Notes

Existing Knowledge of Molecular Phase Splitting up in Chromosomes.
Therefore, the EHA process was recommended for efficient biomethane conversion in co-digestion of CS and CM. This study investigated the removal efficiency of micro- and nanoplastics (180 nm-125 μm) during drinking water treatment, particularly coagulation/flocculation combined with sedimentation (CFS) and granular filtration under ordinary working conditions at water treatment plants (WTPs). It also studied the interactions between biofilms and microplastics and the consequential impact on treatment efficiency. Generally, CFS was not sufficient to remove micro- and nanoplastics. The sedimentation rate of clean plastics was lower than 2.0% for all different sizes of plastic particles with coagulant Al2(SO4)3. Even with the addition of coagulant aid (PolyDADMAC), the highest removal was only 13.6% for 45-53 μm of particles. In contrast, granular filtration was much more effective at filtering out micro- and nanoplastics, from 86.9% to nearly complete removal (99.9% for particles larger than 100 μm). However, there existed a critical size (10-20 μm) where a significant lower removal (86.9%) was observed. Biofilms were easily formed on microplastics. In addition, biofilm formation significantly increased the removal efficiency of CFS treatment from less then 2.0% to 16.5%. This work provides new knowledge to better understand the fate and transport of emerging micro- and nanoplastic pollutants during drinking water treatment, which is of increasing concern due to the potential human exposure to micro- and nanoplastics in drinking water. Forskolin Complete degradation of azo dye has always been a challenge due to the refractory nature of azo dye. An innovative hybrid system, constructed wetland-microbial fuel cell (CW-MFC) was developed for simultaneous azo dye remediation and energy recovery. This study investigated the effect of circuit connection and the influence of azo dye molecular structures on the degradation rate of azo dye and bioelectricity generation. The closed circuit system exhibited higher chemical oxygen demand (COD) removal and decolourisation efficiencies compared to the open circuit system. The wastewater treatment performances of different operating systems were ranked in the decreasing order of CW-MFC (R1 planted-closed circuit) > MFC (R2 plant-free-closed circuit) > CW (R1 planted-open circuit) > bioreactor (R2 plant-free-open circuit). The highest decolourisation rate was achieved by Acid Red 18 (AR18), 96%, followed by Acid Orange 7 (AO7), 67% and Congo Red (CR), 60%. The voltage outputs of the three azo dyes were ranked in the decreasing order of AR18 > AO7 > CR. The results disclosed that the decolourisation performance was significantly influenced by the azo dye structure and the moieties at the proximity of azo bond; the naphthol type azo dye with a lower number of azo bond and more electron-withdrawing groups could cause azo bond to be more electrophilic and more reductive for decolourisation. Moreover, the degradation pathway of AR18, AO7 and CR were elucidated based on the respective dye intermediate products identified through UV-Vis spectrophotometry, high-performance liquid chromatography (HPLC), and gas chromatograph-mass spectrometer (GC-MS) analyses. The CW-MFC system demonstrated high capability of decolouring azo dyes at the anaerobic anodic region and further mineralising dye intermediates at the aerobic cathodic region to less harmful or non-toxic products. The combustion and emission characteristics of sludge, biomass (rice husk, peanut husk, pine sawdust) and their blends were studied by non-isothermal thermogravimetry, tube oven method and SEM. The results showed that the combustion process of sludge, biomass and their blends could be divided into three stages the evaporation of water, the release and combustion of volatile, combustion of char. Combustion characteristics of 80% biomass +20% sludge were improved compared with the theoretical value. NOx and SO2 emission of 80% biomass +20% sludge was lower than that of sludge combustion. There was synergistic effect between sludge/biomass combustion. Comparing combustion and emission characteristics comprehensively, the mixed fuels of 80% biomass +20% sludge could promote combustion and reduce emissions, which was a better method to treat municipal sludge. Seasonal allergic rhinitis (AR), also known as hay fever, is a common respiratory condition brought on by a range of environmental triggers. Previous work has characterised the relationships between community-level AR symptoms collected using mobile apps in two Australian cities, Canberra and Melbourne, and various environmental covariates including pollen. Here, we build on these relationships by assessing the skill of models that provide a next-day forecast of an individual's risk of developing AR and that nowcast ambient grass pollen concentrations using crowd-sourced AR symptoms as a predictor. Categorical grass pollen forecasts (low/moderate/high) were made based on binning mean daily symptom scores by corresponding categories. Models for an individual's risk were constructed by forward variable selection, considering environmental, demographic, behaviour and health-related inputs, with non-linear responses permitted. Proportional-odds logistic regression was then applied with the variables selected, modelling the symptom scores on their original five-point scale. AR symptom-based estimates of today's average grass pollen concentration were more accurate than those provided by two benchmark forecasting methods using various metrics for assessing accuracy. Predictions of an individual's next-day AR symptoms rated on a five-point scale were correct in 36% of cases and within one point on this scale in 82% of cases. Both outcomes were significantly better than chance. This large-scale AR symptoms measurement program shows that crowd-sourced symptom scores can be used to predict the daily average grass pollen concentration, as well as provide a personalised AR forecast. Predictive capability of landslide susceptibilities is assumed to be varied with different sampling techniques, such as (a) the landslide scarp centroid, (b) centroid of landslide body, (c) samples of the scrap region representing the scarp polygon, and (d) samples of the landslide body representing the entire landslide body. However, new advancements in statistical and machine learning algorithms continuously being updated the landslide susceptibility paradigm. This paper explores the predictive performance power of different sampling techniques in landslide susceptibility mapping in the wake of increased usage of artificial intelligence. We used logistic regression (LR), neural network (NNET), and deep learning neural network (DNN) model for testing and validation of the models. The tests were applied to the 2018 Hokkaido Earthquake affected areas using a set of 11 predictor variables (seismic, topographic, and hydrological). We found that the prediction rates are inconsequential with the DNN model irrespective of the sampling technique (AUC 0.
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