Miracle-dz/UniFAR-FTUs huggingface dataset:
International Dental Research

Aim: Composite resins contain different monomers and fillers that are directly affected by polymerization shrinkage. Therefore, an accurate and reliable method is needed to measure the volume changes. The aim of this study was to evaluate the amount of volume change associated with polymerization shrinkage in four different bulk-fill composite materials in class II restorations using a micro-CT device, which has high resolution and provides 3-dimensional images. Methodology: A total of 40 human 3rd molar teeth were used. First, standard class II cavities were opened on the mesial surfaces of all the teeth, and then the first micro-CT images were obtained. The same adhesive material was applied to all the teeth. The teeth were then separated into four groups, and a different bulk-fill composite was applied to each group; Filtek (FTK), X-tra Fil (XTF), Tetric Evo Ceram (TEC), and Filtek One (FLO) and the second micro-CT images were obtained. Then after polymerization of the materials, the final micro-CT images were taken, and analyses were made according to the scanning results. The Kruskal Wallis and Mann Whitney U-tests were used in the statistical evaluation of the data. Results: The volumetric gap formed after polymerization of the composite resins was not determined to be statistically significant (p>0.05). Conclusion: The volumetric difference (%) between the composite resin and the dental tissue following polymerization was seen to be greatest in XTF and least in FTK. It was concluded that the volumetric gap caused by the polymerization shrinkage of the tested materials may be due to the structure of the material.
International Dental Research

Aim: Coronavirus disease 2019 (COVID-19) can cause stress, anxiety, and fear, especially in healthcare workers as it has a high risk of transmission. The aim of this study was to investigate the levels of stress and anxiety during the COVID-19 pandemic among dentists working in our country. Methodology: The study included 164 dentists. Sociodemographic data and the stress and anxiety levels of the individuals who participated in the study were recorded. The COVID-19 Stress Scale and the Perceived Stress Scale-10 were used for evaluation of the stress levels, and the Generalized Anxiety Disorder 7 was used to evaluate the anxiety levels. All data were considered statistically significant at p < 0.05. Results: Among those who participated in the study, 112 were women and 52 were men, and 50% of them were 20 to 30 years of age. Based on the information these dentists provided, 63% did not have any expertise and 66% had worked in the normalization process. Their scores in the COVID-19 Stress Scale, the Generalized Anxiety Disorder Scale 7, and Perceived Stress Scale-10 were 64.4 ± 25.2, 5.46 ± 4.8, and 18.8 ± 5.9, respectively, and there was a positive correlation between all scales. When evaluating the stress levels according to gender, it was observed that those of the female dentists were higher than those of the male dentists (p < 0.05). There was no significant difference in the anxiety levels between the male and female dentists. While there were no significant differences between the dentists who worked during the pandemic process and those who did not work (p > 0.05), the COVID-19 stress levels were found to be lower in the normalization process (p < 0.05). Conclusion: The COVID-19 pandemic has caused stress and anxiety in dentists and has affected female dentists more than male dentists. In addition, working during the pandemic process can benefit dentists by helping to reduce the stress caused by the pandemic.
Mean First Passage Time and Stochastic Resonance in a Transcriptional Regulatory System with Non-Gaussian Noise

The mean first passage time (MFPT) in a phenomenological gene transcriptional regulatory model with non-Gaussian noise is analytically investigated based on the singular perturbation technique. The effect of the non-Gaussian noise on the phenomenon of stochastic resonance (SR) is then disclosed based on a new combination of adiabatic elimination and linear response approximation. Compared with the results in the Gaussian noise case, it is found that bounded non-Gaussian noise inhibits the transition between different concentrations of protein, while heavy-tailed non-Gaussian noise accelerates the transition. It is also found that the optimal noise intensity for SR in the heavy-tailed noise case is smaller, while the optimal noise intensity in the bounded noise case is larger. These observations can be explained by the heavy-tailed noise easing random transitions.
Hemophagocytic Lymphohistiocytosis and Gastrointestinal Bleeding: What a Surgeon Should Know

This paper presents to the surgical community an unusual and often ignored cause of gastrointestinal bleeding. Hemophagocytic syndrome or hemophagocytic lymphohistiocytosis (HLH) is a rare medical entity characterized by phagocytosis of red blood cells, leucocytes, platelets, and their precursors in the bone marrow by activated macrophages. When intestinal bleeding is present, the management is very challenging with extremely high mortality rates. Early diagnosis and treatment seem to be the most important factors for a successful outcome. We present two cases and review another 18 from the literature.
A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome

Convolutional neural networks (CNNs) are easily spoofed by adversarial examples which lead to wrong classification results. Most of the defense methods focus only on how to improve the robustness of CNNs or to detect adversarial examples. They are incapable of detecting and correctly classifying adversarial examples simultaneously. We find that adversarial examples and original images have diverse representations in the feature space, and this difference grows as layers go deeper, which we call Adversarial Feature Separability (AFS). Inspired by AFS, we propose a defense framework based on Adversarial Feature Genome (AFG), which can detect and correctly classify adversarial examples into original classes simultaneously. AFG is an innovative encoding for both image and adversarial example. It consists of group features and a mixed label. With group features which are visual representations of adversarial and original images via group visualization method, one can detect adversarial examples because of ASF of group features. With a mixed label, one can trace back to the original label of an adversarial example. Then, the classification of adversarial example is modeled as a multi-label classification trained on the AFG dataset, which can get the original class of adversarial example. Experiments show that the proposed framework not only effectively detects adversarial examples from different attack algorithms, but also correctly classifies adversarial examples. Our framework potentially gives a new perspective, i.e., a data-driven way, to improve the robustness of a CNN model.
Value Relevance of Accounting Information in Different Stock Market Segments: The Case of Chinese A‐, B‐, and H‐Shares

This study provides empirical evidence on the value relevance of accounting information in different stock market segments of China. Using the available data of listed A‐, B‐, and H‐shares from 1999 to 2003, and based on the modified Ohlson model, the value relevance of accounting information is compared among different Chinese stock market segments. Further, important factors that might influence the value relevance of accounting information in the Chinese stock market (e.g., the percentage of total tradable shares [PTS] and the B‐share opening event of 2001) are studied in this paper. Four primary results are drawn from this study. First, the accounting information is value‐relevant in all the A‐, B‐, and H‐share markets, and the accounting information in the B‐ and H‐share markets is more value‐relevant than that of the A‐share market. Second, within the A‐share market, the value relevance of accounting information does not differ between firms issuing both AB‐ and AH‐shares and firms issuing only A‐sh.
A Multiscale Nested Modeling Framework to Simulate the Interaction of Surface Gravity Waves with Nonlinear Internal Gravity Waves

Abstract : Our long-term goal is to develop a multiscale nested modeling framework that simulates, with the finest resolution being centimeter scale, surface mixed layer processes arising from the combined actions of tides, winds and mesoscale currents. Issues related to nesting of models designed to study phenomena at different scales will be studied, including how to nest the regional and nonhydrostatic SUNTANS model within the global HYCOM model, as well as nesting of small-scale LES codes into the SUNTANS model.
Convergence Rate Analysis of Inertial Krasnoselskii–Mann Type Iteration with Applications

Abstract It is well known that the Krasnoselskii–Mann iteration of nonexpansive operators find applications in many areas of mathematics and known to be weakly convergent in the infinite dimensional setting. In this present paper, we provide a nonasymptotic convergence rate result for a Krasnoselskii–Mann iteration with inertial extrapolation step in real Hilbert spaces. We give some applications of our results to the Douglas–Rachford splitting method and the alternating projection method by John von Neumann. Our result serves as supplement to many existing results on convergence rate of Krasnoselskii–Mann iteration in the literature.
Biological Variation and Reference Change Values of TSH, Free T3, and Free T4 Levels in Serum of Healthy

Aim: Thyrotropin (TSH), thyroxine (T4) and triiodothyronine (T3) levels in serum are important for diagnosis of thyroid dysfunction. Thyroid function tests have considerable biological variations. Evaluation of the components of biological variation is essential to assess the usefulness of reference values and to evaluate significance of changes in serial results from an individual. Published estimates may show disagreement about the values of biological variations. Not many studies exist at present that evaluate the reference change values (RCV) of these parameters. The aim of this study was to determine the biological variations of TSH, free T3, and free T4 and to calculate their RCV values in serum of healthy individuals in our population. Materials and Methods: The study group included 46 healthy volunteer subjects (mean age 33.5 ± 9 years). During six consecutive weeks, four blood samples were obtained for each subject at two-week intervals. Serum TSH, free T3, and free T4 levels were measured by an immunoluminometric assay on a random-access analyzer. Results: The intra-individual/inter-individual biological variations (defined in terms of percent coefficient of variation, CV) for serum TSH, fT3, and fT4 were 37.21/37.6%, 22.32/2.43%, and 13.26/7.47% respectively. The RCVs for TSH, fT3, and fT4 were 104.0%, 63.1%, and 38.8%, respectively (P < 0.05). Conclusions: TSH, fT3, and fT4 showed a significant intra-individual biological variation. The utility of population-based reference intervals for TSH would be of limited use. During evaluation, especially when monitoring thyroid functions, the estimates of biological variations and the RCV values should be considered.
Progressive Memory Banks for Incremental Domain Adaptation

This paper addresses the problem of incremental domain adaptation (IDA). We assume each domain comes one after another, and that we could only access data in the current domain. The goal of IDA is to build a unified model performing well on all the domains that we have encountered. We propose to augment a recurrent neural network (RNN) with a directly parameterized memory bank, which is retrieved by an attention mechanism at each step of RNN transition. The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity. We learn the new memory slots and fine-tune existing parameters by back-propagation. Experimental results show that our approach achieves significantly better performance than fine-tuning alone, which suffers from the catastrophic forgetting problem. Compared with expanding hidden states, our approach is more robust for old domains, shown by both empirical and theoretical results. Our model also outperforms previous work of IDA including elastic weight consolidation (EWC) and the progressive neural network.1
Illness Perception and Medication Adherence Among Patients with Primary Hypothyroidism in Al Qassim, Saudi Arabia

Purpose: Poor adherence to levothyroxine is a potential risk factor for treatment failure that ultimately leads to increased healthcare costs and serious health outcomes. This study aimed to investigate the correlation between illness perception and medication adherence among primary hypothyroidism patients. Patients and Methods: We conducted a cross-sectionalsurvey among primaryhypothyroidism patients who met the inclusion criteria in the Qassim region of Saudi Arabia using a questionnaire withdemographiccharacteristics,clinicalfactors,the12-ItemMedicationAdherenceScale,andthe Brief Illness Perception Questionnaire (BIPQ). We included 400 patients in the analysis. Results: We found a signi ﬁ cant positive correlation between BIPQ subscales – personal control, treatment control, and understanding – with all domains of the 12-Item Medication Adherence Scale. The other dimensions of BIPQ subscales did not signi ﬁ cantly correlate with medication adherence. However, a high BIPQ overall score was signi ﬁ cantly correlated with poor medication adherence. Conclusion: This study found a signi ﬁ cant correlation between a high BIPQ overall score and poor medication adherence. This ﬁ nding suggests the need to consider the emotional and cognitive representation of hypothyroidism in hypothyroid patients to improve their treatment adherence.
FAKTOR- FAKTOR YANG MEMPENGARUHI MINAT PERILAKU WAJIB PAJAK UNTUK MENGGUNAKAN E FILLING

Sistem pelaporan SPT mulai mengikuti perkembangan teknologi informasi (online dan real time) yaitu E-filling. Penggunaan E Filling memberikan kemudahan wajib pajak dalam pelaporan SPT. Penelitian ini bertujuan untuk mengetahui faktor faktor yang mempengaruhi minat wajib pajak orang pribadi untuk menggunakan E Filling. Penelitian ini dilakukan pada wajib pajak orang pribadi (pengusaha) di Kabupaten Pati. Metode yang digunakan adalah metode analisis regresi. Data yang digunakan dalam penelitian ini adalah data primer, dengan menggunakan kuesioner. Hasil penelitian ini menunjukkan bahwa Kesukarelaan menggunakan (voluntariness), Pengalaman menggunakan (experience), Persepsi kegunaan (perceived usefulness), sikap terhadap penggunaan (attitude toward using), Kompleksitas penggunaan (complexity, berpengaruh terhadap minat Wajib Pajak dalam menggunakan E-Filling.
A Unified Framework for One-shot Achievability via the Poisson Matching Lemma

We introduce the Poisson matching lemma and apply it to prove one-shot achievability results for channels with state information at the encoder, lossy source coding with side information at the decoder, joint source-channel coding, broadcast channels, and distributed lossy source coding. Our one-shot bounds improve upon the best known bounds in the aforementioned settings, with shorter proofs in some settings even when compared to the conventional asymptotic typicality approach. The Poisson matching lemma replaces both the packing and covering lemmas. This paper extends the work of Li and El Gamal on Poisson functional representation for variable-length source coding settings, showing that the Poisson functional representation is a viable alternative to typicality for most problems in network information theory.
Land cover characterization for a watershed in Kenya using MODIS data and Fourier algorithms

Abstract. A time series algorithm was being presented that classifies vegetation in the Njoro Watershed, Kenya, according to the shapes of temporal normalized difference vegetation index (NDVI) profiles representing growing cycles for different vegetation. We present a two-step approach that includes noise reduction using discrete Fourier filtering and a clustering algorithm that uses the Fourier components of magnitude and phase to identify phenological differences. The classification considers possible variations in shape that may be imposed by climate, soil, topography, or human impacts. The primary input to the classification is a user-defined set of reference cycles to which pixels are assigned depending on a set of shape criteria. The output is a consistent classification of NDVI cycles representing vegetation classes with similar phenologies. The algorithm allows the creation of classification mosaics without typical boundary offsets and temporally comparable classification products. It identifies vegetation more accurately than single image classification methods, because it exploits the temporal variability in spectral reflectance due to phenological responses. We produced a classified land cover map at two hierarchical levels with five classes in a level I classification and seven classes in a level II classification that represents a refinement of the level I data. These map products compare favorably to previously published land cover maps that were developed using more standard supervised classification. The level I map has an overall accuracy of 94% compared to field data, while the level II map as an overall accuracy of 77%.
Nonlinear Nonhomogeneous Robin Problems with Superlinear Reaction Term

Abstract We consider a nonlinear Robin problem driven by a nonlinear, nonhomogeneous differential operator, and with a Carathéodory reaction term which is (p-1)${{(p-1)}}$-superlinear near ±∞${\pm\infty}$ without satisfying the Ambrosetti–Rabinowitz condition and which does not have a standard subcritical polynomial growth. Using a combination of variational methods and Morse theoretic techniques, we prove a multiplicity theorem producing three nontrivial solutions (two of which have constant sign). In the process we establish some useful facts about the boundedness of the weak solutions of critical equations and the relation of Sobolev and Hölder local minimizers for functionals with a critical perturbation term.
Finding Anomalies in Sequential Data using Local Outlier Factor

Anomaly detection has many applications in many different ﬁelds. Examples of practical applications are credit card fraud detection, network intrusion detection and health monitoring. Many of the applications in these ﬁelds rely on knowing what anomalies look like, which they acquire via supervised learning. In cybersecurity however, an anomaly can have many different forms. While we do have examples of cyber attacks, it is no use to apply supervised learning if one attack is not similar to another. Hence, we shift to unsupervised methods. In this thesis we propose an unsupervised method of ﬁnding anomalies in sequential network data. This method performs a statistical analysis per user on the network to ﬁnd behavior that is not in line with a user’s regular behavior. It dynamically identiﬁes small sequences of actions on the network and compares these with each other. Using a real world dataset by the Los Alamos National Laboratory, we demonstrate the advantages of using this method over static sequence identiﬁcation. Finally, we do a careful performance evaluation and show the potential of this methodology.
ANALISIS KEMAMPUAN PENALARAN MATEMATIS SISWA SMA PADA MATERI LIMIT FUNGSI

This research is a qualitative descriptive research that aims to analyze the level of students' reasoning ability in solving the problem on material function limit by categorizing high, medium, and low mathematical reasoning ability. The subjects of the study were 20 students of class XII. A form of data retrieval is the provision of 4 items about written tests and interviews on selected subjects. Based on the results of the analysis, the average value of the whole matter of the instrument of mathematical reasoning ability derives 89% for the indicator performing calculations based on the applicable mathematical formula or rules, 84% and 64% of the indicators draw conclusions and 93% for the indicator to make the estimate so that if totaled and averaged 83%. Then the mathematical reasoning ability of high school students of class XII is appertaining high.
High-Fidelity Face Manipulation With Extreme Poses and Expressions

Face manipulation has shown remarkable advances with the flourish of Generative Adversarial Networks. However, due to the difficulties of controlling structures and textures, it is challenging to model poses and expressions simultaneously, especially for the extreme manipulation at high-resolution. In this article, we propose a novel framework that simplifies face manipulation into two correlated stages: a boundary prediction stage and a disentangled face synthesis stage. The first stage models poses and expressions jointly via boundary images. Specifically, a conditional encoder-decoder network is employed to predict the boundary image of the target face in a semi-supervised way. Pose and expression estimators are introduced to improve the prediction performance. In the second stage, the predicted boundary image and the input face image are encoded into the structure and the texture latent space by two encoder networks, respectively. A proxy network and a feature threshold loss are further imposed to disentangle the latent space. Furthermore, due to the lack of high-resolution face manipulation databases to verify the effectiveness of our method, we collect a new high-quality Multi-View Face (MVF-HQ) database. It contains 120,283 images at $6000\times 4000$ resolution from 479 identities with diverse poses, expressions, and illuminations. MVF-HQ is much larger in scale and much higher in resolution than publicly available high-resolution face manipulation databases. We will release MVF-HQ soon to push forward the advance of face manipulation. Qualitative and quantitative experiments on four databases show that our method dramatically improves the synthesis quality.
Propensity for spontaneous relaxor-ferroelectric transition in quenched (Na1/2Bi1/2)TiO3-BaTiO3 compositions

Recently, quenching lead-free non-ergodic relaxor Na1/2Bi1/2TiO3-BaTiO3 (NBT-BT) materials has been reported to increase the thermal depolarization temperature and enhance the lattice distortion. Driven by the conjecture that enhanced lattice distortion is typically associated with the onset of ferroelectric order, two non-ergodic relaxor NBT-BT compositions at the morphotropic phase boundary were investigated. As evident from the temperature-dependent permittivity, both compositions exhibit a stabilization of ferroelectric order upon quenching. An increase in the depolarization temperature by 40–60 °C is observed. Moreover, the composition with higher tetragonality undergoes a spontaneous relaxor-ferroelectric transition upon quenching. Annealing in oxygen atmosphere is shown to revert back the quenching-induced ferroelectric order to the relaxor state.Recently, quenching lead-free non-ergodic relaxor Na1/2Bi1/2TiO3-BaTiO3 (NBT-BT) materials has been reported to increase the thermal depolarization temperature and enhance the lattice distortion. Driven by the conjecture that enhanced lattice distortion is typically associated with the onset of ferroelectric order, two non-ergodic relaxor NBT-BT compositions at the morphotropic phase boundary were investigated. As evident from the temperature-dependent permittivity, both compositions exhibit a stabilization of ferroelectric order upon quenching. An increase in the depolarization temperature by 40–60 °C is observed. Moreover, the composition with higher tetragonality undergoes a spontaneous relaxor-ferroelectric transition upon quenching. Annealing in oxygen atmosphere is shown to revert back the quenching-induced ferroelectric order to the relaxor state.