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Background: Peritoneal carcinomatosis was the main reason leading to gastric cancer (GC)-related death. We aimed to explore the roles of dysregulated microRNAs (miRNAs) and related immune regulation activities in GC-associated malignant ascites. Methods: GSE126399 were downloaded from GEO database. Differentially expressed miRNAs in GC ascites samples was firstly screened, and critical miRNAs were further investigated by LASSO (least absolute shrinkage and selection operator) logistic regression and random forest (RF) algorithm. Receiver operating characteristic of critical miRNAs was also constructed. Moreover, functional analysis, immune cell infiltration associated with differentially expressed mRNAs were further analyzed. After selecting key modules by weighted gene co-expression network analysis, mRNAs related with survival performance and transcription factor (TF)-miRNA-mRNA network were constructed. Results: Hsa-miR-181b-5p was confirmed as critical differentially expressed miRNAs in GC ascites. Then, the tumor samples were divided into high- and low- expression groups divided by mean expression levels of hsa-miR-181b-5p, and subjects with high hsa-miR-181b-5p levels had better survival outcomes. In total, 197 differentially expressed mRNAs associated with hsa-miR-181b-5p levels were obtained, and these mRNAs were mainly enriched in muscle activity and vascular smooth muscle contraction. Hsa-miR-181b-5 was positively related with activated CD4 T cells and negatively related with eosinophil. 17 mRNAs were selected as mRNAs significantly related with prognosis of GC, such as PDK4 and RAMP1. Finally, 75 TF-miRNA-mRNA relationships were obtained, including 15 TFs, hsa-miR-181b-5p, and five mRNAs. Conclusion: Our data suggest that the differentially expressed hsa-miR-181b-5p in ascites samples of GC patients may be a valuable prognostic marker and a potential target for therapeutic intervention, which should be validated in the near future. [ABSTRACT FROM AUTHOR]

Objective: At present, it is still uncertain whether single screw has the same stability as double screws in the treatment of ulnar coronal process basal fracture (Regan–Morry type III). So, we aimed to compare the pull‐out force and anti‐rotation torque of anterior single/double screw‐cancellous bone fixation (aSSBF, aDSBF) in this fracture, and further study the influencing factors on anatomical and biomechanical stability of smart screw internal fixations. Methods: A total of 63 adult volunteers with no history of elbow injury underwent elbow CT scanning with associated three‐dimensional reconstruction that enabled the measurements of bone density and fixed length of the proximal ulna and coronoid. The models of coronal process basal fracture, aSSBF and aDSBF, were developed and validated. Using the finite element model test, the sensitivity analysis of pull‐out force and rotational torque was carried out. Results: The pull‐out force of aSSBF model was positively correlated with the density of the cancellous bone and linearly related to the fixed depth of the screw. The load pattern of pull‐out force of aDSBF model was similar to that of aSSBF model. The ultimate torque of aDSBF model was higher than that of aSSBF model, but the load pattern of ultimate torque of both models was similar to each other when the fracture reset was satisfactory, and the screw nut attaches closely to coronoid process. Moreover, with enhancement of initial pre‐tightening force, the increase of ultimate torque of both models was small. Conclusions: In addition to three pull‐out stability factors of smart screw fixations, fracture surface fitting degree and nut fitting degree are the other two important anatomical and biomechanical stability factors of smart screw fixations both for rotational stability. When all pull‐out stability and rotational stability factors meet reasonable conditions simultaneously, single or double screw fixation methods are stable for the treatments of ulnar coronoid basal fractures. [ABSTRACT FROM AUTHOR]

Data-driven modeling of nonlinear dynamical systems is essential because of the need for a trade-off among complexity, efficiency, and reliability in analytical or numerical studies as well as the difficulty of deriving fully physics-based models. An important limitation is commonly seen in existing works: modeling of a new dynamical system typically starts from scratch, requiring a large amount of data and intensive computation, though some prior experience or knowledge is available from a previously collected database of similar but different systems. However, on the one hand, the data amount for the new dynamical system is often limited, especially for real-world dynamical systems. On the other hand, the computational resource is also limited and a data-driven modeling task is usually computationally expensive, especially for large-scale systems. To improve data efficiency and computational efficiency in data-driven modeling of nonlinear systems, we present an enhanced data-driven modeling approach by incorporating metalearning into a physics-integrated deep learning framework. The core idea is to learn the metaknowledge about how to model a new system from a previously collected database of similar but different systems. Then this metaknowledge is leveraged to enable efficient modeling of a new system with limited data. For validations we conducted numerical experiments on three sets of fundamental nonlinear systems, including Duffing oscillators, nonlinear pendulums, and van der Pol oscillators. We performed both interpolation and extrapolation modelings to investigate the generalization ability of the presented approach. Furthermore, we conducted a quantitative analysis on data efficiency, addressing two critical issues: how few data are sufficient for the new system modeling and how much prior experience (previously collected database of similar but different systems) is needed for the metalearning. The results show that the presented approach improves both data efficiency and computational efficiency, compared with the conventional data-driven modeling approach (without leverage of the prior database) and the pretraining-based approach (simply using the prior database but without metalearning idea). We also discuss the limitations of this work and potential future study. [ABSTRACT FROM AUTHOR]

To study the difference of volatile substances and their relative contents in different fermentation conditions, the optimal fermentation conditions were determined according to the important aroma volatile compounds profiling. The fermentation time, the amounts of SO2, and yeast addition were used as fermentation conditions, using the headspace solid phase microextraction (HS‐SPME) combined with gas chromatography–mass spectrometry (GC–MS) analysis, a total of 182 volatile substances were identified in nine treatments. In nine treatments, a total of 182 volatile compounds were detected. However, the majority of detected volatile compounds were esters, with a total number of 37. Each treatment had the maximum quantity and relative concentration of esters; moreover, five esters (ethyl acetate, isobutyl acetate, and others) were discovered in all treatments. PCA results showed a large positive relation between esters and fermentation treatments (85.79%). The addition of 40 mg/L SO2, 100 mg/L yeast, and a fermentation time of 2 days (T7) were recognized as the optimum fermentation conditions, where 14 ester compounds were detected and a relative ester content of 65.65% was achieved. Ultimately, the results indicate that with the optimized fermentation conditions of this study, grape wines with more fruity, sweet, milky, and better ester concentrations can be produced and could also provide a certain reference for the production of other fruit wines. Novelty impact statement: A total of 182 volatile substances were identified in nine treatments. The number and relative content of esters were the highest in each treatment. Five esters (ethyl acetate, isobutyl acetate, and others) were detected in all the treatments. [ABSTRACT FROM AUTHOR]

Objectives: Tetralogy of Fallot (TOF) is the most common deformity combined with the unilateral absence of the mediastinal pulmonary artery (UAMPA), and its treatment strategy remains controversial. In this study, we analyzed the effect of bilateral pulmonary reconstruction in patients with TOF combined with UAMPA. Methods: This was a single‐center, retrospective review of 1713 patients with TOF between January 2009 and November 2021. Overall, eight patients were diagnosed with TOF combined with UAMPA. Among them, seven underwent surgery: three underwent one‐stage TOF correction with bilateral pulmonary artery reconstruction; three patients underwent bilateral pulmonary artery reconstruction, followed by two‐stage TOF correction after several months; and one patient underwent two procedures of left pulmonary artery reconstruction, and the ventral septal defect remained open. The left pulmonary arteries were reconstructed with a Goretex conduit in three cases, direct anastomosis in two cases, and the modified autologous tissue extension technique in two cases. Results: All seven patients survived during the postoperative follow‐up and showed good cardiac function and normal oxygen saturation of >97%. During follow‐up echocardiography, we noted that the left pulmonary arteries reconstructed with a Goretex conduit or direct anastomosis had thrombosis or stenosis. However, those reconstructed using the modified autologous tissue extension technique was unobstructed. Conclusions: In patients with TOF and UAMPA, if there is a pulmonary artery confluence in the affected hilum, it is feasible to implement bilateral pulmonary artery reconstruction for one‐stage TOF correction. The use of the pulmonary artery extension technique and autologous tissue for bilateral pulmonary reconstruction could reduce the incidence of anastomotic stenosis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Flower-like Bi 2 S 3 assembled on rGO nanosheets by a one-step hydrothermal process. • The optimal sensor shows a high sensitivity (R g /R a = 9.8) with a rapid response time of 22 s to 1 ppm NO 2. • The synergetic effect of heterojunction and hybrid in rGO/Bi 2 S 3 enhances NO 2 sensing performance. • The wireless sensing platform shows potential for NO 2 detection in Internet of Things. Bismuth sulfide (Bi 2 S 3) is a promising material for detecting NO 2 molecules at room temperature thanks to its narrow and tunable bandgap. However, pure Bi 2 S 3 suffers from some sensing blemish because of its low electroconductivity and poor charge transfer efficiency. Herein, we synthesized the high sensing response room temperature NO 2 detection materials composed of Bi 2 S 3 nanoflowers and reduced graphene oxide (rGO). The formed conductive network and heterojunctions between Bi 2 S 3 nanoflowers and rGO nanosheets dramatically improve the conductivity and electron transfer efficiency. The excellent rGO/Bi 2 S 3 sensor shows a high sensing response of 9.8 (approximately two times higher than that of the pure Bi 2 S 3) and rapid response speed of 22 s (almost half of the pure Bi 2 S 3) upon exposure to 1 ppm NO 2 at room temperature. Meanwhile, the rGO/Bi 2 S 3 sensor displays superb humidity-independent, selectivity, and stability. These improved NO 2 sensing behaviors can be attributed to the heterointerfaces and the hybrid effects, which accelerate charge transfer efficiency between NO 2 molecules and the surface of rGO/Bi 2 S 3 heterostructure. To enable on-line detection of ambient hazardous gas, we designed a portable wireless NO 2 detector based on rGO/Bi 2 S 3 heterostructure. This study offer an insightful guidance for improving the NO 2 sensing behaviors of metal chalcogenides. [ABSTRACT FROM AUTHOR]

In general, mixed modes and dispersion of guided wave occur at any excitation frequency, while single modes are needed for effective nondestructive evaluation of structures. In this work, we present an approach to generate a selective single-mode guided wave in plate-like structures by exploiting the unique directionality of the d36 type piezoelectric wafer and the symmetry of fundamental guided wave modes. Specifically, we devise a unified fundamental shear horizontal (SH0) wave/fundamental antisymmetric mode (A0) wave directional transducer pair by attaching the d36 type piezoelectric wafers back to back on the opposite sides of the plate to provide selective, directional single-mode guided waves for actuation and sensing. Originally, the d36-type piezoelectric wafer, poled and cut from the lead magnesium niobate-lead titanate crystal, produces a mixture of the in-plane SH0 (symmetric) mode and out-of-plane A0 (asymmetric) and S0 (symmetric) modes. By applying the in-phase or out-of-phase applied electrical field to the coupled d36-type piezoelectric wafer pair, our devised approach generates selective single modes, SH0 or A0, respectively, to propagate in the structure. We describe the theoretical development of our approach and conduct both numerical simulations and laboratory experiments for validations. [ABSTRACT FROM AUTHOR]

Studying structure–activity correlations of electrocatalysts is essential for improving the conversion of electrical to chemical energy. Recently, increasing evidence obtained by operando characterization techniques reveal that the structural evolution of catalysts caused by the interplay with electric fields, electrolytes and reactants/intermediates brings about the formation of real active sites. Hence, it is time to summarize structural evolution related research advances and envisage future developments. In this Minireview, we first introduce the fundamental concepts associated with structural evolution (e.g. catalysts, active sites/centers and stability/lifetime) and their relevance. Then, the multiple triggers of structural evolution and advanced operando characterizations are discussed. Significantly, a brief overview of structural evolution and its reversibility in heterogeneous electrocatalysis is provided, especially for the representative electrocatalytic oxygen evolution reaction (OER) and CO2 reduction reaction (CO2RR) processes. Lastly, key challenges and opportunities in this exciting field are highlighted. [ABSTRACT FROM AUTHOR]

Studying structure–activity correlations of electrocatalysts is essential for improving the conversion of electrical to chemical energy. Recently, increasing evidence obtained by operando characterization techniques reveal that the structural evolution of catalysts caused by the interplay with electric fields, electrolytes and reactants/intermediates brings about the formation of real active sites. Hence, it is time to summarize structural evolution related research advances and envisage future developments. In this Minireview, we first introduce the fundamental concepts associated with structural evolution (e.g. catalysts, active sites/centers and stability/lifetime) and their relevance. Then, the multiple triggers of structural evolution and advanced operando characterizations are discussed. Significantly, a brief overview of structural evolution and its reversibility in heterogeneous electrocatalysis is provided, especially for the representative electrocatalytic oxygen evolution reaction (OER) and CO2 reduction reaction (CO2RR) processes. Lastly, key challenges and opportunities in this exciting field are highlighted. [ABSTRACT FROM AUTHOR]

Electrocatalysis is at the heart of many significant chemical transformation processes and advanced clean energy technologies. Traditional noble/transition metal oxides are widely used as electrocatalysts; however, they often suffer from intrinsic disadvantages, including low atom utilization, small surface area, and unfavorable tunability. Metal–organic frameworks (MOFs), as a new family of catalytic materials, are attracting extensive attention due to their unique physicochemical properties. The tremendous pristine MOF‐based materials are created using various synthetic approaches and further used for important energy conversions. Herein, a systematic overview on the unique merits and the state‐of‐the‐art design of MOF‐based electrocatalysts is offered. This review also presents recent advances in the development of various pristine MOFs and MOF‐based host–guest composite catalysts for electrocatalysis (i.e., oxygen reduction reaction, hydrogen oxidation reaction, hydrogen evolution reaction, oxygen evolution reaction, and CO2 reduction reaction) and discusses the future challenges and opportunities in this emerging field. [ABSTRACT FROM AUTHOR]

Core‐shell structured bimetallic platinum‐metal (Pt−M) nanoparticles, as a new class of active and stable nanocatalysts, have shown many advantages in increasing the utilization of precious Pt and improving electrocatalytic performances. Here, a core‐shell Pt3Co@Pt supported on porous graphitic carbon (denoted as Pt3Co@Pt/C) is synthesized via a simple thermal method, and further used as an efficient electrocatalyst for oxygen reduction reaction (ORR) in the direct methanol fuel cell. An atomic‐resolution high‐angle annular dark‐field scanning transmission electron microscopy (HAADF‐STEM) measurement combining with line‐profile analysis reveals that the average thickness of Pt‐shell is around 0.4–0.6 nm, forming an ultrathin catalytic layer. Given its unique geometric and electronic structure, the as‐prepared Pt3Co@Pt/C displays highly enhanced electrocatalytic ORR activity and stability, boosted anti‐methanol poisoning ability with a high onset potential and an exceptional half‐wave potential in 0.1 M HClO4 solution. Impressively, its mass activity (0.71 mA mgPt−1) and specific activity (2.75 mA cmPt−2) for ORR are 3.7‐ and 8.1‐fold higher than those of commercial Pt/C catalyst, respectively. The Pt3Co@Pt/C nanocatalysts show remarkable tolerance against methanol poisoning, evidenced by the in situ Fourier‐transform infrared (FTIR) spectroscopy. This work points out a path for the design of high‐performance nanocatalysts for accelerating the development of clean energy technologies involving ORR. [ABSTRACT FROM AUTHOR]

The special physicochemical properties of Bi 2 S 3 nanomaterial endow it to be exceptional NO 2 sensing properties. However, sensors based on pure Bi 2 S 3 cannot detect trace NO 2 at room temperature effectively due to the scanty active sites and poor charge transfer efficiency. Herein, vacancy defect and heterostructure engineering are rationally integrated to explore BiOCl/Bi 2 S 3−x heterostructure with rich S vacancies to enhance NO 2 sensing performance. The optimized sensor based on S-vacancy-rich BiOCl/Bi 2 S 3−x heterostructure exhibited a high response value (R g /R a = 29.1) to 1 ppm NO 2 at room temperature, which was about 17 times compared to the pristine Bi 2 S 3. Meanwhile, the BiOCl/Bi 2 S 3−x sensor also exhibited a short response time (36 s) towards 1 ppm NO 2 and a low theoretical detection limit (2 ppb). The superior response value of S-vacancy-rich BiOCl/Bi 2 S 3−x heterostructures was ascribed to the improved electron migration at the heterointerface and the additional exposed active sites caused by the S vacancies in Bi 2 S 3−x. Additionally, the sensors based on S-vacancy-rich BiOCl/Bi 2 S 3−x heterostructures showed good long-term stability, outstanding selectivity, and good flexibility. This study offers an effective method for synergistically engineering defect and heterostructure to enhance gas sensing properties at room temperature. [Display omitted] • S-vacancy-rich BiOCl/Bi 2 S 3−x heterostructure is prepared by a hydrothermal process. • The BiOCl/Bi 2 S 3−x sensor shows ultrahigh response value (R g /R a = 29.1) to 1 ppm NO 2. • Enhanced sensing response is due to the synergy of S vacancies and heterostructures. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Crystallinity decreased with increasing annealing temperature or saturation pressure. • Ring-banded spherulites were formed by crystallization under compressed CO 2. • Cellular structure was affected by both crystallinity and crystal morphology. • A suitable foaming window as a function of temperature was proposed. Abstract Using a batch foaming technique, the effects of supercritical carbon dioxide on the formation of crystals under different saturation conditions (i.e. temperature or pressure) and subsequently on the cellular structure of the PLA foams were investigated. The crystallinity decreased with increasing annealing temperature or pressure, meanwhile, crystal morphologies were varied by controlling the temperature or pressure. It was found that a uniform closed cell structure was obtained by specifically controlling the foaming temperature in the range of (80–100) °C, in which only the ring-banded spherulites were formed. A suitable foaming window as a function of temperature was proposed. A number of stamen-like cell structure was observed because of formation of irregular-banded spherulites under 8 MPa. However, an opened and interconnected cell structure was obtained at 24 MPa. The results indicated that not only the crystallinity but also the crystalline morphology played an important role in the cellular structure. [ABSTRACT FROM AUTHOR]

Stem is a special short bright zone for the streamer to leader transition in long air gap discharges, whose characteristics play an important role in leader initiation and development. For the purpose of observing the number of stems around the H.V. electrode at the initial corona discharge, experiments were conducted under positive switching and lightning impulses with different crest values, and the schlieren photographs of stems and the discharge current were recorded synchronously. The associated analysis of the experimental results shows that the number of stems around the electrode at the initial corona discharge is always more than one in most cases. In addition, the average number of stems increases with the radius of the electrode and the crest value of applied impulse. If we focus on the characteristic of only one stem for special study, the conditions with a lower crest value under switching impulse and the electrode with a radius as small as possible are preferable. [ABSTRACT FROM AUTHOR]

Summary: Structures with complex geometries, material properties, and boundary conditions exhibit spatially local dynamic behaviors. A high‐spatial‐resolution model of the structure is thus required for high‐fidelity analysis, assessment, and prediction of the dynamic phenomena of the structure. The traditional approach is to build a highly refined finite element computer model for simulating and analyzing the structural dynamic phenomena based on detailed knowledge and explicit modeling of the structural physics such as geometries, materials properties, and boundary conditions. These physics information of the structure may not be available or accurately modeled in many cases, however. In addition, the simulation on the high‐spatial‐resolution structural model, with a massive number of degrees of freedom and system parameters, is computationally demanding. This study, on a proof‐of‐principle basis, proposes a novel alternative approach for spatiotemporal video‐domain high‐fidelity simulation and realistic visualization of full‐field structural dynamics by an innovative combination of the fundamentals of structural dynamic modeling and the advanced video motion manipulation techniques. Specifically, a low‐modal‐dimensional yet high‐spatial (pixel)‐resolution (as many spatial points as the pixel number on the structure in the video frame) modal model is established in the spatiotemporal video domain with full‐field modal parameters first estimated from line‐of‐sight video measurements of the operating structure. Then in order to simulate new dynamic response of the structure subject to a new force, the force is projected onto each modal domain, and the modal response is computed by solving each individual single‐degree‐of‐freedom system in the modal domain. The simulated modal responses are then synthesized by the full‐field mode shapes using modal superposition to obtain the simulated full‐field structural dynamic response. Finally, the simulated structural dynamic response is embedded into the original video, replacing the original motion of the video, thus generating a new photo‐realistic, physically accurate video that enables a realistic, high‐fidelity visualization/animation of the simulated full‐field vibration of the structure. Laboratory experiments are conducted to validate the proposed method, and the error sources and limitations in practical implementations are also discussed. Compared with high‐fidelity finite element computer model simulations of structural dynamics, the video‐based simulation method removes the need to explicitly model the structure's physics. In addition, the photo‐realistic, physically accurate simulated video provides a realistic visualization/animation of the full‐field structural dynamic response, which was not traditionally available. These features of the proposed method should enable a new alternative to the traditional computer‐aided finite element model simulation for high‐fidelity simulating and realistically visualizing full‐field structural dynamics in a relatively efficient and user‐friendly manner. [ABSTRACT FROM AUTHOR]

Nonlinear dynamical systems in applications such as design and control generally depend on a set of variable parameters that represent system geometry, boundary conditions, material properties, etc. Modeling of such parameterized nonlinear systems from first principles is often challenging due to insufficient knowledge of the underlying physics (e.g., damping), especially when the physics-associated parameters are considered to be variable. In this study, we present a dynamics-embedded conditional generative adversarial network (Dyn-cGAN) for data-driven modeling and identification of parameterized nonlinear dynamical systems, capturing transient dynamics conditioned on the system parameters. Specifically, a dynamics block is embedded in a modified conditional generative adversarial network, thereby identifying temporal dynamics and its dependence on the system parameters, simultaneously. The data-driven Dyn-cGAN model is learned to perform long-term prediction of the dynamical response of a parameterized nonlinear dynamical system (equivalently a family of nonlinear systems with different parameter values), given any initial conditions and system parameter values. The capability of the presented Dyn-cGAN is evaluated by numerical studies on a variety of parameterized nonlinear dynamical systems including pendulums, Duffing, and Lorenz systems, considering various combinations of initial conditions and system (physical) parameters as inputs and different ranges of nonlinear dynamical behaviors including chaotic. It is observed that the presented data-driven framework is reasonably effective for predictive modeling and identification of parameterized nonlinear dynamical systems. Further analysis also indicates that its prediction accuracy degrades gracefully as the complexity of the nonlinear system increases, such as strongly nonlinear systems and systems with multiple parameters changing. The limitations of this work and potential future work are also discussed. [ABSTRACT FROM AUTHOR]

We propose and fabricate a dual-emitter light-induced neuromorphic device composed of two light-induced devices with a common collector and base. Two InGaN multiple-quantum-well diodes (MQW-diodes) are used as the emitters to generate light, and one InGaN MQW-diode is used as the common collector to absorb the emitted light. When the presynaptic voltages are synchronously applied to the two emitters, the collector demonstrates an adding together of the excitatory postsynaptic voltage (EPSV). The width and period of the two input signals constitute the code to generate spatial summation and recognition effects at the same time. Experimental results confirm that temporal summation caused by the repetitive-pulse facilitation could significantly strengthen the spatial summation effect due to the adding together behavior when the repetitive stimulations are applied to the two emitters in rapid succession. Particularly, the resonant summation effect occurs at the cosummation region when the two repetitive-pulse signals have a resonant period, which offers a more sophisticated spatiotemporal EPSV summation function for the dual-emitter neuromorphic device. [ABSTRACT FROM AUTHOR]

To optimize the aseismic performance of nonlinear fluid viscous dampers (FVD) of cable-stayed bridge in the highly seismic zone, Xigu Yellow River Bridge in northwest China is taken as an example. Nonlinear time-history analysis method is used to research on the relation among the internal forces, displacements, and damping parameters of the 650 tonnage FVD. The method of getting the minimum of binary functions is used to obtain the optimal parameters of FVD. Also, the 1 : 1 full-scale FVD model is made and used in the constitutive relation test. Then the test result of the damping parameters can be got by normal equation method. The optimized method to obtain the damping parameters is further verified. The results indicate that seismic response in key positions of the cable-stayed bridge can be reduced by installing longitudinal nonlinear FVD between the towers and girders if choosing reasonable damping parameters C and ξ. The optimal damping parameters can be calculated accurately by the proposed method of optimizing damping parameters of nonlinear FVD, and the constitutive relation test verifies the correctness of the optimization analysis method. Conclusions concerned can be applied to the design of nonlinear FVD for cable-stayed bridges. [ABSTRACT FROM AUTHOR]

Identifying modal parameters from vibration measurements is an essential step for modal analysis and modeling of structural dynamics. A critical challenge in modal parameter identification is the determination of the physical modes from spurious modes, especially with noisy measurement data. In this study, an approach is presented to enable automated identification of modal parameters by quantifying the spatial features of full-field, high-spatial-resolution response measurements. Specifically, it is derived that the local variances of the physical and spurious mode shapes are drastically distinguishing, especially when the spatial resolution of the response measurement is high (i.e., full-field with dense spatial measurement points). This allows an effective identification of the physical modes from spurious. Experimental studies are conducted on a few structural models and detailed comparisons are performed and discussed between the presented method and existing methods, including parametric and non-parametric. • A new approach for automatically distinguishing spurious and physical modes. • Full-field measurements allow spatial and statistical analysis of mode shapes. • Local variances of physical and spurious mode shapes form well-separated clusters. • Performance is compared to existing methods both numerically and experimentally. [ABSTRACT FROM AUTHOR]

To solve the multi-objective, flexible job-shop scheduling problem, the biogeography-based optimization (BBO) algorithm can easily fall into premature convergence, local optimum and destroy the optimal solution. Furthermore, the symbiotic organisms search (SOS) strategy can be introduced, which integrates the mutualism strategy and commensalism strategy to propose a new migration operator. To address the problem that the optimal solution is easily destroyed, a parasitic natural enemy insect mechanism is introduced, and predator mutation and parasitic mutation strategies with symmetry are defined, which can be guided according to the iterative characteristics of the population. By comparing with eight multi-objective benchmark test functions with four multi-objective algorithms, the results show that the algorithm outperforms other comparative algorithms in terms of the convergence of the solution set and the uniformity of distribution. Finally, the algorithm is applied to multi-objective, flexible job-shop scheduling (FJSP) to test its practical application value, and it is shown through experiments that the algorithm is effective in solving the multi-objective FJSP problem. [ABSTRACT FROM AUTHOR]

Structural damage is typically a local phenomenon that initiates and propagates within a limited area. As such high spatial resolution measurement and monitoring is often needed for accurate damage detection. This requires either significantly increased costs from denser sensor deployment in the case of global simultaneous/parallel measurements, or increased measurement time and labor in the case of global sequential measurements. This study explores the feasibility of an alternative approach to this problem: a computational solution in which a limited set of randomly positioned, low-resolution global strain measurements are used to reconstruct the full-field, high-spatial-resolution, two-dimensional (2D) strain field and rapidly detect local damage. The proposed approach exploits the implicit low-rank and sparse data structure of the 2D strain field: it is highly correlated without many edges and hence has a low-rank structure, unless damage—manifesting itself as sparse local irregularity—is present and alters such a low-rank structure slightly. Therefore, reconstruction of the full-field, high-spatial-resolution strain field from a limited set of randomly positioned low-resolution global measurements is modeled as a low-rank matrix completion framework and damage detection as a sparse decomposition formulation, enabled by emerging convex optimization techniques. Numerical simulations on a plate structure are conducted for validation. The results are discussed and a practical iterative global/local procedure is recommended. This new computational approach should enable the efficient detection of local damage using limited sets of strain measurements. [ABSTRACT FROM AUTHOR]

Here, we propose, fabricate and characterize suspended photonic integration of InGaN multiple-quantum-well light-emitting diode (MQW-LED), waveguide and InGaN MQW-photodetector on a single chip. The unique light emission property of InGaN MQW-LED makes it feasible to establish multi-dimensional spatial data transmission using visible light. The in-plane light communication system is comprised of InGaN MQW-LED, waveguide and InGaN MQW-photodetector, and the out-of-plane data transmission is realized by detecting the free-space light emission via a commercial photodiode module. Moreover, a full-duplex light communication is experimentally demonstrated at a data transmission rate of 50 Mbps when both InGaN MQW-diodes operate under simultaneous light emission and detection mode. The in-plane superimposed signals are able to be extracted through the self-interference cancellation method, and the out-of-plane superimposed signals are in good agreement with the calculated signals according to the extracted transmitted signals. These results are promising for the development of on-chip InGaN photonic integration for diverse applications. [ABSTRACT FROM AUTHOR]

Enhancing the spatial and temporal resolution of vibration measurements and modal analysis could significantly benefit dynamic modelling, analysis, and health monitoring of structures. For example, spatially high-density mode shapes are critical for accurate vibration-based damage localization. In experimental or operational modal analysis, higher (frequency) modes, which may be outside the frequency range of the measurement, contain local structural features that can improve damage localization as well as the construction and updating of the modal-based dynamic model of the structure. In general, the resolution of vibration measurements can be increased by enhanced hardware. Traditional vibration measurement sensors such as accelerometers have high-frequency sampling capacity; however, they are discrete point-wise sensors only providing sparse, low spatial sensing resolution measurements, while dense deployment to achieve high spatial resolution is expensive and results in the mass-loading effect and modification of structure's surface. Non-contact measurement methods such as scanning laser vibrometers provide high spatial and temporal resolution sensing capacity; however, they make measurements sequentially that requires considerable acquisition time. As an alternative non-contact method, digital video cameras are relatively low-cost, agile, and provide high spatial resolution, simultaneous, measurements. Combined with vision based algorithms (e.g., image correlation or template matching, optical flow, etc.), video camera based measurements have been successfully used for experimental and operational vibration measurement and subsequent modal analysis. However, the sampling frequency of most affordable digital cameras is limited to 30–60 Hz, while high-speed cameras for higher frequency vibration measurements are extremely costly. This work develops a computational algorithm capable of performing vibration measurement at a uniform sampling frequency lower than what is required by the Shannon-Nyquist sampling theorem for output-only modal analysis. In particular, the spatio-temporal uncoupling property of the modal expansion of structural vibration responses enables a direct modal decoupling of the temporally-aliased vibration measurements by existing output-only modal analysis methods, yielding (full-field) mode shapes estimation directly. Then the signal aliasing properties in modal analysis is exploited to estimate the modal frequencies and damping ratios. The proposed method is validated by laboratory experiments where output-only modal identification is conducted on temporally-aliased acceleration responses and particularly the temporally-aliased video measurements of bench-scale structures, including a three-story building structure and a cantilever beam. [ABSTRACT FROM AUTHOR]

Experimental or operational modal analysis traditionally requires physically-attached wired or wireless sensors for vibration measurement of structures. This instrumentation can result in mass-loading on lightweight structures, and is costly and time-consuming to install and maintain on large civil structures, especially for long-term applications (e.g., structural health monitoring) that require significant maintenance for cabling (wired sensors) or periodic replacement of the energy supply (wireless sensors). Moreover, these sensors are typically placed at a limited number of discrete locations, providing low spatial sensing resolution that is hardly sufficient for modal-based damage localization, or model correlation and updating for larger-scale structures. Non-contact measurement methods such as scanning laser vibrometers provide high-resolution sensing capacity without the mass-loading effect; however, they make sequential measurements that require considerable acquisition time. As an alternative non-contact method, digital video cameras are relatively low-cost, agile, and provide high spatial resolution, simultaneous, measurements. Combined with vision based algorithms (e.g., image correlation, optical flow), video camera based measurements have been successfully used for vibration measurements and subsequent modal analysis, based on techniques such as the digital image correlation (DIC) and the point-tracking. However, they typically require speckle pattern or high-contrast markers to be placed on the surface of structures, which poses challenges when the measurement area is large or inaccessible. This work explores advanced computer vision and video processing algorithms to develop a novel video measurement and vision-based operational (output-only) modal analysis method that alleviate the need of structural surface preparation associated with existing vision-based methods and can be implemented in a relatively efficient and autonomous manner with little user supervision and calibration. First a multi-scale image processing method is applied on the frames of the video of a vibrating structure to extract the local pixel phases that encode local structural vibration, establishing a full-field spatiotemporal motion matrix. Then a high-spatial dimensional, yet low-modal-dimensional, over-complete model is used to represent the extracted full-field motion matrix using modal superposition, which is physically connected and manipulated by a family of unsupervised learning models and techniques, respectively. Thus, the proposed method is able to blindly extract modal frequencies, damping ratios, and full-field (as many points as the pixel number of the video frame) mode shapes from line of sight video measurements of the structure. The method is validated by laboratory experiments on a bench-scale building structure and a cantilever beam. Its ability for output (video measurements)-only identification and visualization of the weakly-excited mode is demonstrated and several issues with its implementation are discussed. [ABSTRACT FROM AUTHOR]

Digital cameras are cost-effective vision sensors and able to directly provide two-dimensional information of structural condition in monitoring and assessment applications. For example, digital cameras are essential components of unmanned aerial vehicles (UAVs) and robotic agents for mobile sensing and inspection of pipelines, buildings, transportation infrastructure, etc, especially in post-natural disaster and man-made extreme events assessment. Additionally, while surveillance cameras have been widely used for transportation systems (e.g., traffic monitoring), if appropriately mounted on the large-scale structures such as the bridges, they can continuously monitor the structural condition under operational loads and hazards, complementing the regular visual inspection and assessment conducted by experts. In these or other applications, efficiently and reliably transferring the structural images or videos, which are as such large-scale, are important and challenging, especially in wireless platform that is either required (e.g., UAVs and robotic agents) or more suitable (e.g., camera monitoring networks) with only limited power and communication resources. This paper studies the computational algorithms for efficient and reliable transmission of the structural monitoring images; in particular, the compressed sensing (CS) technique is explored for robust data transmission and recovery. The sparse representation or data structure of the structural images is exploited, leading to the CS based central strategy: on some sparse domain, randomly encode large-scale image data into few relevant coefficients, which are then transferred (robust to random data loss) and recovered (in base station) for subsequent structural health diagnosis. Image data of bench scale pipe structure, concrete structure and full scale stay cable are employed for validation of the CS based method. Its performance is also compared with traditional transform coding and low-dimensional encoding (sampling), and their advantages and drawbacks are discussed. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a new paradigm of explicitly modeling and harnessing the data structure to address the inverse problems in structural dynamics, identification, and data-driven health monitoring. In particular, it is shown that the structural dynamic features and damage information, intrinsic within the structural vibration response measurement data, possesses sparse and low-rank structure, which can be effectively modeled and processed by emerging mathematical tools such as sparse representation and compressed sensing, low-rank matrix decomposition and completion, as well as the unsupervised multivariate blind source separation. It is also discussed that explicitly modeling and harnessing the sparse and low-rank data structure could benefit future work in developing data-driven approaches toward rapid, unsupervised, and effective system identification, damage detection, as well as massive SHM data sensing and management. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The unique features of Bi 2 S 3 enable it to be a promising candidate for preparing high-performance NO 2 gas sensors. However, pristine Bi 2 S 3 is usually inert for effective detection of ppb-level NO 2 at room temperature owing to the lack of active sites. Herein, urchin-like Bi 2 S 3 nanostructures with rich-defects were designed and prepared by a one-step hydrothermal method to realize ppb-level NO 2 detection at room temperature. The optimal sensor based on urchin-like Bi 2 S 3 with sulfur defects demonstrated an ultrahigh sensing response of 21.7 (almost 13 times higher than that of pristine Bi 2 S 3) and a short response/recovery time of 42/36 s to 1 ppm NO 2. In addition, an ultralow theoretical detection limit of 3 ppb was achieved for the sensor. The improved sensing performance could be ascribed to the numerous sulfur vacancies and the distinctive hierarchical structures, which promote the adsorption of NO 2 molecules and boost charge transfer between NO 2 and Bi 2 S 3. This study would provide a new perspective on nanostructured materials for NO 2 sensing at room temperature. • Urchin-like Bi 2 S 3 with rich-defects was prepared by a one-step hydrothermal method. • Urchin-like Bi 2 S 3 with defects exhibits an ultrahigh NO 2 sensing at room temperature. • An ultralow theoretical detection limit of 3 ppb was achieved. • High response was attributed to the sulfur vacancies and hierarchical structures. [ABSTRACT FROM AUTHOR]

Super-resolution guided wave array imaging has shown to be a feasible tool to detect and image sub-wavelength defects. However, research gaps remain in effectively quantifying and understanding the uncertainties in super-resolution imaging. In this study, we present a Bayesian deep learning approach to quantify and interpret various uncertainties in super-resolution guided wave array imaging. Specifically, we implement a Monte Carlo (MC) dropout scheme in the multi-scale deep learning models for approximate Bayesian inference to effectively quantify uncertainties in super-resolution subwavelength defect imaging. Furthermore, we decompose the total predictive uncertainty into distinct uncertainty sources: aleatoric uncertainty inherent in the data and epistemic uncertainty associated with the Bayesian deep learning model. From the experimental study, we observe that the two types of uncertainty (aleatoric and epistemic) in super-resolution guided wave array imaging can be successfully quantified using the multi-scale Bayesian deep learning approach. We further discuss the effectiveness of the Bayesian deep learning approach in small-data cases and compare the super-resolution imaging performance with a non-Bayesian (deterministic) deep learning approach. [ABSTRACT FROM AUTHOR]

Osmotic stress adversely affects the growth, fruit quality and yield of watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai). Increasing the tolerance of watermelon to osmotic stress caused by factors such as high salt and water deficit is an effective way to improve crop survival in osmotic stress environments. Roots are important organs in water absorption and are involved in the initial response to osmosis stress; however, few studies have examined the underlying mechanism of tolerance to osmotic stress in watermelon roots. For better understanding of this mechanism, the inbred watermelon accession M08, which exhibits relatively high tolerance to water deficits, was treated with 20% polyethylene glycol (PEG) 6000. The root samples were harvested at 6 h after PEG treatment and untreated samples were used as controls. Transcriptome analyses were carried out by Illumina RNA sequencing. A total of 5246 differentially expressed genes were identified. Gene ontology enrichment and biochemical pathway analyses of these 5246 genes showed that short-term osmotic stress affected osmotic adjustment, signal transduction, hormone responses, cell division, cell cycle and ribosome, and M08 may repress root growth to adapt osmotic stress. The results of this study describe the watermelon root transcriptome under osmotic stress and propose new insight into watermelon root responses to osmotic stress at the transcriptome level. Accordingly, these results allow us to better understand the molecular mechanisms of watermelon in response to drought stress and will facilitate watermelon breeding projects to improve drought tolerance. [ABSTRACT FROM AUTHOR]

As magnetic fields constantly act on living and biochemical processes, it is reasonable to hypothesize that magnetic field treatment of plant seeds would enhance the uptake capacity of non-essential elements. To verify this hypothesis, seeds of Brassica juncea were treated with 50, 100, 150, 200, and 400 mT fields, and the dry weight, Cd uptake capacity, ferritin content, antioxidant enzyme activity, and phytoremediation effects of the plant were compared at the end of the experiment. Relative to the control, low- and moderate-intensity fields (50–200 mT) enhanced the dry weight of plant leaves by 15.1%, 24.5%, 35.8%, and 49.1%, respectively, whereas the high-intensity field (400 mT) decreased the biomass yield by 18.9%. The content of Cd in the above-ground tissues of B. juncea enhanced with the increasing field intensity, accompanied by an increase in oxidative damage. The activities of superoxide dismutase (SOD) and ascorbate peroxidase (APX) increased with exposure to low (50 and 100 mT) and moderate (150 and 200 mT) intensities, followed by a reduction at a high intensity (400 mT). Catalase activity (CAT) and ferritin content exhibited an increasing trend with increasing intensity. The Cd decontamination index of B. juncea increased with the increasing magnetic field intensity until it reached a peak at 150 mT, after which the values remained constant. Considering the phytoremediation effect and energy consumption, 150 mT was the optimal scheme for magnetic-field-assisted phytoremediation using B. juncea. This study suggests that a suitable magnetic field can be regarded as an ecologically friendly physical trigger to improve the phytoextraction effect of B. juncea. [Display omitted] • All pre-sowing treatments, except 400 mT increased the biomass of B. juncea. • The 400 mT field increased the content of Cd in B. juncea significantly. • The 400 mT field resulted in the excessive accumulation of MDA in plant shoots. • Slight and moderate fields can increase activities of antioxidant enzyme. • Ferritin is more sensitivity to external fields than other enzymes and proteins. [ABSTRACT FROM AUTHOR]

Randomly missing data of structural vibration responses time history often occurs in structural dynamics and health monitoring. For example, structural vibration responses are often corrupted by outliers or erroneous measurements due to sensor malfunction; in wireless sensing platforms, data loss during wireless communication is a common issue. Besides, to alleviate the wireless data sampling or communication burden, certain accounts of data are often discarded during sampling or before transmission. In these and other applications, recovery of the randomly missing structural vibration responses from the available, incomplete data, is essential for system identification and structural health monitoring; it is an ill-posed inverse problem, however. This paper explicitly harnesses the data structure itself—of the structural vibration responses—to address this (inverse) problem. What is relevant is an empirical, but often practically true, observation, that is, typically there are only few modes active in the structural vibration responses; hence a sparse representation (in frequency domain) of the single-channel data vector, or, a low-rank structure (by singular value decomposition) of the multi-channel data matrix. Exploiting such prior knowledge of data structure (intra-channel sparse or inter-channel low-rank), the new theories of ℓ 1 -minimization sparse recovery and nuclear-norm-minimization low-rank matrix completion enable recovery of the randomly missing or corrupted structural vibration response data. The performance of these two alternatives, in terms of recovery accuracy and computational time under different data missing rates, is investigated on a few structural vibration response data sets—the seismic responses of the super high-rise Canton Tower and the structural health monitoring accelerations of a real large-scale cable-stayed bridge. Encouraging results are obtained and the applicability and limitation of the presented methods are discussed. [ABSTRACT FROM AUTHOR]

The intrinsic low-dimensional structure, which is implicit in the large-scale data sets of structural seismic and typhoon responses, is exploited for efficient data compression. Such a low-dimensional structure, empirically, stems from few modes that are active in the structural dynamic responses. Originally, limited to the sensor and timehistory dimension, the structural seismic and typhoon response data set generally does not have an explicit lowrank representation (e.g., by singular value decomposition or principal component analysis), which is critical in multi-channel data compression. By the proposed matrix reshape scheme, the low-rank structure of the largescale data set stands out, regardless of the original data dimension. Examples demonstrate that the developed method can significantly compress the large-scale structural seismic and typhoon response data sets, which were recorded by the structural health monitoring system of the super high-rise Canton Tower. [ABSTRACT FROM AUTHOR]

Modal identification or testing of structures consists of two phases, namely, data acquisition and data analysis. Some structures, such as aircrafts, high-speed machines, and plate-like civil structures, have active modes in the high-frequency range when subjected to high-speed or broadband excitation in their operational conditions. In the data acquisition stage, the Shannon–Nyquist sampling theorem indicates that capturing the high-frequency modes (signals) requires uniform high-rate sampling, resulting in sensing too many samples, which potentially impose burdens on the data transfer (especially in wireless platform) and data analysis stage. This paper explores a new-emerging, alternative, signal sampling and analysis technique, compressed sensing, and investigates the feasibility of a new method for output-only modal identification of structures in a non-uniform low-rate random sensing framework based on a combination of compressed sensing (CS) and blind source separation (BSS). Specifically, in the data acquisition stage, CS sensors sample few non-uniform low-rate random measurements of the structural responses signals, which turn out to be sufficient to capture the underlying mode information. Then in the data analysis stage, the proposed method uses the BSS technique, complexity pursuit (CP) recently explored by the authors, to directly decouple the non-uniform low-rate random samples of the structural responses, simultaneously yielding the mode shape matrix as well as the non-uniform low-rate random samples of the modal responses. Finally, CS with ℓ 1 -minimization recovers the uniform high-rate modal response from the CP-decoupled non-uniform low-rate random samples of the modal response, thereby enabling estimation of the frequency and damping ratio. Because CS sensors are currently in laboratory prototypes and not yet commercially available, their functionality—randomly sensing few non-uniform samples—is simulated in this study, which is performed on the examples of a numerical structural model, an experimental bench-scale structural model, and a real-world seismic-excited base-isolated hospital buildings. Results show that the proposed method in the CS framework can identify the modes using non-uniform low-rate random sensing, which is far below what is required by the Nyquist sampling theorem. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a novel output-only damage identification method based on the unsupervised blind source separation (BSS) technique termed independent component analysis (ICA). It is discovered that ICA biases to extract sparse component, which typically indicates damage, from the observed mixture signals. The measured structural responses are first preprocessed by wavelet transform (WT). The wavelet-domain signals are then fed as mixtures into the BSS model, which is solved by ICA. The obtained “interesting” source with sharp spike and its associated spatial signature in the recovered mixing matrix reveal damage instant and location respectively. Following which, identification of the time-varying modes is carried out by ICA using the structural responses before and after the identified damage instant. For illustration, numerical simulations are conducted, where damage is modeled by abrupt stiffness variation in the time-varying system. Experimental and real-world seismic-excited structure examples with time-varying stiffness are also presented to illustrate the capability of the developed WT–ICA method. Results show that the WT–ICA algorithm realizes accurate and robust blind identification of damage instant and location in single or multiple damage events. [Copyright &y& Elsevier]

• We propose an accelerated noncontact guided wave array imaging method with sparse scanning measurements; • Our method using sparse array data provides defect imaging performance comparable to the dense scanning case; • We demonstrate the proposed approach on laboratory experiments on composite plate specimens with multiple defects with encouraging results. Noncontact guided wave array imaging with a scanning laser Doppler vibrometer (SLDV) is an effective tool to detect and locate defects within a plate-like structure, as it obviates the need for installing, calibrating, and maintaining a transducer array. However, it requires collecting guided wave signals through scanning across dense spatial grid points to avoid non-defect artifacts in the array image, which is time-consuming. In this paper, we present an accelerated noncontact guided wave array imaging method that does not require dense scanning array while providing defect imaging performance comparable to the dense scanning case. In our approach, sparse scanning measurements at only a small number of points are carried out first for fast guide wave data acquisition. Then, dense guided wave array data is reconstructed from these sparse array measurements using a sparsity-promoting optimization technique, followed by delay-and-sum (DAS) beamforming to image defects within a test structure. We validate this method with laboratory experiments on composite plate specimens with multiple defects. The results demonstrate that defects within a composite plate can be successfully detected and located using sparsely sampled guided wave array measurement data. Such a significant reduction in the number of required measurement points enables accelerated noncontact guided wave array imaging. [ABSTRACT FROM AUTHOR]

SUMMARY Structural vibration responses themselves contain rich dynamic information, exploiting which can lead to tackling the challenging problem: simultaneous denoising of both gross errors (outliers) and dense noise that are not uncommon in the data acquisition of SHM systems. This paper explicitly takes advantage of the fact that typically only few modes are active in the vibration responses; as such, it is proposed to re-stack the response data matrix to guarantee a low-rank representation, through which even heavy gross and dense noises can be efficiently removed via a new technique termed principal component pursuit (PCP), without the assumption that sensor numbers exceed mode numbers that used to be made in traditional methods. It is found that PCP works extremely well under broad conditions with the simple but effective strategy no more than reshaping the data matrix for a low-rank representation. The proposed PCP denoising algorithm overcomes the traditional PCA (or SVD) and low-pass filter denoising algorithms, which can only handle dense (Gaussian) noise. The application of PCP on the health monitoring data of the New Guangzhou TV Tower (Canton Tower) shows its potential for practical usage. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses two problems in structural damage identification: locating damage and assessing damage severity, which are incorporated into the classification framework based on the theory of sparse representation (SR) and compressed sensing (CS). The sparsity nature implied in the classification problem itself is exploited, establishing a sparse representation framework for damage identification. Specifically, the proposed method consists of two steps: feature extraction and classification. In the feature extraction step, the modal features of both the test structure and the reference structure model are first blindly extracted by the unsupervised complexity pursuit (CP) algorithm. Then in the classification step, expressing the test modal feature as a linear combination of the bases of the over-complete reference feature dictionary—constructed by concatenating all modal features of all candidate damage classes—builds a highly underdetermined linear system of equations with an underlying sparse representation, which can be correctly recovered by -minimization; the non-zero entry in the recovered sparse representation directly assigns the damage class which the test structure (feature) belongs to. The two-step CP–SR damage identification method alleviates the training process required by traditional pattern recognition based methods. In addition, the reference feature dictionary can be of small size by formulating the issues of locating damage and assessing damage extent as a two-stage procedure and by taking advantage of the robustness of the SR framework. Numerical simulations and experimental study are conducted to verify the developed CP–SR method. The problems of identifying multiple damage, using limited sensors and partial features, and the performance under heavy noise and random excitation are investigated, and promising results are obtained. [Copyright &y& Elsevier]

Output-only algorithms are needed for modal identification when only structural responses are available. The recent years have witnessed the fast development of blind source separation (BSS) as a promising signal processing technique, pursuing to recover the sources using only the measured mixtures. As the most popular tool solving the BSS problem, independent component analysis (ICA) is able to directly extract the time-domain modal responses, which are viewed as virtual sources, from the observed system responses; however, it has been shown that ICA loses accuracy in the presence of higher-level damping. In this study, the modal identification issue, which is incorporated into the BSS formulation, is transformed into a time-frequency framework. The sparse time-frequency representations of the monotone modal responses are proposed as the targeted independent sources hidden in those of the system responses which have been short-time Fourier-transformed (STFT); they can then be efficiently extracted by ICA, whereby the time-domain modal responses are recovered such that the modal parameters are readily obtained. The simulation results of a multidegree-of-freedom system illustrate that the proposed output-only STFT-ICA method is capable of accurately identifying modal information of lightly and highly damped structures, even in the presence of heavy noise and nonstationary excitation. The laboratory experiment on a highly damped three-story frame and the analysis of the real measured seismic responses of the University of Southern California hospital building demonstrate the capability of the method to perform blind modal identification in practical applications. [ABSTRACT FROM AUTHOR]

Abstract: Blind source separation (BSS) based methods have been shown to be efficient and powerful to perform output-only modal identification. Existing BSS modal identification methods, however, require the number of sensors at least equal to that of sources (active modes). This paper proposes a new modal identification algorithm based on a novel BSS technique termed sparse component analysis (SCA) to handle even the underdetermined problem where sensors may be highly limited compared to the number of active modes. The developed SCA method reveals the essence of modal expansion that the monotone modal responses with disjoint sparsest representations in frequency domain naturally cluster in the directions of the mode matrix's columns (modeshapes), which are readily extracted from the measured system responses using a simple clustering algorithm. Then, in determined case where sensor number equals that of modes, the estimated square mode matrix directly decouples the system responses to obtain the modal responses, whereby computing their frequencies and damping ratios; whereas with limited sensors, the modal responses are efficiently recovered via the -minimization sparse recovery technique from the incomplete knowledge of the partial mode matrix and the system responses of inadequate sensors. Numerical simulations and experimental example show that whether in determined or underdetermined situations, the SCA method performs accurate and robust identification of a wide range of structures including those with closely-spaced and highly-damped modes. The SCA method is simple and efficient to conduct reliable output-only modal identification even with limited sensors. [Copyright &y& Elsevier]

In this study, we present a physics-constrained deep learning method to discover and visualize from data the invariant nonlinear normal modes (NNMs) which contain the spatiotemporal dynamics of the fluid flow potentially containing strong nonlinearity. Specifically, we develop a NNM-physics-constrained convolutional autoencoder (NNM-CNN-AE) integrated with a multi-temporal-step dynamics prediction block to learn the nonlinear modal transformation, the NNMs containing the spatiotemporal dynamics of the flow, and reduced-order reconstruction and long-time future-state prediction of the flow fields, simultaneously. In test cases, we apply the developed method to analyze different flow regimes past a cylinder, including laminar flows with low Reynolds number in transient and steady states (RD = 100) and high Reynolds number flow (RD = 1000), respectively. The results indicate that the identified NNMs are able to reveal the nonlinear spatiotemporal dynamics of these flows, and the NNMs-based reduced-order modeling consistently achieves better accuracy with orders of magnitudes smaller errors in construction and prediction of the nonlinear velocity and vorticity fields, compared to the linear proper orthogonal decomposition (POD) method and the Koopman-constrained-CNN-AE using the same number or dimension of modes. We perform an analysis of the modal energy distribution of NNMs and find that compared to POD modes, the few fundamental NNMs capture a very high level of total energy of the flow, which is advantageous for reduced-order modeling and representation of the complex flows. Finally, we discuss the potentials and limitations of the presented method. [ABSTRACT FROM AUTHOR]

• A novel method for data-driven predictive modeling of nonlinear dynamics is proposed. • This method includes a recurrent neural network framework and a training strategy. • The strategy facilitates the training of recurrent neural network. • The demonstration on various systems shows accuracy and robustness of the method. Long-time prediction of future states has been challenging in data-driven modeling of nonlinear dynamical systems as the prediction error accumulates over the prediction horizon. One of the potential reasons is the lack of robustness for the data-driven model. In this study we present a recurrent neural network (RNN) framework with an adaptive training strategy to model nonlinear dynamical systems from data for long-time prediction of future states. Specifically, we exploit the recurrence of network to improve the model robustness by explicitly incorporating the multi-step prediction with error accumulation into model training. Furthermore, we introduce an adaptive training strategy, where the prediction horizon gradually increases from a small value to facilitate the RNN training. We demonstrate the proposed approach on a family of Duffing oscillators, including autonomous and non-autonomous systems with various attractors, and discuss its advantages and limitations. [ABSTRACT FROM AUTHOR]

Summary: Real‐time health monitoring of stay cables in cable‐stayed bridges is necessary for timely maintenance and to avoid unforeseen fatigue damage due to vortex‐induced vibration—mainly due to combination rain and wind‐related dynamic loads. Conventional contact‐based sensors may often malfunction in harsh weather conditions and are expensive to install and maintain. Therefore, recently, the usage of non‐contact camera‐based measurement is burgeoning in the domain of structural sensing. Non‐contact video‐based sensing provides a higher spatial resolution compared to conventional sensors along with a lower cost. Therefore, in this paper, we present a framework that uses video‐based measurement as multiple sensors to reduce the estimation error in determining the real‐time cable tension. First, we calculate the vibration response using the phase‐based motion estimation algorithm for various locations of interest. We then intuitively fuse the data from all the locations to estimate the real‐time frequency variation using a blind source separation (BSS) technique named complexity pursuit (CP). Finally, the real‐time stay‐cable tension is calculated from the real‐time frequency history using the taut‐string theory. The proposed algorithm is applied to Fred‐Hartman cable‐stayed bridge in Houston, Texas. The algorithm is validated using actual tension in the cable. We also show that the estimation error in the proposed sliding window‐based CP framework is considerably lesser than the conventional real‐time tension estimation technique using Short‐time Fourier Transform (STFT). The accurate estimation of stay‐cable tension from the video‐based measurement shows the significant potential of the proposed framework in the domain of structural health monitoring. [ABSTRACT FROM AUTHOR]

Due to the widespread use of intensive cropping patterns, the problem of continuous cropping obstacle, which is dominated by autotoxicity, has been becoming more and more prominent. Although many methods have been proposed to overcome the continuous cropping obstacle of cucumber, no study has reported the screening and evaluation of cucumber germplasm resistant to autotoxicity. In this study, 28 physiological indices related to the cucumber bud stage under cinnamic acid (CA) treatment were determined. In total, 45 cucumber cultivars were classified into three groups using principal component analysis and cluster analysis, and a model for evaluating cucumber resistance to autotoxicity was developed. The evaluation model was validated using autotoxicity-tolerant and non-autotoxicity-tolerant cultivars. The results showed that the growth of non-autotoxicity-tolerant cultivars was significantly inhibited compared to autotoxicity-tolerant cultivars. This indicated that the evaluation model of cucumber autotoxicity tolerance is reliable. The results of this study provide a valuable reference for the application of cucumber autotoxicity-tolerant germplasm resources and the development of autotoxicity-tolerant genes. [ABSTRACT FROM AUTHOR]

Detecting small, subwavelength defect has known to be a challenging task mainly due to the diffraction limit, according to which the minimum resolvable size is in the order of the wavelength of a propagating wave. In this proof-of-concept study, we present a deep learning-enhanced super-resolution ultrasonic beamforming approach that computationally exceeds the diffraction limit and visualizes subwavelength defects. The proposed super-resolution approach is a novel subwavelength beamforming methodology enabled by a hierarchical deep neural network architecture. The first network (the detection network) globally detects defective regions from an ultrasonic beamforming image. Subsequently, the second network (the super-resolution network) locally resolves subwavelength-scale fine details of the detected defects. We validate the proposed approach using two independent datasets: a bulk wave array dataset generated by numerical simulations and guided wave array dataset generated by laboratory experiments. The results demonstrate that our deep learning super-resolution ultrasonic beamforming approach not only enables visualization of fine structural features of subwavelength defects, but also outperforms the existing widely-accepted super-resolution algorithm (time-reversal MUSIC). We also study key factors of the performance of our approach and discuss its applicability and limitations. [ABSTRACT FROM AUTHOR]

Determination of the model order is a challenging problem in system identification, especially in output-only or operational modal identification where some modes are weakly excited. Although existing methods such as the stabilization diagram method (spectral information) are effective, they do not scale to high-dimensional data, which is usually needed for high-fidelity characterization of structural dynamics and has been made available in the emerging full-field measurement techniques using optical methods such as photogrammetry and laser vibrometers. In this proof-of-concept study we present a new non-parametric, data-driven approach for robust output-only identification of high-spatial-dimensional modal parameters of basic structures by efficiently processing and interactively exploiting the full-field measurement (i.e., very dense spatial measurement points). Specifically, we first over-estimate the system model once , producing a pool of candidate modes associated with their modal frequencies and full-field, high-spatial-dimensional mode shapes. This is accomplished by a data-driven method termed affinity propagation clustering (APC), where the active clusters, which are the active modes in our formulations, emerge from the "message-passing" procedure and does not require a pre-determination of the cluster number (mode or model order). Next, rather than using the spectral information to distinguish the physical and spurious modes in the stabilization diagram method, we exploit and visualize the spatial , full-field mode shape associated with each candidate mode to do so. We conduct extensive experiments on basic structural models with comparisons to a few existing methods. The results indicate that the new method is computationally efficient for identifying high-spatial-dimensional modal parameters, and robust to identify weak modes by exploiting the full-field measurement. We also discuss its applicability and limitations for structures with complex geometry (shapes). • Develops a new method for output-only identification of high-spatial-dimensional (dense spatial measurement points) modal parameters. • Efficient processing of high-dimensional data using a data-driven method affinity propagation clustering. • Robust to extract weak modes via overestimation of model order only once & visualization of full-field spatial information. • Validated by laboratory experiments and comparisons with existing methods (SSI, FDD, and other clustering based method). [ABSTRACT FROM AUTHOR]

The synthesis and characterization of a highly photostable bromo-substituted BODIPY dye (I) fused-ring-expanded with thienopyrrole moieties is reported. The results of MTT assays and flow cytometric analyses in living HeLa cells demonstrate that I has a high singlet oxygen quantum yield (ΦΔ = 0.63) and exhibits photocytotoxicity upon irradiation in the NIR region making it potentially suitable for use in PDT. [ABSTRACT FROM AUTHOR]

• Output-only, simultaneous identification of full-field modal parameters and large rigid-body motion from videos. • Based on a combination of modal analysis and video motion processing techniques. • Data-driven, efficient computation of structural motion. • Validated by laboratory experiments. Many operating structures undergo vibration associated with significant rigid-body motion, such as earthquake-excited structures which vibrate while moving (and/or rocking) along with the shaking ground, rotating structures such as the blades of helicopter, aircraft, wind turbine, and in UAV's measurements (images/videos) of infrastructure response which is coupled with the motion of the UAV itself. In many such cases, the rigid motion is very dominant; measurement and identification of the relatively subtle deformation (flexible) motion of the structure in their operations is very challenging, especially when high-resolution vibration measurement and high-fidelity dynamics modeling are required to capture and characterize spatially local and temporally transient dynamics behaviors and features of the structure. Traditional contact-type wired or wireless sensors, such as accelerometers, strain gauges, and LVDT's, are discrete point-wise sensors that only allow instrumentations at a limited number of places, providing very low spatial resolution vibration measurements. Non-contact interferometry-based optical measurement techniques using laser beams such as laser Doppler vibrometers, electronic speckle pattern interferometry (ESPI) and holographic interferometry, can provide high spatial resolution vibration measurement without the need to install sensors on the structure. However, they are very vulnerable to ambient vibration and rigid-body motion. As an alternative non-contact optical measurement method, photogrammetry uses white-light imaging of digital video cameras that are relatively low-cost, agile, and provides simultaneous measurements with very high spatial resolution where every pixel effectively becomes a sensing unit. Furthermore, it is robust to rigid-body motion. Using image processing algorithms such as optical flow and image correlation, photogrammetry has been successfully used for vibration measurements and experimental modal analysis of structures (including rotating-type) where full-field (or as many points as the pixel number of the image frame on the structure) modal parameters (mode shapes) can be obtained. However, existing such methods typically rely on installing and tracking the optical markers or speckle paints on the structure's surface, which becomes less applicable for large measurement area or in harsh environment (high temperature and corrosion). In addition, they are computationally extensive. Aiming to alleviate these challenges, this study presents the development of adapting a recently-proposed video-based method for output-only, simultaneous identification of both the subtle, full-field deformation modes and the dominant rigid-body motion from only the video of the vibrating while moving structure. Full-field dynamic parameters (deformation modes) of structure, as well as its rigid-body motion can be efficiently estimated, respectively. Laboratory experiments were conducted on a bench-scale building structure with "free-free" boundary condition. Comparisons were made with an analytical finite element model. Results demonstrate that the method can efficiently and accurately extract the full-field, (total) motion of the structure and further identify or separate the subtle deformation modes and large rigid-body motion, respectively. Furthermore, videos reconstructed by the method (provided in the supplementary materials) show that it enables realistic visualization of the subtle deformation modes of the structure in presence of large rigid-body motion. Several practical limitations for implementing the method are also discussed. [ABSTRACT FROM AUTHOR]

Summary: Strain is an essential quantity to characterize local structural behaviors and directly correlates with structural damage initiation and development that is within local regions. Strain measurement at high spatial resolution (density) locations is thus required to characterize local structural behaviors and detect potential local damage. Traditional contact‐type strain gauges are mostly discrete point‐wise sensors that can only be placed in a limited number of positions. Distributed optical fiber sensing techniques can measure strains at spatially dense measurement points, but their instrumentation is a time‐ and labor‐intensive process associated with the issue of the fragility of fibers. Noncontact optical measurement techniques, such as a family of interferometry techniques using laser beams (e.g., laser Doppler vibrometers), can provide vibration measurement at high density spatial points without the need to install sensors on the structure. However, these measurement devices are active sensing methods that are relatively expensive and vulnerable to ambient motion. Photogrammetry is an alternative noncontact optical measurement method using (passive) white‐light imaging of digital video cameras that are relatively low‐cost, agile, and provides simultaneous measurements at high spatial density locations where every pixel becomes a measurement point. Among others, digital image correlation can achieve full‐field deformation measurements and subsequently estimate the full‐field strains. However, it is computationally extensive. This study develops a new efficient approach to estimate the full‐field (as many measurement points as the pixel number of the video frame on the structure) dynamic strains at high‐spatial (pixel)‐resolution/density location points from the digital video measurement of output‐only vibrating structures. The developed approach is based on phase‐based video motion estimation and modal superposition of structural dynamic response. Furthermore, the method is augmented by a high‐fidelity finite element model, which is updated with the full‐field experimental modal parameters "blindly" identified from the video measurement of the output‐only structure. Laboratory experiments are conducted to validate the method on a bench‐scale cantilever beam structure. Results demonstrate that the full‐field dynamic strain estimated by the developed approach from the video measurement of the output‐only vibrating beam match very well those directly measured by the strain gauges (at discrete measurement points). Some factors associated with the effectiveness of the method are experimentally studied and discussed. [ABSTRACT FROM AUTHOR]

Summary: Cables are critical components for a variety of structures such as stay cables and suspenders of cable‐stayed bridges and suspension bridges. When in operational service, they are vulnerable to cumulative fatigue damage induced by dynamic loads (e.g., the cyclic vehicle loads and wind excitation). To accurately analyze and predict their dynamics behaviors and performance that could be spatially local and temporal transient, it is essential to perform high‐resolution vibration measurements, from which their dynamics properties are identified and, subsequently, a high spatial resolution, full‐modal‐order dynamics model of cable vibration can be established. This study develops a physics‐guided, unsupervised machine learning‐based video processing approach that can blindly and efficiently extract the full‐field (as many points as the pixel number of the video frame) modal parameters of cable vibration using only the video of an operating (output‐only) cable. In particular, by incorporating the physics of cable vibration (taut string model), a novel automated modal motion filtering method is proposed to enable autonomous identification of full‐order (as many modes as possible) dynamic parameters, including those weakly excited modes that used to be challenging to identify in operational modal analysis. Therefore, a full‐field, full‐order modal model of cable vibration is established by the proposed method. Furthermore, this new approach provides a low‐cost and noncontact technique to estimate the cable tension using only the video of the vibrating cable where the fundamental frequency is automatically and efficiently estimated to compute the cable tension according to the taut string equation. Laboratory experiments on a bench‐scale cable are conducted to validate the developed approach. [ABSTRACT FROM AUTHOR]