A Bayesian Network Approach to Infer Causality of Sports Spectators' Eco-Friendly Behavioral Intentions

PURPOSE This study explores the factors influencing eco-friendly behavioral intentions during sports spectating and infers the causal structure linking each variable to eco-friendly behavioral intentions. METHODS A total of 364 sports fans participated in the survey that collected data on Knowledge of Climate Change (KCC), Awareness of Climate Change (ACC), Attitude of Climate Change (ATT), Subjective Norm of Climate Change (SN), Perceived Behavior Control of Climate Change (PBC), and Behavioral intention to Reduce Single-Use Plastic (INT) during sports spectating. The validity of the measurement was examined through confirmatory factor analysis. Based on the validated data, latent variables’ average scores were reconstructed as input variables for the Bayesian Network, along with demographic characteristics. RESULTS The results of Bayesian network learning indicated that ACC, ATT, SN, and PBC variables directly influence INT. ACC affects ATT and SN, while ACC is influenced by KCC and sex. Conversely, PBC influenced INT but showed no association with the other input variables. SN was found to have the greatest impact on INT during sports spectating, while the influence of PBC was relatively low. CONCLUSIONS The causal structure inferred in the current study using Bayesian network learning provides insights into the previously underexplored relationship structure explaining eco-friendly behavioral intentions of sports fans in the field of sports science. The findings of this study can serve as empirical evidence for sports-related organizations to develop strategies and decision-making processes to promote sustainable sports spectatorship

Disorder-dependent Li diffusion in Li6PS5Cl\mathrm{Li_6PS_5Cl} investigated by machine learning potential

Solid-state electrolytes with argyrodite structures, such as $\mathrm{Li_6PS_5Cl}$, have attracted considerable attention due to their superior safety compared to liquid electrolytes and higher ionic conductivity than other solid electrolytes. Although experimental efforts have been made to enhance conductivity by controlling the degree of disorder, the underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on ab initio MD simulations have limitations in addressing various types of disorder at room temperature. In this study, we directly investigate Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$ at 300 K using large-scale, long-term MD simulations empowered by machine learning potentials (MLPs). To ensure the convergence of conductivity values within an error range of 10%, we employ a 25 ns simulation using a $5\times5\times5$ supercell containing 6500 atoms. The computed Li-ion conductivity, activation energies, and equilibrium site occupancies align well with experimental observations. Notably, Li-ion conductivity peaks when Cl ions occupy 25% of the 4c sites, rather than at 50% where the disorder is maximized. This phenomenon is explained by the interplay between inter-cage and intra-cage jumps. By elucidating the key factors affecting Li-ion diffusion in $\mathrm{Li_6PS_5Cl}$, this work paves the way for optimizing ionic conductivity in the argyrodite family.Comment: 34 pages, 6 figure

A Millimeter-Wave GaN MMIC Front End Module with 5G NR Performance Verification

This paper proposes a millimeter-wave (mmWave) 5G front end module (FEM) based on multiple gallium nitride (GaN) monolithic microwave integrated circuits (MMICs) with 5G new radio (NR) performance verification. The proposed structure is configured by a wide band GaN single-pole double-throw (SPDT) switch MMIC, a GaN low-noise amplifier (LNA) MMIC, and a GaN power amplifier (PA) MMIC with the target operation band from 26.5 GHz to 29.5 GHz. The LNA and PA MMICs are designed with 150 nm GaN/SiC technology, and the SPDT MMIC is designed with 100 nm GaN/Si. The LNA MMIC shows the measured noise figure less than or equal to 2.52 dB within the operation band. The PA MMIC is based on a two-stage configuration and shows about 35 dBm measured saturated power with power-added efficiency better than 34% within the operation band. Also, the SPDT MMIC is based on an artificial transmission line configuration for wideband performance and shows that the measured insertion loss is less than 1.6 dB, and the measured isolation is higher than 25 dB within the operation band. Furthermore, all MMICs are integrated within a single carrier as an FEM and successfully verified by 5G NR test signals

Anti-Biofouling Features of Eco-Friendly Oleamide-PDMS Copolymers

The biofouling of marine organisms on a surface induces serious economic damage. One of the conventional anti-biofouling strategies is the use of toxic chemicals. In this study, a new eco-friendly oleamide-PDMS copolymer (OPC) is proposed for sustainable anti-biofouling and effective drag reduction. The anti-biofouling characteristics of the OPC are investigated using algal spores and mussels. The proposed OPC is found to inhibit the adhesion of algal spores and mussels. The slippery features of the fabricated OPC surfaces are examined by direct measurement of pressure drops in channel flows. The proposed OPC surface would be utilized in various industrial applications including marine vehicles and biomedical devices. © Copyright © 2020 American Chemical Society.1

Disease-specific induced pluripotent stem cells: a platform for human disease modeling and drug discovery

The generation of disease-specific induced pluripotent stem cell (iPSC) lines from patients with incurable diseases is a promising approach for studying disease mechanisms and drug screening. Such innovation enables to obtain autologous cell sources in regenerative medicine. Herein, we report the generation and characterization of iPSCs from fibroblasts of patients with sporadic or familial diseases, including Parkinson's disease (PD), Alzheimer's disease (AD), juvenile-onset, type I diabetes mellitus (JDM), and Duchenne type muscular dystrophy (DMD), as well as from normal human fibroblasts (WT). As an example to modeling disease using disease-specific iPSCs, we also discuss the previously established childhood cerebral adrenoleukodystrophy (CCALD)- and adrenomyeloneuropathy (AMN)-iPSCs by our group. Through DNA fingerprinting analysis, the origins of generated disease-specific iPSC lines were identified. Each iPSC line exhibited an intense alkaline phosphatase activity, expression of pluripotent markers, and the potential to differentiate into all three embryonic germ layers: the ectoderm, endoderm, and mesoderm. Expression of endogenous pluripotent markers and downregulation of retrovirus-delivered transgenes [OCT4 (POU5F1), SOX2, KLF4, and c-MYC] were observed in the generated iPSCs. Collectively, our results demonstrated that disease-specific iPSC lines characteristically resembled hESC lines. Furthermore, we were able to differentiate PD-iPSCs, one of the disease-specific-iPSC lines we generated, into dopaminergic (DA) neurons, the cell type mostly affected by PD. These PD-specific DA neurons along with other examples of cell models derived from disease-specific iPSCs would provide a powerful platform for examining the pathophysiology of relevant diseases at the cellular and molecular levels and for developing new drugs and therapeutic regimens

Determining crystal structures through crowdsourcing and coursework

We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality

Life cycle resource consumption and land use of photovoltaic (PV) system in South Korea

International audienceThe worldwide demand for fossil and minerals are growing continuously. Also the demand for a number of metals and rare materials are forecast to double over the next 50 years (Muilerman and Blonk 2001). This resource depletion is also related with technologies for harvesting adequate amounts of sustainable energy. Renewable energy technology and systems such as photovoltaic (PV) and wind system are consuming rare materials and using available land as well as providing direct benefits at national and local levels. In South Korea, intensive effort started in 1988 under Promotion Act for New and Renewable Energy Development. According to the 3rd National Plan for Energy Technology Development, the Government is aiming at the supply of 6% of total energy demand by new and renewable energy by 2020 and 11% by 2030. In case of the PV system, the goal is targeted from 59,000 TOE productions in 2008 to 1,364,000 TOE productions in 2030 (about 2,311% increased). In this study, based on the photovoltaic energy production target by 2030, the life cycle resources requirement (e.g., types and amount of input metals and rare materials) and land use for PV system are calculated and showed. Scenario analysis was conducted for the PV production system. By increasing the energy production efficiency of PV system, the future reduced resources and land use can be calculated. Also, having a product-lifespan of over 30 years, we explored that by increasing the recycling rate of significant volumes of end-of-life PV modules, we could see the recovered resource amount

Resource Efficiency and Future Resource Requirement in PV System; Case Study of South Korea

International audienceThe worldwide demand for fossil and minerals are growing continuously. Also the demand for a number of metals and rare materials are forecast to double over the next 50 years (Muilerman and Blonk 2001). This resource depletion is also related with technologies for harvesting adequate amounts of sustainable energy. Renewable energy technology and systems such as photovoltaic (PV) and wind system are consuming rare materials and using available land as well as providing direct benefits at national and local levels. In South Korea, intensive effort started in 1988 under Promotion Act for New and Renewable Energy Development. According to the 3rd National Plan for Energy Technology Development, the Government is aiming at the supply of 6% of total energy demand by new and renewable energy by 2020 and 11% by 2030. In case of the PV system, the production goal is targeted from 59,000 TOE production in 2008 to 1,364,000 TOE production in 2030 (about 2,311% increased). In this study, based on 1 m2 PV module production (Single-crystal silicon (SC-Si), Multi-crystal silicon (MC-Si), CI(G)S thin-film (CI(G)S), CdTe thin-film (CdTe)), the life cycle resources requirement (e.g., types and amount of input ferrous and nonferrous metals and rare materials), resource efficiency and land use are calculated by using material balance data and Eco-invent life cycle inventory data. Also by using the photovoltaic energy production target in South Korea by 2030, future requirement resources and land use amount calculated. As a result, the consumption of ferrous and nonferrous metals, rare earth and critical materials as well as land use were quantified. In the ferrous and nonferrous metal, aluminium (SC-Si; 23kg, MC-Si; 23kg, CI(G)S; 17kg, and CdTe; 16kg) was the most consumed metal and followed by iron and zinc. In the rare materials, cadmium, chromium, manganese, gallium and molybdenum were the most used metals. Also uranium was the most consumed metal in rare earth materials