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Abstract Accurately and efficiently estimating the carbon market risk is paramount for ensuring financial stability, promoting environmental sustainability, and facilitating informed decision‐making. Although classical risk estimation methods are extensively utilized, the implicit pre‐assumptions regarding distribution are predominantly contained and challenging to balance accuracy and computational efficiency. A quantum computing‐based carbon market risk estimation framework is proposed to address this problem with the quantum conditional generative adversarial network‐quantum amplitude estimation (QCGAN‐QAE) algorithm. Specifically, quantum conditional generative adversarial network (QCGAN) is employed to simulate the future distribution of the generated return rate, whereas quantum amplitude estimation (QAE) is employed to measure the distribution. Moreover, the quantum circuit of the QCGAN improved by reordering the data interaction layer and data simulation layer is coupled with the introduction of the quantum fully connected layer. The binary search method is incorporated into the QAE to bolster the computational efficiency. The simulation results based on the European Union Emissions Trading System reveals that the proposed framework markedly enhances the efficiency and precision of Value‐at‐Risk and Conditional Value‐at‐Risk compared to original methods.

Abstract Background DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. Results Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences’ AVITI and Illumina’s NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina’s NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms’ respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. Conclusion These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

Abstract Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers. However, most classical methods suffer from significant time-consuming, memory overhead, and computational complexity issues as the scale of the power system concerned increases. With rapid development of quantum computing technology, the combinatorial optimization method based on quantum computing has shown certain advantages in computational time over existing methods. Given this background, this paper proposes a quantum computing based power system fault diagnosis method with the quantum approximate optimization algorithm. The proposed method reformulates the fault diagnosis problem as a Hamiltonian by using Ising model, which completely preserves the coupling relationship between faulty components and various operations of protective relays and circuit breakers. Additionally, to enhance problem-solving efficiency under current equipment limitations, the symmetric equivalent decomposition method of multi-z-rotation gate is utilized. Furthermore, the small probability characteristics of power system events is utilized to reduce the number of qubits. Simulation results based on the test system show that the proposed methods can achieve the same optimal results with a faster speed compared with the classical higher-order solver provided by D-Wave.

Abstract Extensive inverter‐based power sources (IPS) impose significant challenges on the restoration of high renewable penetrated power systems (HRPPS). To enhance HRPPS resilience, the proper utilization of IPSs must be implemented. Combining frequency dynamics of IPSs and synchronous generators, this paper proposes a coordinated restoration method for multi‐type power sources after a major blackout. First, interactions between synchronous generators and IPSs are systematically analyzed. Based on this, output characteristics and constraints of IPSs in the power sources restoration process are quantified. Second, the dynamic frequency regulation capability (DFRC) of restored systems is quantified based on a unified transfer function structure model. Then the maximum power disturbance that restored systems can bear is derived based on DFRC indices including the maximum frequency deviation and the rate of change of frequency. Third, considering interactions between power sources and the DFRC of restored systems, a coordinated restoration optimization model of multi‐type power sources is proposed. Finally, case studies based on a modified IEEE 39‐bus system are simulated to verify the applicability and superiority of the proposed method. Meanwhile, results show that the proposed method for quantifying DFRC is more suitable for HRPPSs than traditional inertia‐based methods.

Identification of magnitude and orientation for spatially applied loading is highly desired in the fields of not only the machinery components but also human-machine interaction. Despite the fact that the 3-axis force sensor with different structures has been proposed to measure the spatial force, there are still some common limitations including the multi-step manufacturing-assembly processes and complicated testing of decoupling calibration. Here, we propose a rapid fabrication strategy with low-cost to achieve high-precision 3-axis force sensors. The sensor is designed to compose of structural Maltese cross base and sensing units. It is directly fabricated within one step by a hybrid 3D printing technology combining deposition modeling (FDM) with direct-ink-writing (DIW). In particular, a machine learning (ML) model is used to convert the strain signal to the force components. Instead of a mount of calibration tests, this ML model is trained by sufficient simulation data based on programmed batch finite element modeling. This sensor is capable of continuously identifying a spatial force with varying magnitude and orientation, which successfully quantify the applied force of traditional Chinese medicine physiotherapy including Gua Sha and massage. This work provides insight for design and rapid fabrication of multi-axis force sensors, as well as potential applications.

Abstract Non-Intrusive Load Monitoring (NILM) is a valuable technique for breaking down overall power consumption into the energy usage of individual appliances. Understanding power usage patterns through NILM plays an important role in reducing energy costs and achieving carbon reduction goals. However, privacy concerns often deter consumers from sharing their electricity consumption data. To address these privacy concerns, Federated Learning (FL) has been introduced in NILM, which enables the training of NILM models while keeping power consumers’ data locally. However, FL’s reliance on a single global model leads to poor performance on clients with unique power consumption patterns. In response to this challenge, we present a Personalized Federated Learning NILM algorithm (PerFedNILM), a practical personalized FL approach for NILM. PerFedNILM limits the local update bias across clients and trains personalized models for individual clients to improve load-monitoring performance. In addition, it mitigates the negative impact of client dropout, which is a common issue in practice. Our experiments on using real-world energy data demonstrate that PerFedNILM outperforms previous FL-based NILM methods, especially in client dropout scenarios.

Abstract The carbon market plays a critical role in promoting the transition toward renewable energy sources and reducing greenhouse gas emissions in the electricity generation and transmission. Extant research has overlooked the dynamic bilateral causality that exists between electricity and carbon markets. Moreover, these studies have frequently treated the macroeconomic effect as exogenous. To bridge this research gap, this paper presents a holistic modeling framework that comprehensively captures the intertwined nature of electricity and carbon markets and their concomitant interactions with the overarching economy. The suggested modeling framework is an integration of three principal modules, namely, a carbon market, an electricity market, and economic system. This synergistic blend provides an exhaustive understanding of the entire market operation cycle. It offers detailed clearance rules, and most importantly, it adopts a macroeconomic systematic modeling approach for evaluating the impact emanating from the interconnected electricity and carbon markets. To illustrate the practicality and effectiveness of the proposed approach, a case study anchored on empirical data sourced from the electricity and carbon markets in China is conducted. The empirical findings underscore the fact that incorporating a green certificate market into the modeling framework can precipitate a reduction in greenhouse gas emissions. Additionally, the results indicate that expanding the scale of the green certificate market from 1.9% in 2021 to 33% by 2023 will increase the generation of green electricity by 10%.

The nanoscale confinement is of great important for the industrial applications of molecular sieve, desalination, and also essential in biological transport systems. Massive efforts have been devoted to the influence of restricted spaces on the properties of confined fluids. However, the situation of channel-wall is crucial but attracts less attention and remains unknown. To fundamentally understand the mechanism of channel-walls in nanoconfinement, we investigated the interaction between the counter-force of the liquid and interlamellar spacing of nanochannel walls by considering the effect of both spatial confinement and surface wettability. The results reveal that the nanochannel stables at only a few discrete spacing states when its confinement is within 1.4 nm. The quantized interlayer spacing is attributed to water molecules becoming laminated structures, and the stable states are corresponding to the monolayer, bilayer and trilayer water configurations, respectively. The results can potentially help to understand the characterized interlayers spacing of graphene oxide membrane in water. Our findings are hold great promise in design of ion filtration membrane and artificial water/ion channels.

ABSTRACTThis work proposes a rapid manufacturing technique for the conductive lines applied in flexible electronics, which is referred to as the ‘direct wire writing (DWW)’ technique. The fine metal wire is dragged out of the needle by adhesion, and attached to the stick-on substrate synchronously along design paths to form high-quality circuits. This technique overcomes the unstable performance of ink-based conductive lines fabricated by screen printing, spraying, 3D printing, etc., and avoids complex processes for stable metallic circuits mainly manufactured by the photolithography method, etc. Firstly, the forming mechanism of dominating the micro deformation behaviour (local-debonding, slip, warping) is clarified and analysed, which provides guidelines for fabricating in-plane wire patterns and 3D structural circuits rapidly and easily. Subsequently, some practical applications, including strain rosette, wearable sensor patch and light display are presented, showing the promising potential of the DWW technique in the ongoing exploration of flexible electronics.

Abstract Developing renewable energy generation (REG)‐rich power systems could contribute to achieving carbon neutrality. To ensure the secure and economic operation of power systems with high penetration of renewable energy, it is necessary to solve the problem of inefficient utilisation of demand‐side resources by the current electricity market mechanism. The metaverse, an emerging technology attracting widespread attention, is expected to efficiently solve this problem. The metaverse can be regarded as a virtual‐real interactive economic system built on advanced technologies such as blockchain, artificial intelligence, extended reality, avatars, and decentralised autonomous organisations (DAO). This paper first briefly introduces the concept, architecture, technologies, and features of the metaverse. Then, a metaverse‐based DAO for energy systems is proposed and the corresponding business model is explored. The Energy DAO utilises algorithms and user consensus combined with smart contracts to solidify organisational operation rules. In this way, it organises users to directly participate in multiple types of electricity markets and carbon markets, as well as behavioural data production and transactions. Finally, an Energy DAO example for demand‐side sources demonstrates how the Energy DAO could solve the problems of information asymmetry, information opacity, and incentive incompatibility in electricity market mechanisms.

Nowadays, with the wide installation of distributed energy resources and independent energy storage systems, prosumers as a new type of electricity market entity have emerged. Since numerous prosumers can significantly impact the carbon emission of the power grid, this paper proposes an improved carbon emission flow method for the power grid with prosumers. This method can accurately clarify the detailed distribution of electrical carbon emission flow in power grids. First, based on the power flow, prosumers’ impacts on the electrical carbon emission are quantified from three aspects that include the carbon emission sources, the network flow, and the indirect carbon emission individuals. Then, an improved power carbon emission flow model is proposed, in which the complex carbon emission intensity of prosumers is derived emphatically. Finally, case studies based on the IEEE 30-bus system verify the feasibility of the proposed method. This method provides a measurement basis for further research considering electrical carbon emissions.

Abstract Load forecasting is crucial for the economic and secure operation of power systems. Extreme weather events, such as extreme heat and typhoons, can lead to more significant fluctuations in power consumption, making load forecasting more difficult. At present, due to the lack of relevant public data, the research on load forecasting under extreme weather events is still blank, so it is necessary to release a large-scale load dataset containing extreme weather events. The dataset includes electricity consumption data of industrial and commercial users under extreme weather events such as typhoons and extreme heat, which are collected at 15-minute intervals. The data is collected over six years from smart meters installed at the power entry points of users in southern China. The dataset consists of electricity consumption data from 386 industrial and commercial users in 17 industries, with more than 50 million records. During the recording period, extreme weather events such as typhoons and extreme heat are marked to form a total of 5,741 event records.

Abstract Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered significant attention in recent years. The application of artificial intelligence (AI), particularly deep learning (DL), holds great promise for enhancing the cybersecurity of SG. Nevertheless, previous surveys and review articles have failed to comprehensively investigate the intersection between DL and SG cybersecurity. To address this gap, this study presents a survey of the latest advancements in DL technology and their relevance to SG cybersecurity. First, the functional mechanisms and scope of application of common DL techniques are explored. Subsequently, SG cyberthreats are categorised into distinct types of cyberattacks that have not been systematically examined in previous surveys. Based on this, a thorough review of the application of DL techniques in addressing each cyberthreat along with recommendations and a generalised framework for enhancing cyberattack detection using DL is offered. Finally, insights are provided into the emerging challenges presented by DL applications in SG cybersecurity that are yet to be widely acknowledged, and potential research avenues are proposed to address or alleviate these challenges.

Abstract The full-field multiaxial strain measurement is highly desired for application of structural monitoring but still challenging, especially when the manufacturing and assembling for large-area sensing devices is quite difficult. Compared with the traditional procedure of gluing commercial strain gauges on the structure surfaces for strain monitoring, the recently developed Direct-Ink-Writing (DIW) technology provides a feasible way to directly print sensors on the structure. However, there are still crucial issues in the design and printing strategies to be probed and improved. Therefore, in this work, we propose an integrated strategy from layered circuit scheme to rapid manufacturing of strain rosette sensor array based on the DIW technology. Benefit from the innovative design with simplified circuit layout and the advantages of DIW for printing multilayer structures, here we achieve optimization design principle for strain rosette sensor array with scalable circuit layout, which enable a hierarchical printing strategy for multiaxial strain monitoring in large scale or multiple domains. The strategy is highly expected to adapt for the emerging requirement in various applications such as integrated soft electronics, nondestructive testing and small-batch medical devices.

With the rapid development of society and the deteriorating natural environment, there has been an increase in public emergencies. This study aimed to explore how sadness and fear in the context of public emergencies influence moral judgments. This research first induced feelings of sadness and fear by using videos about public emergencies and music, and then used moral scenarios from the CNI model (C parameter: sensitivity to consequences; N parameter: sensitivity to norms; I parameter: general preference for inaction) to assess participants’ moral thinking. In Study 1, participants were divided into a sadness group and a neutral group, while in Study 2, participants were divided into a fear group and a neutral group. During the experiment, participants were exposed to different videos related to public emergencies to induce the corresponding emotions, and emotional music was continuously played throughout the entire experiment. Participants were then asked to answer questions requiring moral judgments. The results showed that based on the CNI model, sadness induced in the context of public emergencies significantly increased the C parameter, without affecting the N or I parameters. Fear increased the I parameter, without affecting the C or I parameters. That is, sadness and fear induced in the context of a public emergency can influence moral judgments. Specifically, sadness increases individuals’ sensitivity to consequences and fear increases the general preference for inaction in moral judgments.

The construction of high-performance n-type semiconductors is crucial for the advancement of organic electronics. As an attractive n-type semiconductor, molecular systems based on perylene diimide derivatives (PDIs) have been extensively investigated over recent years. Owing to the fascinating aggregated structure and high performance, S-heterocyclic annulated PDIs (SPDIs) are receiving increasing attention. However, the relationship between the structure and the electrical properties of SPDIs has not been deeply revealed, restricting the progress of PDI-based organic electronics. Here, we developed two novel SPDIs with linear and dendronized substituents in the imide position, named linear SPDI and dendronized SPDI, respectively. A series of structural and property characterizations indicated that linear SPDI formed a long-range-ordered crystalline structure based on helical supramolecular columns, while dendronized SPDI, with longer alkyl side chains, formed a 3D-ordered crystalline structure at a low temperature, which transformed into a hexagonal columnar liquid crystal structure at a high temperature. Moreover, no significant charge carrier transport signal was examined for linear SPDI, while dendronized SPDI had a charge carrier mobility of 3.5 × 10−3 cm2 V−1 s−1 and 2.1 × 10−3 cm2 V−1 s−1 in the crystalline and liquid crystalline state, respectively. These findings highlight the importance of the structure–function relationship in PDIs, and also offer useful roadmaps for the design of high-performance organic electronics for down-to-earth applications.

Abstract The real‐time carbon emission estimation of generators helps quantify the carbon emission costs and reduce power system emissions of power generation. Accurate estimation relies on the accuracy of the carbon emission factors (EFs) and power generation measurements. The dynamic carbon emission factor (DEF) of generators was proposed recently as a linear function of the output power. However, there is a significant deviation between the modelled DEFs and the actual measurement EFs, especially when the output power is low. This paper first presents the general definition of the DEF to characterize the emissions and focuses on the unit‐level DEF (UDEF). The piecewise non‐linear UDEF (P‐UDEF) model is then proposed, which can better represent the unit emission characteristics. Then an accurate piecewise linear cost approximation method is proposed considering the segment points and extreme points of both P‐UDEF and generation costs function. Last, the system carbon emissions and costs estimation are estimated by combined economic emission dispatch (CEED), and the reduction potential is evaluated. Case studies on an IEEE 30‐bus system with piecewise linear cost functions show that the proposed P‐UDEF can realize real‐time emission and cost estimation as well as reduce the total system emissions by considering the incomplete combustion cost of generating units.

Abstract Carbon nanotube (CNT) networks enable CNTs to be used as building blocks for synthesizing novel advanced materials, thus taking full advantage of the superior properties of individual CNTs. Multiscale analyses have to be adopted to study the load transfer mechanisms of CNT networks from the atomic scale to the macroscopic scale due to the huge computational cost. Among them, fully resolved structural features include the graphitic honeycomb lattice (atomic), inter-tube stacking (nano) and assembly (meso) of CNTs. On an atomic scale, the elastic properties, ultimate stresses, and failure strains of individual CNTs with distinct chiralities and radii are obtained under various loading conditions by molecular mechanics. The dependence of the cohesive energies on spacing distances, crossing angles, size and edge effects between two CNTs is analyzed through continuum modeling in nanoscale. The mesoscale models, which neglect the atomic structures of individual CNTs but retain geometrical information about the shape of CNTs and their assembly into a network, have been developed to study the multi-level mechanism of material deformation and microstructural evolution in CNT networks under stretching, from elastic elongation, strengthening to damage and failure. This paper summarizes the multiscale theories mentioned above, which should provide insight into the optimal assembling of CNT network materials for elevated mechanical performance.

Accurate load forecasting is crucial to improve the stability and cost-efficiency of smart grid operations. However, how to integrate multiple significant factors for enhancing load forecasting performance is insufficiently investigated in previous studies. To fill the gap, this study proposes a novel hybrid deep learning model for short-term load forecasting. First, the long short-term memory network is utilized to capture patterns from historical load data. Second, a time pattern attention (TPA) mechanism is incorporated to improve feature extraction and learning capabilities. By discerning valuable features and eliminating irrelevant ones, the TPA mechanism enhances the learning process. Third, fully-connected layers are employed to integrate external factors such as climatic conditions, economic indicators, and temporal aspects. This comprehensive approach facilitates a deeper understanding of the impact of these factors on load profiles, leading to the development of a highly accurate load forecasting model. Rigorous experimental evaluations demonstrate the superior performance of the proposed approach in comparison to existing state-of-the-art load forecasting methodologies.

Abstract Most mobile battery energy storage systems (MBESSs) are designed to enhance power system resilience and provide ancillary service for the system operator using energy storage. As the penetration of renewable energy and fluctuation of the electricity price increase in the power system, the demand‐side commercial entities can be more profitable utilizing the mobility and flexibility of MBESSs compared to the stational energy storage system. The profit is closely related to the spatiotemporal decision model and is influenced by environmental uncertainties, such as electricity price and traffic conditions. However, solving the real‐time control problem considering long‐term profit and uncertainties is time‐consuming. To address this problem, this paper proposes a deep reinforcement learning framework for MBESSs to maximize profit through market arbitrage. A knowledge‐assisted double deep Q network (KA‐DDQN) algorithm is proposed based on such framework to learn the optimal policy and increase the learning efficiency. Moreover, two criteria action generation methods of knowledge‐assisted learning are proposed for integer actions utilizing scheduling and short‐term programming results. Simulation results show that the proposed framework and method can achieve the optimal result, and KA‐DDQN can accelerate the learning process compared to the original method by approximately 30%.

Measurement(s) carbon dioxide emissions Technology Type(s) fossil fuel consumption

Summary: Backgrounds: Detecting free cancer cells from ascites and peritoneal lavages is crucial for diagnosing gastric cancer (GC). However, traditional methods are limited for early-stage diagnosis due to their low sensitivity. Methods: A label-free, rapid, and high-throughput technique was developed for separating cancer cells from ascites and peritoneal lavages using an integrated microfluidic device, taking advantage of dean flow fractionation and deterministic lateral displacement. Afterward, separated cells were analyzed using a microfluidic single-cell trapping array chip (SCTA-chip). In situ immunofluorescence for EpCAM, YAP-1, HER-2, CD45 molecular expressions, and Wright-Giemsa staining were performed for cells in SCTA-chips. At last, YAP1 and HER-2 expression in tissues was analyzed by immunohistochemistry. Findings: Through integrated microfluidic device, cancer cells were successfully separated from simulated peritoneal lavages containing 1/10,000 cancer cells with recovery rate of 84.8% and purity of 72.4%. Afterward, cancer cells were isolated from 12 patients’ ascites samples. Cytological examinations showed cancer cells were efficiently enriched with background cells excluded. Afterwards, separated cells from ascites were analyzed by SCTA-chips, and recognized as cancer cells through EpCAM+/CD45− expression and Wright-Giemsa staining. Interestingly, 8 out of 12 ascites samples showed HER-2+ cancer cells. At last, the results through a serial expression analysis showed that YAP1 and HER-2 have discordant expression during metastasis. Interpretation: Microfluidic Chips developed in our study could not only rapidly detect label-free free GC cells in ascites and peritoneal lavages with high-throughput, they could also analyze ascites cancer cells at the single-cell level, improving peritoneal metastasis diagnosis and investigation of therapeutic targets. Funding: This research was supported by National Natural Science Foundation of China (22134004, U1908207, 91859111); Natural Science Foundation of Shandong Province of China (ZR2019JQ06); Taishan Scholars Program of Shandong Province tsqn (201909077); Local Science and Technology Development Fund Guided by the Central Government (YDZX20203700002568); Applied Basic Research Program of Liaoning Province (2022020284-JH2/1013).

Abstract The interfacial shear coupling (ISC) governs the relative in-plane deformations of layered two-dimensional (2D) van der Waals (vdW) materials, which is significant for both the fundamental theory of solid mechanics and the stability design of 2D devices. Here we study the representative ISC of 2D vdW stacks using bilayer graphene (BLG) and isotope-labeled Raman spectroscopy. The results show that under uniaxial tensile strain, the ISC between two graphene layers evolves sequentially with bonding, sliding and debonding process, and the corresponding interfacial shear strength is inversely proportional to the sample size. Molecular dynamics (MD) simulations demonstrate the origin of this inverse proportionality as stronger interlayer vdW interaction induced by the edge lattices and atoms of BLG that have more degrees of freedom. These results not only provide new fundamental insights into the multiscale interpretation of macroscopic interfacial shear properties of 2D vdW stacks but also have great potential in guiding the design of graphene-based composite materials and flexible 2D electronics.

Abstract Non‐intrusive load monitoring (NILM) is essential for understanding consumer power consumption patterns and may have wide applications such as in carbon emission reduction and energy conservation. Determining NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners when determining the NILM model. To address these problems, a novel NILM method based on federated learning (FL) called Fed‐NILM is proposed. In Fed‐NILM, instead of local load data, local model parameters are shared among multiple data owners. The global NILM model is obtained by averaging the parameters with the appropriate weights. Experiments based on two measured load datasets are performed to explore the generalization capability of Fed‐NILM. In addition, a comparison of Fed‐NILM with locally trained NILM models and the centrally trained NILM model is conducted. Experimental results show that the Fed‐NILM exhibits superior performance in terms of scalability and convergence. Fed‐NILM out performs locally trained NILM models operated by local data owners and approaches the centrally trained NILM model, which is trained on the entire load dataset without privacy protection. The proposed Fed‐NILM significantly improves the co‐modelling capabilities of local data owners while protecting the privacy of power consumers.

Graphene is a two-dimensional material that can be folded into diverse and yet interesting nanostructures like macro-scale paper origami. Folding of graphene not only makes different morphological configurations but also modifies their mechanical and thermal properties. Inspired by paper origami, herein we studied systemically the effects of creases, where sp2 to sp3 bond transformation occurs, on the thermal properties of graphene origami using molecular dynamics (MD) simulations. Our MD simulation results show that tensile strain reduces (not increases) the interfacial thermal resistance owing to the presence of the crease. This unusual phenomenon is explained by the micro-heat flux migration and stress distribution. Our findings on the graphene origami enable the design of the next-generation thermal management devices and flexible electronics with tuneable properties.

Abstract Coping with the outbreak of Coronavirus disease 2019 (COVID-19), many countries have implemented public-health measures and movement restrictions to prevent the spread of the virus. However, the strict mobility control also brought about production stagnation and market disruption, resulting in a severe worldwide economic crisis. Quantifying the economic stagnation and predicting post-pandemic recovery are imperative issues. Besides, it is significant to examine how the impact of COVID-19 on economic activities varied with industries. As a reflection of enterprises’ production output, high-frequency electricity-consumption data is an intuitive and effective tool for evaluating the economic impact of COVID-19 on different industries. In this paper, we quantify and compare economic impacts on the electricity consumption of different industries in eastern China. In order to address this problem, we conduct causal analysis using a difference-in-difference (DID) estimation model to analyze the effects of multi-phase public-health measures. Our model employs the electricity-consumption data ranging from 2019 to 2020 of 96 counties in the Eastern China region, which covers three main economic sectors and their 53 sub-sectors. The results indicate that electricity demand of all industries (other than information transfer industry) rebounded after the initial shock, and is back to pre-pandemic trends after easing the control measures at the end of May 2020. Emergency response, the combination of all countermeasures to COVID-19 in a certain period, affected all industries, and the higher level of emergency response with stricter movement control resulted in a greater decrease in electricity consumption and production. The pandemic outbreak has a negative-lag effect on industries, and there is greater resilience in industries that are less dependent on human mobility for economic production and activities.

Terahertz (THz) wave has demonstrated a good prospect in recent years, but the resolution is still one of the problems that restrict the application of THz technology in medical imaging. Paraffin-embedded samples are mostly used in THz medical imaging studies, which are thicker and significantly different from the current gold standard slice pathological examination in sample preparation. In addition, THz absorption in different layers of normal and cancerous tissues also remains to be further explored. In this study, we constructed a high-resolution THz imaging system to scan non-tumorous adjacent tissue slices and gastric cancer (GC) tissue slices. In this system, a THz quantum cascade laser emitted a pulsed 3 THz signal and the transmitted THz wave was received by a THz detector implemented in a 65 nm CMOS process. The slice thickness was only 20 μm, which was close to that of the medical pathology examination. We successfully found THz transmittance differences between different layers of normal gastric tissues based on THz images, and the resolution could reach 60 μm for the first time. The results indicated that submucosa had a lower THz transmittance than that of mucosa and muscular layer in non-tumorous adjacent tissue. However, in GC tissue, THz transmittance of mucosa and submucosa was similar, caused by the decreased transmittance of mucosa, where the cancer occurs. Therefore, we suppose that the similar terahertz transmittance between gastric mucosa and submucosa may indicate the appearance of cancerization. The images obtained from our THz imaging system were clearer than those observed with naked eyes, and can be directly compared with microscopic images. This is the first application of THz imaging technology to identify non-tumorous adjacent tissue and GC tissue based on the difference in THz wave absorption between different layers in the tissue. Our present work not only demonstrated the potential of THz imaging to promote early diagnosis of GC, but also suggested a new direction for the identification of normal and cancerous tissues by analyzing differences in THz transmittance between different layers of tissue.

As an important part of renewable energy, wind power is crucial to the realization of carbon neutrality. It is worth studying on how to accurately predict the wind output so that it can be integrated into the power grid as much as possible to enhance its utilization rate. In this article, a data enhancement method and a framework are proposed to assist wind power forecasting. The proposed method uses the super-resolution perception technology to first detect the completeness and correctness of historical meteorological and wind power data collected by industrial devices. Then, the detected errors are corrected and the missing data are recovered to make the data complete. The frequency of the data is then increased using the proposed method so that the data become complete high-frequency data. Based on the enhanced complete high-frequency data with more detailed characteristics, more accurate forecasts of wind power can be achieved, thereby improving the utilization rate of wind power. Experiments based on public datasets are used to demonstrate the effectiveness of the proposed method and framework. With the proposed method and framework, higher frequency data with more detailed information can be achieved, thereby providing support for accurate wind power prediction that was not possible before.

The rapid development of the Industrial Internet-of-Things extends demand response (DR) research to the aspect of low-carbon emission in smart grids. This study proposed the concept of low-carbon DR (LCDR) in the electricity market as well as the price-based LCDR mechanism and its model. First, carbon cost conduction from the generation side to the demand side was analyzed, and then conduction function was quantifiably deduced. Second, the mechanism and model of price-based LCDR were proposed by considering three DR signals, namely, the electricity price, carbon price, and carbon emission intensity of the demand side, based on the traditional price-based DR (PBDR) mechanism. Third, the proposed LCDR mechanism was applied to the environmental–economic dispatch optimization problem. At last, case studies on the modified IEEE 39-bus system verified that the LCDR mechanism can reduce carbon emissions while maintaining the function of the traditional PBDR. Meanwhile, the applicability of LCDR was illustrated based on carbon emission sensitivity to LCDR model parameters. The proposed mechanism can guide participants in the electricity market in reducing electricity carbon emissions.

The electricity spot market plays a significant role in promoting the self-improvement of the overall resource utilization efficiency of the power system and advancing energy conservation and emission reduction. This paper analyzes and compares the potential impacts of spot market operations on system planning, considering the differences between planning methods in traditional and spot market environments through theoretical analysis and model comparison. Furthermore, we conduct research and analysis on grid planning methods under the spot market environment with the goal of maximizing social benefits. Unlike the pricing approach based on historical price data in traditional market simulation processes, a data-driven approach that combines experimental economics and machine learning is proposed, specifically using mixed empirical learning to simulate unit bidding strategies in market transactions. A simulation model for electricity spot market trading is constructed to analyze the performance of the planning results in the spot market environment. The case study results indicate that the proposed planning methods can enable the grid to operate well in the spot market environment, maintain relatively stable nodal prices, and ensure the integration of a high proportion of clean energy.

Nanoscale defects, including cracks, circular holes, and the triangular-shaped defects, often occur in the growth of boron nitride nanosheets (BNNS). In this study, the fracture behavior of chiral BNNS with different crack-tip shapes and the interactions of nanoscale crack-defects are studied using molecular dynamics (MD) simulations and finite element (FE) analysis. Both MD and FE results indicate that the fracture strength of BNNS with two crack tips (t = 2) is significantly higher than that with one crack tip (t = 1), in which the difference in zigzag (ZZ) direction is more obvious than that in armchair (AC) direction, mainly due to the fact that the change of bond angles near the crack tips is more substantial in the ZZ direction than those in the AC direction. Our results show that the fracture strength of BNNS strongly depends on crack-tip shapes, chiral angles, the defect-to-crack tip spacing and deflection angles. Checking against the current MD simulations and FE analysis shows the present results are reasonable. This study should be of great importance for enhancing the fracture performance of BNNS by modulating their crack-tip shapes and the interactions of nanoscale crack-defects.

With the increasingly widespread of advanced metering infrastructure, electric load clustering is becoming more essential for its great potential in analytics of consumers' energy consumption patterns and preference through data mining. Moreover, a variety of electric load clustering techniques have been put into practice to obtain the distribution of load data, observe the characteristics of load clusters, and classify the components of the total load. This can give rise to the development of related techniques and research in the smart grid, such as demand-side response. This paper summarizes the basic concepts and the general process in electric load clustering. Several similarity measurements and five major categories in electric load clustering are then comprehensively summarized along with their advantages and disadvantages. Afterwards, eight indices widely used to evaluate the validity of electric load clustering are described. Finally, vital applications are discussed thoroughly along with future trends including the tariff design, anomaly detection, load forecasting, data security and big data, etc.

The online strain acquisition is highly desired for structural monitoring of key equipment such as aircrafts or high-speed trains. However, there are still enormous challenges to monitor full-field strain in large area with arbitrary shape, especially when the non-contact approaches are not applicable. In this study, we firstly develop an optimization design principle for arbitrary-shape-adaptable sensor array with scalable circuit layout, and then a hierarchical printing strategy by direct-ink-writing is proposed to enable simultaneous monitoring in large-scale area or multiple domains. According to the design principle and printing strategy, the proof-of-concept sensor arrays (e.g. with 8 × 8 and 4 × 16 grids) are designed and printed by bottom-up hierarchies from the layered components (e.g. wires, filaments and isolation intervals) to the main arrays through distributed printing sub-arrays (4 × 4 grids). In particular, the strain field surrounding a hole is monitored in high-precision based on the present strain sensor. This work also provides insights of sensor array from adaptive design to high-efficient DIW-fabrication for other applications including wearable electronics, small-batch equipment and individual medical devices.

Abstract As an important renewable energy resource, wind power has been greatly developed in Southern China to meet the increasing demands. In the context of power industry deregulation and hence electricity market development, wind power introduces new uncertainties for market participants and affects the long‐term planning of the power system concerned. In this paper, we use a simplified Guangdong power system model to simulate the electricity market in Southern China from 2019 to 2028. To study the economic and reliability impacts of installing large‐scale offshore wind farms, case studies are conducted to simulate two targeted scenarios, that is, with and without offshore wind power penetration. Based on the presented model, the well‐established Monte Carol simulation method is applied to assess the reliability of the Guangdong power grid by calculating the expected unsupplied energy (EUE). The experimental results show that the integration of offshore wind power farms will reduce the locational marginal prices (LMPs) as well as the weighted average spot price. The power flow on transmission lines with nodes connected to offshore wind farms will increase and the increase rate is higher in cases with higher demand. In addition, the increased EUE indicates that the integration of large‐scale offshore wind power farms will impair the reliable operation of the power grid. The proposed system model delivers practical references to renewable energy evaluation and regional electricity market operations.

Abstract In this study, water soluble CuO nanostructures having nanobelt, nanorod, or spindle morphologies were synthesized using aqueous solutions of Cu(NO3)2·3H2O and NaOH by adjusting the type of surface modifier and reaction temperature. The effect of morphologies of these various CuO nanostructures as water-based lubricant additives on tribological properties was evaluated on a UMT-2 micro-friction tester, and the mechanisms underlying these properties are discussed. The three different morphologies of CuO nanostructures exhibited excellent friction-reducing and anti-wear properties. Tribological mechanisms differed in the initial stage of frictional interactions, but in the stable stage, a tribochemical reaction film and adsorbed lubricious film on the rubbing surfaces played important roles in hindering direct contact between friction pairs, leading to improved tribological properties.

The smart grid is an evolving critical infrastructure, which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services. To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid, state estimation, which serves as a basic tool for understanding the true states of a smart grid, should be performed with high frequency. More complete system state data are needed to support high-frequency state estimation. The data completeness problem for smart grid state estimation is therefore studied in this paper. The problem of improving data completeness by recovering high-frequency data from low-frequency data is formulated as a super resolution perception (SRP) problem in this paper. A novel machine-learning-based SRP approach is thereafter proposed. The proposed method, namely the Super Resolution Perception Net for State Estimation (SRPNSE), consists of three steps: feature extraction, information completion, and data reconstruction. Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.

Z-DNA-forming CG repeats are mutagenic in mammalian cells but the mechanism has remained unknown so far. Here, the authors show that the nucleotide excision repair complex Rad10-Rad1 (ERCC1-XPF) and the mismatch repair complex Msh2-Msh3 (MSH2-MSH3) are required for Z-DNA-induced genetic instability in yeast and human cells.

Low-velocity impact (LVI) damage of 3D woven composites were experimentally and numerically investigated, considering different off-axis angles and impact energies. The impact responses were examined by LVI tests, and the damage morphology inside the composites was observed by X-ray micro-computed tomography (μ-CT). Yarn-level damage evolution was revealed by developing a hybrid finite element analysis model. The results show that the impact damage has significant directionality determined by the weft/warp orientation of the composites. The damage originates at the bottom of the impacted area and then expands outwards and upwards simultaneously, accompanied by in-plane and out-of-plane stress transfers. The straight-line distributed weft/warp yarns play an important role in bearing loads at the beginning of loading, while the w-shape distributed binder warp yarns gradually absorb impact deformation and toughen the whole structure as the loading proceeds. The effect of directional impact damage on post-impact performance was explored by performing compressing-after-impact (CAI) tests. It is revealed that the CAI properties along principal directions are more sensitive to the low-velocity impact, and the damage mode is significantly affected by the loading direction.

The low-velocity impact behavior of carbon-epoxy cross-ply composites was numerically investigated, examining the effect of impact angle. A plastic continuum damage model, introducing the cohesive interface to describe delamination damage, was established and was validated by available experimental data. Impact histories, progressive deformation, stress transfer, and impact damage are respectively discussed. The results show that an increase in impact angle intensifies the action of tangential force, and gradually transfers energy absorption from normal plastic deformation to tangential deformation and friction, which dissipates more energy through relatively longer contact duration and larger impactor displacement. The delamination damage to upper layers is more affected by tangential loads, intensifying with the increase of the impact angle, and the damage area to the top interface is increased by 132.1% from 0° impact to 60° impact. Meanwhile, the delamination damage to lower layers is mainly determined by normal loads, weakening with the increasing impact angle overall, and the damage area of the lowest interface decreases by 36.6% from 0° impact to 60° impact.

Abstract Background The role of the dose escalation strategy in brain radiotherapy for small cell lung cancer (SCLC) patients with brain metastases (BMs) has not been identified. This study aims to determine whether an additional radiation boost to whole brain radiation therapy (WBRT) has beneficial effects on overall survival (OS) compared with WBRT-alone. Methods A total of 82 SCLC patients who were found to have BMs treated with WBRT plus a radiation boost (n = 33) or WBRT-alone (n = 49) from January 2008 to December 2015 were retrospectively analyzed. All patients were limited-stage (LS) SCLC at the time of the initial diagnosis, and none of them had extracranial metastases prior to detection of BMs. The primary end point was OS. Results The median OS for all of the patients was 9.6 months and the 6-, 12- and 24-months OS rates were 69.1, 42.2 and 12.8%, respectively. At baseline, the proportion of more than 3 BMs was significantly higher in the WBRT group than in the WBRT plus boost group (p = 0.0001). WBRT plus a radiation boost was significantly associated with improved OS in these patients when compared with WBRT-alone (13.4 vs. 8.5 months; p = 0.004). Further, the survival benefit still remained significant in WBRT plus boost group among patients with 1 to 3 BMs (13.4 vs. 9.6 months; p = 0.022). Conclusion Compared with WBRT-alone, the use of WBRT plus a radiation boost may prolong survival in SCLC patients with BMs. The dose escalation strategy in brain radiotherapy for selected BMs patients with SCLC should be considered.

Buildings account for a large proportion of the total energy consumption in many countries and almost half of the energy consumption is caused by the Heating, Ventilation, and air-conditioning (HVAC) systems. The model predictive control of HVAC is a complex task due to the dynamic property of the system and environment, such as temperature and electricity price. Deep reinforcement learning (DRL) is a model-free method that utilizes the “trial and error” mechanism to learn the optimal policy. However, the learning efficiency and learning cost are the main obstacles of the DRL method to practice. To overcome this problem, the hybrid-model-based DRL method is proposed for the HVAC control problem. Firstly, a specific MDPs is defined by considering the energy cost, temperature violation, and action violation. Then the hybrid-model-based DRL method is proposed, which utilizes both the knowledge-driven model and the data-driven model during the whole learning process. Finally, the protection mechanism and adjusting reward methods are used to further reduce the learning cost. The proposed method is tested in a simulation environment using the Australian Energy Market Operator (AEMO) electricity price data and New South Wales temperature data. Simulation results show that 1) the DRL method can reduce the energy cost while maintaining the temperature satisfactory compared to the short term MPC method; 2) the proposed method improves the learning efficiency and reduces the learning cost during the learning process compared to the model-free method.

The failure mechanism of 3D woven composites subjected to compression loading along principal/off-axis direction after low-velocity impact (LVI) was assessed by experimental and numerical methods. The low-velocity impacts under 26.8 J and 80 J energies were applied to the specimens with off-axis angles of 0° and 45°. It can be observed that the impact damages are direction-dependent, which is determined by the weft and warp orientations. By performing the compression-after-impact (CAI) tests, it is found that the CAI strength along principal direction is more sensitive to the low-velocity impact than that along off-axis direction. A finite element dynamic analytical method was established, considering four off-axis angles (0°, 30°, 45° and 60°). The results show that the extension direction of the impact damage changes regularly with the off-axis angle. During the compression, the small off-axis angle can make the specimen prone to produce a sudden crushing failure determined by the fiber failure due to the high axial stress. As the off-axis angle increases, the matrix damage gradually holds the dominant position due to the growing shear effect, which makes the specimen produce a ductile failure governed by the accumulated matrix failure.

Abstract Efficient and sustainable use of water-based lubricants is essential for energy efficiency. Therefore, the use of water-lubricated mechanical systems instead of conventional oil lubricants is extremely attractive from the viewpoint of resource conservation. In this study, water-soluble Cu nanoparticles of size approximately 3 nm were prepared at room temperature (around 25 °C) via in-situ surface modification. The tribological behavior of the as-synthesized Cu nanoparticles as an additive in distilled water was evaluated using a universal micro-tribotester. The results show that the as-synthesized Cu nanoparticles, as a water-based lubricant additive, can significantly improve the tribological properties of distilled water. In particular, the lowest friction coefficient of 0.06 was obtained via lubrication with a concentration of 0.6 wt% of Cu nanoparticles in distilled water, which is a reduction of 80.6% compared with that obtained via lubrication with distilled water alone. It is considered that some Cu nanoparticles entered the contact area of the friction pairs to form a complex lubricating film and prevent direct contact of the friction pairs. Furthermore, some Cu nanoparticles in the solution accelerate the heat transfer process, which also results in good tribological properties.

Abstract An electric vehicle (EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users’ travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics (EE) based simulation method can be used to analyze the behaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EE-based simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users’ travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users’ travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine (ICE) vehicle users.

Summary: Sequences with the capacity to adopt alternative DNA structures have been implicated in cancer etiology; however, the mechanisms are unclear. For example, H-DNA-forming sequences within oncogenes have been shown to stimulate genetic instability in mammals. Here, we report that H-DNA-forming sequences are enriched at translocation breakpoints in human cancer genomes, further implicating them in cancer etiology. H-DNA-induced mutations were suppressed in human cells deficient in the nucleotide excision repair nucleases, ERCC1-XPF and XPG, but were stimulated in cells deficient in FEN1, a replication-related endonuclease. Further, we found that these nucleases cleaved H-DNA conformations, and the interactions of modeled H-DNA with ERCC1-XPF, XPG, and FEN1 proteins were explored at the sub-molecular level. The results suggest mechanisms of genetic instability triggered by H-DNA through distinct structure-specific, cleavage-based replication-independent and replication-dependent pathways, providing critical evidence for a role of the DNA structure itself in the etiology of cancer and other human diseases. : DNA sequences that can adopt alternative structures, such as H-DNA, have been implicated in cancer etiology. Zhao et al. found that such sequences are enriched at translocation breakpoints in human cancer genomes and that repair- and replication-related nucleases cleave H-DNA in both error-free “replication-related” and mutagenic “replication-independent” mechanisms. Keywords: DNA structure, H-DNA, chromosome translocation, mutation hotspot, DNA repair, nuclease, structural simulation

Abstract Background Growing evidence has indicated that some inflammatory markers, including lymphocyte to monocyte ratio (LMR), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and prognostic nutritional index (PNI), can be used as indicators in the prognosis of colorectal cancer (CRC). However, there is controversy concerning what is the best predictor of prognosis in CRC. Methods A cohort of 1744 CRC patients in our institution was analyzed retrospectively. Harrell’s concordance index (c-index) and Bayesian information criterion (BIC) were used to determine the optimal cut-off values of inflammatory markers and compare their predictive capacity. The association of inflammatory markers with overall survival (OS) and cancer-specific survival (CSS) was analyzed using Kaplan-Meier methods with log-rank test, followed by multivariate Cox proportional hazards model. Results The multivariate analysis indicated that among these inflammatory markers, NLR (< 2.0 vs. ≥ 2.0) was the only independent prognostic factor for poor OS [hazard ratio (HR) = 0.758, 95% confidence intervals (CI) = 0.598–0.960, P = 0.021)] and CSS (HR = 0.738, 95% CI = 0.573–0.950, P = 0.018). Among these inflammatory markers, the c-index and BIC value for NLR were maximum and minimum for OS, respectively. In addition, the c-index was higher and the BIC value was smaller in TNM staging combined with NLR compared with the values obtained in TNM staging alone. Conclusion NLR is a superior indicator of prognosis compared with LMR, PLR, and PNI in CRC patients, and NLR may serve as an additional indicator based on the current tumor staging system.

Abstract Background F-18- fluorodeoxyglucose Positron emission tomography (18FDG-PET) has been widely used in clinical practice. However, the prognostic value of the pretreatment standardized uptake value (SUV) for patients with gastric cancer remains controversial. Methods Major databases were systematically searched. The quality of the included studies was assessed using the Newcastle–Ottawa scale; the PET protocols were also evaluated. The pooled hazard ratio (HR) for overall survival (OS) and recurrence-free survival (RFS) were used to estimate the effect size. Data from the included studies were analyzed using Review Manager Software version 5.2. Results Eight studies with 1080 patients were included. The pooled HR for OS of six studies including 672 patients was 1.72 (95% CI [1.28–2.3], p = 0.0004, I2 = 0%), indicating that patients with high SUVs may have poor prognosis. The pooled HR for RFS was 1.70 (95% CI [1.20–2.39], p = 0.003, I2 = 0%). Subgroup analysis based on the cutoff values determining method indicated that the receiver operating characteristic (ROC) method could better define the cutoff value. Subgroup analysis based on the therapeutic strategies used subsequently indicated the significant prognostic value of SUV. Conclusion In conclusion, our meta-analysis indicated that pretreatment SUV in primary lesions can be an important prognostic factor for overall survival and recurrence-free survival in patients with gastric cancer. High SUVs may indicate poor prognosis.

The fast optimisation of transient stability emergency control (TSEC) strategy is a key factor of online safeguard for AC–DC interconnected systems. An engineering applicable TSEC algorithm has to obtain the disturbed trajectories of the system by numerical integration, and then do quantised stability knowledge mining. Theoretical analysis and simulation verification reveal that the premise is to identify the unstable mode correctly, and then emergency control decision can be optimised according to the cost performance of the actions. Small integral step is usually needed for accuracy. In order to coordinate the accuracy and computational burden intelligently, this study proposes an algorithm for optimising generator-tripping control, which can adaptively reduce computational burden. If the unstable mode is insensitive to the change of fault parameters (e.g. fault clearing time), this case's time-varying property is more likely to be weak. Only for such cases, large-step Taylor series expansions (LSTSE) can be taken to replace small-step numerical integration (SSNI) and search for the optimal result. Based on its time-varying property, each case can get disturbed trajectories using LSTSE or SSNI adaptively and obtain optimal solution. The excellent performance of this algorithm is verified by simulation of nine Chinese regional power systems under various operating conditions.

Static security assessment (SSA) is an important procedure to ensure the static security of the power system. Researches recently show that cyber-attacks might be a critical hazard to the secure and economic operations of the power system. In this paper, the influences of false data injection attack (FDIA) on the power system SSA are studied. FDIA is a major kind of cyber-attacks that can inject malicious data into meters, cause false state estimation results, and evade being detected by bad data detection. It is firstly shown that the SSA results could be manipulated by launching a successful FDIA, which can lead to incorrect or unnecessary corrective actions. Then, two kinds of targeted scenarios are proposed, i.e., fake secure signal attack and fake insecure signal attack. The former attack will deceive the system operator to believe that the system operates in a secure condition when it is actually not. The latter attack will deceive the system operator to make corrective actions, such as generator rescheduling, load shedding, etc. when it is unnecessary and costly. The implementation of the proposed analysis is validated with the IEEE-39 benchmark system.