Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis(TB),especially multidrug-resistant TB and extensively drug-resistant TB,has strongly motivated the research and development of new anti-TB drugs.New strategies to facilitate drug combinations,including pharmacokinetics-guided dose optimization and toxicology studies of first-and second-line anti-TB drugs have also been introduced and recommended.Liquid chromatography-mass spectrometry(LC-MS)has arguably become the gold standard in the analysis of both endo-and exo-genous compounds.This technique has been applied successfully not only for therapeutic drug monitoring(TDM)but also for pharmacometabolomics analysis.TDM improves the effectiveness of treatment,reduces adverse drug reactions,and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window.Based on TDM,the dose would be optimized individually to achieve favorable outcomes.Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs,aiding in the discovery of potential biomarkers for TB diagnostics,treatment monitoring,and outcome evaluation.This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades.Besides,we discussed the advantages and disadvantages of this technique in practical use.The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted.Lastly,we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies(pharmacometrics,drug and vaccine developments,machine learning/artificial intelligence,among others)to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

Smart Security Framework for Educational Institutions Using Internet of Things(IoT)

Educational institutions are soft targets for the terrorist with massive and defenseless people.In the recent past,numbers of such attacks have been executed around the world.Conducting research,in order to provide a secure environment to the educational institutions is a challenging task.This effort is motivated by recent assaults,made at Army Public School Peshawar,following another attack at Charsada University,Khyber Pukhtun Khwa,Pakistan and also the Santa Fe High School Texas,USA massacre.This study uses the basic technologies of edge computing,cloud computing and IoT to design a smart emergency alarm system framework.IoT is engaged in developing this world smarter,can contribute significantly to design the Smart Security Framework(SSF)for educational institutions.In the emergency situation,all the command and control centres must be informed within seconds to halt or minimize the loss.In this article,the SSF is proposed.This framework works on three layers.The first layer is the sensors and smart devices layer.All these sensors and smart devices are connected to the Emergency Control Room(ECR),which is the second layer of the proposed framework.The second layer uses edge computing technologies to process massive data and information locally.The third layer uses cloud computing techniques to transmit and process data and information to different command and control centres.The proposed system was tested on Cisco Packet Tracer 7.The result shows that this approach can play an efficient role in security alert,not only in the educational institutions but also in other organizations too.

Lithium-conductive LiNbO_(3) coated high-voltage LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2) cathode with enhanced rate and cyclability

LiNi_(0.5)Co_(0.2)Mn_(0.3)O_(2)(NCM523) cathode materials can operate at extremely high voltages and have exceptional energy density.However,their use is limited by inherent structure instability during charge/discharge and exceptionally oxidizing Ni^(4+)at the surface.Herein,we have developed a citrate-assisted deposition concept to achieve a uniform lithium-conductive LiNbO_(3) coating layer on the NCM523 surface that avoids self-nucleation of Nb-contained compounds in solution reaction.The electrode-electrolyte interface is therefore stabilized by physically blocking the detrimental parasitic reactions and Ni^(4+)dissolution whilst still maintaining high Li+conductivity.Consequently,the modified NCM523 exhibits an encouraging Li-storage specific capacity of 207.4 m Ah g-1at 0.2 C and 128.9 m Ah g-1 at 10 C over the range 3.0-4.5 V.Additionally,a 92% capacity retention was obtained after 100 cycles at 1 C,much higher than that of the pristine NCM523(73%).This surface engineering strategy can be extended to modify other Ni-rich cathode materials with durable electrochemical performances.

Biodegradation of Medicinal Plants Waste in an Anaerobic Digestion Reactor for Biogas Production

Glycyrrhiza glabra,Mint,Cuminum cyminum,Lavender and Arctium medicinal are considered as edible plants with therapeutic properties and as medicinal plants in Iran.After extraction process of medicinal plants,residual wastes are not suitable for animal feed and are considered as waste and as an environmental threat.At present there is no proper management of waste of these plants and they are burned or buried.The present study discusses the possibility of biogas production from Glycyrrhiza Glabra Waste(GGW),Mentha Waste(MW),Cuminum Cyminum Waste(CCW),Lavender Waste(LW)and Arctium Waste(AW).250 g of these plants with TS of 10%were digested in the batch type reactors at the temperature of 35℃.The highest biogas production rate were observed to be 13611 mL and 13471 mL for CCW and GGW(10%TS),respectively.While the maximum methane was related to GGW with a value of 9041 mL(10%TS).The highest specific biogas and methane production were related to CCW with value of 247.4 mL.(g.VS)-1 and 65.1 mL.(g.VS)-1,respectively.As an important result,it was obvious that in lignocellulose materials,it cannot be concluded that the materials with similar ratio of C/N has the similar digestion and biogas production ability.

Optimization Algorithm for Reduction the Size of Dixon Resultant Matrix:A Case Study on Mechanical Application

In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting polynomial.This paper aims at reducing the number of these factors via optimizing the size of Dixon matrix.An optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial systems.To do so,an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity analysis.Moreover,the monomial multipliers are optimally positioned to multiply each of the polynomials.Furthermore,through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated.

A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

Nowadays,air pollution is a big environmental problem in develop-ing countries.In this problem,particulate matter 2.5(PM2.5)in the air is an air pollutant.When its concentration in the air is high in developing countries like Vietnam,it will harm everyone’s health.Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen.This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City,Vietnam.Firstly,this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset.Only variables that affect the results will be selected for PM2.5 concentration prediction.Secondly,an efficient PM25-CBL model that integrates a convolutional neural network(CNN)andBidirectionalLongShort-TermMemory(Bi-LSTM)isdeveloped.This model consists of three following modules:CNN,Bi-LSTM,and Fully connected modules.Finally,this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM,Bi-LSTM,the combination of CNN and LSTM(CNN-LSTM),and ARIMA.The empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).

A Method of Integrating Length Constraints into Encoder-Decoder Transformer for Abstractive Text Summarization

Text summarization aims to generate a concise version of the original text.The longer the summary text is,themore detailed it will be fromthe original text,and this depends on the intended use.Therefore,the problem of generating summary texts with desired lengths is a vital task to put the research into practice.To solve this problem,in this paper,we propose a new method to integrate the desired length of the summarized text into the encoder-decoder model for the abstractive text summarization problem.This length parameter is integrated into the encoding phase at each self-attention step and the decoding process by preserving the remaining length for calculating headattention in the generation process and using it as length embeddings added to theword embeddings.We conducted experiments for the proposed model on the two data sets,Cable News Network(CNN)Daily and NEWSROOM,with different desired output lengths.The obtained results show the proposed model’s effectiveness compared with related studies.

A review of research on spontaneous combustion of coal

Coal oxidation at low temperatures is the heat source liable for the self-heating and spontaneous combustion of coal. This phenomenon has imposed severe problems in coal related industries. Attempts to understand this phenomenon by previous researchers have provided significant progress. It is wellknown that coal oxidation at low temperatures involves oxygen consumption and formation of gaseous and solid oxidation products. This process is majorly influenced by temperature, oxidation history of coal,coal properties, particle size distribution of the coal, etc. The current understanding of the phenomenon of self-heating and spontaneous combustion of coal is discussed along with the different experimental and numerical models established to predict self-heating characteristics of coal. This paper focuses on the global position of the study carried out by academics, research institutes and industries on spontaneous combustion of coal and coal mine fires. Within this framework, the generally used spontaneous combustion techniques to predict the spontaneous combustion liability of coal were evaluated. These techniques are well-known in their usage, but no specific method has become a standard to predict the spontaneous combustion liability. Further study is still needed to indicate a number of impending issues and to obtain a more complete understanding on the phenomenon.

A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate

In this paper,a deep collocation method(DCM)for thin plate bending problems is proposed.This method takes advantage of computational graphs and backpropagation algorithms involved in deep learning.Besides,the proposed DCM is based on a feedforward deep neural network(DNN)and differs from most previous applications of deep learning for mechanical problems.First,batches of randomly distributed collocation points are initially generated inside the domain and along the boundaries.A loss function is built with the aim that the governing partial differential equations(PDEs)of Kirchhoff plate bending problems,and the boundary/initial conditions are minimised at those collocation points.A combination of optimizers is adopted in the backpropagation process to minimize the loss function so as to obtain the optimal hyperparameters.In Kirchhoff plate bending problems,the C^1 continuity requirement poses significant difficulties in traditional mesh-based methods.This can be solved by the proposed DCM,which uses a deep neural network to approximate the continuous transversal deflection,and is proved to be suitable to the bending analysis of Kirchhoff plate of various geometries.

A Nonlocal Operator Method for Partial Differential Equations with Application to Electromagnetic Waveguide Problem

A novel nonlocal operator theory based on the variational principle is proposed for the solution of partial differential equations.Common differential operators as well as the variational forms are defined within the context of nonlocal operators.The present nonlocal formulation allows the assembling of the tangent stiffness matrix with ease and simplicity,which is necessary for the eigenvalue analysis such as the waveguide problem.The present formulation is applied to solve the differential electromagnetic vector wave equations based on electric fields.The governing equations are converted into nonlocal integral form.An hourglass energy functional is introduced for the elimination of zeroenergy modes.Finally,the proposed method is validated by testing three classical benchmark problems.

The Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches

Advances in machine learning(ML)methods are important in industrial engineering and attract great attention in recent years.However,a comprehensive comparative study of the most advanced ML algorithms is lacking.Six integrated ML approaches for the crack repairing capacity of the bacteria-based self-healing concrete are proposed and compared.Six ML algorithms,including the Support Vector Regression(SVR),Decision Tree Regression(DTR),Gradient Boosting Regression(GBR),Artificial Neural Network(ANN),Bayesian Ridge Regression(BRR)and Kernel Ridge Regression(KRR),are adopted for the relationship modeling to predict crack closure percentage(CCP).Particle Swarm Optimization(PSO)is used for the hyper-parameters tuning.The importance of parameters is analyzed.It is demonstrated that integrated ML approaches have great potential to predict the CCP,and PSO is efficient in the hyperparameter tuning.This research provides useful information for the design of the bacteria-based self-healing concrete and can contribute to the design in the rest of industrial engineering.

Spontaneous combustion liability between coal seams: A thermogravimetric study

The spontaneous combustion liability of coal can be determined by using different experimental techniques.These techniques are well-known in their application,but no certain test method has become a standard to prove the reliability of all of them.A general characterisation which included proximate and ultimate analyses,petrographic properties and spontaneous combustion tests(thermogravimetric analysis(TGA)and the Wits-Ehac tests)were conducted on fourteen coal and four coal-shale samples.The spontaneous combustion liability of these samples collected between coal seams(above and below)were predicted using the TGA and the Wits-Ehac tests.Six different heating rates(3,6,9,15,20 and 25
C/min)were selected based on the deviation coefficient to obtain different derivative slopes and a liability index termed the TGspc index.This study found that coal and coal-shale undergo spontaneous combustion between coal seams when exposed to oxygen in the air.Their intrinsic properties and proneness towards spontaneous combustion differ considerably from one seam to the other.The Wits-Ehac test results agreed with the TGspc results to a certain extent and revealed the incidents of spontaneous combustion in the coal mines.

辐射滑移边界条件下多孔板上的传热与纳米流体流动（英文）

考虑多孔表面上的纳米流体在不同时期不同值下可能会发生热性能变化。本研究的目的是研究纳米粒子的体积分数、纳米粒子的种类、吸入或注入、热的产生或吸收、Eckert数、热和速度滑移参数以及辐射对多孔平板上流体流动和传热的影响。研究了4种不同类型的纳米粒子,Cu纳米颗粒、Al2O3纳米粒子和碳基纳米材料(MWCNTs和SWCNTs),分散在水中的情况。利用相似解将控制方程转化为常微分方程,并用RKF45算法进行数值求解。模拟结果与其他研究人员的结果相矛盾,其他研究人员认为,使用具有高导热系数的高体积分数的纳米粒子可提高纳米流体的传热速率;本研究证明,当辐射、热产生和粘滞耗散等参数值发生显著变化时,提高纳米粒子的体积分数可能会降低传热速率。

Fuzzy-Based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

The feedback collection and analysis has remained an important subject matter for long.The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis.However,the student expresses their feedback opinions on online social media sites,which need to be analyzed.This study aims at the development of fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction by assigning proper sentiment score to opinion words and polarity shifters present in the input reviews.Our technique computes the sentiment score of student feedback reviews and then applies a fuzzy-logic module to analyze and quantify student’s satisfaction at the fine-grained level.The experimental results reveal that the proposed work has outperformed the baseline studies as well as state-of-the-art machine learning classifiers.

Static and free vibration analyses of functionally graded porous variable-thickness plates using an edge-based smoothed finite element method

The main purpose of this paper is to present numerical results of static bending and free vibration of functionally graded porous(FGP) variable-thickness plates by using an edge-based smoothed finite element method(ES-FEM) associate with the mixed interpolation of tensorial components technique for the three-node triangular element(MITC3), so-called ES-MITC3. This ES-MITC3 element is performed to eliminate the shear locking problem and to enhance the accuracy of the existing MITC3 element. In the ES-MITC3 element, the stiffness matrices are obtained by using the strain smoothing technique over the smoothing domains formed by two adjacent MITC3 triangular elements sharing an edge. Materials of the plate are FGP with a power-law index(k) and maximum porosity distributions(U) in the forms of cosine functions. The influences of some geometric parameters, material properties on static bending, and natural frequency of the FGP variable-thickness plates are examined in detail.

Mathematical Analysis of Novel Coronavirus (2019-nCov) Delay Pandemic Model

In this manuscript,the mathematical analysis of corona virus model with time delay effect is studied.Mathematical modelling of infectious diseases has substantial role in the different disciplines such as biological,engineering,physical,social,behavioural problems and many more.Most of infectious diseases are dreadful such as HIV/AIDS,Hepatitis and 2019-nCov.Unfortunately,due to the non-availability of vaccine for 2019-nCov around the world,the delay factors like,social distancing,quarantine,travel restrictions,holidays extension,hospitalization and isolation are used as key tools to control the pandemic of 2019-nCov.We have analysed the reproduction number𝐑𝐑𝐧𝐧𝐧𝐧𝐧𝐧𝐧𝐧of delayed model.Two key strategies from the reproduction number of 2019-nCov model,may be followed,according to the nature of the disease as if it is diminished or present in the community.The more delaying tactics eventually,led to the control of pandemic.Local and global stability of 2019-nCov model is presented for the strategies.We have also investigated the effect of delay factor on reproduction number𝐑R_(nCov).Finally,some very useful numerical results are presented to support the theoretical analysis of the model.

纳米流体在各种热管中的应用综述（英文）

纳米技术被广泛应用于传热装置中,以提高热性能。纳米流体应用于热管中,可降低热管的热阻,提高热管的传热能力。文中,对纳米在流体热管中的应用进行了全面的综述。阐述了纳米流体对传统热管、脉动热管、热虹膜等各种类型热管的热性能的影响。此外,发现了纳米颗粒的高效利用必须有一定的浓度;因动力黏度的增加导致颗粒团聚的可能性越高,高浓度的纳米颗粒会产生较高的热阻。热传递性能的增强是晶核位置的增多和纳米流体本身具有的更大热导率的结果。

Flood susceptibility modelling using advanced ensemble machine learning models

Floods are one of nature's most destructive disasters because of the immense damage to land,buildings,and human fatalities.It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods.Therefore,earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters.In this study,we applied and assessed two new hybrid ensemble models,namely Dagging and Random Subspace(RS)coupled with Artificial Neural Network(ANN),Random Forest(RF),and Support Vector Machine(SVM)which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin,the northern region of Bangladesh.The application of these models includes twelve flood influencing factors with 413 current and former flooding points,which were transferred in a GIS environment.The information gain ratio,the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors.For the validation and the comparison of these models,for the ability to predict the statistical appraisal measures such as Freidman,Wilcoxon signed-rank,and t-paired tests and Receiver Operating Characteristic Curve(ROC)were employed.The value of the Area Under the Curve(AUC)of ROC was above 0.80 for all models.For flood susceptibility modelling,the Dagging model performs superior,followed by RF,the ANN,the SVM,and the RS,then the several benchmark models.The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

A comprehensive study of two-phase flow and heat transfer of water/Ag nanofluid in an elliptical curved minichannel

In this research,laminar flow and heat transfer of two-phase water/Ag nanofluid with 0–6%volume fraction of nanoparticles at Re=150–700 in a curved geometry are simulated using finite volume method.Studied geometry is an elliptical curved minichannel with curvature angle of 180°.Forced and natural flow of two-phase nanofluid is simulated at Gr=15000,35000 and 75000.For estimation of nanofluid flow behavior,two-phase mixture method is used.The second-order discretization and SIMPLEC algorithm are used for solving governing equations.The results indicate that the increase of volume fraction of nanoparticles leads to the enhancement of the temperature of central line of flow.The increase of Grashof number(Gr^75000)has a great effect on reduction of dimensionless temperature in central line of flow.Creation of thermal boundary layer at Re=500 and after the angle of 30°becomes significant.In low Grashof numbers(Gr^15000),due to the great effects of temperature gradients close to wall,these regions have significant entropy generation.

Influence of active constrained layer damping on the coupled vibration response of functionally graded magneto-electro-elastic plates with skewed edges

This article makes the first attempt in assessing the influence of active constrained layer damping(ACLD)treatment towards precise control of frequency responses of functionally graded skew-magneto-electroelastic(FGSMEE)plates by employing finite element methods.The materials are functionally graded across the thickness of the plate in terms of modest power-law distributions.The principal equations of motion of FGSMEE are derived via Hamilton’s principle and solved using condensation technique.The effect of ACLD patches are modelled by following the complex modulus approach(CMA).Additionally,distinctive emphasis is laid to evaluate the influence of geometrical skewness on the attenuation capabilities of the plate.The accuracy of the current analysis is corroborated with comparison of previous researches of similar kind.Additionally,a complete parametric study is directed to understand the combined impacts of various factors like coupling fields,patch location,fiber orientation of piezoelectric patch in association with skew angle and power-law index.