Miracle-dz/UniFAR-FTUs huggingface dataset:
FATIGUE PERFORMANCE & FRACTURE MODE ANALYSIS OF TWO AVAILABLE HIGH STRENGTH GLASS CERAMICS

Objective: As the high strength glass ceramics had attained a profound contribution in prosthodontics, it was aimed to evaluate the fatigue performance of two machinable high strength glass ceramics with further fracture mode analysis. Materials and methods : Twenty crowns were fabricated using Cerec in-Lab CAD\CAM system, ten crowns were fabricated from IPS. E.max CAD: group (EM-LDS) as control group and ten crowns were fabricated from VITA SUPRINITY: group (VS-ZLS) . The completed crowns were cemented on epoxy resin dies. The cemented crowns were subjected to thermomechanical cycling for 75000 cycles, then loaded until fracture by using universal testing machine. Failure mode were assesses guided by Brukes’ classification for all specimens. The collected data was statistically analyzed using Student t-test and Paired t-test while the significance level was set at P ≤ 0.05, in addition to descriptive statistics of the fracture mode analysis. Results: There was no statistically significant difference in fatigue performance between the two tested groups with the major fracture occurrence in type III central fracture. Conclusion : both tested high strength glass ceramics are considered as acceptable modalities for restoration of single tooth restoration as both materials have comparable strength and exceeded the reported range of human masticatory forces.
Graph convolutional neural networks‐based assessment of students' collaboration ability

As 21st‐century skills have become increasingly important, collaboration ability is now considered essential in many areas of life. Different theoretical frameworks and assessment tools have emerged to measure this skill. However, more applied studies on its implementation and assessment in current educational settings are required. This research accordingly uses Graph Convolutional Neural Networks (GCNs) to assess students' collaboration ability from students' assignments. The Pearson correlation coefficient is used to measure the similarity of the level of students' collaboration ability, and similar students are linked together to establish an adjacency matrix. By sorting through relevant literature and selecting the feature words that represent the strength of collaboration ability, calculating the similarity between the preprocessed student data and each selected feature word, after which the highest value of the similarity as the feature value of the student for this feature and establish the student feature matrix. Finally, the GCNs are jointly trained by the adjacency matrix and the feature matrix. The results show that this method can effectively assess students' collaboration ability. Moreover, compared with other text classification methods, the GCNs selected in this paper has higher accuracy.
EXPERIMENTAL STUDY ON CONVECTIVE HEAT TRANSFER USING FINS FOR COOLING PV CELLS

Experimental research on the impact of fin cooling on silicon solar cell performance metrics was performed, and it was compared to two other cells, one cooled with water and the other without cooling. Photovoltaic (PV) cell waste heat is dispersed, using aluminum fins. The tests were conducted out at a variety of ambient temperatures and with varying levels of illumination. Three test panel were used, the first one used a freestanding photovoltaic system module as a model. The second has a water-cooling system as well, where the water passes through a serpentine heat exchanger, and the third has a fin cooling system, in which water passes through a rectangular helical heat exchanger and fins put on It is equipped with a water-cooling system. as well, although the water passes through a serpentine heat exchanger. Figures (1) and (2) (Serpentine and rectangular helical with fins). The suggested cooling technique significantly increases the produced power and the photovoltaic cell's electric efficiency (helical form) via fins, according to experimental data. At 6 LPM, power climbed to 76.85W and efficiency increased to 9.97%, a performance gain of 38.7% above the uncooled panel.
Discrete Wigner formalism for qubits and noncontextuality of Clifford gates on qubit stabilizer states

We show that qubit stabilizer states can be represented by non-negative quasi-probability distributions associated with a Wigner-Weyl-Moyal formalism where Clifford gates are positive state-independent maps. This is accomplished by generalizing the Wigner-Weyl-Moyal formalism to three generators instead of two---producing an exterior, or Grassmann, algebra---which results in Clifford group gates for qubits that act as a permutation on the finite Weyl phase space points naturally associated with stabilizer states. As a result, a non-negative probability distribution can be associated with each stabilizer state's three-generator Wigner function, and these distributions evolve deterministically to one another under Clifford gates. This corresponds to a hidden variable theory that is non-contextual and local for qubit Clifford gates while Clifford (Pauli) measurements have a context-dependent representation. Equivalently, we show that qubit Clifford gates can be expressed as propagators within the three-generator Wigner-Weyl-Moyal formalism whose semiclassical expansion is truncated at order $\hbar^0$ with a finite number of terms. The $T$-gate, which extends the Clifford gate set to one capable of universal quantum computation, require a semiclassical expansion of the propagator to order $\hbar^1$. We compare this approach to previous quasi-probability descriptions of qubits that relied on the two-generator Wigner-Weyl-Moyal formalism and find that the two-generator Weyl symbols of stabilizer states result in a description of evolution under Clifford gates that is state-dependent, in contrast to the three-generator formalism. We have thus extended Wigner non-negative quasi-probability distributions from the odd $d$-dimensional case to $d=2$ qubits, which describe the non-contextuality of Clifford gates and contextuality of Pauli measurements on qubit stabilizer states.
The Machine-SUIF Control Flow Analysis Library

The Machine-SUIF control flow analysis (CFA) library builds on the control flow graph (CFG) library. It currently provides dominator analysis and natural-loop analysis. The DominanceInfo class computes dominator sets, the dominator tree, and dominance frontiers in either the forward or reverse graphs. Given dominance information, the NaturalLoopInfo class finds natural loops, computes the loop depth of each CFG node, and provides predicates that identify loop boundaries.
Hollowing and Transfer of Polymethyl Methacrylate Film Propelled by Laser Ablation of Triazeno Polymer Film

Utilizing the laser ablation of a triazeno polymer (TA) film as a driving force, materials transfer and patterning were successfully demonstrated. The prepared overlaid films consist of a source film, which is made of pyrene-doped polymethyl methacrylate (PMMA) and TA films, and a target neat PMMA film on a quartz plate. After pressing them together, a 248 nm laser pulse was irradiated from the back side of the source film. When the thickness of the TA film was 1.0 µm, slightly thicker than the penetration depth of TA of 600 nm, the pyrene-doped PMMA film was perfectly transferred to the target film.
HERSCHEL/HIFI OBSERVATIONS OF IRC+10216: WATER VAPOR IN THE INNER ENVELOPE OF A CARBON-RICH ASYMPTOTIC GIANT BRANCH STAR

We report the results of observations of 10 rotational transitions of water vapor toward the carbon-rich asymptotic giant branch (AGB) star IRC+10216 (CW Leonis), carried out with Herschel's HIFI instrument. Each transition was securely detected by means of observations using the dual beam switch mode of HIFI. The measured line ratios imply that water vapor is present in the inner outflow at small distances (≤few × 1014 cm) from the star, confirming recent results reported by Decin et al. from observations with Herschel's PACS and SPIRE instruments. This finding definitively rules out the hypothesis that the observed water results from the vaporization of small icy objects in circular orbits. The origin of water within the dense C-rich envelope of IRC+10216 remains poorly understood. We derive upper limits on the H172O/H162O and H182O/H162O isotopic abundance ratios of ∼5 × 10−3 (3σ), providing additional constraints on models for the origin of the water vapor in IRC+10216.
Phase diagram of the quadrumerized Shastry-Sutherland model

We determine the phase diagram of a generalized Shastry-Sutherland model, using a combination of dimer- and quadrumer-boson methods and numerical exact diagonalization techniques. Along special lines in the parameter space the model reduces to the standard Shastry-Sutherland model, the 1/5th depleted square lattice and the two-dimensional plaquette square lattice model. We study the evolution of the ordered phases found in the latter two unfrustrated models under the effect of frustration. Furthermore we present exact diagonalization results for the Shastry-Sutherland model on clusters with up to 32 sites, supporting the existence of an intermediate gapped valence bond crystal phase with plaquette long-range order.
Do Private Equity Funds Manipulate Returns? Do Private Equity Funds Manipulate Reported Returns?I

Private equity funds hold assets that are hard to value. Managers may have an incentive to distort reported valuations if these are used by investors to decide on commitments to subsequent funds managed by the same firm. Using a large dataset of buyout and venture funds, we test for the presence of reported return manipulation. We find evidence that some underperforming managers inflate reported returns during times when fundraising takes place. However, those managers are less likely to raise a next fund, suggesting that investors can see through the manipulation on average. In contrast, we find that top-performing funds likely understate their valuations. A simple theoretical framework rationalizes our empirical results as well as those of related papers.
Vers une approche multi-échelle pour l’interaction fluide-structure

La prediction du comportement de structures soumises au chargement d'un guide est un enjeu actuel de l'industrie aeronautique. Les materiaux composites, de plus en plus employes, ont des mecanismes de degradation dont la taille caracteristique est inferieure au dixieme de millimetre. Dans le cadre des methodes multi-echelles developpees au laboratoire pour la simulation des structures, nous proposons une methode de decomposition de domaine mixte pour la simulation d'ecoulements incompressibles. Les equations de Navier-Stokes sont ecrites dans un formalisme eulerien, et resolues de maniere incrementale. La solution du probleme a chaque piquet de temps est obtenue de maniere iterative, par la resolution de problemes poses sur les interfaces et sur les sous-domaines. La non-linearite liee au terme de convection est traitee au niveau des sous-domaines, et les problemes sur les interfaces assurent la verification de l'ensemble des equations du probleme initial. Nous introduisons ensuite un probleme global pose sur l'ensemble des interfaces. Ce probleme macroscopique original base sur la condition d'incompressibilite de l'ecoulement accelere la convergence de la strategie iterative. Nous abordons enfin le couplage par une methode de domaine fictif, dont les premiers developpements ont permis d'obtenir des resultats prometteurs. Les methodes proposees sont implantees dans un code elements finis et illustrees sur des exemples bidimensionnels.
Deep Reinforcement Learning Approach to Portfolio Optimization

This paper evaluates whether a deep reinforcement learning (DRL) approach can be implemented, on the Swedish stock market, to optimize a portfolio. The objective is to create and train two DRL algorithms that can construct portfolios that will be benchmarked against the market portfolio, tracking OMXS30, and the two conventional methods, the naive portfolio, and minimum variance portfolio. We evaluate all the portfolios on a five-year period, from the start of 2016 to the end of 2020, in terms of returns and risk-adjusted returns. The two DRL algorithms implemented are Advantage Actor-Critic (A2C) and Deep Deterministic Policy Gradient (DDPG), they are also compared against each other. The results of this study show that the A2C constructed portfolio significantly outperform the market and all of the other benchmark portfolios, in terms of returns and risk-adjusted returns. The A2C portfolio also outperforms the DDPG constructed portfolio. Even though the DDPG constructed portfolio performs less than the A2C constructed portfolio, it still significantly outperforms all of the other benchmarks on the whole testing period. Thus, concluding that a DRL approach can be implemented, on the Swedish stock market, to optimize a portfolio. Moreover, the study shows that the two DRL agents can pick up on market trends and profit from them. However, applying the methods in a real-world environment does come with some data-processing caveats. Even though the models may come with caveats linked to them, the results of this study underline the usefulness of machine learning methods in portfolio management.
The Effects of Motivational Interview on Healthy Behaviour and Quality of Life in the Uncontrolled Type 2 Diabetes Patients

Objectives: The study aims to determine the impact of motivational interviews, which were conducted with type 2 diabetes patients at high risk of diabetes, on healthy lifestyle behaviors and life qualities of the individuals. Materials and methods: The semi-experimental study was designed with a pre-test and post-test in a single group. In the first phase, the study was conducted with 30 individuals who could be connected among 44 type 2 diabetes patients at high risk of diabetes. Six motivational interviews, each of which lasted 120 minutes, on the management of type 2 diabetes were conducted with individuals. The data of the study were collected through the Healthy Lifestyle Behaviors Scale II and WHO Healthy Lifestyle Behaviors Scale BREF. Results: The study showed that there was a statistically significant difference between the individuals' scores in both scales and the pre-test post-test scores in all their sub-dimensions (p<0.05). Accordingly, it was found that motivational interviews improved individuals' healthy lifestyle behaviors and increased life quality. Conclusion: In line with these results, it is recommended to conduct studies in which motivational interviews are more commonly and comprehensively applied for diabetes management.
Microarchitectural Leakage Templates and Their Application to Cache-Based Side Channels

The complexity of modern processor architectures has given rise to sophisticated interactions among their components. Such interactions may result in potential attack vectors in terms of side channels, possibly available to userland exploits to leak secret data. Exploitation and countering of such side channels requires a detailed understanding of the target component. However, such detailed information is commonly unpublished for many CPUs. In this paper, we introduce the concept of Leakage Templates to abstractly describe specific side channels and identify their occurrences in binary applications. We design and implement Plumber, a framework to derive the generic Leakage Templates from individual code sequences that are known to cause leakage (e.g., found by prior work). Plumber uses a combination of instruction fuzzing, instructions' operand mutation and statistical analysis to explore undocumented behavior of microarchitectural optimizations and derive sufficient conditions on vulnerable code inputs that if hold can trigger a distinguishing behavior. Using Plumber we identified novel leakage primitives based on Leakage Templates (for ARM Cortex-A53 and -A72 cores), in particular related to previction (a new premature cache eviction), and prefetching behavior. We show the utility of Leakage Templates by re-identifying a prefetcher-based vulnerability in OpenSSL 1.1. 0g first reported by Shin et al. [39].
Hybrid Image-/Data-Parallel Rendering Using Island Parallelism

In parallel ray tracing, techniques fall into one of two camps: image-parallel techniques aim at increasing frame rate by replicating scene data across nodes and splitting the rendering work across different ranks, and data-parallel techniques aim at increasing the size of the model that can be rendered by splitting the model across multiple ranks, but typically cannot scale much in frame rate. We propose and evaluate a hybrid approach that combines the advantages of both by splitting a set of N x M ranks into M islands of $N$ ranks each and using data-parallel rendering within each island and image parallelism across islands. We discuss the integration of this concept into four wildly different parallel renderers and evaluate the efficacy of this approach based on multiple different data sets.
ANTIMICROBIAL ACTIVITY OF EXTRACTS OF ZANTHOXYLUM RHETSA (ROXB.) DC. AGAINST FOOD PATHOGENS

The pericarps and seeds of Zanthoxylum rhetsa were collected and powdered. The powdered material was subjected to kinetic maceration and soxhlet extraction with methanol, dichloromethane, ethyl acetate and n-hexane to get respective extracts. The extracts of pericarps and seeds were screened for its antimicrobial activity using agar cup method. The Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) values were also determined. Zanthoxylum rhetsa exhibited narrow spectrum antimicrobial activity with MIC value ranging between 0.312 mg/ml to 12.5 mg/ml. It can be concluded from the antimicrobial data that Z. rhetsa has promising antimicrobial activity against the food pathogens; hence it may be considered as a safe antimicrobial agent. in the organic solvents methanol, dichloromethane, ethyl acetate and n-hexane using kinetic maceration and soxhlet extraction.
A review of statistical methods used for developing large-scale and long-term PM 2.5 models from satellite data Remote Sensing of Environment

Research of PM 2.5 chronic health effects requires knowledge of large-scale and long-term exposure that is not supported by newly established monitoring networks due to their sparse spatial coverage and lack of historical measurements. Estimating PM 2.5 using satellite-derived aerosol optical depth (AOD) can be used to fill the data gap left by the ground monitors and extend the PM 2.5 data coverage to suburban and rural areas over long time periods. Two approaches have been applied in large-scale and long-term satellite remote sensing of PM 2.5 , i.e., the scaling and statistical approaches. Compared to the scaling method, the statistical approach has greater prediction accuracy and has been widely used. There is a gap in the current literature and review papers on how the statistical methods work and specific considerations to best utilize them, especially for large-scale and long-term estimates. In this critical review, we summarize the evolution of large-scale and long-term PM 2.5 statistical models reported in the literature. We describe the framework and guidance of large-scale and long-term satellite-based PM 2.5 modeling in data preparation, model development, validation, and predictions. Sample computer codes are provided to expedite new model-building efforts. We also include useful considerations and recommendations in covariates selection, addressing the spatiotemporal heterogeneities of PM 2.5 -AOD relationships, and the usage of cross validation, to aid the determination of the final model.
Machine Learning of Explicit Order Parameters: From the Ising Model to SU(2) Lattice Gauge Theory

We present a solution to the problem of interpreting neural networks classifying phases of matter. We devise a procedure for reconstructing the decision function of an artificial neural network as a simple function of the input, provided the decision function is sufficiently symmetric. In this case one can easily deduce the quantity by which the neural network classifies the input. The method is applied to the Ising model and SU(2) lattice gauge theory. In both systems we deduce the explicit expressions of the order parameters from the decision functions of the neural networks. We assume no prior knowledge about the Hamiltonian or the order parameters except Monte Carlo–sampled configurations.
Criminal Justice in Serbia: The Effectiveness of Addressing Drug Trafficking

regional collaboration in drug trafficking suppression, further knowledge on functioning of criminal networks and adequate mapping of the affected institutions could offer significant information to future policy making. This study explores the role of state institutions in suppressing drug trafficking in an attempt to identify potential structural holes that hinder the effectiveness of such efforts. Considering that Serbia lies on the Balkan route, examining how these criminal networks can be addressed represents a transnational crime problem. In view of the increased regional and international cooperation against drug trafficking and organized crime, it is of importance to assess the actions un-dertaken by the state, as they may be a vital link for addressing the problem of international drug traffic. Drug trafficking criminal groups operate as loosely-organized networks, interrelated through diverse transnational networks, successfully circumventing law enforcement efforts. Given the impact of the social context on the emergence of criminal networks, social network analysis is deployed an innovative manner to map the network of Serbian institutions designed to address drug trafficking groups and identify potential barriers to their effectiveness. Initial findings indicate the existence of various structural holes at the level of legislation and implementation, with high potential to provide smooth functioning of illicit networks. The study utilizes elite interviewing in a sample of 50 state officials and civil society representatives to explore the effectiveness of state actions and the influence of a particular social context in this process. Mapping of the institutions and positioning their competencies within the network may have the capacity to indicate specific obstacles which can significantly hamper the efforts of the institutions and efficient multiagency and regional cooperation.
Temporal Super-resolution for Wire Position Estimation in Electric Discharge Machines

We present a technique for achieving ultra-high temporal super resolution of the position of the cutting wire in electric discharge machines from low frame rate video. Our approach models the video of the vibrating wire as a frequency sparse signal and formulates a joint-sparsity recovery problem to determine the vibration frequency as well as reconstruct the high frame rate video. The proposed method is capable of reconstructing the high frame rate video at 5000 x super resolution rate. We demonstrate the performance of our method using simulated data mimicking the acquisition setup and wire vibrations in realistic EDM settings. Abstract We present a technique for achieving ultra-high temporal super resolution of the position of the cutting wire in electric discharge machines from low frame rate video. Our approach models the video of the vibrating wire as a frequency sparse signal and formulates a joint-sparsity recovery problem to determine the vibration frequency as well as reconstruct the high frame rate video. The proposed method is capable of reconstructing the high frame rate video at 5000 × super resolution rate. We demonstrate the performance of our method using simulated data mimicking the acquisition setup and wire vibrations in realistic EDM settings.