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International Conference on engineering Technology and their Applications (IICETA)

作者： Mohammad Alslatie Hiam Alquran Wan Azani Mustafa Isam Abu-Qasmieh Ali Mohammad Alqudah Ahmed Alkhayyat King Hussein Medical Center Royal Jordanian Medical Service The Institute of Biomedical Technology Amman Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Department of Electrical and Computer Engineering Biomedical Engineering Program University of Manitoba Canada Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images.

关键词： Image segmentation Visualization Transfer learning Feature extraction Convolutional neural networks Artificial intelligence X-ray imaging

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Marine macro-litter sources and ecological impact: a review

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Environmental Chemistry Letters 2024年第3期22卷 1257-1273页

作者： Le, Van-Giang Nguyen, Hoang-Lam Nguyen, Minh-Ky Lin, Chitsan Hung, Nguyen T. Quang Khedulkar, Akhil P. Hue, Nguyen K. Trang, Phung T. Thu Mungray, Arvind Kumar Nguyen, D. Duc Central Institute for Natural Resources and Environmental Studies Vietnam National University Hanoi111000 Viet Nam Department of Civil Engineering McGill University MontrealQC Canada Faculty of Environment and Natural Resources Nong Lam University Hamlet 6 Linh Trung Ward Thu Duc City Ho Chi Minh City700000 Viet Nam Ph.D. Program in Maritime Science and Technology National Kaohsiung University of Science and Technology Kaohsiung81157 Taiwan Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung81157 Taiwan Department of Biomedical Engineering and Environmental Science National Tsing Hua University Hsinchu30013 Taiwan Environmental Research Center Institute of Hydrology and Meteorology Science and Climate Change Hanoi111000 Viet Nam Department of Chemical Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India Department of Civil and Energy System Engineering Kyonggi University Suwon16227 Korea Republic of Institute of Applied and Sustainable Technology Nguyen Tat Thanh University Ho Chi Minh City700000 Viet Nam

Marine pollution by various debris is rising in the context of increasing urbanization, industrialization and worldwide trade. Here, we review marine macro-litter with focus on sources, abundance, distribution, and impact on aquatic wildlife, human health, and the economy. We observe that about 75–80% of marine litter originates from land-based sources, including poorly managed landfills and improper disposal. Macro-litter ingestion by marine life is of growing concern, in particular concerning the presence of plastics and associated pollutants, e.g., bisphenol A and phthalates, in fishes. These pollutants are accumulated and transferred to human via seafood. Marine plastic pollution induces a substantial rise of economic costs due to the decline of ecosystem services. Mitigation of marine plastic pollution should be done by following the ten Rs principles of refuse, rethink, reduce, re-use, repair, refurbish, remanufacture, repurpose, recycle, and recover. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

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Porous silicon on paper sensors: Factors affecting biosensing performance 17

Porous silicon on paper sensors: Factors affecting biosensin...

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Frontiers in Biological Detection: From Nanosensors to Systems XVII 2025

作者： An, Huijin Laibinis, Paul E. Locke, Andrea K. Weiss, Sharon M. Interdisciplinary Material Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemical and Biomolecular Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemistry Vanderbilt University NashvilleTN37235 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37235 United States

ISBN: (纸本)9781510684249

A paper-based biosensor integrating a functionalized porous silicon (PSi) membrane as the active sensing element for quantifiable protein detection has been developed. For similar short-time exposures to an analyte, improved molecular transport in PSi membranes when on paper leads to larger signal changes compared to traditional PSi films that remain on a silicon substrate. In this work, we discuss controlling the incubation time of the analyte and the overall testing time of the sensor by incorporating different combinations of wicking and absorbent paper beneath the PSi membrane. With this control, the PSi-on-paper sensor platform has the potential to serve as an effective low-cost rapid diagnostic test with highly sensitive, quantitative readout for a wide range of analytes. © 2025 SPIE.

关键词： Porous silicon

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Synergistic Polydopamine/Graphene Oxide Hybrids: Elevating Electrochemical Performance

ACS Electrochemistry

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ACS Electrochemistry 2025年第4期1卷 453-464页

作者： Busra Ozlu Saimon Moraes Silva Simon E. Moulton Bong Sup Shim Program in Biomedical Science & Engineering Inha University 100 Inha-ro Michuhol-gu Incheon 22212 Republic of Korea Department of Biochemistry and Chemistry La Trobe Institute for Molecular Science The Biomedical and Environmental Sensor Technology (BEST) Research Centre La Trobe University Melbourne Victoria 3086 Australia ARC Centre of Excellence for Electromaterials Science Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn Victoria 3122 Australia；Aikenhead Centre for Medical Discovery St Vincent’s Hospital Melbourne Melbourne Victoria 3065 Australia；Iverson Health Innovation Research Institute Swinburne University of Technology Melbourne Victoria 3122 Australia Program in Biomedical Science & Engineering Inha University 100 Inha-ro Michuhol-gu Incheon 22212 Republic of Korea；Department of Chemical Engineering Inha University 100 Inha-ro Michuhol-gu Incheon 22212 Republic of Korea

Melanin, a natural biopolymer with a conjugated backbone, stands out as a promising candidate for bioelectronics owing to its redox activity, metal ion chelation, hydration-dependent electrical response, wet adhesion, potential electronic transport, anti-inflammatory properties, and excellent thermal stability. In this study, we explore the impact of hybridizing liquid crystalline graphene oxide (LCGO) into the conductive melanin-like polymer polydopamine (PDA) on the morphology, stability, and synergistic electrochemical performance. Our findings reveal that the gold electrode coated with PDA:LCGO composites exhibit more than two orders of magnitude lower impedance and more than 100 times higher charge storage capacity compared to both bare gold electrodes and those coated with pure PDA. Furthermore, we demonstrate that the morphology and the electrode performance could be tuned by adjusting the synthesis parameters in the PDA:LCGO electrochemical deposition. This PDA:LCGO composite, with its superior electrochemical performance, holds promise for diverse bioelectronics applications ranging from biosensors to implantable bionic interfaces.

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Comprehensive climatic variability analysis and its significance on flood occurrences in the Middle Ganga Plain, India

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Discover Geoscience 2024年第1期2卷 1-26页

作者： Arora, Aman Siddiqui, Masood A. Geesupalli, Purna Durga Mishra, Varun Narayan Pandey, Manish Université Gustave Eiffel GERS-LEE Bouguenais France Department of Geography Faculty of Natural Sciences Jamia Millia Islamia New Delhi India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai India Amity Institute of Geoinformatics & Remote Sensing (AIGIRS) Amity University Noida India Marwadi University Research Centre Marwadi University Rajkot India Department of Civil Engineering Faculty of Engineering & Technology Marwadi University Rajkot India

Climate variability significantly affects rainfall, air temperature, relative humidity, and solar radiation. In subtropical regions like India, intense monsoon rainfall primarily causes floods. This study aims to analyze the climatic factors contributing to flood occurrence in the Middle Ganga Plain (MGP) by examining meteorological parameters through various analyses, including monthly and yearly trends, spatial variability, exceedance probability, historical highest intensity rainfall, and wind trajectory analysis. By establishing a strong link between climatic variables and flood events, this research provides valuable insights into the impact of climate variability on flood patterns in the MGP. Results indicate that monsoon rainfall peaks from July to October, correlating with major flood events. During these months, high relative humidity increases the likelihood of rainfall. Spatial analysis shows higher rainfall in the northern Middle Ganga Plain (MGP), which aligns with observed flood patterns. Trend detection tests reveal increasing annual rainfall and rising average temperatures. Geostatistical analysis identifies the western and eastern MGP as areas prone to extreme rainfall. Exceedance probability analysis confirms the occurrence of substantial monsoon rainfall events. Wind trajectory analysis uncovers seasonal shifts that lead to low-pressure systems and heavy monsoon rainfall. The study establishes a strong link between climatic variables and flood events in the MGP region.

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Characterization of Electroencephalography (EEG) Responses to Musical Stimuli

Characterization of Electroencephalography (EEG) Responses t...

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biomedical engineering and Bioengineering (CI-IB&BI), IEEE International Congress of

作者： Angélica María Rojas Tocora Oliver Müller Alvaro D. Orjuela-Cañón Victor A. Ortíz Biomedical Engineering Program Universidad del Rosario - Escuela Colombiana de Ingeniería Julio Garavito Bogota D.C. Colombia School of Medicine and Health Sciences Universidad del Rosario Bogota D.C. Colombia Engineering Faculty Unversidad Tecnologica de pereira Pereira Colombia

ISBN: (数字)9798331532352

ISBN: (纸本)9798331532369

This study investigates the effects of different musical genres on electroencephalographic (EEG) responses in healthy subjects. The EEG technique was analyzed for five different music styles, such as classical, rock, jazz, vallenato, and urban, and in this way, to determine the influence in brainwave patterns. For instance, classical music was linked to alpha brainwave patterns associated with relaxation, while genres like rock and reggaeton are associated with beta patterns, indicating of concentration and mental activity. The study also explores how intrinsic factors such as musical lyrics and individual differences like musical training and preferences impact brain activity. Findings suggest that music can evoke strong emotional responses and activate different brain regions depending on the genre and individual factors. This complexity underscores the importance of considering personal and cultural aspects in understanding the neural dynamics of musical experiences.

关键词： Training Brain Music Rocks Particle measurements Electroencephalography Frequency measurement Complexity theory Cultural differences biomedical engineering

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Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network

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Computers, Materials & Continua 2021年第11期69卷 2031-2044页

作者： K.Muthumayil S.Manikandan S.Srinivasan JoséEscorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Information Technology K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Biomedical Engineering Saveetha School of EngineeringSIMATSChennai602105India Electronic and Telecommunicacions Program Universidad Autónoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

White Blood Cell(WBC)cancer or leukemia is one of the serious cancers that threaten the existence of human *** spite of its prevalence and serious consequences,it is mostly diagnosed through manual *** risks of inappropriate,sub-standard and wrong or biased diagnosis are high in manual ***,there is a need exists for automatic diagnosis and classification method that can replace the manual *** is mainly classified into acute and chronic *** current research work proposed a computer-based application to classify the *** the feature extraction stage,we use excellent physical properties to improve the diagnostic system’s accuracy,based on Enhanced Color Co-Occurrence *** study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network(EVNN)*** proposed method achieved optimum accuracy in detection and classification of leukemia from WBC ***,the study results establish the superiority of the proposed method in automated diagnosis of *** values achieved by the proposed method in terms of sensitivity,specificity,accuracy,and error rate were 97.8%,89.9%,76.6%,and 2.2%,***,the system could predict the disease in prior through images,and the probabilities of disease detection are also highly optimistic.

关键词： White blood cells enhanced virtual neural networking segmentation feature extraction chronic lymphocytic leukemia

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Identification of D- and L-Phenylalanine Enantiomeric Mixtures by Employing Deep Neural Network Models

SSRN

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SSRN 2023年

作者： Nigdelioglu, Ebru Toprak, Elif Guney Akkurt, Melike Erol Barkana, Duygun Kazanci, Murat Uyaver, Sahin Calik, Nurullah Istanbul Medeniyet University Nanoscience and Nanoengineering Program Graduate School Istanbul Medeniyet University Istanbul34700 Turkey Istanbul Medeniyet University School of Engineering and Natural Sciences Department of Biomedical Engineering Istanbul Turkey Istanbul34700 Turkey Yeditepe University Faculty of Engineering Department of Electrical and Electronics Engineering Kayışdagı Istanbul34755 Turkey Marmara University Faculty of Technology Department of Computer Engineering Maltepe Istanbul34854 Turkey

Phenylalanine is an aromatic essential amino acid that exhibits the tendency to self-aggregate into fibrillar structures in its enantiomerically pure form. This observation was indicated as the underlying mechanism of phenylketonuria, which is a genetic condition associated with various neurological, physical, and developmental issues, characterized with phenylalanine buildup in the brain. The presence of D-phenylalanine was demonstrated previously to inhibit the formation of fibrils by L-phenlyalanine, indicating its potential use in phenylketonuria treatment. In this study, several combinations of D and L-phenylalanine were examined with the help of state-of-the-art deep learning methods for their fibril forming capacity, demonstrating the usefulness and accuracy of deep learning methods in distinguishing between different self-assembled structures. © 2023, The Authors. All rights reserved.

关键词： Self assembly

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VRGrip: Developing a Grip Strength Training Platform Integrating a Wireless E-Textile Forearm Band with an Adaptive 3D VR Game Environment

VRGrip: Developing a Grip Strength Training Platform Integra...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Lohith Chatragadda Aiden Fletcher Matthew J. Delmonico Kunal Mankodiya Dhaval Solanki Interdisciplinary Neuroscience Program University of Rhode Island Kingston RI USA Department of Kinesiology University of Rhode Island Kingston RI USA Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

Neurodegenerative disease is a growing global problem. Many of these diseases such as Parkinson's disease can cause grip strength weakness. In this work, we focused on developing an e-textile based EMG acquisition system interfaced with an adaptive VR game engine for detecting hand squeeze actions. We prototyped and performed preliminary tests on a grip detection system consisting of: 1) an e-textile forearm band (E-band), 2) a portable data acquisition module (DAM), and 3) an adaptive 3D VR game engine. Digital health solutions such as our VRGrip system widen the scope of in-home rehabilitation and assessment, and we aimed to make the first step in creating a system for grip strength rehabilitation. To validate our concept, we performed preliminary tests. These tests revealed that the Bluetooth communication protocol between DAM and the game engine was reliable, that the adaptive game engine successfully tailors the game's difficulty level to the user's performance, and that the system was reliable at detecting hand squeezes (F=0.96).

关键词： Wireless communication Training Adaptive systems Three-dimensional displays Protocols Dams Games

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Novel Ensemble Machine Learning Paradigms for the Prediction of Antioxidant Activity of Bryophyllum pinnatum (Lam.) Oken

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Proceedings of the National Academy of Sciences India Section B - Biological Sciences 2024年第4期94卷 779-791页

作者： Abdulrahman, Mahmoud Dogara Usman, A.G. Ozsahin, Dilber Uzun Ibrahim, Abdullahi Umar Abba, S.I. Biology Education Department Tishk International University Erbil Iraq Department of Analytical Chemistry Faculty of Pharmacy Near East University Nicosia Turkey Medical Diagnostic Imaging Department College of Health Science University of Sharjah Sharjah United Arab Emirates Operational Research Centre in Healthcare Near East University Nicosia Turkey Department of Biomedical Engineering Near East University Nicosia Mersin 10 Turkey Interdisciplinary Research Center for Membrane and Water Security King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

For herbal and modern drug research, secondary metabolites produced by plants are a valuable source of pharmaceutically active compounds. Northern Nigeria relies heavily on plants as a source of medication. Succulent perennial herb Bryophyllum pinnatum is endemic to Africa and Asia. The current study investigates the in vitro pharmacological potential of the B. pinnatum with the use of predictive data intelligence algorithms to unveil the inhibitory properties of the plant. In vitro biological evaluation of B. pinnatum was carried out. This study explored the utilization of four different data intelligence models composing of two linear models;multi-linear regression (MLR) and step-wise linear regression (SWLR) as well two other nonlinear artificial intelligence (AI)-based models;adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM). Subsequently, four ensemble machine learning approaches including adaptive neuro-fuzzy inference system ensemble (ANFIS-E), support vector machine ensemble (SVM-E), simple average ensemble (SAE) and Gaussian process regression ensemble (GPR-E) were proposed to increase the single model’s accuracy. The ensemble techniques depict an average fitness of 0.999 in both training and testing stages. Moreover, recent and state-of-the-art visualization illustrations were also used for easy grasping and assimilations of the findings. The data-driven approach obtained results showed the AI-based model’s potentiality including SVM and ANFIS in outperforming the classical linear models;SWLR and MLR according to the study's performance metrics. Furthermore, based on the DC assessment criteria investigated in the current work, ensemble machine learning paradigms increased the performance skills of the single algorithms by up to 27% in the testing phase. © The Author(s), under exclusive licence to The National Academy of Sciences, India 2024.

关键词： Antioxidants Chemometrics Ensemble machine learning Herbal Medicinal plants

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