e., 40%) and deep structure usage (i.e., 32%). The paper provides theoretical implications as well as practical implications to healthcare managers and providers to improve patient portal deep structure usage and sustained use for user retention.One realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society's most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpetuate and exacerbate racial and gender biases. Yet, there is limited knowledge about the extent to which individuals might question AI-based recommendations when perceived as biased. To address this gap in knowledge, we investigate the effects of espoused national cultural values on AI questionability, by examining how individuals might question AI-based recommendations due to perceived racial or gender bias. Data collected from 387 survey respondents in the United States indicate that individuals with espoused national cultural values associated to collectivism, masculinity and uncertainty avoidance are more likely to question biased AI-based recommendations. This study advances understanding of how cultural values affect AI questionability due to perceived bias and it contributes to current academic discourse about the need to hold AI accountable.Cyanobacteria have multifaceted ecological roles on coral reefs. Moorena bouillonii, a chemically rich filamentous cyanobacterium, has been characterized as a pathogenic organism with an unusual ability to overgrow gorgonian corals, but little has been done to study its general growth habits or its unique association with the snapping shrimp Alpheus frontalis. Quantitative benthic surveys, and field and photographic observations were utilized to develop a better understanding of the ecology of these species, while growth experiments and nutrient analysis were performed to examine how this cyanobacterium may be benefiting from its shrimp symbiont. Colonies of M. bouillonii and A. frontalis displayed considerable habitat specificity in terms of occupied substrate. Although found to vary in abundance and density across survey sites and transects, M. bouillonii was consistently found to be thriving with A. frontalis within interstitial spaces on the reef. Removal of A. frontalis from cyanobacterial colonies in a laboratory experiment altered M. bouillonii pigmentation, whereas cyanobacteria-shrimp colonies in the field exhibited elevated nutrient levels compared to the surrounding seawater.The ability to accurately and consistently discover anomalies in time series is important in many applications. Fields such as finance (fraud detection), information security (intrusion detection), healthcare, and others all benefit from anomaly detection. Intuitively, anomalies in time series are time points or sequences of time points that deviate from normal behavior characterized by periodic oscillations and long-term trends. For example, the typical activity on e-commerce websites exhibits weekly periodicity and grows steadily before holidays. Similarly, domestic usage of electricity exhibits daily and weekly oscillations combined with long-term season-dependent trends. How can we accurately detect anomalies in such domains while simultaneously learning a model for normal behavior? We propose a robust offline unsupervised framework for anomaly detection in seasonal multivariate time series, called AURORA. A key innovation in our framework is a general background behavior model that unifies periodicity and long-term trends. To this end, we leverage a Ramanujan periodic dictionary and a spline-based dictionary to capture both seasonal and trend patterns. We conduct experiments on both synthetic and real-world datasets and demonstrate the effectiveness of our method. AURORA has significant advantages over existing models for anomaly detection, including high accuracy (AUC of up to 0.98), interpretability of recovered normal behavior ( 100 % accuracy in period detection), and the ability to detect both point and contextual anomalies. In addition, AURORA is orders of magnitude faster than baselines.We present an extensive study on disinformation, which is defined as information that is false and misleading and intentionally shared to cause harm. Through this work, we aim to answer the following questionsCan we automatically and accurately classify a news article as containing disinformation?What characteristics of disinformation differentiate it from other types of benign information? We conduct this study in the context of two significant events the US elections of 2016 and the 2020 COVID pandemic. We build a series of classifiers to (i) examine linguistic clues exhibited by different types of fake news articles, (ii) analyze "clickbaityness" of disinformation headlines, and (iii) finally, perform fine-grained, veracity-based article classification through a natural language inference (NLI) module for automated disinformation verification; this utilizes a manually curated set of evidence sources. For the latter, we built a new dataset that is annotated with generic, veracity-based labels and ground truth evidence supporting each label.  https://www.selleckchem.com/products/apoptozole.html  The veracity labels were formulated based on examining standards used by reputable fact-checking organizations. We show that disinformation derives features from both propaganda and mainstream news, making it more challenging to detect. However, there is significant potential for automating the fact-checking process to incorporate the degree of veracity. We provide error analysis that illustrates the challenges involved in the automated fact-checking task and identifies factors that may improve this process in future work. Finally, we also describe the implementation of a web app that extracts important entities and actions from a given article and searches the web to gather evidence from credible sources. The evidence articles are then used to generate a veracity label that can assist manual fact-checkers engaged in combating disinformation.