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Background Many factors are associated with hypertension, the most prevalent chronic disease, among which, the association of sleep disturbance and hypertension has received wide attention as sleep medicine advances rapidly in recent years. However, relevant studies on sleep disturbance and hypertension have some limitations, and there is no bibliometric analysis of hotspots about sleep disturbance and hypertension. Objective To review and summarize the research hotspots and trends of literature related to sleep disturbances and hypertension. Methods Studies about sleep disturbance and hypertension were searched in Web of Science Core Collection from inception to June 30, 2021 using "hypertension" and "sleep disturbance" "insomnia" "sleep deprivation" "sleep fragmentation" and "short-term sleep" as subject headings. CiteSpace 5.7.R5W was used for visual analysis. Results In total, 4 589 studies were included for analysis. The number of studies generally showed an increasing trend, with a peak in 2018, and a rapider growth rate between 2011 and 2021. The top 10 most frequently used keywords in the studies published between 2011 and 2021 were hypertension, blood pressure, prevalence, obstructive sleep apnea, risk factor, sleep, cardiovascular disease, positive airway pressure, obesity and insomnia. The tag clusters were sleep time, sleep quality, sleep-disordered breathing, obstructive sleep apnea, sleep apnea, insomnia, stress, sleep, epidemiology, heart failure and symptoms. Keyword clustering analysis revealed that major directions in the studies published between 2011 and 2021 were: (1) the association of sleep-disordered breathing, especially obstructive sleep apnea, and hypertension; (2) the association of sleep time and blood pressure; (3) the association of sleep quality and blood pressure. The most frequently cited studies were mainly about sleep apnea, obstructive sleep apnea and short-term sleep. REDLINE was the most prolific author, and the largest group of authors was formed with her as the core. The US was the most prolific country, and the most prolific institution was the University of Pittsburgh. Conclusion The research on sleep disturbance and hypertension had become increasingly popular. The research hotspots of this field had changed greatly in 2011 and 2018. The effects of obstructive sleep apnea and sleep duration on hypertension were the mostly focused hotspots.

Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning processes, and heavy reliance on mathematical models pose substantial obstacles to their application. To address these challenges, this paper proposes an LSTM-based modeling algorithm. First, a maneuvering motion model based on a real USV model was constructed, and typical operating conditions were simulated to obtain data. The Ornstein–Uhlenbeck process and the Hidden Markov Model were applied to the simulation data to generate noise and random data loss, respectively, thereby constructing a sample set that reflects real experiment characteristics. The sample data were then pre-processed for training, employing the MaxAbsScaler strategy for data normalization, Kalman filtering and RRF for data smoothing and noise reduction, and Lagrange interpolation for data resampling to enhance the robustness of the training data. Subsequently, based on the USV maneuvering motion model, an LSTM-based black-box motion prediction model was established. An in-depth comparative analysis and discussion of the model's network structure and parameters were conducted, followed by the training of the ship maneuvering motion model using the optimized LSTM model. Generalization tests were then performed on a generalization set under Zigzag and turning conditions to validate the accuracy and generalization performance of the prediction model. [ABSTRACT FROM AUTHOR]

The unmanned surface vehicles (USV) are required to perform a dynamic obstacle avoidance during fulfilling a task. This is essential for USV safety in case of an emergency and such action has been proved to be difficult. However, little research has been done in this area. This study proposes an emergency collision avoidance algorithm for unmanned surface vehicles (USVs) based on a motion ability database. The algorithm is aimed to address the inconsistency of the existing algorithm. It is proposed to avoid collision in emergency situations by sharp turning and treating the collision avoidance process as a part of the turning movement of USV. In addition, the rolling safety and effect of speed reduction during the collision avoidance process are considered. First, a USV motion ability database is established by numerical simulation. The database includes maximum rolling angle, velocity vector, position scalar, and steering time data during the turning process. In emergency collision avoidance planning, the expected steering angle is obtained based on the International Regulations for Preventing Collisions at Sea (COLREGs), and the solution space, with initial velocity and rudder angle taken as independent variables, is determined by combining the steering time and rolling angle data. On the basis of this solution space, the objective function is solved by the particle swarm optimization (PSO) algorithm, and the optimal initial velocity and rudder angle are obtained. The position data corresponding to this solution is the emergency collision avoidance trajectory. Then, the collision avoidance parameters were calculated based on the afore mentioned model of motion. With the use of MATLAB and Unity software, a semi-physical simulation platform was established to perform the avoidance simulation experiment under emergency situation. Results show the validity of the algorithm. Hence results of this research can be useful for performing intelligent collision avoidance operations of USV and other autonomous ships

Radars and sensors are essential devices for an Unmanned Surface Vehicle (USV) to detect obstacles. Their precision has improved significantly in recent years with relatively accurate capability to locate obstacles. However, small detection errors in the estimation and prediction of trajectories of obstacles may cause serious problems in accuracy, thereby damaging the judgment of USV and affecting the effectiveness of collision avoidance. In this study, the effect of radar errors on the prediction accuracy of obstacle position is studied on the basis of the autoregressive prediction model. The cause of radar error is also analyzed. Subsequently, a bidirectional adaptive filtering algorithm based on polynomial fitting and particle swarm optimization is proposed to eliminate the observed errors in vertical and abscissa coordinates. Then, simulations of obstacle tracking and prediction are carried out, and the results show the validity of the algorithm. Finally, the method is used to simulate the collision avoidance of USV, and the results show the validity and reliability of the algorithm.

Converting CO2 to liquid (C5+) hydrocarbons remains a significant hurdle. Our study shows that CoFe/HZSM-5 boosts C5+ selectivity to 73.4%, up from 59% for Fe/HZSM-5. This study highlights the pivotal roles of zeolite acidity and catalyst proximity in this improvement. These insights pave the way for more effective CO2 utilization. [ABSTRACT FROM AUTHOR]

The path following control problem of the underactuated unmanned surface vessel (USV) is studied in this paper. An improved line-of-sight (LOS) guidance algorithm is proposed which can adjust adaptively based on the path following error. The global asymptotically stable path following controller is designed based on the nonlinear backstepping method and the Lyapunov stability theory. Firstly, the USV path following error model is established in the Serret-Frenet (SF) coordinate frame. The path following error in the inertial coordinate frame is transformed into the SF coordinate frame, which is used to define the path following control problem. Secondly, inspired by the traditional LOS guidance algorithm, the longitudinal path following error in the SF coordinate frame is introduced into the improved LOS guidance algorithm. This allows the algorithm to adjust adaptively to the desired path. Thirdly, in order to solve the underactuated problem of the USV path following control system, the tangential velocity of the desired path is designed as a virtual input. The underactuated problem is converted to a virtual fully actuated problem by designing the virtual control law for the tangential velocity. Finally, by combining backstepping design principles and the Lyapunov stability theory, the longitudinal thrust control law and the yaw torque control law are designed for the underactuated USV. Meanwhile, the global asymptotic stability of the path following error is proved. Simulation experiments demonstrate the effectiveness and reliability of the improved LOS guidance algorithm and the path following controller.

The district energy system has realized the effective consumption of new energy and the cascade utilization of energy, and has been widely used in the continuous and in-depth advancement of China's urbanization process. The district energy system is an organic combination of source-grid-load, and in practical applications, its components are usually in a dynamic change process, which has a certain impact on the matching of supply and demand of the district energy system. This article first puts forward the concept of district energy network, and then describes the typical adjustment scenarios of its energy supply nodes, energy consumption nodes and energy pipe network side respectively, and allocates energy resources of the district energy network according to various typical adjustment scenarios of the district energy network. The impact of the strategy is analysed to provide a reference for the economic construction of the regional energy system and the rational and efficient use of energy.

A contract for difference is a medium and long-term financial contract, which can be used in the electricity market to lock the electricity price in advance and avoid the risk of electricity price fluctuations in the spot market. The construction of the domestic power spot market has just started. With the release of relevant policies and the gradual improvement of the market structure, it is urgent to design a corresponding trading mechanism to ensure the smooth transition of the market. The current day-ahead transactions, real-time transactions and other short-term transactions for distributed power generation, on the one hand power load forecasting, electricity price demand response and other related technologies need to be further improved, on the other hand due to the randomness and uncertainty of distributed energy, participating in the short-term spot market has large price fluctuations, which is not conducive to the stability of the electricity market, and it is also not conducive to the consumption of distributed energy. Aiming at the above problems, this paper uses the characteristics of CFDs to restrain market power to design a distributed energy trading mechanism to achieve the purpose of energy saving and emission reduction.

Emergency power system refers to a system that is composed of self-generating equipment in the building. By providing power support for emergency events such as power outages, it can keep the energy safety assurance. Usually, emergency power systems are independent and redundant. As a backup, their utilization rates are extremely low, resulting in excessive redundancy causing a lot of waste of resources and equipment. In this paper, a model integrating stand-alone emergency power systems with micro-network was proposed to improve power supply reliability in a more economic manner. Integrated emergency power system with the dispatch strategy of self-priority was established based on reliability and power demand of buildings simulated through Monte Carlo method. To evaluate the impact of integration, the total cost of integrated emergency power system was calculated including economic loss during power outages and system costs. Finally, genetic algorithm was used to obtain the optimal capacity and dispatch strategy of the integrated emergency power system by minimizing the total cost. Results showed that the reliability was improved, and the total cost was reduced to 80.05% by integrated emergency power system in the illustrated case. [ABSTRACT FROM AUTHOR]