Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison of Conical and Cylindrical Implants Inserted in an In Vitro Post-Extraction Model Using Low-Density Polyurethane Foam Blocks

Combining tooth extraction and implant placement reduces the number of surgical procedures that a patient must undergo. Thus, the present study aimed to compare the stability of two types of conical implants (TAC and INTRALOCK) and another cylindrical one (CYROTH), inserted with a range of angulation of 15–20 degrees in low-density polyurethane blocks (10 and 20 pounds per cubic foot, PCF) with or without a cortical lamina (30 PCF), which potentially mimicked the post-extraction in vivo condition. For this purpose, a total of 120 polyurethane sites were prepared (10 for each implant and condition) and the Insertion Torque (IT), Removal Torque (RT), and Resonance Frequency Analysis (RFA) were measured, following a Three-Way analysis of variance followed by Tukey’s post hoc test for the statistical analysis of data. The IT and RT values registered for all implant types were directly proportional to the polyurethane density. The highest IT was registered by INTRALOCK implants in the highest-density block (32.44 ± 3.28 Ncm). In contrast, the highest RFA, a well-known index of Implant Stability Quotient (ISQ), was shown by TAC implants in all clinical situations (up to 63 ISQ in the 20 PCF block without the cortical sheet), especially in lower-density blocks. Although more pre-clinical and clinical studies are required, these results show a better primary stability of TAC conical implants in all tested densities of this post-extraction model, with a higher ISQ, despite their IT.
The relationship between negative workplace gossip and thriving at work among Chinese kindergarten teachers: the roles of psychological contract breach and bianzhi

Introduction Exploring the influencing factors and functioning mechanisms of thriving at work is of practical significance both for teachers and kindergartens. Based on the socially embedded model of thriving at work, this study aimed to examine the association between negative workplace gossip and thriving at work. The mediating role of psychological contract breach and the moderating role of bianzhi were also examined. Methods A total of 1105 Chinese kindergarten teachers were chosen to complete a questionnaire on negative workplace gossip, psychological contract breach, and thriving at work. Results The results demonstrated that negative workplace gossip was positively associated with psychological contract breach and negatively associated with thriving at work. In addition, psychological contract breach was negatively associated with thriving at work. According to the mediation model test, psychological contract breach was a mediating factor between negative workplace gossip and thriving at work. The impact of psychological contract breach on thriving at work could be further moderated by bianzhi. Conclusion This study complements knowledge systems about the influential factors and functional mechanisms of thriving at work. In practical terms, this study offers a fresh and innovative perspective for kindergartens seeking to enhance teachers’ thriving at work.
Measuring the Gender Asset Gap: Evidence from Spain

While the income gender gap has been widely researched to date, less attention has been paid to the wealth gender gap. This paper analyses individual-level data of the Spanish tax agency between 2012 and 2017 and documents the existence of an increasing wealth gap in Spain in this period. It also finds a clear difference in wealth accumulation patterns between both genders, especially in what respects holdings of equity in productive companies. Additionally, it finds that both women and men in the highest quantiles of the wealth distribution have increased their wealth and that men hold more assets that women. With respect to income distribution, the data shows men’s income accounts for a larger share of total income, which reinforces the gender gap related to economic resources.
Ones Assignment Method For Solving Traveling Salesman Problem

This paper presents an approach namely, ones assignment method, for solving the traveling salesman problem. We have previously used this method for the assignment problem. We have slightly modified the procedure to get a tour of the traveling salesman problem. First we define the distance matrix, then by using determinant representation we obtain a reduced matrix which has at least one 1 in each row and each column. Then by using the new method, we obtain an optimal solution for traveling salesman problem by assigning ones to each row and each column. The new method is based on creating some ones in the distance matrix and then try to find a complete solution to their ones. At the end, this method is illustrated with some numerical examples.
LC/MS-MS Analysis of Phenolic Compounds in Hyoscyamus albus L. Extract: In Vitro Antidiabetic Activity, In Silico Molecular Docking, and In Vivo Investigation against STZ-Induced Diabetic Mice

This study aimed to investigate the chemical composition and antidiabetic properties of cultivated Hyoscyamus albus L. The ethanol extract was analyzed using LC-MS/MS, and 18 distinct phenolic compounds were identified. Among these, p-coumaric acid (6656.8 ± 3.4 µg/g), gallic acid (6516 ± 1.7 µg/g), luteolin (6251.9 ± 1.3 µg/g), apigenin (6209.9 ± 1.1 µg/g), and rutin (5213.9 ± 1.3 µg/g) were identified as the most abundant polyphenolic molecules. In the in vitro antidiabetic experiment, the ability of the plant extract to inhibit α-glucosidase and α-amylase activities was examined. The results indicated that the extract from H. albus L. exhibited a higher inhibitory effect on α-amylase compared to α-glucosidase, with an IC50 of 146.63 ± 1.1 µg/mL and 270.43 ± 1.1 µg/mL, respectively. Docking simulations revealed that luteolin, fisetin, and rutin exhibited the most promising inhibitory activity against both enzymes, as indicated by their high contrasting inhibition scores. To further investigate the in vivo antidiabetic effects of H. albus L. , an experiment was conducted using STZ-induced diabetic mice. The results demonstrated that the plant extract effectively reduced the levels of cholesterol and triglycerides. These findings suggest that H. albus L. may have therapeutic potential for managing hyperlipidemia, a common complication associated with diabetes. This highlights its potential as a natural remedy for diabetes and related conditions.
Anomaly Detection for Hyperspectral Images Based on Anisotropic Spatial-Spectral Total Variation and Sparse Constraint

A novel anomaly detection method for hyperspectral images (HSIs) is proposed based on anisotropic spatial-spectral total variation and sparse constraint. HSIs are assumed to be not only smooth in spectral dimension but also piecewise smooth in spatial dimension. The proposed method adopts the anisotropic spatial-spectral total variation model which combines 2D spatial total variation and 1D spectral variation to explore the spatial-spectral smooth property of HSIs. Meanwhile, the sparse property of anomalies is exploited for its low probability in the image. To utilize both the spatial and spectral information of HSIs, we preserve the original cubic form of HSIs and divide the HSIs into three 3D arrays, each representing the background, the anomaly, and the noise respectively. By using anisotropic spatialspectral total variation regularization on the background component and sparse constraint on the anomaly component, this anomaly detection problem has therefore been formulated as a constraint optimization problem whose solution has been derived by alternately using Split Bregman Method and Go Decomposition (GoDec) Method. Experimental results on hyperspectral datasets illustrate that our proposed method has a better detection performance than state-of-the-art hyperspectral anomaly detection methods.
The Interaction of Glycemia with Anxiety and Depression Is Related to Altered Cerebellar and Cerebral Functional Correlations

Depression, type 2 diabetes (T2D), and obesity are comorbid, and prevention and treatment of all three diseases are needed. We hypothesized an inverse relationship between the connectivity of the cingulo-opercular task control network with the somatosensory mouth network and the interaction between HbA1c and depression. Three-hundred and twenty-five participants (BMI: 26.11 ± 0.29; Achenbach adult self-report (ASR) DSM depressive problems T-score (depression): 54.60 ± 6.77; Age: 28.26 ± 3.90 y; adult self-report anxiety and depression scale (anxiety and depression): 54.69 ± 7.27; HbA1c: 5.26 ± 0.29; 68% white) were sampled from the Human Connectome Project 1200 subjects PTN release. Inclusion criteria were: four (15 min) resting state fMRI scans; BMI; hemoglobin A1c (HbA1c); and complete adult self-report data. The following models were run to assess the connectivity between 15 independent fMRI components: the interaction of depression with HbA1c; anxiety and depression with HbA1c; depression with BMI; and anxiety and depression with BMI. All models were corrected for a reported number of depressive symptoms, head motion in the scanner, age, and race. Functional connectivity was modeled in FSLNets. Corrected significance was set at pFWE < 0.05. The interaction HbA1c and anxiety and depression was positively related to the connectivity of the cerebellum with the visual network (t = 3.76, pFWE = 0.008), frontoparietal network (t = 3.45, pFWE = 0.02), and somatosensory mouth network (t = 4.29, pFWE = 0.0004). Although our hypotheses were not supported, similar increases in cerebellar connectivity are seen in patients with T2D and overall suggest that the increased cerebellar connectivity may be compensatory for an increasingly poor glycemic control.
Habitat fragmentation drives divergent survival strategies of a cold‐water fish in a warm landscape

Climate change is a global phenomenon, but natural selection occurs within landscapes. Many global analyses predict how climate change will shape behavior and physiology, but few incorporate information from the landscape scales at which animals actually respond to selective pressure. We compared cold-water fish (redband trout Oncorhynchus mykiss newberrii ) from neighboring habitats in a naturally warm, recently fragmented basin to understand how different responses to warming may arise from landscape constraints. Trout in warm, hydrologically connected Upper Klamath Lake fled summer temperatures and sought refuge in cool tributaries, while trout in an equally warm but fragmented reach of the Klamath River endured summer conditions. Trout in the river were more physiologically tolerant of high temperatures than trout in the lake across multiple metrics, including capacity for aerobic
Numerical Investigation of Unsteady Blowing Through Discrete Slots on a Swept Vertical Tail of Infinite Span

Active flow control over the rudder shoulder of a vertical tail plane is applied to increase its side force coefficient. The rudder of the infinite span model is deflected. This leads without actuation to separated flow over the rudder. Several investigation are conducted. First a full-span constant blowing slot is used. With sufficient high jet velocity the flow on the rudder can be attached. By using in a next step several discrete slots, the required mass flow rate can be reduced for a similar increase in the side force coefficient. Using then pulsed blowing from the discrete slots is advantageous since the mass flow rate can be reduced. However, this is only true for quite small momentum coefficients. For cases with high side force coefficients requirements only the constant blowing approach is capable to achieve them, albeit at the correspondingly large mass flow rates and momentum coefficients.
Bayesian variable selection with spike-and-slab priors

ii ACKNOWLEDGMENTS . iii VITA . iv LIST OF FIGURES . viii LIST OF TABLES. x
Emerging armchair travel: Investigation of authentic armchair travel experiences and their effect on armchair travel stickiness

ABSTRACT Armchair travel is a kind of tourism that gives a vicarious travel experience. This study aimed to examine the influence of authentic travel experiences on travel stickiness using the framework of stimulus-organism-response. Our result revealed that flow and functional experiences positively affected authentic travel experiences with armchair travel. Furthermore, authentic travel experiences positively influenced value and engagement. Customer engagement influenced customer stickiness to armchair travel, while overall value did not. This study provides insightful theoretical implications for the extant armchair travel literature and practical implications for developing new tourism products or programs to keep pace with rapidly shifting industry trends.
Machine Learning for the Nondestructive Prediction of Neutral Temperature in Continuous Welded Rails

ABSTRACT This paper describes the application of machine learning (ML) in the framework of a data-driven nondestructive evaluation (NDE) method to estimate the rail neutral temperature (RNT) of continuous welded rails (CWR). The method consists of triggering vibration of the rail of interest and extracting the power spectral densities (PSDs) of the accelerations associated with the lowest modes of vibration. The PSDs then become the input of an ML algorithm trained to associate the PSD to longitudinal stress and then RNT. In the study presented in this article, the proposed NDE method was tested on a tangent track on wood cross-ties. Vibrations were induced with a hammer and detected with several wireless and wired accelerometers. The PSDs across the 0–700 Hz range were extracted from the time-series. These densities in both the lateral and vertical directions constituted part of the input of an artificial neural network trained and tested with experimental data. The predicted neutral temperatures showed very good agreement with the RNT estimated by an independent party and based on conventional strain-gage rosettes.
Early Detection of Avian Diseases Based on Thermography and Artificial Intelligence

Simple Summary From an economic point of view, timely information about the flock state is crucial for poultry farmers. When a flock is infected with a disease, if quick and necessary measures are not taken, the disease will spread and affect the whole flock. Artificial intelligence is one of the popular methods in precision livestock farming and is effective in various fields such as weight measurement, feed intake estimation, and disease diagnosis. So far, chicken disease has been diagnosed using sound signal processing and video recordings. This study attempted to develop a new and rapid method of poultry disease diagnosis based on thermography for data collection and artificial intelligence for data analytics. With the proposed method, Avian Influenza and Newcastle Disease can be detected within 24 h after virus infection. Abstract Non-invasive measures have a critical role in precision livestock and poultry farming as they can reduce animal stress and provide continuous monitoring. Animal activity can reflect physical and mental states as well as health conditions. If any problems are detected, an early warning will be provided for necessary actions. The objective of this study was to identify avian diseases by using thermal-image processing and machine learning. Four groups of 14-day-old Ross 308 Broilers (20 birds per group) were used. Two groups were infected with one of the following diseases: Newcastle Disease (ND) and Avian Influenza (AI), and the other two were considered control groups. Thermal images were captured every 8 h and processed with MATLAB. After de-noising and removing the background, 23 statistical features were extracted, and the best features were selected using the improved distance evaluation method. Support vector machine (SVM) and artificial neural networks (ANN) were developed as classifiers. Results indicated that the former classifier outperformed the latter for disease classification. The Dempster–Shafer evidence theory was used as the data fusion stage if neither ANN nor SVM detected the diseases with acceptable accuracy. The final SVM-based framework achieved 97.2% and 100% accuracy for classifying AI and ND, respectively, within 24 h after virus infection. The proposed method is an innovative procedure for the timely identification of avian diseases to support early intervention.
Discriminating 
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 form factors via polarization observables and asymm

Form factors are crucial theory input in order to extract $|V_{cb}|$ from $B \to D^{(*)}\ell\nu$ decays, to calculate the Standard Model prediction for ${\cal R}(D^{(*)})$ and to assess the impact of New Physics. In this context, the Dispersive Matrix approach, a first-principle calculation of the form factors, using no experimental data but rather only lattice QCD results as input, was recently applied to $B \to D^{(*)}\ell\nu$. It predicts (within the Standard Model) a much milder tension with the ${\cal R}(D^*)$ measurements than the other form factor approaches, while at the same time giving a value of $|V_{cb}|$ compatible with the inclusive value. However, this comes at the expense of creating tensions with differential $B\to D^*\ell\nu$ distributions (with light leptons). In this article, we explore the implications of using the Dispersive Matrix method form factors, in light of the recent Belle (II) measurements of the longitudinal polarization fraction of the $D^*$ in $B\to D^*\ell\nu$ with light leptons, $F_L^{\ell}$, and the forward-backward asymmetry, $A_{\rm FB}^{\ell}$. We find that the Dispersive Matrix approach predicts a Standard Model value of $F_L^{\ell}$ that is in significant tension with these measurements, while mild deviations in $A_{\rm FB}^{\ell}$ appear. Furthermore, $F_L^{\ell}$ is very insensitive to New Physics such that the latter cannot account for the tension between Dispersive Matrix predictions and its measurement. While this tension can be resolved by deforming the original Dispersive Matrix form factor shapes within a global fit, a tension in ${\cal R}(D^*)$ reemerges. As this tension is milder than for the other form factors, it can be explained by New Physics not only in the tau lepton channel but also in the light lepton modes.
WNN Learning Algorithm Based on Unscented Kalman Particle Filter

In order to improve the nonlinear modeling capability of Wavelet Neural Network (WNN), a learning algorithm of WNN based on modified Unscented Kalman Particle Filter (UPF) is proposed. In the algorithm, first a minimal skew strategy is introduced to reduce the number of Sigma sampling points of Unscented Transform (UT), improving Unscented Kalman Filter (UKF), and then the improved UKF is used to select the importance density function of Particle Filter (PF), forming a new UPF (SUPF), finally, SUPF is taken as learning algorithm of WNN for training and test. The simulation results indicate that, for the learning problem of WNN, the model precision of UPF based on new sampling strategy is approximately close to that of simple UPF, but the former has faster training rate and higher learning efficiency, which validate its feasibility and effectiveness.
Microleakage of Restorative Materials Used for Temporization of Endodontic Access Cavities

A tight temporary seal applied to an access cavity is thought to improve endodontic outcomes. This study aims to assess the bacterial and glucose microleakage of different types and combinations of temporary restorations. Human-extracted incisors were instrumented, dressed with a calcium hydroxide paste, and sealed with Cavit W (CW), CW/Ketac Molar (CW/KM), CW/Smart Dentin Replacement (CW/SDR), Intermediate restorative material/KM (IRM/KM), or Clip F (CF). Standardized 3D-printed hollow test specimens were manufactured and temporized in the same manner. The specimens were examined for bacterial and glucose leakage for 28 days. Data were analyzed using a Kaplan–Meier survival analysis. CW/SDR and CF showed the least bacterial and glucose leakage over time. CW, CW/KM, and IRM/KM had similarly high levels of glucose leakage, but CW/KM and IRM/KM provided a tighter seal against bacterial penetration than CW. CW/SDR and CF should be considered for the sealing of access cavities of teeth previously restored with methacrylate-based materials.
Software Reliability Growth Modeling Involving Burr Type XII distribution and Fault Removal Efficiency

In software reliability analysis various authors have used Burr type XII distribution to model the failure pattern of the system due to its wide variety of flexible shapes. In particular cases it can be reduced to Exponential, Normal, Weibull, Log-logistic, Gamma distributions etc. In proposed paper software reliability growth model has been developed incorporating fault removal efficiency (FRE) and Burr type XII based testing effort function. FRE represents fraction of detected faults which are removed completely. Parameters of model are predicted by LSE whereas MSE is used to perform comparison analysis. Results validate better fitting of data set.
Procedural Level Generation Improves Generality of Deep Reinforcement Learning

Over the last few years, deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when networks are trained in a fixed environment, such as a single level in a video game, it will usually overfit and fail to generalize to new levels. When RL agents overfit, even slight modifications to the environment can result in poor agent performance. In this paper, we present an approach to prevent overfitting by generating more general agent controllers, through training the agent on a completely new and procedurally generated level each episode. The level generator generate levels whose difficulty slowly increases in response to the observed performance of the agent. Our results show that this approach can learn policies that generalize better to other procedurally generated levels, compared to policies trained on fixed levels.
Are More Details Better? On the Norms of Completeness for Mechanistic Explanations

Completeness is an important but misunderstood norm of explanation. It has recently been argued that mechanistic accounts of scientific explanation are committed to the thesis that models are complete only if they describe everything about a mechanism and, as a corollary, that incomplete models are always improved by adding more details. If so, mechanistic accounts are at odds with the obvious and important role of abstraction in scientific modelling. We respond to this characterization of the mechanist’s views about abstraction and articulate norms of completeness for mechanistic explanations that have no such unwanted implications. 1 Introduction 2 A Balancing Act: When Do Details Matter? 3 The Norms of Causal Explanation 4 The Norms of Constitutive Explanation 5 Salmon-Completeness 6 From More Details to More Relevant Details 7 Non-explanatory Virtues of Abstraction 8 From Explanatory Models to Explanatory Knowledge 9 Mechanistic Completeness Reconsidered 10 Conclusion 1 Introduction 2 A Balancing Act: When Do Details Matter? 3 The Norms of Causal Explanation 4 The Norms of Constitutive Explanation 5 Salmon-Completeness 6 From More Details to More Relevant Details 7 Non-explanatory Virtues of Abstraction 8 From Explanatory Models to Explanatory Knowledge 9 Mechanistic Completeness Reconsidered 10 Conclusion