Misleading Effects Research Articles

Entertainment-oriented Use of ‘Depression’ on Chinese Social Media and Its Impact on Stigmatization of Depression Groups: A Survey Study

This study investigates the impact of entertainment-oriented uses of the term ‘depression’ on public attitudes and stigmatization. Utilizing a quantitative method collecting data from an online survey of 110 Chinese internet users, the research explores how exposure to casual or humorous references to depression correlates with attitudes towards individuals who actually suffer from depression. Key findings reveal that engagement with entertainment-related content is prevalent, particularly among younger demographics. The study shows that frequent exposure to such content increases the likelihood of using similar statements in casual contexts and decreases people’s trust in others’ depression claim. It is strongly correlated with more negative attitudes toward individuals with depression. Moreover, people who worry more about the misleading effects of entertainment-related term use show stronger willingness to offer support to depression patients.The results are guide for public education and policy. It highlights the need to take steps to address the harmful effects portrayed by recreational use of depression on social media. Recommendations include improving the education system, setting online support groups, and restricting time spent on social media.

Cost of debt financing, stock returns, and corporate strategic ESG disclosure: Evidence from China

AbstractWhether corporate strategic Environmental, Social, and Governance (ESG) disclosure can be effectively screened by external markets still needs more empirical support. Despite numerous studies confirming the positive impact of ESG, the issue of strategic ESG disclosure has yet to receive sufficient attention. This study examines the impact of ESG greenwashing on the cost of debt financing and stock returns using panel data of Chinese A‐share listed corporates from 2012 to 2021. The study finds that external markets fail to recognize ESG greenwashing in the short term, leading to a temporary reduction in the cost of debt financing and an increase in stock returns, but these effects are not lasting. Mechanism analysis shows that ESG greenwashing enhances corporate reputation and reduces the cost of debt financing through the reputation effect, stimulates investor sentiment, and improves stock returns through the sentiment effect. The study identifies management myopia, excessive power, financial distress, and information asymmetry as the main drivers. However, strong oversight by independent directors, involvement of Big Four audit firms, investor site visits, and high industry competition can mitigate these misleading effects. This research expands the understanding of the causes and economic consequences of corporate ESG greenwashing and provides empirical evidence for managing this phenomenon.

Electricity theft detection model based on the CEEMDAN-CNN-ViT

Abstract Most of the traditional power theft detection methods construct the model directly on the basis of the original power sequences, and do not simultaneously consider the long-period dependencies in the long-time sequences and the local connectivity features between periods, which limits the deep excavation of the behavioral laws of power users. In order to further improve the accuracy of power theft detection, this paper proposes a high-precision power theft detection model that integrates local anomaly filtering, energy consumption decomposition, and multi-feature fusion strategies. First, local anomaly filtering is used to eliminate local anomalies in the normalized energy consumption data to avoid the misleading effect of short-term abnormal behavior on the model. Then, the energy consumption decomposition based on CEEMDAN selects specific frequency band data that can accurately characterize the pattern of power theft users to improve the accuracy of power theft detection. Next, the long-time periodic features in the two-dimensional data and the short-time local features in the one-dimensional sequences are integrated by multi-feature fusion to enhance the adaptability of the model. The results show that the proposed model can effectively improve the detection accuracy, detection completeness, F1 score, and accuracy compared with the existing methods.

Theoretical and experimental analysis of sea ice centrifugal desalination and supercooled water production for slurry ice

This study presents a comprehensive investigation of centrifugal desalination of sea ice and the production of slurry ice from supercooled water. A mathematical model for centrifugal desalination was established and validated through experiments. The results displayed a high level of accuracy in both the theoretical and experimental analyses, with an average goodness of fit of approximately 0.94. An experimental platform is established to produce supercooled water, using different types of water as ice-making solutions. The slurry ice is more suitable for centrifugal desalination than solid ice, and it can be obtained by disrupting the supercooled state of supercooled water, with finer ice crystals produced through ultrasonic supercooling release compared to natural methods. Decreasing the refrigerant temperature benefits enhancing the supercooling degree while compromising the stability of supercooled water generation. The stability is investigated with variations in refrigerant temperature to generate a solution temperature 2.0 °C higher than the refrigerant. When the supercooling degree is 1.0 °C, all of these solutions have a high stability of 80 %, however, it becomes worse as the supercooling degree increases. At the supercooling degree of 2.0 °C, saltwater has the highest stability of only 35 %, and seawater has the lowest stability of 15% due to its complicated composition. This phenomenon also reveals that conducting experiments using saline solution instead of seawater can lead to significant misleading effects. Additionally, the impact of flow rate on the supercooling degree is investigated, showing a minor influence.

Susceptibility to poor arguments: The interplay of cognitive sophistication and attitudes

Despite everyday argumentation being crucial to human communication and decision-making, the cognitive determinants of argument evaluation are poorly known. This study examined how attitudes and aspects of cognitive sophistication, i.e., thinking styles and scientific literacy, relate to people’s acceptance of poorly justified arguments (e.g., unwarranted appeals to naturalness) on controversial topics (e.g., genetically modified organisms (GMOs)). The participants were more accepting of poorly justified arguments that aligned with their attitudes compared to those that opposed their attitudes, and this was true regardless of one’s thinking styles or level of scientific literacy. Still, most of the examined aspects of cognitive sophistication were also positively related to fallacy detection. The strongest cognitive predictors of correctly recognizing the fallacies were one’s scientific reasoning ability and active open-mindedness. The results thus imply that decreasing misleading attitude effects, and increasing certain aspects of analytic and scientific thinking, could improve argumentation.

"Trend" Statement Use in the Orthopaedic Literature.

For research to effectively guide clinical decision making, appropriate interpretation of data is paramount. The P -value is a useful tool for guiding the interpretation of data. However, despite its utility, the P -value is not without limitations. Of particular concern is the use of "trend statements" to describe non-statistically significant findings, a practice which introduces subjectivity and variability into data interpretation and can lead to the drawing of undue conclusions. An audit of original research articles published from January 2022 to December 2022 in four high-impact orthopaedic journals was conducted. The selected journals were queried to identify instances in which a non-statistically significant result was labeled as a "trend." The use of trend statements and associated information was recorded and analyzed. One thousand two hundred sixty articles were included in the analysis. 81 articles (6.4%) included a trend statement to describe a non-statistically significant result. Only two articles (2.5%) formally defined what constituted a trend. In 28.8% of cases, the associated P -value was > 0.10. Trend statements are used to describe non-statistically significant findings with moderate frequency in the orthopaedic literature. Given the potentially misleading effects of trend statements, efforts should be made to mitigate their use. If trend statements are to be used, attention should be paid to defining what constitutes a "trend", explicitly acknowledging the lack of statistical significance of the finding to which the trend statement refers, and avoiding drawing undue conclusions from non-statistically significant data.

Contrastive regularization guided label refurbishment for fault diagnosis under label noise

Conventional intelligent fault diagnosis studies require precise labels by default. However, label noise is inevitable in real industrial environments due to human errors, measurement biases, data transmission and storage errors. The performance of existing data-driven methods may be severely affected by mislabels. In this paper, we investigate the fault diagnosis issue of rotating machinery under noisy labels. A generalized framework contrastive regularization guided label refurbishment (CRLR) is proposed, which utilizes contrastive regularization function extracting class prototype to guide label refurbishment. The learned contrastive representations can suppress the misleading effect of noisy labels. The theoretical analysis from an information theoretic perspective also ensures the effectiveness of CRLR. Validation experiments have been conducted on public and private collected datasets using the ResNet-50 as the backbone network. The experiments demonstrate that our proposed framework outperforms the current state-of-the-art methods at different noise rates.

Unraveling Activation-Related Rearrangements and Intrinsic Divergence from Ligand-Specific Conformational Changes of the Dopamine D3 and D2 Receptors.

Effective rational drug discovery hinges on understanding the functional states of the target protein and distinguishing it from homologues. However, for the G protein coupled receptors, both activation-related conformational changes (ACCs) and intrinsic divergence among receptors can be misled or obscured by ligand-specific conformational changes (LCCs). Here, we unraveled ACCs and intrinsic divergence from LCCs of the dopamine D3 and D2 receptors (D3R and D2R), by analyzing their experimentally determined structures and the molecular dynamics (MD) simulation results of the receptors bound with various ligands. In addition to the ACCs common to other aminergic receptors, we revealed unique ACCs for these two receptors, including the extracellular portion of TM5 (TM5e) and TM6e shifting away from TM2e and TM3e, with a subtle rotation of TM5e. In identifying intrinsic divergence, we found more outward tilting of TM6e in the D2R compared to the D3R in both the experimental structures and simulations bound with ligands in different scaffolds. However, this difference was drastically reduced in the simulations bound with nonselective agonist quinpirole, suggesting a misleading effect of LCCs. Further, in the quinpirole-bound simulations, TM1 showed a greater disparity between these receptors, indicating that LCCs may also obscure intrinsic divergence. Importantly, our MD simulations revealed divergence in the dynamics of these receptors. Specifically, the D2R exhibited heightened flexibility compared to the D3R in the extracellular loops and TMs 5e, 6e, and 7e, associated with its greater ligand binding site plasticity. Our results lay the groundwork for crafting ligands specifically targeting the D2R and D3R with more precise pharmacological profiles.

A credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization algorithm

The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk assessment models are inadequate in meeting the risk control requirements of the financial industry. Therefore, this paper proposes a credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization (PSO) algorithm to obtain better credit risk prediction capability. First, a weighted outlier detection method based on the Induced Ordered Weighted Average Operator is proposed to preprocess the data to reduce noisy data’s misleading effect on model training. Then, an undersampling method combined with fuzzy clustering PSO is proposed to overcome the negative effect of category imbalance on model training by resampling the data. In addition, a hyperparameter optimization framework is introduced to adaptively adjust important parameters in the ensemble model considering the impact of parameter settings on the training performance of the model. Based on the evaluation metrics of F-score, AUC, and Kappa coefficient, an empirical analysis was conducted on five credit risk datasets. The results show that the proposed method outperforms the comparative model with an improvement of 10% to 50% in terms of F-score and AUC. The highest achieved F-score is 0.9488, and the maximum AUC is 0.9807, demonstrating the effectiveness of the proposed method. The kappa coefficient results indicate a high level of consistency in the predicted classification results of the model.

A Dynamic Disadvantage? Social Perceptions of Dynamic Morphed Emotions Differ from Videos and Photos

Dynamic face stimuli are increasingly used in face perception research, as increasing evidence shows they are perceived differently from static photographs. One popular method for creating dynamic faces is the dynamic morph, which can animate the transition between expressions by blending two photographs together. Although morphs offer increased experimental control, their unnatural motion differs from the biological facial motion captured in video recordings. This study aimed to compare ratings of emotion intensity and genuineness in video recordings, dynamic morphs, and static photographs of happy, sad, fearful, and angry expressions. We found that video recordings were perceived to have greater emotional intensity than dynamic morphs, and video recordings of happy expressions were perceived as more genuine compared to happy dynamic morphs. Unexpectedly, static photographs and video recordings had similar ratings for genuineness and intensity. Overall, these results suggest that dynamic morphs may be an inappropriate substitute for video recordings, as they may elicit misleading dynamic effects.

Using carbon and oxygen isotopes for global correlation in the Early Triassic Lower Mahil Formation, North Oman, despite potentially misleading diagenetic effect

The study probes into the carbon isotope curves of the Lower Triassic Mahil Formation in two sections situated in the Northern Oman Mountains. The primary objective of the research was to understand the relationship between facies and isotopes, involving the analysis of 174 micrite samples for oxygen and carbon isotopes. Strict measures were implemented during sample selection to mitigate potential influences such as cracks or weathering.The results unveiled diverse δ13C values within the same facies, indicating inconsistencies in carbon isotope trends. Intriguingly, despite notable diagenetic alterations, particularly in the Saiq Plateau section, the Lower Mahil Formation retained carbon isotope patterns with minimal diagenetic impact. However, a note of caution is warranted in the widespread application of these findings, as utilizing whole rock data for carbon isotope analysis may introduce uncertainties.Despite potential diagenetic effects, the carbon isotope records of the Lower Mahil Formation remain valuable for comprehending patterns associated with KS1, facilitating comparisons between local and regional carbon isotope signatures. This research contributes significantly to establishing correlations between outcrop and subsurface data, offering valuable insights into variations in Lower Triassic carbon isotopes.

Antipredator defences in motion: animals reduce predation risks by concealing or misleading motion signals.

Motion is a crucial part of the natural world, yet our understanding of how animals avoid predation whilst moving remains rather limited. Although several theories have been proposed for how antipredator defence may be facilitated during motion, there is often a lack of supporting empirical evidence, or conflicting findings. Furthermore, many studies have shown that motion often 'breaks' camouflage, as sudden movement can be detected even before an individual is recognised. Whilst some static camouflage strategies may conceal moving animals to a certain extent, more emphasis should be given to other modes of camouflage and related defences in the context of motion (e.g. flicker fusion camouflage, active motion camouflage, motion dazzle, and protean motion). Furthermore, when motion is involved, defence strategies are not necessarily limited to concealment. An animal can also rely on motion to mislead predators with regards to its trajectory, location, size, colour pattern, or even identity. In this review, we discuss the various underlying antipredator strategies and the mechanisms through which they may be linked to motion, conceptualising existing empirical and theoretical studies from two perspectives - concealing and misleading effects. We also highlight gaps in our understanding of these antipredator strategies, and suggest possible methodologies for experimental designs/test subjects (i.e. prey and/or predators) and future research directions.

Do We Need an Alternative to the Disenchantment Narrative? Assessing Joas' Criticism of Weber against the Enduring Merit of the Concept and Theory of Disenchantment

Abstract: One of the central claims of Hans Joas' The Power of the Sacred is that Weber's narrative of the disenchantment of the world is in dire need of replacement by a more viable alternative. In his view, the disenchantment narrative not only fails to function as a correct explanation for the genesis and identity of Western modernity, but has a misleading effect on theoretical developments concerning the future of religion as well. Accordingly, Joas aims to liberate academic research from the lasting and disruptive influence of Weber's views of disenchantment. Not only does he reject Weber's term 'disenchantment' as a useful and unambiguous concept , he also discards it as a correct conceptualization of a long and linear historical process . This article expresses appreciation for Joas' challenging endeavor and its deeper intentions, yet ultimately finds his interpretation of Weber untenable on both counts. It critically assesses Joas' position by examining key components of the book where his twofold argumentation is constructed and by developing an alternative interpretation. The upshot of the analysis is to argue that nuance and redefinition are far more fruitful than elimination, and that both the concept and the theory of disenchantment remain meaningful tools for academic research. In one word, while endorsing Joas' invitation for a more in-depth examination of the concept and history of disenchantment, the article defends the enduring theoretical merit of Weber's disenchantment narrative.

Comparison of Cox and Extended Cox Models on Age at First Marriage among Nigerian Women

Cox model is widely used for analysis of factor effects on censored survival time such as age at first marriage. In most practical situations, the fundamental assumption of the proportionality of hazard in Cox model, which implies that the covariates whose effects are to be investigated have a constant impact on the hazard ratio over the time is not always feasible. For example, the values of some of the covariates for individuals may be different over time and these may cause a break-down of proportionality assumption in the hazard model. Ignoring such violation may result in misleading effects of estimates. In this study therefore, Extended Cox models have been used to analyze data on age at first marriage among Nigerian women between 2013 and 2018 waves of Nigerian Demographic and Health Surveys (NDHS). The standard Cox model as well as Extended Cox models under four distinct time functions, namely, t, t2 , log(t) and Heaviside were considered in the analysis and compared using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The standard Cox model was found to perform worst among all the models considered and Extended model with log(t) time function was best fit for the data.

Dimethyl sulfoxide’s impact on epileptiform activity in a mouse model of chronic temporal lobe epilepsy

In the quest for novel treatments for patients with drug-resistant seizures, poor water solubility of potential drug candidates is a frequent obstacle. Literature indicated that the highly efficient solvent dimethyl sulfoxide (DMSO) may have a confounding influence in epilepsy research, reporting both pro- and antiepileptic effects. In this study, we aim to clarify the effects of DMSO on epileptiform activity in one of the most frequently studied models of chronic epilepsy, the intrahippocampal kainic acid (IHKA) mouse model, and in a model of acute seizures. We show that 100 % DMSO (in a volume of 1.5 µl/g corresponding to 1651 mg/kg) causes a significant short-term anti-seizure effect in epileptic IHKA mice of both sexes, but does not affect the threshold of acute seizures induced by pentylenetetrazol (PTZ). These findings highlight that the choice of solvent and appropriate vehicle control is crucial to minimize undesirable misleading effects and that drug candidates exclusively soluble in 100 % DMSO need to be modified for better solubility already at initial testing.

Artificial intelligence suppression as a strategy to mitigate artificial intelligence automation bias.

Incorporating artificial intelligence (AI) into clinics brings the risk of automation bias, which potentially misleads the clinician's decision-making. The purpose of this study was to propose a potential strategy to mitigate automation bias. This was a laboratory study with a randomized cross-over design. The diagnosis of anterior cruciate ligament (ACL) rupture, a common injury, on magnetic resonance imaging (MRI) was used as an example. Forty clinicians were invited to diagnose 200 ACLs with and without AI assistance. The AI's correcting and misleading (automation bias) effects on the clinicians' decision-making processes were analyzed. An ordinal logistic regression model was employed to predict the correcting and misleading probabilities of the AI. We further proposed an AI suppression strategy that retracted AI diagnoses with a higher misleading probability and provided AI diagnoses with a higher correcting probability. The AI significantly increased clinicians' accuracy from 87.2%±13.1% to 96.4%±1.9% (P < .001). However, the clinicians' errors in the AI-assisted round were associated with automation bias, accounting for 45.5% of the total mistakes. The automation bias was found to affect clinicians of all levels of expertise. Using a logistic regression model, we identified an AI output zone with higher probability to generate misleading diagnoses. The proposed AI suppression strategy was estimated to decrease clinicians' automation bias by 41.7%. Although AI improved clinicians' diagnostic performance, automation bias was a serious problem that should be addressed in clinical practice. The proposed AI suppression strategy is a practical method for decreasing automation bias.

Short branch attraction in phylogenomic inference under the multispecies coalescent.

Accurate reconstruction of species trees often relies on the quality of input gene trees estimated from molecular sequences. Previous studies suggested that if the sequence length is fixed, the maximum likelihood may produce biased gene trees which subsequently mislead inference of species trees. Two key questions need to be answered in this context: what are the scenarios that may result in consistently biased gene trees? and for those scenarios, are there any remedies that may remove or at least reduce the misleading effects of consistently biased gene trees? In this article, we establish a theoretical framework to address these questions. Considering a scenario where the true gene tree is a 4-taxon star tree with two short branches leading to the species and , we demonstrate that maximum likelihood significantly favors the wrong bifurcating tree grouping the two species and with short branches. We name this inconsistent behavior short branch attraction, which may occur in real-world data involving a 4-taxon bifurcating gene tree with a short internal branch. If no mutation occurs along the internal branch, which is likely if the internal branch is short, the 4-taxon bifurcating tree is equivalent to the 4-taxon star tree and thus will suffer the same misleading effect of short branch attraction. Theoretical and simulation results further demonstrate that short branch attraction may occur in gene trees and species trees of arbitrary size. Moreover, short branch attraction is primarily caused by a lack of phylogenetic information in sequence data, suggesting that converting short internal branches to polytomies in the estimated gene trees can significantly reduce artifacts induced by short branch attraction.

The reproducibility crisis meets stock assessment science: Sources of inadvertent bias in the stock assessment prioritization and review process

The broader scientific community is struggling with a reproducibility crisis brought on by numerous factors, including “p-hacking” or selective reporting that may increase the rate of false positives or generate misleading effect size estimates from meta-analyses. This results when multiple modeling approaches or statistical tests may be brought to bear on the same problem, and there are pressures or rewards for finding “significant” results. Fisheries science is unlikely to be immune to this problem, with numerous opportunities for bias to inadvertently enter into the process through the prioritization of stocks for assessment, decisions about competing model approaches or data treatments within complex assessment models, and decisions about whether to adopt assessments for management after they are reviewed. I present a simple simulation model of a system where many assessments are performed each management cycle for a multi-stock fishery, and show how asymmetric selection of assessments for extra scrutiny or re-assessment within a cycle can turn a process generating unbiased advice on fishing limits into one that is biased high. I show similar results when sequential assessments receive extra scrutiny if they show large proportional decreases in catch limits compared to a prior assessment for the same stock, especially if there are only small changes in true stock size or status over the interval between assessments. The level of bias introduced by a plausible level of asymmetric scrutiny is unlikely to fundamentally undermine scientific advice, but may be sufficient to compromise the nominal “overfishing probabilities” used in a common framework for accommodating uncertainty, and introduce a level of bias comparable to the difference between buffers corresponding to commonly-applied levels of risk tolerance.

Constraint Linear Model for Period Estimation and Sparse Feature Extraction Based on Iterative Likelihood Ratio Test

This study proposes a constraint linear model (CLM) to represent periodic signals. Different from the conventional linear model, linear constraints are added to the proposed model to fulfill the continuity constraints of signals. To estimate the period of the signals, a hypothesis testing based on the generalized likelihood ratio is proposed to statistically compare misleading peaks exhibited in the likelihood function of CLM. The misleading peaks usually arise because the likelihood function has a similar value in the true period and its multiples, and this misleading effect obstructs the effectiveness of the conventional maximum likelihood estimation method. An iterative likelihood ratio test (ILRT) is proposed, in which hypothesis testing is iteratively conducted to test the significance of an identified period until no candidate period with significant evidence is found. After the period is estimated by ILRT, an L1 penalty is added to CLM to construct a sparse representation of the signals, and an augmented Lagrangian shrinkage algorithm is applied to extract sparse features from the signals. The effectiveness of the proposed method is verified through a simulation study on synthetic signals and a case study on real vibration signals.

The EPPO’s legislative framework: Navigating through EU law, national law and soft law

The legal framework under which the European Public Prosecutor’s Office (EPPO) is called to operate is particularly complex, given the need for this body to exercise its functions in the legal systems of the Member States, applying both Union and national law. While this may justify the many references to national law contained in the EPPO’s founding Regulation (2017/1939), several among these references present relevant interpretative issues, and may in some cases even have a misleading effect. The article aims at showing examples of these different scenarios. It also touches briefly on the relevance of soft law in the legal framework of the EPPO.