Modelling of a seasonally perturbed competitive three species impulsive system

The population of biological species in the ecosystem is known sensitive to the periodic fluctuations of seasonal change, food resources and climatic conditions. Research in the ecological management discipline conventionally models the behavior of such dynamic systems through specific impulsive response functions, but the results of such research are applicable only when the environments conform exactly to the conditions as defined by the specific response functions that have been implemented for specific scenarios. This means that the application of previous work may be somewhat limited. Moreover, the intra and inter competitions among species have been seldom studied for modelling the prey-predator ecosystem. To fill in the gaps this paper models the delicate balance of two-prey and one-predator system by addressing three main areas of: ⅰ) instead of using the specific impulse response this work models the ecosystem through a more general response function; ⅱ) to include the effects due to the competition between species and ⅲ) the system is subjected to the influences of seasonal factors. The seasonal factor has been implemented here in terms of periodic functions to represent the growth rates of predators. The sufficient condition for the local and global asymptotic stability of the prey-free periodic solution and the permanence of the system have been subsequently obtained by using the Comparison techniques and the Floquet theorems. Finally, the correctness of developed theories is verified by numerical simulation, and the corresponding biological explanation is given.2017005,2017019: Shanxi Agricultural University of Science and Technology Innovation Fund Projects

An Intelligent SDWN Routing Algorithm Based on Network Situational Awareness and Deep Reinforcement Learning

To address the challenges of obtaining network state information, flexibly forwarding data, and improving the communication quality of service (QoS) in wireless network transmission environments in response to dynamic changes in network topology, this paper introduces an intelligent routing algorithm based on deep reinforcement learning (DRL) with network situational awareness under a software-defined wireless networking (SDWN) architecture. First, comprehensive network traffic information is collected under the SDWN architecture, and a graph convolutional network-gated recurrent unit (GCN-GRU) prediction mechanism is used to perceive future traffic trends. Second, a proximal policy optimization (PPO) DRL-based data forwarding mechanism is designed in the knowledge plane. The predicted network traffic matrix and topology information matrix are treated as the DRL environment, while next-hop adjacent nodes are treated as executable actions, and action selection policies are designed for different network conditions. To guide the learning and improvement of the DRL agent’s routing strategy, reward functions of different forms are designed by utilizing network link information and different penalty mechanisms. Additionally, importance sampling steps and gradient clipping methods are employed during gradient updating to enhance the convergence speed and stability of the designed intelligent routing method. Experimental results show that this solution outperforms traditional routing methods in network throughput, delay, packet loss rate, and wireless node distance. Compared to value-function-based Dueling Deep Q-Network (DQN) routing, the convergence of the proposed method is significantly faster and more stable. Simultaneously, hardware storage consumption is reduced, and real-time routing decisions can be made using the current network state information. The source code can be accessed at https://github.com/GuetYe/DRL-PPONSA

Outbreak by ventilator-associated ST11 K. pneumoniae with co-production of CTX-M-24 and KPC-2 in SICU of a tertiary teaching hospital in central China

The emergence of carbapenem-resistant Klebsiella pneumoniae (CRKP) often responsible for numerous hospital-associated outbreaks has become an important public health problem. From January 2013 to February 2014, a total of 41 non-duplicate K. pneumoniae isolates with carbapenem resistance, were collected at a tertiary teaching hospital in Nanchang, central China. Among 41 K. pneumoniae isolates, 28 were isolated from hospitalized patients including 19 from the patients in surgery intensive care unit (SICU) and 13 were isolated from ventilators. Twenty-four of 28 patients infected by CRKP have been submitted to mechanical ventilation using ventilator. More than 95% of the CRKP isolates were resistant to 13 antimicrobials tested.All CRKP isolates were confirmed as carbapenemase producer and were positive for blaKPC-2, with one positive for both blaKPC-2 and blaNDM-1. All carbapenemase-producing isolates harbored at least one of extended spectrum βlactamase genes tested, among which95.1% (39/41) of the tested isolates were found to harbor both blaCTX-M-24 and blaKPC-2, Of note, one isolate harbored simultaneously two carbapenemase genes (blaKPC-2 and blaNDM-1) and two ESBL genes (blaCTX-M-3 and blaTEM-104) . To the best of our knowledge, coexistence of blaKPC-2 and blaCTX-M-24 in one isolate is first reported. MLST results showed that 41 CRKP isolates belonged to 4 sequence types (STs) including ST11, novel ST1854, novel ST1855 and ST1224. PFGE results displayed 3 PFGE clusters. Thirty-eight ST11 CRKP isolates (92.7%, 38/41) including all 13 isolates from ventilators and 25 isolates from patients from 7 wards (18 from是ICU) belonged to same PFGE cluster, indicating these isolates were clonally related. Fifteen isolates have an identical undistinguished pattern (100% similarity) forming a single clonal population. Moreover, this clone was exclusively linked to the cases attended in SICU and linked to the Ventilators. Additionally, the other SICU cases were linked to closely related clones (similarity greater than 95%). These data indicated that the occurrence of a clonal outbreak associated with vetilators has been found. In conclusion, outbreak by by ventilator-associated ST11 K. pneumoniae with co-production of CTX-M-24 and KPC-2 is found in a SICU of a tertiary teaching hospital in central China

Visual DSD simulations of AND logic gate with input signal “01”.

<p>Visual DSD simulations of AND logic gate with input signal “01”.</p

Irreversible DNA strand displacement.

<p>Irreversible DNA strand displacement.</p

Fluoroscence data of DNA Neurons execute AND logic with input “00”.

<p>The X-axis is cycling time of Real-time PCR, the time span of each cycle is 10 minutes, temperature of each cycle keeps in 24–25°C. The Y-axis is relative intensity of HEX (red curve) and FAM (blue curve).</p

The detailed amounts of involved DNA molecules in XOR logic gate.

<p>The detailed amounts of involved DNA molecules in XOR logic gate.</p

Fluoroscence data of DNA Neurons execute AND logic with input “11”.

<p>The X-axis is cycling time of Real-time PCR, the time span of each cycle is 10 minutes, temperature of each cycle keeps in 24–25°C. The Y-axis is relative intensity of HEX (red curve) and FAM (blue curve).</p

Fluoroscence data of DNA Neurons execute XOR logic with input “11”.

<p>The X-axis is cycling time of Real-time PCR, the time span of each cycle is 10 minutes, temperature of each cycle keeps in 24–25°C. The Y-axis is relative intensity of HEX (red curve) and FAM (blue curve).</p

Fluoroscence data of DNA Neurons execute XOR logic with input “01”.

<p>The X-axis is cycling time of Real-time PCR, the time span of each cycle is 10 minutes, temperature of each cycle keeps in 24–25°C. The Y-axis is relative intensity of HEX (red curve) and FAM (blue curve).</p