Miracle-dz/UniFAR-FTUs huggingface dataset:
Macro-Economic Determinants of Migration. A Comparative Analysis for Old vs. New European Member States

The objective of this paper is to identify and measure the macroeconomic factors having a significant impact on migration flows in European countries. A comparative analysis is proposed between Old Member States (traditionally receiving countries for migrants) and New Member States (with lower net migration rates). Thus, two panel data regression models are estimated for identifying the determinants of net migration (period 2000 – 2017). The factors considered are consistent with the migration theories: labour market factors (unemployment, income), social factors (Gini inequality coefficient, poverty rate) and other factors (economic freedom, health system). Obtained results confirm the economic theories of migration. Unemployment is a significant and strong supply push factor for migration, while income emerges as a significant factor only for the Old Member States. Inequality is endorsed as a significant push factor for all countries, while economic freedom has a significant positive influence on the net migration rate only for the New Member States. Health related variables are not validated as migration determinants. The findings of this paper can be further used for developing migration projections but also for establishing migration policies recommendations that could lead to better integration of migrants.
3D Reconstruction for Motion Blurred Images Using Deep Learning-based Intelligent Systems

The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images. Generally, during the acquisition of images in real-time, motion blur, caused by camera shaking or human motion, appears. Deep learning-based intelligent control applied in vision can help us solve the problem. To this end, we propose a 3D reconstruction method for motion-blurred images using deep learning. First, we develop a BF-WGAN algorithm that combines the bilateral filtering (BF) denoising theory with a Wasserstein generative adversarial network (WGAN) to remove motion blur. The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image. Then, the blurred image and the corresponding sharp image are input into the WGAN. This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions. Next, we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction. We propose a threshold optimization random sample consensus (TO-RANSAC) algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately. Compared with the traditional RANSAC algorithm, the TO-RANSAC algorithm can adjust the threshold adaptively, which improves the accuracy of the 3D reconstruction results. The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms. In addition, the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.
Fabrication of Magnetic Luminescent Nanocomposites by a Layer-by-Layer Self-assembly Approach

Magnetic luminescent nanocomposites were prepared via a layer-by-layer (LbL) assembly approach. The Fe3O4 magnetic nanoparticles of 8.5 nm were used as a template for the deposition of the CdTe quantum dots (QDs)/polyelectrolyte (PE) multilayers. The number of polyelectrolyte multilayers separating the nanoparticle layers and the number of QDs/polyelectrolyte deposition cycles were varied to obtain two kinds of magnetic luminescent nanocomposites, Fe3O4/PEn/CdTe and Fe3O4/(PE3/CdTe)n, respectively. The assembly processes were monitored through microelectrophoresis and UV−vis spectra. The topography and the size of the nanocomposites were studied by transmission electron microscopy. The LbL technique for fabricating magnetic luminescent nanocomposites has some advantages to tune their properties. It was found that the selection of a certain number of the inserted polyelectrolyte interlayers and the CdTe QDs loading on the nanocomposites could optimize the photoluminescence properties of the nanocomposites.
Development of Program Comprehension Skills by Novice Programmers - Longitudinal Eye Tracking Studies

The article discusses the findings of longitudinal studies (three stages spanning 6 months) which were to investigate the process of acquiring the ability to comprehension program code by the computer science students having started to learn to program. The studies were conducted with the use of a knowledge measurement test, the diagnostic survey, and eye tracking technology that enabled the recording of movement of the subjects’ eyes and an analysis of the patterns of information processing during solving programming problems. The obtained results have shown that the students solved the tasks most effectively in the last stage of the research during which they obtained the highest indicator of correct answers in the significant ly shortest time. In the last stage of the research the dominant form of the algorithmic problem analysis was code, in two previous it was flowchart. The eye tracking data have shown that re gardless of the research stage the code analysis was definitely connected with a greater number of fixations, with very near values of time devoted to solving those two forms of the algorithm. The participants who increased their competences in a scope of the program code analysis had a significantly greater saccade amplitude average (SAA) and a significantly shorter fixa tion duration average (FDA) in the last stage of the research comparing to previous ones. The results suggest that the FDA and SAA are parameters sensitive to the development of program comprehension skills.
Evaluation of Students’ Flow State in an E-learning Environment Through Activity and Performance Using Deep Learning Techniques

Estimating the flow state of students in a course allows evaluating their sentimental state and the challenges they are facing. In e-learning platforms, the evaluation of flow state is a complex task because it depends on the ability to extract the parameters that better reflect the activity and effort of students. In this scope, the current study proposes a method based on flow theory aiming to provide information about the students' flow state in a course that is taught in an e-learning environment. First, the interaction of students with an e-learning platform that comprises classical e-learning pages and a timeline tool is analyzed, using activity heatmaps and deep neural networks. Then, by taking also in account their grades, the flow state of students is calculated. The resulted data are validated with a statistical analysis that also utilizes student surveys. In order to guarantee that this method is applicable to various profiles, students from different faculties participated in this study. In a period where education is rapidly adapting to online lectures and e-learning platforms, the estimation of student's flow state in e-learning environments can provide useful feedback and data to students and educators.
Sparse structure deep network embedding for transforming brain functional network in early mild cognitive impairment classification

Currently, it remains one of the most challenging issues to distinguish brain functional networks of early mild cognitive impairment subjects (eMCIs) and normal control subjects (NCs). Unlike images, functional networks are non‐Euclidean data and not easily classified by dilated convolutional neural network (DCNN). To address this problem, we developed a sparse structure deep network embedding (SSDNE) method which transforms brain functional networks into a double‐channel image in eMCI classification. First, the eigenvector of each node in the functional network was obtained by SSDNE, and principal component analysis (PCA) was employed to sort the importance of eigenvectors. Next, two eigenvector groups with the highest contribution rate were extracted in turn and divided into several equal‐length intervals along its direction respectively, and the numbers of nodes that fall into the intervals were counted to obtain the corresponding two‐dimensional histograms. Then, the both histograms were stacked up and down into a double‐channel image. Finally, a double‐channel image was input into DCNN for feature learning to achieve final classification results. Experimental results show that, SSDNE performed better in maintaining the original structure of brain functional networks, and the transformed double‐channel image achieved comparably identifying results on eMCI classification compared with other network embedding algorithms. This novel method solved the problem that brain functional networks cannot be directly applied to convolutional neural networks for feature extraction and classification. Meanwhile, it can provide a reference for the early auxiliary diagnosis of Alzheimer's disease (AD).
Job characteristics, marital intentions, and partner-seeking actions: Longitudinal evidence from Japan

BACKGROUND Most research linking jobs to marriage formation focuses on how job contexts and prospects affect singles’ paces of entering marriage. Direct evidence on whether job traits shape singles’ desire for marriage and actions toward forming a union remains scarce. OBJECTIVE We examine how changes in a range of job characteristics correspond to alterations in never-married people’s intention to marry and actions taken to meet romantic partners in Japan, a country with increasing inequality in job quality and declining marriage rates. METHODS We use longitudinal data from the Japan Life Course Panel Survey to fit fixed-effects models, which take into account unobserved heterogeneity among people with differing jobs. RESULTS We find that rises in job insecurity and workplace staffing shortages weaken, whereas increases in income and job autonomy strengthen, men’s intention to marry. Moreover, men with a low marriage desire are especially likely to withdraw from partner-seeking activities when they have low-income jobs or face great deadline pressure at work. Job prospects and quality are generally less important to women’s desire for marriage or partner-seeking actions. Nevertheless, being in workplaces where teamwork is prevalent, which could enhance singles’ exposure to married and older coworkers, raises both women’s intention to marry and their probability of using a formal method, such as employing a marriage agency, to find a partner. CONCLUSIONS For Japanese men, our results offer support for the argument that economic stagnation and deterioration of job quality are conducive to later and fewer marriages. The findings for women, however, are more consistent with the narrative focusing on values and social influences. CONTRIBUTIONS This study enriches our understanding of singles’ considerations of marriage and partner search and provides highly rigorous evidence on the roles of job conditions.
Influence of the Length of a Catalyst-Coated Glow Plug on Exhaust Emissions

This paper discusses the application of an in-cylinder catalyst in reducing the exhaust emissions from a diesel engine. This is an additional method of exhaust gas aftertreatment; yet the placement of a catalyst in the combustion chamber (i.e., the closest location to the process of combustion) allows a reduction of the emissions ‘at source’ (the catalyst applied on the glow plugs). For the investigations, we used an engine dynamometer to reproduce the traffic conditions of a homologation test carried out on a chassis dynamometer. We carried out the investigations on a Euro 4 1.3 JTD MultiJet diesel engine. The selection of the research object was followed by an analysis of the number of engines used in the EU meeting individual emission standards. We present results (measurement of carbon monoxide, hydrocarbons, nitrogen oxides, particle number, and carbon dioxide) related to the assessment of the applicability of the in-cylinder catalyst for three types of glow plugs: standard, catalyst-covered, and a prototype plug with an elongated catalyst-covered heating part. Prototype catalytic glow plugs ensure a few percent reduction in the emission of carbon monoxide, hydrocarbons, carbon dioxide, and particle number. The use of such a solution (glow plug replacement) in most diesel engines (easy to retrofit) would improve the environmental performance of combustion engines. It is of particular importance that in-cylinder catalysts are most efficient during cold start and warm-up, which is often the case in urban driving.
PHOTOINDUCED OXIDATION OF HORSERADISH PEROXIDASE

Photoexcited [Ru(bpy)3]2+ (bpy = 2,2‘-bipyridine) can be quenched with [Co(NH3)5Cl]2+ to give [Ru(bpy)3]3+; this photogenerated oxidant (E0 = 1.25 V vs NHE) reacts with horseradish peroxidase isoenzyme c (HRPc) to produce oxidized protein species. Spectra and kinetics measured by laser-flash transient spectroscopy show that oxidation of the trivalent resting state, [PFeIII]+ (P = porphyrin dianion), to ferryl compound II, [PFeIVO], is preceded by generation of a π-cation porphyrin radical intermediate, [PFeIII]•2+. In the interval 7.8 < pH < 9.8, the rate-limiting step for the transformation of the radical intermediate to compound II is the binding of a water molecule to the five-coordinate heme iron, kobsd = (4.1 ± 0.9) s-1; this step is followed by fast proton and electron transfer to give the ferryl species. There is a burst in compound II formation in the pH region (10.3 < pH < 10.8) in which the heme iron changes from a five-coordinate, high-spin species to a six-coordinate, low-spin complex (pKa = 1.
Self-Similarity Principle and the General Theory of Fractal Elements: How to Fit a Random Curve with a Clearly Expressed Trend?

The well-known power-law fractal element was determined to need several important revisions by the authors of this work. It is now possible to demonstrate that any scaling equation associated with a fractal element is actually K-fold degenerated and includes previously unknown but crucial adjustments. These new discoveries have the potential to significantly alter the preexisting theory and create new connections between it and its experimental support, particularly when it comes to measurements of the impedances of diverse metamaterials. It is now easy to demonstrate that any random curve with a clearly stated tendency in a specific range of scales is self-similar using the method involving reduction to three invariant points (Ymx, Ymn, and Ymin). This useful procedure indicates that the chosen random curve, even after being compressed a certain number of times, still resembles the original curve. Based on this common peculiarity, it is now possible to derive “a universal” fitting function that can be used in a variety of applied sciences, particularly those that deal with complex systems, to parametrize many initial curves when a model fitting function derived from a simple model is not present. This self-similarity principle-derived function demonstrates its effectiveness in data linked to photodiode noise and the smoothed integral curves produced from well-known transcendental numbers E and Pi, which are considered in the paper as an example.
Corrigendum to “Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate”

After publication of Esarey and Sumner, we recognized substantively significant errors in the paper. We correct those errors here. We discovered these errors as part of work on another, unrelated paper to which one co-author hoped to apply similar methods. The Brambor et al. procedure is overconfident when separately testing multiple hypotheses, as correctly stated in our paper. However, when conjointly testing multiple hypotheses, the Brambor et al. procedure is appropriate. The procedure we suggest on pp. 1161–1163 to correct for overconfidence in the case of separate testing of multiple hypothesis (based on Benjamini and Hochberg) is (to our knowledge) correct, but subject to several limitations unstated in the paper. This corrigendum lays out all errors and limitations and adds a more robust procedure to our interactionTest software.
Enhancement of the Erbium Concentration in RbTiOPO4 by Codoping with Niobium

A thorough study of the RbTiOPO4(RTP) crystallization in its self-flux and WO3-containing fluxes (10, 20, and 30 mol % WO3) has been performed. The composition regions and isotherms of crystallization were obtained, and most of the crystallized neighboring phases were identified. Afterward, the possibilities of doping and codoping RTP crystals with Er3+ and Nb5+ were studied. Adding Nb2O5 substituting TiO2 in the solution increases the distribution coefficient of Er3+ but changes the crystal morphology toward thin plates significantly. This means it is difficult to grow crystals of sufficient quality and size for research and applications. To optimize the crystal growth process, the conditions for growing doped and codoped RTP single crystals with Er3+ and Nb3+ by the top seeded solution growth technique (TSSG) were studied. For crystal growth from self-flux, stirring the solution with an immersed platinum turbine significantly increased the efficiency of the crystal growth process. These conditions allow.
Cornering Algorithm for a Crawler In-Pipe Inspection Robot

Based on the large-scale wall-pressing three-legged crawler pipeline inspection robot, our team proposed a cornering algorithm based on space constraints, that aims to better control the smooth operation of the pipeline robot in the pipeline. This algorithm is aimed at large robots that use an electric telescopic rod structure to replace the elastic structure on traditional small robots. The electric telescopic rod structure meets the large-scale weight change of the robot and provides sufficient supporting force. However, this structure also makes it difficult for the robot to automatically adapt to the change of pipe diameter and increases the difficulty of the robot’s control. In order to solve this problem and more accurately control the operation of the robot during cornering, this paper analyzes the space constraints of the robot when turning, the optimization analysis of the telescopic rod expansion and the ratio of the speed of each crawler, obtaining a stable turning algorithm for pipeline robots. The algorithm guarantees that the robot can provide sufficient support in the bend pipeline, and that it has good stability and mobility.
Can local governments’ disclosure of pandemic information decrease residents’ panic when facing COVID-19 in China?

Abstract When COVID-19 wreaked havoc around the world and caused widespread panic, Chinese government has taken pandemic information disclosure measures to fight the outbreak. However, there is a theoretical debate about whether information disclosure increases or decreases panic of residents during a pandemic. In response to this debate, the authors conduct a questionnaire survey with 1062 participants in the worst period of COVID-19 in China. The results reveal that both generic information and detailed information disclosure can alleviate panic by enhancing individuals’ perceived prevention effectiveness and increase panic by amplifying individuals’ risk perception. In addition, information disclosure is more effective in reducing panic in conditions with a higher level of trust in local government. These findings provide practical evidence for the role of information disclosure in alleviating panic in a pandemic, and suggest that pandemic information timely disclosed by the local government is feasible to ease the spread of panic.
Considerations for a Revised Adult Cochlear Implant Candidacy Evaluation Protocol

Objective: To compare the current metrics used in adult cochlear implant candidacy evaluations for consistency and applicability, and to make a recommendation for an updated assessment battery. Study Design: Prospective, multicenter, within-subject clinical trial. Setting: North American cochlear implant programs including private practices, universities, and hospital centers. Patients: One hundred adult hearing aid users scoring 40% or less on monosyllabic words in quiet in the aided test ear who presented for cochlear implantation evaluation. Intervention: Subjects underwent unilateral cochlear implantation. Main Outcome Measures: Speech perception measured via Consonant-Nucleus-Consonant words in quiet and AzBio sentences in noise. Patient-reported hearing disability measured via the Speech, Spatial, and Qualities of Hearing Scale administered preoperatively and at 6 months postoperatively. Results: Significant group mean improvement on all speech perception measures and SSQ subscales postoperatively with possible floor effects observed in objective background noise testing preoperatively and a broad range of variability seen postoperatively. Conclusions: Consonant-Nucleus-Consonant words are an effective tool to clinically evaluate hearing ability over time. Adjustment of cochlear implant screening protocols to prioritize monosyllabic words over sentences in noise as the chief determining factor appears justified, and this test can be supplemented by the SSQ for a holistic and applicable assessment of cochlear implant candidacy.
ALMA front-end and digitizer technical requirements for enabling the ALMA board's 2030 vision roadmap

The ALMA 2030 Development Roadmap defines the long-term scientific objectives and serves as a guide for the development activity for the upgrade of hardware, software, and analysis tools in order to enhance the future observing capabilities of ALMA. A working group was established to define a set of consistent system level technical goals in order to guide the ongoing and future ALMA technical development effort. The working group has prepared an updated set of technical goals for Front-end and Digitizer products to enable the scientific needs as stipulated in the ALMA 2030 Development Roadmap. This manuscript describes the working group’s considerations of system trade-offs and feasibility studies and presents tentative specifications arrived at for some of the key technical requirement goals.
Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types

We developed a high-throughput mapping workflow, which centers on deep learning (DL) convolutional neural network (CNN) algorithms on high-performance distributed computing resources, to automatically characterize ice-wedge polygons (IWPs) from sub-meter resolution commercial satellite imagery. We applied a region-based CNN object instance segmentation algorithm, namely the Mask R-CNN, to automatically detect and classify IWPs in North Slope of Alaska. The central goal of our study was to systematically expound the DLCNN model interoperability across varying tundra types (sedge, tussock sedge, and non-tussock sedge) and image scene complexities to refine the understanding of opportunities and challenges for regional-scale mapping applications. We corroborated quantitative error statistics along with detailed visual inspections to gauge the IWP detection accuracies. We found promising model performances (detection accuracies: 89% to 96% and classification accuracies: 94% to 97%) for all candidate image scenes with varying tundra types. The mapping workflow discerned the IWPs by exhibiting low absolute mean relative error (AMRE) values (0.17–0.23). Results further suggest the importance of increasing the variability of training samples when practicing transfer-learning strategy to map IWPs across heterogeneous tundra cover types. Overall, our findings demonstrate the robust performances of IWPs mapping workflow in multiple tundra landscapes.