Exploring the Cation Regulation Mechanism for Interfacial Water Involved in the Hydrogen Evolution Reaction by In Situ Raman Spectroscopy

Interfacial water molecules are the most important participants in the hydrogen evolution reaction(HER).Hence,understanding the behavior and role that interfacial water plays will ultimately reveal the HER mechanism.Unfortunately,investigating interfacial water is extremely challenging owing to the interference caused by bulk water molecules and complexity of the interfacial environment.Here,the behaviors of interfacial water in different cationic electrolytes on Pd surfaces were investigated by the electrochemistry,in situ core-shell nanostructure enhanced Raman spectroscopy and theoretical simulation techniques.Direct spectral evidence reveals a red shift in the frequency and a decrease in the intensity of interfacial water as the potential is shifted in the positively direction.When comparing the different cation electrolyte systems at a given potential,the frequency of the interfacial water peak increases in the specified order:Li+<Na^(+)<K^(+)<Ca^(2+)<Sr^(2+).The structure of interfacial water was optimized by adjusting the radius,valence,and concentration of cation to form the two-H down structure.This unique interfacial water structure will improve the charge transfer efficiency between the water and electrode further enhancing the HER performance.Therefore,local cation tuning strategies can be used to improve the HER performance by optimizing the interfacial water structure.

Cationic ordering transition in oxygen-redox layered oxide cathodes

Understanding the structural origin of the competition between oxygen 2p and transition-metal 3d orbitals in oxygen-redox(OR)layered oxides is eminently desirable for exploring reversible and high-energy-density Li/Na-ion cathodes.Here,we reveal the correlation between cationic ordering transition and OR degradation in ribbon-ordered P3-Na_(0.6)Li_(0.2)Mn_(0.8)O_(2) via in situ structural analysis.Comparing two different voltage windows,the OR capacity can be improved approximately twofold when suppressing the in-plane cationic ordering transition.We find that the intralayer cationic migration is promoted by electrochemical reduction from Mn^(4+)to Jahn–Teller Mn^(3+)and the concomitant NaO_(6) stacking transformation from triangular prisms to octahedra,resulting in the loss of ribbon ordering and electrochemical decay.First-principles calculations reveal that Mn^(4+)/Mn^(3+)charge ordering and alignment of the degenerate eg orbital induce lattice-level collective Jahn–Teller distortion,which favors intralayer Mn-ion migration and thereby accelerates OR degradation.These findings unravel the relationship between in-plane cationic ordering and OR reversibility and highlight the importance of superstructure protection for the rational design of reversible OR-active layered oxide cathodes.

Enabling an Inorganic-Rich Interface via Cationic Surfactant for High-Performance Lithium Metal Batteries

An anion-rich electric double layer(EDL)region is favorable for fabricating an inorganic-rich solid-electrolyte interphase(SEI)towards stable lithium metal anode in ester electrolyte.Herein,cetyltrimethylammonium bromide(CTAB),a cationic surfactant,is adopted to draw more anions into EDL by ionic interactions that shield the repelling force on anions during lithium plating.In situ electrochemical surface-enhanced Raman spectroscopy results combined with molecular dynamics simulations validate the enrichment of NO_(3)^(−)/FSI−anions in the EDL region due to the positively charged CTA^(+).In-depth analysis of SEI structure by X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry results confirmed the formation of the inorganic-rich SEI,which helps improve the kinetics of Li^(+)transfer,lower the charge transfer activation energy,and homogenize Li deposition.As a result,the Li||Li symmetric cell in the designed electrolyte displays a prolongated cycling time from 500 to 1300 h compared to that in the blank electrolyte at 0.5 mA cm^(-2) with a capacity of 1 mAh cm^(-2).Moreover,Li||LiFePO_(4) and Li||LiCoO_(2) with a high cathode mass loading of>10 mg cm^(-2) can be stably cycled over 180 cycles.

A novel cationic collector for silicon removal from collophane using reverse flotation under acidic conditions

We analyzed a novel cationic collector using chemical plant byproducts,such as cetyltrimethylammonium bromide(CTAB)and dibutyl phthalate(DBP).Our aim is to establish a highly effective and economical process for the removal of quartz from collophane.A microflotation test with a 25 mg·L^(−1)collector at pH value of 6-10 demonstrates a considerable difference in the floatability of pure quartz and fluorapatite.Flotation tests for a collophane sample subjected to the first reverse flotation for magnesium removal demonstrates that a rough flotation process(using a 0.4 kg·t−1 new collector at pH=6)results in a collophane concentrate with 29.33wt%P_(2)O_(5)grade and 12.66wt%SiO2 at a 79.69wt%P_(2)O_(5)recovery,providing desirable results.Mechanism studies using Fourier transform infrared spectroscopy,zeta potential,and contact angle measurements show that the adsorption capacity of the new collector for quartz is higher than that for fluorapatite.The synergistic effect of DBP increases the difference in hydrophobicity between quartz and fluorapatite.The maximum defoaming rate of the novel cationic collector reaches 142.8 mL·min−1.This is considerably higher than that of a conventional cationic collector.

Misclassification analysis of discriminant model

This paper extends the criterion of the misclassification ratio of discriminant model and presents a new selection method of discriminant model.For selecting the discriminant model,this method establishes the rule of misclassification degree ratio through misclassification ratio of the discriminant model and misclassification degree of the samples.To test the effect of this method,this work uses seven UCI data sets.Numerical experiments on these examples indicate that this method has certain rationality and has a better effect to select a discriminant model.

Third-Order Nonlinear Optical Responses of Bis(15-crown-5)-stilbenes Binding to One-or Two-Alkali Metal Cation(Li^(+),Na^(+)and K^(+))

Bis(15-crown-5)-stilbenes containing crown ether parts have been widely used in a variety of chemical applications,such as cation detectors,because of their ability to selectively bind to alkali metal cations,Bis(15-crown-5)-stilbenes and its derivatives with complexation of one-or two-alkali metal cation(Li^(+),Na^(+)and K^(+))have been theoretically investigat-ed by quantum chemistry methods.The coordination of alkali cations results in partial shrinkage of crown ethers,which directly affected natural distribution analysis charges and molecular orbital energy levels.The number of alkali metal ions has significant effects on absorption spectra and mean second hyperpolarizability.When one alkali metal ion was added to the anticonformer of bis(15-crown-5)-stilbene,the absorption spectra were obvious-ly redshifted and the mean second hyperpolarizability values were slightly increased;while two alkali metal ions were added to bis(15-crown-5)-stilbene,the absorption spectra were ob-viously blue shifted and the mean second hyperpolarizability values decreased.On the other hand,as the radius of the alkali ions increased,the mean second hyperpolarizability values of the compounds increased gradually.It is indicated that the mean second hyperpolarizability value is sensitive to the number and radius of the alkali metal cations,thus the third order nonlinear optical response can be used as a signal to detect the number and type of alkali met-al ions.

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

Addressing cation mixing in layered structured cathodes for lithium-ion batteries:A critical review

High-performance lithium-ion batteries(LIB)are important in powering emerging technologies.Cathodes are regarded as the bottleneck of increasing battery energy density,among which layered oxides are the most promising candidates for LIB.However,a limitation with layered oxides cathodes is the transition metal and Li site mixing,which significantly impacts battery capacity and cycling stability.Despite recent research on Li/Ni mixing,there is a lack of comprehensive understanding of the origin of cation mixing between the transition metal and Li;therefore,practical means to address it.Here,a critical review of cation mixing in layered cathodes has been provided,emphasising the understanding of cation mixing mechanisms and their impact on cathode material design.We list and compare advanced characterisation techniques to detect cation mixing in the material structure;examine methods to regulate the degree of cation mixing in layered oxides to boost battery capacity and cycling performance,and critically assess how these can be applied practically.An appraisal of future research directions,including superexchange interaction to stabilise structures and boost capacity retention has also been concluded.Findings will be of immediate benefit in the design of layered cathodes for high-performance rechargeable LIB and,therefore,of interest to researchers and manufacturers.

One-step crosslinking preparation of tannic acid particles for the adsorption and separation of cationic dyes

In this study,a new tannic acid adsorbent(ethylene glycol diglycidyl ether crosslinked tannic acid,TAEGDE)for adsorptive removal of dyes from water was prepared using EGDE as a cross-linking agent.The resultant TA-EGDE was in particulate form with rough surface morphology and a diameter ranging from 10 to 30μm.The adsorption performance of the TA-EGDE was evaluated in a flow-through mode using water samples contaminated with methylene blue(MB)and two-component mixed dyes,respectively.The TA-EGDE provided adsorption capacity up to 721.8 mg·g^(-1)at 65°C for MB.It showed a high removal efficiency(99%)of MB(50 mg·L^(-1))from the water sample and could recovery 90%of the adsorbed MB by eluting with acidic ethanol aqueous solution.The excellent adsorption of MB and neutral red on the TA-EGDE may be the result of the synergy of electrostatic interaction andπ-πinteraction.Furthermore,the TA-EGDE could separate dyes from water samples contaminated with twocomponent mixed dyes with a separation coefficient ranging from 1.8 to 36.5.The anionic TA-EGDE would be an effective adsorbent to remove and recycle dyes from the contaminated water.

Divalent nitrogen-rich cationic salts with great gas production capacities

Monocyclic nitrogen-rich 3-(aminomethyl)-4,5-diamine-1,2,4-triazole(1)and fused cyclic 3,7-diamine-6-(aminomethyl)-[1,2,4]triazolo[4,3-b][1,2,4]triazole(9)were synthesized through the convenient cyclization reaction from the readily available reactant.Their energetic salts with high nitrogen content were proved to be rare examples of divalent monocyclic/fused cyclic cationic salts according to the single crystal analyses.The structure of intermediate B was also identified and verified by its trivalent cation crystal 17.5H_2O indirectly.Energetic compounds 2-8 and 10-17 were fully characterized by NMR spectroscopy,infrared spectroscopy,differential scanning calorimetry,elemental analysis.These energetic salts exhibit good thermal stability with decomposition temperatures ranged from 182℃to 245℃.The sensitivity of compounds 2,6,10 and 14 is similar or superior to that of RDX while the others were much more insensitive to mechanical stimulate.Furthermore,detonation velocity of 10(8843 m/s)surpass that of RDX(D=8795 m/s).Considering the high gas production volume(≥808 L/kg)of 2,4,10and 12,constant-volume combustion experiments were conduct to evaluate their gas production capacities specifically.These compounds possess much higher maximum gas-production pressures(P_(max):7.88-10.08 MPa)than the commonly used reagent guanidine nitrate(GN:P_(max)=4.20 MPa),which indicate their strong gas production capacity.

Person-Dependent Handwriting Verification for Special Education Using DeepLearning

Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent.However,in crowded classrooms,it is dif-ficult for a teacher to deal with each student individually.This problem can be overcome by using supportive education applications.However,the majority of such applications are not designed for special education and therefore they are not efficient as expected.Special education students differ from their peers in terms of their development,characteristics,and educational qualifications.The handwriting skills of individuals with special needs are lower than their peers.This makes the task of Handwriting Recognition(HWR)more difficult.To over-come this problem,we propose a new personalized handwriting verification sys-tem that validates digits from the handwriting of special education students.The system uses a Convolutional Neural Network(CNN)created and trained from scratch.The data set used is obtained by collecting the handwriting of the students with the help of a tablet.A special education center is visited and the handwrittenfigures of the students are collected under the supervision of special education tea-chers.The system is designed as a person-dependent system as every student has their writing style.Overall,the system achieves promising results,reaching a recognition accuracy of about 94%.Overall,the system can verify special educa-tion students’handwriting digits with high accuracy and is ready to integrate with a mobile application that is designed to teach digits to special education students.

Styrene epoxidation catalyzed by polyoxometalate/quaternary ammonium phase transfer catalysts: The effect of cation size and catalyst deactivation mechanism

Catalytic epoxidation of alkenes is an important type of organic reaction in chemical industry,and the deep insight into catalyst deactivation will help to develop new epoxidation process.In this work,series of quaternary ammoniums bearing different cationic sizes,i.e.MTOA+(methyltrioctylammonium,[(C_(8)H_(17))_(3)CH_(3)N]+),HTMA+(hexadecyltrimethylammonium,[(C_(16)H_(33))(CH_(3))_(3)N]+) and DMDOA+(dimethyldioctadecylammonium,[(C_(18)H_(37))_(2)(CH_(3))_(2)N]+) were incorporated with polyoxometalate (POM) anions to prepare phase transfer catalysts (PTCs),which were used in the styrene epoxidations.Among them,(MTOA)_(3)PW_(4)O_(24)exhibits the best catalytic performance judged from the highest styrene conversion rate(52%) and styrene oxide selectivity (93%),during which the styrene epoxidation conditions were optimized.Meanwhile,the deactivation mechanism of this kind of PTCs was proposed firstly,i.e.in the case of low H_(2)O_(2) content,the oxidant can only be used in the styrene epoxidation,in which the catalyst can transform into stable Keggin-type POM.But when the content of H_(2)O_(2) is higher,the excess H_(2)O_(2) can reactivate the Keggin-type POM into active (PW_(4)O_(24))_(3)-anions,which can trigger the ring-opening polymerization of styrene oxide.Consequently,the catalyst is deactivated by adhered poly(styrene oxide)irreversibly,which was determined by NMR spectra.In this situation,the active moiety{PO_(4)[WO(O_(2))_(2)]_(4)}_(3)-in phase-transfer catalytic system can break into some unidentified species with low W/P ratio with the presence of epoxides.This work will be beneficial for the design of new PTCs in alkene epoxidation in fine chemical industry.

SF-CNN: Deep Text Classification and Retrieval for Text Documents

Researchers and scientists need rapid access to text documents such as research papers,source code and dissertations.Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords.An efficient classification algorithm for retrieving documents based on keyword words is required.The traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in keywords.To solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text documents.The proposed SF-CNN is based on deep semantic-based bag-of-word representation for document retrieval.Traditional deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for bag-of-words.The experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN method.SF-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.

Study of the Temperature-Programmed Desorption of Carbon Dioxide (CO2) on Zeolites X Modified with Bivalent Cations

Study of physisorbed and chemisorbed carbon dioxide (CO2) species was carried out on the NaX zeolite modified by cationic exchanges with bivalent cations (Ca2+ and Ba2+) by temperature-programmed desorption of CO2 (CO2-TPD). Others results were obtained by infrared to complete the study. The results of this research showed, in the physisorption region (213 - 473 K), that the cationic exchanges on NaX zeolite with bivalent cations increase slightly the interactions of CO2 molecule with adsorbents and/or cationic site. Indeed, the desorption energies of physisorbed CO2 obtained on the reference zeolite NaX (13.5 kJ·mol-1) are lower than that of exchanged zeolites E-CaX and E-BaX (15.77 and 15.17 kJ·mol-1 respectively). In the chemisorbed CO2 region (573 - 873 K), the desorption energies related to desorbed species (bidentate carbonates: CO32-) on the exchanged zeolites E-CaX and E-BaX are about 81 kJ·mol-1, higher than the desorbed species (bicarbonates: HCO32-) on the reference R-NaX (62 kJ·mol-1). In addition, the exchanged E-BaX zeolite develops the secondary adsorption sites corresponding to bicarbonates species with desorption energies of 35 kJ·mol-1 lower to desorption energies of bicarbonates noted on the reference zeolite NaX.

An Automatic Deep Neural Network Model for Fingerprint Classification

The accuracy offingerprint recognition model is extremely important due to its usage in forensic and securityfields.Anyfingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy.To solve this problem in a unified model,this paper proposes a model that can automatically specify itself.So,it is called an automatic deep neural net-work(ADNN).Our algorithm can specify the appropriate architecture of the neur-al network used and some significant parameters of this network.These parameters are the number offilters,epochs,and iterations.It guarantees the high-est accuracy by updating itself until achieving 99%accuracy then it stops and out-puts the result.Moreover,this paper proposes an end-to-end methodology for recognizing a person’s identity from the inputfingerprint image based on a resi-dual convolutional neural network.It is a complete system and is fully automated whether in the features extraction stage or the classification stage.Our goal is to automate thisfingerprint recognition system because the more automatic the sys-tem is,the more time and effort it saves.Our model also allows users to react by inputting the initial values of these parameters.Then,the model updates itself until itfinds the optimal values for the parameters and achieves the best accuracy.Another advantage of our algorithm is that it can recognize people from their thumb and otherfingers and its ability to recognize distorted samples.Our algo-rithm achieved 99.75%accuracy on the publicfingerprint dataset(SOCOFing).This is the best accuracy compared with other models.

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Intemet of things and cloud computing as the core tec hnology and government management and industrial development as the core content,forming a large scale,heterogeneous and dynamic distributed Internet of things environment between different Internet of things.There is a wide demand for cooperation between equipment and management institutions in the smart city.Therefore,it is necessary to establish a trust mechanism to promote cooperation,and based on this,prevent data disorder caused by the interaction between honest terminals and malicious temminals.However,most of the existing research on trust mechanism is divorced from the Internet of things environment,and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of hings devices,resuling in the fact that the research on abstract trust trust mechanism cannot be directly applied to the Internet of things;On the other hand,various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered.Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals,a cross domain trust model(CDTM)based on self-authentication is proposed.Unlike most trust models,this model uses self-certified trust.The cross-domain process of internet of things(IoT)terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction.At the same time,in order to alleviate the collision attack and improve the accuracy of trust evaluation,the overall trust value is calculated by comprehensively considering the quantity weight,time attenuation weight and similarity weight.Finally,the simulation results show that CDTM has good anti collusion attack ability.The success rate of malicious interaction will not increase significantly.Compared with other models,the resource consumption of our proposed model is significantly reduced.

Optimal Deep Belief Network Enabled Malware Detection and Classification Model

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily lives.Portable Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading malware.The recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify malware.With this motivation,this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification(MFODBN-MDC)technique.The major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the PDFs.The proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature subsets.In addition,Adamax optimizer with the DBN model is used for PDF malware detection and classification.The design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-fication demonstrates the novelty of the work.For demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various aspects.The comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

Efficient Object Detection and Classification Approach Using HTYOLOV4 and M^(2)RFO-CNN

Object detection and classification are the trending research topics in thefield of computer vision because of their applications like visual surveillance.However,the vision-based objects detection and classification methods still suffer from detecting smaller objects and dense objects in the complex dynamic envir-onment with high accuracy and precision.The present paper proposes a novel enhanced method to detect and classify objects using Hyperbolic Tangent based You Only Look Once V4 with a Modified Manta-Ray Foraging Optimization-based Convolution Neural Network.Initially,in the pre-processing,the video data was converted into image sequences and Polynomial Adaptive Edge was applied to preserve the Algorithm method for image resizing and noise removal.The noiseless resized image sequences contrast was enhanced using Contrast Limited Adaptive Edge Preserving Algorithm.And,with the contrast-enhanced image sequences,the Hyperbolic Tangent based You Only Look Once V4 was trained for object detection.Additionally,to detect smaller objects with high accuracy,Grasp configuration was observed for every detected object.Finally,the Modified Manta-Ray Foraging Optimization-based Convolution Neural Network method was carried out for the detection and the classification of objects.Comparative experiments were conducted on various benchmark datasets and methods that showed improved accurate detection and classification results.

An Intelligent Deep Neural Sentiment Classification Network

A Deep Neural Sentiment Classification Network(DNSCN)is devel-oped in this work to classify the Twitter data unambiguously.It attempts to extract the negative and positive sentiments in the Twitter database.The main goal of the system is tofind the sentiment behavior of tweets with minimum ambiguity.A well-defined neural network extracts deep features from the tweets automatically.Before extracting features deeper and deeper,the text in each tweet is represented by Bag-of-Words(BoW)and Word Embeddings(WE)models.The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2(TSA2),Twit-ter-Sanders-Apple3(TSA3),and Twitter-DataSet(TDS).TSA2 and TDS consist of positive and negative tweets,whereas TSA3 has neutral tweets also.Thus,the proposed DNSCN acts as a binary classifier for TSA2 and TDS databases and a multiclass classifier for TSA3.The performances of DNSCN architecture are evaluated by F1 score,precision,and recall rates using 5-fold and 10-fold cross-validation.Results show that the DNSCN-WE model provides more accuracy than the DNSCN-BoW model for representing the tweets in the feature encoding.The F1 score of the DNSCN-BW based system on the TSA2 database is 0.98(binary classification)and 0.97(three-class classification)for the TSA3 database.This system provides better a F1 score of 0.99 for the TDS database.

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%).