Impact of COVID-19 on arthritis with generative AI

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.

Exploring the connection between frailty and cardiovascular diseases

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.

Vaccine effects on COVID-19 infection with bivalent boosting by age group

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.

Artificial intelligence universal biomarker prediction tool

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.

An urgent call to action: The absolute necessity to ban asbestos production and sales

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.

Generative AI for analysis and identification of Medicare improper payments by provider type and HCPC code

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.

Clinical Use and Treatment Mechanism of Molecular Hydrogen in the Treatment of Various Kidney Diseases including Diabetic Kidney Disease

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.

Impact of COVID-19 on mental health in the US with generative AI

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.

A time-series COVID-19 policy outcome analysis tool to measure human behavior from a herd instinct perspective

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12553-023-00759-x.

Why the power of diversity does not always produce better groups and societies

Diversity is supposed to create better groups and societies but sometimes fails. It is explained why the power of diversity may not create better groups in the current diversity prediction theory. Diversity may hurt civic life and introduce distrust. This is because the current diversity prediction theory is based on real numbers that ignore individual abilities. Its diversity prediction theory maximizes performance with infinite population size. Contrary to this, collective intelligence or swarm intelligence is not maximized by infinite population size, but by population size. The extended diversity prediction theory using the complex number allows us to express individual abilities or qualities. The diversity of complex numbers always produces better groups and societies. The wisdom of crowds, collective intelligence, swarm intelligence or nature-inspired intelligence is implemented in the current machine learning or artificial intelligence, called Random Forest. The problem of the current diversity prediction theory is detailed in this paper.

Review of hikikomori: A global health issue, identification and treatment

In the early 2000s, Japanese society became aware of the social phenomenon of hikikomori or social withdrawal among Japanese youth. However, the hikikomori phenomenon was not a domestic Japanese social problem, but a global social and health issue or a global silent epidemic. A literature review was conducted on the global silent epidemic issue and identifying hikikomori and effective treatment. This paper will reveal how to identify hikikomori with biomarkers or determinants and treatments for hikikomori. Impact of COVID-19 on hikikomori was briefly investigated.

hiscovid for visualizing and identifying when policymakers made mistakes against COVID-19

There are two types of policy outcome analysis tools: snapshot tool and time-series tool. hiscovid is a time-series policy outcome scoring tool of COVID-19 policies by country where the daily cumulative population mortality is used for scoring the outcomes of COVID-19 country policies to visualize and identify when policymakers made mistakes. hiscovid allows policymakers to observe the progress and transition of scores over time to learn lessons from the past decision-making mistakes for correcting the current policies to reduce unnecessary deaths. The lower the score, the better the policy. hiscovid attracted 1480 users worldwide.

How to build disaster-resilient cities and societies for making people happy

The current disaster approaches and strategies have failed. Our society is also vulnerable to COVID-19 pandemics and natural disasters. This paper surveys the conventional disaster governance regimes and examines whether a sustainable and resilient society against natural and human-induced disasters can be built. This paper aims to shed light on resilient city and country building from the perspective of disaster prevention. Current disaster strategies must be updated in order to mitigate the cost of disaster events and to make people happy. This paper quantifies the scores of the COVID-19 policies for strengthening disaster prevention technology.

Policy analysis and data mining tools for controlling COVID-19 policies

Much research has been done on the efficacy of vaccines against the COVID-19 pandemic, but the claims have not yet been realized in the real world. This paper proposes three COVID-19 policy outcome analysis tools such as jpscore for scoring and revealing the best prefecture policy in Japan, scorecovid for scoring and revealing the best country policy in the world, and finally hiscovid for visualizing and identifying when policymakers made mistakes in time-series scores. Poorly scored countries or prefectures can learn good strategies from the best country or prefecture with excellent scores. Three tools are based on a single metric dividing the number of COVID-19 deaths by the population in millions. Three tools suggest us that the sustainable mandatory test-isolation strategy should be adopted in the world for mitigating the pandemic. This paper also addresses what is lacking in Japan for scientific evidence-based research for mitigating the pandemic. Visualization tools and sorted and time-series scores of policy outcomes help policymakers make the right decisions.

COVID-19 policy analysis for 10 European countries

Aim

The goal of this paper is to analyze the COVID-19 policies of 10 European countries, including Sweden, Finland, Norway, Italy, France, Germany, Poland, Belgium, the Netherlands, and Hungary, with a time-series policy analysis tool.

Subject and methods

The results of the COVID-19 policy analysis are based on a single time-series indicator, or daily population mortality rate: the number of COVID-19 daily cumulative deaths divided by the population in millions. The lower the score, the better the policy. Although many experts believe that the COVID-19 policy outcome analysis is premature, time series analysis is an excellent analysis that can provide information on the progress and transition of policy outcomes. In other words, the proposed time series analysis tool allows policymakers to identify and quantify when mistakes were made during the on-going COVID-19 pandemic.

Results

The COVID-19 policy analysis discovered many useful facts. Sweden failed due to the herd immunity approach. Hungary made a fundamental mistake in COVID-19 tactics. Countries such as Sweden, Hungary, Belgium, and Poland showed time-series changes that differed from the others.

Conclusion

Public health interventions can play a key role in mitigating the COVID-19 pandemic. The proposed policy analysis tool, hiscovid demonstrated the effectiveness of the time-series score behavior for discovering when policymakers made mistakes.

Time-series COVID-19 policymaker analysis of the UAE, Taiwan, New Zealand, Japan and Hungary

There are two types of policy analysis: socioeconomic analysis and public policy outcome analysis. The socioeconomic analysis is used for understanding the relationship between COVID-19 incident and mortality and building effective governance. There are two types of policy outcome analysis: general policy analysis and time series policy analysis. This paper is a policy outcome analysis of COVID-19, not a policy analysis. This paper examines COVID-19 policy outcome analysis of five countries such as the UAE, Taiwan, New Zealand, Japan and Hungary. Two policy outcome analysis tools are used in this paper such as scorecovid to generate a snapshot list of sorted scores and time-series hiscovid to identify when policymakers made mistakes for correcting mistakes in the near future policy update. Scores in both tools are based on the population mortality rate: dividing the number of COVID-19 deaths by the population in millions. The lower the score, the better the policy. The higher the score, the more deaths that make people unhappy. COVID-19 death is the most unfortunate event in life and is caused by policy. The introduced time-series policy analysis tool, hiscovid discovered ten facts of five countries. Discovered ten facts will be detailed in this paper. Visualization of policy outcomes over time will play an important role in mitigating the COVID-19 pandemic.

Discovering COVID-19 state sustainable policies for mitigating and ending the pandemic

COVID-19 policy outcomes are influenced by urban policy and governance. The goal of this paper is to navigate the sustainable solution of the COVID-19 pandemic using evidence-based research for cities. The number of deaths from COVID-19 is one good indicator to evaluate the results of individual policies by country, state and cities. A policy assessment of urban agglomerations is essential to scientific research. Scoring policies with a single determinant is calculated by dividing the number of deaths by the population in millions. The lower the score, the better the policy. The score monotonically increases so that policymakers can suppress it, but they cannot improve or decrease it. Thus, mistakes by policymakers cannot be corrected and are fatal forever. Many countries have used a pharmacological approach alone such as vaccination with boosting, not sustainable, but their scores are poor and their policies are not effective against the pandemic. Sustainable and optimal policies to mitigate the pandemic were discovered by sorting the scores. This paper introduces two new policy scoring tools such as scorev and usscore. Both tools revealing sustainable approaches are designed for policy-poor states or urban agglomerations to learn the good strategies from countries with excellent scores.

Sustainable policy: Don't get infected and don't infect others

COVID-19 is an environmental policy problem. The goal of this paper is to show sustainable policies against the COVID-19 pandemic. Medical professionals tend to stick only to pharmacological approaches such as vaccination and boosting, but that is not sustainable. The scorecovid and hiscovid tools revealed that the sustainable and the best policy against COVID-19 is based on the mandatory test-and-isolation by law. Because COVID-19 variants have the ability to spike mutations and immune escape, pharmacological approaches such as vaccination alone cannot mitigate or end COVID-19. The scorecovid tool is a Python Package Index (PyPI) application for scoring individual policies against COVID-19. In scorecovid, scoring policies is calculated by dividing the number of deaths due to COVID-19 by the population in millions. The hiscovid tool was developed to identify mistakes by policymakers in order to monitor their policies in time-series scores. The lower the score, the better the policy.

The overlooked benefits of hydrogen-producing bacteria

Intestinal bacteria can be classified into "beneficial bacteria" and "harmful bacteria." However, it is difficult to explain the mechanisms that make "beneficial bacteria" truly beneficial to human health. This issue can be addressed by focusing on hydrogen-producing bacteria in the intestines. Although it is widely known that molecular hydrogen can react with hydroxyl radicals, generated in the mitochondria, to protect cells from oxidative stress, the beneficial effects of hydrogen are not fully pervasive because it is not generally thought to be metabolized in vivo. In recent years, it has become clear that there is a close relationship between the amount of hydrogen produced by intestinal bacteria and various diseases, and this report discusses this relationship.

Molecular Hydrogen as a Medical Gas for the Treatment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Possible Efficacy Based on a Literature Review

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disorder that is characterized by fatigue that persists for more than 6 months, weakness, sleep disturbances, and cognitive dysfunction. There are multiple possible etiologies for ME/CFS, among which mitochondrial dysfunction plays a major role in abnormal energy metabolism. The potential of many substances for the treatment of ME/CFS has been examined; however, satisfactory outcomes have not yet been achieved. The development of new substances for curative, not symptomatic, treatments is desired. Molecular hydrogen (H₂) ameliorates mitochondrial dysfunction by scavenging hydroxyl radicals, the most potent oxidant among reactive oxygen species. Animal experiments and clinical trials reported that H₂ exerted ameliorative effects on acute and chronic fatigue. Therefore, we conducted a literature review on the mechanism by which H₂ improves acute and chronic fatigue in animals and healthy people and showed that the attenuation of mitochondrial dysfunction by H₂ may be involved in the ameliorative effects. Although further clinical trials are needed to determine the efficacy and mechanism of H₂ gas in ME/CFS, our literature review suggested that H₂ gas may be an effective medical gas for the treatment of ME/CFS.

Deathdaily: A Python Package Index for predicting the number of daily COVID-19 deaths

This paper presents a new open-source program called deathdaily for predicting the number of daily COVID-19 deaths in the next 7 days. The predictions can be used by policymakers to determine whether current policies should be strengthened/mitigated or new policies should be challenged to mitigate the COVID-19 pandemic. Although vaccines have been mitigating the pandemic initially, the recent resurgence with new variants has been observed in many vaccinated countries. This paper shows how to use deathdaily to detect symptoms of resurgence. The proposed deathdaily is available in public and can be installed by a Python package manager PyPI. The deathdaily has been downloaded by 15,964 users worldwide, according to https://pepy.tech/project/deathdaily. The fact shows that the applicability, practicality, and usefulness of the proposed program have been duly evaluated.

Guidelines for the selection of hydrogen gas inhalers based on hydrogen explosion accidents

Despite the fact that we have reported on the dangers of the explosion of hydrogen gas inhalers, hydrogen gas inhalers with explosive hazards are, as a matter of fact, still being sold today. In this study, we investigated past reports of hydrogen gas inhaler explosion accidents to clarify the causes of these explosion incidents. As a result of this investigation, we found that the central cause was the leakage of hydrogen gas inside the hydrogen gas inhaler. Although it is said that the explosive concentration of hydrogen is between 10% and 75%, and that the gas does not explode above 75% due to the lack of oxygen, we confirmed through a series of ignition experiments that explosions can occur even in hydrogen gas inhalers that produce 100% hydrogen gas. Some manufacturers of such highly concentrated hydrogen gas inhalers claim that the high concentration and purity of hydrogen is safe and that there is no risk of explosion. We believe that manufacturing or selling such products that pose a risk of explosion or detonation is a violation of social justice. This paper presents ideas for selecting safe hydrogen gas inhalers based on a survey of past accident cases.

SCORECOVID: A Python Package Index for scoring the individual policies against COVID-19

This study proposes SCORECOVID, a new Python Package Index (PyPI) for scoring individual policies against covid-19 and mitigating the pandemic. The new PyPI package consists of two modules. The first module automatically scrapes the latest information on the number of deaths and population by COVID-19 to score individual policies for a given country. The second module calculates the score by dividing the number of deaths by the population in millions. The Federal Communications Commission (FCC) in the US estimates the economic value of a statistical life to be $9.5 million per individual. The higher the number of deaths, the greater the economic loss. To use the best policies to reduce the number of deaths, we should adopt measures and methods from exceptional countries with high scores. The proposed method reveals two groups: a high-scored group and a low-scored group. The number of deaths is an indicator of economic and health policy scores. SCORECOVID is the world's first open-source policy scoring tool for COVID-19. It is designed to help many countries utilize state-of-the-art analytics methods to effectively mitigate the COVID-19 pandemic.

Analysis of digital fences against COVID-19

This paper examines individual policies against the covid-19. The number of deaths due to COVID-19 can be divided by the population (in million) to score the performance of individual policies. 19 countries were investigated. Although many experts emphasize pharmaceutical methods including vaccines, the most effective policy against the covid-19 is based on a robust digital fence where the physical isolation strategy reveals the remarkable achievement for mitigating the pandemic. To mitigate and end the pandemic, combined policies with vaccination and digital fencing need to be used.

Molecular Hydrogen as a Novel Protective Agent against Pre-Symptomatic Diseases

Mibyou, or pre-symptomatic diseases, refers to state of health in which a disease is slowly developing within the body yet the symptoms are not apparent. Common examples of mibyou in modern medicine include inflammatory diseases that are caused by chronic inflammation. It is known that chronic inflammation is triggered by the uncontrolled release of proinflammatory cytokines by neutrophils and macrophages in the innate immune system. In a recent study, it was shown that molecular hydrogen (H2) has the ability to treat chronic inflammation by eliminating hydroxyl radicals (·OH), a mitochondrial reactive oxygen species (ROS). In doing so, H2 suppresses oxidative stress, which is implicated in several mechanisms at the root of chronic inflammation, including the activation of NLRP3 inflammasomes. This review explains these mechanisms by which H2 can suppress chronic inflammation and studies its applications as a protective agent against different inflammatory diseases in their pre-symptomatic state. While mibyou cannot be detected nor treated by modern medicine, H2 is able to suppress the pathogenesis of pre-symptomatic diseases, and thus exhibits prospects as a novel protective agent.

A "philosophical molecule," hydrogen may overcome senescence and intractable diseases

It has been revealed that the cause of senescence and diseases is associated with the reactive oxygen species “hydroxyl radicals” (·OH). Senescence and diseases may be overcome as long as we can scavenge •OH mostly produced in mitochondria. It is one and only one “molecular hydrogen” (H₂) that can both penetrate into the mitochondria and scavenge the •OH. The H₂ in the body can function in disease prevention and recovery. H₂ gas is explosive so that a safe hydrogen inhaler has to be developed for home use. We would like to advocate the great use of H₂.

Fourier analysis using the number of COVID-19 daily deaths in the US

Fourier analysis can provide policymakers useful information for analysing the pandemic behaviours. This paper proposes a Fourier analysis approach for examining the cycle length and the power spectrum of the pandemic by converting the number of deaths due to coronavirus disease 2019 in the US to the frequency domain. Policymakers can control the pandemic by using observed cycle length whether they should strengthen their policy or not. The proposed Fourier method is useful for analysing waves in other medical applications.

Protective effects of hydrogen gas inhalation on radiation-induced bone marrow damage in cancer patients: a retrospective observational study

Although intensity-modulated radiation therapy (IMRT) has been developed as an alternative to conventional radiotherapy, reducing bone marrow damage is limited. Thus, a novel technology is needed to further mitigate IMRT-induced bone marrow damage. Molecular hydrogen (H₂) was recently reported as a preventive and therapeutic antioxidant that selectively scavenges hydroxyl radical (·OH) and peroxynitrite (ONOO^-). This observational study aimed to examine whether H₂ gas treatment improves IMRT-induced bone marrow damage in cancer patients. The study was performed at Clinic C4 in Tokyo, Japan between May 2015 and November 2016. During this period, all enrolled patients received IMRT once per day for 1 to 4 weeks. After each time of IMRT, the patients of control group (n = 7, 3 men and 4 women, age range: 26-70 years) received mild hyperbaric oxygen therapy in health care chamber for 30 minutes, and the patients of H₂ group (n = 16, 8 men and 8 women, age range: 35-82 years) received 5% H₂ gas in health care chamber for 30 minutes once per day. Radiation-induced bone marrow damage was evaluated by hematological examination of peripheral blood obtained before and after IMRT, and the data were expressed by the ratio after to before treatment. The total number of radiation times and total exposure doses of radiation were similar between the control and H₂ groups. IMRT with health care chamber therapy significantly reduced white blood cells and platelets, but not red blood cells, hemoglobin and hematocrit. In contrast, H₂ gas treatment significantly alleviates the reducing effects of white blood cells and platelets (P = 0.0011 and P = 0.0275, respectively). Tumor responses to IMRT were similar between the two groups. The results obtained demonstrated that H₂ gas inhalation therapy alleviated IMRT-induced bone marrow damage without compromising the anti-tumor effects of IMRT. The present study suggests that this novel approach of H₂ gas inhalation therapy may be applicable to IMRT-induced bone marrow damage in cancer patients. The study protocol was approved by an Ethics Committee Review of Tokyo Clinic and Research Institute ICVS Incorporated (Tokyo, Japan) on February 1, 2019, and was registered in the University Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN ID: UMIN000035864) on February 20, 2019.

Preventing explosions of hydrogen gas inhalers

Production and excretion of hydrogen (H₂) gas in human was reported in 1969, since then it has been regarded as non-toxic molecule. For preventive and therapeutic medical uses, a possible treatment for cancer was reported and another article was published on how H₂ acts as a therapeutic antioxidant by selectively reducing cytotoxic oxygen radicals. A variety of H₂ gas inhalers have been available in the market for hospital and home uses. However, H₂ is odorless and flammable or explosive ignited by static electricity. We have examined the safety of a variety of H₂ gas concentrations from the viewpoint of flammability and explosion. We have also measured concentrations of H₂ gas inhalers in the market respectively. This paper also details how to control H₂ gas concentration for preventing explosions.

Information Hiding by Machine Learning

In this paper, the authors propose information hiding by machine learning: a method of key generation for information extracting using neural network. The method consists of three layers for information hiding. First, the proposed method prepares feature extraction keys, which are saved by feature extraction attributes like feature coordinates and the region of frequency coefficients. Second, the proposed method prepares hidden patterns in advance to the embedding procedure as a watermark signal of the target contents. Finally, the proposed method generates information extraction keys by using machine learning to output presented hidden patterns. The proper hidden patterns are generated with the proper information extraction key and feature extraction key. In the experiments, the authors show that the proposed method is robust to high pass filtering and JPEG compression. The proposed method contributes to secure visual information hiding without damaging any detailed data of the target content.

Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services

The Service-Oriented Architecture (SOA) demands supportive technologies and new requirements for mobile collaboration across multiple platforms. One of its representative solutions is intelligent information security of enterprise resources for collaboration systems and services. Digital watermarking became a key technology for protecting copyrights. In this article, the authors propose a method of key generation scheme for static visual digital watermarking by using machine learning technology, neural network as its exemplary approach for machine learning method. The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches, herein neural network approach as an exemplary technology. First, the proposed method of key generation is to extract certain type of bit patterns in the forms of visual features out of visual objects or data as training data set for machine learning of digital watermark. Second, the proposed method of watermark extraction is processed by presenting visual features of the target visual image into extraction key or herein is a classifier generated in advance by the training approach of machine learning technology. Third, the training approach is to generate the extraction key, which is conditioned to generate watermark signal patterns, only if proper visual features are presented to the classifier. In the proposed method, this classifier which is generated by the machine learning process is used as watermark extraction key. The proposed method is to contribute to secure visual information hiding without losing any detailed data of visual objects or any additional resources of hiding visual objects as molds to embed hidden visual objects. In the experiments, they have shown that our proposed method is robust to high pass filtering and JPEG compression. The proposed method is limited in its applications on the positions of the feature sub-blocks, especially on geometric attacks like shrinking or rotation of the image.

Microcode optimization with neural networks

Microcode optimization is an NP-complete combinatorial optimization problem. This paper proposes a new method based on the Hopfield neural network for optimizing the wordwidth in the control memory of a microprogrammed digital computer. We present two methodologies, viz., the maximum clique approach, and a cost function based method to minimize an objective function. The maximum clique approach albeit being near O(1) in complexity, is limited in its use for small problem sizes, since it only partitions the data based on the compatibility between the microoperations, and does not minimize the cost function. We thereby use this approach to condition the data initially (to form compatibility classes), and then use the proposed second method to optimize on the cost function. The latter method is then able to discover better solutions than other schemes for the benchmark data set.

O(log2 M) self-organizing map algorithm without learning of neighborhood vectors

In this letter, a new self-organizing map (SOM) algorithm with computational cost O(log2 M) is proposed where M2 is the size of a feature map. The first SOM algorithm with O(M2) was originally proposed by Kohonen. The proposed algorithm is composed of the subdividing method and the binary search method. The proposed algorithm does not need the neighborhood functions so that it eliminates the computational cost in learning of neighborhood vectors and the labor of adjusting the parameters of neighborhood functions. The effectiveness of the proposed algorithm was examined by an analysis of codon frequencies of Escherichia coli (E. coli) K12 genes. These drastic computational reduction and accessible application that requires no adjusting of the neighborhood function will be able to contribute to many scientific areas.