Miracle-dz/UniFAR-FTUs huggingface dataset:
New insights into the support morphology-dependent ammonia synthesis activity of Ru/CeO2 catalysts

Three ceria nanocrystals with different morphologies (nanocubes, nanorods, and nanoparticles) were synthesized and used as supports to evaluate the support morphology-dependent ammonia synthesis activity in the structure-sensitive ammonia synthesis reaction. The Ru/r-CeO2 catalyst shows a higher catalytic reaction rate (3830 μmol g−1 h) than that of Ru/c-CeO2 (1289 μmol g−1 h) and Ru/p-CeO2 (529 μmol g−1 h) under the reaction conditions of 400 °C, 1 MPa, and 60 mL min−1. In addition, promoters (Cs, K, and Ba) can effectively enhance the catalytic activity of the catalysts such as 2Cs–Ru/r-CeO2 (14266 μmol g−1 h) and 2K–Ru/r-CeO2 (11227 μmol g−1 h). The effect of the morphology of the CeO2 support on the metal–support interaction and catalytic performance of the Ru/CeO2 catalysts can be correlated to the exposed crystal planes and surface composition/electronic structure of the CeO2 support with diverse morphologies.
IMPACT OF ON-LINE LEARNING SYSTEM ON HIGHER EDUCATION

The higher education system uses traditional teaching methods in teaching and learning. The emergence of technology encompasses a great challenge for learners, teachers, and the higher education system to attain success in the learning process via the internet. Technology has made numerous changes in the process of teaching and learning. The students and teachers adopted the advent of technology in the progress of learning and teaching respectively. The pandemic situation also necessitated the adoption of the online-based learning system. The higher education system instantly instigated the online learning system into the real-time scenario. The online learning system gave numerous advantages for both the learners and teachers. Though, they faced several challenges in the process of incorporating the new system of teaching and learning. In this paper, the numerous studies and approaches of online learning systems by various researchers are discussed. Mobile-based learning, online learning in the pandemic system, and the challenges of online learning systems are addressed in this paper.
Efficient small molecule organic light-emitting diodes fabricated by brush-coating

With the ongoing progress in exploring novel fabrication methods, topics towards high-performance, simply fabricated organic light-emitting diodes (OLEDs) have received a great deal of attention. Brush-coating is a superior liquid phase thin film deposition technology with nearly 100% material utilization, which has remarkable potential application in depositing large-area uniform films. Here, a uniform and smooth polymer hole-injection layer and a small molecule light-emitting layer are successfully fabricated through brush-coating by controlling the wetting and dewetting processes. White phosphorescent OLEDs with blue and orange complementary colours are constructed by continuously brush-coating the hole-injection layer and small molecule light-emitting layer. The brush-coated white device possesses a peak luminance of 29 190 cd m−2 and a current efficiency of 29.3 cd A−1, which are comparable to those of the spin-coated device with the same structure. In addition, the brush-coated red, green and blue phosphorescent OLEDs exhibit excellent performance in our research with peak current efficiencies of 16.7, 30.4 and 20.9 cd A−1, respectively. These results demonstrate that the brush-coating technology is a simple and effective strategy for depositing uniform organic films and, consequently, fabricating a high-performance device. What's more, brush-coating is expected to be applied to the best-to-date, large-area, flexible or patterned OLEDs in the future.
Values Created from Far and Near: Influence of Spatial Distance on Brand Evaluation

This research shows that spatial distance between visual representations of a product and consumers may enhance or devalue consumers’ perceptions of the brand depending on the brand image (prestigious vs. popular). The authors suggest that spatial distance signals prestige when status and luxury are relevant to the brand image, and decreased distance signals social closeness when popularity and broad appeal are relevant to the brand image. The authors show that for prestigious brands whose brand image is associated with status and luxury, consumers’ attitude toward the product becomes more favorable and their willingness to pay a premium for the product grows as the distance between the visual representations of the product and the consumer increases. In contrast, for popular brands whose brand image is associated with broad appeal and social connectedness, the closer the distance, the more favorable is consumers’ attitude and the higher their willingness to pay a premium. The findings provide useful guidelines to marketers on the use of visual cues in advertising and product displays.
Relative importance of ecological versus biological traits in driving range sizes of African dragonflies

Ecological traits (e.g. biotope specialisation and habitat preference) and biological traits (e.g. mobility and life history) are highly variable among species. In insect conservation, considerable focus has been on ecological generalisation, with species occupying many biotope types (generalists) often assumed to be widely distributed, whereas species occupying few biotope types (specialists) are assumed as rare and localised. Although this may be valid in most cases, there is an increasing recognition that functional traits, such as insect mobility and life history, have similar importance as drivers of range size among species. Here, we investigate ecological and biological traits simultaneously to determine their relative importance as drivers of range size variation. We hypothesise that ecological traits are primary drivers, whereas biological traits related to mobility and life history are less important drivers of range size.
Differential RCS of Modulated Tag

Differential RCS characterizes the aptitude of a tag to modulate the backscattered power. This parameter is classically estimated based on the variation of the in-phase and quadrature (IQ) channels in the time domain. This communication introduces a generalization of the RCS backscattered by a tag and a new definition of the delta RCS in the frequency domain. The analytical model is based on stochastic processes and allows to estimate the delta RCS from the power spectral density of the modulated signal backscattered by the tag. The associated measurement is compared to the classical time-domain-based methods. Results show a good agreement with a difference between the different approaches less than 0.5 dB. Finally, the proposed method relies on simple synchronization between the emitter and the receiver to estimate the delta RCS while maintaining the accuracy.
Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.
Plant community response to fuel break and goat grazing in southern California

Background: California is a global biodiversity hotspot, yet increased urbanization of wildlands, warming temperatures, and invasion of nonnative species pose serious risks to these areas due to an increase in wildfire frequency. Fuel management is a tool for reducing fire risk to neighboring communities and natural resources that involves a two-step process requiring an initial reduction of woody vegetation followed by a repeated control of woody plants and reduction of herbaceous cover. To understand the compositional and structural changes resulting from fuel treatment methods in southern California chaparral, we evaluated the compositional and structural impacts of a recently created fuel break established around the Lake Morena community on the Cleveland National Forest. The area was initially treated with cut and pile burning, then treated with herbicide, and lastly grazed by 1,200 goats. The purpose of this study is to (1) evaluate the compositional and structural differences associated with the initial fuel break, and (2) quantify compositional shifts in herbaceous and woody vegetation caused by goat grazing over time. Results: Plots on fuel breaks and in adjacent wildlands exhibited significantly different species assemblages. Total herbaceous cover (both native and nonnative) was 92 times greater on fuel breaks than in adjacent chaparral-dominated wildlands and native shrub cover was 55.3 times greater in adjacent wildlands than on fuel breaks. Goats had a significant impact on reducing native and nonnative herb cover (87% reduction in cover, 92% reduction in height), but were ineffective at reducing the cover and height of most woody species such as Adenostoma fasciculatum, Eriogonum fasciculatum, Quercus berberidifolia, and Artemisia tridentata. However, goats were found to be effective in controlling nonnative grasses including Bromus diandrus and Bromus madritensis. Conclusion: Initial fuel break creation was effective at reducing wood biomass and height, simultaneously giving rise to an abundance and diversity of native and nonnative herbaceous species. Although targeted goat grazing was successful at reducing herbaceous biomass, it was ineffective at reducing woody biomass which is often one of the most important goals for fuel management in chaparral ecosystems.
Prospects of genome editing using CRISPR/CAS or how to master genetic scissors. Nobel Prize in Chemistry 2020

one of the most powerful genetic technology tools - CRISPR/Cas9, or so-called ‘genetic scissors’. has to many important results of basic research. In particular, plant researchers have managed to create crops resistant to mold, pests and drought. As for medicine, clinical trials of new cancer treatment techniques are underway, and a dream of curing hereditary diseases is about to become a reality. Genetic scissors have brought the life sciences to a new stage of development and greatly contributed to the benefit of mankind.
Emerging interstices in communities of innovation

Online communities are an important source of distributed intelligence and novelty for any firm. Their evolving independence exhibits purpose and progress of the individuals in them. However, research on community innovation is disparate, with literature enshrouded in macroperspectives of a firm's largest external source of innovation. Utilizing critical interpretive synthesis, we uncover three dominant perspectives in literature on community innovation — namely, outside-in, epistemic and competitive. We then propose a multilevel perspective of communities to explore three levels of innovation context. In closing, we examine a missing mesoperspective in literature and present four dynamic stages of exchange in communities — (i) convergence and design, (ii) interaction, (iii) interchange and (iv) productive synergy. With this final and main contribution, we apply a conceptual multilevel perspective to the same sample of 132 articles, previously used to demonstrate the three dominant macroperspectives. In doing so, we reconceptualize diversified knowledge in community literature and acknowledge the innovation context of independent individuals and groups in the community. We hope that scholars of open innovation use variations of the multilevel perspective for further theory construction as a lens into the context of innovation for individual, group and community innovation outcomes. The interconnectivity brought by these levels also creates challenges and opportunities for practitioners, wishing to engage with or design online communities to influence individual, group and community outcomes and motivations.
Multiple Dimensions of Smart Cities’ Infrastructure: A Review

In recent years, there has been significant focus on smart cities, on how they operate and develop, and on their technical and social challenges. The importance of infrastructure as a major pillar of support in cities, in addition to the rapid developments in smart city research, necessitate an up-to-date review of smart cities’ infrastructure issues and challenges. Traditionally, a majority of studies have focused on traffic control and management, transport network design, smart grid initiatives, IoT (Internet of Things) integration, big data, land use development, and how urbanization processes impact land use in the long run. The work presented herein proposes a novel review framework that analyzes how smart city infrastructure is related to the urbanization process while presenting developments in IoT sensor networks, big data analysis of the generated information, and green construction. A classification framework was proposed to give insights on new initiatives regarding smart city infrastructure through answering the following questions: (i) What are the various dimensions on which smart city infrastructure research focuses? (ii) What are the themes and classes associated with these dimensions? (iii) What are the main shortcomings in current approaches, and what would be a good research agenda for the future? A bibliometric analysis was conducted, presenting cluster maps that can be used to understand different research trends and refine further searches. A bibliographic analysis was then followed, presenting a review of the most relevant studies over the last five years. The method proposed serves to stress where future research into understanding smart systems, their implementation and functionality would be best directed. This research concluded that future research on the topic should conceptualize smart cities as an emergent socio-techno phenomenon.
In-Situ XRD Study of Phase Transformation Kinetics in a Co-Cr-W-Alloy Manufactured by Laser Powder-Bed Fusion

The additive manufacturing process of laser powder-bed fusion (L-PBF) is an increasingly popular approach for patient-specific production of dental frameworks made from Co-Cr alloys. Macroscopically, frameworks produced in this way exhibit high anisotropy especially in Young’s modulus, and are missing standardized requirements. Microscopically, pronounced texture and high residual stresses are characteristic. To reduce resulting detrimental effects, the as-built (AB) parts are heat treated. Dependent on the treatment temperature, effects like the transformation of the γ-phase matrix in the AB condition to ϵ-phase, precipitation, stress relief, and grain growth were observed. While the existence of these processes was established in the past, little is known about their kinetics. To fill this gap, these effects were studied with in-situ X-ray diffraction (XRD) methods in isothermal heat treatments (HTs) at four different sample surface temperatures TS reaching from 650∘C to 900∘C. Furthermore, room temperature ex situ XRD and SEM/EDS measurements completed the analysis. An evaluation of the datasets, with single peak fitting and QXRD methods, yielded the following results. In the HTs below a certain threshold, a γ-to-ϵ transformation was observed in the sample bulk and close to the sample surface. In the latter case, evidence for a partially strain-induced transformation related to oxide formation was present. Above this threshold and possibly slightly below, σ- and Laves-phase precipitated. Additionally, peak profile evolutions hinted at a drop of inter- and intragranular stresses within the first 30 to 60 min. Therefore, an HT of about 30 to 60 min slightly above the threshold is proposed as optimal for reducing residual stresses while retaining a predominantly single-phased microstructure, possibly superior in corrosion properties and likewise in bio-compatibility.
New approach for predicting nitrification and its fraction of N2O emissions in global terrestrial ecosystems

Nitrification is a major pathway of N2O production in aerobic soils. Measurements and model simulations of nitrification and associated N2O emission are challenging. Here we innovatively integrated data mining and machine learning to predict nitrification rate ( Rnit ) and the fraction of nitrification as N2O emissions ( fN2ONit ). Using our global database on Rnit and fN2ONit , we found that the machine-learning based stochastic gradient boosting (SGB) model outperformed three widely used process-based models in estimating Rnit and N2O emission from nitrification. We then applied the SGB technique for global prediction. The potential Rnit was driven by long-term mean annual temperature, soil C/N ratio and soil pH, whereas fN2ONit by mean annual precipitation, soil clay content, soil pH, soil total N. The global fN2ONit varied by over 200 times (0.006%–1.2%), which challenges the common practice of using a constant value in process-based models. This study provides insights into advancing process-based models for projecting N dynamics and greenhouse gas emissions using a machine learning approach.
Reproductive Biology of Olive Trees (Arbequina cultivar) at the Northern Limit of Their Distribution Areas

In recent years, North-western Spain has experienced an increase in the cultivated area of olive trees. The main propitious areas for olive groves are the Mino and Sil basins, as a consequence of their Oceanic climate with Mediterranean influence. The objective of this study is to determine the characteristics of reproductive biology, phenological and aerobiological behaviour of olive trees in the most northerly new plantation areas of the Iberian Peninsula. The study was carried out in an olive grove growing Olea europaea L. cv. ‘Arbequina’ located in Quiroga (Lugo) from 2016 to 2018. The phenological observations were based upon the main growth stages following the Biologische Bundesanstalt Bundessortenamt and Chemical industry (BBCH) scale. To predict the onset of flowering, a thermal time model was used in order to quantify the chill requirements, and growing degree-days were applied to determine the heat requirement. The production, viability and germination rates of Olea pollen were evaluated from samples selected in nine individual trees for the phenological survey. The aerobiological study was conducted by means of a Hirst-type pollen trap located in the centre of the olive grove. The vegetative period of the olive tree in the study area lasted an average of 259 days. The important phenological stage 6 (flowering) was the shortest stage. An average of 704 Chilling Hours (CH) with a threshold of 2.5 °C was required to overcome the chilling period, 1139 Growing Degree Days (GDD) for the beginning of flowering, and 4463 GDD for harvest. The pollen production per anther was 82589 grains (± 14084 pollen grains), with a rate of 81% viability and 12% pollen tube germination. The main pollen season started on average on May 20th and ended on June 16th with an average duration of 27 days and an annual pollen integral of 833 pollen grains. The low pollen concentrations could be a consequence of the Northern location of the forest, in a bioclimatic transition zone between the Eurosiberian and the Mediterranean areas, at the limit of olive tree distribution.
In situ scanning–digital image correlation for high-temperature deformation measurement of nickel-based single crystal superalloy

In situ microscopic observation and measurement of deformations for advanced hot-section materials at high-temperature is helpful in understanding their failure mechanisms. Scanning electron microscopy (SEM) and a digital image correlation (DIC) method are combined to capture the failure process. This article reports an in situ high-temperature testing system which can heat the specimen up to 1000 °C and provide clear images simultaneously. The Al2O3 nanoscale particles are developed as high-temperature deformation carriers in SEM–DIC, which are suitable for high-temperature samples with significant advantages of stability, high image contrast and without shedding or melting up to 1000 °C. High-temperature tensile and creep properties of a nickel-based single crystal superalloy (NBSCS) at 750 °C were investigated using this system. In addition, a scanning–DIC (S–DIC) method, which avoids errors introduced by conventional DIC methods, was used to calculate full-field dynamic displacement and strain of high-temperature NBSCS samples. Analysis of the strain fields show that the strain concentrations are generally at the positions of crack initiation or propagation, and the creep cracks interact with each other through the strain field. Finally, the crack opening displacement is obtained using the virtual extensometer.
Structural disavowal and personal inundation of responsibility – a local perspective on pressure on mental health front-line professionalsStrukturell ansvarsfraskrivelse og individuell ansvarsoversvømmelse – kommunalt perspektiv på dilemmaer i profesjonelt arbeidet med rus og psykisk helse

ABSTRACT This article examines how professionals in front-line mental health and addiction services respond to the dilemmas of implementing policies at the local level of the service system. In their daily work, front-line health and social work professionals must find ways to cope with challenges balancing limited resources, professional ethical standards and client needs for varied and flexible services. Front-line professionals have significant space for discretion in finding ways to cope with these dilemmas. We discuss these ways as a drift towards moral dispositions. Drawing on qualitative interviews with local managers and professionals, we find that front-line professionals find themselves at the bottom of the service line where one cannot ‘decentralise’ responsibilities any more. We describe and denote this as ‘structural disavowal of responsibility’, as patients with severe mental health diagnoses are often discharged from specialist services to primary care that lacks the necessary resources. Furthermore, front-line professionals improvise by including measures found in the local community and even their private lives, making their work exceedingly stressful. This is denoted ‘personal inundation of responsibility’. We argue that the moral disposition of front-line professionals hence should be extended to encompass personal inundation as one perversion of their responsibilities.