Transmission Time Research Articles

Joint scheduling and offloading of computational tasks with time dependency under edge computing networks

Mobile edge computing is an emerging technology paradigm that improves mobile devices’ computational power and efficiency by offloading computational tasks to edge servers. However, there are still some problems: (1) Ignoring the complex time dependencies of compute-intensive or latency-sensitive tasks, leading to the omission of offload scheduling opportunities for fine-grained sub-tasks within a task, thus reducing the computational power of the mobile device and the utilization of edge resources; (2) Overly biased towards optimizing task execution latency and ignoring the balance between data transmission latency and energy loss, reducing the scheme versatility in natural computing environments. Aiming at the above problems, this paper models the complex dependencies, transmission time, and execution time between computing tasks, constructs a mathematical model of the scheduling and offloading process of computing tasks, and a joint task scheduling and offloading method (N-JSU) based on Nash equilibrium is designed. This offloading strategy is divided into two steps: first, the average latency of each task is analyzed, and the scheduling order of the computational tasks is determined based on the highest response ratio of the tasks; secondly, the optimal solution is determined by iterative search to offload the most suitable IoT tasks or computing tasks to the edge server. The experimental results show that this offloading strategy can effectively reduce the latency of computationally intensive edge network tasks and improve the resource utilization of the edge server.

Transmission Timing Control among Both Aperiodic and Periodic Flows for Reliable Transfer by Restricted Packet Loss and within Permissible Delay in Wireless Sensor Networks

Nowadays, in various wireless sensor networks, both aperiodically generated packets like event detections and periodically generated ones for environmental, machinery, vital monitoring, etc. are transferred. Thus, packet loss caused by collision should be suppressed among aperiodic and periodic packets. In addition, some packets for wireless applications such as factory IoT must be transferred within permissible end-to-end delays, in addition to improving packet loss. In this paper, we propose transmission timing control of both aperiodic and periodic packets at an upper layer of medium access control (MAC). First, to suppress packet loss caused by collision, transmission timings of aperiodic and periodic packets are distributed on the time axis. Then, transmission timings of delay-bounded packets with permissible delays are assigned within the bounded periods so that transfer within their permissible delays is possible to maximally satisfy their permissible delays. Such control at an upper layer has advantages of no modification to the MAC layer standardized by IEEE 802.11, 802.15.4, etc. and low sensor node cost, whereas existing approaches at the MAC layer rely on MAC modifications and time synchronization among all sensor nodes. Performance evaluation verifies that the proposed transmission timing control improves packet loss rate regardless of the presence or absence of packet's periodicity and permissible delay, and restricts average transfer delay of delay-bounded packets within their permissible delays comparably to a greedy approach that transmits delay-bounded packets to the MAC layer immediately when they are generated at an upper layer.

Stochastic Peak Age of Information Guarantee for Cooperative Sensing in Internet of Everything

This paper focuses on the service freshness guarantee for cooperative sensing in the Internet of Everything. Specifically, the peak age of information (AoI) is first introduced to evaluate the information and service freshness. An analytical model is then constructed to decouple the components of peak AoI into the inter-arrival time and transmission time. With the knowledge of identical increments of update arrival and transmission process, a close bound of peak AoI violation probability is derived based on martingale theory. Furthermore, the impact of source node parameter configuration on the peak AoI violation probability are analyzed and a task allocation scheme is proposed to guide cooperative sensing. Numerical analysis validates the tightness of the peak AoI violation probability bound and the effectiveness of the proposed scheme in service freshness guarantee.

Adaptive NN Fixed-Time Fault-Tolerant Control for Uncertain Stochastic System With Deferred Output Constraint via Self-Triggered Mechanism.

For a class of nonstrict-feedback stochastic nonlinear systems with the injection and deception attacks, this article explores the problem of adaptive neural network (NN) fixed-time control ground on the self-triggered mechanism in a pioneering way. After developing the self-triggered mechanism and the delay-error-dependence function, a neural adaptive delay-constrained fault-tolerant controller is proposed by employing the backstepping technique. The self-triggered mechanism does not require an additional observer to determine the time of the data transmission, which reduces the consumption of the system resources more efficiently. In addition, the whole Lyapunov function with the delay-error-dependence term is developed to solve the deferred output constraint problem. Under the proposed controller, it can be proven that all the signals within the closed-loop system are semiglobally uniformly bounded in probability, while the convergence time is independent of the initial state and the deferred output constraint control performance is achieved. The feasibility and the superiority of the proposed control strategy are shown by some simulations.

A Study to Ensure Communication Reliability of Bus Location System Using LoRa Communication

We have developed a bus location system, “Busdoko,” that utilizes LoRa. Recently, LoRa has been increasingly used in local cities. As a result, it has become difficult to maintain real-time performance. Conventional TDMA does not allow transmission overlap between slots, and requires guard gaps. The transmission time of LoRa covering the city is long. There is a report that it takes about 13 seconds. In this case, the slot interval should be set to about 30 seconds including the guard gap. The distance between bus stops of community buses is short, and there are several places where the running time is about 30 seconds. We can't make the bus location systems in conventional TDMA, because it is necessary to display the position of the bus at least once between bus stops. We propose a communication scheme using multi-channel multiple access communication. In the scheme, the slot interval is designed to allow overlap within a permissible range. We evaluated the proposed scheme on a bus route. We confirmed that the system works correctly even if we allow a few percent transmission overlap between slots. The experimental results showed that the proposed scheme made effective use of the channels and improved reliability.

Efficient Lossless Compression of Integer Astronomical Data

Each new generation of telescope produces increasingly larger astronomical data volumes, which are expected to reach the order of exabytes in the next decade. Effective and fast data compression methods are paramount to help the scientific community contain storage costs and improve transmission times. Astronomical data differs significantly from natural and Earth-observation images, asking for specifically tailored compression approaches. This paper presents a novel lossless compression technique that employs the discrete Haar wavelet transform within the JPEG 2000 standard. Its performance is compared to that of a comprehensive selection of compressors, including fpack, the most common technique in astronomical observatories, as well as other algorithms highly competitive for other types of data. Experiments are performed on a large data set of 16 bit integer images, produced by telescopes around the world and representative of a wide variety of astronomical scenarios. The proposed technique has two modes. The first mode outperforms all the other tested techniques in terms of compression performance. It surpasses the most competitive configuration of fpack by, respectively, 5.3% (about 0.3 bits per sample), having also 4.5% lower compression and decompression times. The second mode is the fastest among all tested techniques. Its compression and decompression times are 2.5 and 3.5 times faster than the fastest configuration of fpack, while also yielding a 2.4% better compression performance (0.15 bits per sample).

Flexible and Innovative Connectivity Solution to Support National Decentralized Infectious Diseases Point-of-Care Testing Programs in Primary Health Services: Descriptive Evaluation Study.

Molecular point-of-care (POC) testing for Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and Trichomonas vaginalis (TV) has been available in regional and remote primary health services in Australia as part of a decentralized POC testing program since 2016 and for SARS-CoV-2 from 2020. As there was no suitable existing connectivity infrastructure to capture and deliver POC test results to a range of end users, a new system needed to be established. The aim of the study is to design, implement, and optimize a connectivity system to meet clinical management, analytical quality management, and public health surveillance needs. We used commercially available e-messaging technology coupled with adapted proprietary software to integrate a decentralized molecular POC testing platform (GeneXpert) in primary health services and interface with end-user databases. This connectivity infrastructure was designed to overcome key barriers to the implementation, integration, and monitoring of these large multijurisdictional infectious disease POC testing networks. Test result messages were tailored to meet end-user needs. Using centrally captured deidentified data, we evaluated the time to receipt of test results and completeness of accompanying demographic data. From January 2016 to April 2020, we operationalized the system at 31 health services across 4 jurisdictions and integrated with 5 different patient management systems to support the real-time delivery of 29,356 CT/NG and TV test results to designated recipients (patient management system and local clinical and central program databases). In 2019, 12,105 CT/NG and TV results were delivered, and the median time to receipt of results was 3.2 (IQR 2.2-4.6) hours, inclusive of test runtime. From May 2020 to August 2022, we optimized the system to support rapid scale-up of SARS-CoV-2 testing (105 services; 6 jurisdictions; 71,823 tests) and additional sexually transmissible infection testing (16,232 tests), including the electronic disease-specific notifications to jurisdictional health departments and alerts for connectivity disruption and positive results. In 2022, 19,355 results were delivered with an overall median transmission time of 2.3 (IQR 1.4-3.1) hours, 2.2 (IQR 1.2-2.3) hours for SARS-CoV-2 (n=16,066), 3.0 (IQR 2.0-4.0) hours for CT/NG (n=1843), and 2.6 (IQR 1.5-3.8) hours for TV (n=1446). Demographic data (age, sex, and ethnicity) were completed for 99.5% of test results in 2022. This innovative connectivity system designed to meet end-user needs has proven to be sustainable, flexible, and scalable. It represents the first such system in Australia established independent of traditional pathology providers to support POC testing in geographically dispersed remote primary health services. The system has been optimized to deliver real-time test results and has proven critical for clinical, public health, and quality management. The system has significantly supported equitable access to rapid diagnostics for infectious diseases across Australia, and its design is suitable for onboarding other POC tests and testing platforms in the future.

OPTIMIZATION OF ENERGY CONSUMPTION IN SENSOR NODE BASED ON RECEIVED SIGNAL STRENGTH INDICATOR (RSSI) AND SLEEP AWAKE METHOD

The transmission power management in Wireless Sensor Networks (WSN) is a critical problem. This research investigated optimizing power consumption based on transmission power (Tx Power) level according to RSSI and periodic transmission time. We investigated the RSSI value by varying Tx Power Level to get the optimum Tx Power Level. We found the optimum periodic transmission time by transmitting the data with various transmission times. By varying the Tx Power Level, we found the optimum Tx Power Level, resulting in the power consumption decreasing by about 42% and the power supply’s lifetime increasing by about 71% in the 280 m distance between the sensor node and gateway, with a 108 Wh power supply. By varying the periodic transmission time, we found that the optimum periodic transmission time is 8 seconds. Combining the optimum Tx Power Level and periodic transmission time, we found that the power supply’s lifetime is 40 times longer. This result is helpful for WSN applications in remote areas.

Research on the integration of online teaching resources in higher education institutions under the perspective of industry-education integration

Abstract The purpose of exploring the development mode of online teaching resources in colleges and universities is to enhance the utilization rate of online teaching resources to promote the diversification of teaching methods. In this paper, based on big data technology, a cloud education platform integrating online teaching resources in colleges and universities is constructed under the perspective of integrating industry and education, and the architecture and functions of the cloud education platform are explained. Then the collaborative filtering algorithm is used to filter and collect the information on online teaching resources, and the Pearson correlation formula is used to pre-process the data and help users to recommend teaching resources. Finally, the pre-processed data are filtered and analyzed using the multilayer perceptron technology under the deep learning model. To verify the practicality of the cloud education platform proposed in this paper, the platform’s performance is evaluated by combining MAE metrics with RMSE metrics. In terms of load capacity, the platform has the best load capacity when the number of users is 200. From the performance test, the average transmission rate of the cloud education platform is 8.1% and 0.93% higher than that of NetEase Cloud Classroom and Tencent Classroom, and the average transmission time is 7.09 s. From the practicality analysis, the average value of the MAE index is 0.317%, and the average value of the RMSE index is 0.232%. This shows that the cloud education platform can effectively integrate university online teaching resources and help universities achieve diversified development of teaching methods.

Static aeroelasticity analysis of a rotor blade using a Gauss-Seidel fluid-structure interaction method

The present study introduces a Gauss-Seidel fluid-structure interaction (FSI) method including the flow solver, structural statics solver and a fast data transfer technique, for the research of structural deformation and flow field variation of rotor blades under the combined influence of steady aerodynamic and centrifugal forces. The FSI method is illustrated and validated by the static aeroelasticity analysis of a transonic compressor rotor blade, NASA Rotor 37. An improved local interpolation with data reduction (LIWDR) algorithm is introduced for fast data transfer on the fluid-solid interface of blade. The results of FSI calculation of NASA Rotor 37 show that when compared with the radial basis function (RBF) based interpolation algorithm, LIWDR meets the interpolation accuracy requirements, while the calculation cost can be greatly improved. The data transmission time is only about 1% of that of RBF. Moreover, the iteration step of steady flow computation within one single FSI has little impact on the converged aerodynamic and structural results. The aerodynamic load-caused deformation accounts for nearly 50% of the total. The effects of blade deformation on the variations of aerodynamic performance are given, demonstrating that when static aeroelasticity is taken into account, the choke mass flow rate increases and the peak adiabatic efficiency slightly decreases. The impact mechanisms on performance variations are presented in detail.

Assessing the Impact of Travel Restrictions on the Spread of the 2020 Coronavirus Epidemic: An Advanced Epidemic Model Based on Human Mobility

Infectious disease transmission can be greatly influenced by human mobility. During the COVID-19 pandemic, the Chinese Government implemented travel restriction policies to mitigate the impact of the disease or even block the transmission chain of it. In order to quantify the impact of these policies on the number of infections and the peak time of transmission, this research modified the traditional SIR model by considering human mobility. The proposed model was validated using a Baidu Qianxi dataset and the results indicate that the number of total infections would have increased by 1.61 to 2.69 times the current value and the peak time would have moved forward by 3 to 8 days if there were no such restriction policies. Furthermore, a mixing index α added in the proposed model showed that the proportion of residents using public transport to travel between different areas had a positive relationship with the number of infections and the duration of the epidemic.

The rates and symptoms of natural and breakthrough infection pre- and post- Covid-19 non-mRNA vaccination at various peaks amongst Iranian healthcare workers

Background/AimsThe aim of this study was to determine the rate of natural and breakthrough infection and related symptoms of Covid-19 amongst Iranian healthcare workers (HCWs) who were vaccinated by different non-mRNA-based vaccines at peak points.MethodsIn this cross-sectional study, the RT-PCR test was performed for a total of 10,581 HCWs suspicious of Covid-19 infection. For each HCW, the frequency of SARS-CoV-2 infection and the time of transmission based on vaccination administration time and schedule were examined during different waves of the pandemic. Based on these findings, the study patients were divided into three groups: natural, natural/breakthrough, and breakthrough.ResultsIn total, 53% of the HCWs were exposed to SARS-CoV-2 infection between 1 and 5 times within two years after the current pandemic, while 20.7% and 32.3% experienced natural and breakthrough SARS-CoV-2 infection, respectively. Only 6% of the breakthrough-infected HCWs had naturally contracted SARS-CoV-2 infection during the initial waves. The highest natural peaks of infection occurred during the interval administration of the first and second dose of the first vaccination series, while the single highest peak of breakthrough infection belonged to the Omicron wave. It occurred simultaneously with the administration of the third vaccination dose. On the other hand, the highest rate of reinfection was observed amongst people who had received the Sinopharm and Bharat vaccines full-doses.ConclusionThis study compared the clinical differences between the two peaks of Omicron and Delta. This study indicates the rates of natural and breakthrough SARS-CoV-2 infections according to vaccination schedules and different waves of the pandemic.

Reinforcement learning based routing for time-aware shaper scheduling in time-sensitive networks

To guarantee real-time performance and quality-of-service (QoS) of time-critical industrial systems, time-aware shaper (TAS) in time-sensitive networking (TSN) controls frame transmission times in a bridged network using a scheduled gate control mechanism. However, most TAS scheduling methods generate schedules based on pre-configured routes without exploring alternatives for better schedulability, and methods that jointly consider routing and scheduling require enormous runtime and computing resources. To address this problem, we propose a TSN Scheduler with Reinforcement Learning-based Routing (TSLR) that identifies improved load balanced routes for higher schedulability with acceptable complexity using distributional reinforcement learning. We evaluate TSLR through TSN simulations and compare it against state-of-the-art algorithms to demonstrate that TSLR effectively improves TAS schedulability and link utilization in TSN with lower complexity. Specifically, TSLR shows a more than 66% increase in schedulability compared to the other algorithms, and TSLR’s scheduling time is reduced by more than 1 h. It also shows flows’ transmission latency is less than 25% of their latency deadline requirement and reduces maximum link utilization by approximately 50%.

Federated Learning Hyperparameter Tuning From a System Perspective

Federated learning (FL) is a distributed model training paradigm that preserves clients’ data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose, an automatic FL hyper-parameter tuning algorithm tailored to applications’ diverse system requirements in FL training. iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of for diverse applications and FL aggregation algorithms, we show that is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of is available at.

Maximizing the stable throughput of heterogeneous nodes under airtime fairness in a CSMA environment

The stability region of non-persistent CSMA is analyzed in a general heterogeneous network, where stations have different mean packet arrival rates, packet transmission times probability distributions and transmission probabilities. The considered model of CSMA captures the behavior of the well known CSMA/CA, at least as far as stability and throughput evaluation are concerned. The analysis is done both with and without collision detection. Given the characterization of the stability region, throughput-optimal transmission probabilities are identified under airtime fairness, establishing asymptotic upper and lower bounds of the maximum achievable stable throughput. The bounds turn out to be insensitive to the probability distribution of packet transmission times. Numerical results highlight that the obtained bounds are tight not only asymptotically, but also for essentially all values of the number of stations. The insight gained leads to the definition of a distributed adaptive algorithm to adjust the transmission probabilities of stations so as to attain the maximum stable throughput.

Combining a parsimonious mathematical model with infection data from tailor-made experiments to understand environmental transmission

Although most infections are transmitted through the environment, the processes underlying the environmental stage of transmission are still poorly understood for most systems. Improved understanding of the environmental transmission dynamics is important for effective non-pharmaceutical intervention strategies. To study the mechanisms underlying environmental transmission we formulated a parsimonious modelling framework including hypothesised mechanisms of pathogen dispersion and decay. To calibrate and validate the model, we conducted a series of experiments studying distance-dependent transmission of Campylobacter jejuni in broilers. We obtained informative simultaneous estimates for all three model parameters: the parameter of C. jejuni inactivation, the diffusion coefficient describing pathogen dispersion, and the transmission rate parameter. The time and distance dependence of transmission in the fitted model is quantitatively consistent with marked spatiotemporal patterns in the experimental observations. These results, for C. jejuni in broilers, show that the application of our modelling framework to suitable transmission data can provide mechanistic insight in environmental pathogen transmission.

DL‐DC: Deep learning‐based deadline constrained load balancing technique

SummaryLoad balancing is a crucial feature of cloud computing that evenly distributes workload between servers, network interfaces, and hard drives. Because of dynamic computing over the internet, cloud computing suffers from request overloading. To address this challenge, this paper proposes a new deep learning‐based deadline constrained (DL‐DC) load balancing technique. The proposed DL‐DC technique will improve resource utilization, reduce cost, latency, and response time, as well as balance load between servers and improve reliability. The proposed DL‐DC technique will direct traffic to a load balancer, which will forward the load with a deadline to a deep Inception ResNet. This network considers some parameters, such as sticky session, content‐based and instance health check and efficiently predicts the schedule for the task. Finally, a predicted task schedule has been derived using the DL‐DC model which is used to distribute the task to the virtual machine. The proposed DL‐DC load balancing algorithm is compared with other existing algorithms such as QMPSO, DQTS, and ACSO in terms of cost, makespan, response time, transmission time and task migration. The proposed method achieves up to 21.08% low response time, 27.3% decrease in make span, 25.5% decrease in task migration, and 38.9% decrease in cost respectively.

Setting Sustainability Goals for Biodiversity Informatics Infrastructure

Biodiversity data are of crucial importance in the implementation of sustainable development strategies (UN Sustainable Development Goals, EU Taxonomy for Sustainable Activities), because they contribute to deepening our understanding of marine and terrestrial ecosystems and our assessment of the impact of human activities on the planet. Yet, biodiversity informatics itself is lacking an overall sustainability plan that addresses “the three E’s of environment, equity, and economics” (Brinkmann 2021) in relation to biodiversity data, their lifecycle, and related technologies. This is especially problematic given that data-driven biodiversity research has had a ten-fold increase (GBIF Secretariat 2022). This implies higher costs for data management, storage and transmission, a larger carbon footprint, and open issues about data access and reuse. SMART (Specific, Measurable, Assignable, Realistic, Time-Related) goals can help to set a sustainability strategy for biodiversity informatics because they bring together the ambitions of an organisation with the action plan necessary to realise these ambitions. When G.T. Doran proposed the use of SMART goals (Doran 1981), his aim was not to create a checklist, but a tool to get results and produce deliverables, which is precisely what is required for implementing an effective sustainability strategy. Three examples are provided here to explain the value of a SMART approach to set sustainability goals in biodiversity informatics. Goal 1, Increase energy efficiency of computing hardware: A large part of the energy consumption in data centres is due to hardware operations. Several strategies can be pursued to increase energy efficiency in this area, such as activity optimisation for Graphic Processing Units (GPUs), improvements in the effectiveness of cooling systems, and replacement of Central Processing Units (CPUs) with more efficient GPUs. This sustainability goal is specific because it clearly identifies what needs to be achieved; it is measurable because pre- and post- energy consumption are known and can be easily compared with the expected improvement; it is assignable because data centre managers can be identified as the agents of change; it is realistic because there are multiple technological solutions to achieve the goal; and time-related because a roadmap for change stretching through months or years can be set up. Achieving this goal is evidently beneficial for both the budget and the carbon footprint of biodiversity informatics, as higher energy efficiency is associated to lower running costs and also contributes to reduce greenhouse gases emissions. Goal 2, Reducing data storage costs per terabyte: Data storage is becoming one of the most significant operating expenses in data-driven organisations due to the considerable increase in the amount of data produced. It is therefore imperative to reduce costs by selecting appropriate storage options (on-premises or cloud services), by adopting compressed formats whenever possible, and by avoiding data redundancy. This goal is SMART because it sets a target (the expected cost reduction can be quantified) and establishes measures (cost per terabyte) to assess its achievement. It can be assigned to data managers, who can choose among different technologies and service providers to lower costs, and can be scheduled over time according to budget needs. The achievement of this goal has clear economic benefits, but also increases the social sustainability of biodiversity informatics, because it makes data-driven research affordable even for organisations with limited financial means. Goal 3, Decreasing API (Application Programming Interface) response time and bandwidth for data transmission: Data sharing is a key activity in biodiversity informatics and APIs have become the standard technology for achieving it. Improving API performance is therefore crucial to enhance the user experience for stakeholders interested in biodiversity data. In particular, API response time and bandwidth required for data transmission control whether users will be able to access immediately and download satisfactorily the data they are interested in. Goal 3 is SMART because API response time and bandwidth are known measures and can be improved using strategies such as caching and concurrency to handle data requests more rapidly and delivering data in compressed formats to reduce bandwidth. Software developers and project managers involved in API design and maintenance are the people in charge of this goal. They decide the best strategy and timeline to pursue changes. Improving API performance increases the social sustainability of biodiversity informatics because it promotes uncomplicated data access for all stakeholders interested in biodiversity data. It also lowers energy consumption for downloading data benefitting the environment.

Vertical transmission of African-lineage Zika virus through the fetal membranes in a rhesus macaque (Macaca mulatta) model.

Zika virus (ZIKV) can be transmitted vertically from mother to fetus during pregnancy, resulting in a range of outcomes including severe birth defects and fetal/infant death. Potential pathways of vertical transmission in utero have been proposed but remain undefined. Identifying the timing and routes of vertical transmission of ZIKV may help us identify when interventions would be most effective. Furthermore, understanding what barriers ZIKV overcomes to effect vertical transmission may help improve models for evaluating infection by other pathogens during pregnancy. To determine the pathways of vertical transmission, we inoculated 12 pregnant rhesus macaques with an African-lineage ZIKV at gestational day 30 (term is 165 days). Eight pregnancies were surgically terminated at either seven or 14 days post-maternal infection. Maternal-fetal interface and fetal tissues and fluids were collected and evaluated for ZIKV using RT-qPCR, in situ hybridization, immunohistochemistry, and plaque assays. Four additional pregnant macaques were inoculated and terminally perfused with 4% paraformaldehyde at three, six, nine, or ten days post-maternal inoculation. For these four cases, the entire fixed pregnant uterus was evaluated with in situ hybridization for ZIKV RNA. We determined that ZIKV can reach the MFI by six days after infection and infect the fetus by ten days. Infection of the chorionic membrane and the extraembryonic coelomic fluid preceded infection of the fetus and the mesenchymal tissue of the placental villi. We did not find evidence to support a transplacental route of ZIKV vertical transmission via infection of syncytiotrophoblasts or villous cytotrophoblasts. The pattern of infection observed in the maternal-fetal interface provides evidence of paraplacental vertical ZIKV transmission through the chorionic membrane, the outer layer of the fetal membranes.

Gain Without Pain: Recycling Reflected Energy From Wireless-Powered RIS-Aided Communications

In this paper, we investigate and analyze energy recycling for a reconfigurable intelligent surface (RIS)-aided wireless-powered communication network. As opposed to the existing works where the energy harvested by Internet of things (IoT) devices only comes from the power station, IoT devices are also allowed to recycle energy from other IoT devices. In particular, we propose group switching-and user switching-based protocols with time-division multiple access to evaluate the impact of energy recycling on the system performance. Two different optimization problems are respectively formulated for maximizing the sum throughput by jointly optimizing the energy beamforming vectors, the transmit power, the transmission time, the receive beamforming vectors, the grouping factors, and the phase-shift matrices, where the constraints of the minimum throughput, the harvested energy, the maximum transmit power, the phase shift, the grouping, and the time allocation are taken into account. In light of the intractability of the above problems, we respectively develop two alternating optimization-based iterative algorithms by combining the successive convex approximation method and the penalty-based method to obtain corresponding sub-optimal solutions. Simulation results verify that the energy recycling-based mechanism can assist in enhancing the performance of IoT devices in terms of energy harvesting and information transmission. Besides, we also verify that the group switching-based algorithm can obtain more sum throughput of IoT devices, and the user switching-based algorithm can harvest more energy.