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Highly Automated Driving (HAD) systems implement new features to improve the performance, safety and comfort of partially or fully automated vehicles. The identification of safety parameters by means of complex systems and the driving environment is a fundamental aspect that require great attention. Therefore, much research has been conducted in the field of collision detection in the development of automated vehicles. However, the development of HAD systems faces the challenge of ensuring zero accidents. For this reason, collision detection in the safety-related concept phase as hazard identification is one of the key research points in HAD system. In this paper, a systematic approach to detect potential collisions for scenario-based hazard analysis of HAD systems is presented by using Multilayer Perceptron (MLP) as a Machine Learning (ML) technique. Moreover, the proposed approach assists in reducing the number of observed scenarios for hazard analysis and risk assessment. Additionally, two simulation-based scenario datasets are examined in the ML model to identify potential hazard scenarios. The results of this study show that MLP can support to detect the collision at safety-related concept phase. Furthermore, this paper contributes to providing arguments and evidence for ML techniques in HAD systems safety by selecting relevant use cases.

We report a case of intratendinous patellar abscess and prepatellar septic bursitis following direct inoculation in a 26-year-old male injection drug user. The patient presented with 2 days of progressive knee pain, swelling, and erythema. Computed tomography demonstrated an enlarged patellar tendon with central low attenuation. Ultrasonography revealed a complex intratendinous fluid collection concerning for abscess. Aspiration of this fluid collection yielded grossly purulent fluid which grew methicillin-resistant staphylococcal aureus. The patient subsequently underwent operative debridement which revealed an intratendinous patellar abscess with extension to involve the prepatellar bursa. This case report demonstrates typical, though nonspecific, radiographic findings of abscess in an atypical location and highlights the importance of clinical history in diagnosing musculoskeletal disorders, particularly in the absence of traditional types of traumatic injury. Keywords: Abscess, Tendon injuries, Methicillin-resistant Staphylococcus aureus, Bursitis

Objective: To determine prevalence of REM sleep behavior disorder (RBD) [prodromal Lewy body disease] in Sun City, Arizona. Patients and methods: We attempted, by telephone and mail, a survey using the RBD single item question for probable RBD (pRBD) and the Innsbruck RBD Inventory. Individuals answering “yes” to 4/5 Inventory questions were considered to have high likelihood RBD (HL-RBD.) Results: Response rate was 484/3000 individuals contacted (16%), mean age 78; 48 (9.9%) endorsed pRBD by RBD1Q; 16 (3.3%) had HL-pRBD. Prevalence of idiopathic cases (without neurodegenerative disease) was 8.8% pRBD and 2.8% HL-RBD. Conclusion: Our estimated definite RBD prevalence of 1.7% (61.3% of HL-RBD) was similar to previous community-based studies.

Many-core platforms are rapidly expanding in various embedded areas as they provide the scalable computational power required to meet the ever-growing performance demands of embedded applications and systems. However, the huge design space of possible task mappings, the unpredictable workload dynamism, and the numerous non-functional requirements of applications in terms of timing, reliability, safety, and so forth. impose significant challenges when designing many-core systems. Hybrid Application Mapping (HAM) is an emerging class of design methodologies for many-core systems which address these challenges via an incremental (per-application) mapping scheme: The mapping process is divided into (i) a design-time Design Space Exploration (DSE) step per application to obtain a set of high-quality mapping options and (ii) a run-time system management step in which applications are launched dynamically (on demand) using the precomputed mappings. This paper provides an overview of HAM and the design methodologies developed in line with it. We introduce the basics of HAM and elaborate on the way it addresses the major challenges of application mapping in many-core systems. We provide an overview of the main challenges encountered when employing HAM and survey a collection of state-of-the-art techniques and methodologies proposed to address these challenges. We finally present an overview of open topics and challenges in HAM, provide a summary of emerging trends for addressing them particularly using machine learning, and outline possible future directions. While there exists a large body of HAM methodologies, the techniques studied in this paper are developed, to a large extent, within the scope of invasive computing. Invasive computing introduces resource awareness into applications and employs explicit resource reservation to enable incremental application mapping and dynamic system management.