Purpose: This study aimed to explore the perception types of workplace spirituality among nurses. Method: To achieve this, Q methodology was applied, selecting 34 Q samples from a total of 102 Q statements extracted. The Q samples were distributed among 40 nurses and categorized into a normal distribution. A 9-point scale was used for measurement, and the data were analyzed using the pc-QUANL program. Results: The four types identified were ‘reflective type’, ‘nursing-oriented type’, ‘relationship-oriented type’, and ‘spirituality-oriented type’. Conclusion: The four types derived in this study classify nurses’ perceptions of workplace spirituality for establishing a nurse’s workplace spirituality that provides integrated nursing care. This categorization can serve as foundational information when planning workplace spirituality programs, considering each type’s characteristics.

Clustering technics, like k-means and its extended version, fuzzy c-means clustering (FCM) are useful tools for identifying typical behaviours based on various attitudes and responses to well-formulated questionnaires, such as among forensic populations. As more or less standard questionnaires for analyzing aggressive attitudes do exist in the literature, the application of these clustering methods seems to be rather straightforward. Especially, fuzzy clustering may lead to new recognitions, as human behaviour and communication are full of uncertainties, which often do not have a probabilistic nature. In this paper, the cluster analysis of a closed forensic (inmate) population will be presented. The goal of this study was by applying fuzzy c-means clustering to facilitate the wider possibilities of analysis of aggressive behaviour which is treated as a heterogeneous construct resulting in two main phenotypes, premeditated and impulsive aggression. Understanding motives of aggression helps reconstruct possible events, sequences of events and scenarios related to a certain crime, and ultimately, to prevent further crimes from happening.

This paper analyzes the characteristics and influence mechanisms of financial support for China’s strategic emerging industries. Using a sample of 356 listed companies across nine major industries, we conduct an in-depth analysis of the efficiency of financial support and its influencing factors. In addition, this paper analyzes the influence mechanism of financial support for strategic emerging industries based on the relevant theory of financial support for industry development. It clarifies the internal and external influencing factors. Based on the theoretical analysis, a two-stage empirical investigation was conducted: The data of 356 listed companies in strategic emerging industries from 2010 to 2022 were selected as a sample, and the data envelopment analysis (DEA) method was applied to measure efficiency. The influencing factors were then analyzed using a Tobit regression and an intermediate effects test.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

The article aims at developing an efficient and stable catalysts for simultaneous hydrogenation of o-chloronitrobenzene to o-chloroaniline and 1,4-butanediol dehydrogenation to γ-butyrolactone. A series of CoO-Cu-MgO catalysts, composed of 10 wt% of copper, various amount of cobalt loadings (1, 5 and 10 wt%) and remaining of MgO were developed by co-precipitation followed by thermal treatment. o-Chloroaniline and γ-butyrolactone were the main products with high yield of 85% and 90%, respectively. The advantage of the coupling process is that the hydrogenation reaction was conducted without external hydrogen, demonstrating minimize the hydrogen consumption known as hydrogen economy route. From N₂O characterization results, the high activity of 5CoO-10Cu-MgO was found that it has high amount of Cu species (Cu⁰/Cu⁺¹) which govern the stable activity and selectivity on time on stream study in presence of cobalt in Cu-MgO.

Objective: The influence of climate on forest stands cannot be ignored, but most of the previous forest stand growth models were constructed under the presumption of invariant climate and could not estimate the stand growth under climate change. The model was constructed to provide a theoretical basis for forest operators to take reasonable management measures for fir under the influence of climate. Methods: Based on the survey data of 638 cedar plantation plots in Hunan Province, the optimal base model was selected from four biologically significant alternative stand basal area models, and the significant climate factors without serious covariance were selected by multiple stepwise regression analysis. The optimal form of random effects was determined, and then a model with climatic effects was constructed for the cross-sectional growth of fir plantations. Results: Richards formula is the optimal form of the basic model of stand basal area growth. The coefficient of adjustment  was 0.8355; the average summer maximum temperature  and the water vapor loss  in Hargreaves climate affected the maximum and rate of fir stand stand growth respectively, and were negatively correlated with the stand growth. The adjusted coefficient of determination  of the fir stand area break model with climate effects was 0.8921, the root mean square error (RMSE) was 3.0792, and the mean relative error absolute value (MARE) was 9.9011; compared with the optimal base model,  improved by 6.77%, RMSE decreased by 19.04%, and MARE decreased by 15.95%. Conclusion: The construction of the stand cross-sectional area model with climate effects indicates that climate has a significant influence on stand growth, which supports the rationality of considering climate factors in the growth model, and it is important for the regional stand growth harvest and management of cedar while improving the accuracy and applicability of the model.