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This edited volume features academic experts using leading policy frameworks to analyze the prominent U.S. public policy issues of the twenty-first century. Readers will learn about the similariti...

Do not walk away from this burgeoning hope of heat and dryness.

From a snowbank I watched the squirrel run.

Grey Sparrow Press, in this landmark book, cherishes the voices of national writing treasures published over ten years; Robert Bly, Robert Wexelblatt, Michael C. Keith, Jules Nyquist, Khem Aryal, Marie Sheppard Williams [posthumously,] Doug Holder, Momila Joshi, William Woolfitt, Thomas R. Smith, M.J. Iuppa, LB Chhetri, John Roche, and Bhisma Upreti to name a few. Grey Sparrow Press was formed as a non-profit 501[c]3 on May 11, 2009.

Stock market forecasting is an essential factor in the daily operations of many companies and individuals. However, the complex and nonlinear nature of the stock market and the unpredictable variations in factors affecting stock prices present significant challenges in accurate forecasting. To address this, we employ four model-based metaheuristic search algorithms (MHs), namely the Crow Search Algorithm (CSA), Particle Swarm Optimizer (PSO), Gray Wolf Optimizer (GWO), and Dandelion Optimizer (DO), to estimate the parameters of stock market prices models. The data utilized in our experiments are extracted from the widely recognized stock index of Standard & Poor's 500 (S&P 500), that serves as a representative benchmark for the United States stock market. Our findings demonstrate that the CSA outperforms other MHs by providing the best combination of parameters for modeling stock market prices. The optimized parameters for the CSA model yielded Variance-Account-For (VAF) values of 97.846% in the training set and 93.483% in the testing set. This suggests that CSA offers promising capabilities for enhancing the accuracy and effectiveness of stock market forecasting models. © (2024), (Research Institute of Intelligent Computer Systems). All rights reserved.

Background: Social determinants of health account for racial inequities in breastfeeding rates in the United States. There is a gap in the role of neighborhood socioeconomic status (NSES) as it relates to breastfeeding disparities. Methods: Using longitudinal data from the Black Women’s Health Study, we assessed associations of NSES with breastfeeding initiation and duration in a cohort of primiparous U.S. Black women. We also explored associations within strata of important economic indicators, including education, occupation, and marital status. Results: Breastfeeding initiation ( n = 2,705) increased with NSES quartile, from 75.2% in the lowest quartile to 88.3% in the highest quartile ( p < 0.0001). Compared with women living in the highest NSES quartile, those in the lowest quartile had a 41% (odds ratio: 0.59 [95% confidence interval: 0.43, 0.81]) decreased odds of initiating breastfeeding. For breastfeeding duration ( n = 2,172), women residing in NSES quartiles 1–3 were significantly less likely ( p < 0.0001) to breastfeed (44.4%) for 6+ months compared with those living in the highest quartile (62.8%). Adjusted relative risks for those in quartiles 1–3 compared with 4 (highest) were 0.63 (0.45, 0.87), 0.50 (0.37, 0.68), and 0.64 (0.47, 0.86), respectively ( p = 0.0001). There was no statistically significant evidence of effect modification by education, occupation, marital status, and region ( P interaction = >0.05). Conclusion: Living in a lower NSES environment was associated with reduced breastfeeding initiation and duration compared with a higher NSES environment. Research is needed to understand the mechanisms by which neighborhood-level factors influence breastfeeding initiation and duration for Black women in the United States.

Diabetes, affecting more than 500 million individuals worldwide, is the most widespread non-communicable disease, globally. The early identification and effective management of diabetes are crucial for controlling its spread. Currently, the HbA1c test is the gold standard for the detection of diabetes with high confidence. But this is an invasive, expensive pathology test. Therefore, alternative non-invasive and inexpensive methods have been proposed in the literature for the early detection of diabetes.

Epidermolysis bullosa acquisita (EBA) represents a big challenge as a rare skin disorder, with no established markers for early detection for patients. Moreover, as a rare disease, it is extremely difficult to acquire good number of patient sample to diagnose accurately with high confidence. EBA has many biomarkers very similar to other bullosa diseases and needs specific clinical expertise to detect it using immunofluorescence microscopy. In this study, we introduce a deep learningbased method, EBAnet, that leveraged Convolutional Neural Network (CNN) based model for the detection of EBA based on Direct immunofluorescence (DIF) microscopy image. The proposed EfficientNet-based model achieved 97.3% sensitivity, 96.1% precision, and 96.7% accuracy in distinguishing EBA from other class and outperformed the existing model for the same purpose. GradCAM based class activation map also highlighted the important region of the DIF images that was focused by the proposed model leveraging the explainability of the model. We believe, EBAnet will add value in the early and accurate detection of EBA, addressing a critical need in clinical practice.

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.