True Information Research Articles

Punishment Trumps Warning in Prebunking Misinformation: Evidence From Survey Experiments in Taiwan

There have been many attempts to use prebunking strategies to address the problem of misinformation. While extant research supports their efficacy, it also finds that they could make people suspicious even of true information. This study employs survey experiments in Taiwan to assess the effect of warning messages, with or without mentioning punishments one might incur if spreading misinformation, on people’s beliefs in false and true news, and their subsequent intention to share the information with others. The findings suggest that warning messages mentioning monetary punishments are the most effective in affecting people’s beliefs and intentions to behave.

Roodmus: a toolkit for benchmarking heterogeneous electron cryo-microscopy reconstructions.

Conformational heterogeneity of biological macromolecules is a challenge in single-particle averaging (SPA). Current standard practice is to employ classification and filtering methods that may allow a discrete number of conformational states to be reconstructed. However, the conformation space accessible to these molecules is continuous and, therefore, explored incompletely by a small number of discrete classes. Recently developed heterogeneous reconstruction algorithms (HRAs) to analyse continuous heterogeneity rely on machine-learning methods that employ low-dimensional latent space representations. The non-linear nature of many of these methods poses a challenge to their validation and interpretation and to identifying functionally relevant conformational trajectories. These methods would benefit from in-depth benchmarking using high-quality synthetic data and concomitant ground truth information. We present a framework for the simulation and subsequent analysis with respect to the ground truth of cryo-EM micrographs containing particles whose conformational heterogeneity is sourced from molecular dynamics simulations. These synthetic data can be processed as if they were experimental data, allowing aspects of standard SPA workflows as well as heterogeneous reconstruction methods to be compared with known ground truth using available utilities. The simulation and analysis of several such datasets are demonstrated and an initial investigation into HRAs is presented.

Methodology comparison of environmental sediment fungal community analysis

Fungi play important roles in ecosystems. Analyzing fungal communities in environments has long been a challenge due to the large difference in compositions retrieved using different methods or sequencing regions, obscuring the true abundance and species information. Our study aimed to compare and determine more accurate approach for evaluating fungal populations in river sediment. To achieve this, different primer sets in the internal transcribed spacer (ITS) (ITS5/ITS1R, ITS1F/ITS2), 18S rRNA gene (0817F/1196R) for High-throughput sequencing (HTS), metagenomic shotgun sequencing (MS) directly from environmental samples, and HTS using ITS primers for the fungal samples collected from plate cultivation were used to characterize the fungal communities. We calculated diversity index and used FungalTraits to analyze methods preferences for fungal species. The study revealed that when analyzing the fungal species directly from environmental samples, amplification and sequencing of ITS region demonstrated more accuracy than MS and 18S rRNA gene sequencing methods, but displayed significant primer preference. Over 30 % fungal species from HTS after plate cultivation were not present in HTS from the environmental samples. NMDS analysis demonstrated significant disparities in species diversity among different methods, suggesting potential complementarity between them. Over 85% species identified by HTS using ITS primers belonged to filamentous fungi, while the MS mostly identified yeast (62%). Therefore, to get more accurate fungal community information in sediment, multiple methods were recommended by using cultivation, molecular biological methods dependent on PCR techniques like ITS1F/ITS2 primer for HTS and PCR independent method such as metagenomic shotgun sequencing techniques.

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.

Mesiri: Mephisto Early Supernovae Ia Rapid Identifier

The early time observations of Type Ia supernovae (SNe Ia) play a crucial role in investigating and resolving longstanding questions about progenitor stars and the explosion mechanisms of these events. Colors of supernovae (SNe) in the initial days after the explosion can help differentiate between different types of SNe. However, the use of true color information to identify SNe Ia at the early-time explosion is still in its infancy. The Multi-channel Photometric Survey Telescope (Mephisto) is a photometric survey telescope equipped with three CCD cameras, capable of simultaneously imaging the same patch of sky in three bands (u, g, i or v, r, z), yielding real-time colors of astronomical objects. In this paper, we introduce a new time-series classification tool named Mephisto Early Supernovae Ia Rapid Identifier (Mesiri), which, for the first time, utilizes real-time color information to distinguish early-time SNe Ia from core-collapse supernovae. Mesiri is based on the deep learning approach and can achieve an accuracy of 96.75% ± 0.79%, and AUC of 98.87% ± 0.53% in case of single epoch random observation before the peak brightness. These values reach towards perfectness if additional data points on several night observations are considered. The classification with real-time color significantly outperforms that with pseudo-color, especially at the early time, i.e., with only a few points of observations. The BiLSTM architecture shows the best performance compared to others that have been tested in this work.

Computer Vision-Driven Movement Annotations to Advance fNIRS Pre-Processing Algorithms.

Functional near-infrared spectroscopy (fNIRS) is beneficial for studying brain activity in naturalistic settings due to its tolerance for movement. However, residual motion artifacts still compromise fNIRS data quality and might lead to spurious results. Although some motion artifact correction algorithms have been proposed in the literature, their development and accurate evaluation have been challenged by the lack of ground truth information. This is because ground truth information is time- and labor-intensive to manually annotate. This work investigates the feasibility and reliability of a deep learning computer vision (CV) approach for automated detection and annotation of head movements from video recordings. Fifteen participants performed controlled head movements across three main rotational axes (head up/down, head left/right, bend left/right) at two speeds (fast and slow), and in different ways (half, complete, repeated movement). Sessions were video recorded and head movement information was obtained using a CV approach. A 1-dimensional UNet model (1D-UNet) that detects head movements from head orientation signals extracted via a pre-trained model (SynergyNet) was implemented. Movements were manually annotated as a ground truth for model evaluation. The model's performance was evaluated using the Jaccard index. The model showed comparable performance between the training and test sets (J train = 0.954; J test = 0.865). Moreover, it demonstrated good and consistent performance at annotating movement across movement axes and speeds. However, performance varied by movement type, with the best results being obtained for repeated (J test = 0.941), followed by complete (J test = 0.872), and then half movements (J test = 0.826). This study suggests that the proposed CV approach provides accurate ground truth movement information. Future research can rely on this CV approach to evaluate and improve fNIRS motion artifact correction algorithms.

Direct Evidence of Breakdown of Spin Statistics in Ion-Atom Charge Exchange Collisions.

Recent experimental studies have questioned the validity of spin statistics assumptions, particularly in charge exchange processes occurring in atomic MeV collisions. Here, we study spin-resolved single electron capture processes in collisions between C^{3+} ions and helium within an energy range of 1.25-400 keV/u. Using high resolution reaction microscope and multielectronic theoretical approaches, we directly measure and calculate the true population information of the C^{2+}(1s^{2}2s2p ^{1,3}P) states at the time of electron capture, overcoming the previous experimental and theoretical difficulties. At the level of integral and scattering angle differential cross sections, our results demonstrate the breakdown of pure spin statistics arguments, especially at high impact energies where they are traditionally expected to be valid. These novel findings and conclusions raise intriguing questions both in the understanding of the electronic dynamics during such fast collisional processes and in exploring quantum manipulation of atomic and molecular reactivity.

Batch effects correction in scRNA-seq based on biological-noise decoupling autoencoder and central-cross loss

Technical or biologically irrelevant differences caused by different experiments, times, or sequencing platforms can generate batch effects that mask the true biological information. Therefore, batch effects are typically removed when analyzing single-cell RNA sequencing (scRNA-seq) datasets for downstream tasks. Existing batch correction methods usually mitigate batch effects by reducing the data from different batches to a lower dimensional space before clustering, potentially leading to the loss of rare cell types. To address this problem, we introduce a novel single-cell data batch effect correction model using Biological-noise Decoupling Autoencoder (BDA) and Central-cross Loss termed BDACL. The model initially reconstructs raw data using an auto-encoder and conducts preliminary clustering. We then construct a similarity matrix and a hierarchical clustering tree to delineate relationships within and between different batches. Finally, we introduce a Central-cross Loss (CL). This loss leverages cross-entropy loss to prompt the model to better distinguish between different cluster labels. Additionally, it employs the Central Loss to encourage samples to form more compact clusters in the embedding space, thereby enhancing the consistency and interpretability of clustering results to mitigate differences between different batches. The primary innovation of this model lies in reconstructing data with an auto-encoder and gradually merging smaller clusters into larger ones using a hierarchical clustering tree. By using reallocated cluster labels as training labels and employing the Central-cross Loss, the model effectively eliminates batch effects in an unsupervised manner. Compared to current methods, BDACL can mitigate batch effects without losing rare cell types.

Disclosure of true medical information: the case of Bangladesh

BackgroundTruth-telling in health care is about providing patients with accurate information about their diagnoses and prognoses to enable them to make decisions that can benefit their overall health. Physicians worldwide, especially in the United Kingdom (U.K.) and the United States (U.S.), openly share such medical information. Bangladesh, however, is a Muslim-majority society with different social norms than Western societies. Therefore, we examined whether Muslim culture supports truth disclosure for patients, particularly how and to what extent medical information about life-threatening diseases is provided to patients in Bangladesh.MethodsThis was a phenomenological qualitative study. We conducted thirty in-depth interviews with clinicians, nurses, patients and their relatives at Shaheed Suhrawardy Medical College Hospital in Dhaka, Bangladesh. We also used observations to explore interactions between patients, families and healthcare professionals regarding their involvement in medical decisions and truth disclosure issues. NVivo software was used to identify common themes, and a thematic analysis method was utilised to analyse the datasets.ResultsThis study identified three recurring themes relevant to the ethics and practice of truth disclosure: best interest rather than autonomy, the ambivalent value of deception and who understands what. The participants revealed that physicians often withhold fatal medical prognoses from terminally ill patients to ensure the best healthcare outcomes. The results indicate that deception towards patients is commonly accepted as a means of reducing burden and providing comfort. The participants opined that true medical information should be withheld from some patients, assuming that such disclosures may create a severe burden on them. Whether or to what extent medical information is disclosed primarily depends on a family’s wishes and preferences.ConclusionsWhile truth disclosure to patients is considered an ethical norm in many cultures, such as in the U.K. and the U.S., the practice of concealing or partially revealing severe medical prognoses to patients is an actual medical practice in Bangladeshi society. This study emphasises the importance of recognising a patient’s active involvement and respecting the cultural values that shape family involvement in medical decision-making. These findings may have significant policy and practical implications for promoting patient autonomy within Bangladeshi family dynamics and religious-based cultural values.

Exposure to detectable inaccuracies makes children more diligent fact-checkers of novel claims.

How do children decide when to believe a claim? Here we show that children fact-check claims more and are better able to catch misinformation when they have been exposed to detectable inaccuracies. In two experiments (N = 122), 4-7-year-old children exposed to falsity (as opposed to all true information) sampled more evidence before verifying a test claim in a novel domain. Children's evidentiary standards were graded: fact-checking increased with higher proportions of false statements heard during exposure. A simulation suggests that children's behaviour is adaptive, because increased fact-checking in more dubious environments supports the discovery of potential misinformation. Importantly, children were least diligent at fact-checking a new claim when all prior information was true, suggesting that sanitizing children's informational environments may inadvertently dampen their natural scepticism. Instead, these findings support the counterintuitive possibility that exposing children to some nonsense may scaffold vigilance towards more subtle misinformation in the future.

Evaluating the damage of collapsed bridges using remote sensing technologies: Case study: Baltimore’s Francis Scott Key Bridge

Bridges are vital for linking communities and facilitating economic activity. However, in the face of disaster, like ship collisions pose a significant threat to bridge infrastructure, causing structural damage and potential safety hazards. Rapid and precise assessment of the damage is essential for effective emergency response and recovery operations. Remote sensing with near-real-time satellite imagery provides the disaster scenario. This paper presents a change detection using pre- and post-disaster satellite data for Baltimore’s Francis Scott Key Bridge to identify structural damage due to the collision of the ship with support pillars on 26 March 2024. Both optical and microwave satellite data were used from open data sources and analyzed based on geospatial techniques such as change detection and surface profiling. It is estimated that an 1100-meter span of bridge was affected due to this collision, which helped to estimate the damage and mobilize the rescue operations. It may need further validation from ground truth information. Hence, the current study emphasizes the potential of remote sensing satellite data to provide near-real-time impact on disaster analysis.

Jurisprudential Rulings Relevant to the Use of Artificial Intelligence in the Medical Field

*This paper of research is sponsored and funded by Kuwait University, Research Project No. (HC03/23) Idea of the Research is to address the topic of using artificial intelligence in the medical field from the Islamic point of view. Its importance lies in focusing on the way Islamic Law keeps pace with latest issues that comply with the provisions and objectives of Islamic Law. Hence, the problematic deals with jurisprudential rulings relevant to the use of AI in the medical field and providing patients’ confidential data to AI software. This research aims to explain some jurisprudential rulings on using AI. Accordingly, extrapolatory, analytical, and comparative methods have been used. The main findings of the research are the following: learning AI is a collective duty provided that it agrees with the Objectives of Islamic Law that firmly stipulates non-disclosure of patients’ medical data except in a few cases to bring benefit or prevent harm. On this basis, practitioners can provide AI software with confidential patients’ information under certain conditions such as achieving public interest, obtaining permission and a written consent from the patient, revealing true information and preventing any kind of harm. In this regard, the researcher urges to keep pace with the latest AI developments and encourages more jurisprudential studies to promote research on emerging medical issues.

Can large language models address unmet patient information needs and reduce provider burnout in the management of thyroid disease?

BackgroundPatient electronic messaging has increased clinician workload contributing to burnout. Large language models can respond to these patient queries, but no studies exist on large language model responses in thyroid disease. MethodsThis cross-sectional study randomly selected 33 of 52 patient questions found on Reddit/askdocs. Questions were found through a “thyroid + cancer” or “thyroid + disease” search and had verified-physician responses. Additional responses were generated using ChatGPT-3.5 and GPT-4. Questions and responses were anonymized and graded for accuracy, quality, and empathy using a 4-point Likert scale by blinded providers, including 4 surgeons, 1 endocrinologist, and 2 physician assistants (n = 7). Results were analyzed using a single-factor analysis of variance. ResultsFor accuracy, the results averaged 2.71/4 (standard deviation 1.04), 3.49/4 (0.391), and 3.66/4 (0.286) for physicians, GPT-3.5, and GPT-4, respectively (P < .01), where 4 = completely true information, 3 = greater than 50% true information, and 2 = less than 50% true information. For quality, the results were 2.37/4 (standard deviation 0.661), 2.98/4 (0.352), and 3.81/4 (0.36) for physicians, GPT-3.5, and GPT-4, respectively (P < .01), where 4 = provided information beyond what was asked, 3 = completely answers the question, and 2 = partially answers the question. For empathy, the mean scores were 2.37/4 (standard deviation 0.661), 2.80/4 (0.582), and 3.14/4 (0.578) for physicians, GPT-3.5, and GPT-4, respectively (P < .01), where 4 = anticipates and infers patient feelings from the expressed question, 3 = mirrors the patient's feelings, and 2 = contains no dismissive comments. Responses by GPT were ranked first 95% of the time. ConclusionsLarge language model responses to patient queries about thyroid disease have the potential to be more accurate, complete, empathetic, and consistent than physician responses.

Identifying the Original Source of Megalithic Stones in Tambunan Using Spectral Signature Technique

Megalithic stones are a remnant of the ancient people. An intriguing megalithic stone legacy ought to emphasize the context in which it fits. Consequently, it is necessary to ascertain each megalithic stone’s historical context. This is significant for the archaeologist to obtain true and valid information. In Tambunan, there are numerous myths about megalithic stones. As a result, it is impossible to verify the story’s validity. These days, remote sensing has emerged as a crucial technical instrument for archaeologists to gather spatial data in both broad and localized locations. Prior research has demonstrated the efficacy of remote sensing in identifying archaeological site remnants. Nevertheless, there have never been any investigations utilizing remote sensing in relation to small-sized megalithic stones. Thus, this study employs spectral signature techniques and remote sensing to identify the original source of the megalithic stones discovered in Tambunan. This study carries out the spectral signature of megalithic stones in Tambunan using Sentinel-2A satellite image data with a 5-meter resolution using the SNAP application. Before that, the coordinates and measurements for each megalithic stone were obtained from the ground. The spectral signature of all the megalithic stones was then compared with the spectral signature of the stones near the river or near the hill. The study’s findings demonstrate that the spectral signature of the megalithic stones in question is identical to the stone found in the river near its location. As a result, this study was successful in providing scientific evidence that the river is the source of the megalithic stones in Tambunan, despite the stones’ considerable distance from their site.

SSM-CloudNet: state space model-based cloud and cloud shadow detection network for Sentinel-2 imagery

Abstract Remote sensing images acquired by satellite sensors are inevitably obscured by clouds and cloud shadows, which obscure true ground reflectance information in those areas. Therefore, to ensure the accuracy and reliability of downstream analyses, detecting clouds and their shadows in satellite images is crucial. This paper proposes a state space model-based cloud and cloud shadow detection network for Sentinel-2 imagery, named SSM-CloudNet. This is the first time that a state space model (SSM) is applied to cloud and cloud shadow detection tasks, combining the efficient feature extraction capability of the symmetric encoder-decoder structure with the accuracy of SSM in cloud feature extraction, and providing an innovative approach to this task. Experiments demonstrate that SSM-CloudNet performs exceptionally well across multiple metrics, proving the high effectiveness of the proposed method.

Classifier surrogates: sharing AI-based searches with the world

In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and rely on experiment-internal data as well as full detector simulations. We show a concrete implementation of a newly proposed strategy, so-called Classifier Surrogates, to be trained inside the experiments, that only utilise publicly accessible features and truth information. These surrogates approximate the original classifier distribution, and can be shared with the public. Subsequently, such a model can be evaluated by sampling the classification output from high-level information without requiring a sophisticated detector simulation. Technically, we show that continuous normalizing flows are a suitable generative architecture that can be efficiently trained to sample classification results using conditional flow matching. We further demonstrate that these models can be easily extended by Bayesian uncertainties to indicate their degree of validity when confronted with unknown inputs by the user. For a concrete example of tagging jets from hadronically decaying top quarks, we demonstrate the application of flows in combination with uncertainty estimation through either inference of a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights.

The Importance Of Public Relations In The Post-Truth Era And The Strategies Followed

‘Fake news’, which are generally created for a specific purpose on different topics, spread rapidly through social media platforms and search engines and prevent the public from accessing real information. Throughout history, fake news and misinformation have been used for various purposes. However, with the introduction of social media into our lives, the dissemination of unproven information or fake news has accelerated, and such content has reached large masses in a short time through both professional content providers and users. Despite the fake news about health and nutrition, raising awareness is a prerequisite for creating healthy societies that consume and eat consciously, and this can be made easier with sustainable public relations. In this study, the function of verification platforms in preventing such information disseminated in the new media environment in the post-truth era is examined. Within the scope of the study, the results of the verification platform teyit.org on health and nutrition claims were analysed. In the study, it was observed that only four out of 14 claims on health and nutrition shared on the internet and social media were found to be true information, and that such information was mostly disseminated through social media.

Evaluating the damage of collapsed bridges using remote sensing technologies: Case study: Baltimore’s Francis Scott Key Bridge

&lt;p class="MsoNormal" style="line-height: 120%; layout-grid-mode: char; mso-layout-grid-align: none; margin: 12.0pt 0cm 6.0pt 147.4pt;"&gt;&lt;span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-fareast-language: JA; mso-no-proof: no;"&gt;Bridges are vital for linking communities and facilitating economic activity. However, in the face of disaster, like ship collisions pose a significant threat to bridge infrastructure, causing structural damage and potential safety hazards. Rapid and precise assessment of the damage is aid for effective emergency response and recovery operations. Remote sensing with near real-time satellite imagery provides the disaster scenario. This paper presents a change detection using pre and post-disaster satellite data for Baltimore&amp;rsquo;s Francis Scott Key Bridge to identify structural damage due to the collision of the ship with support pillars on 26 March 2024. Both optical and microwave satellite data were used from open data sources and analyzed based on geospatial techniques such as change detection and surface profiling. It is estimated that an 1100 m span of bridge was affected due to this collision, which helped to estimate the damage and mobilize the rescue operations. It may need further validation from ground truth information. Hence, the current study emphasizes the potential of remote sensing satellite data to provide near-real-time impact on disaster analysis.&lt;/span&gt;&lt;/p&gt;

Assigning credit where it is due: an information content score to capture the clinical value of multiplexed assays of variant effect

BackgroundA variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of the current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, a gap in this measure of utility is that it underrepresents the information gained from MAVEs. The aim of this study was to develop an improved quantification metric for MAVE utility. We propose adopting an information content approach that includes data that does not reclassify variants will better reflect true information gain. We adopted an information content approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information.ResultsBRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification.ConclusionsAn information content approach will more accurately portray information gained through MAVE mapping efforts than by counting the number of variants reclassified. This information content approach may also help define the impact of guideline changes that modify the information definitions used to classify groups of variants.

Comparative Perceptions Influence Actions on Climate Change Between Eastern and Western Europeans

Climate change is an important issue that countries worldwide are aware of and campaign to reduce. All parties must cooperate, whether the government, the private sector, individuals, etc. Many perception surveys have met the public's understanding and knowledge about climate change. Despite the odds, previous research supported environmental protection and sustainability in surveys, polls, and past research. However, the existing literary gap between intentions and actions and awareness and implementation has recently widened. The objectives of this research include comparing perceptions and climate change mitigation actions between Eastern and Western European countries because both regions are different, as well as searching for and comparing perceptions influencing climate change mitigation actions between them. The research adopts a quantitative methodology, emphasizing exploring secondary data from the European Investment Bank (EIB) climate change survey 2022–2023. It analyzed the data to compare the perceptions and actions of the two regions in mitigating climate change. The statistical tests included a t-test and stepwise multiple regression analysis to predict the influence of perceptions and actions on reducing climate change. The results showed that respondents' perceptions of climate change in Eastern and Western Europe were not significantly different. However, there was a significant difference in the number of reduction actions taken by Western Europeans compared to Eastern Europeans. Perceptions of climate change catastrophes and serious government mitigation actions positively influenced actions in Eastern Europe. This study found that businesses' attention to mitigation and their perception of the catastrophic effects of climate change positively influence mitigation actions in Western Europe. The recommendations guided governments, policymakers, and businesses to take action to reduce climate change, motivating people's adequate perceptions and actions. Stakeholders communicating climate change's impacts on catastrophe should present true information with honesty and transparency. Doi: 10.28991/HEF-2024-05-03-03 Full Text: PDF