Miracle-dz/UniFAR-FTUs huggingface dataset:
Manakov system with parity symmetry on nonzero background and associated boundary value problems

We characterize initial value problems for the defocusing Manakov system (coupled two-component nonlinear Schrödinger equation) with nonzero background and well-defined spatial parity symmetry (i.e., when each of the components of the solution is either even or odd), corresponding to boundary value problems on the half line with Dirichlet or Neumann boundary conditions at the origin. We identify the symmetries of the eigenfunctions arising from the spatial parity of the solution, and we determine the corresponding symmetries of the scattering data (reflection coefficients, discrete spectrum and norming constants). All parity induced symmetries are found to be more complicated than in the scalar (i.e., one-component) case. In particular, we show that the discrete eigenvalues giving rise to dark solitons arise in symmetric quartets, and those giving rise to dark–bright solitons in symmetric octets. We also characterize the differences between the purely even or purely odd case (in which both components are either even or odd functions of x) and the ‘mixed parity’ cases (in which one component is even while the other is odd). Finally, we show how, in each case, the spatial symmetry yields a constraint on the possible existence of self-symmetric eigenvalues, corresponding to stationary solitons, and we study the resulting behavior of solutions.
Generating point clouds for slicing free-form objects for 3-D printing

ABSTRACT3-D printing, also known as additive manufacturing, has gained a lot of attention both within and outside CAD research. Even popular media have touted the technology as one of the game changer technologies of the 21st century. Simply stated, most printing devices add material to an unfinished part, layer by layer, until the entire object is completed. In order to make this happen, the object is sliced into thin layers which are produced and glued together. Since NURBS are the standard form of modeling tools, the process entails converting the NURBS into an STL model (piecewise triangular model) which is then sliced into a set of closed polygonal loops. In order to avoid the many problems with the STL-based slicing, in this paper we investigate a point cloud-based approach to direct slicing of NURBS based models. It uses the original NURBS model and converts the model into a point cloud, based on layer thickness and accuracy requirements, for direct slicing. The only major computational requirement.
Strain engineering in optoelectronic properties of MoSi2N4 monolayer: ultrahigh tunability

Controllable optical properties are important for optoelectronic applications. Recently, the two-dimensional MoSi2N4 monolayer was successfully synthesized by chemical vapor deposition, showing remarkable stability in the ambient condition. Motivated by this achievement, herein, we investigate the electronic and optical properties of MoSi2N4 monolayer under mechanical strain through the first-principle calculations. The considered monolayer is structurally and dynamically stable. It is a semiconductor with an indirect band gap of 1.92 eV so that the size of the band gap is easily tuned under biaxial strain. By increasing the tensile strain up to 6%, the effective mass of holes increases to 3.84 me whereas the effective mass of electrons reduces to 0.43 me. In other words, under the strain of 6%, one can have strongly localized holes together with free electrons simultaneously in MoSi2N4 monolayer, which could bring fascinating features like ferromagnetism and superconductivity. Under the strain from 10% to 18%, a Mexican hat dispersion is observed in the highest valence band in such a manner that its coefficient increases from 0.28 to 2.89 eVÅ, indicating the potential thermoelectric application of MoSi2N4 monolayer under strain. Under the strain of 8%, the light absorption coefficient is improved by almost 70%. More importantly, this monolayer tolerates biaxial strain up to 18% and stays mechanically and dynamically stable, making it very promising for flexible nanoelectronics. The controllable electronic and optical properties of MoSi2N4 monolayer may open up an important path for exploring next-generation optoelectronic applications.
Attachment style and social media fatigue: The role of usage-related stressors, self-esteem, and self-concept clarity

Social media fatigue is a subjective sense of physical and mental exhaustion, lassitude, and irritation, caused by social media use. The current research explored the association between individual differences in attachment styles and the experience of fatigue resulting from extensive social media use. Two studies examined the association between adult attachment style and Facebook fatigue, and the mediating role of stressors related to social media use, self-esteem, and self-concept clarity. The results of the first study (N = 264) revealed an association between attachment anxiety and Facebook fatigue that was mediated by Facebook social comparison and Facebook anxiety. In the second study (N = 294), attachment anxiety was also associated with Facebook fatigue and was mediated by fear of missing out and Facebook anxiety, and these mediation effects were moderated by self-concept clarity. The findings indicate that the experience of social-media fatigue varies in accordance with specific user characteristics. Additionally, they Illustrate the impact of social media use on mental health, and emphasize the need to create a user experience that takes into account the stressors associated with social media use.
Fostering Creative Confidence with SCD in Interaction Design Education

Creative confidence is a concept that has been popularly linked to design thinking in more generalist interaction design education. Based on an analysis of creative confidence, however, we propose that Speculative and Critical Design practices (SCD) can be a better perspective for developing creative confidence in aspiring designers. We present how we have taught two subsequent SCD courses as part of a short 2-year generalist interaction design master’s programme. Based on the outcome of selected student projects, course evaluations, and personal reflections, we articulate the promises and pitfalls of using SCD to foster creative confidence.
Addressing service profit chain with artificial and emotional intelligence

ABSTRACT In view of the prevalence of artificial intelligence (AI) – powered applications in service organizations, this paper draws on service profit chain theory and proposes these applications as a service product for employees (referred to as internal customers) and customers. The outcome of which is to examine the influence of AI service quality on internal and external customer loyalty. Customer satisfaction and engagement are modeled as mediators, whereas emotional intelligence (EI) is a moderator in AI – customer loyalty relationships. Two studies were undertaken with employees and customers in Australian-based hotels to examine these relationships. The results show that AI service quality is significantly related to internal and external customer satisfaction and engagement. Both internal and external customer engagement have significant effects on customer loyalty and play a significant mediation role in the service quality – customer loyalty relationship. EI has only a significant moderation effect on the relationship between AI service quality and customer engagement for internal and external customers. Discussion and implications of the research findings are provided for researchers and practitioners.
Virtual image sensors to track human activity in a smart house

With the advancement of computer technology, demand for more accurate and intelligent monitoring systems has also risen. The use of computer vision and video analysis range from industrial inspection to surveillance. Object detection and segmentation are the first and fundamental task in the analysis of dynamic scenes. Traditionally, this detection and segmentation are typically done through temporal differencing or statistical modelling methods. One of the most widely used background modelling and segmentation algorithms is the Mixture of Gaussians method developed by Stauffer and Grimson (1999). During the past decade many such algorithms have been developed ranging from parametric to non-parametric algorithms. Many of them utilise pixel intensities to model the background, but some use texture properties such as Local Binary Patterns. These algorithms function quite well under normal environmental conditions and each has its own set of advantages and short comings. However, there are two drawbacks in common. The first is that of the stationary object problem; when moving objects become stationary, they get merged into the background. The second problem is that of light changes; when rapid illumination changes occur in the environment, these background modelling algorithms produce large areas of false positives. These algorithms are capable of adapting to the change, however, the quality of the segmentation is very poor during the adaptation phase. In this thesis, a framework to suppress these false positives is introduced. Image properties such as edges and textures are utilised to reduce the amount of false positives during adaptation phase. The framework is built on the idea of sequential pattern recognition. In any background modelling algorithm, the importance of multiple image features as well as different spatial scales cannot be overlooked. Failure to focus attention on these two factors will result in difficulty to detect and reduce false alarms caused by rapid light change and other conditions. The use of edge features in false alarm suppression is also explored. Edges are somewhat more resistant to environmental changes in video scenes. The assumption here is that regardless of environmental changes, such as that of illumination change, the edges of the objects should remain the same. The edge based approach is tested on several videos containing rapid light changes and shows promising results. Texture is then used to analyse video images and remove false alarm regions. Texture gradient approach and Laws Texture Energy Measures are used to find and remove false positives. It is found that Laws Texture Energy Measure performs better than the gradient approach. The results of using edges, texture and different combination of the two in false positive suppression are also presented in this work. This false positive suppression framework is applied to a smart house senario that uses cameras to model ”virtual sensors” to detect interactions of occupants with devices. Results show the accuracy of virtual sensors compared with the ground truth is improved.
Prevention Methods of Fitness and Bodybuilding Exercise Injury Based on Data Mining

Fitness and bodybuilding are becoming the trend of the development of sports projects today, not only to bring health to the exercisers, but also to keep the body in good condition. However, due to the wrong attitudes and methods of exercisers, they often have negative effects on exercisers, such as sprains, strains, and tendon strains. This article is based on data mining technology to realize the research of exercise injury prevention methods, so firstly, it introduces the steps and applications of data mining technology and highlights the cluster analysis method in data mining technology. Then it discusses the injury factors of fitness and bodybuilding and outlines the best measures to prevent exercise injury. At the same time, the C4.5 algorithm was introduced to realize the processing of the data set, and the effectiveness of preventive measures was proved by investigating the damage of fitness and bodybuilding exercises in 15 clubs in a certain city. The results of the study show that the number of people who exercise more than 3 times a week accounts for 65.6% of the total number of people, which shows that exercisers in a city's fitness club exercise more frequently each week.
Phyllanthus muellerianus and C6H15NO3 synergistic effects on 0.5 M H2SO4-immersed steel-reinforced concrete: Implication for clean corrosion-protection of wind energy structures in industrial environment

This paper investigates Phyllanthus muellerianus leaf-extract and C6H15NO3 (triethanolamine: TEA) synergistic effects on reinforcing-steel corrosion-inhibition and the compressive-strength of steel-reinforced concrete immersed in 0.5 M H2SO4. This is to assess suitability of the synergistic admixture usage for wind-energy steel-reinforced concrete structures designed for industrial environments. Steel-reinforced concrete specimens were admixed with individual and synergistic designs of Phyllanthus muellerianus leaf-extract and C6H15NO3 admixtures and immersed in the 0.5 M H2SO4. Electrochemical monitoring of corrosion potential, as per ASTM C876-91 R99, and corrosion current were obtained and statistically analysed, as per ASTM G16-95 R04, for modelling noise resistance. Post-immersion compressive-strength testing then followed, as per ASTM C39/C39M–03, for detailing the admixture effect on load-bearing strength of the steel-reinforced concrete specimens. Results showed that while individual Phyllanthus muellerianus leaf-extract concentrations exhibited better inhibition-efficiency performance than C6H15NO3, synergistic additions of C6H15NO3 to Phyllanthus muellerianus leaf-extract improved steel-rebar corrosion-inhibition. Thus, 6 g Phyllanthus muellerianus + 2 g C6H15NO3 synergistically improved inhibition-efficiency to η = 84.17%, from η = 55.28% by the optimal chemical or from η = 74.72% by the optimal plant-extract admixtures. The study also established that improved compressive strength of steel-reinforced concrete with acceptable inhibition of the steel-rebar corrosion could be attained through optimal combination of the Phyllanthus muellerianus leaf-extract and C6H15NO3 admixtures.
CORRELATION BETWEEN GANGLION CELL COMPLEX AND RETINAL NERVE FIBER LAYER IN DIAGNOSIS OF GLAUCOMA

Background: Glaucoma is a group of diseases of different etiologies with progressive optic nerve degeneration as well as loss of retinal ganglion cells (RGCs) with a subsequent visual impairment. It remains a primary cause of irreversible blindness. So, early diagnosis and treatment of glaucoma has been demonstrated to reduce the rate of disease progression and improve cases’s quality of life. Spectral domain-optical coherence tomography (SD-OCT) technologies play an essential role in glaucoma management. Objective: To determine and compare the use of thickness of macular ganglion cell complex (GCC) and peripapillary retinal nerve fiber layer (ppRNFL) using OCT in diagnosis of glaucoma. Patients and methods: This was a comparative study carried out on 40 eyes. The eyes were classified into two groups: Group A that represented: 10 normal eyes as a control of the study, and Group B that included 30 eyes with glaucoma classified as regards disease severity into: 10 eyes with early glaucoma, 10 eyes with moderate glaucoma, and 10 eyes with severe glaucoma as regards Hodapp, Parrish, and Anderson (H-P-A) classification. Comprehensive ophthalmic examination, white-on-white perimetry and SD-OCT were done for entire cases. The OCT was done using 3D-OCT (Topcon) 2000 to evaluate the following: RNFL parameters average (total, superior and inferior) thickness and GCC parameters average (total, superior and inferior) thickness. Results: There was a positive statistically significant association of high probability between the two groups regarding RNFL thickness, GCC thickness, intraocular pressure (IOP), and vertical and linear cup-to-disc ratio (P<0.001). Conclusion: Early diagnosis of glaucoma and initiation of therapy was of high significance, as additional vision loss can be stopped or slowed down. RNFL and GCC measurement with SD-OCT could provide essential data for detection and assessment of glaucoma. GCC thickness and RNFL thickness demonstrated comparable diagnostic value as markers of early, moderate, and severe glaucoma. There was a strong positive association between the RNFL thickness and the GCC thickness in the glaucomatous cases.
Does Adherence to the Mediterranean Diet Have a Protective Effect against Asthma and Allergies in Children? A Systematic Review

Dietary pattern may potentially impact on the pathogenesis of asthma and allergies. The Mediterranean Diet (MD) has significant health benefits due to its antioxidant and anti-inflammatory properties. The aim of this systematic review was to investigate the effectiveness of adherence to the MD against asthma and allergies in childhood. Hence, a systematic literature search was conducted on PubMed, ESBCO (Cinahl), Scopus, and the Cochrane Library databases up to 26 January 2022. The total number of articles obtained, after the initial search on the databases was conducted, was 301. Twelve studies were included, after the removal of duplicates and screening for eligibility. Our findings indicated a protective role of the MD against childhood asthma, but they also imply that the MD probably does not affect the development of allergies. Nevertheless, the heterogeneity and limitations of the studies highlight the need for randomized controlled trials that will focus on the pediatric population and hopefully provide more robust evidence.
Effect of Thiophene Insertion on X-Shaped Anthracene-Based Hole-Transporting Materials in Perovskite Solar Cells

In this work, two novel tetra-substituted X-shaped molecules X1 and X2 that were constructed with anthracene as the central core and arylamine as the donor groups have been synthesized. The HTMs X1 and X2 were synthesized in two steps from industrially accessible and moderately reasonable beginning reagents. These new HTMs are described in terms of utilization of light absorption, energy level, thermal properties, hole mobility (µh), and film-forming property. The photovoltaic performances of these HTMs were effectively assessed in perovskite solar cells (PSCs). The devices based on these HTMs accomplished an overall efficiency of 16.10% for X1 and 10.25% for X2 under standard conditions (AM 1.5 G and 100 mW cm−2). This precise investigation provides another perspective on the use of HTMs in PSCs with various device configurations.
On-chip separation of spermatozoa using dielectrophoresis

The aim of the fertility project in the BIOS group of the University of Twente is to develop a portable point of care semen quality analyzer. This should enable couples who are staying unwanted childless to perform semen quality tests themselves in a convenient environment. The quality of semen depends on four important factors, these are the concentration, motility, viability and morphology. This master thesis is focused on the separation of spermatozoa based on their motility and viability. The research described in this thesis was focused on the possibility to separate motile and non-motile spermatozoa and to separate viable and non-viable spermatozoa on-chip with a technique called dielectrophoresis. Based on theory, simulations were performed, resulting in six different lab-on-a-chips are designed that have been manufactured. Measurements with boar-semen were performed to verify the simulations and to measure the sorting functioning of these chips. It was not possible to test the separation operation of the chip, due to experimental problems and the limited time available. However, some of the simulation results were confirmed by the experiments. More research is necessary to investigate the separation of spermatozoa.
Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method

The issue of students’ career choice is the common concern of students themselves, parents, and educators. However, students’ behavioral data have not been thoroughly studied for understanding their career choice. In this study, we used eXtreme Gradient Boosting (XGBoost), a machine learning (ML) technique, to predict the career choice of college students using a real-world dataset collected in a specific college. Specifically, the data include information on the education and career choice of 18,000 graduates during their college years. In addition, SHAP (Shapley Additive exPlanation) was employed to interpret the results and analyze the importance of individual features. The results show that XGBoost can predict students’ career choice robustly with a precision, recall rate, and an F1 value of 89.1%, 85.4%, and 0.872, respectively. Furthermore, the interaction of features among four different choices of students (i.e., choose to study in China, choose to work, difficulty in finding a job, and choose to study aboard) were also explored. Several educational features, especially differences in grade point average (GPA) during their college studying, are found to have relatively larger impact on the final choice of career. These results can be of help in the planning, design, and implementation of higher educational institutions’ (HEIs) events.
Dynamics and Determinants of the Grain Yield Gap in Major Grain-Producing Areas: A Case Study in Hunan Province, China

Understanding the dynamics of the grain yield gap (YGAP) and its causative factors is essential for optimizing the layout of grain production and addressing the food crisis, especially in countries with a huge population and less cultivated land, such as China. In the study, a spatial analysis- and machine learning-based framework for YGAP analysis was developed, taking Hunan Province, China, as an application. The results showed that the average YGAP in Hunan Province gradually narrowed from 1990 to 2018, and the YGAPs narrowed in 116 counties. Of which, 26 counties narrowed by more than 4 t ha−1, 58 counties narrowed from 2–4 t ha−1, and 32 counties narrowed within 2 t ha−1. Additionally, we found that the GDP per capita (GDPPC), sunshine hours (SH), per capita annual net income of farmers (PCAI), and rural electricity consumption (REC) play a key role in YGAP change, and the importance of human investment to the YGAP decreased, while socioeconomic environment became the dominant factor that influenced grain production. Comprehensively, the relatively great potential for grain yield growth was generated in sixty-four counties, which are mainly located in the northern, central, and southern Hunan. The findings suggest that it is necessary to consider the trends of economic development in rural areas and population migration in agricultural management. This work provides insights into yield gap dynamics and may contribute to sustainable agricultural management in Hunan Province, China, and other similar regions.
Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study

For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a “harmonized” alongside a “standard” dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on the images with the highest image noise as compared to the images with the lowest image noise. Using the harmonized dataset significantly improved the feature agreement for five of the six investigated feature classes over the standard dataset. For three feature classes, high feature agreement corresponded with higher sensitivity to the different patterns, suggesting a way to select suitable features for predictive models.