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ردیف | عنوان | نوع |
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1 |
Next generation material interfaces for neural engineering
واسط های مواد نسل بعدی برای مهندسی عصبی-2021 Neural implant technology is rapidly progressing, and gaining
broad interest in research fields such as electrical engineering,
materials science, neurobiology, and data science. As the
potential applications of neural devices have increased, new
technologies to make neural intervention longer-lasting and
less invasive have brought attention to neural interface
engineering. This review will focus on recent developments in
materials for neural implants, highlighting new technologies in
the fields of soft electrodes, mechanical and chemical
engineering of interface coatings, and remotely powered
devices. In this context, novel implantation strategies,
manufacturing methods, and combinatorial device functions
will also be discussed. |
مقاله انگلیسی |
2 |
Top management team characteristics and digital innovation: Exploring digital knowledge and TMT interfaces
ویژگی های تیم مدیریت بالا و نوآوری دیجیتال: بررسی دانش دیجیتال و رابط TMT-2021 On their journey toward digital transformation, industrial firms need to embrace digital inno-
vation. The top management team (TMT) is expected to set the course for digital innovation,
which is a challenging endeavour given the novel and cross-functional nature of digital innova-
tion. We draw on role theory to make sense of emerging role requirements for the TMT and
combine this view with upper echelon theory to hypothesize on the specific TMT characteristics
that are needed for digital innovation. We first theorize that firms could benefit from TMT digital
knowledge. Second, we argue that the effective utilization of TMT digital knowledge can be
fostered at internal TMT interfaces, such as between the chief executive officer (CEO), respec-
tively a chief digital officer (CDO), and other top managers. Finally, we consider the TMT hier-
archical structure as a contextual factor in the stimulation of TMT integration processes by
integrative CEOs and CDOs. We employ panel data regressions to a longitudinal dataset of US
industrial firms and find a positive relation between TMT digital knowledge and digital inno-
vation, on average. We additionally find evidence for the integrative roles of CEOs and CDOs.
However, our findings also indicate that the CDO’s integrating role can be hampered by a strong
hierarchical structure in the TMT. keywords: دانش دیجیتال TMT | نوآوری دیجیتال | رابط TMT | افسران ارشد دیجیتال | ساختار سلسله مراتبی TMT | TMT digital knowledge | Digital innovation | TMT interfaces | Chief digital officers | TMT hierarchical structure |
مقاله انگلیسی |
3 |
Universality and scaling laws among fingers at Rayleigh–Taylor and Richtmyer–Meshkov unstable interfaces in different dimensions
جهانی بودن و مقیاس بندی قوانین در میان انگشتان در رابط های ناپایدار ریلی - تیلور و ریچمیتر - مشکوف در ابعاد مختلف-2020 The study of fingers at gravity-induced or shock-induced unstable interfaces, known as Rayleigh–
Taylor instability and Richtmyer–Meshkov instability respectively, is extremely important. It is well
known that many factors can affect the growth rates of fingers in the Rayleigh–Taylor and Richtmyer–
Meshkov instabilities: density ratio of the fluids in difference phases, finger type (spike or bubble)
and the dimension of systems (in two dimensions or in three dimensions). Therefore, conducting
systematic investigations for Rayleigh–Taylor and Richtmyer–Meshkov instabilities in different sets
of physical parameters is very challenging and time-consuming. This is especially true for fingers in
three dimensions. We here present a surprising universality property: once the time and the growth
rate are properly scaled, the dominant behavior of all fingers in systems with different density ratios,
even in different dimensions, approximately follows a universal relation. This universal scaling relation
allows using data for fingers in two dimensions to predict the dominant behavior of fingers in three
dimensions. Both numerical results and experimental data in the literature confirm this universality
property. Keywords: Rayleigh–Taylor instability | Richtmyer–Meshkov instability | Universality | Scaling law |
مقاله انگلیسی |
4 |
Automatic bad channel detection in implantable brain-computer interfaces using multimodal features based on local field potentials and spike signals
تشخیص خودکار کانال بد در رابط های قابل کاشت مغز با کامپیوتر با استفاده از ویژگی های چند حالته بر اساس پتانسیل های محلی و سیگنال های لبه-2020 “Bad channels” in implantable multi-channel recordings bring troubles into the precise quantitative description
and analysis of neural signals, especially in the current “big data” era. In this paper, we combine multimodal
features based on local field potentials (LFPs) and spike signals to detect bad channels automatically using
machine learning. On the basis of 2632 pairs of LFPs and spike recordings acquired from five pigeons, 12
multimodal features are used to quantify each channel’s temporal, frequency, phase and firing-rate properties.
We implement seven classifiers in the detection tasks, in which the synthetic minority oversampling technique
(SMOTE) system and Fisher weighted Euclidean distance sorting (FWEDS) are used to cope with the class
imbalance problem. The results of the two-dimensional scatterplots and classifications demonstrate that correlation
coefficient, phase locking value, and coherence have good discriminability. For the multimodal features,
almost all the classifiers can obtain high accuracy and bad channel detection rate after the SMOTE operation, in
which the Random Forests classifier shows relatively better comprehensive performance (accuracy: 0.9092 �
0.0081, precision: 0.9123 � 0.0100, and recall: 0.9057 � 0.0121). The proposed approach can automatically
detect bad channels based on multimodal features, and the results provide valuable references for larger datasets. Keywords: Bad channel | Multimodal feature | LFP | Spike | Machine learning |
مقاله انگلیسی |
5 |
Time-dependent behaviour of the Callovo-Oxfordian claystone-concrete interface
رفتار وابسته به زمان رابط سنگ خشتی و بتونی Callovo - Oxfordian-2020 In the context of the Cigéo project, the French National Radioactive Waste Management Agency (Andra)
is studying the behaviour of a deep geological facility for radioactive waste deposit in the Callovo-
Oxfordian (COx) claystone. The assessment of durability of this project requires the prediction of irreversible
strain over a large time scale. The mechanical interaction of the host rock and the concrete
support of tunnels must be investigated to ensure the long-term sustainability of the structure. The
instantaneous and time-dependent behaviour of the claystone-concrete interface is experimentally
investigated with direct shear tests and long-duration shear tests of a few months. The mechanical and
structural state of the claystone which is affected after interaction with concrete reflects to the response
of the claystone-concrete interface, and thus different types of COx claystone-concrete interfaces are
tested. The delayed deformation of the interface is found to be linked to the level of the normal loading
and the loading history, while a different response of the interface was observed from the short- and
long-duration tests, indicating a possible progressive modification of interface under long-duration
loadings. Keywords: Callovo-Oxfordian (COx) claystone | Interfaces | Time-dependent behaviour |
مقاله انگلیسی |
6 |
A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence
یک دوقلوی دیجیتال برای آموزش عامل یادگیری تقویتی عمیق برای کارخانه های تولید هوشمند: محیط ، رابط ها و هوش-2020 Filling the gaps between virtual and physical systems will open new doors in Smart Manufacturing. This work
proposes a data-driven approach to utilize digital transformation methods to automate smart manufacturing
systems. This is fundamentally enabled by using a digital twin to represent manufacturing cells, simulate system
behaviors, predict process faults, and adaptively control manipulated variables. First, the manufacturing cell is
accommodated to environments such as computer-aided applications, industrial Product Lifecycle Management
solutions, and control platforms for automation systems. Second, a network of interfaces between the environments
is designed and implemented to enable communication between the digital world and physical
manufacturing plant, so that near-synchronous controls can be achieved. Third, capabilities of some members in
the family of Deep Reinforcement Learning (DRL) are discussed with manufacturing features within the context
of Smart Manufacturing. Trained results for Deep Q Learning algorithms are finally presented in this work as a
case study to incorporate DRL-based artificial intelligence to the industrial control process. As a result, developed
control methodology, named Digital Engine, is expected to acquire process knowledges, schedule manufacturing
tasks, identify optimal actions, and demonstrate control robustness. The authors show that integrating a smart
agent into the industrial platforms further expands the usage of the system-level digital twin, where intelligent
control algorithms are trained and verified upfront before deployed to the physical world for implementation.
Moreover, DRL approach to automated manufacturing control problems under facile optimization environments
will be a novel combination between data science and manufacturing industries. Keywords: Smart manufacturing systems | Robotics | Artificial intelligence | Digital transformation | Virtual commissioning |
مقاله انگلیسی |
7 |
Weaving seams with data: Conceptualizing City APIs as elements of infrastructures
بافتن با داده ها: اندیشه سازی رابط های برنامه های کاربردی (API) شهری به عنوان عناصر زیرساخت-2019 This article addresses the role of application programming interfaces (APIs) for integrating data sources in the context of
smart cities and communities. On top of the built infrastructures in cities, application programming interfaces allow to
weave new kinds of seams from static and dynamic data sources into the urban fabric. Contributing to debates about
‘‘urban informatics’’ and the governance of urban information infrastructures, this article provides a technically informed
and critically grounded approach to evaluating APIs as crucial but often overlooked elements within these infrastructures.
The conceptualization of what we term City APIs is informed by three perspectives: In the first part, we review
established criticisms of proprietary social media APIs and their crucial function in current web architectures. In the
second part, we discuss how the design process of APIs defines conventions of data exchanges that also reflect negotiations between API producers and API consumers about affordances and mental models of the underlying computer
systems involved. In the third part, we present recent urban data innovation initiatives, especially CitySDK and
OrganiCity, to underline the centrality of API design and governance for new kinds of civic and commercial services
developed within and for cities. By bridging the fields of criticism, design, and implementation, we argue that City APIs as
elements of infrastructures reveal how urban renewal processes become crucial sites of socio-political contestation
between data science, technological development, urban management, and civic participation.
Keywords: Application Programming Interface (API) | infrastructure | Internet of Things (IoT) | interface design | social urban data | smart city |
مقاله انگلیسی |
8 |
A recommender system for component-based applications using machine learning techniques
یک سیستم توصیه گر برای برنامه های کاربردی مبتنی بر مؤلفه با استفاده از تکنیک های یادگیری ماشین-2019 Software designers are striving to create software that adapts to their users’ requirements. To this end,
the development of component-based interfaces that users can compound and customize according to
their needs is increasing. However, the success of these applications is highly dependent on the users’
ability to locate the components useful for them, because there are often too many to choose from. We
propose an approach to address the problem of suggesting the most suitable components for each user at
each moment, by creating a recommender system using intelligent data analysis methods. Once we have
gathered the interaction data and built a dataset, we address the problem of transforming an original
dataset from a real component-based application to an optimized dataset to apply machine learning
algorithms through the application of feature engineering techniques and feature selection methods.
Moreover, many aspects, such as contextual information, the use of the application across several devices
with many forms of interaction, or the passage of time (components are added or removed over time),
are taken into consideration. Once the dataset is optimized, several machine learning algorithms are
applied to create recommendation systems. A series of experiments that create recommendation models
are conducted applying several machine learning algorithms to the optimized dataset (before and after
applying feature selection methods) to determine which recommender model obtains a higher accuracy.
Thus, through the deployment of the recommendation system that has better results, the likelihood
of success of a component-based application is increased by allowing users to find the most suitable
components for them, enhancing their user experience and the application engagement. Keywords: Machine learning | Recommender systems | Feature engineering | Feature selection | Component-based interfaces | Interaction information acquisition |
مقاله انگلیسی |
9 |
Towards an accessible use of smartphone-based social networks through brain-computer interfaces
به سمت استفاده در دسترس از شبکه های اجتماعی مبتنی بر تلفن های هوشمند از طریق رابط های مغز و کامپیوتر-2019 This study presents an asynchronous P300-based Brain–Computer Interface (BCI) system for controlling social networking features of a smartphone. There are very few BCI studies based on these mobile devices and, to the best of our knowledge, none of them supports networking applications or are focused on an assistive context, failing to test their systems with motor-disabled users. Therefore, the aim of the present study is twofold: (i) to design and develop an asynchronous P300-based BCI system that allows users to control Twitter and Telegram in an Android device; and (ii) to test the usefulness of the developed system with a motor-disabled population in order to meet their daily communication needs. Row-col paradigm (RCP) is used in order to elicitate the P300 potentials in the scalp of the user, which are immediately processed for decoding the user’s intentions. The expert system integrates a decision-making stage that analyzes the attention of the user in real-time, providing a comprehensive and asynchronous control. These intentions are then translated into application commands and sent via Bluetooth to the mobile de- vice, which interprets them and provides visual feedback to the user. During the assessment, both quali- tative and quantitative metrics were obtained, and a comparison among other state-ofthe-art studies was performed as well. The system was tested with 10 healthy control subjects and 18 motor-disabled sub- jects, reaching average online accuracies of 92.3% and 80.6%, respectively. Results suggest that the system allows users to successfully control two socializing features of a smartphone, bridging the accessibility gap in these trending devices. Our proposal could become a useful tool within households, rehabilitation centers or even companies, opening up new ways to support the integration of motor-disabled people, and making an impact in their quality of life by improving personal autonomy and self-dependence. Keywords: Brain-computer interface (BCI) | Smartphones | Asynchronous control | Social networks | P300 Event-related potentials | Electroencephalography (EEG) |
مقاله انگلیسی |
10 |
مروری بر امنیت در اینترنت اشیاء: تکنولوژی جدید و چالش ها
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 19 اینترنت اشیاء (IoT) به دستگاه های فیزیکی اشاره دارد که اینترنت، تجهیزات الکترونیکی، نرم افزار، حسگرها، محرک ها و اتصال شبکه در آنها تعبیه شده است. این شامل بسیاری از سیستم های مختلف است، به عنوان مثال، مراقبت های بهداشتی، سلامتی، خانه هوشمند، ساختمان، کنتورهای هوشمند، و غیره. معماری فنی مبتنی بر اینترنت، پروتکل های ارتباطی مبتنی بر IP، و فن آوری ها، باعث تسهیل تبادل سرویس های هوشمند بر روی کانال های ناامن می شوند، بنابراین موضوع اصلی، امنیت و حفظ حریم خصوصی ذینفع است. در این مقاله، امنیت IOT موجود را به سه شکل تحلیل می کنیم: (1) امنیت در ارتباطات، (2) امنیت در رابط کاربری و (3) امنیت داده ها. در این مقاله، فناوری ها، رویکردها و مدل های فعلی IoT مورد بررسی قرار گرفته و شکاف امنیتی در فن آوری های ارتباطی موجود، رابط های برنمه های کاربردی و امنیت داده ها کشف می شود. هدف دیگر این مقاله این است که یک بررسی در مورد کار مربوطه در IoT همراه با چالش های باز و مسیرهای تحقیقاتی در آینده ارائه دهد.
واژه های کلیدی: اینترنت اشیاء | شبکه های حسگر بی سیم | امنیت | حریم خصوصی | اعتماد |
مقاله ترجمه شده |