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The purpose of this paper is to apply experimentalist framework to understand self-optimizing efforts within German manufacturing multinationals. Benefits and characteristic obstacles to diffusion are discussed. Mechanisms for combatting obstacles are outlined.

Qualitative case studies, interview-based research, processual and reflexive action theory are applied to the governance of manufacturing-based multinational enterprises.

Uncertainty is an ineradicable element in multinational companies (MNC) FDI operations. Self-optimizing systems, many with an experimentalist character, are a pervasive form of response to this uncertainty. Obstacles to the diffusion and effective operation of self-optimization are chronic and, indeed, endogenously generated. But as a result, so are superordinate efforts to undercut the continuous emergence of obstacles. MNC development is, thus, characterized by continuous self-recomposition.

Implication is that managers and management theorists should focus as much on the management of dynamic process and learning that results in the recomposition of institutional rules as they do on the constraining and enabling effects of those rules.

Superordinate mechanisms for the disruption of incipient insulation and exclusion are crucial for the implementation of successful experimentalist (learning) systems.

Transparency, stakeholder involvement in MNC governance processes has positive implications for learning, innovation and competitiveness.

This paper presents the application of experimentalist learning theory to MNC global governance.

Vertical handover has been grown rapidly due to the mobility model improvements. These improvements are limited to certain circumstances and do not provide the support in the generic mobility, but offering vertical handover management in HetNets is very crucial and challenging. Therefore, this paper presents a vertical handoff management method using the effective network identification method.

This paper presents a vertical handoff management method using the effective network identification method. The handover triggering schemes are initially modeled to find the suitable position for starting handover using computed coverage area of the WLAN access point or cellular base station. Consequently, inappropriate networks are removed to determine the optimal network for performing the handover process. Accordingly, the network identification approach is introduced based on an adaptive particle-based Sailfish optimizer (APBSO). The APBSO is newly designed by incorporating self-adaptive particle swarm optimization (APSO) in Sailfish optimizer (SFO) and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is utilized for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like end to end (E2E) delay, jitter, signal-to-interference-plus-noise ratio (SINR), packet loss, handover probability (HOP) are considered to find the best network.

The developed APBSO-based deep stacked autoencoder outperformed than other methods with a minimal delay of 11.37 ms, minimal HOP of 0.312, maximal stay time of 7.793 s and maximal throughput of 12.726 Mbps, respectively.

The network identification approach is introduced based on an APBSO. The APBSO is newly designed by incorporating self-APSO in SFO and hence, modifying the update rule of the APBSO algorithm based on the location of the solutions in the past iterations. Also, the proposed APBSO is used for training deep-stacked autoencoder to choose the optimal weights. Several parameters, like E2E delay, jitter, SINR, packet loss and HOP are considered to find the best network. The developed APBSO-based deep stacked autoencoder outperformed than other methods with minimal delay minimal HOP, maximal stay time and maximal throughput.

Nowadays there are many digital tools and mediatised ways for self-tracking for the sake of gaining self-knowledge through numbers. In his recent book ‘Resonance’, Hartmut Rosa suggests that artefacts can indeed resonate with people (Rosa, 2016, p. 381ff.) by affecting emotion, intrinsic interests and self-efficacy expectations. In contrast, Rosa characterises self-tracking as an attempt to measure the resource potential of individuals, confounding it with the good life itself (Rosa, 2016, p. 47). That is why we want to challenge Rosa’s concept of a good life and enhance the assertion of individual and social practices that can generate resonance.

With several case studies, we want to study empirically how people ‘resonate’ (or not) with and in self-tracking practices and to which degree Rosa’s hypothesis is verifiable or not. By empirically contrasting the quantifying practices and metric culture of self-tracking with the recently emerging sociological field of ‘world relationships’ and ‘resonance’, new insights on the embedding of the quantified with the qualified self will be gained.

This paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic diagnosis of austenitic stainless steel weld defects. These are insufficient feature extraction and subjective dependence of diagnosis model parameters.

To express the richness of the one-dimensional (1D) signal information, the 1D ultrasonic testing signal was derived to the two-dimensional (2D) time-frequency domain. Multi-scale depthwise separable convolution was also designed to optimize the MobileNetV3 network to obtain deep convolution feature information under different receptive fields. At the same time, the time/frequent-domain feature extraction of the defect signals was carried out based on statistical analysis. The defect sensitive features were screened out through visual analysis, and the defect feature set was constructed by cascading fusion with deep convolution feature information. To improve the adaptability and generalization of the diagnostic model, the authors designed and carried out research on the hyperparameter self-optimization of the diagnostic model based on the sparrow search strategy and constructed the optimal hyperparameter combination of the model. Finally, the performance of the ultrasonic diagnosis of stainless steel weld defects was improved comprehensively through the multi-domain feature characterization model of the defect data and diagnosis optimization model.

The experimental results show that the diagnostic accuracy of the lightweight diagnosis model constructed in this paper can reach 96.55% for the five types of stainless steel weld defects, including cracks, porosity, inclusion, lack of fusion and incomplete penetration. These can meet the needs of practical engineering applications.

This method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defect diagnosis technology.

This paper introduces a new mathematical model for analyzing the economic benefits of incorporating the fourth party logistics (4PL), which is a contractor (i.e. agent) for the supply chain coordination and construction based on the division of community and the outsourcing development. Based on the physical theory and the wave-particle duality, a supply chain is the special organization whose characteristic has wave-particle duality. The mathematical model enriches the connotation of 4PL and it broadens the thought for 4PL development. Secondly, the proposed mathematical model predicated on transaction costs, is supported by Transaction Cost Theory (TCT) and acts as the theoretical analysis tool of 4PL for coordinating 3-party generic supply chain. Through the model, some trendy conclusions can be drawn to provide theoretical support for 4PL’s practices. Finally, a case illustrates our conclusions.

The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency. An accurate monitoring and controlling of the process can improve production yield and efficiency. The purpose of this paper is to propose a real-time optimization (RTO) using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor.

The integration of the esterification process optimization using self-optimization (SO) was developed with classification process was combined with necessary condition optimum (NCO) as gradient adaptive selection, supported with laboratory scaled medium wavelength infrared (mid-IR) sensors, and measured the proposed optimization system indicator in the batch process. Business Process Modeling and Notation (BPMN 2.0) was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase. Next, Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine (SVM) classification and Arduino microcontroller for implementation.

This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent, lower error measurement with percentage error 1.11 percent, reduced the process duration up to 22 minutes, with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210°C which was more efficient, as it consumed less energy.

In this research the authors just have an experiment for the esterification process using glycerol, but as a development concept of RTO, it would be possible to apply for another chemical reaction or system.

This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties. As the methodology is generic, it can be applied to different optimization problems for a batch system in chemical industries.

The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data, applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.

Presents work carried out in the wider context of the IST Adrenalin project whose aim is to facilitate formation and lifecycle management of networked enterprises utilising concepts from two key information research areas. The approach described places heavy emphasis on the notion of mobile agent technologies and their adaptation for achieving the IT realisation of the above theoretical background. It covers specification, design and conceptual realisation of how information supply chains and routes can be organised and navigated across networked enterprise activities within the context of a branch independent model. Builds on the distribute and integrate concept as well as on the fractal idea by supporting self‐similarity, self‐organisation, self‐optimisation and dynamic organisational behavior. The harmonised combination of the concepts formulated in the Adrenalin theoretical framework and their IT realisation are employed within the context of a real‐world case study in an industrial ERP system.

Laser systems are becoming more and more a commodity in many fields of application and this is driving a strong trend towards increasingly efficient production technologies and miniaturized products. A central aspect of laser production is the assembly where the majority of cost is due to manual operations. Resistance soldering of optical components is an upcoming technology for automated assembly, especially for high power laser applications. This technology transfers design and packaging concepts from the electronics industry into photonics. The purpose of this paper is to present recent developments in the field of this soldering technology and to show the first experimental results in combination with robot‐based handling.

Soldering results were examined by resistance measurements, shear testing and optical analysis of the melting zone.

The experiments conducted proved the viability of robot‐based automated resistance soldering of optical components. Analyses of current and voltage profiles gave valuable information for process control possibilities without additional sensors and validated the theoretical considerations of the temperature dependency of material properties. Shear testing underlined the necessity for accurate mechanical contact arrangement and opened fields for further process developments.

Robot‐based resistance soldering of optical components represents a promising joining technology for the automated assembly of laser systems. Existing solutions with mechanical fixtures hamper the miniaturization of optical systems and concurrently increase material and production costs. Manual and semi‐automated gluing of components is an alternative process with significant disadvantages regarding the robustness of the process and the resulting joint connection.