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A precision measurement of the K ⁎0 meson yield is reported in Au+Au collisions at sNN=7.7,11.5,14.6,19.6, and 27 GeV using the high-statistics data sample collected by the STAR experiment during the Beam Energy Scan II (BES-II) program at RHIC. The transeverse momentum ( pT )-integrated yield ratios (K*0+K*0‾)/(K++K−) in central collisions show a suppression relative to peripheral collisions at the (1.7–3.6) σ level, while a thermal model without final-stage rescattering overpredicts this ratio with a deviation of (6.9–8.2) σ . These results indicate a loss of the measured K ⁎0 signal in central collisions due to re-scattering of its hadronic decay products in the hadronic phase. The pT -integrated yield of charged kaons exhibits an approximate scaling with charged-particle multiplicity, independent of collision energy and system size. A similar trend is observed for the short-lived K ⁎0 resonance, although significant deviations emerge at lower energies. At BES energies, the K ⁎0/ K ratio shows stronger suppression than at the highest RHIC and LHC energies within a given multiplicity bin, particularly in central and mid-central collisions. This behavior is consistent with changes in the effective hadronic interaction cross section and is supported by transport model calculations, which indicate dominant meson–baryon interactions at lower energies and meson–meson interactions at higher energies. Copyright © 2026. Published by Elsevier B.V.

The increasing volume of suspicious emails, commonly known as spam, has created a critical need for more reliable and robust anti-spam filters. These suspicious emails can be dangerous and can lead to the loss of personal information, underscoring the necessity for an effective spam filtering system. The application of machine learning methods has enhanced system security and improved the detection of suspicious messages. This research evaluates the effectiveness of seven machine learning algorithms for classifying suspicious email messages: random forest, support vector machine, artificial neural network, decision tree, gradient boosting classifier, and k-nearest neighbor. The primary focus of this evaluation is the accuracy achieved by each algorithm in identifying spam emails. Our analysis revealed that the random forest algorithm outperformed the other evaluated algorithms in terms of accuracy for spam email classification, achieving a remarkable 95%. The accuracy percentages of the various methods ranged from 88% to 93%. Copyright 2025. The Korean Institute of Information Scientists and Engineers.

Measurements of the variation of anisotropic flow-plane angles (Ψn) with rapidity, commonly known as the flow-plane decorrelation, provide important insights into the initial conditions of the matter produced in heavyion collisions. In this paper, using data collected by the STAR experiment, we report the first measurement of the four-plane correlator observable (Formula presented.), where superscripts a, b, c, and d denote sequential pseudorapidity (η) regions with a corresponding to the most backward region, b and c close to midrapidity with nb < 0 and nc > 0, and d being the most forward. The measurement is performed for the elliptic and triangular flow (i.e., n = 2 and 3) in Au + Au and isobar (Ru + Ru, Zr + Zr) collisions at (Formula presented.) = 200 GeV. The goal of calculating the correlation of the flow-plane angle variations from backward to midcentral, and from midcentral to forward regions, is to probe the systematic variation of flow angle over a wide П range. In midcentral collisions (10-30 % centrality), we find T2{ba; dc} = —0.004 ± 0.001 (stat) ± 0.002(syst) independent of the collision system. Such a small value of T2 favors a “random-walk” variation of the flow-plane angles, where the rapidity correlation length is smaller than the entire region under study. These measurements provide new information on the decorrelation patterns in the system and offer a quantitative estimate of possible systematic variations in anisotropic flow angles such as “twist” between forward and backward regions. This opens new opportunities for understanding the three-dimensional structure and the time evolution of the quarkgluon plasma created in heavy-ion collisions. © 2026 American Physical Society

The correlation between the mean transverse momentum, [p T], and the squared anisotropic flow, vn2, on an event-by-event basis has been suggested to be influenced by the initial conditions in heavy-ion collisions. We present measurements of the variances and covariance of [p T] and vn2, along with their dimensionless ratio, for Au+Au collisions at various beam energies: sNN = 14.6, 19.6, 27, 54.4, and 200 GeV. Our measurements reveal a distinct energy-dependent behavior in the variances and covariances. In addition, the dimensionless ratio displays a similar behavior across different beam energies. We compare our measurements with hydrodynamic models and similar measurements from Pb+Pb collisions at the Large Hadron Collider (LHC). These findings provide valuable insights into the beam energy dependence of the specific shear viscosity (η / s) and initial-state effects, allowing for differentiating between different initial-state models. © 2026 The Authors.

Traditional physical biometrics - such as fingerprints, facial recognition, and iris scans - have long been utilized for user identification in areas like border control, military operations, law enforcement, and public safety. However, the rise of smartphone technology has introduced new avenues for security research. One emerging area is behavioral biometrics, particularly the use of touchscreen interaction data as a more accessible and user-friendly identification method. In this context, our study focuses on a novel form of touchscreen input: capacitive swipe gestures for user identification. We compiled a comprehensive dataset of capacitive swipe gestures collected over multiple sessions from 30 participants. To evaluate this modality, we conducted thorough experiments using established machine learning algorithms, including Support Vector Machine, Random Forest, and XGBoost. Additionally, we developed a new preprocessing algorithm tailored for capacitive swipe data. Our findings reveal that this algorithm significantly enhances identification performance compared to existing methods. Overall, our results highlight the strong potential of capacitive swipe gestures as a viable biometric modality for user identification. © 2025 IEEE.

Philosophers have long speculated that individual differences in temperament influence philosophical thinking, yet empirical research has rarely explored the role of neurodivergent traits in this domain. In this large online study (N = 1,254), we investigated whether participants with training in philosophy differ from the general population when it comes to six psychological traits–autism, ADHD, aphantasia, anendophasia, anauralia, and representational manipulation–and also whether these traits correlate with responses to two widely studied philosophical thought experiments: the “trolley problem” and the “rollback deterministic universe.” Compared to the general population, participants with training in philosophy had higher scores on measures of ADHD, internal verbalization, and representational manipulation, but lower scores on measures of visual imagery. These cognitive traits were also correlated with participants’ moral and metaphysical judgments (independent of their level of philosophical training)–e.g. participants who scored higher in visualization were less likely to judge that hitting the switch in the trolley problem is permissible but not obligatory, and also less likely to attribute free will and moral responsibility to agents in the rollback universe. Finally, we employed machine learning to develop predictive models that classify a randomly selected participant as either a philosopher or a non-philosopher. Models trained solely on responses to measures for neurodivergent traits achieved better performance than models trained solely on responses to philosophical thought experiments. This suggests that stable, trait-level neurodivergent characteristics may be more diagnostic of philosophical interest, aptitude, or training than judgments philosophers make on domain-relevant problems. © 2026 Informa UK Limited, trading as Taylor & Francis Group.

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