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This study explores how emotional intelligence, customer orientation, deep acting and surface acting influence job satisfaction among middle managers in their interactions with customers, colleagues and business partners. By examining these factors, we aim to provide insights into their collective impact on job satisfaction and interpersonal dynamics within organizational contexts.

By involving 95 middle managers at Indonesian Internet service providers as respondents, this research used a questionnaire to collect data. Next, the data were analyzed using the partial least square-structural equation modeling (PLS-SEM) technique, which evaluated measurement models and structural models. A total of twelve hypotheses were tested in this study.

This study found that customer orientation does not have a significant effect on deep acting, thereby nullifying its indirect effect on job satisfaction. Conversely, it's demonstrated that both deep acting and surface acting serve as partial mediators in the relationship between emotional intelligence and job satisfaction. Furthermore, surface acting emerges as a partial mediator in the connection between customer orientation and job satisfaction.

By exploring the relationship between customer orientation, emotional intelligence and job satisfaction among employees, this study seeks to reveal novel insights. The study examines the impact of these critical elements, which are necessary for middle managers to effectively manage their emotions and cultivate significant connections, on their overall job satisfaction and interpersonal dynamics in their diverse responsibilities.

The study aims to closely look at the phenomenon of transformational leadership and the psychological capital of followers by using affective process theory (APT). It has empirically tested the mediation of the perceived emotional labor (EL) of a leader and susceptible emotional contagion (EC) of followers when studying the effect of transformational leadership on the psychological capital (PsyCap) of followers.

The method adopted was mixed methodology. The data were collected from the 120 respondents and their perception regarding the construct as identified by previous literature was captured through a structured questionnaire. The relationships and hypotheses were tested by the structural equation modeling (SEM) model using SMART PLS. Further 20 semi-structured interviews were conducted using a qualitative approach.

The current research has empirically shown how specific aspects of transformational leadership, i.e. individual consideration perceived by followers also show high use of perceived deep acting strategy. Deep acting EL strategy is impacting positive EC and positive EC is leading to higher PsyCap of followers generating more work efficacy, hope, optimism and resilience. Mediation of positive EC between Deep acting EL and PsyCap was also observed. In qualitative studies done with the participants, major themes that emerged were transformational leadership, EL strategies, EC and PsyCap.

In times of uncertainty and stress after the post-COVID scenario, employees are facing emotional burnout due to increased work pressure and workload. Transformational leadership has become very critical to manage the PsyCap of followers by using correct EL strategies. Leaders can focus on the optimism and resilience aspect of PsyCap.

The current research has taken affective process theory (APT) as a foundation to understand the connection between transformational leadership and the PsyCap of followers. The study has specifically picked up the fourth mechanism of affective linkage as suggested by Elfenbein (2014) called emotional recognition and seen how emotions are transferred from source (leaders) to recipient (followers). The research has contributed by empirically testing the mediation of the perceived EL of leaders and the susceptible EC of followers and how they affect the PsyCap of followers.

Social media fatigue (SMF) has been widely recognized; however, previous studies have included various concepts into a single fatigue construct. Fatigue has typically been explored from the stressor-strain-outcome (SSO) or stimulus-organism-response (SOR) perspectives. To further investigate SMF, the authors split it into the two constructs of exhaustion and disinterest. Furthermore, the authors introduced the concept of emotional labor and identified rules that may affect surface and deep acting strategies.

The authors designed and conducted a survey to collect data from social networking platform users.

Results from 364 users of social networking platforms supported most of the authors' hypotheses. First, most of the display rules affect the choice of deep or surface acting. Second, both types of acting lead to exhaustion, but only surface acting leads to disinterest. Third, discontinuance intention is affected by both types of fatigue.

This study contributes to SMF research by adding more antecedents (deep and surface acting) based on the emotional labor perspective and showing the impacts of communication rules on emotional labor. In addition, this study also distinguishes disinterest-style fatigue from exhaustion.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

This paper aims to report the study of a fluid buoy system that includes wave effects, with particular emphasis on validating the numerical results with experimental data.

A fluid–solid coupled algorithm is proposed to describe the motion of a rigid buoy under the effects of waves. The Navier–Stokes equations are solved with the open-source finite volume package Code Saturne, in which a free-surface capture technique and equations of motion for the solid are implemented. An ad hoc experiment on a laboratory scale is built. A buoy is placed into a tank partially filled with water; the tank is mounted into a shake table and subjected to controlled motion that promotes waves. The experiment allows for recording the evolution of the free surface at the control points using the ultrasonic sensors and the movement of the buoy by tracking the markers by postprocessing the recorded videos. The numerical results are validated by comparison with the experimental data.

The implemented free-surface technique, developed within the framework of the finite-volume method, is validated. The best-obtained agreement is for small amplitudes compatible with the waves evolving under deep-water conditions. Second, the algorithm proposed to describe rigid-body motion, including wave analysis, is validated. The numerical body motion and wave pattern satisfactorily matched the experimental data. The complete 3D proposed model can realistically describe buoy motions under the effects of stationary waves.

The novel aspects of this study encompass the implementation of a fluid–structure interaction strategy to describe rigid-body motion, including wave effects in a finite-volume context, and the reported free-surface and buoy position measurements from experiments. To the best of the authors’ knowledge, the numerical strategy, the validation of the computed results and the experimental data are all original contributions of this work.

Emotions are an important aspect of work performance but are often overlooked, especially amongst preschool teachers whose work environment is laden with emotional job demands. The present study aims to examine the mediating role of emotional exhaustion in the relationship between emotional labour and contextual performance.

Using a cross-sectional design, data were obtained from 288 preschool teachers in the Tema Metropolis in the Greater Accra region of Ghana. The study's hypotheses were tested using structural equation modelling with maximum likelihood estimation in AMOS 21.0.

The structural equation modelling analyses revealed that deep acting had a direct positive relationship with contextual performance, whereas the direct relationship between surface acting and contextual performance was not statistically significant. Furthermore, deep acting and surface acting were indirectly related to contextual performance via emotional exhaustion.

The study's findings underscore the need for educational institutions and managers to create a supportive environment for teachers engaging in emotional labour, and to ensure that emotional labour is not overburdening teachers.

The study contributes to the literature on teachers' engagement in discretionary behaviours by elucidating emotional exhaustion as a linking mechanism between emotional labour and contextual performance in a non-Western context. This is one of the few studies to link emotional labour to contextual performance in the educational context.

This paper presents the phenomenographic analysis of students' approaches to learning in the first year architectural design coursework; thereby correlating contextualization in the architectural curriculum.

This paper reviews phenomenographic data of first year architecture students' learning experience through a comparative analysis of first- and fourth-year students' approaches to learning in the design studio; further co-relating this analysis to the final classification involving all five years of students' learning approaches in the architecture program.

Five meta-categories of the comparative analysis and nineteen meta-categories of the final classification are evaluated using first-year students' learning approaches – to understand the importance of contextualization in curriculums of architecture.

This phenomenographic analysis of first-year students' learning experience represents the onward journey from surface-to-deep approaches to learning that is encountered in their learning approaches, pertaining to the design process in the design coursework during five years of architectural education.

This paper systematically extends the discussion of first year architecture students' engagement in the design process that leads to deep learning; further delving into the static dimension of knowledge and its extension to the dynamic dimension of knowing architecture.