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Gas insulated systems, such as gas insulated lines (GIL), use insulating gas, mostly sulfur hexalfluoride (SF₆), to enable a higher dielectric strength compared to e.g. air. However, under high voltage direct current conditions, charge accumulation and electric field stress may occur, which may lead to partial discharge or system failure. Therefore, numerical simulations are used to design the system and determine the electric field and charge distribution. Although the gas conduction shows a more complex current–voltage characteristic compared to solid insulation, the electric conductivity of the SF₆ gas is set as constant in most works. The purpose of this study is to investigate different approaches to address the conduction in the gas properly for numerical simulations.

In this work, two approaches are investigated to address the conduction in the insulating gas and are compared to each other. One method is an ion-drift-diffusion model, where the conduction in the gas is described by the ion motion in the SF₆ gas. However, this method is computationally expensive. Alternatively, a less complex approach is an electro-thermal model with the application of an electric conductivity model for the SF₆ gas. Measurements show that the electric conductivity in the SF₆ gas has a nonlinear dependency on temperature, electric field and gas pressure. From these measurements, an electric conductivity model was developed. Both methods are compared by simulation results, where different parameters and conditions are considered, to investigate the potential of the electric conductivity model as a computationally less expensive alternative.

The simulation results of both simulation approaches show similar results, proving the electric conductivity for the SF₆ gas as a valid alternative. Using the electro-thermal model approach with the application of the electric conductivity model enables a solution time up to six times faster compared to the ion-drift-diffusion model. The application of the model allows to examine the influence of different parameters such as temperature and gas pressure on the electric field distribution in the GIL, whereas the ion-drift-diffusion model enables to investigate the distribution of homo- and heteropolar charges in the insulation gas.

This work presents numerical simulation models for high voltage direct current GIL, where the conduction in the SF₆ gas is described more precisely compared to a definition of a constant electric conductivity value for the insulation gas. The electric conductivity model for the SF₆ gas allows for consideration of the current–voltage characteristics of the gas, is computationally less expensive compared to an ion-drift diffusion model and needs considerably less solution time.

The purpose of this paper is to investigate the research landscape of LSS 4.0 papers published in two well-known repositories, Scopus and Web of Science (WoS), in terms of publication trends, article distribution by author, journal, affiliations and country, and article clustering based on keywords, authors and countries. In addition, a literature review was carried out to build a conceptual framework of integrated Lean Six Sigma and Industry 4.0 (LSS 4.0) that encompasses operational, sustainability and human factors or ergonomics aspects.

The literature review of integrated Lean Six Sigma and I4.0 publications published in Scopus and WoS databases in the current decade was conducted for the present study. This study categorizes LSS, I4.0 and related research articles based on publication patterns, journals, authors and affiliations, country and continental-wise distribution and clustering the articles based on keywords and authors from the Scopus and WoS databases from 2011 to 2022 using the search strings “Lean”, “Six Sigma”, “Lean Six Sigma” and “Industry 4.0” in the Title, Abstract and Keywords using Biblioshiny, VOS viewer and Microsoft Excel.

In the recent three years, from 2020 to 2022, LSS 4.0 has been substantially increasing and is seen as an emerging and trending area. This research identifies the most influential authors, most relevant affiliations, most prolific countries and most productive journals and clusters based on keywords, authors and countries. Further, a conceptual framework was developed that includes the impact of operational, sustainability and ergonomic or human factors in LSS 4.0.

This article assists in comprehending the trends and patterns of LSS 4.0. Further, the conceptual framework helps professionals and researchers understand the significance and impact of integrating LSS and Industry 4.0 in the aspects of human factors/ergonomic, sustainability and operations. Also, the research induce professionals to incorporate all these factors while designing and implementing LSS 4.0 in their organization.

This conceptual framework and bibliometric analysis would aid in identifying potential areas of research and providing future directions in the domain of LSS 4.0. It will be beneficial for academicians, professionals and researchers who are planning to apply and integrate techniques of LSS and technologies of I4.0 in their organizations and research.

This article proposes a novel hybrid simulation model for understanding the complex tobacco use behavior.

The model is developed by embedding the concept of the multistage learning-based fuzzy cognitive map (FCM) into the agent-based model (ABM) in order to benefit from advantageous of each methodology. The ABM is used to represent individual level behaviors while the FCM is used as a decision support mechanism for individuals. In this study, socio-demographic characteristics of individuals, tobacco control policies, and social network effect are taken into account to reflect the current tobacco use system of Turkey. The effects of plain package and COVID-19 on tobacco use behaviors of individuals are also searched under different scenarios.

The findings indicate that the proposed model provides promising results for representing the mental models of agents. Besides, the scenario analyses help to observe the possible reactions of people to new conditions according to characteristics.

The proposed method combined ABM and FCM with a multi-stage learning phases for modeling a complex and dynamic social problem as close as real life. It is expected to contribute for both ABM and tobacco use literature.

The purpose of the study is to propose a framework for fleet management and make suitable distribution solution choices in the food industry.

This study reviews the literature to examine food distribution criteria. These criteria are used in the analytic hierarchy process (AHP) assessment and combined with discrete events simulation in a structured framework, which is validated through an empirical study.

The empirical case results demonstrate that both the AHP and discrete events simulation converge toward the same solution in most cases.

This study contributes to the literature on distribution management and develops a framework that can both guide future research and aid logistics practitioners in analysing distribution decision-making systems in dynamic environments.

The purpose of this paper is to examine the impact of affiliate marketing strategies as a tool for increasing customers' engagement and vulnerability over financial services. This is attempted by examining the connection between affiliate marketing factors and customers' brand engagement and vulnerability metrics.

The authors developed a three-staged methodological context, based on the 7 most known centralized payment network (CPN) firms' website analytical data, which begins with linear regression analysis, followed by hybrid modeling (agent-based and dynamic models), so as to simulate brand engagement and vulnerability factors' variation in a 180-day period. The deployed context ends by applying the cognitive modeling method of producing heatmaps and facial analysis of CPN websites to the selected 47 vulnerable website customers, for gathering more insights into their brand engagement.

Throughout the simulation results of the study, it becomes clear that a higher number of backlinks and referral domains tend to increase CPN firms' brand-engaged and vulnerable customers.

From the simulation modeling process, the implication for backlinks and referral domains as factors that enhance website customers' brand engagement and vulnerability has been highlighted. A higher number of brand-engaged website customers could mean that vulnerable categories of customers would be impacted by CPNs' affiliate marketing. Improving those customers' knowledge of the financial services utility is of utmost importance.

The outcomes of the research indicate that online banking service providers can increase their customers' engagement with their brands by adopting affiliate marketing techniques. To avoid the increase in customers' vulnerability, marketers should aim to apply affiliate marketing strategies to domains relevant to the provided financial services.

The paper's outcomes provide a new approach to the literature, where the website customer's brand engagement comes out as a valuable metric for estimating online banking sector customers' vulnerability.

Efforts to implement supplier selection and order allocation (SSOA) approaches in small and medium-sized enterprises (SMEs) are quite restricted due to the lack of affordable and simple-to-use strategies. Although there is a huge amount of literature on SSOA techniques, very few studies have attempted to address the issues faced by SMEs and develop strategies from their point of view. The purpose of this study is to provide an effective, practical, and time-tested integrated SSOA framework for evaluating the performance of suppliers and allocating orders to them that can improve the efficiency and competitiveness of SMEs.

This study was conducted in two stages. First, an integrated supplier selection approach was designed, which consists of the analytic hierarchy process and newly developed measurement alternatives and ranking using compromise solution to evaluate supplier performance and rank them. Second, the Wagner-Whitin algorithm is used to determine optimal order quantities and optimize inventory carrying and ordering costs. The joint impact of quantity discounts is also evaluated at the end.

Insights derived from the case study proved that the proposed approach is capable of assisting purchase managers in the SSOA decision-making process. In addition, this case study resulted in 10.89% total cost savings and fewer stock-out situations.

Criteria selected in this study are based on the advice of the managers in the selected manufacturing organizations. So the methods applied are limited to manufacturing SMEs. There were some aspects of the supplier selection process that this study could not explore. The development of an effective, reliable supplier selection procedure is a continuous process and it is indeed certainly possible that there are other aspects of supplier selection that are more crucial but are not considered in the proposed approach.

Purchase managers working in SMEs will be the primary beneficiaries of the developed approach. The suggested integrated approach can make a strategic difference in the working of SMEs.

A practical SSOA framework is developed for professionals working in SMEs. This approach will help SMEs to manage their operations effectively.

Simulations exist for the prediction of the behaviour of building structural systems under fire, including two-way coupled fire-structure interaction. However, these simulations do not include detailed models of the connections, whereas these connections may impact the overall behaviour of the structure. Therefore, this paper proposes a two-scale method to include screw connections.

The two-scale method consists of (a) a global-scale model that models the overall structural system and (b) a small-scale model to describe a screw connection. Components in the global-scale model are connected by a spring element instead of a modelled screw, and the stiffness of this spring element is predicted by the small-scale model, updated at each load step. For computational efficiency, the small-scale model uses a proprietary technique to model the behaviour of the threads, verified by simulations that model the complete thread geometry, and validated by existing pull-out experiments. For four screw failure modes, load-deformation behaviour and failure predictions of the two-scale method are verified by a detailed system model. Additionally, the two-scale method is validated for a combined load case by existing experiments, and demonstrated for different temperatures. Finally, the two-scale method is illustrated as part of a two-way coupled fire-structure simulation.

It was shown that proprietary ”threaded connection interaction” can predict thread relevant failure modes, i.e. thread failure, shank tension failure, and pull-out. For bearing, shear, tension, and pull-out failure, load-deformation behaviour and failure predictions of the two-scale method correspond with the detailed system model and Eurocode predictions. Related to combined load cases, for a variety of experiments a good correlation has been found between experimental and simulation results, however, pull-out simulations were shown to be inconsistent.

More research is needed before the two-scale method can be used under all conditions. This relates to the failure criteria for pull-out, combined load cases, and temperature loads.

The two-scale method bridges the existing very detailed small-scale screw models with present global-scale structural models, that in the best case only use springs. It shows to be insightful, for it contains a functional separation of scales, revealing their relationships, and it is computationally efficient as it allows for distributed computing. Furthermore, local small-scale non-convergence (e.g. a screw failing) can be handled without convergence problems in the global-scale structural model.

The purpose of this study is to investigate the integrated application of business process modeling and notation (BPMN) and value stream mapping (VSM) to improve knowledge work performance and productivity in police services. In order to explore the application of the hybrid BPMN-VSM approach in police services, this study uses the department of digital crime investigation (DCI) in one Norwegian police district as a case study.

Service process identification was the next step after selecting an appropriate organizational unit for the case study. BPMN-VSM-based current state mapping, including time and waste analyses, was used to determine cycle and lead time and identify value-adding and nonvalue-adding activities. Subsequently, improvement opportunities were identified, and the current state process was re-designed and constructed through future state mapping.

The study results indicate a 44.4% and 83.0% reduction in process cycle and lead time, respectively. This promising result suggests that the hybrid BPMN-VSM approach can support the visualization of bottlenecks and possible causes of increased lead times, followed by the systematic identification and proposals of avenues for future improvement and innovation to remedy the discovered inefficiencies in a complex knowledge-work environment.

This study focused on one department in a Norwegian police district. However, the experience gained can support researchers and practitioners in understanding lean implementation through an integrated BPMN and VSM model, offering a unique insight into the ability to investigate complex systems.

Complex knowledge work processes generally characterize police services due to a high number of activities, resources and stakeholder involvement. Implementing lean thinking in this context is significantly challenging, and the literature on this topic is limited. This study addresses the applicability of the hybrid BPMN-VSM approach in police services with an original public sector case study in Norway.

Short food supply chains have the potential to facilitate the transition to more sustainable food systems. Related distribution processes, however, can be challenging for smallholder and family farmers. To extend the market reach of farmers without the need for extensive investments, crowd logistics (CL) can be used. The purpose of this paper is to explore the benefits and trade-offs of implementing CL platforms in short food supply chains (SFSCs).

A decision support system (DSS) based on agent-based and discrete event simulation (DES) modelling is developed, which closely approximates the behaviour of customers and distribution processes at outlets. Different scenarios are explored to evaluate the potential of CL in rural and urban settings using the example of regions from Bavaria, Germany.

Results show that CL can be used to increase the reach of farmers in SFSCs at the cost of minor food quality losses. Moreover, a difference between urban and rural settings is noted: An urban scenario requires less investment in the driver base, whereas the rural scenario shows a higher potential to increase market reach.

Platform-based food delivery services are still mostly unexplored in the context of SFSCs. This research shows that platform services such as CL can be used to support local agriculture and facilitate the distribution of perishable food items, introducing a simulation-based DSS and providing detailed results on various application settings; this research serves as a steppingstone to facilitate successful real-world implementations and encourage further research.

This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image.

The paper proposes an approximate circuit for 4:2 compressor, which shows a significant amount of improvement in performance metrics than that of the existing designs. This paper also reports a hybrid architecture for the Dadda multiplier, which incorporates proposed 4:2 compressor circuit as a basic building block.

Hybrid Dadda multiplier architecture is used in a median filter for image de-noising application and achieved 20% more PSNR than that of the best available designs.

The proposed 4:2 compressor improves the error metrics of a Hybrid Dadda multiplier.