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1 |
Strategies for ensuring required service level for COVID-19 herd immunity in Indian vaccine supply chain
راهکارهایی برای اطمینان از سطح خدمات مورد نیاز برای مصونیت گله COVID-19 در زنجیره تأمین واکسن هند-2021 Post COVID-19 vaccine development, nations are now getting ready to face another challenge: how to
effectively distribute vaccines amongst the masses to quickly achieve herd immunity against the infection. According to some experts, herd immunity for COVID-19 can be achieved by inoculating 67% of the
population. India may find it difficult to achieve this service level target, owing to several infrastructural
deficiencies in its vaccine supply chain. Effect of these deficiencies is to cause frequent lead time disruptions. In this context, we develop a novel modelling approach to identify few nodes, which require
additional inventory allocations (strategic inventory reserves) to ensure minimum service level (67%) under the possibility of lead time disruptions. Later, through an illustrative case study on distribution of
Japanese Encephalitis vaccine, we identify conditions under which strategic inventory reserve policy cannot be practically implemented to meet service level targets. Nodes fulfilling these conditions are termed
as critical nodes and must be overhauled structurally to make the implementation of strategic inventory
policy practically viable again. Structural overhauling may entail installation of better cold storage facilities, purchasing more quality transport vans, improving reliability of transport network, and skills of cold
storage manager by training. Ideally, conditions for identifying critical nodes for COVID-19 vaccine distribution must be derived separately by substituting COVID-19 specific parametric values in our model. In
the absence of the required data for COVID-19 scenario, JE specific criteria can be used heuristically to
identify critical nodes and structurally overhaul them later for efficiently achieving service level targets. Keywords: Humanitarian logistics (O) | Structural resilience | COVID-19 | Lead time disruption | Herd immunity | Vaccine supply chain |
مقاله انگلیسی |
2 |
AI-based Reference Ankle Joint Torque Trajectory Generation for Robotic Gait Assistance: First Steps
تولید مسیر حرکت گشتاور مفصل مچ پا مبتنی بر هوش مصنوعی برای کمک به راه رفتن رباتیک: اولین قدم ها-2020 Robotic-based gait rehabilitation and assistance
have been growing to augment and to recover motor function in
subjects with lower limb impairments. There is interest in
developing user-oriented control strategies to provide
personalized assistance. However, it is still needed to set the
healthy user-oriented reference joint trajectories, namely,
reference ankle joint torque, that would be desired under healthy
conditions. Considering the potential of Artificial Intelligence (AI)
algorithms to model nonlinear relationships of the walking
motion, this study implements and compares two offline AI-based
regression models (Multilayer Perceptron and Long-Short Term
Memory-LSTM) to generate healthy reference ankle joint torques
oriented to subjects with a body height ranging from 1.51 to 1.83
m, body mass from 52.0 to 83.7 kg and walking in a flat surface
with a walking speed from 1.0 to 4.0 km/h. The best results were
achieved for the LSTM, reaching a Goodness of Fit and a
Normalized Root Mean Square Error of 79.6 % and 4.31 %,
respectively. The findings showed that the implemented LSTM
has the potential to be integrated into control architectures of
robotic assistive devices to accurately estimate healthy useroriented
reference ankle joint torque trajectories, which are
needed in personalized and Assist-As-Needed conditions. Future
challenges involve the exploration of other regression models and
the reference torque prediction for remaining lower limb joints,
considering a wider range of body masses, heights, walking speeds,
and locomotion modes. Keywords: Ankle Joint Torque Prediction | Artificial Intelligence | Control Strategies | Regression Models | Robotic Gait Rehabilitation |
مقاله انگلیسی |
3 |
Unbalance evaluation of a scaled wind turbine under different rotational regimes via detrended fluctuation analysis of vibration signals combined with pattern recognition techniques
ارزیابی عدم تعادل یک توربین بادی مقیاس پذیر تحت رژیمهای مختلف چرخش از طریق تجزیه و تحلیل نوسانات آشکار سیگنال های لرزش همراه با تکنیک های تشخیص الگو-2019 This work aims to propose a different approach to evaluate the operating conditions of a scaled wind
turbine through vibration analysis. The turbine blades were built based on the NREL S809 profile and a
40-cm diameter, while the design blade tip speed ratio (l) is equal to 7. Masses weighing 0.5, 1.0, and
1.5 g were added to the tip of one or two blades in a varying sequence with the intent of simulating
potential problems and producing several scenarios from simple imbalances to severe rotor vibration
levels to be compared to the control condition where the three blades and the system were balanced. The
signals were processed and classified by a combination of detrended fluctuation analysis with Karhunen-
Loeve Transform, Gaussian discriminator, and Artificial Neural Network, which are pattern recognition
techniques with supervised learning. Good results were achieved by employing the above cited recognition
techniques as more than 95% of normal and imbalanced cases were correctly classified. In a
general way, it was also possible to identify different levels of blade imbalance, thus proving that the
present approach may be an excellent predictive maintenance tool for vibration monitoring of wind
turbines. Keywords: Machine learning | Signal processing | Fault detection | Condition monitoring | Non-stationary vibration | Condition based maintenance |
مقاله انگلیسی |
4 |
Is mass classification in mammograms a solved problem? - A critical review over the last 20 years
آیا طبقه بندی دسته جمعی در ماموگرافی ها یک مشکل حل شده است؟ - بررسی انتقادی طی 20 سال گذشته-2019 Breast cancer is one of the most common and deadliest cancers that affect mainly women worldwide, and mammography examination is one of the main tools to help early detection. Several papers have been published in the last decades reporting on techniques to automatically recognize breast cancer by analyzing mammograms. These techniques were used to create computer systems to help physicians and radiologists obtain a more precise diagnosis. The objective of this paper is to present an overview re- garding the use of machine learning and pattern recognition techniques to discriminate masses in digi- tized mammograms. The main differences we found in the literature between the present paper and the other reviews are: 1) we used a systematic review method to create this survey; 2) we focused on mass classification problems; 3) the broad scope and spectrum used to investigate this theme, as 129 papers were analyzed to find out whether mass classification in mammograms is a problem solved. In order to achieve this objective, we performed a systematic review process to analyze papers found in the most im- portant digital libraries in the area. We noticed that the three most common techniques used to classify mammographic masses are artificial neural network, support vector machine and k-nearest neighbors. Furthermore, we noticed that mass shape and texture are the most used features in classification, al- though some papers presented the usage of features provided by specialists, such as BI-RADS descriptors. Moreover, several feature selection techniques were used to reduce the complexity of the classifiers or to increase their accuracies. Additionally, the survey conducted points out some still unexplored research opportunities in this area, for example, we identified that some techniques such as random forest and logistic regression are little explored, while others, such as grammars or syntactic approaches, are not being used to perform this task. Keywords: Mammography | Mammogram | Breast cancer | Classification | Diagnosis | Pattern recognition |
مقاله انگلیسی |
5 |
Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features
یادگیری ماشین برای تمایز لیپوماتیک رحمی T2 با وزنی T2 با استفاده از ویژگیهای تصویربرداری رزونانس مغناطیسی چند پارامتری مغناطیسی از سارکوم رحمی-2019 Rationale and Objective: Uterine leiomyomas with high signal intensity on T2-weighted imaging (T2WI)
can be difficult to distinguish from sarcomas. This study assessed the feasibility of using machine learning
to differentiate uterine sarcomas from leiomyomas with high signal intensity on T2WI on multiparametric
magnetic resonance imaging.
Materials and Methods: This retrospective study included 80 patients (50 with benign leiomyoma and 30
with uterine sarcoma) who underwent pelvic 3 T magnetic resonance imaging examination for the evaluation
of uterine myometrial smooth muscle masses with high signal intensity on T2WI. We used six machine
learning techniques to develop prediction models based on 12 texture parameters on T1WI and T2WI,
apparent diffusion coefficient maps, and contrast-enhanced T1WI, as well as tumor size and age. We calculated
the areas under the curve (AUCs) using receiver-operating characteristic analysis for each model
by 10-fold cross-validation and compared these to those for two board-certified radiologists.
Results: The eXtreme Gradient Boosting model gave the highest AUC (0.93), followed by the random
forest, support vector machine, multilayer perceptron, k-nearest neighbors, and logistic regression
models. Age was the most important factor for differentiation (leiomyoma 44.9 § 11.1 years; sarcoma
58.9 § 14.7 years; p < 0.001). The AUC for the eXtreme Gradient Boosting was significantly higher than
those for both radiologists (0.93 vs 0.80 and 0.68, p = 0.03 and p < 0.001, respectively).
Conclusion: Machine learning outperformed experienced radiologists in the differentiation of uterine
sarcomas from leiomyomas with high signal intensity on T2WI. Key Words: Magnetic resonance imaging | Uterine neoplasm | Leiomyoma | Machine learning | Sarcoma |
مقاله انگلیسی |
6 |
معیارهای رسانه های اجتماعی و تحلیل احساسات برای ارزیابی اثربخشی پست های رسانه های اجتماعی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 14 پژوهش حاضر معیارهای برای موفقیت در خودبازاریابی برای رسانه های اجتماعی ارائه می دهد. شرکت کنندگان شامل گیمرهای یوتیوب بودند. ما بر روی محتوای ارتباطات شان در فیس بوک تمرکز می کنیم تا تفاوت های قابل توجهی ملاک ها و تفسیر احساسات فیس بوک شان را شناسایی کنیم. در این راستا روش تحقيقANOVA و تحليل احساسات مورد استفاده قرار گرفت. تجزیه و تحلیل دسته بندی شده ی پست طبقه بندی شده ANOVA نشان داد که فیلم های یوتیوب لایک ها ، کامنت ها و اشتراک های کم اهمیتر را به دست آورد. از سوی دیگر به عکس ها در مقایسه با سایر انواع نمونه پست ها، تمایل بیشتری نشان دادند. تجزیه و تحلیل احساسات نشان می دهد که منفی بودن احساس فالورها، زمانی بود که فعالیت های تولید شده توسط کاربر نسبتا کم بود. این مسئله نتایج مکمل ارزشمندی را برای تجزیه و تحلیل سایر شاخص های پست مانند تعداد لایک ها، کامنت ها و اشتراک ها فراهم کرد. نتایج پژوهش ضرورت استفاده از تکنیک های پردازش زبان طبیعی برای بهینه سازی برند ارتباطات در مورد رسانه های اجتماعی و اهمیت بررسی نظر توده ها برای درک بهتر بازخورد مصرف کنندگان را نشان داد.
کلمات کلیدی: معیارهای رسانه اجتماعی | خود بازاریابی | تجزیه و تحلیل احساسات |
مقاله ترجمه شده |
7 |
Simulating mining-induced strata permeability changes
شبیه سازی تغییرات نفوذپذیری اقشار ناشی از معادن-2018 Mining processes fracture the surrounding strata and may modify the flow of groundwater by inducing new
fractures or changing the permeability of existing defects. The result of mining-induced permeability changes
can be disturbance to aquifers or other surface or sub-surface water bodies. Traditional methods for predicting
mining-induced fracture connectivity and enhanced permeability based on empirical strain-based criteria may
not satisfy modern regulatory demands, nor adequately reflect local geological, geotechnical and hydro
geological conditions. Standard continuum numerical methods may indirectly estimate permeability enhance
ment from plastic strains however they are not able to track aperture on flow paths or predict fracture con
nectivity. This paper presents a numerical approach that is demonstrated to be capable of representing longwall
mining induced fracturing in sedimentary rock masses. By initiating and propagating fractures, determining
connectivity and calculating aperture in a piecewise manner on flow paths, we have estimated permeability
enhancement from first principles. Fracture intensity and porosity metrics are calculated and identify the height
of the enhanced permeability fractured zone above a longwall goaf. Permeability within the overburden is
estimated from the Kozeny-Carman permeability–porosity equation. At a mine site studied in detail in this paper
a permeability increase from the in situ state is predicted to range from approximately eight orders-of-magnitude
in the caved zone to one to two orders-of-magnitude in the strata above the fractured zone. Realistically si
mulating cracking, fracturing and crushing of rock strata remains numerically intensive and challenging at the
scale of a longwall panel. It is demonstrated in this paper and provides valuable insights into the rockmass
response to mining.
Keywords: Coal mining permeability changes ، Coal mining ، Kozeny-Carman ، PFC ، Aquifer interference ، Fracture propagation |
مقاله انگلیسی |
8 |
Economic and environmental assessment of agro-energy wood biomass supply chains
ارزیابی اقتصادی و زیست محیطی کشاورزی انرژی زیست توده زنجیره تامین چوب-2017 The aim of this study is to conduct an economic and environmental assessment of forest biomass for
heating, in particular two types of firewood and three types of wood chips were analyzed. Regarding
economic aspects, an analysis was made of production costs and revenues (per tonne of biomass),
considering all the stages involved “from the woods to the mouth of the boiler.” For the environmental
analysis, conducted using life cycle assessment, the stages taken into account went from “the woods to
the heat produced”. The wood biomasses were compared to each other and to fuel oil and natural gas.
The economic analysis showed that at current market prices it is more profitable to produce firewood
rather than wood chips. As concerns the environmental aspects, the results of the LCA showed that, for
the same heat output, forest wood-based fuel has an environmental impact lower than fuel oil, but still
higher than natural gas. There are no big differences in the impact of various wood fuels. In the
conclusion, some ways for improvement have been proposed, in terms of both the economic competi
tiveness of the agro-energy supply chains considered and the reducing of their environmental impact.
Keywords:Wood biomass|Agro-energy|Economic analysis|Environmental assessment|Life cycle assessment|Sustainability |
مقاله انگلیسی |
9 |
Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach
تشخیص کامپیوتری توده های ماموگرافی براساس روش بازیابی تصویر مبتنی بر محتوا-2017 Article history:Received 1 September 2016Revised 28 April 2017Accepted 25 May 2017Available online 26 May 2017Keywords: Mammography MassesCBIR CADx SVMIn this work, the incorporation of content-based image retrieval (CBIR) into computer aided diagnosis (CADx) is investigated, in order to contribute to the decision-making process of radiologists in the char- acterization of mammographic masses. The proposed scheme comprises two stages: A margin-specific su- pervised CBIR stage that retrieves images from reference cases along with a decision stage that is based on the retrieved items. The feature set utilized exploits state-of-the-art features along with a newly pro- posed texture descriptor, namely mHOG, targeted to capturing margin and core specific mass properties. Performance evaluation considers the CBIR and diagnosis stages separately and is addressed by using standard measures on an enhanced version of the widely adopted digital database for screening mam- mography (DDSM). The proposed scheme achieved improved performance of CADx of masses in X-ray mammography experimentally compared to the state-of-the-art.© 2017 Elsevier Ltd. All rights reserved. Keywords: Mammography | Masses | CBIR | CADx | SVM |
مقاله انگلیسی |
10 |
Experimental evaluation of a modal parameter based system identification procedure
ارزیابی تجربی از یک روش شناسایی مبتنی بر سیستم پارامتر معین-2016 Correct modelling of the foundation of a rotor bearing foundation system (RBFS) is an invaluable asset for the balancing and efficient running of turbomachinery. Numerical experiments have shown that a modal parameter based identification approach could be feasible for this purpose but there is a lack of experimental verification of the suitability of such a modal approach for even the simplest systems. In this paper the approach is tested on a simple experimental rig comprising a clamped horizontal bar with lumped masses. It is shown that apart from damping, the proposed approach can identify reasonably accurately the relevant modal parameters of the rig; and that the resulting equivalent system can predict reasonably well the frequency response of the rig. Hence, the proposed approach shows promise but further testing is required, since application to identifying the foundation of an RBFS involves the additional problem of accurately obtaining the force excitation from motion measurements.& 2015 Elsevier Ltd. All rights reserved.
Keywords: Experimental evaluation | System identification | Modal parameters |
مقاله انگلیسی |