Search Results (10)

Preeclampsia is a pregnancy syndrome characterized by complex symptoms which cause maternal and fetal problems and deaths. The aim of this study is to achieve preeclampsia risk prediction and early risk prediction in Xinjiang, China, based on the placental growth factor measured using the SiMoA or Elecsys platform. A novel reliable calibration modeling method and missing data imputing method are proposed, in which different strategies are used to adapt to small samples, training data, test data, independent features, and dependent feature pairs. Multiple machine learning algorithms were applied to train models using various datasets, such as single-platform versus bi-platform data, early pregnancy versus early plus non-early pregnancy data, and real versus real plus augmented data. It was found that a combination of two types of mono-platform data could improve risk prediction performance, and non-early pregnancy data could enhance early risk prediction performance when limited early pregnancy data were available. Additionally, the inclusion of augmented data resulted in achieving a high but unstable performance. The models in this study significantly reduced the incidence of preeclampsia in the region from 7.2% to 2.0%, and the mortality rate was reduced to 0%. Full article

The occurrence of tree flash faults in hybrid overhead–underground lines presents a significant challenge to the smooth operation of power systems. However, research on localizing such faults is relatively scarce. This study conducts theoretical analyses on the formation of tree flash faults, constructs a tree flash fault discharge test platform, and simulates the discharge process. The tree flash fault discharge traveling wave signals were obtained through a high-frequency current acquisition system. Additionally, this paper establishes a model for the current traveling wave of tree flash faults and analyzes transmission attenuation. To enhance the bi-terminal traveling wave localization method, we introduce modal decomposition and the Hilbert–Huang transform. Modal decomposition is used to disentangle signals and derive the instantaneous frequencies of modal signal components through the Hilbert–Huang transform. This process helps determine the time at which the initial wavefront reaches the terminals of the mixed-line transmission. The simulation analysis carried out using PSCAD/EMTDC v4.6.3 demonstrates that this method effectively calibrates the wavefront timing of tree flash fault signals without requiring knowledge of their wave velocity along the mixed-line transmission. Therefore, this approach achieves precise localization of tree flash faults efficiently. Full article

The development of multifunctional biomimetic nanozymes with high catalytic activity and sensitive response is rapidly advancing. The hollow nanostructures, including metal hydroxides, metal-organic frameworks, and metallic oxides, possess excellent loading capacity and a high surface area-to-mass ratio. This characteristic allows for the exposure of more active sites and reaction channels, resulting in enhanced catalytic activity of nanozymes. In this work, based on the coordinating etching principle, a facile template-assisted strategy for synthesizing Fe(OH)₃ nanocages by using Cu₂O nanocubes as the precursors was proposed. The unique three-dimensional structure of Fe(OH)₃ nanocages endows it with excellent catalytic activity. Herein, in the light of Fe(OH)₃-induced biomimetic nanozyme catalyzed reactions, a self-tuning dual-mode fluorescence and colorimetric immunoassay was successfully constructed for ochratoxin A (OTA) detection. For the colorimetric signal, 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) can be oxidized by Fe(OH)₃ nanocages to form a color response that can be preliminarily identified by the human eye. For the fluorescence signal, the fluorescence intensity of 4-chloro-1-naphthol (4-CN) can be quantitatively quenched by the valence transition of Ferric ion in Fe(OH)₃ nanocages. Due to the significant self-calibration, the performance of the self-tuning strategy for OTA detection was substantially enhanced. Under the optimized conditions, the developed dual-mode platform accomplishes a wide range of 1 ng/L to 5 μg/L with a detection limit of 0.68 ng/L (S/N = 3). This work not only develops a facile strategy for the synthesis of highly active peroxidase-like nanozyme but also achieves promising sensing platform for OTA detection in actual samples. Full article

In polymerase chain reaction (PCR)-based nucleic acid quantification, the DNA template type, primer/probe sequence, and instrument platform such as real-time quantitative PCR (qPCR) and digital PCR (dPCR) affect the accuracy and reliability of quantitative results. In this study, a plasmid DNA (pDNA) pBI121-screening, genetically modified (GM) rice SDrice genomic DNA (gDNA), and GM rapeseed SDrape gDNA, all carrying the same 11 screening elements, were used to prepare samples of different levels of gDNA and pDNA in a non-GM gDNA background. The comparison of the dPCR assays targeting the 11 screening elements revealed that the primer/probe set is a key factor that affects the accuracy of dPCR quantification. The optimal PCR method for the 11 screening elements was screened out from among the validated qPCR methods. The accuracy of the qPCR quantification of the low-level pDNA and gDNA test samples was low when pDNA was used as a calibrator, whereas that of the dPCR quantification was high and not affected by variations in template type and detection target. The validated dPCR assays targeting one or two elements can be randomly selected to characterize multiple-target pDNA reference materials (RMs). Low-level pDNA RMs with certified values can be used as quality controls for dPCR assays to avoid significant bias in gDNA quantification. Full article

Many women who experience gestational diabetes (GDM), gestational hypertension (GHT), pre-eclampsia (PE), have a spontaneous preterm birth (sPTB) or have an offspring born small/large for gestational age (SGA/LGA) do not meet the criteria for high-risk pregnancies based upon certain maternal risk factors. Tools that better predict these outcomes are needed to tailor antenatal care to risk. Recent studies have suggested that metabolomics may improve the prediction of these pregnancy-related disorders. These have largely been based on targeted platforms or focused on a single pregnancy outcome. The aim of this study was to assess the predictive ability of an untargeted platform of over 700 metabolites to predict the above pregnancy-related disorders in two cohorts. We used data collected from women in the Born in Bradford study (BiB; two sub-samples, n = 2000 and n = 1000) and the Pregnancy Outcome Prediction study (POPs; n = 827) to train, test and validate prediction models for GDM, PE, GHT, SGA, LGA and sPTB. We compared the predictive performance of three models: (1) risk factors (maternal age, pregnancy smoking, BMI, ethnicity and parity) (2) mass spectrometry (MS)-derived metabolites (n = 718 quantified metabolites, collected at 26–28 weeks’ gestation) and (3) combined risk factors and metabolites. We used BiB for the training and testing of the models and POPs for independent validation. In both cohorts, discrimination for GDM, PE, LGA and SGA improved with the addition of metabolites to the risk factor model. The models’ area under the curve (AUC) were similar for both cohorts, with good discrimination for GDM (AUC (95% CI) BiB 0.76 (0.71, 0.81) and POPs 0.76 (0.72, 0.81)) and LGA (BiB 0.86 (0.80, 0.91) and POPs 0.76 (0.60, 0.92)). Discrimination was improved for the combined models (compared to the risk factors models) for PE and SGA, with modest discrimination in both studies (PE-BiB 0.68 (0.58, 0.78) and POPs 0.66 (0.60, 0.71); SGA-BiB 0.68 (0.63, 0.74) and POPs 0.64 (0.59, 0.69)). Prediction for sPTB was poor in BiB and POPs for all models. In BiB, calibration for the combined models was good for GDM, LGA and SGA. Retained predictors include 4-hydroxyglutamate for GDM, LGA and PE and glycerol for GDM and PE. MS-derived metabolomics combined with maternal risk factors improves the prediction of GDM, PE, LGA and SGA, with good discrimination for GDM and LGA. Validation across two very different cohorts supports further investigation on whether the metabolites reflect novel causal paths to GDM and LGA. Full article

From the perspectives of characterized data, calibrated TCAD simulations and compact modeling, we present a deeper investigation of the very high frequency behavior of state-of-the-art sub-THz silicon germanium heterojunction bipolar transistors (SiGe HBTs) fabricated with 55-nm BiCMOS process technology from STMicroelectronics. The TCAD simulation platform is appropriately calibrated with the measurements in order to aid the extraction of a few selected high-frequency (HF) parameters of the state-of-the-art compact model HICUM, which are otherwise difficult to extract from traditionally prepared test-structures. Physics-based strategies of extracting the HF parameters are elaborately presented followed by a sensitivity study to see the effects of the variations of HF parameters on certain frequency-dependent characteristics until 500 GHz. Finally, the deployed HICUM model is evaluated against the measured s-parameters of the investigated SiGe HBT until 500 GHz. Full article

Due to the coastal morphodynamic being impacted by climate change there is a need for systematic and large-scale monitoring. The monitoring of sandy dunes in Pays-de-la-Loire (France) requires a simultaneous mapping of (i) its morphology, allowing to assess the sedimentary stocks and (ii) its low vegetation cover, which constitutes a significant proxy of the dune dynamics. The synchronization of hyperspectral imaging (HSI) with full-waveform (FWF) LiDAR is possible with an airborne platform. For a more intimate combination, we aligned the 1064 nm laser beam of a bi-spectral Titan FWF LiDAR with 401 bands and the 15 cm range resolution on the Hyspex VNIR camera with 160 bands and a 4.2 nm spectral resolution, making both types of data follow the same emergence angle. A ray tracing procedure permits to associate the data while keeping the acquisition angles. Stacking multiple shifted FWFs, which are linked to the same pixel, enables reaching a 5 cm range resolution grid. The objectives are (i) to improve the accuracy of the digital terrain models (DTM) obtained from an FWF analysis by calibrating it on dGPS field measurements and correcting it from local deviations induced by vegetation and (ii) in combination with airborne reflectances obtained with PARGE and ATCOR-4 corrections, to implement a supervised hierarchic classification of the main foredune vegetation proxies independently of the acquisition year and the physiological state. The normalization of the FWF LiDAR range to a dry sand reference waveform and the centering on their top canopy echoes allows to isolate Ammophilia arenaria from other vegetation types using two FWF indices, without confusion with slope effects. Fourteen HSI reflectance indices and 19 HSI Spectral Angle Mapping (SAM) indices based on 2017 spectral field measurements performed with the same Hyspex VNIR camera were stacked with both FWF indices into a single co-image for each acquisition year. A simple straightforward hierarchical classification of all 35 pre-classified co-image bands was successfully applied along 20 km, out of the 250 km of coastline acquired from 2017 to 2019, prefiguring its systematic application to the whole 250 km every year. Full article

The underground air tunnel system shows promising potentials for reducing energy consumption of buildings and for improving indoor thermal comfort, whereas the existing dynamic models using the computational fluid dynamic (CFD) method show computational complexity and are user-unfriendly for parametrical analysis. In this study, a dynamic numerical model was developed with the on-site experimental calibration. Compared to the traditional CFD method with high computational complexity, the mathematical model on the MATLAB/SIMULINK platform is time-saving in terms of the real-time thermal performance prediction. The experimental validation results indicated that the maximum absolute relative deviation was 3.18% between the model-driven results and the data from the on-site experiments. Parametrical analysis results indicated that, with the increase of the tube length, the outlet temperature decreases with an increase of the cooling capacity whereas the increasing/decreasing magnitude slows down. In addition, the system performance is independent on the tube materials. Furthermore, the outlet air temperature and cooling capacity are dependent on the tube diameter and air velocity, i.e., a larger tube diameter and a higher air velocity are more suitable to improve the system’s cooling capacity, and a smaller tube diameter and a lower air velocity will produce a more stable and lower outlet temperature. Further studies need to be conducted for the trade-off solutions between air velocity and tube diameter for the bi-criteria performance enhancement between outlet temperature and cooling capacity. This study proposed an experimentally validated mathematical model to accurately predict the thermal performance of the underground air tunnel system with high computational efficiency, which can provide technical guidance to multi-combined solutions through geometrical designs and operating parameters for the optimal design and robust operation. Full article

Of the cost centres that combine to result in Levelised Cost of Energy (LCOE), O&M costs play a significant part. Several developers have calculated component costs, demonstrating how they can become commercially competitive with other forms of renewable energy. However, there are uncertainties relating to the O&M figures that can only be reduced through lessons learned at sea. This work presents an O&M model calibrated with data from real sea experience of a wave energy device deployed at the Biscay Marine energy Platform (BiMEP): the OPERA O&M Model. Two additional case studies, utilising two other O&M calculation methodologies, are presented for comparison with the OPERA O&M Model. The second case study assumes the inexistence of an O&M model, utilising a Simplified Approach. The third case study applies DTOcean’s (a design tool for ocean energy arrays) O&M module. The results illustrate the potential advantages of utilising real sea data for the calibration and development of an O&M model. The Simplified Approach was observed to overestimate LCOE when compared to the OPERA O&M Model. This work also shows that O&M models can be used for the definition of optimal maintenance plans to assist with OPEX reduction. Full article

Consequences of failure of offshore wind turbines (OWTs) is in general lower than consequences of failure of, e.g., oil & gas platforms. It is reasonable that lower fatigue design factors can be applied for fatigue design of OWTs when compared to other fixed offshore structures. Calibration of appropriate partial safety factors/Fatigue Design Factors (FDF) for steel substructures for OWTs is the scope of this paper. A reliability-based approach is used and a probabilistic model has been developed, where design and limit state equations are established for fatigue failure. The strength and load uncertainties are described by stochastic variables. SN and fracture mechanics approaches are considered for to model the fatigue life. Further, both linear and bi-linear SN-curves are formulated and various approximations are investigated. The acceptable reliability level for fatigue failure of OWTs is discussed and results are presented for calibrated optimal fatigue design factors. Further, the influence of inspections is considered in order to extend and maintain a given target safety level. Full article