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This book is a print version of the online course ILS508 User Services. The course content includes the instructor's lecture notes, assignment instructions, assigned readings, discussions an coursework from the students in the classes.

This books is a print version of the online course ILS 440 Information Service Technology offered at Southern Connecticut State University. The course content includes the instructor's lecture notes, assignment instructions, assigned readings, discussions and coursework from students in the classes.

To determine the present situation regarding services provided to mobile users in US urban libraries, the authors surveyed 138 Urban Libraries Council members utilizing a combination of mobile visits, content analysis, and librarian interviews. The results show that nearly 95% of these libraries have at least one mobile website, mobile catalog, or mobile app. The libraries actively applied new approaches to meet each local community’s remote-access needs via new technologies, including app download links, mobile reference services, scan ISBN, location navigation, and mobile printing. Mobile services that libraries provide today are timely, convenient, and universally applicable.

Areal precipitation estimation directly affects the accuracy of reservoir runoff inflow forecasts and flood dispatching decision-making. Because of the heterogeneous spatial and temporal precipitation distributions in large basins, inadequate precipitation stations normally have a negative impact on forecast accuracy. Using the Panjia-kou reservoir runoff inflow forecast as the research subject, this paper adopts the Thiessen polygon block, square grid computing, and DEM (digital elevation model) methods to estimate average regional areal precipitation. Based on the estimation, a model for the Panjia-kou reservoir runoff forecast is developed. The results indicate that different areal precipitation estimation methods have significantly different effects on the accuracy of the reservoir runoff inflow forecast. When the average regional precipitation estimation from the DEM method is used as an input to the model, the simulation results are accurate and are much better than those from the other two average regional precipitation estimation methods.

This project aimed to develop a valid and reliable scale measuring Chinese preservice physical education teachers’ beliefs about the physical education profession (PPET-BPEP). The domains and items were created from a conceptual analysis of the previous literature and PPETs’ responses to an open-ended survey. Six experts in the field of physical education and educational psychology evaluated the content validity of the scale. The reliability and factorial validity of the scale were examined utilizing a sample of 696 Chinese PPETs. The PPET-BPEP scale with 12 items embedded in two domains revealed acceptable content validity, internal structure validity, and internal consistency. The two domains were labeled as “sense of calling” and “value of physical education profession” based on the shared content of items in each domain. We recommend using PPET-BPEP scale for PPET recruitment and preparation. The scale can also help establish teacher belief scales in other subject matters. Future validation of the scale is needed in different countries and institutions.

We demonstrate that a nonzero strangeness contribution to the spacelike electromagnetic form factor of the nucleon is evidence for a strange-antistrange asymmetry in the nucleon's light-front wave function, thus implying different nonperturbative contributions to the strange and antistrange quark distribution functions. A recent lattice QCD calculation of the nucleon strange quark form factor predicts that the strange quark distribution is more centralized in coordinate space than the antistrange quark distribution, and thus the strange quark distribution is more spread out in light-front momentum space. We show that the lattice prediction implies that the difference between the strange and antistrange parton distribution functions, s(x)-s(x), is negative at small-x and positive at large-x. We also evaluate the strange quark form factor and s(x)-s(x) using a baryon-meson fluctuation model and a novel nonperturbative model based on light-front holographic QCD. This procedure leads to a Veneziano-like expression of the form factor, which depends exclusively on the twist of the hadron and the properties of the Regge trajectory of the vector meson which couples to the quark current in the hadron. The holographic structure of the model allows us to introduce unambiguously quark masses in the form factors and quark distributions preserving the hard scattering counting rule at large-Q2 and the inclusive counting rule at large-x. Quark masses modify the Regge intercept which governs the small-x behavior of quark distributions, therefore modifying their small-x singular behavior. Both nonperturbative approaches provide descriptions of the strange-antistrange asymmetry and intrinsic strangeness in the nucleon consistent with the lattice QCD result. © 2018 authors. Published by the American Physical Society.

Resonance Raman spectroscopy using 532nm excitation was used to distinguish normal brain tissue from different grades of glioma tissues. Principal component analysis was used to analyze the spectral data and achieved high accuracy. © 2018 The Author(s).

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper. © 2018, The Author(s).

VRR spectroscopy was used for BCC and normal skin tissues with 532nm excitation. The spectra showed significant changes in collagen, carotenoids and lipids. These enhanced fingerprints demonstrate a potential use as label-free pathology method. © 2018 The Author(s).

The goal of the research is to determine the prognostic molecular pathological changes in components and composition, for human brain glioma gradings in comparison with normal tissues in three-dimensional Raman imaging profiles by visible Resonance Raman (VRR) imaging. <p> </p>VRR images from twenty-five specimens including three healthy tissues, one normal control, and twenty-one glioma tissues of grades II, II-III and III-IV with histology examination were measured and investigated using WITec300R confocal micro Raman imaging system with laser excitation of 532nm. <p> </p>Two-dimensional RR spectral mappings performed in 20&mu;m x 20&mu;m generated 400 images which integrated the intensity of the specific biochemical bonds as the third dimension. The three-dimension (3D) map demonstrated the spatial distributions of three selected sets of RR spectra of molecular biomarkers, and revealed significant differences in the spectra between normal and glioma tissues of different grades due to the composition changes in key molimageecules. These RR molecular spectral fingerprints have displayed: a clear enhancement of RR vibrational modes at 1129-1131cm^-1 and 2934cm^-1 which are supposed to be arising from lipoproteins; evident decreased RR vibrational modes at 1442cm^-1 and 2854cm^-1 which are from saturated fatty acids bonds in all-grades of glioma brain tissues compared with normal tissues; and the enhanced RR spectral modes of 1129 cm^-1 and 2938cm^-1 which suggest contribution from lactate. These findings may provide a novel proof for anaerobic glycolysis metabolic process in brain glioma cancer tissues that has been explained by Warburg effects.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.

The accurate identification of the human brain tumor boundary and the complete resection of the tumor are two essential factors for the removal of the glioma tumor in brain surgery. We present a visible resonance Raman (VRR) spectroscopy technique for differentiating the brain tumor margin and glioma grading. Eighty-seven VRR spectra from twenty-one human brain specimens of four types of brain tissues, including the control, glioma grade II, III, and IV tissues, were observed. This study focuses on observing the characteristics of new biomarkers and their changes in the four types of brain tissue. We found that two new RR peaks at 1129 cm-1 and 1338 cm-1 associated with molecular vibrational bonds in four types of brain tissues are significantly different in peak intensities of VRR spectra. These two resonance enhanced peaks may arise from lactic acid/phosphatidic acid and adenosine triphosphate (ATP)/nicotinamide adenine dinucleotide, respectively. We found that lactic acid and ATP concentrations vary with glioma gratings. The higher the grade of malignancy, the more the increase in lactic acid and ATP concentrations. These two RR peaks may be considered as new molecular biomarkers and used to evaluate glioma grades and identify the margin of gliomas from the control tissues. The metabolic process of lactic acid and ATP in glioma cells based on the VRR spectral changes may reveal the Warburg hypothesis. © 2018 Author(s).