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Computational experiments are an important method for carrying out the quantitative analysis of complex systems and play a major role in mapping the real world to the virtual world. However, the flexibility of computational experiments leads to arbitrary modeling processes and unconvincing results, which greatly hinder the large-scale application of this method. In this context, the verification of computational models has become an urgent problem in this field. Currently, model evaluation is still in its infancy and the existing evaluation methods are not mature enough. Thus, we took epidemic models as the research object and proposed a capability maturity evaluation framework for computational models of artificial society. The framework differs from previous assessment methods that focus on the validity of results, but instead provides a comprehensive evaluation from two perspectives: 1) evaluation of the model itself--by comparing the expectation with the final implementation, we can obtain whether the model meets the expectation and 2) comparison between different models--by evaluating the implementation process of each model and comparing the results, we can identify more mature models. The implementation of the model is evaluated from input, process, and output. Further, specific analyses and evaluations are conducted for several representative COVID-19 models to verify the validity of this evaluation framework. The results of the case study show that the proposed evaluation framework can help decision-makers identify more mature and referential models, and point out the directions where modelers can improve their models. IEEE

Reduction gears are used in the joints of the robots. While precision and efficiency are required for industrial robot gearboxes, humanoid robots are expected to become more popular in the future. Flexibility must be considered in humanoid robots, and high back drivability shall be required. In this study, we aimed at realizing high efficiency, and high-precision reduction gears with high reduction ratios by maximizing the efficiency equation, hybridizing a 3K compound planetary reduction gearbox and traction drive, experimentally determining the efficiency, backlash, and angular velocity variation, and demonstrating the effectiveness of the proposed mechanism from a control perspective. In the experiments, we achieved a maximum forward driving efficiency of 90.90%, backward driving efficiency of 90.58%, and a minimum backlash of 1.18 arcmin.

Most existing voltage optimization approaches of low voltage distribution networks (LVDNs) assume users follow the regulations unconditionally,which is not always true.To tackle this, we propose a real-time peer to peer flexibility trading scheme for LVDNs. Besides distributed optimization, our scheme offers a way to let users trade their flexibility, in case some users do not want to follow the system regulations at some point. The scheme consists of flexibility allocation and trading. Player compatibility equilibrium and Bernoulli trials are employed to approximate the Pareto optimum in flexibility allocation,while consensus and voltage reference are used to provide real-time offers for flexibility trading. The proposed scheme is computationally tractable and can work with limited communication in a decentralized manner. All the user data is used locally so that privacy is well protected. A network based on a real Belgian semiurban LVDN is employed to validate the proposed scheme with three different scenarios. Besides, the flexibility allocation is benchmarked with a centralized ACOPF algorithm. The case studies result clearly prove the effectiveness and robustness of the proposed scheme. ispartof: Ieee Transactions On Power Systems vol:37 issue:4 pages:2821-2832 status: published

Interactions between different policy instruments in real markets can lead to a mismatch between stakeholders striving for individual benefits and the attainment of an overall system optimum. These distortion effects are of particular relevance for sector-integrated systems with high prosumer shares. Distributed battery storage systems, in particular, could constitute a pivotal source of flexibility in close to 100% renewable energy systems. However, depending on their operation mode, these battery systems can either serve individual profit maximization or the benefit of the system as a whole. We quantify the resulting differences by means of a fundamental linear optimization model of the electricity market, which we stringently link to a stakeholder's complete cost-of-energy analysis including relevant regulatory price components. We demonstrate that the effects encompass both system cost changes and distributional effects for certain stakeholder groups. We quantify the effects and apply an impact variation analysis to demonstrate that they could be relevant in view of the overall acceptance of the energy system transformation.

Security and privacy of data-in-transit are critical issues in Industry 4.0 which are further amplified by the use of faster communication technologies such as 6G. Along with security issues, computation and communication costs, as well as data confidentiality, must be also accommodated. In this work, we design a cybertwin-based cloud-centric network architecture to improve the flexibility and scalability of 6G industrial networks. Cybertwin not only enables the deployment of advanced security solutions but also provides an always-on connection. However, the security of data-in-transit over wireless communication between users/things and cybertwin remains a concern. Hence, a privacy-preserving authentication scheme based on digital signature and authenticated key exchange protocol is designed to address the security concerns of data exchanged. Additionally, we conduct a security analysis that proves that the scheme resists several attacks in the industry 4.0 environment. Moreover, the evaluation performed confirmed the superiority of the proposed work comparing to the existing works.

The growth of autonomous vehicles, ridesharing systems, and self driving technology will bring a shift in the way ride hailing platforms plan out their services. However, these advances in technology coupled with road congestion, environmental concerns, fuel usage, vehicles emissions, and the high cost of the vehicle usage have brought more attention to better utilize the use of vehicles and their capacities. In this paper, we propose a novel multi-hop ride-sharing (MHRS) algorithm that uses deep reinforcement learning to learn optimal vehicle dispatch and matching decisions by interacting with the external environment. By allowing customers to transfer between vehicles, i.e., ride with one vehicle for sometime and then transfer to another one, MHRS helps in attaining 30\% lower cost and 20\% more efficient utilization of fleets, as compared to the ride-sharing algorithms. This flexibility of multi-hop feature gives a seamless experience to customers and ride-sharing companies, and thus improves ride-sharing services., Comment: This is an extended version of the work presented in NeurIPS Workshop 2019. arXiv admin note: text overlap with arXiv:1903.03882

In the early onset of the COVID-19 pandemic in the U.S., consumers experienced surprising shortages of essential goods such as toilet paper, yeast and flour, and some of their favorite meat cuts that appeared to be unrelated to the pandemic at first look. The “usual” explanations attributing these shortages to “demand spikes” (e.g., foreign suppliers, trade wars and tariffs, and “hoarding behavior” of panicked consumers) often failed to explain them, and in the best of cases, predicted only temporary shortages. However, shortages in these supply chains ended up being in many cases real “supply chain struggles,” with their true causes going beyond the “usual” ones, and revealing a set of deeper and “unusual causes.” Our detailed analysis of the affected supply chains identifies these overlooked failure factors and hidden causes. Our underestimated causes include demand shifts among market segments, siloed demand planning, bottlenecks in shared resources such as transportation and distribution, and supply chain structures driven by the economics of an efficiency era that failed in a risk-fraught environment. We conclude on the profound lessons learned from the pandemic crisis on supply chains, and the implied challenges to address for building resilient supply chains for the future. The resilient supply chains of the future require rethinking the relevant “systems” we plan and optimize (e.g., firm, industry, region, global factor resources, etc.). The usual answer of building firm-specific redundancy of assets and operational flexibility might be prohibitive in the level of investment required for any one firm, or their financial stakeholders, to pursue and accept.

Power systems are facing the challenges of integrating massive electric vehicles (EVs) and lacking operational flexibility. An optimal and efficient coordination strategy is needed to coordinate the operations among EV charging stations (CSs) and the distribution system operator (DSO) and to make full use of the EVs charging flexibility for improving the operational flexibility of power systems. To this end, this paper proposes a CSs-DSO distributed coordination strategy. The proposed strategy integrates a supply-curve-based quantification method to quantify the EVs flexibility contribution and a Nash bargaining game-theory-based peer-to-peer (P2P) transactions model for maximizing the profits of entities participating in the coordination. To tackle the CSs-DSO coordination problem that has a bi-level-parallel coupling structure, an analytical target cascading (ATC) and alternating direction method of multipliers (ADMM) jointly-based distributed solution approach is proposed. First, the CSs-DSO bi-level model is decoupled by ATC with tie-line power being the only exchanged information; then, the model is further decomposed for each CS by ADMM for protecting the CSs privacy and autonomy. The effect of coordination and the computational efficiency of the proposed strategy is verified by numerical simulations on a modified IEEE 33-bus distribution system connecting multiple CSs.

Coordinated preheating can potentially enhance the flexibility of the electricity system, thereby driving significant economic savings. In this context, this paper proposes a novel game-theory based preheating coordination scheme aimed at guaranteeing the effectiveness of collective preheating performed by a large population of households. By adopting an innovative two-phase iterative algorithm, individual households autonomously schedule their heating power allocation to seek for savings in energy bills while smartly maintaining thermal comfort. Meanwhile, the total electricity generation cost decreases at each iteration and converges to an equilibrium solution asymptotically. Compared to the centralised optimization-based energy management control methods, the proposed algorithm effectively reduces the information exchange while significantly reduces computational complexity. The simulation results indicate that the proposed coordination strategy can drive 12.30% cost saving when all households participate in the preheating scheme whereas 11.35% cost increase will be incurred if coordination is absent. Overall, the proposed preheating control algorithm simultaneously benefits both individual households and the whole system by intelligently linking local behaviour with global interests.

This work investigates the tracking consensus problem of multiagent systems over directed networks, where the control gains follow certain volatile patterns. Some event-based consensus protocols are formulated so as to reduce the redundant execution of control. By using an extended differential inequality with a time-dependent coefficient, criteria for tracking consensus under time- and state-dependent triggering conditions are constructed, respectively. It is proved that the time average of the control gain, together with the agent dynamics, network topology, and triggering conditions, governs the consensus despite the fluctuation of control gain. The derived theorem can be utilized to ensure consensus with intermittent strategies aiming to lessen the burden in communications, including aperiodic on-off control with periodic perturbation and pulse-modulated on-off control. Unlike existing works, the requirement of a positive lower bound of control ratios is removed and, thus, a wide range of control gain patterns is possible, signifying higher flexibility in intermittent policy design. Finally, numerical examples are provided to further illustrate the theoretical results.

Research on multiple Unmanned Aerial Vehicles (UAVs) cooperative surveillance systems serving Internet of Things (IoT) applications such as smart cities and precision logistics, etc., has become a hot topic. However, the target movement is unpredictable in an uncertain environment, and multiple UAVs are affected by obstacles or inaccessible regions, resulting in the decreased surveillance performance and even the loss of the target. This paper is dedicated to determine the current surveillance environment through the 2-D laser scanner. At the cost of the energy consumption and the transmission unreliability, a Multi-UAV Cooperative Path Optimization (MCPO) model is designed to adjust the surveillance location of each UAV, which improves the target surveillance performance. Specifically, for different types of obstacles or inaccessible regions, we present a novel obstacle avoidance selection strategy with two mechanisms in mind: 1) When some of UAVs encounter obstacles, but others can accurately monitor the target, a strict constraint mechanism is established to promptly adjust the surveillance location of each UAV, which ensures the accuracy of formation surveillance; 2) When all UAVs have to avoid obstacles, a fuzzy constraint mechanism is presented and combined with Lucas-Kanade (LK) method to expand the search range of the multi-UAV and enhance the flexible adjustment capability of the formation. To verify the superiority of the proposed optimization method, we develop a 3D simulation experiment environment based on UE4 platform, and design several groups of experiments to analyze the effectiveness of MCPO. The experimental results demonstrate that MCPO can not only maintain the flight stability of multiple UAVs, but also has satisfactory formation flexibility and surveillance accuracy.

The increase of cyberattacks by using the Internet of Things devices has exposed multiple vulnerabilities not only on devices but also on IoT infrastructures. Now, small devices can affect different networks and their services. This paper presents a review of the vulnerabilities on wireless technologies that gives connectivity to IoT, and the experiences using Software Defined Radio SDR for implementing wireless attacks to IoT technologies are assessed. A systematic literature review was conducted. The types of vulnerabilities and attacks that can affect the wireless technologies that stand the IoT ecosystem and SDR platforms were compared, and recent methods to overcome these attacks were identified. On the IoT reference model, the perception layer was identified as the most vulnerable. Most attacks at this level occur due to hardware limitations, physical device exposure, and heterogeneity of technologies. Future cybersecurity systems based on SDR technologies have notable advantages due to their flexibility and adaptability to new communication technologies and their potential to develop advanced tools. However, the IoT’s cybersecurity challenges are so complex that merging SDR hardware with cognitive and intelligent techniques is highly advisable. Some of these techniques could include deep learning to adapt mitigation systems to rapid technological changes.

This paper focuses on the finite-time guaranteed cost (FTGC) control problem for interval type-2 (IT2) fuzzy semi-Markov switching systems (S-MSSs) with additive disturbances. Based on the IT2 fuzzy S-MSSs and the state feedback control method, a novel IT2 mode-dependent fuzzy GC controller is developed under the mismatched membership functions. Then, some new sufficient conditions are given to ensure that IT2 fuzzy S-MSSs are FT stochastically stable based on the designed IT2 mode-dependent fuzzy GC controller. Furthermore, the transition rates boundaries condition is relaxed, and a new incomplete transition rates boundaries model is constructed to reflect the transition rates information. With a view to both the incomplete transition rates boundaries and the actuator fault phenomenon, the improved stability criteria are provided to stabilize the IT2 fuzzy S-MSSs within a FT interval and determine the gains of the IT2 mode-dependent fuzzy GC controller, resulting in a much greater design flexibility. Distinct from the existing results, the presented FTGC control scheme is more suitable for many practical systems, because the IT2 fuzzy S-MSSs model which is more general compared with the traditional fuzzy system and S-MSSs is considered. Finally, three illustrative examples are offered to explain the effectiveness and feasibility of proposed FTGC control approach.

Bipolar VSC-MTDC is a promising solution to increase power transmission capacity and system reliability for large-scale renewable energy integration. However, traditional interpolar active power control methods cannot comprehensively explore the maximum transmission capability of the bipolar VSC-MTDC under abnormal operating conditions. Considering the flexibility of the bipolar VSC-MTDC that the positive-pole and negative-pole DC networks can be operated independently, a novel fault self-recovering control strategy is proposed in this paper to provide automatic, global and timely recovery solutions under various fault conditions: (a Firstly, a coordinated time-driven and event-driven security monitoring, optimization and recovering control framework is proposed to guarantee the accuracy and speed of the corresponding calculation and control, hence to meet the timeliness requirement of fault self-recovery; (b Secondly, a global optimal power allocation calculation method is proposed to automatically calculate the optimal power allocation between the positive-pole and negative-pole DC networks; (c Thirdly, an interpolar active power dispatch method is proposed to realize the fault self-recovering control strategy based on primary control strategies of bipolar VSC-MTDC. Simulation results using MATLAB and PSCAD/EMTDC show that the proposed control strategy can timely and effectively enhance the overall power transmission capability for large-scale renewable energy integration after the fault.

The dynamic line rating (DLR) is recognized as an effective method to improve the integration of renewable energies, such as wind power by enhancing the flexibility of the system. The principle of DLR is to increase the transmission capacity of a transmission line according to the forecasted weather data. However, the uncertainties of DLR and wind power caused by the forecasting errors might bring in some severe consequences to the power system, such as line overloads. In this paper, we propose a tri-level preventive dispatch model to deal with this issue. To reduce the conservativeness of the solution, we employ the Density-Based Spatial Clustering of Application with Noise (DBSCAN) method to identify and remove the extreme scenarios of DLR. And a decomposition method is proposed to solve the tri-level model.The numerical simulations on several IEEE bus systems demonstrate the effectiveness of the proposed model.

This paper proposes a reinforcement learning approach for nightly offline rebalancing operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse demand in a network, FFEVSS require relocation of electrical vehicles (EVs) to charging stations and demander nodes, which is typically done by a group of drivers. A shuttle is used to pick up and drop off drivers throughout the network. The objective of this study is to solve the shuttle routing problem to finish the rebalancing work in the minimal time. We consider a reinforcement learning framework for the problem, in which a central controller determines the routing policies of a fleet of multiple shuttles. We deploy a policy gradient method for training recurrent neural networks and compare the obtained policy results with heuristic solutions. Our numerical studies show that unlike the existing solutions in the literature, the proposed methods allow to solve the general version of the problem with no restrictions on the urban EV network structure and charging requirements of EVs. Moreover, the learned policies offer a wide range of flexibility resulting in a significant reduction in the time needed to rebalance the network.

Pursuing long-term climate stabilisation scenarios subjects power, heat, industry, and transport sectors to undergo fundamental transitions towards fully renewable energy systems. Cost-efficient strategies involve the integration of energy vectors, introducing additional cross-sectoral electricity demand and flexibility for traditional power sectors. When performing cross-sectoral capacity expansion planning (CEP) analyses of low-carbon energy systems for decision making support, the underlying mathematical optimisation problems easily become intractable. To overcome the computational challenges of optimising real-world instances of future energy systems with adequate representations of temporal, spatial, and technological detail, this work investigates different modelling approaches for flexible heat pump systems at the power and heat sector interface of the long-term energy system model SCOPE SD. A comparative case study carried out for a net-neutral decarbonisation scenario in Europe and Germany highlights the implications of using model aggregation techniques. It is shown that the proposed modelling approaches can present computational benefits at only limited cost of result accuracy. However, poorly implemented grouping strategies can lead to high result inaccuracies and even a lack of performance improvements.

This paper documents two facts: (i) elasticities of substitution in production vary significantly across sectors, with manufacturing sectors being generally less flexible than service sectors, and (ii) during the Great Recession the rise in bond spreads varied systematically with these elasticities. Specifically, more flexible sectors paid lower spreads during the Great Recession. Moreover, among the less-flexible manufacturing sectors, sectors with relatively high flexibility and high debt saw their spreads rise less than average, while among the more-flexible service sectors the sectors with relatively high flexibility and high debt saw their spreads rise more. We interpret these results using a simple two-sector model with working capital constraints, and show that the model replicates these observations if manufacturing sectors face constraints on their purchases of intermediates while services face constraints on their purchases of labor/capital. The dynamics of intermediate prices and quantities support our results, as does a quantitative investigation of a 62-sector version of the US economy.

We study the economic incentives for automation when labor and machines are perfect substitutes. Labor may still be employed in production, even when it is a costlier input than robots on a productivity-adjusted basis. This occurs if firms face idiosyncratic risk, adjusting the stock of machines is costly, and workers can be hired and fired quickly enough. Even though labor survives, jobs become less stable, as workers are hired in short-lived bursts to cope with shocks. We calibrate a general equilibrium, multi-industry version of our model to match data on robot adoption in US manufacturing sectors, and use it to compute the employment and labor share consequences of progress in automation technology. A fall in the relative price of robots leads to relatively few jobs losses, while reductions in adjustment costs, or improvements in relative robot productivity, can be far more disruptive. The model-implied semi-elasticity of aggregate employment to robot penetration (number of robots per thousand employees) ranges between 0.01% and 0.12%, depending on the underlying source of increased robot adoption, consistent with findings in the empirical literature. In an extension, we show that reduced-form hiring and firing costs unambiguously depress long-run employment.

Using distributed hydrological models to evaluate the effectiveness of reducing non-point source pollution by applying best management practices (BMPs) is an important support to decision making for watershed management. However, complex interfaces and time-consuming simulations of the models have largely hindered the applications of these models. We designed and developed a prototype web-based decision support system for watershed management (DSS-WMRJ), which is user friendly and supports quasi-real-time decision making. DSS-WMRJ is based on integrating an open-source Web-based Geographical Information Systems (Web GIS) tool (Geoserver), a modeling component (SWAT, Soil and Water Assessment Tool), a cloud computing platform (Hadoop) and other open source components and libraries. In addition, a private cloud is used in an innovative manner to parallelize model simulations, which are time consuming and computationally costly. Then, the prototype DSS-WMRJ was tested with a case study. Successful implementation and testing of the prototype DSS-WMRJ lay a good foundation to develop DSS-WMRJ into a fully-fledged tool for watershed management. DSS-WMRJ can be easily customized for use in other watersheds and is valuable for constructing other environmental decision support systems, because of its performance, flexibility, scalability and economy.

To model threshold weighted voting systems with multifailure modes, we analyze system performance in terms of system reliability by focusing on indecisive effect. The measurement of system reliability is generated by the decision rule where we apply dynamic process on two major aspects including the indecisive parameter θ and voters' errors resulting from various situations such as weights replacement and inputs discrepancy. The motivation is to make the existing model more applicable via the dynamic process. In this article, two major contributions are addressed as follows. First, the continuous distributed indecisive parameter gives an intuitive idea of how the model adapts from the decision rule, which provides the flexibility of adjustment to satisfy different applications. Second, the time delay degradation process on voters' errors illuminates the opposite system performance for two types of inputs by considering different indecisive effects, which leads to further investigation of model sensitivity of input types.

The widespread presence of contingent generation, when coupled with the resulting volatility of the chronological net-load (i.e., the difference between stochastic generation and uncertain load) in today's modern electricity markets, engender the significant operational risks of an uncertain sufficiency of flexible energy capacity. In this article, we address several operational flexibility concerns that originate from the increase in generation variability captured within a security-constrained unit commitment (SCUC) formulation in smart grids. To quantitatively assess the power grid operational flexibility capacity, we first introduce two reference operation strategies based on a two-stage robust SCUC, one through a fixed and the other via an adjustable uncertainty set, for which the state-of-the-art techniques may not be always feasible, efficient, and practical. To address these concerns and to account for the effects of the uncertainty cost resulting from dispatch limitations of flexible resources, a new framework centered on the adjustable penetration of stochastic generation is proposed. Our hypothesis is that if the SCUC is scheduled with an appropriate dispatch level of stochastic generation, the system uncertainty cost will decrease, and subsequently, the system's ability to accommodate additional uncertainty will improve. Numerical simulations on a modified IEEE 73-bus test system verify the efficiency of the suggested assessment techniques.

An innovative and flexible approach is introduced to address the challenge of self-organizing a group of mobile robots into cubic-spline based patterns without any requirement of control points. Besides the self-organization of mobile robots, the approach incorporates a potential field-based control for obstacle/collision avoidance. This will offer more flexibility to swarm robots to efficiently dealing with many practical situations including smoothly avoiding obstacles during movement, or exploring and covering areas with complex curved patterns. Essentially, this challenge is approached by proposing a formation control model basing on a Smoothed Particle Hydrodynamic estimation technique, which uses special cubic-spline kernel functions applied here to interpolate the density of each robot in the swarm. The moving information is used to weight the distances to the robot’s neighbours available in its field of view. Then an artificial physics mesh is finally built among each robot and its three available neighbours having the smallest weighted distances. Significant results toward emerging cubic-spline patterns are shown with a swarm of foot-bot mobile robots simulated in the ARGoS platform. Analysis results with different metrics are also conducted to assess the performance of the model with different swarm sizes and in the presence of sensory noise as well in the presence of partially faulty robots.

So far, numerous learned models have been pressed to use in microwave imaging problems. These models however, are oblivious to the imaging geometry. It has always been hard to bake the physical setup of the imaging array into the structure of the network, resulting in a data-intensive models that are not practical. This work put forward a graph formulation of the microwave imaging array. The architectures proposed is made cognizant of the physical setup, allowing it to incorporate the symmetries, resulting in a less data requirements. Graph convolution and attention mechanism is deployed to handle the cases of fully-connected graphs corresponding to multi-static arrays. The model works with a modular fashion at node level to strike a trade-off between flexibility and efficient capture of mutual information present in measured signals. Additionally, the modular working fashion endows the model with immunity to overfitting. The graph-treatment of the problem is evaluated on experimental setup in context of anomaly localization with imaging array and has shown higher performance as compared to the popular radar technique. The thin model was realized with a feasibly procured reasonably-sized dataset in the order of few hundreds, thus eliminating the need to resort to simulations for augmentation.

This paper explores national strategies for emerging countries with ambitious policy targets for future offshore wind development. A scenario-based economic cost-benefit analysis of multiple approaches to the development of offshore wind projects is conducted based on (1) who takes the initiative on financing (public sector vs. private sector), (2) whether wind turbines are supplied locally or not, and (3) whether investment plans provide managerial flexibility to respond to changing circumstances or not (real options strategy vs. traditional investment decision-making). The analysis demonstrates that a real options strategy that allows flexibility regarding future uncertainties can significantly increase the economic value of a project regardless of the type of developer or dependence on foreign wind turbine technologies. The study concludes with a recommendation that late-mover countries focus on the combination of public-sector initiatives, a real options strategy, and the purchase of domestic turbines.

It is common that airlines encounter a disruption of a flight schedule, which is mainly caused by resource shortages. In case of a disruption, subject to scarce resources, most of airlines lose flexibility of performing aircraft recovery on the basis of business interest priorities and need to delay, swap, and cancel flights. This paper proposes an air-rail inter-modal strategy to incorporate a High-Speed Rail (HSR) transport mode into an aviation network for aircraft recovery. The air-rail inter-modal strategy focuses on occasionally operational integration of existing airside and HSR infrastructure capacities. It is different from air-rail cooperation implemented in Europe which emphasizes a long-term strategy. In addition to modelling the air-rail inter-modal strategy, an inter-modal time-band network is presented. Modelling is applied to a pure aviation network and the inter-modal network. Comparison results show that the inter-modal air-rail strategy helps to reduce the number of cancelled flights and the total disruption cost.

Synaptic transmission via neurochemical release is the fundamental process that integrates and relays encoded information in the brain to regulate physiological function, cognition, and emotion. To unravel the biochemical, biophysical, and computational mechanisms of signal processing, one needs to precisely measure the neurochemical release dynamics with molecular and cell-type specificity and high resolution. Here we reviewed the development of analytical, electrochemical, and fluorescence imaging approaches to detect neurotransmitter and neuromodulator release. We discussed the advantages and practicality in implementation of each technology for ease-of-use, flexibility for multimodal studies, and challenges for future optimization. We hope this review will provide a versatile guide for tool engineering and applications for recording neurochemical release.

The capability of deep learning (DL) techniques for dealing with non-linear, dynamic and correlated data has paved the way for DL-based fault detection and diagnosis (FDD). Among them, autoencoders (AEs) have shown their potential to serve as the fault detection network. However, misclassifying faulty samples that share similar patterns to normal samples is a common drawback of AEs. In this work, a source-aware autoencoder (SAAE) is proposed as an extension of AEs to incorporate faulty samples in the training stage. In SAAE, flexibility in tuning recall and precision trade-off, ability to detect unseen faults and applicability in imbalanced data sets are achieved. Bidirectional long short-term memory (BiLSTM) with skip connections SAAE is designed as the structure of the fault detection network. Further, a deep network with BiLSTM and residual neural network (ResNet) is proposed for the subsequent fault diagnosis step to avoid randomness imposed by the order of the input features. A framework for combining fault detection and fault diagnosis networks is also presented without the assumption of having a perfect fault detection network. A comprehensive comparison among relevant existing techniques in the literature and SAAE-ResNet is also conducted on the Tennessee-Eastman process, which shows the superiority of the proposed FDD method.

The rising volume of sensitive and personal data being harvested by data controllers has increased the security essentials in the cloud system. The cloud module is not used just to store the data, but also to process them on cloud premises. Security for the cloud premises is essential as the cloud has lot of outsourced, unprotected sensitive data for the public access. This has resulted repeated data violations, and thus there is a need for the advanced legal data protection constraints. Various studies were conducted to adopt the privacy preservation in the cloud, and most of the state-of-the-art techniques fail to handle the optimal privacy when dealing with sensitive data, as it requires separate data sanitization and restoration models. To overcome this challenge, this paper tempts to develop the privacy preservation model in the cloud environment using the advancements of artificial intelligent techniques. Artificial Intelligent capabilities are working in the business cloud computing environment to make organizations more efficient, strategic, and insight-driven. However, by hosting the data, cloud computing offers businesses high flexibility, agility, and cost savings. The two main phases of the proposed privacy preservation system are the data sanitization and restoration. Moreover, the proposed sanitization process depends on the optimal key generation, which is performed by the hybrid meta-heuristic algorithm. This hybrid algorithm merges two well-performed algorithms, such as Shark Smell Optimization (SSO) and Jaya Algorithm (JA), and thus termed as Jaya-based Shark Smell Optimization (J-SSO). The optimal key generation is accomplished by deriving a multi-objective function that involves the parameters, such as the degree of modification, hiding ratio, and information preservation ratio. Finally, the performance analysis has proved the efficiency of the proposed model over the state-of-the-art models in enhancing cloud security.

Natural disasters and accidents have underscored the need for operative solutions that can enhance the resiliency of power distribution network (PDN). This article proposes novel methods of scheduling of battery energy storage system (BESS) and loads of microgrid to enhance the resiliency of PDN. A mixed-integer linear programming model is developed to minimize the energy mismatch between available resources and critical loads during outage period. The proposed model is aimed at minimizing the shedding of high critical loads (hcl) and maximizing the serving time of hcl during outages. In order to increase the longevity of the load serving, the fuzzy-based approach is developed to control the C_rate of the BESS. Moreover, to increase the resiliency of hcl, partitioning of BESS capacity is proposed, which provides more flexibility for charging/discharging control of the BESS. The concept of variable dispatch interval is introduced to reduce the frequent switching of loads. By considering the reactive power, an apparent power resiliency metric is proposed to quantify the resiliency of PDN. The IEEE 33-bus network is tested under various restoration periods in order to verify the performance of the proposed methodology in the MATLAB environment.

Today, in the market with ever-changing consumer preferences, three-dimensional (3D) printing is becoming an overwhelming trend. In this paper, we explore the use of 3D printing in mass customization (MC) programs. We consider the case when 3D printing brings extra self-design fun to consumers, which is highlighted in MC practices of the auto company BMW and the furniture company Poltrona Frau, and also changes the cost formula (i.e., the marginal product variety cost) of the MC product. In addition, the roles played by the risk attitudes of the MC manufacturer and consumers, consumer returns, as well as consumers’ time sensitive behaviors, are also uncovered. We find that under the case with a low consumer's willingness to pay for the traditional “ready-made product variety enjoyment” (RPVE), even the maximized product variety level and the additional self-design fun cannot make the 3D printing based MC more profitable. In addition, compared to the markets with risk-averse and risk-neutral consumers, the 3D printing based MC can increase more MRBs and a higher consumer surplus by simultaneously highlighting the high self-design fun and the high RPVE in a market with risk-seeking consumers. The high flexibility and responsiveness of 3D printing also shows its advantages in remanufacturing the consumer returned MC items and enhancing MC programs’ overall lead-time. Finally, applying 3D printing in creating molds can also help the MC manufacturer tackle demand uncertainties. These findings all provide a good reference to the application of 3D printing in MC operations.

New resource mix, e.g., renewable resources, are imposing operational complexities to modern power systems by intensifying uncertainty and variability in the system net load. This issue has motivated independent system operators (ISOs), e.g., California ISO (CAISO), to add flexible ramping products (FRPs) to their day-ahead (DA) market models. Such structural changes in the DA market formulation require further analyses and detailed design to ensure adequate operational flexibility, market efficiency, and reliability. This paper conducts a comprehensive study to: (a) augment existing DA market models with enhanced FRP design to schedule ramp capabilities that are more adaptive with respect to the real-time (RT) 15-min net load variability and uncertainty, and (b) design corresponding market payment policies that accurately reflect the value of the added flexibility through enhanced FRP design. The proposed FRP design can be implemented in present-day system operations with minimal disruption to existing DA market models. Performance of the proposed DA market model, which includes the enhanced FRP design, is compared against the DA market model with existing FRP design through a validation methodology based on RT unit commitment model. This validation methodology mimics fifteen-minute markets of CAISO. The proposed method is tested on an IEEE 118-bus test system.

Controlling interpenetration and flexibility behaviors is intriguing and fundamental for porous coordination polymers. We report exceptional interpenetration behaviors involving controllable partia...

Energy storage has high application value in the power system, especially in the field of auxiliary services, but the transaction mechanism and process are not yet perfect. Considering the advantages of security and transparency of blockchain technology, this article combines blockchain with energy storage auxiliary services and proposes a blockchain-based grid-side shared energy storage market transaction model and mechanism. This article first introduces the concept and application status of blockchain, and compares the advantages of blockchain technology applied to power transactions. Combined with the existing policies and market rules, the research on the participation of energy storage in auxiliary services was carried out, and the market mechanism for the participation of energy storage in peak and frequency modulation auxiliary services was designed. Finally, a blockchain-based energy storage market-oriented transaction model and mechanism is proposed to improve the flexibility of the power system and promote clean energy consumption.

There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. Existing methods fail to capture these complicated features because they heavily rely on some predefined (often over-simplified) regression expressions. Instead, this paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern. We further develop a two-stage estimation process with Siamese long short-term memory (LSTM) networks. Here, a LSTM network encodes the price response, while the other network estimates the time-varying elasticities. In the case study, the proposed framework and models are validated to achieve higher overall estimation accuracy and better description for various abnormal features when compared with the state-of-the-art methods., Comment: Author copy of the manuscript submitted to IEEE Trans on Power Systems

Robustness enhancement and feature selection are the two crucial issues to be resolved in robust palmprint recognition. However, existing regression-based methods are insufficient to handle outliers and select significant features. From a statistical viewpoint, we present a general framework to intrinsically resolve the two issues. By investigating the role of outliers in the formation of coding errors, we devise a double-Laplacian mixture-error model to faithfully fit the error distribution. Additionally, we design a supervised group-sparse regularizer to enforce the locality and group sparsity of the codes. Integrating the two parts into the framework produces a nonconvex constrained problem, for which we develop an iteratively reweighted l1-l1 minimization algorithm by combining the majorization-minimization strategy and the alternating direction method of multipliers. The weighted vector learned from the error model and the local group sparsity enforced by the regularizer enable our method to better handle outliers and select more significant features than the state-of-the-art methods. Extensive experimental results verify the flexibility and robustness of our method to various contaminations.

Autonomous driving systems (ADSs) need to be able to respond quickly to changes in the dynamic traffic scenario. However, regardless of the changes occurring in traffic scenes, the current local path planning frameworks of ADSs are based on the fixed frequency re-planning path (i.e., running their planning algorithms repeatedly). This planning method makes it difficult to provide a reasonable traveling path, agility, and comfort for driverless vehicles in changing traffic scenarios. Therefore, this article performs an in-depth analysis of the problems of traditional planning frameworks which use a fixed frequency to replan the path and proposes a novel path planning framework that is universal based on multiple-models. The proposed framework divides the planning process into several layers, each of which has different functions. With this framework, the ADS can adaptively adjust the planning process according to the changes in traffic scenes and then provide different path planning algorithms to ensure its safety and flexibility in the process of driving. Moreover, the problems caused by the traditional planning framework can be solved. This framework has been applied to the autonomous vehicle ``Pioneer'', which won first place in the 2019 China Intelligent Vehicle Future Challenge (IVFC). The effectiveness and rationality of the integrated framework of local path planning proposed in this article were verified by a large number of tests in real-world traffic scenarios.

The Interconnections Seam Study examines the potential economic value of increasing electricity transfer between the Eastern and Western Interconnections using high-voltage direct-current (HVDC) transmission and cost-optimizing both generation and transmission resources across the United States, proposing, assessing, justifying, and illustrating a major infrastructure change involving two of the worlds largest power grids. The study conducted a multi-model analysis that used co-optimized generation and transmission expansion planning and production cost modeling. Four transmission designs under eight scenarios were developed and studied to estimate costs and potential benefits. The results show benefit-to-cost ratios that reach as high as 2.5, indicating significant value to increasing the transmission capacity between the interconnections under the cases considered, realized through sharing generation resources and flexibility across regions.

Dendrites are elaborate neural processes which integrate inputs from various sources in space and time. While decades of work have suggested an independent role for dendrites in driving nonlinear computations for the cell, only recently have technological advances enabled us to capture the variety of activity in dendrites and their coupling dynamics with the soma. Under certain circumstances, activity generated in a given dendritic branch remains isolated, such that the soma or even sister dendrites are not privy to these localized signals. Such branch-specific activity could radically increase the capacity and flexibility of coding for the cell as a whole. Here, we discuss these forms of localized and branch-specific activity, their functional relevance in plasticity and behavior, and their supporting biophysical and circuit-level mechanisms. We conclude by showcasing electrical and optical approaches in hippocampal area CA3, using original experimental data to discuss experimental and analytical methodology and key considerations to take when investigating the functional relevance of independent dendritic activity.

Vehicular networks have tremendous potential to improve the road safety and traffic efficiency, and the adoption of the Space-Air-Ground Integrated Network (SAGIN) architecture in vehicular networks can greatly improve the performance of vehicular networks by leveraging the respective advantages of the space, air, and ground segments on coverage, flexibility, reliability, and availability, which results in Space-Air-Ground Integrated Vehicular Networks (SAGIVNs). Trust management is an important tool for constructing trustworthy SAGIVNs, and privacy preservation is also a primary concern in SAGIVNs. They have conflicting requirements and a satisfactory balance between them is urgently required. In this article, we propose a novel Privacy-Preserving Trust Management (PPTM) scheme for the emergency message dissemination in SAGIVNs. The proposed scheme can realize precise trust management and strong conditional privacy preservation simultaneously with low communication overhead, and can provide strong applicability, strong robustness, and multiple other attractive features. Furthermore, the exhaustive theoretical analysis and simulation evaluation are detailed. The results reveal that the proposed scheme is significantly superior to the existing schemes in several aspects.

Batteries can provide valuable operational flexibility to facilitate the system efficiency. However, there lacks market environments to effectively harness and monetize the value of these assets. Most existing works apply a profit-oriented single-signal pricing scheme, which mixes the value of energy and flexibility together and may ultimately raise the electricity bill. Therefore, this paper proposes a profit-neutral double-signal retail pricing scheme that distinguishes elastic market players (i.e., batteries) from inelastic market players (i.e., inflexible loads) and quantifies the value of energy and flexibility separately. Experimental results indicate that under the incentive provided by the proposed retail pricing scheme: 1) The system efficiency benefit aligns with benefits to both batteries and inflexible loads; 2) Value of operational flexibility, contributing to improve the energy efficiency, can be transparently priced and fairly allocated among batteries; and 3) The dominant role in which inflexible loads play on determining the market price is avoided.

Nowadays, the yearning for microwave absorption materials (MAMs) are more and more urgent for dealing with the increasingly serious electromagnetic pollution and the demand of modern military security. Among potential candidates, the graphene (GE) based magnetic hybrids have advantages in structural controllable and designing flexibility, providing opportunities for achieving highly efficiency of microwave absorption (MA). Thus, the structural regulation and MA performances of GE-based magnetic hybrids arouse great attention in related fields. In this review, we summarize the recently progress in MA performance of GE-based magnetic hybrids. Typical absorption process and corresponding mechanism are firstly introduced, for guiding the design of GE-based magnetic MAMs. Then, the magnetic components, synthesis methods, structural features and regulation strategies of these GE-related magnetic materials are reviewed, and their influences on MA performances have also been discussed. Challenges, and prospects of the GE-based magnetic MAMs are suggested. This review provides a brief but systematic introduction to GE-based magnetic MAMs, which may pave the way for the design of MAMs with highly efficient MA performances.

Cascading failure is a ubiquitous phenomenon that can paralyze networked systems in a short time. Many traditional studies of cascading failures have been conducted from the perspective of either an attacker or a defender. In reality, however, malicious attacks on networks are rarely a one-sided process. Instead, both the attacker and defender are actively involved. We use game theory to study the strategies of both sides in terms of an attacker-defender game on complex networks. Based on the concept of the Stackelberg competition, we propose a multi-round attacker-defender game model on complex networks, allowing high flexibility in the available actions for both sides in the game. The model we propose allows the two sides to specify certain parameters of the network to attack/defend and further allocate a certain amount of resources for the attack/defense. Such flexibility allows the model to capture the actions of the attackers and defenders more precisely and simulate the attack process in a more realistic manner. We propose an iterative search algorithm to search for desirable strategies with systematic experiments on various types of networks and associated parameters and in terms of different relative resource owned by the attacker and defender.

Pythagorean fuzzy set (PFS) is an extension of the Intuitionistic fuzzy set (IFS). It has a more wider space of membership degrees. Thus, it is more capable of expressing and handling the fuzzy information in engineering practice and scientific research. However, Pythagorean fuzzy sets lack a mathematical tool to express stochastic or probability information, rendering it unsuitable for application in many scenarios. In this paper, an ordered number pair is used to describe fuzzy information and stochastic information under uncertain environments, namely constrained Pythagorean fuzzy set (CPFS). The CPFS has two components, CPFS=(A,P), A is the classical Pythagorean fuzzy set, while P is a measurement of reliable for A. For PFS, CPFS is the first unified description of fuzzy information and probabilistic information, which is a more flexible way to describe knowledge or thinking. Furthermore, the similarity measure of constrained Pythagorean fuzzy sets (CPFSs) is presented, which meets the similarity measure theorems and can better indicate the flexibility of CPFSs. Numerical examples are used to demonstrate that the CPFSs similarity measure is reasonable and effective. The method of similarity measure can be degenerated to the similarity measure of PFSs under specific case, and can avoid generating counter-intuitive results. In addition, similarity measure of CPFSs is applied to medical diagnosis and target classification of Iris. These experimental results prove the practicability and effectiveness of our model.

Unplanned breakdown of critical equipment interrupts production throughput in Industrial IoT (IIoT), and data-driven Predictive Maintenance (PdM) becomes increasingly important for companies seeking a competitive business advantage. Manufacturers, however, are constantly faced with the onerous challenge of manually allocating suitably competent manpower resources in the event of an unexpected machine breakdown. Furthermore, human error has a negative rippling impact on both overall equipment downtime and production schedules. In this paper, we formulate the complex resource management problem as a resource optimisation problem to determine if a model-free Deep Reinforcement Learning (DRL) based PdM framework can be used to automatically learn an optimal decision-policy from a stochastic environment. Unlike the existing PdM frameworks, our approach considers PdM sensor information and the resources of both physical equipment and human as part of the optimisation problem. The proposed DRL-based framework and Proximal Policy Optimisation Long Short Term Memory (PPO-LSTM) model are evaluated alongside baselines results from human participants using a maintenance repair simulator. Empirical results indicate that our PPO-LSTM efficiently learns the optimal decision-policy for the resource management problem, outperforming comparable DRL methods and human participants by 53% and 65% respectively. Overall, the simulation results corroborate the proposed DRL-based PdM framework’s superiority in terms of convergence efficiency, simulation performance and flexibility.

Despite the advantages of scalability and flexibility, Security Function Virtualization (SFV) raises concerns about its own security. To enhance the security of SFV, a promising approach is to run critical components of off-the-shelf security software inside SGX enclaves. This idea, however, is hardly practical due to the difficulty of detaching components from the monolithic security function and the unacceptable cost of running them in enclaves. In this work, we propose S-Blocks, an architecture to modularize a virtual security function (VSF) and protect its key modules with SGX in an efficient manner. Through systematically decomposing modules of a VSF into related elements, it is easy to put the key modules and elements of the VSF into an enclave. Furthermore, aiming at addressing state consistency and secure migration issues of security function scaling, we design a fine-grained state synchronization and migration mechanism to ensure lose-free, order-preserving, and state security for VSFs. To demonstrate the effectiveness of our approach, we prototype S-Blocks using Fast-Click on a real Skylake platform and implement three main types of virtual security functions based on the S-Blocks architecture. Our evaluation results show that S-Blocks only imposes a manageable performance overhead, and low latency and resource consumption when protecting VSFs.

Currently, there is a beginning of liquefied natural gas (LNG) market transformation, which is deepening global supply chains through the growth of the number of small-scale projects. The main driver of this transformation was the creation of large-scale LNG hubs, which did not meet expectations for improving the efficiency of gas distribution systems (like in Europe). This has led to an understanding of the need of increasing flexibility and decentralization of gas supply projects, which is almost impossible to implement with traditional large and medium scale facilities. Increasing decentralization leads to a change in the role and composition of stakeholders, and, consequently, determines new algorithms and schemes for their interaction, which are poorly described in the scientific literature. This paper systematizes information about the specifics of such project implementation, defines a list of key stakeholders, their area of responsibility and area of interests. Proposed conceptual model of interaction between stakeholders has shown a significant dependence of small-scale projects on regional economic and infrastructure conditions, as well as on the availability of multiple distributed points of gas consumption.

The past several decades have witnessed a growing enthusiasm to implement the market as an information aggregation tool. However, some deep-rooted pricing anomalies frequently observed in various markets loom large, questioning the rationality of such eagerness. One of these anomalies is the favorite-longshot bias, and sometimes its opposite, the reverse favorite-longshot-bias. We propose an equilibrium model consisting of bettors with cumulative prospect theory-based utility and heterogeneous beliefs to explain these two anomalies in a unified framework. Unlike existing results that mostly focus on one aspect of the underlying model, our model enables us to isolate and analyze the impacts of probability weighting, loss aversion, belief concentration, and belief tailedness. We show how these parameters jointly determine the properties of the equilibrium price. Empirical results also suggest this model has adequate flexibility to explain the behavior of actual historical data.

Involving suppliers deep in the supply chain is critical for the success of environmental and social responsibility (ESR) initiatives. Administering ESR programs throughout a complex supply network, however, is challenging. In this paper, we apply a multiunit bilateral bargaining framework to coordinate ESR investments in a general supply network and analyze to what extent an ESR initiator should directly engage the higher-tier suppliers as opposed to delegating that responsibility to the first-tier suppliers. Our bargaining framework not only generalizes the conventional Shapley value approach by allowing the flexibility of modeling imbalanced power distribution among the firms but also provides an explicit way of implementing the resulting gain sharing among the firms through negotiated contract terms. We show that the eventual structure of ESR negotiation relationships can be derived by finding a shortest path tree in the supply network with the arc cost defined as the logarithm of the negotiating parties’ relative bargaining power. These developments allow us to analyze ESR implementation in generally extended supply networks. We find that the ESR initiator tends to delegate ESR negotiations to a supplier that is strong in negotiations with higher-tier suppliers. When the supply network is complex (i.e., wide and deep), directly engaging all suppliers can lead to a larger gain by the initiator than fully delegating the negotiations with higher-tier suppliers to the first-tier ones. However, as the network gets increasingly complex, the ESR initiator tends to directly engage a reduced percentage of higher-tier suppliers. We further extend our analysis to situations where the ESR relationships are sequentially formed in a decentralized manner, where the benefit of ESR depends on the collective choice of the firms’ investment levels, where multiple ESR programs are implemented in the network, and where ESR investments depend on the negotiation relationships. This paper was accepted by David Simchi-Levi, operations management.

Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.