Search for: All records

ABSTRACT Traffic accidents have become a major concern for governments, organizations and individuals worldwide due to the material and moral losses they cause. It is possible to reduce this concern by taking into account the research conducted by relevant institutions and organizations in this field. The main objective of this study is to categorize traffic accidents according to driver violation types and analyse them using machine learning algorithms and feature sensitivity to identify the most influential variables in each category. For this purpose, traffic accident reports that occurred in Erzurum province in the last 1 year were used to categorize and classify driver violation behaviour types. Five different machine learning algorithms, namely k‐nearest neighbour, support vector machines, naive Bayes, multilayer perception and random forest, were used to examine the success performance of the classification. Among these, 91% successful classification was obtained with the random forest algorithm. Based on the classification obtained from this algorithm, sensitivity analysis was used to reveal the variables that most affect each violation category. The results of the analysis revealed that driver age and vehicle type were the most influential variables for many types of violations. Thanks to this study, the problems were clearly identified by going into the details of driver violation behaviours. At the end of the study, measures to reduce driver violation behaviours were proposed. If the recommendations that can reduce driver behaviour are taken into consideration by transportation authorities and policy makers, traffic accidents can be significantly reduced. 

Abstract In an adversarial collaboration, two preregistered U.S.‐based studies (totalN = 6181) tested three hypotheses regarding the relationship between political ideology and belief rigidity (operationalized as less evidence‐based belief updating): rigidity‐of‐the‐right, symmetry, and rigidity‐of‐extremes. Across both studies, general and social conservatism were weakly associated with rigidity (|b| ~ .05), and conservatives were more rigid than liberals (Cohen'sd ~ .05). Rigidity generally had null associations with economic conservatism, as well as social and economic political attitudes. Moreover, general extremism (but neither social nor economic extremism) predicted rigidity in Study 1, and all three extremism measures predicted rigidity in Study 2 (average |bs| ~ .07). Extreme rightists were more rigid than extreme leftists in 60% of the significant quadratic relationships. Given these very small and semi‐consistent effects, broad claims about strong associations between ideology and belief updating are likely unwarranted. Rather, psychologists should turn their focus to examining the contexts where ideology strongly correlates with rigidity. 

Abstract Hardy–Weinberg proportions (HWP) are often explored to evaluate the assumption of random mating. However, in autopolyploids, organisms with more than two sets of homologous chromosomes, HWP and random mating are different hypotheses that require different statistical testing approaches. Currently, the only available methods to test for random mating in autopolyploids (i) heavily rely on asymptotic approximations and (ii) assume genotypes are known, ignoring genotype uncertainty. Furthermore, these approaches are all frequentist, and so do not carry the benefits of Bayesian analysis, including ease of interpretability, incorporation of prior information, and consistency under the null. Here, we present Bayesian approaches to test for random mating, bringing the benefits of Bayesian analysis to this problem. Our Bayesian methods also (i) do not rely on asymptotic approximations, being appropriate for small sample sizes, and (ii) optionally account for genotype uncertainty via genotype likelihoods. We validate our methods in simulations and demonstrate on two real datasets how testing for random mating is more useful for detecting genotyping errors than testing for HWP (in a natural population) and testing for Mendelian segregation (in an experimental S1 population). Our methods are implemented in Version 2.0.2 of thehwepR package on the Comprehensive R Archive Networkhttps://cran.r‐project.org/package=hwep. 

A key challenge facing the use of machine learning (ML) in organizational selection settings (e.g., the processing of loan or job applications) is the potential bias against (racial and gender) minorities. To address this challenge, a rich literature of Fairness-Aware ML (FAML) algorithms has emerged, attempting to ameliorate biases while maintaining the predictive accuracy of ML algorithms. Almost all existing FAML algorithms define their optimization goals according to a selection task, meaning that ML outputs are assumed to be the final selection outcome. In practice, though, ML outputs are rarely used as-is. In personnel selection, for example, ML often serves a support role to human resource managers, allowing them to more easily exclude unqualified applicants. This effectively assigns to ML a screening rather than a selection task. It might be tempting to treat selection and screening as two variations of the same task that differ only quantitatively on the admission rate. This paper, however, reveals a qualitative difference between the two in terms of fairness. Specifically, we demonstrate through conceptual development and mathematical analysis that miscategorizing a screening task as a selection one could not only degrade final selection quality but also result in fairness problems such as selection biases within the minority group. After validating our findings with experimental studies on simulated and real-world data, we discuss several business and policy implications, highlighting the need for firms and policymakers to properly categorize the task assigned to ML in assessing and correcting algorithmic biases. 

Abstract Enhancing the resilience and reliability of power grids is crucial amid rising cyber threats and system complexities. To address these challenges, this paper proposes an energy‐efficient, consortium blockchain‐based global alarm system for power grid management. Using smart contracts and the proof of‐authority consensus algorithm, the alarm system triggers global alarms upon detecting local anomalies, ensuring a prompt response to partition the power grid and mitigate failures. The effectiveness is validated by simulating the Iberian power system with 15 providers from various regions. Key metrics, such as load shedding, damage reduction, energy consumption, latency, and transaction costs, are used to assess the performance. Through simulations, we show that the blockchain‐based system effectively limits the damage propagation and the load shedding during cascading failures by delaying the onset of instability and maintaining lower damage levels compared to non‐blockchain scenarios. Our investigations reveal that the proposed global alarm mechanism reduces the damage and load shedding by up to 29% and 87%, respectively, showcasing its potential for preventing widespread outages. 

Abstract Steroid hormones are often synthesized in multiple tissues, affect several different targets, and modulate numerous physiological endpoints. The mechanisms by which this modulation is achieved with temporal and spatial specificity remain unclear. 17β‐estradiol for example, is made in several peripheral tissues and in the brain, where it affects a diverse set of behaviors. How is estradiol delivered to the right target, at the right time, and at the right concentration? In the last two decades, we have learned that aromatase (estrogen‐synthase) can be induced in astrocytes following damage to the brain and is expressed at central synapses. Both mechanisms of estrogen provision confer spatial and temporal specificity on a lipophilic neurohormone with potential access to all cells and tissues. In this review, I trace the progress in our understanding of astrocytic and synaptic aromatization. I discuss the incidence, regulation, and functions of neuroestradiol provision by aromatization, first in astrocytes and then at synapses. Finally, I focus on a relatively novel hypothesis about the role of neuroestradiol in the orchestration of species‐specific behaviors.