Browsing by Author "Zhang B"

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    Factors Influencing the Quality of Life of Empty Nesters: Empirical Evidence from Southwest China
    (MDPI (Basel, Switzerland), 2021-03-02) Wang C; Zhang B; Oláh J; Hasan M; Appolloni A
    Quality of life is a widely accepted concept based on the notion that people’s lives have been subject to rapid development and industrialization. This study aims to explore the impact of different factors on the quality of life of empty nesters in Southwest China. The main factors explored are resilience and social supports (SS), highlighted here from different perspectives. Moreover, the correlations between other variables and quality of life are shown here. This study experimented with a hierarchical multiple regression model from survey data with 3583 valid responses. It argued that both resilience and social supports, including family members and friends, are significantly correlated with the Chinese empty nesters’ quality of life. Family support and friend support play a significant mediating role in the association between resilience and quality of life. However, neither government nor nongovernmental support significantly influences the quality of life. Therefore, the hypothetical recommendations of this study have been partially confirmed. The findings of this study provide a more comprehensive understanding of the overall mental and physical health of Chinese empty nesters.
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    Global fine-resolution data on springtail abundance and community structure
    (Springer Nature Limited, 2024-01-03) Potapov AM; Chen T-W; Striuchkova AV; Alatalo JM; Alexandre D; Arbea J; Ashton T; Ashwood F; Babenko AB; Bandyopadhyaya I; Baretta CRDM; Baretta D; Barnes AD; Bellini BC; Bendjaballah M; Berg MP; Bernava V; Bokhorst S; Bokova AI; Bolger T; Bouchard M; Brito RA; Buchori D; Castaño-Meneses G; Chauvat M; Chomel M; Chow Y; Chown SL; Classen AT; Cortet J; Čuchta P; de la Pedrosa AM; De Lima ECA; Deharveng LE; Doblas Miranda E; Drescher J; Eisenhauer N; Ellers J; Ferlian O; Ferreira SSD; Ferreira AS; Fiera C; Filser J; Franken O; Fujii S; Koudji EG; Gao M; Gendreau-Berthiaume B; Gers C; Greve M; Hamra-Kroua S; Handa IT; Hasegawa M; Heiniger C; Hishi T; Holmstrup M; Homet P; Høye TT; Ivask M; Jacques B; Janion-Scheepers C; Jochum M; Joimel S; Jorge BCS; Juceviča E; Kapinga EM; Kováč Ľ; Krab EJ; Krogh PH; Kuu A; Kuznetsova N; Lam WN; Lin D; Lindo Z; Liu AWP; Lu J-Z; Luciáñez MJ; Marx MT; Mawan A; McCary MA; Minor MA; Mitchell GI; Moreno D; Nakamori T; Negri I; Nielsen UN; Ochoa-Hueso R; Oliveira Filho LCI; Palacios-Vargas JG; Pollierer MM; Ponge J-F; Potapov MB; Querner P; Rai B; Raschmanová N; Rashid MI; Raymond-Léonard LJ; Reis AS; Ross GM; Rousseau L; Russell DJ; Saifutdinov RA; Salmon S; Santonja M; Saraeva AK; Sayer EJ; Scheunemann N; Scholz C; Seeber J; Shaw P; Shveenkova YB; Slade EM; Stebaeva S; Sterzynska M; Sun X; Susanti WI; Taskaeva AA; Tay LS; Thakur MP; Treasure AM; Tsiafouli M; Twala MN; Uvarov AV; Venier LA; Widenfalk LA; Widyastuti R; Winck B; Winkler D; Wu D; Xie Z; Yin R; Zampaulo RA; Zeppelini D; Zhang B; Zoughailech A; Ashford O; Klauberg-Filho O; Scheu S
    Springtails (Collembola) inhabit soils from the Arctic to the Antarctic and comprise an estimated ~32% of all terrestrial arthropods on Earth. Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. This data paper describes a static version of the database at the publication date, but the database will be further expanded to include underrepresented regions and linked with trait data.
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    How Reproducible are Surface Areas Calculated from the BET Equation?
    (Wiley-VCH GmbH, 2022-05-23) Osterrieth JWM; Rampersad J; Madden D; Rampal N; Skoric L; Connolly B; Allendorf MD; Stavila V; Snider JL; Ameloot R; Marreiros J; Ania C; Azevedo D; Vilarrasa-Garcia E; Santos BF; Bu X-H; Chang Z; Bunzen H; Champness NR; Griffin SL; Chen B; Lin R-B; Coasne B; Cohen S; Moreton JC; Colón YJ; Chen L; Clowes R; Coudert F-X; Cui Y; Hou B; D'Alessandro DM; Doheny PW; Dincă M; Sun C; Doonan C; Huxley MT; Evans JD; Falcaro P; Ricco R; Farha O; Idrees KB; Islamoglu T; Feng P; Yang H; Forgan RS; Bara D; Furukawa S; Sanchez E; Gascon J; Telalović S; Ghosh SK; Mukherjee S; Hill MR; Sadiq MM; Horcajada P; Salcedo-Abraira P; Kaneko K; Kukobat R; Kenvin J; Keskin S; Kitagawa S; Otake K-I; Lively RP; DeWitt SJA; Llewellyn P; Lotsch BV; Emmerling ST; Pütz AM; Martí-Gastaldo C; Padial NM; García-Martínez J; Linares N; Maspoch D; Suárez Del Pino JA; Moghadam P; Oktavian R; Morris RE; Wheatley PS; Navarro J; Petit C; Danaci D; Rosseinsky MJ; Katsoulidis AP; Schröder M; Han X; Yang S; Serre C; Mouchaham G; Sholl DS; Thyagarajan R; Siderius D; Snurr RQ; Goncalves RB; Telfer S; Lee SJ; Ting VP; Rowlandson JL; Uemura T; Iiyuka T; van der Veen MA; Rega D; Van Speybroeck V; Rogge SMJ; Lamaire A; Walton KS; Bingel LW; Wuttke S; Andreo J; Yaghi O; Zhang B; Yavuz CT; Nguyen TS; Zamora F; Montoro C; Zhou H; Kirchon A; Fairen-Jimenez D
    Porosity and surface area analysis play a prominent role in modern materials science. At the heart of this sits the Brunauer-Emmett-Teller (BET) theory, which has been a remarkably successful contribution to the field of materials science. The BET method was developed in the 1930s for open surfaces but is now the most widely used metric for the estimation of surface areas of micro- and mesoporous materials. Despite its widespread use, the calculation of BET surface areas causes a spread in reported areas, resulting in reproducibility problems in both academia and industry. To prove this, for this analysis, 18 already-measured raw adsorption isotherms were provided to sixty-one labs, who were asked to calculate the corresponding BET areas. This round-robin exercise resulted in a wide range of values. Here, the reproducibility of BET area determination from identical isotherms is demonstrated to be a largely ignored issue, raising critical concerns over the reliability of reported BET areas. To solve this major issue, a new computational approach to accurately and systematically determine the BET area of nanoporous materials is developed. The software, called "BET surface identification" (BETSI), expands on the well-known Rouquerol criteria and makes an unambiguous BET area assignment possible.