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1 |
A Novel Application of Educational Management Information System based on Micro Frontends
260-S1877050920320688-2020 With the launch of the Education Informatization 2.0 action plan by the Ministry of Education, a large number of college information systems have been born in China. Most of these systems are single page web applications (SPA) based on traditional MVC structures. Due to the complex logic and high coupling between educational businesses, developers need to write a lot of code. The education information system has many businesses and high coupling between businesses that the system often face problems such as bloated frontend businesses, iterative system updates, and difficult incremental function developments. Combined with the idea of service-oriented architecture, this paper proposes a micro frontends solution and applies it to the new generation of graduate information platform of East China Normal University, which has better agile development capabilities. From the aspects of service separation, efficient development, and incremental upgrade, this paper verifies that the architecture can well adapt to the needs of future educational management information system. The design of the micro frontends provides a new idea for the development of a new generation of education information system.© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the KES International. Keywords: Educational Management information system | Micro Frontends | Education Informatization | Graduate information system; |
مقاله انگلیسی |
2 |
Adaptive AI-based auto-scaling for Kubernetes
مقیاس گذاری خودکار مبتنی بر هوش مصنوعی تطبیقی برای Kubernetes-2020 Kubernetes, the prevalent container orchestrator for
cloud-deployed web applications, offers an automatic scaling
feature for the application provider in order to meet the everchanging
amount of demand from its clients. This auto-scaling
service, however, requires a seemingly difficult parameter set to
be customized by the application provider, and those management
parameters are static while incoming web request dynamics
often change, not to mention the fact that scaling decisions
are inherently reactive, instead of being proactive. Therefore we
set the ultimate goal of making cloud-based web applications’
management easier and more effective.
We propose a Kubernetes scaling engine that makes the
auto-scaling decisions apt for handling the actual variability
of incoming requests. In this engine various AI-based forecast
methods compete with each other via a short-term evaluation
loop in order to always give the lead to the method that suits
best the actual request dynamics, as soon as possible. We also
introduce a compact management parameter for the cloud-tenant
application provider in order to easily set their sweet spot in the
resource over-provisioning vs. SLA violation trade-off.
The multi-forecast scaling engine and the proposed management
parameter are evaluated both in simulations and with
measurements on our collected web traces to show the improved
quality of fitting provisioned resources to service demand.We find
that with just a few competing forecast methods, our auto-scaling
engine, implemented in Kubernetes, results in significantly less
lost requests with slightly more provisioned resources compared
to the default baseline. Keywords: cloud computing | artificial intelligence | autoscaling | Kubernetes | forecast | resource management |
مقاله انگلیسی |
3 |
TUORIS: A middleware for visualizing dynamic graphics in scalable resolution display environments
TUORIS: واسط برای تجسم گرافیک پویا در محیطهای با وضوح مقیاس پذیر-2020 In the era of big data, large-scale information visualization has become an important challenge. Scalable resolution display environments
(SRDEs) have emerged as a technological solution for building high-resolution display systems by tiling lower resolution
screens. These systems bring serious advantages, including lower construction cost and better maintainability compared to other
alternatives. However, they require specialized software but also purpose-built content to suit the inherently complex underlying
systems. This creates several challenges when designing visualizations for big data, such that can be reused across several SRDEs
of varying dimensions. This is not yet a common practice but is becoming increasingly popular among those who engage in
collaborative visual analytics in data observatories. In this paper, we define three key requirements for systems suitable for such
environments, point out limitations of existing frameworks, and introduce Tuoris, a novel open-source middleware for visualizing
dynamic graphics in SRDEs. Tuoris manages the complexity of distributing and synchronizing the information among different
components of the system, eliminating the need for purpose-built content. This makes it possible for users to seamlessly port existing
graphical content developed using standard web technologies, and simplifies the process of developing advanced, dynamic
and interactive web applications for large-scale information visualization. Tuoris is designed to work with Scalable Vector Graphics
(SVG), reducing bandwidth consumption and achieving high frame rates in visualizations with dynamic animations. It scales
independent of the display wall resolution and contrasts with other frameworks that transmit visual information as blocks of images Keywords: distributed visualization | large-scale visualization | SVG |
مقاله انگلیسی |
4 |
Progress of paludiculture projects in supporting peatland ecosystem restoration in Indonesia
پیشرفت پروژه های باغداری و پشتیبانی از ترمیم اکوسیستم مزارع گل در اندونزی-2020 Sustainable peatland management practices such as 14 paludiculture are
15 crucial for restoring degraded peatland ecosystems. Paludiculture involves wet
16 cultivation practices in peatland and can maintain peat bodies and sustaining
17 ecosystem services. However, information about paludiculture effects on tropical
18 peatlands is limited in the literature. Therefore, this study aimed to analyse the
19 effectiveness and progress of paludiculture projects in supporting peatland
20 ecosystem restoration in Indonesia that uses approaches of soil rewetting,
21 revegetation of peat soil/forest, and the revitalisation of rural livelihoods around
22 peatlands. We obtained qualitative and quantitative data from field measurements,
23 observations, document reviews, spatial data from open-source web applications,
24 and interviews with key stakeholders in two projects (agri-silviculture and agro25
sylvofishery) that adapt paludiculture principles to Indonesia’s South Sumatra
26 Province. We found that the limited use of paludiculture principles in both projects
27 has a different contribution to peatland restoration. The agri-silviculture project
28 has been utilising jelutung (Dyera polyphylla), ramin (Gonystylus bancanus), and
29 balangeran (Shorea balangeran) for (forest) revegetation. These species are 3 of
30 the 534 paludiculture species that are adaptive to peat soils and tolerant to acidic
31 conditions and inundation. The revegetation resulted in effective results that
32 supported peatland restoration despite the delayed application of rewetting
33 activities in the initial phase of the project. Additionally, in the agro-sylvofishery
34 project, trade-offs between soil rewetting to maintain high peat water tables and
35 the need to provide short-term economic benefits for local communities through
36 horticulture and fishery practices were noted. During the 2019 El Niño, the involvement of a closed-loop canal to support fishery practices appeared to
38 contribute to affecting the water table, which was also influenced by the open
39 canals dug in nearby palm oil plantations. Keywords: paludiculture | peatland restoration | Indonesia | tropical peatland | trade41 off |
مقاله انگلیسی |
5 |
Development of machine learning algorithms for prediction of mortality in spinal epidural abscess
توسعه الگوریتم های یادگیری ماشین برای پیش بینی مرگ و میر در آبسه اپیدورال ستون فقرات-2019 BACKGROUND CONTEXT: In-hospital and short-term mortality in patients with spinal epidural
abscess (SEA) remains unacceptably high despite diagnostic and therapeutic advancements.
Forecasting this potentially avoidable consequence at the time of admission could improve patient
management and counseling. Few studies exist to meet this need, and none have explored methodologies
such as machine learning.
PURPOSE: The purpose of this study was to develop machine learning algorithms for prediction
of in-hospital and 90-day postdischarge mortality in SEA.
STUDY DESIGN/SETTING: Retrospective, case-control study at two academic medical centers
and three community hospitals from 1993 to 2016.
PATIENTS SAMPLE: Adult patients with an inpatient admission for radiologically confirmed
diagnosis of SEA.
OUTCOME MEASURES: In-hospital and 90-day postdischarge mortality.
METHODS: Five machine learning algorithms (elastic-net penalized logistic regression, random
forest, stochastic gradient boosting, neural network, and support vector machine) were developed
and assessed by discrimination, calibration, overall performance, and decision curve analysis.
RESULTS: Overall, 1,053 SEA patients were identified in the study, with 134 (12.7%) experiencing
in-hospital or 90-day postdischarge mortality. The stochastic gradient boosting model achieved the best
performance across discrimination, c-statistic=0.89, calibration, and decision curve analysis. The variables
used for prediction of 90-day mortality, ranked by importance, were age, albumin, platelet count,
neutrophil to lymphocyte ratio, hemodialysis, active malignancy, and diabetes. The final algorithm was
incorporated into a web application available here: https://sorg-apps.shinyapps.io/seamortality/.
CONCLUSIONS: Machine learning algorithms show promise on internal validation for prediction
of 90-day mortality in SEA. Future studies are needed to externally validate these algorithms inindependent populations. Keywords: Artificial intelligence | Healthcare | Machine learning | Mortality | Spinal epidural abscess | Spine surgery |
مقاله انگلیسی |
6 |
Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining
چارچوب های یادگیری ماشین و داده کاوی برای پیش بینی پاسخ به دارو در سرطان: یک مرور کلی و رمان در فرآیند غربالگری سیلیکون بر اساس قاعده قاچاق انجمن-2019 A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized
basis. The success of such a task largely depends on the ability to develop computational resources that integrate
big “omic” data into effective drug-response models. Machine learning is both an expanding and an evolving
computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various
supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the
strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into
these frameworks and 4) pitfalls and challenges tomaximizemodel performance. In this contextwe also describe
a novel in silico screening process, based on Association RuleMining, for identifying genes as candidate drivers of
drug response and compare it with relevant data mining frameworks, for which we generated a web application
freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large samplespaces,
while is able to detect low frequency events and evaluate statistical significance even in the multidimensional
space, presenting the results in the form of easily interpretable rules. We conclude with future prospects
and challenges of applying machine learning based drug response prediction in precision medicine. Key words: Drug Response Prediction | Precision Medicine | Data mining | Machine Learning | Association Rule Mining |
مقاله انگلیسی |
7 |
Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining
چارچوب های یادگیری ماشین و داده کاوی برای پیش بینی پاسخ به دارو در سرطان: یک مرور کلی و رمان در فرآیند غربالگری سیلیکون بر اساس کاوش قوانین انجمنی-2019 A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized
basis. The success of such a task largely depends on the ability to develop computational resources that integrate
big “omic” data into effective drug-response models. Machine learning is both an expanding and an evolving
computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various
supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the
strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into
these frameworks and 4) pitfalls and challenges tomaximizemodel performance. In this contextwe also describe
a novel in silico screening process, based on Association RuleMining, for identifying genes as candidate drivers of
drug response and compare it with relevant data mining frameworks, for which we generated a web application
freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large samplespaces,
while is able to detect low frequency events and evaluate statistical significance even in the multidimensional
space, presenting the results in the form of easily interpretable rules. We conclude with future prospects
and challenges of applying machine learning based drug response prediction in precision medicine. Key words: Drug Response Prediction | Precision Medicine | Data mining | Machine Learning | Association Rule Mining |
مقاله انگلیسی |
8 |
پلت فرم eTRIKS: مفهوم و بهره برداری از پلت فرم مبتنی بر ابر بسیار مقیاس پذیر برای تحقیق و توسعه برنامه های کاربردی-2018 We describe the genesis, design and evolution of a computing platform designed and built to improve the success
rate of biomedical translational research. The eTRIKS project platform was developed with the aim of building a
platform that can securely host heterogeneous types of data and provide an optimal environment to run tranS
MART analytical applications. Many types of data can now be hosted, including multi-OMICS data, preclinical
laboratory data and clinical information, including longitudinal data sets. During the last two years, the platform
has matured into a robust translational research knowledge management system that is able to host other data
mining applications and support the development of new analytical tools.
Keywords: Computing ، Cloud ، eTRIKS ، tranSMART ، Hosting ، Analysis ، Security ، Translational research ، Authentication ، Platform ، Storage ، Web application ، Knowledge management |
مقاله انگلیسی |
9 |
Great Basin land managers provide detailed feedback about usefulness of two climate information web applications
مدیران زمین های بزرگ بازخوردهای جزئیاتی درباره قابل مفید بودن برنامه کاربردی اینترنتی اطلاعات آب و هوایی فراهم می کنند-2018 Land managers in the Great Basin are working to maintain or restore sagebrush ecosystems as climate change exacerbates existing threats. Web applications delivering climate change and climate impacts information have the potential to assist their efforts. Although many web applications containing climate information currently exist, few have been co-produced with land managers or have incorporated information specifically focused on land managers’ needs. Through surveys and interviews, we gathered detailed feedback from federal, state, and tribal sagebrush land managers in the Great Basin on climate information web applications targeting land management. We found that a) managers are searching for weather and climate information they can incorporate into their current management strategies and plans; b) they are willing to be educated on how to find and understand climate related web applications; c) both field and administrative-type managers want data for timescales ranging from seasonal to decadal; d) managers want multiple levels of climate information, from simple summaries, to detailed descriptions accessible through the application; and e) managers are interested in applications that evaluate uncertainty and provide projected climate impacts.
keywords: Great Basin |Sagebrush |Land management |Climate change |Web application |Co-production |
مقاله انگلیسی |
10 |
توسعه سیستم اطلاعات جغرافیایی سازمانی (GIS) یکپارچه با شبکه هوشمند
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 34 این مقاله از یک سیستم اطلاعات جغرافیایی (GIS) برای ایجاد یک سیستم نظارت آنلاین استفاده می کند تا داده های عملیاتی در زمان واقعی را در نقاط مختلف شبکه برق نشان دهد. شبکه با تمام دارایی های آن بر روی نقشه جغرافیایی نمایش داده شده است. این مقاله ابزاری برای نظارت، کنترل، مدیریت امکانات، و مدیریت تقاضا در یک شبکه هوشمند را در صنعت آب و برق و انرژی فراهم می کند. این سیستم به عنوان یک سیستم پیشرو با استفاده از شبکه های ولتاژ پایین در دانشگاه سلطان قابوس (SQU)، عمان آزمایش شده است. این مقاله منعکس کننده اجرای آزمایشی GIS سازمانی و کاربرد آن در شبکه های هوشمند است، هر چند مراحل فنی مرحله های توسعه مدل داده GIS و برنامه وب GIS با جزئیات شرح داده شده است. این سیستم توسعه یافته یک نمایندگی مکانی شبکه برق و دارایی های آن را مشتمل بر سیستم های انرژی تجدید پذیر با داده های عملیاتی اش را بر روی نقشه شبکه توزیع برق موجود ایجاد می کند. این رویکرد ابزارهایی با توانایی نظارت بر مولفه های سیستمی و عملکرد آنها را در زمان واقعی بر مبنای مکان آنها بر روی نقشه فراهم می کند.
کلمات کلیدی: سیستم اطلاعات جغرافیایی (GIS) | شبکه هوشمند | سیستم سازمانی | منابع انرژی تجدیدپذیر | نظارت و کنترل آنلاین |
مقاله ترجمه شده |