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This study addresses the environmental problem of PET plastic through in silico bioprospecting for the identification and experimental validation of novel PET degrading eukaryotes through the in silico bioprospectingI of PETases, employing a methodology that combines Hidden Markov Models (HMMs), clustering techniques, molecular docking, and dynamic simulations. A total of 424 putative PETase sequences were identified from 219 eukaryotic organisms, highlighting six sequences with low affinity energies. The Aspergillus luchuensis sequence showed the lowest Gibbs free energy and exhibited stability at different temperatures in molecular dynamics assays. Experimental validation, through a plate clearance assay and HPLC, confirmed PETase activity in three wild-type fungal strains, with A. luchuensis showing the highest efficiency. The results obtained demonstrate the effectiveness of combining computational and experimental approaches as proof of concept to discover and validate eukaryotes with PET-degrading capabilities opening new perspectives for the sustainable management of this type of waste and contributing to its environmental mitigation.

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We introduce a model that can be used for the description of the distribution of species when there is scarcity of data, based on our previous work (Ballesteros et al. J Math Biol 85(4):31, 2022). We address challenges in modeling species that are seldom observed in nature, for example species included in The International Union for Conservation of Nature's Red List of Threatened Species (IUCN 2023). We introduce a general method and test it using a case study of a near threatened species of amphibians called Plectrohyla Guatemalensis (see IUCN 2023) in a region of the UNESCO natural reserve "Tacaná Volcano", in the border between Mexico and Guatemala. Since threatened species are difficult to find in nature, collected data can be extremely reduced. This produces a mathematical problem in the sense that the usual modeling in terms of Markov random fields representing individuals associated to locations in a grid generates artificial clusters around the observations, which are unreasonable. We propose a different approach in which our random variables describe yearly averages of expectation values of the number of individuals instead of individuals (and they take values on a compact interval). Our approach takes advantage of intuitive insights from environmental properties: in nature individuals are attracted or repulsed by specific features (Ballesteros et al. J Math Biol 85(4):31, 2022). Drawing inspiration from quantum mechanics, we incorporate quantum Hamiltonians into classical statistical mechanics (i.e. Gibbs measures or Markov random fields). The equilibrium between spreading and attractive/repulsive forces governs the behavior of the species, expressed through a global control problem involving an energy operator.

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This paper extends the concept of metrics based on the Bayesian information criterion (BIC), to achieve strongly consistent estimation of partition Markov models (PMMs). We introduce a set of metrics drawn from the family of model selection criteria known as efficient determination criteria (EDC). This generalization extends the range of options available in BIC for penalizing the number of model parameters. We formally specify the relationship that determines how EDC works when selecting a model based on a threshold associated with the metric. Furthermore, we improve the penalty options within EDC, identifying the penalty ln(ln(n)) as a viable choice that maintains the strongly consistent estimation of a PMM. To demonstrate the utility of these new metrics, we apply them to the modeling of three DNA sequences of dengue virus type 3, endemic in Brazil in 2023.

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Information theory explains how systems encode and transmit information. This article examines the neuronal system, which processes information via neurons that react to stimuli and transmit electrical signals. Specifically, we focus on transfer entropy to measure the flow of information between sequences and explore its use in determining effective neuronal connectivity. We analyze the causal relationships between two discrete time series, X:=Xt:t∈Z and Y:=Yt:t∈Z, which take values in binary alphabets. When the bivariate process (X,Y) is a jointly stationary ergodic variable-length Markov chain with memory no larger than k, we demonstrate that the null hypothesis of the test-no causal influence-requires a zero transfer entropy rate. The plug-in estimator for this function is identified with the test statistic of the log-likelihood ratios. Since under the null hypothesis, this estimator follows an asymptotic chi-squared distribution, it facilitates the calculation of p-values when applied to empirical data. The efficacy of the hypothesis test is illustrated with data simulated from a neuronal network model, characterized by stochastic neurons with variable-length memory. The test results identify biologically relevant information, validating the underlying theory and highlighting the applicability of the method in understanding effective connectivity between neurons.

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INTRODUCTION: Predicting response to inhaled corticosteroids (ICSs) + long-acting ß2-agonist (LABA) by previously detecting the presence of Arg16Gly ADRB2 genotype is a strategy that could reduce and optimize the management of asthmatic patients. There is a need for economic evaluations to facilitate the implementation of such tests. This research aims to evaluate the cost-effectiveness of Arg16Gly ADRB2 screening in children with asthma in Colombia. METHODS: From the perspective of a third-party payer, we conducted a cost-effectiveness analysis to determine the cost and quality-adjusted life-years (QALYs) of genotype-driven asthma prescribing based on the Arg16Gly ADRB2 genotype versus current treatment based on no genetic testing. Using four state-transition models, we estimate cost and QALYs employing micro-simulation modeling with a time horizon of 10 years and a cycle length of 1 week. Cost-effectiveness was assessed at a willingness-to-pay (WTP) value of US$5180. RESULTS: The mean incremental cost of strategy genetic testing versus no genetic testing is US$ -6809. The mean incremental benefit of strategy genetic testing is 16 QALYs. The incremental net monetary benefit of strategic genetic testing versus no genetic testing is US$ 88,893. Genetic testing is the strategy with the highest expected net benefit. The outcomes derived from our primary analysis remained robust when subjected to variations in all underlying assumptions and parameter values. CONCLUSION: Genetic testing of Arg16Gly ADRB2 is a cost-effective strategy to address asthma management in asthmatic children requiring ICS+LABA. This result should encourage the generation of more evidence and the incorporation of such evidence into clinical practice guidelines for pediatric asthma.

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INTRODUCTION: Recent evidence indicates that Maternal Supplementation with Long-Chain n-3 Fatty Acids During Pregnancy Substantially Mitigates Offspring's Asthma. Adding information regarding its cost-utility will undoubtedly allow its adoption, or not, in clinical practice guidelines. This research aimed to determine the cost-utility of LCPUFA supplementation in the third trimester of pregnancy to reduce the risk of wheezing and asthma in infants in Colombia. METHODS: A Markov model was formulated to estimate the cost and quality-adjusted life-years (QALYs) attributed to individuals with severe asthma in Colombia, with a time horizon of five years and a cycle length of two weeks. Probabilistic sensitivity analysis and a value of information (VOI) analysis were conducted to evaluate the uncertainties in the case base. Cost-utility was assessed at a willingness-to-pay (WTP) value of US$5180. All costs were adjusted to 2021 with a 5% annual discounting rate for cost and QALYs. RESULTS: The mean incremental cost of LCPUFA supplementation versus no supplementation was US-43.65. The mean incremental benefit of LCPUFA supplementation versus no supplementation was 0.074 QALY. The incremental cost-utility ratio was estimated at US$590.68 per QALY. The outcomes derived from our primary analysis remained robust when subjected to variations in all underlying assumptions and parameter values. CONCLUSION: Supplementation strategy supplementation with long-chain n-3 fatty acids during pregnancy is cost-effective in reducing the risk of developing asthma during childhood in Colombia.

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The present work intends to discuss parameter estimation and statistical analysis in adsorption. The Langmuir and Tóth isotherm models are compared for a set of carbon dioxide adsorption data on 13X zeolite from literature at different temperatures: 303, 323, 373, and 423 K. Statistical analyses were performed under frequentist and Bayesian perspectives. Under the frequentist statistical view, parameters were estimated using Maximum Likelihood estimation (MLE). Statistical analyses of parameters were performed by confidence regions in terms of elliptical approximation and likelihood region, while the evaluation of models was performed by chi-square statistics. The results showed that, for these nonlinear models, the elliptical region offers a poor approximation of the parameter estimates' confidence region, especially for the most correlated parameter pairs. Additionally, the four-parameter Tóth's equation yields less correlated parameters than the three-parameter Langmuir model. From a Bayesian perspective, the Markov chain Monte Carlo (MCMC) technique facilitated the reconstruction of the probability density functions of parameters as well as enabled the propagation of parametric uncertainties in the model responses. Finally, the accurate assessment of experimental uncertainty significantly influences the evaluation of models and their respective parameters.

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Add-on therapy with tiotropium was cost-effective when added to usual care in patients who remain uncontrolled despite treatment with medium or high-dose ICS/LABA in a middle-income country. BACKGROUND: A significant proportion of asthma patients remain uncontrolled despite inhaled corticosteroids and long-acting beta-agonists. Some add-on therapies, such as tiotropium bromide, have been recommended for this subgroup of patients. This study aimed to assess the cost-effectiveness of tiotropium as an add-on therapy to inhaled corticosteroids and long-acting b2 agonists for patients with severe asthma. METHODS: A probabilistic Markov model was created to estimate the cost and quality-adjusted life-years (QALYs) of patients with severe asthma in Colombia. Total costs and QALYs of two interventions include standard therapy with inhaled corticosteroids and long-acting bronchodilators versus add-on therapy with tiotropium. Multiple sensitivity analyses were conducted. Cost-effectiveness was evaluated at a willingness-to-pay value of $5180. RESULTS: The expected incremental cost per QALY (ICER) is estimated at US$-2637.59. There is a probability of 0.77 that tiotropium + ICS + LABA is more cost-effective than ICS + LABA at a threshold of US$5180 per QALY. The strategy with the highest expected net benefit is Tiotropium, with an expected net benefit of US$800. Our base-case results were robust to parameter variations in the deterministic sensitivity analyses. CONCLUSION: Add-on therapy with tiotropium was cost-effective when added to usual care in patients who remain uncontrolled despite treatment with medium or high-dose inhaled corticosteroids and long-acting bronchodilators. Our study provides evidence that should be used by decision-makers to improve clinical practice guidelines and should be replicated to validate their results in other middle-income countries.

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Resumo Objetivo Caracterizar as teses e dissertações brasileiras que usaram métodos de análise de custo-efetividade em tecnologias de saúde e os respectivos modelos de decisão. Métodos Este estudo documental e quantitativo foi realizado em fevereiro de 2023 com busca no Catálogo de Teses e Dissertações (Coordenação de Aperfeiçoamento de Pessoas de Nível Superior) no período 2011-2022 usando o termo "custo-efetividade". Após a aplicação dos critérios de inclusão e exclusão, 81 produções foram incluídas no estudo e então foi realizada a análise quantitativa das teses e dissertações. Resultados A maioria das produções era de teses de doutorado (54,3%) defendidas em 2018 (22,2%) na região sudeste do Brasil (60,5%) e conduzidas em ambiente hospitalar (42,0%). Foi adotado o modelo de Árvore de Decisão (37,0%) de Markov (28,4%), seguido de outros modelos. Além disso, a maioria das teses seguiu as recomendações de boas práticas na análise econômica de custo-efetividade: elas atenderam grande parte (55,6%), minimamente (25,9%) e parcialmente (18,5%) os itens necessários e recomendações. Conclusão Os estudos de custo-efetividade realizados nas teses analisadas usaram o modelo de Árvore de Decisão e seguiram as recomendações de boas práticas em seu desenvolvimento.

Resumen Objetivo Caracterizar las tesis de doctorado y de maestría brasileñas que utilizaron métodos de análisis de costo-efectividad en tecnologías de salud y los respectivos modelos de decisión. Métodos Este estudio documental y cuantitativo fue realizado en febrero de 2023 con búsqueda en el Catálogo de Tesis de Doctorado y de Maestría (Coordinación de Perfeccionamiento de Personas de Nivel Superior) durante el período 2011-2022 usando el término "costo-efectividad". Después de aplicar los criterios de inclusión y exclusión, se incluyeron 81 producciones en el estudio y luego se realizó el análisis cuantitativo de las tesis de doctorado y de maestría. Resultados La mayoría de las producciones fue de tesis de doctorado (54,3 %) defendidas en 2018 (22,2 %) en la región Sudeste de Brasil (60,5 %) y llevadas a cabo en ambiente hospitalario (42,0 %). Se adoptó el modelo de Árbol de Decisión (37,0 %) de Markov (28,4 %), seguido por otros modelos. Además, la mayoría de las tesis siguió las recomendaciones de buenas prácticas en el análisis económico de costo-efectividad: cumplieron gran parte (55,6 %), mínimamente (25,9 %) y parcialmente (18,5 %) los ítems necesarios y recomendaciones. Conclusión Los estudios de costo-efectividad realizados en las tesis analizadas utilizaron el modelo de Árbol de Decisión y siguieron las recomendaciones de buenas prácticas en su desarrollo.

Abstract Objective To characterize Brazilian theses and dissertations that used cost-effectiveness analysis methods in health technologies and the respective decision models. Method This documentary and quantitative study was carried out in February 2023 with a search in the Catalog of Theses and Dissertations (Coordination for the Improvement of Higher Education Persons) in the period 2011-2022 using the term "cost-effectiveness". After applying the inclusion and exclusion criteria, 81 studies were included in the study and then a quantitative analysis of theses and dissertations was carried out. Results The majority of studies were doctoral theses (54.3%), defended in 2018 (22.2%) in southeastern Brazil (60.5%) and conducted in a hospital setting (42.0%). The Markov Decision Tree model (37.0%) (28.4%) was adopted, followed by other models. Furthermore, most theses followed the recommendations of good practices in economic cost-effectiveness analysis: they met a largely (55.6%), minimally (25.9%) and partially (18.5%) of the necessary items and recommendations. Conclusion The cost-effectiveness studies carried out in the theses analyzed used the Decision Tree model and followed best practice recommendations in their development.

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Introduction: The management of patient flows influences hospital performance, and Markov chains are used to model them, helping to plan capacity, allocate resources and schedule admissions. Objective: To evaluate the scientific activity related to the application of Markov chains in the improvement of patient flows in hospital institutions. Methods: An observational, descriptive and retrospective bibliometric study was applied; the ScienceDirect database was used. The strategy was divided into three: evolution of the application of Markov chains in hospitals; specifically for management; and for the improvement of patient flows; 520, 331 and 9 documents were located, respectively. Results: Research articles predominated, which accounted for 87.91 % of the scientific production. A total of 58.24 % of the articles were in the area of decision science. An analysis of the journals shows that 85.71 % were located in quartile 1, of which the one with the highest production was the European Journal of Operational Research. Four main lines of research were identified: resource optimization; capacity planning; policy development for activity sequencing; and modeling for improvement and decision making. Conclusions: Future research should focus on collaborative analysis, country-specific productivity and generalization to other international impact databases.

Introducción: La gestión de flujos de pacientes influye en el rendimiento hospitalario. Para su modelación, se implementan las cadenas de Markov que contribuyen a planificar la capacidad, asignar recursos y programar ingresos. Objetivo: Evaluar la actividad científica relacionada con la aplicación de las cadenas de Markov en la mejora de los flujos de pacientes en instituciones hospitalarias. Métodos: Se aplicó un estudio bibliométrico de tipo observacional, descriptivo y retrospectivo. Se utilizó la base de datos ScienceDirect. La estrategia se dividió en tres: evolución de la aplicación de las cadenas de Markov en hospitales, específicamente para la gestión, y para la mejora de los flujos de pacientes. Se localizaron 520, 331 y 9 documentos respectivamente. Resultados: Predominaron los artículos de investigación, los cuales representaron el 87,91 % de la producción científica. El 58,24 % de los artículos se encontraron en el área de la ciencia de la decisión. Un análisis de las revistas evidencia que el 85,71 % se encontró ubicado en el cuartil 1; de ellas, la de mayor producción fue European Journal of Operational Research. Se identificaron cuatro líneas de investigación principales: optimización de recursos, planificación de la capacidad, desarrollo de políticas para la secuenciación de las actividades, y modelación en función de la mejora y toma de decisiones. Conclusiones: Las investigaciones futuras deben centrarse en el análisis de la colaboración, la productividad en función del país y la generalización en otras bases de datos de impacto internacional.

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Objetivo: Desenvolver uma análise de custo-utilidade da implementação do teste farmacogenético como uma ferramenta adicional para orientar a escolha do melhor tratamento medicamentoso para indivíduos com depressão. Métodos: Para a realização desta análise, criou-se um modelo analítico de decisão baseado em um modelo de Markov. A avaliação foi realizada sob a perspectiva do Sistema de Saúde Suplementar brasileiro, com horizonte temporal de 10 anos, incluindo custos médicos diretos e custos da tecnologia utilizada, além de ter como comparador o tratamento empírico tradicional para a depressão. As probabilidades de transição foram obtidas por meio de análise da literatura disponível. Também foram realizadas análises de sensibilidade probabilística e univariada. Adicionalmente, foi realizada uma avaliação sob a perspectiva da sociedade, incluindo os custos de tratamento medicamentoso realizados pelos pacientes. Resultados: De acordo com a análise realizada, o emprego do teste farmacogenético como guia do tratamento para depressão mostrou-se favorável, proporcionando economia de -R$ 3.439,97 por paciente e aumento de 0,39 QALY ao longo do horizonte temporal. Assim, evidencia-se uma economia significativa a favor do teste farmacogenético, correspondendo a -R$ 8.776,78 por QALY salvo. Além disso, a robustez do modelo foi comprovada por meio das análises de sensibilidade. No cenário sob perspectiva da sociedade, o resultado foi ainda mais favorável, proporcionando economia de -R$ 9.381,49 por paciente e aumento de 0,39 QALY, correspondendo a -R$ 23.936,05 por QALY salvo. Conclusão: Os resultados encontrados neste estudo demonstraram que o uso de testes farmacogenéticos no tratamento da depressão é economicamente vantajoso, com aumento no valor de QALY e redução nos custos médicos diretos, em comparação ao tratamento empírico tradicional. Essa descoberta alinha-se à tendência atual de personalização no cuidado da saúde mental, sugerindo implicações práticas na reavaliação de protocolos, com potencial incorporação dos testes farmacogenéticos como padrão de cuidado.

Objective: To evaluate the cost-utility of pharmacogenetic testing incorporation as an additional tool in guiding the selection of optimal drug treatments for individuals with depression. Methods: A decision analytical model was created based on the Markov model for this analysis. The evaluation was conducted from the perspective of the Brazilian Supplementary Health System, with a time horizon of 10 years. The study included direct medical and technology costs and a comparison with traditional empirical treatment for depression was performed. Transition probabilities were derived from an analysis of available literature. Probabilistic and univariate sensitivity analyses were also carried out. Additionally, an evaluation was conducted from the perspective of Society, including the costs of drug treatment carried out by patients. Results: The application of pharmacogenetic testing as a guide for depression treatment demonstrated favorable outcomes, yielding savings of -R$ 3,439.97 per patient and an increase of 0.39 QALY over the specified time frame. Thus, significant savings were evident, corresponding to -R$ 8,776.78 per QALY saved. The sensitivity analyses confirmed the model's robustness. In the Society's perspective scenario, the outcome was even more favorable, resulting in savings of -R$ 9,381.49 per patient and a 0.39 increase in QALYs, equivalent to -R$ 23,936.05 per QALY saved. Conclusion: The study findings reveal that incorporating harmacogenetic tests in depression treatment offers economic benefits, evidenced by an increase in QALY value and a decrease in direct medical costs compared to conventional empirical treatment. This aligns with the ongoing trend towards personalized mental health care, implying practical considerations for protocol reassessment and the possible integration of pharmacogenetic tests as a standard of care.

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We derive asymptotic formulae in the limit when population size N tends to infinity for mean fixation times (conditional and unconditional) in a population with two types of individuals, A and B, governed by the Moran process. We consider only the case in which the fitness of the two types do not depend on the population frequencies. Our results start with the important cases in which the initial condition is a single individual of any type, but we also consider the initial condition of a fraction [Formula: see text] of A individuals, where x is kept fixed and the total population size tends to infinity. In the cases covered by Antal and Scheuring (Bull Math Biol 68(8):1923-1944, 2006), i.e. conditional fixation times for a single individual of any type, it will turn out that our formulae are much more accurate than the ones they found. As quoted, our results include other situations not treated by them. An interesting and counterintuitive consequence of our results on mean conditional fixation times is the following. Suppose that a population consists initially of fitter individuals at fraction x and less fit individuals at a fraction [Formula: see text]. If population size N is large enough, then in the average the fixation of the less fit individuals is faster (provided it occurs) than fixation of the fitter individuals, even if x is close to 1, i.e. fitter individuals are the majority.

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This paper characterizes the legislators voting behavior in the Colombian Senate 2010-2014, by implementing a one-dimensional standard Bayesian ideal point estimator via Markov chain Monte Carlo algorithms. Our main goal is to retrieve the political preferences of legislators from their roll-call voting records, which individualizes the electoral behavior of the legislative chamber. Furthermore, we conclude about the nature of the latent trait underlying the deputies voting decisions and the legislators locations in political space. Finally, we also offer several methodological and theoretical tools to guide the analysis of nominal voting data in the context of unbalanced parliaments (multi-party systems), taking as reference the particular case of the Colombian Senate.

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The main objective of this paper is to propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a setup under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate comprise the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly more straightforward and tractable than semi-Markov models with a similar level of generality. Based on stochastic stability, we derive a sufficient condition for a shrinking epidemic regarding the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil, and assess the effect of different stabilising strategies in the latter setting. Results suggest that the proposed approach can curb the epidemic with various occupation rate levels if the mitigation is timely.

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Protein-protein association events are involved in many physiological and pathological processes. Cataract disease is a pathology that manifests protein aggregation of crystallins. ß-Crystallins are present in a high proportion in the eye lens. Therefore, the structural study of the dimerization properties of crystallins can shed light on the first stages of protein aggregation. In the present work, we examine the protein-protein association profiles of the human ßB2-crystallin by employing extensive coarse-grained molecular dynamics (CG-MD) and the Markov state analysis. Interestingly, our results clearly show important changes in the protein dimerization kinetics between wt-HßB2C and the deamidated systems. The two systems show dimeric conformations. However, the association and dissociation rates are very different. Our results show that the deamidated system can associate faster and dissociate slower than the wt- HßB2C. The deamidated system is in a slightly opened conformation with the Greek-key motifs well folded, suggesting that a complete unfolding of the protein is not required for aggregation. Our results describe the first stages of crystallin aggregation due to post-translational modifications.

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The study aims to determine SARS-CoV-2 RNA in sewage of Cancun wastewater treatment plants, the main touristic destination of Mexico, and to estimate the infected persons during the sampling period. SARS-CoV-2 RNA traces were detected in the inlet of the five plants during almost all the sampling months. However, there is no presence of SARS-CoV-2 RNA traces in the effluent of the five WWTPs during the study period. ANOVA analysis showed differences in the concentrations of RNA traces of SARS-CoV-2 between the sample dates, but no differences were found from one WWTP to another. Estimated infected individuals by Markov chain Monte Carlo simulation are higher (between 77% and 91%) than the cases reported by the health authority. Wastewater monitoring and the estimation of infected individuals are a helpful tool, because estimation provides early warning signs on how broadly SARS-CoV-2 is circulating in the city, and led to the authorities to take measures wisely. PRACTITIONER POINTS: There is no presence of SARS-CoV-2 RNA traces in the effluent of the facilities, suggesting the effectiveness of treatment. Surveillance of viral RNA concentrations at treatment plants revealed presence in the influent of five plants Estimated infected individuals by MCMC simulation are higher than cases reported by health authority Environmental surveillance approach in wastewater influent is helpful to identify the clusters and to take informed decisions.

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Introduction: In the past years, robotic lower-limb exoskeletons have become a powerful tool to help clinicians improve the rehabilitation process of patients who have suffered from neurological disorders, such as stroke, by applying intensive and repetitive training. However, active subject participation is considered to be an important feature to promote neuroplasticity during gait training. To this end, the present study presents the performance assessment of the AGoRA exoskeleton, a stance-controlled wearable device designed to assist overground walking by unilaterally actuating the knee and hip joints. Methods: The exoskeleton's control approach relies on an admittance controller, that varies the system impedance according to the gait phase detected through an adaptive method based on a hidden Markov model. This strategy seeks to comply with the assistance-as-needed rationale, i.e., an assistive device should only intervene when the patient is in need by applying Human-Robot interaction (HRI). As a proof of concept of such a control strategy, a pilot study comparing three experimental conditions (i.e., unassisted, transparent mode, and stance control mode) was carried out to evaluate the exoskeleton's short-term effects on the overground gait pattern of healthy subjects. Gait spatiotemporal parameters and lower-limb kinematics were captured using a 3D-motion analysis system Vicon during the walking trials. Results and Discussion: By having found only significant differences between the actuated conditions and the unassisted condition in terms of gait velocity (ρ = 0.048) and knee flexion (ρ ≤ 0.001), the performance of the AGoRA exoskeleton seems to be comparable to those identified in previous studies found in the literature. This outcome also suggests that future efforts should focus on the improvement of the fastening system in pursuit of kinematic compatibility and enhanced compliance.

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We studied the dolomite modified using an ultrasound bath and its application in phosphate removal. The modification was applied to improve the physicochemical properties of the dolomite and then to enhance its suitability as an adsorbent solid. The settings for analyzing the adsorbent modification were bath temperature and sonication time. The modified dolomite was characterized by electron microscopy, N2 adsorption/desorption, pore size, and X-ray diffraction. To grasp the pollutant's adsorption mechanism more precisely, we used experimental research and mathematical model analysis. Design of Experiments was conducted to determine the ideal circumstances. In addition, the Bayesian method of Markov Chain Monte Carlo was used to estimate the isotherm and kinetic model parameters. A thermodynamic study was done to investigate the adsorption mechanism. Results show that the surface area of the modified dolomite was greater, enhancing its adsorption properties. To remove more than 90% of the phosphate, the optimal operational parameters for the adsorption were pH 9, 1.77 g of adsorbent mass, and 55 minutes of contact time. The pseudo-first-order, Redlich-Peterson and Sips models presented a good fit to the experimental data. Thermodynamics suggested a spontaneous and endothermic process. The mechanism suggested that physisorption and chemisorption could be involved in phosphate removal.

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The purpose of this article is to study new non-Archimedean pseudo-differential operators whose symbols are determined from the behavior of two functions defined on the p-adic numbers. Thanks to the characteristics of our symbols, we can find connections between these operators and new types of non-homogeneous differential equations, Feller semigroups, contraction semigroups and strong Markov processes.

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Early detection of the COVID-19 virus is an important task for controlling the spread of the pandemic. Imaging techniques such as chest X-ray are relatively inexpensive and accessible, but its interpretation requires expert knowledge to evaluate the disease severity. Several approaches for automatic COVID-19 detection using deep learning techniques have been proposed. While most approaches show high accuracy on the COVID-19 detection task, there is not enough evidence on external evaluation for this technique. Furthermore, data scarcity and sampling biases make difficult to properly evaluate model predictions. In this paper, we propose stochastic gradient Langevin dynamics (SGLD) to take into account the model uncertainty. Four different deep learning architectures are trained using SGLD and compared to their baselines using stochastic gradient descent. The model uncertainties are also evaluated according to their convergence properties and the leave-one-out predictive densities. The proposed approach is able to reduce overconfidence of the baseline estimators while also retaining predictive accuracy for the best-performing cases.