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This research aims to develop an ecological carrying capacity index for the ecological environment under the principle of the pressure-state-response framework model and discusses the coupling coordination of ecological environment pressure, state, and response system of the urban region in china from 2011 to 2020. Furthermore, predicated the degree of security warning for each year while establishing the ecological carrying capacity index and the urban ecological coupling model. Results specify that the coordination degree of urban ecological development in Nanchang, Jiangxi has been improved year by year, the ecological carrying capacity security index shows an upward tendency from the overall view, and the security warning degree rises from comparative insecurity to comparative security. The analysis demonstrates that in the process of national ecological civilization construction demonstration zone construction, the ecological resources and environmental policies adopted by Nanchang city have played a great role in promoting ecological security. However, the ecological security index in Nanchang is still below 0.6, far from the high-security level (0.77), and the government and the public still need to do a lot to improve ecological security.

With the expanding adoption of technology and intelligent applications in every aspect of our life, energy, resource, data, and product management are all improving. So, modern management has recently surged to cope with modern societies. Numerous optimization approaches and algorithms are used to effectively optimize the literature while taking into account its many restrictions. With their dependability and superior solution quality for overcoming the numerous barriers to generation, distribution, integration, and management, nature-inspired meta-heuristic optimization algorithms have stood out among these methods. Hence, this article aims to review the application of nature-inspired optimization algorithms to modern management. Besides, the created clusters introduce the top authors in this field. The results showed that nature-inspired optimization algorithms contribute significantly to cost, resource, and energy efficiency. The genetic algorithm is also the most important and widely used method in the previous literature.

Attaining Sustainable Development Goals (SDGs) is important to control the adverse impacts of climate change and achieve sustainable development. Among the 17 SDGs, target 13 emphasizes enhancing urgent actions to combat climate-related changes. This target is also dependent on target 7, which advocates enhancing access to cheap alternative sustainable energy. To accomplish these targets, it is vital to curb the transport CO

Establishing effective climate control and reducing the ecological footprint (EF) are necessary for pursuing Sustainable Development Goals (SDGs), in particular Goal 13. In this context, it is required to enhance the understanding of various factors that can either decrease or enhance the EF. In the literature to date, limited studies on external conflicts (EX) have reported diverse results, and also the impacts of government stability (GS) on EF are less explored. This study explores the roles of external conflicts, economic growth, and government stability on EF in the context of SDG-13. The study also contributes to the literature by examining the environmental effects of government stability and external conflicts for the first time in Pakistan. This research uses time-series methodologies on data from Pakistan from 1984 to 2018 for exploring the long-run relations and causal dynamics. The results unfolded that external conflicts stimulate and Granger cause EF and therefore expand environmental deterioration. Thus, limiting conflicts is in the favor of Pakistan to achieve SDG-13. Surprisingly, government stability also poses harmful impacts on environmental quality by enhancing the EF, indicating that stable governments focus on improving economic conditions rather than environmental quality. Moreover, the study proves the validity of the environmental Kuznets curve. Policy suggestions are made to move forward in achieving SDG-13 and to evaluate the effectiveness of government environmental policies.

This research aims to investigate the effect of foreign direct investment on carbon emissions through the panel ARDL method using annual data for the 1990-2016 period for the newly industrialized countries (NICs), including China, Malaysia, Mexico, Philippines, Thailand, Turkey, India, and Brazil. The stationarity of the series was obtained through LLC, IPS, and Fisher ADF panel unit root tests, the cointegration relationship with the panel ARDL-PMG approach, and the causality relationship with Dumitrescu and Hurlin (DH) tests. As a result of the long-term analysis, the foreign direct investment, energy consumption, and trade openness have a positive and significant impact on carbon emissions, whereas economic growth has a negative and significant impact on carbon emissions. The result shows that a percent increase in foreign direct investment increases carbon emissions by 0.03%. As a result of the short-term analysis, it was seen that the coefficient of the error correction term (ECT) was negative and statistically significant. According to DH panel causality test results, there exists a bidirectional causality relationship among energy consumption and carbon emissions, and a unidirectional causality relationship from economic growth and trade openness to carbon emissions and from carbon emissions to foreign direct investment. As policy implication, in industrialized countries especially China and India, there is a greater need to invest in green energy consumption at a larger scale to achieve future sustainable development goals.

Decision Support Systems (DSS) have widely provided on intelligent human–machine interactions like the Internet of Things (IoT). As the dimensions and complexity of the IoT communications between intelligent devices, industrial equipment, sensors, and mobile applications are continuously increasing, supporting Service Level Agreements (SLA) for a large amount of cloud data centers and a huge number of user requests is going to be more challenging. This challenge would be more complicated when the energy consumption of the industrial IoT ecosystems increases exponentially as well. Therefore, DSS models are required for automated decision-making in critical IoT environments like intelligent industrial systems and smart cities. This paper provides a comprehensive review of the DSS strategies for energy-efficient IoT applications in intelligent urban computing. The main objective of this review is (1) presenting a technical taxonomy to categorize existing energy-efficient DSS models for industrial and smart environments in IoT ecosystems; (2) recognizing significant research trends in the field of energy-aware DSS strategies for intelligent urban computing in IoT and (3) presenting a technical and statistical analysis of reviewed studies and evaluation factors. Finally, innovations as forthcoming issues and new challenges of energy-based DSS strategies for intelligent urban computing in IoT are presented.

With the rapid growth of Web services, recommending suitable services to users has become a big challenge. The existing service recommendation works by Quality of Service (QoS) prediction fail to fully consider the influence of situation information, such as time, location, and user relations thoroughly. Two issues must be resolved to consider situation information: issue one, rating scarcity, is that there are less data to learn when importing more situations; issue two is that an effective approach is needed to adapt many situational factors. Our solution is a two-phase method: first, to overcome rating scarcity, data is augmented with estimations of unknown QoS values by learning from observable factors. The augmented data is then used to learn the important latent factors associated with the situational factors for QoS prediction. Experiments on data of real service invocations in different situations show improvement of our method in terms of QoS prediction accuracy over several existing methods, especially in the severe rating scarcity condition. In addition, analysis on parameter selection of proposed method can further assist in obtaining better QoS prediction in practical use.

Chongqing must attach great importance to the construction of teacher talents' quality in the process to achieve "Western Education Heights" and the problem of primary teachers training is increasingly attracted widespread attention. This paper thorough thinking the training model of Chongqing primary teachers and put forward reform measures for them.

In the detection process of large random network intrusion signal, the signal detection method is key to guarantee the network security, the traditional detection method is simple, time-consuming, and the error is big, the detection precision is low. In view of this situation, a ORB wave intrusion signal detection method is proposed based on three B spline wavelet transform, discrete orthogonal wavelet filtering algorithm is used for decomposition filtering, according to the relationship between wavelet transform and signal singularity, the signal processing method is used for intrusion signal detection, time-frequency analysis is taken for intrusion signal, by extracting the characteristics of the signal, distinguish the jamming signal characteristics of intrinsic signal characterization, the R wave peak is recognized in 3 2 scale, the starting point and end point of QRS wave are detected in the 1 2 scale, for each intrusion signal, IMF spectrum analysis with Hilbert transform is taken for mode decomposition, and the network intrusion database is used in simulation. Simulation results show that this algorithm can improve the detection accuracy, and effectively improve the work efficiency in network security defense.