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.
Rapid discrimination of Salvia miltiorrhiza according to their geographical regions by laser induced breakdown spectroscopy (LIBS) and particle swarm optimization-kernel extreme learning machine (PSO-KELM)
تبعیض سریع miltiorrhiza مریم گلی با توجه به مناطق جغرافیایی خود را با طیف سنجی شکست ناشی از لیزر (LIBS) و یادگیری ماشین افراطی بهینه سازی ازدحام ذرات (PSO-KELM)-2020
Laser-induced breakdown spectroscopy (LIBS) coupled with particle swarm optimization-kernel extreme learning machine (PSO-KELM) method was developed for classification and identification of six types Salvia miltiorrhiza samples in different regions. The spectral data of 15 Salvia miltiorrhiza samples were collected by LIBS spectrometer. An unsupervised classification model based on principal components analysis (PCA) was employed first for the classification of Salvia miltiorrhiza in different regions. The results showed that only Salvia miltiorrhiza samples from Gansu and Sichuan Province can be easily distinguished, and the samples in other regions present a bigger challenge in classification based on PCA. A supervised classification model based on KELM was then developed for the classification of Salvia miltiorrhiza, and two methods of random forest (RF) and PSO were used as the variable selection method to eliminate useless information and improve classification ability of the KELM model. The results showed that PSO-KELM model has a better classification result with a classification accuracy of 94.87%. Comparing the results with that obtained by particle swarm optimization-least squares support vector machines (PSO-LSSVM) and PSO-RF model, the PSO-KELM model possess the best classification performance. The overall results demonstrate that LIBS technique combined with PSO-KELM method would be a promising method for classification and identification of Salvia miltiorrhiza samples in different regions.
Keywords: Laser-induced breakdown spectroscopy | Particle swarm optimization | Kernel extreme learning machine | Salvia miltiorrhiza | Classification
Leading successful government-academia collaborations using FLOSS and agile values
پیشرو همکاریهای موفق دولت و آکادمی با استفاده از FLOSS و مقادیر چابک-2020
Government and academia share concerns for efficiently and effectively servicing societal demands, which includes the development of e-government software. Government-academia partnerships can be a valu- able approach for improving productivity in achieving these goals. However, governmental and academic institutions tend to have very different agendas and organizational and managerial structures, which can hinder the success of such collaborative projects. In order to identify effective approaches to overcome collaboration barriers, we systematically studied the case of the Brazilian Public Software portal project, a 30-month government-academia collaboration that, using Free/Libre/Open Source Software practices and agile methods for project management, developed an unprecedented platform in the context of the Brazil- ian government. We gathered information from experience reports and data collection from repositories and interviews to derive a collection of practices that contributed to the success of the collaboration. In this paper, we describe how the data analysis led to the identification of a set of three high-level decisions supported by the adoption of nine best practices that improved the project performance and enabled professional training of the whole team.
Keywords: Project management | Government-Academia collaboration | Free software | Open source software | Agile methodologies | e-Government
Refined composite multivariate multiscale symbolic dynamic entropy and its application to fault diagnosis of rotating machine
آنتروپی پویای نمادین چند متغیره کامپوزیت تصفیه شده و کاربرد آن در تشخیص خطای ماشین چرخشی-2020
Accurate and efficient identification of various fault categories, especially for the big data and multisensory system, is a challenge in rotating machinery fault diagnosis. For the diagnosis problems with massive multivariate data, extracting discriminative and stable features with high efficiency is the significant step. This paper proposes a novel feature extraction method, called Refined Composite multivariate Multiscale Symbolic Dynamic Entropy (RCmvMSDE), based on the refined composite analysis and multivariate multiscale symbolic dynamic entropy. Specifically, multivariate multiscale symbolic dynamic entropy can capture more identification information from multiple sensors with superior computational efficiency, while refine composite analysis guarantees its stability. The abilities of the proposed method to measure the complexity of multivariate time series and identify the signals with different components are discussed based on adequate simulation analysis. Further, to verify the effectiveness of the proposed method on fault diagnosis tasks, a centrifugal pump dataset under constant speed condition and a ball bearing dataset under time-varying speed condition are applied. Compared with the existing methods, the proposed method improves the classification accuracy and F-score to 99.81% and 0.9981, respectively. Meanwhile, the proposed method saves at least half of the computational time. The result shows that the proposed method is effective to improve the efficiency and classification accuracy dealing with the massive multivariate signals.
Keywords: Multivariate multiscale symbolic dynamic | entropy | Random forest | Time-varying speed conditions | Fault diagnosis
Functional urban area delineations of cities on the Chinese mainland using massive Didi ride-hailing records
توصیف های کاربردی منطقه شهری از شهرها در سرزمین اصلی چین با استفاده از سوابق گسترده تگرگ سوار بر دیدنی Didi-2020
The problem associated with a citys administrative boundary being “under-” or “over-bounded” has become a global phenomenon. A citys administrative boundary city does not effectively represent the actual size and impact of its labor force and economic activity. While many existing case studies have investigated the functional urban areas of single cities, the problem of how to delineate urban areas in geographic space relating to large bodies of cities or at the scale of an entire country has not been investigated. This study proposed a method for FUA identification that relies on ride-hailing big data. In this study, over 43 million anonymized 2016 car-hailing records were collected from Didi Chuxing, the largest car-hailing online platform in the world (to the best of our knowledge). A core-periphery approach is then proposed that uses nationwide and fine-grained trips to understand functional urban areas in Mainland China. This study examined 4456 out of all 39,007 townships in an attempt to provide a new method for the definition of urban functional areas in Chinese Mainland. In addition, four types of cities are identified using a comparison of functional urban areas with their administrative limits, and a further evaluation is conducted using 23 Chinese urban agglomerations. With the rapidly increasing use of internet-based ride-hailing services, such as Didi, Grab, Lyft, and Uber, globally, this study provides a practical benchmark for the delineation of functional urban areas at larger scales..
Keywords: Functional urban area | Car-hailing records | |National level | Delineating standards | City system
Delineating urban hinterland boundaries in the Pearl River Delta: An approach integrating toponym co-occurrence with field strength model
ترسیم مرزهای مناطق شهری در دلتای رود مروارید: رویکرد ادغام همزمان وقایع توپومی با مدل مقاومت میدانی-2020
Urban development requires the support of its surrounding areas. Accurate identification of urban hinterlands can help to scientifically evaluate strength and potential of urban development. The field strength model is regarded as an effective way to identify hinterlands, but the revision of friction coefficient has still not reached a consensus. With the application of big data in urban planning, it is possible to improve the field strength model. Toponym co–occurrence data, as a timely data source directly obtained from the Internet, can be used to reflect the spatiotemporal changes in urban connections, and provide an approach to quantifying the friction coefficient for the division of urban hinterlands. In this study, a new approach was developed by integrating toponym co–occurrence and improved field strength model. We considered the Pearl River Delta urban agglomeration as a case and identified the urban hinterland of each city. The results showed that the friction coefficient among cities fluctuated within a range of 1.25–2.50, the urban hinterlands were no longer confined to their own administrative divisions, and there was fierce competition with other cities. In particular, the urban hinterland of Guangzhou was 3699 km2 larger than its actual administrative area. In addition, the proposed approach was more reliable in urban hinterland identification compared with the traditional fixed friction coefficient method. This study provides an improved field strength model based on toponym co–occurrence, which can identify urban hinterlands more accurately and objectively as well as promote the application of big data in urban planning.
Keywords: Urban hinterland | Toponym co–occurrence | Improved field strength model | Pearl River Delta
A model for big spatial rural data infrastructure in Turkey: Sensor-driven and integrative approach
یک مدل برای زیرساخت های داده های بزرگ فضایی روستایی در ترکیه: رویکرد حسگر محور و یکپارچه-2020
A Spatial Data Infrastructure (SDI) enables the effective spatial data flow between providers and users for their prospective land use analyses. The need for an SDI providing soil and land use inventories is crucial in order to optimize sustainable management of agricultural, meadow and forest lands. In an SDI where datasets are static, it is not possible to make quick decisions about land use. Therefore, SDIs must be enhanced with online data flow and the capabilities to store big volumes of data. This necessity brings the concepts of the Internet of Things (IoT) and Big Data (BD) into the discussion. Turkey needs to establish an SDI to monitor and manage its rural lands. Even though Turkish decision-makers and scientists have constructed a solid national SDI standardization, a conceptual model for rural areas has not been developed yet. In accordance with the international agreements, this model should adopt the INSPIRE Directive and Land Parcel Identification System (LPIS) standards. In order to manage rural lands in Turkey, there are several legislations which characterize the land use planning, land classification and restrictions. Especially, the Soil Protection and Land Use Law (SPLUL) enforces to use a lot and a variety of land use parameters that should be available in a big rural SDI. Moreover, this model should be enhanced with IoT, which enables to use of smart sensors to collect data for monitoring natural occurrences and other parameters that may help to classify lands. This study focuses on a conceptual model of a Turkish big rural SDI design that combines the sensor usage and attribute datasets for all sorts of rural lands. The article initially reviews Turkish rural reforms, current enterprises to a national SDI and sensor-driven agricultural monitoring. The suggested model integrates rural land use types, such as agricultural lands, meadowlands and forest lands. During the design process, available data sets and current legislation for Turkish rural lands are taken into consideration. This schema is associated with food security databases (organic and good farming practices), non-agricultural land use applications and local/ European subsidies in order to monitor the agricultural parcels on which these practices are implemented. To provide a standard visualization of this conceptual schema, the Unified Modeling Language (UML) class diagrams are used and a supplementary data dictionary is prepared to make clear definitions of the attributes, data types and code lists used in the model. This conceptual model supports the LPIS, ISO 19156 International Standard (Geographic Information: Observations and Measurements) catalogue and INSPIRE data theme specifications due to the fact that Turkey is negotiating the accession to EU; however, it also provides a local understanding that enables to manage rural lands holistically for sustainable development goals. It suggests an expansion for the sensor variety of Turkish agricultural monitoring project (TARBIL) and it specifies a rural theme for Turkish National SDI (TUCBS).
Keywords: Spatial data infrastructures | Big data | Internet of things | Rural land use | INSPIRE | LPIS
Initial data identification in conservation laws and Hamilton–Jacobi equations
شناسایی داده های اولیه در قوانین حفاظت و معادلات همیلتون-ژاکوبی-2020
In the scalar 1D case, conservation laws and Hamilton–Jacobi equations are deeply related. For both, in the case of a uniformly convex flux/Hamiltonian, we characterize those profiles that can be attained as solutions at a given positive time corresponding to at least one initial datum. Then, for each of the two equations, we precisely identify all those initial data yielding a solution that coincide with a given profile at that positive time. Various topological and geometrical properties of the set of these initial data are then proved.
Keywords: Inverse design | Conservation laws | Hamilton–Jacobi equations | Entropy solutions | Viscosity solutions
Vascularized neural constructs for ex-vivo reconstitution of blood-brain barrier function
سازه های عصبی عروقی برای بازسازی قبلی در داخل بدن و عملکرد سد خونی مغز-2020
Ex-vivo blood-brain barrier (BBB) model is of great value for studying brain function and drug development, but it is still challenging to engineer macroscale three-dimensional (3D) tissue constructs to recapitulate physiological and functional aspects of BBB. Here, we describe a delicate 3D vascularized neural constructs for ex-vivo reconstitution of BBB function. The tissue-engineered tissue construct is based on a multicomponent 3D coculture of four types of cells, which typically exist in the BBB and were spatially defined and organized to mimic the in vivo BBB structure and function. A porous polycaprolactone/poly (D,L-lactide-co-glycolide) (PCL/PLGA) microfluidic perfusion system works as the vasculature network, which was made by freeze-coating a 3D-printed sacrificial template. Endothelial cells were seeded inside the channels of the network to form 3D interconnected blood vessels; while other types of cells, including pericytes, astrocytes, and neurons, were co-cultured in a collagen matrix wrapping the vasculature network to derive a vascularized neural construct that recapitulates in vivo BBB function with great complexity and delicacy. Using this model, we successfully reconstituted BBB function with parameters that are similar to the in vivo condition, and demonstrated the identification of BBBpenetrating therapeutics by examining the molecular delivery to neuronal cells when relevant biologic molecules were applied to the vasculature circulation system of the neural construct.
Keywords: Tissue engineering | 3D printing | Organ on a chip | Blood-brain barrier | Vasculature network | Neuro-engineering | Drug screening
The Salak Field, Indonesia: On to the next 20 years of production
میدان سالاک ، اندونزی: تولید تا 20 سال آینده -2020
The Salak Geothermal Field is the largest producing geothermal field in Indonesia with an installed power generation capacity of 377 MWe. After more than 23 years of commercial operations and through vigilant resource management, the Salak Field is still performing well. Since 1994, when commercial production started, the net capacity factor has averaged about 91% annually. After the last turbine uprating in 2005, the net capacity factor has improved to an annual average of 95%. By 2019, a substantial injection realignment will be implemented which will move all condensate injection outside the field production boundary and shift most brine injection outside and to the southeast margin. This project will mitigate brine injection impact to the AWI 7 and 8 wells and hasten development of the steam cap in the western portion of the field. With this injection realignment, reservoir simulation forecasts show the Salak geothermal resource will likely be able to continue steam production at its current level in the foreseeable future. Similar to other long-producing geothermal fields, Salak has encountered resource management challenges, such as, injection breakthrough, influx of marginal recharge, wellbore scaling, and production of significant amounts of non-condensable gas (“NCG”), in response to commercial production. To address these challenges, an extensive reservoir monitoring program and integration of new make-up drilling results enabled updates and fine-tuning of the conceptual model of the field. Key updates include increased understanding of the distribution of the producing effective fractures and their permeability in the reservoir, identification of injection capacity outside the commercial field boundary in the Cianten Caldera, and improved understanding of NCG interference in the steam cap and injection and natural recharge impacts on the field. These insights and the updated conceptual understanding of the Salak geothermal system coupled with reservoir simulation have provided a credible forecast of the Salak reservoir’s future performance for decision-makers to have the necessary confidence to fund the injection realignment project. Additionally, the refined conceptual model provides the reservoir management team with a means to plan and target development make-up wells and effectively focus future data gathering and reservoir monitoring activities
Keywords: Salak | Injection breakthrough | Non-Condensable gases | Marginal recharge | Effective fractures | Wellbore scale
A new approach for identifying the Kemeny median ranking
یک روش جدید برای شناسایی رتبه بندی متوسط Kemeny-2020
Condorcet consistent rules were originally developed for preference aggregation in the theory of social choice. Nowadays these rules are applied in a variety of fields such as discrete multi-criteria analysis, defence and security decision support, composite indicators, machine learning, artificial intelligence, queries in databases or internet multiple search engines and theoretical computer science. The cycle issue, known also as Condorcets paradox, is the most serious problem inherent in this type of rules. Solutions for dealing with the cycle issue properly already exist in the literature; the most important one being the identification of the median ranking, often called the Kemeny ranking. Unfortunately its identification is a NP-hard problem. This article has three main objectives: (1) to clarify that the Kemeny median order has to be framed in the context of Condorcet consistent rules; this is important since in the current practice sometimes even the Borda count is used as a proxy for the Kemeny ranking. (2) To present a new exact algorithm, this identifies the Kemeny median ranking by providing a searching time guarantee. (3) To present a new heuristic algorithm identifying the Kemeny median ranking with an optimal trade-off between convergence and approximation .
Keywords : Decision analysis | Combinatorial optimisation | Social choice| Multiple criteria | Artificial intelligence| Defence and security| Big data