Miracle-dz/UniFAR-FTUs huggingface dataset:
Regularization of voxel art

Voxel based modeling is an attractive way to represent complex multi-material objects. Multi-labeled voxel models are also ubiquitous in material sciences, medical imaging or numerical simulations. We present here a variational method to regularize interfaces in multi-labeled digital images. It builds piecewise smooth quadran-gulated surfaces efficiently, with theoretical guarantees of stability. Complex topological events when several materials are considered are handled naturally. We show its usefulness for digital surface approximation, for voxel art regularization by transferring colorimetric information, and for upscaling voxel models to speed up coarse-to-fine modeling.
Enhancement of cellular glucose uptake by reactive species: a promising approach for diabetes therapy

It is generally known that antidiabetic activity is associated with an increased level of glucose uptake in adipocytes and skeletal muscle cells. However, the role of exogenous reactive oxygen and nitrogen species (RONS) in muscle development and more importantly in glucose uptake is largely unknown. We investigate the effect of RONS generated by cold atmospheric plasma (CAP) in glucose uptake. We show that the glucose uptake is significantly enhanced in differentiated L6 skeletal muscle cells after CAP treatment. We also observe a significant increase of the intracellular Ca++ and ROS level, without causing toxicity. One of the possible reasons for an elevated level of glucose uptake as well as intracellular ROS and Ca++ ions is probably the increased oxidative stress leading to glucose transport.
A contrast restoration method for effective single image rain removal algorithm

This paper introduces an efficient rain removal algorithm by including the contrast restoration method. Since the rain streaks in rainy image exist in the high frequency parts, many conventional rain removal algorithms first decompose the input rainy image into the base (low-frequency) layer and the detail (high-frequency) layer and then perform rain removal on the detail layer rather than on the entire image. However, the conventional algorithms have a problem that they cannot remove the contrast variation. For this reason, our method also considers the contrast enhancement on the base layer. We further estimate and eliminate the contrast variation on the base layer by low-pass filtering the approximated rain streaks. Although the low frequency components contain not only the contrast variation but also the basic information of the image, our method can distinguish the contrast variation from the base layer and remove them automatically. Visual and quantitative experiments demonstrate our approach has the high efficiency and outperforms previous rain removal algorithm.
1 Systems Pharmacology for Biomarker Discovery

Biomarkers are cornerstones of healthcare. Their applications include disease diagnosis and prediction, indication of disease progression, and monitoring of treatment response. Despite significant efforts that have identified thousands of potential biomarkers, their translation into clinical practice remains poor: less than two approvals per year across all diseases. In part, this inefficiency arises from both experimental and statistical limitations on the biomarker discovery pipeline. Widely used mass-spectrometry approaches suffer from sample throughput whereas targeted approaches such as single and multiplex immunoassays suffer from the number of interrogated proteins and antibody quality. On top of the technological limitations, the current single-biomarker-to-single-disease approach does not capture the multifactorial nature of complex diseases. A disease can be originated from diverse pathological mechanisms, while a pathological mechanism can lead to diverse diseases. Hence, mechanism based biomarker discovery aims to identify signatures that capture the diversity of the disease’s origin and deliver more precise diagnostic and predictive information.
Defects in lamellar diblock copolymers: Chevron- and Ω-shaped tilt boundaries

The lamellar phase in diblock copolymer systems appears as a result of a competition between molecular and entropic forces, which selects a preferred periodicity of the lamellae. Grain boundaries are formed when two grains of different orientations meet. We investigate the case where the lamellae meet symmetrically with respect to the interface. The form of the interface strongly depends on the angle, \ensuremath{\theta}, between the normals of the grains. When this angle is small, the lamellae transform smoothly from one orientation to the other, creating the chevron morphology. As \ensuremath{\theta} increases, a gradual transition is observed to an omega morphology characterized by a protrusion of the lamellae along the interface between the two phases. We present a theoretical approach to find these tilt boundaries in two-dimensional systems, based on a Ginzburg-Landau expansion of the free energy, which describes the appearance of lamellae. Close to the tips at which lamellae from different grains meet, these lamellae are distorted. To find this distortion for small angles, we use a phase variation ansatz in which one assumes that the wave vector of the bulk lamellar phase depends on the distance from the interface. Minimization of the free energy gives an expression for the order parameter $\ensuremath{\varphi}(x,y). $ The results describe the chevron morphology very well. For larger angles, a different approach is used. We linearize \ensuremath{\varphi} around its bulk value ${\ensuremath{\varphi}}_{L}$ and expand the free energy to second order in their difference. Minimization of the free energy results in a linear fourth-order differential equation for the distortion field, with proper constraints, similar to the Mathieu equation. The calculated monomer profile and line tension agree qualitatively with transmission electron microscope experiments, and with full numerical solution of the same problem.
Barriers and Enablers to Accessing Mental Health Services for People With Intellectual Disability: A Scoping Review

ABSTRACT Background: It is well established that people with an intellectual disability have high rates of mental health problems, yet rates of uptake of services do not match need. Aim: To identify the current literature pertaining to the barriers and facilitators to access to mental health services for people with an intellectual disability. Method: A systematic search identified English-language articles that addressed barriers or enablers to access, mental health services, and intellectual disability from 2005 to 2016. Results were synthesized according to Gulliford et al.’s four dimensions of access: availability, utilization, relevance and effectiveness, and equity. Results: Barriers and enablers were identified across all the dimensions. Organizational barriers, lack of services, and poor-quality services related to deficits in knowledge were among the barriers discussed in the literature. Facilitators included emphasis on interagency collaboration, and training and education. Substantial gaps were also identified, particularly in relation to the lived experience of these barriers. Conclusions: Further research and evaluation across all aspects of access to mental health care for people with an intellectual disability is needed.
Artificial Intelligence for Estimation of Future Claim Frequency in Non-Life Insurance

The essential feature of an insurance practice is to set the premium at the beginning of the insurance contract. To determine the correct premium for next year in an insurance company, precise and reliable estimate of the number of occurrence of claims and the total claim amounts is extremely important. Different methods are available in the literature for predicting the claim frequency of a policy for forthcoming years such as Generalized linear models (GLMs), Poisson regression models, Credibility models, Bayesian Models etc. But due to the heterogeneous nature of policies these methods does not produce exact and reliable prediction of future claim frequencies. Besides these conventional statistical methods depends largely on some limiting assumptions such as linearity, normality, independence among predictor variables and a pre-existing functional form relating the criterion variable and predictive variables etc. Recent studies in Artificial Intelligence show that Artificial Neural Networks (ANN) is powerful tools for prediction tasks due to their nonlinear nonparametric adaptive learning properties. In this paper, we developed the procedure for predicting the future claim frequency of insurance portfolio in general insurance using ANN with use of Bayesian credibility inputs with suitable illustration.
Uncertainty in Stated Choice Experiments : Will Eye-Tracking provide useful measures ?

In this study, we conduct a Stated Choice Experiment (SCE) using eye-tracking technology to investigate if eye movements during the completion of choice sets reveal information about response uncertainty. We hypothesise that the number of times a respondent's eyes switch focus between the alternatives in a choice set reflects the respondent's choice uncertainty. Based on one of the largest samples of eye-tracking data in a SCE survey to date, we find evidence in favor of our hypothesis. We also link eye-tracking observations to model-based choice uncertainty – as expressed in the unobserved variance – through parameterization of the scale function in a random parameters logit model. We find that choices characterized by more frequent switching do indeed exhibit a higher degree of unobserved variance. While improving model fit, the Willingness to Pay (WTP) estimates and confidence intervals for the attributes are not significantly different in models where variations in scale due to uncertainty is adjusted for compared to a benchmark model. Overall, findings suggest that eye-tracking can provide an observable and exogenous variable for respondent uncertainty, potentially improving the handling of respondent uncertainty and thus the performance of the choice models in SCE studies.
Encapsulation of date palm pit extract via particulation of starch nanocrystals in a microemulsion

The participation of bioactive food compounds including phenolics in various metabolisms and their counteractions with disease-developing mechanisms (Rostagno et al., 2010) has caused an increasing attraction to functional foods. Date palm pit extract is a rich source of polyphenols (Ardekani & Kanavi, 2010) that show metal scavengering, antimutagenes, and antimicrobial activities (Bagheri et al., 2013). The utilisation of encapsulated polyphenols in formulation of health-promoting functional foods instead of free counterparts can overcome the drawbacks of their instability, alleviate unpleasant tastes or flavours, as well as improve the bioavailability and half-life of these compounds in blood stream (Fang & Bhandari, 2010). Starch being a natural, inexpensive, biodegradable, and biocompatible polymer has attracted much attention in encapsulation processes. Starch nanocrystals are amylopectin-originated nanoscalar blocklets obtained through acidic hydrolysis of starch granules amorphous regions at ambient temperatures (Angellier et al., 2004). The acid-resistant crystals are intrinsically rigid, highly crystalline, and low permeable (Wang et al., 2003). These characteristics nominate the nanocrystals as promising candidates for preparation of acid-resistant and mechanically strong carriers for nutraceuticals. Microemulsions are thermodynamically stable and optically transparent dispersion of two immiscible liquids in which small droplets of one or both liquids are stabilised by surfactant molecules (Destr ee & Nagy, 2006). These systems offer the possibility to synthesise nanoscalar supramolecular assemblies from monomers or tiny starting bricks (Lopez-Quintela, 2003). Zhang & Zhong (2009, 2010) implemented a water-in-oil (W/O) microemulsion system to generate whey protein isolate nanoparticles gelled by heat-treatment. There is no report in the literature on preparation of particles from starch nanocrystals. The objective of the present study was therefore to prepare date palm pit extract-loaded spheres from starch nanocrystals using a microemulsion reactor.
Highly Integrable Microfluidic Impulsion System for Precise Displacement of Liquids on Lab on PCBs

In this paper, an impulsion system for laboratory on printed circuit board (LOP) is described. The proposed system is intended to place the working liquids in a designed location of the LOP. The system is composed of a pressurization system, a microvalve, and a damping chamber. All these parts have been integrated in a microfluidic system to test the behavior of the whole system. The pressurization system allows the storage of mechanical energy to impulse liquids samples. The microvalve releases the pressure of the commented pressurization system toward the damping chamber. The blast wave effect of the microvalve opening has to be dampened. In this regard, the function of the damping chamber is to reduce the effects of the microvalve activation, resulting in good behavior. The materials used for fabricating the device are polymethylmethacrylate and printed circuit board. These materials and the fabrication process can be considered as mass production. The fabricated devices impulse 32.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{L}$ </tex-math></inline-formula> during about 24s, and 21.3 <inline-formula> <tex-math notation="LaTeX">$\mu \text{L}$ </tex-math></inline-formula> during 10 s, for the proposed microfluidic configurations. The errors for those impulsions are 5.3% and 6.5%, respectively. Finally, the experimental results and simulations show a good behavior of the system regarding liquid placement and time response. [2017-0293]
Linearly Polarized Shaped Power Pattern Synthesis With Sidelobe and Cross-Polarization Control by Using Semidefinite Relaxation

In this communication, the problem of synthesizing a linearly polarized shaped power pattern with accurate control on both sidelobe and cross-polarization (XP) levels is considered. For a user-defined desired polarization direction, the definitions of realizable co-polarization (COP) and XP directions for an arbitrary propagation direction in the shaped pattern are presented. With the help of such definitions, the considered problem is formulated as finding appropriate excitations so as to produce a shaped power pattern in which the realizable COP component meets prescribed lower and upper bounds, the realizable XP component and the total power pattern are less than their upper bounds in the regions of interest. The semidefinite relaxation method in the literature is then extended to solve this vectorial pattern synthesis problem. The proposed method can include the mutual coupling and platform effects by using vectorial active element patterns of an antenna array. A set of synthesis examples with different array geometries and radiation requirements are conducted to validate the effectiveness and advantages of the proposed method.
In-building coverage for UMTS using radio over fibre technology

This paper presents a bi-directional optical link for a wireless indoor coverage system operating in the UMTS band, using radio over fibre technology. It shows the feasibility of a dual loop architecture in which only one optical source for both directions and an electroabsorption modulator (EAM) are applied. The EAM is used as a simultaneous receiver as well as transmitter at the remote end of a bi-directional analogue optical fibre link in the UMTS band. On the basis of measurements the distortion performance of the basic dual loop architecture is evaluated A promising alternative problem solution is demonstrated which may also be cost efficient.
A machine learning-based approach for virtual network function modeling

Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. The application of ML to networking brings several use-cases as well as challenges. The objective of this paper is to explore the feasibility of applying different models and ML techniques to model complex networks elements, such as Virtual Network Functions (VNFs). In particular, we focus on the characterization of the CPU consumption of the VNF as a function of the characteristics of the input traffic. The traffic is represented by a set of features describing characteristics from the transport layer to the application layer in small time batches. The CPU consumption is observed from the hypervisor and corresponds to the average CPU consumption when the traffic batch is processed. We experimentally demonstrate that we can learn the behavior of different VNF in order to model its CPU consumption. We conclude that the behavior of different VNF can be modeled using ML techniques.
Auto MPG Prediction using Tree Architectures of Fuzzy Neural Networks

For decades, computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are applied to Auto MPG prediction problem. The dataset concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, Auto MPG dataset obtained from the UCI Machine Learning Repository Database is considered.
Shoes recommendation system based on clustering of 3D shoes data

There are some problems in internet shopping such as products return due to size mismatch. This problem is serious in internet shopping of shoes because wearing feeling are particularly important for shoes. So, we aim to make a shoes recommendation system using three-dimensional data of foot and shoe in order to decrease problem of size mismatch. This paper proposes a visualization method for fitting condition of foot and shoe and feature extraction method of shoe three-dimensional data. We also create a linear regression model that predicts the features of the reference shoes. Standard size table of shoe is made by applying the regression model of reference shoes to other shoes.
Evolutionary Optimization of Graphene-Metal Metasurfaces for Tunable Broadband Terahertz Absorption

Tunable terahertz absorbers based on graphene frequency selective surfaces are designed using well-established evolutionary optimization techniques. Randomly initialized hill climbing (RHC) algorithm is applied to find the design with the broadest bandwidth. The effects of three factors including the substrate loss, structuring the substrate and using double layer patches are examined. The optimum structure with broadest operation bandwidth consists of a perforated lossy substrate with a single layer patch and offers 1.95 THz absorption bandwidth around 2 THz central frequency leading to 100% fractional bandwidth.
Two-port measurement system with coupled-line sensor for detection of material permittivity change

A novel method of dielectric sample detection utilizing two-port microwave system with coupled-line sensor has been presented. The proposed measurement setup is composed of two identical baluns and differentially-excited coupled-line sensor in-between which can be covered with an unknown material. The setup has been theoretically analyzed and formulas allowing for calculation of sensor-sample effective permittivity have been derived along with simplified calibration procedure. The method allows for detection of significantly small dielectric sample having minimal size comparable with separation between sensor’s coupled strips. The presented approach has been theoretically and experimentally investigated. The obtained results prove the usefulness of the proposed technique.
A Guide to Magnetic Tweezers and Their Applications

Magnetic force spectroscopy is a rapidly developing single molecule technique that found numerous applications at the interface of physics and biology. Since the invention of the first magnetic tweezers, a number of modifications were incorporated into the approach that helped relieve the limitations of the original design and amplified its strengths. Inventive molecular biology solutions further advanced the technique by expanding its possible applications. In its present form, the method can be applied to single molecules and live cells without resorting to intense sample irradiation, can be easily multiplexed, accommodates multiple DNAs, displays impressive resolution, and allows a remarkable ease in stretching and twisting macromolecules. In this review, we describe the architecture of magnetic tweezers, key requirements to the experimental design and analysis of data, and outline several applications of the method that illustrate its versatility.
Statistical Inference of the Reliability for Generalized Exponential Distribution Under Progressive Type-II Censoring Schemes

This paper discusses the estimation of <inline-formula><tex-math notation="LaTeX">$R= P(Y<X)$</tex-math> </inline-formula> when <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Y$</tex-math></inline-formula> are independent generalized exponential random variables with different scale parameters and the same shape parameter based on progressive type-II censoring samples. The maximum likelihood estimator (MLE) of the parameter <inline-formula><tex-math notation="LaTeX">$R$</tex-math> </inline-formula> is obtained. The approximate MLE (AMLE) of <inline-formula><tex-math notation="LaTeX">$R$</tex-math> </inline-formula> by the Taylor series expansion is proposed. The asymptotic distribution of <inline-formula> <tex-math notation="LaTeX">$R$</tex-math></inline-formula> is derived to construct the asymptotic MLE and AMLE confidence intervals. Also, the bootstrap-p and bootstrap-t confidence intervals are constructed. In addition, using the Metropolis-Hasting procedure, we derive the Bayes estimate and the corresponding credible interval. Finally, a Monte Carlo simulation and an analysis of two real datasets are presented to evaluate the various proposed methods.
Effect of Multilayer Windings on Rotor Losses of Interior Permanent Magnet Generator With Fractional-Slot Concentrated-Windings

Recently, the interior permanent magnet (IPM) machines have become a promising candidate for more and more applications, for their nice performances-wide speed range, considerable power density, and several other advantages. However, there are still some obstacles to overcome to improve the performances of the IPM machines with fractional-slot concentrated-winding (FSCW). Among which, the rotor losses, due to the large amounts of the magnetomotive force harmonics produced by FSCW, is a big threat to safe operation. In this paper, the effect of multilayer windings on rotor losses, especially the eddy-current losses in the permanent magnets of IPM machines with FSCW, is mainly investigated using the 2-D finite element analysis. Side effects on torque performances is also studied and compared with the double-layer windings.