Financial Aid Research Articles

“I had no idea”: At-Risk College Student Knowledge of Financial Aid and Resources

“I had no idea”: At-Risk College Student Knowledge of Financial Aid and Resources

Artificial Intelligence in Finance: Applications and Implications

The definition and uses of Artificial Intelligence, often abbreviated as AI, has come a long way since its inception. This technology is rapidly changing some business operations in the areas of trading, identification of frauds, and customer service in particular, the modern finance sector, and this article highlights the importance of technology in these processes. Significant Applications of Artificial Intelligence in Finance: From Algorithmic Trading to Robo-Advisors. Explaining, the paper analyzes the current trends and practices in the industries, addressing the positive and negative aspects of AI in the finance sector, with respect to the visions of the future. One that relates to the socio-technical implications that include the use of such technology in finance and the associated risks of data breach, availability, and loss of control and trust in processes.

Harnessing artificial intelligence for sustainable development in emerging markets: Exploring opportunities and challenges in Thailand

The current study investigates how artificial intelligence (AI) impacts sustainable development in emerging markets, with a focus on Thailand. Following a systematic review approach, research designs are configured for reviewing empirical evidence within peer-reviewed papers and reports. It also presents an overview of the adoption of AI in agriculture, health, and urban planning. The key findings lend credence to the potential use of AI in achieving resource optimality, reducing environmental damage, and urging social equity in its use. But these very initiatives are hampered by the digital divide, concerns about data privacy, and bias in algorithms. The way forward should be to establish solid, regulatory frameworks geared towards more investment in infrastructure with ethical AI practices that will lead to optimal gains. The huge potentials for AI to enable sustainability in Thailand are there; hence, it is very important to reduce the associated risks that require equitably distributed results in all sectors.

Google news initiative innovation challenge in Latin America: business models between path dependence and power relations

ABSTRACT In response to growing regulatory pressures, tech companies have partnered with media organizations to support news production through programs, grants, and financial aid for journalism. Notably, the Google News Initiative (GNI) Innovation Challenge has invested millions in Latin American news projects. This study explores the characteristics of these projects and their impact on the business models of Latin American media organizations. By examining project descriptions, a typology clarifies the connection between these initiatives and their effects on publishers’ business models. This analysis is complemented by 20 semi-structured interviews conducted from January 2020 to March 2023 with key regional informants. Results show three types of projects emerging through the GNI Innovation Challenge: audience-building, revenue generation, and technological innovation. It becomes evident that these media organizations are trapped in a dependent cycle, significantly contributing to their challenges in establishing sustainable business models. Furthermore, the study suggests that media organizations perceive these grants as a temporary financial resource, enabling them to hire additional staff and implement technological advancements within their newsrooms for a short time. Meanwhile, Google exerts its influence across the media industry by promoting cutting-edge technologies, which increases media dependence on its infrastructure. Ultimately, this research highlights the complex dynamics between big tech firms and the news industry, raising questions about the journalism sustainability and the potential consequences of its path dependence and capture by platforms.

Perceptions of Artificial Intelligence in Medicine Among Newly Graduated Interns: A Cross-Sectional Study

Perceptions of Artificial Intelligence in Medicine Among Newly Graduated Interns: A Cross-Sectional Study

Medical Education and Artificial Intelligence: Web of Science-Based Bibliometric Analysis (2013-2022).

Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted. This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013-2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education. Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer. A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was "Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey." Keyword analysis revealed that "radiology," "medical physics," "ehealth," "surgery," and "specialty" were the primary focus, whereas "big data" and "management" emerged as research frontiers. The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.

Staff Perspectives on the Role of Public Libraries in Facilitating College Literacy

The decision-making process for potential college students in the United States is overwhelming because of the volume of information available and the complexity of higher education systems. Prospective students must consider institutional rankings, academic programs, and financial aid opportunities in addition to completing numerous forms. For some students, there exist personal and professional advisors and resources to assist with navigating the process. Others with less social and cultural capital must figure out the system with little or no assistance while facing other barriers. Public libraries can be a resource for the college-bound community; however, research shows that public libraries are not actively engaged in supporting this user group. To provide focused, quality information services to any demographic, it is important to understand the perspectives of public-facing information workers. Through a cross-sectional online questionnaire of library staff across four states, this study sought to learn staff perspectives on the role of public libraries in serving prospective college students. The survey received 160 responses of which 100 open-ended responses pertained to the role of public libraries. Qualitative inductive analysis point to three core themes on the role of public libraries in serving this population, that of connector, provider, and supporter. A fourth theme suggests staff see the role as one for others to meet. This article provides insight into past examinations of public library support of college literacy offering an opening to further discussions on serving this overlooked user group.

Artificial Intelligence in Medicine

Artificial intelligence in medicine refers to the use of machine learning models to help process medical data and provide medical professionals with important insights, improving health outcomes and patient experience. Thanks to recent advances in computer science and informatics, artificial intelligence (AI) is rapidly becoming an integral part of modern healthcare. Therefore, artificial intelligence algorithms and other AI-powered applications are now used to support medical professionals in clinical settings and in ongoing research. There are several applications of Artificial intelligence in medicine, including applications to help detect and diagnose diseases; applications to treat diseases with the help of an AI-powered virtual assistant; AI applications in medical imaging; applications to increase the efficiency of clinical trials; and applications to accelerate drug development. The benefits of Artificial intelligence in medicine can be summarized in providing informed patient care, reducing errors, reducing care costs, and increasing doctor-patient engagement.

Awareness and Implications of the Rice Tariffication Law (Republic Act 11203) among Irrigation Association Members in Dipolog City, Philippines

The Rice Tariffication Law, enacted in the Philippines in 2019, aimed to reduce rice prices for consumers while supporting rice growers through various assistance measures. This study assessed the awareness and implications of the Rice Tariffication Law (Republic Act 11203) among irrigated rice farmers in selected barangays of Dipolog City, Philippines. Interviews with 82 farmers revealed that although 71% were aware of the law, their understanding of its objectives (71%) and key provisions (70%-77%) was limited. Nevertheless, all participants received government assistance in the form of farm mechanization, fertilizers, financial aid, and seeds. Farmers unanimously acknowledged both the positive and negative effects of the law. Statistical analysis showed a significant difference in the average income per hectare of rice farmers before and after the law's implementation. The study recommends educational initiatives, particularly through institutions like Jose Rizal Memorial State University, to enhance farmers' understanding and dissemination of the law's objectives and key provisions.

The role of artificial intelligence in optimizing management of atrial fibrillation in acute ischemic stroke.

Atrial fibrillation (AF) is a severe condition associated with high morbidity and mortality, including an increased risk of stroke and poor outcomes poststroke. Our understanding of the prognosis in AF remains poor. Machine learning (ML) has been applied to the diagnosis, management, and prognosis of AF in the context of stroke but remains suboptimal for clinical use. This article endeavors to provide a comprehensive overview of current ML applications to AF patients at risk of stroke, as well as poststroke patients without AF. Strategies to develop effective ML involve the validation of a variety of ML algorithms across internal and external datasets as well as exploring their predictive powers in hypothetical and realistic settings. Recent literature of this rapidly evolving field has displayed much promise. However, further testing and innovation of medical artificial intelligence are required before its imminent introduction to ensure complete patient trust within the community. Prioritizing this research is imperative for advancing the optimization of ongoing care for AF patients, as well as the management of stroke patients with AF.

Does the European Union Start-Up Aid Help Young Farmers to Innovate and to Join Networks?

The endurance and vitality of European agriculture are threatened by the aging of farmers, together with the lack of generational change. The small share of young farmers also impacts on the innovative capacity and competitiveness of the sector. The Common Agricultural Policy of the European Union started addressing the issue long ago by providing financial aid to help young farmers to start up. Over time, the aid increased and packages of measures were set to reinforce the aid and to promote investments, innovations, and networks at the farm level. While the literature focuses on analyzing the effectiveness of the start-up aid in fostering new entries, this paper assumes an original perspective as it seeks to assess whether this aid has actually promoted innovations and networks in the beneficiary farms. The analysis relies on sample data collected in 2021 in Italy and Poland via interviews of 500 farmers under 40 who had entered the sector both with and without the aid. A probit model estimates whether the beneficiaries have a higher probability to innovate and network. Then, the contribution of the aid to the intensity of the adoption of innovations and networks is estimated via an Average Treatment Effect on the Treated model (ATT). Results suggest that the start-up aid increased the adoption of innovations and networks. The ATT indicates that this positive effect holds even after correcting for self-selection bias. By adopting an original perspective, our analysis suggests that the start-up aid for young farmers goes beyond rejuvenating agriculture by fostering innovation at the farm level and by promoting networking, thus enhancing agricultural change. However, farmer behaviors in the two countries are different, suggesting quite complex patterns for the impact of this measure.

Технологизация аграрного сектора как основа продовольственной безопасности страны

The article discusses concepts and approaches to the definition of the concept of "food security" in the scientific literature and documents of the Food and Agriculture Organization of the United Nations (FAO). Under the influence of these ideas, a research boom began in daily nutrition practices and the study of their role in social health and active longevity of people. The problem of youth nutrition culture as an innovative group of society that determines the future state of the world has become, according to the authors, a trending research area. The analysis of the effectiveness of the use of information technologies and artificial intelligence in agriculture as drivers of industry development and food security is actively studied by domestic and foreign scientists. The dialogue of the scientific community is focused around the most relevant research topics: the problems of changing the nature of agricultural labor in the context of digitalization, robotization and the introduction of artificial intelligence; the impact of the technologization of agricultural enterprises on the specific activities of farmers, the existing risks of unintended destruction of ecosystems as a result of intensification of agriculture. Successful practices of using artificial intelligence in crop production and animal husbandry, the possibility of preserving traditional forms of farming on earth and many other issues are being actively studied. It is concluded that it is important to study the problems of agricultural technologization as a way to improve the efficiency of the industry and a way to ensure the country's food security.

The future of medicine or a threat? Artificial intelligence representation in Chicago Med

The eighth season of the American medical drama series Chicago Med (2015–) represented the application of artificial intelligence (AI) in a hospital environment with multiple storylines. Born in the 1950s, medical dramas are among the most popular forms of serial television. Traditionally, the genre aims for a certain amount of accuracy and has educational goals. Former studies investigated the entertainment education and cultivation effects of these series, concluding that these dramas have the potential to contribute information and shape viewers’ opinions on various health-related topics. Chicago Med is a long-running broadcast production with a worldwide audience and considerable viewership. This paper analyzes the series’ representation of medical AI and discusses how this portrayal potentially shapes the audience’s opinion. The research started by identifying artificial intelligence-related storylines in the 22 episodes of the season. The analysis focused on the reasons and outcomes of AI applications, the character’s attitudes, and the ethical issues, including transparency, selective adherence, automation bias, responsibility gap, hallucination, unequal access, and political dimensions. The storyline analysis concluded that Chicago Med provided thought-provoking positive and negative scenarios about applying different types of AI in the surgical and emergency departments. The complex portrayal included groundbreaking opportunities, challenges, dangers, and ethical considerations. The main characters’ attitudes varied, from strong support or opposition to more nuanced, shifting opinions. The educative and engaging content has a potential for knowledge transfer and encourages critical thinking about medical AI.

Interdisciplinary research in artificial intelligence: Lessons from COVID-19

Abstract Artificial intelligence (AI) is widely regarded as one of the most promising technologies for advancing science, fostering innovation, and solving global challenges. Recent years have seen a push for teamwork between experts from different fields and AI specialists, but the outcomes of these collaborations have yet to be studied. We focus on approximately 15,000 papers at the intersection of AI and COVID-19—arguably one of the major challenges of recent decades—and show that interdisciplinary collaborations between medical professionals and AI specialists have largely resulted in publications with low visibility and impact. Our findings suggest that impactful research depends less on the overall interdisciplinary of author teams and more on the diversity of knowledge they actually harness in their research. We conclude that team composition significantly influences the successful integration of new computational technologies into science and that obstacles still exist to effective interdisciplinary collaborations in the realm of AI.

Exploring the role of artificial intelligence technology in empowering women-led startups

The study aims to investigate how artificial intelligence (AI) influences women-led startups in Saudi Arabia, aiming to understand their unique experiences, challenges, and opportunities within the AI technology landscape. This study used a qualitative method, conducting 16 in-depth interviews with women entrepreneurs operating businesses in Saudi Arabia. The analysis was performed using thematic analysis with NVivo 12, uncovering key themes and insights. The findings reveal that cultural norms, societal expectations, limited awareness, and financial constraints are directly associated with women’s involvement in AI-driven businesses. Cultural biases emerged as impediments, underscoring the need for targeted interventions such as awareness campaigns and educational initiatives to dismantle ingrained biases and foster an environment that recognizes and celebrates the contributions of women in the tech and AI sectors. Educational programs, collaborations between academia and industry, and mentorship initiatives were identified as pivotal components to prepare women entrepreneurs to navigate the intricate landscape of AI adoption. Financial inclusion emerged as a critical pillar, advocating for equitable access to funding and resources tailored specifically for women-led AI startups. The study further emphasizes the importance of fostering supportive ecosystems that extend beyond financial aid. Creating networks for mentorship, guidance, and collaboration provides women entrepreneurs with platforms to share experiences and resources, enhancing resilience and the potential for success in the AI landscape. Acknowledgment The authors extend their appreciation to the Arab Open University for funding this work through research fund No. AOUKSA-524008.

Financial Aid and Educational Outcomes: A Study of Grant Effectiveness in Rural China

This paper investigates the effectiveness of financial grants in rural China. The financial grant system’s selection criteria are difficult to quantify and subjective because they vary between school to school. Statistical methods such as ANOVA are utilized to explores correlations between student academic performances with their socio-economic backgrounds. Based on the experiments conducted, it can be shown that certain factors such as attitude towards study and grade contribute more than other factors to the overall academic performances. Furthermore, machine learning methods are incorporated into the study to try to determine non-linear relations between academic performances with socio-economic factors. According to the results, it can be concluded that the current allocation model of financial grants in rural China is insufficient. Thus, in this article the author proposes a more scientific and statistical approach of such allocation.

Accuracy of ChatGPT responses on tracheotomy for patient education.

To investigate the accuracy of information provided by ChatGPT-4o to patients about tracheotomy. Twenty common questions of patients about tracheotomy were presented to ChatGPT-4o twice (7-day intervals). The accuracy, clarity, relevance, completeness, referencing, and usefulness of responses were assessed by a board-certified otolaryngologist and a board-certified intensive care unit practitioner with the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool. The interrater reliability and the stability of the ChatGPT-4o responses were evaluated with intraclass correlation coefficient (ICC) and Pearson correlation analysis. The total scores of QAMAI were 22.85 ± 4.75 for the intensive care practitioner and 21.45 ± 3.95 for the otolaryngologist, which consists of moderate-to-high accuracy. The otolaryngologist and the ICU practitioner reported high ICC (0.807; 95%CI: 0.655-0.911). The highest QAMAI scores have been found for clarity and completeness of explanations. The QAMAI scores for the accuracy of the information and the referencing were the lowest. The information related to the post-laryngectomy tracheostomy remains incomplete or erroneous. ChatGPT-4o did not provide references for their responses. The stability analysis reported high stability in regenerated questions. The accuracy of ChatGPT-4o is moderate-to-high in providing information related to the tracheotomy. However, patients using ChatGPT-4o need to be cautious about the information related to tracheotomy care, steps, and the differences between temporary and permanent tracheotomies.

Artificial intelligence in agriculture

Artificial intelligence in agriculture

Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review.

In recent years, ophthalmology has emerged as a new frontier in medical artificial intelligence (AI) with multi-modal AI in ophthalmology garnering significant attention across interdisciplinary research. This integration of various types and data models holds paramount importance as it enables the provision of detailed and precise information for diagnosing eye and vision diseases. By leveraging multi-modal ophthalmology AI techniques, clinicians can enhance the accuracy and efficiency of diagnoses, and thus reduce the risks associated with misdiagnosis and oversight while also enabling more precise management of eye and vision health. However, the widespread adoption of multi-modal ophthalmology poses significant challenges. In this review, we first summarize comprehensively the concept of modalities in the field of ophthalmology, the forms of fusion between modalities, and the progress of multi-modal ophthalmic AI technology. Finally, we discuss the challenges of current multi-modal AI technology applications in ophthalmology and future feasible research directions. In the field of ophthalmic AI, evidence suggests that when utilizing multi-modal data, deep learning-based multi-modal AI technology exhibits excellent diagnostic efficacy in assisting the diagnosis of various ophthalmic diseases. Particularly, in the current era marked by the proliferation of large-scale models, multi-modal techniques represent the most promising and advantageous solution for addressing the diagnosis of various ophthalmic diseases from a comprehensive perspective. However, it must be acknowledged that there are still numerous challenges associated with the application of multi-modal techniques in ophthalmic AI before they can be effectively employed in the clinical setting.

Quantifying the impact of centralised neonatal care following interhospital transfer of preterm infants on families.

Evaluate the additional burden of centralised neonatal care on families of extremely preterm infants cared for away from their planned hospital of birth. Retrospective cohort study using national data for infants 23+0 to 27+6 weeks of gestation admitted for neonatal care from 2011 to 2016. The number of transfers on the first day of life (potential maternal-infant separation), time away from the maternal booking hospital (BH) and distance from the maternal residence were quantified. Of 14 719 included infants, 2803 (19%) underwent postnatal transfer on the first day of life. A total of 8622 (59%) infants were cared for away from their BH for a median of 39 days (interquartile range [IQR] 15-69), 30% spending >60 days away over a median of two episodes (range 1-12). Median return road travelling distances for parents to their local BH was 13 km (IQR 8-26), but this increased to 74 km (IQR 32-148) for those cared for in a non-BH. Centralised neonatal care improves infant outcomes but introduces additional burdens on the families of extremely preterm infants cared for away from their BH. Additional support, including accommodation and financial aid, could help minimise the impact on these families, facilitate family integrated care and improve parental wellbeing.