5-Chloro­methyl-1,3-dimethyl-1H-pyrazole

The pyazole ring in the title compound, C6H9ClN2, is almost planar (r.m.s. deviation = 0.003 Å). In the crystal, mol­ecules are linked by C—H⋯N inter­actions, forming [100] chains

Ethyl 3-bromo-1-(3-chloro-2-pyrid­yl)-4,5-dihydro-1H-pyrazole-5-carboxyl­ate

The title compound, C11H11BrClN3O2, contains two mol­ecules in the asymmetric unit in which the dihedral angles between the pyrazole and pyridine rings are 30.0 (2) and 22.3 (2)°

(E)-2-[(4-Chloro-1,3-dimethyl-1H-pyrazol-5-yl)methyl­eneamino]benzamide

In the title compound, C13H13ClN4O, the dihedral angle between the aromatic rings is 33.47 (9)° and an intra­molecular N—H⋯N hydrogen bond generates an S(6) ring. In the crystal, inversion dimers linked by pairs of N—H⋯O hydrogen bonds occur, resulting in R 2 2(8) loops

2-(3-Methyl-2-nitro­phen­yl)-4,5-dihydro-1,3-oxazole

In the title compound, C10H10N2O3, an inter­mediate in the synthesis of anthranilamide insecticides, all the non-H atoms except the nitro-group O atom lie on a crystallographic mirror plane. The H atoms of the methyl group are disordered over two sets of sites with equal occupancies. In the crystal structure, C—H⋯N links lead to chains of mol­ecules propagating in [100]

Enhancing cutting tool sustainability based on remaining useful life prediction

As a critical part of machining, cutting tools are of great importance to sustainability enhancement. Normally, they are underused, resulting in huge waste. However, the lack of reliable support leads to a high risk on improving the cutting tool utilization. Aiming at this problem, this paper proposes an approach to enhance the cutting tool sustainability. A non-linear cutting tool remaining useful life prediction model is developed based on tool wear historical data. Probability distribution function and cumulative distribution function are used to quantize the uncertainty of the prediction. Under a constant machining condition, a cutting tool life is extended according to its specific remaining useful life prediction, rather than a unified one. Under various machining conditions, machining parameters are optimized to improve efficiency or capability. Cutting tool sustainability is assessed in economic, environmental and social dimensions. Experimental study verifies that both material removal rate and material removal volume are improved. Carbon emission and cutting tool cost are also reduced. The balance between benefit and risk is achieved by assigning a reasonable confidence level. Cutting tool sustainability can be enhanced by improving cutting tool utilization at controllable risk.©2020 Elsevier. This manuscript version is made available under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International (CC BY–NC–ND 4.0) license, https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models

Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk (RR) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health

Upregulation of SMAD4 inhibits thyroid cancer cell growth via MAPK/JNK pathway repression

Purpose: To investigate whether the effect of mothers against decapentaplegic homolog 4 (SMAD4) on thyroid cancer cell survival was via the MAPK/JNK pathway. Methods: Papillary thyroid cancer (TPC)-1 cells were cultured and transfected with SMAD4 overexpression plasmid or siRNA to achieve SMAD4 overexpression or knockdown, respectively. In TPC-1 cells, the mRNA and protein expression levels of SMAD4, mitogen-activated protein kinase (MAPK), and c-Jun N-terminal kinase (JNK) were quantified using reverse transcription-quantitative polymerase chain reaction and western blotting, respectively. Cell viability and apoptosis were measured using MTT assay and flow cytometry, respectively. MAPK and JNK inhibitors (U0126 and SP600125) were used for rescue experiments. The sensitivity of TPC-1 cells to chemotherapeutic drugs, cisplatin and doxorubicin, was also assessed. Results: A reduction in viability and an enhancement in apoptosis (p < 0.01) were found when SMAD4 was overexpressed in TPC-1 cells. Knockdown of SMAD4 elicited opposite results (p < 0.01). Overexpression of SMAD4 caused a decrease in the activation of MAPK and JNK, as evidenced by lower levels of phosphorylated MAPK and phosphorylated JNK (p < 0.05). Results from rescue experiments indicate that the increase in cell viability after SMAD4 knockdown was reversed by MAPK/JNK inhibitors (p < 0.05 and p < 0.01). Finally, overexpression of SMAD4 increased cytotoxic susceptibility of thyroid cancer cells to cisplatin/doxorubicin. Conclusion: These results indicate that SMAD4 inhibits thyroid cancer cell growth via inactivation of MAPK/JNK pathway. Overexpression of SMAD4 also increased thyroid cancer cell sensitivity to cisplatin/doxorubicin

Strong Tracking Filtering Algorithm of Randomly Delayed Measurements for Nonlinear Systems

This paper focuses on the filtering problems of nonlinear discrete-time stochastic dynamic systems, such as the model simplification, noise characteristics uncertainty, initial conditions uncertainty, or system parametric variation. Under these circumstances, the measurements of system have one sampling time random delay. A new method, that is, strong tracking filtering algorithm of randomly delayed measurements (STF/RDM) for nonlinear systems based on recursive operating by analytical computation and first-order linear approximations, is proposed; a principle of extended orthogonality is presented as a criterion of designing the STF/RDM, and through the residuals between available and predicted measurements, the formula of fading factor is obtained. Under the premise of using the extended orthogonality principle, STF/RDM proposed in this paper can adjust the fading factor online via calculating the covariance of residuals, and then the gain matrices of the STF/RDM adjust in real time to enhance the performance of the proposed method. Lastly, in order to prove that the performance of STF/RDM precedes existing EKF method, the experiment of tracking maneuvering aircraft is carried out

Few-shot remote sensing scene classification based on multi subband deep feature fusion

Recently, convolutional neural networks (CNNs) have performed well in object classification and object recognition. However, due to the particularity of geographic data, the labeled samples are seriously insufficient, which limits the practical application of CNN methods in remote sensing (RS) image processing. To address the problem of small sample RS image classification, a discrete wavelet-based multi-level deep feature fusion method is proposed. First, the deep features are extracted from the RS images using pre-trained deep CNNs and discrete wavelet transform (DWT) methods. Next, a modified discriminant correlation analysis (DCA) approach is proposed to distinguish easily confused categories effectively, which is based on the distance coefficient of between-class. The proposed approach can effectively integrate the deep feature information of various frequency bands. Thereby, the proposed method obtains the low-dimensional features with good discrimination, which is demonstrated through experiments on four benchmark datasets. Compared with several state-of-the-art methods, the proposed method achieves outstanding performance under limited training samples, especially one or two training samples per class