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Objectives: To compare messenger RNA (mRNA)–based and adenovirus-vectored vaccines (ADVVs) with inactivated virus vaccines (IVVs) using real-world aggregate data. Methods: We performed longitudinal analyses of publicly accessible epidemiological, clinical, virological, vaccine-related, and other public health data from 41 eligible countries during the first half of 2021. The relationships between vaccination coverage and clinical outcomes were analyzed using repeated measures correlation analyses and mixed-effects modeling to adjust for potential mediating and confounding factors. Results: Countries that used mRNA and/or ADVV (n = 31) vs IVV, among other vaccine types (n = 10), had different distributions of age (42.4 vs 33.9 years, respectively; P-value = 0.0006), gross domestic product per capita ($ 38,606 vs $ 20,422, respectively; P

Abstract The substantial number of wearable devices in the healthcare industry and the continuous growth of the market procreates the demand for computational offloading. Despite major development of wearable devices and offloading techniques, there are several concerns such as latency, battery power, and computation capability that requires significant development. In this paper, we focus on the fact that most smart wearable devices have Bluetooth pairing with smartphones, and Bluetooth communication is significantly energy-efficient compare to 3G/LTE or Wi-Fi. We propose a computation offloading technique that offloads from the smartphone to the cloud server considering the decision model of both wearable devices and smartphones. Mobile cloud computing can elevate the capacity of smartphones considering the battery state and efficient communications with the cloud. In our model, we increase the energy efficiency of smart devices. To accomplish this, a Dhrystone Millions of Instructions per Second (DMIPS)-based workload measurement model along with a computation offloading decision model were created. According to the performance evaluation, offloading from wearable devices to smartphones and offloading once to cloud server can reduce energy consumption significantly.

Multi-task learning provides plenty of room for performance improvement to single-task learning, when learned tasks are related and learned with mutual information. In this work, we analyze the efficiency of using a single-task reinforcement learning algorithm to mitigate jamming attacks with frequency hopping strategy. Our findings show that single-task learning implementations do not always guarantee optimal cumulative reward when some jammer’s parameters are unknown, notably the jamming time-slot length in this case. Therefore, to maximize packet transmission in the presence of a jammer whose parameters are unknown, we propose deep multi-task conditional and sequential learning (DMCSL), a multi-task learning algorithm that builds a transition policy to optimize conditional and sequential tasks. For the anti-jamming system, the proposed model learns two tasks: sensing time and transmission channel selection. DMCSL is a composite of the state-of-the-art reinforcement learning algorithms, multi-armed bandit and an extended deep-Q-network. To improve the chance of convergence and optimal cumulative reward of the algorithm, we also propose a continuous action-space update algorithm for sensing time action-space. The simulation results show that DMCSL guarantees better performance than single-task learning by relying on a logarithmically increased action-space sample. Against a random dynamic jamming time-slot, DMCSL achieves about three times better cumulative reward, and against a periodic dynamic jamming time-slot, it improves by 10% the cumulative reward.

Neural tube (NT) formation in the spinal region of the mammalian embryo involves a wave of "zippering" that passes down the elongating spinal axis, uniting the neural fold tips in the dorsal midline. Failure of this closure process leads to open spina bifida, a common cause of severe neurologic disability in humans. Here, we combined a tissue-level strain-mapping workflow with laser ablation of live-imaged mouse embryos to investigate the biomechanics of mammalian spinal closure. Ablation of the zippering point at the embryonic dorsal midline causes far-reaching, rapid separation of the elevating neural folds. Strain analysis revealed tissue expansion around the zippering point after ablation, but predominant tissue constriction in the caudal and ventral neural plate zone. This zone is biomechanically coupled to the zippering point by a supracellular F-actin network, which includes an actin cable running along the neural fold tips. Pharmacologic inhibition of F-actin or laser ablation of the cable causes neural fold separation. At the most advanced somite stages, when completion of spinal closure is imminent, the cable forms a continuous ring around the neuropore, and simultaneously, a new caudal-to-rostral zippering point arises. Laser ablation of this new closure initiation point causes neural fold separation, demonstrating its biomechanical activity. Failure of spinal closure in pre-spina bifida Zic2Ku mutant embryos is associated with altered tissue biomechanics, as indicated by greater neuropore widening after ablation. Thus, this study identifies biomechanical coupling of the entire region of active spinal neurulation in the mouse embryo as a prerequisite for successful NT closure. [ABSTRACT FROM AUTHOR]

This article aims to investigate the performance of the IEEE 802.11-based tree-topology network, where a wireless node is within the others' carrier sensing ranges. In such a network, the concurrent transmission is a dominant cause of frame collisions. Moreover, the relay nodes (RN) (i.e., in the tree) likely cause the coexistence of non-saturated and saturated nodes in the networks. Those conditions have not been addressed in the previous works yet. As a solution, this work proposes new analytical expressions of delay and throughput in the investigated scenario. The presented analytical model incorporates the Bianchi model and airtime concept to formulate operations of the IEEE 802.11 nodes. First, by leveraging Bianchi-based analysis, the proposed model gives the frame collision probability caused by concurrent transmission. Second, by using airtime concept analysis, the ratio of frame numbers of each flow in a relay node (RN) is expressed to represent the buffer state of RN. As a result, we can obtain the network throughput, the throughput, and the delay of each flow. The validity of the analytical expressions is confirmed by the quantitative agreement between the analytical and simulation results.

In cognitive radio networks (CRNs), two secondary users (SUs) need to meet on a channel among multiple channels within a finite time to establish a link, which is called rendezvous. For blind rendezvous, researchers have devised ample well-grounded channel hopping (CH) sequences that guarantee smaller time-to-rendezvous. However, the best part of these works lacks the impact of network factors, particularly channel availability and collision during rendezvous. In this study, a new CH scheme is investigated by jointly considering the medium access control (MAC) protocol for single-hop multi-user CRNs. The analysis of our new variable hopping sequence (V-HS) guarantees rendezvous for the asymmetric channel model within a finite time. Although this mathematical concept guarantees rendezvous between two SUs, opportunities can be overthrown because of the unsuccessful exchange of control packets on that channel. A successful rendezvous also requires the exchange of messages reliably while two users visit the same channel. We propose a MAC protocol, namely ReMAC, that can work with V-HS and CH schemes. This design allows multiple rendezvous opportunities when a certain user visits the channel and modifies the conventional back-off strategy to maintain the channel list. Both simulation and analytical results exhibited improved performance over the previous approaches.

Although the application of a wide range of sensors has been generalized through the development of technology, the processing of massive alerts generated through data analysis and monitoring remains a challenge. This problem is also found in cyber security because the intrusion detection system (IDS) produces a tremendous number of alerts. Massive alerts not only significantly increase resources for analysis, but also make it difficult to analyze the overall situation of the system. In order to handle massive alerts, we propose using an indicator as a frequency-based representation. The proposed indicator is generated from categorical parameters of alerts that occur within a unit time utilizing frequency and is used for situational awareness with machine learning to detect whether there is a threat or not. The advantage of using indicators is that they can determine the situation for a period without analyzing individual alerts, which helps security experts to recognize the situation in the system and focus on targets that require in-depth analysis. In addition, the conversion from the categorical parameters which is highly related to analysis to numeric parameter allows for applying machine learning. For performance evaluation, we collect data from an HAI testbed similar to real critical infrastructure and conduct experiments using indicators and XGBoost, a classification machine learning algorithm against five famous vulnerability attacks. Consequently, we show that the proposed method can detect attacks with more than 90 percent accuracy, and the performance is enhanced using heterogeneous intrusion detection systems.

In an IEEE 802.11 Distributed Coordination Function–based wireless network with multiple hops, a node operates on its own with several predefined data rates (i.e. following modulation and coding schemes). Moreover, the IEEE 802.11 Distributed Coordination Function node’s communication is characterized by transmission and carrier-sensing distances. The transmission one is, in general, reverse proportional to the data rate. Meanwhile, the carrier distance keeps constant regardless of the modulation and coding scheme. Therefore, when a node has a high transmission rate, within its carrier-sensing range, the number of nodes may increase. The previous works have not yet extensively investigated the impact of data rates on such a scenario. This article addresses that issue aiming to quantify the network performance of the multi-hop IEEE 802.11 networks. As a solution, we propose the mathematical expressions, which consider data rates, for end-to-end throughputs, as well as delays in the network with string topology. We confirm the expressions’ correctness by presenting the quantitative agreements between the analytical and simulation results.

We are becoming more reliant on multi-hop wireless networks (MWNs) for public safety, tactical/military, and last mile Internet communications. The architecture of an MWN can be flat, as in ad-hoc networks or hierarchical, as in mesh or sensor networks. Today, the lower cost and smaller size of radios enables multiple radios to exist in a device. Hence, simultaneous transmissions on orthogonal channels are possible, which multiplies the performance of MWNs. However, an efficient communication is only possible if every device has reliable connection. A connected MWN can be created using a meticulous channel assignment (CA) scheme that reduces congestion and interference. In addition, CA needs to handle jamming attacks. The medium access control (MAC) layer is responsible for CA related tasks. However, CA results in topology changes that affect routing decisions taken at the routing layer, which could lead to unreliable network. This strong interrelationship requires a cross-layered approach to ensure reliable connectivity. In this paper, we formulate this cross-layer problem as network-path cost optimization. Because the problem is NP-hard, we propose a dynamic and distributed heuristic CA and routing scheme that is applicable for both flat and hierarchical MWNs and that is also resistant to jamming attacks. We call it distributed jamming resilient channel assignment and routing (DJ-CAR) scheme. The performance of DJ-CAR, with that of existing schemes, is compared using an OPNET simulator.

Background: To characterize the temporal dynamics of mumps epidemiology according to the different vaccine strains used, sex-specific trends were decomposed in an age–period–cohort (APC) analysis for mumps cases reported in South Korea. Methods: National surveillance data were used to describe the epidemiology of mumps cases from 2001 to 2015. An APC model was used to break down the reported mumps cases into the effects of age, period, and birth cohort. Results: From 2001 to 2015, the incidence started to increase from fewer than 10 cases to more than 100 cases per 100 000. The incidence rate was highest among males aged 15–17 years during 2013–2015, reaching 508.7 per 100 000 persons. There was an increased incidence during the late teenage years in the 1998–2000 cohort. An age shift towards the earlier teenage years was observed across the 2001–2003 and 2004–2006 cohorts. The risk of mumps increased according to the birth cohort; the net drift from 2001 to 2015 was 27.67 (95% confidence interval 27.5.47–29.90) for males and 27.25 (95% confidence interval 24.91–29.65) for females. Conclusions: The increase in mumps seen in Korea may have been affected by the birth cohort exposed to the Rubini strain; however other factors may have contributed to the increase in non-exposed cohorts.

5G and Beyond 5G mobile networks use several high-frequency spectrum bands such as the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity. However high-frequency bands do not cover larger distances. The coverage problem is addressed by using a heterogeneous network which comprises numerous small and macrocells, defined by transmission and reception points (TRxPs). For such a network, random access is considered a challenging function in which users attempt to select an efficient TRxP by random access within a given time. Ideally, an efficient TRxP is less congested, minimizing delays in users’ random access. However, owing to the nature of random access, it is not feasible to deploy a centralized controller estimating the congestion level of each cell and deliver this information back to users during random access. To solve this problem, we establish an optimization problem and employ a reinforcement-learning-based scheme. The proposed scheme estimates congestion of TRxPs in service and selects the optimal access point. Mathematically, this approach is beneficial in approximating and minimizing a random access delay function. Through simulation, we demonstrate that our proposed deep learning-based algorithm improves performance on random access. Notably, the average access delay is improved by 58.89% from the original 3GPP algorithm, and the probability of successful access also improved.

It is desirable for alarm packets to be forwarded to the sink as quickly as possible in wireless sensor networks. In this paper, we initially analyze the theory of the relationships between network configurations and network lifetime as well as transmission delay. Then, we propose an approximate optimization approach to minimize the end-to-end delay with a reduced complexity of configuration under the condition that the network lifetime remains greater than the specified target value. A local forwarding approach named Fast data collection for nodes Far away from the sink and slow data collection for nodes Close to the Sink (FFSC) is proposed. This approach is energy efficient. Moreover, it can further reduce the end-to-end delay. Both the comprehensive theoretical analysis and the experimental results indicate that the performance of FFSC is better than the methods proposed by previous studies. Relative to the direct forwarding strategy, the FFSC approach can reduce the delay by 7.56%-23.16% and increase the lifetime by more than 25%. It can also increase the energy efficiency as much as 18.99%. Relative to the single fixed threshold strategy, the FFSC approach can reduce the delay by 4.16%-9.79% and increase the energy efficiency by 19.28% while still guaranteeing the same lifetime as those previous methods.

Owing to ad hoc wireless networks’ properties, the implementation of complex security systems with higher computing resources seems troublesome in most situations. Therefore, the usage of anomaly or intrusion detection systems has attracted considerable attention. The detection systems are implemented either as host-based, run by each node; or as cluster/network-based, run by cluster head. These two implementations exhibit benefits and drawbacks, such as when cluster-based is used alone, it faces maintaining protection when nodes delay to elect or replace a cluster head. Despite different heuristic approaches that have been proposed, there is still room for improvement. This work proposes a detection system that can run either as host- or as cluster-based to detect routing misbehavior attacks. The detection runs on a dataset built using the proposed routing-information-sharing algorithms. The detection system learns from shared routing information and uses supervised learning, when previous network status or an exploratory network is available, to train the model, or it uses unsupervised learning. The testbed is extended to evaluate the effects of mobility and network size. The simulation results show promising performance even against limiting factors.

Recently, we often see the environment where many one-to-one Wireless Local Area Networks (WLANs) exist in a small area. In this environment, the network throughput of certain WLAN reduces significantly because of the interference from other networks (i.e., inter-network interference). The inter-network interference is the effect of carrier-sensing activities when there are ongoing transmissions in neighbor networks. This paper presents analytical expressions using airtime concept, which newly take into account the inter-network interference, for network throughputs of WLANs. There are existing works that similarly address the WLAN’s carrier-sensing duration. However, they either consider a simple interference model or assume the simultaneous transmission time is negligible. Different from them, we consider the significant impact of simultaneous transmission. As a result, our analytical model can precisely express each network carrier-sensing duration by subtracting the simultaneous transmission time. More specifically, we have successfully obtained each network throughput by expressing frame-existence probabilities concerning each network’s End Device (ED). We also confirm the validity of the analysis by comparison with simulation. The analytical results and the simulation results agree well.

Clustering is vital for lengthening the lives of resource-constrained wireless sensor nodes. In this work, we propose a cluster-based energy-efficient router placement scheme for wireless sensor networks, where the K-means algorithm is used to select the initial cluster headers and then a cluster header with sufficient battery energy is selected within each cluster. The performance of the proposed scheme was evaluated in terms of the energy consumption, end-to-end delay, and packet loss. Our simulation results using the OPNET simulator revealed that the energy consumption of our proposed scheme was better than that of the low-energy adaptive clustering hierarchy, which is known to be an energy-efficient clustering mechanism. Furthermore, our scheme outperformed low-energy adaptive clustering hierarchy in terms of the end-to-end delay, throughput, and packet loss rate.

Wearable devices such as smartwatches are critical to energy consumption due to their small battery size. To reduce possible energy consumption for high computational tasks, task offloading is a solution, but smartwatches are usually connected to smartphones that are also exposed to energy-saving issues. To resolve this problem, we propose a task offloading model that compares the energy efficiency of two cases: offloading or no offloading. When a task is offloaded, energy of a smartphone is sacrificed instead of a smartwatch, and additional energy is used for the communication. To derive the condition of an offloading, the size of a task could be considered, but it is impossible to estimate accurate energy due to various computational specifications of smartphones as well as smartwatches. To generalize the task size for different CPU conditions, we propose to use Dhrystone million instructions per second. The proposed model was evaluated through our testbed and confirmed that it shows a reasonable offloading condition for both the smartphone and the smartwatch.

Information Retrieval system plays an essential role in web services. However, the web services in which users can upload files as attachments typically do not support enough search conditions and often rely only on the title or the description that the users provide during upload. We present a topic-model based framework for fast and effective Content Based Document Information Retrieval that retrieves the information from the actual contents in the attachment. The proposed systems is analyzed and compared with conventional methods in various aspects. In particular, we propose an efficient keyword extraction method based on Latent Dirichlet Allocation which is compared with the Term Frequency Inverse Document Frequency approach typically used in conventional systems. Moreover, a per-category indexing structure is also proposed and compared with the existing total indexing scheme. Our experimental results validate the utility of the proposed system for web services that can upload document attachments.

Reducing unnecessary and redundant handshake frame is considered as a promising way to reduce the end-to-end delay and energy consumption. Here, a protocol named compressed handshake media access control (CH-MAC) is presented. CH-MAC introduces a new handshake mechanism with implicit RTS to achieve the object of low delivery latency and an initiative sleeping mechanism to settle the hidden terminal problem. Simulation results show that the proposed protocol outperforms SMAC protocol in both energy consumption and packet latency.

In this paper, an algorithm named stepwise subspace pursuit (SSP) is proposed for sparse signal recovery. Unlike existing algorithms that select support set from candidate sets directly, our approach eliminates useless information from the candidate through threshold processing at first and then recovers the signal through the largest correlation coefficients. We demonstrate that SSP significantly outperforms conventional techniques in recovering sparse signals whose nonzero values have exponentially decaying magnitudes or distribution of N ( 0,1 ) . Experimental results of Lena show that SSP is better than CoSaMP, OMP, and SP in terms of peak signal to noise ratio (PSNR) by 5.5 db, 4.1 db, and 4.2 db, respectively.