A new genetic algorithm framework based on Expected Annual Loss for optimizing seismic retrofitting in reinforced concrete frame structures

The design of seismic retrofitting for existing reinforced concrete frame structures concerns the determination of the position and the arrangement of reinforcements. Currently, this design practice is mainly based on trial-and-error attempts and engineers' experience, without a formal implementation of cost/performance optimization. Though, the implementation of this intervention is associated with significant costs, noticeable downtimes, and elevated invasiveness. This paper presents a new genetic algorithm-based framework for the optimization of two different retrofitting techniques (FRP column wrapping and concentric steel braces) that aims at minimizing costs considering indirectly the lessening of expected annual values. The feasibility of each tentative solution is controlled by the outcomes of static pushover analyses in the framework of the N2 method, achieved by a 3D fiber-section model implemented in OpenSees. Application of the framework in a realistic case study structure will show that the sustainability of retrofitting intervention is achievable by employing artificial intelligence aided structural design

A novel genetic algorithm-based optimization framework for minimizing seismic retrofitting interventions costs in existing masonry structures

The pressing necessity of enhancing the seismic safety of existing masonry structures in earthquake-prone areas has led, in recent years, the research to propose a vast amount of new retrofitting techniques. However, retrofitting interventions are generally associated with important costs. Currently, there are no formal methods to optimize these interventions thus, their design is entrusted only to engineers' intuition. This paper presents a novel optimization framework aimed at the minimization of seismic retrofitting-related costs by an optimal placement (topological optimization) of reinforced plasters in masonry structures. In the proposed framework a 3D equivalent masonry model implemented in OpenSees is handled by a genetic algorithm developed in MATLAB® routine that iterates reinforcement configurations to match the optimal solution. The feasibility of each solution is controlled by the outcomes of a seismic static equivalent analysis by controlling the safety check of masonry walls with respect to both flexural and shear collapse. It is also shown, through a case study, that the proposed approach is efficient to pinpoint optimal retrofitting configurations, significantly reducing invasiveness and downtime

Cost and EAL based optimization for seismic reinforcement of RC structures

In this paper, a new genetic algorithm-based framework aimed at efficiently design multiple seismic retrofitting interventions is proposed. The algorithm focuses on the minimization of retrofitting intervention costs of reinforced concrete (RC) frame structures. The feasibility of each tentative solution is assessed by considering in an indirect way the expected annual loss (EAL), this evaluation is performed by referring to different limit states whose repairing costs are expressed as a percentage of reconstruction costs and evaluating the respective mean annual frequency of exceedance. As the EAL takes into account the overall structural performances, to involves both serviceability and ultimate limit states, two different seismic retrofitting techniques are considered. In particular, FRP wrapping of columns is employed to increase the ductility of RC elements managing life safety and collapse limit state demands. On the other hand, steel bracings are used to increase the global stiffness of the structure and mainly increase operational and damage limit states performances. The optimization procedure is carried out by the novel genetic algorithm-based framework developed in Matlab® that is connected to a 3D RC frame fiber-section model implemented in OpenSees. For both the retrofitting systems, the algorithm provides their position within the structure (topological optimization) and their sizing. Results will show that seismic retrofitting can be effectively designed to increase the overall structural safety by efficaciously optimizing the intervention costs

Autonomic responses to emotional linguistic stimuli and amplitude of low-frequency fluctuations predict outcome after severe brain injury

An accurate prognosis on the outcome of brain-injured patients with disorders of consciousness (DOC) remains a significant challenge, especially in the acute stage. In this study, we applied a multiple-technique approach to provide accurate predictions on functional outcome after 6 months in 15 acute DOC patients. Electrophysiological correlates of implicit cognitive processing of verbal stimuli and data-driven voxel-wise resting-state fMRI signals, such as the fractional amplitude of low-frequency fluctuations (fALFF), were employed. Event-related electrodermal activity, an index of autonomic activation, was recorded in response to emotional words and pseudo-words at baseline (T0). On the same day, patients also underwent a resting-state fMRI scan. Six months later (T1), patients were classified as outcome-negative and outcome-positive using a standard functional outcome scale. We then revisited the baseline measures to test their predictive power for the functional outcome measured at T1. We found that only outcome-positive patients had an earlier, higher autonomic response for words compared to pseudo-words, a pattern similar to that of healthy awake controls. Furthermore, DOC patients showed reduced fALFF in the posterior cingulate cortex (PCC), a brain region that contributes to autonomic regulation and awareness. The event-related electrodermal marker of residual cognitive functioning was found to have a significant correlation with residual local neuronal activity in the PCC. We propose that a residual autonomic response to cognitively salient stimuli, together with a preserved resting-state activity in the PCC, can provide a useful prognostic index in acute DOC

Frequency and duration of SARS-CoV-2 shedding in oral fluid samples assessed by a modified commercial rapid molecular assay

Background: RT-PCR on nasopharyngeal (NPS)/oropharyngeal swabs is the gold standard for diagnosis of SARS-CoV-2 infection and viral load monitoring. Oral fluid (OF) is an alternate clinical sample, easy and safer to collect and could be useful for COVID-19 diagnosis, monitoring viral load and shedding. Methods: Optimal assay conditions and analytical sensitivity were established for the commercial Simplexa™ COVID-19 Direct assay adapted to OF matrix. The assay was used to test 337 OF and NPS specimens collected in parallel from 164 hospitalized patients; 50 bronchoalveolar lavage (BAL) specimens from a subgroup of severe COVID-19 cases were also analysed. Results: Using Simplexa™ COVID-19 Direct on OF matrix, 100% analytical detection down to 1 TCID50/mL (corresponding to 4 × 103 copies (cp)/mL) was observed. No crossreaction with other viruses transmitted through the respiratory toute was observed. Parallel testing of 337 OF and NPS samples showed highly concordant results (κ = 0.831; 95 % CI = 0.771–0.891), and high correlation of Ct values (r = 0.921; p < 0.0001). High concordance and elevated correlation was observed also between OF and BAL. Prolonged viral RNA shedding was observed up to 100 days from symptoms onset (DSO), with 32% and 29% positivity observed in OF and NPS samples, respectively, collected between 60 and 100 DSO. Conclusions: Simplexa™ COVID-19 Direct assays on OF have high sensitivity and specificity to detect SARS-CoV-2 RNA and provide an alternative to NPS for diagnosis and monitoring SARS-CoV-2 shedding

Frequency and Duration of SARS-CoV-2 Shedding in Oral Fluid Samples Assessed by a Modified Commercial Rapid Molecular Assay

Background: RT-PCR on nasopharyngeal (NPS)/oropharyngeal swabs is the gold standard for diagnosis of SARS-CoV-2 infection and viral load monitoring. Oral fluid (OF) is an alternate clinical sample, easy and safer to collect and could be useful for COVID-19 diagnosis, monitoring viral load and shedding. Methods: Optimal assay conditions and analytical sensitivity were established for the commercial Simplexa™ COVID-19 Direct assay adapted to OF matrix. The assay was used to test 337 OF and NPS specimens collected in parallel from 164 hospitalized patients; 50 bronchoalveolar lavage (BAL) specimens from a subgroup of severe COVID-19 cases were also analysed. Results: Using Simplexa™ COVID-19 Direct on OF matrix, 100% analytical detection down to 1 TCID50/mL (corresponding to 4 × 103 copies (cp)/mL) was observed. No crossreaction with other viruses transmitted through the respiratory toute was observed. Parallel testing of 337 OF and NPS samples showed highly concordant results (κ = 0.831; 95 % CI = 0.771–0.891), and high correlation of Ct values (r = 0.921; p < 0.0001). High concordance and elevated correlation was observed also between OF and BAL. Prolonged viral RNA shedding was observed up to 100 days from symptoms onset (DSO), with 32% and 29% positivity observed in OF and NPS samples, respectively, collected between 60 and 100 DSO. Conclusions: Simplexa™ COVID-19 Direct assays on OF have high sensitivity and specificity to detect SARS-CoV-2 RNA and provide an alternative to NPS for diagnosis and monitoring SARS-CoV-2 shedding

Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness

Abstract: Cognitively impaired and spared patient subgroups were identified in psychosis and depression, and in clinical high-risk for psychosis (CHR). Studies suggest differences in underlying brain structural and functional characteristics. It is unclear whether cognitive subgroups are transdiagnostic phenomena in early stages of psychotic and affective disorder which can be validated on the neural level. Patients with recent-onset psychosis (ROP; N = 140; female = 54), recent-onset depression (ROD; N = 130; female = 73), CHR (N = 128; female = 61) and healthy controls (HC; N = 270; female = 165) were recruited through the multi-site study PRONIA. The transdiagnostic sample and individual study groups were clustered into subgroups based on their performance in eight cognitive domains and characterized by gray matter volume (sMRI) and resting-state functional connectivity (rsFC) using support vector machine (SVM) classification. We identified an impaired subgroup (N ROP = 79, N ROD = 30, N CHR = 37) showing cognitive impairment in executive functioning, working memory, processing speed and verbal learning (all p < 0.001). A spared subgroup (N ROP = 61, N ROD = 100, N CHR = 91) performed comparable to HC. Single-disease subgroups indicated that cognitive impairment is stronger pronounced in impaired ROP compared to impaired ROD and CHR. Subgroups in ROP and ROD showed specific symptom- and functioning-patterns. rsFC showed superior accuracy compared to sMRI in differentiating transdiagnostic subgroups from HC (BACimpaired = 58.5%; BACspared = 61.7%, both: p < 0.01). Cognitive findings were validated in the PRONIA replication sample (N = 409). Individual cognitive subgroups in ROP, ROD and CHR are more informative than transdiagnostic subgroups as they map onto individual cognitive impairment and specific functioning- and symptom-patterns which show limited overlap in sMRI and rsFC. Clinical trial registry name: German Clinical Trials Register (DRKS). Clinical trial registry URL: https://www.drks.de/drks_web/ . Clinical trial registry number: DRKS00005042

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life

Waveform Modelling for the Laser Interferometer Space Antenna

LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectacular variety of interesting new sources abound: from millions of ultra-compact binaries in our Galaxy, to mergers of massive black holes at cosmological distances; from the beginnings of inspirals that will venture into the ground-based detectors' view to the death spiral of compact objects into massive black holes, and many sources in between. Central to realising LISA's discovery potential are waveform models, the theoretical and phenomenological predictions of the pattern of gravitational waves that these sources emit. This white paper is presented on behalf of the Waveform Working Group for the LISA Consortium. It provides a review of the current state of waveform models for LISA sources, and describes the significant challenges that must yet be overcome.Comment: 239 pages, 11 figures, white paper from the LISA Consortium Waveform Working Group, invited for submission to Living Reviews in Relativity, updated with comments from communit