Mercedes-Benz (Germany)

OBJECTIVE: The 1987 American College of Rheumatology (ACR; formerly, the American Rheumatism Association) classification criteria for rheumatoid arthritis (RA) have been criticized for their lack of sensitivity in early disease. This work was undertaken to develop new classification criteria for RA. METHODS: A joint working group from the ACR and the European League Against Rheumatism developed, in 3 phases, a new approach to classifying RA. The work focused on identifying, among patients newly presenting with undifferentiated inflammatory synovitis, factors that best discriminated between those who were and those who were not at high risk for persistent and/or erosive disease--this being the appropriate current paradigm underlying the disease construct "rheumatoid arthritis." RESULTS: In the new criteria set, classification as "definite RA" is based on the confirmed presence of synovitis in at least 1 joint, absence of an alternative diagnosis that better explains the synovitis, and achievement of a total score of 6 or greater (of a possible 10) from the individual scores in 4 domains: number and site of involved joints (score range 0-5), serologic abnormality (score range 0-3), elevated acute-phase response (score range 0-1), and symptom duration (2 levels; range 0-1). CONCLUSION: This new classification system redefines the current paradigm of RA by focusing on features at earlier stages of disease that are associated with persistent and/or erosive disease, rather than defining the disease by its late-stage features. This will refocus attention on the important need for earlier diagnosis and institution of effective disease-suppressing therapy to prevent or minimize the occurrence of the undesirable sequelae that currently comprise the paradigm underlying the disease construct "rheumatoid arthritis."

Cardiovascular-kidney-metabolic health reflects the interplay among metabolic risk factors, chronic kidney disease, and the cardiovascular system and has profound impacts on morbidity and mortality. There are multisystem consequences of poor cardiovascular-kidney-metabolic health, with the most significant clinical impact being the high associated incidence of cardiovascular disease events and cardiovascular mortality. There is a high prevalence of poor cardiovascular-kidney-metabolic health in the population, with a disproportionate burden seen among those with adverse social determinants of health. However, there is also a growing number of therapeutic options that favorably affect metabolic risk factors, kidney function, or both that also have cardioprotective effects. To improve cardiovascular-kidney-metabolic health and related outcomes in the population, there is a critical need for (1) more clarity on the definition of cardiovascular-kidney-metabolic syndrome; (2) an approach to cardiovascular-kidney-metabolic staging that promotes prevention across the life course; (3) prediction algorithms that include the exposures and outcomes most relevant to cardiovascular-kidney-metabolic health; and (4) strategies for the prevention and management of cardiovascular disease in relation to cardiovascular-kidney-metabolic health that reflect harmonization across major subspecialty guidelines and emerging scientific evidence. It is also critical to incorporate considerations of social determinants of health into care models for cardiovascular-kidney-metabolic syndrome and to reduce care fragmentation by facilitating approaches for patient-centered interdisciplinary care. This presidential advisory provides guidance on the definition, staging, prediction paradigms, and holistic approaches to care for patients with cardiovascular-kidney-metabolic syndrome and details a multicomponent vision for effectively and equitably enhancing cardiovascular-kidney-metabolic health in the population.

BACKGROUND AND PURPOSE: Population-wide reductions in cardiovascular disease incidence and mortality have not been shared equally by African Americans. The burden of cardiovascular disease in the African American community remains high and is a primary cause of disparities in life expectancy between African Americans and whites. The objectives of the present scientific statement are to describe cardiovascular health in African Americans and to highlight unique considerations for disease prevention and management. METHOD: The primary sources of information were identified with PubMed/Medline and online sources from the Centers for Disease Control and Prevention. RESULTS: The higher prevalence of traditional cardiovascular risk factors (eg, hypertension, diabetes mellitus, obesity, and atherosclerotic cardiovascular risk) underlies the relatively earlier age of onset of cardiovascular diseases among African Americans. Hypertension in particular is highly prevalent among African Americans and contributes directly to the notable disparities in stroke, heart failure, and peripheral artery disease among African Americans. Despite the availability of effective pharmacotherapies and indications for some tailored pharmacotherapies for African Americans (eg, heart failure medications), disease management is less effective among African Americans, yielding higher mortality. Explanations for these persistent disparities in cardiovascular disease are multifactorial and span from the individual level to the social environment. CONCLUSIONS: The strategies needed to promote equity in the cardiovascular health of African Americans require input from a broad set of stakeholders, including clinicians and researchers from across multiple disciplines.

A growing appreciation of the pathophysiological interrelatedness of metabolic risk factors such as obesity and diabetes, chronic kidney disease, and cardiovascular disease has led to the conceptualization of cardiovascular-kidney-metabolic syndrome. The confluence of metabolic risk factors and chronic kidney disease within cardiovascular-kidney-metabolic syndrome is strongly linked to risk for adverse cardiovascular and kidney outcomes. In addition, there are unique management considerations for individuals with established cardiovascular disease and coexisting metabolic risk factors, chronic kidney disease, or both. An extensive body of literature supports our scientific understanding of, and approach to, prevention and management for individuals with cardiovascular-kidney-metabolic syndrome. However, there are critical gaps in knowledge related to cardiovascular-kidney-metabolic syndrome in terms of mechanisms of disease development, heterogeneity within clinical phenotypes, interplay between social determinants of health and biological risk factors, and accurate assessments of disease incidence in the context of competing risks. There are also key limitations in the data supporting the clinical care for cardiovascular-kidney-metabolic syndrome, particularly in terms of early-life prevention, screening for risk factors, interdisciplinary care models, optimal strategies for supporting lifestyle modification and weight loss, targeting of emerging cardioprotective and kidney-protective therapies, management of patients with both cardiovascular disease and chronic kidney disease, and the impact of systematically assessing and addressing social determinants of health. This scientific statement uses a crosswalk of major guidelines, in addition to a review of the scientific literature, to summarize the evidence and fundamental gaps related to the science, screening, prevention, and management of cardiovascular-kidney-metabolic syndrome.

Abstract Measurement of the likely magnitude of the economic impact of climate change on African agriculture has been a challenge. Using data from a survey of more than 9,000 farmers across 11 African countries, a cross-sectional approach estimates how farm net revenues are affected by climate change compared with current mean temperature. Revenues fall with warming for dryland crops (temperature elasticity of −1.9) and livestock (−5.4), whereas revenues rise for irrigated crops (elasticity of 0.5), which are located in relatively cool parts of Africa and are buffered by irrigation from the effects of warming. At first, warming has little net aggregate effect as the gains for irrigated crops offset the losses for dryland crops and livestock. Warming, however, will likely reduce dryland farm income immedia-tely. The final effects will also depend on changes in precipitation, because revenues from all farm types increase with precipitation. Because irrigated farms are less sensitive to climate, where water is available, irrigation is a practical adaptation to climate change in Africa.

In the 2011 "Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents," several medical conditions among youth were identified that predispose to accelerated atherosclerosis and early cardiovascular disease (CVD), and risk stratification and management strategies for youth with these conditions were elaborated. Major depressive disorder (MDD) and bipolar disorder (BD) among youth satisfy the criteria set for, and therefore merit inclusion among, Expert Panel tier II moderate-risk conditions. The combined prevalence of MDD and BD among adolescents in the United States is ≈10%, at least 10 times greater than the prevalence of the existing moderate-risk conditions combined. The high prevalence of MDD and BD underscores the importance of positioning these diseases alongside other pediatric diseases previously identified as moderate risk for CVD. The overall objective of this statement is to increase awareness and recognition of MDD and BD among youth as moderate-risk conditions for early CVD. To achieve this objective, the primary specific aims of this statement are to (1) summarize evidence that MDD and BD are tier II moderate-risk conditions associated with accelerated atherosclerosis and early CVD and (2) position MDD and BD as tier II moderate-risk conditions that require the application of risk stratification and management strategies in accordance with Expert Panel recommendations. In this scientific statement, there is an integration of the various factors that putatively underlie the association of MDD and BD with CVD, including pathophysiological mechanisms, traditional CVD risk factors, behavioral and environmental factors, and psychiatric medications.

OBJECTIVE: To estimate and compare the prevalence of fibromyalgia by 2 different methods in Olmsted County, Minnesota. METHODS: The first method was a retrospective review of medical records of potential cases of fibromyalgia in Olmsted County using the Rochester Epidemiology Project (from January 1, 2005, to December 31, 2009) to estimate the prevalence of diagnosed fibromyalgia in clinical practice. The second method was a random survey of adults in Olmsted County using the fibromyalgia research survey criteria to estimate the percentage of responders who met the fibromyalgia research survey criteria. RESULTS: Of the 3,410 potential patients identified by the first method, 1,115 had a fibromyalgia diagnosis documented in the medical record by a health care provider. The age- and sex-adjusted prevalence of diagnosed fibromyalgia by this method was 1.1%. By the second method, of the 2,994 people who received the survey by mail, 830 (27.6%) responded and 44 (5.3%) met the fibromyalgia research survey criteria. The age- and sex-adjusted prevalence of fibromyalgia in the general population of Olmsted County by this method was estimated at 6.4%. CONCLUSION: To the best of our knowledge, this is the first report of the rate at which fibromyalgia is being diagnosed in a community. This is also the first report of prevalence as assessed by the fibromyalgia research survey criteria. Our results suggest that patients, particularly men, who meet the fibromyalgia research survey criteria are unlikely to have been given a diagnosis of fibromyalgia.

Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk of performance degradation over time. With each task of the process, this work proposes quality assurance methodology that is suitable to address challenges in machine learning development that are identified in the form of risks. The methodology is drawn from practical experience and scientific literature, and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support, but fails to address machine learning specific tasks. The presented work proposes an industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance.

Demands for better fuel economy and more electric power are driving cars to multiple higher voltages. In the next 10 years the electrical systems in some luxury automobiles will be so changed as to be almost unrecognizable. Although they will doubtless employ the old reliable 12 V lead-acid battery, their loads will be driven by a variety of voltages, both AC and DC, perhaps derived from a single AC distribution network. Designers will be able to match voltages to individual loads for best efficiency and performance-lights perhaps at 6 V AC, electronics at 5 V DC, active suspension at 350 V DC, and motors and actuators at 42 V DC. The digital signals controlling those loads will be carried by a separate communications network. The enabling technology for these advances are semiconductors. The authors discuss the future development of automobile electrical systems.

Driving can occupy a large portion of daily life and often can elicit negative emotional states like anger or stress, which can significantly impact road safety and long-term human health. In recent decades, the arrival of new tools to help recognize human affect has inspired increasing interest in how to develop emotion-aware systems for cars. To help researchers make needed advances in this area, this article provides a comprehensive literature survey of work addressing the problem of human emotion recognition in an automotive context. We systematically review the literature back to 2002 and identify 63 peer-review published articles on this topic. We overview each study’s methodology to measure and recognize emotions in the context of driving. Across the literature, we find a strong preference toward studying emotional states associated with high arousal and negative valence, monitoring the different states with cardiac, electrodermal activity, and speech signals, and using supervised machine learning to automatically infer the underlying human affective states. This article summarizes the existing work together with publicly available resources (e.g., datasets and tools) to help new researchers get started in this field. We also identify new research opportunities to help advance progress for improving driver emotion recognition.

FIGURE 53. Map of parts of Fresno, Merced, and San Benito Counties, Calif., showing area discussed in this paper____ 54. Relations of fan area and slope to drainage-basin area

Lidar sensors are frequently used in environment perception for autonomous vehicles and mobile robotics to complement camera, radar, and ultrasonic sensors. Adverse weather conditions are significantly impacting the performance of lidar-based scene understanding by causing undesired measurement points that in turn effect missing detections and false positives. In heavy rain or dense fog, water drops could be misinterpreted as objects in front of the vehicle which brings a mobile robot to a full stop. In this letter, we present the first CNN-based approach to understand and filter out such adverse weather effects in point cloud data. Using a large data set obtained in controlled weather environments, we demonstrate a significant performance improvement of our method over state-of-the-art involving geometric filtering. Data is available at https://github.com/rheinzler/PointCloudDeNoising.

Potassium (K(+)) is a major osmoticum of plant cells, and the vacuolar accumulation of this element is a especially crucial feature for plants under high-salt conditions. Emerging evidence indicates that cation/proton transporters of the NHX family are instrumental in the H(+)-linked K(+) transport that mediate active K(+) uptake at the tonoplast for the unequal partitioning of K(+) between vacuole and cytosol. However, and in spite of tenuous supporting evidence, NHX proteins are widely regarded as key players in the sequestration of sodium (Na(+)) into vacuoles to avert ion toxicity in the cytosol of plants under salinity stress. Here, we propose an updated model positing that NHX proteins fulfill a protective function to minimize salt-related stress mainly through the vacuolar compartmentalization of K(+) and, in some cases, of Na(+) as well thereby preventing toxic Na(+)-K(+) ratios in the cytosol while accruing solutes for osmotic balance.

This paper reports on a study of manager perceptions of the cost to employers of on-the-job employee illness, sometimes termed 'presenteeism,' for various types of jobs. Using methods developed previously, the authors analyzed data from a survey of more than 800 US managers to determine the characteristics of various jobs and the relationship of those characteristics to the manager's view of the cost to the firm of absenteeism and presenteeism. Jobs with characteristics that suggest unusually high cost (relative to wages) were similar in terms of their 'absenteeism multipliers' and their 'presenteeism multipliers.' Jobs with high values of team production, high requirements for timely output, and high difficulties of substitution for absent or impaired workers had significantly higher indicators of cost for both absenteeism and presenteeism, although substitution was somewhat less important for presenteeism.

Abstract High‐energy‐density lithium metal batteries (LMBs) are widely accepted as promising next‐generation energy storage systems. However, the safety features of practical LMBs are rarely explored quantitatively. Herein, the thermal runaway behaviors of a 3.26 Ah (343 Wh kg −1 ) Li | LiNi 0.5 Co 0.2 Mn 0.3 O 2 pouch cell in the whole life cycle are quantitatively investigated by extended volume‐accelerating rate calorimetry and differential scanning calorimetry. By thermal failure analyses on pristine cell with fresh Li metal, activated cell with once plated dendrites, and 20‐cycled cell with large quantities of dendrites and dead Li, dendrite‐accelerated thermal runaway mechanisms including reaction sequence and heat release contribution are reached. Suppressing dendrite growth and reducing the reactivity between Li metal anode and electrolyte at high temperature are effective strategies to enhance the safety performance of LMBs. These findings can largely enhance the understanding on the thermal runaway behaviors of Li metal pouch cells in practical working conditions.

Generalized zero-shot action recognition is a challenging problem, where the task is to recognize new action categories that are unavailable during the training stage, in addition to the seen action categories. Existing approaches suffer from the inherent bias of the learned classifier towards the seen action categories. As a consequence, unseen category samples are incorrectly classified as belonging to one of the seen action categories. In this paper, we set out to tackle this issue by arguing for a separate treatment of seen and unseen action categories in generalized zero-shot action recognition. We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category. To train our out-of-distribution detector, video features for unseen action categories are synthesized using generative adversarial networks trained on seen action category features. To the best of our knowledge, we are the first to propose an out-of-distribution detector based GZSL framework for action recognition in videos. Experiments are performed on three action recognition datasets: Olympic Sports, HMDB51 and UCF101. For generalized zero-shot action recognition, our proposed approach outperforms the baseline with absolute gains (in classification accuracy) of 7.0%, 3.4%, and 4.9%, respectively, on these datasets.

Conventional sensor systems record information about directly visible objects, whereas occluded scene components are considered lost in the measurement process. Non-line-of-sight (NLOS) methods try to recover such hidden objects from their indirect reflections - faint signal components, traditionally treated as measurement noise. Existing NLOS approaches struggle to record these low-signal components outside the lab, and do not scale to large-scale outdoor scenes and high-speed motion, typical in automotive scenarios. In particular, optical NLOS capture is fundamentally limited by the quartic intensity falloff of diffuse indirect reflections. In this work, we depart from visible-wavelength approaches and demonstrate detection, classification, and tracking of hidden objects in large-scale dynamic environments using Doppler radars that can be manufactured at low-cost in series production. To untangle noisy indirect and direct reflections, we learn from temporal sequences of Doppler velocity and position measurements, which we fuse in a joint NLOS detection and tracking network over time. We validate the approach on in-the-wild automotive scenes, including sequences of parked cars or house facades as relay surfaces, and demonstrate low-cost, real-time NLOS in dynamic automotive environments.

A new automotive radar data set with measurements and point-wise annotations from more than four hours of driving is presented. Data provided by four series radar sensors mounted on one test vehicle were recorded and the individual detections of dynamic objects were manually grouped to clusters and labeled afterwards. The purpose of this data set is to enable the development of novel (machine learning-based) radar perception algorithms with the focus on moving road users. Images of the recorded sequences were captured using a documentary camera. For the evaluation of future object detection and classification algorithms, proposals for score calculation are made so that researchers can evaluate their algorithms on a common basis. Additional information as well as download instructions can be found on the website of the data set: www.radar-scenes.com.

The increasing change of production leads to differences between the current shop floor and the state of planning. This difference causes significant challenges for production planners while integrating new products into existing production systems. To tackle this issue, this paper presents a concept for the automated creation of a digital twin of a body-in-white production system based on current resources, products as well as process information from the cyber-physical system. The paper focuses on the different data sources and information in cyber-physical systems necessary for integration planning. Furthermore, major parts of the concept are evaluated in a real body-in-white production system. The resulting digital twin enables faster product integration and Industry 4.0 concepts.

The usage of high-strength steels for structural components and reinforcement parts is inevitable for modern car-body manufacture in reaching lightweight design as well as increasing passive safety. Depending on their microstructure these steels show differing damage mechanisms and various mechanical properties which cannot be classified comprehensively via classical uniaxial tensile testing. In this research, damage initiation, evolution and final material failure are characterized for commercially produced complex-phase (CP) and dual-phase (DP) steels in a strength range between 600 and 1000 MPa. Based on these investigations CP steels with their homogeneous microstructure are characterized as damage tolerant and hence less edge-crack sensitive than DP steels. As final fracture occurs after a combination of ductile damage evolution and local shear band localization in ferrite grains at a characteristic thickness strain, this strain measure is introduced as a new parameter for local formability. In terms of global formability DP steels display advantages because of their microstructural composition of soft ferrite matrix including hard martensite particles. Combining true uniform elongation as a measure for global formability with the true thickness strain at fracture for local formability the mechanical material response can be assessed on basis of uniaxial tensile testing incorporating all microstructural characteristics on a macroscopic scale. Based on these findings a new classification scheme for the recently developed high-strength multiphase steels with significantly better formability resulting of complex underlying microstructures is introduced. The scheme overcomes the steel designations using microstructural concepts, which provide no information about design and production properties.