检索结果-内蒙古大学图书馆

Isolation of 24-homoscalarane sesterterpenoids from a sponge of the genus Lendenfeldia

Phytochemistry Letters 2024年 61卷 177-181页

作者： Zheng, Li-Guo Chen, You-Yan Liaw, Chia-Ching Lin, Yu-Chi Chien, Su-Ying Lo, Yi-Hao Tsai, Yu-Chi Hu, Hao-Chun Chen, You-Ying Hwang, Tsong-Long Ou, Tzu-Ming Sung, Ping-Jyun Doctoral Degree Program in Marine Biotechnology National Sun Yat-sen University Kaohsiung 804201 Taiwan National Museum of Marine Biology and Aquarium Pingtung 944401 Taiwan School of Traditional Chinese Medicine College of Medicine Chang Gung University Taoyuan 333323 Taiwan Division of Chinese Materia Medica Development National Research Institute of Chinese Medicine Ministry of Health and Welfare Taipei 112304 Taiwan Department of Biochemical Science and Technology National Chiayi University Chiayi 600355 Taiwan Instrumentation Center National Taiwan University Taipei 106319 Taiwan Department of Family Medicine Zuoying Armed Forces General Hospital Kaohsiung 813204 Taiwan Shu-Zen Junior College of Medicine and Management Kaohsiung 821004 Taiwan Institute of Medical Science and Technology National Sun Yat-sen University Kaohsiung 804201 Taiwan School of Chinese Medicine College of Chinese Medicine China Medical University Taichung 404333 Taiwan Graduate Institute of Natural Products College of Medicine Chang Gung University Taoyuan 333323 Taiwan Research Center for Chinese Herbal Medicine College of Human Ecology Chang Gung University of Science and Technology Taoyuan 333324 Taiwan Graduate Institute of Health Industry Technology College of Human Ecology Chang Gung University of Science and Technology Taoyuan 333324 Taiwan Department of Anesthesiology Chang Gung Memorial Hospital Taoyuan 333423 Taiwan Department of Chemical Engineering Ming Chi University of Technology New Taipei City 243303 Taiwan Division of Gastroenterology Department of Internal Medicine Kaohsiung Armed Forces General Hospital Kaohsiung 802301 Taiwan Department of Marine Biotechnology and Resources National Sun Yat-sen University Kaohsiung 804201 Taiwan Chinese Medicine Research and Development Center China Medical University Hospital Taichung 404394 Taiwan Graduate Institute of Natural Products Kaohsiung Medical University Kaohsiung 807378 Taiwan Program in Ph

The chemical screening of an algae-containing marine sponge identified as Lendenfeldia species has isolated two 24-homoscalarane sesterterpenoids, including a known compound, felixin A (1) and a new analogue, lendenfeldarane V (2). The structure of 1, obtained in a previous study, was cited for the first time in this study via single-crystal X-ray diffraction analysis. The structure of 2 was ascertained via 2D NMR experiments and a literature review. The absolute configurations of 1 and 2 were delineated with DP4+ computation and specific optical rotation. Homoscalarane 1 exhibited cytotoxicity towards MG63 human osteosarcoma cells. © 2024 Phytochemical Society of Europe

关键词： Felixin Homoscalarane Lendenfeldarane Lendenfeldia MG63

来源：评论

学校读者我要写书评

暂无评论

ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy

引用

Sustainable Computing: Informatics and Systems 2024年 41卷

作者： Masdari, Mohammad Band, Shahab S. Qasem, Sultan Noman Sayed, Biju Theruvil Pai, Hao-Ting Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Riyadh11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz6803 Yemen Department of Computer Science Dhofar University PO Box 2509 SalalahPCode 211 Oman Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan

Wireless Body Area Networks (WBANs) are integral components of e-healthcare systems, responsible for monitoring patients' physiological states through intelligent implantable or wearable sensor nodes. These nodes collect medical data, which is then transmitted to remote healthcare facilities for thorough evaluation. Securing medical data within WBANs is paramount due to its central role in preserving patient privacy and confidentiality. Notably, Electrocardiogram (ECG) signals have recently gained prominence as pivotal elements within diverse security frameworks. Incorporating ECG signals strategically enhances the security and reliability of WBANs and broader e-healthcare systems, instilling greater trustworthiness. This survey article provides an in-depth exploration of contemporary ECG-based security schemes, adding to the scholarly discourse. The imperative to categorize these security paradigms revolves around their use of ECG signals. This categorization identifies three key domains: the first involves schemes that utilize ECG signals for cryptographic operations, encompassing key generation, agreement, management, and authentication. The second category employs steganography-based techniques, using ECG signals to conceal patients' sensitive medical data. The third category focuses on enhancing ECG signal security during data transmission. Each category is meticulously elaborated, detailing architectural foundations, notable contributions, and intrinsic security services. Furthermore, each section presents a comprehensive overview of the attributes characterizing ECG-based security frameworks. This includes insights into employed datasets, simulation environments, evaluation metrics, and inherent advantages and limitations. Expanding on this, a thorough analysis of distinctive attributes underpinning these security frameworks concludes by shedding light on potential directions for future research. © 2023 Elsevier Inc.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Multimodal Sentiment Analysis using Kernel Based Support Vector Machine

Multimodal Sentiment Analysis using Kernel Based Support Vec...

引用

Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： V Sunil Kumar Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Ashish Khanna Deepak Gupta Madhumala R B Department Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceará Brazil CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

There has been a steady expansion in the number of social websites, electronic media outlets, and blogging services, all of which contribute a large number of user messages such as comments, reviews, and opinions from customers. The quantity of these signals, which are used to evaluate the quality of a wide variety of goods and services, is rather substantial. As a result, it is challenging to monitor and identify the feelings of customers. As a result, a method known as Sentiment Analysis (SA), also known as Opinion Mining (OM), was developed. This method gathers data from the internet by using Natural Language Processing (NLP) strategies and then analyses people's thoughts. It does this by extracting subjective information from the words, assessing opinions, and quantifying positive and negative evaluations. The end result is a conclusion about the specific entity, which may be an organisation, the public, or a product. The NLP first gathers the views, and then those opinions are sorted into positive, neutral, and negative categories. Both the marketing promotion and the customer's ability to choose the most appropriate product for their needs may be improved by doing an analysis. The purpose of this study is to evaluate the use of different machine learning and swarm intelligence optimization methods to perform sentiment analysis on data derived from movie reviews. This study helps many businesses increase their profits while simultaneously lowering the number of problems caused by a certain product. Within the scope of this investigation, three distinct models of sentiment analysis were investigated. An effective method for doing sentiment analysis known as a Hybrid Kernel based Support Vector Machine (HKSVM) has been presented. The preprocessing that is described here is meant to cut down on the data inconsistencies and get rid of the data that isn't needed. After that, the Expectation Maximization (EM) technique is used in order to identify an outlier. After t

关键词： Support vector machines Sentiment analysis Analytical models Sensitivity Veins Tagging Probabilistic logic

来源：评论

学校读者我要写书评

暂无评论

Inner City Problem-Inner City Solution: Remote Sensing, A Critical Tool for Empowering Underserved Communities

Inner City Problem-Inner City Solution: Remote Sensing, A Cr...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Reginald Blake Hamidreza Norouzi Marzieh Azarderakhsh Abdou Bah Kip Nielsen Ashley Grey Julia Rivera Provost Office New York City College of Technology New York USA NASA Goddard Institute for Space Studies New York City College of Technology New York USA Construction Management and Civil Engineering Technology Department New York City College of Technology New York USA CUNY Graduate Center New York City College of Technology New York USA Department of Geography & Atmospheric Science New York City College of Technology University of Kansas New York USA Baruch College Campus High School New York City College of Technology New York USA Improving Undergraduate STEM Education Program New York City College of Technology New York USA

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Inner City communities have traditionally been overlooked and often forgotten in the satellite and ground-based remote sensing revolution. For too long, undergraduate minority students from these communities have been marginalized, ostracized, and unprepared to participate in the plethora of remote sensing applications that yet remain new, foreign, and mysterious. Even basic understanding of the amazing tool of remote sensing is unknown to them. These students lack the exposure, the awareness, the understanding, and the engagement in the foray of remote sensing and the unique lens it provides to probe and unearth new ways of gaining knowledge about the environment. To adequately prepare inner-city students to become members of the next generation of geoscientists, to empower and sustain underserved neighborhoods, and to assist in replenishing the geoscience workforce, unique paradigms of teaching and learning about remote sensing are *** the Center for Remote Sensing and Earth System Sciences (ReSESS) at the New York City College of technology (City Tech), remote sensing is being used as a catalyst to attract and to engage students from underserved communities in studying urban climate within their local neighborhoods. Results indicate that this approach of using remote sensing to attract students to urban climate increased awareness and understanding in the geosciences, and it motivated students to share their newfound knowledge in environmental sustainability initiatives with their fellow local citizens.

关键词： Satellites Geology Urban areas Green products Education Probes Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Patient Confidential Data Hiding and Transmission System Using Amplitude Quantization in the Frequency Domain of ECG Signals

引用

Sensors 2023年第22期23卷 9199-9199页

作者： Chen, Shuo-Tsung Ye, Ren-Jie Wu, Tsung-Hsien Cheng, Chun-Wen Zhan, Po-You Chen, Kuan-Ming Zhong, Wan-Yu Department of Medical Informatics Chung Shan Medical University Taichung40201 Taiwan Department of Information Center Chung Shan Medical University Hospital Taichung40201 Taiwan Graduate School of Applied Chinese Studies National Yunlin University of Science and Technology Yunlin 64002 Taiwan Bachelor’s Program in Business Management Fu Jen Catholic University New Taipei City242062 Taiwan

The transform domain provides a useful tool in the field of confidential data hiding and protection. In order to protect and transmit patients’ information and competence, this study develops an amplitude quantization system in a transform domain by hiding patients’ information in an electrocardiogram (ECG). In this system, we first consider a non-linear model with a hiding state switch to enhance the quality of the hidden ECG signals. Next, we utilize particle swarm optimization (PSO) to solve the non-linear model so as to have a good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative root mean square error (rRMSE). Accordingly, the distortion of the shape in each ECG signal is tiny, while the hidden information can fulfill the needs of physiological diagnostics. The extraction of hidden information is reversely similar to a hiding procedure without primary ECG signals. Preliminary outcomes confirm the effectiveness of our proposed method, especially an Amplitude Similarity of almost 1, an Interval RMSE of almost 0, and SNRs all above 30. © 2023 by the authors.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Towards Better Cyber Security Consciousness: The Ease and Danger of OSINT Tools in Exposing Critical Infrastructure Vulnerabilities

Towards Better Cyber Security Consciousness: The Ease and Da...

引用

International Conference on Computer Science and Engineering (UBMK)

作者： Muhammad Hasban Pervez Mert Ilhan Ecevit Najiba Zainab Naqvi Reiner Creutzburg Hasan Dag Dept. of Management Information Systems Kadir Has University Istanbul Türkiye Cyber Security and Critical Infrastructure Research Center Kadir Has University Istanbul Türkiye Cyber Security Graduate Program Kadir Has University Istanbul Türkiye Berlin School of Technology SRH Berlin University of Applied Sciences Berlin Germany

This article explores open-source intelligence (OS-INT) to identify the vulnerabilities and loopholes in power grid systems, focusing on an electrical distribution company operating in Turkey. The study emphasizes the potential risks of sharing publicly available information on social media accounts, websites, reports, and press releases which most companies overlook. It highlights that individuals or adversaries can exploit this information to harm companies and countries that may not be fully aware of these vulnerabilities. OSINT tools can efficiently gather interpretable data on a company, which companies unknowingly share. By refining the collected data, the study aims to understand the technologies used, their software versions, and any associated vulnerabilities. Web scraping tools extract data from the company's website, which may contain critical information about updates, ongoing systems, and technologies. The article provides a comprehensive understanding of the potential risks and vulnerabilities associated with sharing sensitive information and the various OSINT tools and techniques that can be used to identify and address these vulnerabilities. The importance of vigilance against the potential harm that remote or unrelated individuals can inflict using OSINT capabilitiesis underscored. This study shows how easy it is to detect vulnerabilities in a critical infrastructure system using OSINT tools.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE

Air Quality Prediction Based on Air Pollution Emissions in t...

引用

International Conference of Computer Science and Information technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Reproducing ATT&CK Techniques and Lifecycles to Train Machine Learning Classifier

引用

IEEE Network 2025年

作者： Yudha, Fietyata Lin, Ying-Dar Lai, Yuan-Cheng Sudyana, Didik Hwang, Ren-Hung EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu City300 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu City300 Taiwan The Department of Information Management National Taiwan University of Science and Technology Tapei106 Taiwan The Computer and Network Center National Cheng Kung University Tainan701 Taiwan The College of Artificial Intelligence National Yang Ming Chiao Tung University Tainan711 Taiwan

he MITRE ATT&CK framework categorizes threat actor behaviors into a sequence of techniques called the attack lifecycle. Based on this, Our work introduces a dual-labeled dataset that is accurately labeled with distinct techniques and lifecycles defined by ATT&CK. The dataset offered a more thorough perspective than previous datasets that employed binary or attack classification. It encompassed 17 distinct techniques throughout five lifecycles and was generated through an automated method that guarantees reproducibility and learnability. Reproducibility guarantees the dataset’s consistency, whereas learnability signifies its use in training machine learning models. Our analysis produced a positive result. The dataset achieved a Pearson correlation coefficient of 0.7 for reproducibility. Regarding distinguishing classes, it exhibits an average AUC-ROC score of 0.92 for techniques and 0.82 for lifecycles. The model training yielded an average F1 score of 0.95 for technique classification and 0.9 for lifecycle classification, but only for the traffic dataset. © 1986-2012 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Preliminary Exploration on the Application of Saussure Sign Concept in Bio-Inspired Design: A Case of Tiantoushui Doll-Design

引用

World Journal of Engineering and technology 2021年第3期9卷 548-554页

作者： Yu-Chun Yao Shiu-Hua Wu Ai-Li Wang Graduate Program of Sustainable Tourism and Recreation Management National Taichung University of Education Taichung Department of Digital Media Design Hsiuping University of Science and Technology Taichung School of Art and Design Dalian Polytechnic University Dalian

Simply, shape bionics is to extract an image feature of natural objects, and most of the techniques for transforming and presenting are focused on metaphorical effects. In particular, the extraction process of metaphor design seems to be related to the concept of Saussure signs. Therefore, this research took the doll design of the Tiantoushui community in Changhua County, Taiwan as an example and attempted to use the Saussure concept to extract and evaluate the signs of bio-inspired design. The results showed that a total of three doll works symbolized respectively rice, guava, and pomelo implied the signification of local region by integrating the Saussure code image with the sign-image of the regional nature and transforming its content into “commodity design” and further transferring into “the product value of cultural and creative”. In addition, the specific extraction process included 1) Extraction of image—adjectives in creative products;2) Meaning space of image—adjec- tives in creative products;3) Extraction and determination of design elements of creative products, and 4) Combined the sensibility of creative products and design elements to derive design rules. After preliminary exploration of the extraction steps above, a concrete and efficient bio-inspired design process was proposed in this work. Besides, it was inferred that if further combined with the post-modern design style, it may not only simplify the design elements of creative products but also enhance the design connotations in the future.

关键词： Bio-Inspired Design Saussure Sign Concept Doll-Design Code-Image

来源：评论

学校读者我要写书评

暂无评论

Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

引用

Engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：