Optimum design of composite prestressed concrete girder railway bridges

This paper deals with the formulation of design optimisation of pretsressed concrete bridges. The bridge is of a slab-on-girder type, hence modeled as an equivalent orthotropic plate. The whole bridge system is considered as a simply supported right angle plate. Following linear elastic behaviour, the governing fourth order differential equation of the plate for patch load is solved in order to find out load distribution on the girders forming the bridge as well as the deflections and internal forces at critical sections of the whole bridge. The optimisation problem is formulated for various cross sectional geometries including rectangular, symmetrical I, unsymmetrical I, box, T and inverted T sections. The design variables are the main cross sectional dimensions, prestressing force and tendon eccentricity. The objective function comprises the cost of concrete material, formwork and prestressing steel tendons. The constraint functions are set to satisfy design requirements as per British Standards for bridges (BS 5400). Nonlinear optimisation method based on sequential unconstrained minimisation technique (SUMT) is employed to achieve optimum bridge configuration for specific design parameters of span length, concrete compressive strength and railway loading patterns. A purpose built computer program is set up to carry out the solution of the design optimisation problem efficiently in terms of time and effort. A typical example of unsymmetrical I-section having a small bottom flange as compared to the top flange width with composite deck effect is presented. The results show that the total cost increases as the span increases due to the increase of the initial prestressing force. Furthermore, the total cost decreases as the concrete compressive strength increases in spite of the increasing of the prestressing force. This is due to decrease of the overall depth, top and bottom flange widths, hence leading to a smaller girder size. Such finding will encourage engineers to adopt high strength concrete for bridges as it will help reducing not only the initial cost but also the life cycle cost of the bridge over its entire life

Salivary microRNA 155, 146a/b and 203: A pilot study for potentially non-invasive diagnostic biomarkers of periodontitis and diabetes mellitus

Dysregulated expression of MicroRNAs (miRNAs) plays substantial role in the initiation and progression of both diabetes and periodontitis. The aim of the present study was to validate four miRNAs in saliva as potential predictive biomarkers of periodontal disease among patients with and without diabetes mellitus (DM). MiRNAs were extracted from the saliva of 24 adult subjects with DM and 29 healthy controls. Each group was subdivided into periodontally healthy or having periodontitis. In silico analysis identified 4 miRNAs (miRNA 155, 146 a/b and 203) as immune modulators. The expression of miRNAs-146a/b, 155, and 203 was tested using quantitative PCR. The expression levels in the study groups were compared to explore the effect of diabetes on periodontal status and vice versa. In our cohort, the four miRNAs expression were higher in patients with periodontitis and/or diabetes. miRNA-155 was the most reliable predictors of periodontitis among non-diabetics with an optimum cut-off value of < 8.97 with accuracy = 82.6%. MiRNA 146a, on the other hand, was the only reliable predictor of periodontitis among subjects with diabetes with optimum cut-off value of ≥11.04 with accuracy = 86.1%. The results of the present study concluded that MiRNA-146a and miRNA155 in saliva provide reliable, non-invasive, diagnostic and prognostic biomarkers that can be used to monitor periodontal health status among diabetic and non-diabetic patients

Hybrid verification technique for decision-making of self-driving vehicles

The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed

Smart cities and smart tourism: what future do they bring?

We have sought to understand the current state of the art on smart tourism and on smart cities. Furthermore, we have sought to understand community awareness and the will to embrace innovation, as they are decisive factors to acquire base knowledge and overcome barriers in (soon to be) overpopulated cities and for those who are looking for a limited time culture experience - known as tourists. We live in an age where technology is increasingly present in our lives and provides us solutions to societal problems. Problems such as traffic, infrastructure and natural resources management, or even increasing citizens’ participation in governance, bringing them closer to decision-making. The objective is to understand the current level of people’s knowledge about the impact that technologies have on the society in which we live and their perception of the usefulness in solving these same problems. Therefore, an anonymous questionnaire was carried out (176 valid answers were received), as well as a focus group with two experts on the Smart Cities subject. What future is brought by those who live and breathe technology? Are people willing to accept a paradigm shift?This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-031309 entitled “PromoTourVR - Promoting Tourism Destinations with Multisensory Immersive Media”.info:eu-repo/semantics/publishedVersio

The Role of Tricellulin in Epithelial Jamming and Unjamming via Segmentation of Tricellular Junctions

Collective cellular behavior in confluent monolayers supports physiological and pathological processes of epithelial development, regeneration, and carcinogenesis. Here, the attainment of a mature and static tissue configuration or the local reactivation of cell motility involve a dynamic regulation of the junctions established between neighboring cells. Tricellular junctions (tTJs), established at vertexes where three cells meet, are ideally located to control cellular shape and coordinate multicellular movements. However, their function in epithelial tissue dynamic remains poorly defined. To investigate the role of tTJs establishment and maturation in the jamming and unjamming transitions of epithelial monolayers, a semi-automatic image-processing pipeline is developed and validated enabling the unbiased and spatially resolved determination of the tTJ maturity state based on the localization of fluorescent reporters. The software resolves the variation of tTJ maturity accompanying collective transitions during tissue maturation, wound healing, and upon the adaptation to osmolarity changes. Altogether, this work establishes junctional maturity at tricellular contacts as a novel biological descriptor of collective responses in epithelial monolayers

Anti-proteinase 3 anti-neutrophil cytoplasm autoantibodies recapitulate systemic vasculitis in mice with a humanized immune system.

Evidence is lacking for direct pathogenicity of human anti-proteinase-3 (PR3) antibodies in development of systemic vasculitis and granulomatosis with polyangiitis (GPA, Wegener's granulomatosis). Progress in study of these antibodies in rodents has been hampered by lack of PR3 expression on murine neutrophils, and by different Fc-receptor affinities for IgG across species. Therefore, we tested whether human anti-PR3 antibodies can induce acute vasculitis in mice with a human immune system. Chimeric mice were generated by injecting human haematopoietic stem cells into irradiated NOD-scid-IL2Rγ⁻/⁻ mice. Matched chimera mice were treated with human IgG from patients with: anti-PR3 positive renal and lung vasculitis; patients with non-vasculitic renal disease; or healthy controls. Six-days later, 39% of anti-PR3 treated mice had haematuria, compared with none of controls. There was punctate bleeding on the surface of lungs of anti-PR3 treated animals, with histological evidence of vasculitis and haemorrhage. Anti-PR3 treated mice had mild pauci-immune proliferative glomerulonephritis, with infiltration of human and mouse leukocytes. In 3 mice (17%) more severe glomerular injury was present. There were no glomerular changes in controls. Human IgG from patients with anti-PR3 autoantibodies is therefore pathogenic. This model of anti-PR3 antibody-mediated vasculitis may be useful in dissecting mechanisms of microvascular injury

Accurate detection of spontaneous seizures using a generalized linear model with external validation

Objective Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes. Methods We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold-out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures. Results From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave-one-out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held-out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures. Significance This method achieves the highest performance published for seizure detection on multiple independent data sets. This method of seizure detection can be applied to automated EEG analysis pipelines as well as closed loop interventional approaches, and can be especially useful in the setting of research using animals in which there is an increased need for standardization and high-throughput analysis of large number of seizures