Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method for Measuring Total Factor Productivity Growth Based on the Full Industry Equilibrium Approach: The Case of the Greek Economy

A new method of identifying the sources of output growth and measuring total factor productivity (TFP) is proposed, with an application to data from the Greek economy. The price accounting approach, based on the full industry equilibrium (FIE) framework introduced by Opocher and Steedman, where technical progress not only increases outputs relative to inputs but also reduces output prices relative to input rewards, is used. The contributions of this paper are that, first, it amends the FIE TFP measurement approach to account for heterogeneous labor inputs, imported inputs, and indirect taxes, and applies the method to real-world data from the Greek economy; second, it provides a comparison of the results with those found by the use of the neoclassical approach to TFP measurement arguing that the FIE approach measures better sectoral TFP change, and third, it provides an estimate of the effects of sectoral research and development (R&D) expenditures and R&D diffusion from other sectors on TFP change for the Greek economy.
Why and When Supervisor Developmental Feedback Impact Innovative Behavior: Perspective of Self-Regulation Theory

In this study, we examined how supervisor developmental feedback influences employees’ innovative behavior. A multi-time survey method was used in this study to collect data from 310 employees in Chinese enterprises. The research results show that supervisor developmental feedback has positive effects on employee innovative behavior via the mediating effect of self-goal-setting. We further found that uncertainty avoidance strengthens the positive relationship between supervisor developmental feedback and the self-goal-setting of employees. Our study offers a new account based on self-regulation perspective for understanding feedback.
Mesoscale biosimulations within a unified framework: from proteins to plasmids

ABSTRACT The simulation of large biomolecules and biological assemblies requires to achieve an optimal balance between physical realism and computational efficiency. We describe how an effective strategy is based on a dual representation, whereby macromolecules are described by means of a coarse grained, particle-based representation, while aqueous solvent and electrolytes are represented by a mesoscopic, kinetic treatment. The joint usage of the Molecular Dynamics and Lattice Boltzmann techniques provides an optimal combination for these representations and allows simulating from proteins to polynucleotides under a wide range of equilibrium and off-equilibrium conditions.
Which Facial Features Are Central in Impression Formation?

Which facial characteristics do people rely on when forming personality impressions? Previous research has uncovered an array of facial features that influence people’s impressions. Even though some (classes of) features, such as resemblances to emotional expressions or facial width-to-height ratio (fWHR), play a central role in theories of social perception, their relative importance in impression formation remains unclear. Here, we model faces along a wide range of theoretically important dimensions and use machine learning techniques to test how well 28 features predict impressions of trustworthiness and dominance in a diverse set of 597 faces. In line with overgeneralization theory, emotion resemblances were most predictive of both traits. Other features that have received a lot of attention in the literature, such as fWHR, were relatively uninformative. Our results highlight the importance of modeling faces along a wide range of dimensions to elucidate their relative importance in impression formation.
RADIATION USE EFFICIENCY OF MAIZE UNDER INFLUENCE OF DIFFERENT LEVELS OF NITROGEN FERTILIZATION AND TWO DIFFERENT SEASONAL CONDITIONS

The objective of this study to estimate the Radiation Use Efficiency under effect of three levels of nitrogen (N1 = 80 Kg N ha-1, N2 =120 Kg N ha-1, and N3=160 Kg N ha-1) for a set of maize hybrids include (Gloria, Market, Cruze and Draxma) in two different seasons in spring and fall of 2017 in Qlyasn-Sulaimani. The experiment was conducted using split plot design with three replicates . The results revealed higher intensity of PAR in autumn season than that of spring (15.672 and 10.555)MJm-2d-1 respectively that led to higher RUE in autumn revealing (4.311,3.546, and 4.702)g MJ-1 m-2 under the effect of (N1,N2, and N3) respectively, while the results of the autumn were higher (5.237, 4.681, and 6.019) g MJ-1 m-2 under the same levels of nitrogen fertilization, the highest application of nitrogen was more effective in autumn. The RGR and the rate of total dry matter accumulation were higher in autumn season displaying positive performance of studied maize hybrids to the higher intercepted of solar energy as well higher temperature.
Does openness enable or hinder innovation performance? The moderating effect of appropriability mechanisms

ABSTRACT Open innovation has been recognised as being important for innovation performance. However, previous studies have reported inconsistent findings regarding the impact of collaboration on innovation performance. Therefore, this study aims to reexamine this issue using recent data. We empirically examine the impact of the breadth and depth of collaboration and geographical proximity on product and process innovation, along with the moderating effect of appropriability mechanisms. The empirical analysis utilises data from the 2018 Korean Innovation Survey with a sample of 2,392 Korean firms. The results show that the breadth and depth of collaboration and geographical proximity have differential effects on product and process innovation. We also demonstrate the differential moderating impact of appropriability mechanisms. This study contributes to offering a better understanding of how firms can enhance innovation performance in the context of external collaboration and appropriability.
Male Allies at Work: Gender-Equality Supportive Men Reduce Negative Underrepresentation Effects Among Women

Does commitment to allyship from a dominant group member cue identity-safety for women in male-dominated environments? We examine this question by assessing women’s perceptions of workplaces that included the presence (vs. absence) of a male ally (Studies 1–3) or a female ally (Study 3), and determine the impact of Black versus White allies for Black and White women. Across three studies (N = 1,032) and an integrative data analysis, we demonstrate that an equality-supportive male ally reduces anticipated isolation and workplace hostility and increases anticipated support, respect, and gender-equality norms for women in general populations (Studies 1 and 2) and women in science, technology, and math (Study 3). These results represent a possible strategy to help retain women in male-dominated fields.
Dehydrogenation of Ethanol to Acetaldehyde over Different Metals Supported on Carbon Catalysts

Recently, the interest in ethanol production from renewable natural sources in Thailand has been receiving much attention as an alternative form of energy. The low-cost accessibility of ethanol has been seen as an interesting topic, leading to the extensive study of the formation of distinct chemicals, such as ethylene, diethyl ether, acetaldehyde, and ethyl acetate, starting from ethanol as a raw material. In this paper, ethanol dehydrogenation to acetaldehyde in a one-step reaction was investigated by using commercial activated carbon with four different metal-doped catalysts. The reaction was conducted in a packed-bed micro-tubular reactor under a temperature range of 250–400 °C. The best results were found by using the copper doped on an activated carbon catalyst. Under this specified condition, ethanol conversion of 65.3% with acetaldehyde selectivity of 96.3% at 350 °C was achieved. This was probably due to the optimal acidity of copper doped on the activated carbon catalyst, as proven by the temperature-programmed desorption of ammonia (NH3-TPD). In addition, the other three catalyst samples (activated carbon, ceria, and cobalt doped on activated carbon) also favored high selectivity to acetaldehyde (>90%). In contrast, the nickel-doped catalyst was found to be suitable for ethylene production at an operating temperature of 350 °C.
High temporal resolution hydrometeorological data collected in the tropical 1 Cordillera Blanca, Peru (2004-2020) 2

This article presents a comprehensive hydrometeorological dataset collected over the past two decades throughout the Cordillera Blanca, Peru. The data recording sites, located in the 27 upper portion of the Rio Santa valley, also known as the Callejon de Huaylas, span an elevation 28 range of 3738 - 4750 m a.s. l. As many historical hydrological stations measuring daily discharge 29 across the region became defunct after their installation in the 1950s, there was a need for new 30 stations to be installed and an opportunity to increase the temporal resolution of the streamflow 31 observations. Through inter-institutional collaboration the hydrometeorological network 32 described in this paper was deployed with goals to evaluate how progressive glacier mass loss 33 was impacting stream hydrology, and to better understand the local manifestation of climate 34 change over diurnal to seasonal and interannual time scales. The four automatic weather stations 35 supply detailed meteorological observations, and are situated in a variety of mountain 36 landscapes, with one on a high-mountain pass, another next to a glacial lake, and two in glacially 37 carved valleys. Four additional temperature and relative humidity loggers complement the 38 weather stations within the Llanganuco valley by providing these data across an elevation 39 gradient. The six streamflow gauges are located in tributaries to the Rio Santa and collect high 40 temporal resolution runoff data. The datasets presented here are available freely from (Mateo et al., 2021). Combined, 42 the hydrological and meteorological data collected throughout the Cordillera Blanca enable 43 detailed research of atmospheric and hydrological processes in tropical high-mountain terrain. 44 45 46 47 48 49 50 51 52
Influence of agricultural managed aquifer recharge on nitrate transport: The role of soil texture and flooding frequency

Agricultural managed aquifer recharge (Ag‐MAR) is a concept in which farmland is flooded during the winter using excess surface water to recharge the underlying groundwater. In this study, we show how different recharge practices affect NO3− leaching and mineralization–denitrification processes in different soil systems. Two contrasting soil textures (sand and fine sandy loam) from the Central Valley, California, were repeatedly flooded with 15 cm of water at varying time intervals in field and soil column experiments. Nitrogen species (NO3–, NH4+, total N), total C, dissolved O2, and moisture content were measured throughout the experiments. Results show that when flooding occurs at longer intervals (every 1–2 wk), N mineralization increases, leading to an increase of mobile NO3− in the upper root zone and leaching of significant quantities of NO3− from both soil textures (137.3 ± 6.6% [sand] and 145.7 ± 5.8% [fine sandy loam] of initial residual soil NO3−) during subsequent flooding events. Laboratory mineralization incubations show that long flooding intervals promote mineralization and production of excess NO3− at rates of 0.11–3.93 mg N kg–1 wk–1 (sand) and 0.08–3.41 mg N kg–1 wk–1 (fine sandy loam). Decreasing the flooding frequency to 72 h reduces potential mineralization, decreasing the amount of NO3− leached during flooding events (31.7 ± 3.8% [sand] and 64.7 ± 10.4% [fine sandy loam] of initial residual soil NO3–). The results indicate that implementing recharge as repeated events over a long (multiple‐week) time horizon might increase the total amount of NO3− potentially available for leaching to groundwater.
Machine Learning-Based Models for Accident Prediction at a Korean Container Port

The occurrence of accidents at container ports results in damages and economic losses in the terminal operation. Therefore, it is necessary to accurately predict accidents at container ports. Several machine learning models have been applied to predict accidents at a container port under various time intervals, and the optimal model was selected by comparing the results of different models in terms of their accuracy, precision, recall, and F1 score. The results show that a deep neural network model and gradient boosting model with an interval of 6 h exhibits the highest performance in terms of all the performance metrics. The applied methods can be used in the predicting of accidents at container ports in the future.
Effects of crosslinking degree on the coating properties of arabinoxylan

Arabinoxylan (AX) was extracted from sugarcane bagasse and modified through crosslinking with glutaraldehyde (GA). The effects of crosslinking degree on the rheological and coating properties of glutaraldehyde crosslinked arabinoxylan (GAX) were investigated. To better evaluate the degree of crosslinking, the crosslink index was used to represent the degree of crosslinking for the GAX in this study. The viscosity of the GAX solution increased when the degree of crosslinking increased, and the solution demonstrated non-Newtonian flow behavior. A high degree of crosslinking was detrimental to the film and coating properties of the GAX. At an optimum degree of crosslinking, the tensile strength of the GAX films increased by approximately 170% and 60% compared with that of the AX and GAX with the highest degree of crosslinking, respectively; the tensile strength of the GAX-coated paper increased by approximately 15% compared with that of the GAX with the highest degree of crosslinking. When calcium carbonate was mixed with the paper coating adhesives, the GAX showed comparable coating properties to that of polyvinyl alcohol, demonstrating its potential to substitute petroleum-based paper coating adhesives.
Fundamental resource specialization of herbivorous butterflies decreases towards lower latitudes

It is generally assumed that the degree of resource specialization in herbivorous insects increases towards lower latitudes. However, latitudinal patterns in herbivore diet breadth at large spatial scales remain poorly understood. In this work, we investigated the drivers of latitudinal variation in lepidopteran ‘fundamental’ resource specialization, which we defined as the host breadth when not limited by interspecific interactions at the same trophic level.
Improved proton conductivity and methanol permeability of PVA-based proton exchange membranes using diphenylamine-4-sulfonic acid sodium salt and silica nanoparticles

ABSTRACT A novel series of PVA/DPA-4-SASS/SiO2 composite membranes were fabricated and characterized in the present study. Compared to the neat PVA, water uptake, proton conductivity, and ion exchange capacity of the membranes were enhanced. The membrane containing 5 Wt. % of SiO2 nanoparticles and 80 Wt. % of the DPA-4-SASS showed the highest values of water uptake, proton conductivity (1.5 × 10−1 S/cm) and ion exchange capacity (1.47 mmol/g). The results also indicated that methanol permeability was decreased by increasing the DPA-4-SASS content in the hybrid membranes. Thermal stability and mechanical properties of the cross-linked membranes were also improved. Graphical Abstract
Alternative diffusion anisotropy measures for the investigation of white matter alterations in 22q11.2 deletion syndrome

Diffusion MRI (dMRI) is widely used to study the brain’s white matter (WM) microstructure in a range of psychiatric and neurological diseases. As the diffusion tensor model has limitations in brain regions with crossing fibers, novel diffusion MRI reconstruction models may offer more accurate measures of tissue properties, and a better understanding of the brain abnormalities in specific diseases. Here we studied a large sample of 249 participants with 22q11.2 deletion syndrome (22q11DS), a neurogenetic condition associated with high rates of developmental neuropsychiatric disorders, and 224 age-matched healthy controls (HC) (age range: 8-35 years). Participants were scanned with dMRI at eight centers worldwide. Using a meta-analytic approach, we assessed the profile of group differences in four diffusion anisotropy measures to better understand the patterns of WM microstructural abnormalities and evaluate their consistency across alternative measures. When assessed in atlas-defined regions of interest, we found statistically significant differences for all anisotropy measures, all showing a widespread but not always coinciding pattern of effects. The tensor distribution function fractional anisotropy (TDF-FA) showed largest effect sizes all in the same direction (greater anisotropy in 22q11DS than HC). Fractional anisotropy based on the tensor model (FA) showed the second largest effect sizes after TDF-FA; some regions showed higher mean values in 22q11DS, but others lower. Generalized fractional anisotropy (GFA) showed the opposite pattern to TDF-FA with most regions showing lower anisotropy in 22q11DS versus HC. Anisotropic power maps (AP) showed the lowest effect sizes also with a mixed pattern of effects across regions. These results were also consistent across skeleton projection methods, with few differences when projecting anisotropy values from voxels sampled on the FA map or projecting values from voxels sampled from each anisotropy map. This study highlights that different mathematical definitions of anisotropy may lead to different profiles of group differences, even in large, well-powered population studies. Further studies of biophysical models derived from multi-shell dMRI and histological validations may help to understand the sources of these differences. 22q11DS is a promising model to study differences among novel anisotropy/dMRI measures, as group differences are relatively large and there exist animal models suitable for histological validation.
From Arms to Trees: Opportunity Costs and Path Dependence and the Exploration-Exploitation Tradeoff

The literature on the exploration-exploitation tradeoff has anchored on the n-armed bandit problem as its canonical formal representation. This structure, however, omits a fundamental property of evolutionary dynamics. Contrary to a bandit formulation, foregoing an opportunity may negate the possibility of engaging in that opportunity in the future, not just modifying the beliefs about the attractiveness of engaging in that opportunity. Thus, the bandit structure only incorporates path dependence with respect to beliefs and not with regard to capabilities as our usual conceptions of dynamics of learning and capabilities would suggest. Furthermore, the consideration of opportunity cost is rather static and does not address the dynamic unfolding of opportunity structures. The nature of path dependence and opportunity costs are used to frame many of our existing conceptualizations of search processes and firm dynamics, including bandit models, real options, pivoting, the “secretary problem,” and “island” models of firm diversification. The discussion points to the need to develop canonical models of what evolutionary biologists’ term phylogenetic trees and opens up a set of new questions, such as what is the degree of parallelism of trajectories that is possible within an organization, what is the fecundity of different trajectories in terms of likelihood of branching possibilities arising, and how are these latent branching opportunities accessed?
A convolution neural network based semi-parametric dynamic model for industrial robot

Robot dynamic model is widely applied to control, collision detection and motion planning. Accurate dynamic model can achieve better performance for the above applications. Traditional dynamic models have several limitations, such as the complex hypotheses for friction model and the requirement of additional joint torque sensors. This article constructs a convolution neural network (CNN) based semi-parametric dynamic (SPD) model by only using the motor encoder signals and motor currents. The SPD model not only contains the physically feasible parameters but also compensates the dynamic model by CNN. The parametric and non-parametric parts constitute the SPD model. A lightweight CNN is proposed to simultaneously ensure the accuracy and computational efficiency. To effectively train the CNN model, a dataset generation method, which expands the excitation trajectory and only uses a continuous trajectory to record data, is proposed. The CNN-based SPD model is verified on a 6-DoF laboratory-developed industrial robot only with the proprioceptive sensors. Compared with the traditional rigid body dynamics (RBD) model, the average error of the CNN-based SPD model is reduced by 9.23% in terms of the experimental results. Meanwhile, the proposed CNN-based method achieves better performance than other supervised methods.
Studies on prevalence of malaria and its adverse fetal outcomes in Federal Medical Centre (FMC), Owerri, IMO State, Nigeria

Despite a massive increase in private and public efforts over the last years, malaria remains one of the most salient global health concerns. The study adopted cross sectional and descriptive survey design to assess the prevalence of maternal malaria and its adverse fetal outcomes in Federal Medical Centre (FMC), Owerri, Imo State, Nigeria from September, 2020 to March, 2021. The study population were 814 consented pregnant women in their reproductive ages (16 - 55years) who attended ante natal clinic or delivered of their babies at FMCO during the time of study. Data collection involved administration of closed ended questionnaire to illicit information on biographic data. Clinical assessments/examinations (laboratory investigations) of maternal peripheral blood, and fetal birth weight were utilized. Shortly before child birth maternal peripheral blood was obtained from each participant into sterile container for laboratory analysis. Statistical analysis of generated data was carried out using descriptive analysis and of percentages and presented using tables. Statistical comparisons and test of significance between positive and negative groups were calculated using the non-parametric Chi-square test. Differences were considered significant at P< 0.05. The study revealed that 65.6% had malaria during pregnancy. Malaria prevalence is significantly associated with maternal age bracket ( x2= 16.27; P < 0.05), gravidity (x2 = 14.9; P < 0.05) and level of education (x2= 24.69; P < 0.05). There is significant relationship between maternal malaria and perinatal mortality (x2 = 23.14; P < 0.05). There is significant effect of maternal malaria on perinatal mortality based on maternal age (x2= 40.11; P < 0.05) and gravidity (x2= 48.67; P < 0.05). An overall prevalence of preterm deliveries were 19.7%. There is significant relationship between maternal malaria and preterm delivery (x2 = 27.58; P < 0.05). There is significant effect of maternal malaria on preterm delivery based on maternal age (x2 = 49.2; P < 0.05) and gravidity (x2= 56.94; P < 0.05). An overall prevalence of low birth weight were 23.6%. There is significant relationship between maternal malaria and fetal birth weight (x2 = 34.06; P < 0.05). There is significant effect of maternal malaria on fetal birth weight based on maternal age (x2 = 53.82; P < 0.05) and gravidity (x2= 65.94; P < 0.05). The study suggests effective therapy since perinatal mortality due to maternal malaria was recorded in this study. Preterm deliveries and low fetal birth weight based on gravidity and maternal age groups associated with maternal malaria as identified is a call for program managers to make haste and implement new strategies for malaria control.