School climate and left-behind children’s achievement motivation: The mediating role of learning adaptability and the moderating role of teacher support

School climate has been reported to have an important impact on children’s achievement motivation, but the mechanism for the impact of school climate on left-behind children has not been fully explored. The purpose of this study is to investigate the roles of left-behind children’s learning adaptability and teacher support in mediating and moderating the relationship between school climate and achievement motivation. In this study, 1,417 left-behind children were surveyed. The results showed that: (1) after controlling for gender and age, the school climate still had a positive effect on the achievement motivation of left-behind children (c′ = 0.177, p < 0.001). (2) School climate perceived by left-behind children directly predicted their achievement motivation, and indirectly through their learning adaptability (a1 = 0.338, p < 0.001; b = 0.341, p < 0.001). In other words, left-behind children’s learning adaptability may play an intermediary role between school climate and achievement motivation. (3) The indirect effect of school climate on achievement motivation through learning adaptability was moderated by teacher support (a2 = 0.153, p < 0.001), and this indirect effect was more significant for left-behind children who perceived high teacher support. The research reveals the importance of school climate and teacher support to the growth and development of left-behind children, thus holding theoretical significance for improving the achievement motivation of left-behind children

Exploring the nano-wonders: unveiling the role of Nanoparticles in enhancing salinity and drought tolerance in plants

Plants experience diverse abiotic stresses, encompassing low or high temperature, drought, water logging and salinity. The challenge of maintaining worldwide crop cultivation and food sustenance becomes particularly serious due to drought and salinity stress. Sustainable agriculture has significant promise with the use of nano-biotechnology. Nanoparticles (NPs) have evolved into remarkable assets to improve agricultural productivity under the robust climate alteration and increasing drought and salinity stress severity. Drought and salinity stress adversely impact plant development, and physiological and metabolic pathways, leading to disturbances in cell membranes, antioxidant activities, photosynthetic system, and nutrient uptake. NPs protect the membrane and photosynthetic apparatus, enhance photosynthetic efficiency, optimize hormone and phenolic levels, boost nutrient intake and antioxidant activities, and regulate gene expression, thereby strengthening plant’s resilience to drought and salinity stress. In this paper, we explored the classification of NPs and their biological effects, nanoparticle absorption, plant toxicity, the relationship between NPs and genetic engineering, their molecular pathways, impact of NPs in salinity and drought stress tolerance because the effects of NPs vary with size, shape, structure, and concentration. We emphasized several areas of research that need to be addressed in future investigations. This comprehensive review will be a valuable resource for upcoming researchers who wish to embrace nanotechnology as an environmentally friendly approach for enhancing drought and salinity tolerance

Validity and applicability of the global leadership initiative on malnutrition criteria in non-dialysis patients with chronic kidney disease

IntroductionThere are no standardized assessment criteria for selecting nutritional risk screening tools or indicators to assess reduced muscle mass (RMM) in the Global Leadership Initiative on Malnutrition (GLIM) criteria. We aimed to compare the consistency of different GLIM criteria with Subjective Global Assessment (SGA) and protein-energy wasting (PEW).MethodsIn this study, nutritional risk screening 2002 first four questions (NRS-2002-4Q), Nutritional Risk Screening 2002 (NRS-2002), Malnutrition Universal Screening Tool (MUST), and Mini-Nutritional Assessment Short-Form (MNA-SF) tools were used as the first step of nutritional risk screening for the GLIM. The RMM is expressed using different metrics. The SGA and PEW were used to diagnose patients and classify them as malnourished and non-malnourished. Kappa (κ) tests were used to compare the concordance between the SGA, PEW, and GLIM of each combination of screening tools.ResultsA total of 157 patients were included. Patients with Chronic kidney disease (CKD) stage 1–3 accounted for a large proportion (79.0%). The prevalence rates of malnutrition diagnosed using the SGA and PEW were 18.5% and 19.7%, respectively. The prevalence of GLIM-diagnosed malnutrition ranges from 5.1% to 37.6%, depending on the different screening methods for nutritional risk and the different indicators denoting RMM. The SGA was moderately consistent with the PEW (κ = 0.423, p < 0.001). The consistency among the GLIM, SGA, and PEW was generally low. Using the NRS-2002-4Q to screen for nutritional risk, GLIM had the best agreement with SGA and PEW when skeletal muscle index (SMI), fat-free mass index (FFMI), and hand grip strength (HGS) indicated a reduction in muscle mass (SGA: κ = 0.464, 95% CI 0.28–0.65; PEW: κ = 0.306, 95% CI 0.12–0.49).ConclusionThe concordance between the GLIM criteria and the SGA and PEW depended on the screening tool used in the GLIM process. The inclusion of RMM in the GLIM framework is important. The addition of HGS could further improve the performance of the GLIM standard compared to the use of body composition measurements

The ALMA Survey of Star Formation and Evolution in Massive Protoclusters with Blue Profiles (ASSEMBLE): Core Growth, Cluster Contraction, and Primordial Mass Segregation

The ALMA Survey of Star Formation and Evolution in Massive Protoclusters with Blue Profiles (ASSEMBLE) aims to investigate the process of mass assembly and its connection to high-mass star formation theories in protoclusters in a dynamic view. We observed 11 massive (Mclump>1000 Msun), luminous (Lbol>10,000 Lsun), and blue-profile (infall signature) clumps by ALMA with resolution of 2200-5500 au at 350 GHz (870 um) in continuum and line emission. 248 dense cores were identified, including 106 cores showing protostellar signatures and 142 prestellar core candidates. Compared to early-stage infrared dark clouds (IRDCs) by ASHES, the core mass and surface density within the ASSEMBLE clumps exhibited significant increment, suggesting concurrent core accretion during the evolution of the clumps. The maximum mass of prestellar cores was found to be 2 times larger than that in IRDCs, indicating evolved protoclusters have the potential to harbor massive prestellar cores. The mass relation between clumps and their most massive core (MMCs) is observed in ASSEMBLE but not in IRDCs, which is suggested to be regulated by multiscale mass accretion. The mass correlation between the core clusters and their MMCs has a steeper slope compared to that observed in stellar clusters, which can be due to fragmentation of the MMC and stellar multiplicity. We observe a decrease in core separation and an increase in central concentration as protoclusters evolve. We confirm primordial mass segregation in the ASSEMBLE protoclusters, possibly resulting from gravitational concentration and/or gas accretion.Comment: 37 pages, 13 figures, 5 tables; accepted for publication in ApJ

New member of the V1V2-directed CAP256-VRC26 lineage that shows increased breadth and exceptional potency.

CAPRISA, 2016.Abstract available in pdf

Incomplete Fuzzy Soft Sets and Their Application to Decision-Making

The research of incomplete fuzzy soft sets is of paramount importance in fuzzy soft sets, where the combination of incomplete fuzzy soft set and decision-making problem is of great significance. Incomplete information in fuzzy soft sets leads to more uncertainty and ambiguity in decision-making. The focus of this paper to propose an algorithm of fuzzy soft set based decision-making problems under incomplete information. On the basis of the weighted function, we introduce the notions of weighted incomplete soft sets and weighted incomplete fuzzy soft sets, and show an approach to weighted incomplete fuzzy soft sets for dealing with decision-making. Considering the missing weight function, the concept of incomplete weighted fuzzy soft sets is presented. Meanwhile, we apply the incomplete weighted fuzzy soft sets to solve the decision-making problem. As modal-style operators for fuzzy soft sets have a precise description of attributes possessed by objects, we apply modal-style operator for incomplete fuzzy soft set to deal with decision-making and propose a new algorithm to make it more accurate and simple