Excessive Reliance Research Articles

The Therapeutic Approach To Complexes Created By The Primary Drive Of Intense Desire (Prospatheia): An Introduction To Neptic Psychology

This paper examines the concept of "prospatheia" and its relationship to the creation and management of psychological complexes, drawing on elements from both Neptic Theology and psychology. Prospatheia, as a powerful longing for connection, is considered a primary drive leading to emotional attachment and contributing to the formation of complexes. In psychology, complexes are interpreted as unconscious patterns that guide behavior, reinforcing dependence on external sources to fulfill fundamental emotional needs. The approach of Neptic Theology proposes a therapeutic process that differs from the psychological acceptance or integration of complexes. Specifically, Neptic Psychology emphasizes the prevention of complex formation from childhood through training the individual to develop a more measured relationship with the material world, avoiding excessive pleasure. In adulthood, Neptic Psychology supports controlling prospatheia through the conscious avoidance of pleasure, which sustains dependence. Thus, the treatment of complexes involves three phases: prevention, pleasure control, and gradual weakening of the complexes. Focusing on the abandonment complex, the paper illustrates how excessive reliance on security can transform into a fear of abandonment, leading to emotional insecurity and alienation from one’s spiritual nature. Neptic Theology advocates for self-control and the avoidance of pleasure that reinforces complexes, thereby fostering an inner balance. This methodology differs from modern psychological methods, as it aims at the weakening rather than the acceptance of complexes, offering a unique framework for understanding the human soul and its relationship to the material world.

Latent Profile Analysis of AI Literacy and Trust in Mathematics Teachers and Their Relations with AI Dependency and 21st-Century Skills

Artificial Intelligence (AI) technology, particularly generative AI, has positively impacted education by enhancing mathematics instruction with personalized learning experiences and improved data analysis. Nonetheless, variations in AI literacy, trust in AI, and dependency on these technologies among mathematics teachers can significantly influence their development of 21st-century skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. This study aims to identify distinct profiles of AI literacy, trust, and dependency among mathematics teachers and examines how these profiles correlate with variations in the aforementioned skills. Using a cross-sectional research design, the study collected data from 489 mathematics teachers in China. A robust three-step latent profile analysis method was utilized to analyze the data. The research revealed five distinct profiles of AI literacy and trust among the teachers: (1) Basic AI Engagement; (2) Developing AI Literacy, Skeptical of AI; (3) Balanced AI Competence; (4) Advanced AI Integration; and (5) AI Expertise and Confidence. The study found that an increase in AI literacy and trust directly correlates with an increase in AI dependency and a decrease in skills such as self-confidence, problem-solving, critical thinking, creative thinking, and collaboration. The findings underscore the need for careful integration of AI technologies in educational settings. Excessive reliance on AI can lead to detrimental dependencies, which may hinder the development of essential 21st-century skills. The study contributes to the existing literature by providing empirical evidence on the impact of AI literacy and trust on the professional development of mathematics teachers. It also offers practical implications for educational policymakers and institutions to consider balanced approaches to AI integration, ensuring that AI enhances rather than replaces the critical thinking and problem-solving capacities of educators.

Enhancing Efficacy in Breast Cancer Screening with Nesterov Momentum Optimization Techniques

In the contemporary landscape of healthcare, machine learning models are pivotal in facilitating precise predictions, particularly in the nuanced diagnosis of complex ailments such as breast cancer. Traditional diagnostic methodologies grapple with inherent challenges, including excessive complexity, elevated costs, and reliance on subjective interpretation, which frequently culminate in inaccuracies. The urgency of early detection cannot be overstated, as it markedly broadens treatment modalities and significantly enhances survival rates. This paper delineates an innovative optimization framework designed to augment diagnostic accuracy by amalgamating momentum-based optimization techniques within a neural network paradigm. Conventional machine learning approaches are often encumbered by issues of overfitting, data imbalance, and the inadequacy of capturing intricate patterns in high-dimensional datasets. To counter these limitations, we propose a sophisticated framework that integrates an adaptive threshold mechanism across an array of gradient-based optimizers, including SGD, RMSprop, adam, adagrad, adamax, adadelta, nadam and Nesterov momentum. This novel approach effectively mitigates oscillatory behavior, refines parameter updates, and accelerates convergence. A salient feature of our methodology is the incorporation of a momentum threshold for early stopping, which ceases training upon the stabilization of momentum below a pre-defined threshold, thereby pre-emptively preventing overfitting. Leveraging the Wisconsin Breast Cancer Dataset, our model achieved a remarkable 99.72% accuracy and 100% sensitivity, significantly curtailing misclassification rates compared to traditional methodologies. This framework stands as a robust solution for early breast cancer diagnosis, thereby enhancing clinical decision making and improving patient outcomes.

Opportunities and challenges in higher education arising from AI: A systematic literature review (2020–2024)

With society’s continuous development and progress, artificial intelligence (AI) technology is increasingly utilized in higher education, garnering increased attention. The current application of AI in higher education impacts teachers’ instructional methods and students’ learning processes. While acknowledging that AI advancements offers numerous advantages and contribute significantly to societal progress, excessive reliance on AI within education may give rise to various issues, students’ over-dependence on AI can have particularly severe consequences. Although many scholars have recently conducted research on artificial intelligence, there is insufficient analysis of the positive and negative effects on higher education. In this paper, researchers examine the existing literature on AI’s impact on higher education to explore the opportunities and challenges presented by this super technology for teaching and learning in higher educational institutions. To address our research questions, we conducted literature searches using two major databases—Scopus and Web of Science—and we selected articles using the PRISMA method. Findings indicate that AI plays a significant role in enhancing student efficiency in academic tasks and homework; However, when considering this issue from an ethical standpoint, it becomes apparent that excessive use of AI hinders the development of learners’ knowledge systems while also impairing their cognitive abilities due to an over-reliance on artificial technology. Therefore, our research provides essential guidance for stakeholders on the wise use of artificial intelligence technology.

Point cloud segmentation method based on an image mask and its application verification

Abstract Accurately perceiving three-dimensional (3D) environments or objects is crucial for the advancement of artificial intelligence (AI) interaction technologies. Currently, various types of sensors are employed to obtain point cloud data for 3D object detection or segmentation tasks. While this multi-sensor approach provides more precise 3D data than monocular or stereo cameras, it is also more expensive. The advent of RGB-D cameras, which provide both RGB images and depth information, addresses this issue.
In this study, we propose a point cloud segmentation method based on image masks. By using an RGB-D camera to capture color and depth images, we generate image masks through object recognition and segmentation. Given the mapping relationship between RGB image pixels and point clouds, these image masks can be further used to extract the point cloud data of the target objects. The experimental results revealed that the average accuracy of target segmentation was 84.78%, which was close to that of PointNet++. Compared with three traditional segmentation algorithms, the accuracy was improved by nearly 23.97%. The running time of our algorithm is reduced by 95.76% compared to the PointNet++ algorithm, which has the longest running time; and by 15.65% compared to the LCCP algorithm, which has the shortest running time among traditional methods. Compared with PointNet++, the segmentation accuracy was improved. This method addressed the issues of low robustness and excessive reliance on manual feature extraction in traditional point cloud segmentation methods, providing valuable support and reference for the accurate segmentation of 3D point clouds.

Reasons for Potential Unreliability of DNA Evidence: A Critical Examination

DNA evidence is widely regarded as the "gold standard" in forensic science because of its capacity to accurately identify individuals and exonerate the innocent. However, several factors cast doubt on its reliability in criminal investigations. This study offers a comprehensive exploration of critical issues influencing the trustworthiness of DNA evidence, including errors in evidence collection, overlooking additional DNA samples at the crime scene, the absence of credible witnesses during searches, and the lack of video documentation throughout the processes of seizure, transportation, and lab analysis. Other concerns include human errors, inconsistent DNA analysis protocols among forensic laboratories, and potential contamination during sample collection. Furthermore, the research delves into laboratory errors, subjective interpretations, and existing biases. There is an increasing worry regarding the excessive reliance on DNA evidence without sufficient investigative corroboration, along with the potential misinterpretation of statistical probabilities by jurors. The article also examines ethical issues surrounding the expansion of DNA databases, privacy violations, and familial DNA searches. Citing pivotal academic studies and landmark legal cases like those of O.J. Simpson and Amanda Knox, this analysis highlights the pressing need for rigorous standards to govern the ethical use of DNA evidence in the criminal justice system.

A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion

In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity of their algorithms or excessive reliance on a priori knowledge and expert experience. To address these issues, this study presents a novel defect quantification method based on multi-sensor signal fusion (MSSF). The method employs a multi-sensor probe to fuse the MFL signals under multiple lift-off values, enhancing the diversity of defect information. This enables defect-opening profile recognition using the characteristic approximation approach (CAA). Subsequently, the MSSF method is based on a 3D magnetic dipole model and integrates the structural features of multi-sensor probes to develop an algorithm. This algorithm iteratively determines the defect depth at multiple data acquisition points within the defect region to obtain the maximum defect depth. The feasibility of the MSSF quantification method is validated through finite element simulation and physical experiments. The results demonstrate that the proposed method achieves accurate defect quantification while enhancing efficiency, with the number of iterations for each defect depth calculation point consistently requiring fewer than 15 iterations. For rectangular metal loss, perforation, and conical defects, quantification errors are less than 10%, meeting practical inspection requirements.

Problems and prospects of the application of artificial intelligence in the combat of criminal offenses by The Security Service of Ukraine at critical infrastructure facilities

The article is devoted to the study of the problems and prospects of the use of artificial intelligence in countering criminal offenses by the Security Service of Ukraine at critical infrastructure facilities. The peculiarities of state policy, the current state of legal regulation of the use of innovative technology in Ukraine are considered, and problematic issues of the use of artificial intelligence in the course of the tasks of prevention, detection, termination, and pre­trial investigation of criminal proceedings in the relevant field are analyzed. The main areas of application of artificial intelligence in combating criminal offenses at critical infrastructure facilities have been worked out, such as: intelligent detection of threats in the process of countermeasures; automation of algorithms for combating illegal encroachments; information and analytical support. The key methods and technologies of artificial intelligence that can be used in the process of combating criminal offenses at critical infrastructure facilities by the Security Service of Ukraine are highlighted, in particular: deep learning, machine learning, natural language processing and computer vision. The potential risks and problematic issues of introducing artificial intelligence into the automated process of combating criminal offenses at critical infrastructure facilities are outlined. Attention is drawn to compliance with the principles of transparency and accountability of activity, legality, responsibility for erroneous decisions, ensuring human rights and freedoms, prevention of risks of discrimination, excessive reliance on artificial intelligence. The need to improve the legal basis for the use of artificial intelligence in combating criminal offenses at critical infrastructure facilities, as well as introducing changes and additions to the legislation of Ukraine concerning the criminal-procedural activity of the Security Service of Ukraine, was noted. The introduction of artificial intelligence technologies into the system of combating criminal offenses at critical infrastructure facilities is identified as a promising direction for further research.

ECG signal analysis and detection system based on deep learning

In view of the problems of large workload and easy error in traditional manual recognition of ECG signals, and the existing ECG monitoring equipment still has few ECG signal recognition types, low diagnostic accuracy, and excessive reliance on network services, in order to improve the performance of ECG monitoring equipment, an ECG (Electrocardiogram Signals) analysis and detection system is designed based on deep learning technology. The SENet-LSTM (Squeeze-and-Excitation Networks Long Short Term Memory) network model is built to realize automatic diagnosis of 7 categories of ECG signals. The model is built on an intelligent hardware platform that uses ADS1292R as ECG acquisition module, STM32F103 as data processing module, and Raspberry Pi as central processing module. The system uses the integrated high- performance microcomputer Raspberry Pi for calculation and analysis, providing users with offline artificial intelligence (AI: Artificial Intelligence) services. At the same time, the accuracy and precision of the model are and respectively 98.44%, 90.00%thereby realizing real-time detection and accurate classification of ECG, providing patients with accurate disease diagnosis.

Selection and adaptation to weed management methods: implications for non-chemical and integrated weed management approaches.

Herbicide-resistant weeds are a growing global concern, threatening food security. Non-chemical weed management approaches are becoming increasingly important. Furthermore, the adoption of non-conventional agricultural practices is on the rise, with regenerative farming practices aimed at rebuilding soil organic matter, restoring biodiversity, and reducing chemical use gaining traction. Consequently, non-chemical weed management methods are seen as essential solutions. However, excessive reliance on these methods may inadvertently lead to weed selection and adaptation, reducing their effectiveness. Here, we explore the mechanisms driving changes in efficacy due to selectivity and adaptation to non-chemical weed management practices. Additionally, we discuss potential integrated weed management (IWM) strategies that combine chemical and non-chemical methods to mitigate the risks of weed adaptation. This study highlights the role of evolutionary processes in shaping weed adaptation to non-chemical weed management methods and underscores the need to understand these processes to develop IWM approaches that remain effective over time. Monitoring phenological shifts and adaptations in the field should be a key component of decision support systems, tailored to the unique conditions of each site. Furthermore, a deeper understanding of weed adaptation mechanisms can enhance the efficacy of IWM strategies and help delay the inevitable adaptation to these control methods. © 2024 Society of Chemical Industry.

Human Subjectivity in Information Practice and AI Governance

ABSTRACTThe rise of Artificial Intelligence (AI) introduces a notable tension in the realm of traditional, human‐centric information practices, where human subjectivity has been pivotal in both influencing and being influenced by our interactions with information. An excessive reliance on AI distances humans from practices, potentially diminishing human subjectivity. Additionally, as AI takes on roles once exclusively human, it might constrict opportunities for personal growth and the cultivation of unique insights. Moreover, this technological dependency could dilute the richness of direct human interactions, weakening the fabric of social bonds. These issues—increased AI dependence, AI's encroachment on human roles, and the degradation of social ties—underscore the urgent necessity to revisit our interaction with technology, ensuring it serves to enrich rather than undermine the human experience. In light of this, our panel gathers experts to explore strategies for preserving human subjectivity through cognitive autonomy, creative agency, and social connectivity in the age of AI‐driven information practices. Our dialogue also aims to develop a comprehensive AI governance framework, scrutinized from an interdisciplinary perspective, continually refined in collaboration with academic communities, such as ASIS&T, to solidify and enhance our approaches.

Dual-channel minimally invasive percutaneous cerclage for femoral shaft and metaphysis fractures during the internal fixation with intramedullary nails: case reports and technical description.

When treating femoral fractures with closed reduction intramedullary nail fixation, excessive residual displacement of the fracture end can affect the fixation effect and prolong the healing time, which is difficult to manage intraoperatively. This article introduces a simple steel wire cerclage fixation technique, which is percutaneous and can control the incision within 1 cm without excessive reliance on specially designed surgical instruments. It is suitable for the reduction and fixation of oblique femoral metaphysis fractures and butterfly-shaped femoral shaft fracture blocks.

Is digitalization essential for abating carbon emission growth in South Asia?

Output generation processes across South Asia are deemed unclean (highly emission-intensive) due to excessive reliance on fossil fuels. Thus, decarbonizing the growth processes within this region has critical emphasis among policymakers. However, since the South Asian countries are yet to be ready to undergo the renewable energy transition, it is unlikely that “their annual carbon discharge levels will subside” anytime soon. Under such circumstances, these countries need to limit the rate at which their carbon emission levels rise each year. Therefore, the impacts of digitalization on annual carbon emission growth rates in selected South Asian countries are assessed in this study. Overall, considering internet penetration rate as a proxy of digital technology adoption, the results confirm that policies aimed at digitalizing the concerned South Asian economies not only exert emission growth rate-reducing impacts via a direct channel, but also indirectly reduce emission growth rates by making natural resources sectors more productive, expediting the energy sector greening processes, greening the financial sectors, making corruption controlling measures within institutions more effective, and limiting urbanization-induced environmental quality-worsening effects. Therefore, it is relevant for the South Asian government to emphasize on digitalization while simultaneously ensuring that green digital technologies are adopted within the major macroeconomic sectors across this region.

EFL Students’ Perception in Indonesia and Taiwan on Using Artificial Intelligence to Enhance Writing Skills

Technological advancements in education have introduced Artificial Intelligence (AI) as a transformative tool to enhance academic writing skills in English as a Foreign Language (EFL). This study explored EFL students’ perceptions in Indonesia and Taiwan regarding the role of AI in improving their writing abilities. Using qualitative research methods, data were collected through semi-structured interviews with 20 second-year students specializing in English at an Islamic State University in Indonesia and a National University in Taiwan, who actively use AI tools in their writing processes. Analysis based on Creswell’s thematic framework revealed positive perceptions of AI, with benefits such as improved grammar, sentence structure, paraphrasing skills, vocabulary enrichment, and efficiency in topic generation. However, concerns also emerged about excessive reliance on AI, reduced creativity, and issues of plagiarism and authenticity. These findings highlighted the dual-edged nature of AI in academic writing, emphasizing the need for careful integration and risk mitigation to fully leverage AI’s educational benefits. This research enriched the discourse on technological advancements in EFL education and provides insights for curriculum developers, educators, and policymakers to optimize AI use in academic settings. The practical implications of these findings included strategies to enhance the effectiveness of AI in education and foster innovation in EFL teaching.

Nomophobia syndrome and its impact on morale and behavior change in early childhood

Nomophobia has become a prevalent issue which affects individuals today. This leads many to feel constantly connected to their devices. The term, derived from "No Mobile Phone Phobia," describes an excessive reliance on gadgets. This psychological condition is recognized as a global psychosocial phenomenon that influences mobile phone users and affects their morals and behavior. Alarmingly, this issue has also emerged among young children, with many preschoolers showing signs of addiction. This study is aimed at exploring the effects of nomophobia on early childhood, focusing on its moral and behavioral consequences in daily life. The study employed a qualitative case study approach and it involved two 5-year-old children. Data were collected through observations and unstructured interviews with their parents. The findings indicated that excessive gadget used in early childhood could significantly alter children's morals and behaviors. This was primarily due to insufficient parental supervision of their app usage. The study emphasized the need for intervention from adults, including both parents and teachers, who are the closest guardians to children in monitoring smartphone usage. These collaborative preventive measures to monitor and mitigate the negative impacts of smartphone usage are important to do. Future studies for exploring nomophobia in early childhood contexts are encouraged to delve deeper into the surrounding events and circumstances that affect children.

Impacto de la Automatización en la Auditoría: Ventajas y Desafíos

Automation in auditing represents a fundamental transformation, allowing for improved operational efficiency and accuracy in error detection. This study examines its advantages, challenges and impact on the auditor's role, highlighting the need to acquire new technological competencies and redefine the professional function. Through a qualitative approach, a literature review was conducted to identify the associated benefits, such as the ability to audit 100% of transactions and free auditors for higher value-added activities. However, the risks of excessive reliance on technology and information overload, which could negatively affect critical judgment, are cautioned. For a successful implementation, it is recommended to integrate automation in a phased manner, manage technology risks through adequate controls, and ensure continuous training of staff. Constant monitoring and adjustment of automated systems is essential to ensure their effectiveness in an environment of constant regulatory change. In conclusion, automation presents a unique opportunity to modernize auditing, but it requires a comprehensive approach that balances technological innovation with professional judgment.

The Impacts and Challenges of ChatGPT on Translation Teaching in Universities

ChatGPT, as an artificial intelligence tool, has played an important role in various fields, especially in the field of education. However, there are still shortcomings in translation teaching in universities. This paper analyzes firstly the impacts and challenges of ChatGPT in translation teaching in universities and concludes that ChatGPT can help reduce teachers' teaching burden, provide a rich corpus, and improve translation efficiency. But at the same time, this paper also finds out that the abuse of ChatGPT will bring certain risks, such as causing alienation of the relationship between teaching subjects, and students' excessive reliance on it is not conducive to the cultivation of innovative and critical consciousness, ChatGPT will also lead to academic misconduct such as cheating and plagiarism. Based on this, this paper puts forward the following suggestions: Establish two correct tripartite relationships, cultivate students' innovative consciousness, and formulate more perfect laws and regulations to regulate the use of ChatGPT.

The Issues of Psychological Health Tests in Chinese High Schools

At present, adolescents are facing many challenges in mental health, and there are many problems with psychological test questions, so this paper aims to strengthen mental health education and interventions. There are many problems in the current psychological test questions, including insufficient professionalism, insufficient consideration of cultural differences, insufficient privacy protection, insufficient validity and reliability, excessive reliance on self-report, oversimplification of the interpretation of results, lack of timely updating and revision, suggestiveness of the questions, insufficient verification of reliability and validity, and failure to fully cover important aspects of mental health. These issues may affect the accuracy and reliability of test results and need to be continuously improved and resolved through scientific research and professional practice. When addressing mental health issues, schools should work with professionals to ensure that psychological tests are scientific and culturally appropriate, while updating test questions regularly. In addition, it combines quantitative and qualitative assessments, provides professional training, uses information technology to improve testing convenience, and establishes a feedback mechanism to optimize test questions. Resources and support are also needed after the exam to ensure that tests are designed without bias to help students address mental health challenges. Through these measures, the effectiveness of psychological testing can be improved, and the mental health and well-being of students can be promoted.

Understanding Digital Dementia and Cognitive Impact in the Current Era of the Internet: A Review.

Dementia encompasses symptoms resulting from brain damage that impairs cognitive functions, surpassing natural aging effects. This condition affects emotional regulation, behavior, and motivation while preserving consciousness. Dr. Manfred Spitzer coined the term 'digital dementia,' highlighting the cognitive decline associated with excessive reliance on digital devices such as smartphones and Google, potentially exacerbating attention deficit hyperactivity disorder (ADHD) and memory loss. This condition mirrors terms like 'digital amnesia' and 'the Google Effect,' highlighting the brain's tendency to offload peripheral information, leading to panic and forgetfulness. Spitzer's book, Digital Dementia, focuses on gaming effects on children and has thus popularized the term. Teenagers are known to use electronic devices regularly, correlating with rising cognitive impairments. The advent of the internet's fifth generation (5G) has transformed technology use, impacting mental health treatments and clinical practices globally. Digital media's influence on the developing brain encompasses motor skills, language, and cognition. Excessive digital media use in young adults correlates with lower cognitive empathy, affecting interpersonal understanding and facial recognition. Studies link heavy reliance on web-based media to decreased white matter integrity, crucial for language skills. Adolescents may be more vulnerable to anxiety and unrealistic expectations due to digital media overuse. Digital media overuse impacts brain development, especially cognitive and inhibitory control, attention, memory, and reasoning, essential for adapting to dynamic environments. Early exposure to fast-paced media can impair motor skills, spatial awareness, problem-solving, and language learning. Neuroimaging studies reveal that environmental factors like screen usage affect brain networks controlling social-emotional behavior and executive functions. Overreliance on smartphones diminishes gray matter in key brain regions, affecting cognitive and emotional regulation. The internet generation, characterized by advancements such as Web 3.0, introduces artificial intelligence and semantic web technologies, reshaping digital content processing. The neurobiological basis of digital dementia involves changes in the brain structure and function, with excessive screen exposure linked to cognitive impairments. Neuroplasticity, or the brain's adaptability, plays a role in cognitive decline from digital media overuse. Early childhood and adolescent brain development stages exhibit significant plasticity, influencing cognitive trajectories. Addressing digital dementia requires strategies to reduce screen time, promote cognitive exercises, and enhance awareness. Parents should regulate children's screen usage, encourage digital detox periods, and substitute screen time with other activities. Cognitive training programs such as Cogmed (Neural Assembly Int AB, Stockholm, SWE) and CogniFit (San Francisco, CA, USA) can improve memory and attention in older adults. Promoting balanced technology use and educating on the risks of excessive digital media consumption is crucial for maintaining cognitive health in the digital age.

Medical Foods for the management of Chronic Illness and their Regulations in India, US and European Union

As many chronic illnesses are incurable and require repeated treatments, this problem contributes to long-term drug use. But when it comes to managing and treating chronic illnesses, medical foods can provide an alternative to natural medications. Medical foods are foods that have been particularly prepared to meet the unique dietary needs of people with specific illnesses. When it comes to therapeutic applications such as deglutition, dyspepsia, or eating disorders, they are crucial in providing patients with nutritional support. In addition, they significantly improve patients' quality of life by lowering drug use, averting problems that could result from an excessive reliance on pharmaceuticals, and saving treatment costs. Depending on the illness, medical foods' nutritional content can be adjusted and customized. They are exempt from laws pertaining to drugs since they are not drugs. Every medical food is created especially for the chronic condition in question. As a result, the purpose of this review is to clarify the use of medical foods for conditions such as Parkinson's, Alzheimer's, anxiety and sleep disorders, pain syndrome, cancer, congenital metabolic disorders, and diabetes mellitus, and to suggest their use as a dietary supplement for these long-term illnesses.