Examining the Relationship between Energy Consumption and Unfavorable CO2 Emissions on Sustainable Development by Going through Various Violated Factors and Stochastic Disturbance–Based on a Three-Stage SBM-DEA Model

The article applies a three-stage Slacks-Based Measure-Data Envelopment Analysis (SBM-DEA) pattern to examine the relationship between energy consumption and unfavorable CO2 emissions on green sustainable development, for the 11 cities of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during 2010–2016, by going through various violated factors and stochastic disturbance. Labor, capital and energy resource are chosen as input variables, while GDP and CO2 emission as output variables. During the three phases consisting of the SBM-DEA model (first stage and third stage) and SFA analysis (second stage), CO2 emission is considered as an unfavorable outcome, while stochastic statistical disturbances and external environmental influences are identified. The results show that the average efficiency of the GBA cities is 0.708, with only Shenzhen, Macao SAR and Hong Kong SAR having an efficiency of 1 during the whole study period. Based on the findings, suggestions are made for the GBA cities’ sustainable development aspects.

Robustness of Hopfield Neural Networks Described by Differential Algebraic Systems of Index-1 under the Conditions of Deviation Argument and Stochastic Disturbance

Robustness refers to the ability of a system to maintain its original state under a continuous disturbance conditions. The deviation argument (DA) and stochastic disturbances (SDs) are enough to disrupt a system and keep it off course. Therefore, it is of great significance to explore the interval length of the deviation function and the intensity of noise to make a system remain exponentially stable. In this paper, the robust stability of Hopfield neural network (VPHNN) models based on differential algebraic systems (DAS) is studied for the first time. By using integral inequalities, expectation inequalities, and the basic control theory method, the upper bound of the interval of the deviation function and the noise intensity are found, and the system is guaranteed to remain exponentially stable under these disturbances. It is shown that as long as the deviation and disturbance of a system are within a certain range, there will be no unstable consequences. Finally, several simulation examples are used to verify the effectiveness of the approach and are described below.

Full reconstruction of simplicial complexes from binary time-series data

Previous efforts on data-based reconstruction focused on complex networks with pairwise or two-body interactions. There is a growing interest in networks with high-order or many-body interactions, raising the need to reconstruct such networks based on observational data. We develop a general framework combining statistical inference and expectation maximization to fully reconstruct 2-simplicial complexes with two- and three-body interactions based on binary time-series data from social contagion dynamics. We further articulate a two-step scheme to improve the reconstruction accuracy while significantly reducing the computational load. Through synthetic and real-world 2-simplicial complexes, we validate the framework by showing that all the connections can be faithfully identified and the full topology of the 2-simplicial complexes can be inferred. The effects of noisy data or stochastic disturbance are studied, demonstrating the robustness of the proposed framework.

Extended Kalman Filter Based Estimations for Satellite Attitude Control System

This paper mainly focuses on the maneuver of the satellite in orbit. A non-linear multi-inputs multi-outputs model has been derived from Newton-Euler equations of motion. The dynamics is presented with control methodologies allowing the Extended Kalman Filter (EKF) to iteratively provide improved data sets with zero errors. As the system is distracted from the atmospheric swings which are random hence the problem of stochastic disturbance is furnished. A set of differential equations of two dimensional Ito stochastic type is used for modeling the said disturbances (before t = 4s is recorded). The attitude parameters are recorded in RT-LAB setup with the Extended Kalman Filter (EKF) providing adequately superior estimation outcome which thereby makes the filter more appealing. With the presence of Gaussian noise in both dimension and system, Extended Kalman Filter gives the correct estimates. It’s collaboration with hardware setup RT-LAB is commendable. Hence, an Extended Kalman Filter which deals with such nonlinear models proves to be a higher choice for achieving best online results. A comparison reflecting the tracking and stable control of the satellite for the designed advanced adaptive robust controller (AARC) for two situations is plotted. The priority of making the system stable in the presence of stochastic disturbance is also visited. Also, the use of three different values of the confounding variables revealed that the control weighting line is completely diminished thereby boosting the tracking when the satellite is in orbit. Moreover, the previous research involves methods to improve satellite communication on ground station, this paper deals with exact positioning of concerned satellite attitude parameters and its validation tested experimentally on OPAL-RT hardware. To sum up, the development of advanced adaptive robust controllers have encouraged the stability and accuracy of systems considering the varying atmospheric conditions. The simulation results predict perfect tracking of output with respect to the desired set-point in the presence of stochastic disturbance for the proposed controller.

Automatic Generation Control for Distributed Multi-Region Interconnected Power System with Function Approximation

Solving the energy crisis and environmental pollution requires large-scale access to distributed energy and the popularization of electric vehicles. However, distributed energy sources and loads are characterized by randomness, intermittence and difficulty in accurate prediction, which bring great challenges to the security, stability and economic operation of power system. Therefore, this paper explores an integrated energy system model that contains a large amount of new energy and combined cooling heating and power (CCHP) from the perspective of automatic generation control (AGC). Then, a gradient Q(σ,λ) [GQ (σ,λ)] algorithm for distributed multi-region interconnected power system is proposed to solve it. The proposed algorithm integrates unified mixed sampling parameter and linear function approximation on the basis of the Q(λ) algorithm with characteristics of interactive collaboration and self-learning. The GQ (σ,λ) algorithm avoids the disadvantages of large action spaces required by traditional reinforcement learning, so as to obtain multi-region optimal cooperative control. Under such control, the energy autonomy of each region can be achieved, and the strong stochastic disturbance caused by the large-scale access of distributed energy to grid can be resolved. In this paper, the improved IEEE two-area load frequency control (LFC) model and the integrated energy system model incorporating a large amount of new energy and CCHP are used for simulation analysis. Results show that compared with other algorithms, the proposed algorithm has optimal cooperative control performance, fast convergence speed and good robustness, which can solve the strong stochastic disturbance caused by the large-scale grid connection of distributed energy.

Domestic Livestock and Rewilding: Are They Mutually Exclusive?

Human influence extends across the globe, from the tallest mountains to the deep bottom of the oceans. There is a growing call for nature to be protected from the negative impacts of human activity (particularly intensive agriculture); so-called “land sparing”. A relatively new approach is “rewilding”, defined as the restoration of self-sustaining and complex ecosystems, with interlinked ecological processes that promote and support one another while minimising or gradually reducing human intervention. The key theoretical basis of rewilding is to return ecosystems to a “natural” or “self-willed” state with trophic complexity, dispersal (and connectivity) and stochastic disturbance in place. However, this is constrained by context-specific factors whereby it may not be possible to restore the native species that formed part of the trophic structure of the ecosystem if they are extinct (e.g., mammoths, Mammuthus spp., aurochs, Bos primigenius); and, populations/communities of native herbivores/predators may not be able to survive or be acceptable to the public in small scale rewilding projects close to areas of high human density. Therefore, the restoration of natural trophic complexity and disturbance regimes within rewilding projects requires careful consideration if the broader conservation needs of society are to be met. In some circumstances, managers will require a more flexible deliberate approach to intervening in rewilding projects using the range of tools in their toolbox (e.g., controlled burning regimes; using domestic livestock to replicate the impacts of extinct herbivore species), even if this is only in the early stages of the rewilding process. If this approach is adopted, then larger areas can be given over to conservation, because of the potential broader benefits to society from these spaces and the engagement of farmers in practises that are closer to their traditions. We provide examples, primarily European, where domestic and semi-domestic livestock are used by managers as part of their rewilding toolbox. Here managers have looked at the broader phenotype of livestock species as to their suitability in different rewilding systems. We assess whether there are ways of using livestock in these systems for conservation, economic (e.g., branded or certified livestock products) and cultural gains.