با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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1 |
Head tremor in cervical dystonia: Quantifying severity with computer vision
لرزش سر در دیستونی دهانه رحم: کمی کردن شدت با دید کامپیوتری-2022 Background: Head tremor (HT) is a common feature of cervical dystonia (CD), usually quantified by subjective
observation. Technological developments offer alternatives for measuring HT severity that are objective and
amenable to automation.
Objectives: Our objectives were to develop CMOR (Computational Motor Objective Rater; a computer vision-
based software system) to quantify oscillatory and directional aspects of HT from video recordings during a
clinical examination and to test its convergent validity with clinical rating scales.
Methods: For 93 participants with isolated CD and HT enrolled by the Dystonia Coalition, we analyzed video
recordings from an examination segment in which participants were instructed to let their head drift to its most
comfortable dystonic position. We evaluated peak power, frequency, and directional dominance, and used
Spearman’s correlation to measure the agreement between CMOR and clinical ratings.
Results: Power averaged 0.90 (SD 1.80) deg2/Hz, and peak frequency 1.95 (SD 0.94) Hz. The dominant HT axis
was pitch (antero/retrocollis) for 50%, roll (laterocollis) for 6%, and yaw (torticollis) for 44% of participants.
One-sided t-tests showed substantial contributions from the secondary (t = 18.17, p < 0.0001) and tertiary (t =
12.89, p < 0.0001) HT axes. CMOR’s HT severity measure positively correlated with the HT item on the Toronto
Western Spasmodic Torticollis Rating Scale-2 (Spearman’s rho = 0.54, p < 0.001).
Conclusions: We demonstrate a new objective method to measure HT severity that requires only conventional
video recordings, quantifies the complexities of HT in CD, and exhibits convergent validity with clinical severity
ratings. keywords: لرزش سر | ویدیو | بینایی کامپیوتر | درجه بندی شدت | TWSTRS | Head tremor | Video | Computer vision | Severity rating | TWSTRS |
مقاله انگلیسی |
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Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network
طبقه بندی مبتنی بر بینایی کامپیوتری شدت پاشش بتن با استفاده از ماشین تقویت کننده گرادیان قویا بهینه شده فراابتکاری و شبکه عصبی پیچیده عمیق-2022 This paper presents alternative solutions for classifying concrete spall severity based on computer vision ap-
proaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are
employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the
properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center sym-
metric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern
(ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is
enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification
directly without the need for texture descriptors. Experimental results with a dataset containing real-world
concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the
Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a
classification accuracy rate of roughly 99%. keywords: شدت ریزش بتن | دستگاه افزایش گرادیان | الگوی باینری محلی | فراماسونری | یادگیری عمیق | Concrete spall severity | Gradient boosting machine | Local binary pattern | Metaheuristic | Deep learning |
مقاله انگلیسی |
3 |
Detection of loosening angle for mark bolted joints with computer vision and geometric imaging
تشخیص زاویه شل شدن اتصالات پیچ شده با بینایی ماشین و تصویربرداری هندسی-2022 Mark bars drawn on the surfaces of bolted joints are widely used to indicate the severity of loosening. The
automatic and accurate determination of the loosening angle of mark bolted joints is a challenging issue that has
not been investigated previously. This determination will release workers from heavy workloads. This study
proposes an automated method for detecting the loosening angle of mark bolted joints by integrating computer
vision and geometric imaging theory. This novel method contained three integrated modules. The first module
used a Keypoint Regional Convolutional Neural Network (Keypoint-RCNN)-based deep learning algorithm to
detect five keypoints and locate the region of interest (RoI). The second module recognised the mark ellipse and
mark points using the transformation of the five detected keypoints and several image processing technologies
such as dilation and expansion algorithms, a skeleton algorithm, and the least square method. In the last module,
according to the geometric imaging theory, we derived a precise expression to calculate the loosening angle using
the information for the mark points and mark ellipse. In lab-scale and real-scale environments, the average
relative detection error was only 3.5%. This indicated that our method could accurately calculate the loosening
angles of marked bolted joints even when the images were captured from an arbitrary view. In the future, some
segmentation algorithms based on deep learning, distortion correction, accurate angle and length measuring
instruments, and advanced transformation methods can be applied to further improve detection accuracy. keywords: Mark bolted joint | Loosening detection | Keypoint-RCNN | Image processing | Geometric imaging |
مقاله انگلیسی |
4 |
A combined real-time intelligent fire detection and forecasting approach through cameras based on computer vision method
یک رویکرد تشخیص و پیشبینی حریق هوشمند ترکیبی در زمان واقعی از طریق دوربینها بر اساس روش بینایی کامپیوتری-2022 Fire is one of the most common hazards in the process industry. Until today, most fire alarms have had very
limited functionality. Normally, only a simple alarm is triggered without any specific information about the fire
circumstances provided, not to mention fire forecasting. In this paper, a combined real-time intelligent fire
detection and forecasting approach through cameras is discussed with extracting and predicting fire development
characteristics. Three parameters (fire spread position, fire spread speed and flame width) are used to charac-
terize the fire development. Two neural networks are established, i.e., the Region-Convolutional Neural Network
(RCNN) for fire characteristic extraction through fire detection and the Residual Network (ResNet) for fire
forecasting. By designing 12 sets of cable fire experiments with different fire developing conditions, the accu-
racies of fire parameters extraction and forecasting are evaluated. Results show that the mean relative error
(MRE) of extraction by RCNN for the three parameters are around 4–13%, 6–20% and 11–37%, respectively.
Meanwhile, the MRE of forecasting by ResNet for the three parameters are around 4–13%, 11–33% and 12–48%,
respectively. It confirms that the proposed approach can provide a feasible solution for quantifying fire devel-
opment and improve industrial fire safety, e.g., forecasting the fire development trends, assessing the severity of
accidents, estimating the accident losses in real time and guiding the fire fighting and rescue tactics. keywords: ایمنی آتش سوزی صنعتی | تشخیص حریق | پیش بینی آتش سوزی | تجزیه و تحلیل آتش سوزی | هوش مصنوعی | Industrial fire safety | Fire detection | Fire forecasting | Fire analysis | Artificial intelligence |
مقاله انگلیسی |
5 |
A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination
یک سیستم بینایی کامپیوتری برای تشخیص زودهنگام آنتراکنوز در انبه قندی (Mangifera indica) بر اساس نور UV-A-2022 The present work describes the development of a computer vision system for the early detection of anthracnose in sugar mango based on Ultraviolet A illumination (UV-A). Anthracnose, a disease caused by the fungus Colletotrichum sp, is commonly found in the fruit of sugar mango (Mangifera indica). It manifests as surface defects including black spots and is responsible for reducing the quality of the fruit. Consequently, it decreases its commercial value. In more detail, this study poses a system that begins with image acquisition under white and ultraviolet illumination. Furthermore, it proposes to analyze the Red, Green and Blue color information (R, G, B) of the pixels under two types of illumination, using four different methods: RGB-threshold, RGB-Linear Discriminant Analysis (RGB-LDA), UV-LDA, and UV-threshold. This analysis produces an early semantic segmentation of healthy and diseased areas of the mango image. The results showed that the combination of the linear discriminant analysis (LDA) and UV-A light (called UV-LDA method) in sugar mango images allows early detection of anthracnose. Particularly, this method achieves the identification of the disease one day earlier than by an expert with respect to the scale of anthracnose severity implemented in this work.
keywords: انبه قندی | آنتراکنوز | LDA | نور UV-A | درجه بندی | پردازش تصویر | Sugar mango | Anthracnose | LDA | UV-A light | Grading | Image processing |
مقاله انگلیسی |
6 |
شیوع، همبستگیهای اجتماعی-جمعیتی و دانشگاهی اختلال وسواسی جبری در دانشجویان دانشکده علوم پزشکی کاربردی دانشگاه ام القرا
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 21 مقدمه: مطالعاتی که شیوع وسواس جبری را در منطقه عربستان سعودی نشان میدهد بسیار اندک است و بیشتر در نمونه جمعیتی دانشجویان پزشکی و پیراپزشکی وجود دارد. هدف از این مطالعه برآورد شیوع علائم وسواس اجباری در یک نمونه جامعه دانشجویان علوم پزشکی کاربردی بود. علاوه بر این، ارتباط بین علائم وسواسی جبری و متغیرهای اجتماعی-جمعیتی و چندین جنبه از زندگی دانشگاهی بررسی شد.
روشها: در این مطالعه مقطعی 404 دانشجوی دانشگاه متعلق به چهار بخش به کار گرفته شدند. ابزارهایی که در این مطالعه استفاده شد، شامل معیارهای ارزیابی وسواس جبری (OCI - R) ، DSM - IV برای تشخیص مقیاس درجه بندی شدت OCD و Y - BOCS بود. نتیجه اصلی اختلال وسواس جبری احتمالی است (امتیاز OCI - R> 21). دانشجویان با نمره بیشتر از 21 بیشتر از نظر وجود اختلال وسواس جبری با استفاده از معیارهای DSM - IV و Y - BOCS ارزیابی شدند. یافته ها: شیوع OCS با ابزار غربالگری OCI-R 20% بود [95% CI(19.902-20.098)]. شیوع واقعی OCD تأیید شده 5.06٪ بود [95% CI(4.39-6.12)]. وجود OCD احتمالی در دانشجویان گروه آزمایشگاه پزشکی بسیار زیاد بود [002/0 = p و95% CI(31.3-3.33) [. ارتباط مهمی بین حضور OCS و عدم رضایت از انتخاب دوره [001/0 = p ، 95٪ CI (1.38 - 3.92)] ، احساس طرد شدن [0.004 = p ، 95٪ CI (1.39 - 5.88]) و علائم افسردگی [0001/0 = p و CI (8/1 - 89/1)] وجود داشت. نمونه ما به زنان در سن دانشگاه محدود بود، بنابراین تفسیر شیوع قابل تعمیم نیست. نتیجه گیری: وجود چنین اختلالی احتمالاً بر عملکرد تحصیلی ، کیفیت زندگی و روابط بین فردی تأثیر می گذارد ، شناسایی و درمان در زمان مناسب به بهبود عملکرد تحصیلی و کیفیت زندگی کمک می کند. کلمات کلیدی: وسواس جبری | علائم وسواسی جبری | دانشجویان پزشکی و پیراپزشکی | اختلال روانی |
مقاله ترجمه شده |
7 |
Knowledge of healthcare providers in the management of anaphylaxis
آگاهی از ارائه دهندگان خدمات بهداشتی در مدیریت آنافیلاکسی-2021 Introduction: Anaphylaxis is defined as a severe, life-threatening systemic hypersensitivity reaction. Early diagnosis and treatment of a severe allergic reaction requires recognition of the signs
and symptoms, as well as classification of severity. It is a clinical emergency, and healthcare
providers should have the knowledge for recognition and management. The aim of the study is to
evaluate the level of knowledge in the management of anaphylaxis in healthcare providers.
Methods: It is an observational, descriptive, cross-sectional study conducted among healthcare providers over 18 years old via a Google Forms link and shared through different social media platforms. A 12-item questionnaire was applied which included the evaluation of the management of anaphylaxis, from June 2020 to May 2021. Results: A total of 1023 surveys were evaluated; 1013 met inclusion criteria and were included in the statistical analysis. A passing grade was considered with 8 or more correct answers out of 12; the overall approval percentage was 28.7%. The group with the highest percentage of approval in the questionnaire was health-care providers with more than 30 years of work experience. There was a significant difference between the proportions of approval between all specialty groups, and in a post-hoc analysis, allergy and immunology specialists showed greater proportions of approval compared to general medicine practitioners (62.9% vs 25%; p¼<0.001). Conclusions: It is important that healthcare providers know how to recognize, diagnose, and treat anaphylaxis, and later refer them to specialists in Allergy and Clinical Immunology in order to make a personalized diagnosis and treatment. Keywords: Anaphylaxis | Epinephrine | Healthcare providers | Knowledge |
مقاله انگلیسی |
8 |
Effects of a symptom management intervention based on group sessions combined with a mobile health application for persons living with HIV in China: A randomized controlled trial
اثرات مداخله مدیریت علائم بر اساس جلسات گروهی همراه با یک برنامه بهداشتی همراه برای افراد مبتلا به HIV در چین: یک آزمایش تصادفی کنترل شده-2021 Objective: This study aims to evaluate the effects of a symptom management intervention (SMI) based on
symptom management group sessions combined with a mobile health (mHealth) application (app) on
the knowledge of symptom management, the certainty of symptom self-management, symptom
severity, symptom distress, medication adherence, social support, and quality of life among persons
living with HIV (PLWH) in China.
Methods: A parallel randomized controlled trial with 61 PLWH was conducted in Shanghai, China. The participants in the control group (n ¼ 30) downloaded the Symptom Management (SM) app according to their needs and preferences, and received routine follow-ups. The participants in the intervention group (n ¼ 31) were guided to download and use the SM app, and received four tailored weekly group sessions at routine follow-ups. Each group session lasted for approximately 2 h and targeted one of the major modules of the SM app. All the outcomes were assessed at baseline and post-intervention. The study was registered with the Chinese Clinical Trial Registry (ChiCTR1900024821). Results: The symptom management knowledge and certainty of symptom self-management were significantly improved after the intervention (all P < 0.01). Compared with the control group, the scores of symptoms reasons knowledge score improved 11.47 points (95% CI: 3.41, 19.53) and scores of symptoms self-management knowledge score improved 12.80 points (95% CI: 4.55, 21.05) in the intervention group after controlling for covariates. However, other outcomes did not show statistically significant differences between the intervention group and the control group (P > 0.05). Conclusion: The SMI could improve PLWH’s symptom management knowledge and certainty of symptom self-management. Multi-center studies with larger sample sizes and longer follow-ups are needed to further understand the effects of SM app on ameliorating symptom severity and symptom distress. More innovative strategies are also needed to promote and maintain the sustainability of the SM app. keywords: چین | عفونت های HIV | برنامه های موبایل | پیروی از دارو | کیفیت زندگی | خود مدیریت | حمایت اجتماعی | China | HIV Infections | Mobile applications | Medication adherence | Quality of life | Self-management | Social support |
مقاله انگلیسی |
9 |
Efficiency of postfire hillslope management strategies: Gaps of knowledge
کارآیی راهبردهای مدیریت هیپلپن پس ازفایر: شکاف دانش-2021 Fire regimes have changed due to environmental and socioeconomic factors which have led to an increase in the number,
frequency, intensity, extension, and severity of fire events. On
this context, postfire management is crucial for preserving
forest ecosystems’ functions and biodiversity, and returning
them to prefire levels (both in the short- and long-term) after
wildfires. Currently, there is a lack of available research evaluating the impacts of plant and soil hillslope restoration strategies on the ecosystem properties of burned forests, leading
to much uncertainty among land managers. The lack of availability information may be explained by undue weight given to
the influence of microclimatic conditions in each burned area,
insufficient monitoring activities and ineffective technical
design related to postfire management strategies. Hence, the
continuing need for more studies evaluating the relative
importance of these strategies on returning the structure and
functions of forest ecosystems after fire.
keywords: Wildfire | Soil erosion | Runoff | Ecosystem restoration. |
مقاله انگلیسی |
10 |
Computer vision techniques on magnetic resonance images for the non-destructive classification and quality prediction of chicken breasts affected by the White-Striping myopathy
تکنیک های بینایی رایانه ای بر روی تصاویر رزونانس مغناطیسی برای طبقه بندی غیر مخرب و پیش بینی کیفیت سینه های مرغ تحت تاثیر میوپاتی White-Striping-2021 This study was designed to assess the capability of MRI-computer vision algorithms, as a non-destructive technique, to classify and predict quality characteristics of chicken breast affected by White-Striping (WS) myopathy. Samples showing moderate and severe degrees of the myopathy were analyzed together with normal samples (no WS symptoms). The influence of the computational algorithms to analyze the MRI images and the techniques of data analysis on the classification and prediction results was aimed. Computational features from both texture (GLCM) and fractal (OPFTA) algorithms were useful to i) classify WS chicken breast by means of different classification technique, Principal Component Analysis and Decision Tree, and ii) predict physico-chemical characteristics of these chicken breast with high accuracy, applying Multiple Linear Regression. The results show the feasibility of objectively classifying chicken breasts without sample destruction into two degrees of severity. This is of remarkable relevance in large processing plants where WS incidence is high and a quick decision-making is required for the fate of affected samples. Keywords: Chicken breast | White-striping | Classification | MRI | Meat quality | Non-destructive technology |
مقاله انگلیسی |