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Studies have suggested that the hippocampus (Hp) plays an important role in spatial learning and avian Hp is thought to have similar functions with mammals. However, the dynamic neural pattern of hippocampal activity is still unclear in the continuous spatial learning processes of birds. In this study, we recorded the behavioral data and local field potential (LFP) activity from Hp of pigeons performing goal-directed behavior. Then the spectral properties and time-frequency properties of the LFPs are analyzed, comparing with the behavioral changes during spatial learning. The results indicated that the power of the LFP signal in the gamma band shown decreasing trend during spatial learning. Time-frequency analysis results shown that the hippocampal gamma activity was weakened along with the learning process. The results indicate that spatial learning correlated with the decreased gamma activity in Hp and hippocampal neural patterns of pigeons were modulated by goal-directed behavior.

Motivated by the growing interests in Bitcoin blockchain technology, we build a Monte-Carlo simulation model to study the miners’ and mining pool managers’ decisions in the Bitcoin blockchain network. Our simulation model aims to capture the dynamics of participants of these two different parties and how their decisions collectively affect the system dynamics. Given the limited amount of monetary budget and mining power capacity, individual miners decide on which mining pools to join and determine how much hashing power to invest. Mining pool managers need to determine how to appropriately allocate the mining reward and how to adjust the membership fee. In addition to the aforementioned miner and pool behavior, we also characterize the system-level dynamics of the blockchain in terms of mining difficulty level and total hashing power.

Simulation models are integral to modern scientific research, national defense, industry and manufacturing, and in public policy debates. These models tend to be extremely complex, often with thousands of factors and many sources of uncertainty. To understand the impact of these factors and their interactions on model outcomes requires efficient, high-dimensional design of experiments. Unfortunately, all too often, many large-scale simulation models continue to be explored in ad hoc ways. This suggests that more simulation researchers and practitioners need to be aware of the power of designed experiments in order to get the most from their simulation studies. In this tutorial, we demonstrate the basic concepts important for designing and conducting simulation experiments, and provide references to other resources for those wishing to learn more. This tutorial (an update of previous WSC tutorials) will prepare you to make your next simulation study a simulation experiment.

Package strength becomes challenging and issue for thin package used in mobile application. Package with low strength may result in package failure such as inside die cracking or package cracking through epoxy mold compound (EMC) when package is subjected to external loading from assembly process or field application. Package design and strength assessment methodology are essential for robust package used in mobile application. In this study, uMCP package is selected to demonstrate package strength investigation and improvement. Three-point bend (3PB) testing approach and finite element analysis (FEA) method are adopted for package strength study. A strain-controlled methodology is developed for package strength assessment. FEA simulation results show that mold cap thickness and mold clearance are 2 key factors for package strength. Die strength is another key factor for robust package strength design. In addition, effect of EMC material on package strength is also investigated.

Many researches in neuroscience rely on the analysis of neuronal spike activities recorded under different behavioral conditions, due to the fact that different types of spikes recorded by multi-channel microelectrode arrays may show specific firing in different cognitive tasks. Therefore, accurate and reliable sorting of spikes plays an important role in the related researches in neuroscience. Based on the nonstationarity and local amplitude jump characteristics of spikes, a novel spikes sorting algorithm based on unsupervised local adaptive projection (LAP) feature selection algorithm and Gaussian mixture model (GMM) clustering algorithm (LAGM) was proposed in this work. Firstly, the adaptive similarity matrix and projection matrix were learned by iterative method to select the features of the original spike waveforms using LAP, and the low-dimensional feature set of the spikes was obtained. Then, these low-dimensional features were clustered for spikes sorting based on GMM. Finally, the feasibility and effectiveness of the LAGM was assessed by simulation data from the publish database, and the practicability was validated by the real signals recorded from the hippocamp region of pigeons under the free-foraging with the GMM clustering based on principal component analysis (PCA) dimension reduction and K-means clustering based on LAP feature selection as the algorithms for comparison. Experimental results indicated that the proposed LAGM in this paper shown high accuracy for spike sorting, which provided an effective means for the reliable analysis of spike signals.

Drought is one of the most common natural disasters which may harm ecosystem and economy, and thus, it is important to accurately grasp the change of drought. In this study, an innovative multisource remote sensing drought index improved Temperature-Vegetation-Soil Moisture Dryness Index (iTVMDI) based on passive microwave remote sensing data of FengYun (FY)3B and optical/infrared data is proposed to monitor the drought in Shandong Province, China in 2016. The monitoring results were verified by meteorological data. Results showed that iTVMDI has a negative correlation $(\mathrm{R}=-0.73)$ with precipitation and the average correlation coefficient with temperature is 0.76. Overall, iTVMDI can be applied to monitor the temporal and spatial variation of drought conditions.

Electrical stimulation of the nervous system using brain-computer interface technology can treat certain diseases, control robo-animals, and so on. At present, the robo-animals mainly use radio frequency communication for real-time control, or offline control according to preset procedures. However, existing robo-animals still have certain limitations in practical applications because of the short communication distance. Herein, we introduce a wearable remote behavior control system for robo-animal, which consists of three parts: the remote behavior control device carried by the animal, the server data transmission program and the human-computer interaction software of the operator. In order to verify that the system can meet the long-distance real-time monitoring and behavior control of the animal, we conducted a test of pigeon movement behavior control for long-distance. When the operator monitors the flight position of the pigeons at a long distance, the stimulation parameters are sent to successfully induce the pigeon to perform the left-circling behavior. The system can be used for long-distance behavior control and monitor the motion behavior of animals and provides powerful help for the practical use of robo-animals.

The avian nidupallium caudolaterale (NCL), a key region of cognitive learning process, it is believed that it play an important role in the process of operational conditioned learning. To reveal the changes of neural activity in operant conditioning learning process, the experimental paradigms of operant conditioning are designed in this paper. The behavioral data and local field potential (LFP) signals of three experimental periods (induction, acquisition and extinction) from the NCL of three pigeons are recorded. Then, on the basis of coherence calculation, NCL functional networks in specific frequency bands (30–60 Hz) during three experimental periods are constructed, then we analyze and compare the topology characteristics of the network. The experimental results of behavioral and LFP data analysis indicate that during the period of induction, pigeons do not establish stable operant conditioning relationship, and the connection of the network is sparse, in which the connection strength and information transmission efficiency are at a low level. In the acquisition period, pigeons can establish this relationship through learning, the functional network connection becomes tighter, and the connection strength and information transmission efficiency are significantly improved. In the extinction period in which the operant conditioning relationship is destroyed, the functional connection is weakened. These results show that pigeons can establish stable operant conditioning relationship through learning, and strengthen the functional connection of NCL, making the information transmission more efficient. This paper provides basic electrophysiological experimental evidence for further revealing the dynamic functional connection mechanism in NCL of pigeons.

In order to enhance the performance of bearing fault identification, a fault feature analysis and recognition model of rolling bearings, combined with maximum overlap discrete wavelet packet transform (MODWPT) and deep belief network (DBN), is proposed in this paper. The execution of the proposed procedure is divided into four steps, including signal processing, features extraction, features reduction and patterns recognition. In the first step, by using Maximum overlap discrete wavelet packet transform (MODWPT) method, the initial statistical characteristics, which can represent the fault condition under different frequency bands, were calculated from reconstructed signals obtained by decomposing original vibration signal. Due to the information redundancy of the initial statistical characteristics, in the following steps, this paper presents a novel features selection method that combines the Dunn Validity Index (DVI) and Standard deviation (STD) based on the selection of the most sensitive features. In addition, with the advantage of deep learning methods in high-dimensional data processing and nonlinear data analysis, this paper used deep belief network (DBN) method to analyze the fault characteristics of rolling bearings and identify the fault status. Finally, four cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed procedure, where vibration signals were freely provided by Bearing Data Center of Case Western Reserve University. In our cases study, the results show that the FSDVSD method can effectively screen out the fault-sensitive features. The DBN model can improve the accuracy of fault status recognition. To summarize, the effectiveness, adaptability and superiority of the proposed method of which the fault diagnosis thought can serve as an intelligent bearing fault diagnosis system.

Drought is one of the major natural disasters which not only causes great damage to ecosystems and the environment, but also seriously affects social and economic activities and residents' lives. In this study, the MODIS- Albedo, LST and NDVI data was used to downscale the FY-3B soil moisture product at 25km to 1km by multivariate statistical regression method. Then the drought of Shandong Province was monitored using the downscaling results in 2016. The results indicate that drought monitoring using multisource remote sensing data is very useful for drought monitoring.

The air conditioning (A/C) system is the largest auxiliary load in electric urban buses that operate in tropical climates. Reducing the energy consumption of the A/C system is therefore vital in reducing the energy demand from the traction battery hence improving vehicle range and in reducing the operational costs. This paper focuses on improving the refrigerant side performance by investigating a multi-stage vapour injection cycle. System modelling of the cycles was done in GT-Suite to investigate the improvement in the energy efficiency of the A/C system with multiple vapour injections in comparison to the conventional vapour compression system. The results showed an improvement in coefficient of performance (COP) enhancement of 23 %. Additional challenges of the proposed cycle are discussed to address the feasibility of implementing a multi-stage vapour injection system in urban electric buses.

To remove the redundant samples and reduce sample set size of big data analysis while ensuring classification accuracy, a sample set reduction method based on Neighborhood Non-dominated Crowding-distance Sorting (NNCS) is proposed. The proposed method is based on the idea of neighborhood, in which the distance between the samples of one class and the other classes is taken as the evaluation criterion. Furthermore, optimization algorithm NSGA-II is adapted to select the key samples by non-dominated crowding-distance sorting and the purpose of reducing the sample set is achieved finally. The experimental results on nine UCI standard data sets show that the method has good generalization performance and reduces the sample set size effectively under the premise of fully guaranteed classification accuracy.

This paper presents a Multi-region Distributed Control Scheme for Multi-UAV Formation. A multi-region distributed control scheme is proposed by the feedback control method which adopts the hierarchical control method. The whole area can be divided into multiple regions which are regarded as the first-level followers in the formation and track the desired trajectory. In multi-region formation flight control, one region only needs to communicate with it’s neighboring regions rather than the entire community. Simultaneously, each UAV in the region is regarded as a second-level follower, and tracks the first-level follower. Collision avoidance is applied so that a swarm of UAVs can fly in the desired formation while maintaining a safe distance from each other. A Like-Lyapunov function is presented for convergence analysis of the multi-regions UAV systems. Simulation results illustrate the performance of the proposed controller.

Pigeons have excellent spatial cognition and navigation ability. The hippocampus $(\text{Hp})$ is the key brain area for navigation behavior. However, the difference of the neural cluster electrical activity in the goal-directed task in the left and right hippocampus of the pigeon brain is still unclear. In this paper, the function network of the left hemisphere and the right hemisphere local field potential (LFP)signals is constructed using Pearson correlation method, the LFP functional networks and their topological characteristics of different Hp are analyzed in different task areas. The results show that the connection density of LFP functional networks in different areas is different, the clustering coefficient (CC)and global efficiency (E)of the pigeons in the straight area(SA)are higher than the waiting area(WA)and the turning area(TA) $(\mathrm{P} , at the same time, the topological characteristics of the LFP functional network in the left Hp were significantly higher than that in the right Hp $(\mathrm{P} . Further research also found it is not affected by the goal location $(\mathrm{P} . This study further analyzes the role of the hippocampus functional network in pigeon navigation behavior, and makes a useful attempt to analyze the brain neural mechanisms of bird spatial cognition and navigation.

The rhythmic oscillation research of neural signal under different non-task state is crucial to understand the inner information processing and encoding mechanisms of birds, but the difference of specific rhythms is unclear. In this paper, we selected pigeon as model animal and nidopallium caudolaterale (NCL)as the target brain area to implant the microelectrode array. The neural signals under: awake resting, sleep resting and anesthesia state are recorded and analyzed to assess their difference of rhythmic oscillation. Firstly, the power spectral density (PSD)analysis of local field potential (LFP)signal are carried out and compared. Then, functional networks of NCL area are constructed within different frequency band based on coherence calculation. Finally, network topology characteristics are quantitatively analyzed and compared. The results show that power of Delta(0-4Hz)rhythm under sleep resting state is significantly higher than the other states and its network connection is also intenser than others, which shows Delta rhythm may have a close relationship with nighttime sleep. For awake resting state, the power and network connections in the Beta(12-30Hz)and Low-Gamma(30-100Hz)bands are significantly higher than others, which may be related to cognitive thinking in awaking state. Under the anesthesia state, the network topology characteristics results in the Theta(4-12Hz)rhythm show that the network connection intensity and information transmission efficiency are lower than the other states, this rhythmic inhibition may be used to characterize the neural signal pattern of the birds' NCL under anesthesia.

Blockchain-based systems such as cryptocurren-cies are achieving widespread usage, with a market capitalization of $150B (USD)as of September 2017. However, the most prominent platforms that account for over 70% of this market - Bitcoin & Ethereum - are exhibiting increasingly lower levels of decentralization. This poses the problem of concentrating levers of consensus to a select group of agents in the system. At the same time, attempts at higher levels of decentralization poses the problem of limiting scalability of such systems. In this paper, using mechanism design we propose a way of designing payoffs in order to disincentivize certain exhibited behaviors and incentivize desired behaviors of miners in such systems. Our approach indicates potential for research in this area for solving the much larger problem of centralization in decentralized systems such as blockchain.

With the concept of Industry 4.0 spreading, smart manufacturing is going to work in a brand new production process, in which a number of technologies are integrated, including cyber-physical systems, the Internet of Things, and the Internet of Services, to facilitate a new business model of “smart manufacturing and service”. The key to an intelligent automation system is the capability of software/hardware system integration regarding various equipments and controllers, where response time of the equipment plays an important role. However, in an industrial transmission network, the hardware specification of the receiver is probably not good enough, so that the processor in the receiver may be unable to process multiple requests simultaneously. This paper proposes a method for scheduling multiple periodic requests. This method can reduce the burden on the device when the device is high loading in order to maintain the effective operation of the device.

This paper presents an area-based shape controller for a swarm of unmanned aerial vehicles (UAVs). In this control method, the UAVs move as a group inside a desired area while maintaining a minimum distance among themselves. Various shapes of the desired area can be formed by choosing the appropriate objective functions. In multi-UAV formation flight control, each UAV only needs to communicate with neighboring UAVs rather than the entire community. The UAVs do not have specific identities within the group. Therefore, the proposed method does not require specific positions or attitude information of the UAVs inside the area and yet different formations can be formed for a swarm of UAVs. A Lyapunov function is presented for convergence analysis of the multi-UAV systems. Simulation results illustrate the performance of the proposed controller.

This paper proposes a fixed-time nonsingular terminal sliding mode control methodology for Unmanned Aerial Vehicles with the presence of nonlinearity and external disturbance. A fixed-time terminal sliding surface is constructed and the settling time is independent of initial system states. Nonsingular terminal sliding mode control strategy based on switching saturation function is employed to enable avoidance of the singularity problem. The control method is proved to be effective by simulation results.

Network analysis of signals originating from different parts of brain during motor imagery (MI) has gained lots of interest recently. In this paper, we used EEG to construct the brain network during MI, and analyzed the topological characteristics of the EEG function network. It is found that the node degree and clustering coefficient of the right hand MI is higher than the left hand MI on the nodes of right sensorimotor cortex, and the characteristic path length of the network is shorter for right hand MI than left hand MI. As for the right hand MI task, most subjects showed higher node degree and clustering coefficient on the right sensorimotor cortex than the left sensorimotor cortex. We conclude that EEG network based measures, which captures the brain information integrating characteristics during MI, may serve as useful features for classification in MI-BCI application, especially for BCI inefficiency subject.

In mammals, prefrontal cortex (PFC) plays an important role in goal-directed navigation task. The avian nidopallium caudolaterale (NCL) and the mammalian PFC are similar in many aspects, such as anatomical connectivity, functional characteristics, and so on. However, it is unclear that whether the avian NCL plays the same or similar role in the goal-directed navigation. In this study, based on the plus-maze device, we recorded and analyzed local field potential (LFP) signals in the NCL when the pigeons performed a goal-directed decision-making task. And the LFP properties in the NCL during the goal-directed behavior of pigeons were studied. The results have shown that the LFP power in gamma-band (40–60 Hz) has significantly difference between the turning begin and the turning end. Moreover, it can effectively decode the behavioral outcomes of pigeons (i.e., the left-turning, the forward, and the right-turning). These results imply that the NCL may be an important brain region for the goal-directed navigation of pigeons.