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This study deals with the core design of a PM vernier machine considering modulation flux effects, and the comparative investigation on volume and performance characteristics of the vernier over conventional PM machines are addressed. To these ends, for a PM vernier machine in operation at the base-speed, the flux density equations for teeth and yokes considering the flux modulation effects are derived, where the air gap harmonic permeance function is used. Using the derived equations, a PM vernier motor with specified yoke flux densities is designed. To identify the predicted flux yoke densities, the flux distribution and iron losses in core parts are analyzed through time-step finite element (FE) simulations. Through Fourier series expansion of the air gap flux waves obtained by FE analysis at several specified times, the harmonic components constituting the flux waves are investigated and their speeds are also evaluated in numerical ways. Finally, to estimate the competitiveness of vernier machines versus conventional machines, the designed PM vernier motor is compared against two different conventional PM motors designed through the same design procedures in various aspects such as volume, torque capacity, efficiency, and power factor, in which, in particular, the core losses are included in efficiency calculation.

As smart cities become a global topic, interest in smart mobility, the core of smart cities, is also growing. The technology that comes closest to general users is “autonomous driving”. In particular, the successful market entry and establishment of some private companies proved that “autonomous driving” is not technology of the future but imminent reality. However, safety in autonomous vehicles that rely on sensors instead of the driver’s five senses has been the focus of attention from the beginning and continues to be so. In this study, we attempted to counter this interest. Based on the actual data of thirty traffic accidents, assuming the AEBS (Autonomous Emergency Braking System) was installed to assist the driver in safe driving, it was reinterpreted through simulation to see what changes occurred in the accident. In the computer program, PC-Crash, the results were first analyzed through simulation using Euro NCAP (New Car Assessment Program)’s AEBS test standards. Subsequently, the other variables in the AEBS were controlled and the accident was reinterpreted by changing only the angle of the radar detection sensor. As a result, it was confirmed that a total of 27 accidents out of thirty accidents could have been prevented with the AEBS. In addition, it proved that the crash avoidance rate of vehicles gradually increased as the radar angle increased.

The leaves of Aster yomena (Kitam.) Honda have long been used as a traditional herb for treating disorders including coughs, asthma, and insect bites. According to recent studies, A. yomena leaf extracts have several pharmacological properties, including anti-inflammatory, antioxidant, and anti-asthmatic activities. However, little information is available regarding their anti-obesity effect. In this study, we investigated the inhibitory effect of the ethanol extracts of A. yomena leaves (EEAY) on adipocyte differentiation and adipogenesis using 3T3-L1 preadipocytes. When 3T3-L1 preadipocytes were treated with various concentrations of EEAY (ranging from non-toxic), the number of lipid droplets, lipid content, and triglyceride production, the typical characteristics of adipocytes, were suppressed in a concentration-dependent manner. During this process, EEAY significantly reduced the expression of adipogenic transcription factors, including peroxisome proliferator-activated receptor-γ, CCAAT/enhancer-binding protein α and β, and sterol regulatory element-binding protein-1c. In addition, EEAY was also found to potently inhibit the expression of adipocyte-specific genes, including adipocyte fatty acid-binding protein and leptin. In particular, EEAY treatment effectively enhanced the activation of the AMP-activated protein kinase (AMPK) signaling pathway; however, the co-treatment with compound C, an inhibitor of AMPK, significantly restored the EEAY-induced inhibition of pro-adipogenic transcription factors and adipocyte-specific genes. These results indicate that EEAY may exert an anti-obesity effect by controlling the AMPK signaling pathway, suggesting that the leaf extract of A. yomena may be a potential anti-obesity agent.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.NIMS-KMA.KACE-1-0-G.ssp370' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The KACE1.0-GLOMAP climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: MOM4p1 (tripolar primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE-HadGEM3-GSI8 (tripolar primarily 1deg; 360 x 200 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.

A noncompartmented microbial fuel cell (NCMFC) composed of a Mn(IV)-carbon plate and a Fe(III)-carbon plate was used for electricity generation from organic wastewater without consumption of external energy. The Fe(III)-carbon plate, coated with a porous ceramic membrane and a semipermeable cellulose acetate film, was used as a cathode, which substituted for the catholyte and cathode. The Mn(IV)-carbon plate was used as an anode without a membrane or film coating. A solar cell connected to the NCMFC activated electricity generation and bacterial consumption of organic matter contained in the wastewater. More than 99 degrees of the organic matter was biochemically oxidized during wastewater flow through the four NCMFC units. A predominant bacterium isolated from the anode surface in both the conventional and the solar cell-linked NCMFC was found to be more than 99 degrees similar to a Mn(II)-oxidizing bacterium and Burkeholderia sp., based on 16S rDNA sequence analysis. The isolate reacted electrochemically with the Mn(IV)-modified anode and produced electricity in the NCMFC. After 90 days of incubation, a bacterial species that was enriched on the Mn(IV)-modified anode surface in all of the NCMFC units was found to be very similar to the initially isolated predominant species by comparing 16S rDNA sequences.

To meet international efforts to prevent climate change, the South Korean government is seeking to transform its main power source from coal to natural gas (NG), which emits less carbon dioxide (CO2) than coal. The government needs information about public preferences for increasing NG-fired generation. Therefore, the purpose of this paper is to provide the government with this information by gathering the data on public preferences for increasing NG-fired generation for reducing CO2 emissions, analyzing these data, and obtaining the implications from the analysis. To this end, a survey of 1000 people, after explaining the merits and demerits of NG-fired power, were asked to determine the proper ratio of NG-fired generation by choosing one of six examples: lower than 10%, 10%–20%, 20%–30%, 30%–40%, 40%–50%, and higher than 50%. An interval data model was employed to deal with the data. The average value of the ratio was estimated to be 26.7% with statistical significance, which is greater than the actual value of 22.2% from 2017. Finally, whether and how much some variables such as education level, age, gender, and income level of the respondent affected their preferences for NG-fired generation were investigated.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.NIMS-KMA.KACE-1-0-G.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The KACE1.0-GLOMAP climate model, released in 2018, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: MOM4p1 (tripolar primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE-HadGEM3-GSI8 (tripolar primarily 1deg; 360 x 200 longitude/latitude). The model was run by the National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Seoho-bukro 33, Seogwipo-si, Jejudo 63568, Republic of Korea (NIMS-KMA) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.