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One of the primary challenges of magnetic drug targeting is to achieve efficient and accurate delivery of drug particles to the desired sites in complex physiological conditions. Though a majority of drugs are delivered through intravenous administration, until now, the kinematics and dynamics of drug particles influenced by the magnetic field, vascular topology and blood flows are still less understood. In this work, a multi-physics dynamical model which captures transient particle motions in both artificial andin vivo-like 3D vascular networks manipulated by the time-varying magnetic field is developed. Based on the model, it is found that particles which perform a random walk with correlated speed and persistence (RWSP motion) inspired by the migratory motion of immune and metastasis cells have higher mobility and navigation ability in both 2D and 3D tree-like and web-like networks. Moreover, to steer particles to perform the efficient RWSP motion, a stochastic magnetic steering strategy which uses time-varying gradient magnetic field is proposed. Parameters of the steering strategy is optimized and the capability of controlling particles to achieve fast spreading and transport in the vascular networks is demonstrated. In addition, the influence of heterogeneous flows in the vascular networks on the particle steering efficiency is discussed. Overall, the numerical model and the magnetic steering strategy can be widely used in the drug delivery systems for precise medicine research.

In this article, a new procedure is proposed on the basis of Hilbert-Huang Transform and deep learning for cyber-attacks detection in direct current (DC) micro-grids (MGs) as well as detection of the attacks in distributed generation (DG) units and its sensors. An advanced elective group deep learning method with Krill Herd Optimization (KHO) algorithm is proposed. At first, Hilbert-Huang Transform is used with the aim of extracting the signals feature and next these features are applied as the multiple deep input basis models are made with the aim of capturing automatically sentient traits from raw fluctuation signals. At third, to make sure the variety of the basis patterns, linear decoder, denoising autoencoder and sparse autoencoder are applied to make various deep autoencoders, respectively. Further, Bootstrap is applied with the aim of designing separate educational data subsets for any base model. Fourth, for implementing selective ensemble learning, a combination strategy of enhanced weighted voting (EWV) with class-particular thresholds is studied. Eventually, KHO algorithm is applied with the aim of adaptive selecting the optimal class-specific thresholds. In the offered tactic, firstly, a DC micro-grid is functioned and controlled with the lack of any false data injection attacks (FDIAs) to collect adequate information within the usual operation needed for the educating of deep learning networks. It is noteworthy that, in the procedure of datum production, load variable is also determined with the aim of having distinctive datasets for cyber-attack scenarios and load variables. Also, to provide more realistic method, the smart plug-in electric vehicle is also considered in the model. Outcomes of Simulation in various scenarios are applied with the aim of verifying the benefit of the offered procedure. The outcomes propose that the offered procedure is able to more accurate and robust know various type of false data injection attack over than 93.76% accuracy detection of true rate.

The RMG-database for chemical property prediction is presented. The RMG-database consists of curated datasets and estimators for accurately predicting parameters necessary for constructing a wide variety of chemical kinetic mechanisms, including thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG-database contains 45 libraries of thermochemical parameters with a combined 4564 entries, a group additivity scheme with nine types of corrections including radical, polycyclic and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural net trained using transfer learning from a set of >130,000 DFT calculations to 10,000 high-quality values. Correction schemes for solvent-solute effects, important for thermochemistry in the liquid phase, are available. They include tabled values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation the database contains 92 libraries of kinetic parameters containing a combined 21,000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG-database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction and by providing information to check other kinetic parameters against.

SARS-CoV-2 belongs to the coronavirus family. Comparing genomic features of viral genomes of coronavirus family can improve our understanding about SARS-CoV-2. Here we present the first pan-genome analysis of 3,932 whole genomes of 101 species out of 4 genera from the coronavirus family. We found that a total of 181 genes in the pan-genome of coronavirus family, among which only 3 genes, the S gene, M gene and N gene, are highly conserved. We also constructed a pan-genome from 23,539 whole genomes of SARS-CoV-2. There are 13 genes in total in the SARS-CoV-2 pan-genome. All of the 13 genes are core genes for SARS-CoV-2. The pan-genome of coronaviruses shows a lower level of diversity than the pan-genomes of other RNA viruses, which contain no core gene. The three highly conserved genes in coronavirus family, which are also core genes in SARS-CoV-2 pan-genome, could be potential targets in developing nucleic acid diagnostic reagents with a decreased possibility of cross-reaction with other coronavirus species.

N-doped blue-fluorescence carbon dots (N-CDs) were fabricated via a one-pot hydrothermal method using folic acid and p-phenylenediamine. The obtained N-CDs exhibited strong fluorescence (FL) with a considerable quantum yield (QY) of 21.8% and exceptional optical stability under different conditions. Upon introducing Cr(vi), blue FL of N-CDs was distinctly quenched. On subsequent addition of l-AA, the FL of N-CDs could be partially recovered. The fluorescence changes of N-CDs have been utilized to detect Cr(vi) and l-AA in aqueous solutions with linear ranges of 0.10-150 μM and 0.75-2.25 mM, respectively, as well as limit of detection values of 9.4 nM and 25 μM, respectively. Furthermore, as-obtained N-CDs can be extended to monitor the fluctuation of intracellular Cr(vi) and l-AA. More intriguingly, N-CDs can target lysosomes with a satisfactory Pearson correction coefficient of 0.87, which indicates a promising application prospect in the biomedical field.

Light alkenes are common combustion intermediates for a variety of fuels. Therefore, understanding their oxidation and pyrolysis chemistry is key to building detailed mechanisms for heavier fuels. This work was focused on the development and evaluation of a detailed kinetic mechanism suitable for the combustion of light alkenes up to C4 without the use of tuned parameters, instead, the parameter values come from first principles or direct measurements. The generated mechanism accurately estimates the laminar burning velocity (Su) and ignition delay time (IDT) of light alkenes available in the literature, which represent fundamental combustion properties at a wide range of conditions. Because each parameter is thought to have a physically realistic value, not tuned to these measurements, the new model could be used as a sub-mechanism in models for other applications. The reaction network was generated with the open-source Reaction Mechanism Generator (RMG) software. Sensitivity analyses were performed under wide ranges of temperatures and pressures, allowing for the identification of the most impactful species and reactions. Based on these, a comprehensive thermochemistry database, including calculations on 550 molecules performed in this work at the CBS-QB3 level of theory, and a kinetic library, including theoretically-derived reaction rates retrieved from the literature, were built and used in the mechanism generation. The developed mechanism was compared against several existing detailed kinetic mechanisms for ethene, propene, 1-butene, 2-butene, and isobutene. The newly generated model is the most accurate among the ones analyzed, in terms of fractional bias and normalized mean square error. Hence, this new model was used to analyze the chemistry of alkene combustion. Key rate coefficients were compared, to identify the cause of deviations between the models and possible areas for further improvements. © 2022

Aim Pre-eclampsia (PE) is the usual complication during pregnancy. Long noncoding RNAs are essential regulatory factors in many diseases. Nevertheless, the role of LINC00511 in the development of PE has not been fully elucidated. Methods The expression of LINC00511, homeobox protein A7 (HOXA7) and miR-31-5p was determined by quantitative real-time polymerase chain reaction. The levels of HOXA7 protein and autophagy-related proteins were measured by western blot analysis. Besides, cell proliferation was evaluated using cell counting kit 8 and colony formation assays. The apoptosis and invasion of cells were detected via flow cytometry and transwell assay, respectively. Further, the interaction between miR-31-5p and LINC00511 or HOXA7 was confirmed by dual-luciferase reporter assay. Results The LINC00511 and HOXA7 expression levels were decreased in placental tissues of PE patients, and the expression levels of both were positively correlated. LINC00511 knockdown suppressed proliferation, invasion and autophagy, while enhanced apoptosis in trophoblast cells. Meanwhile, the elevated HOXA7 expression promoted proliferation, invasion, autophagy, and inhibited the apoptosis of trophoblast cells. Besides, overexpression of HOXA7 also could reverse the effect of LINC00511 knockdown on the biological function of trophoblast cells. Further experiments confirmed that miR-31-5p could be sponged by LINC00511 and could target HOXA7. Also, miR-31-5p mimic could invert the promoting effect of LINC00511 overexpression on the biological function of trophoblast cells. Conclusion LINC00511 expression was crucial for maintaining the normal function of trophoblast cells, and the decreased its expression might promote the progress of PE, which might provide some theoretical strategies for reducing the development of PE.

Dual-excitation and dual-emission carbon dots (CDs) have been prepared by simple one-step hydrothermal treatment of

Sensor drift is one of the important problems affecting the stability of gas sensing and detection system. We proposed a novel memory mechanism, which can be combined with the traditional machine learning classifier to deal with and improve the sensor drift problem. Based on the memory mechanism, we established recognition model, which adopts support vector machine as base-classifier, to achieve better classification effect and eliminate the imbalance of data set. The Gas Sensor Array Drift Dataset from UCI Machine Learning Repository was selected for experimental verification. Through experiments, the method proposed in this paper can improve the classification effect in the short and medium term, postpone the negative effects brought by drift to a certain extent, and then extend the service life of the gas sensor.

Interpreting functional analysis results derived from environmental samples using direct sequencing meta-omics data, including metagenomics and meta-transcriptomics data, is challenging due to their complexity. Visualization of functional analysis results can help researchers discover relevant biological insights. Despite the availability of many R packages, there lacks interactive and comprehensive graphic systems for displaying functional terms and corresponding genes in meta-omics analysis results. Here, we present ivTerm, an R-shiny package with a user-friendly graphical interface that enables users to inspect functional annotations, compare results across multiple experiments, create customized charts, and download these charts. It provides various basic and innovative chart types to visualize functional terms and involved genes. Users can also browse the description of terms obtained from the database web servers automatically. Two examples, including a metagenome analysis data for human gut and a meta-transcriptome data for coral symbiomes, are given to show the usage of ivTerm. In the end, we compared ivTerm with existing tools with similar functions, such as GOplot, ViSEAGO, and Chordomics. The tool ivTerm is convenient and efficient for biologists to gain an integrated view and develop deep insights by interactive analysis of meta-omics data. It can accelerate the procedure to develop insights from complex meta-omics data. The code for ivTerm is freely available at https://github.com/SJTU-CGM/ivTerm.

In proteomics, it is important to detect, analyze, and quantify complex peptide components and differences. The key is to match the elution time peaks (LC peaks) produced by the same peptide in replicate experiments. Warping functions are currently widely used to correct the mean of time shifts among replicates. However, they cannot reduce the ambiguity to distinguish the corresponding peak pairs and the non-corresponding ones because the time shifts are random based on each extracted-ion-chromatogram (XIC). In this paper, besides time feature, isotope distribution pattern similarity is considered. The novelty is that compared with other feature based methods including the isotope feature, the algorithm is not based on the peak profile similarity as usual, but on the isotope distribution similarity. First, the training set of peptides including the corresponding and non-corresponding peak pairs were selected from the MS/MS results. Second, we generated time difference and isotope distribution pattern similarities for each peak pair. Third, Support Vector Machine (SVM) classification was used based on the two features. Finally, the accuracy was measured along with final coverage. We first used a 10-fold cross validation to test the effectiveness of the SVM learning model. The accuracy of correct matching could reach 97%. Second, we evaluated the coverage based on the learning model, which could be from 75% to 91% in different datasets. Thus, this matching algorithm based on time and isotope distribution pattern features could provide a high accuracy and coverage for the corresponding peak identification.

Thermal efficiency is a very important index for heating furnace in chemical industry. In order to predict the complicated variables, the Bagging-ELM network based on ensemble learning and Extreme Learning Machine is adopted. The proposed model is applied to predicting the net hourly electrical energy output in Combined Cycle Power Plant data set and the thermal efficiency of the heating furnace. There is a comparison between the results generated by Bagging-ELM model and those generated by BP model, RBF model, ELM model. Compared with other models, the Bagging-ELM can obtain higher accuracy and stability.

Electronic nose is one of the research hotspots in the field of engineering. In order to solve the chemical gas recognition problem of electronic nose (that is, sensor array), we designed and established a recognition model based on random forest, entropy weight and bootstrap aggregating. The model has been successfully applied to the UCI Gas Sensor Array Drift Dataset and achieved excellent effect and reliability, avoiding the adverse effects caused by drift problem and unbalanced data distribution to a certain extent. The design and implementation method of the recognition model has certain reference value to the research of related fields.

Zombie enterprise portrait and classification is one of the urgent problems in the current society, which has important practical significance and research value. This paper presents an adaptive weighted Bagging integrated learning method, which integrates 5 regular models and 8 pattern recognition models. The weight of the base classifier in integrated learning can be adjusted adaptively according to the training process to reduce the subjectivity and limitation of the regular model as much as possible. The accuracy, precision and recall rate of the model are all up to 1.0 in the experiment. At the same time, the strategy of data cleaning and missing item completion for problem domain is proposed. The research methods and results proposed in this paper have certain reference significance for the study of zombie enterprise portrait and classification and its related fields.

Cyclopentanone (CPO) is a promising biofuel for spark-ignition engines due to its ring strain and high auto-ignition resistance. Understanding CPO decomposition is crucial for building a high-temperature combustion model. Here we present a comprehensive kinetic model for high-temperature pyrolysis of CPO with verified results from high-pressure shock tube (HPST) measurements. The time-histories of carbon monoxide (CO), ethylene (C₂H₄), and CPO absorbances over the temperature range of 1156–1416 K and pressure range of 8.53–10.06 atm were measured during current experiments. A corresponding detailed kinetic model was generated using the Reaction Mechanism Generator (RMG) with dominant unimolecular/radical-involved decomposition pathways from either previous studies or quantum calculations within the current work. The obtained model containing 821 species and 79,859 reactions exhibited a good agreement with the experimental results. In this study, the absorbance ratio between C₂H₄ and CO was used as an important factor to validate models and to prove that radical-involved bimolecular pathways were as significant as unimolecular decomposition of CPO. The rate of production (ROP) analysis showed H radicals play a major role in the decomposition, and the whole decomposition process could be divided into three stages based on the H radical concentration. The insights from present work can be used to generate a better CPO combustion model and help evaluate CPO as an advanced biofuel., Department of Energy (Grant DE-EE007982)

Government WeChat matrix, which has played an important role in the openness of government affairs, public opinion supervision and public services, is one of the main forms of e-government in contemporary China. We took prefecture-level city WeChat public account as the core research object, analyzed the matrix operation mode of government affairs WeChat public account, and proposed a star matrix model. In the meantime, we also designed the corresponding information management system, using first normal form and third normal form mixed database design mode, using dynamic generated static web page as the front-end user interface, to ensure the stability and security of the system, as far as possible to reduce the waiting time for system access, improve the efficiency of implementation. This matrix structure construction and information system implementation method proposed in this paper has certain reference value for the research of related fields.

With the development of industrial interconnection, Customer Relationship Management plays an important role on airlines' servicing and marketing as core competence and brand reputation is determined by customer relationship. In order to fit the globalization and promote the profit from group customer efficiently, airline companies urgently need to establish the personalized Group-CRM, which will be fully applied in controlling service process and making great profit. Aimed at this goal, we proposed a Group-CRM microservice architecture in Unified Modeling Language. Rabbit MQ and ETL are applied in data interaction with related service. Meanwhile, solution in this paper can be widely referenced for other large-scale airline companies and related industry.

Government microblog is one of the important online communication modes of socialized participation, discussion and inquiry in politics in contemporary China. The matrix mode of government microblog is helpful to strengthen the inter-departmental, cross-level and cross-regional linkage of government microblog, which is one of the hot research directions at present. Based on the operation process, we used UML technology to propose a system architecture and database design method of microblog matrix management system, which established the reasonable granularity of functional modules and found the balance between data redundancy and data correlation to ensure the efficiency and reliability of the whole system. The architecture design method proposed in this paper has certain reference value for the research of related fields.

Pre-eclampsia (PE) is the usual complication during pregnancy. Long noncoding RNAs are essential regulatory factors in many diseases. Nevertheless, the role of LINC00511 in the development of PE has not been fully elucidated.The expression of LINC00511, homeobox protein A7 (HOXA7) and miR-31-5p was determined by quantitative real-time polymerase chain reaction. The levels of HOXA7 protein and autophagy-related proteins were measured by western blot analysis. Besides, cell proliferation was evaluated using cell counting kit 8 and colony formation assays. The apoptosis and invasion of cells were detected via flow cytometry and transwell assay, respectively. Further, the interaction between miR-31-5p and LINC00511 or HOXA7 was confirmed by dual-luciferase reporter assay.The LINC00511 and HOXA7 expression levels were decreased in placental tissues of PE patients, and the expression levels of both were positively correlated. LINC00511 knockdown suppressed proliferation, invasion and autophagy, while enhanced apoptosis in trophoblast cells. Meanwhile, the elevated HOXA7 expression promoted proliferation, invasion, autophagy, and inhibited the apoptosis of trophoblast cells. Besides, overexpression of HOXA7 also could reverse the effect of LINC00511 knockdown on the biological function of trophoblast cells. Further experiments confirmed that miR-31-5p could be sponged by LINC00511 and could target HOXA7. Also, miR-31-5p mimic could invert the promoting effect of LINC00511 overexpression on the biological function of trophoblast cells.LINC00511 expression was crucial for maintaining the normal function of trophoblast cells, and the decreased its expression might promote the progress of PE, which might provide some theoretical strategies for reducing the development of PE.

To take full advantage of the waste heat from a diesel engine, a set of dual loop organic Rankine cycle (ORC) system is designed to recover exhaust energy, waste heat from the coolant system, and released heat from turbocharged air in the intercooler of a six-cylinder diesel engine. The dual loop ORC system consists of a high temperature loop ORC system and a low temperature loop ORC system. R245fa is selected as the working fluid for both loops. Through the engine test, based on the first and second laws of thermodynamics, the performance of the dual loop ORC system for waste heat recovery is discussed based on the analysis of its waste heat characteristics under engine various operating conditions. Subsequently, the diesel engine-dual loop ORC combined system is presented, and the effective thermal efficiency and the brake specific fuel consumption (BSFC) are chosen to evaluate the operating performances of the diesel engine-dual loop ORC combined system. The results show that, the maximum waste heat recovery efficiency (WHRE) of the dual loop ORC system can reach 5.4% under engine various operating conditions. At the engine rated condition, the dual loop ORC system achieves the largest net power output at 27.85 kW. Compared with the diesel engine, the thermal efficiency of the combined system can be increased by 13%. When the diesel engine is operating at the high load region, the BSFC can be reduced by a maximum 4%.