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Abstract This study aims to analyze time-series measurements encompassing rainstorm events with over a century of datasets to identify rainstorm evolution and dimensional transitions in non-stationarity. Rainstorm events are identified using partial duration series (PDS) to extract changes in rainstorm characteristics, namely maximum intensity (MAXI), duration (D), total rainfall (TR), and average rainfall intensity (ARI), in response to climate change. Ensemble empirical mode decomposition is used for trend filtering and non-stationary identification to explore spatiotemporal insight patterns. Trend models for the first–second-order moments of rainstorm characteristics are used to formulate the identified mean–variance trends using combined multi-dimensional linear-parabolic regression. Best-fitting combinations of various distributions (probability density functions) and trend models for multiple characteristic series are identified based on the Akaike information criterion. We analyze the dimensional transition in rainfall non-stationarity based on sensitivity analysis using PDS to determine its natural geophysical causes. The integrated methodology was applied to the data retrieved from nine weather stations in Taiwan. Our findings reveal rainstorm characteristics of “short D but high rainfall intensity” or “lower MAXI but high TR” across multiple stations. The parabolic trend of the first-order moment (i.e., mean) of ARI, D, and TR appears at the endpoint of the mountain ranges. Areas receiving monsoons and those on the windward plain show a rising parabolic trend in the first- and second-order moments (i.e., mean–variance) characterizing MAXI, implying that the occurrence frequency and magnitude of extreme MAXI increases. Non-stationary transitions in MAXI appear for mountain ranges exposed to the monsoon co-movement effect on both windward and leeward sides. Stations in the plains and rift valleys show upgraded and downgraded transitions in the non-stationary dimensions for D, respectively.

To precisely identify multi-dimensional spatiotemporal rain-making parameters, generate an approximate Hessian matrix, and solve the nonlinear ill-posed problem, this study uses composite logical tangent hyperbolic functions to construct the rain-generating simulation model as nonlinear algebraic equations with designed key–lock quasi-Newton optimization for deriving multi-order objective functional derivatives for rainstorm causal decomposition into advanced functional, analytical solution (lock) and Newton's conditional constraints. Specifically, the rank-two approximate structure of the Levenberg–Marquardt and Broyden–Fletcher–Goldfarb–Shanno quasi-Newton algorithms are modified as the symmetric rank-four structure to efficiently calculate a positive definite stable Hessian and solve the constrained nonlinear rain-making threshold. The model projects various rain-making factors into multi-rank loading scores, characterizing rain-generating mechanisms and causal components as associated DNAs. To accelerate/modify directional convergence, avoid local minimum, and detect global optimum, the devised vectorized limited switchable step sizes are optimized using advanced double-bracketing approaches combined with candidate parameters' correction vectors (key) and referenced step-size distributions solved by Newton's constrained analytical solution to reduce heterogeneous differences and eliminate the conventional overestimated Hessian. The identified rain-making DNAs reveal that typhoons with similar DNAs move in similar directions. Specifically, rain-making DNAs in Taipei Category 1 were correlated with wind force/direction and cloud height along PCs 1, 3, 4, and 7, and those in Category 2 were correlated with cloud-cover distribution along PCs 1, 2, and 5. The identified rain-making thresholds of typhoons with constant direction/structure showed a weaker steady state, whereas the unsteady rest produced multi-peak rainfall hydrographs. Rain evolution analysis reveals that cloudy rainbands, carried by the wind field, move along the Tamsui River valley when traveling between northeast and south-southeast of Taipei; converge with gradient and geostrophic winds when traveling between east-northeast and southwest; merge with southwest monsoon when traveling between west-southwest and northeast of Kaohsiung.

Abstract At 1:47 a.m. on September 21, 1999, the Mw 7.5 Chi-Chi earthquake struck Taiwan. The purpose of this study is to (1) apply multiple spatiotemporal-frequency analysis to filter the post-seismic change in groundwater head to explore the implicit drawdown associated with excess pore-water pressure release and effective stress relief during post-seismic evolution and (2) establish a stochastic experimental optimization model for identifying the hydrogeological evolution. The approaches used for post-seismic drawdown filtration include multi-rank principal components decomposition, multi-frequent wavelet transforms decomposition, and multi-level wavelet de-noising. This study especially evaluates the following advanced post-seismic evolving parameters: (1) harmonic average leaking/injecting rate, (2) distance between the acting position and monitoring well, (3) storage coefficient under effective stress relief and formation compression, and (4) transmissivity for excess-pore-water pressure release. This study applies the integrated methodology on 179 monitoring wells in the Chou-Shui River alluvial fan. Results show that the overlying principal components PCs and low-level wavelet de-noising can filter additional sources/sinks, in which the extracted drawdown from PC1+PC3 was related to the excess pore-water pressure relaxation process, that from PC2+PC5+PC6+PC7 and high-frequency wavelet de-noised detail cD 2 related to the earth tidal fluctuation effect, and that from PC4+PC9 and cD 3 related to the barometric effect. According to the Riemann integral and an objective function value duration curve, calculated occurrence probability from the stochastic optimization for SC2, the storage coefficient was reduced from pre-seismic pumping test value 0.00107, post-seismic 27th hour evolving value 0.000826 to post-seismic pumping test value 0.000578 in 2004, and the transmissivity increased from pre-seismic test value 92.4 m2/h, post-seismic 27th hour evolving value 98.6 m2/h to post-seismic test value 147.6 m2/h. The results demonstrate that the SC2 and GH3 zones suffer from crustal compression and the permeability was increased to dissipate excess pore-water pressure and effective stress.

Nail gun injuries usually occur at the extremities due to working accidents. Intracranial or intrathoracic injuries are relatively rare, and cases combined with both injuries are even rarer. Such situations pose challenges for surgeons due to their uniqueness during operation. Radiologic imaging findings in our case were significant in indicating surgical findings. Herein, we report a patient who shot himself using a nail gun, damaging his brain and heart. Keywords: Nail gun injury, Computed tomography, Trauma

Abstract This report describes the case of a 17‐year‐old man who developed mediastinal growing teratoma syndrome following two cycles of chemotherapy, after an initial diagnosis of primary mediastinal nonseminomatous germ cell tumor. The large, rapidly‐growing mediastinal tumor was completely resected in conjunction with right pneumonectomy, using simultaneous clamshell thoracotomy and median sternotomy. The salvage surgery with perioperative management involved in mediastinal growing teratoma syndrome is presented here. Key points Significant findings of the study The diagnosis and surgical management are challenging for patients with mediastinal growing teratoma syndrome. Nevertheless, with proper operative planning, favorable outcomes can be attained with complete resection despite the characteristic rapid growth and massive size of these neoplasms. What this study adds In spite of postoperative sternal dehiscence, we believe that a simultaneous clamshell thoracotomy with median sternotomy approach remains a viable option for an extremely large mediastinal growing teratoma, when tumor size prevents safe resection using other approaches due to limited visualization.

This study aims to examine the properties of composites that different carbon materials with different measurements can reinforce. Using a melt compounding method, this study combines polypropylene (PP) and graphene nano-sheets (GNs) or carbon fiber (CF) to make PP/GNs and PP/CF conductive composites, respectively. The DSC results and optical microscopic observation show that both GNs and CF enable PP to crystalize at a high temperature. The tensile modulus of PP/GNs and PP/CF conductive composites remarkably increases as a result of the increasing content of conductive fillers. The tensile strength of the PP/GNs conductive composites is inversely proportional to the loading level of GNs. Containing 20 wt% of GNs, the PP/GNs conductive composites have an optimal conductivity of 0.36 S/m and an optimal EMI SE of 13 dB. PP/CF conductive composites have an optimal conductivity of 10−6 S/m when composed of no less than 3 wt% of CF, and an optimal EMI SE of 25 dB when composed of 20 wt% of CF.

Taiwan is located on a route where typhoons often strike. Each year, the strong winds accompanying typhoons are a substantial threat and cause significant damage. However, because the terrains of high mountains in Taiwan vary greatly, when a typhoon passes the Central Mountain Range (CMR), the wind speed of typhoons becomes difficult to predict. This research had two primary objectives: (1) to develop data-driven techniques and a powerful artificial neural network (ANN) model to predict the highly complex nonlinear wind systems in western Taiwan; and, (2) to investigate the accuracy of wind speed predictions at various locations and for various durations in western Taiwan when the track of westward typhoons is affected by the complex geographical shelters and disturbances of the CMR. This study developed a typhoon wind speed prediction model that evaluated various typhoon tracks (covering Type 2, Type 3, and Type 4 tracks, as defined by the Central Weather Bureau), and evaluated the prediction accuracy at Hsinchu, Wuqi, and Kaohsiung Stations in western Taiwan. Back propagation neural networks (BPNNs) were employed to establish wind speed prediction models, and a linear regression model was adopted as the benchmark to evaluate the strengths and weaknesses of the BPNNs. The results were as follows: (1) The BPNNs generally had favorable performance in predicting wind speeds and their performances were superior to linear regressions; (2) when absolute errors were adopted to evaluate the prediction performances, the predictions at Hsinchu Station were the most accurate, whereas those at Wuqi Station were the least accurate; however, when relative errors were adopted, the predictions at Hsinchu Station were again the most accurate, whereas those at Kaohsiung were the least accurate; and, (3) regarding the relative error rates for the maximum wind speed of Types 2, 3, and 4 typhoons, Wuqi, Kaohsiung, and Wuqi had the most accurate performance, respectively; as for maximum wind time error (ETM) for Types 2, 3, and 4 typhoons, Kaohsiung, Wuqi, and Wuqi correspondingly performed the most favorably.

This study aims to construct a typhoon precipitation forecast model providing forecasts one to six hours in advance using optimal model parameters and structures retrieved from a combination of the adaptive network-based fuzzy inference system (ANFIS) and artificial intelligence. To enhance the accuracy of the precipitation forecast, two structures were then used to establish the precipitation forecast model for a specific lead-time: a single-model structure and a dual-model hybrid structure where the forecast models of higher and lower precipitation were integrated. In order to rapidly, automatically, and accurately retrieve the optimal parameters and structures of the ANFIS-based precipitation forecast model, a tabu search was applied to identify the adjacent radius in subtractive clustering when constructing the ANFIS structure. The coupled structure was also employed to establish a precipitation forecast model across short and long lead-times in order to improve the accuracy of long-term precipitation forecasts. The study area is the Shimen Reservoir, and the analyzed period is from 2001 to 2009. Results showed that the optimal initial ANFIS parameters selected by the tabu search, combined with the dual-model hybrid method and the coupled structure, provided the favors in computation efficiency and high-reliability predictions in typhoon precipitation forecasts regarding short to long lead-time forecasting horizons.

Complicated environmental problems inevitably arise when technology advances. One major environmental problem is the presence of electromagnetic radiation. Long-term exposure to electromagnetic radiation can damage people’s health in many ways. Therefore, this study proposes producing composite boards with electromagnetic shielding effectiveness and thermal insulation by utilizing the structures and properties of materials. Different combinations of flame-retardant polyester fiber (FR fiber), recycled far-infrared polyester fiber (FI fiber), and 4D low-melting-point fibers (LM fiber) were made into flame-retardant and thermally insulating matrices. The matrices and carbon fiber (CF) woven fabric in a sandwich-structure were needle-punched in order to be tightly compact, and then circularly heat dried in order to have a heat set and reinforced structure. The test results indicate that Polyester (PET)/CF composite boards are mechanically strong and have thermal insulation and electromagnetic shielding effectiveness at a frequency between 0.6 MHz and 3 GHz.

Neural network algorithms are becoming more commonly used to model big data, such as images and speech. Although they often offer superior performance, they require more training time than traditional approaches. Graphics processing units (GPUs) are an excellent solution for reducing training time. The use of multiple GPUs, in addition to a single GPU, can further improve computing power. Training DNNs with algorithm and computer hardware support can be challenging when selecting an appropriate learning strategy. In this work, we investigate various learning strategies for training DNNs using multiple GPUs. Experimental data show that using six GPUs with the suggested approach results in a speed boost of approximately four times that of using a single GPU. Moreover, the precision of the suggested method using six GPUs is similar to that of using a single GPU. [ABSTRACT FROM AUTHOR]