Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

Effects of Dual-Frequency Ultrasound-Assisted Thawing Technology on Thawing Rate, Quality Properties and Microstructure of Large Yellow Croaker (Pseudosciaena crocea)

This research evaluated the effects of dual-frequency ultrasound-assisted thawing (UAT) on the thawing time, physicochemical quality, water-holding capacity (WHC), microstructure, and moisture migration and distribution of large yellow croaker. Water thawing (WT), refrigerated thawing (RT), and UAT (single-frequency: 28 kHz (SUAT-28), single-frequency: 40 kHz (SUAT-40), dual-frequency: 28 kHz and 40 kHz (DUAT-28/40)) were used in the current research. Among them, the DUAT-28/40 treatment had the shortest thawing time, and ultrasound significantly improved the thawing rate. It also retained a better performance from the samples, such as color, texture, water-holding capacity and water distribution, and inhibited disruption of the microstructure. In addition, a quality property analysis showed that the pH, total volatile basic nitrogen (TVB-N), and K value were the most desirable under the DUAT-28/40 treatment, as well as this being best for the flavor of the samples. Therefore, DUAT-28/40 treatment could be a possible thawing method because it improves the thawing rate and maintains the quality properties of large yellow croaker.

Investigation of edge radiation intensification effect generated by periodic alignment of three-pole wigglers

The intensification effect of edge radiation due to the periodic alignment of three-pole wigglers was analytically and numerically investigated. The radiation properties were studied using a simple model that had an alternating alignment of straight sections and large gradient orbit sections due to the use of three-pole wigglers. The angular distribution of the radiation was concentrated on a concentric circle. The peak intensity of the radiation was roughly on the same order as that of the peak radiation of a planar undulator. The spectrum of the radiation had a characteristic structure that was rather similar to the higher harmonic structure of undulator radiation. A numerical study showed that a planar undulator with a specific K value satisfies approximately the radiation intensification condition due to the periodic alignment of the three-pole wigglers. The intensified edge radiation is included in the undulator radiation.

KNNHI: Resilient KNN algorithm for heterogeneous incomplete data classification and K identification using rough set theory

The original K-nearest neighbour ( KNN) algorithm was meant to classify homogeneous complete data, that is, data with only numerical features whose values exist completely. Thus, it faces problems when used with heterogeneous incomplete (HI) data, which has also categorical features and is plagued with missing values. Many solutions have been proposed over the years but most have pitfalls. For example, some solve heterogeneity by converting categorical features into numerical ones, inflicting structural damage. Others solve incompleteness by imputation or elimination, causing semantic disturbance. Almost all use the same K for all query objects, leading to misclassification. In the present work, we introduce KNNHI, a KNN-based algorithm for HI data classification that avoids all these pitfalls. Leveraging rough set theory, KNNHI preserves both categorical and numerical features, leaves missing values untouched and uses a different K for each query. The end result is an accurate classifier, as demonstrated by extensive experimentation on nine datasets mostly from the University of California Irvine repository, using a 10-fold cross-validation technique. We show that KNNHI outperforms six recently published KNN-based algorithms, in terms of precision, recall, accuracy and F-Score. In addition to its function as a mighty classifier, KNNHI can also serve as a K calculator, helping KNN-based algorithms that use a single K value for all queries that find the best such value. Sure enough, we show how four such algorithms improve their performance using the K obtained by KNNHI. Finally, KNNHI exhibits impressive resilience to the degree of incompleteness, degree of heterogeneity and the metric used to measure distance.

A Highly Fluorescent Pyrene-based Sensor for Selective Detection of Fe3+ Ion in Aqueous Medium: DFT and Molecular Docking Studies

In this work, we introduce a highly selective and sensitive fluorescent sensor based on pyrene derivative for Fe(III) ion sensing in DMSO/water media. 2-(pyrene-2-yl)-1-(pyrene-2-ylmethyl)-1H-benzo[d]imidazole (PEBD) receptor was synthesized via simple condensation reaction and confirmed by spectroscopic techniques. The receptor exhibits fluorescence quenching in the presence of Fe(III) ions at 440 nm. ESI-MS and Job’s method were used to confirm the 1:1 molar binding ratio of the receptor PEBD to Fe(III) ions. Using the Benesi-Hildebrand equation the binding constant value was determined as 8.485×103 M-1. Furthermore, the limit of detection (LOD, 3σ/K) value was found to be 1.81µM in DMSO/water (95/5, v/v) media. According to the Environmental Protection Agency (EPA) of the United States, it is lower than the acceptable value of Fe3+ in drinking water (0.3 mg/L). The presence of 14 other metal ions such Co2+, Cr3+, Cu2+, Fe2+, Hg2+, Pb2+, K+, Ni2+, Mg2+, Cd2+, Ca2+, Mn2+, Al3+, and Zn2+ did not interfere with the detection of Fe(III) ions. Computational studies of the receptor PEBD were carried out with density functional theory (DFT) using B3LYP/ 6-311G (d, p), LANL2DZ level of theory. Finally, molecular docking studies have been performed to investigate the Cytochrome P450 1A1(CYP1A1) protein inhibitory action of the receptor PEBD.

Influence of Lower Extremity Deep Venous Thrombosis in Cerebral Infarction on Coagulation Index and Thromboelastogram and Its Risk Factors

Cerebral infarction is a serious brain injury disease, which is mainly caused by the blockage of blood circulation in patients’ brains; thus, the patient’s brain appears ischemia and hypoxia state, and large-scale nerve cell death occurs immediately. The aim of this study was to explore the influence of lower extremity deep venous thrombosis (LEDVT) on coagulation indexes and thromboelastogram (TEG) after cerebral infarction. Altogether, 67 patients with cerebral infarction complicated with LEDVT in our hospital from April 2017 to August 2019 were collected as the observation group (OG) and 58 patients with cerebral infarction without lower extremity deep venous thrombosis as the control group (CG). The R, K, angle, and MA values in PT, APTT, TT, FIB, and TEG indexes were compared between the two groups. The ROC curve was applied to analyze the diagnostic value of R value, K value, angle value, and MA value in the occurrence of LEDVT in patients with cerebral infarction. Logistic regression analysis was applied to analyze the independent risk factors of lower extremity deep venous thrombosis in cerebral infarction. PT, APTT, and TT in the OG were evidently lower than those in the CG, while FIB in the OG was evidently higher than that in the CG, R value and K value of the OG were evidently lower than those of the CG, and angle and MA values were higher than those in the CG. The AUC of R value, K value, angle value, and MA value of the ROC curve of LEDVT in patients with cerebral infarction was 0.735, 0.713, 0.790, and 0.819. Multivariate analysis showed that high FIB, angle, and MA were risk factors, while R and K values were protective factors. PT, APTT, and TT are lower and FIB is higher in patients with cerebral infarction with LEDVT. TEG has a certain diagnostic value. FIB value, angle value, and MA value are independent risk factors of LEDVT in patients with cerebral infarction, while R value and K value are protective factors.

Low density rigid polyurethane foam incorporated with renewable polyol as sustainable thermal insulation material

Palm olein-based polyol (PP) was used as a partial replacement for commercial sucrose/glycerine initiated polyether polyol (GP) for the production of low density rigid polyurethane foams (RPUFs). The hydroxyl value (OHV) of the GP was 380 mg KOH/g, whereas the OHV for PP was 360 mg KOH/g. The RPUFs were prepared by replacing the GP with PP up to 50 parts per hundred parts of polyols (pph). Characterisation of the RPUFs, including density, compressive strength and strain, cell morphology and thermal conductivity ( k-value), were conducted. The dimensional stability of the foams was also evaluated. The study showed improvement in the compressive strength and strain for palm-based RPUFs with the incorporation of up to 30 pph PP as compared to GP foams. The lowest k-value (0.0232 W/m.K) of RPUF with density below 30 kg/m3 was obtained with the incorporation of 10 pph PP. This was due to the smallest and uniform pore size distribution observed using SEM images. The dimensional stability of the RPUF prepared from PP was within the acceptable range. Thus, the RPUFs made from PP are potential candidates to be used as insulation for refrigerators, freezers and piping.

IDENTIFICATION OF HOAX BASED ON TEXT MINING USING K-NEAREST NEIGHBOR METHOD

At this time, information is very easy to obtain, information can spread quickly to all corners of society. However, the information that spreaded are not all true, there is false information or what is commonly called hoax which of course is also easily spread by the public, the public only thinks that all the information circulating on the internet is true. From every news published on the internet, it cannot be known directly that the news is a hoax or valid one. The test uses 740 random contents / issue data that has been verified by an institution, where 370 contents are hoaxes and 370 contents are valid. The test uses the K-Nearest Neighbor algorithm, before the classification process is performed, the preprocessing stage is performed first and uses the TF-IDF equation to get the weight of each feature, then classified using K-Nearest Neighbor and the test results is evaluated using 10-Fold Cross Validation. The test uses the k value with a value of 2 to 10. The optimal use of the k value in the implementation is obtained at a value of k = 4 with precision, recall, and F-Measure results of 0.764856, 0.757583, and 0.751944 respectively and an accuracy of 75.4%

Analysis and Evaluation of the Development of Various k-Values for Use in Design

Inconsistency exists between common conversions from soil index properties (e.g., CBR) to a design k-value and a widespread nomograph that has become the definitive industry reference on the topic in the United States. Propagation of these inconsistencies into guidance from groups like the American Concrete Pavement Association (ACPA) and American Concrete Institute (ACI) Committees 330 and 360 has contributed to confusion in the industry. Advancements between the pavement and slab-on-ground communities have occurred in parallel but are inconsistent with each other, thus adding more confusion. ACPA developed a conversion set to better align the industry on a static k-value for design. While the ACPA model is included in StreetPave, PavementDesigner.org, and the ACPA App Library, outdated conversion equations are frequently used due to familiarity and lack of understanding of the underlying principles. This paper presents a summary of the industry's prior practices and recommendations, a detailing of the approach proposed by ACPA, and guidance on which k-value is recommended for design of concrete pavements and slabs-on-ground.

Indonesian Online News Topics Classification using Word2Vec and K-Nearest Neighbor

News is information disseminated by newspapers, radio, television, the internet, and other media. According to the survey results, there are many news titles from various topics spread on the internet. This of course makes newsreaders have difficulty when they want to find the desired news topic to read. These problems can be solved by grouping or so-called classification. The classification process is carried out of course by using a computerized process. This study aims to classify several news topics in Indonesian language using the KNN classification model and word2vec to convert words into vectors which aim to facilitate the classification process. The use of KNN in this study also determines the optimal K value to be used. In addition to using the classification model, this study also uses a word embedding-based model, namely word2vec. The results obtained using the word2vec and KNN models have an accuracy of 89.2% with a value of K=7. The word2vec and KNN models are also superior to the support vector machine, logistic regression, and random forest classification models.