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For a drive unit for axes of robots and robot-like systems (RLS) usually a motor-gearbox arrangement is chosen due to its high-power density. The combination of a high-ratio gearbox and a high-speed electric motor ensures a very compact and efficient design of the drive train. The transmission properties primarily determine the properties of the axes and the whole robot system. Robots and RLS use various types of high-ratio precision gearboxes based on different operating principles. Due to the different operating principles, it is difficult to describe comparable properties across all different types. In addition, there are many influences on the properties which significantly determine their shapes and values. These influencing parameters are insufficiently documented and are often poorly accessible for profound comparability and further consideration. In this paper, an overview of the properties of robot gearboxes is given. Based on these properties, different robot gearboxes can be systematically evaluated and compared to one another. The properties are influenced by various design, operating or manufacturing factors such as the gearbox size, the operating torque and speed or the manufacturing process. In a further step, these influences on the most relevant properties, efficiency and stiffness, are determined and systematically evaluated. This evaluation is based on the specification data of various robot gearbox manufacturers. The properties efficiency and stiffness show a dependency on the gearbox size, the operating torque, speed as well as the ambient temperature and on the transmission ratio. The shown procedure can also be adapted to other properties.

IntroductionHigh-risk percutaneous coronary interventions (HRPCI) are a potential treatment option for patients with reduced left ventricular ejection fraction (LVEF) and coronary artery disease. The extent to which such intervention is coupled with improvement in LVEF and associated with favorable outcomes is unknown.MethodsWe aimed to characterize the incidence and correlates of LVEF improvement after Impella-guided HRPCI, and compare clinical outcomes in patients with versus without LVEF improvement. Data on consecutive patients undergoing Impella-guided HRPCI from a single center registry were analyzed. LVEF-improvement was defined as an absolute increase of LVEF of ≥10% measured at ≥30‐days after intervention. The primary outcome was a composite of all‐cause death, myocardial infarction or target vessel revascularization within 1-year.ResultsOut of 161 consecutive patients undergoing Impella-guided HRPCI from June 2008 to December 2017, 43% (n = 70) demonstrated LVEF-improvement (baseline LVEF of 25.09 ± 6.19 to 33.30 ± 11.98 post intervention). Patients without LVEF-improvement had higher frequency of previous MI (61.5% vs. 37.1%, p = 0.0021), Q-waves on ECG (17.6% vs. 5.7%, p = 0.024) and higher SYNTAX scores (30.8 ± 17.6 vs. 25.2 ± 12.2; p = 0.043). After correction of these confounders by multivariable analysis, no significant differences were found regarding the composite endpoint in patients with versus without LVEF-improvement (34.9% vs. 38.3%; p = 0.48).DiscussionIn this single-center retrospective analysis, we report the following findings. First, LVEF improvement of at least 10% was documented in over 40% of patients undergoing Impella supported high-risk PCI. Second, a history of MI, Q-waves on admission ECG, and higher baseline SYNTAX scores were independent correlates of no LVEF improvement. Third, one year rates of adverse CV events were substantial and did not vary by the presence or absence of LVEF improvement Prospective studies with longer follow-up are needed to elucidate the impact of LVEF improvement on clinical outcomes.

Abstract The distributed blocking flowshop scheduling problem (DBFSP) with new job insertions is studied. Rescheduling all remaining jobs after a dynamic event like a new job insertion is unreasonable to an actual distributed blocking flowshop production process. A deep reinforcement learning (DRL) algorithm is proposed to optimise the job selection model, and local modifications are made on the basis of the original scheduling plan when new jobs arrive. The objective is to minimise the total completion time deviation of all products so that all jobs can be finished on time to reduce the cost of storage. First, according to the definitions of the dynamic DBFSP problem, a DRL framework based on multi‐agent deep deterministic policy gradient (MADDPG) is proposed. In this framework, a full schedule is generated by the variable neighbourhood descent algorithm before a dynamic event occurs. Meanwhile, all newly added jobs are reordered before the agents make decisions to select the one that needs to be scheduled most urgently. This study defines the observations, actions and reward calculation methods and applies centralised training and distributed execution in MADDPG. Finally, a comprehensive computational experiment is carried out to compare the proposed method with the closely related and well‐performing methods. The results indicate that the proposed method can solve the dynamic DBFSP effectively and efficiently.

In high-income countries, ischaemic heart disease is the leading cause of death in women and men, accounting for more than 20% of deaths in both sexes. However, women are less likely to receive guideline-recommended percutaneous coronary intervention (PCI) than men. Women undergoing PCI have poorer unadjusted outcomes because they are older and have greater comorbidity than men, but uncertainty remains whether sex affects outcome after these differences in clinical characteristics are considered. In this paper, we review recent published evidence comparing outcomes between men and women undergoing PCI. We focus on the sex differences in PCI outcomes in different scenarios: acute coronary syndromes, stable angina and complex lesions, including the approach of left main coronary artery. We also review how gender is considered in recent guidelines and offer a common clinical scenario to illustrate the contemporary management strategies an interventional cardiologist should consider when performing PCI on a female patient.

Abstract The dependability characteristic of the control software of manufacturing systems is highlighted more than before, going through repeated changes to cope with various and varying requirements. Formal methods are researched to be applied to automation system engineering to obtain a more effective and efficient quality assurance. One of the approaches, a formal specification language named Generalised Test Tables has been developed with the aim of intuitiveness and accessibility for automation application developers. The result of the experiments conducted to assess the usability of this language is presented here. Focussing on evaluating effectiveness and user satisfaction, three paper‐based experiments have been conducted with students at the bachelor and master level. The evaluation results point to positive usability in both comparative effectiveness to conventional language, that is, Petri Nets, and subjective perception of user satisfaction.

Liquid leakage from pipelines is a critical issue in large-scale process plants. Damage in pipelines affects the normal operation of the plant and increases maintenance costs. Furthermore, it causes unsafe and hazardous situations for operators. Therefore, the detection and localization of leakages is a crucial task for maintenance and condition monitoring. Recently, the use of infrared (IR) cameras was found to be a promising approach for leakage detection in large-scale plants. IR cameras can capture leaking liquid if it has a higher (or lower) temperature than its surroundings. In this paper, a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant. Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid, it is applicable for any type of liquid leakage (i.e., water, oil, etc.). In this method, subsequent frames are subtracted and divided into blocks. Then, principle component analysis is performed in each block to extract features from the blocks. All subtracted frames within the blocks are individually transferred to feature vectors, which are used as a basis for classifying the blocks. The k-nearest neighbor algorithm is used to classify the blocks as normal (without leakage) or anomalous (with leakage). Finally, the positions of the leakages are determined in each anomalous block. In order to evaluate the approach, two datasets with two different formats, consisting of video footage of a laboratory demonstrator plant captured by an IR camera, are considered. The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos. The proposed method has high accuracy and a reasonable detection time for leakage detection. The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.

The quality of teaching in mechanical engineering greatly depends on the practical application of the fundamental theory taught. During COVID-19, limited access to in-class lectures, practical courses with demonstrator plants, and active student participation forced us, an institute of a mechanical engineering faculty in the German higher education system, to transform the teaching strategy in a limited time of one month. For class sizes of between ~25 and ~700 participants, mainly addressing mechanical, electrical and software engineering students, we share challenges and successes in our transformation strategy during April-October 2020. Furthermore, we present a collection of suitable tools, their functions, advantages, disadvantages, and feedback from three sources to provide profound assessment and adaptation criteria in digital teaching. Using quantitative evaluations of four lectures in automation and information systems over the past five years, we highlight the experience from the students’ perspective and evaluate our digital teaching methods compared to well-established in-class formats. This compilation of causes and consequences clarifies the effort involved in achieving the result and discusses the potential of digital teaching or hybrid teaching in engineering.

Abstract Background We investigated the association of insulin resistance (IR) with coronary plaque morphology and the risk of cardiovascular events in patients enrolled in the Providing Regional Observations to Study Predictors of Events in Coronary Tree (PROSPECT) study. Methods Patients with acute coronary syndromes (ACS) were divided based on DM status. Non-DM patients were further stratified according to homeostasis-model-assessment IR (HOMA-IR) index as insulin sensitive (IS; HOMA-IR ≤ 2), likely-IR (LIR; 2

BackgroundNeurotensin is involved in fatty acid and glucose metabolism and promotes the development of obesity and diabetes. These associations appear to be more pronounced in women. We investigated the association of neurotensin with long-term major adverse cardiovascular events (MACE) in patients presenting with acute coronary syndrome (ACS) or chronic coronary syndrome (CCS) undergoing percutaneous coronary intervention (PCI).MethodsWe included 452 consecutive patients [144 (31.9%) females] undergoing PCI for ACS or CCS. Plasma samples drawn after PCI were analyzed for neurotensin with an enzyme-linked immunoassay. As primary endpoint, a composite of MACE including all-cause death, non-fatal myocardial infarction and non-fatal stroke during 7 years of follow-up was investigated. As secondary endpoint, we investigated all-cause death.ResultsNeurotensin levels did not differ between male and female patients (p = 0.560). MACE occurred in 150 (33.2%) patients. Restricted cubic splines demonstrated a U-shaped association of log-transformed neurotensin with the primary and secondary endpoint. Therefore, we dichotomized our cohort according to tertiles of log-transformed neurotensin. In Kaplan-Meier analysis including the total cohort and restricted to male patients log- neurotensin tertiles were not associated with MACE (both p > 0.05). Moreover, in the overall cohort and in male patients multivariable Cox regression analysis log-neurotensin tertiles were not associated with MACE or with all-cause death (all p > 0.05). However, in female patients log-neurotensin was associated with MACE in Kaplan-Meier analysis (log-rank p = 0.013). Also, after multivariable adjustment female patients in the first tertile had a significantly increased risk for MACE compared to female patients in the second tertile [HR 3.84 (95% CI 1.71–8.60), p = 0.001]. There was tendency for increased risk in female patients in the third tertile compared to the second tertile [HR 2.14 (95% CI 0.97–4.73), p = 0.058]. Moreover, in female patients the [first and the third tertile of log- neurotensin were associated with all-cause death 1s vs. 2nd tertile: HR 3.03 (95% CI 1.21–7.63), p = 0.018; 3rd vs. 2nd tertile: HR 3.01 (95% CI 1.22–7.44), p = 0.016].ConclusionIn female patients with CAD undergoing PCI, neurotensin has a U-shaped relationship with adverse outcomes. These data suggest a sex specific association between neurotensin and long-term adverse events after PCI.

Interdisciplinary engineering of cyber physical production systems (CPPS) are often subject to delay, cost overrun and quality problems or may even fail due to the lack of efficient information exchange between multiple interdisciplinary teams working in complex networks within and across companies. We propose a direct integration of multiteam and organisational aspects into the graphical notation of the systems engineering workflow. BPMN++, with eight new notational elements and two subdiagrams, enables the modelling of the required cooperation aspects. BPMN++ provides an improved overview, uniform notation, more compact presentation and easier modifiability from an engineering point of view. We also included a first set of empirical studies and historical qualitative and quantitative data in addition to subjective expert-based ratings to increase validity. The use case introduced to explain the procedure and the notation is derived from surveys in plant manufacturing focussing on the start-up phase and decision support at site. This, in particular, is one of the most complex and critical phases with potentially high economic impact. For evaluation purposes, we compare two alternative solutions for a short-term management decision in the start-up phase of CPPS using the BPMN++ approach.

New York City was one of the epicenters of the COVID-19 pandemic. The management of peripheral artery disease (PAD) during this time has been a major challenge for health care systems and medical personnel. This document is based on the experiences of experts from various medical fields involved in the treatment of patients with PAD practicing in hospitals across New York City during the outbreak. The recommendations are based on certain aspects including the COVID-19 infection status as well as the clinical PAD presentation of the patient. Our case-based algorithm aims at guiding the treatment of patients with PAD during the pandemic in a safe and efficient way.

This paper aims at identifying ontological categories as higher-order knowledge structures that underlie engineering students' thinking about technical systems. Derived from interviews, these ontological categories include, inter alia, a focus on the behavior, structure, or purpose of a technical system. We designed and administered a paper-based test to assess these ontological categories in a sample of N = 340 first-year students in different engineering disciplines. Based on their activation patterns across ontological categories, students clustered into six different ontological profiles. Study program, gender as well as objective and self-perceived cognitive abilities were associated with differences in jointly activated ontological categories. Additional idiosyncratic influences and experiences, however, seemed to play a more important role. Our results can inform university instruction and support successful co-operation in engineering.

In the context of cross-disciplinary and cross-company cooperation, several challenges in developing manufacturing systems are revealed through industrial use cases. To tackle these challenges, two propositions are used in parallel. First, coupling technical models representing different content areas facilitates the detection of boundary crossing consequences, either by using a posteriori or a priori connection. Second, it is necessary to enrich these coupled technical models with team and organizational models as interventions focusing on the collaboration between individuals and teams within broader organizational conditions. Accordingly, a combined interdisciplinary approach is proposed. The feasibility and benefits of the approach is proven with an industrial use case. The use case shows that inconsistencies among teams can be identified by coupling engineering models and that an integrated organizational model can release the modelling process from communication barriers.

The demand for individualized and efficient production yields more capable but also more complex manufacturing environments. Virtual training systems were proposed to train the operators who control and maintain industrial machines. This article reviews such training systems with a focus on their teaching styles and identifies new research directions in the field of adaptive training systems. The review applies a classification scheme that describes the instructions, the presentation of the instructions, and the interaction possibilities of a virtual training system. Another aspect of the classification scheme are the capabilities to adapt a training system to the characteristics of the trainee. The review indicates that existing training systems apply similar teaching styles and provide limited adaptive capabilities. The development of training systems that adapt their teaching style to the disposition and the qualification of individual trainees is proposed, to address the increasingly diverse workforce.

Background: Elevated levels of troponin are associated with future major adverse cardiac events (MACE). Data on the prognostic value of high sensitive troponin T (hs-TnT) compared to high sensitive troponin I (hs-TnI) in diabetic and non-diabetic patients are sparse.Methods: We analyzed patients of a single-center registry undergoing coronary stenting between 2003 and 2006. As a primary endpoint we assessed MACE, a composite of cardiovascular death, nonfatal myocardial infarction and nonfatal stroke according to sex and diabetes status using log-rank. As a second endpoint, we assessed the prognostic impact of hs-TnT and hs-TnI on MACE, adjusting for known confounders using Cox regression analysis.Results: Out of 818 investigated patients, 267 (32.6%) were female. Diabetes mellitus type 2 (T2DM) was diagnosed in 206 (25.2%) patients.After a mean follow-up of 6.6 ± 3.7 years, MACE occurred in 235 (28.7%) patients. The primary endpoint components of cardiovascular death occurred in 115 (14.1%) patients, MI in 75 (9.2%), and ischemic stroke in 45 (5.5%). Outcomes differed significantly according to sex and diabetes status (p = 0.003). In descending order, MACE rates were as follows: female diabetic patients (40.8%), female non-diabetic patients (32.7%), male diabetic patients (28.9%), and male non-diabetic patients (24.8%). Additionally, females with diabetes were at higher risk of cardiovascular death compared to diabetic men (28 vs. 15%). Hs-TnI (HR 1.477 [95% CI 1.100–1.985]; p = 0.010) and hs-TnT (HR 1.615 [95%CI 1.111–2.348]; p = 0.012) above the 99th percentile were significantly associated with MACE. Both assays showed tendency toward association with MACE in all subgroups.Conclusion: Diabetic patients, particularly females, with known coronary artery disease had a higher risk of subsequent MACE. Both, hs-TnI and hs-TnT significantly correlated with MACE.

The so-called ‘Industrie 4.0’ provides high potential for data-driven methods in automated production systems. However, sensor and actuator data gathered during normal operation of the system is often limited to a narrow range of single, specific operating points. This limitation also restricts the significance of condition-based maintenance models, which are trained to the narrow data. In order to reveal the structure of such multi-dimensional data sets and detect deficiencies, this paper derives a data quality metric and visualization. The metric observes the feature space and evaluates the completeness of data. In the best case, the observations utilize the whole feature space, meaning all different combinations of the variables are present in the data. Low metric values indicate missing combinations, reducing the representativeness of the data. In this way, appropriate countermeasures can be taken if relevant data is missing. For evaluation, a data set of an industrial test bed for condition monitoring of control valves is used. It is shown that the state-of-the-art metrics and visualizations cannot detect deficiencies of completeness in multi-dimensional data sets. In contrast, the proposed heat map enables the expert to locate limitations in multi-dimensional data sets.

Data collection from distributed automated production systems is one of the main prerequisites to leverage information gain from data analysis in the context of Industrie 4.0, e.g., for the optimization of product quality. However, the realization of data collection architectures is associated with immense implementation efforts due to the heterogeneity of systems, protocols, and interfaces, as well as the multitude of involved disciplines in such projects. Therefore, this paper contributes with an approach for the model-driven generation of data collection architectures to significantly lower manual implementation efforts. Via model transformations, the corresponding source code is automatically generated from formalized models that can be created using a graphical domain-specific language. The automatically generated architecture features support for various established IIoT protocols. In a lab-scale evaluation and a unique generalized extrapolation study, the significant effort savings compared to manual programming could be quantified. In conclusion, the proposed approach can successfully mitigate the current scientific and industrial challenges to enable wide-scale access to industrial data.

In advanced industrial automation, industrial robots have been widely utilized on assembly lines in order to reduce labor dependence. However, many related layout design approaches proposed are prone to generating unsafe layouts: there generally lacks a consideration regarding robots’ heights and assembly range, which will lead to costly collisions in the operation stage. In order to address the problem, we propose a three-dimensional (3D) optimization approach to a safe layout design for an assembly line with robots. We define modeling rules for robots to judge assembly ranges. A quantitative safety indicator is employed as a trigger for 3D collision detection in order to determine the positional relationship and status of the safe assembly collaboration. The optimization goals are established for minimizing the logistical cost and layout area in the model. A combined algorithm of differential evolution and nondominated sequencing genetic II is applied, which effectively enhances the poor diversity and convergence of the mainstream optimization method when solving this model. The benchmark tests and validation proved that our approach yields excellent convergence and distribution performance. The case study verifies that the safe layout model is valid and our approach can generate a safe layout in order to optimize economics and safety.

Liquid leakage from pipelines is a critical issue in large-scale chemical process plants since it can affect the normal operation of the plant and pose unsafe and hazardous situations. Therefore, leakage detection in the early stages can prevent serious damage. Developing a vision-based inspection system by means of IR imaging can be a promising approach for accurate leakage detection. IR cameras can capture the effect of leaking drops if they have higher (or lower) temperature than their surroundings. Since the leaking drops can be observed in an IR video as a repetitive phenomenon with specific patterns, motion pattern detection methods can be utilized for leakage detection. In this paper, an approach based on the Kalman filter is proposed to track the motion of leaking drops and differentiate them from noise. The motion patterns are learned from the training data and applied to the test data to evaluate the accuracy of the method. For this purpose, a laboratory demonstrator plant is assembled to simulate the leakages from pipelines, and to generate training and test videos. The results show that the proposed method can detect the leaking drops by tracking them based on obtained motion patterns. Furthermore, the possibilities and conditions for applying the proposed method in a real industrial chemical plant are discussed at the end.

System intrusions violate the security of a system. In order to maintain it, it is necessary to decrease the chances of intrusions occurring or by detecting them as soon as they ensue in order to respond to them in a timely manner. These responses are divided in two types: passive or reactive responses. Passive responses are limited to only notification and alerting; whereas, reactive responses influence the intrusion by undoing or diminishing its consequences. Unfortunately, some reactive responses may influence the underlying system where the intrusion has occurred. This is especially a concern in the field of Industrial Automation Systems, as these systems are critical and have a well-defined set of operational requirements that must be maintained. Hence, automatic reactive responses are often not considered or are limited to human intervention. This paper addresses this issue by introducing a concept for reactive protection that integrates the automatic execution of active responses that do not influence the operation of the underlying Industrial Automation System. This concept takes into consideration architectural and security trends, as well as security and operational policies of Industrial Automation Systems. It also proposes a set of reactive actions that can be taken in the presence of intrusions in order to counteract them or diminish their effects. The feasibility and applicability of the presented concept for Industrial Automation Systems is supported by the implementation and evaluation of a prototypical Reactive Protection System.

The benefit of modular concepts in plant automation is seen ambivalent. On one hand it offers advantages, on the other hand it also sets requirements on the system structure as well as discipline of designer. The main reasons to use modularity in systems design for automation applications in industry are reusability and reduction of complexity, but up to now modular concepts are rare in plant automation. This paper analyses the reasons and proposes measures and solution concepts. An analysis of the work flow and the working results of some companies in several branches show different proposals of modularity. These different proposals in production and process engineering are integrated in one model and represent different perspectives of an integrated system.