these applications poses a unique challenge to the retrieval and data mining of enterprise unstructured information. However, the study of EIM as a research field has long been hampered by the lack of availability of enterprise corpora. Due to this paucity of enterprise datasets, much of the research on information retrieval and mining that is meant for EIM is benchmarked on corpora that are vastly different in their structure than a typical enterprise corpus. An important category of enterprise corpora are those that arise during technology creation in an enterprise. Such corpora take center stage, for example, during eDiscovery requests arising from technology patent related litigation: an area of immense commercial impact. In this paper, we highlight the primary characteristics of enterprise corpora that arise during technology creation. At a high-level, these properties are project structure, heterogeneity, collaborations, and skew ness along various axes. We then study these characteristics in a carefully chosen enterprise corpus from a technology creation effort at a Fortune 10 corporation. In summary, we study the salient features of enterprise corpora and emphasize their structural properties. We hope that our study will spur effort in devising retrieval and mining techniques that are designed for EIM.