Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceptual Translucency in 3D Printing Using Surface Texture

We propose a method of reproducing perceptual translucency in three-dimensional printing. In contrast to most conventional methods, which reproduce the physical properties of translucency, we focus on the perceptual aspects of translucency. Humans are known to rely on simple cues to perceive translucency, and we develop a method of reproducing these cues using the gradation of surface textures. Textures are designed to reproduce the intensity distribution of the shading and thus provide a cue for the perception of translucency. In creating textures, we adopt computer graphics to develop an image-based optimization method. We validate the effectiveness of the method through subjective evaluation experiments using three-dimensionally printed objects. The results of the validation suggest that the proposed method using texture may increase perceptual translucency under specific conditions. As a method for translucent 3D printing, our method has the limitation that it depends on the observation conditions; however, it provides knowledge to the field of perception that the human visual system can be cheated by only surface textures.
Design and implementation of adaptive scheduling management for user-mode thread pool

Progress in network and storage technology enables data center services to provide high-concurrency and low-latency processing responses. The user-mode thread pool is one of the technologies that can be used to improve the overall concurrent responsiveness of the service system while ensuring its stability of the system. In the system of multicore processor architecture, the scheduling management of the user-mode thread pool is much more complicated than that of the thread pool. First, the existing method cannot fully utilize the advantage of parallel execution of multicore processor architecture, and it is difficult to implement unified resource provisioning management among multiple user-mode thread pools. Second, the existing user-mode thread scheduling method cannot distinguish the performance requirements of different data processing tasks but treat all user-mode thread tasks in the same way. Last, the existing user-mode thread scheduling method controls the execution of all kernel threads, which is difficult to isolate host threads and other kernel threads in the system. It fails to reflect the performance requirements of different user-mode thread tasks, or to provide finer-grained execution timing control of the user-mode thread. This paper provides a two-level scheduling management method for user-mode thread pool, which can effectively integrate OS scheduling mechanism and user-mode thread scheduling mechanism, with a view to distinguishing the needs of different data processing tasks, enabling more targeted and fine-grained scheduling and execution control of user-mode threads, achieving isolation of host threads from non-host threads and differentiated scheduling control without affecting the scheduling mechanism of the OS itself, and enabling the global unified resource provisioning management of multi-user thread pools for multicore processor architecture. Generally, the new method can greatly improve the efficiency and performance of user thread pools on multicore processor architecture, enable the overall performance of the system to scale freely according to data processing task requirements, and provide more efficient user-mode thread pool operation management for highly concurrent and low-latency service system.
LncRNA H19 Regulates Breast Cancer DNA Damage Response and Sensitivity to PARP Inhibitors via Binding to ILF2

Although DNA damage repair plays a critical role in cancer chemotherapy, the function of lncRNAs in this process remains largely unclear. In this study, in silico screening identified H19 as an lncRNA that potentially plays a role in DNA damage response and sensitivity to PARP inhibitors. Increased expression of H19 is correlated with disease progression and with a poor prognosis in breast cancer. In breast cancer cells, forced expression of H19 promotes DNA damage repair and resistance to PARP inhibition, whereas H19 depletion diminishes DNA damage repair and increases sensitivity to PARP inhibitors. H19 exerted its functional roles via direct interaction with ILF2 in the cell nucleus. H19 and ILF2 increased BRCA1 stability via the ubiquitin-proteasome proteolytic pathway via the H19- and ILF2-regulated BRCA1 ubiquitin ligases HUWE1 and UBE2T. In summary, this study has identified a novel mechanism to promote BRCA1-deficiency in breast cancer cells. Therefore, targeting the H19/ILF2/BRCA1 axis might modulate therapeutic approaches in breast cancer.
Migration behavior of photoinitiators in polyethylene-coated paper for takeaway fast food

The migration of photoinitiators (PIs): benzophenone (BP), ethyl 4-dimethylaminobenzoate (EDAB), and 4-chlorobenzophenone (CBP), from polyethylene (PE)-coated paper to 4% (v/v) acetic acid and 10% (v/v) saltsimulated food solution was quantitatively analyzed using gas chromatography–mass spectrometry. The migration behavior of the three PIs was studied under three specific working conditions: conventional heating, microwave power, and random vibration level. The migration rate of the three PIs in acidic food simulants was ranked from BP > EDAB > CBP under the specific working conditions. The mobility of the three PIs increased with increasing temperature, microwave power, and random vibration acceleration in 4% (v/v) acetic acid by 10, 10, and 15%, respectively. The experimental migration data of three PIs in PE-coated paper under different temperatures and microwave powers were used to establish the Crank monolayer migration model according to Fick’s second law. The diffusion coefficient D increased with an increase in experimental conditions. According to the migration data under the condition of random vibration level, a mathematical model conforming to the Fick model was established, fitting the migration process. These results showed that conventional heating, microwave heating, and vibration could affect the food safety of PE-coated paper.
Impacts of the tropical western Pacific on the East Asian summer monsoon

In this paper, the impacts of the convective activities in the western Pacific warm pool on the interannual and intraseasonal variations of the summer monsoon in East Asia are analyzed by using the observed data for 12 summers from 1978 to 1989. The analyzed results show that both interannual and intraseasonal variabilities of the East Asian summer monsoon are greatly influenced by the convective activities in the warm pool. Generally, the monsoon rainfall is below normal in East Asia and the abrupt change of the monsoon circulation is obvious in the summer of strong convective activities around the Philippines. The impacts of the convective activities in the warm pool on the summer monsoon in East Asia and the East Asia/Pacific teleconnection pattern of summer circulation anomalies due to the convection are discussed by using the theory of planetary wave propagation and the numerical modelling by the IAP-GCM, respectively.
Estimating Regression Coefficients for Balanced Growth Curve Model When Time Trend of Baseline is Not Specified

SYNOPTIC ABSTRACT This article presents a method for estimating the regression coefficients for a growth curve model when the time trend of the baseline has not been specified. The concept of this method is similar to that of the Cox proportional hazard model. No particular shape is assumed for the baseline time trends, or, alternatively, it can be assumed that they are estimated nonparametrically. Because of these nuisance parameters for the baseline trends, we do not have to pay attention to model those shapes. In addition to the simplicity of modeling baseline curves, we can also nonparametrically describe the baseline trends by using the residuals after the regression coefficients have been estimated.
Universal Compressed Sensing of Markov Sources

The main promise of compressed sensing is accurate recovery f high-dimensional structured signals from an underdetermined set of randomized linear projections. Sev eral types of structure such as sparsity and low-rankness have already been explored in the literature. For each type o f structure, a recovery algorithm has been developed based on the properties of signals with that type of structur e. Such algorithms recover any signal complying with the assumed model from its sufficient number of linear projectio ns. However, in many practical situations the underlying structure of the signal is not known, or is only partially kno wn. Moreover, it is desirable to have recovery algorithms that can be applied to signals with different types of struct ure. In this paper, the problem of developing “universal” algori thms for compressed sensing of stochastic processes is studied. First, Rényi’s notion of information dimension i s generalized to analog stationary processes. This provide s a measure of complexity for such processes and is connected t o the number of measurements required for their accurate recovery. Then a minimum entropy pursuit (MEP) alg orithm is proposed, and it is proven that it can reliably and robustly recover any Markov process of any order from suf ficient number of randomized linear measurements, without having any prior information about the distributio n f the process. An implementable version of MEP with the same performance guarantees is also provided. I. I NTRODUCTION Consider the fundamental problem of compressed sensing (CS ): a signalxo ∈ R is observed through a data acquisition system that can be modeled as a linear projectio n systemy o = Ax n o , whereA ∈ Rm×n denotes the measurement matrix. The decoder is interested in recoverin g the signalxo from the measurements y m o . In CS we are usually interested in the case where xo is high-dimensional and the number of measurements m i much smaller than the ambient dimension of the signal, i.e., m ≪ n. In such a setup, clearly, without any extra information, it is impossible to recover xo from y m o , and the system of linear equations described by y m o = Ax n has infinitely many solutions. However, if some extra information about the str ucture ofxo is available, for instance it is known that xo is sparse, i.e., ‖xo‖0 ≪ n, then it is known that this additional information can be exp loited andxo can be recovered fromy o . For sparse signals, it can be proved that in fact the signal r ecovery can be achieved efficiently and robustly [1]–[3]. This result can be extended to several other structures as well [4]–[11]. In each of these cases, the signal is known to have some specific structure, and the de coder takes advantage of its knowledge about the structure of the signal to recover it from its linear project ions.
Critical Evaluation and Development of One-Equation Near-Wall Turbulence Models

Title of dissertation: CRITICAL EVALUATION AND DEVELOPMENT OF ONE-EQUATION NEAR-WALL TURBULENCE MODELS Ricardo Heinrich Diaz, Doctor of Philosophy, 2003 Dissertation directed by: Professor Jewel B. Barlow Department of Aerospace Engineering A systematic evaluation of one-equation near-wall turbulence models is completed and a new model is developed. The study includes five one-equation nearwall models and one two-equation model such that the performance of the one-equation models can be viewed in context of the performance of this more widely used class of models. It is found that the majority of one-equation near-wall models do not reproduce the variation of the Reynolds shear stress near the wall, do not reproduce the dissipation at the wall, and do not predict the dissipation well in the region near the wall for a boundary layer flow. The new model is found to provide improved performance for the boundary layer and a wavy-wall channel. Specifically, it is found that the new model predicts the turbulent kinetic energy and dissipation in closer agreement with direct numerical simulation data than existing one-equation models for the boundary layer and provides improved predictions of the shear stress distribution for the wavy-wall channel. It is found that the one-equation near-wall models generally predict the shear stress distribution for the wavy-wall channel with greater accuracy than the two-equation model. In addition, it is shown that computations using the one-equation models are less sensitive to wall spacing than those using the two-equation model. This suggests that oneequation near-wall models, and in particular the new model, are ideal for engineering computations of practical flows where computational expense may be a significant factor entering into the choice of turbulence model. CRITICAL EVALUATION AND DEVELOPMENT OF ONE-EQUATION NEAR-WALL TURBULENCE MODELS
Effect of Heating Temperature of High-Quality Arbequina, Picual, Manzanilla and Cornicabra Olive Oils on Changes in Nutritional Indices of Lipid, Tocopherol Content and Triacylglycerol Polymerization Process

The aim of the study was to determine the stability and heat resistance of extra premium olive oil. The study material consisted of six extra virgin olive oils (EVOO) obtained from Spain. Four samples were single-strain olive oils: Arbequina, Picual, Manzanilla, and Cornicabra. Two samples were a coupage of Arbequina and Picual varieties: Armonia (70% Arbequina and 30% Picual) and Sensation (70% Picual and 30% Arbequina). Olive oil samples were heated at 170 °C and 200 °C in a pan (thin layer model). In all samples, changes in indexes of lipid nutritional quality (PUFA/SFA, index of atherogenicity, index of thrombogenicity, and hypocholesterolemic/hypercholesterolemic ratio), changes in tocopherol, total polar compounds content, and triacylglycerol polymers were determined. Heating olive oil in a thin layer led to its degradation and depended on the temperature and the type of olive oil. Increasing the temperature from 170 to 200 °C resulted in significantly higher degradation of olive oil. At 200 °C, deterioration of lipid nutritional indices, total tocopherol degradation, and formation of triacylglycerol polymers were observed. A twofold increase in the polar fraction was also observed compared to samples heated at 170 °C. The most stable olive oils were Cornicabra and Picual.
On testing for a stochastic unit root in financial time series: The case of a bilinear unit root process

This paper considers a particular member of the class of stochastic (or randomized) unit root (STUR) process given by a simple bilinear process with a unit root. Under a certain reparameterization of the bilinear parameter, we use the recently proposed stochastic limit for this process to show the consistency of some commonly used nonparametric tests of the null hypothesis of stationarity against the alternative of a unit root under this form of nonstationarity, other than difference stationarity. Also, as an alternative to the existing pseudo T-ratio test for the null of a fixed unit root against a bilinear unit root, we propose a new testing procedure based on a simple modification of the KPSS test statistic that has the advantage to allow for more general forms of the deterministic component and that seems to have good size and power in finite samples to discriminate between a fixed (or linear) and a bilinear unit root. We derive the asymptotic null and alternative distributions and also we present an application to the series of log-prices of some stock market indexes with distinct time frequencies: IBEX 35, SP500, and Dow Jones Composite Average (daily), and CAC40 (weekly).
Context-Aware Neural Fault Localization

Numerous fault localization techniques identify suspicious statements potentially responsible for program failures by discovering the statistical correlation between test results (i.e., failing or passing) and the executions of the different statements of a program (i.e., covered or not covered). They rarely incorporate a failure context into their suspiciousness evaluation despite the fact that a failure context showing how a failure is produced is useful for analyzing and locating faults. Since a failure context usually contains the transitive relationships among the statements of causing a failure, its relationship complexity becomes one major obstacle for the context incorporation in suspiciousness evaluation of fault localization. To overcome the obstacle, our insight is that leveraging the promising learning ability may be a candidate solution to learn a feasible model for incorporating a failure context into fault localization. Thus, we propose a context-aware neural fault localization approach (CAN). Specifically, CAN represents the failure context by constructing a program dependency graph, which shows how a set of statements interact with each other (i.e., data and control dependencies) to cause a failure. Then, CAN utilizes graph neural networks to analyze and incorporate the context (e.g., the dependencies among the statements) into suspiciousness evaluation. Our empirical results on the 12 large-sized programs show that CAN achieves promising results (e.g., 29.23% faults are ranked within top 5), and it significantly improves the state-of-the-art baselines with a substantial margin.
Simulated dynamics of net primary productivity (NPP) for outdoor livestock feeding coefficients driven by climate change scenarios in México

In this paper the concept of Net Primary Productivity (NPP) is used as a way to estimate the capacity of the ecosystem to produce dry matter which may be available for livestock to meet the forage requirements. The method allows the simulation of the possible impact on NPP and dry matter (DM), under climate change conditions observable for the country in a given time horizon. The concept was also used for current coefficients of rangeland and under current climate change scenarios, thus allowing to observe possible changes on the required surface for a sustainable cattle nutrition. It was found that Mexico has NPP values ranging from 0 to 50 000 kgDM / ha / year. Dry matter is in the range that goes from 0 to 25 000 kgDM / ha / year. The coefficients of rangeland for the current scenario do not change significantly compared to those presented by COTECOCA, the official body determining such values. The states that present a greater impact due to current conditions under climate change scenarios were Baja California, Baja California Sur, Coahuila, Colima, Jalisco, Nuevo Leon, Puebla, Queretaro, San Luis Potosi, Sonora, Tamaulipas, Veracruz and Yucatan; this group will also have changes in its coefficients of rangeland in the future. Considering all of the above we discuss the implications that can be observed and we analyze the alternatives proposed by the federal government for the country’s livestock sector.
Wafer-scale controlled exfoliation of metal organic vapor phase epitaxy grown InGaN/GaN multi quantum well structures using low-tack two-dimensional layered h-BN

Recent advances in epitaxial growth have led to the growth of III-nitride devices on 2D layered h-BN. This advance has the potential for wafer-scale transfer to arbitrary substrates, which could improve the thermal management and would allow III-N devices to be used more flexibly in a broader range of applications. We report wafer scale exfoliation of a metal organic vapor phase epitaxy grown InGaN/GaN Multi Quantum Well (MQW) structure from a 5 nm thick h-BN layer that was grown on a 2-inch sapphire substrate. The weak van der Waals bonds between h-BN atomic layers break easily, allowing the MQW structure to be mechanically lifted off from the sapphire substrate using a commercial adhesive tape. This results in the surface roughness of only 1.14 nm on the separated surface. Structural characterizations performed before and after the lift-off confirm the conservation of structural properties after lift-off. Cathodoluminescence at 454 nm was present before lift-off and 458 nm was present after. Electrolumi.
Obstacle Problems and Maximal Operators

Abstract Fix two differential operators L1${L_{1}}$ and L2${L_{2}}$, and define a sequence of functions inductively by considering u1${u_{1}}$ as the solution to the Dirichlet problem for an operator L1${L_{1}}$ and then un${u_{n}}$ as the solution to the obstacle problem for an operator Li${L_{i}}$ (i=1,2${i=1,2}$ alternating them) with obstacle given by the previous term un-1${u_{n-1}}$ in a domain Ω and a fixed boundary datum g on ∂⁡Ω${\partial\Omega}$. We show that in this way we obtain an increasing sequence that converges uniformly to a viscosity solution to the minimal operator associated with L1${L_{1}}$ and L2${L_{2}}$, that is, the limit u verifies min⁡{L1⁢u,L2⁢u}=0${\min\{L_{1}u,L_{2}u\}=0}$ in Ω with u=g${u=g}$ on ∂⁡Ω${\partial\Omega}$.
Supporting Depressed Mothers of Young Children with Intellectual Disability: Feasibility of an Integrated Parenting Intervention in a Low-Income Setting

As a lifelong condition, intellectual disability (ID) remains a public health priority. Parents caring for children with ID experience serious challenges to their wellbeing, including depression, anxiety, stress and health-related quality of life. Integrated parenting interventions, which have been well evidenced for depressed mothers, may also effectively support depressed parents with a child with ID in low-resource settings such as Pakistan, and in turn optimise child outcomes. We conducted a mixed-method rater-blind feasibility randomised controlled trial, which assessed the feasibility and acceptability of the Learning Through Play in My Own Way Plus (LTP-IMOW Plus) intervention. Mothers who screened positive for depression (n = 26) with a young child (age 3–6 years) with ID were recruited from two low-resource community settings. Participants in the intervention arm (n = 13) received 12 group sessions of LTP-IMOW Plus and others (n = 13) received routine care. The intervention was feasible and acceptable with 100% retention and 100% session attendance. The intervention improved depression, anxiety, parenting stress and child socialisation score outcomes relative to the routine care arm. The framework utilised to analyse the qualitative interviews with seven participants at pre-intervention identified a range of struggles experienced by the mothers, and at post-intervention, found improved knowledge of child development and practices, improved mother–child relationships, recommendations for the intervention and perceived practical barriers and facilitators. The findings highlight the prospects for a clinical and cost-effective trial of an integrated parenting intervention to manage long-term parental mental health needs and improve child outcomes.
Prediction of Heart Diseases Using Data Mining Algorithms

Data mining has been successfully used in numerous businesses and sectors as a result of its success in great visible areas like e-commerce and marketing. Healthcare is one of the recently identified industries. The healthcare sector continues to be "information-rich. " Healthcare systems have access to a multitude of datasets and can use them to find hidden links and trends in data. There aren't enough efficient analysis tools, though. The dataset is analyzed using various machine learning algorithms, i