Analysis of Causal Relationships for Nutrient Removal of Activated Sludge Process Based on Structural Equation Modeling Approaches

The removal process of activated sludge in sewage treatment plants is very nonlinear, and removal performance has a complex causal relationship depending on environmental factors, influent load, and operating factors. In this study, how causal relationships are expressed in collected data was identified by structural equation modeling. First, path modeling was attempted as a preliminary step in structural equation model (SEM) construction and, as a result, the nutrient-removal mechanism could not be sufficiently represented as a direct causal relationship between measured variables. However, as a result of the deduced SEMs for effluent total nitrogen (T-N) and total phosphorus (T–P) concentrations, accompanied by exploratory factor analysis to extract latent variables, a causal network was formed that describes the direct or indirect effect of the latent factors and measured variables. Hereby, this study suggests that it is possible to construct an SEM explaining the nutrient-removal mechanism of the activated-sludge process with latent variables. Moreover, nonlinear features embedded in the mechanism could be represented by SEM, which is a model based on linearity, by including causal relations and variables that were not derived by path analysis. This attempt to model the direct and indirect causalities of the process could enhance the understanding of the process, and help decision making such as changing the driving conditions that would be required

Analysis of Causal Relationships for Nutrient Removal of Activated Sludge Process Based on Structural Equation Modeling Approaches

The removal process of activated sludge in sewage treatment plants is very nonlinear, and removal performance has a complex causal relationship depending on environmental factors, influent load, and operating factors. In this study, how causal relationships are expressed in collected data was identified by structural equation modeling. First, path modeling was attempted as a preliminary step in structural equation model (SEM) construction and, as a result, the nutrient-removal mechanism could not be sufficiently represented as a direct causal relationship between measured variables. However, as a result of the deduced SEMs for effluent total nitrogen (T-N) and total phosphorus (T–P) concentrations, accompanied by exploratory factor analysis to extract latent variables, a causal network was formed that describes the direct or indirect effect of the latent factors and measured variables. Hereby, this study suggests that it is possible to construct an SEM explaining the nutrient-removal mechanism of the activated-sludge process with latent variables. Moreover, nonlinear features embedded in the mechanism could be represented by SEM, which is a model based on linearity, by including causal relations and variables that were not derived by path analysis. This attempt to model the direct and indirect causalities of the process could enhance the understanding of the process, and help decision making such as changing the driving conditions that would be required.</jats:p

RuO2 nanocluster as a 4-in-1 electrocatalyst for hydrogen and oxygen electrochemistry

Partially hydrous RuO2 nanocluster embedded in a carbon matrix (x-RuO2@C with x = hydration degree = 0.27 or 0.27@C) is presented as a bifunctional catalyst for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) for water splitting. Symmetric water electrolyzers based on 0.27-RuO2@C for both electrodes showed smaller potential gaps between HER and OER at pH 0, pH 14 and even pH 7 than conventional asymmetric electrolyzers based on two different catalysts (Pt/C || Ir/C) that have been known as the best catalysts for HER and OER respectively. Moreover, 0.27-RuO2@C showed another bifunctional electroactivity for fuel cell electrochemistry involving hydrogen oxidation reaction (HOR) and oxygen reduction reaction (ORR) that are the backward reactions of HER and OER respectively. Pt-level HOR electroactivity was obtained from 0.27-RuO2@C, while its ORR activity was inferior to that of Pt with 200 mV higher overpotential required. The tetra-functionality of 0.27-RuO2@C showed the possibility of realizing single-catalyst regenerative fuel cells

Discriminating between Terminal- and Non-Terminal Respiratory Unit-Type Lung Adenocarcinoma Based on MicroRNA Profiles.

Lung adenocarcinomas can be classified into terminal respiratory unit (TRU) and non-TRU types. We previously reported that non-TRU-type adenocarcinoma has unique clinical and morphological features as compared to the TRU type. Here we investigated whether micro (mi)RNA expression profiles can be used to distinguish between these two subtypes of lung adenocarcinoma. The expression of 1205 human and 144 human viral miRNAs was analyzed in TRU- and non-TRU-type lung adenocarcinoma samples (n = 4 each) by microarray. Results were validated by quantitative real-time (qRT-)PCR and in situ hybridization. A comparison of miRNA profiles revealed 29 miRNAs that were differentially expressed between TRU- and non-TRU adenocarcinoma types. Specifically, hsa-miR-494 and ebv-miR-BART19 were up regulated by > 5-fold, whereas hsa-miR-551b was down regulated by > 5-fold in the non-TRU relative to the TRU type. The miRNA signature was confirmed by qRT-PCR analysis using an independent set of paired adenocarcinoma (non-TRU-type, n = 21 and TRU-type, n = 12) and normal tissue samples. Non-TRU samples showed increased expression of miR-494 (p = 0.033) and ebv-miR-BART19 (p = 0.001) as compared to TRU-type samples. Both miRNAs were weakly expressed in the TRU type but strongly expressed in the non-TRU type. Neither subtype showed miR-551b expression. TRU- and non-TRU-type adenocarcinomas have distinct miRNA expression profiles, suggesting that tumorigenesis in lung adenocarcinoma occur via different pathways

Electrochemically Induced Crystallite Alignment of Lithium Manganese Oxide to Improve Lithium Insertion Kinetics for Dye-Sensitized Photorechargeable Batteries

The insertion of lithium into lithium manganese oxide spinel (LiMn2O4 (LMO) to Li2Mn2O4 (L2MO)) was used to store light energy as a form of chemical energy in a dye-sensitized photorechargeable battery (DSPB). Herein, we investigate the effect of crystallite size of LMO on DSPB performance. The crystallite size of graphene-wrapped sub-micrometer-sized LMO (LMO@Gn) was tuned electrochemically from 26 to 34 nm via repeated LMO-to-L2MO transitions. The different crystallite orientations in LMO@Gn particles were ordered in an identical direction by an electric stimulus. The LMO@Gn having a 34 nm crystallite size (L-34 and L-34*) improved DSPB performances in dim light, compared with the smaller-crystallite LMO@Gn (L-26). The overall energy efficiency (eta(overall)) of 13.2%, higher than ever reported, was achieved by adopting the fully crystallized and structure-stabilized LMO@Gn (L-34*) for DSPB. The phase transition between the cubic and tetragonal forms during the LMO-to-L2MO reaction was suspected to be responsible for the structural ordering