A visual analysis of the usage efficiency of library books

The monographic collections in academic libraries have undergone a period of tremendous growth in volume, in subject diversity, and in formats during the recent several decades. Readers may find it difficult to prioritize which book(s) should be borrowed for a specific purpose. The log data of book loan record may serve as a visible indicator for the more sought-after books by the readers. This paper describes our experimental efforts in works in a university library setting. The visual analysis is thought to provide an effective way to extract the book usage information, which may yield new insights into a host of other related technical as well as user behavior issues. Initial experiment has demonstrated that the proposed approach as articulated in this article can actually benefit end-users as well as library collection development personnel in their endeavor of book selections with effective measure.</p

PULSED AND CW LASER TREATMENTS OF IMPLANTED POLYSILICON SOLAR CELLS

Conventional ion implantation and unanalyzed ion bombardment have been used to elaborate the rectifying N+ contact of polycrystalline silicon (Wacker, HEM, CGE) solar cells. Two surface laser annealing in the liquid phase (Nd : YAG laser) and in the solid phase (CO2 laser) regimes have been used. The properties of the solar cells so processed have been investigated. For both doping procedures and both annealing techniques, the cells (conversion) efficiencies under AM1 illumination exceeded 11% for the various polysilicon substrates

Fermi LAT AGN classification using supervised machine learning

Classifying Active Galactic Nuclei (AGN) is a challenge, especially for BL Lac Objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the 4th Fermi Catalog, Data Release 3. Missing data hinders the use of machine learning to classify AGN. A previous paper found that Multiple Imputation by Chain Equations (MICE) imputation is useful for estimating missing values. Since many AGN have missing redshift and the highest energy, we use data imputation with MICE and K-nearest neighbor (kNN) algorithm to fill in these missing variables. Then, we classify AGN into the BLLs or the Flat Spectrum Radio Quasars (FSRQs) using the SuperLearner, an ensemble method that includes several classification algorithms like logistic regression, support vector classifiers, Random Forests, Ranger Random Forests, multivariate adaptive regression spline (MARS), Bayesian regression, Extreme Gradient Boosting. We find that a SuperLearner model using MARS regression and Random Forests algorithms is 91.1% accurate for kNN imputed data and 91.2% for MICE imputed data. Furthermore, the kNN-imputed SuperLearner model predicts that 892 of the 1519 unclassified blazars are BLLs and 627 are Flat Spectrum Radio Quasars (FSRQs), while the MICE-imputed SuperLearner model predicts 890 BLLs and 629 FSRQs in the unclassified set. Thus, we can conclude that both imputation methods work efficiently and with high accuracy and that our methodology ushers the way for using SuperLearner as a novel classification method in the AGN community and, in general, in the astrophysics community.Comment: 15 pages, 8 figures, to be published in Monthly Notices of the Royal Astronomical Societ

Analyse protéomique des glioblastones: Quel est le meilleur contrôle?

National audienc

The research on nutrients release during the decomposition of Chaetomorpha sp.

通过室内模拟,研究了不同环境条件下绿潮硬毛藻的分解速率,以及死亡藻体内营养盐的释放规律,以阐明硬毛藻大量衰亡对天鹅湖水质的潜在影响。结果显示,温度对硬毛藻分解速率的影响显著(P沉积物>营养盐水平;N释放为:沉积物>温度>营养盐水平。高温条件下,死亡藻..

NUMERICAL SIMULATION OF CONFINED NANO-IMPINGING JET IN MICROSCALE COOLING APPLICATION USING DSMC METHOD

ABSTRACT In this study, we simulate rarefied gas flow through a confined nano-impinging jet using direct simulation Monte Carlo (DSMC) method. The effects of geometrical parameters, pressure ratio, and wall conditions on the heat transfer from a hot surface are examined. Hot surface modeled via diffusive constant wall temperature. Various inlet/confining surface conditions such as specular, adiabatic, and constant temperature are implemented and the effects of them on the wall heat flux rates are studied. The results show that Knudsen number, velocity slip, and temperature jump are main reasons which specify magnitudes of wall heat flux rates. Among all geometrical parameters, H/W ratio has the greatest effect on heat transfer, where H is jet distance from the hot surface and W is the jet width. For different values of pressure ratio, the biggest quantity of wall heat flux rate relates to the lowest velocity slip case. Also for inlet/confining walls with constant temperature condition equal to coolant flow temperature, heat transfer from the hot surface was the maximum

Automated software for streamlining optimisation of resource planning for an additive manufacturing system

Abstract. The use of additive manufacturing (AM) systems in scale production has rapidly increased in recent years. The growing tendency to adopt AM technologies into established manufacturing systems has led to research that considers the optimisation of both process and resource planning. In order to maximise the outputs of such a production process, planning must be conducted rigorously. This paper proposes an automated software tool, called EasyPlan, which streamlines the optimisation of resource planning. The algorithm is developed using LabVIEW and is demonstrated for an AM component from the medical industry. For the evaluation process, parameters such as stock levels, delivery terms and technical charts of the products are provided. A user friendly interface is developed, making EasyPlan versatile to all types of environments