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Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety ofpredictions from dynamical, physics-based models - such as numerical weather prediction, climate, land, hydrology, and Earth system models - into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporatinginitial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes., Hydrology and Earth System Sciences, 27 (9), ISSN:1027-5606, ISSN:1607-7938

Historically, Switzerland and the nearby alpine countries have not been associated with major droughts. However, in recent years, the European Alpine space has experienced several unprecedented low-flow conditions and drought events. As many economic sectors in the region depend heavily on sufficient water availability, such as hydropower production, navigation and transportation, agriculture, and tourism, it is important for decision-makers to have early warnings of drought tailored to their needs and geographical conditions.The European Flood Awareness System (EFAS) has been in operation since 2012 providing flood risk overviews for Europe up to 15 days in advance. More recently, it has also run long-range hydrological outlooks for sub-seasonal to seasonal horizons. While EFAS early flood warnings have been extensively evaluated in the past years, less attention has been paid to evaluating the system’s ability to detect upcoming drought conditions. In this study, we turn our focus to this other extreme of the spectrum and on EFAS’ predictability of drought events in large Alpine catchments. Our goal is to investigate how hydrological patterns of skill at a large spatial scale can be combined with local model outputs to more accurately inform decision makers on droughts and their spatio-temporal evolution.For this, we evaluate the performance of EFAS comparatively to that of a local model in terms of the ability to simulate drought conditions. The Precipitation-Runoff-Evapotranspiration HRU (PREVAH) local model was set up for 59 stations in Switzerland. The PREVAH model is a distributed conceptual hydrological model that accounts for processes such as evapotranspiration, interception, snow- and ice-melt, soil moisture storage, groundwater storage, and runoff generation. We analyse 25 overlapping stations between the local model and the EFAS reporting stations (river network points where EFAS outputs are available to users), and compare the drought simulation performances of the two models. We focus on evaluating the duration, deficit, and magnitude of the drought events, as well as metrics including Nash–Sutcliffe model efficiency coefficient (NSE) and Kling-Gupta efficiency (KGE).The outcome of this study will lay a foundation for how a large-scale hydrological model like EFAS can complement a local model like PREVAH to improve the predictability of sub-seasonal drought forecasting and provide more reliable early warnings for better water resources management.

Medium to subseasonal hydrological forecasts contain more information relevant to water and environmental management tasks than climatological forecasts. However, extracting this information at the most appropriate level of accuracy and spatiotemporal resolution remains a difficulty. Many studies show that the skill of the extended range forecasts with daily resolution tends toward zero after 7–14 days for small mountainous catchments. Beyond that forecast horizon the application of highly sophisticated pre- and postprocessing methods generally produce limited gains. Consequently, current forecasting techniques cannot effectively represent forecast extremes at extended ranges such as anomalously high and low runoff or soil moisture. To tackle these deficiencies, this study analyzes the value of tercile forecasts for weekly aggregates of runoff and soil moisture forecasts available at a daily resolution for Switzerland. The forecasts are classified into three categories: below, above, and normal conditions, which are derived from long-term simulations and correspond approximately to climatological conditions. To achieve improved reliability and skill of the predicted tercile probabilities, a nonparametric probabilistic classification method has been tested. It is based on Gaussian process (GP), which is attractive in machine learning (ML) applications because of its ability to estimate the predictive uncertainty. The outcome of these postprocessed forecasts was compared to preprocessing methods where the meteorological predictions are statistically corrected before passing to the hydrological model. Our results indicate that tercile forecasts of weekly aggregates produce a suitable skill up to 3 weeks lead time using the preprocessed input and up to 4 weeks lead time using the GP method.

The skill of hydro-meteorological forecasts usually drops to zero at horizons beyond 10 to 14 days and predictions of daily values with a lead-time of more than two weeks deceive an unrealistic reliability and accuracy. However, the advances of ensemble forecast systems, the aggregation of daily information to weeks and the classification of variables (e.g. classifying the runoff in terciles, i.e. below, above or within “normal” conditions in view of climatology) extend the skill of forecasts up to three weeks. With the help of post-processing methods, the skill can even be further extended up to four weeks.Machine Learning (ML) based post-processing methods are becoming more and more popular. Several ML methods have been compared for classifying hydrological forecasts into terciles ranging from Multinomial to complex Deep Neural Networks models (DNN). For the verification of the predicted classes and their associated probabilities the accuracy measure and the cross-entropy (log-loss) have been calculated. The models have been applied to monthly forecasts for Switzerland divided into 300 catchments and run operationally with a 500mx500m resolution (www.drought.ch).All the analyzed ML methods showed good results and are able to improve the accuracy and the cross-entropy equally well. The focus of further investigations has been laid on the Gaussian Process (GP) model. This method has the advantage that class probabilities can be calculated directly. Furthermore, several studies highlighted recently the linkage between the GP and Bayesian DNN. The results of this study confirmed these similarities favoring the less complex GP model. In order to take the uncertainty of the measurements and simulations into account, a multi-label classification (MLC) method has been introduced. Contrary to classical classification methods, the classes in MLC are not mutually exclusive. Thus, it avoids the sharp assignment to discrete classes, but allows that for example a runoff value measured (possibly with some error) at the border of one class could belong to the neighboring classes at the same time. The MLC method applied with a GP model showed the best results regarding accuracy and cross-entropy for all 300 catchments and significantly improves the skill over the whole forecast horizon of four weeks.

Switzerland and the nearby alpine countries are not commonly associated with the occurrence of droughts, but in recent years, Switzerland has experienced several unprecedented drought events. As many sectors in the European Alps depend heavily on the water resources, e.g. for hydropower production, navigation and transportation, agriculture, and tourism, it is important for decision makers to have early warnings of drought. Machine learning (ML) approaches have shown potential to compete with traditional hydro-meteorological models. By combining a traditional hydrological model and a ML model in a hybrid setup, the forecasting system is able to benefit from the statistical power of ML while maintaining the understanding of physical processes from the traditional model.The objective of this study is to investigate the predictability of a hybrid forecasting system composed of a traditional hydrological model PREVAH and an ensemble of ML algorithms to provide sub-seasonal streamflow and lake level predictions for major rivers and lakes in Switzerland. Uncertainty of the hydro-meteorological prediction chain is accounted for by using 51 hydrological ensemble members, and the ML uncertainty is accounted for by performing different rounds of initial randomization. We also investigate different drivers of the drought predictability by considering input features such as initial conditions, European weather regime forecasts and a hydropower proxy.We are able to demonstrate that the proposed hybrid forecasting system is able to perform runoff routing scheme and provide sub-seasonal forecasts of streamflow and lake level for Swiss basins. Informed ML models with additional input features achieve better performance than those obtained using hydrological model outputs only. In the first half of the forecast period (weeks 1 and 2), model performance is improved by the initial conditions and in the second half (weeks 3 and 4) by the hydropower proxy. Lake level prediction shows promising skill for different basin sizes, whereas the streamflow prediction skill is linked to basin size. This study shines light on the use of hybrid forecasting for sub-seasonal drought prediction to provide useful information for medium- to long-term planning from an integrated risk management perspective.

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology and Earth system models – into a final prediction product. They are recognised as a promising way of enhancing prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at sub-seasonal to decadal scales, a better appreciation of the strengths of machine learning, plus expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally-expensive offline land model, can minimize the effect of biases that exist within dynamical outputs without explicit bias correction and downscaling, benefit from the strengths of machine learning models, and can learn from large datasets, while combining different sources of predictability with varying time-horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities. These include obtaining physically-explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating modelled initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

In compromised bone conditions such as osteoporosis, developments of the implant surface are necessary to secure the stability of implants. This study investigated the effect of the surface porous titanium structure (PS) on the osseointegration of implants in osteoporotic bone. Bilateral ovariectomy (OVX) was performed in 4 female beagle dogs to induce osteoporosis for 32 wk. Success of induction was based on the evaluation of bone mineral density by Hounsfield units (HU) in computed tomography images. Posterior teeth in both mandibles were extracted 1 wk after OVX, and a total of 30 implants (15 implants in each group) were placed after 32 wk of osteoporosis induction. The control group implant underwent resorbable blast media (RBM) surface treatment, whereas the test group underwent RBM surface treatment in the coronal two-thirds and a PS added to the apical 3-mm portion. HU values in the mandibular trabecular bone, lumbar, and femoral head significantly decreased 32 wk after OVX, confirming osteoporotic condition after induction. Resonance frequency analysis and removal torque test showed comparable values between the 2 groups at 4 wk after implant placement. The surface topography of the implant after removal showed hard tissue integration at the PS in the test group. Bone-to-implant contact length was greater in the apical portion of the test group, although statistical significance was not found between the groups. Interthread bone area in the apical portion of the test group showed a significant increase compared to the control group (control: 0.059 ± 0.041 mm2, test: 0.121 ± 0.060 mm2, P = 0.028) with the histological feature of bone ingrowth at the PS. The findings of the study demonstrated that the surface PS could improve osteoconductivity in the osteoporotic trabecular bone by bone ingrowth at the pore space, thereby enhancing the osseointegration and stability of the implants.

Flood and drought events can lead to severe socio-economic impact and damages. Thus, there is a need for early warnings of such extreme events, especially for decision-makers in sectors like hydropower production, navigation and transportation, agriculture, and hazard management. To improve the predictability of sub-seasonal streamflow, we propose the approach of a hybrid forecasting system, where a conceptual hydrological model PREVAH is combined with a machine learning (ML) model. The PREVAH model provides catchment level hydrological forecasts and the role of the ML model is to emulate a runoff routing scheme. Such a hybrid setup allows the forecasting system to benefit from the statistical power of ML while maintaining the understanding of physical processes from the hydrological model.The objective of this study is to investigate the predictability of a hybrid forecasting system to provide monthly streamflow predictions for three recent extreme events. These include the drought event in summer 2018, the drought event in spring 2020, and the flood event in summer 2021 in selected large Swiss rivers. We also investigate different predictability drivers by considering additional input features to the ML model, such as initial streamflow, European weather regime indices, and a hydropower proxy.We demonstrate that the proposed hybrid forecasting system has the potential to provide skillful monthly forecasts of the interested events. Informed ML models with additional input features achieve better performance than results obtained using hydrological model outputs only. This study sheds light on using hybrid forecasting for sub-seasonal hydrological predictions to provide useful information for medium-term planning at a monthly time horizon and reduce the impact of flood and drought events.

Large regions with seagrass meadows have been discovered on Dongsha Atoll located in the South China Sea. The aim of this study is the estimation of acoustic propagation loss in the frequency range of individual bubble resonance due to the gas content of seagrass in seagrass meadows. Specifically, this study seeks to (a) investigate the attenuation of the mid-frequency (2-8 kHz) signal induced by the gas content in a seagrass meadow and (b) present the diurnal variation of sound attenuation. The results demonstrate that >40 dB diurnal variations of mid-frequency propagation loss was observed in shallow seagrass meadow region.

The flow and heat transfer characteristics of a pulsed jet impinging on a flat plate under various excitation conditions were experimentally studied. The flow behaviors and time-averaged velocity fields of the characteristic flow structures were examined using a laser-assisted smoke flow visualization method and particle image velocimetry (PIV). The heat transfer of a pulsed impinging jet was measured by a thermal imaging camera. Two primary flow modes were observed in the domain of the jet pulsation intensity and excitation Strouhal number. The coherent vortices and vortex breakup modes appeared in the regimes of low and high jet pulsation intensities, respectively. In the coherent vortices mode, two submodes (contiguous vortices and discrete vortices) were additionally classified. The vortex ring maintained its coherency before impinging on the flat plate in the coherent vortices mode, while the vortex ring lost its coherency and broke up quickly before impinging on the flat plate in the vortex breakup mode. The increase rates of the axial traveling distance and axial convective velocity of the vortex rings in the vortex breakup mode were greater than those in the coherent vortices mode. Pulsating the impinging jet in vortex breakup mode, the effect of a large convective velocity of the vortex rings caused large mean axial velocities and axial velocity fluctuation intensities. The convective heat transfer was enhanced by increasing the convective velocity of the vortex ring and fluctuation intensity.

This article re-examines China’s proposed Belt and Road Initiative (BRI) taking into account historical and philosophical narratives. It assumes that the BRI has crucial strategic implications; in particular, that it is not as altruistic as claimed but rather a self-interested proposal aiming to restore China’s grandeur and influence. The Chinese Dream (中國夢) and the concept of Tianxia (天下), ‘all under heaven’) are discussed to illustrate how the initiative is ‘marketed’. It ends with an interpretation of the impacts that the BRI might have on other parts of the world.

Detection of sub-GeV dark matter candidates requires sub-keV detector thresholds on deposited energy. We provide an update on a gram-scale phonon-mediated KID-based device that was designed for a dark matter search in this mass range at the Northwestern Experimental Underground Site. Currently, the device is demonstrating 6 eV resolution on the energy absorbed by the resonator. With some important assumptions, this translates to 20 eV baseline resolution on energy deposited in the substrate. We show that TLS noise dominates this energy resolution estimate. After modifying the design to mitigate TLS noise, we project 5 eV baseline resolution on energy deposited in the substrate (1.5 eV on energy absorbed by the resonator) for an amplifier-white-noise-dominated device. Finally, we present a clear path forward to sub-eV thresholds, which includes installation of a quantum-limited superconducting parametric amplifier and adjustments to the material makeup of our resonators.

Recommendation systems have been developed for online services with a simple product mix. This study extended its application to an offline retailer with a more complex product mix. Purchasing records of two thousand members within one year from an offline retailer in Taiwan were used as the dataset for the study. Datasets of the first 9 months were used for training and models were tested by the records of the last 3 months. This study developed a recommendation system by integrating a matrix factorization to uncover latent factors from both customers and items, and an evolutionary program to optimize the parameter settings of duration adjustment functions that were applied to assign weights so that past purchasing records closer to the testing period would receive higher weights. The objective of the system is to predict the likelihood of customers’ purchasing behavior toward items they never purchased during the training period. By measuring the average percentage-ranking of items for two thousand members, the recommendation system developed in this study outperformed two other approaches, popularity and item-based nearest neighborhood systems. In addition, academic and practical contributions were also discussed.

To clarify the acoustic propagations and seabed properties in the northern South China Sea, the South China Sea Upper-Slope Sand Dunes Experiments were conducted in 2013–2014. To estimate the geacoustic properties of the surficial sediments in the experimental area, geoacoustic inversion with chirp sonar data, predictive model, and matched field technique were applied and compared with one another. Firstly, a predictive model based on sediment samples and the Biot theory with fluid approximation, the effective density fluid model (EDFM), was presented and utilized. The sediment samples were collected using a Shipek sediment sampler and were analyzed using a GEOTEK Multi-Sensor Core Logger and a Beckman Coulter LS particle size analyzer. The relationship between porosities and mean grain sizes in this area with shoaling internal waves has been discussed and compared to the Bachman's regression equation for SAX99. Propagating shoaling internal waves with huge amplitudes observed in the medium over the slope, shelf break, plateau, and continental shelf in the South China Sea are expected to cause strong amplification of near - bottom velocities. Hence, it may cause movement of an exposed bed particle on the bottom sediment, resulting in different grain-packing configurations. The measured porosities and mean grain sizes are inputted to the geoacoustic model to estimate the properties of the surficial sediments. The estimated bulk density is verified by core sample analysis, and the estimated sound speed and attenuation are verified by the match field geoacoustic inversion using the acoustic propagation data. Furthermore, the regression between the measured porosities and mean grain sizes is utilized in the geoacoustic inversion model, and the geoacoustic properties along the chirp sonar survey track are obtained.

Background: skin tears are acute wounds that are frequently misdiagnosed and under-reported. A standardized and globally adopted skin tear classification system with supporting evidence for diagnostic validity and reliability is required to allow assessment and reporting in a consistent way. Objectives To measure the validity and reliability of the International Skin Tear Advisory Panel (ISTAP) Classification System internationally. Methods: a multicountry study was set up to validate the content of the ISTAP Classification System through expert consultation in a two-round Delphi procedure involving 17 experts from 11 countries. An online survey including 24 skin tear photographs was conducted in a convenience sample of 1601 healthcare professionals from 44 countries to measure diagnostic accuracy, agreement, inter-rater reliability and intrarater reliability of the instrument. Results: a definition for the concept of a 'skin flap' in the area of skin tears was developed and added to the initial ISTAP Classification System consisting of three skin tear types. The overall agreement with the reference standard was 0 center dot 79 [95% confidence interval (CI) 0 center dot 79-0 center dot 80] and sensitivity ranged from 0 center dot 74 (95% CI 0 center dot 73-0 center dot 75) to 0 center dot 88 (95% CI 0 center dot 87-0 center dot 88). The inter-rater reliability was 0 center dot 57 (95% CI 0 center dot 57-0 center dot 57). The Cohen's Kappa measuring intrarater reliability was 0 center dot 74 (95% CI 0 center dot 73-0 center dot 75). Conclusions: the ISTAP Classification System is supported by evidence for validity and reliability. The ISTAP Classification System should be used for systematic assessment and reporting of skin tears in clinical practice and research globally. What's already known about this topic? Skin tears are common acute wounds that are misdiagnosed and under-reported too often. A skin tear classification system is needed to standardize documentation and description for clinical practice, audit and research. What does this study add? The International Skin Tear Advisory Panel Classification System was psychometrically tested in 1601 healthcare professionals from 44 countries. Diagnostic accuracy was high when differentiating between type 1, 2 and 3 skin tears using a set of validated photographs., International Skin Tear Advisory Panel (ISTAP)

Objective Air pollution had been reported to be associated with the reproductive health of women. However, the association of particulate matter (PM) and acid gases air pollution with premenstrual syndrome (PMS) warrants investigation. This study investigated the effects of air pollution on PMS risk. Population We combined data from the Taiwan Air Quality-Monitoring Database and the Longitudinal Health Insurance Database. In total, an observational cohort of 85 078 Taiwanese women not diagnosed as having PMS. Methods Air pollutant concentrations were grouped into four levels based on the concentration quartiles of several types of air pollutants. Main outcome measures We then applied univariable and multivariable Cox proportional hazard regression models to assess PMS risk in association with each pollutant type. Results Women exposed to Q4-level SO2 exhibited a 7.77 times higher PMS risk compared with those to Q1-level SO2 (95% confidence interval [CI] = 6.22–9.71). Women exposed to Q4-level NOx exhibited a 2.86 times higher PMS risk compared with those exposed to Q1-level NOx (95% CI = 2.39–3.43). Women exposed to Q4-level NO exhibited a 3.17 times higher PMS risk compared with women exposed to Q1-level NO (95% CI = 2.68–3.75). Finally, women exposed to Q4-level PM with a ≤2.5-µm diameter (PM2.5) exhibited a 3.41 times higher PMS risk compared with those exposed to Q1-level PM2.5 (95% CI = 2.88–4.04). Conclusions High incidences of PMS were noted in women who lived in areas with higher concentrations of SO2, NOx, NO, NO2 and PM2.5.

In the present investigation antipsychotic drug-induced miRNA modulations in glial cells were analyzed through miRNA expression in antipsychotic drugs-treated C6 cells using a miRNA microarray. Seventeen miRNAs were upregulated by haloperidol and 10 miRNAs were upregulated by risperidone in C6 cells. Thirty-six miRNAs were downregulated by clozapine in C6 cells. Six miRNAs (rno-let-7a-5p, rno-miR-221-3p, rno-miR-152-3p, rno-miR-345-5p, rno-miR-3473 and rno-miR-551b-5p) were selected for validation by reverse transcription-quantitative polymerase chain reaction, the results of which showed almost the same expression trends as the microarray results. mRNA and protein expression of potential targets for some validated miRNAs was examined using reverse transcription-quantitative polymerase chain reaction, western blot analysis and immunofluorescence staining. Reduction of miRNA (rno-let-7a-5p, rno-miR-152-3p and rno-miR-221-3p) caused by clozapine treatment could further induce expressions of predicted target genes/proteins (atg12, cdkn1b, c-myc and K-ras) in C6 cells. miRNAs regulation induced by haloperidol or risperidone were not closely related to predicted target genes/proteins expressions. It was concluded that the regulatory trends between antipsychotic drug-induced miRNA and the mRNA/protein expression of potential targets of the examined miRNAs were not quite the same. Clozapine, but not haloperidol or risperidone, could modulate miRNA expression to further regulate protein expression in C6 cells.

Here, we review the results of Southern blotting analyses of the FMR1 gene performed in our reference laboratory in Taiwan over a 15-year period. In total, 725 high-risk women with a family history of fragile X syndrome (FXS) or idiopathic intellectual disability, 3911 low-risk pregnant women without such family history, and prenatal diagnosis data for 32 foetuses from 24 carrier mothers were included. Only 2 carriers were in the low-risk group, which indicated a prevalence of 1 of 1955 women (95% confidence interval: 1/7156-1/539). A total of 100 carriers were found to be in the high-risk group, thus revealing a significantly higher frequency than the low-risk group (100/725 vs 2/3911, P

We present a new investigation of the dependence of H2O maser detection rates and properties on the mid-IR AGN luminosity, L_AGN, and the obscuring column density, N_H, based on mid-IR and hard X-ray photometry. Based on spectral energy distribution fitting that allows for decomposition of the black hole accretion and star-formation components in the mid-infrared, we show that the megamaser (disk maser) detection rate increases sharply for galaxies with 12 micron AGN luminosity L^{AGN}_{12 micron} greater than 10^42 erg/s, from ~= 10^23 cm^{-2} and N_H >= 10^{24} cm^{-2}, respectively. Combining these column density cuts with a constraint for high L^{AGN}_{12 micron} (>=10^42 erg/s) predicts further increases in the megamaser (disk maser) detection rates to 19%(8%) and 27%(14%), revealing unprecedented potential boosts of the megamaser and disk maser detection rates by a factor of 7-15 relative to the current rates, depending on the chosen sample selection criteria. A noteworthy aspect of these new predictions is that the completeness rates are only compromised mildly, with the rates remaining at the level of ~95%(~50%) for sources with N_H >= 10^{23} cm^{-2} (N_H >= 10^24 cm^-2). Applying these selection methods to the current X-ray AGN surveys predicts the detection of >~15 new megamaser disks., 22 pages, 11 figures, 4 tables, Accepted by ApJ

International audience; The current filamentation instability is a key phenomenon underpinning various processes in astrophysics, laboratory laser-plasma, and beam-plasma experiments. Here we show that the ultrafast dynamics of this instability can be explored in the context of relativistic laser-solid interactions through deflectometry by low-emittance, highly relativistic electron bunches from a laser wakefield accelerator. We present experimental measurements of the femtosecond timescale generation of strong magnetic-field fluctuations, with a measured line-integrated B field of 2.70 ± 0.39 kT μm. Three-dimensional, fully relativistic particle-in-cell simulations demonstrate that such fluctuations originate from the current filamentation instability arising at submicron scales around the irradiated target surface, and that they grow to amplitudes strong enough to broaden the angular distribution of the probe electron bunch a few tens of femtoseconds after the laser pulse maximum. Our results open a branch of physics experiments investigating the femtosecond dynamics of laser-driven plasma instabilities by means of synchronized, wakefield-accelerated electron beams.

Recent progress in observing sun-induced Chl fluorescence (SIF) provides an unprecedented opportunity to advance photosynthesis research in natural environments. However, we still lack an analytical framework to guide SIF studies and integration with the well-developed active fluorescence approaches. Here, we derive a set of coupled fundamental equations to describe the dynamics of SIF and its relationship with C3 and C4 photosynthesis. These equations show that, although SIF is dynamically as complex as photosynthesis, the measured SIF simplifies photosynthetic modeling from the perspective of light reactions by integrating over the dynamic complexities of photosynthesis. Specifically, the measured SIF contains direct information about the actual electron transport from photosystem II to photosystem I, giving a quantifiable link between light and dark reactions. With much-reduced requirements on inputs and parameters, the light-reactions-centric, SIF-based biophysical model complements the traditional, dark-reactions-centric biochemical model of photosynthesis. The SIF-photosynthesis relationship, however, is nonlinear. This is because photosynthesis saturates at high light whereas SIF has a stronger tendency to keep increasing, as fluorescence quantum yield has a relatively muted sensitivity to light levels. Successful applications of the SIF-based model of photosynthesis will depend on a predictive understanding of several previously underexplored physiological and biophysical processes. Advances can be facilitated by coordinated efforts in plant physiology, remote sensing, and eddy covariance flux observations.

Detailed considerations of how to construct inclined-incidence hard X-ray resonators are presented. Owing to the symmetry of the crystals used, the Bragg back reflection usually employed in normal-incidence two- and multi-plate resonators to reflect the X-ray beam is often accompanied by unavoidable multiple-beam diffraction, and thus the reflectivity and cavity finesse are quite low. In contrast, crystal-based Fabry–Perot (FP) resonators at inclined incidence utilize multiple-beam diffraction to excite the back reflection inside the resonator to generate FP resonance with high efficiency, while avoiding the incident beam suffering from crystal absorption. The useful characteristics of inclined-incidence resonators are derived from numerical calculations based on the inclined-incidence diffraction geometry and the dynamical theory. Experimental results with Laue inclined incidence are in accordance with the simulation. The sub-millielectronvolt energy resolution and ultra-high efficiency of the transmission spectrum of the proposed resonators are also described.

The large subaqueous sand dunes in the South China Sea are expected to produce the coupling of energy between acoustic normal modes. In this letter, resonant interaction between acoustic propagating modes and subaqueous bedforms are numerically investigated as a function of bedform wavelength, acoustic frequency and bedform packet length. The results demonstrate that bedform wavelength impacts acoustic mode coupling behavior, with the principal transfer of energy occurring between acoustic modes whose eigenvalue difference is equal to the peak value in the bedform wavenumber spectrum. The observed effect of wavelength is greater than that of acoustic frequency and bedform packet length.

Normal incidence echo data (bottom reflection) can provide acoustic reflectivity estimates for seabed classification and predicting sediment properties with the use of seabed sediment models. However, the accuracy of normal-incident reflection coefficients significantly affects the geoacoustic inversion result. In this study, a multi-core platform with an acoustic recording system, which can perform a near-scope photographical survey of the seafloor, was used to acquire sound data and collect surficial sediment samples in the southwestern sea off Taiwan. The acquired acoustic data was used to analyze the potential errors in acoustic reflectivity estimation, which were induced by erroneous assumptions in acoustic propagation and sea surface reflection. The subsequent data analysis led to the following conclusions. First, theoretical spherical spreading loss compensation is appropriate in seabed reflection coefficient estimates, and relatively small uncertainties in propagation occur. Second, the assumption of perfect reflection of the sea surface is invalid for seabed reflection coefficient estimates. The resulting errors caused by sea-surface effects are described in detail. Moreover, the collected seabed reflection coefficients were used to invert the sediment properties, which were verified using core samples and camera images.

Objectives Tigecycline non-susceptible Acinetobacter nosocomialis (TNAN) has been discovered in clinical isolates. The resistance-nodulation-cell division (RND)-type efflux system plays a major role in tigecycline non-susceptible Acinetobacter baumannii, but the mechanism in A. nosocomialis remains unknown. Our aim was to analyse the contribution of efflux-based tigecycline resistance in clinical A. nosocomialis isolates collected from multiple medical centres in Taiwan. Methods A total of 57 A. nosocomialis isolates, including 46 TNAN and 11 tigecycline-susceptible A. nosocomialis (TSAN) isolates, were analysed. Of these, 46 TNAN isolates were clustered to ST410 (43 isolates) and ST68 (three isolates) by multi-locus sequence typing. Results The relationship between the RND efflux pump and tigecycline resistance was indirectly verified by successfully reducing tigecycline resistance with NMP, an efflux pump inhibitor. The three RND efflux systems (AdeABC, AdeIJK and AdeFGH) were detected in all clinical isolates. The transcript level of adeB gene increased significantly and was correlated with tigecycline resistance. Moreover, the AdeRS two-component system was further classified into four different types of AdeRS patterns considering the amino acid sequence. Further analysis showed that tigecycline resistance was related to the transcript level of adeB gene and the AdeRS pattern. Conclusion This study showed that the dissemination of TNAN isolates in Taiwan is attributable mainly to the spread of ST410. The AdeABC efflux pump appeared to play an important role in the tigecycline resistance of A. nosocomialis.

Objectives This study aimed to develop and validate a new instrument based on the health belief model and to use the instrument to investigate the determinants of regular dental attendance among primary schoolchildren. Methods A cross-sectional study was conducted using a newly developed measurement scale based on the HBM, 4 health-promoting schools participated in the study and 958 students studying in grades 4–6 completed the questionnaire. The psychometric properties of the instrument were analysed, and a path analysis model was used to identify the determinants of regular dental attendance. Results The instrument had good internal consistency (Cronbach's α = 0.826–0.925) and a factor structure identical to HBM. Overall, the schoolchildren's health beliefs on caries treatment were positive. The determinants of regular dental visit were school location (β = −0.13), mother's education level (β = 0.15), susceptibility (β = −0.18) and barriers (β = −0.11). Conclusion This study provided evidence that HBM is applicable to children's dental visiting behaviour and their health beliefs towards adherence to caries treatment. Although children had a positive attitude towards dental visits, environmental obstacles would interfere with dental visits. The newly developed instrument could be used to identify high-risk children and help design oral health interventions for these children. Moreover, policy makers should increase the accessibility of dental resources to enhance the utilization of dental care among schoolchildren.

The UFFO/Lomonosov has been successfully launched into Sun synchronous orbit and is operational through tests and calibrations since its launch on Apr. 28, 2016. As a pathfinder of UFFO, it will be the first space instrument to use a fast slewing mirror which reduce the trigger latency of optical telescope, less than a second, to explore early time domain of GRB evolution. In this article, we will report in detail the first mission, UFFO/Lomonosov, for the rapid response to observe early photons from GRBs in orbit.

Previous work has demonstrated the use of a plasma mirror (PM), after a laser-plasma accelerator (LPA), for generating Compton γ-rays by retro-reflecting the spent laser pulse into the just-accelerated electrons. Here, we investigate the use of a PM to stimulate Compton backscatter (CBS) by retro-reflecting a spent pulse from the Texas Petawatt (TPW) laser after it has driven a cm-scale, GeV LPA. A comparative analysis between the electron and CBS pointing and divergence reveals strong agreement, from shot-to-shot, suggesting a reliable, non-invasive extension for GeV-beam metrology. Our observations confirm the self-aligning PM method is scalable to GeV LPAs, while also suggesting a technique with unique advantages and a robustness that can potentially be exploited for investigations of nonlinear Compton backscatter from ultralow divergence, GeV electrons using the Texas Petawatt Laser.

We exploited the Faraday effect to imprint polarization shifts on a transverse probe that captured the structure of a GeV laser-plasma accelerator. Our measurements suggested a plasma bubble diameter ≈ 50.8±10.1 μm.

Stem segments of the tissue culture-grown seedlings of the Victoria grape were soaked 24 or 48 h using an aqueous solution of colchicine (0.05% or 0.1%). The best treatment for chromosome duplication efficiency was the use of the third and fourth buds treated for 48 h in 0.05% colchicine or 24 h in 0.1% colchicine. The doubling rates of primary generation cells were 33% and 31%, respectively. The polyploid cell percentage increases about 10% by subculture of the chimeras. The polyploid cell percentage could reach 75.8%-96.6% by subculturing the chimeras for four to six generations. Through root tip cell chromosome observations, there was little effect on the division cycle of root-tip cells in terms of the cultured seedlings with or without illumination in the short term, and the optimal sampling time of root tips for chromo-some observation was at 9:30 am.

Based on the IEC 61400-3 offshore wind turbine(OWT) design guidelines. The NREL 5MW wind turbine with Taiwan domestic jacket support structure in 18 meters water depth was selected to perform the fatigue strength calculation for the support structure. Commercial software Bladed and SACS are used to perform the required structural responses and fatigue strength calculations. The stress concentration factors(SCF) and S-N curves for the stress calculations of tubular joints are adopted based on the recommendation of DNV GL guidelines. The magnitude of the stress variation range and the corresponding number of counts are obtained by the rainflow counting algorithm. Finally under the assumption of Palmgren-Miner linear cumulative damage rule, the cumulative damage of the structure component can be calculated and then the fatigue life can be estimated. The numerical evaluation results can also be available for the further reference on the determination of sensor locations for the structural monitor system.