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Understanding the feeding behavior of Portunidae crabs with different baits can improve bait selection and is crucial for improving the effectiveness of crab fishing gear. This study, conducted in indoor experimental tanks, used trajectory tracking software and two types of natural baits (mackerel (Scomber australasicus) and squid (Uroteuthis chinensis)) to understand the behavior of Portunus sanguinolentus and Charybdis natator. Spatial distribution results showed that P. sanguinolentus was frequently present in the starting area (S1) and bait area (S3) in the control and treatment groups. However, C. natator was frequently present and concentrated in the S1 area compared to the middle areas S2 and S3, and only in the mackerel treatments were they observed to move to the S3 areas. The spatial distribution results indicate that P. sanguinolentus shows a stronger willingness to explore its surroundings, while C. natator is generally in a stationary, wait-and-see state. The swimming speeds of P. sanguinolentus and C. natator showed different trends. P. sanguinolentus showed continuous movement with no fixed speed when no bait was present in the control groups. However, when treated with mackerel and squid, the average swimming speed of P. sanguinolentus was faster (>5 cm/s) in the first 10 min and showed a more stable movement speed when searching for the baits. C. natator showed a stationary or low movement speed when no bait was present in the control groups. However, when C. natator perceived the presence of the baits in the treatment groups, their movement speed increased in the first 10 min. In addition, there was no significant difference between male and female crabs of P. sanguinolentus and C. natator in movement speed in the control and treatment groups. Compared to C. natator, P. sanguinolentus might be more sensitive to natural baits, as shown by its movement from S1 to S3. The results indicate that the species of Portunidae crabs show different bait selections. Natural baits (mackerel and squid) are recommended for catching P. sanguinolentus in crab fisheries.

Vortex-induced vibration is a type of wind-induced vibration occurring frequently in large-span sea-crossing bridges under relatively low wind speeds, posing a threat to the structural fatigue performance and driving comfort. Identifying the instantaneous occurrence moments of vortex-induced vibration is a prerequisite for establishing a data-driven prediction model for vortex-induced vibration, and it is of great significance for the monitoring and early warning of vortex-induced vibration performance in bridges. To automatically detect the occurrence moments of vortex-induced vibration and establish a correlation model between vortex-induced vibration amplitude and environmental factors, this study proposes a fuzzy C-means clustering-based classification method. In order to detect the occurrence moments of vortex-induced vibration more finely, only short-term or even instantaneous structural vibration indicators were selected and transformed for distribution as clustering features. The entire detection process could be carried out unsupervised, reducing the manual cost of obtaining vortex-induced vibration information from massive monitoring data. Finally, actual vortex-induced vibration test data from a certain overseas bridge was utilized to verify the feasibility of this method. Based on the classification results, the correlation between vortex-induced vibration amplitude and environmental variables was determined, providing valuable guidance for predicting vortex-induced vibration amplitudes.

In this study, a polyvinylidene fluoride (PVDF)/graphene nanoplatelet (GNP) micro-nanocomposite membrane was fabricated through electrospinning technology and was employed in the fabrication of a fiber-reinforced polymer composite laminate. Some glass fibers were replaced with carbon fibers to serve as electrodes in the sensing layer, and the PVDF/GNP micro-nanocomposite membrane was embedded in the laminate to confer multifunctional piezoelectric self-sensing ability. The self-sensing composite laminate has both favorable mechanical properties and sensing ability. The effects of different concentrations of modified multiwalled carbon nanotubes (CNTs) and GNPs on the morphology of PVDF fibers and the β-phase content of the membrane were investigated. PVDF fibers containing 0.05% GNPs were the most stable and had the highest relative β-phase content; these fibers were embedded in glass fiber fabric to prepare the piezoelectric self-sensing composite laminate. To test the laminate’s practical application, four-point bending and low-velocity impact tests were performed. The results revealed that when damage occurred during bending, the piezoelectric response changed, confirming that the piezoelectric self-sensing composite laminate has preliminary sensing performance. The low-velocity impact experiment revealed the effect of impact energy on sensing performance.

Three typhoons, Meranti, Malakas, and Megi, occurred successively in eastern Taiwanese waters in September 2016, causing extreme waves (significant wave height > 10.0 m), and these events were selected to investigate the effect of model grid resolution and wave–wave interaction on simulating typhoon-driven waves. The WAVEWATCH III (WW3) model, with 0.50 deg, 0.25 deg, 0.20 deg, 0.10 deg, and 0.05 deg grid resolutions, and two reanalysis wind fields were adopted to simulate ocean waves during these three typhoons. The results indicated that the exertion of the Climate Forecast System version 2 (CFSv2) winds over the WW3 model with 0.10 deg grid resolution yielded optimum simulations of typhoon waves in a compromise between accuracy and elapsed time. In the present study, the WW3 model modeled nonlinear wave–wave interactions using discrete interaction approximation (DIA). The numerical experiments revealed that the underestimations of typhoon waves were significant when the WW3 model excluded nonlinear wave–wave interactions, especially when employing a higher grid resolution. This study also found that the WW3 model is superior to the Wind Wave Model III (WWM-III) using the CFSv2 winds because the WWM-III tended to overestimate the extreme waves in all three of these eastern Taiwan typhoon events that occurred in September 2016.

Recently, miRNAs have become a promising biomarker for disease diagnostics. miRNA-145 is closely related to strokes. The accuracy determination of miRNA-145 (miR-145) in stroke patients still remains challenging due to its heterogeneity and low abundance, as well as the complexity of the blood matrix. In this work, we developed a novel electrochemical miRNA-145 biosensor via subtly coupling the cascade strand displacement reaction (CSDR), exonuclease III (Exo III), and magnetic nanoparticles (MNPs). The developed electrochemical biosensor can quantitatively detect miRNA-145 ranging from 1 × 102 to 1 × 106 aM with a detection limit as low down as 100 aM. This biosensor also exhibits excellent specificity to distinguish similar miRNA sequences even with single-base differences. It has been successfully applied to distinguish healthy people from stroke patients. The results of this biosensor are consistent with the results of the reverse transcription quantitative polymerase chain reaction (RT-qPCR). The proposed electrochemical biosensor has great potential applications for biomedical research on and clinical diagnosis of strokes.

Infectious keratitis (IK) represents a major cause of corneal blindness. This study aims to investigate the demographics, risk factors, microbiological characteristics and antibiotic susceptibility patterns of IK in Taiwan over the past 15 years. A retrospective population-based study was conducted using the Chang Gung Research Database. Patients with IK were identified by diagnostic codes for corneal ulcer from 2004 to 2019. Of 7807 included subjects, 45.2% of patients had positive corneal cultures. The proportion of contact lens-related IK declined, while that of IK related to systemic diseases grew. The percentage of isolated gram-positive bacteria surpassed that of gram-negative bacteria in the 15-year period. The prevalence of Pseudomonas aeruginosa showed a decreasing trend (p = 0.004), whereas coagulase-negative Staphylococcus (CNS) and Propionibacterium species were increasingly detected (p < 0.001). Overall, the trend of antibiotic susceptibility of both gram-positive and gram-negative bacteria did not change throughout the study period. The susceptibility to the test antibiotics maintained over 90% in gram-negative isolates over 15 years. Vancomycin preserved 100% susceptibility to all gram-positive isolates. Since most tested antibiotics exhibited stable susceptibility over decades, this study reinforced that fluoroquinolones and fortified vancomycin continue to be good empiric therapies for treating bacterial keratitis in Taiwan.

Exosomal MicroRNA-21 (miRNA-21, miR-21) is significantly up-regulated in blood samples of patients with lung cancer. Exosomal-derived miR-21 can be used as a promising biomarker for the early diagnosis of lung cancer. This paper develops a fluorescent biosensor based on the combination of magnetic nanoparticles (MNPs), cascade strand displacement reaction (CSDR) and CRISPR/Cas12a to detect the exosomal miR-21 from lung cancer. The powerful separation performance of MNPs can eliminate the potential interference of matrix and reduce the background signal, which is very beneficial for the improvement of specificity and sensitivity. The CSDR can specifically transform one miR-21 into plenty of DNA which can specifically trigger the trans-cleavage nuclease activity of Cas12a, resulting in the cleavage of ssDNA bi-labeled with fluorescent and a quencher. Under the optimized experimental conditions, the developed fluorescence biosensor exhibited high sensitivity and specificity towards the determination of exosomal-derived miR-21 with a linear range from 10 to 1 × 105 fM and a low detection limit of about 0.89 fM. Most importantly, this method can be successfully applied to distinguish the exosomal miR-21 from the lung cancer patients and the healthy people.

Coronavirus disease-2019 (COVID-19) causes severe pneumonia and acute respiratory distress syndrome. According to the current consensus, immunosuppressants, such as dexamethasone and anti-interleukin-6 receptor monoclonal antibodies, are therapeutic medications in the early stages of infection. However, in critically ill patients, viral, fungal, and bacterial coinfection results in higher mortality. We conducted a single-center, retrospective analysis of 29 mechanically ventilated patients with artificial airways. Patients were adults with confirmed COVID-19 infection and severe pneumonia. Acute respiratory distress syndrome was diagnosed according to the Kigali modification of the Berlin definition. Six patients had invasive pulmonary aspergillosis coinfection based on elevated serum galactomannan levels and/or bronchoalveolar lavage fluid. We present two cases with brief histories and available clinical data. We also conducted a literature review to determine whether immunosuppressants, such as tocilizumab, increase infection risk or invasive aspergillosis in patients with COVID-19. There is no conclusive evidence to suggest that tocilizumab increases coinfection risk. However, further studies are needed to determine the optimal dose, between-dose interval, and timing of tocilizumab administration in patients with COVID-19.

Super Typhoons Maria (2018) and Lekima (2019) were adopted for this case study, although they only passed the northern offshore waters of Taiwan without making landfall. A direct modification technique was employed to create the atmospheric conditions for a wave-circulation model to hindcast large typhoon-driven waves. The radius of the modified scale (Rtrs) for a hybrid typhoon wind plays an important role in the significant wave height (SWH) simulations during the passage of typhoons. The maximum increment in peak SWH reached 3.0 m and 5.0 m in the deep ocean for Super Typhoons Maria (2018) and Lekima (2019), respectively if the Rtrs was increased from 4 × Rmax (radius of the maximum wind) to 7 × Rmax. The SWHs induced by the typhoon winds in the surf zone were more sensitive to different wave-breaking formulations used in the wave-circulation model. The maximum difference in peak SWH reached 2.5 m and 1.2 m for Super Typhoons Maria (2018) and Lekima (2019), respectively, when the wave-breaking formulations of BJ78 (proposed by Battjes and Janssen in 1978) and CT93 (proposed by Church and Thornton in 1993) were introduced to the wave-circulation model. The SWH simulations in the surf zone were insensitive to the wave-breaking criterion (γ) during the passage of typhoons. In shallow nearshore waters, the utilization of a constant γ for the wave-circulation model always produces peak SWHs that are smaller than those using γ based on local steepness or peak steepness.

In this study, long-term wind fields during 1991–2010 from the Climate Forecast System Reanalysis (CFSR) were dynamically downscaled over Taiwan and its offshore islands at a 5 km horizontal resolution using the Weather Research and Forecasting (WRF) model. Simulations of the 10 m (above sea level) dynamically downscaled winds served as the atmospheric forcing for driving a fully coupled wave-circulation model. The sea states of the waters surrounding Taiwan during 1991–2010 were hindcasted to evaluate the offshore wave energy resources and optimal wave energy hotspots. This study reveals that the southeastern offshore waters of Taiwan and the Central Taiwan Strait exhibited the highest mean wave power density (WPD), exceeding 20 kW/m. The annual mean WPD, incidence of the hourly WPD greater than or equal to 4 kW/m, monthly variability index and coefficient of variation of the WPD indicated that the sea areas located between Green Island and Orchid Island (OH_1), southeast of Orchid Island (OH_2), south of the Hengchun Peninsula (OH_3), and north of the Penghu Islands (OH_4) were the optimal hotspots for deploying wave energy converters. The most energetic months were October for OH_1 and OH_2 and November for OH_3 and OH_4, while the wave power was weak from March to June for OH_1, OH_2 and OH_3 and in May for OH_4. The wave direction is prevailingly east-northeast for OH_1, OH_2 and OH_3 and nearly northeast for OH_4. These phenomena reveal that wave power in the waters offshore Taiwan is induced primarily by the northeast (winter) monsoon. The exploitable annual WPD was estimated to be 158.06, 182.89, 196.39 and 101.33 MWh/m for OH_1, OH_2, OH_3 and OH_4, respectively.

The sensitivity of storm wave simulations to storm tides and tidal currents was investigated using a high-resolution, unstructured-grid, coupled circulation-wave model (Semi-implicit Cross-scale Hydroscience Integrated System Model Wind Wave Model version III (SCHISM-WWM-III)) driven by two typhoon events (Typhoons Soudelor and Megi) impacting the northeastern coast of Taiwan. Hourly wind fields were acquired from a fifth-generation global atmospheric reanalysis (ERA5) and were used as meteorological conditions for the circulation-wave model after direct modification (MERA5). The large typhoon-induced waves derived from SCHISM-WWM-III were significantly improved with the MERA5 winds, and the peak wave height was increased by 1.0–2.0 m. A series of numerical experiments were conducted with SCHISM-WWM-II and MERA5 to explore the responses of typhoon wave simulations to tidal elevation and current. The results demonstrate that the simulated significant wave height, mean wave period and wave direction for a wave buoy in the outer region of the typhoon are more sensitive to the tidal current but less sensitive to the tidal elevation than those for a wave buoy moored in the inner region of the typhoon. This study suggests that the inclusion of the tidal current and elevation could be more important for typhoon wave modeling in sea areas with larger tidal ranges and higher tidal currents. Additionally, the suitable modification of the typhoon winds from a global atmospheric reanalysis is necessary for the accurate simulation of storm waves over the entire region of a typhoon.

Despite numerous studies on the effect of a cold weather disaster on fisheries in 2008, no operational systems have been developed to monitor the threat of such an event to mariculturists in the Penghu Islands (PHI) region of Taiwan. The present study employed a suitable aquaculture site selection map of the inner bay of the PHI to reduce aquaculture losses and mortality rates of cobia (Rachycentron canadum) during extreme events. Daily marine environmental data, including sea surface temperature (SST), chlorophyll-a concentration (chl-a), and wind speed in the winter, were collected. An extreme event was defined as a period of over 11 days in a month of strong winds (>6 m/s). Four parameters in the PHI inner bay, including SST, cold-water intrusion days, chl-a, and offshore distance to the PHI coastline, were used to evaluate suitable aquaculture sites for cobia culture. The results indicated that La Niña events could not be used as a factor to detect cold-water intrusion events and select suitable aquaculture sites in the PHI. The evaluated suitable aquaculture site selection map, obtained using an arithmetic mean model and a geometric mean model, revealed that the avoidance sites during extreme events were concentrated in the northern and northwestern PHI. Suitable areas were concentrated in the southeastern areas. We further suggested that commercial cobia aquaculture operations in the PHI inner bay could be moved to the suitable sites in southeastern PHI during extreme events.

The protection of water resources is of paramount importance to human beings’ practical lives. Monitoring and improving water quality nowadays has become an important topic. In this study, a novel Bayesian probabilistic neural network (BPNN) improved from ordinary Bayesian probability methods has been developed to quantitatively predict water quality parameters including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a. The proposed method, based on conventional Bayesian probability methods, involves feature engineering and deep neural networks. Additionally, it extracts significant information for each endmember from combinations of spectra by feature extraction, with spectral unmixing based on mathematical and statistical analysis, and calculates each of the water quality parameters. The experimental results show the great performance of the proposed model with all coefficient of determination R 2 over 0.9 greater than the values (0.6–0.8) from conventional methods, which are greater than ordinary Bayesian probability analysis. The mean percent of absolute error (MPAE) is taken into account as an important statistical criterion to evaluate model performance, and our results show that MPAE ranges from 4% (nitrogen) to 10% (COD). The root mean squared errors (RMSEs) of phosphorus, nitrogen, COD, BOD, and chlorophyll-a (Chla) are 0.03 mg/L, 0.28 mg/L, 3.28 mg/L, 0.49 mg/L, and 0.75 μg/L, respectively. In comparison with other deep learning methods, this study takes a relatively small amount of data as training data to train the proposed model and the proposed model is then tested on the same amount of testing data, achieving a greater performance. Thus, the proposed method is time-saving and more effective. This study proposes a more compatible and effective method to assist with decomposing combinations of hyperspectral signatures in order to calculate the content level of each water quality parameter. Moreover, the proposed method is practically applied to hyperspectral image data on board an unmanned aerial vehicle in order to monitor the water quality on a large scale and trace the location of pollution sources in the Maozhou River, Guangdong Province of China, obtaining well-explained and significant results.

Super Typhoon Nepartak (2016) was used for this case study because it is the most intense typhoon that made landfall in Taiwan in the past decade. Winds extracted from the Climate Forecast System version 2 (CFSV2) and ERA5 datasets and merged with a parametric typhoon model using two hybrid techniques served as the meteorological conditions for driving a coupled wave-circulation model. The computed significant wave heights were compared with the observations recorded at three wave buoys in the eastern waters of Taiwan. Model performance in terms of significant wave height was also investigated by employing the CFSV2 winds under varying spatial and temporal resolutions. The results of the numerical experiments reveal that the simulated storm wave heights tended to decrease significantly due to the lower spatial resolution of the hourly winds from the CFSV2 dataset; however, the variations in the storm wave height simulations were less sensitive to the temporal resolution of the wind field. Introducing the combination of the CFSV2 and the parametric typhoon winds greatly improved the storm wave simulations, and similar phenomena can be found in the exploitation of the ERA5 dataset blended into the parametric wind field. The overall performance of the hybrid winds derived from ERA5 was better than that from the CFSV2, especially in the outer region of Super Typhoon Nepartak (2016).

Protection of water environments is an important part of overall environmental protection; hence, many people devote their efforts to monitoring and improving water quality. In this study, a self-adapting selection method of multiple artificial neural networks (ANNs) using hyperspectral remote sensing and ground-measured water quality data is proposed to quantitatively predict water quality parameters, including phosphorus, nitrogen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), and chlorophyll a. Seventy-nine ground measured data samples are used as training data in the establishment of the proposed model, and 30 samples are used as testing data. The proposed method based on traditional ANNs of numerical prediction involves feature selection of bands, self-adapting selection based on multiple selection criteria, stepwise backtracking, and combined weighted correlation. Water quality parameters are estimated with coefficient of determination R 2 ranging from 0.93 (phosphorus) to 0.98 (nitrogen), which is higher than the value (0.7 to 0.8) obtained by traditional ANNs. MPAE (mean percent of absolute error) values ranging from 5% to 11% are used rather than root mean square error to evaluate the predicting precision of the proposed model because the magnitude of each water quality parameter considerably differs, thereby providing reasonable and interpretable results. Compared with other ANNs with backpropagation, this study proposes an auto-adapting method assisted by the above-mentioned methods to select the best model with all settings, such as the number of hidden layers, number of neurons in each hidden layer, choice of optimizer, and activation function. Different settings for ANNS with backpropagation are important to improve precision and compatibility for different data. Furthermore, the proposed method is applied to hyperspectral remote sensing images collected using an unmanned aerial vehicle for monitoring the water quality in the Shiqi River, Zhongshan City, Guangdong Province, China. Obtained results indicate the locations of pollution sources.

Conducting electrophysiological measurements from human brain function provides a medium for sending commands and messages to the external world, as known as a brain–computer interface (BCI). In this study, we proposed a smart helmet which integrated the novel hygroscopic sponge electrodes and a combat helmet for BCI applications; with the smart helmet, soldiers can carry out extra tasks according to their intentions, i.e., through BCI techniques. There are several existing BCI methods which are distinct from each other; however, mutual issues exist regarding comfort and user acceptability when utilizing such BCI techniques in practical applications; one of the main challenges is the trade-off between using wet and dry electroencephalographic (EEG) electrodes. Recently, several dry EEG electrodes without the necessity of conductive gel have been developed for EEG data collection. Although the gel was claimed to be unnecessary, high contact impedance and low signal-to-noise ratio of dry EEG electrodes have turned out to be the main limitations. In this study, a smart helmet with novel hygroscopic sponge electrodes is developed and investigated for long-term usage of EEG data collection. The existing electrodes and EEG equipment regarding BCI applications were adopted to examine the proposed electrode. In the impedance test of a variety of electrodes, the sponge electrode showed performance averaging 118 kΩ, which was comparable with the best one among existing dry electrodes, which averaged 123 kΩ. The signals acquired from the sponge electrodes and the classic wet electrodes were analyzed with correlation analysis to study the effectiveness. The results indicated that the signals were similar to each other with an average correlation of 90.03% and 82.56% in two-second and ten-second temporal resolutions, respectively, and 97.18% in frequency responses. Furthermore, by applying the proposed differentiable power algorithm to the system, the average accuracy of 21 subjects can reach 91.11% in the steady-state visually evoked potential (SSVEP)-based BCI application regarding a simulated military mission. To sum up, the smart helmet is capable of assisting the soldiers to execute instructions with SSVEP-based BCI when their hands are not available and is a reliable piece of equipment for strategical applications.

Objective: A model of Near Infrared Diffuse Reflectance Spectroscopy (NIR-DRS) was established for the first time to determine the content of Shengmaxinside I in the honey-fried processing of Rhizoma Cimicifugae. Methods: Shengmaxinside I content was determined by high-performance liquid chromatography (HPLC), and the data of the honey-fried processing of Rhizoma Cimicifugae samples from different batches of different origins by NIR-DRS were collected by TQ Analyst 8.0. Partial Least Squares (PLS) analysis was used to establish a near-infrared quantitative model. Results: The determination coefficient R2 was 0.9878. The Cross-Validation Root Mean Square Error (RMSECV) was 0.0193%, validating the model with a validation set. The Root Mean Square Error of Prediction (RMSEP) was 0.1064%. The ratio of the standard deviation for the validation samples to the standard error of prediction (RPD) was 5.5130. Conclusion: This method is convenient and efficient, and the experimentally established model has good prediction ability, and can be used for the rapid determination of Shengmaxinside I content in the honey-fried processing of Rhizoma Cimicifugae.

With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platforms like Flickr have provided a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists and then extends the concept of motifs from topological spaces, to temporal spaces and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns.