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Scholarship exploring Black women-loving women's (Black WLW) romantic attraction to one another is scarce. Using a mixed methods design, this qualitative analysis reveals that Black WLW wield unwavering expectations as it pertains to dating qualifications. Characteristically, Black WLW prefer potential mates to have ethnic pride, intelligence, confidence, communication skills, and career ambition. Physically, Black WLW prefer potential mates who are curvaceous, and brown and/or dark-skinned in skin tone—especially if they themselves share the same features. Congruent with previous scholarship, Black WLW assign more value to an individual's character, aura, and personality over physical attributes when assessing attractiveness and desirability.

The Philippines has had high levels of unemployment for years. During the 2000s, the unemployment rate hovered between seven and ten percent. High unemployment can have adverse effects on individuals and society. The question that this paper analyses is how unanticipated money growth affect the unemployment situation in the Philippines. There has been literature on the relationship between unanticipated growth on the money supply and unemployment. The paper proposes that only unanticipated money movements will affect real economic variables like unemployment and the output level. In order to test our hypothesis, it is important that we need to quantify the concepts of anticipated and unanticipated money movements. This paper uses time-series data on several economic variables as well as a model based on Geetha et al. (2023). Using an error-correction model, the results show that an unanticipated increase in M2 money is a factor that contributes to unemployment in Philippines.

Mexican artistic influence at the beginning of the twentieth century was prevalent in America, most notably muralism, but also talent associated with the movies. This influenced the way Mexico was perceived in the United States, and by extension the way Lupe Vélez and Dolores del Río, the most successful Mexican actresses in early Hollywood, were appreciated. Both actresses had come to Hollywood to fulfill their dream of a career in the movies. At first, they were successful playing a variety of roles in silent movies, but once sound arrived, their accents made obvious their foreignness at a time when American culture was beginning to spread around the world and to establish its preeminence. They were aware of the challenges they faced and knew there was a price to pay if they wanted to continue in a business where the public decided, at a time when Americans distrusted foreigners. So, if that price was to overplay her earthy Mexicaness, in the case of Vélez, or emphasize her aristocratic image, in the case of del Río, they were ready to pay it. Mexico became the other par excellence in the early twentieth century, and these heavily accented actresses were evidence of that.

Leo Kanner’s classic description of autism appeared eight decades ago. Although the pace of research has increased dramatically, research specifically focused on adolescents and adults remains limited in many respects. Numerous outcome studies have been conducted and suggest potential for markedly improved outcome with intervention. Unfortunately, studies on adults after early adulthood are sparse and, for old age, almost nonexistent, reflecting a lack of support for research in this population. This is in stark contrast to other developmental disorders in which considerable information on adult life is often available. This book summarizes work across areas, focusing on what is known and not yet known, highlighting important areas for future research. While overall outcome has improved, a small group of individuals remain in need of high levels of adult care. For all individuals, the field requires new approaches to both research and clinical service.

Despite the ethical concerns over the datafication and surveillance of individuals and groups, companies are making ever greater investments in big data. The assumptions underpinning this movement are: (1) organizations are passive implementers of big data—more data is the inevitable consequence of technology and a competitive necessity for business, (2) more data offers a more objective and accurate picture of reality and (3) more data enables better prediction. We argue that this perspective is strategically unsustainable and abdicates ethical responsibility.

Communicated Stereotypes at Work highlights the pervasiveness and complexity of stereotypes in the workplace by analyzing the role they play in a variety of professional settings. Contributors exp...

Purpose: In the United States, 15 states maintain policies and 25 states represent some level of edTPA affiliation. This study investigated how the edTPA was integrated and aligned into different physical education teacher education (PETE) programs in New Jersey. It also sought to understand teacher educators’ perceptions and experiences in preparing teacher candidates for the edTPA. Methods: This study utilized three data sources: semistructured interviews (n = 4), one focus group interview (n = 1), and documents (n = 17). Data analysis reflected a conventional approach toward qualitative content analysis. Results: In analyzing the data, four themes were established: (a) benefits and drawbacks of edTPA in PETE, (b) goals and success of edTPA in PETE, (c) integrating edTPA into PETE—macro- and microperspectives, and (d) analytic insights into edTPA and future recommendations. Discussion/Conclusion: In states requiring the edTPA, early exposure, scaffolding, curriculum mapping, and a shared mission and vision are critical. In states not requiring the edTPA, programs may want to consider indicators of performance, such as artifacts, reports, elements of the edTPA, university-based assessments, or a portfolio. Regardless of the type of assessment, “a” performance-based assessment may help to determine teacher candidates’ ability to plan, instruct, assess, and reflect.

We present an investigation into the rotation and stellar activity of four fully convective M dwarf “twin” wide binaries. Components in each pair have (1) astrometry confirming they are common-proper-motion binaries, (2) Gaia BP, RP, and 2MASS J, H, and K s magnitudes matching within 0.10 mag, and (3) presumably the same age and composition. We report long-term photometry, rotation periods, multiepoch Hα equivalent widths, X-ray luminosities, time series radial velocities, and speckle observations for all components. Although it might be expected for the twin components to have matching magnetic attributes, this is not the case. Decade-long photometry of GJ 1183 AB indicates consistently higher spot activity on A than B, a trend matched by A appearing 58% ± 9% stronger in L X and 26% ± 9% stronger in Hα on average—this is despite similar rotation periods of A = 0.86 day and B = 0.68 day, thereby informing the range in activity for otherwise identical and similarly rotating M dwarfs. The young β Pic Moving Group member 2MA 0201+0117 AB displays a consistently more active B component that is 3.6 ± 0.5 times stronger in L X and 52% ± 19% stronger in Hα on average, with distinct rotation at A = 6.01 days and B = 3.30 days. Finally, NLTT 44989 AB displays remarkable differences with implications for spindown evolution—B has sustained Hα emission while A shows absorption, and B is ≥39 ± 4 times stronger in L X, presumably stemming from the surprisingly different rotation periods of A = 38 days and B = 6.55 days. The last system, KX Com, has an unresolved radial velocity companion, and is therefore not a twin system.

Shallow mantle processes such as edge-driven convection are thought to play an important role in shaping the passive margin setting. Previous geophysical investigations of northern New England suggest this process is likely operating beneath this portion of the eastern North American margin today. In western Vermont and eastern New York, USA, Early Cretaceous magmatism dated at ca. 140–130 Ma in the Burlington lobe and at ca. 110–100 Ma in the Taconic lobe of the New England–Quebec igneous province may represent the upper crustal expression of edge-driven convection operating beneath the eastern North American margin in the geologic past. This investigation addresses the potential relationship between these two episodes of magmatism and upper crustal deformation in northern New England through paleostress analysis of Early Cretaceous sheet intrusions and mesoscale normal faults, and geochemical analysis of sheet intrusions. The two episodes of magmatism are geochemically similar, display typical characteristics of intraplate alkaline magmatism, and are likely the product of a common source. Paleostress analysis and crosscutting relationships indicate that Burlington lobe magmatism was associated with a subhorizontal N–S extensional stress field, and Taconic lobe magmatism was associated with a subhorizontal NW–SE extensional stress field. Both stress fields represent short-term perturbations to the regional Early Cretaceous subhorizontal NE–SW extensional stress field. Each perturbation coincided with and likely continued following magmatism. The magmatism, geographic and temporal scale of the stress field changes, and return to regional subhorizontal NE–SW extension following these events are consistent with the periodic nature of edge-driven convection and associated small-scale delamination events. This field-based documentation of intraplate magmatism and its association with short-term changes in the stress field improves our understanding of the upper crustal expression of edge-driven convection at passive margins.

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.

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.

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.

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.