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The aim of this research work is to build a regression model of air quality by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (northern Spain) at a local scale. To accomplish the objective of this study, the experimental data set made up of nitrogen oxides (NO x ), carbon monoxide (CO), sulfur dioxide (SO 2 ), ozone (O 3 ), and dust (PM 10 ) was collected over 3 years (2006–2008). The US National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of these numerical calculations, using the MARS technique, conclusions of this research work are exposed.

The main aim of this research work is to build a new practical hybrid regression model to predict the milling tool wear in a regular cut as well as entry cut and exit cut of a milling tool. The model was based on particle swarm optimization (PSO) in combination with support vector machines (SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, a PSO-optimized SVM (PSO–SVM)-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the duration of experiment, depth of cut, feed, type of material, etc. The second aim is to determine the factors with the greatest bearing on the milling tool flank wear with a view to proposing milling machine’s improvements. Firstly, regression with optimal hyperparameters was performed and a determination coefficient of 0.95 was obtained. Secondly, the main advantages of this PSO–SVM-based model are its capacity to produce a simple, easy-to-interpret model; its ability to estimate the contributions of the input variables; and its computational efficiency. Finally, the main conclusions of this study are exposed.

Cyanobacteria also known as blue-green algae can be found in almost every conceivable environment. Cyanobacteria blooms occur frequently and globally in water bodies and they are a major concern in terms of their effects on other species such as plants, fish and other microorganisms, but especially by the possible acute and chronic effects on human health due to the potential danger from cyanobacterial toxins produced by some of them in recreational or drinking waters. Consequently, anticipation of cyanotoxins presence is a matter of importance to prevent risks. The aim of this study is to build a cyanotoxin diagnostic model by using support vector machines and multilayer perceptron networks from cyanobacterial concentrations determined experimentally in the Trasona reservoir (recreational reservoir used as a high performance training centre of canoeing in the Northern Spain). The results of the present study are two-fold. In the first place, the significance of each biological and physical-chemical variables on the cyanotoxins presence in the reservoir is presented through the model. Secondly, a predictive model able to forecast the possible presence of cyanotoxins is obtained. The agreement of the model with experimental data confirmed its good performance. Finally, conclusions of this innovative research work are exposed.

This paper studies the stress state of a threaded fastening by using Finite Element (FE) models, applied to surgical screws in cortical bone. There is a general interest in studying the stress states induced in the different elements of a joint caused by the thread contact. Analytical models were an initial approach, and later FE models allowed detailed studies of the complex phenomena related to these joints. Different studies have evaluated standard threaded joints in machinery and structures, being the thread symmetric. However, surgical screws employ asymmetric thread geometry, selected to improve the stress level generated in the bone. Despite the interest and widespread use, there is scarce documentation on the actual effect of this thread type. In this work, we discuss the results provided by FE models with detailed descriptions of the contacts comparing differences caused by the materials of the joint, the thread geometry and the thread’s three-dimensional helical effects. The complex contacts at the threaded surfaces cause intense demand on computational resources that often limits the studies including these joints. We analyze the results provided by one commercial software package to simplify the threaded joints. The comparison with detailed FE models allows a definition of the level of uncertainty and possible limitations of this type of simplifications, and helps in making suitable choices for complex applications.

In this paper, we numerically study a dynamic viscoelastic problem. The variational formulation is written as a linear parabolic variational equation for the velocity field. An existence and uniqueness result is recalled. Then, fully discrete approximations are introduced using the implicit Euler scheme and the finite element method, for which some a priori error estimates are derived, leading to the linear convergence of the algorithm under suitable additional regularity conditions. Finally, some one- and three-dimensional numerical simulations are presented to show the accuracy of the algorithm and the behaviour of the solution, including a comparison with an experimental study.

Current anchorage systems for poles, lighting columns, etc., have an inherent strength that causes serious injuries, and even death, in cases of vehicle impact. This article explores the deficiency of traditional anchorage systems and explains the development of a new anchorage system over the different phases, such as design conditions, FEM analysis, dynamic testing and actual crash tests. The behaviour of the traditional and new anchorage systems are compared, including the use of a data acquisition system and a high-speed camera. The aim of this study was to develop a new non-energy-absorbing anchorage system, adaptable to lighting poles, signposts, etc., and to evaluate the decrease in primary injuries with the employment of this system.

Influenza A virus’s (IAV’s) frequent genetic changes challenge vaccine strategies and engender resistance to current drugs. We sought to identify conserved and essential RNA secondary structures within IAV’s genome that are predicted to have greater constraints on mutation in response to therapeutic targeting. We identified and genetically validated an RNA structure (packaging stem–loop 2 (PSL2)) that mediates in vitro packaging and in vivo disease and is conserved across all known IAV isolates. A PSL2-targeting locked nucleic acid (LNA), administered 3 d after, or 14 d before, a lethal IAV inoculum provided 100% survival in mice, led to the development of strong immunity to rechallenge with a tenfold lethal inoculum, evaded attempts to select for resistance and retained full potency against neuraminidase inhibitor-resistant virus. Use of an analogous approach to target SARS-CoV-2, prophylactic administration of LNAs specific for highly conserved RNA structures in the viral genome, protected hamsters from efficient transmission of the SARS-CoV-2 USA_WA1/2020 variant. These findings highlight the potential applicability of this approach to any virus of interest via a process we term ‘programmable antivirals’, with implications for antiviral prophylaxis and post-exposure therapy. Targeting conserved secondary structure of RNA viruses offers the potential for a customizable therapeutic approach to viral variants.

Background Infants born extremely preterm are at high risk of dying or suffering from severe brain injuries. Treatment guided by monitoring of cerebral oxygenation may reduce the risk of death and neurologic complications. The SafeBoosC III trial evaluates the effects of treatment guided by cerebral oxygenation monitoring versus treatment as usual. This article describes the detailed statistical analysis plan for the main publication, with the aim to prevent outcome reporting bias and data-driven analyses. Methods/design The SafeBoosC III trial is an investigator-initiated, randomised, multinational, pragmatic phase III trial with a parallel group structure, designed to investigate the benefits and harms of treatment based on cerebral near-infrared spectroscopy monitoring compared with treatment as usual. Randomisation will be 1:1 stratified for neonatal intensive care unit and gestational age (lower gestational age (< 26 weeks) compared to higher gestational age (≥ 26 weeks)). The primary outcome is a composite of death or severe brain injury at 36 weeks postmenstrual age. Primary analysis will be made on the intention-to-treat population for all outcomes, using mixed-model logistic regression adjusting for stratification variables. In the primary analysis, the twin intra-class correlation coefficient will not be considered. However, we will perform sensitivity analyses to address this. Our simulation study suggests that the inclusion of multiple births is unlikely to significantly affect our assessment of intervention effects, and therefore we have chosen the analysis where the twin intra-class correlation coefficient will not be considered as the primary analysis. Discussion In line with the Declaration of Helsinki and the International Conference on Harmonization Good Clinical Practice guidelines, we have developed and published this statistical analysis plan for the SafeBoosC III trial, prior to any data analysis. Trial registration ClinicalTrials.org, NCT03770741 . Registered on 10 December 2018.

Background: Early recognition of neonatal seizures secondary to pathogenic variants in potassium or sodium channel coding genes is crucial, as these seizures are often resistant to commonly used anti-seizure medications, but respond well to sodium-channel blockers. We report a unique aEEG pattern in neonatal seizures caused by SCN2A and KCNQ3 pathogenic variants, as well as adding regular EEG description. Methods: International multicentre descriptive study, reporting clinical characteristics, aEEG and conventional EEG findings of 10 newborns with seizures due to pathogenic SCN2A and KCNQ3 gene variants. Results: Seizures started in the first postnatal week. Seizure semiology typically included tonic posturing with apnea and desaturation. The aEEG showed a characteristic sequence of brief onset with a decrease, followed by a quick rise, and then postictal amplitude attenuation. This pattern correlated with bilateral attenuation in the EEG at onset, followed by rhythmic discharges ending in several seconds of post-ictal amplitude suppression. The majority of patients became seizure free upon initiation of a sodium-channel blocker. Conclusions: Neonatal seizures caused by SCN2A and KCNQ3 mutations can be recognized by a characteristic ictal aEEG pattern and clinical semiology. Awareness of this pattern facilitates the prompt initiation of precision treatment with sodium-channel blockers even before genetic test results are available.