Distribution Method Research Articles

Investigating the closure stress and crack initiation stress in fractured rocks using the student t distribution and Monte Carlo simulation method.

Traditional method of determining closure and initiation stress of fractured rocks by analyzing the stress-strain curve has problems such as strong subjectivity and large errors. This study utilized the rock closure stress values and onset stress values determined by three traditional methods, namely, axial strain method, fracture volume method and empirical value taking method, as the base database. The Student t distribution theory was used to obtain a confidence interval based on its overall distribution of values and to achieve a combination of the advantages of multiple methods. Within confidence interval, the Monte Carlo stochastic simulation was used to determine the convergence interval of the second stage to further improve the accuracy. Finally, mean value of the randomly sampled values after reaching the convergence stage was taken as the probability value of rock closure and crack initiation stress. The results showed that the 3 traditional methods for calculating rock closure and initiation stresses are significantly different. In contrast, the proposed method biases more towards multi-numerical distribution intervals and also considers the preference effects of different calculation methods. In addition, this method does not show any extreme values that deviate from the confidence intervals, and it has strong accuracy and stability compared to other methods.

Benefit Distribution Mechanism of a Cooperative Alliance for Basin Water Resources from the Perspective of Cooperative Game Theory

At present, global water resource security is facing serious threats, and the construction of a cooperative, open, and mutually beneficial water resource community is a potential solution to the global water resource crisis and water resource hegemony. Previous studies on the formation and beneficial distribution of water resources in cooperative alliances have more often focused on the idea that participants take all of their water resources when they join a cooperative alliance (i.e., a crisp cooperative alliance), while fewer studies have focused on participants including different proportions of their water resources and joining multiple cooperative alliances (i.e., fuzzy cooperative alliances), and even fewer comparative studies concern the use of different benefit-sharing mechanisms. In this paper, in order to improve the efficiency of water use, allocate water resources more optimally, and generate higher returns for water users in a given basin, we propose the establishment of a traditional crisp and improved fuzzy cooperative alliance for water resources in the basin from the perspective of cooperative game theory; we examine the water resource allocation mechanism within the alliance based on the principle of priority; we construct a benefit allocation mechanism for the cooperative alliance based on the core, least core, weak least core, and Shapley value method; and we carry out empirical research using the example of the Tarim River Basin. Our findings are as follows: (1) A cooperative alliance based on the perspective of cooperative game theory can effectively improve overall benefits and individual benefits, and a fuzzy cooperative alliance is more effective than a crisp cooperative alliance in improving the overall water benefits of the region. (2) The participants in the fuzzy cooperative alliance can obtain more benefits than in the crisp cooperative alliance, and the benefit distribution mechanism of the cooperative alliance helps the participants to determine the object of cooperation while maintaining the sustainable existence of the alliance. (3) The different methods of benefit distribution within the cooperative alliance directly affect the overall water benefits of the region. (4) The different methods of benefit distribution directly affect the stability of the alliance, and the players in the game have heterogeneous preferences for different distribution schemes. The resource-sharing mechanism and benefit distribution mechanism of a water resource cooperative alliance have good applicability as solutions to the problem of water resource optimization and allocation in river basins, and they may provide policy references for the efficient use of water resources and optimization of water resource allocation and management in areas with a shortage of water resources, such as arid and semi-arid zones.

WATERHYPERNET: a prototype network of automated in situ measurements of hyperspectral water reflectance for satellite validation and water quality monitoring

This paper describes a prototype network of automated in situ measurements of hyperspectral water reflectance suitable for satellite validation and water quality monitoring. Radiometric validation of satellite-derived water reflectance is essential to ensure that only reliable data, e.g., for estimating water quality parameters such as chlorophyll a concentration, reach end-users. Analysis of the differences between satellite and in situ water reflectance measurements, particularly unmasked outliers, can provide recommendations on where satellite data processing algorithms need to be improved. In a massively multi-mission context, including Newspace constellations, hyperspectral missions and missions with broad spectral bands not designed for “water colour”, the advantage of hyperspectral over multispectral in situ measurements is clear. Two hyperspectral measurement systems, PANTHYR (based on the mature TRIOS/RAMSES radiometer) and HYPSTAR® (a newly designed radiometer), have been integrated here in the WATERHYPERNET network with SI-traceable calibration and characterisation. The systems have common data acquisition protocol, data processing and quality control. The choice of validation site and viewing geometry and installation considerations are described in detail. Three demonstration cases are described: 1. PANTHYR data from two sites are used to validate Sentinel-2/MSI (A&B); 2. HYPSTAR® data at six sites are used to validate Sentinel-3/OLCI (A&B); 3. PANTHYR and HYPSTAR® data in Belgian North Sea waters are used to monitor phytoplankton parameters, including Phaeocystis globosa, over two 5 month periods. Conclusion are drawn regarding the quality of Sentinel-2/MSI and Sentinel-3/OLCI data, including indications where improvements could be made. For example, a positive bias (mean difference) is found for ACOLITE_DSF processing of Sentinel-2 in clear waters (Acqua Alta) and clues are provided on how to improve this processing. The utility of these in situ measurements, even without accompanying hyperspectral satellite data, is demonstrated for phytoplankton monitoring. The future evolution of the WATERHYPERNET network is outlined, including geographical expansion, improvements to hardware reliability and to the measurement method (including uncertainty estimation) and plans for daily distribution of near real-time data.

Improvement of inert-gas distribution for fuel tanks with the objective of minimum bleed flowrate

PurposeTo ensure the safety of aircraft fuel tanks, the FAA issued an airworthiness clause (25.981(b)) suggesting that the risk of combustion and explosion be reduced by installing a Flammability Reduction Means or an Ignition Mitigation Means. The airflow distribution method has a significant effect on the inerting performance. Therefore, this study aims to determine an optimum airflow distribution method of the inerting system.Design/methodology/approachThis paper establishes the calculation model of the oxygen concentration in the ullage of a multi-bay fuel tank, calculates the oxygen concentration in the ullage of an aircraft tank in single-flow and dual-flow modes under series and parallel ventilation methods and analyses the inerting performance of the tank under different airflow distribution methods.FindingsThe results show that: (1) the bleed flow rate required to achieve whole process inerting of multi-bay fuel tank in dual-flow mode is lower than that in single-flow mode; (2) under the parallel ventilation method, the decrease of oxygen concentration and the uniformity of each bay are better than that in the series ventilation method; (3) dual-flow mode staged ventilation method can be used to achieve the whole process inerting of the tanks under the minimum engine bleed consumption.Originality/valueThe novelty of this paper is to analyze and optimize the airflow distribution method of the inerting system under the whole flight envelope to minimize the engine bleed consumption.

Intelligent Computational Methods for Optimal Distribution of Friction Dampers in Seismic Protection of Buildings

Intelligent Computational Methods for Optimal Distribution of Friction Dampers in Seismic Protection of Buildings

Study on calculation method for dynamic load distribution of thread pair of planetary roller screw mechanism

To reveal the dynamic load distribution characteristics of planetary roller screw mechanism under the high-speed operating conditions, based on the spiral surface equation and deformation coordination relationship, a static contact force calculation method of the roller-screw and roller-nut thread at the contact point is proposed, and a formula for calculating the contact angle after bearing is derived. On this basis, further considering the inertial force generated by using high-speed operation, a dynamic load distribution model for planetary roller screw mechanism thread was established. By comparing with the finite element contact model of planetary roller screw mechansim, the error is below 5%, which verifies the correctness of the model established. Finally, the influence of the parameters such as screw speed, axial load, pitch, and tooth flank angle on the distribution of dynamic load was analyzed. The results show that the inertia force causes the roller to have a tendency to move closer to the nut and away from the screw resulting in a decrease in dynamic contact force on the roller screw side and an increase in dynamic contact force on the roller nut side. The degree of uneven distribution of dynamic load is positively correlated with the screw speed and pitch, and negatively correlated with the tooth side angle. The size of axial load has a significant impact on the distribution of dynamic load.

The Influence of Financial Knowledge and Locus of Control on Financial Management Behavior with Financial Literacy as an Intermediary : A Case Study on Generation Z Students Using ShopeePay on Java Island

This study explores the effects of financial knowledge and locus of control on financial management behavior, with financial literacy as a mediating variable, among Generation Z students using ShopeePay in Java Island. Involving 387 students, the research employs SEM-PLS 3 for data analysis and utilizes a snowball sampling method for questionnaire distribution. The findings reveal that both financial knowledge and locus of control significantly influence financial literacy and financial management behavior. Financial literacy mediates the relationship between financial knowledge and financial management behavior, enhancing the impact of financial knowledge on financial decision-making. Similarly, financial literacy mediates the relationship between locus of control and financial management behavior, reinforcing the effect of locus of control on effective financial management. The results indicate that while both financial knowledge and locus of control positively affect financial management behavior, the presence of financial literacy amplifies these effects. The study contributes to understanding how financial knowledge and locus of control, supported by financial literacy, influence financial management practices among students, offering insights into the role of financial literacy in bridging these relationships.

Energy security and its determinants in New Zealand

New Zealand relies on imported fossil fuels for about 38% of its primary energy. The country’s energy demand is expected to grow due to population and economic growth, which will put more pressure on the energy system. Besides, resource scarcity, energy price volatility, and environmental challenges have made energy security a major concern for New Zealand and other countries. Given the lack of significant research on the effects of energy security factors in New Zealand, this study aims to shed light on the primary determinants of energy security using the dynamic autoregressive distributed lag method based on time series data from 1978 to 2021. The study found that a long-run link exists between energy security and energy intensity (energy efficiency), renewable energy use, fossil fuel consumption, and global oil prices. Real GDP, renewable energy consumption, and energy security were found to improve energy security, while fossil fuel consumption and world oil prices had a negative impact. The study also revealed a one-way causality from real GDP, fossil fuel consumption, and renewable energy use to energy security. In contrast, the relationship between energy intensity and energy security is bidirectional. Simulation results showed that global crude oil prices have a lower impact on energy security compared to other variables and are most responsive to a 5% shock in fossil fuel consumption, followed by economic growth.

A new data-driven distributed power grid load storage resource-wide area voltage self-optimization control method for distribution system

Abstract Different from the conventional energy distribution network, which solely relies on energy distribution, the novel distribution system gradually exhibits a complex and interconnected form of various resources, such as source network load storage. These resources possess characteristics of “multi-point, wide area, and small amount” distribution. Investigating the potential for wide-area voltage regulation by using distributed resources like renewable energy power generation, distributed energy storage, and flexible loads is crucial for constructing a highly integrated source network load storage in the new type of distribution system. This paper proposes a novel data-driven method for optimizing control of wide-area voltage through distributed power network load storage resources. By comprehensively and cooperatively controlling renewable energy power generation, energy storage systems, and flexible loads, this approach enhances the quality of wide-area voltage in the distribution network while ensuring optimal utilization of energy storage capacity and minimizing network losses. The effectiveness and advanced nature of this method are demonstrated by applying it to an urban distribution network example. Compared to traditional voltage regulation methods, this approach aligns better with the requirements for voltage regulation in the new type of distribution network.

Total-Body Dynamic Imaging and Kinetic Modeling of [18F]F-AraG in Healthy Individuals and a Non-Small Cell Lung Cancer Patient Undergoing Anti-PD-1 Immunotherapy.

Immunotherapies, especially checkpoint inhibitors such as anti-programmed cell death protein 1 (anti-PD-1) antibodies, have transformed cancer treatment by enhancing the immune system's capability to target and kill cancer cells. However, predicting immunotherapy response remains challenging. 18F-arabinosyl guanine ([18F]F-AraG) is a molecular imaging tracer targeting activated T cells, which may facilitate therapy response assessment by noninvasive quantification of immune cell activity within the tumor microenvironment and elsewhere in the body. The aim of this study was to obtain preliminary data on total-body pharmacokinetics of [18F]F-AraG as a potential quantitative biomarker for immune response evaluation. Methods: The study consisted of 90-min total-body dynamic scans of 4 healthy subjects and 1 non-small cell lung cancer patient who was scanned before and after anti-PD-1 immunotherapy. Compartmental modeling with Akaike information criterion model selection was used to analyze tracer kinetics in various organs. Additionally, 7 subregions of the primary lung tumor and 4 mediastinal lymph nodes were analyzed. Practical identifiability analysis was performed to assess the reliability of kinetic parameter estimation. Correlations of the SUVmean, the tissue-to-blood SUV ratio (SUVR), and the Logan plot slope (K Logan) with the total volume of distribution (V T) were calculated to identify potential surrogates for kinetic modeling. Results: Strong correlations were observed between K Logan and SUVR with V T, suggesting that they can be used as promising surrogates for V T, especially in organs with a low blood-volume fraction. Moreover, practical identifiability analysis suggested that dynamic [18F]F-AraG PET scans could potentially be shortened to 60 min, while maintaining quantification accuracy for all organs of interest. The study suggests that although [18F]F-AraG SUV images can provide insights on immune cell distribution, kinetic modeling or graphical analysis methods may be required for accurate quantification of immune response after therapy. Although SUVmean showed variable changes in different subregions of the tumor after therapy, the SUVR, K Logan, and V T showed consistent increasing trends in all analyzed subregions of the tumor with high practical identifiability. Conclusion: Our findings highlight the promise of [18F]F-AraG dynamic imaging as a noninvasive biomarker for quantifying the immune response to immunotherapy in cancer patients. Promising total-body kinetic modeling results also suggest potentially wider applications of the tracer in investigating the role of T cells in the immunopathogenesis of diseases.

Cooperating Graph Neural Networks With Deep Reinforcement Learning for Vaccine Prioritization.

This study explores the vaccine prioritization strategy to reduce the overall burden of the pandemic when the supply is limited. Existing vaccine distribution methods focus on macro-level or simplified micro-level assuming homogeneous behavior within populations without considering mobility patterns. Directly applying these models for micro-level vaccine allocation leads to sub-optimal solutions. To address the issue, we first proposed a Trans-vaccine-SEIR model to incorporate mobility heterogeneity in disease propagation. Then we develop a novel deep reinforcement learning to seek the optimal vaccine allocation strategy for the disease evolution system. The graph neural network is used to effectively capture the structural properties of the mobility network and extract disease features. In our evaluation, the proposed framework reduces 7%-10% of infections and deaths compared to the baseline strategies. Extensive evaluation shows that the proposed framework is robust to seek the optimal vaccine allocation with diverse mobility patterns. In particular, we find transit usage restriction is significantly more effective than restricting cross-zone mobility for the top 10% age-based and income-based zones under optimal vaccine allocation strategy. These results provide valuable insights for areas with limited vaccines and low logistic efficacy.

CBIL-VHPLI: a model for predicting viral-host protein-lncRNA interactions based on machine learning and transfer learning

Virus‒host protein‒lncRNA interaction (VHPLI) predictions are critical for decoding the molecular mechanisms of viral pathogens and host immune processes. Although VHPLI interactions have been predicted in both plants and animals, they have not been extensively studied in viruses. For the first time, we propose a new deep learning-based approach that consists mainly of a convolutional neural network and bidirectional long and short-term memory network modules in combination with transfer learning named CBIL‒VHPLI to predict viral–host protein‒lncRNA interactions. The models were first trained on large and diverse datasets (including plants, animals, etc.). Protein sequence features were extracted using a k-mer method combined with the one-hot encoding and composition–transition–distribution (CTD) methods, and lncRNA sequence features were extracted using a k-mer method combined with the one-hot encoding and Z curve methods. The results obtained on three independent external validation datasets showed that the pre-trained CBIL‒VHPLI model performed the best with an accuracy of approximately 0.9. Pretraining was followed by conducting transfer learning on a viral protein–human lncRNA dataset, and the fine-tuning results showed that the accuracy of CBIL‒VHPLI was 0.946, which was significantly greater than that of the previous models. The final case study results showed that CBIL‒VHPLI achieved a prediction reproducibility rate of 91.6% for the RIP-Seq experimental screening results. This model was then used to predict the interactions between human lncRNA PIK3CD-AS2 and the nonstructural protein 1 (NS1) of the H5N1 virus, and RNA pull-down experiments were used to prove the prediction readiness of the model in terms of prediction. The source code of CBIL‒VHPLI and the datasets used in this work are available at https://github.com/Liu-Lab-Lnu/CBIL-VHPLI for academic usage.

Current review on nail drug delivery system and their future perspectives

Human nails are not only beautiful and protective, but they may also be utilized as a vehicle for drug delivery, particularly in the treatment of nail disorders such as psoriasis and onychomycosis. Even though nails develop at a somewhat moderate rate, their highly keratinized structure presents unique opportunities and challenges for medication penetration. Achieving therapeutic doses might be difficult even with topical pharmaceutical delivery, which offers advantages like non-invasiveness and customized effect. Many physical and chemical methods, such as acid etching and permeation enhancers, have been studied to improve drug penetration. It has recently been demonstrated that nail lacquers have potential for effectively delivering medication through the nails. Nail anatomy, common nail diseases, nail medication administration methods, and the advantages and disadvantages of nail lacquers are all covered in this overview. In addition, it discusses factors influencing drug diffusion through nails, draws comparisons between topical nail lacquers and oral antifungal medications, and looks at recent studies and patents on the topic. Future prospects for transungual medicine administration are promising, thanks to innovations such as photodynamic therapy and water-based lacquers. To fully realize the promise of this distribution method, more research is necessary.

Prospects for Legal Regulation of Quantum Communication

The leading countries across the world have entered the race to develop quantum technologies that will enable them to ensure their continued economic prosperity. Among these technologies, a special place is occupied by quantum communication, which is designed to ensure information security in an era where a quantum computer is capable of compromising a number of cryptography algorithms. In this article, this new digital technology includes quantum key distribution and encryption methods that are cryptographically resistant to a quantum computer. The study does not consider the regulation of the quantum communication sub-technology, the so-called “quantum internet,” due to the technical limitations of the existing equipment. The authors note that their predictions about the cryptographic strength of encryption algorithms are based solely on modern knowledge about the capabilities of quantum computing and do not take into account its hidden potential, for example, in terms of cryptanalysis information systems based on a machine learning model generated by a quantum computer. Currently, the only data protection system that is not subject to quantum threats is the technology of quantum key distribution. In today’s information and digital age, information security systems are an important element of critical infrastructure. Given the importance of these technologies, different states use different methods to regulate this field. This article puts forward and substantiates the hypothesis that the implementation of a combination of regulatory legal acts could have a greater positive impact on the development of quantum communication and ensure an acceptable level of information security in the post-quantum era. The analysis showed that a significant number of states and interstate associations are conducting research in this area, relying only on investment growth. This strategy has prevented any country from achieving the competencies of the People’s Republic of China. The authors also analyzed the methods of legal support used by China, Russia, and other countries in the field of quantum communication, which made it possible to identify a model of legal regulation of quantum communication that stimulates this technology’s development.

Time-Series Feature Extraction by Return Map Analysis and Its Application to Bearing-Fault Detection

Developing new features for time-series characterization is a current challenge in data science and machine learning. In this paper, we propose a new metric based on a simple and efficient algorithm, namely, the return map. The return map analysis is well established in the field of non-linear dynamics, in particular, for fitting the parameters of a chaotic system from a waveform, or to attack a chaotic communication channel. We show that our metrics work well for both non-linear dynamics and time-series feature extraction problems in the field of machine learning. In an experiment aiming to classify vibration signals of normal and damaged bearings, we compare our method with two other methods that reported to have excellent accuracy, based on entropy and statistical feature distribution, respectively. We show that our method achieves higher accuracy with almost the lowest time costs, which was confirmed in experiments with two different datasets containing three main classes of bearings: normal, with inner race faults, and with outer race faults, having different damage origins and recorded in various conditions. In particular, for the dataset supplied by Case Western Reserve University, our method reached an accuracy of 100% at signals of 5000 sample points length, with a total time of 0.4 s required for feature estimation, while the entropy-based method reached an accuracy of 95% with a time of 100 s, and a statistical feature distribution method reached an accuracy of 93% with a total time of 1.9 s. Results show that the developed method is better suited to real-time bearing condition monitoring applications than most of the methods reported to date.

Optimization of Fuel Consumption by Controlling the Load Distribution between Engines in an LNG Ship Electric Propulsion Plant

Due to growing environmental concerns and stringent emissions regulations, optimizing the fuel consumption of marine propulsion systems is crucial. This work deals with the potential in an LNG ship propulsion system to reduce fuel consumption through controlled load distribution between engines in Dual-Fuel Diesel Electric (DFDE) plant. Based on cyclical data acquisition measured onboard and using an optimization model, this study evaluates different load distribution strategies between setups according to the optimization model results and automatic (equal) operation to determine their effectiveness in improving fuel efficiency. The analysis includes scenarios with different fuel types, including LNG, MDO and HFO, at different engine loads. The results indicate that load distribution adjustment based on the optimization model results significantly improves fuel efficiency compared to conventional methods of uniform load distribution controlled by power management systems in almost all load intervals. This research contributes to the maritime industry by demonstrating that strategic load management can achieve significant fuel savings and reduce environmental impact, which is in line with global sustainability goals. This work not only provides a framework for the implementation of more efficient energy management systems on LNG vessels, but also sets a benchmark for future innovations in maritime energy optimization as well as in the view of exhaust emission reduction.

Types of stress and methods of stress reduction in cattle

Recently, stress has become the most urgent problem of modern animal husbandry. With the industrialization of agriculture, this problem is becoming more and more acute, as a result, livestock enterprises and farms suffer significant economic losses. Throughout its life, the animal is exposed to numerous stressors that have a completely different nature of occurrence, but invariably lead to the same changes in the body. The animal noticeably loses weight, weakens, its resistance to diseases decreases. A review of scientific sources on some types of stress loading on cattle: heat stress, transportation stress, pre-slaughter stress is given. According to many studies, the stress state of an animal depends on feeding and maintenance by 70-80% and only 20-30% on genetics. The body of farm animals is constantly affected by various environmental factors. These include production technology, method of maintenance, density of accommodation, size of groups, microclimate of premises, type and level of feeding, biological full-value of diets, methods of preparation and distribution of feed, quality of drinking water, veterinary and preventive and zootechnical measures (vaccination, sanitary treatment of animals, weighing, castration, etc.). When the above factors change, certain reactions occur in the animal body. The nature of stress can be different: mechanical, physical, chemical, biological, mental. Knowledge of the causes of stress, the patterns of its course, its consequences, outlined in the mentioned scientific sources, will allow rational use of the proposed ways to reduce the adverse effects of various stress factors on animals to preserve their health and high productivity.

Distributed sparsity constrained optimization over the Stiefel manifold

Distributed optimization aims to effectively complete specified tasks through cooperation among multi-agent systems, which has achieved great success in large-scale optimization problems. However, it remains a challenging task to develop an effective distributed algorithm with theoretical guarantees, especially when dealing with nonconvex constraints. More importantly, high-dimensional data often exhibits inherent structures such as sparsity, which if exploited accurately, can significantly enhance the capture of its intrinsic characteristics. In this paper, we introduce a novel distributed sparsity constrained optimization framework over the Stiefel manifold, abbreviated as DREAM. DREAM innovatively integrates the ℓ2,0-norm constraint and Stiefel manifold constraint within a distributed optimization setting, which has not been investigated in existing literature. Unlike the existing distributed methods, the proposed DREAM not only can extract the similarity information among samples, but also more flexibly determine the number of features to be extracted. Then, we develop an efficient Newton augmented Lagrangian-based algorithm. In theory, we delve into the relationship between the minimizer, the Karush–Kuhn–Tucker point, and the stationary point, and rigorously demonstrate that the sequence generated by our algorithm converges to a stationary point. Extensive numerical experiments verify its superiority over state-of-the-art distributed methods.

Distributed control for DC microgrids with cyber attacks and constraints: A fault-tolerant model predictive controller

Communication security issues pose severe challenges to stability of microgrid system. This paper designs a cooperative control method based on fault-tolerant distributed model predictive control (DMPC) for DC microgrid against possible false information injection (FDI) attacks and communication constraints problems. First, a state variable is defined based on the voltage and current information, thereby deriving a system model of the microgrid under the cyber-physical framework. Then, since a combined uncertainty term consisting of attacks and disturbances exists in the constructed system model, an expanded state observer (ESO) is proposed to observe the uncertainty term. Further, based on the constructed system model and the designed ESO, a fault-tolerant distributed model predictive controller is designed. In the controller, DMPC is utilized to constrain the communication information and ensure the boundedness of the communication state and physical state. Simulation results indicate that the proposed DMPC-based distributed control method can achieve stable voltage regulation and accurate current sharing, and maintain smooth operation even under FDI attack, which verifies the feasibility for the proposed method.

Numerical methods for distributed stochastic compositional optimization problems with aggregative structure

ABSTRACT The paper studies the distributed stochastic compositional optimization problems over networks, where all the agents' inner-level function is the sum of each agent's private expectation function. Focusing on the aggregative structure of the inner-level function, we employ the hybrid variance reduction method to obtain the information on each agent's private expectation function, and apply the dynamic consensus mechanism to track the information on each agent's inner-level function. Then by combining with the standard distributed stochastic gradient descent method, we propose a distributed aggregative stochastic compositional gradient descent method. When the objective function is smooth, the proposed method achieves the convergence rate O ( K − 1 / 2 ) . We further combine the proposed method with the communication compression and propose the communication compressed variant distributed aggregative stochastic compositional gradient descent method. The compressed variant of the proposed method maintains the convergence rate O ( K − 1 / 2 ) . Simulated experiments on decentralized reinforcement learning verify the effectiveness of the proposed methods.