University of Houston - Victoria

The National Association of School Psychologists (NASP) emphasizes mental and behavioral health service as an important domain within the field and practice of school psychology (NASP, 2020). This is an important role for school psychologists as the social-emotional and behavioral health needs of students are on the rise. Although a variety of social-emotional and behavioral services are offered at schools across tiered systems of support, there is limited information on how families engage with the services offered at their child’s school or their perception of how important these services are. This pilot study investigated families’ engagement and perception of social-emotional and behavioral services in schools. A total of 67 participants were included in the study, with majority identifying as white, non-Hispanic or Latinx, monolingual, and female. Participants were from 12 different states across the U.S. representing all four regions of the U.S. Results indicated that over 50% of families reported that their child participated in universal social-emotional/behavioral programming at school. Families’ most common engagement with services took the form of working with school staff to address concerns and participation in the IEP process. Families reported working with multiple school personnel for all services they participated in. Families’ perception of the most important social-emotional/behavioral services offered in schools were individual and school-wide crisis intervention. Implications and future research are discussed.

Millions of lives could be saved annually if liver tumours could be detected early with computed tomography. But it's a huge strain for radiologists to read hundreds or even tens of these CT scans. Therefore, developing an autonomous, rapid, and reliable method of reading, detecting, and assessing CT scans is important. However, extracting the liver region from CT scans is a bottleneck for any approach. This paper introduces a three-part automatic process. Initial processing includes noise suppression and image enhancement. Optimized Bi-lateral Filtering is used to carry it out; in this case, the process's control parameters are optimized using the Monarch butterfly optimization method. After that, automatic liver segmentation and lesion identification are performed. Mask-Region-based Convolutional Neural Network segment liver from the pre-processed images. Then a new generator network named LiverNet is used to detect tumors within the liver. Finally, an Enhanced Swin Transformer Network employing Adversarial Propagation distinguishes between malignant and benign liver lesions. Positive developments were discovered as a result of the inquiry. Expert results are associated with the consequences of segmentation and analysis. The classifier makes a relatively accurate tumour differentiation and gives the radiologist a second opinion.

Polycystic ovary syndrome (PCOS), a reproductive endocrine disorder with quintessential features of metabolic dysfunction, affects millions of women worldwide. Hyperinsulinemia (i.e., elevated insulin without hypoglycemia) is a common metabolic feature of PCOS that worsens its reproductive symptoms by exacerbating pituitary hormone imbalances and increasing levels of bioactive androgens. Hyperinsulinemia in PCOS is often attributed to insulin resistance, based on the concept that impaired insulin-mediated glucose disposal would induce compensatory insulin hypersecretion. However, it is challenging to define the sequential relationship between insulin sensitivity and insulin secretion, as they are tightly interlinked, and evidence suggests that hyperinsulinemia can alternatively precede insulin resistance. Notably, other drivers of hyperinsulinemia (outside of insulin resistance) may be highly relevant in the context of PCOS. For instance, high androgen levels can augment both hyperinsulinemia and insulin resistance, generating a self-perpetuating cycle of reproductive and metabolic dysfunction. In this review, we evaluate the cause-and-effect relationships between insulin resistance and hyperinsulinemia in PCOS. We examine evidence for the prevailing theory of insulin resistance as the primary defect that causes secondary compensatory hyperinsulinemia, and an alternative framework of hyperinsulinemia as the earlier defect that perpetuates reproductive and metabolic features of PCOS. Considering the heterogenous nature of PCOS, it is improbable that its metabolic characteristics always follow the same progression. Comprehensively examining all mechanistic regulators of hyperinsulinemia and insulin resistance in PCOS might thereby lead to improved prevention and management strategies, and address critical knowledge gaps in the progression of PCOS pathogenesis.

Background Students must be prepared for the transference of medication administration (pharmacology knowledge and clinical skills) to clinical practice. The Preparedness for Medication Oral Administration questionnaire has been used in several studies and demonstrated strong internal reliability and consistency. The questionnaire has been revised to align with updated medication competencies. The factor structure or dimensionality of the questionnaires has not been examined. Aim To examine the psychometric properties of the Preparedness for Medication Administration (Revised) Questionnaire. Method Data from a previous study were used to determine the psychometric properties of the Preparedness for Medication Administration (Revised) Questionnaire. Three new items were added to the revised questionnaire, and the focus shifted from the oral route of medication administration. An exploratory factor analysis was conducted to assess the reliability and dimensionality of the revised questionnaire, using principal axis factoring and Oblique rotation on the 20 items. Results Two hundred fourteen final-year undergraduate nursing students completed the questionnaire; the Kaiser–Meyer–Olkin measure confirmed sampling adequacy (.96) and Bartlett's test of sphericity χ²(214) 3003.534 p < .001 adequate sample size-to-variable ratio and inspection of the correlation matrix for loadings > 0.30. The 20 items produced a 2-factor solution, which was also confirmed by parallel analysis, with the deletion of 4 items not meeting item loadings of > 0.4. The final revised version of the questionnaire titled the Preparedness for Medication Administration Revision2 (PMAR2) contained 16 items loading onto one of the 2 factors titled Clinical Reasoning and Confidence to Practice Safely. Cronbach alpha coefficients for the factors were .89 to .95, respectively. Conclusion This research provides information for a psychometrically sound tool to assess students' preparedness for medication administration once they graduate and become independent practitioners.

Lateral flow assay has been extensively used for at-home testing and point-of-care diagnostics in rural areas. Despite its advantages as convenient and low-cost testing, it suffers from poor quantification capacity where only yes/no or positive/negative diagnostics are achieved. In this study, machine learning and deep learning models were developed to quantify the analyte load from smartphone-captured images of the lateral flow assay test. The comparative analysis identified that random forest and convolutional neural network (CNN) models performed well in classifying the lateral flow assay results compared to other well-established machine learning models. When trained on small-size images, random forest models excelled CNN models in image classification. Contrarily, CNN models outperformed random forest models in classifying noisy images.

Magnetic hyperthermia using heat generation of magnetic nanoparticles has great potential as a non-invasive cancer treatment with minimal side effects. Despite its promising results from basic research, the limited success in clinical translation stems from a lack of systematic nanoparticle optimization. To bridge this gap, machine learning models that predict the heating efficiency of magnetic nanoparticles composed of different compositions from the physical and magnetic properties of nanoparticles and external magnetic field conditions were developed in this study. The comparative analysis showed that the artificial neural network (ANN), gradient boosting regressor, and random forest regressor outperformed compared to support vector machine and k-nearest neighbor. The ANN models with 2 hidden layers showed good prediction performances comparative to deeper ANN models. Furthermore, the feature importance analysis revealed the contributions of each input features towards the heating efficiency of MNPs based on the real experimental data.

This research aims to explore the impact of machine learning (ML) on the evolution and efficacy of recommendation systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data sourcing, feature engineering, and the importance of evaluation metrics, thereby highlighting the iterative nature of enhancing recommendation algorithms. The deployment of recommendation engines (RE), driven by advanced algorithms and data analytics, is explored across various domains, showcasing their significant impact on user experience and decision-making processes. These engines not only streamline information discovery and enhance collaboration but also accelerate knowledge acquisition, proving vital in navigating the digital landscape for businesses. They contribute significantly to sales, revenue, and the competitive edge of enterprises by offering improved recommendations that align with individual customer needs. The research identifies the increasing expectation of users for a seamless, intuitive online experience, where content is personalized and dynamically adapted to changing preferences. Future research directions include exploring advancements in deep learning models, ethical considerations in the deployment of RS, and addressing scalability challenges. This study emphasizes the indispensability of comprehending and leveraging ML in RS for researchers and practitioners, to tap into the full potential of personalized recommendation in commercial business prospects.

The purpose of this paper is to investigate the levels of extensive (wider set of goods) and intensive (largerquantities of each good) margins, as well as price, quantity, GDP, employment and GDP per worker for 126countries grouped by human development, region and income hierarchies. Analysis of variance and thecoefficient of variation were the tools of statistical analysis. In most cases the groups of countries differsubstantially between them more so than countries within the groups.

We study the profitability of technical trading rules based on 9 popular technical indicators. To further examine whether investors can design technical trading strategies that can beat the buy-and-hold strategy, we establish 13 trading models based on one indicator, 25 models based on two indicators, and 28 models based on three indicators. The empirical results show that 58 out of 66 models reject the null hypothesis of equality of the mean returns between buy days and sell days. Our findings provide support for the predictive power of technical trading rules. Finally we employ Hansen’s (2005) Superior Predictive Ability to investigate data snooping problem. Overall we observe an inverse association between the number of technical indicator combinations and trading profitability.

We investigate the predictive power of various trading rules with different combinations of the most popular indicators in technical analysis for the Brazilian stock index (BOVESPA) over the period of 5/1/1996 to 3/1/2011, or 14.83 years. The empirical results show that all the buy-sell differences under single, double and triple-indicator combinations are insignificant in t-test; that is, technical trading models cannot beat the buy and hold strategy. Although few multiple-indicator trading models show profitability, their predictive power is eliminated after considering the possible interest earning from money market in the days out of stock market. The results support strongly the weak form of market efficiency for the Brazilian stock market.

International remittances comprise significant financial inflow for many Sub-Saharan African (SSA) countries and provide considerable disposable income for the receiving households. There has, however, been no consensus on the motivation in the part of sending migrants in which explanations are divided between altruism and self-interest. The study employs Autoregressive Distributed Lag (ARDL) model with co-integration approach to investigate whether international remittances to Kenya can be explained by either altruistic or self-interest motive. We process the World Bank annual data from 1970 to 2010 and find that self-interest, not altruism, as the dominant motivation to determine remittances. The analysis also indicates that demand on housing and exchange rates are the two strong drivers of international remittances to Kenya in both short-run and long-run. The Kenyan government is supposed to facilitate savings from remittances through financial institutions to invest more in the small business sector for economic growth.

Utilizing panel data methods and applying Granger causality tests within a framework of a panel cointegration and error correction model, this paper investigates the relationship between financial development and economic growth for 14 Latin American countries from 1978 to 2011. The empirical results show that when banking sector development indicators are used as proxies for financial development, there is evidence of uni-directional causality from economic growth to banking sector development. On the other hand, when stock market development indicators are used as proxies for financial development, the empirical results show that there is a bi-directional causality between stock market development and economic growth.

Recent research has highlighted the effect of culture on the decisions entrepreneurs make and the behaviours they exhibit. Building on this research stream, we suggest that a comparison of native-born and immigrant entrepreneurs is a rich setting in which to study the effect of culture on entrepreneurship in general and opportunity recognition in particular. Using the qualitative analysis method of matched pairs of seven immigrant and seven native-born entrepreneurs, this exploratory study reveals that immigrant entrepreneurs have a higher tendency to recognize entrepreneurial opportunities through the opportunity discovery process rather than the opportunity creation process.

In this essay, I explore and analyze Zora Neale Hurston’s autoethnography The Dust Tracks on a Road (1942) as an alternative to the modern anthropological simplification of nature and the African American indigenous identity. Hurston’s indigenous feminist subjectivity is focused on environmental sustainability and her resistance to anthropocentrism and racialized labor. Hurston’s subjectivity as an indigenous feminist scholar can be articulated as minoritarian in the critical posthumanities since it is not about margin; rather, it revolutionizes anthropological discourse by re-signifying the African American lumberer identity and environmental sustainability in a robust, appealing, and unique manner. She reconceptualizes Black indigenous lumberers by articulating their historical and cultural relationality with the forests owned by forest industries in Polk County, Florida.

This paper explores the policymaking for generative AI (GAI) use. While GAI's promising potential is far-reaching, it has been hyped and is difficult to gauge accurately due to its rapid evolution. Also, its application across different business functions comes with risks and potential unintended consequences. This paper summarizes the existing literature on GAI use and organizational policymaking. We introduce a qualitative framework to evaluate GAI's maturity and adoption levels within organizations. Utilizing this framework, we outline a proposed examination of GAI usage and policy development in the pharmaceutical industry, presenting initial observations and insights from industry practitioners.

The study employs dynamic system GMM estimations to investigate factors influencing tourist travel choices from within Sub-Saharan Africa (SSA). Key determinants include GDP per capita, exchange rates, relative prices, and transportation costs. Globalization, border policies, and regional trade agreements also impact tourism patterns in SSA. Technology development, specifically internet accessibility, plays a pivotal role in destination selection. Surprisingly, results indicate a preference for less urbanized areas among international intra-SSA tourists. The study suggests that motivations for traveling from within SSA are largely similar to those for international tourists. This research provides valuable insights for stakeholders in the tourism industry and lays the foundation for further exploration of this dynamic sector in the region.

Introduction Despite an evolving e-cigarette environment, few studies have looked at adolescent exposure to e-cigarette advertising over time and its associations with curiosity about and susceptibility to using e-cigarettes. We examined e-cigarette advertising exposure and its associations with curiosity and susceptibility across multiple years among adolescents who have never used e-cigarettes. Methods We obtained data from the National Youth Tobacco Surveys (NYTSs), 2014-2020 (N = 97,496). The NYTS identified e-cigarette advertising exposure from four channels: Internet, newspapers and magazines, convenience stores, and TV. Logistic regressions explored e-cigarette advertising exposure over time and the associations between exposure from the four channels and both curiosity and susceptibility to using e-cigarettes. Results Youth exposure to e-cigarette advertising on the Internet and in convenience stores formed an increase-decrease-increase pattern from 2014 to 2020, whereas exposure in newspapers and magazines and on TV generally decreased over this period. Exposure on the Internet and in convenience stores was consistently associated with curiosity and susceptibility; but exposure in newspapers and magazines and on TV was sporadically associated with the outcomes. Conclusions Despite a changing e-cigarette marketplace, youth were consistently exposed to e-cigarette advertising, especially on the Internet and in convenience stores. This pattern is worrisome, as it may increase youth curiosity and susceptibility to using e-cigarettes. Comprehensive tobacco prevention efforts to prevent e-cigarette use in adolescents should continue to restrict e-cigarette advertising and marketing, thereby reducing exposure and discouraging e-cigarette use. Regular efforts should also be made to educate adolescents about the risks of using e-cigarettes to counteract the impact of high e-cigarette advertising exposure.