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Because of the importance of having enough sleep in life and health, this study aims to determine the effect of vitamin D supplementation on sleep quality and pregnancy symptoms (primary outcomes) and side effects (secondary outcome).

In this triple-blind randomized controlled clinical trial, 88 pregnant women with gestational age of 8–10 weeks and serum vitamin D concentration less than 30 ng/ml were allocated into vitamin D (n = 44) and control (n = 44) groups by blocked randomization method. The vitamin D group received a 4,000 IU vitamin D pill, and the control group received a placebo pill daily for 18 weeks. Independent t-, Mann–Whitney U and ANCOVA tests were used to analyze the data.

The post-intervention mean (SD: standard deviation) of total sleep quality score in the vitamin D and placebo group were 1.94 (2.1) and 4.62 (1.71), respectively. According to the Mann–Whitney U test, this difference between the two groups was statistically significant (p < 0.001). The mean (SD) of pregnancy symptoms in the vitamin D and placebo groups was 23.95 (16.07) and 26.62 (13.84), respectively, and there was no significant difference between the two groups based on ANCOVA test (p = 0.56). Considerable side effects were not observed in any groups.

This study was conducted due to the contradictory results of the effect of vitamin D on sleep quality and the high prevalence of sleep disorders and pregnancy symptoms.

The paper aims to expand the scope of application of the lattice Boltzmann method (LBM), especially in the field of aircraft engineering. The traditional LBM is usually applied to incompressible flows at a low Reynolds number, which is not sufficient to satisfy the needs of aircraft engineering. Devoted to tackling the defect, the paper proposes a developed LBM combining the subgrid model and the multiple relaxation time (MRT) approach. A multilayer adaptive Cartesian grid method to improve the computing efficiency of the traditional LBM is also employed.

The subgrid model and the multilayer adaptive Cartesian grid are introduced into MRT-LBM for simulations of incompressible flows at a high Reynolds number. Validated by several typical flow simulations, the numerical methods in this paper can efficiently study the flows under high Reynolds numbers.

Some numerical simulations for the lid-driven flow of cavity, flow around iced GLC305, LB606b and ONERA-M6 are completed. The paper presents the investigation results, indicating that the methods are accurate and effective for the separated flow after icing.

LBM is developed with the addition of the subgrid model and the MRT method. A numerical strategy is proposed using a multilayer adaptive Cartesian grid method and its treatment of boundary conditions. The paper refers to innovative algorithm developments and applications to the aircraft engineering, especially for iced wing simulations with flow separations.

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

Present study aims to extend the lattice Boltzmann method (LBM) to simulate radiation in geometries with curved boundaries, as the first step to simulate radiation in complex porous media. In recent years, researchers have increasingly explored the use of porous media to improve the heat transfer processes. The lattice Boltzmann method (LBM) is one of the most effective techniques for simulating heat transfer in such media. However, the application of the LBM to study radiation in complex geometries that contain curved boundaries, as found in many porous media, has been limited.

The numerical evaluation of the effect of the radiation-conduction parameter and extinction coefficient on temperature and incident radiation distributions demonstrates that the proposed LBM algorithm provides highly accurate results across all cases, compared to those found in the literature or those obtained using the finite volume method (FVM) with the discrete ordinates method (DOM) for radiative information.

For the case with a conduction-radiation parameter equal to 0.01, the maximum relative error is 1.9% in predicting temperature along vertical central line. The accuracy improves with an increase in the conduction-radiation parameter. Furthermore, the comparison between computational performances of two approaches reveals that the LBM-LBM approach performs significantly faster than the FVM-DOM solver.

The difficulty of radiative modeling in combined problems involving irregular boundaries has led to alternative approaches that generally increase the computational expense to obtain necessary radiative details. To address the limitations of existing methods, this study presents a new approach involving a coupled lattice Boltzmann and first-order blocked-off technique to efficiently model conductive-radiative heat transfer in complex geometries with participating media. This algorithm has been developed using the parallel lattice Boltzmann solver.

This paper aims to develop an efficient algorithm combining straightforward response surface functions with Monte Carlo simulation to conduct seismic reliability analysis in a systematical way.

The representative slip surfaces are identified and based on to calibrate multiple response surface functions with acceptable accuracy. The calibrated response surfaces are used to determine the yield acceleration in Newmark sliding displacement analysis. Then, the displacement-based limit state function is adopted to conduct seismic reliability analysis.

The calibrated response surface functions have fairly good accuracy in predicting the yield acceleration in Newmark sliding displacement analysis. The seismic reliability is influenced by such factors as PGA, spatial variability and threshold value. The proposed methodology serves as an effective tool for geotechnical practitioners.

The multiple sources of a seismic slope response can be effectively determined using the multiple response surface functions, which are easily implemented within geotechnical engineering.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

This study aims to propose a series of numerical and surrogate models to investigate the aerodynamic pressure inside cracks in high-speed railway tunnel linings and to predict the stress intensity factors (SIFs) at the crack tip.

A computational fluid dynamics (CFD) model is used to calculate the aerodynamic pressure exerted on two cracked surfaces. The simulation uses the viscous unsteady κ-ε turbulence model. Using this CFD model, the spatial and temporal distribution of aerodynamic pressure inside longitudinal, oblique and circumferential cracks are analyzed. The mechanism behind the pressure variation in tunnel lining cracks is revealed by the air density field. Furthermore, a response surface model (RSM) is proposed to predict the maximum SIF at the crack tip of circumferential cracks and analyze its influential parameters.

The initial compression wave amplifies and oscillates in cracks in tunnel linings, resulting from an increase in air density at the crack front. The maximum pressure in the circumferential crack is 2.27 and 1.76 times higher than that in the longitudinal and oblique cracks, respectively. The RSM accurately predicts the SIF at the crack tip of circumferential cracks. The SIF at the crack tip is most affected by variations in train velocities, followed by the depth and length of the cracks.

The mechanism behind the variation of aerodynamic pressure in tunnel lining cracks is revealed. In addition, a reliable surrogate model is proposed to predict the mechanical response of the crack tip under aerodynamic pressures.

The purpose of this paper is to investigate the impact of early-stage maintenance clustering. Few researchers have previously studied early-stage maintenance clustering. Experience from product and service development has shown that early stages are critical to the development process, as most decisions are made during these stages. Similarly, most maintenance decisions are made during the early stages of maintenance development. Developing maintenance for clustering is expected to increase the potential of clustering.

A literature study and three case studies using the same data set were performed. The case studies simulate three stages of maintenance development by clustering based on the changes available at each given stage.

The study indicates an increased impact of maintenance clustering when clustering already in the first maintenance development stage. By performing clustering during the identification phase, 4.6% of the planned work hours can be saved. When clustering is done in the planning phase, 2.7% of the planned work hours can be saved. When planning is done in the scheduling phase, 2.4% of the planned work hours can be saved. The major difference in potential from the identification to the scheduling phase came from avoiding duplicate, unnecessary and erroneous work.

The findings from this study indicate a need for more studies on early-stage maintenance clustering, as few others have studied this.

This study aims to examine social determinants and social strains of cyberbullying victimization among expatriate populations in high-income countries such as Qatar. The authors argue that expatriate students will be exposed to stains and pressures due to several factors, such as feeling alienated, lonely, homesick, insecure and helpless. This study examines a partial assumption of general strain theory (GST), which posits that expats’ cyberbullying victimization can create negative stimuli that lead to negative feelings and, as a result, to delinquent behavior. The delinquent behavior can be seen as a coping method in encountering strains. Logistic regression analysis is applied (using SPSS v. 21) to test the hypothesis that the victimization of expats’ cyberbullying could lead to strain and delinquency.

A sample of 2,428 expatriate students (46% males and 54% females) was randomly selected from seven public schools in Qatar. The average number of siblings in the household was 2.7. Around 10.8% of the sample reported victimization by cyberbullying. Most came from intact families (80.6%) compared to 19.4 living in broken homes. Concerning the birth order of the student in the family, 25% of the sample were firstborn, and 20% were middle-born. Students’ mother relationships were higher than students’ father relationships (69% vs 51%, respectively). Fathers were higher in undergraduate education than mothers (60% vs 49%, respectively). Considering that 58% of women did not have a job, most fathers work in administrative positions (47% in administrative work and 39% in professional positions) as opposed to mothers’ positions (15% and 21%, respectively). The survey was distributed among expatriate students by trained researchers, teachers and social workers in public schools. The researchers explained the purpose of the study, confidentiality and potential risks and provided directions for completing the survey. Parents and their children provided signed informed consent before participation, following the institutional review boards of Qatar University’s Human Subject Research Committee and the Ministry of Social Development. The victimization of cyberbullying is measured at the dichotomy level by asking a general cyberbullying question (In the last 12 months, had you been bullied by other students using mobile messages, email, voice or video messages? Responses were 1 – yes and 0 – no). A definition of cyberbullying was included in the questionnaire. Independent measures are as follows: (1) demographic variables: gender, belief, health, number of friends and exposure to violence; (2) family variables: delinquent family, coercive parenting, family relations, family disputes, father absence and family ties; (3) school variables: school satisfaction, school violence, student fighting, teachers' violence, school truancy and going late to school; (4) imprudent behavior: smoking, alcohol, not using a seat belt, gambling, stealing less than 100 QR, chewing tobacco, stimuli, volatile drugs, sexual harassment, throwing garbage out of the car, cheating, vandalism and victimization.

Findings showed that n = 255, 10.8% of the expats’ sample N = 2,428 reported victimization of cyberbullying, of whom 46% were males and 54% were females. A total of 24% of the victims of cyberbullying were males, and 5.8% were females. About a third of the sample were cyberbullying perpetrators (n = 716, 29.5%). Victims of cyberbullying came from intact families (62.4%) compared to broken families (37.6%). A round third of the victims reported the absence of a father during their childhood (n = 78, 31.7%). Most of the victims came from a typical community compared to the delinquent community (71.2% vs 28.2%). About a third of the victims reported it was easy to talk with the father when needed (32% compared to 42.7%), who said it was easy. More than half of the victims do not use seat belts. A low percentage of them were gambling (n = 68, 27% or 2.9% of the total sample). Regarding escaping imprudent and delinquent behaviors, findings showed that a third and more than a tenth of the cyberbullying were smokers and alcoholics (n = 69, 27.4% and n = 42, 16.7% respectively). Moreover, among the most serious, widely spread student problems, around a quarter of the victims reported using chewing tobacco (Sweeka is the local name) (n = 54, 21.4%). Finally, drug use among victims was more than a tenth of them (n = 41, 16.3%). About a quarter of the victims reported stealing less than 100 QR (n = 67, 26.8% or 2.9% of the total sample). Concerning the crimes committed by victims, findings showed that more than forth of them committed assault (n = 71, 28.3%), student fights (n = 202, 80.8%), school violence (n = 117, 46.6%) and half of the victims (50.2%) were also victims of face-to-face bullying. Little below half of the sample was exposed to violence (n = 120, 48.6%) and was the victim of violence in the past 12 months (n = 100, 40.8%). A significant gender differences was found in the victimization of cyberbullying (1 = yes, 0 = no) (a = 000). Moreover, significant mean differences were found between expatriates student victimization of cyberbullying at (a = 000) in all strain variables. Using binary regression analysis to examine the equality of probability of being a victim of cyberbullying as accounted for by the independent variables, the model’s predictability was 89.4%. The Hosmer and Lemeshow test and omnibus tests support the model’s fit (a = 000). Nagelkerke R² shows that the stain variables accounted for 24% of the variance in the expat's student victimization of cyberbullying.

Conceptualization and determination of what constitutes cyberbullying from the criminal law perspective are needed. Cyberbullying is defined as a form of violence and crime involving new technologies (Sun et al., 2016, p. 62). Policymakers should provide accessible and equitable access to the criminal justice system, the integration of expatriates and the provision of support services to avoid delinquency.

A social cohesion, inclusion and well-being policy is needed for expatriate students and their families in Qatar. In particular, cultural diversity policy and programs, a source of strength and enrichment should be promoted in educational settings. Social inclusion and cultural diversity programs could ease the alienation and marginalization that expatriate students may encounter in their host societies to prevent stains and negative emotions that lead to delinquency and criminal behaviors. Access to support services without discrimination to avoid health and psychological strains and risk factors. This includes but is not limited to avoidance of stigmatization, embarrassment, sense of helplessness, humiliation and other negative feelings toward expatriates. Awareness and promotion of cultural diversity values are needed to enhance cultural acceptance to reduce victimization among the expatriate population. Criminal law and security implications conceptualization and determination of what constitutes cyberbullying from the criminal law perspective are needed. Cyberbullying is a form of violence and crime involving new technologies (Sun et al., 2016). Policymakers should provide accessible and equitable access to the criminal justice system, integrating expatriates and providing support services to avoid delinquency. There is a pressing need for further research within the realm of crime and law to establish the precise legal boundaries surrounding cyberbullying and to delineate the potential scope for legislative measures aimed at safeguarding against victimization in Qatar. Although the state of Qatar has taken steps to address cyberbullying by incorporating it into relevant legal frameworks, there remains a gap in terms of specifically targeting cyberbullying involving children and adolescents. Despite the introduction of the new Cybercrime Prevention Law, the issue of cyberbullying among these vulnerable groups is not adequately addressed.

Social implication includes but is not limited to avoidance of stigmatization, embarrassment, sense of helplessness, humiliation and other negative feelings towards expatriate. Awareness and promotion of cultural diversity values is needed to enhance cultural acceptance to reduce victimization among expatriate population.

The present study examines some strain theory assumptions and the victimization of the cyberbullying expatriate population. This study tests a partial assumption of GST, which states that the expatriate population’s exposure to the victimization of cyberbullying can lead to negative stimuli that, in return, create negative feelings and, as a result, imprudent and delinquent behavior. The cyberbullying behavior can be seen as an escaping and coping method in encountering strains. This study underscores the presence of cyberbullying within high schools among expatriate students, exerting significant effects on their personal, social and emotional behaviors. The novel insights gleaned from this investigation contribute substantively to the comprehension of both the pervasiveness and repercussions of cyberbullying on the well-being of expatriate students. This contribution is particularly vital, given the dearth of research in the field, largely attributable to the growing dependence of students on the internet for various cyber activities. This study examines a partial assumption of GST (in non-Western country). Moreover, it uses an advanced statistical analysis and large sample.