Bidirectional regulation of glial potassium buffering - glioprotection versus neuroprotection

Glia modulate neuronal excitability and seizure sensitivity by maintaining potassium and water homeostasis. A salt inducible kinase 3 (SIK3)-regulated gene expression program controls the glial capacity to buffer

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks

A proposed framework for sustainable development in an industrial lowcarbon economy

Along with the rapid increase in the size of the global economy, anthropogenic carbon dioxide (CO2) emissions have also increased over the years, intensifying the greenhouse gas effect and increasing the tension between human and natural environments. Governments have set national and industrial targets for reducing CO2 emissions, while research is being carried out to provide theories to investigate sustainable approaches to achieving low-carbon emission to support these strategies. Pigouvian Tax Theory and the Coase Theorem provide theoretical backing for restraining CO2 emissions through economic methods, such as taxation. The Environmental Kuznets Curve (EKC), the Theory of Coupling-Decoupling, and the IPAT Function investigate the relationship between economic growth and CO2 emissions; however, their conclusions do not provide sufficient guidance to the industrial activities of low-carbon emission in a lowcarbon economy. Most of the studies in this field are still focusing on individual factors within a low carbon economy; their conclusions represent only part of an overall system. In fact, the industrial low-carbon economy is a complex system with inter-disciplinary elements. We therefore carried out research from the perspective of systems thinking, where industrial low-carbon economy is treated as a holistic system. Based on this principle, this research analyses the low-carbon economy with an improved philosophical and theoretical foundations, building up the research methodology, then selecting and optimising the dimensions and factors for representing this system. Seven dimensions are identified: policy and law, macro-economics, society, industrial technology, industrial economy, carrying capacity and industrial goal. These dimensions and the logic interrelationships among them comprise the dimensional structure model, qualitatively representing this system. Further analysis applying Interpretative Structural Modelling method to the factors from each dimension identified a causal relationships model and a hierarchical structure model, presenting the logic and structure of this system. Population, industrial production technology and industrial technology for CO2 treatment are the key factors for achieving a system to determine the goal of maintaining industrial net profit in the low-carbon economy. The population affects the system’s goal through its influences on industrial GDP and industrial policy for low-carbon emission, while industrial production technology and industrial technology for CO2 pollution treatment influence the system’s goal through their causal effects on the industrial GDP and the amount of CO2 emissions from industrial production. From the hierarchical structure model, the logical relational model is constructed, qualitatively representing the logic within this system, with five sub-models. The models for the industrial sustainable development and the optimal approach to low-carbon emission are constructed to identify the approaches to achieve industrial sustainable development and low-carbon emission, which include maintaining net profit after the cost of reducing CO2 emissions, and improving production technology. The theoretical model for economic growth and CO2 emissions in an industrial low-carbon economy is constructed to illustrate the relationships between economic growth and CO2 emissions, which is a correlation but not causal. Therefore, none of the theories of EKC, Coupling-Decoupling and IPAT Function is tenable. The decision-making models for industrial low-carbon emission policy and industrial fiscal and monetary policy are constructed to indicate the policy-making process and their support in achieving the system’s goal. Together with the first two models, they indicate that policies do not directly determine the amount of reduction of CO2 emissions; therefore, neither Pigouvian Tax Theory nor the Coase Theorem can directly lead to reduction of CO2 emissions. These models are applied to and validated in the Chinese thermal electricity generation industry. They indicate that improvement in industrial production technology can lead to the achievement of both the industrial target for CO2 emissions and this industry’s sustainable development. Although the industrial target of CO2 emissions for 2020 was calculated to be achieved early, by 2016, the 2020 national target for China will not be achieved following current practices. Moreover, there is no causal relationship between Chinese economic growth and the amount of CO2 emissions from this industry. Therefore, there is no causal relationship between GDP and the sum of every industry’s CO2 emissions. The development of the models provides the foundation for this study to be used to investigate analytical and managerial methods towards the reduction of carbon emissions and the achievement of sustainable development for industry in a low-carbon economy, and to identify the relationship between economic growth and CO2 emissions. Most importantly, the research methodology constructed here can be applied as a general paradigm for future research and related policymaking regarding an industrial low-carbon economy. Therefore, this study will fulfill the knowledge gaps in the field of industrial low-carbon economy

Object recognition using enhanced particle swarm optimization.

The identification of the most discriminative features in an explainable AI decision-making process is a challenging problem. This research tackles such challenges by proposing Particle Swarm Optimization (PSO) variants embedded with novel mutation and sampling iteration operations for feature selection in object recognition. Specifically, five PSO variants integrating different mutation and sampling strategies have been proposed to select the most discriminative feature subsets for the classification of different objects. A mutation strategy is firstly proposed by randomly flipping the particle positions in some dimensions to generate new feature interactions. Moreover, instead of embarking the position updating evolution in PSO, the proposed PSO variants generate offspring solutions through a sampling mechanism during the initial search process. Two offspring generation sampling schemes are investigated, i.e. the employment of the personal and global best solutions obtained using the mutation mechanism, respectively, as the starting positions for the subsequent search process. Subsequently, several machine learning algorithms are used in conjunction with the proposed PSO variants to perform object classification. As evidenced by the empirical results, the proposed PSO variants outperform the original PSO algorithm, significantly, for feature optimization

Computational Insight into Protein Tyrosine Phosphatase 1B Inhibition: A Case Study of the Combined Ligand- and Structure-Based Approach

Protein tyrosine phosphatase 1B (PTP1B) is an attractive target for treating cancer, obesity, and type 2 diabetes. In our work, the way of combined ligand- and structure-based approach was applied to analyze the characteristics of PTP1B enzyme and its interaction with competitive inhibitors. Firstly, the pharmacophore model of PTP1B inhibitors was built based on the common feature of sixteen compounds. It was found that the pharmacophore model consisted of five chemical features: one aromatic ring (R) region, two hydrophobic (H) groups, and two hydrogen bond acceptors (A). To further elucidate the binding modes of these inhibitors with PTP1B active sites, four docking programs (AutoDock 4.0, AutoDock Vina 1.0, standard precision (SP) Glide 9.7, and extra precision (XP) Glide 9.7) were used. The characteristics of the active sites were then described by the conformations of the docking results. In conclusion, a combination of various pharmacophore features and the integration information of structure activity relationship (SAR) can be used to design novel potent PTP1B inhibitors