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The reasons for and experiences during migration, as well as additional stressors in the new host country, may give rise to mental health problems and additional need for public services. The purpose of the study was to investigate factors related to service utilization among newly arrived refugee youth.

Cross-sectional data were gathered in Sweden where 37 youth aged between 19 and 23 reported on factors related to service utilization, encompassing health-care and support services in school. These factors included predisposition (demographic), need (migration status and mental wellbeing) and enablement (living situation). Service utilization was estimated using multiple logistic regression analysis.

About a fourth of the sample used psychosocial services. Use of general support was more common. Neither predisposing, need nor enabling factors were associated with the use of psychosocial or general health-related services.

Self-reported factors related to use of health-related services have previously not been investigated for refugee youth, which is important in assuring access to appropriate services for this exposed youth population.

This paper aims to identify the relationship between participation motivation, satisfaction and exercise adherence intention of golf range users on the basis of self-determination theory.

For this purpose, the authors proposed research questions and a conceptual research model as well. Then, the authors surveyed users of golf ranges located in Seoul Metropolitan City and Gyeonggi-do province.

By applying convenience sampling, the authors received a total of 313 questionnaires. Results were as follows. First, among the participation motivation sub-factors, health-oriented motivation, achievement motivation, pleasure-oriented motivation and self-displayed motivation had a significant effect on emotional satisfaction, while achievement motivation and pleasure-orientation motivation had a significant effect on performance satisfaction. Second, the following participation motivation factors had a significant effect on exercise adherence intention: health-orientation motivation, achievement motivation and pleasure-orientation motivation. Third, among the satisfaction factors, emotional satisfaction and performance satisfaction both had a significant effect on exercise adherence intention.

This is one of the first papers to examine the relationships that exist between golf range users’ participation motivation, satisfaction and exercise adherence intention.

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 show the aligned development that took place in public administration and public financial management toward serving public values. By analyzing the mode of institutions’ interaction, the paper attempts to pinpoint the changing trends in budget institutions in Egypt, probing the extent to which they can be read from an administrative perspective and the possibility of enhancing budgetary outcomes under the existing administrative arrangements.

An analytical framework for public management administrative and budgetary institutions’ alignment is presented. A ladder analysis is developed to highlight the consistency of rationale between the two sets of institutions. The alignment is demonstrated at three consecutive levels: control and discipline, efficiency and effectiveness and openness and communication.

The international experience reveals that the alignment of administrative and budgetary institutions is both theoretically traceable and practically applicable in the case of developed economies. Whereas, in the case of Egypt, both sets of institutions have been exposed to best practices; yet, they are not seen as complementary and enforcing each other. The internalization of the benefits of reforms in the two tracks into an integrated public management context in the case of Egypt is not reached.

Egypt needs to ensure the alignment of both dimensions to maximize the benefits of reform.

The ladder approach sorts the developments in both administrative and budgetary institutions into three levels to help assessing the maturity and conformity in countries’ public management systems.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.