Accurate identification, quantification, and continual monitoring of carbon emissions constitute pivotal elements for proactive climate interventions. Conventional methodologies like direct measurement, including point-source assessments or remote sensing, often face challenges related to high costs or limited accuracy. Especially in low- and middle-income nations experiencing escalating emissions, carbon monitoring heavily relies on the administrative capabilities of local governments, which frequently lack adequate monitoring infrastructures. Addressing this predicament, our study introduces a computational framework to forecast CO 2 emissions by leveraging comprehensive observable human activity data from third-party sources. Our findings elucidate a robust correlation between multi-origin CO 2 emissions and human mobility ( r = 0.89). Notably, machine learning models adeptly predict these emissions by integrating characteristics extracted from temporally aggregated, anonymized mobility networks (R 2 ≈1.0). We demonstrate that the model effectively captures the notable reduction in CO 2 emissions during the COVID-19 lockdown, with both human mobility and CO 2 emissions in China decreasing by 56.97% and 32.45%, respectively. The prediction accuracy remains high for countries with varying social economic development, such as the U.S., Italy and Mexico. This study presents an inexpensive, real-time, and robust method of quantifying CO 2 emissions on a large scale with high precision, and it could facilitate tailored CO 2 emission reduction strategies, grounded in robust scientific evidence derived from the dynamics of human mobility.

Accurate identification, quantification, and continuous monitoring of anthropogenic carbon emissions are fundamental to effective climate mitigation. However, existing approaches often face trade-offs among temporal resolution, spatial coverage, data availability, and update frequency. Here, we propose a computational framework to estimate anthropogenic CO2 emissions by leveraging large-scale, observable proxies of human activity derived from mobility data. We find that human mobility shows a strong correlation with multi-source CO2 emissions in China (r = 0.89). By integrating mobility-derived network features with static spatial attributes such as geographic coordinates and population, machine-learning models capture both the broad spatial organization and dynamic temporal variation of CO2 emissions, achieving high predictive performance. The framework performs consistently across diverse socioeconomic contexts (China, Italy, the United States, and Mexico) and across independent CO2 emission inventories (GRACED and ODIAC), and it remains robust under both stable and turbulent periods. This work proposes a scalable and generalizable approach for large-scale anthropogenic CO2 emissions estimation, providing a behavior-informed foundation for timely and adaptive climate policy.

The growing usage of online crowdfunding platforms has fundamentally changed the traditional modes of fundraising and donation. Previous studies have mainly focused on the performance and ethical issues of online crowdfunding. In contrast, there is a dearth of information about the complexity of online donation behaviors. To explore the characteristics of fundraising and donation in online crowdfunding campaigns, we conduct a comprehensive analysis of fundraising and donation behaviors based on 151 163 campaigns, with 188 955 849 donations created from 2016 to 2020 in one of the most popular medical crowdfunding (MCF) platforms called Easy Fundraising in China. We propose four indicators, namely, diversity, uncertainty, concentration, and consistency, to characterize the preferences of individual donors in choosing the donation amounts. Furthermore, we investigate the fundraising temporal dynamics and collective donation characteristics of crowdfunding campaigns using statistical methods. Results show that the first three days after the creation of a crowdfunding campaign is the most efficient fundraising period that largely determines the completion of the campaign. Donors who donate early are more generous than those who donate later. Individual donors prefer donation amounts in multiples of five, such as 5, 10, 20, and 50, and rarely change their donation amounts, which is irrelevant to the patients’ locations. The empirical results obtained in this study provide valuable insights to improve crowdfunding management, public welfare systems’ construction, and human donation behaviors’ understanding.

Vehicle routing problems (VRP) are a kind of typical combinational optimization problem, particularly in the logistics industry. This paper proposes a constrained evolutionary optimization algorithm, called CEOA, for solving many-objective VRP with simultaneous delivery, pickup, and time windows (VRPSDPTW). Specifically, we first define the weight value vectors based on the constraint satisfaction situation, which can adaptively adjust according to the feedback of population solutions during the search process. Subsequently, based on the feedback from the weight value vectors, the environmental selection strategy is employed to identify promising solutions for both infeasible and feasible situations. Furthermore, considering the data characteristics of the problem at hand, the crossover and mutation operations are tailored to better align with the VRPSDPTW, which is further explained and illustrated in detail regarding solution construction. The experimental results demonstrate the effectiveness of the proposed algorithm for VRPSDPTW in comparison with other state-of-the-art methods.

The digital twin workshop is an important scene of intelligent manufacturing in the future, which improves the production efficiency of the workshop with its advantage of a combination of virtuality and reality. However, there is still a lack of study on the whole life cycle of digital twin workshop production and the interaction mechanism between the various systems in the workshop. In this view, our paper aims at the architecture of the digital twin workshop. Firstly, we reviewed the works in various business fields of digital twin workshops. Then we introduced the connotation of Unified Architecture Framework (UAF) and proposed the ideas and steps of digital twin workshop architecture design. Finally, the multi-view models of the digital twin workshop architecture were constructed from the perspectives of overview, requirement, operation, strategy, personnel, and resources. This work aims to provide theoretical and application support for developing the digital twin workshop.

The Gamma and Erlang distributions are commonly utilized. The Gamma distribution is easier to use for parameter estimation and provides a better fit for the data, but it has poorer analytical computational properties. On the other hand, the Erlang distribution has better analytical computational properties, but it provides a weaker fit for the data than the Gamma distribution and is more difficult to use for parameter estimation. The paper aims to address the problem of harnessing the benefits of both the Gamma distribution for its ease of parameter estimation and the Erlang distribution for its good analytical properties. Our study focuses on utilizing the Erlang distribution to approximate the Gamma distribution efficiently for a given parameter. We provide a rigorous verification of the approximation method and validate its effectiveness using several examples.

Moment distributions can be used to study many important problems in the field of social sciences, such as the Gini coefficient of residents’ income, Lorenz curve, etc. The set of Hyper-Erlang distributions is a dense subset of the set of non-negative probability distribution functions, which has a wide applicability, so Hyper-Erlang distributions are chosen as the object of this study. In this paper, we give the analytical expression of the moment distribution of HyperErlang distribution and prove that the moment distribution function of Hyper-Erlang distribution is closed within the set of Hyper-Erlang distribution. Combined with the HyperErlang distribution parameter estimation algorithm, eight commonly used distributions are fitted, and the practicality and effectiveness of the Hyper-Erlang moment distribution method proposed in this paper are verified through the comparison of the original moment distribution of the commonly used distributions and the approximation effect of the Hyper-Erlang moment distribution. The approach proposed in this paper can be used to give a moment distribution expression for any data, and valid approximate moment distribution expressions for all types of probability distributions for which no analytical moment distribution expression can be derived.

Selecting the appropriate probability distribution can be a challenging task when using parametric methods for statistical analysis of specific data. To address this challenge, this paper proposes a method of analyzing the data using the Hyper-Erlang distribution. This distribution is a dense subset of all probability distributions on the non-negative real axis and is both easy to analyze and compute. This paper discusses the dense properties of the Hyper-Erlang distribution and the parameter estimation method based on the expectation maximization algorithm, and verifies the validity of the proposed method using the actual income data.

The hesitant fuzzy set (HFS) is an important tool to deal with uncertain and vague information.In equipment system portfolio selection, the index attribute of the equipment system may not be expressed by precise data; it is usually described by qualitative information and expressed as multiple possible values.We propose a method of equipment system portfolio selection under hesitant fuzzy environment.The hesitant fuzzy element (HFE) is used to describe the index and attribute values of the equipment system.The hesitation degree of HFEs measures the uncertainty of the criterion data of the equipment system.The hesitant fuzzy grey relational analysis (GRA) method is used to evaluate the score of the equipment system, and the improved HFE distance measure is used to fully consider the influence of hesitation degree on the grey correlation degree.Based on the score and hesitation degree of the equipment system, two portfolio selection models of the equipment system and an equipment system portfolio selection case is given to illustrate the application process and effectiveness of the method.

In the military field, decision making has become the core of the new operational concept, known as the “kill web”. Although the theory of kill web has been widely recognized by many countries, the decision-making methods for the kill web are still in the early stage. Therefore, there is a need for a new decision-making method for the kill web. Firstly, different from the traditional scheme decision, the kill web is a complex system. The method of complex network provides a new perspective on complex systems, so the kill web was modeled based on complex network. Secondly, the kill web relies on artificial intelligence to provide decision-makers with operation loop solutions, and then decision-makers rely on the experience to make a final decision. However, the current decision-making methods only consider one of the intelligent and human decision-making methods, while the kill web needs to consider both. Hence, we combined intelligent decision making with human decision making through multi-objective optimization and the prospect theory. Finally, we designed a nondominated sorting ant colony genetic algorithm-II (NSACGA-II) to solve large-scale problems, since the kill web is a large-scale system. In addition, an illustrative case was used to verify the feasibility and effectiveness of the proposed model. The results showed that, compared with other classical multi-objective optimization algorithms, the NSACGA-II is superior to other superior algorithms in terms of the hypervolume (HV) and spacing (SP), which verifies the effectiveness of the method and greatly improves the quality of commanders’ decision-making.

This paper proposes an individual-based self-learning prediction method for dynamic multi-objective optimization problems, called ISPM, to effectively track the time-varying Pareto-optimal set (POS) in a dynamic environment. The ISPM adjusts the reference points by individual-based self-learning, which differs from existing approaches based on fixed reference points. The self-learning reference points are given according to the information from the previous population to divide the Pareto-optimal front (POF) into the objective space as uniformly as possible. One of the ISPM’s advantages is it can improve the influence of the corresponding non-uniform POF for the population’s prediction. Furthermore, it is known that each reference point and the original point can form a vector in the objective space. Each vector can present a subregion in the objective space. The moving direction of each subregion in the last two environments is the reference when improving the population in the new environment to realize local search. Meanwhile, we roughly calculate the change degree of the environment using the self-learning reference point sets at the last two environments to realize the global search. The comprehensive experimental results show that the proposed algorithm can effectively balance convergence and diversity compared with other state-of-the-art methods.

In the process of military operation, the choice of action plan is a typical decision-making control problem. Decision makers' judgment and subjective preference for combat situation have a great impact on the selection results. The action plan is represented in this study as an operation loop model based on OODA theory. Therefore, this paper mainly studies the influence of decision-maker preference on operation loop selection control from the perspective of prospect theory. Firstly, the network modeling of kill-web is carried out based on operation loop, in which the preference attributes of decision maker and three kinds of command & control relation are described. Secondly, considering decision preference, the judgment method of damage degree and hit probability of operation loop is analyzed, and an operation loop decision method is proposed. Finally, we verify the feasibility and effectiveness of this method through a case study, and analyze the sensitivity of decision preference attributes. The results show that reference point and risk attitude of decision maker are the key factors that affect the decision of operation loop. Prospect theory is an effective method to analyze the behavior of decision-makers in military combat control.

Aiming at the situation that the dynamic changes of reference points at different stages may lead to changes in the decision-making results of the operation loop, we propose a multistage operation loop decision-making method based on dynamic reference points. Firstly, according to the concept of the operation loop, the network model of the kill-web is carried out through node, edge and meta-path modeling. On this basis, the multi-stage operation loop decision problem is described. Secondly, a decisionmaking method based on prospect theory is proposed. This method sets up the determination method of the reference point and establishes the update mechanism of the reference point, and comprehensively uses the prospect theory to calculate the comprehensive prospect value of the operation loop, which is used as the decision-making factor of the operation loop. Finally, taking a kill-web to perform a three-stage combat mission as an example, the optimal operation loop of each stage is selected to verify the feasibility and effectiveness of the method proposed in this paper.

In recent years, with the rapid development of science and technology, network science research has entered a new stage. Community mining, as an important aspect of network science research, is playing an increasingly important role. It is of great significance for beginners, relevant researchers, and even policy makers to grasp the whole context of community mining and keep up with the latest developments. Based on Web of Science data, this paper first presents the community mining field as a whole by means of bibliometrics, from the perspectives of time distribution, geographical distribution, and discipline distribution. Then, seven indexes for the published scientific papers are selected from the four aspects of quantity, quality, timeliness, and balance to explore the core research institutions and core authors in the field of community mining. The results show that the core research institutions are Indiana University, the University of New South Wales, the Chinese Academy of Sciences, etc., and the core authors are Olaf Sporns, Santo Fortunato, Santo Fortunato, and so on. Finally, based on the word frequency analysis and topic clustering, this study conducted a topic analysis in the community mining field. The main research topics in this field are applications in the fields of biology and social networks, and research on the theories and methods, along with their application in emerging fields. The analysis results could be used as significant references for further research in the field of community mining.

Under the auspices of structural balance theory, the individuals in signed social networks will adjust their relations with their neighbours to reduce the stress induced by imbalanced triangles. In small networks individuals may be able to observe the impact of their adjustments upon the global balance of the network. But in larger networks this will not prove feasible, their local adjustments will only respond to their immediate neighbourhoods. Furthermore, adjustments may only centre upon the more important neighbours. We study whether limited cognition (i.e. variations in the number of important neighbours) affects the convergence of generated networks to balance. We find that limited cognition adjustment can drive the complete networks to global balance as long as the number of recognized neighbours exceeds an even number critical value. But under relaxed increase requirement, the complete network will go global balance only if the number (even number) of recognized neighbours exceeds a critical value. Critical values varies with network size, network density (d) and the initial ratio of positive links. But for incomplete network, less links among node will thwart the network converge to global balance. The sparser the incomplete network is and the lower ratio of initial positive edges, the harder for the network to converge to global balance. Some special imbalance substructures like the attractors can prevent the incomplete network to global balance. The experimental in a real email network also verify this results. (C) 2021 Published by Elsevier B.V.

The weapon system of systems (WSoS) is a complex system composed of independent systems. The capability of WSoS is the ability of the WSoS to accomplish its specific mission. As the scale of the WSoS continues to increase and the types of systems included continue to rise, the capabilities of the WSoS become more and more complex. The measurement of the complexity of the capabilities of the WSoS is conducive to the overall control of the WSoS, which is beneficial to provid a basis for the correct use of the WSoS. In this paper, the capability spacess of the WSoS are described, and their hierarchical structure is analyzed through the capability tree. The capacity decomposition and capacity dependence relationship of the capability spacess are analyzed. Based on the capability decomposition and capacity dependence relationship, the complexity measurement method of the capability spacess of WSoS based on entropy is given. Finally, the feasibility of the method is verified by an example.

针对集成电路规模扩大、片内寄存器数量激增,导致验证难度加大的问题,提出一种轻量级寄存器模型.首先,设计精简的底层结构,配合参数化设置减少寄存器模型在运行时的内存消耗;然后,分析模块级、系统级等不同层次的寄存器验证需求,使用SystemVerilog语言实现验证所需的各项功能;最后,开发内建测试用例和寄存器模型自动生成工具,缩短寄存器模型所处验证环境的建立时间.实验结果表明,在运行时内存消耗方面,该寄存器模型为通用验证方法学(UVM)寄存器模型的21.65％;在功能方面,可应用于传统的UVM验证环境和非UVM验证环境,对25类寄存器的读写属性、复位值、后门访问路径等功能进行检查.该轻量级寄存器模型在工程实践中拥有良好的通用性和灵活性,满足寄存器验证需求,能有效提高寄存器验证的效率.

In previous studies of the command and control (C2) modeling and optimization, the C2 process tends to be simplified to a large extent, resulting in a relatively large deviation to actual operation. The paper adopts a simulation method and surrogate technology to transform the black box C2 system into a white box model and try to optimize the C2 seat allocation problem by maximizing the C2 efficiency. Firstly, the dynamic C2 process is modeled based on the simulation tool of ExtendSim, by mining key elements and relations in C2 process. Secondly, with training data generated from the ExtendSim, the artificial neural network (ANN) based surrogate model is adopted to approximate the relation between allocation solution inputs and efficiency outputs. Then, by treating the trained ANN as the objective function, the non-dominated sorting differential evolution (NSDE) algorithm is used to obtain the Pareto set of the problem. Finally, a case is studied to verify the feasibility and effectiveness of the proposed idea and methods.

Focusing on the decision-making confusion of aerospace components manufacturers in market entry order and product development strategy, a market entry game theory model based on the conversion cost and learning curve effects, as well as a product differentiation with downward compatibility game theory model is established. Game analysis shows there is a significant first-mover advantage in the aerospace components market, while adopting a downward compatible strategy may provide a second-mover advantage. The study has two implications. First, companies must strive to be the first mover into the market; second, the incomers should adopt a downward compatible product differentiation strategy.

It is imperative and arduous to acquire product and business intelligence of global technical market. In this paper, a deep learning methodology is proposed to automatically extract and discover vital technical information from large-scale news dataset. More specifically, six kinds of technical elements are first defined to provide the concrete syntax information. Next, the CRF-BiLSTM approach is used to automatically extract technical entities, in which a conditional random field (CRF) layer is added on top of bidirectional long short-term memory (BiLSTM) layer. Then, three indicators including timeliness, influence and innovativeness are designed to evaluate the value of intelligence comprehensively. Finally, as a case study, technical news on three military-related websites is utilized to illustrate the efficiency and effectiveness of the foregoing methodology with the result of 80.82 (F-score) in comparison to four other models. In more detail, data on unmanned systems are extracted to summarize the state-of-the-art, and track up-to-the-minute innovations and developments in this field.