Numerical Investigation on the Thermo-Mechanical Behavior of HTS Tapes and Experimental Testing on Their Critical Current

This work extends to second generation Rare-Earth Barium-Copper-Oxide ((Re)BCO) tapes an experimental proce- dure previously developed to analyze the impact of double bending at room temperature on the performance of Bismuth-Strontium- Calcium-Copper-Oxide (BSCCO) tapes. The modified procedure is applied to measure the critical current of a commercial (Re)BCO tape subjected to bending around a cylindrical mandrel first on one side, then on the other side, followed by the cooldown to cryogenic temperature. In the bending phase, mandrels of decreasing diame- ter are used to identify the minimum curvature leading to a signif- icant reduction of the tape critical current. Furthermore, a novel finite element model is developed to complement the experimental results. The model simulates the double bending at room tempera- ture, the following straightening of the sample, and its cooldown to cryogenic conditions. The coupled thermo-mechanical numerical model together with the temperature-dependent mechanical prop- erties allow investigating the combination of thermal contraction effects and bending loads in the whole domain of the problem. The experimental and numerical results obtained help to give a better insight in the distribution of the strain and stress components inside the (Re)BCO tape and to evaluate their impact on the conductor electrical performance in relevant operating condition

Klimageographische Inhalte des Geographieunterrichts erfahrungsbasiert verstehen : eine didaktische Rekonstruktion der Passatzirkulation

[no abstract

Off-label long acting injectable antipsychotics in real-world clinical practice: a cross-sectional analysis of prescriptive patterns from the STAR Network DEPOT study

Introduction Information on the off-label use of Long-Acting Injectable (LAI) antipsychotics in the real world is lacking. In this study, we aimed to identify the sociodemographic and clinical features of patients treated with on- vs off-label LAIs and predictors of off-label First- or Second-Generation Antipsychotic (FGA vs. SGA) LAI choice in everyday clinical practice. Method In a naturalistic national cohort of 449 patients who initiated LAI treatment in the STAR Network Depot Study, two groups were identified based on off- or on-label prescriptions. A multivariate logistic regression analysis was used to test several clinically relevant variables and identify those associated with the choice of FGA vs SGA prescription in the off-label group. Results SGA LAIs were more commonly prescribed in everyday practice, without significant differences in their on- and off-label use. Approximately 1 in 4 patients received an off-label prescription. In the off-label group, the most frequent diagnoses were bipolar disorder (67.5%) or any personality disorder (23.7%). FGA vs SGA LAI choice was significantly associated with BPRS thought disorder (OR = 1.22, CI95% 1.04 to 1.43, p = 0.015) and hostility/suspiciousness (OR = 0.83, CI95% 0.71 to 0.97, p = 0.017) dimensions. The likelihood of receiving an SGA LAI grew steadily with the increase of the BPRS thought disturbance score. Conversely, a preference towards prescribing an FGA was observed with higher scores at the BPRS hostility/suspiciousness subscale. Conclusion Our study is the first to identify predictors of FGA vs SGA choice in patients treated with off-label LAI antipsychotics. Demographic characteristics, i.e. age, sex, and substance/alcohol use co-morbidities did not appear to influence the choice towards FGAs or SGAs. Despite a lack of evidence, clinicians tend to favour FGA over SGA LAIs in bipolar or personality disorder patients with relevant hostility. Further research is needed to evaluate treatment adherence and clinical effectiveness of these prescriptive patterns

The Role of Attitudes Toward Medication and Treatment Adherence in the Clinical Response to LAIs: Findings From the STAR Network Depot Study

Background: Long-acting injectable (LAI) antipsychotics are efficacious in managing psychotic symptoms in people affected by severe mental disorders, such as schizophrenia and bipolar disorder. The present study aimed to investigate whether attitude toward treatment and treatment adherence represent predictors of symptoms changes over time. Methods: The STAR Network \u201cDepot Study\u201d was a naturalistic, multicenter, observational, prospective study that enrolled people initiating a LAI without restrictions on diagnosis, clinical severity or setting. Participants from 32 Italian centers were assessed at three time points: baseline, 6-month, and 12-month follow-up. Psychopathological symptoms, attitude toward medication and treatment adherence were measured using the Brief Psychiatric Rating Scale (BPRS), the Drug Attitude Inventory (DAI-10) and the Kemp's 7-point scale, respectively. Linear mixed-effects models were used to evaluate whether attitude toward medication and treatment adherence independently predicted symptoms changes over time. Analyses were conducted on the overall sample and then stratified according to the baseline severity (BPRS < 41 or BPRS 65 41). Results: We included 461 participants of which 276 were males. The majority of participants had received a primary diagnosis of a schizophrenia spectrum disorder (71.80%) and initiated a treatment with a second-generation LAI (69.63%). BPRS, DAI-10, and Kemp's scale scores improved over time. Six linear regressions\u2014conducted considering the outcome and predictors at baseline, 6-month, and 12-month follow-up independently\u2014showed that both DAI-10 and Kemp's scale negatively associated with BPRS scores at the three considered time points. Linear mixed-effects models conducted on the overall sample did not show any significant association between attitude toward medication or treatment adherence and changes in psychiatric symptoms over time. However, after stratification according to baseline severity, we found that both DAI-10 and Kemp's scale negatively predicted changes in BPRS scores at 12-month follow-up regardless of baseline severity. The association at 6-month follow-up was confirmed only in the group with moderate or severe symptoms at baseline. Conclusion: Our findings corroborate the importance of improving the quality of relationship between clinicians and patients. Shared decision making and thorough discussions about benefits and side effects may improve the outcome in patients with severe mental disorders

A combined Finite Element - Artificial Neural Network approach for multiscale modeling of hierarchical structures

In this paper we present a combined finite element (FE) \u2013 artificial neural network (ANN) approach for the multi-scale modeling of cables made of LTS materials. At first different aspects of ANN use in non-linear analysis of hierarchical composite are shown. The possibility to model via ANNs the *homogenized material behavior* starting from a relatively small set of suitable virtual or real experiments is discussed. ANN based procedures can also be exploited in a multi-scale analysis as a tool for the *stress-strain recovery* at the structure lower levels. For example, in SC cables a map of the strain state at the wire and filament scale is needed. The related unsmearing procedure is numerically very costly. An ANN, acting in recall mode during the execution of the homogenization loops, allows for a considerably improved computational efficiency. Secondly, the *cable mechanical behavior* is analyzed, namely the influence of the hierarchical helix geometry on the stiffness of the cable. It is proven how the stiffness matrix of these structures is different from the usual matrix of Euler-Bernoulli beams. Finally, a significant application for the *design of cables* is shown. ANNs can be used to investigate the dependence of the stiffness coefficients upon the twist pitches of the multi-level helixes. The final goal of this research is to substitute, at each level, a bundle of wires with a single equivalent wire, having the characteristics computed on the bundle of the previous scale. The presented hybrid finite element\u2013artificial neural network approach is exploited to this aim, showing that suitably trained ANNs can replace the module that usually provides the stiffness matrix in an FE code. In the end, some real cable examples are shown, where results obtained via the FE method are compared with those calculated by an ANN-FE procedure

Identification of contamination flux in a domain of porous media as an inverse problem solved with Artificial Neural Networks

After a short introduction and main issues associated with inverse problem, three examples are chosen to illustrate the application of Artificial Neural Networks in the inverse problems solution. For a steady state convection problem, assuming given concentration field values in a few measurement points and values of hydraulic head in the same piezometers, the source of the concentration and its intensity are deduced using Artificial Neural Networks (ANNs). The same method is used for identification of diffusivity vector. To illustrate the reliability of the procedure, the case of randomly perturbed data is presented. The main conclusion states that the soft method seems to be very automatic and convenient in solving a large family of inverse problems