Coupled thermo-mechanical damage modelling for structural steel in fire conditions

This paper aims at developing a coupled thermo-mechanical damage model for structural 6 steel at elevated temperatures. The need for adequate modelling of steel deterioration behaviour 7 remains a challenging task in structural fire engineering because of the complexity inherent in 8 the damage states of steel under combined actions of mechanical and fire loading. A fully three9 dimensional damage-coupled constitutive model is developed in this work based on the hypothesis 10 of effective stress space and isotropic damage theory. The new coupling model, adapted from 11 an enhanced Lemaitre’s ductile damage equation and taking into account temperature-dependent 12 thermal degradation, is a phenomenological approach where the underlying mechanisms that govern 13 the damage processes have been retained. The proposed damage model comprises a limited number 14 of parameters that could be identified using unloading slopes of stress-strain relationships through 15 tensile coupon tests. The proposed damage model is successfully implemented in the finite element 16 software ABAQUS and validated against a comprehensive range of experimental results. The 17 damage-affected structural response is accurately reproduced under various loading conditions and 18 a wide temperature range, demonstrating that the proposed damage model is a useful tool in giving a 19 realistic representation of steel deterioration behaviour for structural fire engineering applications

Current status of hepatocellular carcinoma detection: screening strategies and novel biomarkers.

Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide. Delayed diagnosis is a major factor responsible for the poor prognosis of HCC. Several advances have been made in the field of liver imaging with the use of novel imaging contrasts, improving current imaging techniques with contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI), introduction of new technologies such as contrast liver ultrasound, and development of novel biomarkers with the goal of early detection of HCC and improving outcomes of patients with HCC. This review focuses on current surveillance strategies and development of biomarkers with the goal of early detection of HCC

A randomized trial of brief intervention strategies in patients with alcohol-related facial trauma as a result of interpersonal violence

Facial trauma is associated with male gender, low socioeconomic status, alcohol misuse, and violence. Brief intervention (BI) for alcohol is effective at reducing consumption in patients presenting with facial trauma. Singlesession control of violence for angry impulsive drinkers(SS-COVAID) is a new intervention that attempts to address alcohol-related violence. This study assessed the effect of SS-COVAID and BI on drinking and aggression in facial trauma patients. Male facial trauma patients who sustained their injuries as a result of interpersonal violence while drinking and who had Alcohol Use Disorders Identification Test (AUDIT) scores of ≥8 were randomized to either BI or SS-COVAID. Patients were followed up at six and 12 months, and drinking and aggression outcomes were analyzed. One hundred ninety-nine patients entered the trial, and 187 were included in the analysis. Of these, 165 (89%) considered themselves to be victims, 92 (51%) had sustained a previous alcohol-related injury, and 28 (15%) had previous convictions for violence. Both interventions resulted in a significant decrease in negative drinking outcomes over 12 months of follow-up (p<0.001). Neither intervention had a significant effect on aggression scores, nor was there a significant difference between interventions in terms of either outcome. Both SS-COVAID and BI had a significant effect on drinking variables in this patient cohort. No effect on aggressionwas seen despite the fact that SS-COVAID specifically addresses the relationship between alcohol and violence. One reason for this may be that the facial trauma patients in this study considered themselves to be victims rather than aggressors. Another possibility is that, while BI may successfully address lifestyle factors such as hazardous or harmful drinking, it may not be effective in modifying personality traits such as aggression

Sensorimotor adaptation to auditory perturbation of speech is facilitated by noninvasive brain stimulation

Repeated exposure to disparity between the motor plan and auditory feedback during speech production results in a proportionate change in the motor system’s response called auditory-motor adaptation. Artificially raising F1 in auditory feedback results in a concomitant decrease in F1 during speech production. Transcranial direct current stimulation (tDCS) can be used to alter neuronal excitability in focal areas of the brain. The present experiment explored the effect of noninvasive brain stimulation applied to the speech premotor cortex on the timing and magnitude of adaptation responses to artificially raised F1 in auditory feedback. Participants (N = 18) completed a speaking task in which they read target words aloud. Participants' speech was processed to raise F1 by 30% and played back to them over headphones in real time. A within-subjects design compared acoustics of participants’ speech while receiving anodal (active) tDCS stimulation versus sham (control) stimulation. Participants' speech showed an increasing magnitude of adaptation of F1 over time during anodal stimulation compared to sham. These results indicate that tDCS can affect behavioral response during auditory-motor adaptation, which may have translational implications for sensorimotor training in speech disorders

Concepts for radically increasing the numerical convergence rate of the Euler equations

Integral equation and finite difference methods have been developed for solving transonic flow problems using linearized forms of the transonic small disturbance and Euler equations. A key element is the use of a strained coordinate system in which the shock remains fixed. Additional criteria are developed to determine the free parameters in the coordinate straining; these free parameters are functions of the shock location. An integral equation analysis showed that the shock is located by ensuring that no expansion shocks exist in the solution. The expansion shock appears as oscillations in the solution near the sonic line, and the correct shock location is determined by removing these oscillations. A second objective was to study the ability of the Euler equation to model separated flow

Federated-Learning-Assisted Failure-Cause Identification in Microwave Networks

Machine Learning (ML) adoption for automated failure management is becoming pervasive in today's communication networks. However, ML-based failure management typically requires that monitoring data is exchanged between network devices, where data is collected, and centralized locations, e.g., servers in data centers, where data is processed. ML algorithms in this centralized location are then trained to learn mappings between collected data and desired outputs, e.g., whether a failure exists, its cause, location, etc. This paradigm poses several challenges to network operators in terms of privacy as well as in terms of computational and communication resource usage, as a massive amount of sensible failure data is transmitted over the network. To overcome such limitations, Federated Learning (FL) can be adopted, which consists of training multiple distributed ML models at multiple decentralized locations (called 'clients') using a limited amount of locally-collected data, and of sharing these trained models to a centralized location (called 'server'), where these models are aggregated and shared again with clients. FL reduces data exchange between clients and a server and improves algorithms' performance thanks to sharing knowledge among different domains (i.e., clients), leveraging different sources of local information in a collaborative environment. In this paper, we focus on applying FL to perform failure-cause identification in microwave networks. The problem is modeled as a multi-class ML classification problem with six pre-defined failure causes. Specifically, using real failure data from an operational microwave network composed of more than 10000 microwave links, we emulate a multi-operator scenario in which one operator has partial knowledge of failure causes during the training phase. Thanks to knowledge sharing, numerical results show that FL achieves up to 72% precision in identifying an unknown particular class concerning traditional ML (non- FL) approaches where training is performed without knowledge sharing

Finite-Element Model for Pretensioned Prestressed Concrete Girders

This paper presents a nonlinear model for pretensioned prestressed concrete girders. The model consists of three main components: a beam-column element that describes the behavior of concrete, a truss element that describes the behavior of prestressing tendons, and a bond element that describes the transfer of stresses between the prestressing tendons and the concrete. The model is based on a two-field mixed formulation, where forces and deformations are both approximated within the element. The nonlinear response of the concrete and tendon components is based on the section discretization into fibers with uniaxial hysteretic material models. The stress transfer mechanism is modeled with a distributed interface element with special bond stress-slip relation. A method for accurately simulating the prestressing operation is presented. Accordingly, a complete nonlinear analysis is performed at the different stages of prestressing. Correlation studies of the proposed model with experimental results of pretensioned specimens are conducted. These studies confirmed the accuracy and efficiency of the model

A Systematic Approach to Justifying Sufficient Confidence in Software Safety Arguments

Safety arguments typically have some weaknesses. To show that the overall confidence in the safety argument is considered acceptable, it is necessary to identify the weaknesses associated with the aspects of a safety argument and supporting evidence, and manage them. Confidence arguments are built to show the existence of sufficient confidence in the developed safety arguments. In this paper, we propose an approach to systematically constructing confidence arguments and identifying the weaknesses of the software safety arguments. The proposed approach is described and illustrated with a running example