Excessive ultraprocessed foods (UPFs) and poor nutrition tied to poor health

New American Heart Association Science Advisory reviews current evidence about UPFs and their impact on adverse health outcomes and outlines opportunities for research, policy and regulatory reform to improve dietary intake and overall health

American Heart Association

Science Advisory Highlights:

• Most ultraprocessed foods (UPFs) are characterized by poor nutritional quality, contributing to excessive calories, and are typically high in saturated fats, added sugars and sodium (salt), the combination of which is often abbreviated as HFSS, which contribute to adverse cardiometabolic health outcomes, including heart attack, stroke, obesity, inflammation, Type 2 diabetes and vascular complications.
• Observational studies have found links between eating higher amounts of UPFs and an increased risk of cardiovascular disease, chronic illness and mortality.
• Emerging evidence also suggests certain additives and industrial processing techniques may have negative health effects.
• However, not all UPFs are junk foods or have poor nutritional quality; some UPFs have better nutritional value and can be part of an overall healthy dietary pattern.
• Experts recommend multilevel strategies, including more research to uncover how UPFs specifically impact the body, refining dietary guidance to discourage excessive consumption of nutrient-poor UPFs, clarifying the impact of the limited number of UPFs with more favorable nutrition profiles, more research on the health impacts of food additives and evidence-based policies to evaluate and regulate food additives.

Embargoed until 4:00 a.m. CT / 5:00 a.m. ET, Friday, Aug. 8, 2025

DALLAS, Aug. 8, 2025 — Ultraprocessed foods or UPFs are a growing concern due to their widespread consumption and impact on potential health risks. Most UPFs, particularly those commonly seen in U.S. dietary patterns, are high in saturated fat, added sugars and sodium (salt), the combination of which is often abbreviated as HFSS, and contribute to excess calories. These include sugar-sweetened drinks, ultraprocessed meats, refined grains, candy and commercial baked goods, among others. A limited number of ultraprocessed foods, such as certain commercial whole grains, low-fat-low-sugar dairy, and some plant-based items, have positive nutritional value and, therefore, can be part of an overall healthy dietary pattern. This overlap is confusing for health care professionals and the public.

A new Science Advisory from the American Heart Association, “Ultraprocessed Foods and Their Association with Cardiometabolic Health: Evidence, Gaps and Opportunities,” summarizes current knowledge about UPFs and their impact on cardiometabolic health, and outlines opportunities for research, policy and regulatory reform to improve dietary intake and overall health. The manuscript published today in Circulation, the flagship journal of the American Heart Association.  

“The relationship between UPFs and health is complex and multifaceted,” said Maya K. Vadiveloo, Ph.D., R.D., FAHA, volunteer chair of the writing group for this Science Advisory. “We know that eating foods with too much saturated fat, added sugars and salt is unhealthy. What we don’t know is if certain ingredients or processing techniques make a food unhealthy above and beyond their poor nutritional composition. And if certain additives and processing steps used to make healthier food like commercial whole grain breads have any health impact.”

The rapid rise in UPF consumption since the 1990s disrupted traditional dietary patterns, potentially contributing to adverse health effects. It is estimated that 70% of grocery store products in the U.S. contain at least one ultraprocessed ingredient. As detailed in a CDC report published yesterday, 55% of calories consumed by people ages 1 and older in the U.S. were UPFs. Among youth ages 1-18 years of age, total UPF calories jumped to nearly 62%, and among adults ages 19 and older total UPF calories was 53%. In addition, families with lower mean income had a higher percentage of UPFs consumed per day: 54.7% for the lowest income group vs. 50.4% for highest income group.[1]

UPFs are relatively inexpensive, convenient for use and aggressively marketed, particularly toward youth and under-resourced communities, often displacing healthier alternatives. This shift resulted in lowering the overall nutritional quality of typical eating patterns in the U.S. and is misaligned with the American Heart Association’s dietary guidance.

This new Science Advisory reinforces current dietary guidelines from the American Heart Association to:

• Reduce the intake of most UPFs, especially those high in saturated fat, added sugars and sodium, and those that contribute to excessive calories; and
• Replace UPF consumption with healthier options like vegetables, fruits, whole grains, beans, nuts, seeds and lean proteins.

How are ultraprocessed foods classified?

UPFs are multi-ingredient foods containing additives (likely intended to enhance shelf life, appearance, flavor or texture) widely used in industrial food production and not commonly used in home cooking. Human diets are increasingly including more industrially processed foods, leading to various systems for classifying foods based on processing criteria. Multiple food classification systems exist currently; this Science Advisory focuses on the Nova framework for food classification. The Nova system, the most widely used, is based on the nature, extent and purpose of the food’s industrial food processing. However, the Nova categorization does not consider the nutritional quality of foods. Certain types of industrial food processing are beneficial for preservation and safety, and/or lowering cost, such as techniques that extend shelf life, control microbial growth, mitigate chemical toxicants, preserve functional, nutritional and sensory (taste) qualities, and reduce food loss and waste.

Efforts to understand UPFs are hindered by differing definitions, limitations in dietary assessment tools and food composition databases, which often lack detailed information on additives and processing methods. Currently, U.S. manufacturers are not required to disclose processing techniques or cosmetic additive quantities, which contributes to the variability in risk estimates and confusion for consumers.

The writing group cautions that an overreliance on the degree of processing as a proxy for healthfulness of foods could sway the food industry to reduce or remove the markers of ultraprocessing from foods that are high in saturated fats, added sugars and sodium and promote them as “better-for-you alternatives.”

Health Impact of UPFs

A meta-analysis of prospective studies cited in the advisory found a dose-response relationship between UPF consumption and cardiovascular events, such as heart attack, transient ischemic attack and stroke, Type 2 diabetes, obesity and all-cause mortality. High versus low UPF intake was linked to a 25%-58% higher risk of cardiometabolic outcomes and a 21%-66% higher risk of mortality. More research is needed to understand the appropriate thresholds for daily consumption of UPFs—what a safe amount is and the incremental risks of eating more UPFs.

Research has also found that there may be underlying mechanisms that affect eating behaviors and obesity for some people, and that UPFs may promote obesity. UPFs frequently contain combinations of ingredients and additives that are uncommon in whole foods to enhance palatability and reduce cost, and these may influence reward-related brain activity. For example, ingredients like artificial flavors may mimic sweetness without sugar, and this disruption in flavor-nutrient relationships often leads to irregular eating habits, and results in weight gain.

Opportunities for research and policy

Balancing multiple priorities, including the practical need for a nutrient-dense, affordable food supply, current evidence supports the following key research and policy changes to improve public health and reduce risks related to UPFs:

1. Introduce approaches for individuals, food manufacturers and the retail industry that help shift eating patterns away from UPFs high in saturated fat, added sugars and sodium toward patterns high in vegetables, fruits, nuts, seeds, legumes, whole grains, nontropical liquid plant oils, fish and seafood, low-fat-low-sugar dairy, and, if personally desired, lean poultry and meats.
2. Enact multipronged policy and systems-change strategies (e.g., front-of-package labels) to help reduce intake of HFSS products.
3. Increase research funding to explore critical questions about UPFs: To what extent is it the ultraprocessing itself that makes a UPF unhealthy vs. the fact that ultraprocessed foods tend to have unhealthy ingredients? Most UPFs overlap with HFSS foods that are already targeted for cardiometabolic risk reduction, so a better understanding of the root causes of UPFs’ link to poor health is fundamental to effective reduction strategies.
4. Enhance ongoing efforts to improve food additive science, including streamlined and efficient evaluation and regulation of food additives.

”More research is needed to better understand the mechanisms of how UPFs impact health. In the meantime, the Association continues to urge people to cut back on the most harmful UPFs that are high in saturated fats, added sugars and sodium, and excessive calories and instead follow a diet rich in vegetables, fruits, nuts, seeds and whole grains, low-fat-low-sugar dairy, and lean proteins like fish, seafood or poultry—for better short- and long-term health,” said Vadiveloo.

This Science Advisory was prepared by the volunteer writing group on behalf of the American Heart Association Council on Lifestyle and Cardiometabolic Health; the Council on Cardiovascular and Stroke Nursing; the Council on Clinical Cardiology; the Council on Genomic and Precision Medicine; and the Stroke Council. American Heart Association scientific statements and advisories promote greater awareness about cardiovascular diseases and stroke issues and help facilitate informed health care decisions. Scientific statements outline what is currently known about a topic and what areas need additional research. While scientific statements inform the development of guidelines, they do not make treatment recommendations. American Heart Association guidelines provide the Association’s official clinical practice recommendations.

Additional co-authors and members of the writing group include Vice Chair Christopher D. Gardner, Ph.D., FAHA; Sara N. Bleich, Ph.D.; Neha Khandpur, Sc.D.; Alice H. Lichtenstein, D.Sc., FAHA; Jennifer J. Otten, Ph.D., R.D.; Casey M. Rebholz, Ph.D., M.S., M.P.H., FAHA; Chelsea R. Singleton, Ph.D., M.P.H.; Miriam B. Vos, M.D., M.S.P.H., FAHA; and Selina Wang, Ph.D. Authors’ disclosures are listed in the manuscript.

The Association receives more than 85% of its revenue from sources other than corporations. These sources include contributions from individuals, foundations and estates, as well as investment earnings and revenue from the sale of our educational materials. Corporations (including pharmaceutical, device manufacturers and other companies) also make donations to the Association. The Association has strict policies to prevent any donations from influencing its science content and policy positions. Overall financial information is available here.

Additional Resources:

• Available multimedia is on right column of release link including a video interview with Christopher D. Gardner, Ph.D., FAHA, the Vice Chair of the writing group on the Science Advisory.
• After 5 a.m. ET, August 8, 2025, view the manuscript online.
• American Heart Association Scientific Statement: Popular Dietary Patterns: Alignment with American Heart Association 2021 Dietary Guidance (April 2023)
• American Heart Association Scientific Statement: 2021 Dietary Guidance to Improve Cardiovascular Health (Nov. 2021)
• American Heart Association Report: 2025 Heart Disease and Stroke Statistics: A Report of U.S. and Global Data (Jan. 2025)
• American Heart Association health information: Can processed foods be part of a healthy diet?
• Follow AHA/ASA news on X @HeartNews
• Follow news from the AHA’s flagship journal Circulation @CircAHA 

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About the American Heart Association

The American Heart Association is a relentless force for a world of longer, healthier lives. Dedicated to ensuring equitable health in all communities, the organization has been a leading source of health information for more than one hundred years. Supported by more than 35 million volunteers globally, we fund groundbreaking research, advocate for the public’s health, and provide critical resources to save and improve lives affected by cardiovascular disease and stroke. By driving breakthroughs and implementing proven solutions in science, policy, and care, we work tirelessly to advance health and transform lives every day. Connect with us on heart.org, Facebook, X or by calling 1-800-AHA-USA1.

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[1] Ultra-processed Food Consumption in Youth and Adults: United States, August 2021-August 2023. National Center for Health Statistics. National Health and Nutrition Examination Survey. Data Brief No. 536. August 2025. U.S. Centers for Disease Control and Prevention https://www.cdc.gov/nchs/products/index.htm.

 

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Journal

Circulation

DOI

10.1161/CIR.0000000000001365 

Article Title

Ultraprocessed Foods and Their Association With Cardiometabolic Health: Evidence, Gaps, and Opportunities: A Science Advisory From the American Heart Association

 

Rising carbon dioxide level disrupts insects' ability to choose optimal egg-laying sites

Moths rely on carbon dioxide emissions from host plants to identify optimal egg-laying sites. As atmospheric CO₂ levels rise, this natural signal becomes confused, leading the moths to make maladaptive choices that threaten the survival of their offspri

Science China Press

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image: 

Schematic representation of the sensory mechanisms underlying CO2-induced oviposition behavior in Helicoverpa armigera. Abbreviations: LPO, labial pit organ; LPOG, labial pit organ glomerulus; CB, central body; Ca, calyx of the mushroom body; LH, lateral horn; AN, antennal nerve.

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Credit: ©Science China Press

Climate change is rapidly reshaping ecosystems across the globe, and new research has identified a previously unrecognized consequence: disrupted insect reproductive behavior. A recent study published in National Science Review reveals that rising atmospheric carbon dioxide (CO2) levels are interfering with how agricultural pests choose egg-laying sites—posing significant risks to biodiversity, food security, and pest management strategies.

  Insects, despite their adaptability, are especially sensitive to shifts in environmental conditions. As global temperatures rise and atmospheric composition changes, their behavior is changing in ways that ripple through ecosystems. CO2, the primary greenhouse gas driving global warming, has increased from 278 ppm in 1750 to approximately 420 ppm in 2023. Emerging evidence shows that elevated CO2 levels—alongside pollutants such as ozone and nitrogen oxides—are disrupting insects' ability to detect chemical cues essential for reproduction and survival. Until now, the underlying mechanisms remained poorly understood.

  Now, an international collaborative study by scientists from the Chinese Academy of Agricultural Sciences, the Norwegian University of Science and Technology, and the Max Planck Institute has provided key insights. Focusing on Helicoverpa armigera—the cotton bollworm, a major global crop pest—the team discovered that females normally use plant-emitted CO2 to locate suitable egg-laying sites, particularly favoring younger leaves that emit higher CO2 gradients. These sites are critical for larval survival and development. However, under elevated atmospheric CO2 concentrations, this behavior is significantly disrupted. The study found that moths’ CO2-sensing ability is impaired, causing them to lay eggs in less suitable locations. “This disruption is akin to confusing a key olfactory cue from a GPS system,” said Prof. Guirong Wang, lead author of the study. “Without accurate CO2 signals, the insects struggle to find ideal egg-laying sites, which could affect pest population dynamics and agricultural damage.”

  To understand the biological basis for this disruption, the researchers identified three CO2-detecting gustatory receptors—HarmGR1, HarmGR2, and HarmGR3. When any of these receptors were genetically deleted, the moths’ ability to detect CO2 impaired, resulting in disoriented egg-laying behavior.

  The study’s simulations paint a worrying future: if atmospheric CO2 reaches 1000 ppm by 2100, moths’ preference for optimal egg-laying sites could drop by up to 75%. This would likely reduce larval survival, destabilize pest populations, and alter biodiversity and ecological balance.

  Beyond the alarming ecological implications, these findings point to new opportunities. “By targeting the CO2 receptors, we can explore novel, eco-friendly pest control strategies,” said Dr. Qiuyan Cheng, first author of the paper. One promising approach is RNA interference (RNAi), a gene-silencing technique already used in mosquito control, which could disrupt pest reproduction without harmful chemicals.

  The study adds to growing evidence that climate change is influencing insect behavior in complex and unexpected ways—not only through temperature shifts but also via direct changes to atmospheric chemistry. With global CO2 levels on track to exceed 1000 ppm by the end of the century, researchers stress the urgent need for both emissions reductions and innovative agricultural adaptation.

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Journal

National Science Review

DOI

10.1093/nsr/nwaf270 

Method of Research

Experimental study

 

Decoding sweetpotato DNA: New research reveals surprising ancestry

Boyce Thompson Institute

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‘Tanzania’ variety of sweetpotato

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Credit: Benard Yada at National Crops Resources Research Institute (NaCRRI), Uganda

The sweetpotato feeds millions worldwide, especially in sub-Saharan Africa, where its natural resilience to climate extremes makes it crucial for food security. But this humble root vegetable has guarded its genetic secrets for decades. Now, scientists have finally decoded its complex genome, revealing an intricate origin story and providing powerful tools to help improve this vital crop.

Sweetpotato DNA is extraordinarily complex. While humans have two sets of chromosomes, one from each parent, sweetpotatoes have six. This condition, called hexaploidy, made deciphering their genetic code like trying to reconstruct six different, yet similar, sets of encyclopedias that have been shuffled together.

A team led by Professor Zhangjun Fei at the Boyce Thompson Institute achieved a significant breakthrough, as reported in Nature Plants. Using cutting-edge DNA sequencing, along with other advanced techniques, they created the first complete genetic makeup of 'Tanzania'—a sweetpotato variety prized in Africa for its disease resistance and high dry matter content.

The central challenge was to untangle the plant's 90 chromosomes and organize them into their six original sets, called haplotypes. The team succeeded in fully separating, or 'phasing,' this complex genetic puzzle, something that had never been achieved before.

"Having this complete, phased genome gives us an unprecedented level of clarity," explains Fei. "It allows us to read the sweetpotato's genetic story with incredible detail."

The research revealed surprising complexity. The sweetpotato genome is a mosaic assembled from multiple wild ancestors, some of which have yet to be identified. About one-third comes from Ipomoea aequatoriensis, a wild species found in Ecuador that appears to be a direct descendant of a sweetpotato progenitor. Another significant portion resembles a wild Central American species called Ipomoea batatas 4x, though the actual donor may still remain undiscovered in the wild.

"Unlike what we see in wheat, where ancestral contributions can be found in distinct genome sections," says Shan Wu, the study's first author, "in sweetpotato, the ancestral sequences are intertwined on the same chromosomes, creating a unique genomic architecture."

This intertwined genetic heritage means that sweetpotato can be tentatively classified as a "segmental allopolyploid"—essentially a hybrid that arose from different species but behaves genetically as if it came from a single one. This genomic merging and recombination gives sweetpotato its remarkable adaptability and disease resistance, traits crucial for subsistence farmers worldwide.

“The sweetpotato’s six sets of chromosomes also contribute to its enhanced resilience,” adds Fei. “With multiple versions of important genes, the plant can maintain backup copies that help it survive drought, resist pests, and adapt to different environments—a feature known as polyploid buffering.”

However, achieving a full understanding of sweetpotato's genetic potential will require decoding multiple varieties from different regions, as each may carry unique genetic features that have been lost in others.

The work by Fei and his team represents more than just an academic milestone. Equipped with a clearer understanding of sweetpotato’s complex genetics, breeders can now more efficiently identify genes responsible for key traits like yield, nutritional content, and resistance to drought and disease. This precision could accelerate the development of improved varieties.

Beyond sweetpotato, this research demonstrates how modern genomic tools can help decode other complex genomes. Many important crops, including wheat, cotton, and banana, have multiple sets of chromosomes.

As climates shift and pest and disease pressures increase, understanding these genetic puzzles is critical for breeding resilient crops and addressing challenges in food security.

About the Boyce Thompson Institute (BTI)
Founded in 1924 and located in Ithaca, New York, BTI is at the forefront of plant science research. Our mission is to advance, communicate, and leverage pioneering discoveries in plant sciences to develop sustainable and resilient agriculture, improve food security, protect the environment, and enhance human health. As an independent nonprofit research institute, we are committed to inspiring and training the next generation of scientific leaders. Learn more at BTIscience.org.

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Journal

Nature Plants

DOI

10.1038/s41477-025-02079-6 

Method of Research

Experimental study

Subject of Research

Cells

Article Title

Phased chromosome-level assembly provides insight into the genome architecture of hexaploid sweetpotato

Article Publication Date

8-Aug-2025

 

Towards better earthquake risk assessment with machine learning

Researchers utilize geological survey data and machine learning algorithms for accurately predicting liquefaction risk in earthquake-prone areas.

Shibaura Institute of Technology

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By including geographic coordinates, elevation, and stratigraphic information as input variables, researchers from Shibaura Institute of Technology have compared three different ML algorithms (RF, ANN, and SVM) for predicting the bearing layer depth. Compared to ANN and SVM, RF showed significantly higher prediction accuracy for the bearing layer depth.

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Credit: Shinya Inazumi from Shibaura Institute of Technology Source Link: https://www.mdpi.com/2504-4990/7/3/69

“A building is only as strong as its foundation” is a common adage to signify the importance of having a stable and solid base to build upon. The type and design of foundation are important for ensuring the structural safety of a building. Among several factors that can affect the design and laying of a foundation, bearing stratum depth, namely the depth at which the underlying layer of soil or rock has adequate strength to support a foundation, is one of the most crucial. This is because in regions that are prone to earthquakes or landslides, the bearing stratum depth, also known as bearing layer depth, serves as an indirect indicator of soil liquefaction risk, or the risk of soil collapsing and losing its stiffness, and behaving like a liquid. Understandably, an accurate estimation of the bearing layer depth is key to designing robust foundations, limiting soil liquefaction risks, and mitigating soil-related disasters.

Traditional methods to assess the bearing layer depth, notably the standard penetration test (SPT), are generally reliable but involve both time- and labor-intensive processes for obtaining subsurface soil samples and are expensive. A cost-effective alternative is, therefore, imperative.

To address this issue, scientists from Shibaura Institute of Technology (SIT), Japan recently turned their attention to machine learning (ML). A team of researchers led by Professor Shinya Inazumi from the College of Engineering at SIT utilized 942 geological survey records and SPT data from the Tokyo metropolitan area and employed three ML algorithms, random forest (RF), artificial neural network (ANN), and support vector machine (SVM), to predict the bearing layer depth. Their research findings were published in volume 7, Issue 69 of the journal Machine Learning and Knowledge Extraction on July 21, 2025.

“The inspiration for this research stemmed from the pressing challenges in geotechnical engineering within earthquake-vulnerable urban landscapes like Tokyo. As a region with a history of devastating seismic events, such as the 1923 Great Kanto Earthquake, accurate prediction of bearing layer depth is vital.  Through our research study, we hope to empower urban planners and engineers with efficient tools for sustainable development, reducing costs and enhancing safety”, shares Inazumi, explaining the motivation behind the present study.

In their study, the researchers initially trained and optimized the chosen ML models using the SPT dataset. Thereafter, they developed two experimental case scenarios depending on the set of explanatory variables utilized for the assessment. While the first case scenario (Case-1) employed latitude, longitude, and elevation as explanatory variables, the second scenario (Case-2) included stratigraphic classification data, namely information about the underground soil layer, in addition to the other three geographical parameters.

During the comparative evaluation, the researchers found that the RF model consistently outperformed ANN and SVM, particularly in terms of depth prediction accuracy (a mean absolute error of 0.86 m for Case-2 vs. 1.26 m for Case-1) and robustness to noisy data. Moreover, the prediction accuracy of all three models in the Case-2 scenario, which includes stratigraphic classification data as an additional explanatory variable, was markedly improved.

Inspired by their research findings, the researchers went a step further and investigated the impact of spatial data density on prediction performance. To this end, they generated six different data subsets with varying spatial densities: 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 points/km2. They found that the prediction accuracy of RF model in case-2 improved with increasing data density, indicating that spatially denser datasets are valuable for accurate predictions of bearing layer depth.

Overall, the team’s study demonstrates that ML, especially RF, can offer a much-needed alternative to traditional methods for regional disaster risk assessment. Moreover, unlike SPT, ML models are cost-effective and, with further improvements to the computing architecture and integration with advanced real-time platforms, could revolutionize infrastructure planning in seismically active areas, reducing reliance on expensive, localized tests while improving safety and efficiency.

Emphasizing the potential applications of the study, Inazumi concludes, ”Our findings highlight the transformative real-world potential of ML models in geotechnical engineering and urban planning, especially in earthquake-prone regions like Tokyo. By combining ML with existing geological data, stakeholders can optimize site selection for resilient smart cities and other infrastructure projects, such as bridges or subways, with rapid, scalable simulations”.

 

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Reference
DOI: 10.3390/make7030069

 

About Shibaura Institute of Technology (SIT), Japan
Shibaura Institute of Technology (SIT) is a private university with campuses in Tokyo and Saitama. Since the establishment of its predecessor, Tokyo Higher School of Industry and Commerce, in 1927, it has maintained “learning through practice” as its philosophy in the education of engineers. SIT was the only private science and engineering university selected for the Top Global University Project sponsored by the Ministry of Education, Culture, Sports, Science and Technology and had received support from the ministry for 10 years starting from the 2014 academic year. Its motto, “Nurturing engineers who learn from society and contribute to society,” reflects its mission of fostering scientists and engineers who can contribute to the sustainable growth of the world by exposing their over 9,500 students to culturally diverse environments, where they learn to cope, collaborate, and relate with fellow students from around the world.

Website: https://www.shibaura-it.ac.jp/en/

 

About Professor Shinya Inazumi from SIT, Japan
Dr. Shinya Inazumi serves as a professor in the College of Engineering, Shibaura Institute of Technology (SIT), Japan. He received his Ph.D. from Kyoto University. His main research interests include social infrastructure engineering, civil engineering, geo-disaster engineering, geotechnical analysis studies, and artificial intelligence. In addition to leading the Geotechnical Engineering Laboratory at SIT, he has published 320 papers in high-impact factor journals and has also received several prestigious awards for his research excellence.

 

Funding Information
This research received no external funding.

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Journal

Machine Learning and Knowledge Extraction

DOI

10.3390/make7030069 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Prediction of Bearing Layer Depth Using Machine Learning Algorithms and Evaluation of Their Performance

Advanced computing solutions for healthcare

Bentham Science Publishers

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Advanced Computing Solutions for Healthcare, published by Bentham Science, is an authoritative reference that explains how emerging computing technologies are transforming healthcare delivery, diagnostics, and monitoring. Through 22 expertly curated chapters, the book provides a rich overview of innovations such as artificial intelligence, machine learning, IoT, wearable devices, and real-time communication systems like 5G.

Showcasing real-world case studies and advanced hardware solutions like FPGA-based accelerators, the book emphasizes practical implementation in healthcare scenarios merging theoretical concepts with clinical relevance, The book also addresses topics such as neuromorphic computing, federated learning, augmented reality, and deep learning models for disease detection while also providing timely discussions on data privacy, ethical implications, and inclusivity through assistive technologies.

The book is designed for a multidisciplinary audience including computer scientists, healthcare professionals, biomedical engineers, and educators. It offers a forward-looking perspective on how technology continues to reshape patient care and health systems.

For review copies, interviews, or media inquiries, please contact Bentham Science Publishers.

Access the book here  http://bit.ly/4osSKfv

About the Editors:

 Dr. Sivakumar is a researcher and educator with a Ph.D. in Information and Communication Engineering. His work focuses on bio-signal processing, VLSI, and wireless body sensor networks. He is a senior IEEE member and an experienced academic speaker.

Prof. Dr. Shamala K. Subramaniam is the Dean of the Faculty of Computer Science and Information Technology at Universiti Putra Malaysia. With over 20 years of experience, she specializes in distributed computing and plays a key role in national sports technology initiatives.

Dr. Prakasam P., Professor at VIT, Vellore, is known for his contributions in signal processing and wireless communication. He has authored over 150 publications and holds multiple patents. He serves on editorial boards for major international journals.

Dr. Ali Safaa Sadiq is Associate Professor of Cybersecurity at Nottingham Trent University and leads several industry-funded projects. He specializes in AI, IoT, and cybersecurity and has founded TYMLO Technology Ltd.

Dr. B. Prabadevi is an Associate Professor at VIT, Vellore, with rese

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DOI

10.2174/97898152741341250101