Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges

Community assembly of crop-associated fungi is thought to be strongly influenced by deterministic selection exerted by the plant host, rather than stochastic processes. Here we use a simple, sorghum system with abundant sampling to show that stochastic forces (drift or stochastic dispersal) act on fungal community assembly in leaves and roots early in host development and when sorghum is drought stressed, conditions when mycobiomes are small. Unexpectedly, we find no signal for stochasticity when drought stress is relieved, likely due to renewed selection by the host. In our experimental system, the host compartment exerts the strongest effects on mycobiome assembly, followed by the timing of plant development and lastly by plant genotype. Using a dissimilarity-overlap approach, we find a universality in the forces of community assembly of the mycobiomes of the different sorghum compartments and in functional guilds of fungi.

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Fungal community assembly in drought-stressed sorghum shows stochasticity, selection, and universal ecological dynamics

Incipient fault detection with smoothing techniques in statistical process monitoring

We discuss recent advances in understanding of gut bacterial microbiota composition in HIV-infected subjects and comment on controversies. We discuss the putative effects of microbiota shifts on systemic inflammation and HIV disease progression and potential mechanisms, as well as ongoing strategies being developed to modulate the gut microbiota in humans for amelioration of infectious and inflammatory diseases. Lifestyle and behavioral factors relevant to HIV infection studies have independent effects on the microbiota. Microbial metabolism of immunomodulatory compounds and direct immune stimulation by translocation of microbes are putative mechanisms contributing to HIV disease. Fecal microbiota transplantation, microbial enzyme inhibition, phage therapy, and rationally selected probiotic cocktails have emerged as promising strategies for microbiota modulation. Numerous surveys of the HIV gut microbiota matched for lifestyle factors suggest consistent shifts in gut microbiota composition among HIV-infected subjects. Evidence exists for a complex pathogenic role of the gut microbiota in HIV disease progression, warranting further study.

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HIV and the Gut Microbiota: Composition, Consequences, and Avenues for Amelioration.

In order to deeply exploit intrinsic data feature information hidden among the process data, an improved kernel principal component analysis (KPCA) method is proposed, which is referred to as deep principal component analysis (DePCA). Specifically, motivated by the deep learning strategy, we design a hierarchical statistical model structure to extract multilayer data features, including both the linear and nonlinear principal components. To reduce the computation complexity in nonlinear feature extraction, the feature-samples’ selection technique is applied to build the sparse kernel model for DePCA. To integrate the monitoring statistics at each feature layer, Bayesian inference is used to transform the monitoring statistics into fault probabilities, and then, two probability-based DePCA monitoring statistics are constructed by weighting the fault probabilities at all the feature layers. Two case studies involving a simulated nonlinear system and the benchmark Tennessee Eastman process demonstrate the superior fault detection performance of the proposed DePCA method over the traditional KPCA-based methods.

Deep Principal Component Analysis Based on Layerwise Feature Extraction and Its Application to Nonlinear Process Monitoring

An improved weighted recursive PCA algorithm for adaptive fault detection

HIV infection has a tremendous impact on the immune system's proper functioning. The mucosa-associated lymphoid tissue (MALT) is significantly disarrayed during HIV infection. Compositional changes in the gut microbiota might contribute to the mucosal barrier disruption, and consequently to microbial translocation. We performed an observational, cross-sectional study aimed at evaluating changes in the fecal microbiota of HIV-infected individuals from Colombia. We analyzed the fecal microbiota of 37 individuals via 16S rRNA gene sequencing; 25 HIV-infected patients and 12 control (non-infected) individuals, which were similar in body mass index, age, gender balance and socioeconomic status. To the best of our knowledge, no such studies have been conducted in Latin American countries. Given its compositional nature, microbiota data were normalized and transformed using Aitchison's Centered Log-Ratio. Overall, a change in the network structure in HIV-infected patients was revealed by using the SPIEC-EASI MB tool. Genera such as Blautia, Dorea, Yersinia, Escherichia-Shigella complex, Staphylococcus, and Bacteroides were highly relevant in HIV-infected individuals. Differential abundance analysis by both sparse Partial Least Square-Discriminant Analysis and Random Forest identified a greater abundance of Lachnospiraceae-OTU69, Blautia, Dorea, Roseburia, and Erysipelotrichaceae in HIV-infected individuals. We show here, for the first time, a predominantly Lachnospiraceae-based signature in HIV-infected individuals.

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A Lachnospiraceae-dominated bacterial signature in the fecal microbiota of HIV-infected individuals from Colombia, South America

Performance assessment and economic perspectives of integrated PEM fuel cell and PEM electrolyzer for electric power generation.

Dynamic Modeling of Solar Radiation Disturbances Based on a Biomimetic Cloud Shading Model

Given the increase in population and energy demand worldwide, alternative methods have been adopted for the production of hydrogen as a clean energy source. This energy offers an alternative energy source due to its high energy content, and without emissions to the environment. In this bibliometric analysis of energy production using electrolysis and taking into account the different forms of energy production. In this analysis, it was possible to evaluate the research trends based on the literature in the Scopus database during the years 2011–2021. The results showed a growing interest in hydrogen production from electrolysis and other mechanisms, with China being the country with the highest number of publications and the United States TOP in citations. The trend shows that during the first four years of this study (2011–2014), the average number of publications was 74 articles per year, from 2015 to 2021 where the growth is an average of 209 articles, the journal that published the most on this topic is Applied Energy, followed by Energy, contributing with almost 33% in the research area. Lastly, the keyword analysis identified six important research points for future discussions, which we have termed clusters. The study concludes that new perspectives on clean hydrogen energy generation, environmental impacts, and social acceptance could contribute to the positive evolution of the hydrogen energy industry.

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Ivan Portnoy

Papers

An improved weighted recursive PCA algorithm for adaptive fault detection

A Lachnospiraceae-dominated bacterial signature in the fecal microbiota of HIV-infected individuals from Colombia, South America

Performance assessment and economic perspectives of integrated PEM fuel cell and PEM electrolyzer for electric power generation.

Dynamic Modeling of Solar Radiation Disturbances Based on a Biomimetic Cloud Shading Model

Bibliometric Analysis of Global Trends around Hydrogen Production Based on the Scopus Database in the Period 2011–2021