There has been an increasing interest in the conversion of biomass to biofuels, energy, and chemicals due to an increase in meeting environmental demands and price and decrease in the potential availability of crude oil. Among the biofuels postulated as viable alternatives due to their physicochemical characteristics is butanol. Given its high energy content, it is projected as a potential substitute for ordinary gasoline. However, butanol production process through fermentation of lignocellulosic material has shown some disadvantages. Another way of producing butanol is by reduction of volatile fatty acids (from waste streams of organic matters) with hydrogen. An effluent with a high content of water and butanol is obtained. In that sense, thermodynamic interactions make the separation process challenging. On the other hand, current policies and needs have guided the proposals for chemical processes to meet various sustainability metrics, for example, high profit margins and low environmental impact, with inherent safety and robust operation in the presence of disturbances. With this in mind, this work proposes purification schemes to obtain butanol of high purity, from a butanol–water mixture, in the compositions generated by reduction of volatile fatty acids, using pervaporation, pressure swing distillation, and azeotropic distillation. Comparing the results obtained, the pervaporation scheme turned out to be the most promising alternative as it presents reductions in all the “green” indicators (compared to the other purification alternatives) in percentages between 27 and 52%. The general indices for such alternative were 0.0392 ($/kgbutanol), 0.0066 (ecopoints/kgbutanol), 8274, 2.772 × 10–04 (probability/year), and 0.4281 $/kgbutanol regarding the total annual cost, ecological indicator 99, condition number, individual risk, and minimum selling price, respectively.
Speech emotion recognition (SER) refers to the technique of inferring the emotional state of an individual from speech signals. SERs continue to garner interest due to their wide applicability. While the domain is mainly founded on signal processing, machine learning and deep learning methods, generalizing over languages continues to remain a challenge. To improve performance over languages, in this paper we propose a denoising autoencoder with semi-supervision using a continuous metric loss. The novelty of this work lies in our proposal for continuous metric learning, which is among the first proposals on the topic to the best of our knowledge. Furthermore, we contribute labels corresponding to the dimensional model, that were used to evaluate the quality of embedding (the labels will be made available by the time of the publication). We show that the proposed method consistently outperforms the baseline method in terms of the classification accuracy and correlation with respect to the dimensional variables.
Background Artificial intelligence tools have the potential to objectively identify youth in need of mental health care. Speech signals have shown promise as a source for predicting various psychiatric conditions and transdiagnostic symptoms. Objective We designed a study testing the association between obsessive-compulsive disorder (OCD) diagnosis and symptom severity on vocal features in children and adolescents. Here, we present an analysis plan and statistical report for the study to document our a priori hypotheses and increase the robustness of the findings of our planned study. Methods Audio recordings of clinical interviews of 47 children and adolescents with OCD and 17 children and adolescents without a psychiatric diagnosis will be analyzed. Youths were between 8 and 17 years old. We will test the effect of OCD diagnosis on computationally derived scores of vocal activation using ANOVA. To test the effect of OCD severity classifications on the same computationally derived vocal scores, we will perform a logistic regression. Finally, we will attempt to create an improved indicator of OCD severity by refining the model with more relevant labels. Models will be adjusted for age and gender. Model validation strategies are outlined. Results Simulated results are presented. The actual results using real data will be presented in future publications. Conclusions A major strength of this study is that we will include age and gender in our models to increase classification accuracy. A major challenge is the suboptimal quality of the audio recordings, which are representative of in-the-wild data and a large body of recordings collected during other clinical trials. This preregistered analysis plan and statistical report will increase the validity of the interpretations of the upcoming results. International Registered Report Identifier (IRRID) DERR1-10.2196/39613
MotivationBehavioral observations are an important resource in the study and evaluation of psychological phenomena, but it is costly, time-consuming, and susceptible to bias. Thus, we aim to automate coding of human behavior for use in psychotherapy and research with the help of artificial intelligence (AI) tools. Here, we present an analysis plan. Methods Videos of a gold-standard semi-structured diagnostic interview of 25 youth with obsessive-compulsive disorder (OCD) and 12 youth without a psychiatric diagnosis (no-OCD) will be analyzed. Youth were between 8 and 17 years old. Features from the videos will be extracted and used to compute ratings of behavior, which will be compared to ratings of behavior produced by mental health professionals trained to use a specific behavioral coding manual. We will test the effect of OCD diagnosis on the computationally-derived behavior ratings using multivariate analysis of variance (MANOVA). Using the generated features, a binary classification model will be built and used to classify OCD/no-OCD classes. DiscussionHere, we present a pre-defined plan for how data will be pre-processed, analyzed and presented in the publication of results and their interpretation. A challenge for the proposed study is that the AI approach will attempt to derive behavioral ratings based solely on vision, whereas humans use visual, paralinguistic and linguistic cues to rate behavior. Another challenge will be using machine learning models for body and facial movement detection trained primarily on adults and not on children. If the AI tools show promising results, this pre-registered analysis plan may help reduce interpretation bias. Trial registration: ClinicalTrials.gov -H-18010607 Keywords machine learning • visual signals from video data • human behavioral coding • children and adolescents • obsessive-compulsive disorder
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