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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 (Pinteraction = >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.

We investigated whether more arterial stiffness changes could be induced by fragmentation of Swiss ball balance, and examined the role exercise order played in the modulation of arterial stiffness when on-ball kneeling and sitting were combined. Twenty-three healthy young adults (23.8 ± 0.3 years) performed 7 trials in a randomized crossover fashion: CON (non-exercise control), K (on-ball kneeling, 5 min), fK (fragmented on-ball kneeling, 2 × 2.5 min), S (on-ball sitting, 5 min), fS (fragmented on-ball sitting, 2 × 2.5 min), SK (5-min sitting before 5-min kneeling) and KS (5-min kneeling before 5-min sitting). Arterial stiffness in Cardio-ankle vascular index (CAVI) was measured at baseline (BL), immediately (0 min), and every 10 min after exercise, and its changes from BL (ΔCAVI) were calculated. Area under curve (AUC) of ΔCAVI was calculated for SK and KS. The results showed that relative to CON, ΔCAVI decreased at 0 min and 10 min in K and fK, and remained decreased at 20 min in fK only. However, ΔCAVI in S and fS increased with time similarly, with no difference relative to CON. Though ΔCAVI decreased at 10 min in SK, it decreased at both 0 min and 10 min in KS, relative to CON. AUC of ΔCAVI was greater in KS than in SK. The study indicated that compared to continuous mode, fragmented kneeling results in more arterial stiffness improvements, while fragmented sitting exerts no additional effects. When kneeling and sitting are combined, kneeling before sitting elicits more arterial stiffness improvements than sitting before kneeling. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

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.

Herein, CuO and ZnO nanoparticles (NPs) were biogenically synthesized using plant (Artemisia vulgaris) extracts. The biogenic NPs were subsequently evaluated in vitro for antifungal activity (200 mg/L) against Fusarium virguliforme (FV; the cause of soybean sudden death), and for crop protection (200–500 mg/L) in FV-infested soybean. ZnONPs exhibited 3.8-, 2.5-, and 4.9 -fold greater in vitro antifungal activity, compared to Zn or Cu acetate salt, the Artemisia extract, and a commercial fungicide (Medalion Fludioxon), respectively. The corresponding CuONP values were 1.2-, 1.0-, and 2.2 -fold, respectively. Scanning electron microscopy (SEM) revealed significant morpho-anatomical damage to fungal mycelia and conidia. NP-treated FV lost their hyphal turgidity and uniformity and appeared structurally compromised. ZnONP caused shriveled and broken mycelia lacking conidia, while CuONP caused collapsed mycelia with shriveled and disfigured conidia. In soybean, 200 mg/L of both NPs enhanced growth by 13%, compared to diseased controls, in both soil and foliar exposures. Leaf SEM showed fungal colonization of different infection sites, including the glandular trichome, palisade parenchyma, and vasculature. Foliar application of ZnONP resulted in the deposition of particulate ZnO on the leaf surface and stomatal interiors, likely leading to particle and ion entry via several pathways, including ion diffusion across the cuticle/stomata. SEM also suggested that ZnO/CuO NPs trigger structural reinforcement and anatomical defense responses in both leaves and roots against fungal infection. Collectively, these findings provide important insights into novel and effective mechanisms of crop protection against fungal pathogens by plant-engineered metal oxide nanoparticles, thereby contributing to the sustainability of nano-enabled agriculture.

Introduction: Intimate partner violence (IPV) is an important determinant of poor sexual and reproductive health. One’s sense of sexual autonomy may be an important concept in the context of IPV and sexual and reproductive health outcomes. Compromised sexual autonomy may explain the risk of poor sexual and reproductive health among individuals who experienced IPV; yet few studies have examined the role of sexual autonomy. The current study examined the mediating effects of sexual autonomy on the association between recent IPV, sexual risk and HIV-related worry. Methods: One hundred ninety-eight sexually active women and men involved in past-year romantic partnerships completed an online survey in 2016. Path analysis was used to test the direct and indirect effects of sexual autonomy. Results: Recent IPV predicted lower sexual autonomy (B = −.29, SE =.15, p <.05), unwanted condomless sex (aOR = 3.38, 95% CI 1.63–7.02), coercive sexual risk (aOR = 25.91, 95% CI 5.02–133.75), and HIV-related worry (aOR = 5.44, 95% CI 1.44–20.57). Lower sexual autonomy predicted unwanted condomless sex (aOR =.98, 95% CI.96–.99), coercive sexual risk (aOR =.95, 95% CI.90–.99), and HIV-related worry (aOR =.92, 95% CI.90–.97). Sexual autonomy mediated the association between IPV and HIV-related worry (indirect effect OR = 1.39, 95% CI 1.01–3.63). Conclusions: Recent IPV experiences can weaken one’s sexual autonomy, which in turn creates concerns about acquiring HIV. HIV prevention programming should address the implications of IPV, promote sexual safety strategies, and develop tailored support to increase sexual autonomy among individuals navigating violence. Policy Implications: Findings can inform the integration of trauma-informed policies and IPV screening practices in comprehensive sexual health programmatic initiatives. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

Climate change analyses and subsequent communications largely focus on the global and continental spatial scales while most individuals experience climate change at the state and local scales. The aggregation of local weather data into broader spatial scales causes local trends to be lost or poorly represented. This study examines climate trends for the New England region of the United States from 1940 to 2019 at the local scale to determine how they match or differ from the trends described for the region from studies at broader spatial scales. An observed increase in precipitation and average minimum temperature matches previous studies when aggregated for the New England region, but mask local variation of the variables. The increase in precipitation is greatest in upland areas and some local areas have not observed increased precipitation. Average minimum temperature has broadly increased in the region, but not universally, and average temperature and maximum temperature show weaker increasing trends along with local variation. The variation of climate trends at the local scale highlights the need for clear communication of climate change that emphasizes the spatial scale of specific statements and the acknowledgment of different observed experiences at different scales.

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.

Meaningful student–teacher relationships form a crucial foundation for teachers to deliver effective interventions leading to better outcomes for students with challenging behavior. By implementing simple recommendations for facilitating genuine and intentional interactions with students and regulating their own emotional responses, teachers can establish, maintain, and reinforce meaningful relationships with students. This article describes and provides school-based examples of recommendations for building and sustaining meaningful student–teacher relationships with students who exhibit challenging behaviors.

As one of the three dimensions of the Next Generation Science Standards (NGSS), the crosscutting concepts (CCCs) have a presumably equal role in science teaching and learning as the science and engineering practices (SEPs) and disciplinary core ideas (DCIs). While much research has focused on teachers' understanding and use of the DCIs and SEPs, less research has characterized how teachers use the CCCs in combination with the SEPs and DCIs to plan lessons aligned with the three-dimensional vision of the NGSS. This study examined the lesson plans of elementary preservice teachers (n = 53) in two science methods courses; one course which asked the PSTs to explicitly use CCCs in their lesson plans and one course that did not. We analyzed how the teachers' use of CCCs in their lesson plans influenced alignment to the three-dimensional vision of the NGSS. While we found higher CCC use in the course that explicitly asked the preservice teachers to use them, the difference between courses was not significant. More importantly, regardless of context, when the CCCs were used often, across the entire lesson, and were a good “fit” with a lesson's DCI, and anchoring phenomenon those lessons were more aligned with the NGSS.

Stereotypes about aging and aging anxieties are common and when internalized are related to poor physical and psychological outcomes. As a result, older adults may view themselves as having their best years behind them. The present study investigates ageism and aging anxiety as barriers to positive self-development. Participants (n = 360) between ages of 19 and 77 years old (M = 39, SD = 15.9) were recruited using Amazon Mechanical Turk (MTurk) and completed measures of Ageism, Aging Anxiety, and were asked to identify when they have been or will be their Best Self. With increasing age, adults with more internalized ageism and more aging anxiety, specifically physical appearance and fear of loss, identified their Best Self with a time in the past. These findings support the idea that internalization of ageism and aging anxiety can be counterproductive for expectations for growth as one ages.

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.

Purpose Cardiopulmonary rehabilitation, which often follows major acute cardiac events, is traditionally focused on aerobic exercise and has been associated with decreased morbidity and mortality. Its benefit among cardiac surgery patients is less clear, as is the role of resistance-based exercise programs and their sex-specific effects. This study seeks to evaluate the safety and feasibility of a 12-week resistance training program in patients post cardiac surgery through a sex-specific lens. Methods We conducted a nonrandomized feasibility trial with a 12-week strength training exercise intervention. The primary outcome was safety and feasibility. Secondary outcomes included changes in strength, endurance, and functional capacity; and sex differences among these. Adult participants post open-heart surgery who had completed traditional cardiac rehabilitation were consented. Both patients who completed (cases) or did not complete (controls) a tailored 12-week resistance training program underwent comprehensive assessment of physiologic and physical fitness measures pre- and postintervention. Findings Nine participants enrolled in the trial, including 6 in the intervention arm (median age 61 years; 67% male) and 3 in the control arm (median age 66 years; 67% male). No serious adverse events were noted, indicating safety of the intervention. Participants completed a mean of 34.8/36 (96.7%) of sessions, indicating the feasibility of the program. Although not powered for statistical significance, patients experienced positive trends of improvement in measures of hand grip strength, endurance, and functional capacity with the intervention. When stratified, females experienced greater gains than males in these measures. Implications This proof-of-concept study found that resistance-based exercise after cardiac surgery is well tolerated and feasible. Although all patients experienced improvements in exercise parameters, females reported greater relative improvement than males.

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.