Asynchronous Features Research Articles

EEG correlates of static and dynamic face perception: The role of naturalistic motion

Much of our understanding of how the brain processes dynamic faces comes from research that compares static photographs to dynamic morphs, which exhibit simplified, computer-generated motion. By comparing static, video recorded, and dynamic morphed expressions, we aim to identify the neural correlates of naturalistic facial dynamism, using time-domain and time-frequency analysis. Dynamic morphs were made from the neutral and peak frames of video recorded transitions of happy and fearful expressions, which retained expression change and removed asynchronous and non-linear features of naturalistic facial motion. We found that dynamic morphs elicited increased N400 amplitudes and lower LPP amplitudes compared to other stimulus types. Video recordings elicited higher LPP amplitudes and greater frontal delta activity compared to other stimuli. Thematic analysis of participant interviews using a large language model revealed that participants found it difficult to assess the genuineness of morphed expressions, and easier to analyse the genuineness of happy compared to fearful expressions. Our findings suggest that animating real faces with artificial motion may violate expectations (N400) and reduce the social salience (LPP) of dynamic morphs. Results also suggest that delta oscillations in the frontal region may be involved with the perception of naturalistic facial motion in happy and fearful expressions. Overall, our findings highlight the sensitivity of neural mechanisms required for face perception to subtle changes in facial motion characteristics, which has important implications for neuroimaging research using faces with simplified motion.

Unveiling the secrets of non-evaporable getter films: Activation temperature, activation time, and achievable activation degree

Non-evaporable getter films play an important role in the creating and maintaining of high or ultra-high vacuum environments in advanced scientific disciplines. Prior to application, thermal activation in a vacuum environment is essential. Despite numerous attempts to reduce the film activation temperature, uncertainty remains as to whether extending the activation time at a relatively lower activation temperature will ensure a fully-activated state of the film. To address this issue, the microstructure and activation performance of the film has been investigated. Techniques, such as scanning electron microscopy, grazing incidence X-ray diffraction, energy dispersive spectroscopy, and in-situ X-ray photoelectron spectroscopy were employed. Experimental investigations show that a fully-activated state of the film cannot be achieved at around 150°C, regardless of the activation time. Further analysis shows that, at activation temperatures below about 200°C, the migration trend of oxygen atoms within TiZrV/TiZrHfV films exhibits asynchronous features. This process was accompanied by boundary effects of foreign atom migration channels. In addition, the achievable activation degree of the film shows a step characteristic. These results improve the understanding of the relationship between activation temperature, activation time, and the achievable activation degree of the film.

IInception-CBAM-IBiGRU based fault diagnosis method for asynchronous motors

Aiming at the problems of insufficient extraction of asynchronous motor fault features by traditional deep learning algorithms and poor diagnosis of asynchronous motor faults in robust noise environments, this paper proposes an end-to-end fault diagnosis method for asynchronous motors based on IInception-CBAM-IBiGRU. The method first uses a signal-to-grayscale image conversion method to convert one-dimensional vibration signals into two-dimensional images and initially extracts shallow features through two-dimensional convolution; then the Improved Inception (IInception) module is used as a residual block to learning features at different scales with a residual structure, and extracts its important feature information through the Convolutional Block Attention Module (CBAM) to extract important feature information and adjust the weight parameters; then the feature information is input to the Improved Bi-directional Gate Recurrent Unit (IBiGRU) to extract its timing features further; finally, the fault identification is achieved by the SoftMax function. The primary hyperparameters in the model are optimized by the Weighted Mean Of Vectors Algorithm (INFO). The experimental results show that the method is effective in fault diagnosis of asynchronous motors, with an accuracy rate close to 100%, and can still maintain a high accuracy rate under the condition of low noise ratio, with good robustness and generalization ability.

Asynchronous changes in terrestrial near-surface wind speed among regions across China from 1973 to 2017

Understanding the characteristics and causes of asynchronous changes in near-surface wind speed (NSWS) improves the ability of climate projection, but yet to be comprehended are the asynchronous features in NSWS and possible causes over China. In this study, the asynchronous variations in NSWS among regions are considerable across China. The non-synchronization of variations in intra-annual NSWS was manifested in the amplitudes and periods of NSWS. The amplitudes and periods of intra-annual NSWS among regions were in the ranges of 0.08–0.20 m s−1 and 0.24–0.32 years, respectively. The intra-annual changes in NSWS in different regions were influenced by different large–scale ocean–atmosphere circulations (LOACs). The non-synchronization of variations in interannual NSWS was also manifested in the amplitudes and periods of NSWS, and the strongest and weakest interannual variations in NSWS occurred over northeast and northwest China. The interannual variations in NSWS could be induced by El Niño–Southern Oscillation (ENSO). At intra-annual and interannual scales, LOACs mainly controlled the changes in the phase of NSWS, which could not be responsible for the amplitudes of NSWS. The interdecadal NSWS also showed regional differences, and it mainly exhibited an increase after the 1990s in all regions except for South China. The interdecadal changes in NSWS among regions were also dominated by LOACs, which contributed reached 50.0% to the NSWS changes. Furthermore, the LOACs dominated not only the changes in the phase of interdecadal NSWS but also the changes in amplitudes of interdecadal NSWS.

Community-Centered Epidemic and Pandemic Information and Engagement Platform, the Hive.

The COVID-19 pandemic underlined that communities are key in sharing trusted, timely and relevant information especially during a health emergency where the overabundance of information makes it difficult to make decisions to protect one's health. The WHO Hive project grew out of the desire to create a community-centered solution with the potential to change the way credible health information is shared, adapted, understood and used for health-related decision making before, during and after an epidemic or pandemic. The Hive online platform provides a safe space for knowledge-sharing, discussion, and collaboration, including access to timely scientific information through direct engagement with WHO subject matter experts, and the true innovation lies within the platform's ability to leverage the power of communities to crowdsource solutions to community concerns and needs. The platform is equipped with a set of synchronous and asynchronous features and tools to encourage coworking and facilitate cross-sectorial collaboration. The Hive seeks to leverage the expert communities to share resources and knowledge for epidemic and pandemic preparedness and provide an environment that is able to respond to the challenges faced in a complex information ecosystem.

Synchronisation Model of Campus-Industry Partnership Through Smart Expert System Hybrid Advisory for Industrial Internship Students and Teaching Assistance in the Era of Independent Learning

The objectives of this research include (1) developing smart expert system innovation integrated hybrid advisory; and (2) Testing the attractiveness and feasibility of the hybrid advisory integrated smart expert system innovation prototype. The method in this research is in the form of research and development (Re-search and Development). Where the method includes a preliminary study; model design; development of model prototypes; model prototype testing; model prototype revision; validated model prototype results; model prototype experimentation; tested models; and dissemination. all state universities in East Java (students,equipped with lecturers, officials in PT and stakeholders). The research instrument used questionnaires, documents, structured surveys, observations, and in-depth interviews. The results of this study include: (1) the innovations developed have synchronous and asynchronous online guidance features, submit reports and daily assignments, student progress reports, live chat with supervisors and instructors, daily attendance, to review report results; and (2) the Smart Expert System Integrated Hybrid Advisory innovation developed has valid results without revision with an average score of 90.1 (user benefit aspect), an average score of 90.5 (application display aspect), an average score of 93 ,8 (newness of information aspect), average score of 97.6 (content aspect), average score of 90.6 (user-friendliness aspect), average score of 93.8 (competency achievement aspect).

BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.

The Brain-Computer Interface (BCI) was envisioned as an assistive technology option for people with severe movement impairments. The traditional synchronous event-related potential (ERP) BCI design uses a fixed communication speed and is vulnerable to variations in attention. Recent ERP BCI designs have added asynchronous features, including abstention and dynamic stopping, but it remains a open question of how to evaluate asynchronous BCI performance. In this work, we build on the BCI-Utility metric to create the first evaluation metric with special consideration of the asynchronous features of self-paced BCIs. This metric considers accuracy as all of the following three - probability of a correct selection when a selection was intended, probability of making a selection when a selection was intended, and probability of an abstention when an abstention was intended. Further, it considers the average time required for a selection when using dynamic stopping and the proportion of intended selections versus abstentions. We establish the validity of the derived metric via extensive simulations, and illustrate and discuss its practical usage on real-world BCI data. We describe the relative contribution of different inputs with plots of BCI-Utility curves under different parameter settings. Generally, the BCI-Utility metric increases as any of the accuracy values increase and decreases as the expected time for an intended selection increases. Furthermore, in many situations, we find shortening the expected time of an intended selection is the most effective way to improve the BCI-Utility, which necessitates the advancement of asynchronous BCI systems capable of accurate abstention and dynamic stopping.

Energy efficient framework to implement next generation network protocol using ATM technology

Real-time and multimedia apps on an integrated service internet are quickly becoming a realization. In the meanwhile, ATM technology is becoming available. Integrating ATM networks into this internet of integrated services is a significant challenge. Traditional TCP over ATM is one of the ways that is currently extensively used, effectively overcoming the problem of accessing network and compatibility. Providing quality-of-service (QoS) assurances for internet traffic flowing over ATM subnets is still a significant challenge. Integrating TCP/IP and Asynchronous Transfer Mode (ATM) is a problematic systemic issue in today's global networks. Asynchronous Transfer Mode's particular features must be integrated with the TCP/IP framework. A precise assessment of TCP/IP over ATM efficiency will be a valuable tool for combining the two approaches. When not used properly, TCP errors and traffic regulation algorithms were designed for best-effort networks and may degrade results on ATM networks. Quality of Service (QoS) is also hidden from programmed using standard overlay technologies such as LAN Emulation or Classic TCP over ATM. For optimal communication, this paper provides complete and standardized research and analysis of Protocol via ATM in a 5G network environment. Our proposed novel framework, TCP applications from QoS, provides Enhanced Energy Zam-Zam Transmission Control Protocol (EEZZTCP). The performance metrics like an end-to-end delay, throughput, Energy utilized, and packet delivery ratio are compared to another method.

REFLECTION OF TRANSFORMING DESIGN WORKSHOP DURING PANDEMIC USING ONLINE EDUCATION PLATFORM, SCHOOLOGY, AT SECOND-YEAR STUDIO

Design workshops are one of the phases in studio-based learning that helps architecture students to accomplished projects in the studio through interactive and intense interactions between lecturers and students. Therefore, this phase is carried out through the face-to-face method and done in the studio. Since March 2020, for the first time, courses at the Architecture Program of Universitas Syiah Kuala (USK) have been conducted by distance learning, including a second-year studio, Architectural Design III. Long before the pandemic COVID-19, several courses at the Architecture Program of Universitas Syiah Kuala (USK) had been carried out by blended learning through USK e-learning platform and Schoology. As a case study, the Architecture Program (PSA) of USK uses Schoology as an online education platform to facilitate design workshops. This paper aims to reflect on the results of using Schoology as an online education platform that is fully utilized in second-year studio course. Using descriptive analysis method, this paper explores the strategies to maneuver from face-to-face into distance learning method in order to achieve learning objectives. As the result, Schoology is able to attempt synchronous and asynchronous features when conducting design workshops but it is not fully utilized, especially interactions between students.

Exploring two-trace two-dimensional (2T2D) correlation spectroscopy as an effective approach to improve accuracy of discriminant analysis by highlighting asynchronous features in two separate spectra of a sample

This study aims to demonstrate two-trace two-dimensional (2T2D) correlation spectroscopy as an effective tool for improving the accuracy of discriminant analysis. Because 2T2D correlation analysis allows sensitive capturing of asynchronous spectral behaviors between two compared spectra of a sample, the subsequent asynchronous correlation features are expected to reveal more sample-to-sample characteristics and discriminants than the original spectral feature. Initially, near-infrared (NIR) spectroscopic authentication of pure olive oil was performed using the spectra collected at 20 °C and 41 °C. When the 2T2D slice spectra of the samples were used for the discriminant analysis, the authentication accuracy reached to 100%, while became degraded in the cases of using the spectra collected either at 20 °C or 41 °C. Furthermore, a simple strategy of utilizing the average spectrum of one sample group as the reference spectrum in the 2T2D correlation analysis was proposed for two-group discrimination and evaluated for the NIR identification of the geographical origins of agricultural products (milk vetch root (MVR) and perilla seed samples). Because the average spectrum of one sample group was used for comparison, dissimilar constituent compositions of the samples in another group were better observed, thereby improving the accuracy of discrimination of the geographical origins of the samples in both cases. The overall results demonstrated that 2T2D correlation analysis is effective for highlighting the minute asynchronous spectral features of a sample and can be expanded for diverse vibrational spectroscopy-based discriminant analyses.

Program package MPGOS: Challenges and solutions during the integration of a large number of independent ODE systems using GPUs

Challenges and efficient solution techniques during the integration of a large number of independent ordinary differential equations (ODEs) using the massively parallel architecture of graphics processing units (GPUs) are presented. One of the main difficulties is the minimisation of the memory transactions through the PCI-E bus between the host (CPU) and the device (GPU) required frequently, for instance, during the calculation of the Lyapunov exponent, winding number or maximum response diagram. The second difficulty is the minimisation of the slow global memory transactions and memory usage by exploiting the memory hierarchy of the GPU architecture. Finally, a good GPU solver has to treat the possible asynchronous features of the ODE systems efficiently; for instance, event detection occurring at distinct time instances or handling the orders of magnitude difference in the required number of time steps of the different ODE systems. The program package MPGOS (written in C++ and CUDA C software environments) can address the aforementioned issues easily via the addition of user-defined functions that must be implemented similarly to the right-hand side of the system; via the possibility of the definition of shared parameters common to all instances of the independent ODE systems; via user-programmable parameters to store only the desired properties of the trajectories; and via an easy was to overlap GPU and CPU computations. This paper focuses on the detailed description of the implementation strategies of the program package.

Overcoming Technological Inequity in Synchronous Online Learning

The recent coronavirus disease of 2019 (COVID-19) pandemic possibly represents a catalyst for broader changes looming ahead for higher education, which includes a shift to online learning. Researchers have proven synchronous learning components to reduce the transactional distance that students experience in online learning environments, and such components play an important role in many information systems (IS) courses. However, students who cannot reliably access the technological resources that they need for synchronous learning remain left behind in these learning environments. We summarize strategies that individual IS faculty and institutional information technology (IT) departments can implement to assist such students. Faculty-level strategies include implementing complementary asynchronous features, clearly communicating expectations, and implementing intervention strategies to foster collaborative work. Institutional solutions include providing software applications via the cloud for students who need to access them remotely and loaning computer resources such as laptops to students who lack them and effectively training faculty and students in online learning. These measures will help higher education institutions to bridge the transactional distance that students experience as online learning becomes more prevalent in the months and years ahead.

An Integrated Control and Protection Scheme Based on FBSM-MMC Active Current Limiting Strategy for DC Distribution Network

DC faults can easily lead to overcurrent in dc distribution networks; these faults pose serious threats to the safe operation of the system. The blocking of a modular multilevel converter based on the full-bridge submodule (FBSM-MMC) is mostly utilized to cut off the fault current. However, the blocking causes short-term blackouts in the entire dc distribution network, and there are presently no effective solutions to address this problem. In this study, an integrated control and protection scheme based on the FBSM-MMC active current limiting strategy is proposed. The project includes three stages: first, MMC active current limiting strategy is used to limit the output current of the converter to about 1.2 p.u. after the occurrence of the fault (Stage 1); second, faulty lines are identified based on the asynchronous zero-crossing features of the dc currents of the two ends of the line (Stage 2); and finally, a fault isolation scheme based on the cooperation of converters, dc circuit breakers, and high-speed switches is proposed to isolate the faulty line (Stage 3). The distribution network can restart quickly via control of the converters. Finally, the simulation of a four-terminal flexible dc distribution network in PSCAD/EMTDC demonstrates the effectiveness of the proposed integrated scheme.

Brain Networks Processing Temporal Information in Dynamic Facial Expressions.

This fMRI study examines the role of local and global motion information in facial movements during exposure to novel dynamic face stimuli. We found that synchronous expressions distinctively engaged medial prefrontal areas in the rostral and caudal sectors of anterior cingulate cortex (r/cACC) extending to inferior supplementary motor areas, as well as motor cortex and bilateral superior frontal gyrus (global temporal-spatial processing). Asynchronous expressions in which one part of the face unfolded before the other activated more the right superior temporal sulcus (STS) and inferior frontal gyrus (local temporal-spatial processing). These differences in temporal dynamics had no effect on visual face-responsive areas. Dynamic causal modeling analysis further showed that processing of asynchronous expression features was associated with a differential information flow, centered on STS, which received direct input from occipital cortex and projected to the amygdala. Moreover, STS and amygdala displayed selective interactions with cACC where the integration of both local and global motion cues could take place. These results provide new evidence for a role of local and global temporal dynamics in emotional expressions, extracted in partly separate brain pathways. Importantly, we show that dynamic expressions with synchronous movement cues may distinctively engage brain areas responsible for motor execution of expressions.

Extending Impression Management Theory: The Need for Privacy vs. the Need to Express Information on Instant Messaging Apps

Impression management (IM) is a central assertion in interpersonal communication. There is increasing interest in exploring IM in new media. However, studies exploring IM in popular instant-messaging apps are lacking. Our main aims in the present study are to fill this gap and extend IM theory toward an innovative theoretical and analytical framework of computer-mediated communication (CMC). This study analyzed the popular instant-messaging app WhatsApp, the largest app for social chatting that has users all over the world.We utilized a multi-variant design of quantitative and qualitative analysis, and conducted content analysis of WhatsApp profiles (n=546); an online survey of WhatsApp users (n=600); and in-depth interpersonal interviews (n=30). The findings demonstrate the structure, motivations, and communication functions of CMC IM. The proposed framework provides composite theoretical explanations, of psychological and sociological perspectives, for the IM’s conflicting motivations: the need for privacy and the need to express information. Moreover, this study delineates the effect of gender and marital status on IM through instant-messaging app. The theoretical and analytical framework develops an impression construct that both reduces and increases information, which activates synchronous and asynchronous features of instant-messaging apps, which affects interpersonal communication.

Non-weighted [formula omitted] gain and asynchronous [formula omitted] control for continuous-time switched T–S fuzzy systems

This article considers the non-weighted L2 gain and asynchronous H∞ control issues for continuous-time switched nonlinear systems. Firstly, Takagi–Sugeno (T–S) fuzzy models are employed to describe the nonlinear subsystems. By taking full advantage of the asynchronous features, a novel time-varying fuzzy Lyapunov function (TVFLF) and the corresponding controllers are all constructed separately in synchronous and asynchronous intervals. Based on the TVFLF approach, sufficient conditions of asynchronous H∞ controller are established. Finally, two simulation examples are discussed to certify the effectiveness of the proposed results.

A Metadata-Driven Approach for Testing Self-Organizing Multiagent Systems

Multiagent Systems (MASs) have multiple different characteristics, such as autonomy, and asynchronous and social features, which make these systems difficult to understand. Thus, there is a lack of procedures guaranteeing that multiagent systems once implemented would behave as desired. Determining the reliability of such systems is further complicated by the fact that current agent-based approaches may also involve non-deterministic characteristics, such as learning, self-adaptation and self-organization (SASO). Nonetheless, there is a gap in the literature regarding the testing of systems with these features. This paper presents an approach based on metadata and the publish-subscribe paradigm to develop test applications that address the process of failure diagnosis in a self-organizing MAS. The novelty of the proposed approach involves its ability to test self-organizing MAS systems in the context of local and global behavior. To illustrate the use of this approach, we developed a self-organizing MAS system based on the Internet of Things (IoT), which simulates a set of smart street lights, and we performed functional ad-hoc tests. The street lights need to interact with each other in order to achieve the global goals of reducing energy consumption and maintaining the maximum value of visual comfort in illuminated areas. To achieve these global behaviors, the street lights develop local behaviors automatically through a self-organizing process based on machine-learning algorithms.

Feature Representations for Neuromorphic Audio Spike Streams.

Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging results on some datasets but remains challenging. While the lack of effective spiking networks to process the spike streams is one reason, the other reason is that the pre-processing methods required to convert the spike streams to frame-based features needed for the deep networks still require further investigation. This work investigates the effectiveness of synchronous and asynchronous frame-based features generated using spike count and constant event binning in combination with the use of a recurrent neural network for solving a classification task using N-TIDIGITS18 dataset. This spike-based dataset consists of recordings from the Dynamic Audio Sensor, a spiking silicon cochlea sensor, in response to the TIDIGITS audio dataset. We also propose a new pre-processing method which applies an exponential kernel on the output cochlea spikes so that the interspike timing information is better preserved. The results from the N-TIDIGITS18 dataset show that the exponential features perform better than the spike count features, with over 91% accuracy on the digit classification task. This accuracy corresponds to an improvement of at least 2.5% over the use of spike count features, establishing a new state of the art for this dataset.

Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets

We present a scheme for online, unsupervised state discovery and detection from streaming, multi-featured, asynchronous data in high-frequency financial markets. Online feature correlations are computed using an unbiased, lossless Fourier estimator. A high-speed maximum likelihood clustering algorithm is then used to find the feature cluster configuration which best explains the structure in the correlation matrix. We conjecture that this feature configuration is a candidate descriptor for the temporal state of the system. Using a simple cluster configuration similarity metric, we are able to enumerate the state space based on prevailing feature configurations. The proposed state representation removes the need for human-driven data pre-processing for state attribute specification, allowing a learning agent to find structure in streaming data, discern changes in the system, enumerate its perceived state space and learn suitable action-selection policies.

Bio-inspired self-aware fault-tolerant routing protocol for network-on-chip architectures using Particle Swarm Optimization

This research paper proposes a bio-inspired self-aware fault-tolerant routing protocol for network-on-chip architecture using particle swarm optimization (PSO), which considers synchronous, asynchronous, and self-organizing communication mechanisms to intelligently load-balance the traffic on the entire network in the presence of faulty components. By way of experimentation and simulation, this study demonstrates that the proposed scheme can converge to a global optimum, minimal routing path in real time, in the presence of network congestion and faulty routers and links. The basic PSO algorithm was improved to implement the proposed routing scheme, named bio-inspired self-aware fault-tolerant routing protocol (BISFTRP). This scheme uses the synchronous, asynchronous, and self-organizing features of PSO to create a global routing table and intelligent adaptation, which gives rise to scalable, real-time, and dynamic routing decisions with high throughput, low latency, and minimum power consumption. A cycle-accurate simulation system to demonstrate the flexibility and efficiency of the proposed scheme is used. Comparison results with state-of-the-art fault-tolerant routing algorithms show that the BISFTRP routing protocol achieves high routing performance without routing oscillations and throughput degradation. Furthermore, the hardware implementation results show that the BISFTRP router achieves an efficient area and power utilization, compared with state-of-the-art routers.