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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.

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.

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.

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.

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.