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This study aims to identify appropriate innovative strategies for solar energy investments for both commercial and non-commercial users. For this purpose, TRIZ-based 8 different innovative strategies are identified. Later, these strategies are weighted with the interval type-2 fuzzy DEMATEL (IT2 FDEMATEL) method to define more important strategies. Thus, the main motivation of this study is to figure out the weights of the strategies for solar energy investments The findings indicate that the replacement of mechanical system is found as the most effective TRIZ-based investment strategy for solar energy projects regarding both commercial and non-commercial customers. Thus, it is recommended that solar energy companies should give special support to their customers with respect to the security issues. Moreover, local quality is another significant strategy for commercial users. Therefore, it will be appropriate to provide special services according to the characteristics of the customers. Within this framework, having distribution centers close to customers and employing customer representatives who know well the culture of that region can be very helpful. Additionally, cushion in advance is found as another appropriate TRIZ-based innovative strategy for commercial customers. Hence, it would be appropriate to ensure the security in solar panels. Within this context, necessary measures should be taken for problems such as electric shock, material destruction, theft, and risk of destroying data on the computer.

A substantial number of authentication protocols are designed to safeguard vehicular ad-hoc network (VANET) communication from potential attacks; however, they experienced an inability to provide a balance between lightweight and security. Existing security and privacy-preserving based authentication protocols in vehicular networks mostly rely on the trusted authority and signatures to validate the communication on road. Accomplishing the quick validation and correspondence is difficult in such methodologies and further, they endure execution obliges from coming about overhead. To overcome these issues, we have developed an enhanced lightweight and secure authentication protocol (ELSAP) for V2V Communication in VANETs. Moreover, the scheme withheld with self-authentication prior to communication that enhances the network feasibilities, which in return needs less message transfer during authentication as well as communication, indicates lightweight features. Furthermore, two or more vehicles can securely perform mutual authentication, proven by Burrow–Abadi–Needham (BAN) logic. Additionally, the competency of the proposed protocol against the current updated threats is shown via security analysis and comparisons tools such as Automated Validation of Internet Security Protocols and Applications (AVISPA). The result of the performance analysis shows that the communication cost and computational cost outperformed the earlier authentication schemes alongside the security features of the proposed protocol.

Penalty function is well-known for constrained evolutionary optimization. An open question in the penalty function is how to tune the penalty coefficient. This paper proposes an adaptive fuzzy penalty method to address this issue, where the coefficient is adjusted at both the individual level and the population level. At the individual level, each individual chooses a penalty coefficient from a predefined domain according to some fuzzy rules. At the population level, the domain of the crisp output is adjusted adaptively by using population information. To enhance the population diversity, an effective mutation scheme is developed. Due to its numerous merits, differential evolution is used to design a search algorithm. By the above processes, a constrained optimization evolutionary algorithm called AFPDE is proposed. Since the objective function value and the degree of constraint violation are normalized, AFPDE is less problem-dependent than the seminal work of the fuzzy penalty method. AFPDE introduces a lower penalty value in the early stage of AFPDE while a higher one in the later stage. Thus, it can escape local optima in the infeasible region. Experiments on three well-known benchmark test sets and two mechanical design problems validate that AFPDE is competitive.

Lithium-ion batteries are widely used as power sources in industrial applications. Electrochemical models and simulations are crucial to disclose many details that cannot be directly measured through experiments. Parameter identification of an accurate electrochemical model is much more cost-effective than direct and destructive measurement methods. However, the complex structure and strong nonlinearity of electrochemical models will make the parameter identification very difficult. Additionally, time-consuming electrochemical simulations can significantly limit the identification efficiency. This article proposes a surrogate-model-based scheme to achieve high-efficiency parameter identification of an electrochemical battery model. To be specific, the proposed method is implemented by the close integration of an evolutionary algorithm and a surrogate model. A sensitivity-based identification strategy is first designed to alleviate the difficulty of optimization. Then, a surrogate model is developed from historical data to gradually approach the objective function used for parameter evaluations. Finally, an evolutionary algorithm is employed to find promising solutions by minimizing the output of the surrogate model. Simulations and experimental studies demonstrate the effectiveness and high efficiency of the proposed method.

Recent technological evolution in the Internet of Things (IoT) age supports better solutions to magnify the management of the power quality and reliability concerns, and imposes the measures of a smart grid. In smart grid environment, a smart meter needs to securely access the services from a service provider via insecure channel. However, since the communication is via public channel, it imposes various security threats by an adversary. To deal with this, in this article we design a new anonymous signature-based authenticated key exchange scheme for IoT-enabled smart grid environment, called AAS-IoTSG. The dynamic smart meter addition phase is also permissible in AAS-IoTSG after initial deployment. The security of AAS-IoTSG has been tested rigorously using formal security analysis under the real-or-random (ROR) model which is one of the broadly-accepted standard random oracle models, formal security verification under the broadly-used automated validation of Internet security protocols and applications (AVISPA) tool and also using informal security analysis. Finally, an exhaustive comparative study unveils that AAS-IoTSG supports better security and functionality features and requires less communication and computation overheads as compared to the existing state-of-art authentication mechanisms in smart grid systems.

Low-cost and efficient bifunctional catalysts are urgently needed for overall water splitting used in large-scale energy storage. In this study, we develop a nickel and iron (di)sulfide (Ni-Fe-S) composite catalyst that is in situ synthesized and fixed within the intergranular nanopores inside high pure polycrystalline graphite. Two precursor solutions (reactants) may permeate the graphite intergranular pores to a depth of more than 3.5 mm. The nanoscale pores serve as an array of nanoreactors for the synthesis of the Ni-Fe-S nanoparticles under conditions much milder than usual. The prepared catalyst efficiently catalyzes both the hydrogen and oxygen evolution reactions (HER and OER) in 1.0 M KOH. It delivers a current density of 400 mA cm-2 at a full cell voltage of around 2.3 V without considerable activity decay over 24 h electrolysis. The active species of the catalyst are different for the HER and OER and discussed accordingly. The synthesis strategy based on the nanopores in a monolithic conductive substrate proves to be a simple, efficient, and promising way to prepare electrocatalysts that are cheap, abundant, and industrially attractive.

For complex distributed parameter systems (DPSs) with strong nonlinearities and time-varying dynamics, the conventional spatiotemporal modeling methods become ill-suited since the elementary assumption that the process data follow a unimodal Gaussian distribution usually becomes invalid. In this paper, a multimode method is proposed for modeling of such systems. First, the original operating space is partitioned along the time dimension into several subspaces via modified dissimilarity analysis. Each subspace represents the local spatiotemporal characteristics of the original system. Second, the Karhunen–Loeve decomposition (KLD)-based spatiotemporal modeling approach is applied to approximate the local dynamics of each subspace. Finally, an ensemble model is obtained using the soft weighting sum of the local ones, where the corresponding weights are calculated by principal component regression. By properly decomposing the original space into several local parts, the ensemble model is capable of handling the strong nonlinearities and time-varying dynamics of the system. The validity and efficiency of the proposed method are verified on two representative applications: 1) a one-dimensional parabolic catalytic rod and 2) a two-dimensional curing thermal process. The experimental results show that the proposed method provides a superior performance regarding modeling accuracy compared to several baselines.

This article presents a control scheme for stabilizing a vibrating flexible hose used for aerial refueling subject to bounded actuators for the rate and magnitude. A dynamical model of hose systems is captured by partial differential equations (PDEs). Based on the PDE model, a novel boundary control law is proposed to dampen the flexible hose's vibration. The backstepping approach is utilized to devise the control scheme, with the bounded input magnitude and derivative handled by adopting the smooth hyperbolic tangent function. The Lyapunov criterion is exploited to demonstrate the stability of the controlled system. Finally, simulation results are used to evaluate the validity of the derived scheme.

In computed tomography (CT) images, pulmonary lobe segmentation is an arduous task due to its complex structures. To remedy the problem, we introduce a new framework based on lung anatomy knowledge for lung lobe segmentation. Firstly, the priori knowledge of lung anatomy is used to identify the fissure region of interest. Then, an oriented derivative of stick filter is applied to isolate plate-like structures from clutters for lobar fissure verification. Finally, a surface fitting model is employed to complete the incomplete fissure surface for lung lobe segmentation. Compared with manually segmented fissure references, the designed approach obtained a high median F1-score of 0.8865 in the left lung and obtained a high median F1-score of 0.9200 in the right lung. The average percentages of the segmented lung lobes in the lung lobe ground truth are 0.960, 0.989, 0.973, 0.920, and 0.985 for the left upper, left lower, right upper, right middle, and right lower lobes, respectively. The perfect performance of the proposed scheme is tested by visual inspection and quantitative evaluation.

This special issue starts with an article on high seismicity detection, namely “Fluid-Induced High Seismicity in Songliao Basin of China,” co-authored by Sheng et al. (2020). The authors attempt to...

This paper introduces a fuzzy control (FC) under spatially local averaged measurements (SLAMs) for nonlinear-delayed distributed parameter systems (DDPSs) represented by parabolic partial differential-difference equations (PDdEs), where the fast-varying time delay and slow-varying one are considered. A Takagi-Sugeno (T-S) fuzzy PDdE model is first derived to exactly describe the nonlinear DDPSs. Then, by virtue of the T-S fuzzy PDdE model and a Lyapunov-Krasovskii functional, an FC design under SLAMs, where the membership functions of the proposed FC law are determined by the measurement output and independent of the fuzzy PDdE plant model, is developed on basis of spatial linear matrix inequalities (SLMIs) to guarantee the exponential stability for the resulting closed-loop DDPSs. Lastly, a numerical example is offered to support the presented approach.

As an important topic of IoT, wireless sensor network (WSN) data transmission is popular nowadays. It is widely accepted that the wireless channel is hazard, and multifactor authentication schemes are proposed to save the hazard of wireless communication circumstance. To overcome the problems, we give a fresh three-factor authentication scheme providing session keys for WSNs. Formal verification given by Proverif illustrates that the new scheme keeps security properties. At the same time, the informal analysis also denotes that the proposed scheme is practical and satisfies general needs, such as counteraction against various attacks and meeting security properties. Compared to some recent similar schemes, the proposed scheme performs better in security and is suitable for application. At last, we use NS-3 for simulation. The results from the simulation show that the scheme can run in IoT environment normally and has practical perspective.

In continuation of our efforts toward the discovery of potent HIV-1 NNRTIs with diverse structures, a series of novel S-DACO analogues of 6-(2-cyclohexyl-1-alkyl)-2-(2-oxo-2-phenyl-ethylsulfanyl)pyrimidin-4(3H)-ones were designed, synthesized and evaluated for their antiviral activities in MT-4 cells. Most of these new compounds showed moderate to good activities against wild type HIV-1 with IC50 values ranging from 7.55 μmol/L to 0.018 μmol/L. Among them, compound 5c was identified as the most promising inhibitor against HIV-1 replication with an IC50 = 0.018 μmol/L, CC50 = 194 μmol/L, and SI = 12791, which was much more potent than the reference drugs NVP and DLV and comparable to AZT and EFV. In addition, 5c also exhibited improved activity against double mutant HIV-1 strain RES056 compared to that of the reference drugs NVP/DLV and DB02. The preliminary structure-activity relationship (SAR) and molecular modeling studies were also discussed, which provides some useful indications for guiding the further rational design of new S-DACO analogues. keywords: NNRTIs, -DABOs, -DACOs, Anti HIV-1 activity, SAR ispartof: Chinese Chemical Letters vol:32 issue:3 pages:1020-1024 status: published

Structured illumination microscopy (SIM) is one of the most powerful and versatile super-resolution methods due to its live-cell imaging ability of subcellular structures with high speed. In this paper, we propose an alternative SIM assisted by Bloch surface wave (BSW). Through replacing the conventional laser interference fringes with sub-diffraction BSW counterparts with higher spatial frequency, the imaging resolution of SIM could be enhanced greatly and the super-resolution imaging capability down to 80 nm could be achieved. Compared with traditional wide-field fluorescence microscopy, this BSW SIM demonstrates 2.78 times enhancement in imaging resolution, which surpasses general SIM with only 2 times improvement. Moreover, the structured illumination intensity could be boosted drastically, which is beneficial to nonlinear super-resolution imaging techniques, such as saturated SIM. This BSW SIM would provide a super-resolution, wide field of view approach for surface super-resolution fluorescent imaging while maintaining high imaging speed and good bio-compatibility.

We propose, in this paper, a framework for time series and nonlinear system modeling, called the basis function matrix-based flexible coefficient autoregressive (BFM-FCAR) model. It has very flexible nonlinear structure. We show that many famous nonlinear time series models can be derived under this framework by choosing the proper basis function matrices. Some probabilistic properties (the conditions of geometrical ergodicity) of the BFM-FCAR model are investigated. Taking advantage of the model structure, we present an efficient parameter estimation algorithm for the proposed framework by using the variable projection method. Finally, we show how new models are generated from the proposed framework.

Bulk and interfacial nonradiative recombination hinder the further enhancement of the power conversion efficiency (PCE) and stability of SnO2-based planar perovskite solar cells (PSCs). To date, it is still a huge challenge to minimize the bulk and interfacial nonradiative recombination losses, and thus maximize the potentials of PCE and stability. Herein, a novel and effective multifunctional modification strategy through incorporating Girard's Reagent T (GRT) molecules with multiple functional groups to modify SnO2 nanoparticles (NPs), which significantly reduces the bulk and interfacial nonradiative recombination losses through the simultaneous achievement of suppressing nanoparticle agglomeration, improving the electronic property of SnO2 films, facilitating the vertical growth and enlarging the grain size of perovskite crystals, and passivating interfacial defects is reported. As a result, the device based on GRT modification delivers a much higher PCE of 21.63%, along with significantly suppressed hysteresis, as compared to the control device (19.77%). The device stability is ameliorated after GRT modification. The unencapsulated device with GRT maintains 99.5% of its initial PCE after aging at 60 °C for 720 h and 58.5% after illumination for 672 h under one sun, respectively. The present work provides guidance for the design of multifunctional modification molecules toward efficient and stable PSCs.

As a promising way to modulate the photoluminescence properties and stability of perovskites and perovskite variants, doping has attracted wide attention in the field of optical and optoelectronic applications. In this work, a series of all inorganic lead-free Sb3+-doped Cs2ZrCl6 vacancy-ordered perovskite variants were prepared by the hydrothermal method, and the Sb3+-doping leads to singlet and triplet state emissions, which are strongly dependent on the temperature and surrounding molecular environment. Furthermore, efficient energy transfer can be observed from self-trapped excitons (STEs) of [ZrCl6]2− octahedra to dopant Sb3+. Our results will shed light on the ns2 Sb3+-ion-doping-induced emissive mechanism and expand the understanding of the optoelectronic properties of the perovskite variant family.

Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary optimization during the last two decades. However, as another famous multiobjective optimization framework, decomposition-based multiobjective optimization has not received sufficient attention from constrained evolutionary optimization. In this paper, we make use of decomposition-based multiobjective optimization to solve constrained optimization problems (COPs). In our method, first of all, a COP is transformed into a biobjective optimization problem (BOP). Afterward, the transformed BOP is decomposed into a number of scalar optimization subproblems. After generating an offspring for each subproblem by differential evolution, the weighted sum method is utilized for selection. In addition, to make decomposition-based multiobjective optimization suit the characteristics of constrained evolutionary optimization, weight vectors are elaborately adjusted. Moreover, for some extremely complicated COPs, a restart strategy is introduced to help the population jump out of a local optimum in the infeasible region. Extensive experiments on three sets of benchmark test functions, namely, 24 test functions from IEEE CEC2006, 36 test functions from IEEE CEC2010, and 56 test functions from IEEE CEC2017, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods.

Identifying reliable information from the information ocean is a natural talent of human being, which can be hardly formalized by machines. In this paper, we show how to measure the degree of uncertainty in terms of interpoint similarity. For applications under complex uncertainty, it is desirable that we provide a systematic way for users to identify reliable information. In our approach, the similarity uncertainty for different kinds of data sets is defined according to the Shannon entropy theory. Then, similarity constrained models are designed to guarantee superior learning performance under uncertainty. Experiments using both simulation data set and several public data sets, can clearly demonstrate significant improvements of the proposed method under large uncertainty.

The construction of spatial basis functions (BFs) is critical to the time/space separation of the distributed parameter system (DPS). The spatial BFs constructed by traditional Karhunen–Loeve may not work satisfactorily for two spatial-dimensional (2-D) DPS, because of a distorted mapping of the original sensor array in the row-wise vectorization process. In this article, a novel time/space separation based method is proposed to construct dimension embedded BFs (DE-BFs) for modeling 2-D DPS. The DE-BFs are first formulated according to the spatial sensor array structure, and sequentially optimized with alternating least squares by minimizing the reconstruction error. The mapping relationship between the DE-BFs and the spatial sensor array is well preserved. In addition, the coupling across temporal and different spatial dimensions is sufficiently captured. A satisfactory model accuracy can be achieved by the DE-BFs, even with limited training data. Experiments of a 2-D curing thermal process are used to verify the effectiveness of the proposed method.

With the emergence of the concept of smart city and the increase of vehicles, the vehicular ad-hoc network (VANET) is widely accepted for the communication of vehicles to get information including road condition, traffic congestion description, speed, and location of vehicles. Naturally, the security of the data transmission in VANET has become one of the urgent tasks of researchers. Many privacy-preserving authentication protocols for VANETs have been presented. However, heavy computations and security flaws exist in them. Thus, we put forward a lightweight authentication protocol in a suitable communication model for VANET that meets the privacy protection needs, with only hash functions and exclusive-OR operations. Formal security analysis using BAN logic shows that our protocol achieves the security goals. Proverif is used to verify the security of the protocol and the results show that the privacy can be guaranteed under the simulated attacker. Moreover, through security properties analysis, our protocol is robust enough to get rid of common attacks and keep the communication data secretly. The performance comparison results show that our protocol is lightweight and efficient. Furthermore, we simulate the network with SUMO and NS-3, and all show that our protocol is efficient and practical for VANETs.

Spatial differentiator is the key element for edge detection, which is indispensable in image processing, computer vision involving image recognition, image restoration, image compression, and so on. Spatial differentiators based on metasurfaces are simpler and more compact compared with traditional bulky optical analog differentiators. However, most of them still rely on complex optical systems, leading to the degraded compactness and efficiency of the edge detection systems. To further reduce the complexity of the edge detection system, a monolithic metasurface spatial differentiator is demonstrated based on asymmetric photonic spin-orbit interactions. Edge detection can be accomplished via such a monolithic metasurface using the polarization degree. Experimental results show that the designed monolithic spatial differentiator works in a broadband range. Moreover, 2D edge detection is experimentally demonstrated by the proposed monolithic metasurface. The proposed design can be applied at visible and near-infrared wavelengths by proper dielectric materials and designs. We envision this approach may find potential applications in optical analog computing on compact optical platforms.

A vehicular delay-tolerant network (VDTN) allows mobile vehicles (MVs) to collect data from widely deployed delay-tolerant sensors in a smart city through opportunistic routing, which has proven to be an efficient and low-cost data collection method. However, malicious MVs may report false data to obtain rewards, which will compromise applications. In this paper, the Active Trust Verification Data Collection (ATVDC) scheme is proposed for efficient, cheap, and secure data collection. In this scheme, an unmanned aerial vehicle (UAV) is adopted to collect baseline data from sensors to evaluate the trust of MVs, and a high-trust MV priority recruitment (HTMPR) strategy is proposed to recruit credible MVs at a low cost. In addition, a genetic-algorithm-based trajectory planning (GATP) algorithm is proposed to allow the UAV to collect more baseline data at the minimum flight cost. After sufficient experiments, the strategy proposed in this paper is seen to greatly improve performance in terms of the error-free ratio EF, the symbol error ratio ES, and the data coverage ratio ϑ.

Modeling of the parabolic distributed parameter system (DPS) with the Karhunen-Loeve (KL) method under sparse sensing will become very difficult because the information from the measurements is inc...

The energy-aware MAC protocol is the basic communication protocol for device-to-device communication in sensor-cloud computing and facilitates data sensing, computing, and sharing for applications. Transmitting high-priority data to control centers quickly to manage emergencies is essential for sensor-cloud applications. In this paper, a Dynamic Wait time-based MAC (DWT-MAC) protocol is proposed for data differentiated services of a sensor-cloud system (SCS). The DWT-MAC protocol is novel in that it changes the receiver wait time, which has been fixed in previous MAC protocols, and it reduces the delay in data transmission by dynamically adjusting the wait time. In the DWT-MAC protocol, the wait time changes according to the number of senders, which can ensure that it always approaches the optimal value. A dynamic time adjustment algorithm, which causes the wait time to always trail the optimal value, is proposed to reduce delays. While the DWT-MAC protocol cannot ensure wait times of the optimal value, it can ensure that it quickly comes close to the optimal value, which is suitable for dynamically changing networks. Extensive experiments show that the DWT-MAC protocol reduces the average delay in the transmission of data of the highest priority by 49.3%.

Rapid advances in the Internet and various computer-based algorithms have partly contributed to the proliferation of cyber–physical system (CPS). On the strength of reliability, usability and adaptability, cyber–physical systems (CPSs) bring a lot of positive potential value for life in the future (e.g., automation manufacturing, energy saving). Whereas, how to handle the darker aspects of CPSs such as security, privacy and trust issues is still a challenge. In this paper, an encryption scheme is proposed to support secure, efficient and reliable CPS. By leveraging the Fibonacci Q-matrix, the proposed scheme is efficient, which makes it adequate for resource-constrained and real-time CPS. Moreover, the proposed scheme is not only proven and designed theoretically but also coded and verified in a realistic environment. Both theory and practice demonstrate that the proposed scheme is valid, efficient and secure.

This paper considers the estimator-based $H_{\infty }$ sampled-data fuzzy control (SDFC) problem of nonlinear parabolic partial differential equation (PDE) systems. First, a Takagi–Sugeno (T–S) fuzzy parabolic PDE model is proposed to represent the nonlinear PDE system. Second, with the aid of the T–S fuzzy PDE model, an estimator-based SDFC design ensuring the exponential stability of the closed-loop fuzzy PDE system with an $H_{\infty }$ performance is developed via a Lyapunov functional. The outcome of the estimator-based $H_{\infty }$ SDFC problem is formulated as a bilinear matrix inequality optimization problem, which is solved by an iterative algorithm on the basis of the linear matrix inequalities. Finally, for demonstrating the effectiveness of the proposed method, simulation results are provided to control the diffusion equation and the FitzHugh–Nagumo equation.

The aim of this article is to establish a novel boundary antidisturbance control to dampen the oscillation of a spatially nonlinear flexible string system influenced by unknown disturbances. To realize this, new disturbance rejection controllers are constructed to effectively suppress the external disturbances for vibration reduction with the help of a newly designed disturbance observer. Adopting Lyapunov direct method, we ensure that the derived control system is uniformly bounded stable. Simulations are also performed to demonstrate the feasibility and validity of the suggested strategy.

Modeling influencing factors of battle damage is one of essential works in implementing military industrial logistics simulation to explore battle damage laws knowledge. However, one of key challenges in designing the simulation system could be how to reasonably determine simulation model input and build a bridge to link battle damage model and battle damage laws knowledge. In this paper, we propose a novel knowledge-oriented modeling method for influencing factors of battle damage in military industrial logistics, integrating conceptual analysis, conceptual modeling, quantitative modeling and simulation implementation. We conceptualize influencing factors of battle damage by using the principle of hierarchical decomposition, thus classifying the related battle damage knowledge logically. Then, we construct the conceptual model of influencing factors of battle damage by using Entity-Relationship approach, thus describing their interactions reasonably. Subsequently, we extract the important influencing factors by using social network analysis, thus evaluating their importance quantitatively and further clarifying the elements of simulation. Finally, we develop an agent-based military industry logistics simulation system by taking the modeling results on influencing factors of battle damage as simulation model input, and obtain simulation model output, i.e., new battle damage laws knowledge, thus verifying feasibility and effectiveness of the proposed method. The results show that this method can be used to support human decision-making and action.

A reinforcement learning-based method is proposed for optimal sensor placement in the spatial domain for modeling distributed parameter systems (DPSs). First, a low-dimensional subspace, derived by Karhunen-Loève decomposition, is identified to capture the dominant dynamic features of the DPS. Second, a spatial objective function is proposed for the sensor placement. This function is defined in the obtained low-dimensional subspace by exploiting the time-space separation property of distributed processes, and in turn aims at minimizing the modeling error over the entire time and space domain. Third, the sensor placement configuration is mathematically formulated as a Markov decision process (MDP) with specified elements. Finally, the sensor locations are optimized through learning the optimal policies of the MDP according to the spatial objective function. The experimental results of a simulated catalytic rod and a real snap curing oven system are provided to demonstrate the feasibility and efficiency of the proposed method in solving the combinatorial optimization problems, such as optimal sensor placement.

Self-accelerating beams show considerable captivating phenomena and applications owing to their transverse acceleration, diffraction-free and self-healing properties in free space. Metasurfaces consisting of dielectric or metallic subwavelength structures attract enormous attention to acquire self-accelerating beams, owing to their extraordinary capabilities in the arbitrary control of electromagnetic waves. However, because the self-accelerating beam generator possesses a large phase gradient, traditional discrete metasurfaces suffer from insufficient phase sampling, leading to a low efficiency and narrow spectral band. To overcome this limitation, a versatile platform of catenary-inspired dielectric metasurfaces is proposed to endow arbitrary continuous wavefronts. A high diffraction efficiency approaching 100% is obtained in a wide spectral range from 9 to 13 μm. As a proof-of-concept demonstration, the broadband, high-efficiency and high-quality self-accelerating beam generation is experimentally verified in the infrared band. Furthermore, the chiral response of the proposed metasurfaces enables the spin-controlled beam acceleration. Considering these superior performances, this design methodology may find wide applications in particle manipulation, high-resolution imaging, optical vortex generation, and so forth.

This article provides a framework of dead zone compensation and robust adaptive vibration control for uncertain spatial flexible riser systems. First, nonsymmetric dead zone nonlinearity is represented in the form of the desired control input with the addition of an extra nonlinear input error. Second, by visualizing those input errors and extrinsic disturbances as an unknown “disturbance-like” term, a new robust adaptive vibration control technology and online updating laws can be constructed for riser systems to guarantee the oscillation reduction and compensation of uncertainties and dead zone. Third, the constructed control ensures and achieves bounded Lyapunov stability in the controlled system. Ultimately, control performances are demonstrated with appropriate design parameters.

In addition to changing service management, smart devices connect people and objects around them and collect data from them on and on, in order to construct the notion of a smart city. Such data produced by embedded devices and automatically transmitted over the Internet provides people with the information to make decisions. A smart grid is one of the most popular applications for a smart city. Due to the insecurity of the wireless channels, the security of data transmission in a smart grid has become a hot issue nowadays. Many schemes for data protection have been proposed, but weaknesses exist generally. We present a new data transmission scheme for a smart grid among the smart meter (SM), the electricity utility (EU), and the trusted authority (TA). The EU can obtain the power consumption of each SM, but cannot get the real identity of the SM. To keep the privacy of the user, if the consumption value is over the threshold in special time span or identity of SM is required for public affairs, TA could track the identity in time. Formal proof with random oracle model and security analysis are expressed to show the security of the proposed scheme. Via the performance and network simulation, it is easy to see that our scheme is practical for a smart city.

Active layer morphology optimization and electrode buffer layer interface modification are commonly used strategies in improving the performance of polymer solar cells (PSCs). In this study, we prepared PTB7: PC71BM bulk heterojunction PSCs with 1,8-diiodooctane (DIO) and polyethylene glycol (PEG) additives, and studied the influence of binary additives on exciton dissociation, charge transport and charge extraction. DIO facilitates donor/acceptor phase separation for efficient exciton dissociation and charge transport. The migration of PEG from active layer to the PEDOT:PSS layer improves the crystallinity of PTB7, optimizes charge transport pathway, and enhances the conductivity of PEDOT:PSS layer. With the combined advantages of binary additives in active layer morphology optimization and anode buffer layer modification, the device exhibits a high short-circuit current density of 20.03 mA/cm2 and an improved power conversion efficiency. Binary additive provides a promising method to optimize active layer morphology and improve interfacial buffer layer of PSCs simultaneously.

A low-rate denial of service (LDoS) attack is a precise network attack that aims at reducing the quality of the network service. Many networks do not have an effective mechanism for defending against LDoS attacks, including the emerging Internet of Things. In this paper, we propose an LDoS attack detection method that is based on multiple features of network traffic and an improved Adaboost algorithm (MF-Adaboost). Based on an analysis of the network traffic, we construct a network feature set that is used for feature calculation and feature selection of network traffic data. Feature calculation can extract the most useful information from the network traffic data and reduce the scale of the network data. Feature selection is used to select the optimal classification features to ensure that the detection algorithm can be effectively trained. This method utilizes the Adaboost algorithm, which is a classification algorithm in the field of machine learning. The well-trained Adaboost algorithm can effectively identify LDoS attack traffic. We improve the Adaboost algorithm to alleviate the imbalance of the sample weights. Experiments are conducted on the NS2 simulation platform and a test-bed platform to evaluate the performance of our method. The experimental results demonstrate that our method can detect LDoS attacks effectively.

A novel data-driven approach is proposed to localize the abnormality for distributed parameter systems (DPSs) in this paper. The cross-correlation order of DPSs in the space domain is first obtained by the cumulants-based identification method. Then, a spatial augmented matrix of the spatial–temporal distribution data is formed and a dynamic spatial independent component analysis method is proposed for independent decomposition. The dominant spatial independent components will be extracted and the spatial residuals can be produced for spatial reference statistics. Through the kernel density estimation method, the confidence bounds of these statistics in normal condition (abnormality free) can be established as the spatial references. These unique two references will guarantee the reliable spatial localization of abnormality. Different from model-based methods that rely on an explicit system model of the process, the proposed approach is model free and only uses recorded process data. Experiments on two typical DPSs demonstrate the effectiveness of the proposed approach.

Smart city is a hot aim, and is organized gradually in many developed countries. People can understand the variation of city clearly via the collected data from sensors and RFID chips all over the city. As one of the most important applications for smart city, smart grid is a popular technology which has been already used in some developed countries. Wireless circumstance is the main way for the transmission data in collection process, and it is undoubtedly insecure. The problems that protecting the consumption data in smart grid and tracking some special users simultaneously are still open issues. We propose a novel scheme for smart grid involving three kinds of entities: Smart Meters (SMs), Electricity Utilities (EUs), and a Trustful Anchor (TA). In this scheme, SM encrypts the periodical consumptions and its identity, and sends the information to the relative electricity utility, and then EU can get the consumptions of smart meters in its scope. If necessary, TA may disclose the identities of the special smart meters. The security of this scheme is illustrated by formal proof and security property analysis. After expressions of performance and network simulation, it is easy to see that our scheme is suitable for practicality.

The thermal effect has a significant impact on the performance of a lithium-ion battery. Thus, modeling the thermal process, which always involves unknown boundary heat exchange, is significant to battery management. Two critical issues should be addressed for the thermal process modeling: 1) the nominal model, which is constructed offline, can be updated efficiently to compensate for any online disturbances; and 2) the influence of previous and recent spatiotemporal dynamics may be varying and should be handled properly. Bearing these in mind, in this paper, a spatial correlation-based incremental learning technique is designed for spatiotemporal modeling. First, the incremental learning technique is developed to update the dominant spatial basis functions of the nominal model, which is constructed by a time/space separation-based method. Then, a forgetting factor is incorporated into the incremental learning technique to handle time-varying dynamics. Additionally, the popular approximator, that is, the radial basis function neural network, is utilized to identify the low-dimensional temporal model. Simulations and experiments on a pouch type battery with boundary heat exchange have demonstrated the accuracy and efficiency of the proposed modeling method.

In a wide-area control system (WACS) of power systems, delays will affect the control performance. Dealing with randomly distributed delays besides achieving desired control performance via the WACS control strategy will make the control strategy design more challenging. Moreover, delays from the centralized controller to actuators in WACS are unknown for the controller when calculating the control strategy. A prediction-based hierarchical delay compensation (PHDC) technique enhanced by an increment autoregressive prediction (IAR) algorithm is proposed in this paper to solve these problems. With PHDC, the WACS inputs and outputs are approximated by IAR and thus available in real time for the control strategy by taking advantage of the WACS synchronization mechanism. Meanwhile, the IAR algorithm is proposed with characteristic analysis by which IAR is proved to be an enhancement to PHDC. The combination of PHDC and IAR thus achieves the ideal compensation performance. A Real-Time Digital Simulator (RTDS) hardware-in-the-loop simulation of a wide-area damping control system in an actual power grid is carried out to verify the performance of PHDC under the actual conditions and extreme conditions, respectively. The results demonstrate that PHDC compensates the randomly distributed delay effectively as well as mitigates the packet congestion and packet loss.

The Internet of Things (IoT) enables all objects to connect to the Internet and exchange data via different emerging technologies, which makes the intelligent identification and management a reality. Wireless sensor networks (WSNs), as a crucial basis of IoT, have been applied in many fields like smart health care and smart transportation. With the development of WSNs, data security has attracted more and more attention, and user authentication is a popular mechanism to ensure the information security of WSNs. Recently, many authentication mechanisms for wireless medical sensor networks (WMSNs) have been proposed, but most of the protocols cannot achieve the features of local password change and forward secrecy while resisting stolen smart card attack. To enhance the security based on previous work, an ECC-based secure three-factor authentication protocol with forward secrecy for WMSN is proposed in this paper. It utilizes a fuzzy commitment scheme to handle the biometric information. Meanwhile, fuzzy verifier and honey_list techniques are used to solve the contradiction of local password verification and mobile device lost attack. The security of our protocol is evaluated by provable security, Proverif tool, and information analysis. Besides, the comparisons with the relevant protocols are given, and the results indicate that our protocol is robust and secure for WMSN systems.

Jetting technology has become popular in microelectronic packaging due to its high efficiency and noncontact property. Its performance is significantly affected by the structure design. However, it is very difficult and time-consuming to conduct the optimal design under the traditional approaches, especially when more than one performance criteria is required. A surrogate model-based design method is proposed to optimize structure parameters under two different performance criteria. The surrogate model can serve for the quick evaluation of the process performance, upon which the process can be optimally designed via iterative search. As two performance indexes are required in the jetting process, a biobjective optimization method is used to design multiple parameters concurrently. Under this new design approach, the design can be easier to complete, and the jetting performance is significantly improved in both simulation study and experiment testing.

The transformation behavior of ferrous sulfate was examined during hematite precipitation for iron removal in hydrometallurgical zinc. Specifically, the effects of the method used for oxygen supply (pre-crystallization or pre-oxidation of ferrous sulfate) and temperature (170–190 °C) on the redissolution and oxidation–hydrolysis of ferrous sulfate were studied. The precipitation characteristics and phase characterization of the hematite product were investigated. The results showed that the solubility of ferrous sulfate was considerably lower at elevated temperatures. The dissolution behavior of ferrous sulfate crystals was influenced by both the concentrations of free acid and zinc sulfate and the oxydrolysis of ferrous ions. Rapid oxydrolysis of ferrous ions may serve as the dissolution driving force. Hematite precipitation proceeded via the following sequential steps: crystallization, redissolution, oxidation, and precipitation of ferrous sulfate. The dissolution of ferrous sulfate was slow, which helped to maintain a low supersaturation environment, thereby affording the production of high-grade hematite.

Electronic warfare is a modern combat mode, in which predicting digital material consumption is a key for material requirements planning (MRP). In this paper, we introduce an insensitive loss function (e) and propose a e-SVR-based prediction approach. First, we quantify values of influencing factors of digital equipments in electronic warfare and a small-sample data on real consumption to form a real combat data set, and preprocess it to construct the sample space. Subsequently, we establish the e-SVR-based prediction model based on “wartime influencing factors - material consumption” and perform model training. In case study, we give 8 historical battle events with battle damage data and predict 3 representative kinds of digital materials by using the proposed approach. The results illustrate its higher accuracy and more convenience compared with other current approaches. Taking data acquisition controller prediction as an example, our model has better prediction performance (RMSE = 0.575 7, MAPE (%) = 12.037 6 and R2 = 0.996 0) compared with BP neural network model (RMSE = 1.272 9, MAPE (%) = 23.577 5 and R2 = 0.980 3) and GM (1, 1) model (RMSE = 2.095 0, MAPE (%) = 24.188 0 and R2 = 0.946 6). The fact shows that the approach can be used to support decision-making for MRP in electronic warfare.

A quinine-derived thiourea-catalyzed inter-/intramolecular Michael cycloaddition of chromone-oxindole/benzofuranone synthons with 3-substituted methylenebenzofuranones has been established, which constructed enantiomerically pure bispiro[benzofuran-oxindole/benzofuran-chromanone]s bearing five consecutive stereocenters including two spiro quaternary carton centers in good yields (up to 93%) with high diastereoselectivities (up to >20:1 dr) and good enantioselectivities (up to >99% ee). Moreover, this is the first example of bifunctional chromone-benzofuranone synthon directed organocatalytic tandem reaction, and also the first example of the bispiro[benzofuran-oxindole] and bispirobenzofuranone, potentially useful in medicinal chemistry.

To satisfy the distinct requirements of different evolutionary stages, a dynamic multi-swarm global particle swarm optimization (DMS-GPSO) is proposed in this paper. In DMS-GPSO, the entire evolutionary process is segmented as an initial stage and a later stage. In the initial stage, the entire population is divided into a global sub-swarm and multiple dynamic multiple sub-swarms. During the evolutionary process, the global sub-swarm focuses on the exploitation under the guidance of the optimal particle in the entire population, while the dynamic multiple sub-swarms pour more attention on the exploration under the guidance of the neighbor’s best-so-far position. Moreover, a store operator and a reset operator applied in the global sub-swarm are used to save computational resource and increase the population diversity, respectively. At the later stage, some elite particles stored in an archive are combined with the DMS sub-swarms as a single population to search for optimal solutions, intending to enhance the exploitation ability. The effect of the new introduced strategies is verified by extensive experiments. Besides, the comparison results among DMS-GPSO and other 9 peer algorithms on CEC2013 and CEC2017 test suites demonstrate that DMS-GPSO can effectively avoid the premature convergence when solving multimodal problems, and yield more favorable performance in complex problems.

The encapsulation of living cells in the transformation of contaminants into green energy has great potential for solving energy and environmental pollution problems. However, some unresolved challenges related to the maintenance of the capsules' mechanical stability, biomass harvesting without leaking and necessary high activities hinder its practicability. Here, we develop a novel “interior division-external capsulation” dual-immobilization method to anchor photosynthetic bacteria into self-assembled fiber macrospheres and coating with polymer layers by phase inversion. The functional sites (C–N, C–OH and N–H) of the modified macrospheres cause more cells to adhere in the cage and its large void volume provides sufficient space for cell harvesting. Micro/nanoporous polymer shells can not only facilitate the rapid diffusion of substrates into the cavity, but also effectively reduce cell leakage. This strategy provides a facile method to significantly enhance the chemical/physical stability of the host and quickly achieve much higher activity of the encapsulated cells in the degradation of organics and H2 production as compared to free cells. Importantly, the core–shell confined encapsulation concept enables the cells to have considerable salt tolerance as high as 80 g L−1 and wide pH stability. The long-term reusability of the self-assembled macrospheres provides extensive application prospects for cell encapsulation as a clean and sustainable alternative in broader fields.

Benefiting from its efficiency and quick convergence, the feasibility rule (FR) is well-known for its ability to solve constrained optimization problems (COPs). However, it is highly criticized for its heavy preference for constraints. Thus, an individual-dependent feasibility rule (IDFR) was designed by alleviating the preference from two aspects. Some information of constraints, which might be nonsignificant, is depressed. By contrast, some promising information of objective function is leveraged. The extent of information is individual-dependent. To further enhance the diversity, a two-phase diversity strategy was developed. Due to their numerous merits, two differential evolution (DE) operators were selected as components of the search algorithm. By the above process, we proposed a constrained DE (i.e., IDFRDE). To the best of our knowledge, we made the first attempt to improve FR from the individual perspective. IDFR is more robust than FR while keeping the same computational time complexity. However, it would converge slower than FR on some easy COPs. Experiments on three widely used benchmarks show that: 1) IDFRDE outperforms or gets similar results comparing with other known algorithms; 2) IDFR is more effective than FR on complex COPs; 3) both, the search algorithm and the diversity strategy are important to IDFRDE.

Wave plates as important polarization control devices are widely used in the applications of biomedical imaging, fiber optical communication, astronomy, semiconductor industry and aerospace etc. To make the wave plate actively tunable, we propose a rectangular antenna-based metasurface based on the phase-changing material Ge2Sb2Te5 (GST) with high transmittance and polarization conversion rate. Before the GST phase transition, the metasurface operates as a quarter-wave plate in the wavelength range of 10.0-11.9 μm. After the GST turning into crystalline state, the metasurface operates as a half-wave plate in the wavelength range of 10.3-10.9 μm. The physical mechanism for the high transmittance efficiency and polarization conversion rate attributes to the combination of the electric dipole and magnetic dipole resonances. Moreover, the adjustable wavelength range can be further extended by changing the geometry of antenna-based GST to selectively enhancing or suppressing the electric and magnetic resonances. This work provides a new strategy for tunable wave plate, which would have the potential in the application of multi-functional integrated optical systems.

In natural environments, multiple metal ions are concomitant, and their adsorption at clay mineral/water interfaces is competitive and selective. In this study, the competitive mechanisms and impac...

An incremental spatiotemporal learning scheme is proposed for online modeling of distributed parameter systems (DPSs). A novel incremental learning method is developed to recursively update the spatial basis functions and the corresponding temporal model based on the Karhunen–Loeve decomposition for time-space separation. The time-space synthesis continually evolves by adding new increment data with more updated information and revising the existing parameters of the dynamic system. In this way, the spatiotemporal structure is inherited and updated efficiently as output data increases over time. The adaptive nature of this evolving structure makes it promising for online modeling of DPSs under streaming data environment. The proposed incremental modeling scheme is evaluated on the classical benchmark of a catalytic rod problem. The simulation results demonstrate the viability and efficiency of the proposed method for online modeling of DPSs.