Your search

Libraries are adapting to the changing times by providing mobile services. One hundred fifty-one libraries were chosen based on circulation, with at least one library or library system from each state, to explore the diverse services provided to mobile users across the United States. According to the data, mobile apps, mobile reference services, mobile library catalogs, and mobile printing are among public libraries’ most-frequently offered services, as determined by mobile visits, content analysis, and librarian survey responses. Every library examined had at least one mobile website, mobile catalog, mobile app, or webpage adapted for a mobile device. Following the COVID-19 outbreak, services such as mobile renewal, subscriber database access, mobile reservations, and the ability to interact with a librarian were expanded to allow better communication with customers—all from the comfort and safety of their own homes. Libraries are continually looking for innovative methods to assist their mobile customers as the world changes.

This study explores whether US post-secondary institutions (PPI) follow philosophies to foster inclusive communities, providing resources for those individuals with disabilities thrive socially, personally, and academically, while there have been no thorough studies conducted to determine web accessibility of the nation’s top-ranked PPI library webpages. Additionally, this study pioneers in comparison with the accessibility of PPI’s library homepages fighting COVID-19. The study evaluated the library homepages of the premium PPIs based on Money.com’s 2019 list of “The Best Colleges in America” via the WAVE web accessibility evaluation tool. The outcomes determined that most of the library homepages analyzed were littered with numerous errors, and the shift to online-based research in learning had no significant impact on the number of errors WAVE detected. The disconcerting findings of this study demonstrate the overall failure to recognize the importance of web accessibility or perhaps even the indifference toward accessibility on the part of the PPI community.

This paper analyzes the concept of think tanks and concludes that think tanks have three basic characteristics. Firstly, they are based on academic research, relatively independent operation, and aim to serve scientific decision-making. Taking Ivy League think tanks as an example, this paper provides a preliminary discussion of ways for think tanks to maintain the scientific nature of their research, maintain the independence of their operations, and disseminate research results to enhance their influence. It covers institutional mission, research team construction, institutional governance, fundraising, achievements and activities, and alumni networks. This paper proposes a framework of university think tank generation paths associated with the essential characteristics of think tanks. The paper points out that, as a research consulting organization grown out of universities, university think tanks must maintain the scientific nature and independence of research while providing support and services for decision-making. Only in this way can they truly serve scientific and democratic decision-making, gain the trust of the public and have a real lasting influence.

In modern-day computing, cloud services are widely used in every aspect of life. So, user satisfaction depends on the effectiveness and efficiency of cloud services. Service broker policy of the cloud maintains the effectiveness and efficiency of cloud services. Service broker policy provides the rules and norms based on which a data center is selected for a userbase request. This paper proposes a genetic algorithm-based service broker policy that provides the optimal sequence of data centers for different userbases based on their requirements. This research aims to find an optimal data center for userbases that can achieve user satisfaction by minimizing the cloud service's response time and data processing time. We have experimented with our proposed genetic algorithm-based service broker policy in the CloudAnalyst platform based on different real-world scenarios. Simulation results indicate that our proposed genetic algorithm outperforms existing traditional algorithms. © 2023 IEEE.

Whole-body dynamic fluoro-D-glucose (FDG)-positron emission tomography (PET) imaging through continuous-bed-motion (CBM) mode multi-pass acquisition protocol is a promising metabolism measurement. However, inter-pass misalignment originating from body movement could degrade parametric quantification. We aim to apply a non-rigid registration method for inter-pass motion correction in whole-body dynamic PET. 27 subjects underwent a 90-min whole-body FDG CBM PET scan on a Biograph mCT (Siemens Healthineers), acquiring 9 over-the-heart single-bed passes and subsequently 19 CBM passes (frames). The inter-pass motion correction was executed using non-rigid image registration with multi-resolution, B-spline free-form deformations. The parametric images were then generated by Patlak analysis. The overlaid Patlak slope K_i and y-intercept V_b images were visualized to qualitatively evaluate motion impact and correction effect. The normalized weighted mean-squared Patlak fitting errors (NFEs) were compared in the whole body, head, and hypermetabolic regions of interest (ROIs). In K_i images, ROI statistics were collected and malignancy discrimination capacity was estimated by the area under the receiver operating characteristic curve (AUC). After the inter-pass motion correction was applied, the spatial misalignment appearance between K_i and V_b images was successfully reduced. Voxel-wise normalized fitting error maps showed global error reduction after motion correction. The NFE in the whole body ( p \,\,= 0.0013), head ( p \,\,= 0.0021), and ROIs ( p \,\,= 0.0377) significantly decreased. The visual performance of each hypermetabolic ROI in K_i images was enhanced, while 3.59% and 3.67% average absolute percentage changes were observed in mean and maximum K_i values, respectively, across all evaluated ROIs. The estimated mean K_i values had substantial changes with motion correction ( p \,\,= 0.0021). The AUC of both mean K_i and maximum K_i after motion correction increased, possibly suggesting the potential of enhancing oncological discrimination capacity through inter-pass motion correction.

Measurement of students’ peer assessment motivation is critical to understand how they participate in such activities in higher education. The current study was conducted to develop and validate a brief scale that measures student peer assessment motivation in higher education using the Expectancy-Value Theory (EVT). Initial items were developed, revised, and administered to 369 students. Exploratory factor analyses suggested a three-factor model structure (ability belief, expectancy, and task value) aligning with EVT. Confirmatory factor analyses (n = 399) supported a higher-order factor structure with the three first-order factors (i.e. ability belief, expectancy, and task value) with a decent model fit. The 20-items Peer Assessment Motivation Scale (PAMS) had decent internal reliability, test-retest reliability, convergent validity, and discriminant validity, suggesting that it is a high-quality measure. This scale is beneficial for instructors and researchers who are interested in investigating peer assessment motivation in higher education.

There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.

Alzheimer’s disease (AD) pathogenesis is widely believed to be associated with the production and deposition of the β-amyloid peptide (Aβ) and neurofibrillary tangles (NFTs) which are composed of a highly-phosphorylated form of the microtubule-associated protein tau. Based on the above hypothesis, there are currently no sufficiently effective technologies and drugs for early detection and treatment of AD. Even the most promising new drug Lecanemab that is based on an anti-amyloid monoclonal antibody therapy, has only partially slowed down the cognitive performance of patients with mild impairment caused by Alzheimer's disease. The main symptoms of AD brain tissue lesions in patients are the deposition of β-amyloid peptide and the hyperphosphorylation of tau protein, which aggregates the microtubule structure of neurons. Therefore, Aβ deposition and hyperphosphorylation of Tau are important pathological biomarkers of Alzheimer's disease. Therefore, the main targets of research for AD prevention, detection and pharmaceuticals are still Aβ and Tau protein. The aim of this study was to detect the changes of Aβ and Tau proteins in the mouse brain tissue with AD and control samples using Visible Resonance Raman (VRR) spectroscopic technology. An attempt was made to develop criteria for the detection of early AD lesions by optical spectroscopy technology. The VRR spectra of AD, the control mouse brain tissues, and Aβ and Tau proteins were recorded and analyzed. The AD and the control mouse brain tissue samples were selected from the thalamus, frontal lobe cortex and hippocampus brain areas. VRR technology with high spatial resolution and the resonance-enhanced features of certain protein molecules is first used in this study to detect and characterize the changes of Aβ and Tau proteins in AD mouse brain model. The optical spectroscopy biomarkers of AD and Control brain tissue were identified in fingerprint and the high-wavenumber regions. The Raman spectra of the secondary structure of protein in amide (I-II-III-B-A) are detected and analyzed. The results indicate that the intensity of Amide I decreased at the 1666 cm^-1 corresponding to the β-sheet structure, and the intensity of the amide III bands (1220- 1320 cm^-1) increased in all AD brain tissues. It was also observed that the Raman peaks of 1448 and 980 cm^-1 related to the abundance of proline, serine, and threonine at tau phosphorylation sites were significantly enhanced in the frontal lobe cortex and hippocampus of AD brain tissues. The intensity ratio biomarker of high phosphorylation in the high wavenumber range from 2898 to 2932 cm-1 increased in all AD brain tissues. Changes of protein secondary conformation and abnormally phosphorylated tau or tauopathies were observed. In summary, VRR is a sensitive tool for characterizing protein structural changes and monitoring the tau phosphorylation. It may potentially be used for early detection of AD.