In order to realize high speed, long range, reliable transmission in millimeter-wave 60 GHz wireless personal area networks (60 GHz WPANs), we propose a beamforming (BF) protocol realized in media ...access control (MAC) layer on top of multiple physical layer (PHY) designs. The proposed BF protocol targets to minimize the BF set-up time and to mitigate the high path loss of 60 GHz WPAN systems. It consists of 3 stages, namely the device (DEV) to DEV linking, sector-level searching and beam-level searching. The division of the stages facilitates significant reduction in setup time as compared to BF protocols with exhaustive searching mechanisms. The proposed BF protocol employs discrete phase-shifters, which significantly simplifies the structure of DEVs as compared to the conventional BF with phase-and-amplitude adjustment, at the expense of a gain degradation of less than 1 dB. The proposed BF protocol is a complete design and PHY-independent, it is applicable to different antenna configurations. Simulation results show that the setup time of the proposed BF protocol is as small as 2% when compared to the exhaustive searching protocol. Furthermore, based on the codebooks with four phases per element, around 15.1 dB gain is achieved by using eight antenna elements at both transmitter and receiver, thereby enabling 1.6 Gbps-data-streaming over a range of three meters. Due to the flexibility in supporting multiple PHY layer designs, the proposed protocol has been adopted by the IEEE 802.15.3c as an optional functionality to realize Gbps communication systems.
Spiking neuron network (SNN) attaches much attention to researchers in neuromorphic engineering and brain-like computing because of its advantages in Spatio-temporal dynamics, diverse coding ...mechanisms, and event-driven properties. This paper is a review of SNN in order to help researchers from other areas to know and became familiar with the field of SNN or even became interested in SNN. Neuron models, coding methods, training algorithms, and neuromorphic computing platforms will be introduced in this paper. This paper analyzes the disadvantages and advantages of several kinds of neural models, coding methods, learning algorithms, and neuromorphic computing platforms, and according to these to propose some expected development, such as improving the balance between bio-mimicry and cost of computing for neuron models, compounding coding methods, unsupervised learning algorithms in SNN, and digital-analog computing platform.
Augmented reality (AR) applications are increasingly being used in various fields (e.g., design, maintenance, assembly, repair, training, etc.), as AR techniques help improve efficiency and reduce ...costs. Moreover, collaborative AR systems extend applicability, allowing for collaborative environments for different roles. In this paper, we propose a multi-user collaborative AR system (aptly called the "multi-user collaborative system", or MUCSys); it is composed of three ends-MUCStudio, MUCView, and MUCServer. MUCStudio aims to construct industrial content with CAD model transformation, simplification, database update, marker design, scene editing, and exportation, while MUCView contains sensor data analysis, real-time localization, scene loading, annotation editing, and virtual-real rendering. MUCServer-as the bridge between MUCStudio and MUCView-presents collaborative and database services. To achieve this, we implemented the algorithms of local map establishment, global map registration, optimization, and network synchronization. The system provides AR services for diverse industrial processes via three collaborative ways-remote support, collaborative annotation, and editing. According to the system, applications for cutting machines were presented to improve efficiency and reduce costs, covering cutting head designs, production line sales, and cutting machine inspections. Finally, a user study was performed to prove the usage experience of the system.
It has been reported that SARS-CoV-2 may use ACE2 as a receptor to gain entry into human cells, in a way similar to that of SARS-CoV. Analyzing the distribution and expression level of ACE2 may ...therefore help reveal underlying mechanisms of viral susceptibility and post-infection modulation. In this study, we utilized previously uploaded information on ACE2 expression in various conditions including SARS-CoA to evaluate the role of ACE2 in SARS-CoV and extrapolate that to COVID-19. We found that the expression of ACE2 in healthy populations and patients with underlying diseases was not significantly different. However, based on the elevated expression of ACE2 in cigarette smokers, we speculate that long-term smoking may be a risk factor for COVID-19. Analysis of ACE2 in SARS-CoV infected cells suggests that ACE2 is not only a receptor but is also involved in post-infection regulation, including immune response, cytokine secretion, and viral genome replication. Moreover, we constructed Protein-protein interaction (PPI) networks and identified hub genes in viral activity and cytokine secretion. Our findings may help clinicians and researchers gain more insight into the pathogenesis of SARS-CoV-2 and design therapeutic strategies for COVID-19.
•SARS-CoV-2 may use ACE2 as a receptor to gain entry into human cells, in a way similar to that of SARS-CoV.•PPI networks identified RPS3, RPS8 and RPS9 as hub genes in viral activity and SRC and CASP1 in cytokine secretion.•RPS3 plays a key role in viral replication.•SRC non-receptor protein kinase has a role in macrophage mediated innate immunity and cytokine release.•Blocking the ACE2 receptor may be protective or lead to worse lung injury due to the unmitigated action of angiotensin 2.
This paper investigates the problem of sampled-data synchronization for Markovian neural networks with generally incomplete transition rates. Different from traditional Markovian neural networks, ...each transition rate can be completely unknown or only its estimate value is known in this paper. Compared with most of existing Markovian neural networks, our model is more practical because the transition rates in Markovian processes are difficult to precisely acquire due to the limitations of equipment and the influence of uncertain factors. In addition, the time-dependent Lyapunov-Krasovskii functional is proposed to synchronize drive system and response system. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilize the upper bound of variable sampling interval and the sawtooth structure information of varying input delay. Moreover, the desired sampled-data controllers are obtained. Finally, two examples are provided to illustrate the effectiveness of the proposed method.
This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an ...enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.
This paper investigates the stochastic synchronization problem for Markovian hybrid coupled neural networks with interval time-varying mode-delays and random coupling strengths. The coupling ...strengths are mutually independent random variables and the coupling configuration matrices are nonsymmetric. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is proposed, where some terms involving triple or quadruple integrals are considered, which makes the LKF matrices mode-dependent as much as possible. This gives significant improvement in the synchronization criteria, i.e., less conservative results can be obtained. In addition, by applying an extended Jensen's integral inequality and the properties of random variables, new delay-dependent synchronization criteria are derived. The obtained criteria depend not only on upper and lower bounds of mode-delays but also on mathematical expectations and variances of the random coupling strengths. Finally, two numerical examples are provided to demonstrate the feasibility of the proposed results.
Exosomes derived from cells have been found to mediate signal transduction between cells and to act as efficient carriers to deliver drugs and small RNA. Hepatocyte growth factor (HGF) is known to ...promote the growth of both cancer cells and vascular cells, and the HGF‐cMET pathway is a potential clinical target. Here, we characterized the inhibitory effect of HGF siRNA on tumor growth and angiogenesis in gastric cancer. In addition, we showed that HGF siRNA packed in exosomes can be transported into cancer cells, where it dramatically downregulates HGF expression. A cell co‐culture model was used to show that exosomes loaded with HGF siRNA suppress proliferation and migration of both cancer cells and vascular cells. Moreover, exosomes were able to transfer HGF siRNA in vivo, decreasing the growth rates of tumors and blood vessels. The results of our study demonstrate that exosomes have potential for use in targeted cancer therapy by delivering siRNA.
HGF siRNA packed in exosomes can be transported into cancer cells, and down‐regulates HGF expression, and suppress proliferation and migration of both cancer cells and vascular cells.
Hematite (α-Fe2O3) is widely used in sensor sensitization due to its excellent optical properties. In this study, we present a sensitivity-enhanced surface plasmon resonance alcohol sensor based on ...Fe2O3/Au. We describe the fabrication process of the sensor and characterize its structure. We conduct performance testing on sensors coated multiple times and use solutions with the same gradient of refractive indices as the sensing medium. Within the refractive index range of 1.3335–1.3635, the sensor that was coated twice achieved the highest sensitivity, reaching 2933.2 nm/RIU. This represents a 30.26% enhancement in sensitivity compared to a sensor with a pure gold monolayer film structure. Additionally, we demonstrated the application of this sensor in alcohol concentration detection by testing the alcohol content of common beverages, showing excellent agreement with theoretical values and highlighting the sensor’s potential in food testing.
Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic ...scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep reinforcement learning (DRL). A system architecture for solving dynamic PFSP using DRL is proposed, and the mathematical model to minimize total tardiness cost is established. Additionally, the intelligent scheduling system based on DRL is modeled, with state features, actions, and reward designed. Moreover, the advantage actor-critic (A2C) algorithm is adapted to train the scheduling agent. The learning curve indicates that the scheduling agent learned to generate better solutions efficiently during training. Extensive experiments are carried out to compare the A2C-based scheduling agent with every single action, other DRL algorithms, and meta-heuristics. The results show the well performance of the A2C-based scheduling agent considering solution quality, CPU times, and generalization. Notably, the trained agent generates a scheduling action only in 2.16 ms on average, which is almost instantaneous and can be used for real-time scheduling. Our work can help to build a self-learning, real-time optimizing, and intelligent decision-making scheduling system.