Birhythmicity, Synchronization, and Turbulence in an Oscillatory System with Nonlocal Inertial Coupling

We consider a model where a population of diffusively coupled limit-cycle oscillators, described by the complex Ginzburg-Landau equation, interacts nonlocally via an inertial field. For sufficiently high intensity of nonlocal inertial coupling, the system exhibits birhythmicity with two oscillation modes at largely different frequencies. Stability of uniform oscillations in the birhythmic region is analyzed by means of the phase dynamics approximation. Numerical simulations show that, depending on its parameters, the system has irregular intermittent regimes with local bursts of synchronization or desynchronization.Comment: 21 pages, many figures. Paper accepted on Physica

Sex-specific-differences in cardiovascular risk in type-1-diabetes : a cross sectional study

Background: Little is known about the impact of sex-specific differences in the management of type 1 diabetes (T1DM). Thus, we evaluated the influence of gender on risk factors, complications, clinical care and adherence in patients with T1DM. Methods: In a cross-sectional study, sex-specific disparities in glycaemic control, cardiovascular risk factors, diabetic complications, concomitant medication use and adherence to treatment recommendations were evaluated in 225 consecutive patients (45.3% women) who were comparable with respect to age, diabetes duration, and body mass index. Results: Although women with T1DM had a higher total cholesterol than men, triglycerides were higher in obese men and males with HbA1c>7% than in their female counterparts. No sex differences were observed in glycaemic control and in micro- or macrovascular complications. However, the subgroup analysis showed that nephropathy was more common in obese men, hyperlipidaemic women and all hypertensive patients, whereas peripheral neuropathy was more common in hyperlipidaemic women. Retinopathy was found more frequently in women with HbA1c>7%, obese men and in both sexes with a long duration of diabetes. The multivariate analysis revealed that microvascular complications were associated with the duration of disease and BMI in both sexes and with hyperlipidaemia in males. The overall adherence to interventions according to the guidelines was higher in men than in women. This adherence was concerned particularly with co-medication in patients diagnosed with hypertension, aspirin prescription in elderly patients and the achievement of target lipid levels following the prescription of statins. Conclusions: Our data showed sex differences in lipids and overweight in patients with T1DM. Although glycaemic control and the frequency of diabetic complications were comparable between the sexes, the overall adherence to guidelines, particularly with respect to the prescription of statins and aspirin, was lower in women than in men

Digitalisation And Its Impact On Leadership Competences In Production Work

This contribution tackles the effects of digitalisation on leadership in production work. Based on the assumption that production work in the future will be characterised as more flexible, networked and digitalised, the digital transformation will lead to changes in business models, organisations and work design. Accordingly, changed and new competences are needed by executives. There is a new generation of executives who have to view business differently and use different sets of competences to lead employees. In this paper, an overview will be given about digital trends and their impact on leadership. Secondly, definitions of terms about leadership and the difference between traditional management and digital leadership will be illustrated. Furthermore, competence sets for leadership in digital transformation in production work will be outlined. These competence sets can be described both by several competences subsumed under the term - interactional competences - and by competences needed to establish a work design conducive to learning, facilitating and enabling competence development of employees. We think that these two competence bunches depict the core of future competences of executives in production

Anticipatory Inventory Management For Realizing Robust Production Processes In Engineer-To-Order Manufacturing: A Modeling Approach

At ever shorter intervals, manufacturing and processing companies of all industries are confronted with external or internal disruptions and crises that need to be managed. Consequently, a corporate focus on robust supply chains and processes is essential. At the same time, crises and their impact on supply chains cannot be predicted. To be able to act anticipatively, it is necessary to link product and production system design to take suitable measures to safeguard production at an early stage. In this context, a monetary conflict of objectives arises concerning when a company should position itself robustly and when it is sufficient to react flexibly to disruptions. The production planning and control (PPC) task inventory management is an essential lever for realizing robust order fulfilment processes. Inventory management aims to ensure that production and assembly within the company are supplied in the right quantities and without lateness. In particular, companies that operate according to the engineer-to-order strategy (ETO) face specific challenges in dimensioning stocks for materials or components - for example, due to the low level of standardization or lack of supplier diversity. This paper presents an approach for anticipatory inventory management using product portfolio characteristics. A new modeling approach for dimensioning safety stocks under the increasing influence of crises is also developed and integrated into the process

Entrainment of randomly coupled oscillator networks by a pacemaker

Entrainment by a pacemaker, representing an element with a higher frequency, is numerically investigated for several classes of random networks which consist of identical phase oscillators. We find that the entrainment frequency window of a network decreases exponentially with its depth, defined as the mean forward distance of the elements from the pacemaker. Effectively, only shallow networks can thus exhibit frequency-locking to the pacemaker. The exponential dependence is also derived analytically as an approximation for large random asymmetric networks.Comment: 4 pages, 3 figures, revtex 4, submitted to Phys. Rev. Let

Towards A Design Of A Software-Defined Manufacturing System Based On A Systematic Literature Review For Enabling A Decentralised High-Rate Electrolyser Production

Hydrogen is critical for the transition to an environmentally sound and reliable energy supply. This transition requires large capacities of performant and cost-effective electrolysers. Although performant electrolysers already exist, they cannot yet be manufactured at a high rate in series production. The project H2Giga-FRHY is researching a reference factory for large-scale production of electrolysers, developing new production and testing modules. As an essential building block of the reference factory, a research group at Fraunhofer IPA is designing and implementing a comprehensive software-defined manufacturing system (SDMS), which supports the decentralized high-rate production of electrolysers and allows for far-reaching insights regarding high-rate capability, quality, and cost of products, processes, and technologies involved. For the SDMS implementation, different enterprise architecture (EA) approaches are considered and evaluated in the scope of a structured literature review with respect to criteria arising from the project context and related research questions. In this paper, an approach to designing a software-defined manufacturing system is described, and its necessity is based on the use case-specific criteria discussed

Deriving Digital Energy Platform Archetypes for Manufacturing – A Data-Driven Clustering Approach

External factors such as climate change and the current energy crisis due to global conflicts are leading to the increasing relevance of energy consumption and energy procurement in the manufacturing industry. In addition to the growing call for sustainability, companies are increasingly struggling with rising energy costs and the reliability of the power grid, which endangers the competitiveness of companies and regions affected by high energy prices. Appropriate measures for energy-efficient and, not least, energy-flexible production are necessary. In addition to innovations and optimizations of plants and processes, digital energy platforms for the visualization, analysis, optimization, and control of energy flows are becoming essential. Over time, several digital energy platforms emerged on the market. The number and the different functionalities of the platforms make it challenging for classic manufacturing companies to keep track and select the right digital energy platform. In literature, the characteristics and functionalities of digital energy platforms have already been identified and structured. However, a classification of existing platforms into archetypes makes it easier for companies to select the platforms providing the missing functionality. To tackle this issue, we conducted an explorative and data-driven cluster analysis based on 49 existing digital energy platforms to identify digital energy platform archetypes and derive implications for research and practice. The results show five different archetypes that differ primarily in terms of functionalities on energy market integration. The identified archetypes provide a well-founded overview of the similarities and differences of digital energy platforms. Decision makers in manufacturing companies will benefit from the archetypes in future analyses as decision support in procurement processes and modifications of digital energy platforms

Force weighting approach to calculate spinal cumulative loading for ergonomic workforce planning in production

For the prevention of musculoskeletal disorders (MSD), the evaluation of manual materials handling (MMH) is important. In this context, cumulative loading can be used as an exposure index for the ergonomic assessment of workplaces. However, it was shown in previous empirical studies that most existing methods for calculating cumulative loading fail to completely capture the resulting physiological effects of working conditions on human workers. Therefore, this contribution outlines the development and validation of a novel force weighted approach to calculated spinal cumulative loading that reflects the muscular exposure. Empirical data from 36 individuals were used as the data basis for deriving and validating the calculation method. The results of the validation show a high prediction quality on the basis of the hold-out method. Hence, the method provides relevant indicators for the ergonomic assessment of MMH activities. Thus, it might be a useful tool for workforce planning in production

Autonomous Load Profile Recognition in Industrial DC-Link Using an Audio Search Algorithm

Industrial manufacturing plants, including machine tools, robots, and elevators, perform dynamic acceleration and braking processes. Recuperative braking results in an increased voltage in the machines' direct current (DC) links. In the case of a diode rectifier, a braking resistor turns the surplus of energy into lost heat. In contrast, active rectifiers can feed the braking energy back to the AC grid, though they are more expensive than diode rectifiers. DC link-coupled energy storage systems are one possible solution to downsize the supply infrastructure by peak shaving and to harvest braking energy. However, their control heavily depends on the applied load profiles that are not known in advance. Especially for retrofitted energy storage systems without connection to the machine control unit, load profile recognition imposes a major challenge. A self-tuning framework represents a suitable solution by covering system identification, proof of stability, control design, load profile recognition, and forecasting at the same time. This paper introduces autonomous load profile recognition in industrial DC links using an audio search algorithm. The method generates fingerprints for each measured load profile and saves them in a database. The control of the energy storage system then has to be adapted within a critical time range according to the identified load profile and constraints given by the energy storage system. Three different load profiles in four case studies validate the methodology