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Sexism, Religion, And Politics: An Examination Of Rape Myth Acceptance
Get PDFThe literature is inconclusive about the relationship between religion, politics, sexism, rape myth acceptance, and the behavioral implications of the latter. Therefore, the goal of this study was to develop an understanding of the relationship between religion, political affiliation, sexism, and rape myth acceptance. The study utilized primary data collection through surveys of youth using MTurk. The survey involved the administration of the Faith Activities Scale, Moral Foundations Scale, Religious Fundamentalism Scale, Modern Sexism Scale, Ambivalent Sexism Scale, Gender Stereotypes Scale, and the Gender Inclusive Rape Myth Acceptance Scale. Data were analyzed through structural equation modeling to indicate which political affiliations and major US religious adherence predicted various gender stereotypes or sexist beliefs which were also predictive of rape myth acceptance. It was expected that those youth who described being more religious and conservative politically would evidence more sexism and in turn, rape myth acceptance. Although previous research indicated that those who identified as Republican often held stronger rape myth acceptance compared to Democrats (Conroy, 2019; NRP, 2019), the current study did not support these results. Possibly, the results of this study may be attributed to its narrow demographic, or that
more young adults are becoming less affiliated with their parents’ religion and political beliefs systems and are thinking differently from them on these issues. Nevertheless, the findings offer implications for correcting gender miseducation amongst youth toward holding perpetrators accountable, encouraging victims to pursue justice, and reducing instances of sexual victimization in religious organizations.
Keywords: juvenile, sexual assault, religious affiliation, political affiliatio
An Examination Of Risk Factors For Suicidality Among Adolescents In The United States
Get PDFSuicide is considered a significant public health issue, identified as the second leading cause of early mortality in the United States among adolescents (CDC, 2023). The National Institute of Mental Health (2023) defined it as “death induced by self-directed destructive behavior with intent to die” (p. 4). For youth aged 10-14 suicide increased by 36% from 2000 to 2021 in the United States. In recent decades, suicide among adolescents has increased despite estimates of stable or dropping suicide rates in developed countries. Every year, 703,000 youth worldwide commit suicide, and many more attempt (WHO, 2022).
This study used a nationally representative sample of adolescents from the 2021 High School Youth Risk Behavior surveys. Data were examined with a General Strain theoretical framework utilizing logistic regression, and linear regression to understand the impact of empirical risk factors, physical, dating, sexual victimization, bullying, and cyberbullying on youth mental health issues, physical well-being that includes exercises and workouts, and suicidality among high school students. During the early months of the COVID-19 pandemic (2020 into 2021), youth experiences of physical and sexual violence/victimization may have increased, given their isolation. While dating violence increased, it is not clear how this impacted suicides. Youth likely experienced more cyberbullying, given their increased interactions online.
This study revealed significant relationships between various forms of victimization, that is physical, sexual, dating violence, bullying, and cyberbullying, and the mental health and physical well-being of high school students, which in turn influenced their risk of suicidality. The regression analyses highlighted that these forms of victimization were predictors of increased mental health issues, which were directly linked to higher suicidality rates among adolescents. The findings are consistent with the literature, as victimization indicators are expected to be related to mental health issues and physical wellness. Sexual victimization was more impactful than physical victimization and dating victimization on mental health and cyberbullying was more impactful than more traditional bullying on mental health. Relatedly mental health issues significantly affected high school students\u27 physical wellness. This study\u27s results offer empirical details that should be informative for policymakers in prioritizing their efforts to reduce youth health risk and victimization.
Keywords: suicidality, victimization, mental health issues, physical wellness, adolescents
Social Media Influencers: The Effects Of Information Richness And Follower Size On Purchase Intent
Get PDFThis study investigated the dynamics of social media influence on marketing with a focus on two key variables, information richness and influencer follower count, to determine how these characteristics influenced customer purchase intents. In the search for more successful advertising strategies, current marketing trends have capitalized on social media influencers’ expanding role in determining customer behavior. In some cases, this has led to unethical behavior and subsequent mistrust, underlining the challenge of maintaining openness and ethical standard in digital marketing.
This quantitative study used surveys to investigate consumer experiences with various influencer content forms and follower counts. The research specifically investigated two main questions: (1) How does social media influencer channel richness affect purchase intent? (2) How does the number of followers affect purchase intent? The survey looked at how different media types, that is, videos, images, and text posts, and follower sizes affected customer purchasing intents.
The findings revealed that content richness—characterized by quality and interactivity—significantly boosted engagement and purchasing intent, underscoring the critical role of content quality in influencer effectiveness. Conversely, increased audience size did not consistently enhance an influencer’s impact, illustrating the complex interplay between follower count and consumer trust. The study illuminates how influencer marketing strategies can be designed to increase purchase intents. Future studies should consider the cultural and technological elements influencing consumer behavior as well as ways for adapting to fast changing social media landscapes. In summation, this research lays the framework for more effective, consumer-focused influencer marketing methods.
Keywords: social media, information richness, purchase intent, social media influencer, social media content, consumer engagement, information richness theor
Interaction Of Cellulose Model (D-Cellobiose) With Some Selected Sodium Salts
Get PDFThis work focused on employing sodium salts to modify cellulose, a crucial biopolymer found in plant cell walls, to improve its characteristics. Three methods were used to study cellulose-salt interaction. Task 1 employed Fourier-transform infrared spectroscopy (FT-IR) to examine infrared absorption frequencies and discovered that the highest peak shift was caused by sodium borate (Na3BO3). In Task 2, thermal stability was evaluated using thermogravimetric analysis. The results showed that while sodium sulfate (Na2SO4) decreased stability and combustion temperature, sodium bicarbonate (NaHCO3) increased both. Task 3 investigated interactions between cellobiose and sodium salts using density functional theory and computational techniques, with a particular emphasis on bond lengths ≤ 3.5 Å. Sodium borate (Na3BO3) had strong binding at 1.780 Å, whereas sodium azide (NaN3) had the maximum binding activity with a bond length of 1.882 Å. According to estimations of reaction energy, sodium borate, and β-cellobiose had the maximum energy at 30.88 Kcal/mol, while sodium nitrite and α-cellobiose had the lowest energy at -97 Kcal/mol. This study shows the influence of sodium salt on cellulose.
Keywords: cellulose, biopolymer, Cellobiose, sodium salts, bond lengt
Enhancing Black Student Success At Hbcus: The Impact Of Black Faculty Representation On Graduation Rates
Get PDFThis study examined the pivotal role Historically Black Colleges and Universities (HBCUs) play in the United States economy by producing skilled and inventive graduates who contribute significantly to the workforce. Despite representing a small fraction of higher education institutions, HBCUs are instrumental in the educational achievement of Black students. This research specifically investigated the impact of Black faculty representation on the graduation rates of Black students at HBCUs, amidst concerns of lower graduation rates compared to other Title IV institutions. Employing a linear regression model to analyze empirical data, the study found a significant, positive correlation between the presence of Black faculty and the academic success of Black students at HBCUs, contrasting with a negative correlation at non-HBCU institutions.
The dissertation offers strategic and operational recommendations for HBCU administrators and policymakers to improve institutional effectiveness and enhance Black student success. These include comprehensive evaluations of hiring practices, focused recruitment efforts for Black faculty, competitive compensation packages, the implementation of engagement and feedback mechanisms, and the development of community and mentorship programs. Furthermore, the study underscores the importance of future research on financial challenges facing higher education, the impact of institutional location on graduation rates, and the operational disparities between HBCUs and non-HBCUs.
By providing empirical evidence and data-driven recommendations, this study contributes significantly to the literature on higher education and policy making, emphasizing the critical role of HBCUs in fostering academic excellence and diversity in the U.S. labor market. The findings highlight the need for ongoing support and strategic planning to sustain HBCUs as bastions of Black excellence, ensuring they continue to play a crucial role in the economic success of Black students in the U.S.
Keywords: Historically Black Colleges and Universities (HBCUs), HBCU Graduation Rates, Black students, HBCU Black Facult
Game-Theory Application In Co-Resident Security Of Function-As-A-Service Cloud Environments
Get PDFThe cloud is a shared computing environment with a value beyond the sum of its parts. The number of customers that data-centers can serve, comparative advantages, and the ability to manage depreciation allow computing at economies of scale. The cloud allows for every element of factors of production to translate into goods and services. This shared environment spans across a vast clientele, introducing self-sustaining security risks. The vulnerabilities extend beyond the traditional gaps in computer security, through exploitation of the cloud’s efficiency structures.
Shared computing resources enable the existence of co-resident attack vectors on cloud platforms. This study considered the result of modeling co-resident threats in simulation at the boundaries of game-theory using real-world workloads, scalable hardware specifications, and recognized attack parameters. Both attacker and benign user variables were adapted to an extended-time and geographically defined game-space and the results of the co-resident risk determined on an ecological scale.
This study sought to determine the applicability of this technique to emergent cloud structures. The current cloud trend is toward finer granularity programming of applications, where decoupling of data and algorithms into developer customized programming is ceded to by monolithic applications. This phasing into micro-service based limited purpose coding is called Functions-as-a-Service (FaaS). Supporting this feature is provider management, configuration, and patching which anchors FaaS in a serverless interface.
This cloud evolution of code, storage, and presentation into distinct sectors has altered the security environment into discrete sectors by reducing state, ephemeral hosting, and transient runtimes to enable the sought after economic efficiency. Where this increased the cloud dynamism, it also redistributed the cost to benefit analysis. The effective implementation of the game-theory principles required validation on this economic structure.
Index Terms – Cloud computing, data leakage, game-theory, mutli-tenancy, securit
PV Panther April 1977 Requiem
No full texthttps://digitalcommons.pvamu.edu/dr-robert-alphonso-henry-professional/1002/thumbnail.jp
Analysis And Optimization Of Machine Learning Models For Network Intrusion Detection
Get PDFNetwork intrusion detection NID) is a technology that monitors network traffic and identifies abnormal activity. The ability to identify malicious activity can be manifested by application of artificial intelligence and machine learning (ML). This research delved into analysis, development, and optimization of supervised, unsupervised, and reinforcement learning approaches to network anomaly detection. In the past 10 years, there has been a substantial amount of research on supervised learning, a type of ML that is trained from a prelabeled network dataset that tags each sample with benign or abnormal labels. Unsupervised and reinforcement learning research has been minimal.
This study evaluated supervised, unsupervised, and reinforcement learning approaches to anomaly detection. ML employs statistical algorithms that learn the underlying data characteristics and use this learning to detect abnormal activity. From the literature review, it is clear that the most important attributes for an effective NIDs are data quality and efficient ML algorithms, including runtimes and memory usage. Most research in this domain has been conducted using obsolete network datasets that do not reflect the type of malicious traffic encountered today.
This study used a modern dataset that captured traffic from an “Internet of Things” (IoT) test network with modern attack types. In one experiment, a dataset created in the SECURE Center, Prairie View A&M University, was used with scanning attack types, as well as a common utility for converting network traffic called NFStream. NFStream uses deep packet inspection to convert the header/data portion of network packets, preserves Transmission Control Protocol (TCP) Flag states, and calculates statistical features from the raw data in packets. The research was conducted through several experiments covering data pre-processing, dataset labeling, feature selection/ reduction, synthetic data generation, and unsupervised learning. The aim was to develop an optimal ensemble of feature selection/ML algorithms and to establish a framework for future research in Learning Automata and Reinforcement Learning. Based on the results, several contributions to the research domain are provided: a pre-processing framework, several feature selection techniques, a synthetic data generator, a new type of neural network based on Kolmogorov Arnold Networks, and a new clustering approach for unsupervised learning.
Index Terms—Feature selection, generative adversarial networks, Kolmogorov Arnold Networks, learning automata, reinforcement learning, supervised learning, unsupervised learning