Sejong University

Energy production and storage technologies have attracted a great deal of attention for day-to-day applications. In recent decades, advances in lithium-ion battery (LIB) technology have improved living conditions around the globe. LIBs are used in most mobile electronic devices as well as in zero-emission electronic vehicles. However, there are increasing concerns regarding load leveling of renewable energy sources and the smart grid as well as the sustainability of lithium sources due to their limited availability and consequent expected price increase. Therefore, whether LIBs alone can satisfy the rising demand for small- and/or mid-to-large-format energy storage applications remains unclear. To mitigate these issues, recent research has focused on alternative energy storage systems. Sodium-ion batteries (SIBs) are considered as the best candidate power sources because sodium is widely available and exhibits similar chemistry to that of LIBs; therefore, SIBs are promising next-generation alternatives. Recently, sodiated layer transition metal oxides, phosphates and organic compounds have been introduced as cathode materials for SIBs. Simultaneously, recent developments have been facilitated by the use of select carbonaceous materials, transition metal oxides (or sulfides), and intermetallic and organic compounds as anodes for SIBs. Apart from electrode materials, suitable electrolytes, additives, and binders are equally important for the development of practical SIBs. Despite developments in electrode materials and other components, there remain several challenges, including cell design and electrode balancing, in the application of sodium ion cells. In this article, we summarize and discuss current research on materials and propose future directions for SIBs. This will provide important insights into scientific and practical issues in the development of SIBs.

We present cosmological results from the final galaxy clustering data set of the Baryon Oscillation Spectroscopic Survey, part of the Sloan Digital Sky Survey III. Our combined galaxy sample comprises 1.2 million massive galaxies over an effective area of 9329 deg 2 and volume of 18.7 Gpc 3 , divided into three partially overlapping redshift slices centred at effective redshifts 0.38, 0.51 and 0.61. We measure the angular diameter distance D M and Hubble parameter H from the baryon acoustic oscillation (BAO) method, in combination with a cosmic microwave background prior on the sound horizon scale, after applying reconstruction to reduce non-linear effects on the BAO feature. Using the anisotropic clustering of the

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.

The metaverse has the potential to extend the physical world using augmented and virtual reality technologies allowing users to seamlessly interact within real and simulated environments using avatars and holograms. Virtual environments and immersive games (such as, Second Life, Fortnite, Roblox and VRChat) have been described as antecedents of the metaverse and offer some insight to the potential socio-economic impact of a fully functional persistent cross platform metaverse. Separating the hype and “meta…” rebranding from current reality is difficult, as “big tech” paints a picture of the transformative nature of the metaverse and how it will positively impact people in their work, leisure, and social interaction. The potential impact on the way we conduct business, interact with brands and others, and develop shared experiences is likely to be transformational as the distinct lines between physical and digital are likely to be somewhat blurred from current perceptions. However, although the technology and infrastructure does not yet exist to allow the development of new immersive virtual worlds at scale - one that our avatars could transcend across platforms, researchers are increasingly examining the transformative impact of the metaverse. Impacted sectors include marketing, education, healthcare as well as societal effects relating to social interaction factors from widespread adoption, and issues relating to trust, privacy, bias, disinformation, application of law as well as psychological aspects linked to addiction and impact on vulnerable people. This study examines these topics in detail by combining the informed narrative and multi-perspective approach from experts with varied disciplinary backgrounds on many aspects of the metaverse and its transformational impact. The paper concludes by proposing a future research agenda that is valuable for researchers, professionals and policy makers alike.

The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All of the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 deg 2 of sky, more than triples the number of H -band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 stars from the Multi-object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). The APOGEE outputs now include the measured abundances of 15 different elements for each star. In total, SDSS-III added 5200 deg 2 of ugriz imaging; 155,520 spectra of 138,099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2) survey; 2,497,484 BOSS spectra of 1,372,737 galaxies, 294,512 quasars, and 247,216 stars over 9376 deg 2 ; 618,080 APOGEE spectra of 156,593 stars; and 197,040 MARVELS spectra of 5513 stars. Since its first light in 1998, SDSS has imaged over 1/3 of the Celestial sphere in five bands and obtained over five million astronomical spectra.

The RENO experiment has observed the disappearance of reactor electron antineutrinos, consistent with neutrino oscillations, with a significance of 4.9 standard deviations. Antineutrinos from six $2.8\text{ }\text{ }{\mathrm{GW}}_{\mathrm{th}}$ reactors at the Yonggwang Nuclear Power Plant in Korea, are detected by two identical detectors located at 294 and 1383 m, respectively, from the reactor array center. In the 229 d data-taking period between 11 August 2011 and 26 March 2012, the far (near) detector observed 17102 (154088) electron antineutrino candidate events with a background fraction of 5.5% (2.7%). The ratio of observed to expected numbers of antineutrinos in the far detector is $0.920\ifmmode\pm\else\textpm\fi{}0.009(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}0.014(\mathrm{syst})$. From this deficit, we determine ${sin}^{2}2{\ensuremath{\theta}}_{13}=0.113\ifmmode\pm\else\textpm\fi{}0.013(\mathrm{stat})\ifmmode\pm\else\textpm\fi{}0.019(\mathrm{syst})$ based on a rate-only analysis.

Abstract We present the first Event Horizon Telescope (EHT) observations of Sagittarius A* (Sgr A*), the Galactic center source associated with a supermassive black hole. These observations were conducted in 2017 using a global interferometric array of eight telescopes operating at a wavelength of λ = 1.3 mm. The EHT data resolve a compact emission region with intrahour variability. A variety of imaging and modeling analyses all support an image that is dominated by a bright, thick ring with a diameter of 51.8 ± 2.3 μ as (68% credible interval). The ring has modest azimuthal brightness asymmetry and a comparatively dim interior. Using a large suite of numerical simulations, we demonstrate that the EHT images of Sgr A* are consistent with the expected appearance of a Kerr black hole with mass ∼4 × 10 6 M ⊙ , which is inferred to exist at this location based on previous infrared observations of individual stellar orbits, as well as maser proper-motion studies. Our model comparisons disfavor scenarios where the black hole is viewed at high inclination ( i > 50°), as well as nonspinning black holes and those with retrograde accretion disks. Our results provide direct evidence for the presence of a supermassive black hole at the center of the Milky Way, and for the first time we connect the predictions from dynamical measurements of stellar orbits on scales of 10 3 –10 5 gravitational radii to event-horizon-scale images and variability. Furthermore, a comparison with the EHT results for the supermassive black hole M87* shows consistency with the predictions of general relativity spanning over three orders of magnitude in central mass.

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is based on the social value of Generation Z that online and offline selves are not different. With the technological development of deep learning-based high-precision recognition models and natural generation models, Metaverse is being strengthened with various factors, from mobile-based always-on access to connectivity with reality using virtual currency. The integration of enhanced social activities and neural-net methods requires a new definition of Metaverse suitable for the present, different from the previous Metaverse. This paper divides the concepts and essential techniques necessary for realizing the Metaverse into three components (i.e., hardware, software, and contents) and three approaches (i.e., user interaction, implementation, and application) rather than marketing or hardware approach to conduct a comprehensive analysis. Furthermore, we describe essential methods based on three components and techniques to Metaverse’s representative Ready Player One, Roblox, and Facebook research in the domain of films, games, and studies. Finally, we summarize the limitations and directions for implementing the immersive Metaverse as social influences, constraints, and open challenges.

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multitenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.

ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model. It is designed to address the physics of strongly interacting matter and the quark-gluon plasma at extreme values of energy density and temperature in nucleus-nucleus collisions. Besides running with Pb ions, the physics programme includes collisions with lighter ions, lower energy running and dedicated proton-nucleus runs. ALICE will also take data with proton beams at the top LHC energy to collect reference data for the heavy-ion programme and to address several QCD topics for which ALICE is complementary to the other LHC detectors. The ALICE detector has been built by a collaboration including currently over 1000 physicists and engineers from 105 Institutes in 30 countries. Its overall dimensions are 16 × 16 × 26 m 3 with a total weight of approximately 10 000 t. The experiment consists of 18 different detector systems each with its own specific technology choice and design constraints, driven both by the physics requirements and the experimental conditions expected at LHC. The most stringent design constraint is to cope with the extreme particle multiplicity anticipated in central Pb-Pb collisions. The different subsystems were optimized to provide high-momentum resolution as well as excellent Particle Identification (PID) over a broad range in momentum, up to the highest multiplicities predicted for LHC. This will allow for comprehensive studies of hadrons, electrons, muons, and photons produced in the collision of heavy nuclei. Most detector systems are scheduled to be installed and ready for data taking by mid-2008 when the LHC is scheduled to start operation, with the exception of parts of the Photon Spectrometer (PHOS), Transition Radiation Detector (TRD) and Electro Magnetic Calorimeter (EMCal). These detectors will be completed for the high-luminosity ion run expected in 2010. This paper describes in detail the detector components as installed for the first data taking in the summer of 2008.

Abstract We describe the Sloan Digital Sky Survey IV (SDSS-IV), a project encompassing three major spectroscopic programs. The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) is observing hundreds of thousands of Milky Way stars at high resolution and high signal-to-noise ratios in the near-infrared. The Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey is obtaining spatially resolved spectroscopy for thousands of nearby galaxies (median ). The extended Baryon Oscillation Spectroscopic Survey (eBOSS) is mapping the galaxy, quasar, and neutral gas distributions between and 3.5 to constrain cosmology using baryon acoustic oscillations, redshift space distortions, and the shape of the power spectrum. Within eBOSS, we are conducting two major subprograms: the SPectroscopic IDentification of eROSITA Sources (SPIDERS), investigating X-ray AGNs and galaxies in X-ray clusters, and the Time Domain Spectroscopic Survey (TDSS), obtaining spectra of variable sources. All programs use the 2.5 m Sloan Foundation Telescope at the Apache Point Observatory; observations there began in Summer 2014. APOGEE-2 also operates a second near-infrared spectrograph at the 2.5 m du Pont Telescope at Las Campanas Observatory, with observations beginning in early 2017. Observations at both facilities are scheduled to continue through 2020. In keeping with previous SDSS policy, SDSS-IV provides regularly scheduled public data releases; the first one, Data Release 13, was made available in 2016 July.

Abstract Purpose Structural equation modeling (SEM) depicts one of the most salient research methods across a variety of disciplines, including hospitality management. Although for many researchers, SEM is equivalent to carrying out covariance-based SEM, recent research advocates the use of partial least squares structural equation modeling (PLS-SEM) as an attractive alternative. The purpose of this paper is to systematically examine how PLS-SEM has been applied in major hospitality research journals with the aim of providing important guidance and, if necessary, opportunities for realignment in future applications. Because PLS-SEM in hospitality research is still in an early stage of development, critically examining its use holds considerable promise to counteract misapplications which otherwise might reinforce over time. Design/methodology/approach All PLS-SEM studies published in the six SSCI-indexed hospitality management journals between 2001 and 2015 were reviewed. Tying in with the prior studies in the field, the review covers reasons for using PLS-SEM, data characteristics, model characteristics, the evaluation of the measurement models, the evaluation of the structural model, reporting and use of advanced analyses. Findings Compared to other fields, the results show that several reporting practices are clearly above standard but still leave room for improvement, particularly regarding the consideration of state-of-the art metrics for measurement and structural model assessment. Furthermore, hospitality researchers seem to be unaware of the recent extensions of the PLS-SEM method, which clearly extend the scope of the analyses and help gaining more insights from the model and the data. As a result of this PLS-SEM application review in studies, this research presents guidelines on how to accurately use the method. These guidelines are important for the hospitality management and other disciplines to disseminate and ensure the rigor of PLS-SEM analyses and reporting practices. Research limitations/implications Only articles published in the SSCI-indexed hospitality journals were examined and any journals indexed in other databases were not included. That is, while this research focused on the top-tier hospitality management journals, future research may widen the scope by considering hospitality management-related studies from other disciplines, such as tourism research or general management. Originality/value This study contributes to the literature by providing hospitality researchers with the updated guidelines for PLS-SEM use. Based on a systematic review of current practices in the hospitality literature, critical methodological issues when choosing and using the PLS-SEM were identified. The guidelines allow to improve future PLS-SEM studies and offer recommendations for using recent advances of the method.

This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2 had unknown downscaling operators (blur kernel and decimation) but learnable through low and high res train images. Each competition had ∽100 registered participants and 20 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

The National Health Insurance Service–National Sample Cohort (NHIS-NSC) is a population-based cohort established by the National Health Insurance Service (NHIS) in South Korea. The sole purpose of constructing the cohort was to provide public health researchers and policy makers with representative, useful information regarding citizens’ utilization of health insurance and health examinations. Korea’s universal coverage health insurance system for all citizens was initiated in 1963, based on the National Medical Insurance Act, and was introduced for companies with over 500 employees in 1977. Universal healthcare coverage was achieved in 1989, only 12 years after its introduction, which is the fastest this has been achieved globally.1 In 2000 a single-insurer system, the NHIS, was launched by integrating more than 366 medical insurance organizations, for efficient system operation in Korea.1 The NHIS provides benefits for prevention, diagnosis, disease and injury treatment, as well as rehabilitation, births, deaths and health promotion. Currently the NHIS maintains and stores national records for healthcare utilization and prescriptions. The NHIS records have garnered academic interest due to the effectiveness of the system and relevance to public health and medical research. To meet this interest, a population database has been developed, the ‘National Health Information Database’ (NHID)2 containing personal information, demographics and medical treatment data for Korean citizens, who were categorized as insured employees, insured self-employed individuals or medical aid beneficiaries. The NHID was generated using participants’ medical bill expenses claimed by medical service providers. Data were rearranged according to date of medical treatment rather than date of claim. However, due to limited useability of the NHID’s unavoidably large volume and the lack of confidentiality regarding personal information, the NHIS decided to construct a representative sample database, the NHIS-NSC, with a substantial volume of representative information that does not require privacy regulation for research and policy development. To construct the NHIS-NSC, we first built a target population of 46 605 433 individuals using 47 851 928 individuals in the 2002 NHID by excluding non-citizens and special-purpose employees with an unidentifiable income level. From the target population a representative sample cohort of 1 025 340 participants was randomly selected, comprising 2.2% of the total eligible Korean population in 2002, and followed for 11 years until 2013 unless participants’ eligibility was disqualified due to death or emigration. Systematic stratified random sampling with proportional allocation within each stratum was conducted using the individual’s total annual medical expenses as a target variable for sampling.3 First, 1476 strata were constructed by age group, sex, participant’s eligibility status and income level. Specifically, strata were defined by 18 age groups (infants under 1 year, ages 1–4, 5-year age groups between 5 and 79, and 80 years and above), two groups according to sex (male, female) and 41 groups based on participant’s income level (upper 20 percentiles for insured employees, lower 20 percentiles for insured self-employed individuals, and the lowest level of income for medical aid beneficiaries). Next, within each stratum, systematic sampling was conducted after sorting population data by the value of total annual medical expenses and maintaining a sampling rate of 2.2%. Stratum samples were iteratively drawn until a maximum absolute percentage error—defined as a relative percentage difference between population and sample averages of total annual medical expenses to the population average—reached a predefined value of less than 5%. This technique was used to compensate for the severely positively skewed total annual medical expenses of the entire cohort and each stratum. During the follow-up period, the cohort was refreshed annually by adding a representative sample of newborns, sampled across 82 strata (two for sex, combined with 41 for parents’ income levels) using the 2.2% sampling rate (Figure 1). Participant’s residential information was not used as a stratum variable because the NHIS maintained records of workplace addresses until 2005 and residential addresses after 2006. A schematic representation of the cohort data construction. DB, database. Although the representativeness for follow-up years is not guaranteed, using an appropriate sampling design and sufficient sample size for the initial cohort can help ensure representativeness. The sample’s representativeness was, therefore, evaluated by examining whether a 95% confidence interval for the sample’s average total annual medical expenses contained the population average; it was satisfied in every stratum. Further, the sample cohort was compared with the population according to residence distribution across 16 regions in Korea. Moreover, the mean and standard deviation of health insurance premiums for the sample and population for each cohort year were compared; these were not used as a stratification or target variable for sampling. The difference in the proportion of residence is negligible for 2002, and changed slightly during the follow-up years 2003–13 by 0–0.3%. The difference in average health insurance premium is also negligible during cohort years. The cohort sampled in the 2002 NHID database was followed until 2013, provided that participants were still eligible for health insurance. The total numbers of participants in each of cohort years are presented in Table 1. The number of infants (age 0) in the initial cohort and those added annually are also given in the table. Currently the NHIS plans to maintain regular annual cohort updates for the NHIS-NSC. Number of participants in each cohort year and number of infants added annually (unit: person) Number of participants in each cohort year and number of infants added annually (unit: person) The cohort comprises four databases on participants’ insurance eligibility, medical treatments, medical care institutions and general health examinations. The insurance eligibility database contains 14 variables including information on participant’s identity and socioeconomic variables such as gender, residential area, type of health insurance, level of income, type and grade of disability registered, birth and death. Variables for cause of death and residential area details are provided upon request (see the ‘Can I get hold of the data?’). The medical treatment database consists of 57 variables containing information about participants’ medical bills claimed by medical service providers. It comprises four databases: participant’s electronic medical treatment bills, bill details, details of diseases and details of prescriptions. All four databases are further classified according to type of medicine: ‘medical’ and ‘dental & Chinese medicine’ tables. A pharmacy table is also included in the first two databases. In the medical care institution database, information regarding the type of institution, establishment, location, number of beds, facilities and physicians are recorded under 10 variables. The general health examination database comprises information regarding nationwide health examinations conducted by the NHIS in 2002–13, including major health examination results and information about lifestyles and behaviours obtained from questionnaires. In Korea, nationwide health examinations are conducted for citizens aged 40 years and above.4 Two types of examinations are performed, a general and a life-transition health examination. The former, initiated in 1995, is administered biennially to citizens aged 40 years or older who are dependants of the insured employee or householder/family members of insured self-employed individuals. An insured employee and a householder of the insured self-employed can receive the general health examination regardless of his/her age. For blue-collar employees, this examination is conducted annually. According to the 2013 NHIS statistics, 72.1% of eligible beneficiaries had received general health examinations.5 The more comprehensive life-transition health examination, initiated in 2008, is given to individuals on reaching age 40 and age 66, twice in a lifetime, who are eligible for general health examinations. Both nationwide health examinations involve a screening and a confirmatory test. Examination details are summarized in Table 2. The NHIS-NSC database contains only the first-stage (screening) examination data for those who took the examination during cohort years, with two separate datasets for 2002–08 and 2009–13 because major changes were made to the content of health examinations and questionnaires in 2009 in accordance with a system reformation. Thus, the general health examination database contains 37 variables in the 2002–08 datasets and 41 in the 2009–13 datasets. The numbers of participants who received health examinations during the cohort years are presented in the fourth column in Table 1; 11% received an examination in the initial cohort year (2002), whereas this is more than doubled in 2013, reaching 23%. A detailed list of NHIS-NSC database variables is included in Appendix Table 1 (available as Supplementary data at IJE online). Types and content of general health examinations provided by the NHIS aEligibility: An insured employee and a householder of the insured self-employed regardless of his/her age, a dependant of the insured self-employed individual over 40 years old, or a dependant of the insured employee, over 40 years old. bEligibility: individuals aged 40 and 66. This examination was started in 2008. cThe second-stage examination is performed if an examinee is categorized with suspected hypertension or diabetes or if a 70- or 74-year-old examinee is classified into a high-risk cognitive impairment category from his/her first-stage examination. dThe second-stage examination is performed on all examinees who received the first-stage examination regardless of its result. Types and content of general health examinations provided by the NHIS aEligibility: An insured employee and a householder of the insured self-employed regardless of his/her age, a dependant of the insured self-employed individual over 40 years old, or a dependant of the insured employee, over 40 years old. bEligibility: individuals aged 40 and 66. This examination was started in 2008. cThe second-stage examination is performed if an examinee is categorized with suspected hypertension or diabetes or if a 70- or 74-year-old examinee is classified into a high-risk cognitive impairment category from his/her first-stage examination. dThe second-stage examination is performed on all examinees who received the first-stage examination regardless of its result. To protect participants’ privacy, the Resident Registration Number (RRN, a unique identification number in Korea) which was initially used to construct the cohort, has been replaced with a newly-assigned eight-digit personal ID. Furthermore, to prevent the possibility of identifying a participant by merging information about rare disease status, age and residence, we replaced ICD-10 codes of 114 sensitive diseases with an asterisk except for the code’s initial. A comparison of socio-demographic variables in the NHIS-NSC database and population in 2002, as well as in 2013 (the most recent year of available data), are presented in Tables 3 and 4, respectively; and a comparison of health examination variables in 2002 and 2013 are presented in Appendix Table 1 (available as Supplementary data at IJE online) and Appendix Table 2 (available as Supplementary data at IJE online), respectively. A 95% confidence interval of each variable is also presented. For all demographic variables in 2002, the intervals contained the population average, indicating that the difference between the cohort and population was not significant (Table 3). Comparison of socio-demographic variables between the general population and sample cohort in 2002 [number of subjects (percentage)] CI, confidence interval. a95% confidence interval for the sample proportion. Comparison of socio-demographic variables between the general population and sample cohort in 2002 [number of subjects (percentage)] CI, confidence interval. a95% confidence interval for the sample proportion. Comparison of socio-demographic variables between the population and sample cohort in 2013, number of subjects (percentage) a95% confidence interval for the sample proportion. bThe population value has not been included in the 95% confidence i interval of the sample proportion. Comparison of socio-demographic variables between the population and sample cohort in 2013, number of subjects (percentage) a95% confidence interval for the sample proportion. bThe population value has not been included in the 95% confidence i interval of the sample proportion. In the year 2013, after 11 years of follow-up, the cohort proportion of insured employees underestimated that of the general population, whereas the cohort proportions of self-employed insured and medical-aid beneficiaries overestimated the population proportions for both males and females; however, the differences of less than 0.3% were trivial (Table 4). For smoking status—as a health examination variable—the cohort overestimated the proportion of male and female non-smokers compared with the general population at the time of data collection in the initiation year. The cohort included a significantly higher proportion of men who did not exercise and a lower proportion of men engaging in mild–moderate exercise, compared with the population. No statistical differences for other health variables between the cohort and population in 2002 were found (Appendix Table 2, available as Supplementary data at IJE online). In 2013, for males only the sample proportion of ex-smokers was 0.7% higher than that of the general population. There were differences between the cohort and the population in frequency of exercise (intensive physical activity more than 20 minute per week and moderate exercise more than 30 minute per week, variables that were surveyed since 2009; see Appendix Table 3, available as Supplementary data at IJE online). This finding implies that the cohort’s representativeness regarding some general health examination variables for health behaviour could be inadequate, requiring a periodic adjustment for future cohort years. We also would like to mention that the NHIS is currently preparing to build a special-purpose cohort, specific to general health examination data, using a population database of the NHID. Providing public access to the NHIS-NSC database can support research in auxiliary fields such as sociology, economics, environment policy and industry, besides evidence-based academic research in public health and medicine. As of March 2015, 8 months after becoming publicly available in July 2014, 109 studies (99 academic and 10 political researches) are being conducted using the NHIS-NSC database. Among these, Rim et al. found that the risk of stroke after retinal vein occlusion (RVO) was significantly higher especially for ischaemic stroke patients.6 They also showed that those with RVO had an approximately 2-fold higher hazard ratio among younger, compared with older, adults: suggesting that ophthalmologists need to specifically attend to this population.6 Kwon et al. examined the association between bisphosphonate exposure and osteonecrosis of the jaw (ONJ) in Korean patients with osteoporosis.7 They performed a nested case-control study using the NHIS-NSC database and found a positive relationship between the two, arguing that this relationship must be acknowledged for older adults requiring dental integration, to ensure that the benefits and risks are evaluated and that symptoms suggestive of ONJ are monitored.7 The NHIS-NSC database contains representative population-based cohort data, which is a major strength as it ensures its applicability in research—for example, when evaluating the effects of medical practice on health outcomes. Moreover, the data are large-scale, extensive and stable because it is constructed based on nationwide health insurance data generated by the government or public institutions’ involvement. Therefore, the cohort can also be used by policy makers to create higher value-added policies. Similar databases such as the Healthcare Cost and Utilization Project-National Inpatient Sample (NIS)8 in the USA or the National Health Insurance Research Database (NHIRD)9 in Taiwan, are available. Because the primary sampling unit of the NIS database, however, is the hospital, overlapping participants may introduce a selection bias. The NHIRD database uses a simple random sampling strategy; hence, the representativeness of major health-related indicators including the population’s demographic characteristics may have been lost. Moreover, they may not free of inherent limitations of cross-sectional data in evaluating, for example, an effect of medical practice on a health outcome. However, since the NHIS-NSC is a cohort based on nationwide health insurance data, it is both representative of the population and overcomes the limitations of cross-sectional data. The NHIS-NSC database has several limitations. Although the cohort comprises over one million participants, information on rare diseases may not be sufficient. Therefore, it is necessary to conduct a pre-evaluation of study size when using the NHIS-NSC database. The NHIS is currently preparing special-purpose cohort databases such as a cohort of older adults and of female workers, as well as customized databases for policy development/evaluation and academic research. Disease codes listed in the cohort may not represent participant’s true disease status because the code was created to claim health insurance serviced to participants, an inherent limitation of insurance databases. Hence, it warrants careful use by researchers. In this cohort, non-insurance benefits data such as cosmetic surgeries and information for over-the-counter drugs have not been included. Moreover, evaluating details of a participant’s specific medical treatment is difficult if his/her insurance claims were made under the diagnosis-related-group (DRG) policy. In contrast to the traditional fee- for-service payment system, the DRG system reimburses a fixed amount of medical fees to all hospitalized patients, depending on the patient’s illness and regardless of the type or cost of medical services provided during hospitalization.10,11 In Korea, nearly all types of healthcare providers follow the fee-for-service payment system and the DRG is applied only to seven disease groups (for details, see Health Insurance Review & Assessment Service of Korea website) [http://kostat.go.kr/portal/english/index.action].12 Currently, the NHIS-NSC database consists of 156 SAS® data files, comprising 13 files—for participants’ insurance eligibility (1 file), medical treatments (10 files), medical care institutions (1 file) and health examination (1 file)—for each of the 12 years of the cohort between 2002 and 2013. The total cohort file size is approximately 211 gigabytes with 2619 million cases in 2002–13. Data can be accessed through the NHIS’ National Health Insurance Data Sharing Service website [http://nhiss.nhis.or.kr/bd/ab/bdaba021eng.do]. To gain access to NHIS-NSC data, a completed application form, a research proposal and the applicant’s institutional review board (IRB) approval document should be submitted to and reviewed by the Review Committee of Research Support in NHIS. After granting approval, data are provided to an applicant for a fee. The data application process is described in Figure 2. Upon request, causes of death prepared by Statistics Korea12 and information regarding participant’s district of residence can be provided by the NHIS after the committee’s review. The process for accessing the NHIS-NSC database. IRB, Institutional Review Board. The NHIS-NSC profile in a nutshell The NHIS-NSC database is a population-based sample cohort. Its purpose is to provide representative, useful health insurance and health examination data to public health researchers and policy makers. A total of 1 025 340 participants of the cohort, 2.2% of the total eligible population, were randomly sampled from the 2002 Korean (nationwide) health insurance database to obtain baseline data. Cohort participants were followed for 11 years, until 2013. During the follow-up period, a representative sample of newborns (age 0) was added annually and deceased or emigrated participants were excluded. In 2013, the database included 1 014 730 participants. Information about participants’ insurance eligibility, medical treatment history, healthcare provider’s institution and general health examination are included. The NHIS-NSC database access on [http://nhiss.nhis. or.kr/bd/ab/bdaba021eng.do] requires a completed application form, a research proposal and the institutional review board’s approval document. A list of variables and other NHIS-NSC data are included in the Appendix, available as Supplementary data at IJE online. This work was supported by the NHIS in South Korea. This study used NHIS-NSC data (NHIS-2014-2-001) from the National Health Insurance Service (NHIS). Conflict of interest: None declared.

We present the cosmological implications from final measurements of clustering using galaxies, quasars, and $\mathrm{Ly}\ensuremath{\alpha}$ forests from the completed Sloan Digital Sky Survey (SDSS) lineage of experiments in large-scale structure. These experiments, composed of data from SDSS, SDSS-II, BOSS, and eBOSS, offer independent measurements of baryon acoustic oscillation (BAO) measurements of angular-diameter distances and Hubble distances relative to the sound horizon, ${r}_{d}$, from eight different samples and six measurements of the growth rate parameter, $f{\ensuremath{\sigma}}_{8}$, from redshift-space distortions (RSD). This composite sample is the most constraining of its kind and allows us to perform a comprehensive assessment of the cosmological model after two decades of dedicated spectroscopic observation. We show that the BAO data alone are able to rule out dark-energy-free models at more than eight standard deviations in an extension to the flat, $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model that allows for curvature. When combined with Planck Cosmic Microwave Background (CMB) measurements of temperature and polarization, under the same model, the BAO data provide nearly an order of magnitude improvement on curvature constraints relative to primary CMB constraints alone. Independent of distance measurements, the SDSS RSD data complement weak lensing measurements from the Dark Energy Survey (DES) in demonstrating a preference for a flat $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ cosmological model when combined with Planck measurements. The combined BAO and RSD measurements indicate ${\ensuremath{\sigma}}_{8}=0.85\ifmmode\pm\else\textpm\fi{}0.03$, implying a growth rate that is consistent with predictions from Planck temperature and polarization data and with General Relativity. When combining the results of SDSS BAO and RSD, Planck, Pantheon Type Ia supernovae (SNe Ia), and DES weak lensing and clustering measurements, all multiple-parameter extensions remain consistent with a $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model. Regardless of cosmological model, the precision on each of the three parameters, ${\mathrm{\ensuremath{\Omega}}}_{\mathrm{\ensuremath{\Lambda}}}$, ${H}_{0}$, and ${\ensuremath{\sigma}}_{8}$, remains at roughly 1%, showing changes of less than 0.6% in the central values between models. In a model that allows for free curvature and a time-evolving equation of state for dark energy, the combined samples produce a constraint ${\mathrm{\ensuremath{\Omega}}}_{k}=\ensuremath{-}0.0022\ifmmode\pm\else\textpm\fi{}0.0022$. The dark energy constraints lead to ${w}_{0}=\ensuremath{-}0.909\ifmmode\pm\else\textpm\fi{}0.081$ and ${w}_{a}=\ensuremath{-}0.4{9}_{\ensuremath{-}0.30}^{+0.35}$, corresponding to an equation of state of ${w}_{p}=\ensuremath{-}1.018\ifmmode\pm\else\textpm\fi{}0.032$ at a pivot redshift ${z}_{p}=0.29$ and a Dark Energy Task Force Figure of Merit of 94. The inverse distance ladder measurement under this model yields ${H}_{0}=68.18\ifmmode\pm\else\textpm\fi{}0.79\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$, remaining in tension with several direct determination methods; the BAO data allow Hubble constant estimates that are robust against the assumption of the cosmological model. In addition, the BAO data allow estimates of ${H}_{0}$ that are independent of the CMB data, with similar central values and precision under a $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model. Our most constraining combination of data gives the upper limit on the sum of neutrino masses at $\ensuremath{\sum}{m}_{\ensuremath{\nu}}<0.115\text{ }\text{ }\mathrm{eV}$ (95% confidence). Finally, we consider the improvements in cosmology constraints over the last decade by comparing our results to a sample representative of the period 2000--2010. We compute the relative gain across the five dimensions spanned by $w$, ${\mathrm{\ensuremath{\Omega}}}_{k}$, $\ensuremath{\sum}{m}_{\ensuremath{\nu}}$, ${H}_{0}$, and ${\ensuremath{\sigma}}_{8}$ and find that the SDSS BAO and RSD data reduce the total posterior volume by a factor of 40 relative to the previous generation. Adding again the Planck, DES, and Pantheon SN Ia samples leads to an overall contraction in the five-dimensional posterior volume of 3 orders of magnitude.

Abstract This paper documents the 16th data release (DR16) from the Sloan Digital Sky Surveys (SDSS), the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the Southern Hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey and new data from the SPectroscopic IDentification of ERosita Survey programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library “MaStar”). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17).

Future generations of electric vehicles require driving ranges of at least 300 miles to successfully penetrate the mass consumer market. A significant improvement in the energy density of lithium batteries is mandatory while also maintaining similar or improved rate capability, lifetime, cost, and safety. The vast majority of electric vehicles that will appear on the market in the next 10 years will employ nickel-rich cathode materials, LiNi 1– x – y Co x Al y O 2 and LiNi 1– x – y Co x Mn y O 2 ( x + y < 0.2), in particular. Here, the potential and limitations of these cathode materials are critically compared with reference to realistic target values from the automotive industry. Moreover, we show how future automotive targets can be achieved through fine control of the structural and microstructural properties.

International audience

Purpose The purpose of this study is to propose an integrated model that examines the impact of three elements of foodservice quality dimensions (physical environment, food, and service) on restaurant image, customer perceived value, customer satisfaction, and behavioral intentions. Design/methodology/approach Data were collected from customers at an authentic upscale Chinese restaurant located in a Southeastern state in the USA via a self‐administered questionnaire. Anderson and Gerbing's two‐step approach was used to assess the measurement and structural models. Findings Structural equation modeling shows that the quality of the physical environment, food, and service were significant determinants of restaurant image. Also, the quality of the physical environment and food were significant predictors of customer perceived value. The restaurant image was also found to be a significant antecedent of customer perceived value. In addition, the results reinforced that customer perceived value is indeed a significant determinant of customer satisfaction, and customer satisfaction is a significant predictor of behavioral intentions. Research limitations/implications The proposed model and study findings will greatly help researchers and practitioners understand the complex relationships among foodservice quality (physical environment, food, and service), restaurant image, customer perceived value, customer satisfaction, and behavioral intentions in the restaurant industry. Originality/value This study is the first to develop an integrated model that explicitly accounts for the influence of three restaurant service quality factors on restaurant image and customer perceived value. Using structural equation modeling, this study empirically confirms that the model with the causality from quality, in particular three dimensions of foodservice quality in this study, to restaurant image is superior to the one with causality from image to quality in the context of restaurant.

Soil contamination by potentially toxic elements (PTEs) has led to adverse environmental impacts. In this review, we discussed remediation of PTEs contaminated soils through immobilization techniques using different soil amendments with respect to type of element, soil, and amendment, immobilization efficiency, underlying mechanisms, and field applicability. Soil amendments such as manure, compost, biochar, clay minerals, phosphate compounds, coal fly ash, and liming materials are widely used as immobilizing agents for PTEs. Among these soil amendments, biochar has attracted increased interest over the past few years because of its promising surface properties. Integrated application of appropriate amendments is also recommended to maximize their use efficiency. These amendments can reduce PTE bioavailability in soils through diverse mechanisms such as precipitation, complexation, redox reactions, ion exchange, and electrostatic interaction. However, soil properties such as soil pH, and clay, sesquioxides and organic matter content, and processes, such as sorption/desorption and redox processes, are the key factors governing the amendments' efficacy for PTEs immobilization in soils. Selecting proper immobilizing agents can yield cost-effective remediation techniques and fulfill green and sustainable remediation principles. Furthermore, long-term stability of immobilized PTE compounds and the environmental impacts and cost effectiveness of the amendments should be considered before application.