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Since autonomous vehicles (AV) are in the testing process, it is an open question of how pedestrians will communicate with self-driving cars. Nowadays, explicit communication pattern is the main way of pedestrian-driver interaction, however, AV may use implicit communication when making crossing decisions. This study aims to analyze pedestrians' gaze behavior when crossing the road using an eye camera and find the most applicable location for the LED interface on AVs. 10 pedestrian crossings in Gyor, Hungary were analyzed using the synchronized eye-tracking (ET) technology and regular video cameras for combined data processing. The data were analyzed using digital image processing techniques and statistical methods to identify where pedestrians looked and whether a pedestrian-driver interaction was captured during the crossing maneuver. [ABSTRACT FROM AUTHOR]

This study aims to provide a conceptual analysis of the dynamic transformations occurring in an autonomous vehicle (AV), placing a specific emphasis on the safety implications for pedestrians and passengers. AV, also known as self-driving automobiles, are positioned as potential disruptors in the contemporary transportation landscape, offering heightened safety and improved traffic efficiency. Despite these promises, the intricate nature of road scenarios and the looming specter of misinformation pose challenges that can compromise the efficacy of AV decision-making. A crucial aspect of the proposed verification process is the incorporation of the stop, investigate the source, find better coverage, trace claims, quotes, and media to the original context (SIFT) method. The SIFT method, originally designed to combat misinformation, emerges as a valuable mechanism for enhancing AV safety by ensuring the accuracy and reliability of information influencing autonomous decision-making processes. [ABSTRACT FROM AUTHOR]

Since autonomous vehicles (AV) are in the testing process, it is an open question of how pedestrians will communicate with self-driving cars. Nowadays, explicit communication pattern is the main way of pedestrian-driver interaction, however, AV may use implicit communication when making crossing decisions. This study aims to analyze pedestrians' gaze behavior when crossing the road using an eye camera and find the most applicable location for the LED interface on AVs. 10 pedestrian crossings in Gyor, Hungary were analyzed using the synchronized eye-tracking (ET) technology and regular video cameras for combined data processing. The data were analyzed using digital image processing techniques and statistical methods to identify where pedestrians looked and whether a pedestrian-driver interaction was captured during the crossing maneuver.

Supraventricular tachycardia (SVT) with ventriculoatrial (VA) block can represent a diagnostic challenge. We present a case of SVT where His-His interval shortening was repeatedly observed during episodes of VA block. This novel observation is more diagnostically suggestive of atrioventricular nodal re-entrant tachycardia, as opposed to orthodromic re-entry using a nodofascicular or nodoventricular pathway where a constant His-His is recorded during episodes of VA block. (Level of Difficulty: Intermediate.).

Understanding the complexities of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technologies is critical for road safety, especially concerning their adoption by drivers. Effective training is a crucial element in ensuring the safe and competent operation of these technologies. This study emphasises the critical role of training methodologies in shaping drivers' mental models, defined as an individual's cognitive frameworks for understanding and interacting with ADAS and AV systems. Their mental models substantially influence their interactions with those technologies. A comparative analysis of text-based and video-based training methods has been conducted to assess their influence on participants' performance and the development of their mental models of ADAS and AV functionalities. Performance is evaluated in terms of the accuracy and reaction time of the participants as they interacted with ADAS and AV functions in a driving simulation. The findings reveal that video-based training yielded better performance outcomes, more accurate mental models, and a deeper understanding of ADAS functionalities among participants. These findings are crucial for policy makers, automotive manufacturers, and educational institutions involved in driver training. They underscore the necessity of developing tailored training programs to facilitate the proficient and safe operation of increasingly complex automotive technologies. [ABSTRACT FROM AUTHOR]

Automated vehicles (AV) can increase safety, reduce congestion, and provide environmental benefits to the urban transport system. Nonetheless, AV’s governance in the urban transport system is challenging. This paper proposes a novel interdisciplinary methodology and contributes to the governance debate from the ‘policy’ and ‘polity’ dimensions. We attempt to draw insight from the Norwegian cultural context and a willingness to use AV survey in Oslo, Norway. We then attempt to provide insight into the workable governance approach for AV deployment in the Oslo region, Norway. Our result suggests that less than half of the people in the Oslo region are willing to use AV. Based on our analytical insight, we argue that a workable governance approach for AV that will allay fears of privacy loss while making people feel safe and secure, manage uncertainty, pessimism about technological innovation, and embrace time-honoured Norwegian traditions should be considered to support the deployment of AV in Norway.

Autonomous vehicles (AV) are expected to improve road safety and reduce traffic congestion by optimizing routes and reducing human errors. AVs have the potential to increase accessibility for people with disabilities and reduce the environmental impact of transportation. AVs require radio transmitters to communicate with other vehicles and infrastructure, external charging to power their electric motors, and communication equipment to receive real-time data about traffic and road conditions. Additionally, these requirements must be met for AVs to operate efficiently and conserve energy. Therefore, this work introduces a novel technique called energy-conservation guided route adaptation (EC-GRA) that aims to enhance the energy efficiency of connected vehicles. With the balance in energy adaptation for distinct purposes, the utilization rate is adjusted for communication and navigation. The complex decisions are confined to the energy availability and conservation factors required in an adaptive driving condition. This technique employs transfer learning to update the available and adaptable energy ratios under displacement-based route adaptations. In the learning process, the transfer and update states for displacement-aware decisions under varying scenarios are modeled. This study validates the state transitions involved in recommending energy utilization during both autonomous and guided driving scenarios. The results show that the proposed methodology exhibits superior performance compared to the currently available techniques. The EC-GRA under consideration has demonstrated an average energy conservation ratio of 45.58. The decision rate for this method is 0.63/navigation, while its energy utilization is 126.37 Joules. The number of failures observed in the proposed EC-GRA is 6/navigation, which represents an improvement over the existing approach.

This study aims to provide a conceptual analysis of the dynamic transformations occurring in an autonomous vehicle (AV), placing a specific emphasis on the safety implications for pedestrians and passengers. AV, also known as self-driving automobiles, are positioned as potential disruptors in the contemporary transportation landscape, offering heightened safety and improved traffic efficiency. Despite these promises, the intricate nature of road scenarios and the looming specter of misinformation pose challenges that can compromise the efficacy of AV decision-making. A crucial aspect of the proposed verification process is the incorporation of the stop, investigate the source, find better coverage, trace claims, quotes, and media to the original context (SIFT) method. The SIFT method, originally designed to combat misinformation, emerges as a valuable mechanism for enhancing AV safety by ensuring the accuracy and reliability of information influencing autonomous decision-making processes.

ObjectivesTo compare echocardiographic and invasive mean gradients obtained concomitantly in degenerated bioprosthetic surgical aortic valves (SAVRs).MethodsIn a multicenter study, we compared concomitant echocardiographic and invasive mean gradients of SAVR, obtained before valve-in-valve transcatheter aortic valve replacement in all patients, patients with primary stenosis (AS), primary aortic regurgitation (AR), and mixed aortic valve disease (MAVD), and in small versus large valves (≤ or >23 mm). Dimensionless index (DI) was calculated in all groups.ResultsIn total, 74 patients were included and data presented as median (interquartile range). Echocardiography-catheterization mean gradient discordance was observed in all patients (invasive = 22 mm Hg [11-34] vs echocardiographic = 32 mm Hg [21-42], P = .013), small valves (invasive = 15 mm Hg [8-34] vs echocardiographic = 28 mm Hg [21-41], P = .013), and large valves (invasive = 20 mm Hg [8.5-27.13] vs echocardiographic = 32 mm Hg [25.5 - 41.5], P

Effects of a frontal brake light (FBL, a potential external human–machine interface for automated vehicles) on participants’ self-reported willingness to cross a vehicle’s path were investigated. In a mixed design online study (vehicles in the experimental group were equipped with FBLs, there were no FBLs in the control group), participants observed videos of a vehicle approaching at different speeds from the perspective of a pedestrian standing at the curb. The vehicles exhibited either yielding behavior (braking onset 55 m or 32 m before standstill in front of the pedestrian’s position) or non-yielding behavior (approach speed was maintained). Participants specified their willingness to cross the vehicle’s path at different distances. When the vehicle yielded (i.e., FBL was activated), willingness to cross was significantly higher in the experimental group than the control group. Notably, we further observed a significantly lower willingness to cross in the experimental group than the control group when the vehicle did not yield (i.e., FBL was deactivated). Novel external human–machine interfaces might therefore influence the interaction with vehicles not only when they are activated but also when they are deactivated.

Autonomous vehicles (AV) are expected to improve road safety and reduce traffic congestion by optimizing routes and reducing human errors. AVs have the potential to increase accessibility for people with disabilities and reduce the environmental impact of transportation. AVs require radio transmitters to communicate with other vehicles and infrastructure, external charging to power their electric motors, and communication equipment to receive real-time data about traffic and road conditions. Additionally, these requirements must be met for AVs to operate efficiently and conserve energy. Therefore, this work introduces a novel technique called energy-conservation guided route adaptation (EC-GRA) that aims to enhance the energy efficiency of connected vehicles. With the balance in energy adaptation for distinct purposes, the utilization rate is adjusted for communication and navigation. The complex decisions are confined to the energy availability and conservation factors required in an adaptive driving condition. This technique employs transfer learning to update the available and adaptable energy ratios under displacement-based route adaptations. In the learning process, the transfer and update states for displacement-aware decisions under varying scenarios are modeled. This study validates the state transitions involved in recommending energy utilization during both autonomous and guided driving scenarios. The results show that the proposed methodology exhibits superior performance compared to the currently available techniques. The EC-GRA under consideration has demonstrated an average energy conservation ratio of 45.58. The decision rate for this method is 0.63/navigation, while its energy utilization is 126.37 Joules. The number of failures observed in the proposed EC-GRA is 6/navigation, which represents an improvement over the existing approach. [ABSTRACT FROM AUTHOR]

Understanding the complexities of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technologies is critical for road safety, especially concerning their adoption by drivers. Effective training is a crucial element in ensuring the safe and competent operation of these technologies. This study emphasises the critical role of training methodologies in shaping drivers’ mental models, defined as an individual’s cognitive frameworks for understanding and interacting with ADAS and AV systems. Their mental models substantially influence their interactions with those technologies. A comparative analysis of text-based and video-based training methods has been conducted to assess their influence on participants’ performance and the development of their mental models of ADAS and AV functionalities. Performance is evaluated in terms of the accuracy and reaction time of the participants as they interacted with ADAS and AV functions in a driving simulation. The findings reveal that video-based training yielded better performance outcomes, more accurate mental models, and a deeper understanding of ADAS functionalities among participants. These findings are crucial for policy makers, automotive manufacturers, and educational institutions involved in driver training. They underscore the necessity of developing tailored training programs to facilitate the proficient and safe operation of increasingly complex automotive technologies.

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• Experimental study comparing the effect of two different light-based eHMIs on pedestrian' crossing decisions in test-track environment. • No effect of the eHMIs on pedestrians' crossing decision time or deceleration perception time. • Participants were able to perceive a partial light band, consisting of a moving block of cyan coloured light, at a further distance than a full light band. • Both eHMI solutions received positive ratings in terms of acceptance, perceived safety, and confidence around the AV. The development of increasingly automated vehicles (AVs) is likely to lead to new challenges around how they will interact with other road users. In the future, it is envisaged that AVs, manually driven vehicles, and vulnerable road users such as cyclists and pedestrians will need to share the road environment and interact with one another. This paper presents a test track study, funded by the H2020 interACT project, investigating pedestrians' reactions towards an AV's movement patterns and external Human Machine Interfaces (eHMIs). Twenty participants, standing on the side of a test-track road and facing an approaching AV, were asked to raise their arm to indicate: (1) when they could perceive the AV's eHMI, which consisted of either a Full Light Band (FLB) or a Partial Light Band (PLB); (2) when they perceived the deceleration of the AV (with eHMI vs. no eHMI); and (3) when they felt safe to cross the road in front of the approaching AV (with eHMI vs. no eHMI). Statistical analyses revealed no effects of the presence of an eHMI on the pedestrians' crossing decision or deceleration perception, but significant differences were found regarding the visibility of the FLB and PLB designs. The PLB design could be perceived at further distances than the FLB design. Both eHMI solutions were generally well-received, and participants provided high ratings of perceived safety, and confidence around the AV. [ABSTRACT FROM AUTHOR]

There is a phenomenon of attaching importance to technique and neglecting emotion in the training of sports dance (SP), which leads to the lack of integration between movement and emotion and seriously affects the training effect. Therefore, this article uses the Kinect 3D sensor to collect the video information of SP performers and obtains the pose estimation of SP performers by extracting the key feature points. The Arousal-Valence (AV) emotion model, based on the Fusion Neural Network model (FUSNN), is also combined with theoretical knowledge. It replaces long short term memory (LSTM) with gate recurrent unit (GRU), adds layer-normalization and layer-dropout, and reduces stack levels, and it is used to categorize SP performers’ emotions. The experimental results show that the model proposed in this article can accurately detect the key points in the performance of SP performers’ technical movements and has a high emotional recognition accuracy in the tasks of 4 categories and eight categories, reaching 72.3% and 47.8%, respectively. This study accurately detected the key points of SP performers in the presentation of technical movements and made a major contribution to the emotional recognition and relief of this group in the training process.

Automated vehicles (AVs) are expected to improve safety by gradually reducing human decisions while driving. However, there are still questions on their effectiveness as we transition from almost 0% AVs to 100% AVs with different levels of vehicle autonomy. The focus of this study, therefore, is on synthesizing and identifying risk factors influencing fatal crashes involving partially automated vehicles (PAVs) i.e., level 1 AVs and level 2 AVs, in the United States. Fatal crashes involving non-AVs (level 0 vehicles) within their vicinity were used for comparison, to minimize unobserved heterogeneity and randomness associated with the influencing risk factors. The fatal crash data for the years 2016 to 2019 is used for the analysis. A partial proportional odds model is developed using crash, road, and vehicle characteristics as independent variables while the fatal crash involving a vehicle with a specific level of automation (0, 1, or 2) is used as the dependent variable. The level of automation was captured by developing tools and identifying advanced features in each vehicle based on the vehicle identification number (VIN). The odds ratios varied for PAVs compared to non-AVs. PAVs are safer but are more likely to be involved in a fatal crash with non-motorists. Pedestrian automatic emergency braking (PAEB) and lane-keeping assistance (LKA) were observed to improve safety by reducing possible collision with a pedestrian and roadside departure, respectively. Contrarily, vehicles with other smart features are still highly likely to be involved in fatal crashes, demanding further research and attention.

In the urban landscape, parking structures have evolved to become lean, level- of-service driven objects that occupy valuable space. Recent urban trends have caused planners to rethink the definition of parking structures and their contribution to the functions of the city. In light of the new narrative on the urban parking structure, how can the City of Los Angeles better support efforts to construct parking garages in a manner that enables them to be repurposed for some use other than parking in the future?

To prepare for the arrival of autonomous vehicles, the Los Angeles Department of City Planning is exploring land-use strategies to optimize the potential safety and environmental and access benefits of this new technology. Self-driving or autonomous vehicles have captured the imagination of many, including transportation officials, the media, technology entrepreneurs, and travelers. At the same time, this new technology raises complicated questions related to ethics, liability, accessibility, and safety.Technology companies and auto manufacturers are testing autonomous vehicles on public roads today.Although self-driving technology is still under development, advancements are occurring rapidly. In order to capture the benefits and minimize the costs of autonomous vehicles, cities must take advantage of this testing period to prepare for an autonomous future. Autonomous vehicles have the potential to transform mobility in Los Angeles by providing populations previously unable or unwilling to drive with access to vehicles, eliminating human error in traffic crashes, and increasing efficiency through connected vehicle technology; however, these benefits are not assured. In the absence of advanced preparation and planning, there may be unintentional adverse effects, including increased sprawl, vehicle miles traveled, commute distances, and automobile-related pollution.

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The most well-known research on driverless vehicles at the moment is connected autonomous vehicles (CAVs), which reflects the future path for the self driving field. The development of connected autonomous vehicles (CAVs) is not only increasing logistics operations, but it is also opening up new possibilities for the industry’s sustainable growth. In this review, we will explore the cloud-controlled wireless network-based model of cyber physical aspect of the autonomous vehicle, which is coupled with unmanned aerial vehicles (UAVs). Additionally, this model is Internet of Things (IoT) managed and AI-based, with a blockchain-based security mechanism. Additionally, we’ll focus on lateral control in autonomous driving, particularly the lane change maneuver, taking social behavior into account. Here, we briefly reviewed Vehicle-to-Everything (V2X) communication, which is carried out by on-board sensors and connected wireless medium that enhance the lane departure processes while retaining human driver behavior relying on obstacle avoidance.

Aim: to evaluate the efficacy of azoximer bromide along with a combined antimicrobial drug in treatment of patients with aerobic vaginitis (AV).Materials and Methods. There was conducted a prospective, randomized, parallel group study with enrolled 60 patients diagnosed with AV and 10 apparently healthy women (control group III). Group I (n = 30) received azoximer bromide (10 vaginal insertion according to the scheme, course of 10 days) and a combined antimicrobial drug, while Group II (n = 30) received only antimicrobial drug (course of 10 days). Microscopy of vaginal discharge smears, polymerase chain reaction method was used, detection of cytokines – interleukins (IL) and tumor necrosis factor-alpha (TNF-α) in vaginal washings was performed by ELISA. The study of neutrophil extracellular traps (NETs) was performed by ELISA, and DNA-containing strands ejected by neutrophils were determined by fluorescence method.Results. Recovery from disease was found in 100 % (30/30) and 86.7 % (26/30) in group I and group II, respectively. After treatment, serum level of IL-8 was revealed to decline in group I from 35.2 to 5.5 pg/ml (p = 0.05), in group II – from 33.4 to 5.3 pg/ml (p = 0.04), also not differ (p > 0.05) from the control values (5.2 pg/ml). The level of IL-1β also decreased after treatment in group I from 51.5 to 15.1 pg/ml (p = 0.002), in group II – from 57.9 to 20.1 pg/ml (p = 0.03), which also did not differ (p > 0.05) from the control values (16.7 pg/ml). The IL-10 level in both main groups decreased slightly (from 0.26 and 0.24 to 0.16 pg/ml in both groups), which was higher by 1.8-fold than in the control group (0.09 pg/ml; p < 0.001). The number of ejected NETs in both main groups decreased significantly after treatment, reaching control level. No side effects were observed, compliance and acceptability were 100 % in both groups. The frequency of AV relapses within 3 months was observed in 3.3 % (1/30) and 14.3 % (4/28) patients, after 6 months – in 14.3 % (4/28) and in 20.0 % (5/25) patients in groups I and II, respectively. Using the logit regression model, demonstrated that AV relapse might occur with a 50 %-probability at TNF-α level < 0.1 pg/ml after treatment (sensitivity – 67 %, specificity – 79 %). The risk of relapses in group I, in contrast to group II, showed a downward trend.Conclusions. The use of azoximer bromide and a combined broad-spectrum antimicrobial drug is effective in AV treatment, exerts a modulating effect on immune response parameters contributes to lowering relapse rate.

This article presents a low phase noise (PN) voltage-controlled oscillator (VCO) based on a current-reused structure with inductive-transformer feedback (ITF) technique. With the proposed topology, the drain-to-gate voltage gain ($A_{v}$), negative conductance ($G_{m}$), loaded- $\mathbf {\mathit {Q}}$ , and the impulse sensitivity function (ISF) are improved, thus better PN. Besides, the ITF can improve the transistors’ switch synchronization by eliminating the waveform offset of the waveforms at the gate nodes, leading to smaller amplitude errors. As a proof of concept, the VCO is designed and fabricated in a 65-nm CMOS process consuming 0.25 mm2 die area and 7.7 mW from the 1.2-V supply. The measurement results exhibit a PN of −115.35 dBc/Hz at 1 MHz offset when the oscillation frequency is 6.85 GHz, and a tuning range of 29.9% varying from 6.27 to 8.47 GHz. The VCO achieves a figure-of-merit (FoM) of −183.11 dBc/Hz and a −192.6-dBc/Hz tuning range included FoM(FoMT). [ABSTRACT FROM AUTHOR]

Private Autonomous Vehicles (PAV) and Shared Autonomous Vehicles (SAV) are known to improve safety, mobility, roadway capacity, driver productivity and reduce parking costs (due to better utilization of space and self-parking to less expensive spots). However, the increased vehicle miles traveled (VMT), might increase overall emission production, system travel time, and operating costs. Also, the purchase price of autonomous vehicle (AV) is expected to be higher than that of human-driven (conventional) vehicles. A multi-objective mathematical model is proposed to minimize the purchase and operating costs, time spent, and emission production. The proposed model captures the tradeoff between the benefits of increased mobility, reduction in the value of travel of time (VOTT), efficient driving pattern, and the negative impacts of increased VMT, ownership cost due to the adoption of AVs. The proposed framework assists with the development of simplified adoption models that can be used by the policymakers and/or investors. SAVs would be the optimal solution if the replacement rate or CO2 costs are significantly low, or CO costs are sufficiently high. SAVs can also be the optimal solution if the travel time is used efficiently, or the purchase price is below certain relative threshold while minimizing system cost. Considering the private mobility system, PAVs can be the optimal solution only if the on-board amenities are improved, lifetime mileage is increased, AV technology is installed in luxurious cars, and is being adopted by people with high VOTT. [ABSTRACT FROM AUTHOR]

• Investigation of how kinematic information and eHMI designs affect pedestrian crossing behaviours. • Comparison of how quickly a novel light-based eHMI and a more conventional Flashing Headlights eHMI were learned. • FH led to earlier crossings than the novel eHMI. • It took participants longer for the novel eHMI to affect crossing behaviour, indicating a longer learning process. • eHMI visibility was not the only reason for earlier crossings, with message familiarity playing a role. Introduction: In current urban traffic, pedestrians attempting to cross the road at un-signalized locations are thought to mostly use implicit communication, such as deceleration cues, to interpret a vehicle's intention to yield. There is less reliance on explicit driver- or vehicle-based messages, such as hand/head movements, or flashing lights/beeping horns. With the impending deployment of Automated Vehicles (AV), especially those at SAE Level 4 and 5, where the driver is no longer in control of the vehicle, there has been a surge in interest in the value of new forms of communication for AVs, for example, via different types of external Human Machine Interfaces (eHMIs). However, there is still much to be understood about how quickly a novel eHMI affects pedestrian crossing decisions, and whether it provides any additional aid, above and beyond implicit/kinematic information from the vehicle. The aim of this between-participant study, funded by the H2020 interACT project, was to investigate how the combination of kinematic information from a vehicle (e.g., Speed and Deceleration), and eHMI designs, play a role in assisting the crossing decision of pedestrians in a cave-based pedestrian simulator. Method: Using an existing, well-recognized, message for yielding (Flashing Headlights - FH) as a benchmark, this study also investigated how quickly a novel eHMI (Slow Pulsing Light Band – SPLB) was learned. To investigate the effect of eHMI visibility on crossing decisions, the distance at which each eHMI was perceivable was also measured. Results: Results showed that, compared to SPLB, the FH led to earlier crossings during vehicle deceleration, especially at lower approaching speeds, and smaller time gaps. However, although FH was visible earlier than SPLB, this visibility does not appear to be the only reason for earlier crossings, with message familiarity thought to play a role. Participants were found to learn the meaning conveyed by FH relatively quickly, crossing around 1 second earlier in its presence (compared to the no eHMI condition), across the three blocks of trials. On the other hand, it took participants at least one block of 12 trials for the new SPLB signal to affect crossing, which only accelerated crossing initiations by around 200 ms, compared to the no eHMI condition. The role of comprehension, long-term exposure, and familiarity of novel messages in this context is therefore important, if AVs are to provide safe, trustworthy communication messages, which will enhance traffic flow and efficiency. [ABSTRACT FROM AUTHOR]

Safety-critical systems such as Advanced Driving Assistance Systems and Autonomous Vehicles require redundancy to satisfy their safety requirements and to be classified as fail-operational. Introducing redundancy in a system with high data rates and processing requirements also has a great impact on architectural design decisions. The current self-driving vehicle prototypes do not use a standardized system architecture but base their design on existing vehicles and the available components. In this work, we provide a novel analysis framework that allows us to qualitatively and quantitatively evaluate an in-vehicle architecture topology and compare it with others. With this framework, we evaluate different variants of two common topologies: domain and zone-based architectures. Each topology is evaluated in terms of total cost, failure probability, total communication cable length, communication load distribution, and functional load distribution. We introduce redundancy in selected parts of the systems using our automated process provided in the framework, in a safety-oriented design process that enables the ISO26262 Automotive Safety Integrity Level decomposition technique. After every design step, the architecture is re-evaluated. The advantages and disadvantages of the different architecture variants are evaluated to guide the designer towards the choice of correct architecture, with a focus on the introduction of redundancy.

Myocardial abscess is a suppurative infection of myocardium, endocardium, native or prosthetic valves, perivalvular structures and cardiac conduction system. It develops in about 20% of patients with infective endocarditis. Due to avascular and fibrous structures, valvular regions are commonly involved. More precisely, aortic valve (AV) rings area; native or prosthetic valve is usually affected. Occurrence of myocardial abscess within free wall of the left ventricle (LV) without any evidence of infective endocarditis is a rare phenomenon; and infrequently reported in medical literature. We report a case of myocardial abscess cavity within the anterior wall of the LV, in a patient who underwent open heart surgery for severe AV stenosis. This was an incidental intraoperative transesophageal echocardiography (TEE) finding without any other evidence of infective endocarditis. The stenotic AV was replaced, along with surgical drainage and closure of the cavity. Postoperatively, patient was managed on empirical antibiotics according to infective endocarditis guidelines.

In an autonomous vehicle (AV), in order to efficiently exploit the acquired resources, big data analyses will be a reliable source for extracting valuable information from various sensors and actuators. The data extracted with the combined ability of telematics and real‐time investigation forms the vibrant asset for self‐driving cars. To demonstrate the significances of big data analysis, this study proposes a competent architecture for real‐time big data analysis for an AV, which indeed keeps pace with the latest trends and advancement concerning an emerging paradigm. There are a massive amount of sensors and independent systems needed to be realised for better competence in an AV, and the proposed model focuses on independent sensors that distinguish objects and handles visual information to decide the path. In order to attain the objective as mentioned above, a sensor fusion mechanism is proposed, which combines 3D camera sensor data and Lidar sensor information to provide an optimised solution for path selection. Furthermore, three algorithms, namely overlapping algorithm, sequential adding algorithm, the distance‐focused algorithm is designed for higher efficiency in sensor fusion mechanism. The proposed methodology is for the best exploitation of the enormous dataset, meant for real‐time processing for an AV.

Myocardial abscess is a suppurative infection of myocardium, endocardium, native or prosthetic valves, perivalvular structures and cardiac conduction system. It develops in about 20% of patients with infective endocarditis. Due to avascular and fibrous structures, valvular regions are commonly involved. More precisely, aortic valve (AV) rings area; native or prosthetic valve is usually affected. Occurrence of myocardial abscess within free wall of the left ventricle (LV) without any evidence of infective endocarditis is a rare phenomenon; and infrequently reported in medical literature. We report a case of myocardial abscess cavity within the anterior wall of the LV, in a patient who underwent open heart surgery for severe AV stenosis. This was an incidental intraoperative transesophageal echocardiography (TEE) finding without any other evidence of infective endocarditis. The stenotic AV was replaced, along with surgical drainage and closure of the cavity. Postoperatively, patient was managed on empirical antibiotics according to infective endocarditis guidelines. [ABSTRACT FROM AUTHOR]

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After the Cold War, certain processes were still going on by inertia, including clashes of military systems. In particular, these models manifested themselves in Yugoslavia in 1999 and Iraq in 2003. Subsequently, these new military-science realities and technical solutions were found in conventional as well as non-conventional conflicts. The model of American air supremacy in the long-running Syrian war, as well as other conflicts, has clearly demonstrated its vitality. Here, let us attempt to clearly present what rules are in question with respect to this model.

BackgroundA secondary rise of intracellular Ca(2+) (Cai) and an upregulation of apamin-sensitive K(+) current (I(KAS)) are characteristic findings of failing ventricular myocytes. We hypothesize that apamin, a specific I(KAS) blocker, may induce torsades de pointes (TdP) ventricular arrhythmia from failing ventricles exhibiting secondary rises of Cai.ObjectiveTo test the hypothesis that small conductance Ca(2+) activated IKAS maintains repolarization reserve and prevents ventricular arrhythmia in a rabbit model of heart failure (HF).MethodsWe performed Langendorff perfusion and optical mapping studies in 7 hearts with pacing-induced HF and in 5 normal control rabbit hearts. Atrioventricular block was created by cryoablation to allow pacing at slow rates.ResultsThe left ventricular ejection fraction reduced from 69.1% [95% confidence interval 62.3%-76.0%] before pacing to 30.4% [26.8%-34.0%] (N = 7; P < .001) after pacing. The corrected QT interval in failing ventricles was 337 [313-360] ms at baseline and 410 [381-439] ms after applying 100 nmol/L of apamin (P = .01). Apamin induced early afterdepolarizations (EADs) in 6 ventricles, premature ventricular beats (PVBs) in 7 ventricles, and polymorphic ventricular tachycardia consistent with TdP in 4 ventricles. The earliest activation site of EADs and PVBs always occurred at the site with long action potential duration and large amplitude of the secondary rises of Ca(i). Apamin induced secondary rises of Ca(i) in 1 nonfailing ventricle, but no EAD or TdP were observed.ConclusionsIn HF ventricles, apamin induces EADs, PVBs, and TdP from areas with secondary rises of Ca(i). I(KAS) is important in maintaining repolarization reserve and preventing TdP in HF ventricles.

The advent of road transport automation is suggested to be one of four key technological transitions that could amount to a major transformation in mobility practices. Specifically, fully Automated Vehicles (AVs) might replace the current private car owner user model with fleets of on-demand synchronously-shared automated taxis. However, significant barriers to this vision becoming the norm remain. This paper examines two critical user-acceptance aspects of the transition: willingness to adopt AVs, and willingness to share an AV with others, particularly strangers. Our novel survey (n = 899) included a choice experiment featuring four future full automation transport services (private, synchronously/asynchronously shared, and public). Cluster analysis examined respondents' preferences and their demographic and psycho-social characteristics. We uncover significant uncertainty about willingness to adopt automation and sharing, and important differences between clusters within our sample. For example, under 50% of participants report willingness to use an AV over their normal mode, or would prefer an automated option to a current human-driven option. Our findings raise critical questions for policymakers and transport authorities. Not least, how can AV technologies help realise the environmental and social benefits of widespread vehicle sharing in a context of a travelling public that still prefers its privacy on-the-move? [ABSTRACT FROM AUTHOR]