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Bouncing, which refers to the up-and-down movement of the human body with both feet remaining on the ground, is one of the most common human-induced dynamic loads for civil structures such as sports stadiums and concert halls. Although researchers have focused on the modeling of bouncing-induced loads, studies on the excitation source (the bouncing person) have not gained much attention. The spring-mass-damper model with a pair of internal biomechanical forces is the simplest model to represent a bouncing person. The model parameters, such as stiffness and damping, though very important for application and for HSI (Human Structure Interaction) analysis, have been rarely reported. This study utilized the particle filter (a step-by-step system identification method) to identify human SMD (Spring-Mass-Damper) model parameters from measured bouncing forces obtained by a wireless insole system. A total of 173 continuous bouncing force signals were recorded and divided into 6800 bouncing cycles, from which the SMD model parameters were identified. The results indicated that human natural frequency and high-order biomechanical load factors have a linear trend against the bouncing frequency, while the damping ratio and first-order BLF (Biomechanical Load Factor) share a parabolic relation. For each model parameter, a skew normal distribution was fitted, and the fitting parameters were found to be dependent on the bouncing frequency range. The influence of HSI on the model parameters was also experimentally investigated, indicating that a vibrating surface leads to a lower human natural frequency and a higher damping ratio value. • Particle filter is used to identify SMD model parameters for a bouncing person. • This is a parameter identification problem with unknown excitation. • Results are given for all recorded bouncing cycles. • The dependence of human system parameters on the bouncing frequency is analyzed. • Influence from human-structure-interaction is experimentally investigated. [ABSTRACT FROM AUTHOR]

Highlights • An augmented Kalman filter estimates dynamic tire forces from vehicle responses. • Smartphones are used to record vehicle body responses. • Numerical examples with observability and sensitivity analysis prove the feasibility of the study. • A field measurement validates the proposed method. Abstract Bridge modal parameters, including modal mass, are important factors that directly affect the bridge responses under various excitations. To identify these parameters, the simultaneous measurement of the bridge excitation and its corresponding bridge responses is typically required. However, traditional methods of exciting a bridge using a hammer or a shaker have the limitations of small impact energy, narrow frequency range, and other site-specific difficulties. However, the vehicle-induced load, which excites the bridge through the vehicle-bridge interaction, can be used in bridge parameter identification if the vehicle-bridge contact force is estimated well. In this paper, an algorithm is proposed to estimate the dynamic components of the vertical tire contact forces by measuring the vehicle body acceleration and angular velocity at selected locations on an ordinary vehicle. A Kalman filter with augmented tire forces in the system state vector is employed. Observability and sensitivity analyses provide the method's theoretical foundation. Numerical examples for different road roughness conditions demonstrate that the proposed algorithm has good accuracy and robustness against noise and modelling error. A commercial light vehicle was used in the conduct of field measurement to validate the proposed method. The estimated dynamic tire forces are in good agreement with reference measurements. [ABSTRACT FROM AUTHOR]

A bridge's natural frequencies are important dynamic properties reflecting the structural condition of the bridge. Numerous studies have been conducted in the field to extract a bridge's natural frequencies from responses of passing vehicles. The bridge frequency peaks are, however, not easily observed, because pavement roughness often influences the spectra of vehicle responses. In this research, a method that extracts the fundamental frequency of a bridge from the responses of an ordinary vehicle with its parameters calibrated in advance is proposed. The method is based on the idea that the vehicle passing across a bridge is excited by two sources, i.e., pavement roughness and bridge vibration. The excitation inputs to the vehicle, i.e., displacement inputs at the front and rear tire locations, are estimated from vehicle responses using a particle filter method. The estimated displacement inputs at the front and rear tires are then subtracted from each other after shifting by a wheel–base distance to eliminate the roughness influence, which commonly appears in both signals. The signal after the subtraction contains only the bridge vibration influence and is used to extract the fundamental frequency of the bridge. This indirect method of bridge frequency extraction is investigated through numerical simulations. A field measurement was also conducted, and it showed that the bridge's fundamental frequency was successfully extracted with a good accuracy for several driving-speed cases. [ABSTRACT FROM AUTHOR]

The spring-mass-damper (SMD) model with a pair of internal biomechanical forces is the simplest model for a walking pedestrian to represent his/her mechanical properties, and thus can be used in human-structure-interaction analysis in the vertical direction. However, the values of SMD stiffness and damping, though very important, are typically taken as those measured from stationary people due to lack of a parameter identification methods for a walking pedestrian. This study adopts a step-by-step system identification approach known as particle filter to simultaneously identify the stiffness, damping coefficient, and coefficients of the SMD model's biomechanical forces by ground reaction force (GRF) records. After a brief introduction of the SMD model, the proposed identification approach is explained in detail, with a focus on the theory of particle filter and its integration with the SMD model. A numerical example is first provided to verify the feasibility of the proposed approach which is then applied to several experimental GRF records. Identification results demonstrate that natural frequency and the damping ratio of a walking pedestrian are not constant but have a dependence of mean value and distribution on pacing frequency. The mean value first-order coefficient of the biomechanical force, which is expressed by the Fourier series function, also has a linear relationship with pacing frequency. Higher order coefficients do not show a clear relationship with pacing frequency but follow a logarithmic normal distribution. [ABSTRACT FROM AUTHOR]

Tick-borne encephalitis virus (TBEV) is a medically important representative of the Flaviviridae family. The TBEV genome encodes a single polyprotein, which is co/post-translationally cleaved into three structural and seven non-structural proteins. Of the non-structural proteins, NS5, contains an RNA-dependent RNA polymerase (RdRp) domain that is highly conserved and is responsible for the genome replication. Screening for potential antivirals was done using a hybrid receptor and ligand-based pharmacophore search likely targeting the RdRp domain. For the identification of pharmacophores, a mixture of small probe molecules and nucleotide triphosphates were used. The ligand/receptor interaction screenings of structures from the ZINC database resulted in five compounds. Zinc 3677 and 7151 exhibited lower cytotoxicity and were tested for their antiviral effect against TBEV in vitro. Zinc 3677 inhibited TBEV at micromolar concentrations. The results indicate that Zinc 3677 represents a good target for structure-activity optimizations leading potentially to a discovery of effective TBEV antivirals. [ABSTRACT FROM AUTHOR]