Small Root Research Articles

Research on Personalized Recommendation of Higher Education Resources Based on Multidimensional Association Rules

The personalized recommendation method of higher education resources currently cannot carry out multidimensional association analysis of learners, situations, and resources and cannot extract accurate resources for learners, resulting in a large error. This study constructs a personalized recommendation method for higher education resources based on multidimensional association rules. This algorithm clarifies the multidimensional association rules, extracts the key data from massive educational resources, and groups the same kind of data by using a frequent itemset algorithm for mining association rules, namely, the Apriori algorithm. Combined with traditional data mining technology, this study constructs a new personalized recommendation model for education resources based on multidimensional association rules, which achieves the accurate extraction of higher education resources and ensures the matching degree between learners and resources. The experimental results show that the personalized recommendation model of educational resources in this study effectively makes up for the disadvantages of the traditional data mining algorithms, with a small root mean square error and short data mining time, within 20 ms.

Evaluation of tetrahydrocannabinolic acid (THCA) synthase polymorphisms for distinguishing between marijuana and hemp.

The Controlled Substances Act (CSA) classifies marijuana (Cannabis sativa) as a Schedule I illicit drug. However, the recent Agriculture Improvement Act of 2018 (U.S. Farm Bill) removed hemp from the definition of marijuana in the CSA, making it a legal crop. As a result, many hemp products are now available, including strains of hemp buds high in other cannabinoids such as cannabidiol (CBD) or cannabigerol (CBG). The genetic inheritance of chemical phenotype (chemotype) has been widely studied, with the tetrahydrocannabinolic acid (THCA) synthase gene at the forefront. Previous studies have speculated that there are two forms of the THCA gene, one that produces an active enzyme (present in marijuana) and one that cannot produce a functional enzyme (present in hemp). A DNA analysis method is desirable for determining crop type in sample types inconducive to chemical analysis, such as immature crops, trace residues, small leaf fragments, seeds, and root material. This study optimized and evaluated a previously reported single nucleotide polymorphism (SNP) assay for determining C.sativa crop type. Furthermore, the presence or absence of 15 cannabinoids, including THC and THCA, was reported in cannabis reference materials and 15 legal hemp flower samples. The SNP assay correctly identified crop type in most samples. However, several marijuana samples were classified as hemp, and several hemp seeds were classified as marijuana. Two strains of legal CBG hemp flowers were also classified as marijuana, indicating that factors other than the genetic variation of the THCA synthase gene should be considered when determining crop type.

Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources.

Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based approach. To tackle this challenge, we develop and investigate the usefulness of a novel deep learning method called tower networks. This method is able to learn from multiple input data sources at once. We apply the tower network to the problem of short-term temperature forecasting. First, we compare our method to a number of meteorological baselines and simple statistical approaches. Further, we compare the tower network with two core network architectures that are often used, namely the convolutional neural network (CNN) and convolutional long short-term memory (convLSTM). The methods are compared for the task of weather forecasting performance, and the deep learning methods are also compared in terms of memory usage and training time. The tower network performs well in comparison both with the meteorological baselines, and with the other core architectures. Compared with the state-of-the-art operational Norwegian weather forecasting service, yr.no, the tower network has an overall 11% smaller root mean squared forecasting error. For the core architectures, the tower network documents competitive performance and proofs to be more robust compared to CNN and convLSTM models.

Friction velocity estimation using a 2D sonic anemometer in coastal zones

Friction velocity (u*) is an important velocity scale used in the study of engineering and geophysical flows. The widespread use of 2D sonic anemometers in modern meteorological stations makes the estimation of u* from just the horizontal components of the velocity a very attractive possibility. The presence of different wind regimes (such as sea breezes in or near coastal zones) causes the turbulent parameters to be dependent on the wind direction. Additionally, u* depends on atmospheric stability, whch makes the estimation of u* from 2D measurements very difficult. A simple expression is proposed, and then tested with data from six independent experiments located in coastal zones. The results show that it is possible to estimate friction velocity from 2D measurements using the turbulence intensity as a proxy for u*, reducing substantially the sensitivity to the wind direction or atmospheric stability, with small root mean squared errors (0.06 < RMSE < 0.097) and high correlation coefficients (0.77 < r2 < 0.95).

Prediction of the Compressive Strength of Fly Ash Geopolymer Concrete by an Optimised Neural Network Model.

This article presents a regression tool for predicting the compressive strength of fly ash (FA) geopolymer concrete based on a process of optimising the Matlab code of a feedforward layered neural network (FLNN). From the literature, 189 samples of different FA geopolymer concrete mix-designs were collected and analysed according to ten input variables (all relevant mix-design parameters) and the output variable (cylindrical compressive strength). The developed optimal FLNN model proved to be a powerful tool for predicting the compressive strength of FA geopolymer concrete with a small range of mean squared error (MSE = 10.4 and 15.0), a high correlation coefficient with the actual values (R = 96.0 and 97.5) and a relatively small root mean squared error (RMSE = 3.22 and 3.87 MPa) for the training and testing data, respectively. Based on the optimised model, a powerful design chart for determining the mix-design parameters of FA geopolymer concretes was generated. It is applicable for both one- and two-part geopolymer concretes, as it takes a wide range of mix-design parameters into account. The design chart (with its relatively small error) will ensure cost- and time-efficient geopolymer production in future applications.

Optimizing Parameters for the Downscaling of Daily Precipitation in Normal and Drought Periods in South Korea

One important factor that affects the performance of statistical downscaling methods is the selection of appropriate parameters. However, no research on the optimization of downscaling parameters has been conducted in South Korea to date, and existing parameter selection methods are dependent on studies conducted in other regions. Moreover, several large-scale predictors have been used to predict abnormal phenomena such as droughts, but in the field of downscaling, parameter optimization methods that are suitable for drought conditions have not yet been developed. In this study, by using the K-nearest analog methodology, suitable daily precipitation downscaling parameters for normal and drought periods were derived. The predictor variables, predictor domain, analog date size, time dependence parameters, and parameter sensitivity values that are representative of South Korea were presented quantitatively. The predictor variables, predictor domain, and analog date size were sensitive to the downscaling performance in that order, but the time dependency did not affect the downscaling process. Regarding calibration, the downscaling results obtained based on the drought parameters returned smaller root mean square errors of 1.3–28.4% at approximately 70% of the stations compared to those of the results derived based on normal parameters, confirming that drought parameter-based downscaling methods are reasonable. However, as a result of the validation process, the drought parameter stability was lower than the normal parameter stability. In the future, further studies are needed to improve the stability of drought parameters.

Y-incision Aortic Root Enlargement With Modified Aortotomy Upsizing the Annulus by 5 Valve Sizes

A very small aortic root and annulus (≤17 mm) needs extensive aortic annular enlargement in adult patients. This report describes a technique that enlarged the aortic annulus by 5 valve sizes from 16 to 17 mm to size 27 bovine pericardial valve, as well as a modification of the aortotomy with the roof technique to make the aortotomy closure easier and more hemostatic while enlarging the sinotubular junction and proximal ascending aorta effectively for future valve-in-valve transcatheter aortic valve replacement.

An adjusted partial least squares regression framework to utilize additional exposure information in environmental mixture data analysis

In a large-scale environmental health population study that is composed of subprojects, often different fractions of participants out of the total enrolled have measures of specific outcomes. It’s conceptually reasonable to assume the association study would benefit from utilizing additional exposure information from those with a specific outcome not measured. Partial least squares regression is a practical approach to determine the exposure-outcome associations for mixture data. Like a typical regression approach, however, the partial least squares regression requires that each data observation must have both complete covariate and outcome for model fitting. In this paper, we propose novel adjustments to the general partial least squares regression to estimate and examine the association effects of individual environmental exposure to an outcome within a more complete context of the study population’s environmental mixture exposures. The proposed framework takes advantage of the bilinear model structure. It allows information from all participants, with or without the outcome values, to contribute to the model fitting and the assessment of association effects. Using this proposed framework, incorporation of additional information will lead to smaller root mean square errors in the estimation of association effects, and improve the ability to assess the significance of the effects.

A REVIEW ON DROUGHT TOLERANCE AND EFFECTS IN MAIZE

Drought is a situation that occurs when there is a prolonged period of exceptionally low rainfall, resulting in water scarcity. It is one of the most significant factors contributing to serious crop output deficits in developing countries. Tiny, broad branches, tiny tassels, elevated leaves, delayed senescence, low root biomass, and a deep root system all seem to be characteristics of this plant. with a small lateral root are all features that seem to be likely selected in maize breeding for enhanced drought tolerance. Tolerant genotypes should have strong spikelet and kernel growth, as well as adequate osmotic adjustment to help retain water in cells under drought, during the cell division, and expansion-growth stages. This does not completely stifle root and ear growth, and leaf survival is better despite the lack of water. As a consequence of comprehensive dissections of drought tolerance features in maize over the last century, numerous QTLs linked to drought tolerance in maize have been revealed(Ribaut et al., 2009).Drought-related yield reduction in maize (Zea mays L.) arise anticipated to boost when temperatures rise and rainfall distribution changes as a result of global climate change in popular traditional producing areas. The effectiveness of traditional crop development for drought tolerance has served as a benchmark against which new genetic approaches must be measured for the past 50 years. Resulting in higher yield potential and kernel set, faster silk effort, and less barrenness, selection based on performance in multi-environment trials (MET) has increased grain output under drought, though at a slower rate than under optimal conditions.

Zero-Tillage Effects on Durum Wheat Productivity and Soil-Related Variables in Future Climate Scenarios: A Modeling Analysis

Adoption of zero-tillage practices with residue retention in field crops has been introduced as an alternative soil-management technique to counteract the resource degradation and high production costs derived from intensive tillage. In this sense, the biophysical models are valuable tools to evaluate and design the most suitable soil-management technique in view of future climate variability. The aim of this study was to use the ARMOSA process-based crop model to perform an assessment of tillage (T) and no-tillage (No-T) practices of durum-wheat-cropping systems in the Campania region (South of Italy) under current and future climate scenarios. First, the model was calibrated using measurements of soil water content at different depths, leaf area index, and aboveground biomass in the T and No-T treatments during the 2013–2014 season. Then, the model was further applied in the T and No-T treatments to future climate data for 2020–2100 that was generated by the COSMO-CLM model using the RCP4.5 and 8.5 paths. Results of the calibration depicted that the model can accurately simulate the soil-crop-related variables of both soil-management treatments, and thus can be applied to identify the most appropriate conservation agricultural practices in the durum-wheat system. The simulation of soil water content at different depths resulted in small relative root mean square errors (RRMSE < 15%) and an acceptable Pearson’s correlation coefficient (r > 0.51); and the goodness-of-fit indicators for simulated LAI and AGB resulted in acceptable RRMSE (RRMSE < 28%), and high r (r > 0.84) in both soil-management treatments. Future climate simulations showed that No-T management will deliver 10% more wheat yield than the T, with an annual average 0.31% year−1 increase of soil organic carbon, and an increase of 3.80% year−1 for N uptake, which can diminish the N leaching. These results suggest that No-T could be implemented as a more resilient management for farming system in view of climate uncertainty and scarcity of resources. Therefore, these findings support the potential of the ARMOSA model to evaluate the soil-crop response of the durum-wheat system under different management conditions and to design appropriate soil-management practices for current and future climate predictions.

Multitarget nociceptor sensitization by a promiscuous peptide from the venom of the King Baboon spider

The King Baboon spider, Pelinobius muticus, is a burrowing African tarantula. Its impressive size and appealing coloration are tempered by reports describing severe localized pain, swelling, itchiness, and muscle cramping after accidental envenomation. Hyperalgesia is the most prominent symptom after bites from P. muticus, but the molecular basis by which the venom induces pain is unknown. Proteotranscriptomic analysis of P. muticus venom uncovered a cysteine-rich peptide, δ/κ-theraphotoxin-Pm1a (δ/κ-TRTX-Pm1a), that elicited nocifensive behavior when injected into mice. In small dorsal root ganglion neurons, synthetic δ/κ-TRTX-Pm1a (sPm1a) induced hyperexcitability by enhancing tetrodotoxin-resistant sodium currents, impairing repolarization and lowering the threshold of action potential firing, consistent with the severe pain associated with envenomation. The molecular mechanism of nociceptor sensitization by sPm1a involves multimodal actions over several ion channel targets, including NaV1.8, KV2.1, and tetrodotoxin-sensitive NaV channels. The promiscuous targeting of peptides like δ/κ-TRTX-Pm1a may be an evolutionary adaptation in pain-inducing defensive venoms.

A novel regression method for harmonic analysis of time series

Harmonic analysis of time series is an important technique to reveal seasonal land surface dynamics using remote sensing information. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other hand, it is challenging to obtain temporally continuous satellite data with high quality because of atmospheric contamination. We developed a novel regression method named Harmonic Adaptive Penalty Operator (HAPO) for harmonic analysis of unevenly distributed time series. We introduced a new penalty function to minimize unexpected fluctuations in the model, which can substantially reduce the overfitting issue of regression in time series with temporal gaps. Specifically, the new penalty function minimizes the length of the model curve and the value range difference between the model and time series observations. We compared HAPO with three widely used regression methods (OLS: Ordinary Least Squares; LASSO: Least Absolute Shrinkage and Selection Operator; and Ridge) with different scenarios using Landsat time series data across the United States. First, we evaluated methods using Landsat surface reflectance time series within a single year. HAPO showed small and consistent monthly Root Mean Square Deviation (RMSD) values, in which most of the time RMSD values of predicted reflectance were less than 0.04. More importantly, HAPO showed consistent and less bias given varying density and irregularity of time series. Second, we evaluated methods using multi-year time series and the result suggested that HAPO was a better predictor of relatively short time series (less than4 years) with steady small RMSD values. When a longer time series (≥4 years) was used, all four methods disclosed similar RMSD values, but HAPO outperformed other three methods when there were temporal gaps. Last, we preliminarily tested how regression methods affected change detection and classification accuracy. HAPO showed the highest change detection accuracy of all tests in terms of F1 score when using the change threshold of 0.9999. In classification, HAPO produced the highest accuracy for short time series segments (one- or two-year time series). In contrast, all methods reached similar accuracy for 5-year time series. These results suggest that for areas that have large seasonal observation gaps or for time series that have less than 4 years records, HAPO can provide more consistent and accurate analytical results than other regression methods for harmonic analysis of time series.

Transient Aeroelastic Response of a Rotor During Rotor Speed Transition in Forward Flight

Compound helicopters often have to reduce their rotor tip speeds to achieve higher forward speeds. The rotors, therefore, have to undergo a transient process of rotor speed changes. To investigate the transient loads and the related transformation from the rotating frame to the nonrotating frame, a rotor model is used to predict the transient aeroelastic responses. The rotor model is validated by using test data of a teetering model rotor during engagement and disengagement operations. The investigations indicate that a small transient lagwise root bending moment can result in a large transient contribution to rotor torque. This transient component is triggered by the force related with the sudden change of the angular acceleration of the rotor speed. Increasing the blade damping in the lagwise direction can significantly decrease the transient components of the lagwise root bending moment and the corresponding rotor torque. Making the angular acceleration continuous will not trigger the transient components, suggesting that this is a better way for the reduction of the transient rotor loads during speed changes.

Aortic Root Replacement of Small Roots to Prepare for Transcatheter Aortic Valve Replacement

Small aortic root is a significant limitation of optimal hemodynamics for surgical aortic valve replacement due to a small annulus and application of transcatheter aortic valve replacement due to low coronary ostia and limited space at the root. We sutured a bioprosthetic valve that was up-sized by 2 to 3 valve sizes in the skirt portion of a Valsalva graft (Terumo Medical Corp) and implanted the coronary button high, close to sinotubular junction, to achieve optimal hemodynamics and prepare patients for future transcatheter aortic valve replacement.

不同林分密度杉木林生态系统碳密度及其垂直空间分配特征

以不同林分密度(1800、3000、4500株/hm²)杉木林为研究对象, 通过野外调查、样品采集和室内分析, 研究不同林分密度杉木林生态系统碳密度及其分配特征。结果表明：1)三种林分密度杉木林生态系统碳密度分别为131.54、161.42、172.69 t/hm², 随林分密度增大而升高, 且具有显著差异(P < 0.05)。杉木林碳密度表现为土壤层>乔木层>林下地被物层。土壤有机碳储量占总碳储量的比例最大(53.11%-67.37%), 其次是树干、树根、树皮(25.89%-35.74%), 高密度杉木林分有利于树干、树皮、树根碳密度分配比例的增加。2)乔木层, 树干、树皮、宿留枯枝及宿留枯叶碳密度随林分密度增大而升高, 鲜枝及鲜叶碳密度随林分密度增大先升高后降低, 且均具显著差异(P < 0.05)；树干、树皮碳密度随树体高度的升高而降低, 鲜枝鲜叶碳密度集中于树体中上部(8 m≤h≤10 m), 宿留枯枝枯叶碳密度集中分布于树体中部(4 m≤h≤8 m)。3)随着林分密度的增大, 不同径级根碳密度呈上升趋势, 且不同径级根碳密度随林分密度变化差异达显著水平(P < 0.05)；不同径级间碳密度：根头>粗根>大根>中根>小根>细根, 根头和粗根占比最大(57.38%-70.84%)。4)林下植被碳密度随林分密度增大不断降低, 而林下凋落物碳密度随林分密度增大呈逐渐上升趋势, 且具显著差异(P < 0.05)。5)土壤层碳密度随土壤深度增加逐渐降低, 不同林分密度土壤层碳密度没有显著差异(P>0.05)。综上所述, 适当增加林分密度有利于提高杉木林生态系统的碳密度。;Cunninghamia lanceolata plantation with densities of 1800, 3000 and 4500 plants/hm² were employed to investigate the carbon density and its distribution characteristics of C. lanceolata forest ecosystem under different stand densities through field investigation, sample collection and analysis. The results showed that: 1) the carbon density in the C.lanceolata ecosystem with stand densities of 1800, 3000 and 4500 plants/hm² were 131.54 t/hm², 161.42 t /hm² and 172.69 t/hm², respectively, and increased significantly with the increase of stand densities (P < 0.05). The carbon density of C. lanceolata forest followed the order of soil layer > tree layer > understory layer. Soil organic carbon storage accounted for the largest proportion of total carbon storage (53.11%-67.37%), followed by trunk, root and bark (25.89%-35.74%). High stand density could increase the carbon density distribution portion in trunk, bark and root. 2) The carbon density of tree layer, trunk, bark and persistent withered branches and leaves increased with the increase of stand densities, while the carbon density of fresh branches and leaves displayed first increase and then decrease tendency with the increasing stand densities, which all showed significantly decreased with the increase of stand densities (P < 0.05). The carbon density of trunk and bark decreased with the increase of tree height. The carbon density of fresh branches and leaves were relatively high in the middle and upper part of tree (8 m≤h≤10 m), while for withered branches and leaves, higher carbon density was observed in the middle of the tree (4 m≤h≤8 m). 3) The carbon density in roots of different diameter increased significantly with the increase of stand densities (P < 0.05). The carbon density among different roots diameter was as follows: root head > coarse root > big root > middle root > small root > fine root, and root head and coarse root accounted for the largest portion (57.38%-70.84%). 4) The carbon density of undergrowth vegetation decreased with the increase of stand densities, while the opposite trend was observed for litter, which showed significantly decreased with the increase of stand densities (P < 0.05). 5) The carbon density of soil layer decreased with the increase of soil depth, and no significant difference was observed for different stand densities (P>0.05). 6) In summary, appropriate increase stand density is beneficial to improve the carbon density of C. lanceolata forest ecosystem.

Toward Online Removal of Cardiac Interference From Trunk Electromyography by Morphological Modeling of the Electrocardiography

Trunk electromyography (EMG) has been widely used in many biomedical applications, which is usually contaminated by electrocardiography (ECG) interference. Several methods have been proposed for ECG removal from trunk EMG. However, most of them are either inaccurate or unsuitable for online applications, e.g., prosthesis control. The aim of the present study is therefore to develop an accurate ECG removal algorithm suitable for online applications. Each ECG wave was modeled by Gaussian kernel functions and subtracted from the trunk measurement to obtain a clean EMG. Two synthetic datasets were generated by mixing a real EMG with a healthy ECG and a dysrhythmia ECG, respectively. Average rectified value (ARV) and mean frequency (MF) were calculated from the reconstructed EMG and the clean EMG for performance evaluation. Moreover, real trunk EMG was recorded under isometric contractions with different forces. Correlation coefficient (CC) between the amplitude of the reconstruct EMG and the contraction force was calculated as performance metric. Small root mean square errors were observed in ARV and MF between the clean EMG and reconstructed EMG, i.e., 2.5&#x00B1;0.7 &#x03BC;v and 2.0&#x00B1;0.4 Hz for the synthetic dataset containing healthy ECG and 3.1&#x00B1;1.7 &#x03BC;v and 3.0&#x00B1;1.2 Hz for that containing dysrhythmia ECG. High CC (0.91&#x00B1;0.12) between EMG amplitude and contraction force was observed for real trunk EMG. Our algorithm outperforms many of the state-of-the-art algorithms and is implemented in each cardiac cycle, enabling possible online applications such as prosthesis control.

Different plant species exhibit contrasting root traits and penetration to variation in soil bulk density of clayey red soil

AbstractHigh bulk density reduces yields in almost every soil and management system worldwide. Using pioneer plants was one of the practical solutions to remediate the adverse effects from high bulk density. The objectives of this research were to investigate the effect of soil bulk density on root traits and penetration of different pioneer plants (rape [Brassica napus L. cv. Huashuang 4]; deep rape [Brassica napus L. cv. Xinan 28]; lucerne [Medicago sativa L. cv. Ladino]; vetiver [Chrysopogon zizanioides L. cv. Wild]) under controlled and field conditions. Replicated field and laboratory studies were conducted in a soil composition of high clay percentage, high oxides content, and low organic matter content. Seedling and matured root traits of four plants under contrasting bulk density in controlled study and field were analyzed. Under controlled situations, soil with a bulk density of 1.4 g cm–3 had thicker roots (higher diameter) than soil with a bulk density of 1.2 g cm–3, whereas soil with a bulk density of 1.6 g cm–3 had reduced root lengths (RL), branch number, root tip angle, and root elongation rate than soil with a bulk density of 1.4 g cm–3. Similar changes of root diameter (RD) and root length density (RLD) with bulk density also appeared in the field. Generally, the penetration ability of plant species displayed in the order of vetiver &gt; lucerne &gt; deep rape &gt; rape. Vetiver showed the highest penetrating ability due to its high RD (0.7 mm in field), percentage of medium roots (20–50%) and RLD, and small root tip angle to ensure penetration through hard layers. The results demonstrated that vetiver will be an effective pioneer plant for red soil management.

Craniofacial and Oral Manifestations of Non-Syndromic Hypodontia: A Review

AbstractPatients with hypodontia appear to have a different craniofacial morphology as compared with controls. They tend to have more class III skeletal pattern and retroclined upper and lower soft tissue lips as a consequence of retroclined upper and lower incisors. These features are more pronounced as the severity of the hypodontia increases. In addition to changes in craniofacial and soft tissue morphology, various dental anomalies are commonly associated with hypodontia such as microdontia of the remaining dentition, anomalies in tooth shape, particularly peg-shaped upper lateral incisors, smaller root dimensions of some of the permanent teeth, and decreased maxillary and mandibular dental arch widths and lengths measurements. Other dental anomalies which are seen in association with hypodontia are taurodontism of molars, retained deciduous molars, delayed development of the permanent teeth, distoangulation of the mandibular second premolar, and infraocclusion of the deciduous molars.

Determination of coagulant dosages for process control using online UV-vis spectra of raw water

Traditionally, coagulant doses are determined by the operators for the coagulation process at water treatment plants which is a multi-factor approach based on raw and treated water quality and in some situations relies heavily on their decisions. It can be challenging to determine appropriate coagulant doses proactively for tight coagulation control with the traditional method. Therefore, this study looked for alternative approaches for coagulation control and maybe the first to build coagulant dose determination models using only online raw water quality data (UV–Vis spectra) combined with chemometrics to determine coagulant doses for a drinking water treatment plant (WTP). Online UV–Vis spectral data at the raw water intake and alum dose data from a drinking WTP were used for building coagulant dose determination models. Three modelling techniques, including multiple linear regression (MLR), partial least squares (PLS) and artificial neural networks (ANNs), were applied in this work. The results show that MLR and PLS models had almost identical performances with small root mean square errors (RMSE) and high correlation coefficients (R2). Both MLR and PLS had slightly better performance than the ANNs for alum dose predictions. This study shows that the combination of online UV–Vis spectra and a chemometric method (MLR or PLS) was able to mimic operators' decisions in the determination of coagulant doses with a pH target of 6 to achieve a target DOC level of less than 5 mg/L for treated water quality.

Root CT Segmentation Using Incremental Learning Methodology on Improved Multiple Resolution Images

The real-time issue of reliability segmenting root structure while using X-Ray Computed Tomography (CT) images is addressed in this work. A deep learning approach is proposed using a novel framework, involving decoders and encoders. The encoders-decoders framework is useful to improve multiple resolution by means of upsampling and downsampling images. The methodology of the work is enhanced by incorporating network branches with individual tasks using low-resolution context information and high-resolution segmentation. In large volumetric images, it is possible to resolve small root details by implementing a memory efficient system, resulting in the formation of a complete network. The proposed work, recent image analysis tool developed for root CT segmented is compared with several other previously existing methodology and it is found that this methodology is more efficient. Quantitatively and qualitatively, it is found that a multiresolution approach provides high accuracy in a shallower network with a large receptive field or deep network in a small receptive field. An incremental learning approach is also embedded to enhance the performance of the system. Moreover, it is also capable of detecting fine and large root materials in the entire volume. The proposed work is fully automated and doesn’t require user interaction.