We want a clear technique in vaccination

1007/s10796-021-10155-3.
The online version contains supplementary material available at 10.1007/s10796-021-10155-3.Sustaining patient portal use is a major problem for many healthcare organizations and providers. If this problem can be successfully addressed, it could have a positive impact on various stakeholders. Through the lens of cognitive dissonance theory, this study investigates the role of health professional encouragement as well as patients' security concerns in influencing continuous use intention and deep structure usage among users of a patient portal. The analysis of data collected from 177 patients at a major medical center in the Midwestern region of the United States shows that health professional encouragement helps increase the continuous use intention and deep structure usage of the patient portal, while security concerns impede them. Interestingly, health professional encouragement not only has a direct positive influence on continuous use intention and deep structure usage but also lowers the negative impact of security concerns on them. The research model explains a substantial variance in continuous use intention (i.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. selleck chemicals 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. link2 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. link3 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. 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.An understanding of human attitudes towards wildlife can be an essential element in the success or failure of a conservation initiative, policy or practice and represents one of the main conservation problems for wildlife species. Despite the ecosystem services bats provide, they often are a socially stigmatized group, misperceived and even hunted. This problem has been on the increase as a result of the Covid-19 pandemic. We examined how aesthetic appeal and informational factors could influence human attitudes towards bats in a survey of 1966 participants from Spanish-speaking countries. Gender, educational level, religiousness and previous experiences with bats were relevant variables to understand attitudes towards them. The results indicate that both aesthetic and informational stimuli increase the positive responses, reducing the negatives on the participants' attitudes. Our results show the importance of public attitudes to achieve conservation goals, especially in the context of human-wildlife conflict. Bats are not charismatic animals and are still surrounded in mystery; however, our findings could benefit bat conservation plans, allowing the development of new communication strategies both locally and nationally and increasing public acceptance that will facilitate bat conservation.The COVID-19 pandemic has seriously impacted the air transport network (ATN) globally. Policies to restrict international passenger arrivals adopted by many countries are effective responses to control the spread of the virus. This paper studies the impact of two entry restriction policies implemented by some countries against international travelers during COVID-19, i.e., direct flight suspension and complete entry suspension, on the international connectivity (IC) of ATNs. Firstly, the concept of international air transport network (IATN) is defined, and a novel weighted IC index for ATNs is proposed considering flight frequency. Furthermore, to systematically analyze the difference between two policies, the hierarchical structure of the IATN is investigated, followed by studying the change of the IC index assuming different countries impose the two policies. Taking China as an example, this paper evaluates the influence of two policies based on real policy implementation of some countries against travelers from China. Besides, the critical countries affecting the IC are identified, and the network robustness is assessed. Implications for assessing and ranking the impact of different countries under different policies are provided and discussed. Lastly, two extensions are presented to discuss the impact of partial suspension and response actions such as air travel bubble. This work is one of the first to study the impact of country-to-country disconnection on air transport connectivity and deepens our understanding of the performance of ATNs during emergencies.Accurate line lists are important for the description of the spectroscopic nature of small molecules. While a line list for CN (an important molecule for chemistry and astrophysics) exists, no underlying energy spectroscopic model has been published, which is required to consider the sensitivity of transitions to a variation of the proton-to-electron mass ratio. Here we have developed a Duo energy spectroscopic model as well as a novel hybrid style line list for CN and its isotopologues, combining energy levels that are derived experimentally (Marvel), using the traditional/perturbative approach (Mollist), and the variational approach (from a Duo spectroscopic model using standard ExoMol methodology). The final Trihybrid ExoMol-style line list for 12C14N consists of 28 004 energy levels (6864 experimental, 1574 perturbative, the rest variational) and 2285 103 transitions up to 60 000 cm-1 between the three lowest electronic states (X 2Σ+, A 2Π, and B 2Σ+). The spectroscopic model created is used to evaluate CN as a molecular probe to constrain the variation of the proton-to-electron mass ratio; no overly promising sensitive transitions for extragalactic study were identified.