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Abstract In this article, the dynamic thermal rating assessment method of oil‐immersed power transformer with multiple operating conditions is proposed, considering the constraints of hot spot temperature (HST), top oil temperature, losses of life and the maximum allowable current of on‐load tap changer (OLTC) or bushing, which can determine the dynamic load curves under different operating conditions and give the most sensitive constraints to limit the dynamic load capacity. To improve the accuracy of HST estimation, the temperature estimation model is also improved and the thermal parameters are optimised using the HST measured by optical fibre. Finally, several application examples are studied for transformers in different scenarios. The results show that the normal cyclic dynamic transformer rating (DTR) is mainly limited by the losses of life when the ambient temperature is high, and the average load factor can be increased to 0.82 with a maximum load capacity of 1.23. The main limiting factor of short‐term DTR is the OLTC or bushing current constraint, and the average load factor can be increased to 1.00. The maximum load capacity of the transformer under both operating conditions is 23% and 50% higher than its rated load capacity, indicating that the transformer still has a large load potential available.

This paper presents an improved sensorless controller scheme for a robust permanent magnet synchronous motor. Firstly, a sliding mode observer based on a variable rate approach rate is designed to observe the back electromotive force, enhance the robustness of the back electromotive force acquisition process, and reduce the system buffeting. Secondly, the concept of finite control set model predictive control is introduced, and the position angles are divided into small angles to form finite control sets. Then the position Angle of the finite control set is entered into the sliding mode observer to calculate the back electromotive force. At the same time, the optimal position angle is obtained based on the cost function of the back electromotive force. In order to reduce the number of iterations, the dichotomy is adopted to reduce unnecessary calculations. The proposed finite position-set phase-locked loop (PLL) avoids the PI controller in the traditional PLL and further improves the robustness of the system. Finally, the effectiveness of the proposed scheme is verified by relevant experiments.

This study introduces a nonfragile observer-based control strategy for power-line inspection robots (PILRs) that adeptly tackles input time delays while integrating passivity and $H_{\infty }$ performance. The research commences with the development of a nonlinear system model for PILRs, where the equilibrium manifold linearization method is employed to transform these underactuated systems into a linear framework using scheduling variables. Given the challenges posed by input delays, parameter disturbances, and uncertainties in real-world applications, this study proposes a robust observer-based control framework. Utilizing Lyapunov-Krasovskii functions and linear matrix inequalities (LMIs), the strategy confirms the system’s robustness in maintaining mixed passivity and $H_{\infty }$ performance. Simulation assessments substantiate the effectiveness of the proposed control approach, demonstrating its capacity to uphold system stability under diverse operational conditions. The strategy significantly enhances the reliability and safety of PILRs operating in complex environments, marking a pivotal advancement in robotic technologies for maintaining and inspecting utility infrastructures. This contribution not only strengthens the operational capabilities of PILRs but also provides a scalable approach to handling uncertainties and disturbances in robotic control systems.

Control technology for wheeled mobile robots is one of the core focuses in the current field of robotics research. Within this domain, model-free adaptive control (MFAC) methods, with their advanced data-driven strategies, have garnered widespread attention. The unique characteristic of these methods is their ability to operate without relying on prior model information of the control system, which showcases their exceptional capability in ensuring closed-loop system stability. This paper extensively details three dynamic linearization techniques of MFAC: compact form dynamic linearization, partial form dynamic linearization and full form dynamic linearization. These techniques lay a solid theoretical foundation for MFAC. Subsequently, the article delves into some advanced MFAC schemes, such as dynamic event-triggered MFAC and iterative learning MFAC. These schemes further enhance the efficiency and intelligence level of control systems. In the concluding section, the paper briefly discusses the future development potential and possible research directions of MFAC, aiming to offer references and insights for future innovations in control technology for wheeled mobile robots.

Interstitial diffusion is important for radiation defect evolution in zirconium alloys. This study employed molecular dynamics simulations to investigate interstitial diffusion in α-Zr and its alloys with 1.0 at.% Nb and 1.0 at.% Sn using a variety of interatomic potentials. Pronounced differences in diffusion anisotropy were observed in pure Zr among the employed potentials. This was attributed to the considerable differences in migration barriers among the various interstitial configurations. The introduction of small concentrations of Nb and Sn solute atoms was found to significantly influence diffusion anisotropy by either directly participating in the diffusion process or altering the chemical environment around the diffusing species. Based on the moderate agreement of interstitial energetics in pure Zr, accurately describing interstitial diffusion in Zr alloys is expected to be more complex. This work underscores the importance of the careful validation and selection of interatomic potentials and highlights the need to understand the effects of solute atoms on interstitial diffusion.

Bimetallic composites have a wide range of application prospects in various industries. Different bonding temperatures, as one of the influencing factors, directly affect the bonding effectiveness as well as the performance and application of the materials. Using metallurgical bonding techniques ensures a strong bond at the interface of bimetallic materials, resulting in high-quality composite pipe billets. This paper describes an Incoloy825/P110 steel bimetal composite material made by the solid–liquid composite method. By utilizing ProCAST 14.5 software for simulation and deriving theoretical formulas, an initial range of temperatures for bimetallic preparation has been tentatively determined. And this temperature range will be utilized for on-site experiments to prepare bimetallic samples. After the preparation process is completed, samples will be selected. The influence of the external mold temperature on the interface bonding of Incoloy825/P110 steel solid–liquid composite material is studied using an ultra-depth three-dimensional morphology microscope and a scanning electron microscope. Through research, the optimal preheating temperature range for the solid–liquid composite outer mold of Incoloy825/P110 bimetallic composite material has been determined. The casting temperature of the inner mold has a significant impact on the interface bonding of this bimetal composite material. As the casting temperature of the inner mold increases, the interface thickness gradually increases. At lower temperatures, the interface thickness is lower and the bonding is poorer. At higher temperatures, melting may occur, leading to coarse grains at the interface. When the temperatures of the inner and outer molds are within a certain range, a new phase appears at the interface. Indeed, it increases the strength of the interface bonding. Due to co-melting of the bimetal near the interface, element migration occurs, resulting in increased Ni and Cr content at the interface and enhanced corrosion resistance.

In bridge maintenance, assessing structural performance requires adherence to rules outlined in safety and regulatory standards which can be effectively and formally represented in both human and machine-readable formats using ontologies. However, ontology-based semantic inference alone falls short when faced with the complicated mathematical operations required for structural analysis. The increasing digitization of bridge engineering has opened doors to data-driven prediction methods. Machine learning (ML)-based models, in particular, have the capacity to learn from historical data and forecast future structural performance with remarkable accuracy. This paper introduces an innovative approach that integrates ML models with an ontological knowledge base for evaluating bridge corrosion. Web Ontology Language and Semantic Web Rule Language are combined to develop the knowledge base. Random forest algorithm is used to train the ML model with a good agreement (coefficient of determination of 0.989 and root mean square error of 1.200). A Python-based module is designed to seamlessly integrate ML predictions with ontology-based semantic inference. The proposed approach not only infers the corrosion ratings based on the rules defined in the Network Rail standard, but also infers the structural safety performance based on predicted structural response under the action of corrosion. To demonstrate the effectiveness of the developed method in enabling accurate and rational evaluations, a real bridge in the UK is showcased as a practical application.

The English grammatical error correction system is suitable for the English learning environment, with the goal of accurately correcting errors in learners’ writing. However, false corrections are often generated in practical applications, and many errors cannot be corrected, thus misleading learners. The quality estimation model is beneficial to ensure that learners obtain accurate grammatical error correction results and avoid misleading sentences caused by error corrections. Grammatical error correction models can generate multiple hypotheses of higher quality, but existing quality estimation models do not consider interactions between different hypotheses. Based on this, we propose a model based on multiple hypotheses interaction and self-attention, BGANet, for English grammatical error correction quality estimation. BGANet builds interactions between multiple hypotheses, extracts and aggregates grammatical error correction evidence in hypotheses through two kinds of self-attention mechanisms, and evaluates the quality of the generated hypotheses. Experiments on four grammatical error correction datasets show that BGANet has better quality estimation performance.

Power grid enterprises have a great number of projects in a high value. Their investment decisions need to be fully evaluated according to the project’s critical characteristics and it is necessary to realize the reasonable allocation of funds through optimization of investment packages. In this paper, the new internal and external situations and new requirements facing the development of power grid are considered to propose the new principle of index system establishment, i.e. “define characteristics macroscopically and define indexes microscopically”; adapt to the different policies, economies, safety conditions and strategies of different regions; establish an advanced, systematic and operable system of critical characteristics and indexes; and compile the calculation methods and steps to apply the index system for project evaluation. Finally, the calculation results of practical examples of power grid projects are referenced to prove the scientificity and accuracy of the index system and evaluation method and effectively support the investment decision of grid construction.

The deployment of medium-duty and heavy-duty (MDHD) electric vehicles (EVs), characterized by their substantial battery capacity and high charging power demand, poses a potential threat to voltage stability within distribution networks. One possible solution to voltage instability is reactive power compensation from charging MDHD EVs. However, this process must be carefully facilitated in order to be effective. This paper introduces an innovative distribution network voltage stability solution by first identifying the network’s weakest buses and then utilizing a metaheuristic algorithm to schedule reactive power compensation from MDHD EVs. In the paper, multiple metaheuristic algorithms, including genetic algorithms, particle swarm optimization, moth flame optimization, salp swarm algorithms, whale optimization, and grey wolf optimization, are subjected to rigorous evaluation concerning their efficacy in terms of voltage stability improvement, power loss reduction, and computational efficiency. The proposed methodology optimizes power flow with the salp swarm algorithm, which was determined to be the most effective tool, to mitigate voltage fluctuations and enhance overall stability. The simulation results, conducted on a modified IEEE 33 bus distribution system, convincingly demonstrate the algorithm’s efficacy in augmenting voltage stability and curtailing power losses, supporting the reliable and efficient integration of MDHD EVs into distribution networks.

Understanding the infiltration and solidification processes of liquid 5083Al alloy into Al2O3 three-dimensional reticulated porous ceramic (Al2O3(3D) RPC) is essential for optimizing the microstructure and properties of Al2O3(3D)/5083Al interpenetrating phase composites (IPCs) prepared by low-pressure infiltration process (LPIP). This study employs ProCAST software to simulate the infiltration and solidification processes of liquid 5083Al with pouring velocities (PV) of 0.4 m/s infiltrating into Al2O3(3D) RPC preforms with varying porosities at different pouring temperatures (PT) to prepare Al2O3(3D)/5083Al IPCs using LPIP. The results demonstrate that pore diameter of Al2O3(3D) RPC preforms and PT of liquid 5083Al significantly influence the of the infiltration. Solidification process analysis reveals that the Al2O3(3D) RPC preform with smaller pore diameters allows the lower pouring velocity of 5083Al to solidify faster compared to the preform with larger pore diameters. Al2O3(3D)/5083Al IPCs were prepared successfully from Al2O3(3D) RPC porosity of 15 PPI with liquid 5083Al at PV 0.4 m/s and PT 800 °C using LPIP, resulting in nearly fully dense composites, where both Al2O3(3D) RPCs and 5083Al interpenetrate throughout the microstructure. The infiltration and solidification defects were reduced under air pressure of 0.3 MPa (corresponding to PV of 0.4 m/s) during LPIP. Finite volume method simulations are in good agreement with experimental data, validating the suitability of the simplified model for Al2O3(3D) RPCs in the infiltration simulation.

In breast cancer diagnoses, antagonist compounds are used to resist $\alpha $ Subtype of Estrogen Receptor (ER $\alpha$ ) bioactivity. However, those compounds are difficult to be obtained in the process of drug screening. In this paper, an efficient bioactivity prediction model is proposed with consideration of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties as an auxiliary prediction to achieve the minimization of the damage caused to human body whereas maximizing the efficacy of estrogen activity inhibitors. In the proposed prediction model, Pearson correlation analysis and Bayesian regularization algorithm under neural network are specifically used to train and analyze the data of bioactivity. To calculate the loss of neural network, cross-entropy and supervise learning are applied. The results show that the accuracy of our proposed compound prediction model can reach up to 92.2%, 94.3%, and 90.2% for Caco2, CYP3A4, and hERG, respectively. Moreover, the Area Under the Curve (AUC) value of our proposed model is 0.9752, which is 13-14% higher than that of SVM method. In addition, the Matthews Correlation Coefficient (MCC) can reach up to 0.93 ± 0.03, 37-40% higher than that of SVM method. Due to correlation analysis and valid data screening, our model can shorten the prediction time by 15%. Due to its advantages in accuracy and speed of prediction, this research can contribute to the upgrading of diagnosis and treatment of breast cancer.

This research investigates the effects of iron tailings content on the mechanical properties and durability of concrete under dry–wet cycling and negative temperature conditions (−10 °C), where iron tailings replace river sand at rates of 0%, 10%, 20%, and 30%. A variety of tests were conducted on the iron tailings concrete, including compressive strength, flexural strength, splitting tensile strength, mass loss, and relative dynamic modulus, and its pore characteristics were analyzed using low-field nuclear magnetic resonance (NMR) experiments. The results reveal that when 20% of the river sand was replaced with iron tailings, the concrete achieved optimal splitting strength, compressive strength, and flexural strength at 28 days, improving by 0.46 MPa, 3.14 MPa, and 0.41 MPa, respectively, compared to conventional concrete. Furthermore, the concrete containing this proportion of iron tailings demonstrated superior mechanical properties and durability in both negative temperature conditions and dry–wet cycling experiments. Due to the excellent physical and chemical properties of iron tailings, they enhance the performance of concrete when incorporated in appropriate quantities. The fine granularity of iron tailings helps to compensate for the granularity defects in concrete aggregates by filling internal voids, optimizing the pore structure, and improving the concrete’s density and integrity. This enhances the concrete’s mechanical properties and its resistance to external solutions and harmful ion penetration. Additionally, the active substances in iron tailings promote the hydration reaction of cement, leading to the formation of an increased amount of C-S-H gel and other hydration products in the cement system.

Titanium alloys are extensively used in various industries due to their excellent corrosion resistance and outstanding mechanical properties. However, titanium alloys are difficult to machine due to their low thermal conductivity and high chemical reactivity with tool materials. In recent years, there has been increasing interest in the use of titanium components produced by additive manufacturing (AM) for a range of high-value applications in aerospace, biomedical, and automotive industries. The machining of additively manufactured titanium alloys presents additional machining challenges as the alloys exhibit unique properties compared to their wrought counterparts, including increased anisotropy, strength, and hardness. The associated higher cutting forces, higher temperatures, accelerated tool wear, and decreased machinability lead to an expensive and unsustainable machining process. The challenges in machining additively manufactured titanium alloys are not comprehensively documented in the literature, and this paper aims to address this limitation. A review is presented on the machining characteristics of titanium alloys produced by different AM techniques, focusing on the effects of anisotropy, porosity, and post-processing treatment of additively manufactured Ti-6Al-4V, the most commonly used AM titanium alloy. The mechanisms resulting in different machining performance and quality are analysed, including the influence of a hybrid manufacturing approach combining AM with conventional methods. Based on the review of the latest developments, a future outlook for machining additively manufactured titanium alloys is presented.

In this paper, a low profile, dual-band dual-polarized patch antenna is proposed for antenna-filter function with a relatively low profile of 0.032 wavelength. A simple symmetric open/short-circuited stub (O/SCS) is proposed to excite a square radiating patch, and an additional resonant mode is generated except for the patch-mode. At the lower band, the proposed antenna works as a dual-polarized patch antenna. At the upper band, transmission characteristic is obtained between the two ports, and the antenna structure can be used as a bandpass filter. A $1\times 2$ array is also designed to validate its application potential in MIMO system. The two antennas can operate as radiators with high port isolation levels at the lower band. Meanwhile, at the upper band, the two antenna elements can act as one bandpass filter as the good transmission characteristic. In addition, a $2\times 2$ array is also developed meet dual-function demands in dual-polarized MIMO systems. The low profile, simple structures and excellent performances indicate that the proposed antenna can be a good candidate in future wireless communication applications with shared functions.

Random forest (RF) is an ensemble classifier method, all decision trees participate in voting, some low-quality decision trees will reduce the accuracy of random forest. To improve the accuracy of random forest, decision trees with larger degree of diversity and higher classification accuracy are selected for voting. In this paper, the RF based on Kappa measure and the improved binary artificial bee colony algorithm (IBABC) are proposed. Firstly, Kappa measure is used for pre-pruning, and the decision trees with larger degree of diversity are selected from the forest. Then, the crossover operator and leaping operator are applied in ABC, and the improved binary ABC is used for secondary pruning, and the decision trees with better performance are selected for voting. The proposed method (Kappa+IBABC) are tested on a quantity of UCI datasets. Computational results demonstrate that Kappa+IBABC improves the performance on most datasets with fewer decision trees. The Wilcoxon signed-rank test is used to verify the significant difference between the Kappa+IBABC method and other pruning methods. In addition, Chinese haze pollution is becoming more and more serious. This proposed method is used to predict haze weather and has achieved good results.

This paper proposes an adaptive neural network (NN) output-feedback synchronization controller for the nonlinear stochastic systems driven by Lévy processes, which consist of Wiener and compensated Poisson process. By employing the generalized It&ohat;’s formula combined with Lyapunov function method, it is proved that the proposed controller can ensure that all signals of closed system are bounded in probability. Under the backstepping control framework of stochastic process including Lévy noises, the event-triggered control technology is used to reduce the utilization of communication resources. Moreover, the command filtered control technology is introduced into the controller to avoid “explosion of complexity” and obtain the derivatives for virtual control functions continuously. Simulation proves the feasibility of the proposed control method.

With the rapid development of deep learning techniques, the performance of object detection has increased significantly. Recently, several approaches on joint learning of object detection and semantic segmentation have been proposed to exploit the complementary benefits of the two highly correlated tasks. In this work, we propose a weakly-annotated auxiliary multi-label segmentation network that boosts object detection performance without additional computational cost at inference. The proposed auxiliary segmentation network is trained using weakly-annotated dataset and therefore does not require expensive pixel-level annotations for training. Different from the previous approaches, we use multi-label segmentation to jointly supervise auxiliary segmentation and object detection for better occlusion handling. The proposed method can be integrated with any one-stage object detector such as RetinaNet, YOLOv3, YOLOv4, or SSD. Our experimental results on the MS COCO dataset show that the proposed method can improve the performance of popular one-stage object detectors without slowing down the inference speed regardless of the sub-optimal training sample selection schemes.

Recently, small-signal stability of the modular multilevel converter (MMC) based high-voltage direct current (HVDC) transmission for wind farm integration has attracted great attentions. The impedance-based frequency-domain method is an effective tool for such studies, in which the accuracy of the impedance model is of significant importance. Currently, the decoupled single-input single-output (SISO) sequence impedance of the MMC-HVDC system with wind farm is commonly used due to the simplicity in stability analysis. However, since both the MMC and wind farm exhibit frequency coupling behaviors, the decoupled SISO sequence impedance model may lead to inaccurate stability conclusion under certain conditions. In order to improve the model accuracy, based on the harmonic state-space (HSS) modeling approach, this paper proposes a generalized multi-input multi-output (MIMO) sequence impedance model of the MMC-wind farm system. The proposed MIMO impedance model can effectively capture the frequency couplings in the interconnected system, based on which the coupling mechanism of multi-frequency components within the MMC is then revealed. To facilitate the interconnection analysis, the model truncation criteria is proposed and validated by comparing the accuracy of the MIMO model with different truncated dimensions. In addition, this paper considers the internal impedance network of the wind farm when developing the aggregated wind farm impedance model for interaction stability analysis. Finally, the proposed MIMO sequence impedance model is applied to accurately predict the instability of the MMC-HVDC connected wind farm system.

Short-term passenger flow prediction of urban rail transit is very important to traffic management. The prediction result is a key input for the crowd early warning system. However, a reliable approach of short-term passenger flow prediction is still greatly desired. At present, most researches focus on single-site prediction (SSP), and its technique cannot be improved due to its maturity, and any further breakthrough is difficult to make. In this paper, a multi-sites prediction method (MSP) of passenger flow in subway station is proposed. Real time passenger flow data collected from multi-sites in a subway station is used as inputs, and delay parameter is introduced to identify the correlation between measurements at multiple sites in this paper. In order to achieve a stable predictive effect, wavelet decomposition and reconstruction are used to process the data. Dynamic weights combination of Support Vector Machines (SVM) and Radial Basis Function Neural Network (RBF) are applied to obtain the prediction of each frequency component. A case study of a station in Beijing subway shows that the proposed MSP method reduces the average absolute prediction error by 15.8% compared with the SSP. It is concluded that the proposed method has good performance in the case.

With the wide application of vegetable insulating oil in power transformers, it is essential to investigate the influence of moisture on the frequency domain dielectric spectrum (FDS) analysis in order to propose an accurate moisture assessment method for vegetable oil-paper insulation system. In this regard, various vegetable oil-paper samples are prepared in a laboratory environment. FDS curves for all samples are obtained at different moisture contents and operating temperatures. A new method to estimate the moisture content in insulation paper is proposed based on the low-frequency characteristic quantity. Also, the effect of operating temperature and water migration between the oil and paper insulation on the FDS analysis is investigated. Based on this analysis, a compensation factor is introduced to eliminate the influence of temperature on paper moisture assessment. Furthermore, an improved normalization method for the temperature effect on the FDS of vegetable oil-paper insulation with different moisture contents is proposed. The proposed moisture assessment and temperature normalization methods are validated thorough their application on an operating power transformer.

The phenomenon of crossing the forbidden zone is unavoidable and wasteful when scheduling the hydro units. In this paper, the effect of crossing the forbidden zone is taken into account using the strategic offering model to help the price-maker hydro producers (PMHPs) devise more economic offering strategies in this paper. This effect is economically quantified and formulated in the form of the mixed integer linear program (MILP) with the use of the big M method and the piecewise linear technique. Therefore, the effect of crossing the forbidden zone therefore can easily be incorporated into the strategic offering model. Based on the residual demand curve (RDC) scenarios, the strategic offering model, which consists of offering and self-scheduling models, is established according to the real-world data from the Three Gorges Project (TGP). The whole optimization model is then converted into the form of MILP formulations by properly discretizing the net head. A test case based on the TGP is presented, in which the strategic offering model is set as a comparable test while ignoring the effect of crossing the forbidden zone. The results provide insight on the offering strategy and physical system operations during a single day. The outcomes indicate that the model that incorporates the effect of crossing the forbidden zone will effectively prevent the crossing phenomenon from happening and that the proposed strategic offering model is more useful for assisting a PMHP in developing profitable offering strategy.

Multi-task learning (MTL) is a learning paradigm which can improve generalization performance by transferring knowledge among multiple tasks. Traditional collaborative filtering recommendation methods suffer from cold start, sparsity and scalability problems. The latest research has shown that applying side information of knowledge graph can not only solve the problems above, but also improve the accuracy of recommendation. However, existing multi-task methods for knowledge graph enhanced recommendation expose obvious issues of disclosing the private information of training samples. In order to solve these problems, we put forward a privacy-preserving multi-task framework for knowledge graph enhanced recommendation. In specific, Laplacian noise is added into the recommendation module to guarantee the privacy of sensitive data and knowledge graph is utilized to improve the accuracy of recommendation. Extensive experimental results on three datasets demonstrate that the proposed method can not only preserve the privacy of sensitive training data, but also have little effect on the prediction accuracy of the model.

In the past decades, a huge number of large-scale photovoltaic (PV) plants have been constructed all around the world. However, the capital and operational costs can be high, which limits their widespread applications. Because the installed PV modules often operate in harsh environments (i.e. storm, high temperature, dust, hail, etc.), non-uniform aging phenomena of PV modules cannot be avoided and it impacts adversely on the performance of PV plants, especially in the middle and late periods of their service life. This paper develops an offline PV module reconfiguration strategy for the non-uniform aging PV array to mitigate this effect. In order to maximize the economic benefit, electricity price and labor cost are considered in the offline reconfiguration. The Branch-and-Bound based optimization algorithm is proposed to find the highest economic benefit. In order to verify the proposed algorithm, MATLAB software-based modelling and simulation have been performed in four case studies, in which a typical 42kW PV array with 7 × 20 connection is employed in a testing benchmark, and the manpower cost and electricity price in USA, UK, China and Turkey are considered in the case study. The results demonstrate that the economic benefit from a non-uniformly aged PV array is further enhanced with the proposed reconfiguration method.

The certified emission reduction (CER) carbon trading market promoted by the clean development mechanism (CDM) has become an important platform for the development of the international carbon market. However, the CER carbon market has shown unsteady development with the present phenomena of price decrease, transaction inactivity, and recession. Against this backdrop, this study aims to explore the intuition behind CER price volatility from the new perspective of internal and external market dynamic linkages. By introducing three homogeneous carbon products of CER futures, namely, the daily dataset of CER spot, EUA (European Union Allowance) spot and EUA futures, and taking five heterogeneous market drivers comprising stock, exchange rates, coal, crude oil, and natural gas into account, we analyze the dynamic correlations and volatility spillovers between CER futures returns and these influencing factors using the DGC-MSV model. With sample data from January 2013 to May 2019, our empirical results show a persistent dynamic dependence between CER futures price and its factors. The homogeneous and heterogeneous markets have significant positive and negative spillover effects, respectively, on the CER futures market. The decline of CER futures price in the post-Kyoto era is due to two aspects: fluctuation of the exchange rate market, which is closely connected to the settlement of currency, and coal price volatility in energy markets. However, the CER futures market has no obvious spillover effect on other markets, except for its strong impact on the CER spot market and weak information spillover to the exchange rate market. Overall, this finding indicates the feeblest financial property of CER carbon futures market.

In order to effectively extract image features highly related to visual perception quality, improve the image quality evaluation method, under the framework of deep learning, combining the optical flow method and the edge detection algorithm, a multi-feature fusion motion based on improved optical flow is proposed target detection algorithm. First, a video fluid model is proposed. The fluid model decomposes the video object area changes into sub-area zoom, rotation and translation movements. The rigid body area and the area hierarchy describe the spatial relationship of pixels, and the rigid body motion describes the time domain relationship of pixels. It provides a region-based video processing. The associated spatiotemporal is association method. Secondly, a video fluid model is proposed. The video fluid model treats all pixels of the same surface imaged in the video as a fluid, using streamlines to represent the regional motion of the object, and streamlines to represent the pixel motion of the video object, using rotators and translation lines to simplify the streamlines when necessary. The streamline of the same fluid is smooth in the time domain, and the flow pattern is smooth in both the time domain and the space domain. Finally, the top-down deep learning generation model conversion is carried out, and finally through continuous adjustment between different levels, the generation model can reconstruct the original sample with lower error, so that the essential characteristics of this sample are obtained, namely the highest abstract representation of the depth model. After processing the deep learning model, the sample features after dimensionality reduction can be obtained, and the recognition module is used on this basis. Experiments show that the optical flow estimation method based on deep learning and multi-grid, optical flow field estimation method based on variational model and desiccation method proposed in this paper are effective, and it is suitable for moving image analysis, target tracking and 3D reconstruction Such research has certain theoretical significance and practical application value.

One of the major challenges in video object detection is drastic scale changes of objects due to camera motion. In this paper, we propose a two-path Convolutional Long Short-Term Memory (convLSTM) pyramid network designed to extract and convey multi-scale temporal contextual information in order to handle object scale changes efficiently. The proposed two-path convLSTM pyramid consists of a stack of multi-input convLSTM modules. It is updated in top-down and bottom-up pathways so that the temporal contextual information for small-to-large and large-to-small scale changes is exploited. The proposed multi-input convLSTM module uses two input feature maps of different resolutions to store and exchange temporal contextual information of different scales between neighboring convLSTM modules. The outputs of the proposed convLSTM pyramid network constitute a feature pyramid where each feature map contains multi-scale temporal contextual information from earlier frames. The proposed convLSTM pyramid can be combined with various still-image object detectors to improve the performance of video object detection. Experimental results on ImageNet VID dataset show that the proposed method achieves state-of-the-art performance and can handle scale changes efficiently in video object detection.

In this study, an Al2O33D/5083 Al composite was fabricated by infiltrating a molten 5083 Al alloy into a three-dimensional alumina reticulated porosity ceramics skeleton preform (Al2O33D) using a pressureless infiltration method. The corrosion resistance of 5083 Al alloy and Al2O33D/5083 Al in NaCl solution were compared via electrochemical impedance spectroscopy (EIS), dynamic polarization potential (PDP), and neutral salt spray (NSS) tests. The microstructure of the two materials were investigated by 3D X-ray microscope and scanning electron microscopy aiming at understanding the corrosion mechanisms. Results show that an Al2O33D/5083 Al composite consists of interpenetrating structure of 3D-continuous matrices of continuous networks 5083 Al alloy and Al2O33D phase. A large area of strong interfaces of 5083 Al and Al2O33D exist in the Al2O33D/5083 Al composite. The corrosion development process can be divided into the initial period, the development period, and the stability period. Al2O33D used as reinforcement in Al2O33D/5083 Al composite improves the corrosion resistance of Al2O33D/5083 Al composite via electrochemistry tests. Thus, the corrosion resistance of Al2O33D/5083 Al is higher than that of 5083 Al alloy. The NSS test results indicate that the corrosion resistance of Al2O33D/5083 Al was lower than that of 5083 Al alloy during the initial period, higher than that of 5083 Al alloy during the development period, and there was no obvious difference in corrosion resistance during the stability period. It is considered that the elements in 5083 Al alloy infiltrated into the Al2O33D/5083 Al composite are segregated, and the uniform distribution of the segregated elements leads to galvanic corrosion during the corrosion initial period. The perfect combination of interfaces of Al2O33D and the 5083 Al alloy matrix promotes excellent corrosion resistance during the stability period.

Non-renewable resources such as natural stone and asphalt are in short supply. Recycling technology, with its lower cost, has been used as the primary approach to asphalt pavement maintenance engineering. The inclusion of reclaimed asphalt pavement materials in producing new asphalt pavements may increase the risk of cracking. The strength and toughness of the asphalt mixture can be reduced. In this study, Hamburg wheel tracking tests (HWTT) were performed on rejuvenated asphalt mixtures with distinct maintenance processes. Different kinds of fibers have been used as additives to reinforce the rejuvenated asphalt mixtures. The HWTT rutting curve was identified as having three stages, including the post-compaction stage, the creep stage, and the stripping stage. The three-stage rutting curve model was used to determine the intersection point between the creep stage and stripping stage. The other two feature points (i.e., the post-compaction point and the stripping inflection point) were redefined with a new calculation method. Then, the rutting effect and stripping effect were separated with these feature points. The performance and economic benefits of fiber-reinforced rejuvenated asphalt mixtures were investigated through grey correlation analysis under the three maintenance processes. The feature points of the HWTT curve and the cost of the corresponding maintenance process were selected as the impact factors. Finally, the optimal scheme was developed by analyzing the influence of each factor on both performance and economic benefits.

The 3D object reconstruction from depth image streams using Kinect-style depth cameras has been extensively studied. In this paper, we propose an approach for accurate camera tracking and volumetric dense surface reconstruction, assuming that a known cuboid reference object is present in the scene. Our contribution is threefold. First, we maintain the drift-free camera pose tracking by incorporating the 3D geometric constraints of the cuboid reference object into the image registration process. Second, we reformulate the problem of depth stream fusion as a binary classification problem, enabling high-fidelity surface reconstruction, especially in the concave zones of objects. Third, we further present a surface denoising strategy to mitigate the topological inconsistency (e.g., holes and dangling triangles), which facilitates the generation of a noise-free triangle mesh. We extend our public dataset CU3D with several new image sequences, test our algorithm on these sequences, and quantitatively compare them with other state-of-the-art algorithms. Both our dataset and our algorithm are available as open-source content at https://github.com/zhangxaochen/CuFusion for other researchers to reproduce and verify our results.

In this paper, a novel method to design a multifunctional metasurface (MS) is proposed and demonstrated. Inspired by shared aperture technology, the proposed MS is integrated with both radiation and low backward scattering performance. Each MS unit cell consists of two parts, polarization conversion surface part and radiation part. Different from traditional MS design, the proposed two parts are integrated together, and thus, bi-functions are realized in one surface. To acquire radiation performance, the feeding structure is added to each radiation in part by introducing an antenna design concept. The peak gain of the MS attains 18.1 dBi and main-beam patterns are all along the normal direction. To obtain low backward scattering performance, the polarization of an incident electromagnetic wave is transformed by the reflected MS. The reflected wave will have a 180° phase difference through rotating the polarization conversion surface structure. Thus, two different types of MS units are arranged in chessboard array to achieve reflection suppression. The radar cross section reduction band is from 3.0 to 4.3 GHz with peak values of 7.2 and 16.8 dB. Both the simulated and experimental results prove that our method offers a feasible strategy for the multifunctional MS design which can lead to many exciting applications in different frequency domains.

With the increasing penetration of wind power, the effective inertia of the power system reduces. Besides, a series of interactive instability issues including sub-synchronous oscillations and harmonic oscillations were reported due to the weak grid effects. Broadly speaking, those issues are closely related to the PLL and its tuning. Therefore, recent literature make efforts to avoid using the PLL for converter control, e.g., the virtual synchronous generator (VSG) control. However, the VSG control usually employs multiple loops, where a fast switching frequency of the converter is required to decouple each loop's dynamic. This prerequisite is usually not met for wind power converters with high capacity and low switching frequency. To address these issues but also inherit the merits of a VSG on the grid-integration, this paper employs a new concept of PLL-less control and applies it to the Type-IV wind turbine, in which the grid-synchronization is realized by the dynamics of dc capacitor voltage. The virtual capacitor control is designed and added to the machine-side converter to eventually deliver adequate inertia to the grid. To justify the effectiveness of the proposed control, both the soft start-up validation and the thorough analysis of the overall small-signal stability are presented. Several concerns of vital importance regarding the virtual capacitor design and stabilization control are discussed, where the mechanism of stability is revealed through the complex-power coefficient-based analysis. On this basis, a stabilization control method is proposed, which can enlarge the stable range of virtual capacitor coefficient and enhance the inertial response effect. Finally, the performance of the proposed method on the inertial response and the weak grid operation is evaluated by time domain simulations in PSCAD/EMTDC, which is proven effective overall.

Presently, condition monitoring and fault diagnostics in the three-phase voltage-source inverter (VSI) system are already considered essential to improve maintenance efficiency and increase reliability levels. This paper presents a new open-switch fault real-time diagnosis method in the VSI based on the phase currents. First, on the basis of real-time measured two phase currents, the sum of the absolute value of the normalized phase currents is calculated, whose average value is less relevant to the transient condition such as load variations and current frequency variations. Second, the current zero-cross detection method is employed to avoid the influence of current distortion caused by the transient condition. Then, the fault detection signal is built to precisely localize the faulty switches, and the real-time fast diagnosis for three-phase VSI with the open-switch fault is realized. Finally, the experiment results verify the reliability and effectiveness of the proposed method.

SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-known sampling algorithms for addressing class imbalance learning problem. The merits of SMOTE reflect at that in comparison with the random oversampling technique, it can alleviate the problem of overfitting to a large extent. However, two drawbacks of SMOTE have also been observed as follows, 1) it tends to propagate the noisy information in the procedure of oversampling; 2) it always assigns a global neighborhood parameter K but neglects the local distribution characteristics. To synchronously deal with these two problems, a grouped SMOTE algorithm with noise filtering mechanism (GSMOTE-NFM) is presented in this article. The algorithm firstly adopts Gaussian-Mixture Model (GMM) to explore the real distributions of the majority and minority classes, respectively. Then, most noisy instances can be removed by comparing the probability densities of the same instance in two different classes. Next, two new GMMs are constructed on the rest majority and minority class instances, respectively. Furthermore, all minority class instances can be divided into three different groups: safety, boundary and outlier, based on the corresponding probability density information. Finally, we assign an individual parameter K to the instances belonging to each specific group to generate new instances. We tested GSMOTE-NFM algorithm on 24 benchmark binary-class data sets with three popular classification models, and compared it with several state-of-the-art oversampling algorithms. The results indicate that our algorithm is significantly superior than the original SMOTE algorithm and several SMOTE-based modified methods.

Motifs have been recognized as basic network blocks and are found to be quite powerful in modeling certain patterns. Generally speaking, local characteristics of big networks could be reflected in network motifs. Over the years, motifs have attracted a lot of attention from researchers. However, most current literature reviews on motifs generally focus on the field of biological science. In contrast, here we try to present a comprehensive survey on motifs in the context of big networks. We introduce the definition of motifs and other related concepts. Big networks with motif-based structures are analyzed. Specifically, we respectively analyze four kinds of networks, including biological networks, social networks, academic networks, and infrastructure networks. We then examine methods for motif discovery, motif counting, and motif clustering. The applications of motifs in different areas have also been reviewed. Finally, some challenges and open issues in this direction are discussed.

One of the major threats to cybersecurity is the emergence of new computer viruses. By emergence of new viruses, the cybersecurity companies assign a team of security experts and programmers to study the behavior of the virus and develop the corresponding antivirus program to secure networks. Sometimes, more than one new virus is identified that requires the cybersecurity team to make a tradeoff on the allocation of programmers, and thus leads to two-antivirus-program development problem under double-virus attack. In this paper, we propose DOWNHILL algorithm to address the outlined challenge. We model the time evolution of the expected state of the network as a differential dynamical system to measure the total loss caused by the viruses. Then, we propose a DOWNHILL algorithm, three heuristic algorithms and a random algorithm to solve the problem, respectively. We study the computational complexities of the proposed algorithms as well. Through numerous comparative experiments, we confirm the DOWNHILL algorithm is the most effective method to this problem. Finally, the influence of different factors on the DOWNHILL strategy and its potential total loss are also researched.

In such an information society, the Internet of Things (IoT) plays an increasingly important role in our daily lives. With such a huge number of deployed IoT devices, Cyber-Physical System (CPS) calls for powerful distributed infrastructures to supply big data computing, intelligence, and storage services. With the increasingly complex distributed software infrastructures, new intricate bugs continue to manifest, causing huge economic loss. Synchronization performance problems, which means that improper synchronizations may degrade the performance and even lead to service exception, heavily influence the entire distributed cluster, imperiling the reliability of the system. As one kind of performance problems, the synchronization performance problems are acknowledged as difficult to diagnosis and fix. We collect 26 performance issues in three real-world distributed systems: HDFS, Hadoop MapReduce, and HBase, and do analysis on their root cause, fix strategy, and algorithm complexity in order to understand these synchronization performance bugs better. Then, we implement a static detection tool including critical section identifier, loop identifier, inner loop identifier, expensive loop identifier, and pruning component. After that, we evaluate our detection tool on these three distributed systems with sampled bugs. In the evaluation, our detection tool accurately finds out all the target bugs. Besides, it points out more new potential performance problems than the previous works. With the strict performance overhead, our detection tool is proved to be greatly efficient.

Binary fault localization is important for vulnerability analysis, but many current techniques face problems in locating vulnerability accurately and effectively, especially for real-world programs. In this paper, we propose a novel gradient-guided vulnerability locating method named DeepVL, which leverages deep neural networks to diagnose the root cause of weakness in binaries and provide guidance information for further analysis. DeepVL collects sufficient amounts of crashed execution traces and normal execution traces as input of the constructed neural networks. Based on trained neural network, DeepVL calculates the gradient information for each basic block in traces and filter out the vulnerable basic blocks according to corresponding gradients. To demonstrate the applicability of DeepVL, we perform plenty of experiments on different datasets. According to the experimental results on Common Weakness Enumeration (CWE) dataset, DeepVL could locate different types of vulnerabilities accurately and effectively, with recall@10 reaching 96.9% and precision@10 reaching 70.1%. Additionally, the results on Cyber Grand Challenge (CGC) program and LibTIFF 4.0.10 show that DeepVL is capable of locating vulnerable basic blocks in large-scale programs. As a fault localization tool, DeepVL could greatly reduce the manual effort of finding vulnerabilities in binaries.

To understand the microstructure evolution of hydrogels swollen in seawater, freeze-drying technology was used to fix and preserve the swollen three-dimensional microstructure. By this method, we revealed the swelling behavior of hydrogels in seawater, and elucidated the mechanism of the swelling process. Meanwhile, we also used Fourier-transform infrared spectroscopy; laser confocal microscopy; field emission scanning electron microscopy, and swelling performance tests to research the structure and properties of PS-PEG hydrogels, before and after seawater swelling, and analyzed the structure and properties of PEG-based hydrogels with different contents of polystyrene microspheres. Results showed that PS-PEG hydrogels went through three stages during the swelling process, namely ‘wetting-rapid swelling-swelling equilibrium’. Due to the capillary effect and hydration effect, the surface area would initially grow tiny pores, and enter the interior in a free penetration manner. Finally, it formed a stable structure, and this process varied with different content of polystyrene microspheres. In addition, with the increase of polystyrene microsphere content, the roughness of the hydrogel before swelling would increase, but decrease after swelling. Appropriate acquisition of polystyrene microspheres could enhance the three-dimensional network structure of PEG-based hydrogels, with a lower swelling degree than hydrogels without polystyrene microspheres.

(1) Background: the applications of ceramic materials in a friction pair and a moving pair are limited, just because of their poor toughness and unsatisfactory tribological characteristics. In view of this, Mo as a soft metal layer was added into a Si3N4 matrix to improve its toughness and tribological characteristics. (2) Methods: The microstructure and metal/ceramic transition layer were examined using X-ray diffraction, scanning electron microscope, electron dispersive X-ray spectroscopy, and Vickers hardness. Bending strength and fracture toughness were also measured. Tribological characteristics were obtained on the pin-on-disc wear tester. (3) Results: It can be found that the multilayer structure could improve the fracture toughness of laminated composite compared with single-phase Si3N4, but the bending strength was significantly reduced. Through microstructure observation, the transition layer of Si3N4/Mo-laminated composite was revealed as follows: Si3N4→MoSi2→Mo5Si3→Mo3Si→Mo. Moreover, the addition of the Mo interface to silicon nitride ceramic could not significantly improve the tribological properties of Si3N4 ceramic against titanium alloy in seawater, and the friction coefficients and wear rates of the sliding pairs increased with the increase in load. (4) Conclusions: The process failed to simultaneously improve the comprehensive mechanical properties and tribological performance of Si3N4 ceramic by adding Mo as the soft interfacial layer. However, the utilization of metal interfacial layers to enhance the toughness of ceramics was further recognized and has potential significance for the optimization of ceramic formulation.

The microstructure, interfacial characteristics, and corrosion resistance of Fe-W-Mn-Al-B alloys in molten zinc at 520 °C have been investigated using scanning electron microscopy (SEM), X-ray diffractometry (XRD), and electron probe micro-analysis (EPMA). The experimental results indicate that the Fe-B alloy with 11 wt.% W, 7 wt.% Mn, and 4 wt.% Al addition displays a lamellar eutectic microstructure and excellent corrosion resistance to molten zinc. The toughness of M2B-type borides in the hyper-eutectic Fe-4.2B-11W-7Mn-4Al alloy can be more than doubled, reaching 10.5 MPa·m1/2, by adding Mn and Al. The corrosion layer of the Fe-3.5B-11W-7Mn-4Al alloy immersed in molten zinc at 520 °C comprises Fe3AlZnx, δ-FeZn10, ζ-FeZn13, and η-Zn. The lamellar borides provide the mechanical protection for α-(Fe, Mn, Al), and the thermal stability of borides improves as the fracture toughness of the borides increases, which jointly contribute to the improvement of the corrosion resistance to the molten zinc.

In this paper, a metasurface antenna with ultra-broadband radar cross section (RCS) reduction was systematically presented and evaluated based on the spoof surface plasmon polariton (SSPP) and transmission. A circular metasurface consisted of twelve gradually increasing two-sided metallic grooves and an ultra-thin dielectric film. The evolutions of dispersion characteristic and near electric-field distributions were adequately demonstrated based on SSPP in simulation. Furthermore, the SSPP metasurface had been loaded on a common waveguide slot antenna. The simulation and laboratory measurements were performed to characterize RCS reduction, radiation patterns and scattering performance. A prototype of metasurface antenna had been fabrication and measurement to demonstrate the characteristics. Experiment data were carried out to verify the simulation results and measured results showed that the metasurface antenna exhibited ultra-wideband RCS reduction from 1 GHz to 10 GHz.

Person re-identification refers to matching images of pedestrians from camera networks distributed at different locations. It involves multiple challenging problems, especially large variations in illumination, viewpoint, and occlusion which exert negative affect on recognition rate. In this paper, we propose the partial least square (PLS) algorithm based on the Riemannian manifold. The covariance matrix is established by extracting the feature information of the region of the torso and leg images of a pedestrian. To find a suitable tangent plane in the covariance matrix of the Riemannian manifold, we create a relation between the torso and leg image by using the PLSs algorithm to obtain the correlative feature information of pedestrian images. Using unsupervised learning, this method performs well in the single-shot scenario. Experimental results on publicly available benchmarks show better performance of the proposed method.

A wideband and high gain cavity-backed 4 × 4 patch antenna array is proposed in this paper. Each patch antenna element of the array is enclosed by a rectangular cavity and differentially-fed by the slot underneath. By optimizing the geometry of the radiating patch and the cavity, a very uniform E-field distribution at the antenna aperture is achieved, leading to the high array aperture efficiency and thus the gain. Taking advantages of the higher-order substrate integrated cavity excitation, the elements of the array are efficiently fed with the same amplitude and phase in a simplified feeding mechanism instead of the conventional bulky and lossy power-splitter-based feeding network. Measured results show the antenna bandwidth is from 56 to 63.1-GHz (16.1%) with the peak gain reaching 21.4 dBi. The radiation patterns of the array are very stable over the entire frequency band and the cross-polarizations are as low as -30 dB. These good characteristics demonstrate that the proposed array can be a good candidate for the future 60-GHz communication system applications.

Interface problems and the destruction of the continuity of the oxide film in the Al matrix usually reduce the corrosion resistance of the material. In this paper, the corrosion resistance of Al matrix composites (AMCs) was improved by introducing the silicon carbide skeletons (SiC3D) obtained with polymer replica technology. SiC3D/6061Al was fabricated by infiltrating molten 6061Al alloy in the oxidized SiC3D using the low-pressure casting method. The corrosion resistance performances of 6061Al and SiC3D/6061Al in NaCl solution were studied by electrochemical, neutral salt spray corrosion (NSS), and salt leaching (SL) tests. Results show corrosion resistance of SiC3D/6061Al is higher than that of 6061Al alloys by open circuit potential (OCP), potentio-dynamic polarization (PDP), and electrochemical impedance spectroscopy (EIS) tests. However, NSS and SL tests show the corrosion resistance of SiC3D/6061Al is lower than that of 6061Al alloy. The reason is a corrosion resistant and anti-oxidation network macrostructure with large interface recombination, few concentrated interfaces, and a small specific area that formed in SiC3D/6061Al. SiC3D cannot damage the continuity of the Al2O3 passivating film, and the network macrostructure greatly improves the corrosion resistance performance.

To provide a new method for the evaluation of the compaction quality of asphalt mixture, a real-time data acquisition and processing system (RDAPS) for the motion state of aggregate with a small volume and high precision is developed. The system consists of an intelligent aggregate (IA), analysis software and hardware equipment. The performance of the IA was tested by regarding data sensitivity, high-temperature resistance, and mechanical properties. A new evaluation method was proposed for evaluating the compaction quality of AC-25 and SMA-25 asphalt mixtures based on an IA. The results show that the best transmission baud rate for the IA was 9600 bps, and the corresponding signal transmission distance was 380 m. Only one IA was needed to complete the state data collection for the aggregate within the asphalt mixture in a circular area, with the IA layout point as the center of the circle and a radius of 5 m. The IA conducted reliable data transmission up to 200 °C; however, its compressive strength decreased with increasing temperature until reaching stability. Traditional aggregate could be replaced by an IA to withstand external forces and internal load transfer. Embedding an IA into AC-25 or SMA-25 asphalt mixtures did not have a significant impact on the original mechanical properties of the mixture. The effect of the gradation type of the asphalt mixture on the IA motion state was not significant. When the compaction degree met the specification requirements, the motion data of the IA did not reach a stable state, and the interlocking effect between aggregates in the asphalt mixture could be further optimized. An evaluation method is proposed based on the IA for the compaction quality of AC-25 and SMA-25 asphalt mixtures with the compaction degree as the main index and the spatial attitude angle and spatial acceleration of the IA as the auxiliary indexes.

A design for low radar cross section (RCS) and broadband magnetic-electric (ME) dipole antenna is proposed. Minkowski-like fractal metal patches printed on the substrate form the electric dipoles, four metallic vias connected to the radiation patches and the metal ground form the magnetic dipoles. The whole antenna is connected with an L-shaped feeding structure which excites electric and magnetic dipoles simultaneously. Meanwhile, two different structure AMC cells with a 180° (±30°) phase difference in a broadband frequency region are designed as a chessboard and loaded around the ME antenna radiation patch. Numerical and experimental results incident the antenna bandwidth is 42.4% from 8.0GHz to 12.3GHz, covering the whole X band. Moreover, the RCS is reduced remarkable in a broad frequency range from 6.5GHz to 15.5GHz (81.8% relative bandwidth) when compared to conventional ME antenna. After loading AMC structures, the antenna still keeps advanced performances such as stable gain and almost consistent pattern in E and H plane.

We proposed a novel ultra-broadband meta¬material screen with controlling the electromagnetic scat¬tering fields based on the three layers wideband polariza¬tion converter (TLW-PC). The unit cell of TLW-PC was composed of a three layers substrate loaded with double metallic split-rings structure and a metal ground plane. We observed that the polarization converter primarily per¬formed ultra-broadband cross polarization conversion from 5.71 GHz to 14.91 GHz. Furthermore, a metamaterial screen, which contributed to the low scattering charac¬teristics, had been exploited with the orthogonal array based on TLW-PC. The near scattering electronic fields are controlled due to the change of phase and amplitude for incident wave. The metamaterial screen significantly exhibited low scattering characteristics from 5.81 GHz to 15.06 GHz. To demonstrate design, a metamaterial device easily implemented by the common printed circuit board method has been fabricated and measured. Experimental results agreed well with the simulated results.

As carbon dioxide (CO2) adsorbents, porous materials with high specific surface areas and abundant CO2-philic groups always exhibit high CO2 capacities. Based on this consensus, a category of oxygen-rich macroporous carbon foams was fabricated from macroporous resorcinol-formaldehyde resins (PRFs), which were obtained via an oil-in-water concentrated emulsion. By the active effect of potassium hydroxide (KOH) at high temperatures, the resultant carbon foams (ACRFs) possessed abundant micropores with rich oxygen content simultaneously. At the same time, most of the ACRFs could retain the marcoporous structure of their precursor. It is found that porosity of ACRFs was mainly determined by carbonization temperature, and the highest specific surface areas and total pore volume of ACRFs could reach 2046 m2/g and 0.900 cm3/g, respectively. At 273 K, ACRFs showed highest CO2 capacity as 271 mg/g at 1 bar and 91.5 mg at 15 kPa. Furthermore, it is shown that the ultra-micropore volume was mainly responsible for the CO2 capacities of ACRFs at 1 bar, and CO2 capacities at 15 kPa were mainly affected by the oxygen content. It is also found that the presence of macropores would accelerate ACRFs adsorbing CO2. This study provides ideas for designing a porous CO2 adsorbent.

The residual stress of machined surface has a crucial influence on the performance of parts. It results in large deviations in terms of the position accuracy, dimension accuracy and service life. The purpose of the present study is to provide a novel semi-empirical residual stress prediction approach for turning Inconel 718. In the method, the bimodal Lorentz function was originally applied to express the residual stress distribution. A statistical model between the coefficients of the bimodal Lorentz function and cutting parameters was established by the random forest regression, in order to predict the residual stress distribution along the depth direction. Finally, the turning experiments, electrolytic corrosion peeling, residual stress measurement and correlation analysis were carried out to verify the accuracy of predicted residual stress. The results show that the bimodal Lorentz function has a great fitting accuracy. The adjusted R2 (Ad-R2) are ranging from 95.4% to 99.4% and 94.7% to 99.6% in circumferential and axial directions, respectively. The maximum and minimum errors of the surface residual tensile stress (SRTS) are 124.564 MPa and 18.082 MPa, those of the peak residual compressive stress (PRCS) are 84.649 MPa and 3.009 MPa and those of the depth of the peak residual compressive stress (DPRCS) are 0.00875 mm and 0.00155 mm, comparing three key feature indicators of predicted and simulated residual stress. The predicted residual stress is highly correlated with the measured residual stress, with correlation coefficients greater than 0.8. In the range of experimental measurement error, the research in the present work provides a quite accurate method for predicting the residual stress in turning Inconel 718, and plays a vital role in controlling the machining deformation of parts.