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Social and behavioral science researchers who use survey data are vigilant about data quality, with an increasing emphasis on avoiding common method variance (CMV) and insufficient effort responding (IER). Each of these errors can inflate and deflate substantive relationships, and there are both a priori and post hoc means to address them. Yet, little research has investigated how both IER and CMV are affected with the use of these different procedural or statistical techniques used to address them. More specifically, if interventions to reduce IER are used, does this affect CMV in data? In an experiment conducted both in and out of the laboratory, we investigate the impact of attentiveness interventions, such as a Factual Manipulation Check (FMC) on both IER and CMV in same-source survey data. In addition to typical IER measures, we also track whether respondents play the instructional video and their mouse movement. The results show that while interventions have some impact on the level of participant attentiveness, these interventions do not appear to lead to differing levels of CMV.

Artificial intelligence (AI) is a distinct area of computer science that enables machines to handle and interpret complex data effectively. In recent years, there has been a dramatic uptick in studies devoted to AI, with many focusing on healthcare and medical research. This article delves deep into the potential of AI in several areas of healthcare, including the diagnosis and treatment of diseases. In recent years, Machine learning (ML) and deep learning (DL) have emerged as the most widely used artificial intelligence technologies in the healthcare industry. Moreover, this research demonstrates the crucial significance of progressing AI technologies, namely generative AI and large language models (LLMs), highlighting their revolutionary influence on healthcare. Finally, we highlight upcoming innovations and offer profound insights into the significant ethical, medical, and technological challenges associated with AI in healthcare. © 2025 Nova Science Publishers, Inc. All rights reserved.

We report directed flow (v1) of multistrange baryons (Ξ and Ω) and improved v1 data for K−, p¯, Λ¯ and ϕ in Au+Au collisions at sNN=27 and 200 GeV from the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). We focus on particles whose constituent quarks are not transported from the incoming nuclei but instead are produced in the collisions. At intermediate impact parameters, we examine quark coalescence behavior for particle combinations with identical quark content, and search for any departure from this behavior (“splitting”) for combinations having non-identical quark content. Under the assumption of quark coalescence for produced quarks, the splitting strength appears to increase with the electric charge difference of the constituent quarks in the combinations, consistent with electromagnetic effect expectations.

An analysis of how national narratives are inevitably forms of epistemic injustice, depriving individuals of epistemic and moral agency. Denying access to knowledge about the past is a tool of all autocratic regimes, commonly used for the purpose of retaining power and exerting dominance over individuals or groups subordinate to the ruling elite. Yet such narratives and the falsifications used to buttress them, are not the exclusive instruments of autocracies but can be found to pervade the national narratives of what we often nominally label as democracies. The denial of crimes against humanity and genocide are the most egregious examples of the harms perpetrated against victims and survivors. Miranda Fricker’s writings on epistemic injustice are employed in the analysis. Turkish and Azerbaijani genocide denial of the Armenian Genocide are used to illustrate how epistemic injustice lies at the heart of denialism. © 2024 Central European Pragmatist Forum. All rights reserved.

We report the differential yields at mid-rapidity of the Breit-Wheeler process (𝛾⁢𝛾→𝑒+⁢𝑒−) in peripheral Au+Au collisions at √𝑠𝑁⁢𝑁=54.4 and 200 GeV with the STAR experiment at the Relativistic Heavy Ion Collider (RHIC), as a function of energy √𝑠𝑁⁢𝑁, 𝑒+⁢𝑒− transverse momentum 𝑝T, 𝑝2T, invariant mass 𝑀𝑒⁢𝑒, and azimuthal angle. In the invariant mass range of 0.4<𝑀𝑒⁢𝑒<2.6GeV/𝑐2 at low transverse momentum (𝑝T<0.15GeV/𝑐), the yields increase while the pair √⟨𝑝2T⟩ decreases with increasing √𝑠𝑁⁢𝑁, a feature that is correctly predicted by the QED calculation. The energy dependencies of the measured quantities are sensitive to the nuclear form factor, infrared divergence and photon polarization. The data are compiled and used to extract the charge radius of the Au nucleus.

The percentage of US youths experiencing mental health and substance use problems has risen rapidly in recent years. Schools are important settings for prevention, but whether preventive programming meets student needs is unknown. This study examined trends in school programming related to mental health and substance use and teacher professional development across US middle and high schools from 2008-2020.

The Gona Paleoanthropological Research Project area in the Afar Region of Ethiopia arguably contains one of the most complete records of archaeological sites anywhere in the world, from the earliest Oldowan dated to 2.6 Ma, to the Later Stone Age (LSA) dated to ca. 12-7 Ka. This makes Gona an ideal place to examine long-term trends in hominin-environment interaction. We revisited archaeological and hominin fossil sites at Gona and characterized the fossil soils using paleopedology and found evidence of paleo-Fluvisols, -Cambisols and -Vertisols. Greater than 70% of those archaeological sites spanning Oldowan to the Later Stone Age are found in buried paleosols with A-C and A-Bk-C paleosol profiles resembling modern-day Fluvisols or Fluvic Cambisols. Fluvisol morphology shows presence of bedding, incipient soil structure development and overprinting after burial. Stratigraphy and lithofacies show that these paleo-Fluvisols were proximal to the ancestral Awash River (Type I depositional system) or a distal fan channel (Type II depositional system). These data suggest that soil burial rates were rapid due to proximal flooding, where this would be a primary factor inhibiting soil development. This style of sedimentation and weathering resembles a narrow (5–10 m width) strip of land in a modern-day channel shelf and bar setting, separating the river from the adjacent gallery forest. A review of the literature shows that the frequent association of artifacts with paleo-Fluvisols may be prevalent throughout eastern Africa and indicates a long history of hominin reliance on a riverine ecosystem edge, proximal stream water and gallery forest resources within broader river valleys. The few older archaeological sites (e.g., Oldowan and Acheulian) found in/on more well-developed paleosols at Gona are an exception to this rule. These latter sites may hint at different land-use patterns and thus differing trajectories of hominin-environmental interactions. Because most paleosol studies at Gona and elsewhere in eastern Africa use paleo-Vertisols or other more well-developed calcareous soils to reconstruct paleoenvironment, there is a potential spatial and temporal decoupling between those well-studied paleosols and the more weakly-developed ones where archaeology is found.

Fieldwork is often cited as one of the most important and effective parts of geography education, despite increasing scrutiny over its environmental and financial cost. As a result, it is imperative that any overseas fieldwork is as impactful as possible, enabling deep experiential learning. Here, we investigate the success of a joint field trip (Liverpool John Moores University, UK and Southern Connecticut State University, USA) to East Iceland. Such field trips are rare but have the potential to be extremely impactful on both cohorts of students. We outline the origins of the field trip, the considerations taken into account during planning, and the student skills we embedded into teaching. Surveys and interviews demonstrated that the field trip was highly successful, with students reporting excellent development of environmental and global awareness as well as research and leadership skills. Students also developed strong, lasting social networks, including those in the alternate university, and in Iceland. Cohorts responded similarly, suggesting that the trip presents similar opportunities to all students. We demonstrate that undertaking a joint field trip can deliver huge benefits to students, becoming a “perspective changing, and a once in a lifetime opportunity” affecting future study and career choices.

In heavy-ion collision experiments, the global collectivity of final-state particles can be quantified by anisotropic flow coefficients (𝑣𝑛). The first-order flow coefficient, also referred to as the directed flow (𝑣1), describes the collective sideward motion of produced particles and nuclear fragments in heavy-ion collisions. It carries information on the very early stage of the collision, especially at large pseudorapidity (𝜂), where it is believed to be generated during the nuclear passage time. Directed flow therefore probes the onset of bulk collective dynamics during thermalization, providing valuable experimental guidance to models of the pre-equilibrium stage. In 2018, the Event Plane Detector (EPD) was installed in STAR and used for the Beam Energy Scan phase-II (BES-II) data taking. The combination of EPD (2.1<|𝜂|<5.1) and high-statistics BES-II data enables us to extend the 𝑣1 measurement to the forward and backward 𝜂 regions. In this paper, we present the measurement of 𝑣1 over a wide 𝜂 range in Au+Au collisions at √𝑠𝑁⁢𝑁= 19.6 and 27 GeV using the STAR EPD. The results of the analysis at √𝑠𝑁⁢𝑁= 19.6 GeV exhibit excellent consistency with the previous PHOBOS measurement, while elevating the precision of the overall measurement. The increased precision of the measurement also revealed finer structures in heavy-ion collisions, including a potential observation of the first-order event-plane decorrelation. Multiple physics models were compared to the experimental results. Only a transport model and a three-fluid hybrid model can reproduce a sizable 𝑣1 at large 𝜂 as was observed experimentally. The model comparison also indicates 𝑣1 at large 𝜂 might be sensitive to the QGP phase transition.

Obstructive Sleep Apnea (OSA) is a prevalent health issue affecting 10-25% of adults in the United States (US) and is associated with significant economic consequences. Machine learning methods have shown promise in improving the efficiency and accessibility of OSA diagnoses, thus reducing the need for expensive and challenging tests. A comparative analysis of Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting (GB), Gaussian Naive Bayes (GNB), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms was conducted to predict Obstructive Sleep Apnea (OSA). To improve the predictive accuracy of these models, Random Oversampling was applied to address the imbalance in the dataset, ensuring a more equitable representation of the minority class. Patient demographics, including age, sex, height, weight, BMI, neck circumference, and gender, were employed as predictive features in the models. The RFC provided outstanding training and testing accuracies of 87% and 65%, respectively, and a Receiver Operating Characteristic (ROC) score of 87%. The GBC and SVM classifiers also demonstrated good performance on the test dataset. The results of this study show that machine learning techniques may be effectively used to diagnose OSA, with the Random Forest Classifier demonstrating the best results.

This chapter explores the impact of implementing a partnership agreement to grant incoming university students credit based on their documented language proficiency. In 2018, an agreement was forged between an urban university and public school district, and, by extension, any high school offering the Seal of Biliteracy (SoBL) to offer students who hold the SoBL university credit for their language proficiency in a world language. This chapter examines the evolution of that agreement over a five-year period and its potential impact on students’ decision to pursue a minor/major in Spanish. Through semi-structured interviews, we also examined multiple university stakeholders’ perceptions and/or experiences about the value of the SoBL, the agreement to grant credit by examination (CBE), as well as other challenges, including the university’s reduction of their language requirement, transitions in leadership, and differing ideas about if and how best to award students credit based on examination.

Objective Preterm birth (PTB) is one of the leading causes of infant and neonatal mortality. Prepregnancy body mass index (BMI; kg/m2) has been linked to PTB but the evidence of this association by weight gain during pregnancy, race, and ethnicity is limited. This study aimed to assess the association between maternal prepregnancy BMI and PTB stratified by weight gain during pregnancy, race, and ethnicity. Study Design The U.S. natality data from 2017 to 2021 were used. In this analysis, we included mothers who had a live singleton birth and available data for prepregnancy BMI, gestational age at birth, weight gain during pregnancy, race, and ethnicity. Logistic regression models were used to assess the association between prepregnancy BMI categories and PTB stratified by weight gain during pregnancy, race, and ethnicity. Results A total of 17,311,509 singleton live births were included of which 1,393,889 (8.05 %) were PTBs. After adjusting for confounders, compared with normal prepregnancy BMI mothers (18.5–24.9), those with underweight BMI (<18.5) were at increased odds of PTB regardless of weight gain during pregnancy, race, and ethnicity. However, for mothers with a prepregnancy BMI above the normal weight (≥25), the association between prepregnancy BMI and PTB differs by weight gain during pregnancy, race, and ethnicity. Asian mothers with obesity II (35.0–39.9) had 93% (odds ratio [OR] = 1.93, 95% confidence interval [CI]: 1.62–2.30) increased odds of PTB for weight gain during pregnancy of 31 to 40 pounds. Their White, Hispanic, and Black counterparts experienced lower odds of PTB for similar weight gain during pregnancy (White: OR = 1.56, 95% CI: 1.51–1.60; Hispanic: OR = 1.48, 95% CI: 1.41, 1.54; and Black: OR = 1.22, 95% CI: 1.17–1.27). Conclusion Mothers with underweight BMI were at increased risk of PTB regardless of weight gain during pregnancy, race, and ethnicity. However, the association between high prepregnancy BMI and PTB varied by weight gain during pregnancy, race, and ethnicity. Key Points

Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing.

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems.

Structured faculty development programs focused on integrating health equity into medical education curricula remain limited.