Takefuji Yoshiyasu

Instructors are always interested in methods to activate learner incentives and motivation to increase learning effectiveness. This paper introduces the Python Package Index (PyPI) as a powerful tool to maximize learner incentives on software and presents an example of its application in agriculture and science. The more useful the PyPI application is, the more it will be downloaded worldwide, providing an external review for the learner, and strengthening their incentive. However, many existing tutorials on PyPI, including the official site, are not updated on the twine library for uploading files to the PyPI site. This paper presents an updated tutorial on using PyPI for counting disaggregated objects such as bugs and pests, and for software reproducibility validation via Code Ocean. Additionally, generative AI is introduced as an indispensable assistant for tasks such as understanding technical terms and providing solutions for encountered problems.

This study explores the significant correlation between frailty and an elevated risk of mortality in COVID-19 patients, suggesting that increased frailty screening could enhance disease management and optimize resource distribution. An analysis of peer-reviewed papers on frailty and cardiovascular diseases (CVD) over a ten-year period reveals a peak of 4480 articles from September 2021 to September 2022. The literature review conducted on frailty and CVD highlights the high prevalence of frailty in older adults with CVD and its role as a predictor of cardiovascular death. The study suggests that frailty can inform treatment decisions, offering more personalized care. However, standardizing frailty assessment in clinical practice and trials is needed. The impact of frailty on coronary artery disease, peripheral artery disease, and atrial fibrillation requires further research. The study also discusses the increasing global burden of CVD among older adults due to aging populations and improved care. It highlights the challenges posed by older age, multiple comorbidities, polypharmacy, frailty, and adverse noncardiovascular outcomes. The review focuses on geriatric conditions that significantly impacted health status, quality of life, and overall prognosis. The study concludes that frailty significantly increases the risk of CVD events and major adverse cardiovascular events in older adults without prior CVD. Screening for frailty could help identify those at higher risk and facilitate targeted preventive measures.

The Michael J. Fox Foundation has been funding research on Parkinson's disease for 35 years, but has yet to find a cure. This is due to a problem with the philosophy behind the development of modern medical treatments. In this paper, we will introduce "smart medicine" with a substance that can solve all the problems of central nervous system drugs. The substance is the smallest diatomic molecule, the hydrogen molecule. Due to their size, hydrogen molecules can easily penetrate the cell membrane and enter the brain. In the midbrain of Parkinson's disease patients, hydroxyl radicals generated by the Fenton reaction cause a chain reaction of oxidation of dopamine, but hydrogen entering the midbrain can convert the hydroxyl radicals into water molecules and inhibit the oxidation of dopamine. In this paper, we focus on the etiology of neurological diseases, especially Parkinson's disease, and present a case in which hydrogen inhalation improves the symptoms of Parkinson's disease, such as body bending and hand tremor. And we confidently state that if Michael J. Fox encountered "smart medicine" that could be realized with molecular hydrogen, he would not be a "lucky man" but a "super-lucky man."

While drug therapy plays a crucial role in cancer treatment, many anticancer drugs, particularly cytotoxic and molecular-targeted drugs, cause severe side effects, which often limit the dosage of these drugs. Efforts have been made to alleviate these side effects by developing derivatives, analogues, and liposome formulations of existing anticancer drugs and by combining anticancer drugs with substances that reduce side effects. However, these approaches have not been sufficiently effective in reducing side effects. Molecular hydrogen (H2) has shown promise in this regard. It directly reduces reactive oxygen species, which have very strong oxidative capacity, and indirectly exerts antioxidant, anti-inflammatory, and anti-apoptotic effects by regulating gene expression. Its clinical application in various diseases has been expanded worldwide. Although H2 has been reported to reduce the side effects of anticancer drugs in animal studies and clinical trials, the underlying molecular mechanisms remain unclear. Our comprehensive literature review revealed that H2 protects against tissue injuries induced by cisplatin, oxaliplatin, doxorubicin, bleomycin, and gefitinib. The underlying mechanisms involve reductions in oxidative stress and inflammation. H2 itself exhibits anticancer activity. Therefore, the combination of H2 and anticancer drugs has the potential to reduce the side effects of anticancer drugs and enhance their anticancer activities. This is an exciting prospect for future cancer treatments.

With the proliferation of the Internet, the number of cyber-attacks has increased worldwide. To prevent cyber-attacks, network administrators need to use analytical tools to disclose six new critical statistics such as Internet Protocol (IP) authenticity and classification, IP ownership, frequency of attacks, time of day, day of the week, and country of attack. There are four SSH attack analysis tools available, but existing tools lack visualization and cannot provide the necessary attack statistics. This paper proposes SSHAA, the first open source PyPI analysis tool that meets the requirement. Text mining technology is used for extracting data from SSH log files, classifying IPs with identified country names and calculating a variety of statistics. PyPI allows SSHAA to run on Windows, MacOS, and Linux operating systems. This paper highlights how to debut a PyPI application using SSHAA which will be the world’s first tutorial in the security journals for maximum software dissemination. According to PePy, SSHAA has been downloaded 7668 times worldwide. The substantial number of downloads signifies the efficacy of the proposed tool. The proposed system offers ten functional statistics for SSH attack analysis. The six new functional statistics to information security are significant.

A comprehensive literature review was conducted to explore the link between COVID-19 and coronary spasms. The findings from the review strongly suggest a significant association between COVID-19 and coronary spasms, as well as related heart diseases.

Objective: The study aims to examine the effects of the COVID-19 pandemic on the prevalence of arthritis in the US using a specific generative AI tool. Methods: The AI tool with Bing.com/copilot, designed to generate Python code, uses data from the Centers for Disease Control and Prevention (CDC) to visualize trends and uncover insights in four key areas: (1) The prevalence of arthritis in adults aged 18 years and older who have diabetes, (2) The prevalence of fair or poor health in adults aged 18 years and older who have arthritis, (3) The prevalence of activity limitations due to arthritis in adults aged 18 years and older with doctor-diagnosed arthritis, (4) The prevalence of arthritis in adults aged 18 years and older who are obese. This research did not require approval from an institutional review board or an ethics committee. Results: The findings reveal a significant decline in the prevalence of arthritis among adults with conditions such as diabetes and obesity during the COVID-19 pandemic. There was also an observed improvement in activity limitations among patients with doctor-diagnosed arthritis. Conclusion: The study highlights the potential impact of the pandemic on chronic disease management, particularly arthritis. It underscores the importance of continued monitoring and care for patients with arthritis, especially during a global health crisis like the COVID-19 pandemic. The use of AI tools in generating insights from health data proves to be valuable in this context.

Purpose: The purpose of this study is to scrutinize the repercussions of the COVID-19 pandemic on the incidence of drug overdose fatalities in the US. Methods: The study utilizes datasets from the CDC and employs a linear regression model to calculate the time-series of excessive deaths spanning from 2020 to 2022. An extensive literature review focusing on overdoses during the pandemic period is also conducted. Results: The findings reveal that the influence of COVID-19 on overdose fatalities in 2020, 2021, and 2022 were 1.18, 1.36, and 1.38 times higher, respectively. The observed demand for urgent mental health care has seen a lesser decline compared to the overall need for emergency services. Conclusions: This study offers critical insights into the correlation between the COVID-19 pandemic and drug overdose deaths in the US, which could serve as a valuable resource for future research and policy-making decisions. Consequently, it is imperative for emergency departments to be equipped and ready to deliver crucial care for adolescents confronted with mental health crises.

This paper investigates the trends in erectile dysfunction research by integrating individual keywords such as COVID-19, mental health, psychiatry, and drug use. The study spans a six-year period from September 21, 2017, to September 21, 2023, and utilizes resources from the National Library of Medicine. For instance, the quantity of relevant documents is determined using the Google search engine. A custom date range can be set on the browser via tools, and the search is limited to the nih.gov site domain. The phrase-site search command used is executed with “erectile dysfunction” COVID-19 site:nih.gov. The result revealed that from Sept 2021 to 2022, research on COVID-19 and erectile dysfunction spiked. However, the previous year saw a rise in studies linking drugs or mental health with erectile dysfunction. Meanwhile, psychiatry-focused publications have consistently grown. The time-series trends of erectile dysfunction linked to COVID-19 are substantiated by a comprehensive literature review.

Objectives: This paper aims to provide a tutorial for diabetologists and endocrinologists on using generative AI to analyze datasets. It is designed to be accessible to those new to generative AI or without programming experience. Methods: The paper presents three examples using a real diabetes dataset. The examples demonstrate binary classification with the ‘Group’ variable, cross-validation analysis, and NT-proBNP regression. Results: The binary classification achieved a prediction accuracy of nearly 0.9. However, the NT-proBNP regression was not successful with this dataset. The calculated R-squared values indicate a poor fit between the predicted model and the raw data. Conclusions: The unsuccessful NT-proBNP regression may be due to insufficient training data or the need for additional determinants. The dataset may be too small or new metrics may be required to accurately predict NT-proBNP regression values. It is crucial for users to verify the generated codes to ensure that they can achieve their desired objectives.

This paper examines time-series vaccine effectiveness on COVID-19 infection with/without a bivalent booster dose by 6 age groups such as 18–29, 30–49, 50–64, 65–79, 80+, and all_ages respectively. CDC's COVID data on rates of COVID-19 cases and deaths by updated (bivalent) booster status was used in this study. This result concludes that there is no difference between vaccines with or without a bivalent booster dose for preventing COVID-19 infection in 6 age groups 18–29, 30–49, 50–64, 65–79, 80+, and all_ages. Vaccination is effective in two age groups of 65-79 and 80+ for preventing COVID-19 infection. However, vaccine effectiveness against COVID-19 infection has not been confirmed in the 18–29 and 30–49 age groups.

Through experiencing cardiopulmonary arrest, an artificial intelligence universal biomarker prediction tool was developed to help patients understand improvement in the trends of their disease. PyPI tool handles two biomarkers, hbA1c for diabetes and NP-proBNP for heart failure, to predict the next hospital visit. Predicting improvement in disease is a great hope for patients.

The 2022 Medicare Fee-For-Service Improper Payments Report reveals an estimated $80.57 billion in improper payments, with a payment error rate of 15.62%. This paper uses generative AI to analyze and identify which provider types and HCPC codes are most strongly associated with these errors. The paper employs generative AI to produce two Python codes: one generates a time-series trend graph of Medicare improper payments from 2010 to 2022, and the other calculates the number of payment errors by provider type and HCPC code. These codes are designed for novice and non-programmers. Three datasets are used, such as Medicare Fee-for-Service Comprehensive Error Rate Testing dataset released on March 8, 2023, merged codes such as HCPC codes and PCT codes. The result suggests what systems should be improved to reduce Medicare improper payments. Generative AI is being introduced to help novice and non-programmers analyze Medicare improper payments with datasets, aiding researchers in conducting similar tasks in the future.

Drawing from a comprehensive Japan-based literature review and the author's personal experience, this article presents findings that highlight potential improvements in clinical outcomes, such as reduced mortality rates, by optimizing the current resuscitation procedure for cardiopulmonary arrest. Many countries have adopted similar procedures for cardiopulmonary arrest. This article presents a prioritized resuscitation method based on scientific evidence, aiming to improve survival rates. The study, which was conducted in Japan, revealed inconsistencies in the current resuscitation procedure for cardiopulmonary arrest. The study did not involve direct participants but relied on literature review for data collection. A literature review was conducted to analyze the survival rates of various resuscitation methods. The interventions reviewed in the literature included cardiopulmonary resuscitation, automated external defibrillator, and automatic mechanical chest compressions. The survival rate of cardiopulmonary arrest in Japan was found to be low. The results of the literature review suggest that cardiopulmonary resuscitation or automatic mechanical chest compressions should be applied before using an automated external defibrillator. The study emphasizes the need to prioritize resuscitation methods with higher survival rates. This article presents a prioritized resuscitation method based on scientific evidence, aiming to improve survival rates. It is hoped that this new approach will lead to a significant improvement in the survival rates of cardiopulmonary arrest patients.

Goal of health policies is to protect and promote the health of communities. We examined COVID-19 policy outcomes of the 50 US states according to policymaker assumptions over time. With daily cumulative population mortality chosen as an indicator to evaluate and score outcomes of individual health policies, Hawaii had the best score and Arizona has the worst score. Our policy outcome analysis tool could identify and quantify policymakers’ faulty assumptions against COVID-19, and concludes that the more COVID-19 deaths, the greater the economic loss.

As diabetes rates surge globally, there is a corresponding rise in the number of patients suffering from diabetic kidney disease (DKD), a common complication of diabetes. DKD is a significant contributor to chronic kidney disease, often leading to end-stage renal failure. However, the effectiveness of current medical treatments for DKD leaves much to be desired. Molecular hydrogen (H2) is an antioxidant that selectively reduces hydroxyl radicals, a reactive oxygen species with a very potent oxidative capacity. Recent studies have demonstrated that H2 not only possesses antioxidant properties but also exhibits anti-inflammatory effects, regulates cell lethality, and modulates signal transduction. Consequently, it is now being utilized in clinical applications. Many factors contribute to the onset and progression of DKD, with mitochondrial dysfunction, oxidative stress, and inflammation being strongly implicated. Recent preclinical and clinical trials reported that substances with antioxidant properties may slow the progression of DKD. Hence, we undertook a comprehensive review of the literature focusing on animal models and human clinical trials where H2 demonstrated effectiveness against a variety of renal diseases. The collective evidence from this literature review, along with our previous findings, suggests that H2 may have therapeutic benefits for patients with DKD by enhancing mitochondrial function. To substantiate these findings, future large-scale clinical studies are needed.

There is no vaccine for norovirus. This paper shows spraying platinum nanoparticles on masks can inactivate airborne noroviruses. Feline calicivirus F-9 strain was used as a surrogate for human norovirus to verify the effectiveness of inactivation under JIS L 1922 and ISO 18184 for testing the ability of textiles to kill virus. The result achieved an R-value = 0.9, which is equivalent to 87.4 % reduction of infectious norovirus.

The COVID-19 pandemic has significantly affected the middle-aged population in the US. Leveraging the CDC dataset, this study quantifies the number of fatalities across various midlife age brackets, specifically 40–44, 45–49, 50–54, 55–59, and 60–64 for both males and females, spanning the years 2015 to 2020. A novel Python Package Index (PyPI) application, midlife was developed to compute and visualize these findings. The PyPI midlife application was also validated via Code Ocean for reproducibility of the application. The analysis revealed that males aged 55–59 and females aged 50–54 experienced the highest excess mortality due to COVID-19, likely due to a previously declining death trend in these groups. This research not only provides a method to visualize and calculate the impact of COVID-19 on midlife mortality by age and sex, but also highlights the potential economic repercussions of rising midlife mortality rates.

The issue with asbestos highlights the shortcomings in the global management of health policies for dangerous substances. The perils of asbestos dust were identified about a century ago. A significant number of individuals succumb to asbestos-related diseases worldwide annually. A considerable portion of occupational cancer fatalities are believed to be due to asbestos. A large population across the globe is exposed to asbestos in their workplaces. To address issues like asbestos, it is crucial for policymakers to prioritize public interest, and third parties should actively participate in scrutinizing the actions of these policymakers.

Context: Programming, particularly with the widely-used scripting language Python, is a powerful tool for expressing ideas in science and technology. PyPI, or the Python Package Index, is a management tool for Python packages. Despite the availability of many tutorials on PyPI, including on its official website, many users have encountered difficulties due to outdated information and issues with the twine upload library. Objectives: This paper demonstrates the use of a gas chromatography program to prototype the PyPI application, with the goal of maximizing its dissemination and validating its reproducibility in science and engineering worldwide. Methods: The peer-reviewed Python program on gas chromatography is converted to the PyPI application. Third-party evaluations motivate learners to boost programming skills. The more the downloads from third-party evaluations, the greater the incentive for learners. Results: The gas chromatography program is successfully converted to agci PyPI application. Learners can be excited to check their progress on external downloads to motivate them to learn and program. This tutorial shows how to verify software reproducibility of the PyPI application via Code Ocean. Conclusion: This paper demonstrated the real gas chromatography program to the PyPI application and showed how to verify software reproducibility for maximizing dissemination skills and boosting the motivation of learners for advancing chemometrics research.

Background In the event of a natural disaster, water is often unavailable. Natural disasters often prevent the existing water infrastructure from functioning to supply water to citizens. Existing water systems are also vulnerable to poisoning such as terrorism and can be destroyed in war because they are centralized infrastructure systems. In the U.S.A, a huge investment of $50 billion is required just to improve infrastructure for drinking water, wastewater, and stormwater. Scope and Approach This paper introduces state-of-the-art technologies for sustainable water harvesting to prepare for natural or human-induced disasters to fulfill the Sustainable Development Goals (SDGs). A literature review was conducted on drinking water technology. Key findings The latest water-harvesting technology uses Metal-Organic Framework materials. The properties of MOFs allow us to survive and efficiently harvest water from air. We can create a sustainable society with MOF materials where the society will become resilient to natural and human-induced disasters for fulfilling the SDGs. Conclusions This paper will show that MOFs play an important role in enhancing urban water sustainability and resilience with the most economical and ecological engineering technology.

There is no tool for calculating a case fatality rate (CFR) and a lag time of COVID-19. This paper proposes a new policymaker tool, covidlag for policymakers to calculate the CFR and the lag time with associated relationship from infection to death. The more the infections, the more the COVID-19 deaths. The less the infections, the less the deaths. We took advantage of this correlation between infection and death. In other words, the number of daily cases and that of daily deaths are used for calculating CFRs and lag times in the US. Scoring policies are based on dividing the number of daily cumulative deaths by the population in millions. The proposed covidlag algorithm with a strong correlation from infection to death with maxima and minima can generate lag times and CFRs in ongoing time series. covidlag allows users to provide the appropriate degree of curve-fitting polynomials of infection and death to generate maxima and minima for calculating time lag and CFR. The introduced hiscovid discovered that New Zealand made a single mistake in March 2022 while the US made multiple major mistakes during the pandemic. The hiscovid tool found that the COVID-19 surge in New Zealand is due to lifting of travel border restrictions. The CFR and lag time calculations play a key role for policymakers in analyzing the COVID-19 pandemic.

This paper investigates the impact of COVID-19 on mental health in the US using a large CDC dataset and a new method with generative AI for automatically generating Python code. The generated code was used to investigate and visualize the time-series impact of COVID-19 on mental health by eight categories over time. The paper aims to activate research on mental health during COVID-19 and demonstrates the use of generative AI in psychiatry research for novice or non-programmer researchers.

Recently, datasets have been discovered for which adaptive optimizers are not more than adequate. No evaluation criteria have been established for optimization as to which algorithm is appropriate. In this paper, we propose a characterization method by implementing backward automatic differentiation and characterizes the optimizer by tracking the gradient and the value of the signal flowing to the output layer at each epoch. The proposed method was applied to a CNN (Convolutional Neural Network) recognizing CIFAR-10, and experiments were conducted comparing and Adam (adaptive moment estimation) and SGD (stochastic gradient descent). The experiments revealed that for batch sizes of 50, 100, 150, and 200, SGD and Adam significantly differ in the characteristics of the time series of signals sent to the output layer. This shows that the ADAM optimizer can be clearly characterized from the input signal series for each batch size.

Purpose: There are 47 municipalities and prefectures in Japan that operate similar COVID-19 policies in a unified manner. There are significant differences regarding their policy outcomes. In order to investigate when the outcomes are different, we made a COVID-19 policy outcome analysis tool, jpcovid for evaluating time-series scores of individual prefectures, not a policy analysis tool. Methods: Scoring policies is based on a single population mortality metric: the number of COVID-19 deaths divided by the population in millions from a demographic perspective. Results: Although uniformed policies have been adopted by the 47 prefectures in Japan, there are significant differences in the calculated scores among the 47 prefectures. This difference can be caused by differences in the herding instincts of the community with COVID-19 variants. The herd instinct is an inherent tendency to associate with others and follow the group's behavior or a behavior wherein people tend to react to the actions of others without considering the reason. The snapshot scoring tool, jpscore showed that Niigata has the best score of 67.9 while Osaka has the worst score of 727.9. jpcovid allows users to identify when herd instincts made changes in time-series scores. Conclusions: This is the world’s first large-scale measurement on the herd instinct of prefectures in Japan. The proposed method can be applied to other countries in general.