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Nondestructive assessment of watermelon flesh color by laser vibrometry

engineering in Agriculture, Environment and Food 2015年第1期8卷 33-37页

作者： Abbaszadeh, Rouzbeh Rajabipour, Ali Mahjoob, Mohammad Mikani, Mohaddeseh Ahmadi, Hojjat Delshad, Mojtaba Agricultural Institute Iranian Research Organization for Science and Technology Iran Faculty of Agricultural Engineering and Technology Tehran University Karaj Iran Faculty of Mechanical Engineering Tehran University Tehran Iran Faculty of Agricultural Sciences and Engineering Tehran University Karaj Iran

Flesh color of watermelon is often used as a major postharvest criterion by consumers to assess fruit quality. In this paper a non-destructive method was studied to determine watermelon (Crimson Sweet) internal color. Responses of samples to excitation vibrations were detected using laser Doppler vibrometry (LDV) technology. Phase shift between input and output vibrations were extracted overall frequency range. The frequencies of phase shifts which had the highest correlation with Hunter color values were selected. Regression models were developed to predict texture color using obtained results. The correlation coefficients of the calibration and cross validation models were 0.9 and 0.86 for a&***1;respectively. Those of L&***;were 0.91 and 0.87. Meanwhile, cut watermelons were sensory evaluated by subjective scoring of color. Comparisons showed that correlations between objective measurements and consumer opinions were significant at the 0.01 level. This study demonstrated feasibility of laser vibrometery for predicting flesh color of watermelon. © 2014 Asian Agricultural and Biological engineering Association.

关键词： Color

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Electrochemical genosensors on-a-chip: Applications in early diagnosis of pathogens

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Sensors and Actuators Reports 2025年 9卷

作者： A. Dehghan M.J. Kiani A. Gholizadeh J. Aminizadeh A. Rahi I. Zare E. Pishbin H. Heli School of Mechanical Engineering Iran University of Science and Technology Tehran Iran Center of Excellence in Energy Conversion (CEEC) School of Mechanical Engineering Sharif University of Technology Tehran Iran Pathology and Stem cell Research Center Kerman University of Medical Sciences Kerman Iran Research and Development Department Sina Medical Biochemistry Technologies Co. Ltd. Shiraz *** Iran Bio-microfluidics Laboratory Department of Electrical Engineering and Information Technology Iranian Research Organization for Science and Technology Tehran Iran Nanomedicine and Nanobiology Research Center Shiraz University of Medical Sciences Shiraz Iran

Electrochemical genosensors have emerged as a powerful tool for the early diagnosis of pathogens, offering advantages such as high sensitivity, rapid response times, low cost, and easy adaptability for point-of-care applications. This review highlights recent advancements in CRISPR-Cas-integrated electrochemical systems, novel nanomaterial architectures, and label-free detection mechanisms. Key innovations include anisotropic gold nanostructures, MXene composites with exceptional conductivity, and poly(ortho-aminophenol) films, which enable attomolar detection limits for pathogens such as bacteria and parasites. We evaluate DNA hybridization-based approaches, emphasizing innovations in signal amplification strategies such as saltatory rolling circle amplification and self-assembled monolayers, which address specificity challenges in complex matrices. Additionally, we highlight the integration of electrochemical genosensors with microfluidic platforms, including automated sample-to-answer workflows and multiplexed detection architectures, which address traditional laboratory bottlenecks. By cataloging advancements in material science, bioreceptor design, and microfluidic automation, this work provides a comprehensive yet focused resource for researchers advancing the frontiers of rapid, portable pathogen diagnostics. Furthermore, we explore the commercial potential of these technologies, providing insights that could guide the development of highly sensitive, field-deployable biosensors for clinical and environmental applications.

关键词： Electrochemical genosensors Pathogen detection Microfluidics Nucleic acid biosensors Point-of-care diagnostics (missDNA) Base mismatched DNA (BSA) Bovine serum albumin ( C. auris ) Candida auris (E-CRISPR) Cas-integrated electrochemical (CeO 2 NRs) Cerium oxide nanorods (CS) Chitosan (CRISPR) Clustered regularly interspaced short palindromic repeat (cDNA) Complementary DNA (CP) Conducting polymers (Cas) CRISPR-associated (DPV) Differential pulse voltammetry (DSS) DNA super-sandwich (dsDNA) Double-stranded DNA ( E. faecalis ) Enterococcus faecalis (EBV) Epstein-Barr Virus ( E. coli ) Escherichia coli (gDNA) Genomic DNA (Au NPs) Gold nanoparticle (Gr) Graphene (HBV) Hepatitis B virus (HRP) Horseradish peroxidase (SA-HRP) HRP-labeled streptavidin (EIS) Impedance spectroscopy (LOC) Lab-on-a-chip ( L. pneumophila ) Legionella pneumophila (L. infantum) Leishmania infantum ( L. major ) Leishmania major (LOD) Limit of detection (LAMP) Loop-mediated isothermal amplification (MP) Magnetic particle (MB) Methylene blue ( M. tuberculosis ) Mycobacterium tuberculosis (NP) Nanoparticle (ncDNA) Non-complementary DNA (NA) Nucleic acid (PNA) Peptide nucleic acid (pPNA) Peptide nucleic acid probe (POC) Point-of-care (PANI) Poly(aniline) (PPy) Poly(pyrrole) (PCR) Polymerase chain reaction (pDNA) Probe DNA (RCA) Rolling circle amplification ( Salmonella typhimuriumS. typhimurium ) S.almonella typhimuriumalmonella Typhimurium (SRCA) Saltatory rolling circle amplification (SPCE) Screen-printed carbon electrode (SAM) Self-assembled monolayer (ssDNA) Single-stranded DNA (SWV) Square wave voltammetry (S. aureus) Staphylococcus aureus (tDNA) Target DNA (tDNA) Target ssDNA (TB) Toluidine blue ( T. vaginalis ) Trichomonas vaginalis

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction 14

An Attention-Based Model for Clinical Time Series Prediction...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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A Robust Framework for Epileptic Seizure Diagnosis: Utilizing GRU-CNN Architectures in EEG Signal Analysis 20

A Robust Framework for Epileptic Seizure Diagnosis: Utilizin...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Nikoupour, Maryam Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran Iranian Research Organization for Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350383942

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository's epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods. © 2024 IEEE.

关键词： Electroencephalography

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A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction

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Genomics, Proteomics & Bioinformatics 2023年第6期21卷 1266-1285页

作者： Farzaneh Esmaili Mahdi Pourmirzaei Shahin Ramazi Seyedehsamaneh Shojaeilangari Elham Yavari Department of Information Technology Tarbiat Modares UniversityTehran 14115-111Iran Department of Biophysics Faculty of Biological SciencesTarbiat Modares UniversityTehran 14115-111Iran Biomedical Engineering Group Department of Electrical Engineering and Information TechnologyIranian Research Organization for Science and Technology(IROST)Tehran 33535-111Iran

Post-translational modifications(PTMs)have key roles in extending the functional diversity of proteins and,as a result,regulating diverse cellular processes in prokaryotic and eukaryotic *** modification is a vital PTM that occurs in most proteins and plays a significant role in many biological *** in the phosphorylation process lead to multiple diseases,including neurological disorders and *** purpose of this review is to organize this body of knowledge associated with phosphorylation site(p-site)prediction to facilitate future research in this *** first,we comprehensively review all related databases and introduce all steps regarding dataset creation,data preprocessing,and method evaluation in p-site ***,we investigate p-site prediction methods,which are divided into two computational groups:algorithmic and machine learning(ML).Additionally,it is shown that there are basically two main approaches for p-site prediction by ML:conventional and end-to-end deep learning methods,both of which are given an ***,this review introduces the most important feature extraction techniques,which have mostly been used in p-site ***,we create three test sets from new proteins related to the released version of the database of protein post-translational modifications(dbPTM)in 2022 based on general and human *** online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release,distinct from those in the dbPTM 2019 release,reveals their *** other words,the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research pape.

关键词： Phosphorylation Machinelearning Deep learning Post-translational modification Database

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Efficiency Analysis of Convolutional Neural Netwoks in Multiclass Recognition of Skin Lesions via Dermoscopy Images 10

Efficiency Analysis of Convolutional Neural Netwoks in Multi...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Monajemi, Mahsa Shahdi, Seyed Omid Haji Nasiri, Seyed Saeed Shojaedini, Seyed Vahab Department of Biomedical Engineering Qazvin Branch Islamic Azad University Qazvin Iran Biomedical Engineering Department Iranian Research Organization for Science & Technology Tehran Iran

ISBN: (纸本)9798350348873

Although melanoma is considered as one of the deadliest types of cancer, however, detecting this disease in its early stages may lead to saving the patient's life. Biopsy is the most accurate method to diagnose this type of cancer, but due to its invasiveness trying to use non-invasive diagnostic modalities has always been of interest to researchers in this field. Dermoscopy is one of the common non-invasive modalities, in which it is determined whether the patient is healthy or cancerous by analyzing the images captured from the patient's skin. However, there may be several lesions on the skin that, despite their appearance, most of them are not cancerous and even harmless. In this way, recognizing melanoma lesions is the most important challenge in the field of analyzing dermoscopic images especially when it is desired to determine the type of other lesions in this classification process. In this article, the performance of the deep learning paradigm in the classification of multi-type lesions in dermoscopic images is evaluated and the challenges facing deep neural networks in this domain are analyzed. For this purpose, two well-known datasets of dermoscopy images known as ISIC2019 and HAM10000, which have 8 and 7 types of labeled lesions, respectively, were selected and the detection rate of lesions in them was evaluated by applying deep neural networks. The obtained results indicate that although the results obtained from the application of deep learning in the separation of lesions in both of examined datasets are significantly better than the conventional methods, but the resultant accuracy is highly dependent on the volume of data and balance between several classes of lesions. In this way, the use of data augmentation and data balancing methods should be considered as a priority for conducting any research in order to improve the analysis of dermoscopic images by using deep learning. ©2024 IEEE.

关键词： Deep neural networks

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A pulse taking device for Persian medicine based on Convolutional Neural Network 27

A pulse taking device for Persian medicine based on Convolut...

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27th National and 5th International iranian Conference of Biomedical engineering, ICBME 2020

作者： Nafisi, Vahid Reza Ghods, Roshanak Mardi, Mahnaz Iranian Research Organization for Science and Technology Biomedical Engineering Group Tehran Iran Iran University of Medical Sciences Research Institute for Islamic and Complementary Medicine Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9781665419550

In Persian Medicine (PM), measuring the wrist pulse is one of the main method for determining a person's health status and temperament. One problem that can arise is the dependence of the diagnosis on the physician's interpretation of pulse wave features. Perhaps this is one reason why this method has yet to be combined with modern medical methods. This paper addresses this concern and outlines a system for measuring pulse signals based on PM. A system that uses data from a customized device that logs the pulse wave on the wrist was designed and clinically implemented based on PM. Seven Convolutional Neural Networks (CNN) have been used for classification. The pulse wave features of 34 participants was assessed by a specialist based on PM principles. Pulse taking was done on the wrist in the supine position (named Malmas in PM) under the supervision of the physician. Seven CNNs were implemented for participants' classification based on seven PM classes. It appears that the design and construction of a customized device that can measure the pulse waves features according to PM, is possible and can increase the reliability of the diagnostic results based on PM. © 2020 IEEE.

关键词： Convolutional neural networks

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Corrosion behavior of severely plastically deformed Mg and Mg alloys

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Journal of Magnesium and Alloys 2022年第10期10卷 2607-2648页

作者： Ahmad Bahmani Mehrab Lotfpour Milad Taghizadeh Woo-Jin Kim Department of Advanced Materials and Renewable Energy Iranian Research Organization for Science and TechnologyTehran ***Iran Department of Mechanical Engineering University of NevadaRenoNV 89557USA Department of Mechanical Engineering Clemson UniversityClemsonSC 29634USA Department of Materials Science and Engineering Hongik UniversityMapo-guSangsu-dong 72-1Seoul 121-791Republic of Korea

Magnesium(Mg)alloys have several advantages,such as low density,high specific strength and ***,they also suffer weak points,such as high corrosion,low formability and easy ignition,which makes their applications *** studies have been conducted to overcome these disadvantages and further improve the advantages of Mg *** plastic deformation(SPD)is one of the most important techniques and has great effects on the microstructure refinement of Mg alloys and improvements in their strength and *** researchers have studied the corrosion behavior of SPD-processed Mg alloys in recent ***,these studies have reported some controversial effects of SPD on the corrosion of Mg alloys,which makes the research roadmap ***,it is important to review the literature related to the corrosion properties of Mg alloys prepared by SPD and understand the mechanisms controlling their corrosion *** grain refinement by SPD improves the corrosion properties of pure Mg and Mg alloys,but control of the processing conditions is a key factor for achieving this goal because texture,dislocation density,size and morphology of secondary phase also importantly affects the corrosion properties of Mg *** grain size in the fine grain-size range can decrease the corrosion rate due to the increased barrier effect of grain boundaries against corrosion and the formation of a stable passivation layer on the surface of fine *** texture reduces the corrosion rate because basal planes with the highest atomic planar density are more corrosion resistant than other *** dislocation density after SPD deteriorates the corrosion resistance of the interior grains and thus proper annealing after SPD is *** fine and uniform distribution of secondary phase particles during SPD is important to minimize the micro-galvanic corrosion effect and retain small grains during annealing treatment f

关键词： Magnesium alloys Corrosion Severe plastic deformation Grain size Secondary phase Dislocation density

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Detection of myocardial infarction using high-frequency QRS components

Detection of myocardial infarction using high-frequency QRS ...

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作者： Heidarnejad, A. Setarehdan, S.K. Nafisi, V.R. Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Iranian Research Organization for Science and Technology Iran

ISBN: (纸本)9789644634284

This paper presents a method for detecting Myocardial Infarction. one hundred subjects were selected from PTB database in which reason for admission of 50 people is myocardial infarction and 50 people is healthy control. In order to extract high-frequency components, signal averaging was applied to the signal with the aid of cross-correlation function. Averaged signal is then passed through a bandpass filter (150-250 Hz). Seven features were extracted from 7 different leads. A linear classifier was used in order to distinguish between patients and healthy people. The accuracy of the proposed method is 91% with 90% sensitivity and 92% specificity. Simplicity and speed of proposed method makes it useful in practical diagnosis. © 2011 AmirKabir Univ of Tech.

关键词： Bandpass filters

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Enhanced oxidation and overheating resistance of the extruded Mg–Zn–Al–Mn magnesium alloy by Calcium addition

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Journal of Magnesium and Alloys 2023年第4期11卷 1276-1291页

作者： Hanieh Yeganeh Ahmad Bahmani Mehrab Lotfpour Mehdi Malekan Masoud Emamy Behzad Nayebi Kwang Seon Shin School of Metallurgy and Materials Engineering College of EngineeringUniversity of TehranP.O.Box 11155-4563TehranIran Department of Advanced Materials and Renewable Energies Iranian Research Organization for Science and TechnologyTehranIran Department of Mechanical Engineering University of NevadaRenoRenoNV 89557USA Advanced Research and Technology of Magnesium(ARTofMag)Co. TehranIran Research Institute of Advanced Materials School of Materials Science and EngineeringSeoul National UniversityGwanak-guSeoul 08826Republic of Korea

The effects of Ca addition on the microstructure and oxidation properties of a new Mg alloy were *** oxidation behavior of the alloys was analyzed by thermal analysis and material characterization of the alloys exposed in flame environment;and both electrical and induction ***,the surface layers were characterized using field emission scanning electron microscopy,and X-ray diffraction *** was found that increasing the Ca addition reduces the grain size and increases the fraction of the secondary phases,and enhances the mechanical ***,increasing the Ca contents resulted in the formation of a dense CaO/MgO layer on the surface prohibited the oxygen diffusion and assisted in protection of the substrate against further ***,ignition temperature was increased from 680℃ to 890℃ after addition of the Ca *** mechanical properties and ignition behavior of the current materials was compared with the literature which it showed an excellent combination of the properties in the developed alloys.

关键词： Mg alloys Oxidation properties Ca addition Microstructure Intermetallic phases

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