The effect of abusive supervision on safety behaviour of Chinese underground miners: a multi-level moderated mediation analysis

Purpose Prior research has suggested that abusive supervision has negative impacts on various work outcomes. However, little attention has been paid to the relationship between abusive supervision and employees’ safety behaviour. The purpose of this study is, therefore, to address these limitations by developing and testing a theoretically based conceptual model that explicitly considers the underlying mechanism and boundary condition of the relationship between abusive supervision and safety behaviour of underground coal miners in China. Design/methodology/approach At Time 1, the authors conducted a survey of 630 employees to assess their supervisors’ abusive leadership behaviours, their own power distance beliefs and their self-reflection. At Time 2, the authros sent questionnaires to the leaders and invited them to evaluate employees’ safety behaviour in the workplace. After cleaning the survey data, the authors tested our model using a multi-level analysis on a sample (n = 458) of underground miners across 96 coal mining sites in China. Findings The authors propose that abusive supervision decreases employees’ safety compliance/participation by reducing reflection but strengthening rumination. The authors further find that the linkage from abusive supervision to reflection/rumination to safety compliance/participation is affected by power distance. Originality/value To the best of the authors’ knowledge, This is one of the first empirical studies to investigate the mediating effects of a deep cognitive processing variable – namely, self-reflection – and the moderating effects of power distance on the relationship between abusive supervision and safety behaviour.

Optimization of Liquid Fuel Production from Microwave Pyrolysis of Used Tyres

Used tyres pose a threat to the environment, especially in developing countries, since the current disposal methods lead to environmental pollution. Pyrolysis liquid from used tyres can be used as a source of fuel to replace petroleum diesel. Microwave pyrolysis is an alternative valorization process that is supposed to save energy and, therefore, is environment friendly. In the current study, microwave pyrolysis was used to produce liquid fuel. Processing variable levels for microwave were power levels of 20, 30, 40, 50, 60, 80, and 100%; the reaction times were 8, 13, 18, 23, and 28 minutes; and the particle sizes were 25, 50, 100, and 200 mm2. Design-Expert 13 was used for data analysis and optimization, and GC-MS was used for chemical composition analysis, while physiochemical properties were tested using standard methods. Response surface methodology (RSM) was used to study the effects of operating variables and identify the points of optimal yields. For microwave pyrolysis, the highest liquid yield of 39.1 wt. % was at 50% power, 18 min reaction time, and particle size of 25 mm2. The yield decreased as the particle size increased. RSM gave conditions for optima in agreement with the experimental results. The calorific value for liquid fuel was 48.99 MJ/kg. GC-MS analysis showed that the oil comprised complex mixtures of organic compounds with limonene, toluene, and xylene as major components. The liquid fuel properties meet the required international standards and can be used as an alternative to diesel fuel.

Effect of Working Memory Load and Typicality on Semantic Processing in Aphasia

Purpose This study evaluated the effects of a linguistic characteristic, typicality, and a processing variable, working memory on the abilities of people with aphasia (PWA) and neurologically intact adults to process semantic representations. This was accomplished using a newly developed assessment task, the Category Typicality Test, which was created for the Temple Assessment of Language and Short-Term Memory in Aphasia. Method A post hoc quasi-experimental design was used. Participants included 27 PWA and 14 neurologically intact adults who completed the picture and word versions of the Category Typicality Test, which required them to determine if two items are in the same category. Memory load was altered by increasing the number of items to be compared, and the typicality of items was altered to increase linguistic complexity. Results A four-way mixed analysis of covariance was conducted. There was a significant interaction between working memory load and category typicality with performance accuracy decreasing as working memory load increased and category typicality decreased. There was also a significant interaction for typicality and stimuli with better performance in the picture condition and a significant interaction for working memory and group with lower performance accuracy for PWA. Post hoc pairwise comparisons revealed differences between memory load, typicality, stimuli conditions, and group. PWA also showed greater magnitude of change than neurologically intact adults when comparing high and low working memory load conditions, but not typicality conditions. Discussion Increasing working memory load had the most substantial impact on the accuracy of category judgments in PWA, but the interaction between increased working memory load and decreased category typicality of items to be compared resulted in reduced accuracy in both groups. These findings suggest that manipulation of processing and linguistic variables in assessment will provide insight into the nature of linguistic breakdown in aphasia. Supplemental Material https://doi.org/10.23641/asha.14781996

Improved Canopy Characterization with Laser Scanning Sensor for Greenhouse Spray Applications

HighlightsAn algorithm was developed to process laser sensor data to make more accurate measurements of canopy dimensions.The algorithm isolated individual canopies, removed distortion, and estimated the occluded portions of the dataset.The algorithm reduced measured error by 46% in terms of root mean square error (RMSE).The RMSE was higher for sensor heights below and above a calculated optimal sensor height.Abstract. Laser-guided intelligent spray technology for greenhouse applications requires sensors that can accurately measure plant dimensions. This study proposed a new method to overcome current limitations by introducing a processing algorithm that manipulates the noisy dataset and determines the optimal sensor height to produce better measurements of the canopy width. The processing algorithm involves a combination of registration, clustering, and mirroring. Registration aligns multiple scans of the same scene to improve resolution. Clustering isolates individual plant canopies from the dataset to enable further processing. Mirroring is used to resolve the problems of distortion and occlusion and predict missing information in the dataset. The performance of the processing algorithm was evaluated by calculating the root mean square error (RMSE) in the canopy width measurements. Its results were compared with the measurements reported in earlier research, where there was limited processing of the laser sensor data. The processing algorithm reduced RMSE values by 46% compared to the earlier research, and the largest improvements were seen for objects placed beyond 1.5 m from the sensor. The sensor height was observed to be inversely proportional to the RMSE values. The average RMSE of the processing algorithm was 25 mm, compared to 47 mm in the earlier research when the laser sensor was at a height of 1 m. Another experimental setup was used to test the limits of the relationship between sensor height and algorithm performance while using objects that were more representative of plant canopy shapes. The accuracy of the processing algorithm decreased when the sensor height was either above or below the optimal sensor height, which was derived from calculations made in earlier research. The processing algorithm has potential to improve spray efficiencies. Keywords: Automation, Clustering, LiDAR, Point cloud data processing, Variable-rate spray.

Ultrasound-assisted extraction and gaseous ozone as preservation method for Roselle (Hibiscus sabdariffa L.) fruit juice

Ultrasound extraction and ozone treatment are promising non-thermal hurdle technologies which can increase the extraction yield while minimizing the loss of nutritional qualities of Roselle fruit juice. Response surface methodology (RSM) was used to investigate the effect of ultrasound time and temperature on juice yield. The optimized points were found at 4.4 min and 22°C, where the extraction yield of the Roselle fruit juice achieved a maximum of 80%, 12% higher than the control sample without ultrasound treatment. Roselle fruit juice (unfiltered and filtered) was then ozone-treated with processing variable of treatment time (0–30 mins). The effects of processing variables on physicochemical characteristics of Roselle fruit juice were determined and no significant differences (P>0.05) in pH and titratable acidity (TA) were observed. However, significant effect (P<0.05) was found in total colour difference (TCD), ascorbic acid (AA), total phenolic content (TPC), and total anthocyanin content (TAC) with increased ozone treatment time. Nevertheless, the degradation of AA was less than 50%, which showed that ozone has the potential to retain a high amount of AA in Roselle fruit juice. Thus, the synergistic effects of ultrasound and ozone treatment on Roselle fruit juice should be carefully considered by processors prior to its adoption as a preservation technique.

Let’s Go to the Movies…for Science!

This study examined whether watching science-related entertainment narratives can influence science learning, interest in science, and information-seeking intentions. Based on the capacity model ( Fisch, 2000 ), a more precise processing variable called engagement with the science content was proposed as an additional mechanism that can account for variance not explained by existing narrative engagement variables. Results of a between-subjects experiment showed that watching a science-related narrative increased all three science outcomes. Engagement with the science content explained significantly more variance in posttest interest and information-seeking than did narrative engagement or transportation. Science knowledge was acquired regardless of preexisting level of interest, demonstrating the benefits of popular entertainment narratives in fostering informal science learning among nonexpert audiences.

Degradation Status Recognition of Axial Piston Pumps under Variable Conditions Based on Improved Information Entropy and Gaussian Mixture Models

Axial piston pumps are crucial for the safe operation of hydraulic systems and usually work under variable operating conditions. However, deterioration status recognition for such pumps under variable conditions has rarely been reported until now. Therefore, it is valuable to develop effective methods suitable for processing variable conditions. Firstly, considering that information entropy has strong robustness to variable conditions and empirical mode decomposition (EMD) has the advantages of processing nonlinear and nonstationary signals, a new degradation feature parameter, named local instantaneous energy moment entropy, which combines information entropy theory and EMD, is proposed in this paper. To obtain more accurate degradation feature, a waveform matching extrema mirror extension EMD, which is used to suppress the end effects of EMD decomposition, was employed to decompose the original pump’s outlet pressure signals, taking the quasi-periodic characteristics of the signals into consideration. Subsequently, given that different failure modes of pumps have different degradation rates in practice, which makes it difficult to effectively recognize degradation status when using the modeling methods that need the normal and failure data, a Gaussian mixture model (GMM), which has no need for failure data when building a degradation identification model, was introduced to capture the new degradation status index (DSI) to quantitatively assess the degradation state of the pumps. Finally, the effectiveness of the proposed approach was validated using both simulations and experiments. It was demonstrated that the defined local instantaneous energy moment entropy is able to effectively characterize the degree of degradation of the pumps under variable operating conditions, and the DSI derived from the GMM is able to accurately identify different degradation states when compared with the previously published methods.

Multilabel Text Classification in News Articles Using Long-Term Memory with Word2Vec

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic relationships. It requires a multilabel text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multilabel text classification techniques. Some of the deep learning methods used for text classification include Convolutional Neural Networks, Autoencoders, Deep Belief Networks, and Recurrent Neural Networks (RNN). RNN is one of the most popular architectures used in natural language processing (NLP) because the recurrent structure is appropriate for processing variable-length text. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. The models are trained based on trial and error experiments using LSTM and 300-dimensional words embedding features with Word2Vec. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features Word2Vec can achieve good performance in text classification. The results show that text classification using LSTM with Word2Vec obtain the highest accuracy is in the fifth model with 95.38, the average of precision, recall, and F1-score is 95. Also, LSTM with the Word2Vec feature gets graphic results that are close to good-fit on seventh and eighth models.

Decode Brain System: A Dynamic Adaptive Convolutional Quorum Voting Approach for Variable-Length EEG Data

The brain is a complex and dynamic system, consisting of interacting sets and the temporal evolution of these sets. Electroencephalogram (EEG) recordings of brain activity play a vital role to decode the cognitive process of human beings in learning research and application areas. In the real world, people react to stimuli differently, and the duration of brain activities varies between individuals. Therefore, the length of EEG recordings in trials gathered in the experiment is variable. However, current approaches either fix the length of EEG recordings in each trial which would lose information hidden in the data or use the sliding window which would consume large computation on overlapped parts of slices. In this paper, we propose TOO (Traverse Only Once), a new approach for processing variable-length EEG trial data. TOO is a convolutional quorum voting approach that breaks the fixed structure of the model through convolutional implementation of sliding windows and the replacement of the fully connected layer by the 1 × 1 convolutional layer. Each output cell generated from 1 × 1 convolutional layer corresponds to each slice created by a sliding time window, which reflects changes in cognitive states. Then, TOO employs quorum voting on output cells and determines the cognitive state representing the entire single trial. Our approach provides an adaptive model for trials of different lengths with traversing EEG data of each trial only once to recognize cognitive states. We design and implement a cognitive experiment and obtain EEG data. Using the data collecting from this experiment, we conducted an evaluation to compare TOO with a state-of-art sliding window end-to-end approach. The results show that TOO yields a good accuracy (83.58%) at the trial level with a much lower computation (11.16%). It also has the potential to be used in variable signal processing in other application areas.