Evolution of Mental Health Online Strategies from the Early Stage of the COVID-19 Pandemic to the Pre-Vaccination Period

Background: The COVID-19 outbreak and its consequent quarantines, containment measures and social distancing imposed by authorities worldwide has caused an increase of psychological responses such as depression, abuse use, insomnia, post-traumatic stress symptoms, anger, anxiety, grief or confusion. This situation has fostered the implementation of new strategies like remote therapy to maintain the continuity of mental health (MH) care. Several international organizations (World Health Organization, the United Nations and the American Psychiatric Association) are focused on addressing the recovery from the COVID-19 pandemic, ensuring availability of emergency MH services, strengthening social cohesion, reducing the isolation, and promoting psychological support, as well as protecting human rights. This research aims to assess the evolution of online MH strategies and recommendations to cope with psychological impact of COVID-19 since early stages of the pandemic to pre-vaccination period. Methods: A sample of 24 online documents was analysed to assess their structural evolution from April 2020 to June 2021. Each document was analysed separately by two researchers. The questionnaire, developed by Almeda et al. (2021), was used to assess the content of these documents. This instrument consists of 39 items organized in seven domains (D) D1) Symptoms, D2) Mental disorders, D3) COVID-19 general information, D4) MH strategies and MH topics, D5) MH strategies and MH-related topics, D6) MH recommendations and MH topics and D7) MH recommendations and MH-related topics. To assess the structural evolution of the document in the selected periods, a T-Student for related samples was used. Results: Statistically significant differences with a negligible effect size were found in D1+D2 domains (t(23) = 3, p = 0.006, d = 0.18). An increasing concern on bereavement, sleeping problems and loneliness symptoms has been highlighted together with a greater interest on schizophrenia, bipolar disorder, chronic pain and obsessivecompulsive disorder. Statistically significant differences with negligible size effect were also found when the questions related to COVID-19 have been analysed (D3-D7;t(23) = 2.24, p = 0.035, d = 0.19). All COVID-19 information items have increased (D3) as also happened in most of the MH strategies and MH-related topics (80%;D5). In D7, D4 and D6 domains, a small increase in the information provided is highlighted. From an international point of view, England, Australia, New Zealand and Mexico are the countries with the highest rate of improvement in their strategies, followed by Ireland and Spain with small improvements. Finally, the information in the online documents of the rest of the countries remains stable. Conclusions: Online MH strategies and recommendations have improved during the pandemic period only in specific countries, especially in Mexico. Due to the high rate of mortality, bereavement has played a key role in the set of symptoms included. Globally speaking, the analysed countries are making efforts to address MH remotely, as it is evidenced in their online strategies

Control Predictivo basado en Modelo Neuroborroso de un Autoclave Industrial

XXVIII JORNADAS DE AUTOMÁTICA. 05/09/2007. HuelvaEn este artículo se presenta un modelo neuroborroso de la temperatura de un autoclave industrial, usado para estrategias basadas en Control Predictivo No-Lineal, permitiendo un bajo coste computacional, las cuales son aptas para implementarse en un autómata programable (PLC) de gama media, muy común en la industria. El modelo se ha validado con datos experimentales obtenidos en una planta real.Ministerio de Eduación y Ciencia DPI2004-07444-C04-0

A robust multi-model predictive controller for distributed parameter systems

12 páginas, 6 figurasIn this work a robust nonlinear model predictive controller for nonlinear convection–diffusion-reaction systems is presented. The controller makes use of a collection of reduced order approximations of the plant (models) reconstructed on-line by projection methods on proper orthogonal decomposition (POD) basis functions. The model selection and model update step is based on a sufficient condition that determines the maximum allowable process-model mismatch to guarantee stable control performance despite process uncertainty and disturbances. Proofs on the existence of a sequence of feasible approximations and control stability are given. Since plant approximations are built on-line based on actual measurements, the proposed controller can be interpreted as a multi-model nonlinear predictive control (MMPC). The performance of the MMPC strategy is illustrated by simulation experiments on a problem that involves reactant concentration control of a tubular reactor with recycle.This work has been also partially founded by the Spanish Ministry of Science and Innovation (SMART-QC, AGL2008-05267-C03-01), the FP7 CAFE project (KBBE-2007-1-212754), the Project PTDC/EQU-ESI/73458/2006 from the Portuguese Foundation for Science and Technology and PI grant 07/IN.1/I1838 by Science Foundation Ireland. Also, the authors acknowledge financial support received by a collaborative grant GRICES-CSIC.Peer reviewe

A robust multi-model predictive controller for distributed parameter systems

12 páginas, 6 figurasIn this work a robust nonlinear model predictive controller for nonlinear convection–diffusion-reaction systems is presented. The controller makes use of a collection of reduced order approximations of the plant (models) reconstructed on-line by projection methods on proper orthogonal decomposition (POD) basis functions. The model selection and model update step is based on a sufficient condition that determines the maximum allowable process-model mismatch to guarantee stable control performance despite process uncertainty and disturbances. Proofs on the existence of a sequence of feasible approximations and control stability are given. Since plant approximations are built on-line based on actual measurements, the proposed controller can be interpreted as a multi-model nonlinear predictive control (MMPC). The performance of the MMPC strategy is illustrated by simulation experiments on a problem that involves reactant concentration control of a tubular reactor with recycle.This work has been also partially founded by the Spanish Ministry of Science and Innovation (SMART-QC, AGL2008-05267-C03-01), the FP7 CAFE project (KBBE-2007-1-212754), the Project PTDC/EQU-ESI/73458/2006 from the Portuguese Foundation for Science and Technology and PI grant 07/IN.1/I1838 by Science Foundation Ireland. Also, the authors acknowledge financial support received by a collaborative grant GRICES-CSIC.Peer reviewe

A model for the biochemical degradation of inosine monophosphate in hake (Merluccius merluccius)

7 páginas, 3 tablas, 3 figuras, 1 apéndiceATP-derived products are typically used as early indicators of fish quality loss during storage. In this work, we explore different biochemical routes that are potentially relevant in contributing to nucleotide degradation in hake (Merluccius merluccius). A major motivation of this study is to get more insight on the biochemical degradation mechanisms of nucleotide catabolites in hake muscle at fish storage and transport conditions. This requires the identification of its relevant pathways. To that purpose, different degradation routes proposed in the literature are considered and a mathematical model for the degradation process is derived. First order kinetics are assumed for all the reactions and temperature dependence is taken into account through the Arrhenius equation. Unknown model parameters, namely activation energies and pre-exponential Arrhenius coefficients, are estimated via fitting to experimental data. From the estimation results, relevant routes are identified. The kinetic study is performed on sterile fish juice to avoid coupling with microbial degradation mechanisms or possible interferences of the food matrix that might hide biochemical interactions. The proposed scheme adequately describes biochemical changes in nucleotide catabolites under variable temperature profiles. It also reveals a pathway which at least seems relevant for nucleotide degradation in hakeThe authors acknowledge financial support from the Spanish Ministry of Science and Innovation (Projects ISFORQUALITY AGL2012-39951-C02-01, PIE 201230E042 and RESISTANCE DPI2014-54085-JIN)Peer reviewe

Towards predictive models in food engineering: Parameter estimation dos and don'ts

1 póster.-- 29th EFFoST International Conference, 10-12 November 2015, Athens, GreeceRigorous, physics based, modeling is at the core of computer aided food process engineering. Models often require the values of some, typically unknown, parameters (thermo-physical properties, kinetic constants, etc). Therefore, parameter estimation from experimental data is critical to achieve desired model predictive properties. Unfortunately, it must be admitted that often experiment design and modeling are fully separated tasks: experiments are not designed for the purpose of modeling and models are usually derived without paying especial attention to available experimental data or experimentation capabilities. When, at some point, the parameter estimation problem is put on the table, modelers use available experimental data to ``manually'' tune the unknown parameters. This results in inaccurate parameter estimates, usually experiment dependent, with the implications this has in model validation. This work takes a new look into the parameter estimation problem in food process modeling. First the common pitfalls in parameter estimation are described. Second we present the theoretical background and the numerical techniques to define a parameter estimation protocol to iteratively improve model predictive capabilities. This protocol includes: reduced order modeling, structural and practical identifiability analyses, data fitting with global optimization methods and optimal experimental design. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods. The model was experimentally validated in the IIM-CSIC pilot plantThe authors acknowledge financial support from the EU (Project SPECTRAFISH), Spanish Ministry of Science and Innovation (Project ISFORQUALITY) and CSIC (Project CONTROLA)Peer reviewe

Triplets of Quasars at high redshift I: Photometric data

We have conducted an optical and infrared imaging in the neighbourhoods of 4 triplets of quasars. R, z', J and Ks images were obtained with MOSAIC II and ISPI at Cerro Tololo Interamerican Observatory. Accurate relative photometry and astrometry were obtained from these images for subsequent use in deriving photometric redshifts. We analyzed the homogeneity and depth of the photometric catalog by comparing with results coming from the literature. The good agreement shows that our magnitudes are reliable to study large scale structure reaching limiting magnitudes of R = 24.5, z' = 22.5, J = 20.5 and Ks = 19.0. With this catalog we can study the neighbourhoods of the triplets of quasars searching for galaxy overdensities such as groups and galaxy clusters.Comment: The paper contains 12 figures and 3 table

Use of a decision support system for benchmarking analysis and organizational improvement of regional mental health care:Efficiency, stability and entropy assessment of the mental health ecosystem of Gipuzkoa (Basque Country, Spain)

Decision support systems are appropriate tools for guiding policymaking processes, especially in mental health (MH), where care provision should be delivered in a balanced and integrated way. This study aims to develop an analytical process for (i) assessing the performance of an MH ecosystem and (ii) identifying benchmark and target-for-improvement catchment areas. MH provision (inpatient, day and outpatient types of care) was analysed in the Mental Health Network of Gipuzkoa (Osakidetza, Basque Country, Spain) using a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence. The unit of analysis was the 13 catchment areas defined by a reference MH centre. MH ecosystem performance was assessed by the following indicators: relative technical efficiency, stability and entropy to guide organizational interventions. Globally, the MH system of Gipuzkoa showed high efficiency scores in each main type of care (inpatient, day and outpatient), but it can be considered unstable (small changes can have relevant impacts on MH provision and performance). Both benchmark and target-for-improvement areas were identified and described. This article provides a guide for evidence-informed decision-making and policy design to improve the continuity of MH care after inpatient discharges. The findings show that it is crucial to design interventions and strategies (i) considering the characteristics of the area to be improved and (ii) assessing the potential impact on the performance of the global MH care ecosystem. For performance improvement, it is recommended to reduce admissions and readmissions for inpatient care, increase workforce capacity and utilization of day care services and increase the availability of outpatient care services

Modelling mental healthcare improvement in highly integrated care systems: the case of the Basque Country (Spain)

Currently there is growing interest in providing integrated mental health care between hospital (acute residential care) and community-based services (… and other health systems). Mental health systems are complex due to the high disorder prevalence, socio-economic burden, stigma associated, and high gap of unmet population needs. Mental health can be considered an ecosystem related to, at least, physical health and social services ones. Decision support systems are robust tools for guiding and improving planning and management of health ecosystems by integrating methods like Bayesian networks. These models identify critical variables, domains and constructs and their corresponding causal relationships. The objective of this research is to design an integrated and integral theoretical Bayesian network for guiding mental health planning and management, and in consequence, improving mental health care delivery