Development of forced degradation and stability indicating studies of drugs——A review

Forced degradation is a degradation of new drug substance and drug product at conditions more severe than accelerated conditions. It is required to demonstrate specificity of stability indicating methods and also provides an insight into degradation pathways and degradation products of the drug substance and helps in elucidation of the structure of the degradation products. Forced degradation studies show the chemical behavior of the molecule which in turn helps in the development of formulation and package. In addition, the regulatory guidance is very general and does not explain about the performance of forced degradation studies. Thus, this review discusses the current trends in performance of forced degradation studies by providing a strategy for conducting studies on degradation mechanisms and also describes the analytical methods helpful for development of stability indicating method.

Understanding host-graft crosstalk for predicting the outcome of stem cell transplantation

Mesenchymal stromal cells(MSCs)hold great promise for tissue regeneration in debilitating disorders.Despite reported improvements,the short-term outcomes of MSC transplantation,which is possibly linked to poor cell survival,demand extensive investigation.Disease-associated stress microenvironments further complicate outcomes.This debate underscores the need for a deeper understanding of the phenotypes of transplanted MSCs and their environment-induced fluctuations.Additionally,questions arise about how to predict,track,and comprehend cell fate post-transplantation.In vivo cellular imaging has emerged as a critical requirement for both short-and long-term safety and efficacy studies.However,translating preclinical imaging methods to clinical settings remains challenging.The fate and function of transplanted cells within the host environment present intricate challenges,including MSC engraftment,variability,and inconsistencies between preclinical and clinical data.The study explored the impact of high glucose concentrations on MSC survival in diabetic environments,emphasizing mitochondrial factors.Preserving these factors may enhance MSC survival,suggesting potential strategies involving genetic modification,biomaterials,and nanoparticles.Understanding stressors in diabetic patients is crucial for predicting the effects of MSC-based therapies.These multifaceted challenges call for a holistic approach involving the incorporation of large-scale data,computational disease modeling,and possibly artificial intelligence to enable deterministic insights.

Azeotropic mixture used for development and validation of Lornoxicam in bulk and its tablet dosage form by spectrophotometric method

A novel,safe,economic and sensitive method of spectrophotometric estimation has been developed using Azeoptropic mixture (water∶methanol:60∶40,v/v) for the quantitative determination of Lornoxicam,a practically water-insoluble drug.Hence,Lornoxicam stock solution was prepared in Azeoptropic mixture.Lornoxicam showed maximum absorbance at 383 nm.Beer's law was obeyed in the concentration range 4-24 μg/mL with regression coefficient of 0.999.The method was validated in terms of linearity (R2=0.999),precision (CV for intra-day and inter-day was 0.28 0.68 and 0.12-0.92,respectively),accuracy (98.03-100.59％ w/w) and specificity.This method is simple,precise,accurate,sensitive and reproducible and can be used for the routine quality control testing of the marketed formulations.

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression(SVR)with GWO,BAT and COA algorithms

In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Strigolactones interact with other phytohormones to modulate plant root growth and development

Strigolactones(SLs),which are biosynthesized mainly in roots,modulate various aspects of plant growth and development.Here,we review recent research on the role of SLs and their cross-regulation with auxin,cytokinin,and ethylene in the modulation of root growth and development.Under nutrientsufficient conditions,SLs regulate the elongation of primary roots and inhibit adventitious root formation in eudicot plants.SLs promote the elongation of seminal roots and increase the number of adventitious roots in grass plants in the short term,while inhibiting lateral root development in both grass and eudicot plants.The effects of SLs on the elongation of root hairs are variable and depend on plant species,growth conditions,and SL concentration.Nitrogen or phosphate deficiency induces the accumulation of endogenous SLs,modulates root growth and development.Genetic analyses indicate cross-regulation of SLs with auxin,cytokinin,and ethylene in regulation of root growth and development.We discuss the implications of these studies and consider their potential for exploiting the components of SL signaling for the design of crop plants with more efficient soil-resource utilization.

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage.Therefore,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open mines.Accordingly,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,respectively.Subsequently,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity method.For developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop software.In which,111 events were randomly selected for the development and optimization of the models.Subsequently,the remaining 25 blasting events were applied to confirm the accuracy of the proposed models.Herein,blast design parameters were regarded as input variables to predict the size of rock in blasting operations.Finally,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting.Among the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks.The other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and efficiency.Hence,the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.

Synchronization Phenomena Investigation of a New Nonlinear Dynamical System 4D by Gardano’s and Lyapunov’s Methods

Synchronization is one of the most important characteristics of dynamic systems.For this paper,the authors obtained results for the nonlinear systems controller for the custom Synchronization of two 4D systems.The findings have allowed authors to develop two analytical approaches using the second Lyapunov(Lyp)method and the Gardanomethod.Since the Gardano method does not involve the development of special positive Lyp functions,it is very efficient and convenient to achieve excessive systemSYCR phenomena.Error is overcome by using Gardano and overcoming some problems in Lyp.Thus we get a great investigation into the convergence of error dynamics,the authors in this paper are interested in giving numerical simulations of the proposed model to clarify the results and check them,an important aspect that will be studied is Synchronization Complete hybrid SYCR and anti-Synchronization,by making use of the Lyapunov expansion analysis,a proposed control method is developed to determine the actual.The basic idea in the proposed way is to receive the evolution of between two methods.Finally,the present model has been applied and showing in a new attractor,and the obtained results are compared with other approximate results,and the nearly good coincidence was obtained.

Optimal treatment of stratified Carreau and Casson nanofluids flows in Darcy-Forchheimer porous space over porous matrix

A comparative three-dimensional(3D)analysis for Casson-nanofluid and Carreau-nanofluid flows due to a flat body in a magnetohydrodynamic(MHD)stratified environment is presented.Flow is estimated to be suspended in a Darcy-Forchheimer medium.Soret and Dufour responses are also accommodated in the flow field.A moving(rotating)coordinate system is exercised to examine the bidirectionally stretched flow fields(flow,heat transfer,and mass transfer).Nanofluid is compounded by taking ethylene glycol/sodium alginate as base fluid and ferric-oxide(Fe3O4)as nanoparticles.Governing equations are handled by the application of optimal homotopy asymptotic method(OHAM),where convergence parameters are optimized through the classical least square procedure.The novel mechanism(hidden physics)due to appearing parameters is explored with the assistance of tabular and graphical expositions.Outcomes reveal the double behavior state for temperature field with thermal stratification/Dufour number,and for concentration field with Soret number due to the presence of turning points.

Security Requirement Management for Cloud-Assisted and Internet of Things—Enabled Smart City

The world is rapidly changing with the advance of information technology.The expansion of the Internet of Things(IoT)is a huge step in the development of the smart city.The IoT consists of connected devices that transfer information.The IoT architecture permits on-demand services to a public pool of resources.Cloud computing plays a vital role in developing IoT-enabled smart applications.The integration of cloud computing enhances the offering of distributed resources in the smart city.Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability,security,performance,condentiality,and privacy.The key reason for cloud-and IoT-enabled smart city application failure is improper security practices at the early stages of development.This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications.Its three-layered architecture includes privacy preserved stakeholder analysis(PPSA),security requirement modeling and validation(SRMV),and secure cloud-assistance(SCA).A case study highlights the applicability and effectiveness of the proposed framework.A hybrid survey enables the identication and evaluation of signicant challenges.

Separation of methane from different gas mixtures using modified silicon carbide nanosheet: Micro and macro scale numerical studies

This research discusses the separation of methane gas from three different gas mixtures,CH4/H2 S,CH4/N2 and CH4/CO2,using a modified silicon carbide nanosheet(Si CNS)membrane using both molecular dynamics(MD)and computational fluid dynamics(CFD)methods.The research examines the effects of different structures of the Si CNSs on the separation of these gas mixtures.Various parameters including the potential of the mean force,separation factor,permeation rate,selectivity and diffusivity are discussed in detail.Our MD simulations showed that the separation of CH4/H2 S,and CH4/CO2 mixtures was successful,while simulation demonstrated a poor result for the CH4/N2 mixture.The effect of temperature on the diffusivity of gas is also discussed,and a correlation is introduced for diffusivity as a function of temperature.The evaluated value for diffusivity is then used in the CFD method to investigate the permeation rate of gas mixtures.

Modeling viscosity of methane,nitrogen,and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas mixtures.It is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K.Also,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the literature.Average absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,respectively.In addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions.Also,using leverage technique,valid,suspected and outlier data points were determined.Finally,trends of gas viscosity models at different conditions were evaluated.

Iron-chelating and anti-lipid peroxidation properties of 1-(N-acetyl-6-aminohexyl)-3-hydroxy-2-methylpyridin-4-one(CM1)in longterm iron loading β-thalassemic mice

Objective:To evaluate the iron—chelating properties and free—radical scavenging activities of1-(N-acetyl-6-aminohexyl)-3-hydroxy-2-methyIpyridin—4-one(CM1) treatment in chronic iron-loaded β-thalassemic(BKO) mice.Methods:The BKO mice were fed with a ferrocene-rich diet and were orally administered with CM1|50 mg/(kg·day)| for 6 months.Blood levels of non-transferrin hound iron,labile plasma iron.ferritin(Ft) and malondialdehyde were determined.Results:The BKO mice were fed with an iron diet for 8 months which resulted in iron overload.Interestingly,the mice showed a decrease in the non—transferrin bound iron,labile plasma iron and malondialdehyde levels,but not the Ft levels after continuous CM1 treatment.Conclusions:CM1 could be an effective oral iron chelator that can reduce iron overload and lipid peroxidation in chronic iron overload β—thalassemic mice.

Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review

Diabetic Retinopathy(DR)is an eye disease that mainly affects people with diabetes.People affected by DR start losing their vision from an early stage even though the symptoms are identified only at the later stage.Once the vision is lost,it cannot be regained but can be prevented from causing any further damage.Early diagnosis of DR is required for preventing vision loss,for which a trained ophthalmologist is required.The clinical practice is time-consuming and is not much successful in identifying DR at early stages.Hence,Computer-Aided Diagnosis(CAD)system is a suitable alternative for screening and grading of DR for a larger population.This paper addresses the different stages in CAD system and the challenges in identifying and grading of DR by analyzing various recently evolved techniques.The performance metrics used to evaluate the Computer-Aided Diagnosis system for clinical practice is also discussed.

Design and Fabrication of a Low-Loss Microstrip Lowpass-Bandpass Diplexer for WiMAX Applications

This work presents a compact lowpass-bandpass microstrip diplexer with a novel configuration.It consists of a lowpass filter integrated with a bandpass filter via a simple compact junction.The proposed bandpass filter consists of four rectangular patch cells and some thin strips.The step impedance structures,with a radial cell,are applied to achieve a lowpass frequency response.The lowpass channel of the introduced diplexer has 2.64 GHz cut-off frequency,whereas,the bandpass channel center frequency is 3.73 GHz for WiMAX applications and covers the frequencies3.31 GHz to 4 GHz.In addition to having novel structures,both filters have other advantages in terms of high return loss,low insertion loss and high selectivity.The presented microstrip diplexer has the compact size of 29 mm×13.8 mm×0.762 mm,calculated at 2.64 GHz.The obtained insertion losses are 0.20 dB(for the first channel)and 0.25 dB(for the second channel),which make the proposed diplexer suitable for energy harvesting.The stopband properties of both bandpass and lowpass filters are improved by creating several transmission zeros.The comparison results show that the lowest insertion losses,the minimum gap between channels,good return losses,and good isolation are obtained.

Numerically simulated behavior of radiative Fe_(3)O_(4) and multi-walled carbon nanotube hybrid nanoparticle flow in presence of Lorentz force

Free convection in hybrid nanomaterial-saturated permeable media is crucial in various engineering applications.The present study aims to investigate the free convection of an aqueous-based hybrid nanomaterial through a zone under the combined effect of the Lorentz force and radiation.The natural convection of the hybrid nanomaterial is modeled by implementing a control volume finite element method(CVFEM)-based code,whereas Darcy assumptions are used to model the porosity terms in the momentum buoyancy equation involving the average Nusselt number Nu_(ave),flow streamlines,and isotherm profiles.A formula for estimating Nu_(ave) is proposed.The results show that the magnetic force retards the flow,and the fluid tends to attract the magnetic field source.Nu_(ave) is directly correlated with the Rayleigh number and radiation;however,it is indirectly dependent on the Hartmann number.Conduction is the dominant mode at larger Darcy and Hartmann numbers.

Modeling and Simulation of Two Axes Gimbal Using Fuzzy Control

The application of the guided missile seeker is to provide stability to the sensor’s line of sight toward a target by isolating it from the missile motion and vibration.The main objective of this paper is not only to present the physical modeling of two axes gimbal system but also to improve its performance through using fuzzy logic controlling approach.The paper is started by deriving the mathematical model for gimbals motion using Newton’s second law,followed by designing the mechanical parts of model using SOLIDWORKS and converted to xml file to connect dc motors and sensors using MATLAB/SimMechanics.Then,a Mamdani-type fuzzy and a Proportional-Integral-Derivative(PID)controllers were designed using MATLAB software.The performance of both controllers was evaluated and tested for different types of input shapes.The simulation results showed that self-tuning fuzzy controller provides better performance,since no overshoot,small steady-state error and small settling time compared to PID controller.

Optical Analysis of a ZnO/Cu_(2)O Subcell in a Silicon-Based Tandem Heterojunction Solar Cell

Research on silicon-based tandem heterojunction solar cells (STHSC) incorporating metal oxides is one of the main directions for development of high-efficiency solar cells. In this work, the optical characteristics of a STHSC consisting of a ZnO/Cu2O subcell on top of a silicon-based subcell were studied by optical modelling. Cu2O is a direct-gap p-type semiconductor which is attractive for application in solar cells due to its high absorptance of ultra-violet and visible light, nontoxicity, and low-cost producibility. Highly Al-doped ZnO and undoped Cu2O thin films were prepared on quartz substrates by magnetron sputter deposition. Thermal annealing of the Cu2O layer at 900°C enhances the electrical properties and reduces optical absorption, presumably as a result of increased grain size. Hall effect measurements show that the majority carrier (hole) mobility increases from 10 to 50 cm2/V×s and the resistivity decreases from 560 to 200 Ω×cm after annealing. A Cu2O absorber layer of 2 μm thickness will generate about 10 mA/cm2 of photocurrent under AM1.5G illumination. The optical analysis of the STHSC involved calculating the spectral curves for absorptance, transmittance, and reflectance for different thicknesses of the thin film layers constituting the ZnO/Cu2O subcell. The complex refractive indices of the thin films were derived from spectroscopic ellipsometry measurements and implemented in the simulation model. The lowest reflectance and highest transmittance for the ZnO/Cu2O subcell are obtained for a thickness of approximately 80 nm for both the top and bottom AZO layers. The SiNx anti-reflection coating for the c-Si bottom subcell must be optimized to accommodate the shift of the photon spectrum towards longer wavelengths. By increasing the thickness of the SiNx layer from 80 nm to 120 nm, the total reflectance for the STHSC device is reduced from 12.7% to 9.7%.