Financial Aid Research Articles

Random forest models reveal academic and financial factors outweigh demographics in predicting completion of a year-round veterinary program

Abstract OBJECTIVE The purpose of this study was to develop random forest classifier models (a type of supervised machine learning algorithm) that could (1) predict students who will or will not complete the DVM degree requirements and (2) identify the top predictors for academic success and completion of the DVM degree. METHODS The study utilized Ross University School of Veterinary Medicine student records from 2013 to 2022. Twenty-four variables encompassing demographic (eg, age, race), academic (eg, grade point average), and financial aid (eg, outstanding balances) data were assessed in 11 cross-validated random forest machine learning models. One model was built assessing all years of data and 10 individual models were developed for each enrollment year to compare how the top predictors of success varied among the years. RESULTS Consistently, only academic and financial factors were identified as being features of importance (predictors) in all models. Demographic factors such as race were not important for predicting student success. All models performed very well to excellently based on multiple performance metrics including accuracy, ranging from 96.1% to 99%, and the areas under the receiver operating characteristic curves, ranging from 98.1% to 99.9%. CONCLUSIONS The random forest algorithm is a powerful machine learning prediction model that performs well with veterinary student academic records and is customizable such that variables important to each veterinary school’s student population can be assessed. CLINICAL RELEVANCE Identifying predictors of success as well as at-risk students is essential for providing targeted curricular interventions to increase retention and achieve timely completion of a DVM degree.

Cybersecurity Framework for Synthetic Data in Training Medical AI

Abstract The development of medical artificial intelligence is dependent on the availability of vast quantities of data, a considerable proportion of which is medical data containing sensitive information pertaining to the health and well-being of patients. The use of such data is subject to extensive legal regulation and is further hindered by financial and organisational constraints, which can result in limitations on accessibility. One potential solution to this problem is the use of synthetic data. This article examines the potential for their use in light of cybersecurity requirements derived from horizontal and sectoral EU legislation. The outcome of this analysis is that EU legislation does not contain specific regulations on the use of synthetic data. Consequently, it cannot be concluded that there is any prohibition on their use. Moreover, while the Medical Device Regulation (MDR) contains some general requirements for cybersecurity, these are further specified by the provisions of the AI Act. It is important to note, however, that the AI Act will not apply to Class I medical devices, which are subject only to the MDR. Furthermore, only indirect obligations within the scope under consideration can be derived from the horizontal regulations, which will apply in a limited number of cases.

Recommendations for Clinicians, Technologists, and Healthcare Organizations on the Use of Generative Artificial Intelligence in Medicine: A Position Statement from the Society of General Internal Medicine

AbstractGenerative artificial intelligence (generative AI) is a new technology with potentially broad applications across important domains of healthcare, but serious questions remain about how to balance the promise of generative AI against unintended consequences from adoption of these tools. In this position statement, we provide recommendations on behalf of the Society of General Internal Medicine on how clinicians, technologists, and healthcare organizations can approach the use of these tools. We focus on three major domains of medical practice where clinicians and technology experts believe generative AI will have substantial immediate and long-term impacts: clinical decision-making, health systems optimization, and the patient-physician relationship. Additionally, we highlight our most important generative AI ethics and equity considerations for these stakeholders. For clinicians, we recommend approaching generative AI similarly to other important biomedical advancements, critically appraising its evidence and utility and incorporating it thoughtfully into practice. For technologists developing generative AI for healthcare applications, we recommend a major frameshift in thinking away from the expectation that clinicians will “supervise” generative AI. Rather, these organizations and individuals should hold themselves and their technologies to the same set of high standards expected of the clinical workforce and strive to design high-performing, well-studied tools that improve care and foster the therapeutic relationship, not simply those that improve efficiency or market share. We further recommend deep and ongoing partnerships with clinicians and patients as necessary collaborators in this work. And for healthcare organizations, we recommend pursuing a combination of both incremental and transformative change with generative AI, directing resources toward both endeavors, and avoiding the urge to rapidly displace the human clinical workforce with generative AI. We affirm that the practice of medicine remains a fundamentally human endeavor which should be enhanced by technology, not displaced by it.

The Gendered Impact of In-State Tuition Policies on Undocumented Immigrants’ College Enrollment, Graduation, and Employment

Executive Summary Assessing the impact of policies offering in-state tuition and financial aid to undocumented immigrant youth is highly pertinent from a policy perspective. Eligible immigrants arrived as children and the United States is often the only home they have ever known. Without the right to stay or a path to citizenship, they cannot achieve their full potential as productive members of society. Policies improving access to higher education have the potential to elevate their social mobility and amplify economic contributions by improving job prospects and bolstering tax revenues. Since 2001, roughly half of the U.S. states have expanded in-state college tuition benefits to undocumented immigrants. Around one-third of states have also provided financial aid; yet approximately one-tenth have imposed heightened barriers to accessing higher education. While early studies found an increase in enrollment in states with in-state tuition policies, recent studies accounting for additional state policies found no impact on enrollment. These results mask varying impacts based on gender and marital status. We assess these policies, control for Deferred Action for Childhood Arrivals (DACA), and analyze the impact of in-state tuition on college enrollment, graduation rates, employment, and self-employment. Our analysis relies on data from the 2005 to 2019 American Community Survey. To identify individuals who are the most likely to be undocumented and affected by the policy, we focus on noncitizen Hispanic youth, excluding those likely authorized (e.g., licensed professionals, veterans, and refugees). That said, we are assuming that state policies are the same for all undocumented residents, not just the Hispanics in our sample. Our findings are as follows: - In our sample, there are 17,649 likely undocumented Hispanic men (14,249 women) aged 18–22 years who completed high school and arrived in the U.S. prior to age 16 years. Approximately 75 percent of them are Mexican. - The data reveals that approximately 30 percent of men are enrolled in college. Access to in-state tuition boosts enrollment by 7.2 percentage points for Hispanic men and by 13.2 percentage points for Mexican men. This response is driven by married men. Men in general do not graduate at higher rates. - Women enroll in college at the same rate across states. However, access to lower tuition raises graduation rates by 4.7 percentage points, representing nearly a 30 percent increase compared to the average graduation rate of 15.7 percent, particularly among single women. - Among men with access to in-state tuition, single men are more likely to be self-employed, while married men respond to the policy by working less, consistent with higher enrollment rates. - On average 55 percent of single women are employed. Access to in-state tuition increases employment by 12–19.8 percentage points for single women. We find no significant impact of the policy on the employment of married women. Our research indicates that while in-state tuition policies do make a difference, there is a need for a more nuanced policy approach, particularly due to gender disparities among the intended beneficiaries, which have previously been overlooked. However, we argue that relying solely on in-state tuition policies is insufficient. Single women are driven by these policies to complete their degrees and pursue employment. If policymakers aim for a broader impact and seek to include a more diverse group of undocumented youth, including men, they should consider enhancing opportunities within formal labor markets after college graduation. In support of this argument, we document higher graduation and employment rates and lower self-employment rates among DACA-eligible youth who have legal access to formal employment.

Financial behavior between male and female: a systematic literature review

Finance is an essential aspect of existence. Handling it is reflected in one’s financial behavior. Poor financial decisions have consequences, therefore it's critical for people of both genders, men and women, to be financially informed in making financial decisions. A synthesis of the differences in financial behavior between men and women is the aim of this systematic literature review. Twenty-seven papers (27) published papers between 2014 and 2024 were extracted from the database using the Preferred Reporting Items for Systematic Review (PRISMA) framework. The findings showed that, in comparison to women, men are more financially knowledgeable; they have a greater risk appetite than women, which allows then to take advantage of financial products and services. Contrarily, females with their risk aversion attitude, leaves them financially susceptible because it prevents them from taking advantage of financial products and services like optimizing the potential for money growth. In terms of spending, women are more conservative, and are price sensitive than men. The findings also indicates that women are more vulnerable to financial misconduct; yet, financial literacy will mitigate this risk. Therefore, programs for gender-sensitive financial education should be taken into consideration to close the gap in financial literacy and give women more power in making financial decisions. With this, it would promote financial inclusion and aid in the realization of Sustainable Development Goals (SDG) 5 and 10 or Gender Equality and Reduce inequalities respectively. Future studies may focus on how financial technology affects gendered financial behavior to promote more equal economic outcomes.

RUSSIA’S EMBRACE OF ATTRITIONAL WARFARE: "WINNING BY NOT LOSING"

Abstract: This paper argues that once President Putin was no longer able to achieve his initial objectives of regime change in Kyiv in 2022 by using blitzkrieg operations, Russia’s grand strategy underwent a reassessment towards narrower strategic objectives. This paper will provide an account of the changing nature of Russia’s strategic aims and goals, as well as exploring the ‘Russian’ way of warfare. The shift in war aims is evident in the partial mobilisation of fresh troops, the annexation of Ukrainian territory and waging of a war of attrition. Russia has increased its military budget, the manufacturing of weaponry, as well as the import of military technology from Iran and North Korea. However, NATO allies, including Romania, have provided military aid to Kyiv to continue fighting. Taking all these factors into account, this paper argues this conflict has become a ‘long war’ of attrition.

Federated machine learning in finance: A systematic review on technical architecture and financial applications

Abstract. With the advancement of technology, an increasing amount of data is stored online, including substantial amounts of personal information. Especially in the financial industry, where large sums of money are often involved, protecting personal privacy is crucial. Federated learning, as a privacy-preserving distributed machine learning method, offers a solution to these challenges by enabling data privacy while addressing the difficulties of data sharing in the financial sectorissues that frequently impede innovation, risk management, and fraud detection. This paper delves into the principles of federated learning algorithms, exploring their mechanisms in detail. Besides, applications and case studies in applications such as financial fraud detection, supply chain financing prediction, and other financial services are examined. Furthermore, sample dataset will be introduced. Moreover, the potential benefits and challenges of implementing federated learning within financial contexts are also assessed. Finally, promising research directions for the application of federated learning in the financial industry are outlined.

Research on agricultural disease detection technology based on artificial intelligence

Abstract. Pests and disease are two major factors that reduce the yield of agricultural products per acre of arable land. A report from the Food and Agriculture Organization of the United Nations suggests that agricultural disease and pests cause a loss of more than 1/3 of agricultural production every year. However, farmers cannot check their crops all day without a break. Artificial Intelligence (AI) is developing rapidly this year, it performs well in image recognition. Thus, it is suitable to integrate AI in agriculture. This paper shows the outcome of the accuracy of the outcome when a model is trained using the ResNet-50 model and by some crop pathogens datasets downloaded from Kaggle and using a platform of mmpretrain. It also discusses the pros, cons, and areas for improvement when using AI to detect agricultural disease. This research aims to enhance crop yields and agricultural stability by helping integrate the model in agricultural production.

Navigating Barriers to Success for Community College Science Students: A Review of Transfer Challenges

This paper examines the challenges community college students encounter when transferring to four-year higher-education institutions, focusing on the gap between their transfer aspirations and actual success. Despite the availability of substantial financial aid in higher education, transfer students continue to face numerous challenges that impede their integration and success at new institutions. These obstacles arise from a combination of institutional factors and individual student characteristics. Utilizing Pascarella’s model, this study underscores the importance of support programs in raising institutional awareness of the diverse challenges transfer students face and the necessity for tailored strategies to facilitate their successful transition and retention.

Effects of Teen Childbearing on the Family Socio-Economic Wellbeing in Ruiru, Kiambu County, Kenya

Purpose: The purpose of the study was to investigate the impact of teenage childbearing on family socio-economic well-being in Ruiru, Kiambu County, Kenya, focusing on financial strain, educational disruption, post-partum experiences, and family support. Methodology: Using family stress, human capital, and social support theories, the study adopted descriptive survey design targeted 378 teenage mothers, with a sample of 195 selected using Yamane’s formula. Data collection involved semi-structured questionnaires and interviews, analyzed quantitatively through descriptive statistics and regression, and qualitatively through thematic analysis. Findings: The findings reveal that 85.5% of respondents faced financial difficulties within six months, and 55% reported disrupted high school education, leading to long-term setbacks. Emotional and health challenges characterized post-partum experiences, and 61.1% of respondents found family support insufficient to meet socio-economic needs. The study concludes that teenage childbearing significantly disrupts family well-being, recommending comprehensive support programs, including financial aid, childcare, healthcare, and community initiatives, to better assist teenage mothers and reduce their socio-economic burdens. Unique Contribution to Theory, Policy and Practice: This research uniquely contributes to theory, policy, and practice by creating a comprehensive model that integrates empirical findings and qualitative insights, thereby enriching existing theories. Additionally, it provides practical recommendations for targeted support programs that guide policymakers in alleviating socio-economic burdens and enhancing family well-being.

Death dealers: Dynamics of Israel’s permanent war economy

This study critically explores Israel’s permanent war economy, examining its core principles and strategic impacts. It analyzes how the war economy is integral to Israel’s settler-colonial structures and functions, fulfilling various ideological, geopolitical, and financial goals through a sustained conflict footing, rather than being a transient occurrence. To this end, the study identifies and analyzes four key dynamics underpinning Israel’s war economy: First, acute societal militarism serves as an economic growth engine, nurturing skilled military capital and advanced technologies that feed the war economy system. Second, the role of American patronage is examined as a key enabler of the Israeli war economy. In the absence of a traditional colonial metropole, the United States functions as an imperial sponsor, providing extensive military aid, technological transfers, financial assistance, and diplomatic backing–without which Israel’s military posture would be inconceivable. Third, the article investigates Israel’s exploitation of the occupied Palestinian territories as a laboratory for testing, optimizing and marketing military systems’ performance to international clients. Fourth, examines how Israel’s arms export multiplies revenues for the Israeli war economy while also providing diplomatic leverage to advance Israel’s geopolitical agendas and foreign policy objectives.

Medical technologies, telemedicine and artificial intelligence for neurotrauma and neurorehabilitation.

Medical technologies, telemedicine and artificial intelligence for neurotrauma and neurorehabilitation.

The Influence of Digital Trading Applications on Financial Markets

The word "fintech" refers to a new technology that aims to make financial services better and more automated. Investment management, retail banking, artificial intelligence, block chains, mobile payments, stock trading, and other industries are increasingly included in the fintech sector. With today's high-tech electronic trading on the stock exchange, ordinary investors can purchase or sell shares in a matter of seconds, giving investors the opportunity to efficiently perform their research before investing in equities. This paper, thus, talks about the emergence of fintech, which has led to the development of various mobile trading applications like Zerodha, Grow, Upstox, Angel Broking, etc. These apps have become popular among young investors in recent years because of their low-cost broking services. In this study, both primary and secondary data have been used. Primary survey-based research has been used to know the experiences of people belonging to different age groups on trading apps, and secondary data for statistical figures and other factual points has been used from verified stock exchange data and websites of trading apps. This paper will also shed light on the various seamless services offered by such broking applications, which have drastically changed the way the market functions. Investors could easily browse through the available investments, order what they wanted, and manage their portfolio with a single tap from their phone, making adjustments right away. The paper will go on to explain how stock trading applications have affected the financial markets by encouraging an increasing number of people to participate in the process of constructing economies by trading in financial assets. The report can assist policymakers in determining the advantages and hazards of using these new technologies, as well as the repercussions of their use. It offers legislative recommendations that are meant to encourage the use of AI in finance while making sure that its application is consistent with fostering market integrity, financial stability, and competition while safeguarding financial consumers. To support and encourage the use of responsible AI, emerging dangers from the deployment of AI techniques need to be detected and mitigated.

Integrating large language models in mental health practice: a qualitative descriptive study based on expert interviews

BackgroundProgress in developing artificial intelligence (AI) products represented by large language models (LLMs) such as OpenAI’s ChatGPT has sparked enthusiasm for their potential use in mental health practice. However, the perspectives on the integration of LLMs within mental health practice remain an underreported topic. Therefore, this study aimed to explore how mental health and AI experts conceptualize LLMs and perceive the use of integrating LLMs into mental health practice.MethodIn February–April 2024, online semi-structured interviews were conducted with 21 experts (12 psychiatrists, 7 mental health nurses, 2 researchers in medical artificial intelligence) from four provinces in China, using snowballing and purposive selection sampling. Respondents’ discussions about their perspectives and expectations of integrating LLMs in mental health were analyzed with conventional content analysis.ResultsFour themes and eleven sub-themes emerged from this study. Firstly, participants discussed the (1) practice and application reform brought by LLMs into mental health (fair access to mental health services, enhancement of patient participation, improvement in work efficiency and quality), and then analyzed the (2) technological-mental health gap (misleading information, lack of professional nuance and depth, user risk). Based on these points, they provided a range of (3) prerequisites for the integration of LLMs in mental health (training and competence, guidelines for use and management, patient engagement and transparency) and expressed their (4) expectations for future developments (reasonable allocation of workload, upgrades and revamps of LLMs).ConclusionThese findings provide valuable insights into integrating LLMs within mental health practice, offering critical guidance for institutions to effectively implement, manage, and optimize these tools, thereby enhancing the quality and accessibility of mental health services.

Following Medical Advice of an AI or a Human Doctor? Experimental Evidence Based on Clinician-Patient Communication Pathway Model

ABSTRACT Medical large language models are being introduced to the public in collaboration with governments, medical institutions, and artificial intelligence (AI) researchers. However, a crucial question remains: Will patients follow the medical advice provided by AI doctors? The lack of user research makes it difficult to provide definitive answers. Based on the clinician-patient communication pathway model, this study conducted a factorial experiment with a 2 (medical provider, AI vs. human) × 2 (information support, low vs. high) × 2 (response latency, slow vs. fast) between-subjects design (n = 535). The results showed that participants exhibited significantly lower adherence to AI doctors’ advice than to human doctors. In addition, the interaction effect suggested that, under the slow-response latency condition, subjects perceived greater health benefits and patient-centeredness from human doctors, while the opposite was observed for AI doctors.

Biomedical Engineering and Measurement Technology

Abstract The intersection of biomedical engineering and measurement technology has transformed the medical landscape, offering innovative solutions for diagnostics, treatment, and patient monitoring. Biomedical engineering encompasses a broad spectrum of applications, from molecular biology and tissue engineering to medical robotics and wearable technologies. Measurement technology, meanwhile, enables precise and real-time data collection through advanced sensing techniques, ensuring that these biomedical systems operate at optimal efficiency. In recent years, significant advancements in medical sensors, imaging technologies, and artificial intelligence have paved the way for improved accuracy and performance in medical diagnostics and treatment. This special issue focuses on the latest developments in these fields, showcasing groundbreaking research that enhances our understanding of physiological phenomena and revolutionizes medical measurement applications. Submissions for this topic in Measurement Science and Technology are open for submission from 03 November 2022 to 31 July 2023 and contains 21 outstanding papers in above research fields.

Enhancing Student Access to Financial Aid: A Web-Based Approach to Scholarship Search and Application

This paper worked with a background of the existing harsh educational competition that leads to many challenges when searching for easily accessible scholarships to support a student’s educational needs. This paper presents the development of a user-friendly Web-based system aimed at simplifying the navigation through the selection of scholarships and the submission of applications. Due to its design, the site allows users to create personal accounts with options to incorporate secure means of logging in. Scholarship seekers are allowed to search for scholarships by various filters like the area of study, region, etc., after they register themselves to the website. That is why the presented platform applies a smart filter to provide users with the most relevant opportunities and to save time. Moreover, we direct them to the official scholarship sites through which they can apply and therefore get the most accurate information. This way of searching and accessing the scholarships is more effective to the users making it easier time to access scholarships they wish to apply for in line with their study goals.

Scientific challenges in governing military AI

Scientific challenges in governing military AI

Bias in medical AI: Implications for clinical decision-making.

Biases in medical artificial intelligence (AI) arise and compound throughout the AI lifecycle. These biases can have significant clinical consequences, especially in applications that involve clinical decision-making. Left unaddressed, biased medical AI can lead to substandard clinical decisions and the perpetuation and exacerbation of longstanding healthcare disparities. We discuss potential biases that can arise at different stages in the AI development pipeline and how they can affect AI algorithms and clinical decision-making. Bias can occur in data features and labels, model development and evaluation, deployment, and publication. Insufficient sample sizes for certain patient groups can result in suboptimal performance, algorithm underestimation, and clinically unmeaningful predictions. Missing patient findings can also produce biased model behavior, including capturable but nonrandomly missing data, such as diagnosis codes, and data that is not usually or not easily captured, such as social determinants of health. Expertly annotated labels used to train supervised learning models may reflect implicit cognitive biases or substandard care practices. Overreliance on performance metrics during model development may obscure bias and diminish a model's clinical utility. When applied to data outside the training cohort, model performance can deteriorate from previous validation and can do so differentially across subgroups. How end users interact with deployed solutions can introduce bias. Finally, where models are developed and published, and by whom, impacts the trajectories and priorities of future medical AI development. Solutions to mitigate bias must be implemented with care, which include the collection of large and diverse data sets, statistical debiasing methods, thorough model evaluation, emphasis on model interpretability, and standardized bias reporting and transparency requirements. Prior to real-world implementation in clinical settings, rigorous validation through clinical trials is critical to demonstrate unbiased application. Addressing biases across model development stages is crucial for ensuring all patients benefit equitably from the future of medical AI.

AI in Agriculture : Revolutionizing Precision Farming and Sustainable Crop Management

This article explores the transformative impact of Artificial Intelligence (AI) on agriculture, focusing on precision farming and sustainable crop management. It highlights how AI-driven technologies are revolutionizing agricultural practices by optimizing resource utilization, enhancing productivity, and promoting sustainability. The article discusses key applications of AI in agriculture, including crop health monitoring, predictive analytics, resource optimization, smart irrigation systems, variable rate application of chemicals, and pest and disease detection. It presents case studies and statistical data demonstrating the significant improvements in crop yields, resource efficiency, and environmental sustainability achieved through AI adoption. The article also addresses the challenges facing widespread AI implementation in agriculture, such as cost barriers, lack of technical expertise, data quality issues, and infrastructure limitations, while exploring future directions including the integration of AI with robotics and blockchain technology.