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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.

Magneto-quasi-static fields emanated by inductive charging systems can be potentially harmful to the human body. Recent projects, such as TALAKO and MILAS, use the technique of wireless power transfer (WPT) to charge batteries of electrically powered vehicles. To ensure the safety of passengers, the exposing magnetic flux density needs to be measured in situ and compared to reference limit values. However, in the design phase of these systems, numerical simulations of the emanated magnetic flux density are inevitable. This study aims to present a tool along with a workflow, based on the Scaled-Frequency Finite Difference Time-Domain and Co-Simulation Scalar Potential Finite Difference schemes, to determine body-internal magnetic flux densities, electric field strengths and induced voltages into cardiac pacemakers. The simulations should be time efficient, with lower computational costs and minimal human workload.

The numerical assessment of the human exposure to magneto-quasi-static fields is computationally expensive, especially when considering high-resolution discretization models of vehicles and WPT systems. Incorporating human body models into the simulation further enhances the number of mesh cells by multiple millions. Hence, the number of simulations including all components and human models needs to be limited while efficient numerical schemes need to be applied.

This work presents and compares four exposure scenarios using the presented numerical methods. By efficiently combining numerical methods, the simulation time can be reduced by a factor of 3.5 and the required storage space by almost a factor of 4.

This work presents and discusses an efficient way to determine the exposure of human beings in the vicinity of wireless power transfer systems that saves computer simulation resources and human workload.

The authors tested the hypothesis that having a high availability of different types of alcoholic drinks (beer, wine or spirits) as compared to being confined to one type only associate with a higher alcohol intake and worse next-day hangover.

This randomized controlled pilot trial took place over one evening and included 35 individuals randomized into two groups. Individuals in the mixed-type group could consume any type of alcoholic drink (beer, wine, cocktails), whereas individuals in the single-type group were to adhere to one type of own choice. Information on number of drinks was obtained continuously during the trial. Hangover symptoms were reported by participants using the Acute Hangover Severity Scale (AHSS) at 8:00, 12:00 and 16:30 the following day. Median regression was used to assess number of drinks and maximum AHSS in two groups.

Participants in the mixed-type group consumed significantly more drinks compared to the single-type group (10.9 vs 5.7, p < 0.001). The maximal AHSS score the day after drinking was higher in the mixed-type group as compared to the single-type group (median 4.3 vs 2.6, p < 0.0001).

This study suggests that having a variety of alcoholic drinks available associates to a higher alcohol intake and more severe hangover as compared to having to adhere to one type only. Larger studies are warranted to validate findings.

The effectiveness of a message and its attributes have become important for digital media. This study aims to investigate how different elements of a website including both argument-oriented and emotional stimuli based on the elaboration likelihood model (ELM) can affect the issue involvement and change the attitude of the website visitors of a healthcare service provider.

The Ministry of Health and Education (MOHME) website was selected to explore how its content and design can persuade visitors. An online survey was conducted on 355 adults engaging in health protection behaviors during the COVID-19 pandemic.

Structural equation modeling (SEM) analysis showed that one design element, i.e. website navigation and one social cue, i.e. social connectedness, have positive impact on issue involvement, while social presence and website satisfaction have a negative effect on issue involvement because of the random fluctuation suppressor effect. In addition, prior knowledge significantly influenced the issue's involvement. Further, website satisfaction has impacted attitudes directly. There was no significant relationship between argument quality and issue involvement.

Previous works have studied health-related behaviors in offline contexts; however, the scholars have not focused on the individuals' persuasion using ELM regarding the healthcare services provided in online communities. The results of the current study have theoretical and practical implications for scholars, website designers and policymakers.

This paper aims to examine and discuss the factors affecting consumers' purchase intentions for ice cream in India.

This study is based on a primary survey of 340 respondents from India, collected through a well-structured questionnaire. The data has been analysed using partial least square-based structural equation modelling (PLS-SEM). The theory of planned behaviour (TBP) has been used to identify the determinants of the purchase intention of ice cream.

The measurement model shows that the constructs of the model have adequate convergent and discriminant validity. The findings of the structural model show the positive and significant impact of subjective norms, perceived behavioural control, and trustworthiness on attitudes and purchase intentions for ice cream. In addition, estimates also revealed that attitude has significant implications on purchase intention.

The study provides insight into the factors that influence ice cream purchase intentions in India. Corporate strategy makers and advertising managers who work for ice cream brands can look at the study to figure out how to market their products to get more customers.

The novelty and value of this study lie in choosing an unexplored product (ice cream) whose demand is continuously increasing. Perhaps this is the first study in an emerging economy like India to consider an extended version of the TPB by including health consciousness and trustworthiness to establish the relationship between ice cream purchase intention and health consciousness.