Misinformation Research Articles

Digital health literacy and health behaviour of schoolchildren: A representative study in Germany

Abstract Background Health information is increasingly available and plays a crucial role in making health decisions and developing healthy behaviours. Digital health literacy (DHL) enables one to distinguish between true and false health information online. Studies have found higher health literacy to be associated with healthier behaviour. However, there is no research on child DHL and health behaviour. This study aims to investigate the relationship between DHL and selected health behaviour of schoolchildren in Germany. Methods This cross-sectional study is based on a representative sample of (N = 1448) secondary schoolchildren (aged 9-18 years) in Germany across all 16 federal states and school forms. DHL was assessed using the child and adolescent version of the Digital Health Literacy Instrument developed and pretested in this study. Selected health behaviours (eating, teeth brushing, smoking, and alcohol consumption) were measured using self-report scales. Chi-square tests were conducted to measure differences between DHL and health behaviour. Results Associations were found between DHL and all eating behaviours (fruits; vegetables and salad; whole-grain products; other animal-based products; chocolate or other sweets; sugary drinks; fast food), except for eating meat and sausages. Schoolchildren with higher levels of DHL reported more often about regular consumption of fruits and vegetables (X2 (2)=17.06, p = <.001). salads (X2 (2)=56.54 p = <.001) and whole-grain-products (X2 (2)=98.81 p = <.001). While DHL was associated with teeth brushing (X2 (2)=44.46, p = <.001), no differences were observed for smoking (X2 (2)=1.93, p=.381) and drinking alcohol within the last 30 days (X2 (2)=2.62, p=.270). Conclusions These findings indicate that most health behaviours of schoolchildren may benefit from enhancing DHL in schools. Programs should build around these topics. However, more research is needed to investigate associations between DHL and health behaviours comprehensively.

Social patterning of health misinformation in two Eastern Mediterranean populations

Abstract Background Health misinformation, the spread of false or misleading information relevant to health, has been proposed as a major global public health thread by the WHO. We aimed to identify the major social determinants of health misinformation in the general Greek and Cypriot populations. Methods Following proportional quota sampling we recruited a nationwide sample of 585 adults from Greece and Cyprus between January 2022 and May 2023. A newly developed, culturally adopted questionnaire comprising 48 items, was used to assess misinformation on COVID-19 vaccines and the pandemic. Sociodemographic determinants of interest included gender, age, marital status, area of residence, educational attainment, and income. Multiple linear regression was used to determine associations between the aforementioned social factors and the two dimensions of health misinformation, applying mutual adjustment. Results Misinformation about both vaccination and the pandemic was higher in younger age groups (p-trend 0.026 and 0.007, respectively), among those separated, divorced, or widowed (mean difference, 95% CI compared to those single: 9.73, 0.45, 19.01), and among those living in semi-urban compared to urban areas (11.14, 1.32; 20.97 and 9.44, 1.96; 16.92, respectively). We also detected strong inverse socioeconomic gradients in health misinformation based on educational attainment and income (p-trend <0.001, for both). Conclusions Younger individuals, those living alone, those residing in semi-urban areas, and those with lower educational attainment and income are particularly prone to health misinformation. Our adjusted models show that this social phenomenon is not simply explained by differences in education, hence more complex, cultural, political, perceptual, and psychosocial dynamics might be in place. Addressing the social patterning of health misinformation would help manage the issue more effectively and also reduce health inequalities. Key messages • Health misinformation is strongly socially patterned in two Eastern Mediterranean European populations. • Targeting efforts to tackle health misinformation to those more vulnerable, would render such programmes more effective, also helping reduce social inequalities in health.

Improved algorithm for fracture-dissolution pore detection in resistivity imaging logging based on dung beetle optimization

Abstract Resistivity imaging logging has become a direct and precise method for visualizing the structural complexities of reservoir fractures and dissolution pores. The current use of Otsu's thresholding for segmentation results in poor segmentation quality and significant noise. Accurate segmentation of sub-images containing fracture and dissolution pore targets is essential for automated structure identification and subsequent parameter calculation. This study leverages the rapid convergence and robust global optimization capabilities of the Dung Beetle Optimizer to develop enhanced image segmentation approaches. Specifically, it introduces a refined K-Means algorithm for multi-category image segmentation and an Otsu algorithm for multi-threshold image segmentation, both optimized by the Dung Beetle Optimizer. Compared to conventional binary segmentation algorithms, this new algorithm effectively isolates noise and extracts multi-category information. Using the segmented sub-images, this paper integrates mathematical morphology techniques to compute parameters such as area, perimeter, tortuosity length, and pore shape factor for identified targets. Additionally, Principal Component Analysis is used to derive recognition factors for fractures and dissolution pores. Applications show that this factor can identify matrix, fracture, and dissolution pore targets in complex background images. By combining parameter information of the target area, the method effectively removes false information in resistivity imaging and segments sub-images of fractures and dissolution pores, calculating fracture area ratio, dissolution pore area ratio, and total area ratio.

COUNTERING DISINFORMATION POLICY: CHALLENGES FOR UKRAINE’S NATIONAL SECURITY

he article examines the problems of combating disinformation in the conditions of a modern full-scale war and overcoming the challenges facing the national security of Ukraine. The concept of disinformation, its impact on public consciousness and the key mechanisms of the spread of false information are outlined. The main directions of the policy of combating disinformation aimed at ensuring information security and resistance to information attacks have been studied. The importance of the development of media literacy among the population as one of the main tools for countering disinformation campaigns through educational programs and social interaction is revealed. The experience of combating disinformation in the developed countries of the world, the USA and the countries of the European Union, is analyzed. The role of state and international initiatives, the cooperation of governments with technology companies, as well as the implementation of legislative mechanisms are highlighted. The main tools aimed at regulating the spread of false information in the information space are revealed. The study emphasizes the importance of active cooperation with international partners to ensure an effective fight against disinformation at the global level. Special attention is paid to the role of international organizations in the formation of a unified strategy for countering disinformation attacks. Prospects for attracting international support for the development of national information protection systems and Ukraine's participation in global information security initiatives are being considered. Proposed means of an effective policy of combating disinformation, which combine legislative, technical and educational measures. The methods of ensuring resistance to external information threats are characterized. Particular attention is paid to media literacy programs, which have become important elements of national strategies in the fight against fakes and disinformation attacks. The article considers the possibilities of implementing international experience in Ukraine with the aim of strengthening national information security. The expediency of developing complex programs, including the strengthening of modern means of monitoring the information space and raising the level of critical thinking among the population, is analyzed. Emphasis is placed on the need to create a national security strategy for combating disinformation, which will allow effective response to new threats and ensure information security in the conditions of modern warfare.

The Problem of AI Hallucination and How to Solve It

AI in education is a topic that has been researched for the last 70 years. However, the last two years have seen very significant changes. These changes relate to the introduction of OpenAI's ChatGPT chatbot in November 2022. The GPT (Generative Pre-trained Transformer) language model has dramatically influenced how the public approaches artificial intelligence. For many, generative language models have become synonymous with AI and have come uncritically viewed as a universal source of answers to most questions. However, it soon became apparent that even generative language models had their limits. Among the main problems that emerged was hallucination (providing answers containing false or misleading information), which is expected in all language models. The main problem of hallucination is that this information is difficult to distinguish from other information, and AI language models are very persuasive in presenting it. The risks of this phenomenon are much more substantial when using language modules to support learning, where the learner cannot distinguish correct information from incorrect information. The proposed paper focuses on the area of AI hallucination in mathematics education. It will first show how AI chatbots hallucinate in mathematics and then present one possible solution to counter this hallucination. The presented solution was created for the AI chatbot Edu-AI and designed to tutor students in mathematics. Usually, the problem is approached so that the system verifies the correctness of the output offered by the chatbot. Within the Edu-AI, checking responses is not implemented, but checking inputs is. If an input containing a factual query is recorded, it is redirected, and the answer is traced to authorised knowledge sources and study materials. If a relevant answer cannot be traced in these sources, a redirect to a natural person who will address the question is offered. In addition to describing the technical solution, the article includes concrete examples of how the system works. This solution has been developed for the educational domain but applies to all domains where users must be provided with relevant information.

The Effectiveness of an Educational Intervention on Countering Disinformation Moderated by Intellectual Humility

<span lang="EN-US">There is an ongoing debate among scholars on how to tackle disinformation. Media education initiatives to increase literacy are effective ways to counter disinformation. Hence, the European Commission (2022) published <em>Guidelines for Teachers and Educators on Tackling Disinformation and Promoting Digital Literacy Through Education and Training</em>. The present research looked at the role of social media literacy in increasing awareness of the role of social media in spreading disinformation. We developed an educational intervention based on the European Commission guidelines. We investigated its impact on perceived social media literacy, the intention to share fake news on social media, and general conspiracy beliefs. We conducted a within-subject (two times measurement: before the educational intervention and one week after) +1 experiment with <em>N</em> = 127 young adults (aged 18 to 23). After filling in an initial survey, the experimental group received a 15-minute educational intervention on the role of social media for disinformation dissemination in complex digital information environments. One week later, all participants completed the second survey to assess perceived social media literacy and general conspiracy beliefs. In both surveys, participants saw three Instagram posts from a fictitious media outlet to express potential intentions to share on social media. Among the three posts, two showed false information. Findings showed that educational intervention produces a significant increase in perceived social media literacy and a decrease in general conspiracy beliefs. Intellectual humility moderates the impact of educational intervention on algorithmic awareness.</span>

A Novel Cloud-Enabled Cyber Threat Hunting Platform for Evaluating the Cyber Risks Associated with Smart Health Ecosystems

The fast proliferation of Internet of Things (IoT) devices has dramatically altered healthcare, increasing the efficiency and efficacy of smart health ecosystems. However, this expansion has created substantial security risks, as cybercriminals increasingly target IoT devices in order to exploit their weaknesses and relay critical health information. The rising threat landscape poses serious concerns across various domains within healthcare, where the protection of patient information and the integrity of medical devices are paramount. Smart health systems, while offering numerous benefits, are particularly vulnerable to cyber-attacks due to the integration of IoT devices and the vast amounts of data they generate. Healthcare providers, although unable to control the actions of cyber adversaries, can take proactive steps to secure their systems by adopting robust cybersecurity measures, such as strong user authentication, regular system updates, and the implementation of advanced security technologies. This research introduces a groundbreaking approach to addressing the cybersecurity challenges in smart health ecosystems through the deployment of a novel cloud-enabled cyber threat-hunting platform. This platform leverages deception technology, which involves creating decoys, traps, and false information to divert cybercriminals away from legitimate health data and systems. By using this innovative approach, the platform assesses the cyber risks associated with smart health systems, offering actionable recommendations to healthcare stakeholders on how to minimize cyber risks and enhance the security posture of IoT-enabled healthcare solutions. Overall, this pioneering research represents a significant advancement in safeguarding the increasingly interconnected world of smart health ecosystems, providing a promising strategy for defending against the escalating cyber threats faced by the healthcare industry.

Crowdsourcing the Mitigation of disinformation and misinformation: The case of spontaneous community-based moderation on Reddit

Community-based content moderation, an approach that utilises user-generated knowledge to shape the ranking and display of online content, is recognised as a potential tool in combating disinformation and misinformation. This study examines this phenomenon on Reddit, which employs a platform-wide content ranking system based on user upvotes and downvotes. By empowering users to influence content visibility, Reddit's system serves as a naturally occurring community moderation mechanism, providing an opportunity to analyse how users engage with this system. Focusing on discussions related to climate change, we observe that in this domain, low-credibility content is spontaneously moderated by Reddit users, although the magnitude of this effect varies across Subreddits. We also identify temporal fluctuations in content removal rates, indicating dynamic and context-dependent patterns influenced by platform policies and socio-political factors. These findings highlight the potential of community-based moderation in mitigating online false information, offering valuable insights for the development of robust social media moderation frameworks.

Paradox of Criminal Provisions Formulation Regarding Reporting of Political Party Campaign Funds in the 2024 General Election

Political parties as a form of organization whose scope is the political field make it a primary position in modern democracy which is required to be in line with democratic norms and election principles. The characteristics of political parties as legal subjects (subjectum juris) of corporations/public legal entities that can also act in the private sphere that are attached to legal consequences for all actions or deeds, and legal relationships, both in the administrative, civil, and criminal spheres. One of the actions or legal acts of political parties in the Election is the legal obligation to report campaign funds honestly, accountably, and transparently in accordance with the principles of organizing elections as a legal act that has the potential to have administrative and criminal consequences. In several formulations of criminal provisions of the Election Law which are specifically formulated for political parties as legal subjects (subjectum juris) of corporations, although the construction of the articles does not explicitly mention political parties, but refers to the phrase Election Participants which consists of 5 Articles, namely Article 496, Article 525 Paragraph (2), Article 526 Paragraph (2), Article 527, and Article 528 Paragraph (1) of the a quo Law. One of the actions or legal acts of political parties that has the potential to be subject to criminal penalties is the provision of Article 496 of the a quo Law related to election participants who intentionally provide false information in the Election campaign fund report, which constructively-holistically reaps problems in the actions or legal actions of political parties as election participants as well as the problem of deadlocked norms in related articles in the a quo Law.

The Silent Saboteur: The Impact and Management of Malicious Word-Of-Mouth in The Digital Age

In the digital era, the impact of malicious word-of-mouth on individuals and businesses is complex. Malicious online reviews and false information can spread quickly, causing serious damage to personal reputation and corporate branding. This article provides an in-depth analysis of the effects of malicious word-of-mouth on various aspects of the economy and society, and proposes effective strategies for response. Through specific case studies, such as the controversy surrounding the BOY LONDON brand, the public relations crisis of the Wahaha brand, the damage to Jiang Ping's personal image, and the "pink-haired girl" incident, this article illustrates how malicious word-of-mouth can erode consumer decision-making autonomy, pose threats to personal physical and mental health, and tarnish the image of brands and enterprises. To address these challenges, companies and governments must take proactive measures, such as monitoring and responding to negative information on the Internet to maintain a positive image and strengthening Internet regulation to ensure the authenticity and transparency of information and to prevent the spread of false information. At the same time, improving public media literacy helps people identify and resist the influence of malicious word-of-mouth.

Have people ‘had enough of experts’? The impact of populism and pandemic misinformation on institutional trust in comparative perspective

ABSTRACT Public trust in institutions is a key prerequisite for effective crisis management. However, the rise of populism and misinformation in recent years made it increasingly difficult to maintain institutional trust. Despite this recognition, we still lack a systematic understanding of how exposure to misinformation and populist political orientation affect people’s trust in institutions. This paper fills this gap by adopting an original approach to trust, focusing on prospective trust rather than trust in the present, and by comparing four countries led by populist leaders during the pandemic – Brazil, Poland, Serbia, and the United States. The comparative design allows us to consider not only the role of individual-level factors (populist attitudes and misinformation exposure) but also the role of different approaches to the COVID-19 pandemic adopted in the four countries. The study utilizes data from a cross-sectional survey, carried out between November and December 2022 (N = 5000). Our findings show that populist attitudes are the most significant predictor of distrust in political institutions in all four countries. Believing in false information related to COVID-19, on the other hand, has a stronger impact on distrust in expert institutions – public health authorities, scientists, and medical professionals. The data also highlight the importance of local context and different approaches to handling the pandemic in the dynamics of trust. In Poland and Serbia, populist voters have more trust in both healthcare authorities as well as in political institutions; however, in Brazil and the United States, populist voters were more likely to distrust expert institutions.

ASCL: Accelerating semi‐supervised learning via contrastive learning

SummarySSL (semi‐supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo‐labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo‐labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug‐and‐play method named Accelerating semi‐supervised learning via contrastive learning (ASCL). This method combines contrastive learning with uncertainty‐based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo‐labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi‐supervised learning. Uncertainty‐based selection provides a robust mechanism for acquiring pseudo‐labels. The combination of the contrastive learning module and the uncertainty‐based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state‐of‐the‐art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR‐10 dataset, the application of ASCL to Pseudo‐label, UDA (unsupervised data augmentation for consistency training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%.

The opioid industry's use of scientific evidence to advance claims about prescription opioid safety and effectiveness

Abstract It is widely recognized that pharmaceutical marketing contributed to the ongoing US opioid epidemic, but less is understood about how the opioid industry used scientific evidence to generate product demand, shape opioid regulation, and change clinician behavior. In this qualitative study, we characterize select scientific articles used by industry to support safety and effectiveness claims and use a novel database, the Opioid Industry Documents Archive, to determine notable elements of industry and non-industry documents citing the scientific articles to advance each claim. We found that 15 scientific articles were collectively mentioned in 3666 documents supporting 5 common, inaccurate claims: opioids are effective for treatment of chronic, non-cancer pain; opioids are “rarely” addictive; “pseudo-addiction” is due to inadequate pain management; no opioid dose is too high; and screening tools can identify those at risk of developing addiction. The articles contributed to the eventual normalization of these claims by symbolically associating the claims with scientific evidence, building credibility, expanding and diversifying audiences and the parties asserting the claims, and obfuscating conflicts of interest. These findings have implications for regulators of industry products and corporate activity and can inform efforts to prevent similar public health crises.

Content analysis of fluoride-related posts on Instagram

BackgroundThe Internet and social media have become many people’s primary sources of health information. Instagram is one of the most popular social media platforms, and users spend a significant amount of time daily on the platform. Therefore, analyzing oral health-related information shared online can help to identify the requirements of specific groups, assisting in the planning and execution of educational public health actions. As a result, this study was carried out to conduct a qualitative evaluation of fluoride-related content on Instagram in 2022–2023.MethodsUsing hashtags associated with fluoride, a search was conducted on the Instagram application in the Persian language. All fluoride-containing posts were saved, and data was collected thrice at two-month intervals. MAXQDA software version 2020 was used to categorize the collected data content based on type, quality, and quantity.ResultsThe qualitative analysis included 1565 (9.45%) posts with fluoride-related content out of 16,475 total posts. After removing the duplicates, 400 posts remained, among which 282 posts (70.5%) and 118 posts (29.5%) were pro- and anti-fluoride, respectively. After qualitative analysis, 270 codes were examined and categorized into three main themes and eight subthemes: “fluoride sources,” “The advantages and disadvantages of fluoride,” and “Invalid beliefs about fluoride.”ConclusionThe frequency of pro-fluoride use was higher than anti-fluoride posts. However, due to the high prevalence of posts containing false information on fluoride toxicity for health, ineffectiveness on caries prevention, and conspiracy theories, there is a need to improve the quality of information on fluoride by providing scientific and unbiased explanations by professionals online or during dental visits and supervising social media content.

Media Literacy Interventions Improve Resilience to Misinformation: A Meta-Analytic Investigation of Overall Effect and Moderating Factors

The widespread dissemination of misinformation has become a global concern. A recommended solution is to improve people’s ability to discern true from false information through appropriate media literacy education programs. This meta-analysis quantitatively synthesized the results of 49 experimental studies ( N = 81,155) that examined the efficacy of media literacy interventions in mitigating misinformation. This study finds that media literacy interventions generally improve resilience to misinformation ( d = 0.60). Specifically, the interventions reduce belief in misinformation ( d = 0.27), improve misinformation discernment ( d = 0.76), and decrease misinformation sharing ( d = 1.04). Moreover, media literacy interventions have stronger effects (1) when multiple sessions rather than a single session are implemented, (2) in high (vs. low) uncertainty avoidance cultures, and (3) among college students than among adults recruited from online crowdsourcing platforms (e.g., Amazon Mechanical Turk). These findings enrich our understanding of inoculation theory and provide valuable guidance for the design of future media literacy intervention programs.

Mengurai Polarisasi Politik di Kalangan Milenial Peran Komunikasi dalam Pemilu

Abstract Political polarization among millennials has become an increasingly prominent challenge in recent years. This generation, which is a significant potential voter in the 2024 Election, is vulnerable to misleading political campaigns, hate speech and false information spread through social media and digital platforms. High levels of polarization can threaten social cohesion and hinder constructive dialogue in the democratic process. This research aims to explore the role of effective political communication and media literacy in unraveling political polarization among millennials in the 2024 elections. By using a mixed method, this research combines quantitative survey methods, qualitative studies with in-depth interviews and focus group discussions, as well as media content analysis to provide a comprehensive picture of this issue. It is hoped that the research results can provide insight into effective political communication strategies, the importance of cross-group dialogue, and the role of media literacy and political education in reducing polarization among millennials. It is hoped that these findings can contribute to efforts to create a healthier and more constructive political environment in the 2024 elections and the democratic process as a whole. Keywords: Political Polarization, Political Communication, Media Literacy, Millennials, 2024 Election

Spoofing countermeasure for fake speech detection using brute force features

Due to the progress in deep learning technology, techniques that generate spoofed speech have significantly emerged. Such synthetic speech can be exploited for harmful purposes, like impersonation or disseminating false information. Researchers in the area investigate the useful features for spoof detection. This paper extensively investigates three problems in spoof detection in speech, namely, the imbalanced sample per class, which may negatively affect the performance of any detection models, the effect of the feature early and late fusion, and the analysis of unseen attacks on the model. Regarding the imbalanced issue, we have proposed two approaches (a Synthetic Minority Over Sampling Technique (SMOTE)-based and a Bootstrap-based model). We have used the OpenSMILE toolkit, to extract different feature sets, their results and early and late fusion of them have been investigated. The experiments are evaluated using the ASVspoof 2019 datasets which encompass synthetic, voice-conversion, and replayed speech samples. Additionally, Support Vector Machine (SVM) and Deep Neural Network (DNN) have been adopted in the classification. The outcomes from various test scenarios indicated that neither the imbalanced nature of the dataset nor a specific feature or their fusions outperformed the brute force version of the model as the best Equal Error Rate (EER) achieved by the Imbalance model is 6.67 % and 1.80 % for both Logical Access (LA) and Physical Access (PA) respectively.

Exploring the Relation Between Health Literacy, Infodemic, and Acceptance of COVID-19 Vaccination in Iran: A Cross-Sectional Study.

Health literacy (HL) plays a crucial role in navigating the coronavirus disease 2019 (COVID-19) pandemic, especially with the abundance of conflicting information from unauthorized sources. This study aims to measure COVID-19 vaccine acceptability, COVID-19 HL, and COVID-19 infodemic and their predictors in Iranians. A cross-sectional study was conducted on the general population in Iran between August 20, 2021 to October 29, 2021. Data were collected using a valid questionnaire with four parts. Univariable and multivariable logistic regression models, generalized linear models, and ordinal regression with the Polytomous Universal Model were performed using SPSS version 26. The study revealed that 68.6% of the participants were willing to accept the COVID-19 vaccine, 45% had sufficient knowledge about COVID-19, and 52.2% were confused by false information during the pandemic. The results showed that having sufficient COVID-19 HL was significantly associated with higher acceptance of the vaccine (adjusted odds ratio = 1.52, 95% confidence interval (CI): 1.02, 2.12). Additionally, higher COVID-19 HL scores were associated with a higher adjusted odds of confusion by false information with an odds ratio of 1.12 (95% CI: 1 .11, 1 .13). This study exhibits low levels of COVID-19 HL in Iran, and the fact that higher COVID-19 HL is associated with higher vaccine acceptance. Because vaccination is an essential preventive measure to stop the COVID-19 pandemic, factors associated with low vaccine acceptance need to be addressed by public health strategies. [HLRP: Health Literacy Research and Practice. 2024;8(4):e184-e193.].

Dancing in the Dark: The Case for Chaos in an Age of AI Generated Answers

ABSTRACTOne of the key aims of information search tools is to organise information and make it accessible and useful, reducing the chaos of an information deluge to ten blue links, perhaps with some context about the utility of the information they represent. Generative AI answer engines, such as ChatGPT, have gone one step further, reducing the ten blue links to a single passage of text: highly digestible, easily accessible, possibly false and ultimately bland. All chaos, including disagreement between sources, possible false information, and the influence of societal biases, and the opportunity to access more information is hidden under a rug of simple, authoritative sounding text. Friction, disagreement, reflections and odd informational juxtapositions have long been known to underpin serendipity, inspiration and creativity, yet these answer engines reduce all of these opportunities, particularly in the context of complex information needs. Using ‘bounded chaos’ as a framework, we critique the trade‐offs made by answer engines, demonstrating that, even as they are non‐deterministic, they hide the wrong parts of the chaos for those who seek learning or inspiration. We make the case against bland, and defend information seekers need for perceptible heterogeneity, friction, and even a little chaos.

Event-triggered sliding mode control for networked T–S fuzzy delayed systems against random injection attacks

This paper investigates the event-triggered sliding mode control (SMC) problem for networked T–S fuzzy delayed systems subject to random injection attacks, where the event-triggered mechanism (ETM) is introduced to alleviate data congestion and improve network efficiency. Under an open network environment, malicious attackers may randomly inject false information into the controller–actuator channel to compromise the integrity of the data. A new fuzzy sliding mode controller is constructed based on a non-parallel distributed compensation strategy, which introduces the distribution information of the attack probability. Then, the membership function dependent (MFD) analysis technique is adopted to reconstruct the membership functions in order to reduce the conservatism caused by the mismatch between the membership functions of fuzzy model and controller. Furthermore, the co-design conditions for the ETM and the fuzzy sliding mode controller are provided, which can ensure that the resulting closed-loop fuzzy system is mean-square exponentially ultimately bounded (EUB) and the sliding surface is reachable. Finally, a simulation experiment with tunnel diode circuit application is utilized to demonstrate the validity of the proposed SMC scheme.