The perpendicular exchange bias effect is observed in the ferromagnetic Co/Ni multilayers adjacent to the antiferromagnetic FeRh layer. It is found that as the antiferromagnetic FeRh thickness increases from 10 Å to 50 Å, the hysteresis loop is gradually changed from the symmetric shape to the asymmetric shape shifted by some amount corresponding to the exchange-biased field at the thickness of 25 Å. Also, the magnetic domain observation experiment confirms that the domain reversals in the increasing and the decreasing field regions of the sample with the thickness of 50 Å exhibit the same single domain wall motion even though they have the different coercivities.

Considering bitemark evidence, forensic dentists must give testimony that could have catastrophic consequences. A bitemark is often the only physical evidence on a body, and odontologists' testimony should be powerful and simple to understand. Given that perpetrators may be executed or imprisoned for life, a defective bitemark analysis is comparable to dentists' most crucial clinical decisions regarding diagnosis. Bias affects human bitemark analysis, and forensic dentists must examine its invisible impacts to avoid making mistakes. The aim of this study was to explore the potential of different types of bias in bitemark analysis and methods during analysis by conducting a scoping review. The majority of the 14 articles that were taken into consideration were published in 2019. USA, UK, Australia, New Zealand, and the Netherlands published the most articles. 36% of the publications addressed contextual bias, while 57% acknowledged cognitive bias. Preventive measures have been recommended to address bias in bitemark analysis. These consist of limiting the availability of unrelated data during the research, employing several comparison samples for a more impartial assessment, and repeating the analysis while being blind to past findings. These preventative measures reduce cognitive and contextual bias and improve bitemark analysis in forensic investigations.

Although ChatGPT promises wide-ranging applications, there is a concern that it is politically biased; in particular, it has a left-libertarian orientation. Nevertheless, in light of recent trends in attempts to reduce such biases, this study re-evaluated the political biases of ChatGPT using political orientation tests and the application programming interface. Moreover, the effects of the languages used in the system as well as gender and race settings were evaluated. The results indicated that ChatGPT had less political bias than previously thought; however, they did not entirely discount the political bias. The languages used in the system and the gender and race settings may induce political biases. These findings enhance our understanding of the political biases of ChatGPT and may be useful for bias evaluation and designing ChatGPT’s operational strategy.

53% of adults in the US acknowledge racial bias as a significant issue, 23% of Asian adults experience cultural and ethnic bias, and more than 60% conceal their cultural heritage after racial abuse. AI models like ChatGPT and Google Bard, trained on historically biased data, inadvertently amplify racial and ethnic bias and stereotypes. This paper addresses the issue of racial bias in AI models using scientific, evidence-based analysis and auditing processes to identify biased responses from AI models and develop a mitigation tool. The methodology involves creating a comprehensive database of racially biased questions, terms, and phrases from thousands of legal cases, Wikipedia, and surveys, and then testing them on AI Models and analyzing the responses through sentiment analysis and human evaluation, and eventually creation of an 'AI-BiasAudit,' tool having a racial-ethnic database for social science researchers and AI developers to identify and prevent racial bias in AI models.

Cognitive biases towards disorder-specific stimuli are suggested to be crucial in the development and maintenance of symptoms in adults with functional gastrointestinal disorders (FGID). Functional abdominal pain disorders (FAPD), a subtype of FGID, are common in children and adolescents, but the influence of cognitive biases is sparsely examined. The aim of this study was to 1) develop a new experimental design for assessing cognitive biases towards gastrointestinal stimuli in children and adolescents (age 8-17 years) and 2) test its feasibility on a healthy sample exploring potential sex- and age-related differences. The online experimental design – BY-FAP (Bias in Youth with Functional Abdominal Pain) includes a word task and a picture task. Stimuli in both tasks are related to general and gastrointestinal symptoms and the design includes three phases: 1) encoding, 2) free recall and 3) recognition. Results from 96 healthy participants (Mage = 12.3, 47.9% female) found no significant difference between sex or age groups in recall and recognition of either words or pictures. The results support that the design is feasible for children and adolescents. In the future, BY-FAP can be used to explore possible cognitive biases in children and adolescents with FAPD.

The objective of this study is to examine different types of bias present in assessment tools and techniques, identify and eliminate any forms of favoritism, and suggest effective approaches for advancing equity and objectivity in assessment procedures. The research utilizes a qualitative methodology, incorporating surveys and focus group discussions with educators and students to collect data. Thematic analysis is a method employed for the purpose of analyzing qualitative data. The results suggest that students hailing from diverse cultural backgrounds perceive partiality in the language and cultural context of evaluations, leading to diminished scores. According to academic professionals, standardized tests are subject to bias, and offering accommodations that cater to linguistic and cultural diversity may improve the academic achievement of students from diverse cultural backgrounds. The findings of this research are of great importance to professionals in the domains of education, policy development, and academia who seek to improve assessment practices and promote equitable educational opportunities for all learners.

A lack of data reproducibility (“reproducibility crisis”) is debated across many scientific and medical disciplines. A systematic analysis of the available evidence on the reliability of scientific data revealed that, although the existence of a reproducibility crisis is widely perceived, conclusive data on the scale of the problem are lacking. Most importantly we found that, although the debate is largely focused on methodological issues, researcher conduct defines research standards and in turn data reliability. The availability of reliable methods itself does not guarantee good practice. Moreover, research is typically characterised by a lack of established methods due to its immanent novelty. Despite the crucial importance of researcher conduct, research and conclusive data on the determinants of researcher behaviour are missing. In conclusion, meta-research is urgently needed that establishes an understanding of the factors that determine researcher behaviour. This knowledge can then be used to implement and iteratively improve measures, which incentivise researchers to apply the highest standards resulting in high quality data.

Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under-represented in the literature: many studies focus solely on measuring accuracy. Methods for assessing bias in single series are relatively well-known and well-researched, but for datasets containing thousands of observations for multiple series, the methodology for measuring and reporting bias is less obvious. We compare alternative approaches against a number of criteria when rolling-origin point forecasts are available for different forecasting methods and for multiple horizons over multiple series. We focus on relatively simple, yet interpretable and easy-to-implement metrics and visualization tools that are likely to be applicable in practice. To study the statistical properties of alternative measures we use theoretical concepts and simulation experiments based on artificial data with predetermined features. We describe the difference between mean and median bias, describe the connection between metrics for accuracy and bias, provide suitable bias measures depending on the loss function used to optimise forecasts, and suggest which measures for accuracy should be used to accompany bias indicators. We propose several new measures and provide our recommendations on how to evaluate forecast bias across multiple series.

Background: Several studies have questioned the reliability of the interview process in predicting resident success. Objective: Statistical analysis of the resident interview structure might guide process improvements and increase the validity of the selection methodologyMethod: We performed a prospective comparative analysis of our general surgery residency interview process over two consecutive academic years (AY 2022 and 2023). We used descriptive statistics as well as a mixed effects ordered logit model to measure bias and guide process improvement. Results: Eighty total students were interviewed. We found numerous statistical biases including leniency bias exacerbated by the unequal distribution of evaluators; significant differences in the variance from evaluator’s scoring (p<0.0001) and non-normal distribution (p<0.001). Rater reliability was “good” in both years: 0.69 (C.I. 0.51-0.82). Using uncorrected means, on average, each student was statistical different from 11.5±1.1 other students. Using our model, we improved the number of statistically distinct groupings as each student now differed by 30±1.0 others (p<0.0001). In AY23, we restructured the interviews so that all the evaluators scored every student which greatly improved the interview accuracy using the same mixed model; R-squared of 0.95 versus 0.70; and a smaller percent of students had a change in their rank using with the improved structure (57% versus 90%, p<0.05), compared to AY22.Conclusions: Due to numerous statistical biases, there is little differentiation between students and thus little validity in the process for scoring students using raw mean scores. This can be overcome by developing a structured interview and correcting for statistical biases.

Immortal-time bias (ITB) is known to be common in cohort studies and distorts the association estimates between treated and untreated groups. We used data from the last of two large studies in an Italian province on COVID-19 vaccines safety and effectiveness incurred this bias, and aligned the entire population on a single index date, to correct the ITB. We considered the "all-cause deaths" outcome to compare the survival curves between the unvaccinated group and the various vaccination statuses. The all-cause deaths Hazard Ratios in univariate analysis for unvaccinated (reference) versus vaccinated with 1, 2, 3/4 doses were 0.88 (CI95: 0.78 –1.00; p-value 0.044), 1.23 (1.16–1.32; p-value ≤0.001) and 1.21 (1.14–1.29; p-value ≤0.001), respectively. The multivariate values were 2.40 (2.00–2.88; p-value <0.0001), 1.98 (1.75–2.24; p-value <0.0001), 0.99 (0.90–1.09; ns). The possible explanations of the trend of the Hazard Ratios as vaccinations increase could be a harvesting effect; a calendar-time bias, accounting for seasonality and pandemic waves; a case-counting windows bias; a healthy-vaccinee bias; or some their combination. With two and even with 3/4 doses the calculated Restricted Mean Survival Time and Restricted Mean Time Lost have shown a small but significant downside for the vaccinated populations.

Consumption of coffee by women early in their pregnancy has been viewed as potentially increasing the risk of miscarriage, low birth weight, and childhood leukemias. Many of these reports of epidemiologic studies have not acknowledged the potential biases inherent in studying the relationship between early-pregnancy-coffee consumption and subsequent events. I discuss five of these biases, recall bias, misclassification, residual confounding, reverse causation, and publication bias. Each might account for claims that attribute adversities to early-pregnancy-coffee consumption. To what extent these biases can be avoided remains to be determined. At a minimum, they need to be acknowledged wherever they might account for what is reported.

Given the widespread use of AI language models such as ChatGPT, there is an urgent need 1 to understand their political biases in different languages. This study aims to uncover such biases. To 2 investigate this issue, we applied IDRLabs’ Political Coordinates Test to ChatGPT based on GPT-4 3 in four languages - English, French, German, and Italian. The results confirm previous studies that 4 identified a left-libertarian bias in ChatGPT. There was remarkable consistency in political orientation 5 across all four languages, although minor variations were found. Differences in ChatGPT response 6 distributions across languages could indicate culturally determined differences in interpretation 7 of political questions. The results highlight the need to diversify and optimize data collection and 8 model training methods to ensure greater fairness and neutrality and minimize the influence of bias. 9 Our study highlights the importance of cultural differences in the processing and interpretation of 10 political information by AI models.

Three typhoons (Rammasun, Kalmaegi, and Sarika) travelled through the deployed stations in the northern South China Sea from 2014–2016. During the passage of typhoons, strong winds and vigorous currents resulted in horizontal displacement of buoy over 2000 m, vertical displacement of ropes on buoys as much as 200 m. The rectification can correct the warm anomaly to cool anomaly of temperature. These movements lead to biases of raw data, with temperature bias as much as 4°C, salinity as much as 0.05 psu, velocity bias as much as 0.4 m/s. The crosscheck of current velocity from different instruments shows that the bias of overlapping velocity and correlation coefficient after depth rectification obviously enhances. The observation shows that temperature cools 1.5 °C, and 0.1 psu saltier in maximum, the near-inertial current increases to 0.4 m/s in the upper layer. The inertial kinetic energy propagates downward with the upward phase, and the maximum depth can reach over 2000 m.

Artificial intelligence (AI) driven language models have seen a rapid rise in development, deployment, and adoption over the last few years. This surge has sparked many discussions about their societal and political impact, including political bias. Bias is a crucial topic in the context of large models due to its far-reaching consequences on technology, politics, and society. It significantly influences public perception, decision-making, political discourse, and AI policy governance and ethics. This study investigates political bias through a comparative analysis of four prominent AI models: ChatGPT-4, Perplexity, Google Gemini, and Claude.Through a comprehensive analysis by systematically and categorically evaluating their responses to politically and ideologically charged tests and prompts, utilizing the Pew Research Center’s Political Typology Quiz, the Political Compass assessment, and ISideWith political party quiz, this study identifies significant ideological leanings and the nature of political bias within these models. The findings revealed that ChatGPT-4 and Claude exhibit a liberal bias, Perplexity is more conservative, while Google Gemini adopts more centrist stances. The presence of such biases underscores the critical need for transparency in AI development and the incorporation of diverse training datasets, regular audits, and user education to mitigate these biases. This analysis also advocates for more robust practices and comprehensive frameworks to assess and reduce political bias in AI, ensuring these technologies contribute positively to society and support informed, balanced, and inclusive public discourse, which will point towards neutrality.The results of this study add to the ongoing discourse about the ethical implications and development of AI models, highlighting the critical need to build trust and integrity in AI models. Additionally, future research directions have been outlined to explore and address the complex issue of bias in AI.

Social biases may concentrate attention of researchers on a small number of well-known molecules/mechanisms leaving others underexplored. In accordance with this view, central to mechanistic toxicology is a narrow range of molecular pathways that are assumed to be involved in significant part of responses to toxicity. It is unclear, however, if there are other molecular mechanisms which play important role in toxicity events but are overlooked by toxicology. To identify overlooked genes sensitive to chemical exposures we used publicly available databases. First, we used data on published chemical-gene interactions for 17,338 genes to estimate their sensitivity to chemical exposures. Next, we extracted data on publication numbers per gene for 19,243 human genes from Find My Understudied Genes database. Threshold were applied to both datasets using our algorithm to identify chemically sensitive and chemically insensitive genes and well-studied and underexplored genes. 1,110 underexplored genes highly sensitive to chemical exposures were used in GSEA and Shiny GO analyses to identify enriched biological categories. Metabolism of fatty acids, amino acids, and glucose were identified as underexplored molecular mechanisms sensitive to chemical exposures. These findings suggest that future effort is needed to uncover the role of xenobiotics in the current epidemics of metabolic diseases.

The development and qualification of CrAlN coatings deposited by DC magnetron sputtering offer new industrial solutions to increase the lifecycle of material forming tools (machining and shaping). This study focuses on the selection and optimization of deposition modes for CrAlN layers to be deposited on a 90CrMoV8 tool steel substrate. The adopted methodology aims to optimize the deposition conditions to meet the customer's requirement of a CrAlN coating thickness of approximately 2-3 µm for use in a hot forming process. Different polarizations using two Al and Cr targets, with both stationary and rotating substrate modes during deposition, were tested and evaluated. Characterizations of the CrAlN coating layers (microhardness, residual stress, structures) and performance in service (wear, friction, adhesion, corrosion) were chosen as selection criteria for a particular coating mode. The aluminum content plays a crucial role in the tribological and mechanical behavior of CrAlN layers. Corrosion resistance is more sensitive to the aluminum content for the stationary deposition mode. As a result, the rotating mode provides higher adhesion (+100%) and less wear (-60%) compared to the stationary mode. Therefore, depending on the likely performance in service, either a stationary or rotating mode could be applied for coating the functional surfaces of hot forming tools.

This study evaluated the potential of ERA5 reanalysis as a reference dataset for snow water equivalent (SWE) modeling in 16 Alpine basins of varying catchment sizes using the semi-distributed snow model (TOPMELT) in the Upper Adige river basin in the Eastern Italian Alps. The study aimed to identify errors in ERA5 meteorological variables and assess their impact on SWE computation from 1992 to 2019. The findings revealed that ERA5 precipitation overestimated low-intensity rainfall (drizzle problem) and underestimated high-intensity rainfall, while ERA5 temperature underestimated observations. The overestimation of low-intensity rainfall created fictitious low-intensity snowfall events, which, when combined with colder ERA5 temperature, resulted in delayed snowmelt and increased fictitious snow cover days over the study area. The Quantile Mapping (QM) technique was used to remove errors in ERA5 variables. For precipitation, a monthly correction factor accounting the calibration period (1992-2005) was considered to correct the entire period. Temperature errors were corrected individually for calibration and validation periods. The corrected ERA5 SWE simulation showed improved performance, with a decrease in fictitious snow cover days. The study also highlighted the importance of temperature correction over precipitation correction in SWE simulation, particularly for smaller basins, while considering the monthly moving mean to remove seasonality.

This paper examines cognitive biases which affect the ability of decision makers to make rational decisions in an organizational context. The motivation for this analysis begins with the observation of catastrophic accidents caused by human error but in an organizational context. This paper expands on the concept of cognitive bias to define organizational biases which are the factors that affect decisions in an organizational context. The paper distinguishes between organizational biases, which are the focus of this paper, and individual biases, which are biases experienced by individuals but may have organizational consequences. The purpose of this paper is to identify methods to mitigate the risks of organizational accidents, accidents which involve many people operating at different levels of an organization. The methodology is to identify those decisions that would address the specific organizational biases. The focus of this paper is the decisions for mitigating the risks associated with decisions in an organizational context. Results are shown for seven organizational biases, six specific case studies, and four decision options. This paper concludes that organizational biases are intrinsically different from individual biases and that these differences lead to different decision options from those that mitigate individual biases; however, they may exist concurrently.

Parkinson’s Disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assist in recognizing the PD with good accuracy. The decorrelation function reduces the non-linear correlation between features and bias in order to learn bias-invariant features. The Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single scanner imbalanced dataset in this study used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new Decorrelated Convolutional Neural Networks (DcCNN) framework by applying decorrelation-based optimization to Convolutional Neural Networks(CNN). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN model performs significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner invariant features without significantly impacting the performance of a model. The obtained dataset is a multi-scanner dataset which leads to scanner bias due to the differences in acquisition protocols and scanners. The multi-scanner dataset is a combination of two datasets, namely PPMI and FTLDNI - frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed Feature Extraction-DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multi-scanner dataset for differentiating PD from healthy control, which is superior to the DcCNN model trained on a single scanner imbalanced dataset.

Aileen Oeberst and Roland Imhoff’s remarkable paper (Perspectives on Psychological Science 2023; 18: 1464-87). should be compulsory reading for all intelligence analysts. It offers a tantalisingly parsimonious explanation comprising the combination of strongly held prior 'beliefs' coupled with subsequent 'belief-consistent information processing' (akin to confirmation bias) - an explanation that simplifies understanding of a vast array of cognitive biases, and thereby suggests that all of these might be attenuated through the application of accessible analytical and assessment practices. What this means for intelligence analysis will be determined by the explicit and implicit 'beliefs' (or, more generally, ‘value[s]’) attributed to any evidence available, and any insight generated, to reduce decision-makers’ uncertainty and unsubstantiated certainty, and thereby offer them future advantage. Analysts are well aware that different sources of evidence (whether empirical, theoretical or entirely speculative) can assign different types and amounts of ‘prior value’ to the information available for intelligence analysis and assessment. Acknowledging such value as a potential driver of subsequent confirmation bias should help analysts guard against any inherent tendency to preference evidence solely on the basis that this was initially considered most ‘valuable’. Instead they should subject all evidence and all insight – regardless of perceived ‘value’ – to a consistent battery of systematic, rigorous and robust evaluation.

Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, trustworthy, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including balancing interpretability and accuracy, integrating XAI into clinical workflows, and ensuring adherence to rigorous regulatory standards. This paper provides a comprehensive review of XAI in healthcare, covering techniques, challenges, opportunities, and advancements, thereby enhancing the understanding and practical application of XAI in healthcare. The study also explores responsible AI in healthcare, discussing new perspectives and emerging trends and providing valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, promoting the development of transparent, trustworthy, and effective AI-driven solutions.

Causal language can be a powerful tool for media outlets to subtly influence public perception and reinforce specific narratives. Therefore, identifying these linguistic patterns can be helpful in assessing the level of bias present in media content. In this paper, we directly compare the prevalence of causal language in headlines pertaining to the coverage of the Israel-Palestine conflict and the Russia-Ukraine war. We specifically look at the BBC and AlJazeera headlines covering the assault on Gaza since October 7, 2023. We use UniCausal, a model for causal text mining, in order to automatically extract the cause and effect of each headline. Our findings reveal that BBC headlines tend to avoid directly attributing causality to Israel for the violence in Gaza, whereas Al Jazeera headlines explicitly identify Israel as the responsible party. This differential use of causal language suggests a potential bias in media reporting.

Language bias stands as a noteworthy concern in Visual Question Answering (VQA), wherein models tend to rely on spurious correlations between questions and answers for prediction. This prevents the models from effectively generalizing, leading to a decrease in performance. To address this bias, we propose a novel modality fusion collaborative de-biasing algorithm (CoD). In our approach, bias is considered as the model’s neglect of information from a particular modality during prediction. We employ a collaborative training approach to facilitate mutual modeling between different modalities, achieving efficient feature fusion and enabling the model to fully leverage multi-modal knowledge for prediction. Our experiments on various datasets, including VQA-CP v2, VQA v2, and VQA-VS, using different validation strategies, demonstrate the effectiveness of our approach. Notably, employing a basic baseline model resulted in an accuracy of 60.14% on VQA-CP v2.

Satellite-derived soil moisture observations typically rely on bias-correction (BC) prior to assimilation in land surface models. Current techniques include rescaling or machine learning approaches to map the observations to the modelled soil moisture climatology. However, these approaches do not allow for non-stationary biases and recalibrations require a long training period, which is not always feasible. In this study we evaluate a two-stage filter to dynamically correct soil moisture biases from satellite-derived active ASCAT C-band and passive L-band SMOS surface soil moisture observations in the European Centre for Medium Range Weather Forecasts (ECMWF) land data assimilation system. This adaptive soil moisture BC approach is designed to complement the operational seasonal rescaling of the ASCAT observations and the SMOS neural network retrieval used at ECMWF, while allowing the assimilation to correct sub-seasonal scale errors. Over a 3-year test period, the adaptive BC reduces the seasonal-scale first guess-observation departures by 20-30% for ASCAT and SMOS. The adaptive BC leads to (i) slight improvements in soil moisture performance against in situ data; and (ii) moderate but statistically significant reductions in the 1-5 day relative humidity forecast errors in the boundary layer of the northern hemisphere midlatitudes.

This study rolls out a robust framework relevant for simulation studies through the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model using the rugarch package. The package is thoroughly investigated, and novel findings are identified for improved and effective simulations. The focus of the study is to provide necessary simulation steps for volatility estimation that involve "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that includes writing the code, setting the seed, setting the true parameters a priori, data generation process and performance assessment through meta-statistics. This novel, easy-to-understand steps are demonstrated on financial returns using illustrative Monte Carlo simulation with empirical verification. Among the findings, the study shows that regardless of the arrangement of the seed values, the efficiency and consistency of an estimator generally remain the same as the sample size increases. The study also derived a new and flexible true-parameter-recovery measure which can be used by researchers to determine the level of recovery of the true parameter by the MCS estimator. It is anticipated that the outcomes of this study will be broadly applicable in finance, with intuitive appeal in other areas, for volatility estimation.

Just 2 years ago, 35% of Americans were involved with gig work. According to Statista, over half of Americans will be freelancing through online gig work by 2027. Research on how consumers select gig workers, or taskers, is still mostly underexplored. The purpose of this study is to investigate the potential impact of a tasker's gender and self-presentation in their profile picture on consumer perceptions and choices. We proposed that taskers who have a professional headshot as the profile picture will receive higher ratings on competence, warmth, and trustworthiness than taskers whose profile picture is a selfie. We also proposed that taskers who smile in their profile picture will receive higher ratings on competency, warmth, and trustworthiness than taskers who do not smile. Furthermore, we predicted that professionalism has a stronger positive impact on perceptions of male taskers than on female taskers for our third hypothesis. Lastly, we hypothesized that gender bias exists on gig platforms, such that smiling will have a stronger positive effect on female taskers’ competence, warmth, and trustworthiness than on male taskers. Our study had 292 Kennesaw State University students who were tested online through Qualtrics. Our first and second hypotheses are not supported but our last two have partial support. There is a significant main effect of gender, and we find that male taskers are rated significantly higher on competence, warmth, and trustworthiness, compared to female taskers. Professional photos give male taskers an advantage over professional female taskers and female taskers are punished more for not smiling than male taskers. Future directions include looking into factors such as Race, Age, or background setting could be investigated. Conducting similar tests in a larger and more externally valid setting could yield more concrete results. Continuation into this new area of gig work could lead the way into revolutionizing how freelancers conduct their public appearance or how gig work platforms could handle this new market.

North American Whites tend to evaluate members of their own race more positively than members of other races. One exception may be White Liberals, who appear politically motivated to evaluate members of (disadvantaged) racial outgroups more positively than members of their own racial group. We confirmed this claim presently, where 75 White Liberals, 95 White Conservatives, 49 Black Liberals and 71 Black Conservatives evaluated biracial faces using explicit and implicit evaluation tests. We instrumentally ‘frustrated’ half our participants to note whether incidental anger influenced ideologically-motivated racial attitudes. Evaluations towards racial outgroups were largely unaffected by negative mood induction -White Liberals were pro-Black relative to White Conservatives, and Black Conservatives were pro-White relative to Black Liberals, independent of mood. Negative mood induction selectively influenced own-race evaluations. Black Liberals and White Conservatives became respectively more pro-Black and pro-White when frustrated.Conversely, Black Conservatives and White Liberals became respectively less pro-Black and pro-White. Frustration significantly amplified negative own-race attitudes across explicit and implicit evaluation measures for White Liberals. We speculate on some social consequences that may follow from frustration-amplified ‘anti-White’ bias across White Liberals.

The editors of several major journals have recently asserted the importance of combating racism and sexism in science. This is especially relevant now, as the COVID-19 pandemic may have led to a widening of the gender and racial/ethnicity gaps. Implicit bias is a crucial component in this fight. Negative stereotypes that are socially constructed in a given culture are frequently associated with implicit bias (which is unconscious or not perceived). In the present article, we point to scientific evidence that shows the presence of implicit bias in the academic community, which contributes to strongly damaging unconscious evaluations and judgments of individuals or groups. Additionally, we suggest several actions aimed at (1) editors and reviewers of scientific journals, (2) people in positions of power within funding agencies and research institutions and (3) members of selection committees to mitigate this effect. These recommendations are based on the experience of a group of Latin American scientists comprising Black and Latin women, teachers and undergraduate students who participate in a women in science working group at universities in the state of Rio de Janeiro, Brazil. With this article, we hope to contribute to reflections, actions and the development of institutional policies that enable and consolidate diversity in science and reduce disparities based on gender and race/ethnicity.

The assessment of positive emotional states in animals has been advanced considerably through the use of judgement bias testing. JBT methods have now been reported in a range of species. Generally, these tests show good validity as ascertained through use of corroborating methods of affective state determination. However, published reports of judgement bias task findings can be counter-intuitive and show high inter-individual variability. It is proposed that these outcomes may arise as a result of inherent inter- and intra-individual differences as a result of biology. This review discusses the potential impact of sex and reproductive cycles, social status, genetics, early life experience and personality on judgement bias test outcomes. We also discuss some aspects of test design that may interact with these factors to further confound test interpretation. There is some evidence that a range of biological factors affect judgement bias test outcomes, but in many cases this evidence is limited and needs further characterisation to reproduce the findings and confirm directions of effect. It is our proposition that researchers should consider dedicated study on these factors and their impact on judgement biasing. This is needed to confirm effect and investigate mechanisms. Alternately, consideration and reporting of these factors in JBT studies through incorporation in statistical analyses will provide much needed additional data on their impact. These actions will enhance the validity and practical applicability of the JBT for welfare assessment.

Automation surprises in aviation continue to be a significant safety concern and the community’s search for effective strategies to mitigate them are ongoing. The literature has offered two fundamentally divergent directions, based on different ideas about the nature of cognition and collaboration with automation. In this paper, we report the results of a field study that empirically compared and contrasted two models of automation surprises: a normative individual-cognition model and a sensemaking model based on distributed cognition. Our data prove a good fit for the sense-making model. This finding is relevant for aviation safety, since our understanding of the cognitive processes that govern the human interaction with automation drives what we need to do to reduce the frequency of automation-induced events.

Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and have inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method incorporating multi-granularity features is proposed for human behavior recognition.Firstly, a skeleton fine-grained division strategy is proposed, which initializes the skeleton data into data streams of different granularities.Using a normalized Gaussian function, an adaptive cross scale feature fusion layer is designed for feature fusion between different granularities, and fine-grained features guide the model to focus on discriminative feature expressions between similar behaviors. Secondly, a sparse multi-scale adjacency matrix is introduced to solve the bias weighting problem that amplifies the multi-scale spatial domain modeling process under multi granularity conditions. Finally, an end-to-end graph convolutional neural network is constructed to improve the feature expression ability of spatio-temporal receptive field information and enhance the robustness of recognition between similar behaviors. The feasibility of the proposed algorithm was verified on the public behavior recognition dataset MSR Action 3D, with a recognition rate of 95.67%, which is superior to most existing behavior recognition methods.

The Fading Affect Bias (FAB) occurs in autobiographical memory when unpleasant emotions fade faster than pleasant emotions and the phenomenon appears to be a form of emotion regulation. As emotion regulation is positively related to problem-solving, the current study examined FAB in the context of problem-solving. In-person and online studies asked participants to provide basic demographics, describe their problem-solving abilities, and rate various healthy and unhealthy variables including emotional intelligence and positive problem-solving attitudes. Participants also completed an autobiographical event memory form for which they recalled and described 2 pleasant and 2 unpleasant problem-solving and non-problem-solving events and rated the initial and current affect and rehearsals for those events. We found a robust FAB effect that was larger for problem-solving events than for non-problem-solving events in Study 1 but not in Study 2. We also found that FAB was positively related to healthy variables, such as grit, and negatively related to unhealthy variables, such as depression. Moreover, many of these negative relations were inverted at high levels of positive problem-solving attitudes, and these complex interactions were partially mediated by talking rehearsals and thinking rehearsals.

The growing interest in fair AI development is evident. The ''Leave No One Behind'' initiative urges us to address multiple and intersecting forms of inequality in accessing services, resources, and opportunities, emphasising the significance of fairness in AI. This is particularly relevant as an increasing number of AI tools are applied to decision-making processes, such as resource allocation and service scheme development, across various sectors such as health, energy, and housing. Therefore, exploring joint inequalities in these sectors is significant and valuable for thoroughly understanding overall inequality and unfairness. This research introduces an innovative approach to quantify cross-sectoral intersecting discrepancies among user-defined groups using latent class analysis. These discrepancies can be used to approximate inequality and provide valuable insights to fairness issues. We validate our approach using both proprietary and public datasets, including EVENS and Census 2021 (England \& Wales) datasets, to examine cross-sectoral intersecting discrepancies among different ethnic groups. We also verify the reliability of the quantified discrepancy by conducting a correlation analysis with a government public metric. Our findings reveal significant discrepancies between minority ethnic groups, highlighting the need for targeted interventions in real-world AI applications. Additionally, we demonstrate how the proposed approach can be used to provide insights into the fairness of machine learning.

Biases in Artificial Intelligence (AI) or Machine Learning (ML) systems due to skewed datasets problematise the application of prediction models in practice. Representation bias is a prevalent form of bias found in the majority of datasets. This bias arises when training data inadequately represents certain segments of the data space, resulting in poor generalisation of prediction models. Despite AI practitioners employing various methods to mitigate representation bias, their effectiveness is often limited due to a lack of thorough domain knowledge. To address this limitation, this paper introduces human-in-the-loop interaction approaches for representation debiasing of generated data involving domain experts. Our work advocates for a controlled data generation process involving domain experts to effectively mitigate the effects of representation bias. We argue that domain experts can leverage their expertise to assess how representation bias affects prediction models. Moreover, our interaction approaches can facilitate domain experts in steering data augmentation algorithms to produce debiased augmented data and validate or refine the generated samples to reduce representation bias. We also discuss how these approaches can be leveraged for designing and developing user-centred AI systems to mitigate the impact of representation bias through effective collaboration between domain experts and AI.

The manuscript presents the applicability of the Gumbel distribution in the frequency analysis of extreme events in hydrology. The advantages and disadvantages of using the distribution are highlighted, as well as recommendations regarding its proper use. A literature review was also carried out regarding the methods of estimating the parameters of the Gumbel distribution. Thus, for the verification of the methods, case studies are presented regarding the determination of the maximum annual flows and precipitations using nine methods for parameter estimation. The influence of the observed data lengths on the estimation of the statistical indicators, the parameters and the quantiles corresponding to the field of low annual exceedance probabilities (p <1%) is also highlighted. The results are compared to those obtained with the Generalized Extreme Value distribution, respectively the Burr and the Wakeby distributions with parameters estimated using the L-moments. The results highlight and reaffirm the statistical, mathematical and hydrological recommendation regarding the avoidance of using the Gumbel distribution in Flood Frequency Analysis and its use with reservations in the case of maximum precipitation, especially when the statistical indicators of the analyzed data are not close to the characteristic ones and unique to the distribution.

To realize precise control of the quantum dots (Qdots) device, multi-channel precision bias source plays the key role. In this paper, the 16-channel high precision bias source with 18-bit resolution for Qdots device was designed. The prototype was made and its performance was tested. The short time fluctuations can reach 50μV. The step response time is less than 3μs. The resolution, stability, linearity and dynamic range of the bias source exhibits good performance. What's more, the bias source can be controlled locally and online. The results show that it is one effective and feasible topology for experiments in Qdots device application.

Individual response to drugs is highly variable and largely influenced by genetic variants and gene-expression profiles. Also, it has been shown that response to drugs is strongly sex-dependent, both in terms of efficacy and toxicity. To expand current knowledge on sex differences in the expression of genes relevant for drug response, we generated a catalogue of differentially expressed human transcripts encoded by 289 genes in 41 human tissues from 838 adult individuals of the Genotype-Tissue Expression project (GTEx, v8 release) and focused our analysis on relevant transcripts implicated in drug response. We have detected significant sex-differentiated expression of 99 transcripts encoded by 59 genes in the tissues most relevant for human pharmacology (Liver, Lung, Kidney, Small intestine terminal ileum, Skin not sun-exposed, and Whole Blood). Among them, as expected, we observed significant differences in the expression of transcripts encoded by the cytochromes in the liver, CYP2B6, CYP3A7, CYP3A5, and CYP1A1. Our systematic investigation on differences between male and female in the expression of drug response related genes, reinforce the need to overcome the sex bias of clinical trials.

(1) Background: There is robust evidence of an attention bias-anxiety relationship in children, but lack of appropriate methods has limited the number of studies with children younger than 8 years old. This study used eye-tracking as a measure of overt attention in young children. The aim of this study was to assess anxiety related attention bias in children aged 4 to 8 years. Age was considered as a moderator and the influence of effortful control was investigated. (2) Method: A community sample of 104 children were shown pairs of happy-neutral and angry-neutral faces. Growth curve analyses were used to examine patterns of gaze over time. (3) Results: Analyses revealed moderation by age and anxiety, with distinct patterns of anxiety-related biases seen in different age groups in the angry-neutral face trials. Effortful control did not account for age related effects. (4) Conclusions: Results support a moderation model of the development of anxiety in children.

Estimation of the reality can easily be flawed, hence, in order to result in accurate and useful estimates the process has to be protected from bias and confounding and should follow other methodological milestones inherent to different types of empirical observations. Candidate-gene association studies are a specific form of observations that have been rather extensively applied in psychiatry yielding valuable information on various aspects – when methodologically adequate and used in appropriate settings. However, certain flaws that may occur in such studies might not be bluntly obvious, at least not at first glance, and may pass unnoticed by researchers and reviewers. This case study uses two recent published candidate-gene association reports suggesting involvement of cannabinoid receptor type 1 and of heat shock protein single nucleotide polymorphisms in development of neurocognitive performance and psychopathology in a cohort of adult first episode psychosis patients to point-out the types of flaws inevitably resulting in inaccurate and useless estimates.

Phylogenies depict shared evolutionary patterns and structures on a tree topology, enabling the identification of hierarchical and historical relationships. Recent analyses indicate that phylogenetic signals extend beyond the primary structure of protein or DNA, and various aspects of codon usage biases are phylogenetically conserved. Several functional biases exist within genes, including the number of codons that are used, the position of the codons, and the overall nucleotide composition of the genome. Codon usage biases can significantly affect transcription and translational efficiencies, leading to differential gene expression. Although systematic codon usage biases originate from the overall GC content of a species, ramp sequences, codon aversion, codon pairing, and tRNA competition also significantly affect gene expression and are phylogenetically conserved. We review recent advances in analyzing codon usage biases and their implications in phylogenomics. We first outline common phylogenomic techniques. Next, we identify several codon usage biases and their effects on secondary structure, gene expression, and implications in phylogenetics. Finally, we suggest how codon usage biases can be included in phylogenomics. By incorporating various codon usage biases in common phylogenomic algorithms, we propose that we can significantly improve tree inference. Since codon usage biases have significant biological implications, they should be considered in conjunction with other phylogenetic algorithms.

The PILAR model provides a dynamical systems perspective on collaboration. Two studies are performed using peer assessment data, both testing empirical support for the five Pillars (prospects, involved, liked, agency, respect) that constitute member’s perceptions of collaboration viability. The first study analyses peer-assessment data collected online from 458 first-year engineering students (404 males; 54 females). A nine-item instrument was inherited from past year’s usage in the course, expanded with four additional items to elaborate upon the agency and liked Pillars. Exploratory factor analysis was conducted on student responses to test whether they thematically aligned to constructs consistent with the five Pillars. As anticipated, twelve of the thirteen items grouped into five components, each aligned with a Pillar, providing empirical evidence that the five Pillars represent perceptions of collaboration. The second study replicated the first study using a retrospective analysis of 87 items included in the Comprehensive Assessment of Team Member Effectiveness (CATME) peer-assessment tool. The associated factor analyses resulted in five components and conceptual alignment of these components with Pillars was evident for three of five CATME components. We recommend a peer-assessment instrument based upon PILAR as potentially more parsimonious and reliable than an extensive list of behaviours, such as employed by CATME. We also recommend including items that target inter-rater bias, which is aligned with the liked Pillar, that instruments such as CATME exclude.

This paper aims to contribute to refining the e-values for testing precise hypotheses, especially when dealing with nuisance parameters, leveraging the effectiveness of asymptotic expansions of the posterior. The proposed approach offers the advantage of bypassing the need for elicitation of priors and reference functions for the nuisance parameters and the multidimensional integration step. For this purpose, starting from a Laplace approximation, a posterior distribution for the parameter of interest only is considered and then a suitable objective matching prior is introduced, ensuring that the posterior mode aligns with an equivariant frequentist estimator. Consequently, both Highest Probability Density credible sets and the e-value remain invariant. Some targeted and challenging examples are discussed.

Artificial Intelligence (AI) tools and techniques are comparatively recent advances of present-day globalized world. With their easy availability and friendly user interface AI has affected every sphere of human being. These tools are increasingly becoming popular because of the numerous benefits occurred to the society in many ways. However, there are not only the positive side of these technologies, but also many negative sides. Along with the development and adoption of AI, cyber-attacks are increased and disrupting the information system. Threat actors may misuse artificial intelligence. Hence, these tool designers are working and applying the ability of generative AI can tip the scales in cybersecurity. So, in the age of AI, there is always tension between the cyber attackers and the defenders to make a balanced information system. It is a general rule that defenders must be well in advance in their favor and keep ahead of adversaries. The United National and several governments for example the US, UK, and the big AI giants like Microsoft along with OpenAI, are working on emerging cyberattacks. This research paper explores the concept of information resilience in ML and AI systems, focusing on strategies to enhance their ability to withstand uncertainties, adversarial attacks, and data perturbations.

When asked to estimate the carbon footprint of a bundle of relatively low carbon footprint items and relatively high carbon footprint items, people typically report a lower value compared to estimating the high carbon footprint items alone. This finding is called the negative footprint illusion and suggests people report the average rather than sum of the combined items’ carbon footprint. In a recent paper by Gorissen et al. (2024), the authors claimed that people are immune to this illusion if the question is framed differently. Specifically, they found that estimates of the environmental friendliness of a set comprising a burger (high carbon footprint) and an organic apple (low carbon footprint) was higher than for a set comprising the burger alone, whereas no difference was found between the two sets when participants were asked to estimate how environmentally damaging they were. Using larger item categories wherein the degree of the items’ environmental impact is clearly communicated, here we show it does not matter whether participants are required to estimate how friendly or damaging the items are. The roles of attribute substitution, dependent variables, and independent variables (stimulus characteristics) in modulating the negative footprint illusion is discussed.

This study addressed algorithmic bias in predictive policing, focusing on the Chicago Police Department's Strategic Subject List (SSL) dataset. We specifically focused on identifying and mitigating age-related biases, a notably underexplored area in prior research. Our research introduced Conditional Score Recalibration as a bias mitigation strategy alongside the well-established Class Balancing technique. Conditional Score Recalibration involved reassessing and adjusting risk scores for individuals initially assigned moderately high-risk scores in the dataset. This recalibration marked such individuals as low risk if they met three conditions, namely: no prior arrests for violent offenses, no previous arrests for narcotic offenses, and having never been involved in shooting incidents. These fairness strategies were implemented on the Random Forest model, and the fairness metrics employed included Equality of Opportunity Difference, Average Odds Difference, and Demographic Parity. The results showed a significant improvement in model fairness, particularly for age biases, without compromising the model's accuracy. These findings challenged the often-assumed trade-off between fairness and accuracy, underscoring the feasibility of achieving fairness without compromising accuracy.

This study focuses on the analyses of nano-twinned Copper (Cu) films deposited through magnetron sputtering on Silicon Carbide (SiC) chips. The investigation encompasses the utilization of Chromium (Cr) adhesive layer coupled with varying voltage bias conditions. The goal is to comprehensively examine the influence of adhesive layer and negative bias voltages, contributing to an enhanced understanding of materials engineering and bonding technologies for advanced applications. The formation of a nanotwinned structure and (111) surface orientation can be properly controlled by applied substrate bias. High density nanotwinned structures were introduced into Cu films sputtered on SiC substrates with 82.3% of (111)-orientation proportion at -150 V much higher than the Cu film sputtered with another substrate bias. It is concluded that the sputtered Cu nanotwinned film formed with -150 V bias voltage has the potential to be employed as the interlayer for low temperature direct bonding.

Nonparametric density estimation for nonnegative data is considered in a situation where a random sample is not directly available but the data are instead observed from the length-biased sampling. Due to the boundary bias problem of the location-scale kernel, the approach in this paper is an application of asymmetric kernel. Two nonparametric density estimators are proposed. The mean integrated squared error, strong consistency, and asymptotic normality of the estimators are investigated. Some simulations illustrate the finite sample performance of the estimators.

Rising incidences of cutaneous melanoma have fueled the development of statistical models that predict the individual melanoma risk. Our aim was to assess the validity of published prediction models for incident cutaneous melanoma using a standardized procedure based on PROBAST (Prediction model Risk Of Bias ASsessment Tool). We included studies that were identified by a recent systematic review and updated the literature search to ensure that our PROBAST rating included all relevant studies. Six reviewers assessed the risk of bias (ROB) for each study using the published “PROBAST Assessment Form” that consists of four domains and an overall rating of ROB. We further examined a temporal effect regarding changes in overall and domain-specific ROB rating distributions. Altogether 42 studies were assessed, of which a vast majority (n=34; 81%) was rated as having high ROB. Only one study was judged as having low ROB. The main reasons for high ROB ratings were the use of hospital controls in case-control studies and the omission of any validation of prediction models. However, our results of the temporal analysis showed a significant reduction in the number of studies with high ROB for the domain analysis. Nevertheless, the evidence base of high-quality studies that can be used to draw conclusions on the prediction of incident cutaneous melanoma is currently much weaker than the high number of studies on this topic would suggest.

Melanoma is considered the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in prognosticating this type of cancer. With the emergence of new therapeutic strategies for metastatic melanoma that have shown improvement in patient survival, we developed a transfer learning-based biomarker discovery model that could help in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results reveal that the genes we found show consistency with other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set, and our methods found novel biomarker genes as well. Our ensemble model achieved Area Under the Receiver Operating Characteristic (AUC) of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB). We also assessed the potential sources of bias for our model and confirmed some of them by the model's performance.

Success and impact metrics in science are based on a system that perpetuates sexist and racist ‘rewards’ through prioritizing citations and impact factors. These metrics are flawed and biased against already marginalized groups and fail to accurately capture the breadth of individuals’ meaningful scientific impacts. We advocate shifting this outdated value system to advance science through principles of justice, equity, diversity, and inclusion. We outline pathways for a paradigm shift in academic values based on multidimensional mentorship and promoting mentee wellbeing. These actions will require collective efforts supported by academic leaders and administrators to drive essential systemic change.

The past years saw the rise of genomic biobanks and mega-scale meta-analysis of genomic data that promise to reveal the genetic underpinnings of health and disease. However, the over-representation of Europeans in genomic studies not only limit the global understanding of disease risk and intervention efficacy, but also inhibit viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared, and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable, computable, operate without access to raw data due to privacy concerns. But they must be comparable, both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of commonly used genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks, locally and internationally, increase the accuracy of association analyses, and inform developmental efforts.

Among humans, simply looking at infants can activate affiliative and nurturant behaviors. However, it remains unknown whether mere exposure to infants also activates other aspects of the caregiving motivational system, such as generalized defensiveness in the absence of immediate threats. Here, we demonstrate that simply viewing faces of infants (especially from the ingroup) may heighten vigilance against social threats and support for institutions that purportedly maintain security. Across two studies, participants viewed and rated one among several image types (between-subjects design): infants, adult males, adult females, and puppies in Study 1, and infants of varying racial/ethnic groups (including one's ingroup) and puppies in Study 2. Following exposure to one of these image types, participants completed measures of intergroup bias from a range of outgroups that differed in perceived threat, belief in a dangerous world, right-wing authoritarianism and social-political conservatism (relative to liberalism). In Study 1 (United States), stronger affiliative reactions to images of infants (but not adults or puppies) predicted stronger perceptions of a dangerous world, endorsement of right-wing authoritarianism, and support for social-political conservatism (relative to liberalism). Study 2 (Italy) revealed that exposure to images of ingroup infants (compared to outgroup infants) increased intergroup bias against outgroups that are characterized as threatening (immigrants and Arabs) and increased conservatism. These findings suggest a predisposed preparedness for social vigilance in the mere suggested presence of infants e.g., viewing images, even in the absence of salient external threats.

We introduce an information theoretic framework for a quantitative measure of originality to model the impact of various classes of biases, errors and error corrections on scientific research. Some of the open problems are also outlined.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly within social media platforms like X (formerly Twitter) and in addressing cyberbullying present on them. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of Transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Leveraging state-of-the-art LLMs such as ChatGPT-4o, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and quantized for multilabel classification (detecting both biases and cyberbullying). The study's outcomes include a prototype of a hybrid application that combines a Bias Data Detector and a Bias Data Generator, validated through extensive testing.

In the field of machine learning, the rapid development of data volume and variety requires ethical data utilization and strict privacy protection standards. Fair Federated Learning (FFL) has emerged as a key solution that aims to ensure fairness and privacy protection in a distributed learning environment. FFL enhances privacy protection and solves the inherent limitations of existing federated learning (FL) by promoting fair model training in diverse participant groups, preventing the exclusion of individual users or minorities, and improving overall model fairness. In this study, FFL discusses the causes of bias and fairness of existing FL, and separates solutions based on data partitioning strategies, privacy mechanisms, applicable machine learning models, communication architectures, and technologies to overcome heterogeneity. In order to improve the causes of bias, fairness, and privacy protection of FL, fairness evaluation indicators and applications and challenges of FFL are discussed. Since it addresses bias, fairness, and privacy issues in FL of all mechanisms, it can be an important resource for practitioners who want to implement efficient FL solutions.

Our article AI, cultural heritage and bias examines the challenges and potential solutions for using machine learning to interpret and classify human memory and cultural heritage artifacts. We argue that bias is inherent in cultural heritage collections (CHCs) and their digital versions, and that AI pipelines may amplify this bias. We hypothesise that effective AI methods require vast, well-annotated datasets with structured metadata, which CHCs often lack due to diverse digitisation practices and limited interconnectivity. The paper discusses the definition of bias in CHCs and other datasets, exploring how it stems from training data and insufficient humanities expertise in generative platforms. We conclude that scholarship, guidelines, and policies on AI and CHCs should address bias as both inherent and augmented by AI technologies. We recommend implementing bias mitigation techniques throughout the process, from collection to curation, to support meaningful curation, embrace diversity, and cater to future heritage audiences.

DSSC devices are among the most widely studied thin-film solar cells because of their cost-effectiveness, low toxicity, and simple fabrication method. However, there is still much scope for replacing current DSSC materials due to their high cost, low volume, and lack of long-term stability. Accordingly, ITO-nanorod films were fabricated by E-beam evaporation using the glancing angle deposition method in this study. Then, the ITO-nanorod was treated by oxygen plasma via a bias-magnetron RF sputtering process to improve the efficiency of DSSC under a varying gas flow rate of 20, 40, 60, 80, and 100 sccm. The FE-SEM investigation of the ITO film structure revealed that the obtained nanorod structures have slightly different diameters. At the same time, an increase in the oxygen flow rate resulted in a rougher film surface structure. In this, the lower sheet resistance was received because of rougher morphology. When comparing the DSSC efficiency test results, we found that at a gas flow rate of 100 sccm, the highest efficiency value showed by 9.5%. On the other hand, the ITO-nanorod without plasma treatment exhibited the lowest efficiency. Hence, plasma technology can be practically applied to improve the efficiency of DSSC devices. This study will be a prototype of a highly advanced solar cell manufacturing method for the solar cell industry.

This paper proposes a novel facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depthwise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while also capturing the dependencies between different facial regions. Additionally, a novel loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets show that the proposed LFNSB model performs excellently in facial expression recognition tasks. It achieves high recognition accuracy while significantly reducing the number of parameters, thus substantially lowering model complexity.

In scientific research, there is a noticeable lack of specific studies focused on inconclusive results, in contrast to the abundance of studies dedicated to conclusive or positive outcomes. While conclusive results offer definitive answers, inconclusive results, often viewed less favorably, do not provide such clarity. However, they play a vital role in the scientific process by identifying gaps and inspiring future research. This paper introduces the pioneering Integrated Inconclusive Results Framework, a significant development that addresses the challenges associated with inconclusive results. The Framework aims to shift the perception of inconclusive results from setbacks to crucial components of scientific progress, ultimately enhancing scientific inquiry's rigor, transparency, and overall quality. The paper delves into the historical treatment of inconclusive results, causes, and strategies for handling them, offering insights into utilizing them to inspire and guide future research, thereby strengthening the scientific community and instilling confidence and reassurance in the direction of scientific research.

In this study, we investigate how dataset composition influences the performance and bias of age estimation models across different ethnic groups. We utilized pre-trained Convolutional Neural Networks (CNNs) such as VGG19, fine-tuning them on two datasets: UTKFace and APPA-REAL. UTKFace comprises 23,705 samples (12,391 males, 11,314 females) including 10,078 White, 4,526 Black, and 3,434 Asian individuals. APPA-REAL consists of 7,591 samples (3,818 males, 3,773 females) with 6,686 White, 231 Black, and 674 Asian individuals. We adjusted dataset composition by oversampling minority groups and reducing samples from the majority group to evaluate effects on model performance, measured using Mean Absolute Error (MAE) and standard deviation. Our findings demonstrate that oversampling alone does not guarantee equitable performance across ethnicities; reducing samples from the majority group often resulted in more balanced performance, evidenced by lower MAE standard deviations. These results underscore the importance of a nuanced approach to achieve fairness, including combining various sampling techniques. This study emphasizes the critical need for tailored dataset compositions to mitigate bias and suggests exploring more refined data processing methods and algorithmic adjustments for enhanced model fairness and accuracy in facial recognition technologies.

Based on the theory of behavioral economics, this study deeply analyzes the principles of behavioral economics involved in the development of mass sports, including psychological expectation, selection deviation and incentive mechanism, rationalize the current situation of group sports development, discusses how to effectively promote the development of mass sports, and puts forward a series of concrete countermeasures. In view of the problem of behavioral expectations, it is suggested to establish a comprehensive publicity and education system to improve the public's awareness and understanding of sports, and to stimulate individuals' active participation through self-setting goals and planning methods. In view of the selection bias, it is suggested that the government and relevant institutions provide diversified and attractive sports activity options, and make full use of technological means to increase the visibility and participation of activities.In addition, in view of the incentive mechanism problem, it is advocated to adopt differentiated incentive measures to meet the needs of different groups. The government can provide some preferential policies and establish incentive mechanisms to provide support for the organization of mass sports activities. It is expected that this research can stimulate the enthusiasm of the masses to participate in sports and promote the healthy development of mass sports.

Directed acyclic graphs (DAGs) are nonparametric causal path diagrams that have substantial utility as principled representations of disease and healthcare pathways, and of the underlying ‘data generating mechanisms’ these pathways involve. As such, DAGs provide a valuable bridge between: the aetiological knowledge, operational insight and professional experience on which clinical training and practice depend; and the more abstract epistemological and analytical considerations required to extract robust statistical insight from health and healthcare data. DAGs are nonetheless vulnerable to imperfect biomedical paradigms, partial clinical knowledge and limited empirical data. DAGs drawn under such circumstances offer limited scope for statistical insight free from cognitive, analytical or inferential bias if: they misrepresent the data generating mechanisms involved; or ignore the important role that omitted variables (whether measured, unmeasured or unacknowledged) might play therein. To address these weaknesses and broaden the appeal and application of DAGs, this chapter provides ten simple steps that educators can use to improve the analytical competence and statistical confidence of the healthcare students, qualified practitioners and experienced researchers they support. These steps use temporal logic to draw DAGs so as to: reduce reliance on uncertain knowledge, incomplete information, flawed assumptions or guesswork; and avoid, mitigate or acknowledge the errors and biases that each of these incur. The chapter comprises an accessible, non-technical overview of the perspective and thoughtfulness required to generate temporally coherent DAGs as objective representations of the probabilistic causal paths involved in context-specific data generating mechanisms. It encourages a focus on those variables operating as potential sources of analytical or inferential bias when estimating the plausible, probabilistic causal relationship between two pre-specified variables; and specifically addresses the challenges posed by: omitted; time-variant; non-asynchronous; and temporally obscure variables. The chapter includes a worked example based on a published clinical study to demonstrate how each of the steps required to generate temporally-informed DAGs can be applied to: critically appraise the analytical decisions made during applied healthcare research; and inform the decisions required when designing, undertaking and analysing primary and secondary, prospective and retrospective research. The appendices include a summary of ten recommendations for improving the reporting and interrogability of DAGs and DAG-informed analyses.

The origins of directed acyclic graphs (DAGs) date back to the emergence of ‘graph theory’ in the early 1700s (Biggs et al. 1986). DAGs are conceptual or literal, diagrammatic representations of causal paths between variables which are constructed – as their name suggests – on the basis of two over-riding principles: first, that all causal paths are ‘directed’ (i.e. for each pair of variables, only one can represent the cause, while the other must be its consequence); and second, that no direct cyclical paths, or indirect cyclical pathways (comprising sequences of consecutive paths) are allowed, such that no consequence can be considered its own direct or indirect cause (hence ‘acyclic’; Law et al., 2012). As such DAGs reflect the knowledge, presumptions, assumptions and/or speculation of the analyst(s) concerned regarding the causal relationships between each of the variables included therein. Current convention dictates that variables are represented as nodes/vertices, and that any causal paths between variables are represented as directed arcs/edges/lines, often in the form of arrows (see Figure 1). Although each arc indicates the presence and direction of a known/presumed/assumed/speculative causal relationship between the two variables concerned, drawing an arc does not require the sign, magnitude, precision or shape of the relationship to be known or declared (Tennant et al., 2021). In this respect, DAGs provide a simple, uncomplicated, accessible and entirely nonparametric approach for postulating causal relationships amongst any variables of interest even when these are uncertain, unknown or entirely speculative (Ellison, 2020). Nonetheless, as a result of the parametric constraints imposed by the presence/absence of possible arcs within any given DAG, these also reflect and support a number of more sophisticated statistical applications which make it possible to use DAGs to inform the design of multivariable statistical models that reflect the causal structure(s) involved – albeit without the need to know or understand the mathematical technicalities on which these are based (Lewis and Kuerbis, 2016). These features make DAGs attractive cognitive, educational and analytical tools for strengthening the epistemological, theoretical and empirical basis of causal inference, and there has been a recent proliferation in the use of DAGs across a range of applied scientific disciplines (e.g. Knight and Winship, 2013), and an associated upsurge in analytical methods training (e.g. Elwert, 2011; Gilthorpe, 2017; Hernán 2018; Roy, 2021; Hünermund, 2021). This Chapter reflects on a decade of delivering medical statistics training to undergraduate medical students at the University of Leeds between 2012-2021 in which the third year research, evaluation and special studies module (‘RESS3’) has used DAGs to support the development of applied statistical skills relevant to the extended student-selected research and evaluation projects (ESREP) students undertake in their fourth and final years (Ellison, 2021; Ellison et al., 2014a,b). Based on successive iterations of the structure and content of the RESS3 module, together with notes made during formal and informal planning and review meetings with module leads, lecturers, tutors and students, we draw on the claims and criticisms made of DAGs in the epidemiological literature to identify a number of explicit strengths (and associated, often implicit. weaknesses) that are central to their use in prediction and causal inference modelling. While using DAGs requires (and benefits from) a clear understanding of their non-parametric nature and parametric implications, the weaknesses of DAGs seem likely to reflect both: the challenges inherent in the modelling of data generating processes when these are imperfectly understood; and troublesome cognitive and heuristic tendencies common to all analytical tools – in which the tool facilitates the task in hand by reducing the necessity (and benefits of) exploring uncertainties and identifying assumptions. These, more epistemological considerations appear particularly challenging for medical undergraduates to grasp (Ellison, 2021), but also appear poorly understood by many established analysts and clinical epidemiologists (Ellison, 2020).

Codon use bias is an important characteristic in the process of genetic information transmission of species. With the publication of the genome of C. panzhihuaensis, it is of great significance to investigate the codon use bias for understanding the genetic evolution of this species and for molecular breeding. In this study, Perl language and CodonW 1.4.2 software was used to systematically analyze the coding gene sequences of the C. panzhihuaensis genome, obtain the characteristics of codon use, and identify the sources of variation affecting codon use. The results showed that GC3s content, GCall content, and ENC value were 46.75%, 47.67%, and 52.8 respectively. As the first seed plant, the GC3 content of Cycas is similar to that of most gymnosperms and most dicotyledons, but considerably different from that of monocotyledons.RSCU value was greater than 1, among which 21 codons ended in U or A. Enc-plot analysis, PR2-plot neutral plot analysis, and correlation coefficient analysis of Axis1 with GC3s and CAI revealed that the codon bias was mainly affected by natural selection, but also by mutation pressure and other factors. Thirty-one optimal codons of Cycas genes were screened. This research can provide a reference for the phylogeny and genome codon evolution of Cycas.

A vivid debate is ongoing in the scientific community about statistical malpractice and the related publication bias. No general consensus exists on the consequences and this is reflected in heterogeneous rules defined by scientific journals on the use and reporting of statistical inference. This paper aims at discussing how the debate is perceived by the agricultural economics community and implications for our roles as researchers, contributors to the scientific publication process, and teachers. We start by summarizing the current state of the p-value debate and the replication crisis, and commonly applied statistical practices in our community. This is followed by motivation, design, results and discussion of a survey on statistical knowledge and practice among the researchers in the agricultural economics community in Austria, Germany and Switzerland. We conclude that beyond short-term measures like changing rules of reporting in publications, a cultural change regarding empirical scientific practices is needed that stretches across all our roles in the scientific process. Acceptance of scientific work should largely be based on the theoretical and methodological rigor and where the perceived relevance arises from the questions asked, the methodology employed, and the data used but not from the results generated. Revised and clear journal guidelines, the creation of resources for teaching and research, and public recognition of good practice are suggested measures to move forward.

In fisheries monitoring, catch is assumed to be a product of fishing intensity, catchability, and availability, where availability is defined as the number or biomass of fish present and catchability refers to the relationship between catch rate and the true population. Ecological monitoring programs use catch per unit of effort (CPUE) to standardize catch and monitor changes in fish populations; however, CPUE is proportional to the portion of the population that is vulnerable to the type of gear that is used in sampling, which is not necessarily the entire population. Programs often deal with this problem by assuming that catchability is constant, but if catchability is not constant, it is not possible to separate the effects of catchability and population size using monitoring data alone. This study uses individual-based simulation to separate the effects of changing environmental conditions on catchability and availability in environmental monitoring data. The simulation combines a module for sampling conditions with a module for individual fish behavior to estimate the proportion of available fish that would escape from the sample. The method is applied to the case study of the well-monitored fish species Delta Smelt (Hypomesus transpacificus) in the San Francisco Estuary, where it has been hypothesized that changing water clarity may affect catchability for long-term monitoring studies. Results of this study indicate that given constraints on Delta Smelt swimming ability, it is unlikely that the apparent declines in Delta Smelt abundance are due to an effect of changing water clarity on catchability.

We explore gaps in Wikipedia's coverage of the visual arts by comparing the representation of 100 artists and 100 artworks from the Western canon against corresponding sets of notable artists and artworks from non-Western cultures. We measure the coverage of these two sets of topics across Wikipedia as a whole and for its individual language versions. We also compare the coverage for Wikimedia Commons and Wikidata, sister-projects of Wikipedia that host digital media and structured data. We show that all these platforms strongly favour the Western canon, giving many times more coverage to Western art. We highlight specific examples of differing coverage of visual art inside and outside the Western canon. We find that European language versions of Wikipedia are generally more "Western" in their coverage and Asian languages more "global", with interesting exceptions. We suggest how both Wikipedia and the wider cultural sector can address this gap in content and thus give Wikipedia a truly global perspective on the visual arts.

The parameters of logistic regression models are usually obtained by the method of maximum likelihood (ML). However, in analyses of small data sets or data sets with unbalanced outcomes or exposures, ML parameter estimates may not exist. This situation has been termed “separation” as the two outcome groups are separated by the values of a covariate or a linear combination of covariates. To overcome the problem of non-existing ML parameter estimates, applying Firth’s correction (FC) was proposed. In practice, however, a principal investigator might be advised to “bring more data” in order to solve a separation issue. We illustrate the problem by means of an examples from colorectal cancer screening and ornithology. It is unclear if such an increasing sample size (ISS) strategy that keeps sampling new observations until separation is removed improves estimation compared to applying FC to the original data set. We performed an extensive simulation study where the main focus was to estimate the cost-adjusted relative efficiency of ML combined with ISS compared to FC. FC yielded reasonably small root mean squared errors and proved to be the more efficient estimator. Given our findings, we propose not to adapt the sample size when separation is encountered but to use FC as the default method of analysis whenever the number of observations or outcome events is critically low.

This paper investigates a range of popular differential evolution (DE) configurations to identify components responsible for emergence of structural bias – a recently identified tendency of algorithms to prefer some regions of search space over others, for reasons unrelated to objective function values. Previous work has explored this tendency for genetic algorithms (GA) and particle swarm optimisation (PSO), finding a relationship between population size and extent of structural bias, hence highlighting potential weaknesses of those algorithms. In current article, we focus on DE, extend the investigation to include consideration of an algorithmic component that is often overlooked – constraint handling mechanism. Towards this end, a wide range of DE configurations was tested here. Results suggest that DE is generally robust to structural bias. Unlike the case with GA and PSO, population size seems to have no influence on DE structural bias. Only one of variants studied – DE/current-to-best/1/bin – shows clear signs of bias, however, we show that this effect is mitigated by a judicious choice of constraint handling technique. These findings contribute towards explaining widespread success of DE variants in algorithm comparison studies; its robustness to structural bias represents the absence of a factor that may confound other algorithms.

Terrestrial LiDAR becomes more and more popular to estimate leaf and plant area density. Voxel-based approaches account for this vegetation heterogeneity and significant work has been done in this recent research field, but no general theoretical analysis is available. Although estimators have been proposed and several causes of biases have been identified, their consistency and efficiency have not been evaluated. Also, confidence intervals are almost never provided. In the present paper, we solve the transmittance equation and use the Maximum Likelihood Estimation (MLE), to derive unbiased estimators and confidence intervals for the attenuation coefficient, which is proportional to leaf area density. The new estimators and confidence intervals are defined at voxel scale, and account for the number of beams crossing the voxel, the inequality of path lengths in voxel, the size of vegetation elements, as well as for the variability of element positions between vegetation samples. They are completed by numerous numerical simulations for the evaluation of estimator consistency and efficiency, as well as the assessment of the coverage probabilities of confidence intervals. • Although commonly used when the beam number is low, the usual estimators are strongly biased and the 95% confidence intervals can be ≈±100% of the estimate. • Our unbiased estimators are consistent in a wider range of validity than the usual ones, especially for the unbiased MLE, which is consistent when the beam number is as low as 5. The unbiased MLE is efficient, meaning it reaches the lowest residual errors that can be expected (for an unbiased estimator). Also the unbiased MLE does not require any bias correction when path lengths are unequal. • When elements are small (or voxel is large), 103 beams entering the voxel leads to some confidence intervals ≈±10%, but when elements are larger (or voxel smaller), it can remain wider than ±50%, even for a large beam number. This is explained by the variability of element positions between vegetation samples. Such a result shows that a significant part of residual error can be explained by random effects. • Confidence intervals are much smaller (±5 to 10%) when LAD estimates are averaged over several small voxels, typically within a horizontal layer or in the crown of individual plants. In this context, our unbiased estimators show a reduction of 50% of the radius of confidence intervals, in comparison to usual estimators. Our study provides some new ready-to-use estimators and confidence intervals for attenuation coefficients, which are consistent and efficient within a fairly large range of parameter values. The consistency is achieved for a low beam number, which is promising for application to airborne LiDAR data. They entail to raise the level of understanding and confidence on LAD estimation. Among other applications, their usage should help determine the most suitable voxel size, for given vegetation types and scanning density, whereas existing guidelines are highly variable among studies, probably because of differences in vegetation, scanning design and estimators.

Aristotle vs. Ringelmann was a discussion between two distinct research teams from the ETH Zürich who argued whether the productivity of Open Source software projects scales sublinear or superlinear with regard to its team size. This discussion evolved around two publications, which apparently used similar techniques by sampling projects on GitHub and running regression analyses to answer the question about superlinearity. Despite the similarity in their research methods, one team around Ingo Scholtes reached the conclusion that projects scale sublinear, while the other team around Didier Sornette ascertained a superlinear relationship between team size and productivity. In subsequent publications, the two authors argue that the opposite conclusions may be attributed to differences in project populations, since 81.7% of Sornette’s projects have less than 50 contributors. Scholtes, on the other hand, sampled specifically projects with more than 50 contributors. This publication compares the research from both authors by replicating their findings, thus allowing for an evaluation of how much project sampling actually accounted for the differences between Scholtes’ and Sornette’s results. Thereby, the discovery was made that sampling bias only partially explains the discrepancies between the two authors. Further analysis led to the detection of instrumentation biases that drove the regression coefficients in opposite directions. These findings were then consolidated into a quantitative analysis, indicating that instrumentation biases contributed more to the differences between Scholtes’ and Sornette’s work than the selection bias suggested by both authors.

The field of microbial pathogenesis seeks to identify the agents and mechanisms responsible for disease causation. Since Robert Koch introduced postulates that were used to guide the characterization of microbial pathogens, technological advances have substantially increased the capacity to rapidly identify a causative infectious agent. Research efforts currently focus on causation at the molecular level with a search for virulence factors (VFs) that contribute to different stages of the infectious process. We note that the quest to identify and characterize VFs sometimes lacks scientific rigor, and this suggests a need to examine the epistemology of VF characterization. We took this premise as an opportunity to explore the epistemology of VF characterization. In this perspective, we discuss how the characterization of various gene products that evolved to facilitate bacterial survival in the broader environment have potentially been prematurely mischaracterized as VFs that contribute to pathogenesis in the context of human biology. Examples of the reasoning that can affect misinterpretation, or at least premature assignment of mechanistic causation, are provided. Our aim is to refine categorization of VFs by emphasizing a broader biological view of their origin.

Boost-derived converters are most popular in Power Factor Correction AC-DC conversion applications and alternative energy generation systems. The proper operation of such converters relies on housekeeping power supplies providing auxiliary power for their control circuitry. The purpose of this study is to present a design-oriented analysis of a simple, passive, isolated, self-bias circuit that can provide a stabilized voltage for the control circuits of boost-derived converters. The circuit’s output voltage inherently follows the main regulated output and therefore, does not require an additional active control. The paper presents a detailed analysis of the circuit and offers design guidelines. Following the presentation of the basic idea several variations of the circuit, that can be used in special applications, are presented. The feasibility of the proposed bias circuits was verified by simulation and experiment on a laboratory prototype.

The reviews usefulness has been the aim of several research studies. However, results regarding the significance of usefulness determinants are often contradicting, thus decreasing the accuracy of reviews’ helpfulness estimation. Also, bias in user reviews attributed to differences e.g. in gender, nationality, etc., may result into misleading judgments thus diminishing reviews’ usefulness. Research is needed for sentiment analysis algorithms that incorporate bias embedded in reviews, thus improving their usefulness, readability, credibility, etc. This study utilizes fuzzy relations and fuzzy synthetic evaluation (FSE) in order to calculate reviews’ usefulness by incorporating users’ biases as expressed in terms of reviews’ articulacy and sentiment polarity. It selected and analysed 95.678 hotel user reviews from Tripadvisor, for five nationalities. The findings indicate that there are differences among nationalities. The British are most consistent in their judgments expressed in titles and review documents. The British and the Greek review titles suffice to convey any negative sentiments. The Dutch use fewer words in their reviews than the other nationalities. This study suggests that fuzzy logic captures subjectivity which is often found in reviews, and it can be used to quantify users’ behavioral differences, calculate reviews usefulness, and provide the means for developing more accurate voting systems.

Spin valves with a synthetic antiferromagnet were fabricated by magnetron sputtering. It was shown that the fabricated spin valves had perfect microstructure of layers and smooth interfaces, therefore RKKY interaction dominates in coupling of ferromagnetic layers separated by a copper spacer. Rhombus-shaped microobjects were fabricated from a single spin valve film. The thermomagnetic treatment procedure was found, to form unidirectional anisotropy in the microobject so, that the values of the exchange bias fields in the rhombus’ nonparallel sides were opposite in sign. For the synthetic antiferromagnet CoFeNi/Ru/CoFeNi the difference between ferromagnetic layers thicknesses was found, at which the thermomagnetic treatment forms the same exchange bias all over the rhombus’ side. The sensor element was fabricated in which each side of the rhombus was a shoulder of a Wheatstone bridge. After the thermomagnetic treatment procedure each shoulder worked as an active magnetosensitive element, thus the device operated as the full Wheatstone bridge. The sensor output has the shape of a step, high sensitivity to field change and significant magnetic hysteresis. Such characteristics are suitable for switching devices.

Water resources planners and policymakers often ask questions about the future projections of drought characteristics (events, intensity, severity, duration, and peak) under different climatic scenarios. This study focused on quantifying the historical (1951-2005) and future (2026-2100) drought characteristics using the Standardized precipitation index (SPI) under RCP 4.5 and RCP 8.5 climate scenarios for the Balochistan province of Pakistan, an arid and drought-vulnerable region. Precipitation data of MPI-ESM-LR_RCA4 RCM was obtained from the Coordinated Regional Climate Downscaling Experiment South Asia (CORDEX-SA). The CORDEX-SA data was interpolated at 12-gauge stations and bias-corrected by the distribution mapping method using Asian Precipitation - Highly-Resolved Observational Data Integration towards Evaluation (APHRODITE) data. The drought characteristics were calculated based on standardized precipitation index (SPI), and intercompared between northern Balochistan (NB) and southern Balochistan (SB). It was found that the northern Balochistan (NB) region has suffered more droughts in the historical period and is also projected to have more severe and intense droughts than SB region. It was also found that with the increase of drought events, the duration reduces, which means that the higher the drought events at a station, the lower the drought duration. Government officials should focus more on managing the already few freshwater resources sustainably, given the increased likelihood of droughts in Balochistan due to climate change.

The manipulation of participant allocation in randomized controlled trials to achieve equal groups sizes may introduce allocation bias potentially leading to larger treatment effect estimates. This study aimed to estimate the proportion of nursing trials that have precisely equal group sizes and examine if there was an association with trial outcome. Data were extracted from a sample of 148 randomized controlled trials published in nursing science journals in 2017. One hundred trials (68%) had precisely equal group sizes. Respectively, a positive outcome was reported in 70% and 58% of trials with equal/unequal groups. Trials from Asia were more likely to have equal group sizes than those from the rest of the world. Most trials reported a sample size calculation (n=105, 71%). In a third of trials (n=36, 34%), the number of participants recruited precisely matched the requirement of the sample size calculation; this was significantly more common in studies with equal group sizes. The high number of nursing trials with equal groups may suggest nurses con-ducting clinical trials are manipulating participant allocation to ensure equal group size increasing the risk of bias.

We investigated the characteristics of surface wind speeds and temperatures predicted by the local data assimilation and prediction system (LDAPS) operated by the Korean Meteorological Administration. First, we classified automated weather stations (AWSs) into four categories [urban flat (Uf), rural flat (Rf), rural mountainous (Rm), and rural coastal (Rc) terrains] based on the surrounding land cover and topography, and selected 25 AWSs representing each category. Then we calculated the mean bias error of wind speed (WE) and temperature (TE) using AWS observations and LDAPS predictions for the 25 AWSs in each category for a period of 1 year (January–December 2015). We found that LDAPS overestimated wind speed (average WE = 1.26 m s–1) and underestimated temperature (average TE = –0.63°C) at Uf AWSs located on flat terrain in urban areas because it failed to reflect the drag and local heating caused by buildings. At Rf, located on flat terrain in rural areas, LDAPS showed the best performance in predicting surface wind speed and temperature (average WE = 0.42 m s–1, average TE = 0.12°C). In mountainous rural terrain (Rm), WE and TE were strongly correlated with differences between LDAPS and actual altitude. LDAPS underestimated (overestimated) wind speed (temperature) for LDAPS altitudes that were lower than actual altitude, and vice versa. In rural coastal terrain (Rc), LDAPS temperature predictions depended on whether the grid was on land or sea, whereas wind speed did not depend on grid location. LDAPS underestimated temperature at grid points on the sea, with smaller TE obtained for grid points on sea than on land.

This paper investigates whether optimisation methods with the population made up of one solution can suffer from structural bias just like their multisolution variants. Following recent results highlighting the importance of choice of strategy for handling solutions generated outside the domain, a selection of single solution methods are considered in conjunction with several such strategies. Obtained results are tested for the presence of structural bias by means of a traditional approach from literature and a newly proposed here statistical approach. These two tests are demonstrated to be not fully consistent. All tested methods are found to be structurally biased with at least one of the tested strategies. Confirming results for multisolution methods, it is such strategy that is shown to control the emergence of structural bias in single solution methods. Some of the tested methods exhibit a kind of structural bias that has not been observed before.

An article's tone and framing not only influence an audience's perception of a story but may also reveal attributes of author identity and bias. Building upon prior media, psychological, and machine learning research, this neural network-based system detects those writing characteristics in ten news agencies' reporting, discovering patterns that, intentional or not, may reveal an agency's topical perspectives or common contextualization patterns. Specifically, learning linguistic markers of different organizations through a newly released open database, this probabilistic classifier predicts an article's publishing agency with 74% hidden test set accuracy given only a short snippet of text. The resulting model demonstrates how unintentional 'filter bubbles' can emerge in machine learning systems and, by comparing agencies' patterns and highlighting outlets' prototypical articles through an open source exemplar search engine, this paper offers new insight into news media bias.

Advances in remote sensing have led to use of satellite-derived rainfall products to complement the sparse rain gauge data. Although globally derived and some regional bias corrected, these products often show large discrepancies with ground measurements attributed to local and external factors that require systematic consideration. Decreasing rain gauge network however inhibits continuous validation of these products. We propose to deal with this problem by the use of Bayesian approach to merge the existing historical rain gauge information to create a consistent satellite rainfall data that can be used for climate studies. Monthly Bayesian bias correction is applied to the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS v2) data to reduce systematic errors using a corresponding gridded (0.05°) rain gauge data over East Africa for a period of 33 (1981–2013) years of which 22 years are utilized to derive error fields which are then applied to an independent CHIRPS data for 11 years for validation. The bias correction is spatially and temporally assessed during the rainfall wet months of March-May (MAM), June-August (JJA) and October–December (OND) in East Africa. Results show significant reduction of systematic errors at both monthly and yearly scales and harmonization of their cumulative distributions. Monthly statistics showed a reduction of RMSD (29–56)% and MAE (28–60)% and an increase of correlations (2–32) %, while yearly ones showed reductions of RMSD (9-23)%, and MAE (7–27)% and increase of correlations (4–77)% for MAM months, reduction of RMSD (15–35)% and MAE (16–41)% and increase in correlations (5–16)% for JJA months, and reduction of RMSD (3–35)% and MAE (9–32)% and increase of correlations (3–65)% for OND months. Systematic errors of corrected data were influenced by local processes especially over Lake Victoria and high elevated areas. Large-scale circulations induced errors were mainly during JJA and OND rainfall seasons and were reduced by the separation of anomalous years during training. The proposed approach is recommended for generating long-term data for climate studies where consistencies of errors can be assumed.

Drainage systems are usually dimensioned for design storms based on intensity-duration-frequency (IDF) curves of extreme precipitation. For each location, different IDF curves are established based on local hydrological conditions. Recent research shows that these curves also vary with time, and should be updated with recent data. The purpose of this study is to evaluate IDF curves obtained from precipitation simulations from the Eta RCM, comparing them with IDF curves obtained from data of a rainfall station. Climate models can be a useful tool for assessing the impacts of climate changes on drainage systems, referring precipitation forecasts. In this study, the Eta RCM was forced by two global climate models: HadGEM2-ES and MIROC5. The bias of the precipitation data, generated by RCM models, was corrected using a Gamma distribution. The Juqueriquerê River Basin, in the cities of Caraguatatuba and São Sebastião, São Paulo State, Brazil, was chosen as a case study. The results show a good correlation between the IDF curves of simulated and observed rainfall for the control period (1960-2005), indicating the strong possibility of using the Eta RCM precipitation forecasts for 2007 - 2099 to establish future IDFs thereby, taking into account climate changes in urban drainage design.

This paper formulates the classic Monty Hall problem as a Bayesian game. Allowing Monty a small amount of freedom in his decisions facilitates a variety of solutions. The solution concept used is the Bayes Nash Equilibrium (BNE), and the set of BNE relies on Monty's motives and incentives. We endow Monty and the contestant with common prior probabilities (p) about the motives of Monty, and show that under certain conditions on p, the unique equilibrium is one where the contestant is indifferent between switching and not switching. This coincides and agrees with the typical responses and explanations by experimental subjects. Finally, we show that our formulation can explain the experimental results in Page (1998) [12]; that more people gradually choose switch as the number of doors in the problem increases.

Background and Objectives: Clinical trials are a critical step in the development of new medicines and medical devices, testing the efficacy and safety of new treatment regimens. However, if the results of clinical trials are not made public, the evidence base on interventions is incomplete and possibly distorted, which lead to suboptimal treatment choices and negatively affect public health. This study analyses and contrasts the laws and regulations governing clinical trial registration and reporting in China and the United States. Methods: We used desk research to compile, assess and compare current laws, regulations, compliance patterns, and enforcement mechanisms and actions in China and the United States (U.S.). Policy documents were downloaded from Chinese and U.S. government websites. A spreadsheet analysis was utilized for direct comparison. Results: Both China and the U.S. have laws and regulations governing clinical trial registration and reporting. Chinese legislation covers a broader range of trials. In the U.S. trial results must be disclosed to both the national regulator and the public, while Chinese law mandates disclosure to the regulator alone. Cross-country comparisons of regulatory compliance by trial sponsors are impossible due to the opacity of the Chinese data platform. Neither country effectively ensures that all clinical trial results are made public as required by the Declaration of Helsinki and recommended by the WHO. Interpretation: While neither country is perfect, both may be able to learn from each other. There is a major gap in the literature regarding the extent of non-publication of clinical trial results by sponsors operating in China.

Homogenization of climatic time series aims to remove non-climatic biases which come from the technical changes of climate observations. The method comparison tests of the Spanish MULTITEST project (2015-2017) showed that ACMANT was likely the most accurate homogenization method available at that time, in spite of the tested ACMANTv4 version gave suboptimal results when the test data included synchronous breaks for several time series. The technique of combined time series comparison has been introduced to ACMANTv5 in order to treat better this specific problem. Tests confirm that ACMANTv5 treats adequately synchronous inhomogeneities, but the accuracy has slightly worsened in some other cases. Results for a known daily temperature test dataset for 4 U.S. regions show that the residual errors after homogenization may be larger with ACMANTv5 than with ACMANTv4. Further tests have been performed to learn more about the efficiencies of ACMANTv4 and ACMANTv5, and to find solutions for the appearing problems with the new version. Planned changes in ACMANTv5 are presented in the paper along with connecting test results. The overall results indicate that the combined time series comparison can be kept in ACMANT, but smaller networks should be generated by the automatic networking process of the method. For the further improvements of homogenization methods and for obtaining more reliable and more solid knowledge about their accuracies, more synthetic test datasets mimicking fairly the true spatio-temporal structures of real climatic data would be in need.

The intricacies of translational strategies make this stage of the implementation of genetic information one of the most difficult to understand and, at the same time, perhaps the most interesting. It also becomes clear that all this variety of strategies is not just a consequence of a long evolutionary history but is of paramount importance for fine-tuning gene expression and metabolic modulation. This is especially true for those organisms that demonstrate predominantly biochemical and physiological reactions with a lack of behavioural ones. Plants are just such a group of organisms. Overcoming unfavourable environmental conditions occupies a key place in plant physiology. This is especially true with the changing conditions of global warming and the irretrievable loss or depletion of natural ecosystems. Conceptually, the response of plants to abiotic stress is a set of complex and intricate strategies. This is dictated both by a variety of abiotic factors that provoke stressful conditions and by a variety of molecular genetic mechanisms that make it possible to fine-tune the metabolic pathways through which the plant organism overcomes non-standard and non-optimal conditions. In the presented review, we tried to focus on the state-of-the-art in the field of translational regulation in plants under abiotic stress conditions. Various regulatory elements and patterns are considered in relative chronological order. We also considered it necessary to pay attention to key high-performance methods for studying the dynamics of genetic information at the stage of translation.

Harsh parenting could be regarded as a harsh form of behavior, feelings and attitude towards children in the process of parenting. According to the theory of intergenerational transmission of violence, harsh parenting is an important factor affecting children's aggressive behavior, but the theory does not clarify the specific action path between the harsh parenting and aggressive behavior. In order to reveal the relationship between harsh parenting and aggressive behavior of juvenile delinquents, 604 male juvenile delinquents (N=604, Mage=16.57 years, SD=0.612 years) were investigated using Harsh Parenting Questionnaire, Buss-Perry Aggression Questionnaire, Multidimensional Peer-Victimization Scale, and the Word Sentence Association Paradigm for Hostility in Chinese version. Analysis using structural equation modeling procedures showed that (a) all variables were positively associated with each others; (b) the partial indirect effect of harsh parenting on aggressive behaviors was realized through the mediator of peer victimization and hostile attribution bias independently, (c) as well as the partial indirect effect through the mediator of peer victimization and hostile attribution bias sequentially. The results suggested that harsh parenting can explain the high aggressive behavior of male juvenile delinquents. Moreover, harsh parenting may also lead to the formation of hostile attribution bias by increasing the risk of peer victimization, and ultimately lead to the formation of highly aggressive behaviors.

Multipartite bacteria have one chromosome and one or more chromid. Chromids are believed to have properties that enhance genomic flexibility, making them a favored integration site for new genes. However, the mechanism by which chromosomes and chromids jointly contribute to this flexibility is not clear. To shed light on this, we analyzed the openness of chromosomes and chromids of the two bacteria, Vibrio and Pseudoalteromonas, both in the Enterobacterales order of gamma-proteobacteria, and compared it with monopartite genomes in the same order. We applied pangenome analysis, codon usage analysis and the HGTector software to detect horizontally transferred genes. Our findings suggest that the chromids of Vibrio and Pseudoalteromonas likely originated from two separate plasmid acquisition events. Bipartite genomes were found to be more open compared to monopartite. We found that the shell and cloud pangene categories drive the openness of bipartite genomes in Vibrio and Pseudoalteromonas. Based on this and our two recent studies, we propose a hypothesis that explains how chromids and the chromosome terminus region contribute to the genomic plasticity of bipartite genomes.

In the field of stochastic optimisation, the so-called structural bias constitutes an undesired behaviour of an algorithm that is unable to explore the search space to a uniform extent. In this paper, we investigate whether algorithms from a subclass of estimation of distribution algorithms, the compact algorithms, exhibit structural bias. Our approach, justified in our earlier publications, is based on conducting experiments on a test function whose values are uniformly distributed in its domain. For the experiment, 81 combinations of compact algorithms and strategies of dealing with infeasible solutions have been selected as test cases. We have applied two approaches for determining the presence and severity of structural bias, namely a visual and a statistical (Anderson-Darling) tests. Our results suggest that compact algorithms are more immune to structural bias than their counterparts maintaining explicit populations. Both tests indicate that strong structural bias is found only in one of the algorithms (cBFO) regardless of the choice of strategy of dealing with infeasible solutions and cPSO mirror. For other test cases, statistical and visual tests disagree on some cases classified as having mild or strong structural bias: the former one tends to make harsher decisions, thus needing further investigation.

This study rigorously investigates the impact of COVID-19 on the Tunisian stock market volatility. The investigation spans from January 2020 to December 2022, employing a GJR-GARCH model, bias-corrected wavelet analysis, and an ARDL approach. Specific variables related to health measures and government interventions are incorporated. The findings highlight that confirmed and death cases contribute significantly to the escalation in TUNINDEX volatility. Interestingly, certain indices related to government interventions exhibit no substantial impact on volatility, indicating a weak resilience of the Tunisian stock market amidst the challenges posed by COVID-19. However, the application of the bias-corrected wavelet analysis yields more nuanced outcomes in terms of correlations of volatility to the same metrics. Notably, the study recognizes the potential influence of uncertainties linked to the duration of lockdowns on financial market volatility. The positive and long-term impact on volatility of the US Equity Market uncertainty and VIX is evident through the Autoregressive Distributed Lag Model (ARDL), whereas economic policy uncertainty shows no significant impact. This indicates a potential vulnerability of the Tunisian stock market to future shocks, emphasizing the need for government efforts to instill investor confidence. This comprehensive exploration contributes to our understanding of the dynamics between the pandemic’ metrics, government interventions, and financial market stability. Our findings provide valuable insights for implementing risk management strategies amid the COVID‐19 crisis.

Objectives: The aim of our current study was to analyze whether the use of important measures of methodological quality and reporting of randomized clinical trials published in the field of cardiovascular disease research have changed over time. Further aim was to investigate whether there was an improvement over time in the ability of these trials to provide a good estimate of the true intervention effect. Methods We conducted two searches in the Cochrane Central Register of Controlled Trials (CENTAL) database to identify cardiovascular clinical research trials published in either 2012 or 2017. Randomized clinical trials (RCTs) trials in cardiovascular disease research with adult participants were eligible to be included. We randomly selected 250 RCTs for both publication year 2012 and 2017. Trial characteristics, data on measures of methodological quality and reporting were extracted and risk of bias for each trial was assessed. Results As compared to 2012 in 2017 there were significant improvements in the reporting of the presence of a data monitoring committee (42.0% in 2017 compared to 34.4% in 2012), and a positive tendency of registering cardiovascular disease research RCTs in clinical trial registries (83.6% in 2017 compared to 72.0% in 2012). On the other hand, we also observed that significantly fewer RCTs reported sample size calculation (60.4% in 2017 compared to 98.4% in 2012) in 2017 as compared to 2012. RCTs in 2017 were more likely to have low overall risk of bias ( RoB ) than in 2012 (29.2% in 2017compared to 21.2% in 2012). Conclusion: As compared to 2012 in 2017 there were significant improvement in some, but not all the important measures of methodological quality. Although more trials in the field of cardiovascular disease research had a lower overall RoB in 2017, the improvement over time was not consistently perceived in all RoB domains.

Climate change is a phenomenon that makes the climate system of a given region to be more unpredictable and increases the risk of water-related problems. GCMs under the new CMIP6 framework holds several climate models with many improvements as compared to past similar efforts. The improvements are mainly in the number of scenarios formulated, setup, parametrization, and resolution. In this study, 10 downscaled climate models from CMIP6 are evaluated by applying statistical and data mining tools and are ranked based on their capability to describe the historical observed series. The result of the analysis showed that the outputs of the MPI-ESM1-2-HR model have a good overall ranking among those 10 models. The output of this top-ranked model is used to understand future climate over UASB after properly bias-corrected using the QM method. Results of the bias correction step show that average annual precipitation has shown an increment of 6.5\% in the middle (SSP2-4.5) and 10.3\% in the worst (SSP5-8.5) case scenarios for the mid-century (2040 - 2069). Similarly, for the end of the century (2070 - 2099) an increment of 4.7\% and 17.5\% was predicted for the two scenarios respectively. Whereas average annual maximum temperature series showed an increment of 1.5 $\degree C$ for middle and 2.6 $\degree C$ for the worst case in the mid-century. At the same time, an increment of 2.2 $\degree C$ and 3.5 $\degree C$ were predicted for the end of the century similarly for those scenarios. Furthermore, it was predicted that the average annual minimum temperature series will have an increment of 2.6 $\degree C$ and 3.1 $\degree C$ for mid-century and 3.1 $\degree C$ and 4.7 $\degree C$ for the end century for the two scenarios respectively. An increase in precipitation with increased land degradation problems in the sub-basin increases the risk of flood events in the future.

Ayurveda as a healthcare system has survived for thousands of years but continues to be dogged by reported lack of efficacy of the treatments in clinical trials. The reported lack of efficacy could be due to a real lack of efficacy (which then contradicts the survival of Ayurveda as a functional medical system enjoying considerable public patronage) or could be attributed to inadequacies in the efforts towards evidence generation or in a larger context the overall scientific conduct of research in Ayurveda. In an effort towards better evidence generation, there is an immediate need for standardizing the design, conduct and reporting of clinical trials of Ayurveda but it is a daunting task. For this effort to benefit the scientific endeavors of Ayurveda researchers, it should allow the researchers to be able to apply Ayurveda’s multi-component, individualized and inherently holistic approach. Statistical principles can benefit this effort. Statistical hypothesis testing (SHT) is central to these statistical principles and also aligns well with conventional scientific principles of evidence generation. Although there are challenges with SHT, good practitioners engaged in it do much more than just apply the mathematical theory behind it. As a particular example, lot of time in clinical trial designing is spent in addressing biases and designing trials prudently by minimizing the effect of such biases. SHT can benefit such an effort objectively. There is a need for Ayurveda researchers to engage deeply and mindfully about biases in study design in order to gain scientific validity and acceptability. The article highlights issues that arise in Ayurveda research, and discusses few ways of dealing with these issues using statistical principles.

Automation technologies are present in almost every domain of human activity and they are now more and more present in our everyday life. The reason for this massive deployment of automated systems would reside in all the benefits they offer to the users. In experimental settings, multiple studies have demonstrated the positive effects the introduction of automation can have on human decision-making and performance. However, studies have also demonstrated that the introduction of automation can have important negative effects as well. Considering that automation is now introduced in sensitive domains like military defense or medicine, more than ever we need a complete understanding of the effects caused by these systems on human performance and decision-making, and particularly in tasks and contexts with social or moral dimension. In this paper we will firstly review the main effects produced on a human agent’s behaviors by the introduction of automation. Then, we will review the conditions identified as underlying factors of these effects, and see how they are currently integrated in models of human – automation interaction. We will conclude this review by highlighting new directions for future investigations on human – automation interaction.

Two opposed p–n diodes are connected with another junction that causes cancellation of the electric field in the depletion layer of each diode by the field of the other diode. This derived quantum diode is called the A system. Another dual diode, constructed by the same process but with the p- and n-types positioned as duality, called the B system. When a bias voltage is applied between the A and B systems, Lorentz conservation imparts a momentum (i.e., a wave number) to the carriers in the absence of any internal voltage. Thus, a superconducting bias current density appears without the need for cooling. The reappearances of electron–hole pairs on the junction surfaces are assumed to be described by entire wavefunctions normalized by the band gap. Based on the bias superconducting current, NOT and NAND gates were constructed from the quantum diode systems. Numerical calculations revealed that the constant phases of the entire wavefunctions of the p-and n-types converged. Accordingly, it was clarified that Bose–Einstein condensation and the Meissner effect (described by the London equation) occurred in the quantum diode systems. Moreover, the systems exhibited rectification characteristics and a switching speed of the order of 10^-14 s. Combining this switching property with the large bias superconducting current (of the order of several V), we developed NOT and NAND gates with direct quantum correlations among many qubits, which are unaffected by random and thermal noises. These gates have memorization and initialization properties and are compatible with existing and accumulating programing algorithms. Moreover, when harvesting a divergent current output from these systems, the bias superconducting current and memorization property preserve the formed quantum correlations.

Sensory evaluation has been widely applied in assessing the quality of many consumer products that directly serve human needs such as food, beverages, clothing, etc. This paper first examined the inherent deficiencies in this approach, due mainly to the essentially subjective nature of human sensory preference. It then argued that instinctively designating certain materials attributes as sensory perceptions is unnecessary; considering every scientific concept is in essence the processed results of our sensory organs/brains, i.e., all quantities were initially human perceptions. Scientific advances have inevitably generated gradual transformation of such human perceptions as warmth and heaviness into objective parameters measurable in physical quantities like temperature and kilograms. Then using existing successful examples, it demonstrated firmly how the sensory attributes can be assessed by more reliable instrumental methods, and envisioned the key steps to turning a perception into scientifically measurable parameter(s).

Aluminum wire is a common material for wire bonding due to its resistance to oxidation and low price. It does not melt when becoming a free air ball (FAB) during the electronic flame-off (EFO) process with wettability, and is applied by wedge bonding. This study used 20μm Zn-Coated Al-0.5wt.%Si (ZAS) wires to improve the FAB shape after the EFO process, while maintaining stability of the mechanical properties, including the interface bonding strength and hardness. In order to test circuit stability after ball bonding, the current-tensile test was performed. During the experiment, it was found that 80nm ZAS with wire bonding has lower resistance and higher fusing current. For the bias tensile test, the thicker Zn film diffused into the Al-Si matrix easily, after which the strength was reduced. The ball-bond interfaces had no change in their condition before and after the bias. Accordingly, ZAS could be a promising candidate for ball bonding in the future.

This article presents a radical reassessment of scientific validation processes, arguing that traditional peer review has become an outdated, inefficient, and ultimately flawed mechanism for ensuring research quality. Modern artificial intelligence systems demonstrate superior capabilities in analyzing methodological rigor, statistical validity, and literature comprehensiveness, while being free from human cognitive biases, professional rivalries, and institutional politics. Through examination of empirical evidence, we demonstrate how AI systems consistently outperform human reviewers in speed, accuracy, and comprehensiveness of research evaluation. The current peer review system, characterized by months-long delays, substantial costs, and demonstrable biases, actively impedes scientific progress. We propose a fully automated AI-driven validation framework that can evaluate research in real-time, identify methodological flaws, verify statistical analyses, and assess significance within the broader scientific context. This transformation would democratize research validation, eliminate publication bottlenecks, and accelerate scientific progress while maintaining higher standards of methodological rigor than currently possible under human review.

We present evidence of the anchoring bias and disposition effect (DE) in investor trading decisions across different asset markets (stable and volatile assets) and market scenarios (stable and volatile markets), as well as how these biases affect investors' portfolio performance. The study employs a quasi-experimental design, allowing subjects to engage in interactive trading with four securities, two with potential negative returns and two with potential positive returns, within a simulated asset market. Our findings reveal the presence of the disposition effect and anchoring bias among individual investors in India. Second, market scenarios or volatility can influence the investors’ behavioral biases. The disposition effect was more prominent in volatile markets and the total market, while the anchoring bias was significant in stable and total markets. Additionally, investors exhibiting the disposition effect exhibit lower portfolio performance, while those demonstrating anchoring bias perform relatively better. The study's findings can help individual investors understand their behavioral biases, avoid them in future trading decisions, and improve portfolio performance. Financial institutions and regulatory agencies could identify investment products and supportive investor interactions towards better market trade decisions.

This study aimed to investigate the relationship between weight-bias internalization and eat-ing-disorder psychopathology in treatment-seeking patients with severe obesity using a network approach. Two thousand, one hundred and thirteen patients with obesity consecutively admitted to a specialist clinical unit for obesity were recruited from January 2016 to February 2023. Body mass index was measured, and each patient completed the Weight Bias Internalization Scale (WBSI) and the Eating Disorder Examination Interview (EDE). Network analysis showed that the most central and highly interconnected nodes in the network were related to the EDE items exposure avoidance, dissatisfaction with shape, and wanting an empty stomach. Bridge nodes were found, but the bootstrap difference test on expected bridge influence indicated non-significant centrality differences. Nevertheless, the eating-disorder psychopathology and weight-bias internalization network structure in patients seeking treatment for obesity indicate the prominent role of body dissatisfaction and control of eating and weight in these psychological constructs. This finding, if replicated, could pave the way for a new understanding of the psychological mechanisms operating in patients with obesity.