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نتیجه جستجو - Identification

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ردیف عنوان نوع
81 کمک درمانی به بیمار کوید 19 با استفاده از تکنولوژی‌های توانمند اینترنت اشیا (‏اینترنت اشیا)
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 17
اینترنت اشیا می‌تواند منجر به نوآوری در مراقبت‌های بهداشتی شود. بنابراین مقاله‌های تحقیقاتی در مورد اینترنت اشیا در حوزه بهداشت و درمان و بیماری‌های کوید 19 به منظور کشف پتانسیل این تکنولوژی مورد تحقیق قرار می‌گیرند. این تحقیق مبتنی بر ادبیات ممکن است به متخصصان کمک کند تا راه‌حل‌هایی برای مسائل مرتبط پیدا کنند و با اپیدمی کوید 19 مبارزه کنند. با استفاده از یک نمودار فرآیند، دستاوردهای مهم اینترنت اشیا به طور خلاصه ارزیابی شدند. سپس هفت تکنولوژی اینترنت اشیا مهم که در مراقبت‌های بهداشتی در طول برنامه کوید 19 مفید به نظر می‌رسند، شناسایی و نشان داده می‌شوند. در نهایت، در برنامه کاربردی COVID۱۹، کاربردهای اساسی اینترنت اشیا بالقوه برای صنعت پزشکی با یک توضیح کوتاه شناسایی شدند. این وضعیت ناگوار راهی تازه برای خلاقیت در زندگی روزمره ما باز کرده‌است. اینترنت اشیا یک تکنولوژی در حال ظهور است که راه‌حل‌های بهتری در حوزه پزشکی، مانند حفظ سوابق پزشکی مناسب، نمونه، یکپارچه‌سازی دستگاه، و علت بیماری ارائه می‌دهد. تکنولوژی مبتنی بر سنسور اینترنت اشیا توانایی قابل‌توجهی در کاهش خطر مداخله در شرایط چالش برانگیز دارد و برای نوع همه‌گیر کوید 19 مفید است. در حوزه پزشکی، تاکید اینترنت اشیا بر کمک به درمان دقیق موقعیت‌های مختلف کوید 19 است. این امر کار جراح را با کاهش خطرات و افزایش عملکرد کلی تسهیل می‌کند. با استفاده از این تکنولوژی، پزشکان می‌توانند به آسانی تغییرات پارامترهای حیاتی COVID - ۱۹ را شناسایی کنند. این خدمات مبتنی بر اطلاعات چشم اندازهای جدیدی را برای مراقبت‌های بهداشتی فراهم می‌کنند زیرا آن‌ها به سمت تکنیک ایده‌آل برای یک سیستم اطلاعاتی پیش می‌روند تا نتایج کلاس جهانی را با بهبود سیستم‌های درمانی بیمارستان تطبیق دهند.
کلمات کلیدی: کوید 19 | اینترنت اشیا | پاندمی | مراقبت های بهداشتی | مدیریت | پشتیبانی زندگی
مقاله ترجمه شده
82 Economic impact of the bioeconomy in Spain: Multiplier effects with a bio social accounting matrix
تأثیر اقتصادی اقتصاد زیستی در اسپانیا: اثرات چند برابری با ماتریس حسابداری زیست اجتماعی-2021
The bioeconomy emerges as a new economic model to help address issues related to environmental care and focus on a more sustainable economy. In the last decade, it has become a global priority and many countries have published their own strategies that clearly refer to the development of the bioeconomy. The symmetric social accounting matrix with basic prices was constructed, including the breakdown of biobased accounts belonging to the bioeconomy to determine which sectors of the bioeconomy are most strategic to promote sustainable economic growth. This constructed matrix was used to analyse the economic influence of the bioeconomy products and their impact on job creation. The analysis was carried out using the diffusion and absorption multipliers, which enabled the interpretation of the linkages between the different economic agents. The results were analysed in depth and the multipliers decomposed into their different effects, own, open and circular, and complemented with the calculation of the employment multiplier to evaluate the most important sectors for employment generation. The analysis was applied to the case of Spain. The results of this research enabled the identification of the strategic sectors where economic policies can be applied since these are the ones that increase economic growth and activities within the bioeconomy and create jobs. The conclusions indicated that the Spanish bioeconomy is still focused on traditional sectors and has not yet developed its potential in more innovative biobased products, demonstrating that the bioeconomy in Spain still has a long way to go.
keywords: زیست توده | اسپانیا | ماتریس حسابداری اجتماعی | مدل های چند منظوره | آنالیز تاثیرات | ضغطه | Bioeconomy | Spain | Social accounting matrix | Multisectoral models | Impact analyses | Multipliers
مقاله انگلیسی
83 Backtracking and prospect on LNG supply chain safety
پیگیری و چشم انداز ایمنی زنجیره تامین LNG-2021
The safety issues of Liquefied Natural Gas (LNG) in production, storage, loading/unloading, transportation/ shipping, and re-gasification have became a major concern, since an accident in the LNG industry would be very costly. Understanding the threat of LNG not only contributes to the process safety and reliability in the research and development (R&D) system, but improves the efficiency of loss prevention, fire protection and emergency responses. As of April 2019, in order to obtain the present status and trend of LNG safety research, basing 1122 documents of the Web of Science database about safety research of LNG as a data source, Cite Space and VOS viewer were used for network knowledge map analysis. A comprehensive knowledge map of LNG safety field was obtained from several research aspects including scientific research power, research hot spots and trends, research knowledge base and frontier. According to the study results, the development of LNG safety research was divided into four stages from 1970s to 2019, China and South Korea made a lot of contributions, and the United States is the most influential. Among them, the research from 2005 to 2019 was the most representative. Current research results indicate that a combination of Formal Safety Assessment (FSA) methodology and Dynamic Procedure for Atypical Scenarios Identification (DyPASI) will fully identify risks; The PHAST and TerEx programs quickly define safety zones. Computational Fluid Dynamics (CFD) software package can provide ac- curate quantitative data for the study of LNG safety. Research on quantitative risk assessment (QRA) and LNG evaporated gas (BOG) has been a hot topic and trend in this field. The application of expansion foam in LNG accident mitigation covers most of the research content in this field, and the optimization of LNG liquefaction process has a great influence on this industry. As the international demand for LNG energy output increases, floating liquefied natural gas (FLNG) will have considerable development, and increasingly researchers attach vital importance to the safety of LNG offshore production integrated unit.
Keywords: Liquefied natural gas | Supply chain | Safety | FLNG | CiteSpace | VOS viewer
مقاله انگلیسی
84 A knowledge graph method for hazardous chemical management: Ontology design and entity identification
یک روش نمودار دانش برای مدیریت مواد شیمیایی خطرناک: طراحی هستی شناسی و شناسایی موجودیت-2021
Hazardous chemicals are widely used in the production activities of the chemical industry. The risk management of hazardous chemicals is critical to the safety of life and property. Hence, the effective risk management of hazardous chemicals has always been important to the chemical industry. Since a large quantity of knowledge and information of hazardous chemicals is stored in isolated databases, it is challenging to manage hazardous chemicals in an information-rich manner. Herein, we prompt a knowledge graph to overcome the information gap between decentralized databases, which would improve the hazardous chemical management. In the implementation of the knowledge graph, we design an ontology schema of hazardous chemicals management. To facilitate enterprises to master the knowledge in the full lifecycle of hazardous chemicals, including production, transportation, storage, etc., we jointly use data from companies and open data from the public domain of hazardous chemicals to construct the knowledge graph. The named entity recognition task is one of the key tasks in the implementation of the knowledge graph, which is of great significance for extracting entity information from unstructured data, namely the hazardous chemical accidents records. To extract useful information from multi-source data, we adopt the pre-trained BERT-CRF model to conduct named entity recognition for incidents records. The model achieves good results, exhibiting the effectiveness in the task of named entity recognition in the chemical industry.
keywords: نمودار دانش | هستی شناسی | مدیریت مواد شیمیایی خطرناک | به رسمیت شناختن نهادها | Knowledge graph | Ontology | Hazardous chemicals management | Named entity recognition
مقاله انگلیسی
85 Modeling and identification of suitable motivational mechanism in the collection system of municipal solid waste supply chain
مدل سازی و شناسایی سازوکار انگیزشی مناسب در سیستم جمع آوری زنجیره تأمین پسماند جامد شهری-2021
Many studies have identified that incentive, subsidy, and reward-penalty mechanisms improve the col- lection rate of recyclables and end of life products. But there is a lack of studies mathematical models and analysis of these mechanisms in the context of municipal solid waste supply chain. Therefore, in this study, models have been formulated for municipal solid waste supply chain (profit) considering government and collectors’ profit under incentive, subsidy, and reward-penalty mechanisms. The study has analysed the models against the non-separation and separation scenario of waste. A numerical analysis is performed and observed that: (i) separation of waste at source along with incentive, subsidy, and reward-penalty mechanisms scenario improve the collection rate by 17%, 23%, 30%, and 45% compared to non-separated MSW. (ii) Incentive, subsidy, and reward-penalty mechanisms increases the total sup- ply chain profit by around 9%, —36% and 18%. (iii) reward-penalty mechanism performs better than incentive and subsidy mechanism by providing the high supply chain profit (18% and 85%) and collection rate (22% and 15%) comparatively. Further, sensitivity analysis carried out to understand the behaviour of the models against the key parameters. The study also develops interesting propositions and proved for a better understanding of the models. From results, some key managerial insights have been drawn and a few future scopes of the study are presented.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Solid waste supply chain | Circular economy | Incentive | Subsidy | Reward-penalty
مقاله انگلیسی
86 A knowledge-based risk management tool for construction projects using case-based reasoning
یک ابزار مدیریت ریسک مبتنی بر دانش برای پروژه های ساختمانی با استفاده از استدلال مبتنی بر مورد-2021
Construction projects are often deemed as complex and high-risk endeavours, mostly because of their vulnera- bility to external conditions as well as project-related uncertainties. Risk management (RM) is a critical success factor for companies operating in the construction industry. RM is a knowledge-intensive process that requires effective management of risk-related knowledge. Although some research has already been conducted to develop tools to support knowledge-based RM processes, most of these tools ignore some critical features, such as live knowledge capture, web-based platform for knowledge sharing and effective case retrieval for learning from past projects. Moreover, several RM phases, such as risk identification, analysis, response and monitoring are not usually integrated. Thus, this study aims to bridge these gaps by developing a knowledge-based RM tool (namely, CBRisk) via case-based reasoning (CBR). CBRisk has been developed as a web-based tool that supports the cyclic RM process and utilises an effective case retrieval method considering a comprehensive list of project similarity features in the form of fuzzy linguistic variables. Finally, the developed tool was evaluated and validated by conducting black-box testing and expert review meeting. Results demonstrated that CBRisk has a considerable potential to enhance the effectiveness of RM in construction projects and may be used in other project-based industries with minimal modifications.
keywords: هوش مصنوعی | فراگیری ماشین | مدیریت ریسک مبتنی بر دانش | مدیریت ریسک | مدیریت دانش | استدلال مبتنی بر مورد | ابزار مبتنی بر وب | Artificial intelligence | Machine learning | Knowledge-based risk management | Risk management | Knowledge management | Case-based reasoning | Web-based tool
مقاله انگلیسی
87 Application of spectral features for separating homochromatic foreign matter from mixed congee
کاربرد ویژگیهای طیفی برای جداسازی مواد خارجی هم رنگ از مخروط مخروطی-2021
Foreign matter (FM) in mixed congee not only reduces the quality of the congee but may also harm consumers. However, the common computer vision methods with poor recognition ability for the homochromatic FM. This study used hyperspectral reflectance images with the pattern recognition model to detect homochromatic FM on the mixed congee surface. First, spectral features corresponding to homochromatic FM and background were extracted from hyperspectral images. Then, based on the optimal spectral preprocessing method, LDA, K-nearest neighbor, backpropagation artificial neural network, and support vector machine (SVM) were used to classify the spectral features. The results revealed that the SVM model input with raw spectra principal components exhibited optimal identification rates of 99.17%. Finally, most of the pixels for homochromatic FM were classified correctly by using the SVM model. To summarized, hyperspectral images combined with pattern recognition are an effective method for recognizing homochromatic FM in mixed congee.
Keyword: Mixed congee | Homochromatic foreign matter | Hyperspectral imaging technology | Pattern recognition | Chemometrics
مقاله انگلیسی
88 Gait recognition based on vision systems: A systematic survey
تشخیص راه رفتن بر اساس سیستم های بینایی: یک مرور سیستماتیک-2021
With the growing popularity of biometrics technology in the pattern recognition field, especially identification of human has gained the attention of researchers from both academia and industry. One such type of biometric technique is Gait recognition, which is used to identify a human being based on their walking style. Generally, two types of approaches are adopted by any algorithm designed for gait recognition, namely model based and model free approaches. The key reason behind the popularity of gait recognition is that it can identify a person from a considerable distance while other biometrics has failed to do so. In this paper, the authors have conducted a survey of extant studies on gait recognition in consideration of gait recognition approaches and phases of a gait cycle. Moreover, some aspects like floor sensors, accelerometer based recognition, the influences of environ- mental factors, which are ignored by exiting surveys, are also covered in our survey study. The information of gait is usually obtained from different parts of silhouettes. This paper also describes different benchmark datasets for gait recognition. This study will provide firsthand knowledge to the researchers working on the gait recognition domain in any real-world field. It has been observed that work done on the gait recognition with sufficiently high accuracy is limited in comparison to research on various other biometric recognition systems and has enough potential for future research.
Keywords: Gait recognition | Surveillance | Biometric | Person identification
مقاله انگلیسی
89 Role and knowledge of critical care nurses in the assessment and management of hypophosphataemia and refeeding syndrome: A descriptive exploratory study
نقش و شناخت پرستاران مراقبت های ویژه در ارزیابی و مدیریت هیپوفسفاتهمی و سندرم ریزش شده:یک مطالعه اکتشافی توصیفی-2021
Objective: To assess the perceived and actual role of critical care nurses in nutritional care, and their knowledge regarding the identification and management of hypophosphataemia and refeeding syndrome. Design and methods: Data were collected in one intensive care unit in Israel, from a self-administered questionnaire completed by 42 critical care nurses. The questionnaire was designed to assess their perceived and actual roles in the administration of nutritional care, and knowledge regarding electrolyte monitoring, hypophosphataemia and refeeding syndrome, including risk factors, consequences, and treatment.
Results: The majority participants that dieticians are solely responsible for nutrition care and follow-up. Most agreed that the measurement of phosphate levels was not important and that patients should receive full nutrition upon admission, while important risk factors for the development of refeeding syndrome were not recognised or considered. This informed their actual practice. A correlation was found between nurses’ knowledge and their actual practice so that the greater the nurses’ knowledge, the more they adhered to current nutrition guidelines (p < 0.05).
Conclusions: This study revealed critical care nurses’ lack of clarity of their role and lack of knowledge regarding nutrition care. We suggest that this complex task is best managed by a multidisciplinary team, including nurses and dieticians, with clear role definitions.
keywords: پرستاری مراقبت های ویژه | مطالعه اکتشافی توصیفی | هیپوفسفاتیه | سندرم ریزش | Critical care nursing | Descriptive exploratory study | Hypophosphataemia | Refeeding syndrome
مقاله انگلیسی
90 Automatic fetal biometry prediction using a novel deep convolutional network architecture
پیش بینی بیومتری خودکار جنین با استفاده از معماری شبکه ای پیچیده عمیق جدید-2021
Purpose: Fetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic segmentation and measurement of fetal biometric parameters, including biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) from ultra- sound images that relies on the attention gates incorporated into the multi-feature pyramid Unet (MFP-Unet) network.
Methods: The proposed approach, referred to as Attention MFP-Unet, learns to extract/detect salient regions automatically to be treated as the object of interest via the attention gates. After determining the type of anatomical structure in the image using a convolutional neural network, Niblack’s thresholding technique was applied as pre-processing algorithm for head and abdomen identification, whereas a novel algorithm was used for femur extraction. A publicly-available dataset (HC18 grand-challenge) and clinical data of 1334 subjects were utilized for training and evaluation of the Attention MFP-Unet algorithm.
Results: Dice similarity coefficient (DSC), hausdorff distance (HD), percentage of good contours, the conformity coefficient, and average perpendicular distance (APD) were employed for quantitative evaluation of fetal anatomy segmentation. In addition, correlation analysis, good contours, and conformity were employed to evaluate the accuracy of the biometry predictions. Attention MFP-Unet achieved 0.98, 1.14 mm, 100%, 0.95, and0.2 mm for DSC, HD, good contours, conformity, and APD, respectively.
Conclusions: Quantitative evaluation demonstrated the superior performance of the Attention MFP-Unet compared to state-of-the-art approaches commonly employed for automatic measurement of fetal biometric parameters.
Keywords: Fetal biometry | Ultrasound imaging | Deep learning | Convolutional neural network | Image classification
مقاله انگلیسی
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