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Computational biology in topical bioactive peptide discovery for cosmeceutical application:a concise review

Biomedical Engineering Communications 2023年第3期2卷 7-15页

作者： Xu-Hui Li Wen-rou Su Fei-Fei Wang Ke Li Jing-Yong Zhu Si-Yu Zhu Si-Ning Kang Cong-Fen He Jun-Xiang Li Xiao Lin AGECODE R&D Center Yangtze Delta Region Institute of Tsinghua UniversityJiaxing 314000China Zhejiang Provincial Key Laboratory of Applied Enzymology Yangtze Delta Region Institute of Tsinghua UniversityJiaxing 314000China Yunnan Botanee Bio-technology Group Co. Ltd.Yunnan 650000China Yunnan Yunke Characteristic Plant Extraction Laboratory Co. Ltd.Yunnan 650000China Shenzhen Accompany Technology Co. LtdShenzhen 518000China College of Life Sciences and Medicine Zhejiang Sci-tech UniversityHangzhou 310018China Harvest Biotech(Zhejiang)Co. Ltd.Jiaxing 314000China School of Beijing Key Lab of Plant Resources Research and Development Beijing Technology and Business UniversityBeijing 100048China School of Life Sciences Northwestern Polytechnical UniversityXi’an 710072China

regenerative medicine and anti-aging research have made great strides at the molecular and cellular levels in dermatology and the medical aesthetic field,targeting potential treatments with skin therapeutic and intervention pathways,which make it possible to develop effective skin regeneration and repair *** the rapid development of computational biology,bioinformatics as well as artificial intelligence(A.I.),the development of new ingredients for regenerative medicine has been greatly accelerated,and the success rate has been *** application cases have appeared in topical skin regeneration and repair *** review will briefly introduce the application of bioactive peptides in skin repair and anti-aging as emerging ingredients in cosmeceutics and emphasize how *** computational biology technology may accelerate the development of innovative peptide molecules and ultimately translate them into potential skin regenerative and anti-aging ***,two research routines have been summarized and current limitations as well as directions were discussed for border applications in future research.

关键词： computational biology artificial intelligence cosmeceutical bioactive peptide regenerative medicine

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day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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Saudi Arabia Smart Grid Conference (SASG)

作者： Yiannis Kamarianakis Yannis Pantazis Evangelia Kalligiannaki Theodoros d. Katsaounis Konstantinos Kotsovos Issam Gereige Marwan Abdullah Aqil Jamal Athanasios Tzavaras Data Science Group Institute of Applied and Computational Mathematics Foundation for Research and Technology-Hellas Greece Dept. of Mathematics and Applied Mathematics University of Crete Greece Carbon Management Division Saudi Aramco R&D Center Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MdP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.

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Plasmonic nanoparticle-based surface-enhanced raman spectroscopy-guided photothermal therapy: emerging cancer theranostics

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Nanomedicine 2023年第6期18卷 555-576页

作者： Anindita das Hsieh-Chih Tsai Tapas Sen rakesh S Moirangthem Department of Physics Nanophotonics Lab Indian Institute of Technology (Indian School of Mines) Dhanbad Jharkhand 826004 India Graduate Institute of Applied Science & Technology National Taiwan University of Science & Technology Taipei 106 Taiwan Advanced Membrane Materials Center National Taiwan University of Science & Technology Taipei 106 Taiwan R&D Center for Membrane Technology Chung Yuan Christian University Chungli Tao-Yuan 320 Taiwan Nano-biomaterials Research Group School of Natural Sciences Faculty of Science & Technology University of Central Lancashire Preston PR1 2HE UK Department of Physics Manipur University Canchipur Imphal Manipur 795003 India

Optical imaging modalities have emerged as a keystone in oncological research, capable of providing molecular and cellular information on cancer with the advantage of being minimally invasive toward healthy tissues. Photothermal therapy (PTT) has shown great potential, with the exceptional advantages of high specificity and noninvasiveness. Combining surface-enhanced raman spectroscopy (SErS)-based optical imaging with PTT has shown tremendous potential in cancer theranostics (therapeutics + diagnosis). This comprehensive review article provides up-to-date information by exploring recent works focused mainly on the development of plasmonic nanoparticles for medical applications using SErS-guided PTT, including the fundamental principles behind SErS and the plasmon heating effect for PTT.

关键词： cancer theranostics photothermal therapy plasmonic nanoparticles surface-enhanced raman spectroscopy surface functionalization toxicity

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Correlation between the bath composition and nanoporosity of dC-electrodeposited Ni-Fe alloy

Correlation between the bath composition and nanoporosity of...

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作者： Maizza, Giovanni Pero, renato Kaciulis, Saulius Bolli, Eleonora Eom, Hyeonjin Lee, Minsu Yim, Tai Hong Department of Applied Science and Technology Politecnico di Torino Torino Italy Department of Industrial Engineering University of Rome "Tor Vergata" Rome Italy Institute for the Study of Nanostructured Materials ISMN — CNR Rome Italy Thermochemical Energy R&D Group Korea Institute of Industrial Technology Cheonan-si Korea Republic of Department of Materials Science and Engineering Hanyang University Seoul Korea Republic of Surface Technology R&D Group Korea Institute of Industrial Technology Incheon Korea Republic of

The outstanding mechanical strength of as-deposited dC-electrodeposited nanocrystalline (nc) Ni-Fe alloys has been the subject of numerous researches in view of their scientific and practical interest. However, recent studies have reported a dramatic drop in ductility upon annealing above 350°C, associated with a concomitant abnormal rapid grain growth. The inherent cause has been ascribed to the presence of a detrimental product or by product in the bath, which affects either the microstructure or causes defects in the concentration and/or distribution of the as-deposited films. The present work has been inspired by the observed abnormal behaviour of annealed electrodeposited nc Ni-Fe alloy, which has here been addressed by considering the relationship between the composition of the bath (iron-chloride, nickel-sulphate solution, saccharin and ascorbic acid) and deposition defects (e.g. grain boundary pores) in the case of an nc Ni-Fe (Fe 48 wt%) alloy. The current investigations have included X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) in both as-deposited and post-annealed conditions (300°C–400°C). XPS depth profiling with Ar ion sputtering showed a significant amount of C and O impurities entrapped in the foils during deposition. As such impurities are often overlooked in common analytical techniques, new scenarios may need to be rationalised to explain the observed drop in tensile ductility of the as-deposited Ni-Fe alloys. © 2020 John Wiley & Sons, Ltd.

关键词： X ray photoelectron spectroscopy

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Finite State Machine based Vehicle System for Autonomous driving in Urban Environments

Finite State Machine based Vehicle System for Autonomous Dri...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Sang-Hyeon Bae Sung-Hyeon Joo Jung-Won Pyo Jae-Seong Yoon Kwanghee Lee Tae-Yong Kuc Sungkyunkwan University Suwon Korea Robot R&D Group Korea Institute of Industrial Technology Ansan Korea

ISBN: (数字)9788993215205

ISBN: (纸本)9781728185620

In autonomous driving systems, many approach have been developed based on high-precision sensors, high computing power, development of various networks in academia and industry. The key point of an autonomous car is that it needs to know what is happening in the driving situation and an action corresponding to this situation must be created and operated. In this paper, we propose the system structure and method of a autonomous driving car consisting of three main parts:perception, planning, control. Each main part is designed to recognize the real-time driving environment, make action plan based on finite state machine, and activate kinematic model based control. The proposed vehicle system copes with six major situations of urban environment driving, and we verified system through the simulation.

关键词： Autonomous vehicles Automobiles Sensors Laser radar Wheels Tires Planning

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Innovative machine learning for drilling fluid density prediction: a novel central force search-adaptive XGBoost in HPHT environments

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Frontiers in Energy research 2024年 12卷

作者： Shanmugasundar, G. Manjunatha, r. Cep, robert Logesh, K. Kaushik, Vikas raju, S. Srinadh Elangovan, Muniyandy Department of Mechanical Engineering Sri Sai Ram Institute of Technology Chennai India Karnataka Bangalore India Department of Machining Assembly and Engineering Metrology Faculty of Mechanical Engineering VSB-Technical University of Ostrava Ostrava Czech Republic Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of Mechanical Engineering Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Punjab Mohali India Department of Computer Science and Engineering Raghu Engineering College Visakhapatnam India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Oil and gas industries are facing a special dilemma when it comes to high-pressure, high-temperature (HPHT) drilling as the accurate forecasting of the drilling fluid density (dFd) is a vital factor for safe and efficient operations. Complicated relationships and inconsistencies in HPHT situations are rarely mapped by current forecasting models, while their buggy performance and safety risks during drilling can be underestimated. In this research, we propose a novel machine learning (ML) approach to enhance the accuracy of dFd anticipation under HPHT conditions: central force search-adaptive extreme gradient boosting (CFS-XGB). This paper uses a dataset that has drilling variables together with the dFd for HPHT situations to examine the accuracy of the CFS-XGB model. Excluding the abnormalities of data or mistakes, the reliability of the original data is maintained by applying min–max normalization. After that, finding the important features with the help of the boosted principal component analysis (BPCA) approach to the normalized data will ensure a major improvement in the CFS-XGB methodology’s prediction efficacy. This research is experimented in the Python platform, and the performance of the proposed CFS-XGB method is analyzed in terms of MSE, r2, and AAPrE metrics. The suggested approach performs better than the current methods in forecasting the drilling fluid concentration in HPHT settings, according to the experimental data. This development in predictive modeling helps increase the productivity and safety of drilling operations, which will eventually help the oil and gas sector manage the challenges posed by HPHT drilling settings. Copyright © 2024 Shanmugasundar, Manjunatha, Cep, Logesh, Kaushik, raju and Elangovan.

关键词： Gas industry

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Instance segmentation based recognition system tracking tomatoes by ripeness in natural light conditions

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Journal of Institute of Control, robotics and Systems 2020年第11期26卷 940-948页

作者： Lee, Woo-Young Ko, Kwang Eun Kang, Jaehyeon Park, Hyun Ji Jang, Inhoon Applied Robot R&D Department Korea Institute of Industrial Technology Korea Republic of Department of computer Science & Engineering Hanyang University Korea Republic of

This paper describes a tomato recognition system to be applied to a harvesting robot used in farms under natural light conditions. The tomato recognition system is composed of a stereo camera, light, deep learning based instance segmentation model and Kalman filter based object tracking algorithm. In order to train the instance segmentation model, a large-scale dataset of the actual smartfarm background is made, and performance verification is performed on the test dataset. The average precision of the deep learning based instance segmentation model achieved 96.8% of performance when the threshold of the prediction score for classifying ripeness is over 0.75 (Table. 3), and the multiple object tracking accuracy of the Kalman filter based tracking method is approximately 74.3%. © ICrOS 2020.

关键词： Kalman filters

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An aptamer-based shear horizontal surface acoustic wave biosensor with a CVd-grown single- layered graphene film for high-sensitivity detection of a label-free endotoxin

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Microsystems & Nanoengineering 2020年第1期6卷 1247-1257页

作者： Junwang Ji Yiquan Pang dongxiao Li Zheng Huang Zuwei Zhang Ning Xue Yi Xu Xiaojing Mu Key Laboratory of Optoelectronic Technology&Systems Ministry of EducationInternational R&D Center of Micro-nano Systems and New Materials TechnologyChongqing University400044 ChongqingChina School of Chemistry and Chemical Engineering Chongqing University400030 ChongqingChina Department of Applied Physics Chongqing University401331 ChongqingChina Chongqing Acoustic-Optic-Electric Corporation China Electronic Technology Group Corporation400060 ChongqingChina Institute of Electronics Chinese Academy of Sciences100190 BeijingChina

The thickness of the sensitive layer has an important influence on the sensitivity of a shear horizontal surface acoustic wave(SH-SAW)biosensor with a delay-line structure and lower layer numbers of graphene produce better sensitivity for biological ***,a label-free and highly sensitive SH-SAW biosensor with chemical vapor deposition(CVd-)-grown single-layered graphene(SLG)for endotoxin detection was developed in this *** this methodology,SH-SAW biosensors were fabricated on a 36°Y-90°X quartz substrate with a base frequency of 246.2MHz,and an effective detection cell was fabricated using acrylic *** increase the surface hydrophilicity,chitosan was applied to the surface of the SLG ***,the aptamer was immobilized on the surface of the SLG film by cross-linking with ***,the sensitivity was verified by endotoxin detection with a linear detection ranging from 0 to 100 ng/mL,and the detection limit(LOd)was as low as 3.53 ng/*** addition,the stability of this type of SH-SAW biosensor from the air phase to the liquid phase proved to be excellent and the specificity was tested and verified by detecting the endotoxin obtained from Escherichia coli(***),the endotoxin obtained from Pseudomonas aeruginosa(***),and ***,this type of SH-SAW biosensor with a CVd-grown SLG film may offer a promising approach to endotoxin detection,and it may have great potential in clinical applications.

关键词： film surface layered

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Effect of shoaling length on rogue wave occurrence

arXiv

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arXiv 2023年

作者： Zhang, Jie Mendes, Saulo Benoit, Michel Kasparian, Jérôme State Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Dalian116023 China Group of Applied Physics University of Geneva Rue de l’École de Médecine 20 Geneva1205 Switzerland Institute for Environmental Sciences University of Geneva Boulevard Carl-Vogt 66 Geneva1205 Switzerland Chatou France Saint-Venant Hydraulics Laboratory LHSV École des Ponts EDF R&D Chatou France

The impact of shoaling on linear water waves is well-known, but it has only been recently found to significantly amplify both the intensity and frequency of rogue waves in nonlinear irregular wave trains atop coastal shoals. At least qualitatively, this effect has been partially attributed to the "rapid" nature of the shoaling process, i.e., shoaling occurs over a distance far shorter than that required for waves to modulate themselves and adapt to the reduced water depth. Through the development of a theoretical model and highly accurate nonlinear simulations, we disentangle the respective effects of the slope length and the slope gradient of a shoal and focus on the slope length to investigate the rapidness of the shoaling process on the evolution of key statistical and spectral sea-state parameters. Provided the shoal slope is 1/10 or steeper, our results indicate that the non-equilibrium dynamics is involved even for rather short shoaling lengths and becomes dominant in the regime of large lengths. When the non-equilibrium dynamics governs the wave evolution, further extending the slope length no longer influences the statistical and spectral measures. Thus, the shoaling effect on rogue waves is mainly driven by the slope magnitude rather than the slope length. Moreover, the simulations show that a higher cut-off frequency of the wave spectrum has a smaller impact on wave statistics than expected for a flat bottom in deep water and that insufficient attenuation of low-frequency waves at the downstream domain boundary has notable influence on wave statistics atop the shoal. © 2023, CC BY-NC-SA.

关键词： Ocean currents

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Object removal and Inpainting from Image using Combined GANs

Object Removal and Inpainting from Image using Combined GANs

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Jeongwon Pyo Yuri Goncalves rocha Arpan Ghosh Kwanghee Lee Gungyo In Taeyoung Kuc College of Information and Communication Engineering Sungkyunkwan University Suwon Korea Robot R&D Group Korea Institute of Industrial Technology Ansan Korea

ISBN: (数字)9788993215205

ISBN: (纸本)9781728185620

As recent research on deep learning methods has been actively conducted, a number of deep learning methods have been proposed. In this paper, we propose a method of removing the desired object from an image using generative adversarial networks(GANs) structure. We composed the network in which two GANs are fused. The first GAN erases the target object from the input image, and the second GAN generates an image that fills the empty space with the background. Through this network, we can erase the desired object from the input image and get an image with the erased part filled with the background without any object detection method. We show that the removal of people and vehicles from images of roads using the CityScapes dataset.

关键词： Generators Generative adversarial networks Training Image segmentation deep learning Annotations Semantics

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