Your search

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.

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.

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

Background: HIV risk behavior in women who use drugs is related to myriad psychosocial issues, including incarceration. The experience of incarceration elevates women’s HIV risk by disrupting social networks, housing, employment, and access to health care. During the COVID-19 pandemic, changes in criminal-legal practices resulted in decreased incarceration, especially among women. These changes may have largely altered HIV risk among women who use drugs, depending on their access to care in the community. Objective: This study seeks to build knowledge about the impact of shifts in criminal-legal practices during the COVID-19 pandemic on HIV risk behaviors of justice-involved women who use drugs. Methods: Qualitative methods are used to gather and analyze women’s narratives about their life experiences before and during the COVID-19 pandemic, with a focus on individual and structural determinants of HIV risk behaviors. Thirty formerly incarcerated women with a history of substance use are being recruited through collaboration with community partners. Each participant completes a sociodemographic survey and two interviews. The first interview uses a life history instrument that invites participants to reflect on key turning points in their lives. The second interview uses a calendar approach to gather information about participants’ lives during the first year of the COVID-19 pandemic (March 2020-March 2021). The interviews (1 hour each) are audio-recorded and transcribed for analysis. Rapid Qualitative Inquiry and thematic analysis are being used to manage, organize, and interpret the data. The study team will collaborate with a subset of participants to develop digital stories about their COVID-19 experiences, a process that allows for member-checking and triangulation. Findings will be disseminated to program and policy makers in academic venues, community settings, and social service agencies. Results: To date, 10 women’s data have been collected. In total, two themes have been identified in this preliminary data: (1)the chaos and instability of participants’ lives increased during the COVID-19 pandemic: participants reported a wide range of psychosocial and health problems and limited engagement with social service systems. Interaction with criminal-legal systems was rife with uncertainty; participants described living in a state of limbo, which was extremely stressful. (2) When asked to describe a “turning point” in their lives, many participants attributed their substance use to the traumatic loss of a child due to death, incarceration, or termination of parental rights. During the COVID-19 pandemic, participants’ struggles to cope with these unresolved experiences of grief and loss were intensified by the widespread death and dying of the pandemic. Conclusions: Preliminary findings suggest that HIV risk factors increased for participants during the COVID-19 pandemic and invite further investment in community-based harm reduction programs, especially housing, that support women who use drugs. Interventions that address experiences of maternal grief and loss may reduce women’s substance use. Trial Registration:

Despite the persistence of breastfeeding racial and ethnic disparities in the United States, little is known about Black fathers' perceptions of breastfeeding and breastfeeding support services (e.g., maternity hospital-based care and lactation management care). This qualitative, community-based participatory research study reports Black fathers' perceptions of barriers and facilitators to breastfeeding, including the provision of breastfeeding support services in Connecticut. A focus group guide was co-developed with community partners and adapted from the Barrier Analysis Tool to identify breastfeeding facilitators, barriers, and service improvement areas. Four focus groups were conducted with 30 Black fathers who were Connecticut residents with a child under 3 years old. Qualitative data were analyzed using rapid template analysis involving deductive and inductive coding. We identified factors influencing breastfeeding and fathers' ability to support breastfeeding across all levels of the Socio-Ecological Model. Facilitators included high paternal breastfeeding knowledge, paternal breastfeeding involvement, parents' shared decision-making, extensive maternity hospital discharge support, ongoing breastfeeding support into the postnatal period, availability of community breastfeeding resources, and designated spaces for public breastfeeding. Barriers included low paternal breastfeeding knowledge, familial discouragement, insufficient prenatal breastfeeding education, exclusion of the father from breastfeeding support services, and stigma against breastfeeding in public. Understanding breastfeeding perceptions among members of a mother's support network, including their partners, is key for developing effective person- and family-centered breastfeeding education and counseling services that are well coordinated from the prenatal to postnatal periods with strong direct engagement from fathers.

This research examines the impact of generative artificial intelligence (AI) on the perception of educational content quality, specifically by comparing AI-generated and human-generated course syllabi in marketing education. Results from four studies indicate a general preference for AI-generated syllabi, attributed to their greater perceived objectivity. This preference is more pronounced in conventional courses but diminishes in unconventional ones, suggesting that the unique aspects of these courses may reduce the advantages of generative AI. In addition, disclosing the AI authorship of syllabi significantly affects their perceived quality negatively, underscoring the impact of transparency on the acceptance of AI-generated educational materials. These findings highlight the potential of generative AI in educational content creation and its limitations in certain contexts. They offer valuable insights for enhancing educational practices and shaping policy decisions to enrich student experiences in the era of AI integration.

The purpose of this study is to learn more about virtual reality (VR) and augmented reality (AR) practices at the United States’ top one hundred university libraries, as well as how they are engaging with the metaverse. We conducted qualitative and descriptive analysis on the websites of the top one hundred university libraries in the United States to determine the application fields and application proportions of VR and AR technologies and found good practice examples of using VR and AR technologies in this field. The findings show that 86 percent of the top one hundred US university libraries have implemented VR and AR technologies, with practice areas focused on: VR/AR studio and VR/AR makerspace; immersive learning services and virtual exhibitions/conference services; visual geographic information system and VR navigation services; virtual reading services and visual retrieval services; and VR reference services. The study provides university library administrators and professionals with the most up-to-date information and best practices of VR and AR engagement areas and the proportion of use, which can aid in the development of strategies to leverage VR and AR technologies to improve patron service and embrace the metaverse for the communities they serve.

This assignment is designed to enhance resilience among students in leadership courses. It leverages the US Army’s Master Resilience Training (MRT) framework and positive psychology to develop resiliency skills.,A three-part experiential workshop integrates academic readings (providing a foundation of resilience concepts), explores the influence of personal identities on leadership and connects leadership skills with resilience concepts.,Participants reflect on self-awareness tools and positive psychology and create personalized action plans. Participants' resilience skills are enhanced with their personalized resiliency plan.,The program provides a structured approach to resilience training, which can be integrated into university curriculums. Students gain self-awareness and psychological tools to manage challenges, which are valuable for personal growth and professional development. There is a persistent gender gap in leadership, and for women to attain greater parity in leadership positions, resilience skills are imperative. By focusing on identity-related factors, the program prepares future leaders for challenges in attaining leadership positions.,This program is uniquely tailored for students aspiring to leadership positions, with an emphasis on the role of identity, such as gender, in leader emergence and overcoming related challenges.

Across three online studies, we examined the relationship between the Fear of Missing Out (FoMO) and moral cognition and behavior. Study 1 (N = 283) examined whether FoMO influenced moral awareness, judgments, and recalled and predicted behavior of first-person moral violations in either higher or lower social settings. Study 2 (N = 821) examined these relationships in third-person judgments with varying agent identities in relation to the participant (agent = stranger, friend, or someone disliked). Study 3 (N = 604) examined the influence of recalling activities either engaged in or missed out on these relationships. Using the Rubin Causal Model, we created hypothetical randomized experiments from our real-world randomized experimental data with treatment conditions for lower or higher FoMO (median split), matched for relevant covariates, and compared differences in FoMO groups on moral awareness, judgments, and several other behavioral outcomes. Using a randomization-based approach, we examined these relationships with Fisher Tests and computed 95% Fisherian intervals for constant treatment effects consistent with the matched data and the hypothetical FoMO intervention. All three studies provide evidence that FoMO is robustly related to giving less severe judgments of moral violations. Moreover, those with higher FoMO were found to report a greater likelihood of committing moral violations in the past, knowing people who have committed moral violations in the past, being more likely to commit them in the future, and knowing people who are likely to commit moral violations in the future.