Algorithms for processing the group K nearest-neighbor query on distributed frameworks

Distributed and Parallel Databases - Given two datasets of points (called Query and Training), the Group (K) Nearest-Neighbor (GKNN) query retrieves (K) points of the Training with the smallest sum...     

数字标牌时代,智能生活的全面升级

正数字标牌的发展可以说是一场技术革命,它在不同的环境下有着不同的应用潜能。如今,凭借智能、互联、精准和可视性强等突出优势,数字标牌正已成为仅次于电视的第二大媒体形式,同时也是零售物联网的重要媒介,帮助实现真正的O2O业务模式。数字标牌,可能对很多人来说还是一个十分陌生的词汇,但它其实已在"不经意"间走入了你的生活:在商场里,装有人脸识别系统的数字标牌成为商家进行精准营销的"虚拟店铺";在街头,具有移动支付功能的自动售货机可提供多种便民服务;在学校内,安装了教学系统的电子白板可帮助师生实时     

Ambient and High-Temperature Stable Fracture Tests in Ceramics: Applications to Yttria-Partially-Stabilized Zirconia

A new technique that provides stable fracture tests in brittle ceramic materials at room and at high temperature is presented. This technique uses the crack mouth opening displacement signal from a laser extensometer to feed the servo control of a hydraulic testing machine. In this way it is possible to obtain—in addition to the fracture toughness—the fracture energy and the R -curve, in a single test.     

A review of the unique features of HDL apoproteins

The human plasma high density lipoproteins (HDL) are a heterogeneous ensemble of five proteins associated with both neutral and polar lipids. The sequences of all five proteins are known. ApoA-I and apoA-II are the major protein components; apoC-I, apoC-II and apoC-III are the minor protein components. All these apoproteins spontaneously recombine with phospholipids to give stable lipid-protein complexes and freely exchange between the two major HDL subclasses, HDL₂ and HDL₃. In addition, ApoC-I, apoC-II, and apoC-III exchange between HDL and very low density lipoproteins. Furthermore, certain HDL apoproteins are activators for plasma enzymes that are important in lipid metabolism. ApoA-I and apoC-I activate lecithin/cholesterol acyltransferase; apoC-II is an activator of lipoprotein lipase. The regions of apoC-I and apoC-II that are involved in the activation of these enzymes have been localized with synthetic peptides. Studies of synthetic and native fragments of apoA-II, apoC-I, apoC-II, and apoC-III as well as model lipid-binding peptides have identified specific regions with structural features common to lipid-binding proteins. These special properties, which include helical potential, sequences with a critical amphipathic length, and high hydrophobicity of the nonpolar side of the amphipathic helix, are the determinants of HDL structure and metabolism.     

Inverse analysis procedure to determine flow stress and friction data for finite element modeling of machining

This paper describes an inverse procedure to determine the constitutive constants and the friction conditions in the machining processes using Finite Elements (FE) simulations. In general, the FE modeling of machining processes is an effective tool to analyze the materials machinability under different cutting conditions. However, the use of reliable rheological and friction models represents the basis of a correct numerical investigation. The presented inverse procedure was based on the numerical results obtained using a commercial FE code and was developed considering a specific optimization problem, in which the objective functions that have to be minimized is the experimental/numerical error. This problem was performed by a routine developed in a commercial optimization software. In order to verify the goodness and the robustness of the methodology, it was applied on a Super Duplex Stainless Steel (SDSS) and on an Austenitic Stainless Steel (AUSS) orthogonal machining processes. This work, then, was focused on the identification of the Johnson-Cook (JC) coefficients (A,B,C, n and m) and on the calibration of a Coulomb friction model, in the specific cases of the SAF2507 SDSS and of an AISI 316 Based AUSS Alloy (AISI 316 ASBA). The identification phases were performed considering forces and temperatures experimental data, collected in two specific experimental tasks in which different orthogonal cutting tests were carried out under different cutting parameters conditions.     

An internet of things–based personal device for diabetes therapy management in ambient assisted living (AAL)

Diabetes therapy management in AAL environments, such as old people and diabetes patients homes, is a very difficult task since many factors affect a patient’s blood sugar levels. Factors such as illness, treatments, physical and psychological stress, physical activity, drugs, intravenous fluids and change in the meal plan cause unpredictable and potentially dangerous fluctuations in blood sugar levels. Right now, operations related to dosage are based on insulin infusion protocol boards, which are provided by physicians to the patients. These boards are not considering very influential factors such as glycemic index from the diet, consequently patients need to estimate the dosage leading to dose error, which culminates in hyperglycemia and hypoglycemia episode. Therefore, right insulin infusion calculation needs to be supported by the next generation of personal-care devices. For this reason, a personal device has been developed to assist and consider more factors in the insulin therapy dosage calculation. The proposed solution is based on Internet of things in order to, on the one hand, support a patient’s profile management architecture based on personal RFID cards and, on the other hand, provide global connectivity between the developed patient’s personal device based on 6LoWPAN, nurses/physicians desktop application to manage personal health cards, glycemic index information system, and patient’s web portal. This solution has been evaluated by a multidisciplinary group formed by patients, physicians, and nurses.     

A proposal for student modeling based on ontologies and diagnosis rules

The advances in the educational field and the high complexity of student modeling have provoked it to be one of the aspects more investigated in Intelligent Tutoring Systems (ITSs). The Student Models (SMs) should not only represent the student’s knowledge, but rather they should reflect, as faithfully as possible, the student’s reasoning process. To facilitate this goal, in this article a new approach to student modeling is proposed that benefits from the advantages of Ontological Engineering, advancing in the pursue of a more granular and complete knowledge representation. It’s focused, mainly, on the SM cognitive diagnosis process, and we present a method providing a rich diagnosis about the student’s knowledge state – especially, about the state of learning objectives reached or not. The main goal is to achieve SMs with a good adaptability to the student’s features and a high flexibility for its integration in varied ITSs.     

Invariant-driven specifications in Maude

This work presents a general mechanism for executing specifications that comply with given invariants, which may be expressed in different formalisms and logics. We exploit Maude’s reflective capabilities and its properties as a general semantic framework to provide a generic strategy that allows us to execute Maude specifications taking into account user-defined invariants. The strategy is parameterized by the invariants and by the logic in which such invariants are expressed. We experiment with different logics, providing examples for propositional logic, (finite future time) linear temporal logic and metric temporal logic.     

Learning a color distance metric for region-based image segmentation

In this paper we describe an experiment where we studied empirically the application of a learned distance metric to be used as discrimination function for an established color image segmentation algorithm. For this purpose we chose the Mumford–Shah energy functional and the Mahalanobis distance metric. The objective was to test our approach in an objective and quantifiable way on this specific algorithm employing this particular distance model, without making generalization claims. The empirical validation of the results was performed in two experiments: one applying the resulting segmentation method on a subset of the Berkeley Image Database, an exemplar image set possessing ground-truths and validating the results against the ground-truths using two well-known inter-cluster validation methods, namely, the Rand and BGM indexes, and another experiment using images of the same context divided into training and testing set, where the distance metric is learned from the training set and then applied to segment all the images. The obtained results suggest that the use of the specified learned distance metric provides better and more robust segmentations, even if no other modification of the segmentation algorithm is performed.