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To date, a number of parameter identification methods have been developed for the purpose of structural health monitoring and vibration control. Among them, the extended Kalman filter (EKF) series methods are attractive in view of the efficient unbiased estimation in recursive manner. However, most of these methods are performed on the premise that the parameters are time-invariant and/or the loadings are known. To circumvent the aforementioned limitations, an online EKF with unknown input (OEKF-UI) approach is proposed in this paper for the identification of time-varying parameters and the unknown excitation. A revised observation equation is obtained with the aid of projection matrix. To capture the changes of structural parameters in real-time, an online tracking matrix (OTM) associated with the time-varying parameters is introduced and determined via an optimization procedure. Then, based on the principle of EKF, the recursive solution of structural states including the time-variant parameters can be analytically derived. Finally, using the estimated structural states, the unknown inputs are identified by means of least-squares estimation (LSE) at the same time-step. The effectiveness of the proposed approach is validated via linear and nonlinear numerical examples with the consideration of parameters being varied abruptly.

Online identification of time-variant parameters without knowledge of external loads is an important but challenging task for structural health monitoring and vibration control. In this study, a two-stage approach, named extended Kalman filter with forgetting factor matrix under unknown inputs (EKF-FFM-UI), is proposed for simultaneously identifying the time-variant parameters and external loads. In stage 1, an extended Kalman filter under unknown inputs (EKF-UI) approach previously proposed by the authors is employed for estimating the structural states and unknown loads. This EKF-UI approach is solely suitable for time-invariant system identification. Therefore, the aim of stage 2 is to improve this approach for the purpose of possessing tracking capability. In this stage, the acceleration responses are first reconstructed by using the differential equation of motion and employed for improving the accuracy of estimated structural states. A forgetting factor matrix is introduced into the priori estimation error covariance matrix to track time-varying parameters. The square errors between the measurements and the corresponding estimates are defined as an index and used for detecting the damage time instant. Then, a covariance resetting technique is employed to assure that such changes in structural parameters can be efficiently captured. A shear-type building structure without/with magneto-rheological (MR) dampers and a fixed beam structure are used as numerical examples for validating the effectiveness of the proposed approach. Experimental tests on a six-story building model are also conducted. Results show the time-varying parameters and unknown inputs can be simultaneously identified with acceptable accuracy.

The development of graphene oxide/rare earth complexes luminescent materials is limited due to the strong quenching ability of graphene oxide. However, the hybrid materials with pyromellitic acid (PMA) as ligand not only have better performance, but also have stronger luminescence intensity. Herein we tested the effect of the carboxylic acid structure of PMA on the luminescence intensity of graphene oxide/rare earth complexes hybrid materials. Phthalic acid (PA), Isophthalic acid (i-PA) and Terephthalic acid (p-PA) are selected as test ligand, representing the o-position, meta-position and para-position effects of PMA. By comparing the luminescence intensity, we found that o-position effect is helpful for the ligand to sensitize rare earth ions, and meta-position and para-position effects are helpful for the graphene oxide sheets (GOSs) to improve the luminescence intensity of hybrid materials and inhibit the quenching effect of the GOSs. Furthermore, we speculate on the relationship between the distance of π-π stacking of GOSs and rare earth complexes and luminescence intensity of hybrid materials. This work provides an important reference for the design of graphene oxide/rare-earth aromatic carboxylic acid hybrid luminescent materials.

It has been proved that the usage of multi-type observations including global and local information for structural health monitoring (SHM) outperforms that of solo-type measurement. Kalman filter (KF) is a simple but powerful tool for the online state estimation. However, external force is required for the classic KF. Moreover, direct implementation of KF in time domain may be time consuming especially for large structures with many degrees-of-freedoms (DOFs) involved. From these points of view, the idea of modal Kalman filter (MKF) is introduced. An MKF-based approach is then proposed for joint estimation of multi-scale responses and unknown loadings with data fusion of multi-type observations. By using a projection matrix and the selected several modes, a modified observation equation in modal domain is derived. Limited multi-type measurements are fused together for online estimating multi-scale responses of structure at its critical locations. The unknown excitation is simultaneously identified by least squares estimation (LSE). The drift problem of the real time estimation of structural responses and unknown loads is avoided. The effectiveness of the proposed approach is numerically demonstrated via several examples. Results show that the proposed approach is capable of satisfactorily estimating the unmeasured multi-scale responses and identifying the unknown loadings.

With the advancement of technology and science, the power system is getting more intelligent and flexible, and the way people use electric energy in their daily lives is changing. Monitoring the condition of electrical energy loads, particularly in the early detection of aberrant loads and behaviors, is critical for power grid maintenance and power theft detection. In this paper, we combine the widely used deep learning model Transformer with the clustering approach K-means to estimate power consumption over time and detect anomalies. The Transformer model is used to forecast the following hour’s power usage, and the K-means clustering method is utilized to optimize the prediction results, finally, the anomalies is detected by comparing the predicted value and the test value. On real hourly electric energy consumption data, we test the proposed model, and the results show that our method outperforms the most commonly used LSTM time series model.

Background An emergency response to a medical situation is generally considered to be a risk decision-making problem. When an emergency event occurs, it makes sense to take into account more than one decision maker’s opinions and psychological behaviors. The existing research tends to ignore these multidimensional aspects. To fill this literature gap, we propose a multi-attribute model. Methods The model is based on cumulative prospect theory (CPT), considering multiple experts’ psychological factors. By not assuming full rationality, we extend existing models to allow multiple experts’ risk preferences to be incorporated into the decision-making process in the case of an emergency. Then, traditional CPT is extended by allowing for multiple attributes. In addition, rather than using crisp data, interval values are adopted to tackle the usual uncertainties in reality. Results The multi-attribute CPT based model proposed can deal with the selection of potential emergency alternatives. The model incorporates interval values to allow more uncertainty and the comparative studies show that the optimal solution changes under different scenarios. Conclusions Our illustrative example and comparative study show that considering multiple experts and multiple attributes is more reasonable, especially in complicated situations under an emergency. In addition, decision-makers’ risk preferences highly affect the selection outcomes, highlighting their importance in the medical decision-making process. Our proposed model can be applied to similar fields with appropriate modifications.

Heteroatom doping and three-dimensional (3D) porous structures are critical to the performance of graphene supercapacitor electrode materials. However, the conventional methods, such as chemical vapor deposition (CVD) and hydrothermal synthesis, require tedious processes and harsh conditions. In this work, we describe a very simple and efficient approach to fabricate a 3D nitrogen/sulfur-co-doped porous graphene hydrogel (3DNS-GH). It is found that thiourea can reduce graphene oxide (GO) obtaining 3DNS-GHs at low temperature and atmospheric pressure by adjusting the pH value, because the reducibility of thiourea significantly increases in acidic or alkaline media. Furthermore, the amount of nitrogen and sulfur doping and nano-pore structure varies with the pH value of solution. The 3DNS-GH exhibits uniform pore structure and can be directly used as electrode materials without additive binder. The binder-free electrode based on the optimum operating conditions (3DNS-GH-12) exhibit a high specific capacitance of 259.2 F g−1 and retain 96.0% of its initial capacitance after 10,000 cycles in 2 M KOH solution at 10 A g−1. Additionally, the symmetrical supercapacitor assembled by the 3DNS-GH-12 showed an impressive energy density of 6.08 W h kg−1 and a high power density of 7.51 kW kg−1. Therefore, these materials demonstrate excellent performance, indicating this route possess potential applied value in the production of supercapacitor electrode materials.

L-Tryptophan (Trp) functionalized graphene quantum dots (Trp-GQDs) were prepared via a thermal cracking method, using Trp and citric acid (CA) as raw material. The as-obtained Trp-GQDs show stronger blue photoluminescence (PL) than simple graphene quantum dots (GQDs). The presence of Trp can obviously improve the luminescent properties of GQDs. The possible mechanism was investigated for the formation and luminescence properties of the Trp-GQDs. Furthermore, the Trp-GQDs exhibit a good liner relationship between fluorescence response and pH variation within the pH range of 4–12. More importantly, Trp-GQDs still perform well in sensitivity to pH values in real water samples. The results indicate that the probe shows great potential in pH detection.

Truth discovery methods aim to identify which piece of information is trustworthy from multi-sourced data. Most existing truth discovery methods, however, are designed for structured data and fail to meet the strong need to extract trustworthy information from raw text data. More specifically, existing methods ignore the semantic information of text answers, i.e., answers may contain multiple factors, the word usages may be diverse, and the answers may be partially correct. In addition, ubiquitous long-tail phenomenon exists in the tasks, i.e., most users provide only a few answers and only a few users provide plenty of answers, which causes the user reliability estimation for small users to be unreasonable. To tackle these challenges, we propose a Graph Convolutional Network (GCN) based truth discovery model to automatically discover trustworthy information from text data. Firstly, Smooth Inverse Frequency (SIF) is utilized to learn real-valued vector representations for answers. Then, we construct undirected graph with these vectors to capture the structural information of answers. Finally, the GCN is utilized to store and update the reliability of these answers, and sums up all the feature vectors of all neighboring answers to improve the accuracy and efficiency of truth discovery. Different from traditional methods, we use vectors to store the reliability of answers which have higher representation capability compared with real numbers, and network is utilized to capture complex relationships among answers rather than simplified functions. The experiment results on real datasets show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art truth discovery methods.

As the need for environmental protection and resource sustainability has increased in recent times, wastewater treatment has become increasingly important. In this paper, a group decision-making model for plans selection in wastewater treatment is proposed. In order to deal with uncertainties and multiple attributes in wastewater treatment, an intuitionistic fuzzy set is employed to evaluate wastewater treatment plans effectively. A distance measure is defined to obtain an objective weight measuring the expert’s judgment. More specifically, experts first use group decision-making on the various plans with an intuitionistic fuzzy set. Meanwhile, Due to the decision-makers psychological behavior, the prospect theory is applied. Next, the various plans are ranked by The Order of Preference by Similarity to Ideal Solution (TOPSIS) method and prospect theory. Finally, an illustrative example of wastewater treatment plans selection is used to verify the proposed model.

In this paper, a novel modified graphene oxide sheets (GOSs) has been synthesized based on pyromellitic acid (PMA) depositing on the surface of GOSs via noncovalent approach. Fourier Transform Infrared spectra (FT-IR), X-ray diffraction patterns (XRD) and scanning electron microscopy (SEM) were adopted to investigate its structure. The as-prepared materials are dispersed in Rhodamine-B (RB) solutions which show highly efficient fluorescence. This phenomenon suggests the quenching effect of graphene oxide can be effectively weakened via rational structural modification. Moreover, this dispersion serving as a fluorescent probe still has a relatively high selectivity and sensitivity (Stern-Volmer constant K SV = 4.58*10 4 L mol −1 ) to Fe 3+ ions.

We report a facile combustion method synthesis of reduced graphene oxide/CuI composites as a photocatalyst, in which CuI nanoparticles were homogeneously distributed on the surface of reduced graphene oxide (rGO), showing a good visible light response. The rGO-supported and unsupported CuI hybrids were tested over the photocatalytic reduction of CO2 for methanol evolution in visible light. In the current study rGO-CuI composites have shown excellent yields (19.91 μmol g-cat−1). rGO provides a light-weight, charge complementary and two-dimensional material that interacts effectively with the CuI nanoparticles.

In this study, the authors developed a novel load-isolation and extendible wireless power transfer system for multi-receiver applications. This system consists of a half-bridge or full-bridge converter power supply, K-inverters, transmitting resonators, and multiple receivers as loads. Only one power supply is needed in this extendible system and the power division ratio of the receivers is controllable. The principle of the authors’ load-isolation scheme is illustrated theoretically in this study and was verified through experimentation, as described below. The results show that it can indeed be applied in extendible multi-receiver household applications. The system features are also stated theoretically and verified experimentally, including the power-distributing of each receiver, the transmission efficiency, and the cross-couplings. Experimental results are in agreement with the theory very well.

Utilization of multitype measurements including local and global information for structural health monitoring (SHM) has typically outperformed that using solo-type measurements. However, in...

Nonlinearity exists widely in civil engineering structures, for example, the initiation and growth of damage under dynamic loadings is a typical nonlinear process. To date, for the purpose of structural evaluation and a better understanding nonlinear characteristics of complicated structures, a number of parametric and nonparametric methods have been developed for the identification of nonlinear restoring force (NRF). However, due to the highly individualistic nature of nonlinear systems, it would be inefficient to attempt to express the structural NRF in a general parametric form. For many nonparametric techniques, their nonparametric models or approximations may result in undesirable results or oscillations around unsmooth points. In this paper, on the basis of extended Kalman filter (EKF), a model-free NRF identification approach is proposed to circumvent the limitations mentioned above. The NRF to be identified was treated as &lsquo, unknown fictitious input&rsquo, and thus, no prior assumptions or approximations for the NRF model were required. With the aid of a projection matrix, a modified version of observation equation was obtained. Based on the principle of EKF, the recursive solution of the proposed approach was analytically derived. The NRFs provided by the nonlinear components were identified by means of least squares estimation (LSE) at each time step. Numerical examples, including building structures equipped with magnetorheological (MR) damper and shape memory alloy (SMA) damper, demonstrated that the proposed approach is capable of satisfactorily identifying NRF without knowledge or intuitive assumptions of any nonlinear model class in advance.

Generally, amorphous alloys are fabricated in an inert atmosphere—Ar to avoid crystallization and gain high glass forming ability (GFA). In this work, the effect of non-inert casting atmospheres (N2, air and O2) on the GFA of Fe76Si9B10P5, Fe76Si8B10P5Mo1, Fe77Mo2P10C4B4Si3 and [(Fe0.8Co0.2)0.75B0.2Si0.05]96Nb4 soft magnetic bulk metallic glasses (BMGs) was systematically investigated. Surprisingly, contrary to existing experience, while the GFA of [(Fe0.8Co0.2)0.75B0.2Si0.05]96Nb4 alloy remains the same as cast in Ar, it was found that the non-inert casting atmospheres can dramatically improve the GFA of P-containing Fe-based BMGs, especially in oxidizing casting atmosphere, with no influence in their thermal properties and magnetic properties. The results also indicated the surface composition of the P-containing Fe-based BMGs varied much with the casting atmospheres whilst the bulk composition remain unchanged. Moreover, plausible reasons were eliminated and underlying mechanism was elucidated. The decreased surface tension resulted from the segregation of P element of the P-containing Fe-based liquids during the melting process leads to the crystallization occurring in the surface layer rather than in the central area. At last, the enhancement of GFA by non-inert casting atmospheres were mainly ascribed to the increased surface tension resulting from the elimination of oxygen element for the oxidizing (air and O2) atmosphere and the alloying effect of nitrogen element for the N2 atmosphere, respectively. The new understanding about the non-inert atmosphere and metallic amorphous formation is expected to benefit the composition design of amorphous materials with better GFA and advances in the manufacture of amorphous alloys.

In this paper, a novel Zn(L1)2(L2) phosphor with yellow light under blue-light excitation was synthesized using simple one pot method by introducing the second ligand 4-dimethylaminopyridine (L2) to the reaction that uses 5-chloro-8-hydroxyquinoline (L1) and zinc acetate to prepare the coordination compound Zn(L1)2. The structure of Zn(L1)2(L2) was determined with single crystal X-ray diffraction techniques. Besides, the compounds Zn(L1)2 and Zn(L1)2(L2) were characterized by Fourier transform infrared spectroscopy (FT-IR), powder X-ray diffraction (PXRD), fluorescence spectroscopy, and thermo-gravimetric analysis (TGA). Compared with the compound Zn(L1)2, the optimal excitation and emission wavelengths of the compound Zn(L1)2(L2) have red-shifted, with the excitation wavelength of 467 nm and the emission wavelength of 574 nm. The special luminescent properties and simple synthesis method of the Zn(L1)2(L2) make it become a promising candidate in the field of white-light-emitting diodes.

Herein, we report an ammonia-responsive of lanthanide (Eu(Sal)3, Sal = salicylic acid) complexes hydrogel sensors based on the amine molecule affecting the energy transfer process. In this system, the ammonia stimulation causes the coordination and decomposition of Eu(Sal)3 in the hydrogel, thus leading the fluorescence quenching and recovery of Sal in the ammonia atmosphere. Furthermore, the EuLn-Fm/PVA hydrogel composed of Eu(Sal)3 and Eu(TTA)3 complexes (TTA = 2-Thenoyltrifluoroacetone, L = Sal and TTA, Fm = formamide, n = 1-3) was prepared by an one-pot process, which achieves the continuous detection for ammonia gas. Meanwhile, EuLn-Fm/PVA hydrogel also achieved diverse responses for ammonia gas. This device could be a promising strategy for developing fluorescence hydrogels with potential applications due to its desirable advantages such as simple preparation, cost-effectiveness, etc..

To clarify the underlying mechanism of effects of P on amorphous forming ability (AFA) for Fe–Si–B–P alloy system, the AFA and melting viscosity of (Fe76Si9B10)(100-x)/95Px (x = 0–3) amorphous alloys measured in the temperature range of 1200–1450 °C by oscillating cup method were investigated. A viscosity hysteresis occurs at about 1350 °C between the heating and cooling processes for all alloys, and the values of viscosity almost increase as P content increases at each measured temperature under cooling process. However, only considering viscosity cannot provide a satisfactory explanation for the trend of AFA for this alloy system. Afterwards, the mechanism was well revealed by calculating the Gibbs free-energy difference and speculating the interfacial tension. In addition, a positive correlation between magnetic properties (coercivity and effective permeability) and the AFA of this alloy system was also founded, which can provide an auxiliary in understanding the mechanism of AFA to some extent. Meanwhile, when x = 3, the alloy shows excellent soft magnetic properties including low coercivity of 0.9 A/m and high effective permeability of 17,000, which is promising for future electromagnetic device application.

Under the background of green and low-carbon economy, comprehensively analyzing the influential factors and apply them to accurately estimate energy-related CO2 emissions make great sense for Chinese green-economy development. In this paper, the grey incidence analysis is employed to identify the influential factors that may generate strong and nonlinear effects on emissions. To consider these nonlinear effects, a new discrete grey power model is designed, through introducing grey power indexes into the model structure. Additionally, one-step ahead rolling mechanism is associated with the proposed technique so as to further improve its forecasting precision. To demonstrate its efficacy, the rolling discrete grey power model is utilized to forecast Chinese energy-related carbon emissions from 2011 to 2015, and then compared to projections provided by diversified competing techniques - the empirical applications show that the newly proposed rolling model clearly outperform other benchmark models in accordance with three measuring indices. Therefore, by using this optimum model, Chinese carbon emissions from 2016 to 2020 are quantified, which infers that it will rise to over 9936 million tons in 2020, and is consistent with those provided by other international agencies and scholars. Eventually, based on the forecasts, suggestions on reducing the CO2 emissions are provided for decision-makers.

Herein, a series of novel dual-mode NaYF4:Ln/NaYF4:Eu (Ln = Yb, Er, Tm) composites involving up-conversion and down-shifting luminescence have been prepared by a facile synthetic method. X-ray diffraction, scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray analysis, up-conversion and down-shifting fluorescence spectrometer were used to characterize the materials. By doping different lanthanide ions and tailoring the molar ratio of the doped lanthanide ions, three kinds of composites were obtained which could exhibit three-primary-color (red-green-blue, abbreviating as RGB) up-conversion luminescence under the irradiation of 980 nm laser, and generate red down-shifting color under the excitation of 393 nm. The composites show excellent photoluminescence properties, which attribute to NaYF4 served as a major matrix for both the up-conversion and down-shifting materials. Moreover, the work provides a promising material for anti-counterfeiting application.

A novel hybrid portfolio selection procedure consisting of three phases is proposed for choosing an appropriate project portfolio based on a set of criteria having fuzzy measurements. Different from other existing selection procedures, in which decisions are based mainly on the projects’ characteristics at decision points, the proposed procedure takes into account projects’ historical performances. In particular, projects are first evaluated and assigned comparable scores, based on which a multi-objective model is built. The model selects a portfolio with a view to maximizing its expected value and development trend, while minimizing its risk with other selected projects. Three important elements are considered: (1) criteria weighting over each other, (2) uncertainty in decision making, and (3) projects’ historical performances. The aim is to select more robust project portfolios in the long run, which lead to fewer replacements. Comparative studies are performed to verify the validity of the approach. It is shown that our method can lead to more robust portfolios in a shorter time, which can aid decision makers throughout the selection process.

Weapons development planning is an unstructured and complex multi-criteria decision-making problem, especially in antagonistic environments. In this paper, the defender’s decision was modelled as a high complexity non-linear optimization problem with limited resources. An operation loop with realistic link rules was first proposed to model the cooperation relationships among weapons in the defense system. The system dynamics principle was used to characterize the dynamic behavior of the nodes in a complex weapons network. Then, we used cumulative threat and development risk to measure different planning solutions by considering the opponent and uncertainties in the development process. Next, an improved Differential Evolution (DE) and Non-Dominated Sorting Differential Evolution (NSDE) were designed to determine the optimal planning solutions for a single objective and multi-objective. The compromise solution, based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), was used to evaluate the Pareto solution set of the multi-objective. Finally, an illustrative case was studied to verify the feasibility and validity of the proposed model.

The continuous-variable version of quantum key distribution (QKD) offers the advantages (over discrete-variable systems) of higher secret key rates in metropolitan areas as well as the use of standard telecom components that can operate at room temperature. An important step in the real-world adoption of continuous-variable QKD is the deployment of field tests over commercial fibers. Here we report two different field tests of a continuous-variable QKD system through commercial fiber networks in Xi'an and Guangzhou over distances of 30.02 km (12.48 dB) and 49.85 km (11.62 dB), respectively. We achieve secure key rates two orders-of-magnitude higher than previous field test demonstrations. This is achieved by developing a fully automatic control system to create stable excess noise and by applying a rate-adaptive reconciliation protocol to achieve a high reconciliation efficiency with high success probability. Our results pave the way to achieving continuous-variable QKD in a metropolitan setting., 19 pages, 6 figures

Corn cob and waste oil were co-pyrolysed in a fixed bed at the temperatures of 500 °C, 550 °C, 600 °C, respectively, under nitrogen atmosphere. Co-pyrolysis products were investigated with focus on the physical and chemical properties of oily products characterized by means of GC–MS and elemental analyser. The results show 550 °C seems to be the optimum temperature considering maximum bio oil yields and bio oil properties. Co-pyrolysis of corn cob and waste oil produced more amount of liquid and less amount of solid residue than that of pyrolysis of corn cob solely. While weight ratio of waste oil: corn cob increases from 0 to 0.87, bio-oil yield increases dramatically from 44.7wt% to 70.62wt% with increasing acids content and decreasing phenols, acohols, ketones content. The upgraded bio-oil has the potential to be an alternative fuel for engine after upgraded by HY zeolite.

A facile and straightforward strategy was developed for the fabrication of highly fluorescent carbon quantum dots (CQDs). The CQDs were synthesized only using pure-solvent tetrahydrofuran (THF) as carbon source via a one-step hydrothermal treatment without further surface passivation or modification. The as-synthesized CQDs emitted bright blue fluorescence and showed a strong emission at 460 nm with excitation at 370 nm. Also, the CQDs displayed a high quantum yield of 11.5%. The CQDs not only showed excellent photoluminescence properties and water solubility, but also exhibited an exceptional multifunctional fluorescence sensor for the applications of pH probing and silver ion (Ag+) sensing. There was a linear correlation between the fluorescence intensity vs. the pH value in the range from 4.0 to 12.0 (R2 = 0.987). In addition, the sensor could detect Ag+ (R2 = 0.998) in the concentration range of 0.2–50 μM with a detection limit of 0.075 μM. Furthermore, the proposed method is applicable to the detection of Ag+ in real aqueous samples, which is a detection system with excellent development prospects.

Sewage sludge is an unavoidable secondary pollution produced in the process of sewage treatment. At present traditional methods of treating sludge (e.g. landfill, incineration or land application) have some disadvantages and shortages. Direct thermochemical liquefaction of sludge is a new treatment method, which has the advantage of both treatment and energy recovery. Research progress and application prospect of sludge liquefaction technology are widely reported, typical liquefaction process with bio-oil production and its main influencing factors are introduced. Besides, the development of this process is illustrated, and resource and energy recovery of this technology are pointed out to be the tendency of sludge treatment in the future.