• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.66-82
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• 2021

Recommender Systems have gained immense popularity due to their capability of dealing with a massive amount of information in various domains. They are considered information filtering systems that make predictions or recommendations to users based on their interests and preferences. The more recent technology, Linked Open Data (LOD), has been introduced, and a vast amount of Resource Description Framework data have been published in freely accessible datasets. These datasets are connected to form the so-called LOD cloud. The need for semantic data representation has been identified as one of the next challenges in Recommender Systems. In a LOD-enabled recommendation framework where domain awareness plays a key role, the semantic information provided in the LOD can be exploited. However, dealing with a big chunk of the data from the LOD cloud and its integration with any domain datasets remains a challenge due to various issues, such as resource constraints and broken links. This paper presents the challenges of interconnecting and extracting the DBpedia data with the MovieLens 1 Million dataset. This study demonstrates how LOD can be a vital yet rich source of content knowledge that helps recommender systems address the issues of data sparsity and insufficient content analysis. Based on the challenges, we proposed a few alternatives and solutions to some of the challenges.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.1
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• pp.117-123
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• 2015

The need for drop simulations for board-leveled flip chips in micro-system packaging has been increasing. There have been many studies on flip chips with various solder ball compositions. However, studies on flip chips with Sn-1.0Ag-0.5Cu and Sn-3.0Ag-0.5Cu have rarely been attempted because of the unknown material properties. According to recent studies, drop simulations with these solder ball compositions have proven feasible. In this study, predictions of the impact lifetime by drop simulations are performed considering Cu and Cu/Ni UBMs using LS-DYNA to alter the design parameters of the flip chips, such as thickness of the flip chip and size of the solder ball. It was found that a smaller chip thickness, larger solder ball diameter, and using the Cu/Ni UBM can improve the drop lifetime of solder balls.

• Computers and Concrete
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• v.22 no.1
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• pp.63-70
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• 2018

The steel fiber reinforced concrete (SFRC) shows better performance under dynamic loading than conventional concrete in virtue of its good ductility. In this paper, a series of quasi-static experiments were carried out on the SFRC with volume fractions from 0 to 6%. The compressive strength increases by 38% while the tension strength increases by 106% when the fraction is 6.0%. The contact explosion tests were also performed on the ${\Phi}40{\times}6cm$ circular SFRC slabs of different volume fractions with 20 g RDX charges placed on their surfaces. The volume of spalling pit decreases rapidly with the increase of steel fiber fraction with a decline of 80% when the fraction is 6%, which is same as the crack density. Based on the experimental results, the fitting formulae are given, which can be used to predict individually the change tendencies of the blast crater volume, the spalling pit volume and the crack density in slabs with the increase of the steel fiber fraction. The new formulae of the thickness of damage region are established, whose predictions agree well with our test results and others. This is of great practical significance for experimental investigations and engineering applications.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.5
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• pp.535-540
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• 2015

We propose an enhanced Green's function approach to predict thermal stresses by considering temperature-dependent material properties. We introduce three correction factors for the maximum stress, the time taken to reach maximum stress, and the time required to attain steady state based on the Green's function results for each temperature. The proposed approach considers temperature-dependent material properties using correction factors, which are defined as polynomial expressions with respect to temperatures based on Green's functions, that we obtain from finite-element (FE) analyses at each temperature. We verify the proposed approach by performing detailed FE analyses on thermal transients. The Green's functions predicted by the proposed approach are in good agreement with those obtained from FE analyses for all temperatures. Moreover, the thermal stresses predicted using the proposed approach are also in good agreement with the FE results, and the proposed approach provides better predictions than the conventional Green's function approach using constant, time-independent material properties.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.

• Journal of the Korean Chemical Society
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• v.16 no.6
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• pp.329-333
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• 1972

Chloramphenicol molecule has an asymmetric carbon, so it has conformational isomer, namely, threo and erythro form. These two forms have great difference in biological activity, that is, only threo (-) form has biological activity. Semiempirical quantum mechanical treatments are applied to these molecules to explain the difference, using the extended Huckel theory. The theoretical predictions for the preferred conformation are in good agreement with the experimental results.

• Nuclear Engineering and Technology
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• v.51 no.5
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• pp.1261-1271
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• 2019

Droplet size and distribution are important parameters determining venturi scrubber performance. In this paper, we proposed physical models for a maximum stable droplet size prediction and upper limit log-normal (ULLN) distribution parameters. For the proposed maximum stable droplet size prediction model, a Eulerian-Lagrangian framework and a Reitz-Diwakar breakup model are solved simultaneously using CFD calculations to reflect the effect of multistage breakup and droplet acceleration. Then, two ULLN distribution parameters are suggested through best fitting the previously published experimental data. Results show that the proposed approach provides better predictions of maximum stable droplet diameter and Sauter mean diameter compared to existing simple empirical correlations including Boll, Nukiyama and Tanasawa. For more practical purpose, we developed the simple, one dimensional (1-D) calculation of Sauter mean diameter.

• Nuclear Engineering and Technology
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• v.31 no.2
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• pp.236-255
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• 1999

An improved mechanistic model was developed to predict a convective boiling critical heat flux (CHF) in the vertical round tubes with uniform heat fluxes. The CHF formula for subcooled and low quality boiling was derived from the local conservation equations of mass, energy and momentum, together with appropriate constitutive relations. The model is characterized by the momentum balance equation to determine the limiting transverse interchange of mass flux crossing the interface of wall bubbly layer and core by taking account of the convective shear effect due to the frictional drag on the wall-attached bubbles. Comparison between the present model predictions and experimental CHF data from several sources shows good agreement over a wide range of How conditions. The present model shows comparable prediction accuracy with the CHF look-up table of Groeneveld et al. Also the model correctly accounts for the effects of flow variables as well as geometry parameters.

• Structural Engineering and Mechanics
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• v.28 no.4
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• pp.443-460
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• 2008

The aim of this paper is to propose a design procedure for predicting the buckling strength of built-up, cold-formed steel columns based on the two well known methods; the effective width method and the Direct Strength Method. Several design approaches, based on different elastic buckling solutions, were considered in this investigation. Traditional hand methods, without interaction effects between the different modes, and a new numerical spline finite strip method were used to predict the buckling stresses. All of the proposed methods were compared with experimental data on plain and lipped, built-up columns. Results have shown that the effective width approaches are more accurate than the Direct Strength Method. However, both methods can be investigated using more experimental data to assess a practical design method for built-up columns.