Treating a Viral Culture: Using Cultural Competency and Social Informatics to Design Contextualized Information Literacy Efforts for Specific Social Information Cultures

This chapter proposes a novel theoretical framework, Social Information Cultural Competency (SICC), that may be used for designing contextualized information literacy efforts. The SICC approach leverages the frameworks of social informatics, cultural competency, and psychosocial understandings of information behavior to encourage information professionals to develop more nuanced understandings of specific social information cultures. After defining this approach, the chapter then applies the SICC framework to a case study considering information literacy interventions addressing a social information culture engaged in sharing COVID-19 misinformation through social media. As part of this case study, the chapter discusses three current information literacy approaches to COVID-19 online misinformation interventions: inoculation or “prebunking” efforts, accuracy prompts before posting or sharing, and online conversation groups. Finally, the SICC framework is compared with each of the three current approaches. The chapter concludes with implications of this novel framework for current and future information literacy efforts aimed at combatting dis/misinformation within social information cultures

WORKING MEMORY IN ALZHEIMER DISEASE: A 5-YEAR SYSTEMATIC REVIEW OF EMPIRICAL EVIDENCES FROM BADDELEY’S WORKING MEMORY MODEL

The Alzheimer’s disease is the most common of theneurogenerative conditions associated with dementia. Itis known as a pathological frame that comes with severalimpairments in cognitive and psychological processes.This study aimed to understand the relationship betweenAlzheimer’s disease and Working Memory impairments.We adopted Baddeley’s Working Memory Model tosystematically review if impairments in the subcomponentsof this theoretical model – phonological loop, visualsketchpad, episodic buffer and central executive – followdistinct or similar paths. The systematic review consultedMedline, Psycinfo and Scielo databases. From 329 articles,only 11 were accepted by the established criteria. Resultssuggested that episodic buffer and central executive,respectively, decline with AD severity. Phonological loopand visual sketchpad are the last of the Baddeley’s WorkingMemory Model subcomponents impaired

Using Rheo-Small-Angle Neutron Scattering to Understand How Functionalised Dipeptides Form Gels

We explore the use of rheo-small-angle neutron scattering as a method to collect structural information from neutron scattering simultaneously with rheology to understand how low-molecular-weight hydrogels form and behave under shear. We examine three different gelling hydrogel systems to assess what structures are formed and how these influence the rheology. Furthermore, we probe what is happening to the network during syneresis and why the gels do not recover after an applied strain. All this information is vital when considering gels for applications such as 3D-printing and injection

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Work-Unit Absenteeism: Effects of Satisfaction, Commitment, Labor Market Conditions, and Time

Prior research is limited in explaining absenteeism at the unit level and over time. We developed and tested a model of unit-level absenteeism using five waves of data collected over six years from 115 work units in a large state agency. Unit-level job satisfaction, organizational commitment, and local unemployment were modeled as time-varying predictors of absenteeism. Shared satisfaction and commitment interacted in predicting absenteeism but were not related to the rate of change in absenteeism over time. Unit-level satisfaction and commitment were more strongly related to absenteeism when units were located in areas with plentiful job alternatives

Dealing with Missing Data and Uncertainty in the Context of Data Mining

Missing data is an issue in many real-world datasets yet robust methods for dealing with missing data appropriately still need development. In this paper we conduct an investigation of how some methods for handling missing data perform when the uncertainty increases. Using benchmark datasets from the UCI Machine Learning repository we generate datasets for our experimentation with increasing amounts of data Missing Completely At Random (MCAR) both at the attribute level and at the record level. We then apply four classification algorithms: C4.5, Random Forest, Naïve Bayes and Support Vector Machines (SVMs). We measure the performance of each classifiers on the basis of complete case analysis, simple imputation and then we study the performance of the algorithms that can handle missing data. We find that complete case analysis has a detrimental effect because it renders many datasets infeasible when missing data increases, particularly for high dimensional data. We find that increasing missing data does have a negative effect on the performance of all the algorithms tested but the different algorithms tested either using preprocessing in the form of simple imputation or handling the missing data do not show a significant difference in performance

Characterization of MKIDs for CMB observation at 220 GHz with the South Pole Telescope

We present an updated design of the 220 GHz microwave kinetic inductance detector (MKID) pixel for SPT-3G+, the next-generation camera for the South Pole Telescope. We show results of the dark testing of a 63-pixel array with mean inductor quality factor $Q_i = 4.8 \times 10^5$, aluminum inductor transition temperature $T_c = 1.19$ K, and kinetic inductance fraction $\alpha_k = 0.32$. We optically characterize both the microstrip-coupled and CPW-coupled resonators, and find both have a spectral response close to prediction with an optical efficiency of $\eta \sim 70\%$. However, we find slightly lower optical response on the lower edge of the band than predicted, with neighboring dark detectors showing more response in this region, though at level consistent with less than 5\% frequency shift relative to the optical detectors. The detectors show polarized response consistent with expectations, with a cross-polar response of $\sim 10\%$ for both detector orientations.Comment: 6 pages, 5 figures, ASC 2022 proceeding

How managers can build trust in strategic alliances: a meta-analysis on the central trust-building mechanisms

Trust is an important driver of superior alliance performance. Alliance managers are influential in this regard because trust requires active involvement, commitment and the dedicated support of the key actors involved in the strategic alliance. Despite the importance of trust for explaining alliance performance, little effort has been made to systematically investigate the mechanisms that managers can use to purposefully create trust in strategic alliances. We use Parkhe’s (1998b) theoretical framework to derive nine hypotheses that distinguish between process-based, characteristic-based and institutional-based trust-building mechanisms. Our meta-analysis of 64 empirical studies shows that trust is strongly related to alliance performance. Process-based mechanisms are more important for building trust than characteristic- and institutional-based mechanisms. The effects of prior ties and asset specificity are not as strong as expected and the impact of safeguards on trust is not well understood. Overall, theoretical trust research has outpaced empirical research by far and promising opportunities for future empirical research exist

Author Correction: A consensus-based transparency checklist.

An amendment to this paper has been published and can be accessed via a link at the top of the paper