Purpose - The purpose of this research is to examine relationships between emotional intelligence and the four factor model of cultural intelligence - metacognitive CQ, cognitive CQ, motivational CQ, ...and behavioral CQ.Design methodology approach - Confirmatory factor analyses and hierarchical regression analyses on data from 381 students in Korea are conducted.Findings - The results support discriminant validity of the four factor model of cultural intelligence scale (CQS) in relation to the emotional intelligence (EQ) construct. This study also demonstrates that the EQ factors related to social competence (social awareness and relationship management) explain CQ over and beyond the EQ factors related to self-competence (self-awareness, and relationship management). Finally, the results present that specific factors of EQ are related to specific factors of CQ.Originality value - The findings of this study demonstrate how CQ and EQ are distinct, but related constructs, which has not been conducted by prior research.
Greenhouses require accurate and reliable data to interpret the microclimate and maximize resource use efficiency. However, greenhouse conditions are harsh for electrical sensors collecting ...environmental data. Convolutional neural networks (ConvNets) enable complex interpretation by multiplying the input data. The objective of this study was to impute missing tabular data collected from several greenhouses using a ConvNet architecture called U-Net. Various data-loss conditions with errors in individual sensors and in all sensors were assumed. The U-Net with a screen size of 50 exhibited the highest coefficient of determination values and the lowest root-mean-square errors for all environmental factors used in this study. U-Net
correctly learned the changing patterns of the greenhouse environment from the training dataset. Therefore, the U-Net architecture can be used for the imputation of tabular data in greenhouses if the model is correctly trained. Growers can secure data integrity with imputed data, which could increase crop productivity and quality in greenhouses.
Purpose
This study aims to examine how workplace incivility (i.e. coworker and customer incivility) affects service employees’ creativity, specifically the way emotional exhaustion at work decreases ...their intrinsic motivation, and, in turn, damages service employees’ creativity. The purpose of this study, therefore, is to show the mechanism by which both coworker and customer incivility at work affects service employee creativity.
Design/methodology/approach
Service employees from a hotel in South Korea were surveyed using a self-administered instrument for data collection. Out of 450 questionnaires, a total of 281 usable questionnaires were obtained after list-wise deletion, for a 62.4 per cent response rate. Structural equation modeling analysis provided support for the hypotheses.
Findings
The results indicate a serial multiple mediator model in which both coworker and customer incivility increase service employees’ emotional exhaustion, which, in turn, reduces their intrinsic motivation at work and ultimately decreases their creativity. That is, the findings of this study reveal a negative relationship between workplace incivility (i.e. coworker and customer incivility) and service employees’ creativity that is fully and sequentially mediated by the service employees’ emotional exhaustion and intrinsic motivation.
Research limitations/implications
The use of cross-sectional self-reports potentially raises concerns about common method bias. Caution is recommended in reaching conclusions concerning the causal relationships between the variables, as the current study did not capture causality variation. For instance, it may be that emotional exhaustion from incivility gradually compounds over time, leading to a greater negative impact on service employees. In contrast, employees may develop strategies to cope with uncivil behavior over time, which attenuates the negative effects on service employees as time passes. A longitudinal design might offer an alternative to overcome this limitation in future research.
Practical implications
Considering the findings about the mediating effect of emotional exhaustion between workplace incivility and employee outcomes (i.e. intrinsic motivation and creativity), firms should consider establishing systematic institutional practices and policies to prevent employees from feeling emotionally exhausted from workplace incivility. Executive and senior management teams would benefit by instituting strict policies and regulations which nurture desirable behaviors among organizational members that protect victims of workplace incivility.
Originality/value
This study is the first to examine the relationship between workplace incivility and creativity. Moreover, the present study attempts to develop an understanding of the underlying mechanism through which both coworker and customer incivility negatively affect service employees’ creativity.
Root-zone environment is considered difficult to analyze, particularly in interpreting interactions between environment and plant. Closed-loop soilless cultures have been introduced to prevent ...environmental pollution, but difficulties in managing nutrients can cause nutrient imbalances with an adverse effect on crop growth. Recently, deep learning has been used to draw meaningful results from nonlinear data and long short-term memory (LSTM) is showing state-of-the-art results in analyzing time-series data. Therefore the macronutrient ion concentrations affected by accumulated environment conditions can be analyzed using LSTM.
The trained LSTM can estimate macronutrient ion concentrations in closed-loop soilless cultures using environmental and growth data. The average training accuracy of six macronutrients was R
= 0.84 and the test accuracy was R
= 0.67 with RMSE = 1.48 meq L
. The used values of input interval and time step were 1 h and 168 (1 week), respectively. The accuracy was improved when the input interval became shorter, but not improved when the LSTM consisted of a multilayer structure. Regarding training methods, the LSTM improved the accuracy better than the non-LSTM. The trained LSTM showed relatively adequate accuracies and the interpolated ion concentrations showed variations similar to those seen during traditional cultivation.
We could analyze the nutrient balance in the closed-loop soilless culture, the model showed potential in estimating the macronutrient ion concentrations using environmental and growth factors measured in greenhouses. Since the LSTM is a powerful and flexible tool used to interpret accumulative changes, it is easily applicable to various plant and cultivation conditions. In the future, this approach can be used to analyze interactions between plant physiology and root-zone environment.
Crop fresh weight and leaf area are considered non-destructive growth factors due to their direct relation to vegetative growth and carbon assimilation. Several methods to measure these parameters ...have been introduced; however, measuring these parameters using the existing methods can be difficult. Therefore, a non-destructive measurement method with high versatility is essential. The objective of this study was to establish a non-destructive monitoring system for estimating the fresh weight and leaf area of trellised crops. The data were collected from a greenhouse with sweet peppers (Capsicum annuum var. annuum); the target growth factors were the crop fresh weight and leaf area. The crop fresh weight was estimated based on the total system weight and volumetric water content using a simple formula. The leaf area was estimated using top-view images of the crops and a convolutional neural network (ConvNet). The estimated crop fresh weight and leaf area exhibited average R2 values of 0.70 and 0.95, respectively. The simple calculation was able to avoid overfitting with fewer limitations compared with the previous study. ConvNet was able to analyze raw images and evaluate the leaf area without additional sensors and features. As the simple calculation and ConvNet could adequately estimate the target growth factors, the monitoring system can be used for data collection in practice owing to its versatility. Therefore, the proposed monitoring system can be widely applied for diverse data analyses.
In existing closed-loop soilless cultures, nutrient solutions are controlled by the electrical conductivity (EC) of the solution. However, the EC of nutrient solutions is affected by both growth ...environments and crop growth, so it is hard to predict the EC of nutrient solution. The objective of this study was to predict the EC of root-zone nutrient solutions in closed-loop soilless cultures using recurrent neural network (RNN). In a test greenhouse with sweet peppers (
L.), data were measured every 10 s from October 15 to December 31, 2014. Mean values for every hour were analyzed. Validation accuracy (
) of a single-layer long short-term memory (LSTM) was 0.92 and root-mean-square error (RMSE) was 0.07, which were the best results among the different RNNs. The trained LSTM predicted the substrate EC accurately at all ranges. Test accuracy (
) was 0.72 and RMSE was 0.08, which were lower than values for the validation. Deep learning algorithms were more accurate when more data were added for training. The addition of other environmental factors or plant growth data would improve model robustness. A trained LSTM can control the nutrient solutions in closed-loop soilless cultures based on predicted future EC. Therefore, the algorithm can make a planned management of nutrient solutions possible, reducing resource waste.
Crop models are tools used to analyze the interaction of crops and the environment. Since crop models can be applied to diverse research scales and purposes, models and their modifications vary. The ...parameters of a crop model could be biased for unseen data; thus, crop models should be calibrated for the adequate simulation of the given data. In this study, we aimed to calibrate food and feed crop models for sweet peppers (
Capsicum annuum
var.
annuum
) using Bayesian optimization. The algorithm does not require domain knowledge because it only considers input and output distributions based on Bayesian probability. For the implementation of Bayesian optimization, HyperOpt, an algorithm for optimizing high-dimensional hyperparameters, was used. The target growth factors were fruit yield and leaf area index, and the loss function was mean squared error (MSE). As a result, the calibrated crop model showed the highest modeling efficiency (EF) of 0.53, compared to − 1.91 and 0.62 from NLopt, a nonlinear optimization methodology, and random walk, respectively. The methodology showed adequate performance with reasonable ranges of convergence. The optimization method can be used for unknown distribution spaces of parameters because it does not require an initial status. Among the selected food crops, the groundnut model was suitable for sweet pepper. Since the optimized crop models yielded reasonable simulations, Bayesian optimization could be introduced for horticultural purposes. However, more data could be required to ensure convergence of the parameters and construct a robust crop model.
In greenhouses, the higher plant density causes poor light environments inside the canopy due to the mutual shading of adjacent plants. Interlighting has been introduced as a countermeasure to ...compensate for the lack of light in the middle and bottom canopies. However, most studies have focused on growth and yield, not light use efficiency (LUE) or water use efficiency (WUE). The objective of this study was to evaluate the LUE and WUE of sweet pepper plants subjected to light-emitting diode (LED) interlighting in greenhouses. Two lighting treatments, natural light (control) and supplemental lighting with red and blue LEDs, were applied. Interlighting began 34 days after transplanting. The ratio of red and blue light to photosynthetic photon flux density (PPFD) was 8:2, and the total PPFD was adjusted to 71 μmol m
−2
s
−1
at a distance of 20 cm. The daily transpiration was measured by subtracting the drainage from the supplied nutrient solution and the weight change of the hydroponic systems. The photosynthetic rate was obtained by measuring light response curves at light intensities of 0, 50, 100, 200, 400, 600, 900, 1200, 1500, and 2000 μmol m
−2
s
−l
. The LUE was calculated based on the simulated light interception obtained by 3D-scanned plant models and ray-tracing simulation. WUE was calculated by dividing the measured dry weight by the accumulated water consumption. Under the interlighting, the LUE increased at the canopy level due to the improved vertical light distributions. The WUE for biomass and fruit yield were higher in the interlighting treatment than in the control. These results were due to the higher increase rates of plant dry weight and fruit yield than that of water consumption by interlighting. In this study, the improvement of LUE and WUE by interlighting could be quantified by optical simulation and a measurement of water consumption throughout the entire growth period.
Far-red light has been included in horticultural light sources due to its physiological activity related to phytochrome-mediated responses. Recent studies have shown that adding far-red light to red ...and blue inter-lighting LEDs improves the growth and production of greenhouse tomatoes. However, in long-term cultivation, the effects of inter-lighting with additional far-red light may vary according to the seasonal light environment. This study aims to investigate the effect of adding far-red light to red and blue inter-lighting on growth and yield of sweet pepper in winter and summer. Sweet pepper (
Capsicum annuum L
. cv. Kori) plants were grown in greenhouses for five months under average daily light integrals of 16.6 and 32.2 mol m
− 2
day
− 1
in winter and summer, respectively. The inter-lighting with adding 55 µmol m
− 2
s
− 1
of far-red light to 71 µmol m
− 2
s
− 1
of red and blue light (red:blue = 8:2). The shoot dry weight and fruit yields were 20% and 17% higher, respectively, with additional far-red in the winter, but no significant differences in growth, yield, or morphology were observed in the summer. In summer, stem length and leaf area of plants increased rapidly due to sufficient sun irradiance, resulting in a higher far-red fraction at the bottom canopy. Thus, the far-red fraction at the middle and bottom canopies would have been sufficient with sunlight alone. Moreover, additional far-red inter-lighting promotes a decrease in leaf photosynthetic capacity in the bottom canopies. Therefore, plant growth and morphology were not influenced by adding far-red to inter-lighting in the summer at our latitude. We concluded that the additional far-red could contribute to growth and yield of sweet peppers in insufficient light conditions, but this effect disappears in conditions with ample sunlight.
Crop models have been developed for wide research purposes and scales, but they have low compatibility due to the diversity of current modeling studies. Improving model adaptability can lead to model ...integration. Since deep neural networks have no conventional modeling parameters, diverse input and output combinations are possible depending on model training. Despite these advantages, no process-based crop model has been tested in full deep neural network complexes. The objective of this study was to develop a process-based deep learning model for hydroponic sweet peppers. Attention mechanism and multitask learning were selected to process distinct growth factors from the environment sequence. The algorithms were modified to be suitable for the regression task of growth simulation. Cultivations were conducted twice a year for 2 years in greenhouses. The developed crop model, DeepCrop, recorded the highest modeling efficiency (= 0.76) and the lowest normalized mean squared error (= 0.18) compared to accessible crop models in the evaluation with unseen data. The t-distributed stochastic neighbor embedding distribution and the attention weights supported that DeepCrop could be analyzed in terms of cognitive ability. With the high adaptability of DeepCrop, the developed model can replace the existing crop models as a versatile tool that would reveal entangled agricultural systems with analysis of complicated information.
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