Performance Analysis and Optimal Allocation of Layered Defense M/M/N Queueing Systems

One important mission of strategic defense is to develop an integrated layered Ballistic Missile Defense System (BMDS). Motivated by the queueing theory, we presented a work for the representation, modeling, performance simulation, and channels optimal allocation of the layered BMDS M/M/N queueing systems. Firstly, in order to simulate the process of defense and to study the Defense Effectiveness (DE), we modeled and simulated the M/M/N queueing system of layered BMDS. Specifically, we proposed the M/M/N/N and M/M/N/C queueing model for short defense depth and long defense depth, respectively; single target channel and multiple target channels were distinguished in each model. Secondly, we considered the problem of assigning limited target channels to incoming targets, we illustrated how to allocate channels for achieving the best DE, and we also proposed a novel and robust search algorithm for obtaining the minimum channel requirements across a set of neighborhoods. Simultaneously, we presented examples of optimal allocation problems under different constraints. Thirdly, several simulation examples verified the effectiveness of the proposed queueing models. This work may help to understand the rules of queueing process and to provide optimal configuration suggestions for defense decision-making

Schottky-Contact Formation between Metal Electrodes and Molecularly Doped Disordered Organic Semiconductors

We study using three-dimensional kinetic Monte Carlo (KMC) simulations to what extent the formation of Schottky contacts between a metal electrode and a molecularly doped disordered organic semiconductor can be understood from the theory for crystalline inorganic semiconductors, adapted to include the effects of the localized nature of the states in which the charge carriers reside and the hopping transport in between these states. The thickness of the Schottky-contact depletion region is found to be significantly smaller than as expected when the energetical disorder is neglected. The presence of energetic disorder is also found to influence the voltage dependence of the width of the depletion regions near the contacts of single-layer double-Schottky-contact devices. The voltage drop over the two depletion regions and the remaining charge-neutral bulk layer is shown to be described successfully by a semianalytical model, based on an accurately parameterized bulk mobility function of the dopant concentration, energetic disorder, and the electric field. We furthermore find that the mobility in the depletion regions is drastically reduced. As a result, the depletion-region formation process can be ultraslow, with a characteristic time scale ranging from microseconds to beyond milliseconds.</p

Seed Mucilage Improves Seedling Emergence of a Sand Desert Shrub

The success of seedling establishment of desert plants is determined by seedling emergence response to an unpredictable precipitation regime. Sand burial is a crucial and frequent environmental stress that impacts seedling establishment on sand dunes. However, little is known about the ecological role of seed mucilage in seedling emergence in arid sandy environments. We hypothesized that seed mucilage enhances seedling emergence in a low precipitation regime and under conditions of sand burial. In a greenhouse experiment, two types of Artemisia sphaerocephala achenes (intact and demucilaged) were exposed to different combinations of burial depth (0, 5, 10, 20, 40 and 60 mm) and irrigation regimes (low, medium and high, which simulated the precipitation amount and frequency in May, June and July in the natural habitat, respectively). Seedling emergence increased with increasing irrigation. It was highest at 5 mm sand burial depth and ceased at burial depths greater than 20 mm in all irrigation regimes. Mucilage significantly enhanced seedling emergence at 0, 5 and 10 mm burial depths in low irrigation, at 0 and 5 mm burial depths in medium irrigation and at 0 and 10 mm burial depths in high irrigation. Seed mucilage also reduced seedling mortality at the shallow sand burial depths. Moreover, mucilage significantly affected seedling emergence time and quiescence and dormancy percentages. Our findings suggest that seed mucilage plays an ecologically important role in successful seedling establishment of A. sphaerocephala by improving seedling emergence and reducing seedling mortality in stressful habitats of the sandy desert environment

Spectator Arrival and Departure Traffic Mode and Influence Factors in Beijing Olympic Games Opening and Closing Ceremony

In the Olympic Games opening and closing ceremonies, participants\u27 arrival and departure transportation demand in small area and short period of time with high volume is a great challenge for transportation services. Analyzing the arrival and departure traffic mode is the basis of traffic organiziation. With the survey data collected from the opening and closing ceremonies, this study explores the traffic mode structure of spectators\u27 arrival and departure. This study compares the different mode structures of opening and closing ceremonies, reveals several factors, and analyzes their impacts on arrival and departure traffic mode choice, namely, management policy, traffic mode timeliness, transportation supply, media propagandize, and the origin of spectators. The study provides suggestions for proper traffic control, management policy isssue, and traffic organization of the Olympic Games and other big events

Morphology Determines Conductivity and Seebeck Coefficient in Conjugated Polymer Blends

The impact of nanoscale morphology on conductivity and Seebeck coefficient in p-type doped all-polymer blend systems is investigated. For a strongly phase separated system (P3HT:PTB7), we achieve a Seebeck coefficient that peaks at S similar to 1100 mu V/K with conductivity sigma similar to 3 x 10(-3) S/cm for 90% PTB7. In marked contrast, for well-mixed systems (P3HT:PTB7 with 5% diiodooctane (DIO), P3HT:PCPDTBT), we find an almost constant S similar to 140 mu V/K and sigma similar to 1 S/cm despite the energy levels being (virtually) identical in both cases. The results are interpreted in terms of a variable range hopping (VRH) model where a peak in S and a minimum in a arise when the percolation pathway contains both host and guest sites, in which the latter acts as energetic trap. For well-mixed blends of the investigated compositions, VRH enables percolation pathways that only involve isolated guest sites, whereas the large distance between guest clusters in phase separated blends enforces (energetically unfavorable) hops via the host. The experimentally observed trends are in good agreement with the results of atomistic kinetic Monte Carlo simulations accounting for the differences in nanoscale morphology.Funding Agencies|China Scholarship Council (CSC)</p

System Frequency Control Method Driven by Deep Reinforcement Learning and Customer Satisfaction for Thermostatically Controlled Load

The intermittence and fluctuation of renewable energy aggravate the power fluctuation of the power grid and pose a severe challenge to the frequency stability of the power system. Thermostatically controlled loads can participate in the frequency regulation of the power grid due to their flexibility. Aiming to solve the problem of the traditional control methods, which have limited adjustment ability, and to have a positive influence on customers, a deep reinforcement learning control strategy based on the framework of soft actor&ndash;critic is proposed, considering customer satisfaction. Firstly, the energy storage index and the discomfort index of different users are defined. Secondly, the fuzzy comprehensive evaluation method is applied to evaluate customer satisfaction. Then, the multi-agent models of thermostatically controlled loads are established based on the soft actor&ndash;critic algorithm. The models are trained by using the local information of thermostatically controlled loads, and the comprehensive evaluation index fed back by users and the frequency deviation. After training, each agent can realize the cooperative response of thermostatically controlled loads to the system frequency only by relying on the local information. The simulation results show that the proposed strategy can not only reduce the frequency fluctuation, but also improve customer satisfaction

System Frequency Control Method Driven by Deep Reinforcement Learning and Customer Satisfaction for Thermostatically Controlled Load

The intermittence and fluctuation of renewable energy aggravate the power fluctuation of the power grid and pose a severe challenge to the frequency stability of the power system. Thermostatically controlled loads can participate in the frequency regulation of the power grid due to their flexibility. Aiming to solve the problem of the traditional control methods, which have limited adjustment ability, and to have a positive influence on customers, a deep reinforcement learning control strategy based on the framework of soft actor–critic is proposed, considering customer satisfaction. Firstly, the energy storage index and the discomfort index of different users are defined. Secondly, the fuzzy comprehensive evaluation method is applied to evaluate customer satisfaction. Then, the multi-agent models of thermostatically controlled loads are established based on the soft actor–critic algorithm. The models are trained by using the local information of thermostatically controlled loads, and the comprehensive evaluation index fed back by users and the frequency deviation. After training, each agent can realize the cooperative response of thermostatically controlled loads to the system frequency only by relying on the local information. The simulation results show that the proposed strategy can not only reduce the frequency fluctuation, but also improve customer satisfaction

Morphology Determines Conductivity and Seebeck Coefficient in Conjugated Polymer Blends

The impact of nanoscale morphology on conductivity and Seebeck coefficient in p-type doped all-polymer blend systems is investigated. For a strongly phase separated system (P3HT:PTB7), we achieve a Seebeck coefficient that peaks at <i>S</i> ∼ 1100 μV/K with conductivity σ ∼ 3 × 10^–3 S/cm for 90% PTB7. In marked contrast, for well-mixed systems (P3HT:PTB7 with 5% diiodooctane (DIO), P3HT:PCPDTBT), we find an almost constant <i>S</i> ∼ 140 μV/K and σ ∼ 1 S/cm despite the energy levels being (virtually) identical in both cases. The results are interpreted in terms of a variable range hopping (VRH) model where a peak in <i>S</i> and a minimum in σ arise when the percolation pathway contains both host and guest sites, in which the latter acts as energetic trap. For well-mixed blends of the investigated compositions, VRH enables percolation pathways that only involve isolated guest sites, whereas the large distance between guest clusters in phase-separated blends enforces (energetically unfavorable) hops via the host. The experimentally observed trends are in good agreement with the results of atomistic kinetic Monte Carlo simulations accounting for the differences in nanoscale morphology

Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution

Accurate regional wind power prediction is of great significance to the wind farm clusters integration and the economic dispatch of the regional power grid. The complex spatiotemporally coupled characteristics between multiple wind farms bring challenges to wind power prediction (WPP) of regional wind farm clusters. In this context, this paper proposes a regional WPP method using spatiotemporally multiple clustering algorithm and hybrid neural network to learn the potential spatial-temporal dependencies of regional wind farms. In which, a long-term daily power curve similarity method is proposed to identify spatially correlative wind power plants in long-term. Furthermore, the spatio-temporal wind farm sub-clusters are dynamically recognized by the similar fluctuation trend of short-term power sequences. On this basis, a spatial-temporal integrated prediction model consisting of the improved convolutional neural network (I-CNN) and the bidirectional long short-term memory (BILSTM) network is established for spatio-temporal sub-cluster based on point clouds distribution. Finally, the effectiveness of the proposed regional wind power forecasting framework is validated by using the Wind Integration National Dataset Toolkit, and the results show that the method improves accuracy effectively. 2022 Elsevier LtdThis work is supported by the National Natural Science Foundation of China (No. 52207121 and No. 52007167 ) and the technology project of Electric Power Research Institute of State Grid Hubei Electric Power Co ., Ltd. (Grant number: B31532225680 ).Scopu