با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
چارچوب حاکمیتی هوش تجاری در دانشگاه: مطالعه موردی دانشگاه دو لا کاستا
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 25
دانشگاه ها و شرکت ها دارای فرآیندهای تصمیم گیری هستند که به آنها اجازه می دهد تا به اهداف سازمانی دست پیدا کنند. در حال حاضر، تحلیل داده ها نقش مهمی در ایجاد دانش، بدست آوردن الگوهای مهم و پیش بینی استراتژی ها ایفا می کنند.این مقاله طراحی چارچوب نظارت هوش تجاری را برای دانشگاه دو لا کاستا ارائه کرده است که به آسانی برای سازمان های دیگر هم قابل استفاده است. برای این منظور، تشخیص انجام شده به منظور شناسایی میزان بلوغ تحلیلی انجام شده است. با استفاده از این چشم انداز، مدلی برای تقویت فرهنگ سازمانی ، زیر ساختارها، مدیریت داده، تحلیل داده و نظارت ارائه شده است.این مدل در بر گیرنده تعریف چارچوب نظارتی، اصول هدایت کننده، استراتژی ها، نهادهای تصمیم گیرنده و نقش ها می باشد. بنابراین، این چارچوب برای استفاده از کنترل های موثر جهت اطمینان از موفقیت پروژه های هوش تجاری و دست یابی به اهداف برنامه توسعه همراه با چسم انداز تحلیلی سازمان ارائه شده است.
کلمات کلیدی: هوش تجاری | نظارت | دانشگاه | تحلیل | تصمیم گیری
|مقاله ترجمه شده|
Forecasting third-party mobile payments with implications for customer flow prediction
پیش بینی پرداخت های تلفن همراه شخص ثالث با پیامدهای پیش بینی جریان مشتری-2020
Forecasting customer flow is key for retailers in making daily operational decisions, but small retailers often lack the resources to obtain such forecasts. Rather than forecasting stores’ total customer flows, this research utilizes emerging third-party mobile payment data to provide participating stores with a value-added service by forecasting their share of daily customer flows. These customer transactions using mobile payments can then be utilized further to derive retailers’ total customer flows indirectly, thereby overcoming the constraints that small retailers face. We propose a third-party mobile-paymentplatform centered daily mobile payments forecasting solution based on an extension of the newly-developed Gradient Boosting Regression Tree (GBRT) method which can generate multi-step forecasts for many stores concurrently. Using empirical forecasting experiments with thousands of time series, we show that GBRT, together with a strategy for multi-period-ahead forecasting, provides more accurate forecasts than established benchmarks. Pooling data from the platform across stores leads to benefits relative to analyzing the data individually, thus demonstrating the value of this machine learning application.
Keywords: Analytics | Big data | Customer flow forecasting | Machine learning | Forecasting many time series | Multi-step-ahead forecasting strategy
Cellular automata Markov chain model based deforestation modelling in the pastoral and agro-pastoral areas of southern Ethiopia
مدل سازی زنجیره مارکوف ماشین سلولی مبتنی بر مدلسازی جنگل زدایی در مناطق روحانی و کلیسایی در جنوب اتیوپی-2020
Permanent conversion of woodland to large-scale commercial agriculture, pastures or urban areas and temporary or partial removal of indigenous trees for shifting cultivation and selective logging remained major environmental challenges in the tropical region. Cognizant of the environmental changes prevailing in the pastoral and agro-pastoral areas of Southern Ethiopia, we have examined the past conversion of woodland to other land uses through the analysis of Landsat Multi-spectral scanner (MSS) 1973, Thematic Mapper(TM) 1986, Enhanced Thematic Mapper (ETMþ) 2003, Operational Land Imagery (OLI) 2017 and then projected the future change in land use/cover (LUC) as well. We have employed Cellular Automata Markov chain model to simulate and predict LUC changes between 2017 and 2060. Four spatial driver variables such as distance to road and settlement, slope and elevation were used to run the simulation. Prior to the prediction, we have simulated the LUC of 2017 using transition potential maps of 2003 and transition matrix between 1973 and 2003. The predictive power of the model was then examined by comparing the reference and simulated LUC maps of 2017and also using the kappa index. A good correlation was obtained between the reference and simulated LUC maps of 2017. In addition, the computed kappa index was above 0.9, which implies that the model is effective in predicting change in LUC. The analysis result revealed that in the entire monitoring period (1973–2017) the area lost 89,875 ha of woodland. The loss is expected to continue during the period 2017–2060, with an estimated loss of 32,423 ha of woodland, if a proper measure is not taken against the continuous loss of woodland. Thus, relentless efforts are needed to rehabilitate the already degraded land and also minimize the potential loss of woodland in the future through the implementation of conservation – livelihood approach, REDD þ project, and sustainable land use management strategies.
Keyterms: Deforestation | Kappa coefficient | CA-Markov | Woodland
Laminar flame speeds of methane/air mixtures at engine conditions: Performance of different kinetic models and power-law correlations
سرعت شعله چند لایه مخلوط های متان / هوا در شرایط موتور: عملکرد مدل های مختلف جنبشی و همبستگی قدرت قانون-2020
The laminar flame speed is an important input in turbulent premixed combustion modelling of spark ignition engines. At engine-relevant temperatures and pressures, its measurement is challenging or not possible and thereby it is usually obtained from simulations based on chemical models or power-law correlations. This work aims to investigate the performance of different models and power-law correla- tions in terms of predicting laminar flame speeds of methane/air at engine conditions. The propagation of spherically expanding laminar flames in a closed chamber was simulated and laminar flame speeds were computed over a broad range of pressures (1-120 atm) and temperatures (30 0-110 0 K) for methane/air mixtures based on seven kinetic models. It was found that at engine conditions, there are notable dis- crepancies among the predictions. GRI Mech. 3.0 and USC Mech. II respectively predict the largest and smallest values at high pressure conditions. This was explained by the difference in CH 3 oxidation and recombination according to reaction pathway analysis. Additionally, laminar flame speeds of methane flames were experimentally determined under engine-relevant conditions. It was shown that the recently developed Foundational Fuel Chemistry Model Version 1.0 model predicts closely the data at high pres- sures and temperatures. Therefore, it was chosen as the reference model for the comparisons. Thirteen published power-law correlations for laminar flame speeds of CH 4 /air were implemented, and their per- formance in predicting the laminar flame speeds at engine conditions was investigated. Most of these correlations have been derived for a narrow range of temperatures and pressures, which are lower than those encountered in engines. A new power-law correlation was derived based on predictions by the Foundational Fuel Chemistry Model Version 1.0. This new correlation is expected to provide reliable pre- dictions at engine conditions for a stoichiometric methane/air mixture and thereby it is recommended to be used in modeling turbulent premixed combustion in spark-ignition engine simulations.
Keywords: Laminar flame speed | engine conditions | methane | power-law correlation | propagating spherical flame
Delamination analysis using cohesive zone model: A discussion on traction-separation law and mixed-mode criteria
تجزیه و تحلیل لایه لایه شدن با استفاده از مدل منطقه منسجم: بحث در مورد قانون جداسازی کشش و معیارهای حالت مختلط-2020
A discussion on cohesive zone model formulation for prediction of interlaminar damage in composite laminates is presented in this paper. The degradation of interlaminar mechanical properties is analysed from a physical point of view. Firstly, the damage evolution is evaluated according to the traction-separation law and it is demonstrated that if a linear elastic unloading/ reloading curve is assumed, the softening function must also be linear. Secondly, issues regarding damage onset and fracture criteria in mixed-mode loading are critically addressed and commented. A new set of criteria is proposed, and the limitations of existing criteria are discussed.
Keywords: Cohesive zone modelling | Fracture mechanics | Finite element analysis (FEA) | Delamination | Interface fracture
Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big dat
آیا می توان از طریق استعلام هوشمند تحت شرایط تجارت الکترونیکی ، وضعیت یک بیمار را پیش بینی کرد؟ پیش بینی خطر ابتلا به بیماری مبتنی بر ویژگی های توالی بر ویژگی های انتخاب شده از تشخیص شناختی داده های بزرگ-2020
The data-driven mode has promoted the researches of preventive medicine. In prediction of disease risks, physicians’ clinical cognitive diagnosis data can be used for early prevention of diseases and, therefore, to reduce medical cost, to improve accessibility of medical services and to lower medical risk. However, researches involved no physicians’ cognition of patients’ conditions in intelligent inquiry under e-health business mode, offered no diagnosis big data, neglected the values of the fused text information generated by joint activities of online and offline medical data, and failed to thoroughly analyze the phenomenon of redundancy-complementarity dispersion caused by high-order information shortage from the online inquiry data-driven perspective. Besides, the risk prediction simply based on offline clinical cognitive diagnosis data undoubtedly reduces prediction precision. Importantly, relevant researches rarely considered temporal relationships of different medical events, did not conduct detailed analysis on practical problems of pattern explosion, did not offer a thought of intelligent portrayal map, and did not conduct relevant risk prediction based on the sub-maps obtained from the map. In consequence, the paper presents a disease risk prediction method with the model for redundancy-complementarity dispersion-based feature selection from physicians’ online cognitive diagnosis big data to realize features selection from the cognitive diagnosis big data of online intelligent inquiry; the obtained features were ranked intelligently for subsequent high-dimensional information shortage compensation; the compensated key feature information of the cognitive diagnosis big data was fused with offline electronic medical record (EMR) to form the virtual electronic medical record (VEMR). The formed VEMR was combined with the method of the sequential feature map for modelling, and a sequential feature map-based model for disease risk prediction was presented to obtain online users’ medical conditions. A neighborhood-based collaborative prediction model was presented for prediction of an online intelligent medical inquiry user’s possible diseases in the future and to intelligently rank the risk probabilities of the diseases. In the experiments, the online intelligent medical inquiry users’ VEMRs were used as the foundation of the simulation experiments to predict disease risks in chronic obstructive pulmonary disease (OCPD) population and rheumatic heart disease (RHD) population. The experiments demonstrated that the presented method showed relatively good metric performances in the VEMR and improved disease risk prediction.
Keywords: Cognitive diagnosis big data | Online intelligent inquiry | Sequential feature map | Disease risk prediction | Redundancy and complementarity dispersion
Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems
مدیریت داده های همکاری مشترک زوج دیجیتال برای سیستم های تولید مواد افزودنی فلز-2020
Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.
Keywords: Metal Additive Manufacturing | Digital Twin | data management | data model | machine learning | product lifecycle management
Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux
مدل سازی فرآیند اسمزوز رو به جلو با استفاده از شبکه های عصبی مصنوعی (ANN) برای پیش بینی شار نفوذ-2020
Artificial neural networks (ANN) are black box models that are becoming more popular than transport-based models due to their high accuracy and less computational time in predictions. The literature shows a lack of ANN models to evaluate the forward osmosis (FO) process performance. Therefore, in this study, a multi-layered neural network model is developed to predict the permeate flux in forward osmosis. The developed model is tested for its generalization capability by including lab-scale experimental data from several published studies. Nine input variables are considered including membrane type, the orientation of membrane, molarity of feed solution and draw solution, type of feed solution and draw solution, crossflow velocity of the feed solution, and the draw solution and temperature of the feed solution and the draw solution. The development of optimum network architecture is supported by studying the impact of the number of neurons and hidden layers on the neural network performance. The optimum trained network shows a high R2 value of 97.3% that is the efficiency of the model to predict the targeted output. Furthermore, the validation and generalized prediction capability of the model is tested against untrained published data. The performance of the ANN model is compared with a transport-based model in the literature. A simple machine learning technique such as a multiple linear regression (MLR) model is also applied in a similar manner to be compared with the ANN model. ANN demonstrates its ability to form a complex relationship between inputs and output better than MLR.
Keywords: Artificial neural network | Forward osmosis | Water treatment | Desalination | Machine learning
Data mining and application of ship impact spectrum acceleration based on PNN neural network
داده کاوی و کاربرد شتاب طیف تأثیر کشتی بر اساس شبکه عصبی PNN-2020
The selection of the smoothing coefficient of the probabilistic neural network directly affects the performance of the network. Traditionally, all the mode layer neurons use a uniform smoothing coefficient, and then the optimal smoothing parameters suitable for this problem are searched by the optimization algorithm. In this study, the smoothing coefficients of the mode layer neurons connected by the same summation layer are set to the same value, which not only reflects the relationship between the training samples of the same pattern, but also highlights the difference between the training samples of different modes. Two probabilistic neural network models are applied to the ship impact environment prediction respectively. The results show that the classification effect of multiple smoothing factors is further improved than the single smoothing factor network.
Keywords: Ship impact environment prediction | Probabilistic neural network | Smoothing coefficient | Optimization algorithm
From chemical structure to quantitative polymer properties prediction through convolutional neural networks
از ساختار شیمیایی گرفته تا پیش بینی کمی از خواص پلیمر از طریق شبکه های عصبی در هم تنیده -2020
In this work convolutional-fully connected neural networks were designed and trained to predict the glass transition temperature of polymers based only on their chemical structure. This approach has shown to successfully predict the Tg of unknown polymers with average relative errors as low as 6%. Several networks with different architecture or hiperparameters were successfully trained using a previously studied glass transition temperatures dataset for validation, and then the same method was employed for an extended dataset, with larger Tg dispersion and polymer’s structure variability. This approach has shown to be accurate and reliable, and does not require any time consuming or expensive measurements and calculations as inputs. Furthermore, it is expected that this method can be easily extended to predict other properties. The possibility of predicting the properties of polymers not even synthesized will save time and resources for industrial development as well as accelerate the scientific understanding of structure-properties relationships in polymer science.
Keywords: QSPR | Properties prediction | Deep learning | Neural network | Smart design