A Framework of AI-Based Approaches to Improving eHealth Literacy and Combating Infodemic

The global COVID-19 pandemic has put everyone in an urgent need of accessing and comprehending health information online. Meanwhile, there has been vast amount of information/misinformation/disinformation generated over the Internet, particularly social media platforms, resulting in an infodemic. This public health crisis of COVID-19 pandemic has put each individual and the entire society in a test: what is the level of eHealth literacy is needed to seek accurate health information from online resources and to combat infodemic during a pandemic? This article aims to summarize the significances and challenges of improving eHealth literacy in both communicable (e.g., COVID-19) and non-communicable diseases [e.g., cancer, Alzheimer's disease, and cardiovascular diseases (CVDs)]. Also, this article will make our recommendations of a general framework of AI-based approaches to improving eHealth literacy and combating infodemic, including AI-augmented lifelong learning, AI-assisted translation, simplification, and summarization, and AI-based content filtering. This general framework of AI-based approaches to improving eHealth literacy and combating infodemic has the general advantage of matching the right online health information to the right people.

Social Elements of Agri-Expo Strategies Because of Momentary Fluctuation in Key Family and Network Prosperity Measures in Bangladesh and Pakistan

High-return social information accumulation can encourage improved review, progressively comprehensive detailing, and improved inconstancy catch over a period. In spite of the fact that there are instances of little investigations that catch explicit high recurrence factors in the sociology writing, there is so far no noteworthy exertion to gather a wide scope of high recurrence factors. We tried our first such endeavors with a deliberately fluctuating cell phone-based information accumulation approach the return of the investigation task and the update time frame, which enables one to dissect the general advantage of high recurrence information gathering for different key factors in family unit overviews. This overview of 482 ranchers from northwesterly Bangladesh for roughly 1 year of persistent information on key examination for family unit and network prosperity can be especially valuable in planning and assessing improvement intercessions and strategies. While the information talked about here give a concise review of what is conceivable, we likewise stress their capacity to give interdisciplinary research in family unit agribusiness, rehearses, regular yearning, and so forth., in a low-salary rural society.

Reasoning strategy modulates gender differences in performance on a spatial rotation task

The dual-strategy model of reasoning has proposed that individual differences in reasoning can be understood as due to two general ways of processing information: an analytic, counterexample strategy that examines information for explicit potential counterexamples and an intuitive, statistical strategy that uses associative access to generate a likelihood estimate of putative conclusions. Previous studies have examined this model in the context of basic conditional reasoning tasks. However, the distinctions that underlie the dual-strategy model can be seen as a basic description of more general differences in information processing. A recent study examining interactions between gender and strategy use in processing of negative emotions found that gender differences were modulated by strategy, with the general advantage of females concentrated within statistical reasoners. Two studies were performed to extend this analysis to performance on a mental rotation task for which there also exist clear gender differences. The initial study presented rotation tasks with unlimited time. Results show that males perform better on more difficult rotation tasks than females, with the difference concentrated among statistical reasoners. The second study replicated this using a restricted time (4 s) to make each judgement and showed an increase in the effect of both gender and strategy. This provides additional evidence that the dual-strategy model captures an important individual difference in the general way that information is processed.

Animals Do Not Induce or Reduce Attentional Blinking, But They Are Reported More Accurately in a Rapid Serial Visual Presentation Task

Evolutionary psychologists have suggested that modern humans have evolved to automatically direct their attention toward animal stimuli. Although this suggestion has found support in several attentional paradigms, it is not without controversy. Recently, a study employing methods customary to studying the attentional blink has shown inconclusive support for the prioritization of animals in attention. This showed an advantage for reporting animals as second targets within the typical window of the attentional blink, but it remained unclear whether this advantage was really due to a reduction of the attentional blink. We reassessed for the presence of a reduced attentional blink for animals compared with artifacts by using three disparate stimuli sets. A general advantage for animals was found but no indication of a reduction of the attentional blink for animals. There was no support for the prediction that animal distractors should lead to spontaneous inductions of attentional blinks when presented as critical distractors before single targets. Another experiment with single targets still showed that animals were reported more accurately than artifacts. A final experiment showed that when animals were first target, they did not generate stronger attentional blinks. In summary, we did find a general advantage for animal images in the rapid serial visual presentation task, but animal images did not either induce or reduce attentional blinks. This set of results is in line with conclusions from previous research showing no evidence for a special role of animals in attention.

The Runge–Kutta method in geometric multiplicative calculus

This paper illuminates the derivation, applicability, and efficiency of the multiplicative Runge–Kutta method, derived in the framework of geometric multiplicative calculus. The removal of the restrictions of geometric multiplicative calculus on positive-valued functions of real variables and the fact that the multiplicative derivative does not exist at the roots of the function are presented explicitly to ensure that the proposed method is universally applicable. The error and stability analyses are also carried out explicitly in the framework of geometric multiplicative calculus. The method presented is applied to various problems and the results are compared to those obtained from the ordinary Runge–Kutta method. Moreover, for one example, a comparison of the computation time against relative error is worked out to illustrate the general advantage of the proposed method.

Grapheme-colour synaesthesia improves detection of embedded shapes, but without pre-attentive ‘pop-out’ of synaesthetic colour

For people with synaesthesia letters and numbers may evoke experiences of colour. It has been previously demonstrated that these synaesthetes may be better at detecting a triangle made of 2s among a background of 5s if they perceive 5 and 2 as having different synaesthetic colours. However, other studies using this task (or tasks based on the same principle) have failed to replicate the effect or have suggested alternative explanations of the effect. In this study, we repeat the original study on a larger group of synaesthetes ( n = 36) and include, for the first time, an assessment of their self-reported colour experiences. We show that synaesthetes do have a general advantage over controls on this task. However, many synaesthetes report no colour experiences at all during the task. Synaesthetes who do report colour typically experience around one third of the graphemes in the display as coloured. This is more consistent with theories of synaesthesia in which spatial attention needs to be deployed to graphemes for conscious colour experiences to emerge than the interpretation based on ‘pop-out’.

AN ADAPTIVE HYBRID PATTERN-MATCHING ALGORITHM ON INDETERMINATE STRINGS

We describe a hybrid pattern-matching algorithm that works on both regular and indeterminate strings. This algorithm is inspired by the recently proposed hybrid algorithm FJS and its indeterminate successor. However, as discussed in this paper, because of the special properties of indeterminate strings, it is not straightforward to directly migrate FJS to an indeterminate version. Our new algorithm combines two fast pattern-matching algorithms, ShiftAnd and BMS (the Sunday variant of the Boyer-Moore algorithm), and is highly adaptive to the nature of the text being processed. It avoids using the border array, therefore avoids some of the cases that are awkward for indeterminate strings. Although not always the fastest in individual test cases, our new algorithm is superior in overall performance to its two component algorithms — perhaps a general advantage of hybrid algorithms.