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Coronary artery disease is a leading cause of death and disability in developing and developed countries. In Vietnam, coronary artery disease has also become one of the leading causes of death. Chronic coronary syndrome is a disease related to the relative stability of coronary atherosclerotic plaque, in the absence of sudden rupture or after the acute phase or after intervention/surgery. As plaque progresses, causing significant narrowing of the coronary lumen (usually stenosis greater than 70% of the lumen diameter), symptoms may be present, most notably angina/dyspnea on exertion. and help when resting. Medical treatment of chronic coronary syndrome is an important issue. Two patients diagnosed as chronic coronary syndrome in 2015 received medical treatment after 5 years of coronary angiography from 80% of the left anterior descending and 60% of the left anterior descending with mild atherosclerosis. coronary artery disease in both patients.

Objectives: Describe the current situation and some associated factors to the adherence to blood pressure treatment of hypertensive patients at hospital E. Methods: This is a cross-sectional study conducted from March 2022 to July 2022. The study involved 101 patients.[1] Results: Out of 101 study participants. The mean adherence score to antihypertensive treatment was 49.3 (4.13) points. Most patients do not make the decision to stop taking their blood pressure medication, do not take their medication carelessly, and do not take other people's medications. Patients regularly make follow-up appointments with medical staff. We did not find any associated factors affecting the practice of controlling hypertension in the patients participating in the study. Patients who used more than 1 drug had higher blood pressure goals than patients who used only 1 drug. Conclusion: Our study provides information on the adherence to treatment for hypertension in the group of patients at the hospital

This paper considers uplink solar-powered cognitive radio networks (CRNs) where multiple secondary users (SUs) transmit data to a secondary base station (SBS) by sharing a licensed channel of a primary system. A deep Q-learning (DQL) algorithm, which combines non-orthogonal multiple access (NOMA) and time division multiple access (TDMA) techniques, is proposed to maximize the long-term throughput of the system. By using our scheme, the agent (i.e. the SBS) can obtain the optimal decision by interacting with the environment to learn about system dynamics. Simulation results validate the superiority of the performance under the proposed scheme, compared with traditional schemes.

The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast system, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the network performance by opportunistically allocating the primary channels to the devices for data transmissions. Firstly, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process with the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.

Physical layer wireless communications are more and more essential, and are vulnerable to malicious users owing to the nature of broadcast channels. Herein, we consider a centralized multi-channel cognitive radio network in the presence of eavesdroppers (EVEs). In the network, the secondary base station (SBS) shares currently free primary channels to simultaneously communicate with secondary users (SUs), while passive eavesdroppers attempt to overhear data in the secondary communications. Each limited-battery SU is equipped with two antennas (one for transmitting signals, and other for receiving signals) and is powered by a solar energy harvester. Meanwhile, the SBS equipped with multiple antennas can operate in full-duplex (FD) transmission mode (simultaneously transmit and receive signals) or in half-duplex (HD) transmission mode (transmit and receive signals in turn during each half of a time slot) with the SUs. In this paper, we propose a novel scheme to maximize the secondary system security of the multi-channel cognitive system in the presence of multiple passive EVEs, in which the EVEs are able to overhear the data of the SBS−SU transmissions on all the primary channels. The problem of decision making is formulated as the framework of a partially observable Markov decision process (POMDP), and an optimal solution is achieved by adopting value iteration−based dynamic programming. Specifically, in each time slot, the SBS allocates optimal channel and optimal action (i.e. either stay silent or employ HD/FD transmission modes with optimal transmission power) for each SU in order to obtain maximum long-term secrecy rate for the secondary system.

Currently, deploying fixed terrestrial infrastructures is not cost-effective in temporary circumstances, such as natural disasters, hotspots, and so on. Thus, we consider a system of caching-based UAV-assisted communications between multiple ground users (GUs) and a local station (LS). Specifically, a UAV is exploited to cache data from the LS and then serve GUs&rsquo, requests to handle the issue of unavailable or damaged links from the LS to the GUs. The UAV can harvest solar energy for its operation. We investigate joint cache scheduling and power allocation schemes by using the non-orthogonal multiple access (NOMA) technique to maximize the long-term downlink rate. Two scenarios for the network are taken into account. In the first, the harvested energy distribution of the GUs is assumed to be known, and we propose a partially observable Markov decision process framework such that the UAV can allocate optimal transmission power for each GU based on proper content caching over each flight period. In the second scenario where the UAV does not know the environment&rsquo, s dynamics in advance, an actor-critic-based scheme is proposed to achieve a solution by learning with a dynamic environment. Afterwards, the simulation results verify the effectiveness of the proposed methods, compared to baseline approaches.

In this paper, we investigate a UAV-assisted communication system based on data caching at the unmanned aerial vehicle (UAV). In the network, two ground users (GUs) request data stored in a local station (LS). However, direct links from the LS to the GUs are assumed to be unavailable due to unexpected or emergency circumstances such as natural disasters or complex terrains. Herein, a UAV is dispatched to cache the data in the LS and then transmit data to the two GUs by using non-orthogonal multiple access (NOMA) technique. In order to address the energy limitation issue, energy harvesting is applied in which the UAV can prolong its operation time by harvesting solar energy from the ambient environment. Besides, the UAV is assumed to move along a circular trajectory around a fixed center. We consider a joint caching and resource allocation algorithm to deal with the Markov decision process problem for maximizing the long-term data transmission rate of the system. In particular, we propose a novel scheme based on value iteration-based dynamic programming to obtain the optimal policy in which the UAV can allocate an optimal transmission power for the GUs in each time slot. The simulation results are presented to verify the effectiveness of the proposed method.

Non-orthogonal multiple access (NOMA) is a promising technique to satisfy a host of access demand and provide higher throughput. It enables that multiple users are multiplexed with different power levels by using superposition coding at the transmitter side and successive interference cancellation at the receiver side. In this paper, we deal with the long-term throughput maximization of an uplink NOMA in the cognitive radio network (CRN). The secondary users (SUs) have limited capacity battery, hence, SUs equipped with an energy harvester can harvest energy from solar sources to prolong their operations. Particularly, a combination of NOMA and time division multiple access (TDMA) is proposed in order to reduce the complexity of massive wireless communication systems. By taking into account the practical applications, a deep Q learning algorithm is employed to maximize the long-term throughput of the system, where the agent (i.e secondary base station (SBS)) can interact with an environment to learn about system dynamics. As a result, the SBS learns how to allocate optimal transmission energy to SUs in each time slot. Simulation results demonstrate that the proposed scheme can achieve better performance than conventional schemes.

Non-orthogonal multiple access (NOMA) is considered a potential technique in fifth-generation (5G). Nevertheless, it is relatively complex when applying NOMA to a massive access scenario. Thus, in this paper, a hybrid NOMA/OMA scheme is considered for uplink wireless transmission systems where multiple cognitive users (CUs) can simultaneously transmit their data to a cognitive base station (CBS). We adopt a user-pairing algorithm in which the CUs are grouped into multiple pairs, and each group is assigned to an orthogonal sub-channel such that each user in a pair applies NOMA to transmit data to the CBS without causing interference with other groups. Subsequently, the signal transmitted by the CUs of each NOMA group can be independently retrieved by using successive interference cancellation (SIC). The CUs are assumed to harvest solar energy to maintain operations. Moreover, joint power and bandwidth allocation is taken into account at the CBS to optimize energy and spectrum efficiency in order to obtain the maximum long-term data rate for the system. To this end, we propose a deep actor-critic reinforcement learning (DACRL) algorithm to respectively model the policy function and value function for the actor and critic of the agent (i.e., the CBS), in which the actor can learn about system dynamics by interacting with the environment. Meanwhile, the critic can evaluate the action taken such that the CBS can optimally assign power and bandwidth to the CUs when the training phase finishes. Numerical results validate the superior performance of the proposed scheme, compared with other conventional schemes.

Cognitive radio networks (CRNs) have a shortcoming in that attackers can increase their ability to disturb secondary users (SUs). This paper focuses on jamming attacks in the physical layer, in which several attackers try to interrupt SUs by injecting the interference into their communications. Once a jammer transmits interfering signals on the channel during the defined time, all ongoing transmissions on this channel will be corrupted. It is quite difficult for SUs to protect a single-hop data transmission from jammers. So, obtaining a solution for secure multi-hop data transmission in the presence of jammers becomes a more challenging task in CRNs. This paper investigates a strategy to find the optimal route and channels for transmission between cognitive transmitters and receivers in the presence of jammers in CRNs. In this scenario, the jammers are located randomly and their jamming behavior is assumed to follow a Gaussian distribution. We provide an optimal link–channel pair allocation scheme in which the secondary transmitter (the source) selects the best relay and a suitable channel for each hop in the source-to-destination route to protect the information intended to the secondary receiver (the destination) from the jammers. Simulation results prove the efficiency of the proposed scheme in a CR network.

We study jamming attacks in the physical layer of multihop cognitive radio networks (MHCRNs) where energy-constrained relays forward information from the source to the destination. Meanwhile, a jammer can transmit interfering signals on a channel such that all ongoing transmissions on this channel will be corrupted. In this paper, all jammers can attack only one of the predefined channels in each time slot. Moreover, they can randomly switch channels to start jamming another channel at the beginning of every time slot. The switching behavior is assumed to follow a Gaussian distribution. Due to limited battery capacity in the relays, energy harvesting is utilized to solve the energy-constrained problem in the cognitive radio network. Subsequently, relays are able to harvest energy from non-radio frequency (non-RF) signals such as solar, wind, or temperature. In this paper, we determine the throughput/delay ratio as a key metric to evaluate the performance in MHCRNs. Owing to the limited battery capacity in the relays and the jamming problem, the source needs to select proper relays and channels for each data transmission frame to optimize overall network performance in terms of end-to-end delay, throughput, and energy efficiency. Therefore, we provide two novel multihop allocation schemes to maximize achievable end-to-end throughput while minimizing delay in the presence of jammers. Through simulation results, we validate the effectiveness of the proposed schemes under multiple jamming attacks in MHCRNs.