Evaluation of the Influence of Different Grades of Reinforcing Steel on the Seismic Performance of Concrete reinforced Frame Structures with Nonlinear Static Analysis

In this investigation, the elasto-plastic behavior and the seismic performance of concrete reinforced frame structures reinforced are evaluated by applying the Pushover method. This evaluation is done on several cases: with high ductility steel (Grade 40), conventional steel (Grade 60) and high strength steel (Grade 75). For the previous, the capacity curve graph obtained from the displacement coefficient method was used to measure the capacity of the structure. In addition, the performance of the structure for different levels of seismic design are evaluated with the resulting values of ductility and rigidity of each case. The results showed that reinforcing a structure with a Grade 40 reinforcing steel increases the energy dissipation capacity, and if reinforced with a Grade 75 reinforcing steel increases the strength capacity in the structure. Finally, the comparative result of the various cases are presented to demonstrate the influence of reinforcing steel on the plastic behavior of concrete reinforced frame structures

Analysis of seismic bidirectionality on response of reinforced concrete structures with irregularities of l-shaped plan and soft story

The seismic design of buildings is usually performed using one-way analysis for each of main axes independently. However, seismic events have fairly random behaviour and impose bidirectional solicitations on structures. In this work, the study of the response in structures subjects to earthquake loads with irregularity of l-shaped plan and soft story is carried out. For this, the linear time-story analysis (LTHA) of these has been carried out imposing seismic solicitations in two orthogonal directions. Thus, the structural response with incidence angle variations of 10 is obtained and compared with the response derived from the unidirectional analysis. Variations of up to 50% and 72% are obtained for model structures with l-shaped plan and soft story respectively

Correction coefficients of distortion and vibration period for buildings due to soil-structure interaction

The present research analyzed the influence of the soil structure interaction (SSI) in buildings, varying geotechnical parameters and height, considering 3 international codes. The responses obtained from the structures taking into account the SSI, were compared with the responses of fixed-base buildings, being the main control variables: the period and the drift. It was determined that the estimated range in which the period of the structure increases is from 30 to 98%, demonstrating the influence of considering soil flexibility. Due to the variability of the responses obtained, an adjustment factor is proposed to predict said amplification of the control variables, depending on the height of the building and the ground

System derived spatial-temporal CNN for high-density fNIRS BCI

An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN

Deep learning-enabled high-speed, multi-parameter diffuse optical tomography

SIGNIFICANCE: Frequency-domain diffuse optical tomography (FD-DOT) could enhance clinical breast tumor characterization. However, conventional diffuse optical tomography (DOT) image reconstruction algorithms require case-by-case expert tuning and are too computationally intensive to provide feedback during a scan. Deep learning (DL) algorithms front-load computational and tuning costs, enabling high-speed, high-fidelity FD-DOT.AIM: We aim to demonstrate a simultaneous reconstruction of three-dimensional absorption and reduced scattering coefficients using DL-FD-DOT, with a view toward real-time imaging with a handheld probe.APPROACH: A DL model was trained to solve the DOT inverse problem using a realistically simulated FD-DOT dataset emulating a handheld probe for human breast imaging and tested using both synthetic and experimental data.RESULTS: Over a test set of 300 simulated tissue phantoms for absorption and scattering reconstructions, the DL-DOT model reduced the root mean square error by 12 % ± 40 % and 23 % ± 40 % , increased the spatial similarity by 17 % ± 17 % and 9 % ± 15 % , increased the anomaly contrast accuracy by 9 % ± 9 % ( μ a ), and reduced the crosstalk by 5 % ± 18 % and 7 % ± 11 % , respectively, compared with model-based tomography. The average reconstruction time was reduced from 3.8 min to 0.02 s for a single reconstruction. The model was successfully verified using two tumor-emulating optical phantoms. CONCLUSIONS: There is clinical potential for real-time functional imaging of human breast tissue using DL and FD-DOT.</p

Compactness in Banach space theory - selected problems

We list a number of problems in several topics related to compactness in nonseparable Banach spaces. Namely, about the Hilbertian ball in its weak topology, spaces of continuous functions on Eberlein compacta, WCG Banach spaces, Valdivia compacta and Radon-Nikod\'{y}m compacta

"Contemplating the next maneuver": functional neuroimaging reveals intraoperative decision-making strategies

OBJECTIVE: To investigate differences in the quality, confidence, and consistency of intraoperative surgical decision making (DM) and using functional neuroimaging expose decision systems that operators use. SUMMARY BACKGROUND DATA: Novices are hypothesized to use conscious analysis (effortful DM) leading to activation across the dorsolateral prefrontal cortex, whereas experts are expected to use unconscious automation (habitual DM) in which decisions are recognition-primed and prefrontal cortex independent. METHODS: A total of 22 subjects (10 medical student novices, 7 residents, and 5 attendings) reviewed simulated laparoscopic cholecystectomy videos, determined the next safest operative maneuver upon video termination (10 s), and reported decision confidence. Video paradigms either declared ("primed") or withheld ("unprimed") the next operative maneuver. Simultaneously, changes in cortical oxygenated hemoglobin and deoxygenated hemoglobin inferring prefrontal activation were recorded using Optical Topography. Decision confidence, consistency (primed vs unprimed), and quality (script concordance) were assessed. RESULTS: Attendings and residents were significantly more certain (P < 0.001), and decision quality was superior (script concordance: attendings = 90%, residents = 78.3%, and novices = 53.3%). Decision consistency was significantly superior in experts (P < 0.001) and residents (P < 0.05) than novices (P = 0.183). During unprimed DM, novices showed significant activation of the dorsolateral prefrontal cortex, whereas this activation pattern was not observed among residents and attendings. During primed DM, significant activation was not observed in any group. CONCLUSIONS: Expert DM is characterized by improved quality, consistency, and confidence. The findings imply attendings use a habitual decision system, whereas novices use an effortful approach under uncertainty. In the presence of operative cues (primes), novices disengage the prefrontal cortex and seem to accept the observed operative decision as correct