Current Issue : October-December Volume : 2023 Issue Number : 4 Articles : 5 Articles
Web API is an efficient way for Service-based Software (SBS) development, and mashup is a key technology which merges several web services to deal with the increasing complexity of software requirements and expedite the service-based system development. The efficient service recommendation method is vital for the software development. However, the existing methods often suffer from data sparsity or cold start issues, which should lead to bad effects. Currently, this paper starts with SBS development, and proposes a service recommendation method based on knowledge graph embedding and collaborative filtering (CF) technology. In our model, we first construct a refined knowledge graph using SBS-service co-invocation record and SBS and service related information to mine the potential semantics relationship between SBS and service. Then, we learn the SBS and service entities in the knowledge graph. These heterogeneous entities (SBS and service, etc.) are embedded into the low-dimensional space through the representation learning algorithms ofWord2vec and TransR, and the distances between SBS and service vectors are calculated. The input of recommendation model is SBS requirement (target SBS), the similarities functional SBS set is extracted from knowledge graph, which can relieve the cold start problem. Meanwhile, the recommendation model uses CF to recommend service to target SBS. Finally, this paper verifies the effectiveness of method on the real-word dataset. Compared with the several state-of-the-art methods, our method has the best service hit rate and ranking quality....
The reliability and lifetime of systems-on-chip (SoCs) are being seriously threatened by thermal issues. In modern SoCs, dynamic thermal management (DTM) uses the thermal data captured by thermal sensors to constantly track the hot spots and thermal peak locations in real time. Estimating peak temperatures and the location of these peaks can play a crucial role for DTM systems, as temperature underestimation can cause SoCs to fail and have shortened lifetime. In this paper, a novel sensor allocation algorithm (called thermal gradient tracker, TGT), based on the recursive elimination of regions that likely do not contain any thermal peaks, is proposed for determining regions that potentially contain thermal peaks. Then, based on an empirical source temperature detection technique called GDS (gradient direction sensor), a hybrid algorithm for detecting the position and temperature of thermal peaks is also proposed to increase the accuracy of temperature sensing while trying to keep the number of thermal sensors to a minimum. The essential parameters, H and R, of the GDS technique are determined using an automated search algorithm based on simulated annealing. The proposed algorithm has been applied in a system-on-chip (SoC) in which four heat sources are present, and for temperatures ranging between 45 °C and 115 °C, in a chip area equal to 25 mm2. The simulation results show that our proposed sensor allocation scheme can detect on-chip peaks with a maximum error of 1.48 °C and an average maximum error of 0.49 °C by using 15 thermal sensors....
Simultaneous and non-destructive quantitative detection of intracellular metal ions holds great promise for improving the accuracy of diagnosis and biological research. Herein, novel multicolor DNAzymes-embedded framework nucleic acids (FNAzymes) were presented, which can easily enter cells and achieve simultaneous and quantitative detection of intracellular physiologically related Cu2+ and Zn2+. Two types of DNAzymes, specific to Cu2+ and Zn2+, were encoded in the framework nucleic acids (FNAs) via self-assembly. With the formation of a well-ordered FNAzyme nanostructure, the fluorophore and the quencher were close to each other; therefore, the fluorescence was quenched. In the presence of Cu2+ and Zn2+, the integrated FNAzymes would be specifically cleaved, resulting in the release of fluorophores in cells. Consequently, the fluorescence in living cells could be observed by a confocal microscope and semi-quantitatively analyzed by flow cytometry with low-nanomolar sensitivity for both metal ions. The FNAzymes have high uniformity and structural accuracy, which are beneficial for intracellular detection with excellent reproducibility. This proposed method offers new opportunities for non-destructive, semi-quantitative, multi-target detection in living cells....
In order to explore complex structures and relationships hidden in data, plenty of graphbased dimensionality reduction methods have been widely investigated and extended to the multiview learning field. For multi-view dimensionality reduction, the key point is extracting the complementary and compatible multi-view information to analyze the complex underlying structure of the samples, which is still a challenging task. We propose a novel multi-view dimensionality reduction algorithm that integrates underlying structure learning and dimensionality reduction for each view into one framework. Because the prespecified graph derived from original noisy high-dimensional data is usually low-quality, the subspace constructed based on such a graph is also low-quality. To obtain the optimal graph for dimensionality reduction, we propose a framework that learns the affinity based on the low-dimensional representation of all views and performs the dimensionality reduction based on it jointly. Although original data is noisy, the local structure information of them is also valuable. Therefore, in the graph learning process, we also introduce the information of predefined graphs based on each view feature into the optimal graph. Moreover, assigning the weight to each view based on its importance is essential in multi-view learning, the proposed GoMPL automatically allocates an appropriate weight to each view in the graph learning process. The obtained optimal graph is then adopted to learn the projection matrix for each individual view by graph embedding. We provide an effective alternate update method for learning the optimal graph and optimal subspace jointly for each view. We conduct many experiments on various benchmark datasets to evaluate the effectiveness of the proposed method....
The effects of reduced gravity on the periodic behavior of convective heat transfer characteristics of fluid flow along the magnetized heated cone embedded in porous medium is studied in the current contribution. The mathematical form of the nonlinear partial differential equations subject to the boundary conditions for the proposed unsteady model is presented. By employing appropriate dimensionless quantities, the mathematical equations are transformed into dimensionless form to get the numerical solutions of the proposed model. The dimensionless form is further condensed to a form that is more straightforward for smooth numerical computations. Later, large simulations are run using the implicit finite difference method for appropriate range of parameters values included in the flow model. The effect of reduced gravity parameter Rg, the Richardson parameter or mixed convection parameter λ, the Prandtl number Pr, and the porosity parameter Ω, on chief physical quantities, that is, velocity profile, temperature distribution, magnetic intensity, transient skin friction, transient rate of heat transfer, and transient current density are simulated and highlighted graphically. Additionally, via careful examination and intentional discussion of physical reasoning, the physical impacts of various factors on the material qualities are examined. Applications that motivate the present work is the reduced gravity effects due to which the other nongravity forces such as thermal volume expansion, density difference, and magnetic field can induce the fluid motion....
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