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Electrospun nanofiber membranes possess high specific surface area with small pores and thus can be developed as wound dressings for absorbing exudate and also preventing bacterial penetration. In this study, hydroxypropyl cellulose (H), chitosan (C) and polyethylene oxide (P) were chosen as membrane materials to increase the hydrophilicity, anti-bacterial property, and yield of nanofibers, respectively. Additionally, graphene (G) was added to enhance the anti-bacterial property of the membranes. As indicated by SEM, the HCP and HCPG solutions (containing H:4.5 wt%, C:4.5 wt%, P:0.75 wt%, without/with G:0.5 wt%) could be electrospun into HCP and HCPG nanofiber membranes with good fiber morphology using a non-toxic solvent system. Further, the membranes were crosslinked by glutaraldehyde vapor to improve the strength. The tensile strength of the membranes was 1.38–1.82 MPa with a swelling ratio up to 1330–1410%. The water vapor transmission rate (WVTR) of wet HCPG membrane was about 3100 g/m2-day, close to the recommended WVTR of wound dressings. The anti-bacterial properties of the membranes were confirmed using three tests against Escherichia coli (Gram-negative bacterium) and Staphylococcus aureus (Gram-positive bacterium). Highly hydrophilic HCP and HCPG membranes prevented the bacterial adherence. The presence of the membranes (especially graphene-embedded HCPG membrane) also greatly reduced bacterial growth. The small pore sizes of HCP and HCPG nanofiber membranes prevented the bacterial penetration to cause infection. Taken together, the HCP and HCPG nanofiber membranes possessed good mechanical properties, appropriate WVTR and high water absorption thus suitable for absorbing wound exudate. Besides, the membranes exhibited nontoxic, anti-fibroblast adhesion and anti-bacterial properties. Therefore, HCP and HCPG nanofiber membranes have the potential to become superior anti-bacterial wound dressings.

Peak ground acceleration (PGA) can be used to estimate the seismic intensity. However, using P-wave features to estimate PGA is a challenging task. One of the reasons for that is that a seismic wave commonly undergoes modification due to various site effects, consequently leading to uncertainty in the predicted PGA. In order to accommodate site effects using site parameters together with P-wave parameters, this paper takes advantage of machine learning to consider multiple parameters simultaneously. Several artificial neural network (ANN) models considering different site effect parameters are constructed. The performances of these ANN models were investigated and compared. In total, 53531 ground motion data obtained from the Taiwan Strong Motion Instrumentation Program were utilized to develop the proposed approach. It was found that the proposed ANN model with horizontal-to-vertical spectral ratio parameters effectively reduces the error of the estimated PGA when compared with either the ANN model without site parameters or the ANN model with other site parameters.

The high-frequency decay parameter kappa (κ) was computed by fitting Fourier amplitude spectra from the seismic network of the Taiwan Strong Motion Instrumentation Program (TSMIP). The κ of shear horizontal waves (SH waves) was calculated for individual recordings, and the relationship between κ values and the epicentral distance (Repi) of each station was derived for each station. The κ value at Repi = 0 (denoted as κ0) can be used as a site parameter. There are totally 26,277 seismograms that have been recorded at 679 TSMIP stations over the period of 1993 through 2014 with local magnitudes of 4.0–7.1 and focal depths less than 30 km. The estimation of κ0 for Taiwan ranges from 0.0208 to 0.147 s, and the spatial distribution of κ0 was closely related to geology and velocity. The site-specific κ0 values from 425 stations were correlated with the averaged shear wave velocity of the top 30 m of strata (VS30), and the relationship could be described as κ0 = (0.125 ± 0.005) − (0.011 ± 0.001) × ln(VS30), and an acceptable linear correlation (R2 = 0.57) was performed. The results may be used in the future application of ground motion prediction equations (GPMEs) and serve as simulation parameters. Also, the relationship between κ0 and the depth to engineering rock (Z1.0) is not well correlated, whereas an acceptable correlation exists between κ0 and other site proxies (i.e., elevation and resonant frequency).

The strong ground motion of 6 February 2018 M 6.2 Hualien Earthquake triggered a series of co-seismic geomagnetic fluctuations and seismo-traveling atmospheric disturbances (STADs) signatures in infrasonic waves and micro-pressures upon the seismic wave arrival. Networks of 9 QuakeFinder systems, 3 infrasound systems, 2 tiltmeters, 2 micro-barometers, and 11 co-located seismometers are used in this study. Each QuakeFinder system consists of a 3-axes induction magnetometer, an air conductivity sensor, a geophone, and temperature/relative humidity sensors. Co-seismic signatures clearly appear in the induction magnetometers, infrasound systems, and micro-barometers data. The magnetometers register both high- and lowfrequency pulsations. Geomagnetic fluctuations occur upon the seismic wave arrival but last a longer duration, while the STADs lag their co-located seismic waves by about 15 - 45 s. The long-lasting fluctuations recorded by both induction and fluxgate magnetometers suggest that the ground/underground water motion play an important role, which is further conformed by low-frequency fluctuations in the tiltmeter data. In general, the amplitude of geomagnetic fluctuations decays as away from the epicenter. However, unusual large co-seismic geomagnetic fluctuations are detected over areas of the abnormal seismic intensity level and/or the magnetic underground structure with anomalously high susceptibilities.

It is seen that strong motion generation area plays an important role in the shaping of strong motion records at the observation point. Strong motion generation areas identified within the rupture plane of the 27 March 2013 Nantou, Taiwan, earthquake (Mw = 5.9) have been modelled in this work. It is seen that all available records are at the surface which include site amplification terms. The modified semi-empirical technique effectively simulates records at rock site. The site amplification terms in all records have been removed using SHAKE 91 program and velocity input at each site. The observed records corrected for site amplification terms are further used for comparison with simulated record at the bedrock from several models. Once the observed records at soil sites are transferred at the bedrock, the next task is selection of final model that gives best fit records. Peak ground acceleration from simulated record at four sites is compared with that from corrected observed data. Since the semi-empirical technique of simulation is strongly dependent on various modelling parameters such as dip, strike, rake, rupture velocity and starting points of rupture, these parameters change iteratively in a specified range in a heuristic way to obtain best modelling parameters. The model giving minimum root mean square error (RMSE) is retained at final model. It is seen that minimum root mean square error of the wave form comparison has been obtained at four stations for the source model having single strong motion generation area. Strong motion records have been simulated at four different recording stations. Comparison of observed and simulated records has been made in terms of RMSE between simulated and observed acceleration records, velocity records and the response spectra at each of four stations. Comparison of waveforms and parameters extracted from observed and simulated records confirms the efficacy of the modified technique to model earthquake characterized by SMGAs.

Hazards from gravity-driven instabilities on hillslope (termed ‘landquake’ in this study) are an important problem facing us today. Rapid detection of landquake events is crucial for hazard mitigation and emergency response. Based on the real-time broadband data in Taiwan, we have developed a near real-time landquake monitoring system, which is a fully automatic process based on waveform inversion that yields source information (e.g., location and mechanism) and identifies the landquake source by examining waveform fitness for different types of source mechanisms. This system has been successfully tested offline using seismic records during the passage of the 2009 Typhoon Morakot in Taiwan and has been in online operation during the typhoon season in 2015. In practice, certain levels of station coverage (station gap 106 m3 and area > 0.20 km2) are required to ensure good performance (fitness > 0.6 for successful source identification) of the system, which can be readily implemented in other places in the world with real-time seismic networks and high landquake activities.

The luminescence of LaPO4:Eu phosphors was improved by coprecipitation with phosphate from different sources and with anionic surfactants. The effects of the method of synthesis on the characteristics of the phosphors were investigated. When H3PO4 was used as the source of phosphate, strong absorption of the charge transfer band (CTB) was observed. A red shift of the CTB absorption was observed when (NH4)2HPO4 and Na5P3O10 were used as sources of phosphate. Phosphors with high crystallographic symmetry of Eu3+ in the lattice were synthesized when (NH4)2HPO4 was used. The optimum crystallinity and the maximum photoluminescence (PL) intensity were obtained when the phosphor surface was modified with 1 mol.% (NaPO3)6 and 3 mol.% sodium dodecyl sulfate (SDS) surfactant, with PL intensity being increased by 10% and 7%, respectively.

Ambient noise is typically used to estimate seismic site effects and velocity profiles instead of earthquake recordings, especially in areas with limited seismic data. The dominant Horizontal to Vertical Spectral Ratio (HVSR) frequency of ambient noise is correlated to Vs30, which is the average S-wave velocity in the top 30 m. Vs30 is a widely used parameter for defining seismic amplification in earthquake engineering. HVSR can detect the vertical discontinuity of velocities, that is, the interfaces between hard bedrock and soft sediments. In southwestern Taiwan most strong motion stations are located in the plains and show a dominant frequency lower than 3 Hz. Several stations near the coast have low dominant frequencies of less than 1 Hz. The dominant frequencies are higher than 4 Hz at piedmont stations. The stations in the mountains with dominant frequencies over 8 Hz are typically located on very hard sites. This study analyzed the HVSR characteristics under different seismic site conditions considering the Vs30 from previous study (Kuo et al. 2012). The result implies that HVSRs are a better tool than Vs30 to classify the sites where bedrock is deeper than 30 m. Furthermore, we found a linear correlation between Vs30 and dominant HVSR frequency which could be used as a proxy of Vs30. The Vs30 map in this area was derived using the Engineering Geological Database for Taiwan Strong Motion Instrumentation Program (EGDT). The comparable distribution pattern between the dominant frequency and Vs30 demonstrate that HVSR can recognize S-wave velocity properties at the shallow subsurface.

The western coastal plain of Taiwan borders the Western Foot hills, which is a fold-and-thrust zone and one of Taiwan's major seismic zones. Earth quakes from the Western Foot hills are a frequent cause of disaster in the western coastal plain. In this study, array measurements of microtremors were conducted at seven sites through out the western coastal plain. At each site, four arrays (S, M, L, and XL arrays) of different sizes were considered. The largest radius of each array ranged from 32 m to 1 km. The Maximum Likelihood Method of Frequency-Wavenumber (F-K) analyses was used to obtain phase velocity dispersion curves. After the inversion of the dispersion curves with the initial models decided by Genetic Algorithm (GA) searching, shallow S-wave velocity structures for the western coastal plain were estimated.

In the Tatun mountain area of northern Taiwan are two strong motion stations approximately 2.5 km apart, TAP056 and TAP066 of the TSMIP network. The accelerometer at station TAP056 is often triggered by earthquakes, but that at TAP066 station is not. Comparisons of vertical and horizontal peak ground accelerations reveal PGA in the vertical, east-west, and north-south components at TAP056 station to be 3.89, 7.57, and 5.45 times those at station TAP066, respectively. The PGA ratio does not seem to be related to earthquake source or path. Fourier spectra of earthquake records at station TAP056 always have approximately the same dominant frequency; however, those at station TAP066 are different due to different sources and paths of different events. This shows that spectra at TAP056 station are mainly controlled by local site effects. The spectral ratios of TAP056/TAP066 show the S-wave is amplified at around 8 ~ 10 Hz. The horizontal/vertical spectral ratios of station TAP056 also show a dominant frequency at about 6 and 8 ~ 10 Hz. After dense microtremor surveying and the addition of one accelerometer just 20 meters away from the original observation station, we can confirm that the top soft soil layer upon which the observation station is constructed generates the local site response at station TAP056.

The horizontal-to-vertical spectral ratio has become increasingly popular in studies of site effect and determination of the predominant period of a site. In this study, this method is extended to identify nonlinear soil responses. To establish this phenomenon, borehole array records that already showed nonlinear site responses using spectral ratios between surface and borehole station pairs were analyzed. Moreover, in this study, the horizontal-to-vertical spectral ratio method was applied to weak and strong motion records from the same dataset. The results demonstrate that nonlinear site responses can be evaluated using horizontal-to-vertical spectral ratios of surface recordings at a single station.