406 research outputs found
Hysteretic behavior of dissipative welded fuses for earthquake resistant composite steel and concrete frames
No full textChannel-embedded porous zirconia surfaces to mimic dentine-cementum functionality in dental Implants: Design, production and characterisation
No full textThis study introduces a novel concept of dental implant integration, fibrointegration, which aims to replicate the natural attachment mechanism of teeth. To achieve this, zirconia samples featuring internal channels and one to three external porous layers were developed using Computer-Aided Design/Manufacturing (CAD/CAM) and dipcoating techniques, with the aim of mimicking the functional properties of natural teeth and thereby promoting the fibrointegration of a zirconia root-analogue implant. Results showed that the channel-embedded porous surfaces exhibited enhanced porosity, increased surface roughness, and superhydrophilic behaviour. Furthermore, the presence of microchannels induced a strong capillary effect, facilitating immediate fluid rise and spreading. The coating thickness increased with the number of dips, with the double-layer porous coating achieving an optimal thickness (approximate to 100 mu m), resembling natural cementum and exhibiting superior scratch resistance. These findings highlight the potential of bioinspired, channel-embedded porous zirconia surfaces to promote cell adhesion, guide growth, and stimulate fibrointegration, thereby improving implant stability
Proceedings 14th international workshop on foundations of coordination languages and self-adaptive systems – Preface
No full textUsing Ant Colony Optimization metaheuristic and Dynamic Time Warping for anomaly detection
No full textAnomaly detection using digital signature of network segment with adaptive ARIMA model and Paraconsistent Logic
No full textSeismic assessment and base isolation retrofit of a modern heritage R/C swimming pool
No full textA modern heritage reinforced concrete (R/C) swimming pool building designed by Pier Luigi Nervi is examined in this paper. The distinguishing feature of the structure is represented by the barrel vault-shaped roof, constituted by prefab R/C curved beams with smoothed-V wavy section, typical of the internationally recognized Nervi’s style. The R/C columns and beams supporting the roof and the lateral aisles — originally designed for gravitational loads only and pursuing a minimal size philosophy with respect to the calculated stress states — have very small cross sections. As a consequence, the seismic performance assessment analysis carried out in this study shows general unsafe response conditions of these members. In order to substantially improve the seismic response capacity of the building, as well as to avoid any intrusive intervention on the exposed structural elements, an advanced retrofit strategy is proposed, which consists in the installation of a base isolation system incorporating double curved surface slider devices at the feet of all columns and below the pool tank
An experimental investigation into model-scale installed jet-pylon-wing noise
No full textA model-scale experimental investigation of an installed jet-pylon-wing configuration was conducted at the University of Southampton, with the scope to study the effect a pylon has on noise generation and to clarify its impact on the fluctuating wall-pressure load. The set-up consisted of two single-stream nozzles, a baseline axisymmetric annular nozzle and a partially blocked annular pylon nozzle. The nozzles were tested first isolated and then installed next to a NACA4415 aerofoil 'wing' at a single nozzle-wing position. The jet Mach number was varied between 0.5 <= M-j <= 0.8 and measurements were performed both under static and in-flight ambient flow conditions up to M-f = 0.2. The jet flow-field qualification was carried out using a single-velocity-component hot-wire anemometer probe. The pressure field on the wing surface was investigated using two miniature wall-pressure transducers that were flush-mounted in the streamwise and spanwise directions within the pressure side of the wing. A linear 'flyover' microphone array was used to record the noise radiated to the far field. The unsteady pressure data were analysed in both time and frequency domains using multi-variate statistics, highlighting a far-field noise reduction provided by the presence of the pylon only in the installed case. Furthermore, the wake field generated behind the pylon is seen to significantly modify the wall-pressure fluctuations, particularly at streamwise locations close to the pylon trailing edge
Unsupervised online anomaly detection in Software Defined Network environments
No full textSoftware Defined Networking (SDN) simplifies network management and significantly reduces operational costs. SDN removes the control plane from forwarding devices (e.g., routers and switches) and centralizes this plane in a controller, enabling the management of the network forwarding decisions by programming the control plane with a high-level language. However, its centralized architecture may be compromised by flooding attacks, such as Distributed Denial of Service (DDoS) and portscan. Facing this challenge, we propose an Intrusion Detection System (IDS) based on online clustering to detect attacks in an evolving SDN network taking advantage of the entropy of source and destination IP addresses and ports. Our proposal is focused on avoiding the demand for labeling and previous knowledge to provide a practical and accurate method to address real-life online scenarios. Moreover, our proposal paves the way for a comprehensive analysis by projecting the cluster's structure over the feature space, providing insights on intensity, seasonality, and attack type. Our experiments were carried out with the DenStream algorithm in several databases attacked by DDoS and portscan with different intensities, durations, and overlapping patterns. When comparing DenStream performance to Half-Space-Trees, an accurate online one-class classification algorithm for anomaly detection, it was possible to expose the capacity of our unsupervised proposal, overcoming the one-class solution, and reaching f-measure rates above 99.60%
Effect of Double Layering and Prolonged Application Time on MTBS of Water/Ethanol-based Self-etch Adhesives to Dentin
No full textOne way of possibly improving bond strength is by changing the application mode of self-etch adhesives. The current study evaluated the resin-dentin microtensile bond strength (MTBS) promoted by two- and one-step self-etching adhesives after different bonding application procedures. Flat dentin surfaces from extracted human molars were bonded: 1) according to the manufacturers' instructions, 2) duplicating the number of adhesive coats and 3) doubling the application time of the acidic primers. Two-step (Clearfil SE Bond/SEB and Resulcin AquaPrime/RE) and one-step (Etch & Prime 3.0/EP and One-Up Bond F/OUB) self-etch adhesives were used. Resin-dentin beams were tested in tension at 0.5 mm/minute. Selected debonded beams were observed under scanning electron microscopy (SEM). MTBS data were analyzed by ANOVA and multiple comparison tests (p<0.05). The highest MTBS was always attained with SEB, regardless of the bonding procedure. RE, EP and OUB showed similar MTBS when bonded as per the manufacturers' instructions. The MTBS of OUB increased after doubling the application time and duplicating the adhesive coats. The two-step self-etch adhesives were insensible to changes in bonding application procedures. Attempts to improve the bonding performance of water/ethanol-based self-etching systems by using different bonding application parameters were system-specific and only effectively detected in one-step adhesive systems
Multiple voice disorders in the same individual: Investigating handcrafted features, multi-label classification algorithms, and base-learners
No full textNon-invasive acoustic analyses of voice disorders have been at the forefront of current biomedical research. Usual strategies, essentially based on machine learning (ML) algorithms, commonly classify a subject as being either healthy or pathologically-affected. Nevertheless, the latter state is not always a result of a sole laryngeal issue, i.e., multiple disorders might exist, demanding multi-label classification procedures for effective diagnoses. Consequently, the objective of this paper is to investigate the application of five multi-label classification methods based on problem transformation to play the role of base-learners, i.e., Label Powerset, Binary Relevance, Nested Stacking, Classifier Chains, and Dependent Binary Relevance with Random Forest (RF) and Support Vector Machine (SVM), in addition to a Deep Neural Network (DNN) from an algorithm adaptation method, to detect multiple voice disorders, i.e., Dysphonia, Laryngitis, Reinke's Edema, Vox Senilis, and Central Laryngeal Motion Disorder. Receiving as input three handcrafted features, i.e., signal energy (SE), zero-crossing rates (ZCRs), and signal entropy (SH), which allow for interpretable descriptors in terms of speech analysis, production, and perception, we observed that the DNN-based approach powered with SE-based feature vectors presented the best values of F1-score among the tested methods, i.e., 0.943, as the averaged value from all the balancing scenarios, under Saarbrücken Voice Database (SVD) and considering 20% of balancing rate with Synthetic Minority Over-sampling Technique (SMOTE). Finally, our findings of most false negatives for laryngitis may explain the reason why its detection is a serious issue in speech technology. The results we report provide an original contribution, allowing for the consistent detection of multiple speech pathologies and advancing the state-of-the-art in the field of handcrafted acoustic-based non-invasive diagnosis of voice disorders
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