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The fiber of paper mulberry, which is mostly grown in the East Asian region, varies in both length and width—even if from the same species—depending on the country, soil, and climate where it is grown. This accounts for differences in the quality of handmade paper between domestic and imported products. Once handmade paper is manufactured as sheets, its origin cannot be determined from a morphological perspective, regardless of macroscopic and microscopic observations. Therefore, this study attempted to determine the origin of Korean and imported products using mulberry bast fiber and handmade paper from a chemometrics perspective. The inorganic components of wood are absorbed from the soil and exist in the bark of trees; thus, metal and rare-earth elements in the bast fibers and handmade papers were quantitatively analyzed using inductively coupled plasma-atomic emission spectroscopy/mass spectrometry (ICP-AES/MS). Following this, a prediction model of their origin was constructed by applying multivariate statistical analysis, i.e., a partial least squares-discriminant analysis (PLS-DA) loading plot was constructed, which helped in identifying the discrimination factors resulting from their origin. According to the ICP-AES results, Pb was only detected in the Gyeongbuk Mungyeong dak sample of the Korean bast fibers. The Chinese bast fiber sample had high contents of Ca, and B and Zn were detected only in the Chinese bast fiber samples. Bast fiber samples from China, Japan, and Thailand showed relatively high contents of Al, Ca, and Na. Meanwhile, Cu and Zn were detected in the handmade paper manufactured from the imported materials. According to the ICP-MS results, among the Korean handmade papers, the products from Gyeongbuk Mungyeong, Andong, and Chungbuk Goesan exhibited a high U content and that from Gapyeong had high Sc, Rb, Sr, and Ba contents. Thus, the elemental analysis employed in this study effectively identified the origin of the papers. The origin prediction by PLS-DA for the domestic and imported products revealed an accuracy of 86.4% and 72.7% for ICP-AES and ICP-MS methods, respectively, which is deemed acceptable to assess the origin. We believe this method significantly contributes to determining the origin of paper mulberry bast fiber and handmade paper manufactured in East Asia.

Small interfering RNA (siRNA)-based therapy has emerged as a powerful therapeutic tool due to its target-specific gene silencing. However, the therapeutic efficacy has been limited by poor cellular uptake, and therefore delivery vehicles are required to improve the cellular uptake. Herein, guanidinium-incorporated polyether-based block copolyelectrolytes were developed for improved cellular uptake and consequently increased gene silencing efficiency. Guanidinium-functionalized poly(ethylene oxide-b-allyl glycidyl ether) (G-PEO-PAGE) were prepared by living anionic ring opening polymerization of allyl glycidyl ether and post-modification to introduce guanidinium groups. G-PEO-PAGE provides efficient complexation with siRNA due to highly cationic charges of the guanidinium groups at physiological pH, resulting in significantly improved cellular uptake of siRNA and consequently increased gene silencing efficiency. In addition, G-PEO-PAGE showed significantly lower cytotoxicity compared to branched polyethylenimine as a commercial counterpart. This study demonstrates that guanidinium-incorporated block copolyelectrolytes have a great potential for efficient and low toxic siRNA delivery.

The relaxation dynamics of poly(ethyl glycidyl ether-co-isopropyl glycidyl ether-b-ethylene oxide-b-ethyl glycidyl ether-co-isopropyl glycidyl ether) triblock copolymer hydrogels were investigated ...

Complex coacervate core hydrogels were formed by simply mixing two oppositely charged polyether-based ABA triblock copolyelectrolyte solutions, where A blocks functionalized with either ammonium or...

The effects of voltage on the methane production rate (k) of various types of anaerobic sludge biomasses are quantitatively demonstrated. Voltage is supplied to the anaerobic digestion (AD) sludges independently and quickly improves the methane production rates and specific methanogenic activities (SMAs), regardless of bio-electrochemical activation of the sludge. This supports previous findings that bio-electrochemical AD (BEAD) shows higher stabilization and methane production rates than those of AD. However, in reactors fed with H2/CO2, SMA and k are not significantly increased by the voltage supply, indicating that direct electron transfer for hydrogenotrophic methanogenesis is not a major methane formation pathway. The voltage supply contributed to indirect electron transfer for H2 production. Therefore, indirect electron transfer via H2 is a significant factor in hydrogenotrophic methanogenesis. These findings indicate changes in the general pathways of AD achieved using a voltage supply and provide a better understanding of previous BEAD studies.

We examined the packing structure of polystyrene-coated gold nanoparticles (Au@PS) as a function of grafting density. A series of Au@PS nanoparticles with grafting densities in the range of 0.51–1.94 chains nm–2 were prepared by a ligand exchange process using thiol-terminated PS and then self-assembled at a liquid–air interface. We observed a transition from disordered to body-centered cubic (bcc) to face-centered cubic (fcc) arrangements with increasing grafting density, even though the ligand length-to-core radius ratio (λ) was as high as 3.0, a condition that typically favors nonclose-packed bcc symmetry in the self-assembly of hard nanoparticles. To explain this phenomenon, we define λeff to include the concentrated polymer brush regime as part of the “hard core”, which predicts that the softness of Au@PS nanoparticles is reduced from 1.53 to 0.14 in a theta solvent as the grafting density increases from 0.51 to 1.94 chains nm–2. This new definition of λ can also predict the effective radii of nanopa...

The tight regulation of local auxin homeostasis and signalling maxima in xylem precursor cells specifies the organising activity of the vascular cambium and consequently promotes xylem differentiation and wood formation. However, the molecular mechanisms underlying the local auxin signalling maxima in the vascular cambium are largely unknown. Here, we reveal that brassinosteroid (BR)-activated WALLS ARE THIN1 (WAT1) facilitates wood formation by enhancing local auxin signalling in the vascular cambium in Solanum lycopersicum. Growth defects and low auxin signalling readouts in the BR-deficient tomato cultivar, Micro-Tom, were associated with a novel recessive allele, Slwat1-copi, created by the insertion of a retrotransposon in the last exon of the SlWAT1 locus. Molecular and genetic studies by generating the gain-of-function and loss-of-function tomato mutants revealed that SlWAT1 is a critical regulator for fine tuning local auxin homeostasis and signalling outputs in vascular cambium to facilitate secondary growth. Finally, we discovered that BR-regulated SlBZR1/2 directly activated downstream auxin responses by SlWAT1 upregulation in xylem precursor cells to facilitate xylem differentiation and subsequent wood formation. Our data suggest that the BR-SlBZR1/2-WAT1 signalling network contributes to the high level of auxin signalling in the vascular cambium for secondary growth.

Honeybees play a crucial role in the agriculture industry because they pollinate approximately 75% of all flowering crops. However, every year, the number of honeybees continues to decrease. Consequently, numerous researchers in various fields have persistently attempted to solve this problem. Acoustic scene classification, using sounds recorded from beehives, is an approach that can be applied to detect changes inside beehives. This method can be used to determine intervals that threaten a beehive. Currently, studies on sound analysis, using deep learning algorithms integrated with various data preprocessing methods that extract features from sound signals, continue to be conducted. However, there is little insight into how deep learning algorithms recognize audio scenes, as demonstrated by studies on image recognition. Therefore, in this study, we used a mel spectrogram, mel-frequency cepstral coefficients (MFCCs), and a constant-Q transform to compare the performance of conventional machine learning models to that of convolutional neural network (CNN) models. We used the support vector machine, random forest, extreme gradient boosting, shallow CNN, and VGG-13 models. Using gradient-weighted class activation mapping (Grad-CAM), we conducted an analysis to determine how the best-performing CNN model recognized audio scenes. The results showed that the VGG-13 model, using MFCCs as input data, demonstrated the best accuracy (91.93%). Additionally, based on the precision, recall, and F1-score for each class, we established that sounds other than those from bees were effectively recognized. Further, we conducted an analysis to determine the MFCCs that are important for classification through the visualizations obtained by applying Grad-CAM to the VGG-13 model. We believe that our findings can be used to develop a monitoring system that can consistently detect abnormal conditions in beehives early by classifying the sounds inside beehives.

In the chemical mechanical polishing process of semiconductor manufacturing, the concentration of ‘large’ particles (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge $$\end{document}≥0.5 μm) in the slurry, which is considerably larger in size than the main abrasives (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx $$\end{document}≈ 0.1 μm), is a critical parameter that strongly influences manufacturing defects, yields, and reliabilities of large-scale-integrated circuits. Various instruments, so-called particle counters, based on light scattering, light extinction, and holography techniques have been developed to measure and monitor the large particle concentration in semiconductor fabs in real time. However, sizeable fluctuation in the measured particle concentration complicates the statistical process control in the fabs worldwide. Here, we show that an inherent fluctuation exists in the counting of large particles, which is universal, independent of instrument type, and quantitatively determined by the instrument’s operation parameters. We analytically derive a statistical theory of the fluctuation based on Poisson statistics and validate the theory through experiments and Monte-Carlo simulation. Furthermore, we provide a strategy to enhance the measurement accuracy by statistically adjusting the instrumental parameters commonly involved in the particle counters. The present results and analyses could be useful for statistical process control in semiconductor fabs to prevent large particle-induced defects such as micro-scratches and pits on wafers.

Various microorganisms are involved in nitrogen removal, and their group compositions depend closely on operating parameters. The structures and functions of nitrification microorganisms in full-scale anaerobic-anoxic–oxic (A2/O) and oxidation ditch processes were analyzed using metagenomics and canonical correspondence analysis. The community structure of ammonia-oxidizing archaea in the oxidation ditch was 3.8 (winter) – 6.3 (summer) times higher than in A2/O, and the complete ammonia oxidizer was only found in the oxidation ditch process. The canonical correspondence analysis of various environmental variables showed that Nitrosomonadales, Crenarchaeota, and Nitrospira inopinata correlate highly with nitrification, and Nitrospira was involved in NO2−-N oxidation rather than Nitrobacter. The longer solid and hydraulic retention times in the oxidation ditch were more effective in achieving a wider range of novel nitrification than A2/O. This result indicates that microbial communities of novel nitrifiers and ammonia-oxidizing archaea improved in the oxidation ditch process, significantly contributing to stable nitrogen removal.

BR signaling pathways facilitate xylem differentiation and wood formation by fine tuning SlBZR1/SlBZR2-mediated gene expression networks involved in plant secondary growth. Brassinosteroid (BR) signaling and BR crosstalk with diverse signaling cues are involved in the pleiotropic regulation of plant growth and development. Recent studies reported the critical roles of BR biosynthesis and signaling in vascular bundle development and plant secondary growth; however, the molecular bases of these roles are unclear. Here, we performed comparative physiological and anatomical analyses of shoot morphological growth in a cultivated wild-type tomato (Solanum lycopersicum cv. BGA) and a BR biosynthetic mutant [Micro Tom (MT)]. We observed that the canonical BR signaling pathway was essential for xylem differentiation and sequential wood formation by facilitating plant secondary growth. The gradual retardation of xylem development phenotypes during shoot vegetative growth in the BR-deficient MT tomato mutant recovered completely in response to exogenous BR treatment or genetic complementation of the BR biosynthetic DWARF (D) gene. By contrast, overexpression of the tomato Glycogen synthase kinase 3 (SlGSK3) or CRISPR-Cas9 (CR)-mediated knockout of the tomato Brassinosteroid-insensitive 1 (SlBRI1) impaired BR signaling and resulted in severely defective xylem differentiation and secondary growth. Genetic modulation of the transcriptional activity of the tomato Brassinazole-resistant 1/2 (SlBZR1/SlBZR2) confirmed the positive roles of BR signaling pathways for xylem differentiation and secondary growth. Our data indicate that BR signaling pathways directly promote xylem differentiation and wood formation by canonical BR-activated SlBZR1/SlBZR2.

Elastin-like polypeptides (ELPs) are stimulusresponsive protein-based biopolymers that exhibit phase transition behavior. Some ELP block copolymers can assemble spherical nanoparticles with thermo-sensitivity. For this reason, thermo-responsive nanoparticles derived from ELP block copolymers have been utilized in the biomedical field. In this study, four different ELP block copolymers constituted three diblocks and one triblock were synthesized. Physical properties of these self-assembled micelles were then investigated according to their block lengths and distributions. In addition, drug capturing capacities of these micelles and aggregates were explored by changing solution temperature.

An excessive increase in algae often has various undesirable effects on drinking water supply systems, thus proper management is necessary. Algal monitoring and classification is one of the fundamental steps in the management of algal blooms. Conventional microscopic methods have been most widely used for algal classification, but such approaches are time-consuming and labor-intensive. Thus, the development of alternative methods for rapid, but reliable algal classification is essential where an advanced machine learning technique, known as deep learning, is considered to provide a possible approach for rapid algal classification. In recent years, one of the deep learning techniques, namely the convolutional neural network (CNN), has been increasingly used for image classification in various fields, including algal classification. However, previous studies on algal classification have used CNNs that were arbitrarily chosen, and did not explore possible CNNs fitting algal image data. In this paper, neural architecture search (NAS), an automatic approach for the design of artificial neural networks (ANN), is used to find a best CNN model for the classification of eight algal genera in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis sp., Oscillatoria sp., and Anabaena sp.), three diatoms (Fragilaria sp., Synedra sp., and two green algae (Staurastrum sp. and Pediastrum sp.). The developed CNN model effectively classified the algal genus with an F1-score of 0.95 for the eight genera. The results indicate that the CNN models developed from NAS can outperform conventional CNN development approaches, and would be an effective tool for rapid operational responses to algal bloom events. In addition, we introduce a generic framework that provides a guideline for the development of the machine learning models for algal image analysis. Finally, we present the experimental results from the real-world environments using the framework and NAS.

Microalgal&ndash, bacterial consortia are considered an alternative method to conventional wastewater treatment processes with several benefits, such as low oxygen production cost and reduced emission of carbon dioxide resulting from photosynthetic activity. Besides, microalgae effectively remove various emerging contaminants and heavy metals that are hardly removed by conventional wastewater treatment processes. The purpose of this study is finding optimal operation conditions (e.g., light wavelengths, light intensity, microalgal&ndash, bacterial consortia biomass) when applying microalgae in wastewater treatment system. Firstly, reduced transmittance was monitored at four different wavelengths (i.e., blue, green, red, and white light) and at various concentrations of microalgal&ndash, bacterial consortia. Light transmittance rates were rapidly reduced as the biomass increased, where the highest transmittance was observed in green light. Secondly, the reduction of oxygen production over time, by the inhibition of the photosynthetic activity, was tested as the light intensity increased at four different wavelengths and at low (100 mg L&minus, 1) and high (500 mg L&minus, 1) concentrations of microalgal&ndash, bacterial consortia. The observations and subsequent statistical analyses verify that microalgal&ndash, bacterial consortia show the strongest resistance to the inhibition of the photosynthetic activity in green light, with white coming next, when the intensity of light is increased.

Despite the growing interest in amphiphilic block copolymers for their application in micelles as ideal drug delivery carriers, there remain some challenges related to biocompatibility, stability, degradability, and loading efficiency of the micelles. Herein, we report a novel hydrophobic, pH-responsive epoxide monomer, tetrahydropyranyl glycidyl ether (TGE). Anionic ring-opening polymerization affords the controlled synthesis of a series of its homopolymers (PTGE) and amphiphilic polymers, poly(ethylene glycol)-block-poly(tetrahydropyranyl glycidyl ether) (PEG-b-PTGE). Interestingly, these block copolymers with cyclic TGE moieties showed superior stability in biological media, high loading capacity, tunable release, and controllable degradation compared to the block copolymers with its acyclic analogue, 1-ethoxyethyl glycidyl ether (EEGE), widely employed in polyether, which satisfy all the required design principles and address the challenges in drug delivery systems. The superior biocompatibility coupled with the high stability of the novel functional epoxide monomer is anticipated to lead to the development of a versatile platform for smart drug delivery systems.

Sensitivity and uncertainty analysis of contaminant fate and transport modeling have received considerable attention in the literature. In this study, our objective is to elucidate the uncertainty pertaining to micropollutant modeling in the sediment-water column interface. Our sensitivity analysis suggests that not only partitioning coefficients of metals but also critical stress values for cohesive sediment affect greatly the predictions of suspended sediment and metal concentrations. Bayesian Monte Carlo is used to quantify the propagation of parameter uncertainty through the model and obtain the posterior parameter probabilities. The delineation of periods related to different river flow regimes allowed optimizing the characterization of cohesive sediment parameters and effectively reducing the overall model uncertainty. We conclude by offering prescriptive guidelines about how Bayesian inference techniques can be integrated with contaminant modeling and improve the methodological foundation of uncertainty analysis. Sensitivity and uncertainty analysis was performed for sediment-metal modeling.Suspended sediment predictions are sensitive to critical erosion stress.Sediment bed-water partitioning coefficient is critical for metal predictions.River flow dynamics affect contaminant fate and model parameter sensitivity.Strategies to improve uncertainty analysis of sediment-metal modeling are discussed.

Railway accidents are critical issues characterized by a large number of injuries and fatalities per accident due to massive public transport systems. This study proposes a new approach for evaluating the damages resulting from railway accidents using the two-part models (TPMs) such as the zero-inflated Poisson regression model (ZIP model) and the zero-inflated negative-binomial regression model (ZINB model) for the non-negative count measurements and the zero-inflated gamma regression model (ZIG model) and the zero-inflated log-normal regression model (ZILN model) for the semi-continuous measurements. The models are employed for the evaluation of the railway accidents on Korea Railroad, considering the accident damages, such as the train delay time, the number of trains delayed and the cost of considering the accident count responses, for the period 2008 to 2016. From the results obtained, we found that the human-related factors, the high-speed railway system or the Korea Train Express (KTX) and the number of casualties, are the main cost-escalating factors. The number of trains delayed and the amount of delay time tend to increase both the probability of incurring costs and the amount of cost. For better evaluation, the railway accident data should contain accurate information with less recurrence of zeros.

This study adopts two approaches to analyze the occurrence of algae at Haman Weir for Nakdong River; one is the traditional statistical method, such as logistic regression, while the other is machine learning technique, such as kNN, ANN, RF, Bagging, Boosting, and SVM. In order to compare the performance of the models, this study measured the accuracy, specificity, sensitivity, and AUC, which are representative model evaluation tools. The ROC curve is created by plotting association of sensitivity and (1-specificity). The AUC that is area of ROC curve represents sensitivity and specificity. This measure has two competitive advantages compared to other evaluation tools. One is that it is scale-invariant. It means that purpose of AUC is how well the model predicts. The other is that the AUC is classification-threshold-invariant. It shows that the AUC is independent of threshold because it is plotted association of sensitivity and (1-specificity) obtained by threshold. We chose AUC as a final model evaluation tool with two advantages. Also, variable selection was conducted using the Boruta algorithm. In addition, we tried to distinguish the better model by comparing the model with the variable selection method and the model without the variable selection method. As a result of the analysis, Boruta algorithm as a variable selection method suggested PO4-P, DO, BOD, NH3-N, Susp, pH, TOC, Temp, TN, and TP as significant explanatory variables. A comparison was made between the model with and without these selected variables. Among the models without variable selection method, the accuracy of RF analysis was highest, and ANN analysis showed the highest AUC. In conclusion, ANN analysis using the variable selection method showed the best performance among the models with and without variable selection method.