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Wireless sequential dual light delivery for programmed PDT in vivo

Light(Science & Applications) 2024年第6期13卷 1047-1061页

作者： Jiayi Liu Bowen Sun Wenkai Li Han-Joon Kim Shu Uin Gan John S.Ho Juwita Norasmara Bte Rahmat Yong Zhang Department of Oncology The Second Xiangya HospitalCentral South UniversityChangshaHunan410011China Department of Biomedical Engineering College of Design and EngineeringNational University of SingaporeSingapore117585Singapore Department of Mechanical Engineering College of Design and EngineeringNational University of SingaporeSingapore117576Singapore Department of Electrical and Computer Engineering College of Design and EngineeringNational University of SingaporeSingapore117583Singapore Department of Medical IT Convergence Engineering Kumoh National Institute of TechnologyGumi39253Republic of Korea Department of Surgery Yong Loo Lin School of MedicineNational University of SingaporeSingapore119228Singapore The N.1 Institute for Health National University of SingaporeSingapore117456Singapore Institute for Health Innovation and Technology National University of SingaporeSingapore119276Singapore Department of Biomedical Engineering College of EngineeringCity University of Hong KongKowloonHong Kong SARChina

Using photodynamic therapy(PDT)to treat deep-seated cancers is limited due to inefficient delivery of photosensitizers and low tissue penetration of *** nanocarriers are widely used for photosensitizer delivery,while the self-quenching of the encapsulated photosensitizers would impair the PDT ***,the generated short-lived reactive oxygen spieces(ROS)can hardly diffuse out of nanocarriers,resulting in low PDT ***,a smart nanocarrier system which can be degraded by light,followed by photosensitizer activation can potentially overcome these limitations and enhance the PDT efficacy.A light-sensitive polymer nanocarrier encapsulating photosensitizer(RB-M)was *** implantable wireless dual wavelength microLED device which delivers the two light wavelengths sequentially was developed to programmatically control the release and activation of the loaded *** transmitter coils with matching resonant frequencies allow activation of the connected LEDs to emit different wavelengths *** irradiation time,dose,and RB-M concentration were determined using an agent-based digital simulation *** vitro and in vivo validation experiments in an orthotopic rat liver hepatocellular carcinoma disease model confirmed that the nanocarrier rupture and sequential low dose light irradiation strategy resulted in successful PDT at reduced photosensitizer and irradiation dose,which is a clinically significant event that enhances treatment safety.

关键词： PDT overcome hardly

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Enhancing Voxel-Based Human Pose Classification Using CNN with Modified VGG16 Method 8

Enhancing Voxel-Based Human Pose Classification Using CNN wi...

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Rahmanti, Farah Zakiyah Pradnyana, Gede Aditra Priyadi, Ardyono Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya60111 Indonesia Universitas Pendidikan Ganesha Department of Informatics Bali81116 Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9798350344691

Human pose classification based on 3D point cloud is a challenging problem in pattern recognition and computer vision. The human pose prediction based on a 3D point cloud is a first step in human monitoring because the advantages are robustness to light and having an accurate location in 3D space. This research proposed a novel method for human pose classification based on a 3D point cloud to overcome that condition. However, with the proposed method of voxel-based feature extraction and Convolutional Neural Network (CNN) modified VGG16 achieved great success in classifying human poses with 3D point cloud inputs. This research proposes a CNN with modified VGG16 network to classify 3D point cloud human poses by present voxel-based feature extraction. This work uses our primary 3D point cloud data from LiDAR 32-channel. Before 3D point cloud learning, the first step is pre-processing data with normalization and extracting features with voxelization. Our experiment uses two types of classification cases, namely the classification for binary-class and multi-class 3D point cloud human poses. Experimental results show that our proposed method performs well and excellently, obtaining an accuracy value of 90% for the binary-class case and outperforming other existing methods. With our proposed method, it will be possible to recognize human poses better. © 2023 IEEE.

关键词： Feature extraction

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Designing an Advanced Situational Awareness Platform Using Intelligent Multi-Drones for Smart Farming Towards Agriculture 5.0 11

Designing an Advanced Situational Awareness Platform Using I...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Hermanus, Davy Ronald Supangkat, Suhono Harso Hidayat, Fadhil Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350389654

With regard to Agriculture 5.0, the goal of this research is to create an intelligent platform for multi-drone collaboration that will improve situational awareness. This study examines the crucial phases in the Sensing, Understanding, and Action processes by fusing cutting-edge drone technology with creative coordination methods. Creating the architecture and foundation for drone collaboration, utilizing artificial intelligence for real-time data processing, and creating user interfaces that work are all part of the study technique. Area coverage, data quality, processing speed, and decision-making effectiveness are some of the performance evaluation measures. The goal of the research is to identify strategies for enhancing large-scale farming methods' resource management and operational effectiveness. The results will greatly enhance Agriculture 5.0 technologies and create avenues for the deployment of multi-drone collaboration platforms in a variety of high-situational awareness businesses. © 2024 IEEE.

关键词： Data quality

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Voltage Control in Distribution Grids Using Topology Aware Deep Reinforcement Learning

Voltage Control in Distribution Grids Using Topology Aware D...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Hossain, Rakib Gautam, Mukesh Mansourlakouraj, Mohammad Livani, Hanif Benidris, Mohammed University of Nevada Reno Department of Electrical & Biomedical Engineering RenoNV89557 United States Michigan State University Department of Electrical and Computer Engineering East LansingMI48824 United States

ISBN: (纸本)9798350320169

With increasing penetration of distributed energy resources (DERs), voltage fluctuations have become a critical challenge in modern distribution grids. Recent studies exhibit that deep reinforcement learning (DRL)-based Volt-VAR control (VVC) approaches can efficiently control the voltage of the distribution grid through corrective and preventive control of the reactive power output of the DERs. However, the DRL approaches cannot capture the topological changes of the power system since it uses a fully-connected network (FCN) to deliver the action. Therefore, in this paper, we have developed a graph convolutional network (GCN)-based DRL approach for controlling the voltage of the network that can better extract the topology variation of the network. The GCN network is utilized to capture and combine the relationship between the topology branches and the features of the nodes. The output GCN is fed as the input of the DRL to take optimal control action for controlling the voltage of the network. The performance of GCN-based DRL approach is tested on a modified IEEE-34 test case with different load and PV profiles. The results are compared with a base case scenario (i.e., no action is taken by the agent), and an FCN-based DRL approach. The results show that the proposed approach outperforms the FCN-based DRL approach in terms of voltage control. © 2023 IEEE.

关键词： Reinforcement learning

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Improving Prediction of Need for Mechanical Ventilation using Cross-Attention 46

Improving Prediction of Need for Mechanical Ventilation usin...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Mohanty, Anwesh Shashikumar, Supreeth P. Lam, Jonathan Y. Nemati, Shamim University of California San Diego Department of Computer Science and Engineering La Jolla United States University of California San Diego Division of Biomedical Informatics La Jolla United States

ISBN: (纸本)9798350371499

In the intensive care unit, the capability to predict the need for mechanical ventilation (MV) facilitates more timely interventions to improve patient outcomes. Recent works have demonstrated good performance in this task utilizing machine learning models. This paper explores the novel application of a deep learning model with multi-head attention (FFNN-MHA) to make more accurate MV predictions and reduce false positives by learning personalized contextual information of individual patients. Utilizing the publicly available MIMIC-IV dataset, FFNN-MHA demonstrates an improvement of 0.0379 in AUC and a 17.8% decrease in false positives compared to baseline models such as feed-forward neural networks. Our results highlight the potential of the FFNN-MHA model as an effective tool for accurate prediction of the need for mechanical ventilation in critical care settings. © 2024 IEEE.

关键词： Contrastive Learning

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Impacts of High Penetration PV Systems on Power System Stability Parameters

Impacts of High Penetration PV Systems on Power System Stabi...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Mishan, Ramkrishna Abdelkader, Abdelrahman Egan, Matthew Benidris, Mohammed University of Nevada Reno Department of Electrical and Biomedical Engineering RenoNV89557 United States Michigan State University Department of Electrical and Computer Engineering East LansingMI48824 United States

ISBN: (纸本)9798350320169

Incorporation of renewable energy sources into electrical grids has the potential to reduce system total inertia, which influences the transient response. Parameters such as critical clearing time (CCT), short-circuit current ratios, machine transient and sub-transient reactances, and time constants are all intrinsically linked to the system's total inertia and serve as vital components of transient stability (TS) assessment. Consequently, these parameters can be employed as comparative metrics to evaluate the relative TS of the power system under varying levels of PQ-controlled photovoltaic (PV) generation penetration. This paper examines the relationship between these parameters and system inertia at different penetration levels of PV. CCT is a vital transient stability indicator, incorporating other TS parameters, and a longer CCT signifies higher system inertia, serving as a relative stability parameter for evaluating the impact of PV penetration. This paper replaces one of the IEEE WSCC-nine bus generators with an equivalent PV plant, then changes PV penetration to compare transient stability with the original system. This paper also confirms that the sub-transient and transient short-circuit currents that occur during faults in a power system depend not only on the structure of the faulted equipment but also on the overall structure and characteristics of the power system, including its inertia. The sensitivity parameters displayed negative trends in response to variations in system inertia, and it was observed that the transient parameters demonstrated greater sensitivity to changes in system inertia compared to the sub-transient. The paper emphasizes the criticality of carefully managing the integration of renewable energy sources to ensure adequate power system stability. © 2023 IEEE.

关键词： Renewable energy

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Genome-wide exploratory analysis for NARAC dataset with preparation for haplotype block partitioning through minor allele frequency quality control viewpoint

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Iran Journal of computer Science 2023年第4期6卷 387-396页

作者： Saad, Mohamed N. Zareef, Galena W. Ibrahim, Fatma S. Said, Ashraf M. Hamed, Hisham F. A. Biomedical Engineering Department Faculty of Engineering Minia University Minia Egypt Biomedical Engineering Department Misr University for Science and Technology 6th of October City Egypt Bioinformatics Department Faculty of Computer and Information Minia University Minia Egypt Computer and Software Engineering Department Misr University for Science and Technology 6th of October City Egypt Department of Electrical Engineering Faculty of Engineering Egyptian-Russian University Cairo Egypt Electrical and Electronics Engineering Department Faculty of Engineering Minia University Minia Egypt

This article provides a detailed description, analysis, and visualization of a case–control genome-wide genotypic dataset from the North American Rheumatoid Arthritis Consortium (NARAC). The data is presented in terms of the number of females and males in both cases and controls, as well as the percentage of missing data. The number of alleles and genotypes is also counted, and the minor allele frequency (MAF) is calculated for each single nucleotide polymorphism (SNP). The data is further classified into four categories based on the SNP's MAF, namely, very rare, rare, low frequency, and common SNPs. The regions of these categories in the chromosome are investigated to determine the proportion of SNPs in coding locations and other regions. It is observed that each category has a different proportion in each region of consequence annotation. The data composition in terms of alleles and genotypes is found to be greatly disproportionate. The results present clear insights into the data and its MAF, which can be compared with other datasets. These findings can aid researchers in gaining a comprehensive understanding of such case–control datasets and bring accurate insights into the data. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Genome-wide association study Genome-wide exploratory analysis Minor allele frequency Single nucleotide polymorphism Variant annotation

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Head Pose Feature for Prediction Pedestrian Intention to Crossing the Road Using LSTM 38

Head Pose Feature for Prediction Pedestrian Intention to Cro...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Sidharta, Hanugra Aulia Ilham Perdana, Muhammad Mulyanto Yuniarno, Eko Al Kindhi, Berlian Hery Purnomo, Mauridhi Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Universitas Muhammadiyah Malang Department of Informatics Malang Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Automation Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350302196

Understanding pedestrian behaviour when crossing the road is an important key to the development of autonomous vehicles. Because pedestrians are considered Vul-nerable Road Users (VRUs), they are likely to be killed if they are involved in an accident. To ensure their safety, it is then necessary to predict the pedestrian's intention based on their behaviour. In this experiment, we propose head pose observation for predicting their intention, by observing pedestrians' head pose data, we can then predict their intention to cross the road. To achieve this purpose, we use human head detector and head pose extraction feature, and the resulting yaw, pitch and roll as three head pose features. To select the most optimal feature is important for predicting pedestrian intention, then we make 7 combination scenarios based on these three features and compare it with the same model. Based on this scenario, it is proved that all these three data are optimal to observe pedestrian intention. There are three behavioural annotation that have been used, there are crossing, not crossing and will crossing. We derive will crossing from the annotation looking and not crossing while waiting at the roadside. Prediction of pedestrian behaviour is done by using LSTM model, and the resulting precision on crossing and not crossing with 0.98, while will crossing is 0.94. © 2023 IEEE.

关键词： Forecasting

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CRISP - DM Process in Smart Building Management 11

CRISP - DM Process in Smart Building Management

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11th International Conference on electrical and Electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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A 3D-Printed Wideband Sensor for Food Complex Permittivity Estimation Based on Double Ridge Waveguide

A 3D-Printed Wideband Sensor for Food Complex Permittivity E...

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2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Macciò, C. Lodi, M.B. Curreli, N. Melis, A. Mazzarella, G. Bozzi, M. Fanti, A. University of Cagliari Department of Electrical and Electronic Engineering Cagliari09123 Italy via Morego 30 Genoa16163 Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia27100 Italy

ISBN: (纸本)9798350312843

This work aims to advance the design of a microwave device capable of detecting, through indirect measurements, variations in the permittivity of the food materials. To this aim, a novel sensor based on double ridge waveguide configuration and a specific sample holder, to be 3D-printed, are designed. To numerically study the microwave signal propagation in the designed device and investigate its wideband performance, simulations with different food materials are performed. In particular, the complex dielectric permittivity of Carasau bread doughs, a traditional food product from Sardinia (Italy), and different distilled water-based solutions with various percentage of solutes in them, are measured with open-ended coaxial probe in the range 0.5-8.5 GHz and the acquired data are then used as material under test. The results show a good ability of the device to discriminate variations of the percentage of solute in solutions. Differences of up to 60MHz in the position of the |S11| peak and 15.8 dB in amplitude are in fact observed between adjacent curves. However, in the case of Carasau bread dough, this device was not capable of clearly discriminating variations in the percentage of ingredients used. © 2023 IEEE.

关键词： Permittivity

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