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Seit dem Schuljahr 2020/21 gilt in Nordrhein-Westfalen ein neuer Kernlehrplan für die Realschule, Gesamtschule und Sekundarschule. Dafür haben wir gemeinsam mit Fachkräften aus dem Bundesland die #-Schulbuchreihen entwickelt.
Mit #Politik Wirtschaft – Nordrhein-Westfalen bieten wir Ihnen innovative und aktuelle Produkte für einen modernen Politik- und Wirtschaftsunterricht. Neben dem neuen Lehrplan sind die Vorgaben des Medienkompetenzrahmens und die besonderen Herausforderungen heterogener Lerngruppen berücksichtigt.
Wir bieten Ihnen einen problemorientierten und schülernahen Unterricht. Die Rubrik ”Gemeinsam aktiv“ ermöglicht ein selbstgesteuertes Lernen. Die Schülerinnen und Schüler erarbeiten sich projektartig größere Einheiten eines Kapitels. Sie können Ihren Unterricht einfach und schnell besonders vielfältig und spannend gestalten.
Durch Fallbeispiele werden die Schülerinnen und Schüler direkt angesprochen. Eine kreative Vielfalt aus Bild-, Grafik- und Textmaterial, aktivierende Aufgaben, Methoden-und Grundwissenseiten und ein Kompetenzcheck zum Abschluss der Großkapitel vervollständigen das Angebot.
Zu jeder Unterrichtseinheit wird passgenau zum Schulbuch unterschiedliches Differenzierungsmaterial (Texte in einfacher Sprache, Vorstrukturierung von Aufgaben u.v.m.) erstellt. Dieses steht Ihnen in unserem digitalen Lehrermaterial click & teach zur Verfügung und kann von Ihnen nach individuellen Bedürfnissen für einzelne digitale Schulbücher click & study freigeschaltet werden.
#WAT
(2022)
#WAT 1
(2022)
#WAT 5/6
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#Wirtschaft – Niedersachsen
(2020)
'Tools' in public management
(2022)
Tools are methods or procedures, and thus operational patterns of action, applied in public administrations to solve standard problems. It is also possible to consider them as structured communication according to professional standards aiming at complexity reduction. Regularly, tools in management stem on a deductive-synoptic rationale offering a seemingly ‘objective’ decision basis. They have a strong formative influence on the organization, regularly also beyond the intended effects. The prominence of tools is sometimes confused with management as such, e.g. introducing tools is mistaken as equivalent to managing for a particular purpose. However, tools have to be closely and carefully managed regarding the objectives and purposes they should serve.
As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can “forget” their present specialization.
Technological advancements are giving rise to the fourth industrial revolution - Industry 4.0 -characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrialproduct-service systems. The purpose of this paper is to propose a theory-based knowledgedynamics model in the smart grid scenario that would provide a holistic view on the knowledge-based interactions among smart objects, humans, and other actors as an underlyingmechanism of value co-creation in Industry 4.0. A multi-loop and three-layer - physical, virtual, and interface - model of knowledge dynamics is developedby building on the concept of ba - an enabling space for interactions and theemergence of knowledge. The model depicts how big data analytics are just one component inunlocking the value of big data, whereas the tacit engagement of humans-in-the-loop - theirsense-making and decision-making - is needed for insights to be evoked fromanalytics reports and customer needs to be met.
Faced with the increasing needs of companies, optimal dimensioning of IT hardware is becoming challenging for decision makers. In terms of analytical infrastructures, a highly evolutionary environment causes volatile, time dependent workloads in its components, and intelligent, flexible task distribution between local systems and cloud services is attractive. With the aim of developing a flexible and efficient design for analytical infrastructures, this paper proposes a flexible architecture model, which allocates tasks following a machine-specific decision heuristic. A simulation benchmarks this system with existing strategies and identifies the new decision maxim as superior in a first scenario-based simulation.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
This work analyzes the saving and consumption behavior of agents faced with the possibility of unemployment in a dynamic and stochastic life cycle model. The intertemporal optimization is based on Dynamic Programming with a backward recursion algorithm. The implemented uncertainty is not based on income shocks as it is done in traditional life cycle models but uses Markov probabilities where the probability for the next employment status of the agent depends on the current status. The utility function used is a CRRA function (constant relative risk aversion), combined with a CES function (constant elasticity of substitution) and has several consumption goods, a subsistence level, money and a bequest function.
Cyber-physical systems (CPS) have shaped the discussion about Industry 4.0 (I4.0) for some time. To ensure the competitiveness of manufacturing enterprises the vision for the future figures out cyber-physical production systems (CPPS) as a core component of a modern factory. Adaptability and coping with complexity are (among others) potentials of this new generation of production management. The successful transformation of this theoretical construct into practical implementation can only take place with regard to the conditions characterizing the context of a factory. The subject of this contribution is a concept that takes up the brownfield character and describes a solution for extending existing (legacy) systems with CPS capabilities.
We use the prolonged Greek crisis as a case study to understand how a lasting economic shock affects the innovation strategies of firms in economies with moderate innovation activities. Adopting the 3-stage CDM model, we explore the link between R&D, innovation, and productivity for different size groups of Greek manufacturing firms during the prolonged crisis. At the first stage, we find that the continuation of the crisis is harmful for the R&D engagement of smaller firms while it increased the willingness for R&D activities among the larger ones. At the second stage, among smaller firms the knowledge production remains unaffected by R&D investments, while among larger firms the R&D decision is positively correlated with the probability of producing innovation, albeit the relationship is weakened as the crisis continues. At the third stage, innovation output benefits only larger firms in terms of labor productivity, while the innovation-productivity nexus is insignificant for smaller firms during the lasting crisis.
The large literature that aims to find evidence of climate migration delivers mixed findings. This meta-regression analysis i) summarizes direct links between adverse climatic events and migration, ii) maps patterns of climate migration, and iii) explains the variation in outcomes. Using a set of limited dependent variable models, we meta-analyze thus-far the most comprehensive sample of 3,625 estimates from 116 original studies and produce novel insights on climate migration. We find that extremely high temperatures and drying conditions increase migration. We do not find a significant effect of sudden-onset events. Climate migration is most likely to emerge due to contemporaneous events, to originate in rural areas and to take place in middle-income countries, internally, to cities. The likelihood to become trapped in affected areas is higher for women and in low-income countries, particularly in Africa. We uniquely quantify how pitfalls typical for the broader empirical climate impact literature affect climate migration findings. We also find evidence of different publication biases.
In a multi-source, lagged design field study of 197 leader-follower dyads, we test a model that predicts positive interactive effects of visionary and empowering leadership on follower performance. Based on the paradox perspective, we argue that visionary and empowering leadership are synergistic in that their combination enables leaders to address a key paradox inherent to leader behavior identified by Waldman and Bowen (2016): Maintaining control while simultaneously letting go of control. We argue that visionary leadership addresses the former and empowering leadership addresses the latter pole of this pair of opposites. Hence, in line with paradox thinking, we posit that leaders will engender more positive effects on follower performance when they enact visionary and empowering leadership behaviors simultaneously and adopt a "both-and" approach, rather than focus on one of these behaviors without the other. Our results support our hypothesized interactive effect of visionary and empowering leadership on goal clarity, as well as a conditional indirect effect such that goal clarity mediates the interactive effect of visionary and empowering leadership on individual follower performance.
Purpose - The purpose of this study is to analyze whether negotiators stick to one single negotiation style or whether their styles vary during the negotiation process. The paper seeks to identify different combinations of phase-specific negotiation styles and investigates the relationship between these combinations and negotiation performance and satisfaction. Design/methodology/approach - The study is based on a large online negotiation simulation that allows a phase-specific analysis of negotiation styles via an elaborate coding scheme. Findings - The findings reveal that negotiators generally do not limit themselves to a single negotiation style. Instead, they vary their style in the course of different negotiation phases. The authors distinguish between five distinct phase-specific negotiation style patterns that differ with regard to their impact on negotiation performance but not negotiation satisfaction. Practical implications - Negotiation practitioners get to know different phase-specific negotiation style patterns and get insights into which pattern is the most promising for negotiation performance. As a result, they can acquire this phase-specific negotiation style pattern to enhance their performance. Originality/value - The paper contributes to existing negotiation style literature, because it is the first to analyze negotiation styles from a phase-specific point of view.