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Fetal cleft lip is a common congenital defect. Considering the delicacy and difficulty of observing fetal lips, we have utilized deep learning technology to develop a new model aimed at quickly and accurately assessing the development of fetal lips during prenatal examinations. This model can detect ultrasound images of the fetal lips and classify them, aiming to provide a more objective prediction for the development of fetal lips. This study included 632 pregnant women in their mid-pregnancy stage, who underwent ultrasound examinations of the fetal lips, collecting both normal and abnormal fetal lip ultrasound images. To improve the accuracy of the detection and classification of fetal lips, we proposed and validated the Yolov5-ECA model. The experimental results show that, compared with the currently popular 10 models, our model achieved the best results in the detection and classification of fetal lips. In terms of the detection of fetal lips, the mean average precision (mAP) at 0.5 and mAP at 0.5:0.95 were 0.920 and 0.630, respectively. In the classification of fetal lip ultrasound images, the accuracy reached 0.925. The deep learning algorithm has accuracy consistent with manual evaluation in the detection and classification process of fetal lips. This automated recognition technology can provide a powerful tool for inexperienced young doctors, helping them to accurately conduct examinations and diagnoses of fetal lips. [ABSTRACT FROM AUTHOR]

Earth Orientation Parameters (EOP) are essential for monitoring Earth’s rotational irregularities, impacting satellite navigation, space exploration, and climate forecasting. This study introduces a hybrid prediction model combining least-squares (LS) and vector autoregression (VAR) to improve Earth’s Pole Coordinates (x, y) forecast accuracy. Using daily sampled IERS EOP 20 C04 data from 2013 to 2023, we conducted 1,000 yearly random trials, performing 48 forecasts per year. Our method evaluates six data combinations, including primary variables (x, y) and their derivatives ( x ̇ , y ̇ $\dot{x},\dot{y}$ ). Results show a systematic improvement in prediction accuracy, especially for ultra-short-term forecasts (10 days into future), with derivative information stabilizing the solutions. The best-performing combination ( x , y , x ̇ , y ̇ $x,y,\dot{x},\dot{y}$ ) achieved a mean absolute prediction error (MAPE) reduction (with respect to the reference data combination – x, y) of up to 8 % for the y and 7 % for the x over a whole 30-day forecast horizon. These findings highlight the effectiveness of incorporating derivatives of polar motion time series into prediction procedure. [ABSTRACT FROM AUTHOR]

To evaluate the differences in vaginal matrix metalloproteinases (MMP) and tissue inhibitors of metalloproteinases (TIMPs) in pregnant patients with a history of prior preterm birth compared with controls. A prospective cohort pilot study recruited patients during prenatal care with history of prior spontaneous preterm birth (high-risk group) or no history of preterm birth (low-risk/controls). Inclusion criteria were singleton gestation at 11–16 weeks and between 18 and 55 years of age. Exclusion criteria were diabetes mellitus, hypertension, diseases affecting the immune response or acute vaginitis. A vaginal wash was performed at time of enrollment, and patients were followed through delivery. Samples were analyzed using semi-quantitative analysis of MMPS and TIMPS. The study was approved by the IRB and a p-value <0.05 was considered significant. A total of 48 pregnant patients were recruited: 16 with a history of preterm birth (high-risk group) and 32 with no history of preterm birth (low-risk group/controls). Groups were similar in age, race, BMI, and delivery mode. The high-risk group had more multiparous women (100 vs. 68.8 %; p=0.02), a greater preterm birth rate (31.2 vs. 6.3 %; p=0.02), and a lower birth weight (2,885 ± 898 g vs. 3,480 ± 473 g; p=0.02). Levels of vaginal MMP-9 were greater in high-risk patients than low-risk patients (74.9 % ± 27.0 vs. 49.4 % ± 31.1; p=0.01). When dividing the cohort into patients that had a spontaneous preterm birth (7/48, 14.6 %) vs. those with a term delivery (41/48, 85.4 %), the vaginal MMP-9 remained elevated in the cohort that experienced a preterm birth (85.46 %+19.79 vs. 53.20 %+31.47; p=0.01). There were no differences in the other MMPS and in TIMPs between high and low-risk groups. There was an increase in vaginal MMP-9 during early pregnancy in those at high risk for preterm birth and in those who delivered preterm, regardless of prior pregnancy outcome. Vaginal MMP-9 may have potential as a marker of increased risk of preterm birth. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore whether fibrinogen (Fib) can be used as a predictor of postpartum hemorrhage (PPH) in parturients with vaginal delivery, and the value of combining Fib with other indexes to predict postpartum hemorrhage in vaginal delivery. A total of 207 parturients who delivered via vagina were divided into PPH group (n=102) and non-PPH group (n=105). The PPH group was further divided into mild PPH group and severe PPH group. The differences of Fib, platelet (PLT), mean platelet volume (MPV), platelet distribution width (PDW), D-dimer (D-D), hemoglobin (HGB) and neonatal weight (Nw) between the two groups were compared to explore the significance of these indexes in predicting PPH. Fib, PLT and PDW in PPH group were significantly lower than those in non-PPH group, while D-D and Nw in PPH group were significantly higher than those in non-PPH group. In the binary logistic regression model, we found that Fib, D-D and Nw were independently related to PPH. The risk of PPH increased by 9.87 times for every 1 g/L decrease in Fib. The cut-off value of Fib is 4.395 (sensitivity 0.705, specificity 0.922). The AUC value of PPH predicted by Fib combined with D-D and Nw was significantly higher than that of PPH predicted by Fib (p<0.05, 95 % CI 0.00313–0.0587). Fib, D-D and Nw have good predictive value for PPH of vaginal delivery, among which Fib is the best. The combination of three indexes of Fib, D-D and Nw can predict PPH more systematically and comprehensively, and provide a basis for clinical prevention and treatment of PPH. [ABSTRACT FROM AUTHOR]

Optimized sugar-added passion fruit juice (OPFJ) was obtained after optimization of thermosonication treatments of passion fruit juice using hybrid Artificial neural network/Genetic algorithm. The quality parameters and shelf life of OPFJ stored at various temperatures were assessed. Baranyi and Ratkowsky's square-root models proved the prediction accuracy of the theoretical model concerning the microbial population during storage. The lag phase value decreased with increasing storage temperature. Temperature rise from 8 to 25 °C decreased the shelf life from 17 to 8 days for OPFJ and 3.5 to 1.5 days for untreated juice. The activation energy and increase in the reaction rate on increase in temperature by 10 °C (Q10 value) were also evaluated. Thermosonication increased the phenolic and β-carotene contents and maintained the pH, titratable acidity, ascorbic acid content, and antioxidant activity during storage. Thermosonication enhanced the quality and shelf life of juice. Predictive modelling effectively predicted the shelf life. [ABSTRACT FROM AUTHOR]

Liquid biopsy (LBx) provides diagnostic, prognostic and predictive insights for malignant diseases and offers promising applications regarding tumor burden, tumor heterogeneity and clonal evolution. The Augsburg Longitudinal Plasma Study (ALPS) is a prospective trial for patients with metastatic cancer that comprises sequential collection of LBx samples, tumor tissue, radiological imaging data, clinical information and patient-reported outcomes. Peripheral blood plasma is collected based on the individual patient's staging intervals and LBx-derived ctDNA analyses are performed using CAncer Personalized Profiling sequencing (CAPP-seq). From April 2021 to October 2023, 419 patients have been enrolled. A total of 1,293 LBx samples were collected, 419 samples (100 %) at the beginning of the study and an average of 3 (range 1–12) during the 30-month follow-up period of the current interim analysis. 380 tissue biopsy (TBx) samples (90.7 %) were available at baseline and 39.6 % had ≥1 TBx samples at follow-up. Lung cancer patients are most prevalent in ALPS (n=147), followed by colorectal (n=38), prostate (n=31) and gastroesophageal cancer (n=28). On average, 12.0 ng/mL plasma cell-free DNA (cfDNA) could be isolated. First CAPP-seq analyses in 60 patients comprised 110 samples and demonstrated a detection sensitivity of 0.1 %. The first interim analysis of ALPS confirms feasibility for comprehensive longitudinal evaluation of LBx and demonstrates suitability for ctDNA evaluation. [ABSTRACT FROM AUTHOR]

To determine if infants with exomphalos had abnormal antenatal lung growth as indicated by lower chest radiographic thoracic areas (CRTA) on day one compared to controls and whether the CRTA could predict the development of bronchopulmonary dysplasia (BPD). Infants with exomphalos cared for between January 2004 and January 2023 were included. The controls were term, newborn infants ventilated for absent respiratory drive at birth, without lung disease and had no supplemental oxygen requirement by 6 h of age. The radiographs were imported as digital image files by Sectra PACS software (Sectra AB, Linköping, Sweden). Free-hand tracing of the perimeter of the thoracic area was undertaken and the CRTA calculated by the software. Sixty-four infants with exomphalos and 130 controls were included. Infants with exomphalos had a lower median (IQR) CRTA (1,983 [1,657–2,471] mm2) compared to controls (2,547 [2,153–2,932] mm2, p<0.001). Following multivariable regression analysis, infants with exomphalos had lower CRTAs compared to controls (p=0.001) after adjusting for differences in gestational age and male sex. In the exomphalos group, the CRTAs were lower in those who developed BPD (n=14, 1,530 [1,307–1,941] mm2) compared to those who did not (2,168 [1,865–2,672], p<0.001). Following multivariable regression analysis, the CRTA was associated with BPD development (p=0.021) after adjusting for male sex and gestational age. Lower CRTAs on day one in the exomphalos infants compared to the controls predicted BPD development. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Among patients with preterm labor and intact membranes (PTL), those with intra-amniotic infection (IAI) present the highest risk of adverse perinatal outcomes. Current identification of IAI, based on microbiological cultures and/or polymerase chain reaction amplification of the 16S ribosomal RNA gene, delay diagnosis and, consequently, antenatal management. The aim to of the study was to assess the performance of a multivariable prediction model for diagnosing IAI in patients with PTL below 34.0 weeks using clinical, sonographic and biochemical biomarkers. From 2019 to 2022, we prospectively included pregnant patients admitted below 34.0 weeks with diagnosis of PTL and had undergone amniocentesis to rule in/out IAI. The main outcome was IAI, defined by a positive culture and/or 16S ribosomal RNA gene in amniotic fluid. Based on the date of admission, the sample (n=98) was divided into a derivation (2019–2020, n=49) and validation cohort (2021–2022, n=49). Logistic regression models were developed for the outcomes evaluated. As predictive variables we explored ultrasound cervical length measurement at admission, maternal C-reactive protein, gestational age, and amniotic fluid glucose and matrix metalloproteinase-8 (MMP-8) levels. The model was developed in the derivation cohort and applied to the validation cohort and diagnostic performance was evaluated. Clinical management was blinded to the model results. During the study period, we included 98 patients admitted with a diagnosis of PTL. Of these, 10 % had IAI. The final model included MMP-8 and amniotic fluid glucose levels and showed an area under the receiver operating characteristic curve to predict the risk of IAI of 0.961 (95 % confidence interval: 0.860–0.995) with a sensitivity of 75 %, specificity of 93.3 %, positive likelihood ratio (LR) of 11.3 and negative LR of 0.27 in the validation cohort. In patients with PTL, a multivariable prediction model including amniotic fluid MMP-8 and glucose levels might help in the clinical management of patients undergoing amniocentesis to rule in/out IAI, providing results within a few minutes. [ABSTRACT FROM AUTHOR]

Mechanical ventilation in prematurely born infants, particularly if prolonged, can cause long term complications including bronchopulmonary dysplasia. Timely extubation then is essential, yet predicting its success remains challenging. Artificial intelligence (AI) may provide a potential solution. A narrative review was undertaken to explore AI's role in predicting extubation success in prematurely born infants. Across the 11 studies analysed, the range of reported area under the receiver operator characteristic curve (AUC) for the selected prediction models was between 0.7 and 0.87. Only two studies implemented an external validation procedure. Comparison to the results of clinical predictors was made in two studies. One group reported a logistic regression model that outperformed clinical predictors on decision tree analysis, while another group reported clinical predictors outperformed their artificial neural network model (AUCs: ANN 0.68 vs. clinical predictors 0.86). Amongst the studies there was an heterogenous selection of variables for inclusion in prediction models, as well as variations in definitions of extubation failure. Although there is potential for AI to enhance extubation success, no model's performance has yet surpassed that of clinical predictors. Future studies should incorporate external validation to increase the applicability of the models to clinical settings. [ABSTRACT FROM AUTHOR]

Numerous prognostic models have been proposed for ovarian cancer, extending from single serological factors to complex gene-expression signatures. Nonetheless, these models have not been routinely translated into clinical practice. We constructed a robust and readily calculable model for predicting surgical outcome and prognosis of ovarian cancer patients by exploiting commonly available clinico-pathological factors and three selected serum parameters. Serum CA125, human epididymis protein 4 (HE4) and mesothelin (MSL) were quantified by Lumipulse® G chemiluminescent enzyme immunoassay (Fujirebio) in a total of 342 serum samples from 190 ovarian cancer patients, including 152 paired pre- and post-operative samples. Detection of pre-operative HE4 and CA125 was the optimal marker combination for blood-based prediction of surgical outcome (AUC=0.86). We constructed a prognostic model, computed by serum levels of pre-operative CA125, post-operative HE4, post-operative MSL and surgical outcome. Prognostic performance of our model was superior to any of these parameters alone and was independent from BRCA1/2 mutational status. We subsequently transformed our model into a prognostic risk index, stratifying patients as "lower risk" or "higher risk". In "higher risk" patients, relapse or death was predicted with an AUC of 0.89 and they had a significantly shorter progression free survival (HR: 9.74; 95 % CI: 5.95–15.93; p<0.0001) and overall survival (HR: 5.62; 95 % CI: 3.16–9.99; p<0.0001) compared to "lower risk" patients. We present a robust predictive/prognostic model for ovarian cancer, which could readily be implemented into routine diagnostics in order to identify ovarian cancer patients at high risk of recurrence. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The flutter of a composite wing reinforced with functionally graded carbon nanotubes (CNTs) has been investigated. A rectangular plate models a supersonic wing with cantilever boundary conditions. To determine displacement fields of a moderately thick plate, shear deformation theory is used. Using the Hamilton principle, a first-order piston theory was used to simulate supersonic airflow. This study examines four types of CNT thickness. Also, four different CNT distribution patterns are investigated. In a two-layer asymmetric composite, the effects of patch mass, mass distribution, fiber orientation angle, and distribution of CNTs were examined. Moreover, the results are compared and verified with other studies. A greater mass ratio led to a smaller flutter boundary, while a longer added mass increased the flutter boundary. A variation in the distribution pattern in CNT fiber orientation results in a distinct behavior of the flutter boundary for asymmetric composites with increasing orientation angles. The artificial neural network is utilized to predict the damping ratio, and the results showed great accuracy compared to the study results. Hyperparameter tuning is employed for better optimizing the predictive models. [ABSTRACT FROM AUTHOR]

Predictive maintenance techniques are increasingly important in Industry 4.0; they can be applied to nonreturn valves to mitigate waste from improper replacement. To improve such predictive maintenance, this study used four nonreturn valves with different outer diameters to investigate the effects of wear on process variables and product quality under different process parameters. The results indicated that melt temperature (process parameter) had the most substantial influence on the amount of melt backflow. The process variables are the screw position at the end of the packing stage, the slope of the screw position during the packing stage, and peak pressure. The study investigated the influence of nonreturn valve wear on the entire molding process over different periods. The findings can be extended to prediction models for developing process windows and realizing the predictive maintenance of nonreturn valves in injection molding machines. [ABSTRACT FROM AUTHOR]

Using supplementary cementitious materials in concrete production makes it eco-friendly by decreasing cement usage and the corresponding CO2 emissions. One key measure of concrete’s durability performance is its porosity. An empirical prediction of the porosity of high-performance concrete with added cementitious elements is the goal of this work, which employs machine learning approaches. Binder, water/cement ratio, slag, aggregate content, superplasticizer (SP), fly ash, and curing conditions were considered as inputs in the database. The aim of this study is to create ML models that could evaluate concrete porosity. Gene expression programming (GEP) and multi-expression programming (MEP) were used to develop these models. Statistical tests, Taylor’s diagram, R 2 values, and the difference between experimental and predicted readings were the metrics used to evaluate the models. With R 2 = 0.971, mean absolute error (MAE) = 0.348%, root mean square error (RMSE) = 0.460%, and Nash–Sutcliffe efficiency (NSE) = 0.971, the MEP provided a slightly better-fitted model and improved prediction performance when contrasted with the GEP, which had R 2 = 0.925, MAE = 0.591%, RMSE = 0.745%, and NSE = 0.923. Binder, water/binder ratio, curing conditions, and aggregate content had a direct (positive) relationship with the porosity of concrete, while SP, fly ash, and slag had an indirect (negative) association, according to the SHapley Additive exPlanations study.

In this research paper, we introduce an extension of the Gamma-Lindley distribution using a particular exponentiation of its cumulative distribution function, which offers a more flexible model for lifetime data. Another attractive feature of this extension is that it has several particular cases: Weibull, generalized Pareto, Lindley, exponential and Gamma-Lindley distributions. Different statistical properties of this distribution are explored, such as the density, failure rate and the rth moments. We attempt to prove that the extended Gamma-Lindley distribution is characterized by its truncated moment of order statistics. One of the main merits of this work lies in the fact that it provides the ability of this characterization to simulate the new distribution compared with the inverse transform sampling method. Estimation of the parameters using the tailed regression method is investigated. The COVID-19 real data evolution in Tunisia illustrates the performance of the EGL distribution compared with its special cases through some criteria such as the Kolmogorov-Smirnov test, Mean Squared error and Kullback-Leibler divergence. The predictive ability of the extended Gamma-Lindley distribution proved to provide a better fit to expect the number of deaths by Corona-virus in Tunisia in the future period. [ABSTRACT FROM AUTHOR]

Vegetable oils are a crucial source of raw materials for many industries. In order to meet the rising demand for oil on global scale, it has become essential to investigate underutilized plant oilseeds. Hura crepitans seeds are one of the underused plant oilseeds from which oil can be produced via solvent-based extraction. For the purpose of predicting the oil yield from Hura crepitans seeds, the extraction process was modelled using a nonlinear autoregressive exogenous neural network (NARX-NN). The input variables to the model are seed/solvent ratio, extraction temperature and extraction time, while oil yield is the response output variable. NARX-NN model is based on 200 data samples, and model architecture comprises of 3 inputs, 1 hidden layer (with 15 neurons) and 1 output with 2 delay elements. The performance evaluation was carried out to examine the accuracy of the developed model in predicting oil yield from Hura crepitans using different statistical indices. The overall correlation coefficient, R (0.80829), mean square error, MSE (0.0120), root mean square error, RMSE (0.1080) and standard prediction error, SEP (0.1666) show that NARX-NN model can accurately be used for the prediction oil yield from Hura crepitans seeds. [ABSTRACT FROM AUTHOR]

Advance process control is a proven control and optimization technology delivering measurable and sustainable improvements in production yield, coupled with the added value of energy savings. In this work, an adaptive based model predictive control (aMPC) is developed and implemented to control the hydrogenation of dimethyl oxide to ethylene glycol (EG) in a plug flow reactor. The aMPC is compared with 3 other control schemes; proportional-integral (PI), decoupled PI (dPI) and linear model predictive control. The aim is to determine the reliability of aMPC in controlling the production rate and reactor temperature for an optimized hydrogenation reactor. The aspects compared include set point tracking, disturbance rejection and robustness test. The analysis concludes that the aMPC offers the best overall performance compared to the other control schemes. [ABSTRACT FROM AUTHOR]

Neutrophil–lymphocyte ratio (NLR), as an indicator of heightened systemic inflammatory response, predicts increased disease burden and poor oncological outcomes in urothelial carcinoma (UC). The study was undertaken with an aim to evaluate the association of NLR with clinicopathological variables and survival outcomes. A total of 80 patients of UC were enrolled in the current retrospective study. Pre-operative NLR (within one month prior to the procedure), patient age, sex, tumour grade, pathological stage, recurrence free survival (RFS), progression free survival (PFS) and cancer specific survival (CSS) were recorded. We chose a cut-off value of 2.7 for NLR and patients were divide into two groups (NLR <2.7 and ≥2.7). NLR ≥2.7 was significantly associated with advanced tumour stage (p=0.001), but not with tumour grade (p=0.116). Progression (p=0.032) and death rates (p=0.026) were high in patients with NLR ≥2.7. Mean RFS (p=0.03), PFS (p=0.04) and CSS (p=0.04) were reduced in patients with NLR ≥2.7. On univariate analysis, NLR ≥2.7 predicted worse RFS (HR=2.928, p=0.007), PFS (HR=3.180, p=0.006) and CSS (HR=3.109, p=0.016). However, it was not an independent predictor of outcomes on multivariate analysis. Tumour stage and grade are the only independent predictors of RFS, PFS and CSS. High NLR at a cut-off value of ≥2.7 is associated with advanced pathological stage, but does not have an independent predictive value for RFS, PFS and CSS. [ABSTRACT FROM AUTHOR]

To explore the predictive value of C-reactive protein (CRP)-to-albumin (ALB) ratio (CAR) for the risk of 28-day mortality in patients with severe pneumonia. A total of 152 patients with severe pneumonia treated from January 2020 to January 2022 were enrolled and assigned into survival group (n=107) and death group (n=45) according to their survival status after treatment for 28 d. Their clinical data were compared, and the influencing factors for 28-day mortality were explored by multiple logistic regression analysis. The receiver operating characteristic (ROC) curve was plotted to assess the value of CAR for predicting 28-day mortality risk. A risk prediction model was constructed, and its prediction efficiency was evaluated. The death group had significantly older age, Acute Physiology and Chronic Health Evaluation (APACHE) II score, Murray Lung Injury Score, Sequential Organ Failure Assessment score, white blood cell count, neutrophil count, red cell volume distribution width, neutrophil-to-lymphocyte ratio (NLR), fibrinogen, procalcitonin, blood lactic acid (Lac), CRP and CAR and significantly lower oxygenation index and ALB than those of the survival group (p<0.05). APACHE II score, NLR, Lac and CAR were independent risk factors for 28-day mortality (p<0.05). AUC of the established prediction model was 0.826, with sensitivity of 88.45 % and specificity of 87.32 %, indicating high discrimination. The nomogram model had clinical value when the risk threshold probability was 11–93 %. CAR is an independent risk factor that shows a high predictive value for the 28-day mortality risk in patients with severe pneumonia. [ABSTRACT FROM AUTHOR]

We combine robust loss functions with statistical boosting algorithms in an adaptive way to perform variable selection and predictive modelling for potentially high-dimensional biomedical data. To achieve robustness against outliers in the outcome variable (vertical outliers), we consider different composite robust loss functions together with base-learners for linear regression. For composite loss functions, such as the Huber loss and the Bisquare loss, a threshold parameter has to be specified that controls the robustness. In the context of boosting algorithms, we propose an approach that adapts the threshold parameter of composite robust losses in each iteration to the current sizes of residuals, based on a fixed quantile level. We compared the performance of our approach to classical M-regression, boosting with standard loss functions or the lasso regarding prediction accuracy and variable selection in different simulated settings: the adaptive Huber and Bisquare losses led to a better performance when the outcome contained outliers or was affected by specific types of corruption. For non-corrupted data, our approach yielded a similar performance to boosting with the efficient L2 loss or the lasso. Also in the analysis of skewed KRT19 protein expression data based on gene expression measurements from human cancer cell lines (NCI-60 cell line panel), boosting with the new adaptive loss functions performed favourably compared to standard loss functions or competing robust approaches regarding prediction accuracy and resulted in very sparse models. [ABSTRACT FROM AUTHOR]

Microalgae are used to produce renewable biofuels and high-value components and in bioremediation and CO2 sequestration tasks. These increasing applications, in conjunction with a desirable constant large-scale productivity, motivate the development and application of practical controllers. This paper addresses the application of robust control schemes for microalgae cultivation in continuous photobioreactors. Due to the model uncertainties and external perturbations, robust control designs are required to guarantee the desired microalgae productivity. Furthermore, simple controller designs are desirable for practical implementation purposes. Therefore, two robust control designs are applied and evaluated in this paper for two relevant case studies of microalgae cultivation in photobioreactors. The first control design is based on an enhanced simple-input output model with uncertain estimation. The second control design is the robust nonlinear model predictive control considering different uncertain scenarios. Numerical simulations of two case studies aimed at lipid production and CO2 capture under different conditions are presented to evaluate the robust closed-loop performance. [ABSTRACT FROM AUTHOR]

Kokoro as a broadly acknowledge maize snack is being consumed every day by both the children and grown-ups, but it is characterized by the low protein content required for survival. The blending of maize flour (MF), sesame flour (SF) and moringa flour (mF) to enhance the nutritional values of kokoro was optimize with response surface technique (RSM) and artificial neural network (ANN). MF, SF and mF were mixed at diverse proportion and the optimal blending ratio was gotten using D-optimal design method. The protein and carbohydrate actual contents were compared with their predicted values using RSM and ANN models. The performance of the developed RSM and ANN models were validated with coefficient of determination (R2) and mean square error (MSE). The optimal blending ratio of MF: SF: mF was 54.11: 37.06: 8.83. The optimal blending ratio gave 25.53% of protein content and 45.99% of carbohydrate content. The statistical analysis of the experimental data obtained using different statistical techniques shows that regression models by RSM gave R2 of 0.999 for protein yield and 0.983 for carbohydrate yield while ANN gave R2 of 0.999 with MSE 9.24184 × 10−1. Therefore, it can be concluded from the results that both the RSM and ANN gave good prediction of the model. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates an environment-centered, state-driven corrosion prognosis framework to predict the long-term atmospheric corrosion loss of metal materials, and this paper takes carbon steel as an example to show the establishment process of the framework. Unlike traditional power-linear prediction models that seldomly consider environmental impacts, the proposed model quantitatively establishes the correlations between corrosion loss and dynamic atmospheric environmental factors. A comprehensive power-linear function model integrating multiple atmospheric environmental factors is constructed, following the corrosion kinetics robustness. Under the proposed framework, the steady-state start time is evaluated, followed by the long-term corrosion loss prediction under different corrosivity categories and test sites. The applicability is justified via a case study of long-term field exposure tests of metal materials in China, as well as the experimental results of the ISO CORRAG program. By comparing with the traditional power model and ISO model, the experimental results demonstrate the capability and effectiveness of the proposed prognosis methodology in acquiring accurate corrosion state information and corrosion loss prediction results with less input corrosion information. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the value of cervical strain elastography and Cervical Sliding Sign (CSS) for predicting spontaneous preterm birth (sPTB). In our case-control study we performed an elastographic assessment of the cervix in 82 cases of preterm birth (preterm group) and 451 control pregnancies (control group) between the 20th and 37th week of gestation. We divided the anterior cervical lip first into two ("Intern2", "Extern2") and into three sectors ("Intern3", "Middle3", "Extern3"). The tissue deformation pattern after local compression with an ultrasound probe was recorded. We distinguished between an irregularly distributed ("Spotting") and homogeneous pattern presentation. Additionally, the presence of a sliding of the anterior against the posterior cervical lip (positive CSS) during compression was evaluated. A logistic regression analysis and the Akaike Information Criterion (AIC) were used to estimate the probability of sPTB and to select a prediction model. Spotting and positive CSS occurred more frequently in the preterm group compared to control group (97.8 vs. 2.2%, p<0.001; 26.8 vs. 4.2%, p<0.001; respectively). The model with the parameters week of gestation at ultrasound examination, Intern3, Middle3 and CSS was calculated as the highest quality model for predicting sPTB. The AUC (Area Under the Curve) was higher for this parameter combination compared to cervical length (CL) (0.926 vs. 0.729). Cervical strain elastography pattern analysis may be useful for the prediction of sPTB, as the combination of Spotting analysis and CSS is superior to CL measurement alone. [ABSTRACT FROM AUTHOR]

This study aims to investigate the applicability and accuracy of different machine learning (ML) methods for predicting paperboard properties based on raw material and process data. The goal is not to find an ML method that can predict all properties simultaneously, but rather the most suitable method for a single property. The examined properties are bending stiffness and the curl. Furthermore, the focus is not on the most accurate prediction model, but on the best model to interpret. The results show that ML methods are applicable for predicting paperboard properties based on raw material and process data, e. g., bending stiffness and curl in machine direction (MD) and cross-machine direction (CD). The feature selection is crucial for the quality of the prediction. Therefore, domain knowledge is needed to select the right features. This selection improves the model and ensures its reliability, comprehensibility, and interpretability. The self-implemented recursive feature addition (RFA) takes the features specified by a domain expert for the feature selection into consideration. It requires less computation time in comparison to the common recursive feature elimination (RFE). The extremely randomized tree (ET) was chosen due to its high interpretability and good results. It achieves a mean absolute error (MAE) for the bending stiffness MD of 0.913 mN m; an MAE for curl MD of 0.058 m−1 (in a value range from 7 mN m to 100 mN m and −1 m−1 to 1 m−1, respectively); 0.474 mN m and 0.163 m−1 (in a value range from 5 mN m to 45 mN m and −1 m−1 to 2 m−1, respectively) for the respective CD properties. [ABSTRACT FROM AUTHOR]

Theoretical predictions were made using TALYS-1.95(G) and EMPIRE 3.2 reaction-model codes for 69Ge, 67Ge, and medically used 68Ge, 67Ga, 68Ga, 65Zn, 64Cu radionuclides produced in the interaction of α-projectile with 66Zn-target at 10–40 MeV α-energies. Pearson's statistical coefficients showed moderate to strong positive correlations between the theoretically predicted and experimentally measured production cross sections for radionuclides with practical medical applications. Furthermore, the present results indicated that a medium-sized cyclotron and a single α + 66Zn system (projectile + target system) might be an option for optimized production of 68Ge, 68Ga, 67Ga, 65Zn, and 64Cu radionuclides. [ABSTRACT FROM AUTHOR]

In the rubber industry, rheometric properties are critical in defining processing times and temperatures. These parameters of rubber compounds are determined by time-consuming and expensive laboratory studies performed in a rheometer. Artificial intelligence approaches, on the other hand, may be used to estimate rheometric properties in seconds without the need for any samples or laboratory experiments. In this research, artificial neural network, Gaussian process regression, and support vector regression techniques are used to predict minimum and maximum torque, 30% and 60% cure time of a rubber compound using both process parameters and raw material composition as input. The dataset comprises 1128 batches of the selected rubber compound. A detailed sensitivity analysis is performed to determine the best performing hyperparameters and the prediction performances are expressed as mean absolute percentage error (MAPE). Minimum, maximum, and average MAPE values are presented for each artificial intelligence technique. Besides this research contributes to fill the gap in rubber industry literature, the results obtained also strongly improve the existing literature results. [ABSTRACT FROM AUTHOR]

This work presents a summary of environmental data from 3600 h of data collected during operations of a U.S. Coast Guard HC-130H aircraft performing its routine missions over a five-year period. The aircraft operated from multiple U.S. and deployed locations during the data collection period in the tropical and mid-latitudes at altitudes up to 8.5 km (28 kft). The aircraft collected pressure, temperature and humidity data during the period. No aerosol components were sampled (chlorides, sulfur dioxide, etc.). While there was no Time of Wetness sensor, the ISO 9223 definition is used for the present work as an estimator. These data are presented in the context of research into corrosion and environmental severity, or the potential conditions for it. About 40% of the data were collected while the aircraft was parked on the ground at various based and deployed locations. The purpose of this work is to present and summarize the flight data as an aid in providing some context for future research into environmental effects as they relate to both corrosion and environmentally assisted cracking (EAC) of aircraft structure, and perhaps for comparisons with existing or future datasets from aircraft having other missions. Prediction models need realistic, real-world inputs, such as those presented here. An example for the use of these data in corrosion prediction based on the ISO 9223 international standard is presented. Perhaps not surprisingly, these data show that, for this aircraft, conditions most favorable to corrosion occurred while the aircraft was on the ground. [ABSTRACT FROM AUTHOR]

The step by step model of innovation is a benchmark model in research investigating the relationship between competition and innovation. The model assumes an industry can be in one of two states; leveled or unleveled. In an unleveled state the lagging firm is the only innovator. In a leveled state firms compete in a patent race. In this patent race successful innovation probabilities are mutually exclusive. This formulation provides mathematical tractability, but it has no other justification. I relax this assumption and use numerical simulation to demonstrate that allowing for non mutually exclusive success in innovation has important consequences for the inverted U relationship. The inverted U relationship is no longer a prediction of the model. In addition, the model predicts that patent measures will under count innovation from the leveled state, allowing for an inverted U relationship between competition and patenting under a narrow set of parameter restrictions. This theoretical exercise has important implications for understanding the current state of the empirical record on this topic. [ABSTRACT FROM AUTHOR]

Numerous predictive models for Parkinson's disease (PD) incidence have been published recently. However, the model performance and methodological quality of those available models are yet needed to be summarized and assessed systematically. In this systematic review, we systematically reviewed the published predictive models for PD incidence and assessed their risk of bias and applicability. Three international databases were searched. Cohort or nested case-control studies that aimed to develop or validate a predictive model for PD incidence were considered eligible. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used for risk of bias and applicability assessment. Ten studies covering 10 predictive models were included. Among them, four studies focused on model development, covering eight models, while the remaining six studies focused on model external validation, covering two models. The discrimination of the eight new development models was generally poor, with only one model reported C index > 0.70. Four out of the six external validation studies showed excellent or outstanding discrimination. All included studies had high risk of bias. Three predictive models (the International Parkinson and Movement Disorder Society [MDS] prodromal PD criteria, the model developed by Karabayir et al. and models validated by Faust et al.) are recommended for clinical application by considering model performance and resource-demanding. In conclusion, the performance and methodological quality of most of the identified predictive models for PD incidence were unsatisfactory. The MDS prodromal PD criteria, model developed by Karabayir et al. and model validated by Faust et al. may be considered for clinical use. [ABSTRACT FROM AUTHOR]

A continuous stirred tank reactor (CSTR) servo and the regulatory control problem are challenging because of their highly non-linear nature, frequent changes in operating points, and frequent disturbances. System identification is one of the important steps in the CSTR model-based control design. In earlier work, a non-linear system model comprises a linear subsystem followed by static nonlinearities and represented with Laguerre filters followed by the LSSVM (least squares support vector machines). This model structure solves linear dynamics first and then associated nonlinearities. Unlike earlier works, the proposed LSSVM-L (least squares support vector machines and Laguerre filters) Hammerstein model structure solves the nonlinearities associated with the non-linear system first and then linear dynamics. Thus, the proposed Hammerstein's model structure deals with the nonlinearities before affecting the entire system, decreasing the model complexity and providing a simple model structure. This new Hammerstein model is stable, precise, and simple to implement and provides the CSTR model with a good model fit%. Simulation studies illustrate the benefit and effectiveness of the proposed LSSVM-L Hammerstein model and its efficacy as a non-linear model predictive controller for the servo and regulatory control problem. [ABSTRACT FROM AUTHOR]

An improved mean streamline loss model for splittered centrifugal/mixed-flow compressor impellers is presented. One-dimensional model predictions, CFD simulations and experimental results for Krain impeller are compared to confirm the model validity. A new description for the interaction between blade number and splitter blade length of a splittered mixed-flow compressor impeller with high hub/tip ratio is discussed and formulized. The optimal principal blade number and optimal splitter blade length corresponding to maximum impeller efficiency are selected by the new formulas studied. The results indicate that the improved model and new formulas have reasonable prediction effects for the impeller efficiency and the selection of blade number and splitter blade length. Blade height/pitch ratio can also be adopted as a selection reference especially for high hub/tip impellers. Effective blade number or equivalent solidity is only suitable for evaluating load capacity rather than efficiency in impeller design processes. [ABSTRACT FROM AUTHOR]

To evaluate the ability of an artificial intelligence (AI) model to predict the risk of cancer in patients referred from primary care based on routine blood tests. Results obtained with the AI model are compared to results based on logistic regression (LR). An analytical profile consisting of 25 predefined routine laboratory blood tests was introduced to general practitioners (GPs) to be used for patients with non-specific symptoms, as an additional tool to identify individuals at increased risk of cancer. Consecutive analytical profiles ordered by GPs from November 29th 2011 until March 1st 2020 were included. AI and LR analysis were performed on data from 6,592 analytical profiles for their ability to detect cancer. Cohort I for model development included 5,224 analytical profiles ordered by GP's from November 29th 2011 until the December 31st 2018, while 1,368 analytical profiles included from January 1st 2019 until March 1st 2020 constituted the "out of time" validation test Cohort II. The main outcome measure was a cancer diagnosis within 90 days. The AI model based on routine laboratory blood tests can provide an easy-to use risk score to predict cancer within 90 days. Results obtained with the AI model were comparable to results from the LR model. In the internal validation Cohort IB, the AI model provided slightly better results than the LR analysis both in terms of the area under the receiver operating characteristics curve (AUC) and PPV, sensitivity/specificity while in the "out of time" validation test Cohort II, the obtained results were comparable. The AI risk score may be a valuable tool in the clinical decision-making. The score should be further validated to determine its applicability in other populations. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper focuses on investigating the steady-state and dynamic performance of the permanent magnet synchronous motor (PMSM) drive using modified model predictive torque control (MPTC) method with multi-level inverters (MLIs). The MPTC method is simple and easy to implement for motor control as they don't involve cascaded PI controllers unlike required the conventional controllers, such as field oriented control (FOC) and direct torque and flux control (DTFC). The proposed modified MPTC method eliminates the use of weighing factors and it is a fusion of model predictive current control (MPCC) and MPTC. Initially, the proposed modified MPTC fed PMSM drive is implemented using two-level inverter. It provides better steady-state and dynamic performance, but the ripple contents of torque, flux, and currents are high. In order to reduce the ripple contents and improve the performance of the PMSM drive, a modified MPTC is implemented using three-level inverter. The proposed modified MPTC fed PMSM drive using two-level and three-level inverters are simulated in MATLAB/SIMULINK environment under different operating conditions, such as no-load, loading, speed reversal, step change in speed operations. In order to show the effectiveness and feasibility of the proposed method, it is also validated with the experimental results. [ABSTRACT FROM AUTHOR]

The performance evaluation of a single-phase shunt-connected custom power device (SC-CPD) for current quality improvement is discussed in this paper. The SC-CPD performance is compared based on the linear triangle-comparison pulse width modulation (TC-PWM) control, hysteresis current control (HCC), and the predictive non-linear switching control strategies. The predictive switching control is implemented using the finite control set-model predictive control (FCS-MPC). The switching control techniques' operational and implementation features are discussed for the given control objectives of the SC-CPD for a particular nonlinear load. Basic functional case studies with sinusoidal and non-sinusoidal supply mains in the presence of non-linear loads are presented to illustrate the appropriateness of the switching techniques. The SC-CPD model and control methodologies are developed in the MATLAB/Simulink environment, including designing of various circuit components. Finally, performance simulation results using the switching techniques have been compared and validated using OPAL-RT 4510-based real-time simulations. [ABSTRACT FROM AUTHOR]

We demonstrate the formation of the near field with non-trivial phase distribution using surface plasmon interference devices, and experimental quantitative imaging of that phase with near-field phase microscopy. The phase distribution formed with a single device can be controlled by the polarization of the external illumination and the area of the device assigned to the object wave. A comparison of the experimental data to a numerical electromagnetic model and an analytical model assigns the origin of the near-field phase to the out-of-plane electric component of surface plasmon polaritons, and also verifies the predictive power of the models. We demonstrate a formation of near-field plane waves with different propagation directions on a single device, or even simultaneously at distinct areas of a single device. Our findings open the way to the imaging and tomography of phase objects in the near field. [ABSTRACT FROM AUTHOR]

In the present paper, we describe a one-dimensional glacier parameterization for use in the numerical weather prediction models. The proposed scheme is implemented into the global atmospheric model SLAV. To avoid inconsistency of surface temperature and turbulent heat fluxes in the lower troposphere, glacier parameterization has been iteratively coupled with both planetary boundary layer and land surface schemes. First results from numerical experiments with the SLAV model show that the introduction of a simplified description of the glacier heat capacity can significantly improve the 2-meter temperature long-range weather forecast skill. [ABSTRACT FROM AUTHOR]

A novel control method combining nonlinear model predictive control (NMPC) with linear kalman filter (LKF) and adaptive notch filter (ANF) for an integrated helicopter/engine system with variable rotor speed is proposed to enhance the response ability of turboshaft engine. Firstly, based on the integrated helicopter/engine model with variable rotor speed, an ANF combined with frequency estimation is introduced. Then, in order to estimate the significant and unmeasurable performance parameters utilized in model predictive control, such as temperature before turbine, a nonlinear model predictive controller based on LKF is presented. The simulation verifications demonstrate that the fundamental frequency of torsional vibration changes from 1.30 Hz to 2.70 Hz when the rotor speed varies continuously by 50 %. In this case, compared with the notch filter, all torsional vibration amplitudes are damped below 0.1 % by ANF. Meanwhile, in comparison with the PID controller, the NMPC can reduce the overshoot and droop of the power turbine speed to less than 1 % with steady-state error no more than 0.5 % at the off-design point of NMPC based on adaptive torsional vibration suppression. The applications of LKF and similar transformation improve the control accuracy and robustness performance of the NMPC designed at a single operating point. [ABSTRACT FROM AUTHOR]

A Min-Max structure combined with several linear limit controllers are utilized for conventional aero-engines safety control, which is complex and conservative, therefore a new nonlinear model predictive control strategy is proposed that owns the ability to manage time-varying input/output limits during acceleration/deceleration transient states of aero-engines. Penalty function items are constructed according to the characteristics of input/output limits, and are incorporated in the cost function of online receding horizon optimization for limit management of aero-engines. Considering the large computational burden of nonlinear component-level aero-engine model directly used as prediction model, a piecewise linear model is built to describe the steady-state and dynamic properties of aero-engines and its design process is explained in detail. Further, computational efforts are investigated as control horizon, prediction horizon are varied in the improved nonlinear predictive controller. Particularly, when penalty function items are activated, control performance and computational burden are examined, thus providing guidelines for key parameters tuning. Simulations show two clear results. One is that computational efforts become larger for bigger control horizon or prediction horizon, but it depends more on control horizon than prediction horizon. The other is that the proposed method can handle time-varying input/output limits effectively without resulting in a great increase of computational burden. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Currently, there are several animal models for vasculitis. Ovalbumin and lipopolysaccharide (OVA, LPS) are well established for causing inflammation and used as an adjunct in the vasculitis induction. However, to date, none has established the effect of OVA and LPS in disease induction. Therefore, in the present study, an attempt has been made to develop a new animal model for vasculitis using OVA/LPS in rats. A total of 42 Wistar rats were divided randomly into seven groups (n=6/group), normal control, and three different doses (0.5, 1, and 5 mg/kg) of OVA and LPS treated groups. Half of the rats in each group received only intraperitoneal sensitization, while the remaining half rats were additionally subjected to a one-week intranasal challenge. Results showed that both OVA/LPS in their respective groups have significantly increased circulating inflammatory cells, C-reactive protein (CRP), Inflammatory cytokines (IL-1β, IL-6, TNF-α), Kidney damage markers (BUN, Creatinine), and liver function enzymes (AST, ALT) in a dose-dependent manner. OVA/LPS induced vascular inflammation in a dose-dependent manner. However, the higher (5 mg/kg) dose of ovalbumin and lipopolysaccharide has contributed to severe vascular inflammation through increasing inflammatory cytokines. These findings suggest that OVA/LPS may contribute as a possible model for vasculitis in rats. [ABSTRACT FROM AUTHOR]

Among patients with placenta retention, the risk factors of massive blood loss remain unclear. In this study, a secondary data analysis was conducted to construct a predictive risk model for postpartum hemorrhage (PPH) in this particular population. A prediction model based on the data of 13 hospitals in the UK, Uganda, and Pakistan, from December 2004, to May 2008 was built. A total of 516 patients and 14 potential risk factors were analyzed. The least absolute shrinkage and selection operator regression (LASSO) model was used to optimize feature selection for the PPH risk model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the LASSO model. Discrimination and calibration were assessed using C-index and calibration plot. Among patients with placenta retention, the incidence of PPH was 62.98% (325/526). Risk factors in the model were country, number of past deliveries, previous manual removal of placenta, place of placenta delivery, and how the placenta was delivered. In these factors, patients in the low-income country (i.e., Uganda) (OR: 1.753, 95% CI=1.055–2.915), retained placentas delivered in the theater (OR: 2.028, 95% CI=1.016–4.050), and having placentas partially removed by controlled cord traction (cct), completely removed manually (OR: 4.722, 95% CI=1.280–17.417) were independent risk factors. The C-statistics was 0.702. By secondary data analysis, our study constructed a prediction model for PPH in patients with placenta retention, and identified the independent risk factors. [ABSTRACT FROM AUTHOR]

The aim of the present study was to evaluate a new ultrasonographic scoring method that could predict spontaneous preterm birth (sPTB) in uncomplicated twin pregnancies during routine anomaly screening in the midtrimester. This prospective study included women with a monochorionic diamniotic (MCDA) or dichorionic diamniotic (DCDA) twin pregnancy gestational ages from 170/7 through 226/7. A new ultrasonographic score between 0 and 5 points was calculated using cervical length (CL), uterocervical angle (UCA), and cervical dilatation status during routine anomaly screening in the midtrimester. The primary outcome of the study was the prediction of sPTB < 32 and <34 gestational weeks. A total of 118 pregnant women with twins were evaluated, 31 of whom delivered before 34 gestational weeks an 87 of whom delivered after 34 gestational weeks. The sensitivity and specificity values were separately calculated for scores of 3.5 and 2.5 for predicting sPTB < 32 gestational weeks. The cut-off value of 3.5 provided a sensitivity of 80% and a specificity of 82%. When the score was 2.5, the sensitivity and specificity were 86 and 71%, respectively. To determine a score for predicting sPTB < 34 gestational week cut-off value of 3.5 provided a sensitivity of 80% and a specificity of 90%. When the score value was 2.5, the sensitivity and specificity of the method were 83 and 81%, respectively. Our results indicated that the midtrimester new scoring is a simple technique that can be easily used as an improved tool for predicting the risk of sPTB in women with a twin pregnancy. [ABSTRACT FROM AUTHOR]

Since rotational wood welding was patented in 1997, this technology has been successfully used in the field of timber structures and furniture. A growing body of literature has investigated the mechanical behaviour of wood-dowel welding joints in tension to study the effect of various factors on welding strength. However, up to now, only two predictive approaches were proposed by researchers, and due to the lack of small samples, they were applied in a limited scope. Therefore, this paper aimed to find a way to establish a multivariate model to compute their carrying capacity. A pull-out test database including 1338 trials from previous literature was used as the basis of modelling. Due to the similarity of joints with glued-in-rod, after evaluating its model validation, the modelling method was applied to the welded joints. The data analysis was carried out to determine the primary influences (such as: rotational speed, predrilled hole diameter, welded length and substrate density). Based on the non-linear regression analysis, a multivariate prediction model was proposed, and results showed positive accuracy with measured values. This study provides a new comprehensive assessment of the withdrawal strength of welded joints, and can help identify future technical guidelines within the wood industry. [ABSTRACT FROM AUTHOR]

Electrochemical fluorination in anhydrous HF, also known as the Simons process, is a widely used industrial method for fluorination of organic compounds. Its mechanism, being not so well understood, has long been debated and is believed to involve higher valent nickel fluorides formed on the nickel-plated anode during the process. One of these is speculated to be Ni2F5, which was previously reported in the literature and assigned via infrared spectroscopy, but its crystal structure is not yet known. We have identified known crystal structures of compounds with similar stoichiometries as Ni2F5 and utilized them as a starting point for our periodic DFT investigations, applying the PBE+U method. Ni2F5 as the most stable polymorph was found to be of the same crystal structure as another mixed valent fluoride, Cr2F5. The calculated lattice parameters are a = 7.24 Å, b = 7.40 Å, c = 7.08 Å and β = 118.9° with an antiferromagnetic ordering of the nickel magnetic moments. [ABSTRACT FROM AUTHOR]

Galvanic corrosion is a type of localized corrosion that widely exists in all kinds of metal structures and causes severe damage, jeopardising the safety and dependability of equipment. This paper reviews the mechanism of galvanic corrosion and related influencing factors, including the material characteristics and geometric factors of the metal pair and environmental factors. As a unique surface testing technique, wire beam electrode (WBE) is widely used in various corrosion research fields represented by galvanic corrosion. On this basis, the research progress of the WBE technique in the field of galvanic corrosion is mainly reviewed from three perspectives. The experiments also proved that the WBE technique can well characterize the corrosion evolution progression and investigate the polarity reversal mechanisms. Finally, it is pointed out that the urgent scientific problems to be solved in the research of galvanic corrosion include: galvanic corrosion research in a variety of special environments; the mechanism of the galvanic couple reversal caused by the change of corrosion factors; the corrosion behavior of a complex coupling system composed of multiple metal connections; the development of corrosion prediction models. [ABSTRACT FROM AUTHOR]