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With the rise of artificial magnetism and metamaterials, toroidal resonance has gained much attention for its special properties. In this paper, we propose a novel hybrid graphene-metal metamolecule consisting of a square bracket-like resonator and two asymmetric U-shaped resonators. By applying various Fermi energies to graphene, the amplitude of electromagnetically induced transparency (EIT) can be efficiently manipulated, and the maximum amplitude modulation depth can attain 81% in the microwave region. Numerical simulations and theoretical analysis demonstrate that the dynamic manipulation is mainly induced by the active tuning toroidal resonance through the recombination effect of the conductive graphene. Also, the maximum group delay of 85 ps can be attained and controlled with the increasing Fermi energy. The proposed hybrid graphene-metal metamolecule and dynamically manipulating mode presents a novel modulating strategy of EIT-like analog based on the toroidal response, which has great application for the design of efficient tunable resonators, filters, and sensors. [ABSTRACT FROM AUTHOR]

Dynamic-vibration-based structural damage identification (SDI) represents the main target for structural health monitoring (SHM). It is significant to consider the unavoidable uncertainties arising from both the structure and measuring noise. On the other hand, nonuniform measurement conditions often appear in actual SHM applications, which consist of two parts, i.e., spatial nonuniform characteristics for noises are induced by various intensities of input noise in every single sampling channel and multisensor stays in a damaged state. This paper proposes a new method for the SDI considering uncertainties in nonuniform measurement conditions integrating convolutional neural network (CNN). Herein, the great ability of feature extraction from the measurement associated with the convolutional network is used to handle the input data, and the mapping connection between the selected features and damage states is established. Time histories of structural responses, such as acceleration, are applied for damage identification. The application and accuracy of the CNN, which is trained with input uncertain parameters contaminated by stochastic noises, are verified by the finite element numerical and experimental results. Both uncertain parameters and measurement conditions are considered in the verification. The responses obtained from the numerical and experimental approach show that the proposed neural network model can identify the structural damage with high accuracy. The great robustness of the proposed method is examined by studying the influence of uncertainties, even considering the nonuniform measurement condition. [ABSTRACT FROM AUTHOR]

The joints between segments represent weak points and introduce discontinuity into structures, therefore they are particularly significant in precast concrete segmental bridges. In this study, a new steel shear key was designed, and 6 full-scale tests were conducted. Various shear keys and joint types were taken as experimental parameters to study crack propagation, failure mode, shear slip, ultimate bearing capacity, and the residual bearing capacity of various joints under direct shear loading. The results show that the stiffness and shear capacity of steel shear keyed joints were higher than concrete key joints, and the structural system was more stable than concrete keyed joints at the moment of cracking. Both the concrete key and steel key epoxied joints suffered direct shear failure. However, different to the concrete epoxied joints which experienced brittle failure, steel key epoxied joints demonstrated a large residual capacity. Based on traditional segmental bridges construction, construction methods involving steel shear keyed joints included short-line match, long-line match, and modular methods are introduced. Finally, the feasibility of steel shear keyed joints construction was verified via engineering tests. [ABSTRACT FROM AUTHOR]

In this paper, a low-loss toroidal dipole metamaterial composed of four metal split ring resonators is proposed and verified at microwave range. Dual-band Fano resonances could be excited by normal incident electromagnetic waves at 6 GHz and 7.23 GHz. Analysis of the current distribution at the resonance frequency and the scattered power of multipoles shows that both Fano resonances derive from the predominant novel toroidal dipole. The simulation results exhibit that the sensitivity to refractive index of the analyte is 1.56 GHz/RIU and 1.8 GHz/RIU. Meanwhile, the group delay at two Fano peaks can reach to 11.38 ns and 12.85 ns, which means the presented toroidal metamaterial has significant slow light effects. The proposed dual-band toroidal dipole metamaterial may offer a new path for designing ultra-sensitive sensors, filters, modulators, slow light devices, and so on. [ABSTRACT FROM AUTHOR]

A double E-shaped toroidal dipole metasurface is designed with the high Q-factor Fano and classical electromagnetically induced transparency (EIT) phenomena in the microwave frequency range. With the introduction of an asymmetric structure, the sharp Fano resonance can be excited and acquired a quite high Q-factor of 134 at a lower frequency of 4.58 GHz. It can be numerically and experimentally demonstrated that the singularity Fano response of designed construction is caused by the intensive toroidal dipole. In addition, due to destructive interference between the intensive toroidal dipole and electric dipole, the transmission peak of EIT can reach 0.95 with a Q-factor of 50 at 10.18 GHz. By calculating and comparing the radiated power of multipoles, the enhanced toroidal dipole response can be further verified. The designed planar toroidal dipole metamaterial with simple construction may have many possible applications in toroidal moment generators, sensing, and slow-light devices. [ABSTRACT FROM AUTHOR]

The stochastic pseudo excitation method (SPEM), which is based on the principle of pseudo excitation method (PEM), is introduced to represent the randomness of dynamic input in which the amplitude of excitation is adopted as a random variable. Based on the mathematic definition of power spectral density, a physical interpolation of the SPEM is discussed. Even if one random variable is involved in calculation, the effects of the uncertainties are required to be investigated. The SPEM offers a simple but quite effective way to solve the dynamic reliability problem. Through integrating the new algorithm into first-order reliability method (FORM), the dynamic reliability of uncertain structure subjected to random excitation is studied. A linear oscillator with three types of white noise is adopted to verify the SPEM for dynamic reliability of linear random vibration analysis. Also, the accuracy and efficiency of SPEM to handle the multi-degree-of-freedom structure is investigated in this paper. [ABSTRACT FROM AUTHOR]

A high-resolution, spatially explicit forest age map is essential for quantifying forest carbon stocks and carbon sequestration potential. Prior attempts to estimate forest age on a national scale in China have been limited by sparse resolution and incomplete coverage of forest ecosystems, attributed to complex species composition, extensive forest areas, insufficient field measurements, and inadequate methods. To address these challenges, we developed a framework that combines machine learning algorithms (MLAs) and remote sensing time series analysis for estimating the age of China's forests. Initially, we identify and develop the optimal MLAs for forest age estimation across various vegetation divisions based on forest height, climate, terrain, soil, and forest-age field measurements, utilizing these MLAs to ascertain forest age information. Subsequently, we apply the LandTrendr time series analysis to detect forest disturbances from 1985 to 2020, with the time since the last disturbance serving as a proxy for forest age. Ultimately, the forest age data derived from LandTrendr are integrated with the result of MLAs to produce the 2020 forest age map of China. Validation against independent field plots yielded an R2 ranging from 0.51 to 0.63. On a national scale, the average forest age is 56.1 years (standard deviation of 32.7 years). The Qinghai–Tibet Plateau alpine vegetation zone possesses the oldest forest with an average of 138.0 years, whereas the forest in the warm temperate deciduous-broadleaf forest vegetation zone averages only 28.5 years. This 30 m-resolution forest age map offers crucial insights for comprehensively understanding the ecological benefits of China's forests and to sustainably manage China's forest resources. The map is available at 10.5281/zenodo.8354262 (Cheng et al., 2023a). [ABSTRACT FROM AUTHOR]

The stochastic pseudo-excitation method (SPEM) is applied to improve the efficiency of the random vibration analysis of an uncertain train–bridge interaction (TBI) system. This method is combined with the first-order reliability method to handle the reliability analysis of the system. It is characterized by the introduction of a random variable of the amplitude coefficient based on the principle of the PEM, establishing stochastic excitation at each selected frequency point. Unlike the traditional PEM, the calculation results by the SPEM can consider the random characteristics of external loads and also reflect the effect of uncertain parameters. Two comparisons were conducted to verify the accuracy and efficiency of the proposed method for the dynamic analysis of an uncertain TBI system. First, the results calculated using the SPEM were compared with those calculated using the PEM, indicating that the SPEM can calculate the dynamic responses of the TBI system without uncertainties. Second, the stochastic responses of the uncertain TBI system obtained by the SPEM were compared with those obtained by the MCM, which exhibited good agreement and verified the efficiency of the proposed method. Third, the system response of the simplified TBI model was analyzed to determine the parametric sensitivity. Finally, the reliability of the TBI model was studied based on the selected sensitive parameters. These results indicated that the uncertain mass of the train played an essential role in the random vibration of the system. In addition, the safety index of the structure became more accurate with increasing number of samples, and the probabilistic distribution of the dynamic responses of the uncertain TBI system followed a Gaussian distribution extremely well, further verifying the appropriateness of the selected amplitude coefficient in the SPEM. [ABSTRACT FROM AUTHOR]

Based on the theory of pseudo excitation method, a so-called stochastic pseudo excitation method is proposed to acquire the sampling results of stochastic dynamic response of uncertain bridge structure directly, which cannot be achieved by the classical pseudo excitation method. In order to reduce the computational cost of the dynamic finite element analysis of the samples to gain the probability density function of structural responses, the response surface method was adopted to handle the sampling results. The stochastic pseudo excitation method combing response surface method is verified by comparing with Monte Carlo method in good agreement. As an example, the stochastic response of a super-high slender bridge tower subjected to the Davenport fluctuating wind excitations is evaluated taking into account the uncertainties of material properties and wind speed. Numerical results show that the effect of uncertainties of structure or excitations plays a constant role in the stochastic response at the different levels of external excitations. [ABSTRACT FROM AUTHOR]

Highlight • We proposed tunable and polarization sensitive electromagnetically induced transparency metamaterial. • When incident angles vary from 0° to 60°, the transmission spectra has two transmission windows in TE mode. • In TM mode, the transmission window has broadened in the transmission spectra. • On the other hand, the proposed metamaterial has slow light effect and the largest value of group index is 30 in the transparency peak. • According to the property of the designed metamaterial, we believe it suggests the future possibility for a new type of applications in sensing and filtering device. Abstract We demonstrate a classical analogue of electromagnetically induced transparency (EIT) metamaterial that consisting of anomalous split resonance ring and cross-shaped wire with simulations and experiments. The transmission spectrum is tunable with the incident angles varies from 0° to 60° in different modes. Under normal incident wave, the metamaterial leads to polarization insensitive EIT-like effect. The distributions of surface current at peaks and dips of transmission have been analyzed. Furthermore, the group index presents maximum at the transparency peak, which verifies the slow light effect. The proposed metamaterial could provide an avenue for various applications, such as refractive index sensor and microwave switch. [ABSTRACT FROM AUTHOR]

A synthetic process was exploited to fabricate patchy CuO evenly planted on cubic NaTaO 3 for photocatalytically reducing CO 2 in isopropanol. The nano patches of CuO with about 15 nm in size were uniformly distributed on the surface of NaTaO 3 via a phase-transfer protocol and solvothermal synthesis. The crystal phase, morphology, composition, optical absorption and charge separation of as-prepared CuO-NaTaO 3 were characterized by XRD, SEM, TEM, EDX, XPS, UV–Vis and PL. The results of photocatalytic reduction of CO 2 confirmed that the CuO patched NaTaO 3 possessed better ability to separate charge carriers and selectively reduce CO 2 to methanol than CuO directly loaded NaTaO 3 using the traditional liquid phase reduction procedure after comparing the methanol yields. Furthermore, 5 wt% CuO patched NaTaO 3 led to the highest methanol yield of 1302.22 μmol g −1 h −1 . A redox mechanism was proposed and illustrated in a schematic diagram. [ABSTRACT FROM AUTHOR]

To save the computational efforts of Monte Carlo simulation together with nonlinear finite element method, the analysis framework combining the response surface method and Monte Carlo simulation is usually adopted to investigate the stochastic nonlinear behavior of structures. It is found that the traditional response surface method could not describe stochastic behavior of cracked concrete beams. In order to overcome the discontinuity caused by cracking nonlinearity, a scheme by introducing a piecewise response surface is proposed in this paper. This scheme is evaluated by the stochastic analysis of several concrete beams. The comparison between the proposed method and some traditional methods, including Monte Carlo simulation and Monte Carlo simulation with traditional response surface, shows that the proposed method could well depict the stochastic behavior of concrete beams. Finally, the probability density evolution of concrete beams from short-term deflection to a long-term deflection is analyzed. [ABSTRACT FROM AUTHOR]

A compact planar construction composed of asymmetric split ring resonators was designed with a low-loss, high Q-factor resonance transparency at microwave frequency. The singularity property of the proposed metamaterial owing to the enhanced toroidal dipole T is demonstrated via numerical and experimental methods. The transmission peak can reach up to 0.91 and the loss is perfectly repressed, which can be testified by radiated power, H-field distributions, and the imaginary parts of effective permittivity and permeability. The designed planar metamaterial may have numerous potential applications at microwave, terahertz, and optical frequency, e.g., for ultrasensitive sensing, slow-light devices, lasing spacers, even invisible information transfer. [ABSTRACT FROM AUTHOR]

Abstract: The flexural behaviors of fully and partially prestressed concrete beams made of high strength and steel fiber reinforced concretes are studied by experiment and non-linear finite element method. Three levels of partial prestress ratio (PPR) are considered, and for each PPR, a pair of two-span continuous beams with box-section are designed. In each pair of the test beams, one is constructed by the high strength concrete completely, and for the other one the steel fiber reinforced concrete is used in the negative moment zone (the mid-support zone). Such structural behaviors as the deflection of beam, the formation and the development of crack, the strain of concrete and reinforcement bar, and the moment redistribution are investigated. A non-linear finite element analysis program is developed and applied. The constitutive behavior of concrete is modeled by the microplane theory. A four-nodal six-DOF (degree of freedom) degenerated shell element is adopted to simulate the structural behaviors of the box-section beam. To obtain the ultimate limit state of the structure, the arc-length method is introduced in the non-linear solution. The analytical results agree well with test ones. The influences of the steel fiber reinforced concrete used in the negative moment zone on the structural behaviors are mainly concerned. [Copyright &y& Elsevier]

• Dopamine receptor agonists were intrastriatally injected into hemiparkinsonism and control rats. • SKF38393 and quinpirole produce a dose-related enhancement in locomotor activity. • SKF38393 and quinpirole induce more ameliorated movement in hemiparkinsonism rats than in control rats. • Quinpirole produced more ameliorated locomotor activity than SKF38393 in hemiparkinsonism rats. Dopamine (DA) in the striatum is essential to influence motor behavior and may lead to movement impairment in Parkinson's disease (PD). The present study examined the different functions of the DA D1 receptor (D1R) and DA D2 receptor (D2R) by intrastriatal injection of the D1R agonist SKF38393 and the D2R agonist quinpirole in 6-hydroxydopamine (6-OHDA)-lesioned and control rats. All rats separately underwent dose-response behavior testing for SKF38393 (0, 0.5, 1.0, and 1.5 μg/site) or quinpirole (0, 1.0, 2.0, and 3.0 μg/site) to determine the effects of the optimal modulating threshold dose. Two behavior assessment indices, the time of latency to fall and the number of steps on a rotating treadmill, were used as reliable readouts of motor stimulation variables for quantifying the motor effects of the drugs. The findings indicate that at threshold doses, SKF38393 (1.0 μg/site) and quinpirole (1.0 μg/site) produce a dose-dependent increase in locomotor activity compared to vehicle injection. The ameliorated behavioral responses to either SKF38393 or quinpirole in lesioned rats were greater than those in unlesioned control rats. Moreover, the dose-dependent increase in locomotor capacity for quinpirole was greater than that for SKF38393 in lesioned rats. These results can clarify several key issues related to DA receptors directly and may provide a basis for exploring the potential of future selective dopamine therapies for PD in humans. [ABSTRACT FROM AUTHOR]

The reliability of vehicle–bridge dynamic interaction is evaluated. A time domain transfer matrix method for dynamic analysis is proposed. Using the idea of the Newmark-β method, the partial differential equation of beam vibration is transformed into a differential equation. Then, this differential equation is solved by the transfer matrix method. By using D’Alembert’s principle, the dynamic equations of vehicle–bridge interaction can be formulated. An iteration scheme is adopted to deal with the coupled terms in dynamic equations of vehicle–bridge system. Through integrating the first-order reliability method and transfer matrix method, the dynamic reliability of the vehicle–bridge system is studied. Through two numerical examples, the effects of system parameters on dynamic reliability of the vehicle–bridge system are investigated. [ABSTRACT FROM AUTHOR]

Abstract: Chloride diffusion is widely used to evaluate the durability of concrete structures. For civil concrete structures, millions of cycles of repeated loads are expected during their intended life. Those fatigue loads may lead to progressive damage of concrete. The objective of this paper is to study the time-variant reliability of chloride diffusion in fatigue damaged concrete structures through integrating damage mechanics, nonlinear analysis, numerical simulation of diffusion processes, and reliability methods. A phenomenological fatigue damage model of concrete is introduced. The strip method is adopted to simulate the nonlinear response of concrete structures. The chloride diffusion process is analyzed by finite difference methods. Using the important sampling method, the time-variant reliability of chloride diffusion in fatigue damaged concrete structures is assessed. [Copyright &y& Elsevier]

Abstract: To meet the application of nonlinear FEM analysis to the real bridge structure, a general and versatile nonlinear analysis program for concrete bridge structures is presented in this paper. The structural effects such as material nonlinear behavior, prestress effect, construction process, temperature, creep of concrete and so on, which are concerned in practical engineering, are covered. To simulate the nonlinear behaviors, an efficient 3D degenerated beam element is adopted. The step-by-step analysis method allows one to comprehend the structural behaviors of the bridges during the construction, service, and failure periods, respectively. Finally, two examples are studied to verify the accuracy of the program and demonstrate its application in practical bridge engineering. [Copyright &y& Elsevier]

Purpose: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. Methods: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. Results: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. Conclusions: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic.clinicaltrials.gov/ct2/show/NCT05867732. [ABSTRACT FROM AUTHOR]

Summary: Aiming to the problems of high circulating current and low efficiency caused by the traditional strategy of dual active bridge (DAB) converter under light load, in this paper, simple graphical optimization method (GSO) is proposed to improve the efficiency of dual active bridge converter. Through the contour plot of transmission power and current stress, optimal efficiency point can be obtained by tangent of contour map under condition of constant power, and heavily calculation or complex iterations of existing conventional control strategies are avoided. Moreover, analysis of proposed graphical current stress optimization is addressed, and controller design of proposed method is given and described. Efficiency comparison between traditional modulation strategy and GSO is given to show the advantage of proposed mothed. An experimental prototype is built to verify the analysis. [ABSTRACT FROM AUTHOR]

Background: High-dose methotrexate (HD-MTX) is a potent chemotherapeutic agent used to treat pediatric acute lymphoblastic leukemia (ALL). HD-MTX is known for cause delayed elimination and drug-related adverse events. Therefore, close monitoring of delayed MTX elimination in ALL patients is essential. Objective: This study aimed to identify the risk factors associated with delayed MTX elimination and to develop a predictive tool for its occurrence. Methods: Patients who received MTX chemotherapy during hospitalization were selected for inclusion in our study. Univariate and least absolute shrinkage and selection operator (LASSO) methods were used to screen for relevant features. Then four machine learning (ML) algorithms were used to construct prediction model in different sampling method. Furthermore, the performance of the model was evaluated using several indicators. Finally, the optimal model was deployed on a web page to create a visual prediction tool. Results: The study included 329 patients with delayed MTX elimination and 1400 patients without delayed MTX elimination who met the inclusion criteria. Univariate and LASSO regression analysis identified eleven predictors, including age, weight, creatinine, uric acid, total bilirubin, albumin, white blood cell count, hemoglobin, prothrombin time, immunological classification, and co-medication with omeprazole. The XGBoost algorithm with SMOTE exhibited AUROC of 0.897, AUPR of 0.729, sensitivity of 0.808, specificity of 0.847, outperforming the other models. And had AUROC of 0.788 in external validation. Conclusion: The XGBoost algorithm provides superior performance in predicting the delayed elimination of MTX. We have created a prediction tool to assist medical professionals in predicting MTX metabolic delay. [ABSTRACT FROM AUTHOR]

Abstract Recent studies on the impact of Parkinson's disease (PD) on the thalamostriatal pathway have mainly focused on the structural and functional changes in the thalamus projection to the striatum. Alterations in the electrophysiological activity of the thalamostriatal circuit in PD have not been intensively studied. To further investigate this circuit, parafascicular nucleus (PF) single-unit spikes and dorsal striatum local field potential (LFP) activities were simultaneously recorded in control and 6-hydroxydopamine (6-OHDA)-lesioned rats during inattentive rest or treadmill walking states. We classified the PF neurons into two predominant subtypes (PF I and PF II). During rest state, after dopamine loss, increased PF I spike and striatal LFP coherence was observed in the beta-frequency (12–35 Hz), with changed PF I neuronal firing pattern and unchanged firing rates of the two neuron subtypes. However, in a treadmill walking state, PF II neurons displayed markedly increased coherence to striatal beta oscillations in the dopamine-depleted rats, as well as an altered PF II neuronal firing pattern and significantly decreased firing rates of the two neuron subtypes. The results indicate that in PD animals, state transition from rest to moving, such as treadmill walking, is associated with different PF neuron types and increased spike-LFP synchronization, which may provide new paradigms for understanding and treating PD. Highlights • PF neurons were classified into two predominant subtypes and dopamine loss altered PF neural firing activities. • The alterations of PF neural firing in PD are state-dependent. • Synchronization between single PF neurons with striatum β-frequency oscillations increased after dopamine loss. • The changes of synchronization are associated with different PF neuron subtypes in different states. [ABSTRACT FROM AUTHOR]

Zhao, N.; Guo, Y.; Xiang, T.; Xia, M.; Shen, Y., and Mi, C., 2018. Container ship stowage based on monte carlo tree search. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 540–547. Coconut Creek (Florida), ISSN 0749-0208. With the development of larger ships, modernized wharves, standardized production, meticulous management, and flexible service in container transportation, port production management is becoming increasingly intelligent. How to improve the efficiency when making stowage plans, the core issue in container terminals, has become a popular topic in research. Starting with the stowage issue, this paper provides rational solutions to stowage location and loading sequence of unloaded containers on the yard, giving overall consideration on ship owners' requirement and dock authority's practical operation. Considering turnover on yard, RTG cross-container operation and RTG shifting, based on loading principles and constraints, this paper establishes an optimized model for multiple targets and creates an MCTS method, where five procedures including extension and option, together with corresponding strategies, are designed according to the search tree. Practical numerical examples indicate that the suggested model and algorithm can produce effective solution in due time. This is the first time that MCTS is applied for loading issues in container terminals, so this paper does not only use and perfect theories on optimizing, system analysis and decision, but also contribute to the intelligentization of container terminals. Relevant methods and thinking are referential and applicable to the study of issues of the same kind. [ABSTRACT FROM AUTHOR]

In order to address the issue of keeping the converter operating at a high efficiency level under different conditions, this paper suggests an adaptive step frequency adjustment online optimization search algorithm, which achieves the goal of fast adjustment and real‐time response to parameter changes. The converter is quickly adjusted to operate at a high efficiency by sampling the input and output current values and modulating the switching frequency utilizing the algorithm. Additionally, the algorithm shifts between two working models to finish the search for the best switching frequency while the converter's characteristics do not change. Moreover, it accomplishes the objective of instantly resynchronizing the algorithm after a change in the converter's parameters and starting a search for the best switching frequency in the new converter state. For various input voltages and output loads, the operating principle, optimal frequency, and efficiency are given. In addition, the principle analysis when the converter parameters are changed is provided, and the choice of two algorithm models is compared. Finally, a prototype buck converter with adjustable input voltage and load is constructed to test the algorithm's efficiency. [ABSTRACT FROM AUTHOR]

We present the design of a new type of irregular metamaterial structure that can achieve ultra-wideband absorption. The structure is created using 3D-printing to create a shell and contains multiple layers of water. The structure can achieve absorption levels greater than 0.9 in the 6.8–21.0 GHz range, with a relative bandwidth of 101.93%. The absorber also works in a wide range of incidence angles with different modes and is polarization insensitive. Measurement results obtained from a microwave experiment coincide well with the simulation results. The proposed metamaterial could be broadly applied in various civilian and military products in the future. [ABSTRACT FROM AUTHOR]

Although there are some studies on the risk management of logistics service supply chain (LSSC), we still know little about the effects of risk evolution and interactional mechanism of subjects in different urgent scenarios of logistics service supply chain from a micro perspective. Based on the structures of the LSSC, this study thus analyzed the risk evolution and the main actors' reactions in the LSSC, including categorizing the urgent scenarios, analyzing their impacts on the diffusion intensity of pricing, profit and demand, and verifying the models' validity with the numerical analysis. The results indicate divergent impacts of the different urgent scenarios on the logistics service supply chain: the price fluctuation can cause similar profit declines of the supplier and integrator, the reduced capacity can cause the supply chain's price increase by 50%, and the demand risks can lead to similar changes of the upstream orders, prices and profits. The findings may provide clues for firms to develop effective countermeasures against the risks. [ABSTRACT FROM AUTHOR]

The dynamic response of a vehicle-bridge system (VBS) has been a key problem in assessments of train-running safety on bridges, ultimately requiring reliability analyses owing to uncertain parameters and random rail irregularities. To improve the computational efficiency and promote the development of a reliability analysis for VBS, this paper proposes a novel approach incorporating a stochastic pseudo-excitation method (SPEM) to optimize a deep learning approach. This approach can more efficiently calculate the dynamic responses of a VBS than the traditional methods. In this study, the dynamic responses of the uncertain system produced by the SPEM were utilized as the training datasets for the deep learning approach. Based on the training datasets, a neural network was established to construct a numerical model consisting of two functional modules: a full convolutional network (FCN) training for the input rail irregularities and gated recurrent unit (GRU) layer for the VBS response prediction, i.e., a SPEM–FCN–GRU (SFG). The GRU cell was simulated by introducing the randomness of the excitation and uncertain parameters of the system into a portion of the cell, thereby enabling the numerical model to convey the randomness of the VBS and to obtain its stochastic response. Addition, long-time-domain samples of the VBS responses were predicted based on the short-time-domain samples. To verify the accuracy and applicability of the proposed method, a train running through a multi-span simply supported railway bridge was investigated. The dynamic responses of the train were analyzed using the training set data and Monte Carlo method (MCM) and the results showed good agreement with each other. Next, by introducing an algorithm combining a convolutional neural network (CNN) with the MCM and GRU (an MCM–CNN–GRU) and comparing it with the method proposed herein, it was verified that the proposed method has better predictions and higher efficiency than those of a traditional neural network. The prediction efficiency of the SFG was analyzed. Based on the shortcomings of the SFG, we proposed an optimized SFG algorithm. The short-time-domain results predicted by the proposed method were input into the training datasets for the deep learning architecture to enhance the number of training data for the prediction of long-time-domain responses. The efficiency and accuracy of the proposed method were verified by comparing them with those of traditional neural networks. As an example, the dynamic responses of a train running through a three-span bridge were computed by the SPEM; these could be adopted to predict those of a train running through a 10-span bridge. Finally, based on the simulated and predicted results, the reliability of the VBS was studied using a first-order reliability method. [ABSTRACT FROM AUTHOR]

The widespread heavy metal contamination in soil induced by extensive human disturbance has been a global significant issue because of its chronic toxic effects on human health. However, the establishment of effective monitoring and assessing methods for heavy metal content in the soil remain a long-term challenge due to the intrinsic limitation of the current multi-band remote sensing technology and field measurement methods. Prompted by this significant technique gap, we represent an implementation of remote-sensing inversion models based on hyperspectral imagery to reconstruct soil heavy metal contents within an experimental farmland located in Mianzhu city, Sichuan, China. We collected soil samples at the pre-defined sites, and measured soil heavy metal contents and soil spectrum in the laboratory condition. Meanwhile, we obtained Orbita Hyperspectral Satellites (OHS) imagery and quantified the associated vegetation indexes. The measured soil spectrum, bands of OHS, and the generated vegetation indexes were mathematically transformed to better represent their relations with soil heavy metal contents. Consequently, the most sensitive variables were selected as potent predictors of soil heavy metal contents in the inversion models. After evaluating the optimal inversion models for each heavy metal element, we implemented them to reconstruct the spatial patterns of soil heavy metal contents over the study landscape. We found obvious benefits of the remote sensing inversion model in predicting the spatial heterogeneity of heavy metal content within this small landscape patch. Specifically, the inversion model unraveled a normal distribution of heavy metal contents within the landscape, while the traditional spatial interpolation based on field measurements may suggest a largely skewed distribution. Compared with airborne-based studies, this study represents an application of spaceborne satellite data, which can be easily applied to a large spatial scale and long-term monitoring. [Display omitted] • A remote-sensing inversion model for soil heavy metal contents was established. • This model shows obvious benefits in predicting the spatial heterogeneity of soil heavy metal contents. • The satellite-based method can be easily adopted to a large spatial-scale application. [ABSTRACT FROM AUTHOR]

Different shape of bismuth sulfide (Bi2S3), including nanoparticles, and urchin-like, microspheres hierarchical nanostructures, have been successfully fabricated using a facile and template-free solvothermal method. Their crystal and porous structures, morphologies, as well as the optical absorption were characterized by powder X-ray diffraction (XRD), scanning electron microscopy (SEM), UV-vis diffuse reflection spectroscopy (DRS) and nitrogen sorption. The electron microscopy observations showed that both the sulfur sources and solvents greatly affected the morphologies of the as-prepared Bi2S3. Compared with Bi2S3 nanoparticles, the hierarchical architectures exhibited higher activity for photocatalytic reduction of CO2 to methyl formate (MF) in methanol, and Bi2S3 microspheres showed the highest activity. This was attributed to their special hierarchical structure, good permeability and high light-harvesting capacity. [ABSTRACT FROM AUTHOR]

A hybrid method combining the stochastic pseudo excitation method (SPEM) and the response surface method (RSM) was presented to handle the effect of both random characteristics of power spectral density of excitation and uncertain parameters. Based on basic principle of pseudo excitation method (PEM), an optimized algorithm was proposed to be SPEM. The effects of the uncertainties of structure and excitations could be considered through SPEM, thus directly acquiring the whole sampling results of random vibration of uncertain structural system. To reduce the cost of computing the total number of required finite element analysis simulations, RSM was adopted to hold calculated sampling results to gain the probability density function (PDF) of random vibration. The proposed method can consider both the effects of random characteristics of power spectrum of random excitations and the uncertainties of target system. Neverthless, Since the proposed method is based on the pseudo excitation method, the SPEM-RSM can handle the stochastic vibration analysis of determine and uncertain structure. In order to verify the accuracy and efficiency of proposed method, taking a single degree of freedom linear uncertain oscillator and a pier construction as two verification cases, the PDF of responses computed by the hybrid method was verified by Monte Carlo method (MCM), and the efficiency of the proposed method was presented. Then, stochastic responses of the railway continuous bridge subjected to earthquake were calculated, several uncertainties are involved in the computation. In addition, sensitivity analysis was solved for uncertain parameters. • A named as stochastic pseudo excitation method (SPEM) is proposed. • A hybrid method combing the SPEM and the response surface method (RSM) was presented. • The hybrid method handle the stochastic vibration of linear structure with various excitation. • The structural uncertainties can increase predictive uncertainty and discreteness for the stochastic response in this case study. • The most significant uncertain parameter in sensitivity of all the random variable is damping ratio. [ABSTRACT FROM AUTHOR]