Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

Accurate estimation of water table depth dynamics is essential for water resource management, especially in areas where groundwater is overexploited. In recent years, as a data-driven model, artificial neural networks (NNs) have been widely used in hydrological modeling. However, due to the non-stationarity of water table depth data, the performance of NNs in areas of over-exploitation is challenging. Therefore, reducing data noise is an essential step before simulating the water table depth. This research proposed a novel method to model the non-stationary time series data of water table depth through combing the advantages of wavelet analysis and Long Short-Term Memory (LSTM) neural network (NN). A typical groundwater over-exploitation area, Baoding, North China Plain (NCP), was selected as a study area. To reflect the impact of anthropogenic activities, the variables harnessed to develop the model includes temperature, precipitation, evaporation, and some socio-economic data. The results show that decomposing the time series of the water table depth into three sub-temporal components by Meyer wavelets can significantly improve the simulation effect of LSTM on the water table depth. The average NSE (Nash-Sutcliffe efficiency coefficient) value of all the sites increased from 0.432 to 0.819. Additionally, a feedforward neural network (FNN) is used to compare forecasts over 12-months. As expected, wavelet-LSTM outperforms wavelet-FNN. As the prediction time increases, the advantages of wavelet-LSTM become more evident. The wavelet-LSTM is satisfactory for forecasting the water table depth at most in 6 months. Furthermore, the importance of input variables of wavelet-LSTM is analysed by the weights of the model. The results indicate that anthropogenic activities influence the water table depth significantly, especially in the sites close to the Baiyangdian Lake, the largest lake in the North China Plain. This study demonstrates that the wavelet-LSTM model provides an option for water table depth simulation and predicting areas of over-exploitation of groundwater.
The Perceived Beauty of Convex Polygon Tilings: The Influence of Regularity, Curvature, and Density

Polygon tilings in natural and man-made objects show great variety. Unlike previous studies that have mainly focused on their classification and production methods, this study aimed at exploring factors that may contribute to the perceived beauty of convex polygon tilings. We analyze the dimensions of regularity, curvature, and density, as well as individual differences. Triangle tilings and hexagon tilings were tested in Experiment 1 and 2, respectively. The results showed that the perceived beauty of convex polygon tilings can be enhanced by higher levels of regularity and nonobvious local curvature. Surprisingly, the effect of density appeared to be different, with the dense triangle tilings and the less dense hexagon tilings scoring higher than the reverse. We discuss a possible explanation based on trypophobia caused by different types of polygons, as well as the observers’ personality trait of agreeableness.
The Importance of Patient Expectations: A Mixed-Methods Study of U.S. Psychiatrists

Objective: To examine how psychiatrists think about and modulate non-specific factors (e.g., hope, expectations) in clinical practice. Methods: U.S. psychiatrists were recruited for two studies assessing attitudes and behaviors related to non-specific factors. Study 1 entailed remote qualitative focus groups (k = 7) with n = 26 participants (36.0% female). Study 2 was a quantitative survey with n = 346 respondents (34.0% female) designed to assess the generalizability of focus group findings. Results: Four themes were identified in Study 1 that were used to inform the survey (Study 2): (1) Expectations (2) Hope, (3) Placebo Effect, and (4) Aesthetic Features. Nearly all surveyed psychiatrists (92.2%) considered patient expectations at least “most of the time” when interacting with a patient. Focus groups revealed that psychiatrists often attempt to balance optimism and realism to improve outcomes. A majority of survey respondents believed office design and physician attire could at least somewhat influence expectations (72.5 and 77.3%, respectively) and even outcomes (51.5 and 58.7%, respectively). Focus group psychiatrists described how physical features may be used as therapeutic tools. Conclusions: Psychiatrists are highly mindful of patient expectations. Although there is variability in the perceived importance of expectations, hope, the placebo effect, and aesthetic features, many utilize these factors in clinical practice.
360MonoDepth: High-Resolution 360° Monocular Depth Estimation

360° cameras can capture complete environments in a single shot, which makes 360° imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360° data, particularly for high resolutions like 2K (2048 × 1 024) and beyond that are important for novel-view synthesis and virtual reality applications. Current CNN-based methods do not support such high resolutions due to limited GPU memory. In this work, we propose aflexible framework for monocular depth estimation from high-resolution 360° images using tangent images. We project the 360° input image onto a set of tangent planes that produce perspective views, which are suitable for the latest, most accurate state-of-the-art perspective monocular depth estimators. To achieve globally consistent disparity estimates, we recombine the individual depth estimates using deformable multi-scale alignment followed by gradient-domain blending. The result is a dense, high-resolution 360° depth map with a high level of detail, also for outdoor scenes which are not supported by existing methods. Our source code and data are available at https://manurare.github. io/360monodepth/.
Mental burden and perception of the study situation among undergraduate students during the COVID-19 pandemic: a cross-sectional study and comparison of dental and medical students

Objectives To investigate levels of distress, depression, anxiety, stress and perception of their current study situation during the COVID-19 pandemic among undergraduate dental and medical students. Design Observational, cross-sectional study including two consecutive surveys (May and July 2020). Setting A large medical school in Germany. Participants All first year dental and medical students were invited. 132 participating first year students (44 dental, 88 medical) from the first survey and 150 students (50 dental, 100 medical) from the second were included in our analyses. Primary and secondary outcome measures Mental burden (distress thermometer, Patient Health Questionnaire-4, Perceived Stress Scale-4) and self-reported changes in mental health and perception of study situation during the COVID-19 pandemic (self-developed items) were compared. Open-ended questions were analysed by conventional content analyses. Results A considerable proportion of students (t1: May 2020: 84.1%; t2: July 2020: 77.3%) reported distress levels above cut-off. In July 2020, dental students reported significantly higher distress scores than medical students (dental: M=7.0, SD=2.3; medical: M=5.7; SD=2.1; p<0.001). More dental than medical students reported mild, moderate and severe levels of anxiety and depression symptoms. The majority stated that their mental health and study motivation had not changed during the pandemic. Logistic regression showed that being a dental student was significantly associated with a higher likelihood for serious worries regarding the study situation during COVID-19 at t1 (OR 4.0; 95% CI 1.1 to 14.2). At t2 higher distress was significantly associated with a higher likelihood for experiencing serious worries (OR 1.8; 95% CI 1.3 to 2.5). Regarding current concerns related to the pandemic, students most frequently reported difficulties with self-regulated learning (15.2%), study-related worries and uncertainty (14.4%), missing feedback of students and lecturers (11.4%) and lack of practical training (9.8%). Conclusion The results suggest that high mental burden and the lack of practical training among medical and dental students is an increasing problem, with a possibly even higher urgency in dental students. Tailored psychological and educational support offers during and after the COVID-19 pandemic might help them as they progress through (medical and) dental school.
Simulation of water entry of a wedge through free fall in three degrees of freedom

The water entry problem of a wedge through free fall in three degrees of freedom is studied through the velocity potential theory for the incompressible liquid. In particular, the effect of the body rotation is taken into account, which seems to have been neglected so far. The problem is solved in a stretched coordinate system through a boundary element method for the complex potential. The impact process is simulated based on the time stepping method. Auxiliary function method has been used to decouple the mutual dependence between the body motion and the fluid flow. The developed method is verified through results from other simulation and experimental data for some simplified cases. The method is then used to undertake extensive investigation for the free fall problems in three degrees of freedom.
Optimal VAr planning by approximation method for recursive mixed-integer linear programming

The authors propose an algorithm for solving reactive power planning problems. The optimization approach is based on a recursive mixed-integer programming technique using an approximation method. A fundamental feature of this algorithm is that the number of capacitor or reactor units can be treated as a discrete variable in solving large-scale VAr (volt-ampere reactive) planning problems. Numerical results have verified the validity and efficiency of the algorithm. >
Passive coherent location system simulation and evaluation

Passive Coherent Location (PCL) is going to be important and perspective system of passive location of non cooperative and stealth targets. It works with the sources of irradiation of opportunity. PCL is intended to be a part of mobile Air Command and Control System (ACCS) as a Deployable ACCS Component (DAC). The company ERA works on PCL system parameters verification program by complete PCL simulator development since the year 2003. The Czech DoD takes financial participation on this program. The moving targets scenario, the RCS calculation by method of moment, ground clutter scattering and signal processing method (the bottle neck of the PCL) are available up to now in simulator tool. The digital signal (DSP) processing algorithms are performed both on simulated data and on real data measured at NATO C3 Agency in their Haag experiment. The Institute of Information Theory and Automation of the Academy of Sciences of the Czech Republic takes part on the implementation of the DSP algorithms in FPGA. The paper describes the simulator and signal processing structure and results both on simulated and measured data.
The Use Of A Radial Basis Function Network For Visual Autonomous Road Following

In this paper, an alternative connectionist architecture, called a Radial Basis Function (RBF) network, is considered for visual road- following. A controller based on each network architecture is built and the performances of the two network controllers are evaluated using a driving simulator. The RBF network was able to overcome many of the problems encountered by ALVINN (Autonomous Land Vehicle In a Neural Network).
Control of Coliform Growth in Drinking Water Distribution Systems

The occurrence of coliforms in drinking water distribution systems may be explained by (i) inadequate water treatment, (ii) post-treatment contamination, or (iii) coliform growth in the network. In order to confirm the third hypothesis, a 24 h-starved dense suspension of Escherichia coli was injected into an experimental water distribution system. Results from this experiment clearly indicate that E. coli may find ecological conditions in drinking water distribution systems which will allow growth, particularly in the biofilm phase. Chlorination is an appropriate tool to limit coliform much more easily than the total heterotrophic bacterial biomass. However, in all cases, biofilm associated bacteria are more difficult to kill than suspended bacteria, because of chlorine consumption by the pipe material, and because of a diffusion-limited reaction between chlorine and the biofilm.
Key Variables in Screening ELs

This study examined the technical adequacy of Picture Word, a type of Writing Curriculum-Based Measurement, with 73 English learners (ELs) with beginning to intermediate English language proficiency in Grades 1, 2, and 3. The ELs in this study attended schools in one midwestern U.S. school district employing an English-only model of instruction and spoke a variety of native languages. ELs completed two forms of Picture Word in the fall, winter, and spring. The criterion measure, a common English language proficiency assessment, was administered in the winter. Results indicated that Picture Word was not appropriate for the first-grade EL participants but showed promise for secondand third-grade ELs.
Control of natural gas catalytic partial oxidation for hydrogen generation in fuel cell applications

A fuel processor that reforms natural gas to hydrogen-rich mixture to feed the anode field of fuel cell stack is considered. The first reactor that generates the majority of the hydrogen in the fuel processor is based on catalytic partial oxidation of the methane in the natural gas. We present a model-based control analysis and design for a fuel processing system that manages natural gas flow and humidified atmospheric air flow in order to regulate (i) the amount of hydrogen in the fuel cell anode and (ii) the temperature of the catalytic partial oxidation reactor during transient power demands from the fuel cell. Linear feedback analysis and design is used to identify the limitation of a decentralized controller and the benefit of a multivariable controller. Further analysis unveils the critical controller cross-coupling term that contributes to the superior performance of the multivariable controller.
Secure typing via BCI system with encrypted feedback

Information transmission security is an important issue in many scenarios such as password input. Traditional approaches such as typing or voice input are prone to peep, leading to a risk of information leakage. Brain computer interface (BCI) can read information directly from the brain, which is confidential inherently, thus it may be an ideal way for secure information input. This paper proposes a novel BCI-based secure input approach with encrypted feedback. The encrypted feedback is specially designed to notify users and confuse peepers at the same time. We give the theoretical guarantee of accuracy and evaluate the system with both simulation and experiments. The results show that our method can transmit messages effectively.
A 1-V 2GHz VLSI CMOS low noise amplifier

A new LNA topology is presented in this paper. Compatible with VLSI technology the architecture is dedicated to the UMTS third generation mobile phone. Operating under 1 V supply voltage the circuit provides a more than 11 dB gain at 2.1 GHz. With a -11 dBm ICP1, the power consumption is lower than 4 mW, making the architecture well suited to very low power applications. The noise figure of 1.8 dB takes advantage of the inductive degeneration technique. The 0 dBm IIP3 fulfils the UMTS requirement whose the circuit is dedicated to.
Model Construction for the Estimation of Healthy Bone Shape and Density Distribution

Statistical models are widely used within biomedical fields for automated segmentation and reconstruction of healthy geometry. In the absence of contralateral geometry, statistical models are a viable alternative for reconstructing healthy bone anatomy. Therefore, statistical models of shape and appearance were constructed from sample data based on the right femur of South African males, and their use in an automated segmentation and density estimation application was investigated. The models reproduced the shape and density distribution of the population with an average error of 1.3 mm and a 90% density fit. These results fall within the acceptable tolerance limits of reconstructive surgery and appear promising for practical use in implant design.Clinical Relevance— Constructing and validating statistical models and registration algorithms provides the groundwork for further investigation into automating the digital reconstruction of pathological bone for use in implant design.
Learning Influence Adoption in Heterogeneous Networks

We study the problem of learning influence adoption in networks. In this problem, a communicable entity (such as an infectious disease, a computer virus, or a social media meme) propagates through a network, and the goal is to learn the state of each individual node by sampling only a small number of nodes and observing/testing their states. We study this problem in heterogeneous networks, in which each individual node has a set of distinct features that determine how it is affected by the propagating entity. We give an efficient algorithm with nearly optimal sample complexity for two variants of this learning problem, corresponding to symptomatic and asymptomatic spread. In each case, the optimal sample complexity naturally generalizes the complexity of learning how nodes are affected in isolation, and the complexity of learning influence adoption in a homogeneous network.
SEMICONDUCTORS AND DIELECTRICS

S.S. RAGIMOV, 2 M.A. MUSAYEV, N.N. HASHIMOVA 3 1 Baku State University, Institute for Physical Problems (AZ1148, Z. Khalilov Str., 23, Baku, Azerbaijan; e-mails: sadiyar.raqimov@bsu.edu.az; sadiyarragimov58@gmail.com) 2 Institute of Physics, National Academy of Sciences of Azerbaijan (AZ-1143, H. Cavid Ave. 131, Baku, Azerbaijan) 3 Azerbaijan State Oil and Industry University (AZ 1010, Azadliq Ave., 20 Baku, Azerbaijan)
Şişmanoğlu & Sengez: Effects of Acidic Beverages on Color Stability of Bulk-Fill Composites with Different Viscosities

Using a bur multiple times to prepare dental structure may produce a smoother final surface on dentin than a new one. This superficial roughness may affect adhesion with resin-based materials by modifying the substrates’ characteristics. The aim of this study was to evaluate the effect of multiple uses of diamond burs on dentin’s superficial microroughness and bond strength with resin composite when using a self-etch adhesive. Diamond dental burs were used to simulate a preparation (dentin flat surface) on extracted third molars. Samples were distributed into groups according to burs’ number of previous uses as follows: 0, 1, 5 and 10. Scanning electron microscopy images at 70x, 350x, and 1000x were used to illustrate burs’ ODOVTOS-International Journal of Dental Sciences Jiménez-Hernández et al: Evaluation of Dentin Microroughness and Composite Bond Strength After Multiple Uses of Diamond Burs ODOVTOS-Int. J. Dent. Sc. | ISSN: 2215-3411. 355 ODOVTOS-Int. J. Dent. Sc. | ISSN: 2215-3411. 354 deformation. Each specimen’s dentin microroughness was measured three times to compare between experimental groups and the micro-shear bond strength test (n=15) was performed for the 0 and 10 uses groups using a universal adhesive in a selfetching mode. Diamond crystals wear and dislodgements were evident among groups where the burs were used more times. As the number of uses increased the mean microroughness of the dentin surface decreased with significant differences between the 0 and 10 uses groups. No statistical differences between experimental groups resulted from bond strength tests. When using a universal adhesive in a self-etching mode, the number of previous uses of a diamond bur seems to have no significant effect on dentin/resin composite bond strength.
Sargassum Detection Using Machine Learning Models: A Case Study with the First 6 Months of GOCI-II Imagery

A record-breaking agglomeration of Sargassum was packed along the northern Jeju coast in Korea in 2021, and laborers suffered from removing them from the beach. If remote sensing can be used to detect the locations at which Sargassum accumulated in a timely and accurate manner, we could remove them before their arrival and reduce the damage caused by Sargassum. This study aims to detect Sargassum distribution on the coast of Jeju Island using the Geostationary KOMPSAT 2B (GK2B) Geostationary Ocean Color Imager-II (GOCI-II) imagery that was launched in February 2020, with measurements available since October 2020. For this, we used GOCI-II imagery during the first 6 months and machine learning models including Fine Tree, a Fine Gaussian support vector machine (SVM), and Gentle adaptive boosting (GentleBoost). We trained the models with the GOCI-II Rayleigh-corrected reflectance (RhoC) image and a ground truth map extracted from high-resolution images as input and output, respectively. Qualitative and quantitative assessments were carried out using the three machine learning models and traditional methods such as Sargassum indexes. We found that GentleBoost showed a lower false positive (6.2%) and a high F-measure level (0.82), and a more appropriate Sargassum distribution compared to other methods. The application of the machine learning model to GOCI-II images in various atmospheric conditions is therefore considered successful for mapping Sargassum extent quickly, enabling reduction of laborers’ efforts to remove them.
Deep learning classification of COVID-19 in chest radiographs: performance and influence of supplemental training

Abstract. Purpose: Accurate classification of COVID-19 in chest radiographs is invaluable to hard-hit pandemic hot spots. Transfer learning techniques for images using well-known convolutional neural networks show promise in addressing this problem. These methods can significantly benefit from supplemental training on similar conditions, considering that there currently exists no widely available chest x-ray dataset on COVID-19. We evaluate whether targeted pretraining for similar tasks in radiography labeling improves classification performance in a sample radiograph dataset containing COVID-19 cases. Approach: We train a DenseNet121 to classify chest radiographs through six training schemes. Each training scheme is designed to incorporate cases from established datasets for general findings in chest radiography (CXR) and pneumonia, with a control scheme with no pretraining. The resulting six permutations are then trained and evaluated on a dataset of 1060 radiographs collected from 475 patients after March 2020, containing 801 images of laboratory-confirmed COVID-19 cases. Results: Sequential training phases yielded substantial improvement in classification accuracy compared to a baseline of standard transfer learning with ImageNet parameters. The test set area under the receiver operating characteristic curve for COVID-19 classification improved from 0.757 in the control to 0.857 for the optimal training scheme in the available images. Conclusions: We achieve COVID-19 classification accuracies comparable to previous benchmarks of pneumonia classification. Deliberate sequential training, rather than pooling datasets, is critical in training effective COVID-19 classifiers within the limitations of early datasets. These findings bring clinical-grade classification through CXR within reach for more regions impacted by COVID-19.