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Introduction The low quality of life in heart failure patients is related to low self-care and treatment adherence. Consequently, innovative strategies are needed to improve them. The objective of this work is to determine the effectiveness of the use of a home telemonitoring system to improve the self-care and treatment adherence of heart failure patients. Methods A randomized clinical trial that compares the efficacy of a home telemonitoring system –intervention group versus usual care control group – among heart failure outpatients over a 90-day monitoring period was carried out. The home telemonitoring system consists of an application that collects measurements of different parameters on a daily basis and provides health education to patients. The home telemonitoring system processes data gathered and generates an alert if a risky situation arises. The outcomes observed were significant changes in patients’ self-care (European Heart Failure Self-care Behaviour Scale), treatment adherence (Morisky Modified Scale) and re-hospitalizations over the follow-up period. Results 104 heart failure patients were screened; 40 met the inclusion criteria; only 30 completed the study. After the follow-up, intragroup analysis of the control group indicated a decrease in treatment adherence ( p = 0.02). The mean European Heart Failure Self-care Behaviour Scale overall score indicated an improved self-care in the intervention group patients ( p = 0.03) and a worsened self-care in the control group ( p = 0.04) with a p value of 0.004 in the intergroup analysis. Thanks to the home telemonitoring system alerts, two re-hospitalizations were avoided. Discussion This study demonstrated that the proposed home telemonitoring system improves patient self-care when compared to usual care and has the potential to avoid re-hospitalizations, even considering patients with low literacy levels. Trial Registration: Home Telemonitoring System for Patients with Heart Failure. clinicaltrials.gov Identifier: NCT04071093

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Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speeding up the development of innovative products and processes. Modeling for optimization is proposed as a systematic approach to bias data gathering for iterative policy improvement through experimental design using first-principles models. Designing dynamic experiments that are optimally informative in order to reduce the uncertainty about the optimal operating conditions is addressed by integrating policy iteration based on the Hamilton-Jacobi-Bellman optimality equation with global sensitivity analysis. A conceptual framework for run-to-run convergence of a model-based policy iteration algorithm is proposed. Results obtained in the fed-batch fermentation of penicillin G are presented. The well-known Bajpai and Reuss bioreactor model validated with industrial data is used to increase on a run-to-run basis the amount of penicillin obtained by input policy optimization and selective (re)estimation of relevant model parameters. A remarkable improvement in productivity can be gain using a simple policy structure after only two modeling runs despite initial modeling uncertainty.

Every finite group G acts as an automorphism group of some non-orientable Klein surfaces without boundary. The minimal genus of these surfaces is called the symmetric crosscap number and denoted by [Formula: see text]. It is known that 3 cannot be the symmetric crosscap number of a group. Conversely, it is also known that all integers that do not belong to nine classes modulo 144 are the symmetric crosscap number of some group. Here we obtain infinitely many groups whose symmetric crosscap number belong to each one of six of these classes. This result supports the conjecture that 3 is the unique integer which is not the symmetric crosscap number of a group. On the other hand, there are infinitely many groups with symmetric crosscap number 1 or 2. For g > 2 the number of groups G with [Formula: see text] is finite. The value of [Formula: see text] is known when G belongs to certain families of groups. In particular, if o(G) < 32, [Formula: see text] is known for all except thirteen groups. In this work we obtain it for these groups by means of a one-by-one analysis. Finally we obtain the least genus greater than two for those exceptional groups whose symmetric crosscap number is 1 or 2.

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