bluuebunny/crossref_metadata_2025_split huggingface dataset:
What makes dynamic strategic problems difficult? Evidence from an experimental study
Authors: Rahmandad Hazhir , Denrell Jerker , Prelec Drazen 
Published: November 2020

AbstractResearch SummaryManagers regularly deal with dynamic tasks, where decisions impact immediate payoffs as well as long‐term capabilities. Research shows that people do poorly in dynamic tasks, but the underlying mechanisms are unclear. These may range from unsystematic problem‐solving to rational learning in complex environments. In a series of experiments, we tease apart alternative explanations, showing that poor performance is due to behavioral difficulties. Remarkably, we find that people do poorly even if provided with complete information about the payoff function, thus, eliminating any need for learning. They unsystematically search among possible solutions and end up with inefficient heuristics. The results show that differences in thinking through a dynamic problem may lead to substantial variation in performance, even if common sources of complexity and ambiguity are excluded.Managerial SummaryWhy do people, including managers, have difficulty managing systems where taking action today impacts future outcomes? Difficulty of learning in a complex environment has been proposed as the key challenge. Using experiments, we show that people find such tasks difficult even when all relevant information is provided to them and there is nothing to learn. Using trial and error most participants learn satisfactory, but inferior, heuristics. Those who systematically think through tradeoffs over time significantly outperform others even in a simple task, suggesting such thinking adds value in realistic managerial settings as well.
Carbon‐Based Quantum Dots with Solid‐State Photoluminescent: Mechanism, Implementation, and Application
Authors: Xu Anli , Wang Gang , Li Yongqiang , Dong Hui , Yang Siwei , He Peng , Ding Guqiao 
Published: November 2020

AbstractCarbon‐based quantum dots (CQDs), including spherical carbon dots and graphene quantum dots, are an emerging class of photoluminescent (PL) materials with unique properties. Great progress has been made in the design and fabrication of high‐performance CQDs, however, the challenge of developing solid‐state PL CQDs have aroused great interest among researchers. A clear PL mechanism is the basis for the development of high‐performance solid‐state CQDs for light emission and is also a prerequisite for the realization of multiple practical applications. However, the extremely complex structure of a CQD greatly limits the understanding of the solid‐state PL mechanism of CQDs. So far, a variety of models have been proposed to explain the PL of solid‐state CQDs, but they have not been unified. This review summarizes the current understanding of the solid‐state PL of solid‐state CQDs from the perspective of energy band theory and electronic transitions. In addition, the common strategies for realizing solid‐state PL in CQDs are also summarized. Furthermore, the applications of CQDs in the fields of light‐emitting devices, anti‐counterfeiting, fingerprint detection, etc., are proposed. Finally, a brief outlook is given, highlighting current problems, and directions for development of solid‐state PL of CQDs.
Role of Dopants on the Local Electronic Structure of Polymeric Carbon Nitride Photocatalysts
Authors: Ren Jian , Lin Lihua , Lieutenant Klaus , Schulz Christian , Wong Deniz , Gimm Thorren , Bande Annika , Wang Xinchen , Petit Tristan 
Published: November 2020

AbstractPolymeric carbon nitride (PCN) is a promising class of materials for solar‐to‐chemical energy conversion. The increase of the photocatalytic activity of PCN is often achieved by the incorporation of heteroatoms, whose impact on the electronic structure of PCN remains poorly explored. This work reveals that the local electronic structure of PCN is strongly altered by doping with sulfur and iron using X‐ray absorption spectroscopy (XAS) and resonant inelastic X‐ray scattering (RIXS). From XAS at the carbon and nitrogen K‐edges, sulfur atoms are found to mostly affect carbon atoms, in contrast to iron doping mostly altering nitrogen sites. In RIXS at the nitrogen K‐edge, a vibrational progression, affected by iron doping, is evidenced, which is attributed to a vibronic coupling between excited electrons in nitrogen atoms and C–N stretching modes in PCN heterocycling rings. This work opens new perspectives for the characterization of vibronic coupling in polymeric photocatalysts.
Molecular‐Level Insight into Correlation between Surface Defects and Stability of Methylammonium Lead Halide Perovskite Under Controlled Humidity
Authors: Akhavan Kazemi Mohammad Ali , Raval Parth , Cherednichekno Kirill , Chotard Jean‐Noel , Krishna Anurag , Demortiere Arnaud , Reddy G. N. Manjunatha , Sauvage Frédéric 
Published: November 2020

AbstractPerovskite‐based photovoltaics (PVs) have garnered tremendous interest, enabling power conversion efficiencies exceeding 25%. Although much of this success is credited to the exploration of new compositions, defects passivation and process optimization, environmental stability remains an important bottleneck to be solved. The underlying mechanisms of thermal and humidity‐induced degradation are still far from a clear understanding, which poses a severe limitation to overcome the stability issues. Herein, in situ X‐ray diffraction (XRD), in operando liquid‐cell transmission electron microscopy (TEM) and ex situ solid‐state (ss)NMR spectroscopy are combined with time‐resolved spectroscopies to reveal new insights about the degradation mechanisms of methylammonium lead halide (MAPbI3) under 85% relative humidity (RH) at different length scales. Liquid‐cell TEM enables the live visualizations from meso‐to‐nanoscale transformation between the perovskite particles and water molecules, which are corroborated by the changes in local structures at sub‐nanometer distances by ssNMR and longer range by XRD. This work clarifies the role of surface defects and the significance of their passivation to prevent hydration and decomposition reactions.
Syringin alleviates ovalbumin‐induced lung inflammation in BALB/c mice asthma model via NF‐κB signaling pathway
Authors: Dai Rui , Niu Manman , Wang Ningling , Wang Yan 
Published: November 2020

AbstractAsthma is an allergic chronic inflammatory disease of the pulmonary airways, characterized by the infiltration of white blood cells and release of inflammatory cytokines of complex pathways linked to its pathogenesis. Syringin extracted from various medicinal plants has been used extensively for the treatment of inflammatory diseases. Hence, this study was conducted to further explore the protective effects of the syringin in ovalbumin (OVA) induced‐asthma mice model. OVA‐sensitized BALB mice were treated intraperitonealy with three doses (25, 50 and 100 mg/kg) of the syringin which was validated by the alteration in the immunoglobulin E (IgE) levels, cytokines levels, histopathological evaluation inflammatory cell count, lung weight, nitrite (NO) levels, oxidative stress biomarkers and gene markers. The treatment of syringin intensely reduced the increased IgE, inflammatory cytokines, WBC count and restored the antioxidant stress markers OVA stimulated animals. In addition, a significant reduction in inflammation and mucus production was evidenced in histopathological analysis which was further validated by suppression NF‐κB pathway activation by syringin. These results suggest that syringin may improve asthma symptoms in OVA‐induced mice by modulating NF‐κB pathway activation.
Predicting land surface temperature with geographically weighed regression and deep learning
Authors: Jia Hongfei , Yang Dehe , Deng Weiping , Wei Qing , Jiang Wenliang 
Published: November 2020

AbstractFor prediction of urban remote sensing surface temperature, cloud, cloud shadow and snow contamination lead to the failure of surface temperature inversion and vegetation‐related index calculation. A time series prediction framework of urban surface temperature under cloud interference is proposed in this paper. This is helpful to solve the problem of the impact of data loss on surface temperature prediction. Spatial and temporal variation trends of surface temperature and vegetation index are analyzed using Landsat 7/8 remote sensing data of 2010 to 2019 from Beijing. The geographically weighed regression (GWR) method is used to realize the simulation of surface temperature based on the current date. The deep learning prediction network based on convolution and long short‐term memory (LSTM) networks was constructed to predict the spatial distribution of surface temperature on the next observation date. The time series analysis shows that the NDBI is less than −0.2, which indicates that there may be cloud contamination. The land surface temperature (LST) modeling results show that the precision of estimation using GWR method on impervious surface and water bodies is superior compared to the vegetation area. For LST prediction using deep learning methods, the result of the prediction on surface temperature space distribution was relatively good. The purpose of this study is to make up for the missing data affected by cloud, snow, and other interference factors, and to be applied to the prediction of the spatial and temporal distributions of LST.This article is categorized under:
Technologies > Machine Learning
Impacts of Climate Change on the Hydro-Climatology and Performances of Bin El Ouidane Reservoir: Morocco, Africa
Authors: Ahbari Abdellatif , Stour Laila , Agoumi Ali 
Published: November 2020

AbstractIn arid and humid contexts, dams’ reservoirs play a crucial role in water regulation and flood control. Under the projected climate change (CC) effects, even a preoptimized management approach (MA) of a reservoir needs to be assessed in this projected climate. This chapter aims to assess the impacts of CC on the Hydroclimatic (HC) variables of the basin upstream the reservoir of Bin El Ouidane (Morocco), and the effects on the performances of its preoptimized MA. The applied Top-Down assessment procedure included CORDEX climate projections, hydrological, siltation, evaporation, and management models. Concerning the HC variables, the results obtained concord with those reported in the literature in terms of trend, but not always in terms of intensity of change. On the other hand, the projections expected a decrease in the performances of the reservoir, except for criterion allocations’ standard deviation, calibrated during the optimization. Also, interesting conclusions have been found like: the change in precipitation dominant form, the accentuation of the pluvial hydrological regime, the advanced snow melting due to the temperature increase. This chapter presents a typical case study on how to use climate projections for reservoir MA adaptation, without being highly and negatively influenced by the climate model uncertainties.
Farmers’ Adaptive Capacity to Climate Change in Africa: Small-Scale Farmers in Cameroon
Authors: Awazi Nyong Princely , Tchamba Martin Ngankam , Temgoua Lucie Felicite , Tientcheu-Avana Marie-Louise 
Published: November 2020

AbstractSmall-scale farmers’ limited adaptive capacity confronted with the adversities of climate change is a major call for concern considering that small-scale farms feed over half of the world’s population. In this light, small-scale farmers’ adaptive choices and adaptive capacity to climate change were assessed. Data were collected from primary and secondary sources using a mixed research approach. Findings revealed that extreme weather events have been recurrent and small-scale farmers perceived access to land, household income, and the planting of trees/shrubs on farms (agroforestry) as the main factors influencing their capacity to adapt to climate change. Agroforestry and monoculture practices were the main adaptive choices of small-scale farmers confronted with climate change. T-test and chi-square test statistics revealed a strong non-cause-effect relationship (p < 0.001) between small-scale farmers’ capacity to adapt to climate change and different socio-economic, institutional, and environmental variables. Parameter estimates of the binomial logistic regression model indicated the existence of a strong direct cause-effect relationship (p < 0.05) between small-scale farmers’ capacity to adapt to climate change and access to credit, household income, number of farms, access to information, and access to land, indicating that these variables enhance small-scale farmers’ capacity to adapt to climate change. It is recommended that policy makers examine the adaptive choices and determinants of farmers’ adaptive capacity unearthed in this chapter when formulating policies geared towards enhancing small-scale farmers’ capacity to adapt to climate change.
Singapore’s Teacher Education Model for the 21st Century (TE21)
Authors: Rajandiran Durgesh 
Published: November 2020

Abstract
Singapore introduced the Teacher Education Model for the 21st Century (TE21) in 2009 as a framework to propose a set of 21st century competencies that Singaporean teachers should be equipped with. The introduction of TE21 catalyzed the reform of existing programs and the implementation of new initiatives in initial teacher preparation programs and lifelong teacher professional development. This chapter first examines the local and international driving forces that led to the conceptualization of TE21 since Singapore’s independence. Then, the recommendations of TE21 are scrutinized along with the implementation of two new initiatives in the initial teacher preparation program. The findings are twofold. First, we find that Singapore has extensively performed a comparative review of global 21st century recommendations over four decades to customize an education system for their local context. Second, by synthesizing information sourced from interviews, government documents, and quantitative data, we find that the progress towards developing a cadre of 21st century teachers and producing holistic students in Singapore is largely successful. However, students are found to be at the receiving end of a generational cultural clash between them and their parents’ beliefs about the core of education.
Complexity measures from geometric actions onVirasoro and Kac-Moody orbits
Authors: Erdmenger Johanna , Gerbershagen Marius , Weigel Anna-Lena 
Published: November 2020

AbstractWe further advance the study of the notion of computational complexity for 2d CFTs based on a gate set built out of conformal symmetry transformations. Previously, it was shown that by choosing a suitable cost function, the resulting complexity functional is equivalent to geometric (group) actions on coadjoint orbits of the Virasoro group, up to a term that originates from the central extension. We show that this term can be recovered by modifying the cost function, making the equivalence exact. Moreover, we generalize our approach to Kac-Moody symmetry groups, finding again an exact equivalence between complexity functionals and geometric actions. We then determine the optimal circuits for these complexity measures and calculate the corresponding costs for several examples of optimal transformations. In the Virasoro case, we find that for all choices of reference state except for the vacuum state, the complexity only measures the cost associated to phase changes, while assigning zero cost to the non-phase changing part of the transformation. For Kac-Moody groups in contrast, there do exist non-trivial optimal transformations beyond phase changes that contribute to the complexity, yielding a finite gauge invariant result. Moreover, we also show that our Virasoro complexity proposal is equivalent to the on-shell value of the Liouville action, which is a complexity functional proposed in the context of path integral optimization. This equivalence provides an interpretation for the path integral optimization proposal in terms of a gate set and reference state. Finally, we further develop a new proposal for a complexity definition for the Virasoro group that measures the cost associated to non-trivial transformations beyond phase changes. This proposal is based on a cost function given by a metric on the Lie group of conformal transformations. The minimization of the corresponding complexity functional is achieved using the Euler-Arnold method yielding the Korteweg-de Vries equation as equation of motion.
Higgs inflation as nonlinear sigma model and scalaron as its σ-meson
Authors: Ema Yohei , Mukaida Kyohei , van de Vis Jorinde 
Published: November 2020

AbstractWe point out that a model with scalar fields with a large nonminimal coupling to the Ricci scalar, such as Higgs inflation, can be regarded as a nonlinear sigma model (NLSM). With the inclusion of not only the scalar fields but also the conformal mode of the metric, our definition of the target space of the NLSM is invariant under the frame transformation. We show that theσ-meson that linearizes this NLSM to be a linear sigma model (LSM) corresponds to the scalaron, the degree of freedom associated to theR2term in the Jordan frame. We demonstrate that quantum corrections inevitably induce thisσ-meson in the large-Nlimit, thus providing a frame independent picture for the emergence of the scalaron. The resultant LSM only involves renormalizable interactions and hence its perturbative unitarity holds up to the Planck scale unless it hits a Landau pole, which is in agreement with the renormalizability of quadratic gravity.
Insulin granule biogenesis and exocytosis
Authors: Omar-Hmeadi Muhmmad , Idevall-Hagren Olof 
Published: November 2020

AbstractInsulin is produced by pancreatic β-cells, and once released to the blood, the hormone stimulates glucose uptake and suppresses glucose production. Defects in both the availability and action of insulin lead to elevated plasma glucose levels and are major hallmarks of type-2 diabetes. Insulin is stored in secretory granules that form at the trans-Golgi network. The granules undergo extensive modifications en route to their release sites at the plasma membrane, including changes in both protein and lipid composition of the granule membrane and lumen. In parallel, the insulin molecules also undergo extensive modifications that render the hormone biologically active. In this review, we summarize current understanding of insulin secretory granule biogenesis, maturation, transport, docking, priming and eventual fusion with the plasma membrane. We discuss how different pools of granules form and how these pools contribute to insulin secretion under different conditions. We also highlight the role of the β-cell in the development of type-2 diabetes and discuss how dysregulation of one or several steps in the insulin granule life cycle may contribute to disease development or progression.
Effects of aluminum sulfate soaking pretreatment on dimensional stability and thermostability of heat-treated wood
Authors: Qu Lijie , Wang Zhenyu , Qian Jing , He Zhengbin , Yi Songlin 
Published: November 2020

AbstractAcidic aluminum sulfate hydrolysis solutions can be used to catalyze the thermal degradation of wood in a mild temperature environment, and thus reduce the temperature required for heat treatment process. To improve the dimensional and thermal stability of Chinese fir during heat treatment at 120 °C, 140 °C and 160 °C, this study investigated the effects of soaking pretreatment with 5%, 10% and 15% aluminum sulfate on the chemical and structural changes of the heat-treated Chinese fir. The results indicated that the samples treated at 15% aluminum sulfate concentration and 160 °C heat treatment achieved the best dimensional and thermal stability. Chemical analyses by Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) indicated that the catalysis of aluminum sulfate resulted in degradation of hemicelluloses during the heat treatment, and an increase in the soaking concentration and heat treatment temperature also affected the thermal degradation of celluloses. The scanning electron microscope (SEM) and mass changes test results proved that the hydrolyzed aluminum flocs mainly adhered to the inner wall of the wood tracheid as spherical precipitates, and when the soaking concentration reached 10% and 15%, a uniform soaking effect could be achieved. The thermogravimetric (TG) analysis revealed the soaking pretreatment effectively improved the thermal stability of the heat-treated wood by physically wrapping and promoting the formation of a carbon layer on the wood surface during heat treatment. Thus, aluminum sulfate soaking pretreatment exerted a great effect on the dimensional and thermal stability of wood, allowing heat treatment to be performed at a lower temperature.
Strategies for simultaneous and successive delivery of RNA
Authors: Moradian Hanieh , Lendlein Andreas , Gossen Manfred 
Published: November 2020

AbstractAdvanced non-viral gene delivery experiments often require co-delivery of multiple nucleic acids. Therefore, the availability of reliable and robust co-transfection methods and defined selection criteria for their use in, e.g., expression of multimeric proteins or mixed RNA/DNA delivery is of utmost importance. Here, we investigated different co- and successive transfection approaches, with particular focus on in vitro transcribed messenger RNA (IVT-mRNA). Expression levels and patterns of two fluorescent protein reporters were determined, using different IVT-mRNA doses, carriers, and cell types. Quantitative parameters determining the efficiency of co-delivery were analyzed for IVT-mRNAs premixed before nanocarrier formation (integrated co-transfection) and when simultaneously transfecting cells with separately formed nanocarriers (parallel co-transfection), which resulted in a much higher level of expression heterogeneity for the two reporters. Successive delivery of mRNA revealed a lower transfection efficiency in the second transfection round. All these differences proved to be more pronounced for low mRNA doses. Concurrent delivery of siRNA with mRNA also indicated the highest co-transfection efficiency for integrated method. However, the maximum efficacy was shown for successive delivery, due to the kinetically different peak output for the two discretely operating entities. Our findings provide guidance for selection of the co-delivery method best suited to accommodate experimental requirements, highlighting in particular the nucleic acid dose-response dependence on co-delivery on the single-cell level.
Persistent and transient inefficiency in adult education
Authors: Badunenko Oleg , Mazrekaj Deni , Kumbhakar Subal C. , De Witte Kristof 
Published: November 2020

AbstractThis paper evaluates the inefficiency of adult education programs. Using an advanced four-component stochastic frontier model on Belgian adult education data, we distinguish between persistent and transient inefficiency of adult education programs. Whereas persistent inefficiency is structural and difficult to tackle because of its time-invariant nature, transient inefficiency can be eliminated somewhat easily without a major structural change. Thus, reduction in different inefficiency components may require different policy measures. Our results indicate that despite the presence of persistent inefficiency, the overall inefficiency is mainly driven by the transient component, and hence, at the control of the adult education management. The findings suggest that social interaction is relevant in adult education as both more sessions and more learners per program increase educational efficiency. Moreover, adult education programs seem to be particularly useful for young less-educated learners.