Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of easy disassembly abutment system based on elastic connection

The abutment system is a necessity for oral implant repair, traditional abutment systems are mostly of an integrated structure, after oral implant repair, it is difficult to disassemble whether there is implant failure or infection, or daily cleaning and maintenance. Based on the analysis of the problems in the clinical use of the traditional abutment system, this paper proposes a design method of easy disassembly abutment system based on elastic connection. This method changes the traditional abutment integrated mechanism into a two-part structure, and designs an elastic connection device to fix the two-part structure together, which can facilitate the connection and separation. Through performance test and clinical verification, it is proved that the system can meet the clinical requirements in terms of performance and function, and the effect of patients using the system after implant repair is good. In addition, compared with the traditional abutment system, this system has the advantages of convenient installation and disassembly, small occlusal hole of the dental crown, and more beautiful after repair, so, it has good application value.
Parametric instability of internal gravity waves

A continuously stratified fluid, when subjected to a weak periodic horizontal acceleration, is shown to be susceptible to a form of parametric instability whose time dependence is described, in its simplest form, by the Mathieu equation. Such an acceleration could be imposed by a large-scale internal wave field. The growth rates of small-scale unstable modes may readily be determined as functions of the forcing-acceleration amplitude and frequency. If any such mode has a natural frequency near to half the forcing frequency, the forcing amplitude required for instability may be limited in smallness only by internal viscous dissipation. Greater amplitudes are required when boundaries constrain the form of the modes, but for a given bounding geometry the most unstable mode and its critical forcing amplitude can be defined. An experiment designed to isolate the instability precisely confirms theoretical predictions, and evidence is given from previous experiments which suggest that its appearance can be the penultimate stage before the traumatic distortion of continuous stratifications under internal wave action. A preliminary calculation, using the Garrett & Munk (197%) oceanic internal wave spectrum, indicates that parametric instability could occur in the ocean at scales down to that of the finest observed microstructure, and may therefore have a significant role to play in its formation.
A DATA-DRIVEN NONPARAMETRIC SPECIFICATION TEST FOR DYNAMIC REGRESSION MODELS

The paper introduces a new nonparametric specification test for dynamic regression models. The test combines chi-square statistics based on Fourier series regression. A data-driven choice of the regression order, which uses the square root of the number of Fourier coefficients, is proposed. The benefits of the new test are (1) the selection procedure produces explicit and chi-square critical values that give a finite-sample size close to the nominal size; (2) the test is adaptive rate-optimal and detects local alternatives converging to the null with a rate that can be made arbitrarily close to the parametric rate. Simulation experiments illustrate the practical relevance of the new test.The first author acknowledges financial support from the Fonds Québécois de la Recherche sur la Société et la Culture (FQRSC). The second author acknowledges financial support from LSTA.
Automatic Operator Performance Tumng in a Machine Learning System on Edge

With the current large scale deployment of machine learning technologies, such as those on cloud servers and edge and IoT hardwares, machine learning systems have been widely prevalence. Practical requirement has driven their performance increase in both academia and industry. However, the application requirement varies greatly across different applications, and directly using off-the-shelf systems might not be sufficient in many cases. In this work, we first propose to implement a series of techniques to optimize performance of convolution operation, one of the most important operations, in constructing deep learning networks. Besides, we also propose to apply the automated empirical optimisation of software approach to improve the performance of operators in machine learning system, most notably across various hardware platforms. Evaluation compared to existing libraries on different hardware devices has proved the efficiency of our proposed method.
D-Wave Superconducting Gap Symmetry as a Model for Nb1-xMoxB2 (x = 0.25; 1.0) and WB2 Diborides

Recently, Pei et al. (National Science Review2023, nwad034, 10.1093/nsr/nwad034) reported that ambient pressure β-MoB2 (space group: R3¯m) exhibits a phase transition to α-MoB2 (space group: P6/mmm) at pressure P~70 GPa, which is a high-temperature superconductor exhibiting Tc=32 K at P~110 GPa. Although α-MoB2 has the same crystalline structure as ambient-pressure MgB2 and the superconducting critical temperatures of α-MoB2 and MgB2 are very close, the first-principles calculations show that in α-MoB2, the states near the Fermi level, εF, are dominated by the d-electrons of Mo atoms, while in MgB2, the p-orbitals of boron atomic sheets dominantly contribute to the states near the εF. Recently, Hire et al. (Phys. Rev. B2022, 106, 174515) reported that the P6/mmm-phase can be stabilized at ambient pressure in Nb1−xMoxB2 solid solutions, and that these ternary alloys exhibit Tc~8 K. Additionally, Pei et al. (Sci. China-Phys. Mech. Astron. 2022, 65, 287412) showed that compressed WB2 exhibited Tc~15 K at P~121 GPa. Here, we aimed to reveal primary differences/similarities in superconducting state in MgB2 and in its recently discovered diboride counterparts, Nb1−xMoxB2 and highly-compressed WB2. By analyzing experimental data reported for P6/mmm-phases of Nb1−xMoxB2 (x = 0.25; 1.0) and highly compressed WB2, we showed that these three phases exhibit d-wave superconductivity. We deduced 2Δm(0)kBTc=4.1±0.2 for α-MoB2, 2Δm(0)kBTc=5.3±0.1 for Nb0.75Mo0.25B2, and 2Δm(0)kBTc=4.9±0.2 for WB2. We also found that Nb0.75Mo0.25B2 exhibited high strength of nonadiabaticity, which was quantified by the ratio of TθTF=3.5, whereas MgB2, α-MoB2, and WB2 exhibited TθTF~0.3, which is similar to the TθTF in pnictides, A15 alloys, Heusler alloys, Laves phase compounds, cuprates, and highly compressed hydrides.
Planar small-size LTE/WWAN tablet computer antenna with eight-band operation

By attaching a C-shaped parasitic monopole strip on the backside of the antenna, a novel compact eight-band monopole antenna (MA) is proposed and embedded in the LTE/WWAN tablet computer. The operating impedance bandwidths of 264/997 MHz can be obtained across the LTE/WWAN bands, respectively. Given that the overall antenna size is only 35 × 10 × 0.8 mm3, the proposed planar MA provides more than 22% reduction in antenna size over that of the conventional ones. Meanwhile, this small-size MA provides the measured peak gains and antenna efficiencies of 2.0/3.2 dBi and 60/66% for the LTE/WWAN bands, respectively. Additionally, to fulfill the limitation of 1.6 W/kg of the 1-g body specific absorption rate (SAR) requirement for mobile device applications, the proposed planar compact MA is deposited inside the tablet computer for the study of radiating performance.
SKELETON EXTRACTION AND SKELETON-DRIVEN PROCESSING OF INCOMPLETE DATA

In the proposed thesis, we address two challenges related to skeletons. The first is to formulate novel ways to define and compute curve skeletons a specific type of skeleton. The second is to employ skeletons to enhance surface reconstruction for geometry affected by severe amounts of missing data. In solving these challenges we discuss three different approaches. In the first approach we focus our attention to watertight geometry. We propose a curve skeletonization algorithm that evolves the shape’s surface towards a curve by means of a motion that accentuates its local shape anisotropy. In the second approach we shift our attention to acquired data, where severe amounts of missing data introduce a significant challenge. To tackle this issue, we propose a technique that robustly extracts curve skeletons by interpreting its branches as local axes of rotational symmetry. We also propose an application that, by exploiting the extracted skeletons, helps to repair the data by performing a skeleton-based volumetric inpainting. Our last approach further explores the idea of volumetric inpainting by replacing curve skeletons with medial skeletons. Thanks to the fact that medial skeletons provide a natural volumetric representation of the shape, we propose a surface reconstruction method that considers volumetric smoothness as a novel and effective shape prior.
Variability, Expression, and Methylation of IL-6 and IL-8 Genes in Bladder Cancer Pathophysiology

Bladder cancer (BC) is the 10th most common form of cancer globally, but its complete aetiology is still unknown. Nevertheless, there is evidence that chronic inflammation plays a role in the development and progression of BC. Therefore, the presented study aimed to detect a potential association between selected single nucleotide polymorphisms (SNPs)—rs1800797 and rs2069845 in IL-6 and rs2227307 in IL-8—and BC development, as well as to identify the impact of BC on the level of expression and methylation of IL-6 and IL-8 promoters in PBMCs with the use of the TaqMan SNP genotyping assay, TaqMan gene expression assay, and methylation-sensitive high-resolution melting techniques. We did not find any association between the genotypes and combined genotypes of all studied polymorphisms and the occurrence of BC. However, we found that BC patients were characterised by decreased IL-6 and IL-8 mRNA expression levels compared to the controls. Additionally, the methylation status of the IL-6 promoter was higher in controls than in BC patients. Our findings suggest that inflammation may be involved in the development and progression of BC.
Air Pollution Prediction using Supervised Machine Learning Technique

Toxins in the air pose a threat to human health and the environment worldwide, a problem known as air pollution. Predicting air quality from pollution using machine learning techniques might be an effective step in mitigating this issue in the transportation sector. Statistical analysis, multiple analyses, variations, missing value treatment, validation, and cleaning/correction of air quality data have all been previously considered. Then, supervised machine learning methods like Logistic Regression, Random Forest, Decision Tree, and Naive Byes are used to make predictions about the air quality. Precision, Recall, and F1 Score are used to evaluate the effectiveness of various machine learning methods. Predictions of air quality using the Decision Tree method are accurate. The Bureau of Meteorology can use this app to improve their forecasts of air quality. The use of Artificial Intelligence methods to enhance this work is a possibility for the future.
Credit Scoring via Kernel Matching Pursuit and its Ensemble

C redit risk is paid more and more attention by financial institutions , and c redit s coring has become an active research topic in computational finance . T his paper proposes to apply k ernel m atching p ursuit (KMP) and its ensemble to credit scoring. KMP originates from m atching p ursuit algorithms that append sequentially basic functions from a basis function dictionary to an initial empty basis using a greedy optimization algorithm, to approximate a given function , and obtain the final solution with a linear combination of chosen functions . KMP is the special m atching p ursuit algorithm using a kernel-based dictionary. An outstanding advantage of KMP in solving classification problems is the sparsity of its solution. Furthermore , we also apply KMP ensemble to credit scoring to model the large-scale data set, which is infeasible for the single KMP . Experimental results based on two data sets f ro m UCI repository and one large data set from individual housing loans in a commercial bank of China show the effectiveness and sparsity of KMP and KMP ensemble in building credit scoring model , compared with the classical classification method － support vector machine .
Location based Context Awareness with Multipoint Transmission by ELAML Algorithm

Wireless network and Communication engineers have a query that when, where and how mmWave will be used in volte+ network? The future generation network depends on multiple speed Radio Access Technology (RAT), but it makes use of different access in downlink with multipoint transmission in the presence of multilink distributed wireless channel information. Context awareness means that systems can sense and react their environment based on location which is capable of identifying nearest network locator. The location of particular system in a distributed network can be determined by Navigational Network Locator (NNL). Next, Vehicular Systems demand high data transfer links of mmWave communication. By the Convolution Neural Networks, it passes the result to the upcoming vehicular signal and multiple links shares the data to all mobile networks. The Enhanced Location Awareness Machine Learning (ELAML) combined with Mobile Edge Computing (MEC) technologies to enable multiple links ultra-high speed data downloads. The real-world implementations of Location based machine learning network architecture using traffic patterns are identified. The main aim of this research paper is about implementing multiple ultra-high data rate with volte+ mmWave vehicular network.
Clustering algorithm based on fuzzy theory in WEKA

The data in Internet has a large scale and dynamic peculiarity and the discovered knowledge or rules are likely to be imprecise or incomplete generally.Fuzzy theory and information entropy were introduced into the clustering analysis to overcome the difficulties and achieve the best results of clustering by looking for Fuzzy similarity upper approximation.The process of embedding the Fuzzy approximation algorithm into the WEKA platform in which the classes and visualization functions of open source WEKA was fully utilized.The Fuzzy approximation algorithms extended the clustering algorithm in WEKA.The experiment proves that it has a higher accuracy and convergence for the large-scale data sets that are anomalous and noise.
Study of Physico-Chemical Properties of Dough and Wood Oven-Baked Pizza Base: The Effect of Leavening Time

The research objective was to investigate the morpho-rheological, chemical, and structural changes of dough and Neapolitan pizza TSG as the leavening time varies and to evaluate their effects on the digestibility of starch and on the formation of acrylamide during baking. Pizza dough leavening was monitored for 48 h at 22 °C/80% RH, and the analyses were conducted at selected leavening times (0, 4, 8, 16, 24, and 48 h). It was observed that in 30 h the volume tripled and the viscoelastic dough relaxed in the first 4 h, as evidenced by the lower value of the relaxation percentage “a” and the higher rate of decay “b”, associated with a high value of the compression work, indicating the presence of a very strong gluten mesh. In the following hours, the dough lost elasticity, and in fact, the G’ modulus decreased due to the weakening of the weak interactions between the gluten proteins and the starch. This suggests that a long leavening improved the extensibility of the pizza disc, facilitating the action of the pizza maker. Thermal (TGA and DSC) and morphological (SEM) analyses evidenced the highest water removal rate from the dough, a wider starch gelatinization temperature range, a ∆H of 0.975 ± 0.013 J/g, and a more open and weak gluten structure in dough balls leavened for 16 h. As the leavening time increased, both dough and pizza base samples showed an increase in reducing sugars and free amino groups, while the rapidly digestible starch decreased in the dough following the metabolism of the yeasts and increased in the pizza base due to the starch gelatinization that occurs during baking, which makes it much more susceptible to α-amylase. Finally, the levels of acrylamide remained at the same values despite the higher availability of reducing sugars and its precursors during leavening.
Disease Prediction and Classification using Intelligent Algorithms

Cardio Vascular Diseases are the major risk factors for human survival. Cardio Vascular Diseases are comprised with many forms such as abnormal heart rhythms, congenital heart disease, deep vein thrombosis, heart attack, heart failure etc. These diseases are identified using scanning devices and doctors can analyze the report by observing heart x-ray images. This research work is carried out using various intelligence algorithms for predicting and classifying the type of heart disease. It is observed that Support Vector Classifier predicts with an accuracy of 99%, Logistic Regression predicts with an accuracy of 85%, Multi Layer Perceptron prediction accuracy is 86.4%, Simple Logistic prediction accuracy is 85.8%, J48 prediction accuracy as 88.62% and Random Forest classification as 100% based on the training data.
Analytic Solution of Black-Scholes-Merton European Power Put Option Model on Dividend Yield with Modified-Log-Power Payoff Function

This paper proposes a framework based on the celebrated transform of Mellin type (MT) for the analytic solution of the Black-Scholes-Merton European Power Put Option Model (BSMEPPOM) on Dividend Yield (DY) with Modified-Log-Power Payoff Function (MLPPF) under the geometric Brownian motion. The MT has the capability of tackling complex functions by means of its fundamental properties and it is closely related to other well-known transforms such as Laplace and Fourier types. The main goal of this paper is to use MT to obtain a valuation formula for the European Power Put Option (EPPO) which pays a DY with MLPPF. By means of MT and its inversion formula, the price of EPPO on DY was expressed in terms of integral equation. Moreover, the valuation formula of EPPO was obtained with the help of the convolution property of MT and final time condition. The MT was tested on an illustrative example in order to measure its performance, effectiveness and suitability. The MLPPF was compared with other existing payoff functions. Hence, the effect of DY on the pricing of EPPO with MLPPF was also investigated.
Comparative Cognition Research Demonstrates the Similarity between Humans and Other Animals

Simple Summary The traditional view of cognition is that it is something that differentiates humans from other species. There is growing evidence, however, that if one “asks” other animals appropriately, they often show evidence that they too are capable of cognitive processes thought to be unique to humans. Furthermore, several behaviors thought to involve complex human cognition that are presumed to be culturally learned can be shown to occur in other species as well. Results such as these suggest that simpler processes are likely to be involved. Together, these lines of research demonstrate that the cognitive distinctions that have been made between humans and other animals may be greatly exaggerated. The present review focuses on several cognitive phenomena that can be demonstrated in animals. The review focuses primarily on pigeons (because they are not thought to be especially cognitive compared to primates) out of convenience, because they are a highly visual species. A better understanding of the similarities between humans and other animals should help foster improved treatment of nonhuman species, as well as, in some cases, improved treatment of disordered human behavior. Abstract The field of comparative cognition represents the interface between the cognitive behavior of humans and other animals. In some cases, research demonstrates that other animals are capable of showing similar cognitive processes. In other cases, when animals show behavior thought to be culturally determined in humans, it suggests that simpler processes may be involved. This review examines research primarily with pigeons (out of convenience because of their visual ability). I start with the concept of sameness and follow with the concept of stimulus equivalence, the building blocks of human language. This is followed by research on directed forgetting, the cognitive ability to maintain or forget information. A hallmark of cognition is transitive inference performance (if A < B, and B < C, the understanding that A < C), but the variety of species that show this ability suggests that there may be simpler accounts of this behavior. Similarly, experiments that demonstrate a form of cognitive dissonance in animals suggest that dissonance may not be necessary to explain this biased behavior. Furthermore, examples of sunk cost in pigeons suggests that the human need to continue working on a failing project may also have a biological basis. Finally, pigeons show a preference for a suboptimal choice that is similar to unskilled human gambling, a finding that may clarify why humans are so prone to engage in this typically losing activity.
Camera Intrinsic Parameter Calibration Using Genetic Algorithms

We propose a method of camera calibration based on genetic algorithms to improve the accuracy of camera calibration.The method extracts corners of chess board images and calculates the coordinates of the reference corners,then estimates intrinsic and external parameters of camera.Genetic algorithm is used to optimize the parameters extracted by the proposed method.Through evolution,selection and variation among generations,optimization is achieved by convergence.The result shows that the average projection deviation with the method is 0.255 9 pixel,and the standard deviation 0.139 3 pixel.
New heavy resonances: from the Electroweak to the Planck scale

The principle of local gauge invariance is a pillar of modern particle physics theories and in the SM relies on the ad-hoc gauge group structure SU(3)_c x SU(2)_L x U(1)_Y. Extending this gauge group is very well motivated in a Grand Unified Theory (GUT) perspective in which the SM is the low-energy limit of a more fundamental theory based on a larger gauge group like SO(10) or E(6). Indeed, the symmetry breaking (SB) of the underlying GUT gauge group, down to the SM one, leaves some additional group factors unbroken, such as U(1) or SU(2). In this spirit, we focus in this manuscript on the phenomenology of extended gauge group models and on the new heavy neutral and charged resonances, generically called Z' and W' predicted by these.In this manuscript we present different aspects of the phenomenology of the G(221) models. After reviewing these extensions, we present a public tool, PyR@TE, that aims at automating the calculation of RGEs at two-loop for arbitrary gauge theories and exemplify its use with the G(221) models. In a second part, we present our results for the calculation of the QCD corrections to the electroweak top-pair production as well as their implementation in a general purpose Monte Carlo generator allowing for a consistent matching of next-to-leading order (NLO) matrix elements with parton shower algorithms, the POWHEG BOX. We then review the status of our calculation of the QCD corrections to the electroweak single-top production. Finally, we present a different aspect of the phenomenology of new heavy resonances, Z', W' by studying their impact on the interaction of ultra-high energy neutrinos in the atmosphere. For definiteness we consider the Pierre Auger Observatory, which is sensible to showers initiated by neutrinos of extreme energies up to 10E12 GeV