The Discriminance for <i>FLDcirc_r</i>Matrices and the Fast Algorithm of Their Inverse and Generalized Inverse

This paper presents a new type of circulant matrices. We call it the first and the last difference r-circulant matrix (FLDcircr matrix). We can verify that the linear operation, the matrix product and the inverse matrix of this type of matrices are still FLDcircr matrices. By constructing the basic FLDcircr matrix, we give the discriminance for FLDcircr matrices and the fast algorithm of the inverse and generalized inverse of the FLDcircr matrices.

Underwater Pulse Waveform Recognition Based on Hash Aggregate Discriminant Network

Underwater pulse waveform recognition is an important method for underwater object detection.Most existing works focus on the application of traditional pattern recognition methods,which ignore the time-and space-varying characteristics in sound propagation channels and cannot easily extract valuable waveform features.Sound propagation channels in seawater are time-and space-varying convolutional channels.In the extraction of the waveform features of underwater acoustic signals,the effect of high-accuracy underwater acoustic signal recognition is identified by eliminating the influence of time-and space-varying convolutional channels to the greatest extent possible.We propose a hash aggregate discriminative network(HADN),which combines hash learning and deep learning to minimize the time-and space-varying effects on convolutional channels and adaptively learns effective underwater waveform features to achieve high-accuracy underwater pulse waveform recognition.In the extraction of the hash features of acoustic signals,a discrete constraint between clusters within a hash feature class is introduced.This constraint can ensure that the influence of convolutional channels on hash features is minimized.In addition,we design a new loss function called aggregate discriminative loss(AD-loss).The use of AD-loss and softmax-loss can increase the discriminativeness of the learned hash features.Experimental results show that on pool and ocean datasets,which were collected in pools and oceans,respectively,by using acoustic collectors,the proposed HADN performs better than other comparative models in terms of accuracy and mAP.

LociScan,a tool for screening genetic marker combinations for plant variety discrimination

To reduce the cost and increase the efficiency of plant genetic marker fingerprinting for variety discrimination,it is desirable to identify the optimal marker combinations.We describe a marker combination screening model based on the genetic algorithm(GA)and implemented in a software tool,Loci Scan.Ratio-based variety discrimination power provided the largest optimization space among multiple fitness functions.Among GA parameters,an increase in population size and generation number enlarged optimization depth but also calculation workload.Exhaustive algorithm afforded the same optimization depth as GA but vastly increased calculation time.In comparison with two other software tools,Loci Scan accommodated missing data,reduced calculation time,and offered more fitness functions.In large datasets,the sample size of training data exerted the strongest influence on calculation time,whereas the marker size of training data showed no effect,and target marker number had limited effect on analysis speed.

Impact pressure of waves generated by landslides on bank slopes

Impulse waves that are generated by landslides in narrow reservoir areas threaten the stability of buildings and bank slopes.To discuss the action process and evolution law of the wave pressure on bank slopes,a three-dimensional physical model test that considers impulse waves generated by landslides was performed,and factors including landslide width,thickness,slope angles of the sliding surface,and bank slope angle were considered.Based on wave forms on the bank slopes,wave pressure curve characteristics,and peak value,the action process of wave pressure could be divided into the following stages:maximum pulsating pressure stage,wave impact stage(when waves break),and stationary pulsation stage.It was found that wave breaking is dependent on the value of the surf similarity parameterξ.The distribution pattern of impact pressure decays linearly on both sides of the maximum impact pressure point,and the attenuation degree decreases when it attains 40%of the maximum value.Thus,it is proposed that the prediction formula for the maximum effective impact pressure of the bank slope be related to the reciprocal of wave steepness,relative water depth,and slope rate.The prediction formula provides strong theoretical support for early safety warning and for predicting the bank slope under impulse waves generated by landslides.

The Analysis of the Correlation between SPT and CPT Based on CNN-GA and Liquefaction Discrimination Research

The objective of this study is to investigate themethods for soil liquefaction discrimination. Typically, predicting soilliquefaction potential involves conducting the standard penetration test (SPT), which requires field testing and canbe time-consuming and labor-intensive. In contrast, the cone penetration test (CPT) provides a more convenientmethod and offers detailed and continuous information about soil layers. In this study, the feature matrix based onCPT data is proposed to predict the standard penetration test blow count N. The featurematrix comprises the CPTcharacteristic parameters at specific depths, such as tip resistance qc, sleeve resistance f s, and depth H. To fuse thefeatures on the matrix, the convolutional neural network (CNN) is employed for feature extraction. Additionally,Genetic Algorithm (GA) is utilized to obtain the best combination of convolutional kernels and the number ofneurons. The study evaluated the robustness of the proposed model using multiple engineering field data sets.Results demonstrated that the proposed model outperformed conventional methods in predicting N values forvarious soil categories, including sandy silt, silty sand, and clayey silt. Finally, the proposed model was employedfor liquefaction discrimination. The liquefaction discrimination based on the predicted N values was comparedwith the measured N values, and the results showed that the discrimination results were in 75% agreement. Thestudy has important practical application value for foundation liquefaction engineering. Also, the novel methodadopted in this research provides new ideas and methods for research in related fields, which is of great academicsignificance.

I-DCGAN and TOPSIS-IFP:A simulation generation model for radiographic flaw detection images in light alloy castings and an algorithm for quality evaluation of generated images

The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.

EDU-GAN:Edge Enhancement Generative Adversarial Networks with Dual-Domain Discriminators for Inscription Images Denoising

Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.However,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character damage.To solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,EDU-GAN.Unlike existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription image.Moreover,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising tasks.The proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure intact.Due to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image denoising.The experimental results show the superiority of our method both in the synthetic and real-inscription datasets.

Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.

A Dual Discriminator Method for Generalized Zero-Shot Learning

Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.

Artificial neural network-based method for discriminating Compton scattering events in high-purity germaniumγ-ray spectrometer

To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the deposition location,we extract four shape parameters from the pulse signals output by the detector.Machine learning is used to input the four shape parameters into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.

High-Precision Doppler Frequency Estimation Based Positioning Using OTFS Modulations by Red and Blue Frequency Shift Discriminator

Orthogonal Time Frequency and Space(OTFS) modulation is expected to provide high-speed and ultra-reliable communications for emerging mobile applications, including low-orbit satellite communications. Using the Doppler frequency for positioning is a promising research direction on communication and navigation integration. To tackle the high Doppler frequency and low signal-to-noise ratio(SNR) in satellite communication, this paper proposes a Red and Blue Frequency Shift Discriminator(RBFSD) based on the pseudo-noise(PN) sequence.The paper derives that the cross-correlation function on the Doppler domain exhibits the characteristic of a Sinc function. Therefore, it applies modulation onto the Delay-Doppler domain using PN sequence and adjusts Doppler frequency estimation by red-shifting or blue-shifting. Simulation results show that the performance of Doppler frequency estimation is close to the Cramér-Rao Lower Bound when the SNR is greater than -15dB. The proposed algorithm is about 1/D times less complex than the existing PN pilot sequence algorithm, where D is the resolution of the fractional Doppler.

Multi‑omics integration identifies regulatory factors underlying bovine subclinical mastitis

Background Mastitis caused by multiple factors remains one of the most common and costly disease of the dairy industry.Multi-omics approaches enable the comprehensive investigation of the complex interactions between mul-tiple layers of information to provide a more holistic view of disease pathogenesis.Therefore,this study investigated the genomic and epigenomic signatures and the possible regulatory mechanisms underlying subclinical mastitis by integrating RNA sequencing data(mRNA and lncRNA),small RNA sequencing data(miRNA)and DNA methylation sequencing data of milk somatic cells from 10 healthy cows and 20 cows with naturally occurring subclinical mastitis caused by Staphylococcus aureus or Staphylococcus chromogenes.Results Functional investigation of the data sets through gene set analysis uncovered 3458 biological process GO terms and 170 KEGG pathways with altered activities during subclinical mastitis,provided further insights into subclin-ical mastitis and revealed the involvement of multi-omics signatures in the altered immune responses and impaired mammary gland productivity during subclinical mastitis.The abundant genomic and epigenomic signatures with sig-nificant alterations related to subclinical mastitis were observed,including 30,846,2552,1276 and 57 differential methylation haplotype blocks(dMHBs),differentially expressed genes(DEGs),lncRNAs(DELs)and miRNAs(DEMs),respectively.Next,5 factors presenting the principal variation of differential multi-omics signatures were identified.The important roles of Factor 1(DEG,DEM and DEL)and Factor 2(dMHB and DEM),in the regulation of immune defense and impaired mammary gland functions during subclinical mastitis were revealed.Each of the omics within Factors 1 and 2 explained about 20%of the source of variation in subclinical mastitis.Also,networks of impor-tant functional gene sets with the involvement of multi-omics signatures were demonstrated,which contributed to a comprehensive view of the possible regulatory mechanisms underlying subclinical mastitis.Furthermore,multi-omics integration enabled the association of the epigenomic regulatory factors(dMHBs,DELs and DEMs)of altered genes in important pathways,such as‘Staphylococcus aureus infection pathway’and‘natural killer cell mediated cyto-toxicity pathway’,etc.,which provides further insights into mastitis regulatory mechanisms.Moreover,few multi-omics signatures(14 dMHBs,25 DEGs,18 DELs and 5 DEMs)were identified as candidate discriminant signatures with capac-ity of distinguishing subclinical mastitis cows from healthy cows.Conclusion The integration of genomic and epigenomic data by multi-omics approaches in this study provided a better understanding of the molecular mechanisms underlying subclinical mastitis and identified multi-omics candidate discriminant signatures for subclinical mastitis,which may ultimately lead to the development of more effective mastitis control and management strategies.

Exploring the Pathways of Leprosy Patients from Their Communities to a Diagnosis in the Districts of Mayuge, Yumbe and Kasese-Uganda

Background: Leprosy is known to cause disability that leads to severe outcomes like stigma, discrimination, mental health problems and participation restriction. Furthermore, in cases of infectious leprosy, longer delays increase the risk for the spread of the disease. Despite being preventable and curable, a significant proportion of new leprosy patients (39%) in 2019 had grade 2 (Described as Visible disability) at the time of diagnosis signifying late presentation. The aim of this study was to describe patient journeys from first symptoms suggestive of leprosy to a diagnosis and individual and community level factors associated with health seeking behavior of leprosy patients. Methods: This was a cross-sectional explorative study implemented in Kasese, Mayuge and Yumbe districts .A structured questionnaire was used to collect quantitative data. Qualitative assessment included patients, family members, health workers, voluntary health teams and the district health team. Descriptive statistics were presented in terms of percentages, frequency tables, pie Charts and graphs for easy interpretation and discussion. Results: The results indicate that 53% of the respondents identified as female. The median age of the respondents being 34 years, with a range of 1 to 76 years (Mean: 44.7, Mode: 65, Standard-Deviation: 19.6, Kurtosis: 0.6). The most common first symptom noticed by respondents was skin lesions (65%) followed by deformities (18%) (P value = 0.05%) occurring mostly in the feet (P-value = 0.48). Majority (52%) of the patients had taken more than 24 months (SD 18.72 OR 2.75) for a diagnosis to be made with a maximum delay of over 60 months. The most common cause of delay in seeking health care was lack of knowledge on leprosy (P value=Conclusions: There was a delay of 2 years in seeking health care for the majority of the patients. Key barriers to early diagnosis were lack of knowledge and infrastructure. Community sensitization and strengthening capacity building are needed to achieve early diagnosis of leprosy and proper management.

The Alzheimer’s Dementia Patients’ Observed Illness Course and Experience in Ghana and Care Lessons to Be Learnt: A Mental Health Professional’s Perspective

Alzheimer’s disease (AD) and associated dementia patient numbers continue to increase globally with associated economic costs to healthcare systems. Of note is the increase in numbers in lower and middle-income countries (LMICs) including Sub-Saharan African (SSA) countries, which already face challenges with their health budgets from communicable and non-communicable diseases. Ghana, an SSA country, faces the problem of healthcare budgetary difficulties and the additional impact of AD as a consequence of increasing population strata of old aged persons (OAPs) due to the demographic transition effect. This article uses examples of known patients’ illness courses to give a perspective on the lived experience of patients with dementia (PWD) in Ghana, living amongst a populace with a culture of stigmatization of PWD, and a relatively fragile public mental health system (PMHS) for those with mental illness, including AD. The lived experience of AD patients is characterised by stigmatisation, discrimination, non-inclusiveness, diminished dignity and human rights abuses in the face of their mental disability, and eventually death. This article is an advocacy article giving voice to the voiceless and all persons suffering from AD and other dementias in Ghana, whilst pleading for a call to action from healthcare professionals and responsible state agencies.

The Influence of Price Discrimination from Airports on the Route Development Behavior of Airlines

Given the prominence and magnitude of airport incentive schemes,it is surprising that literature hitherto remains silent as to their effectiveness.In this paper,the relationship between airport incentive schemes and the route development behavior of airlines is analyzed.Because of rare and often controversial findings in the extant literature regarding relevant influencing variables for attracting airlines at an airport,expert interviews are used as a complement to formulate testable hypotheses in this regard.A fixed effects regression model is used to test the hypotheses with a dataset that covers all seat capacity offered at the 22 largest German commercial airports in the week 46 from 2004 to 2011.It is found that incentives from primary choice,as well as secondary choice airports,have a significant influence on Low Cost Carriers.Furthermore,Low Cost Carriers,in general,do not leave any of both types of airports when the incentives cease.In the case of Network Carriers,no case is found where one joins a primary choice airport and receives an incentive.Insufficient data between Network Carriers and secondary choice airports in the time when incentives have ceased means that no statement can be given.

Methylome and transcriptome data integration reveals potential roles of DNA methylation and candidate biomarkers of cow Streptococcus uberis subclinical mastitis

Background:Mastitis caused by different pathogens including Streptococcus uberis(S.uberis)is responsible for huge economic losses to the dairy industry.In order to investigate the potential genetic and epigenetic regulatory mecha‑nisms of subclinical mastitis due to S.uberis,the DNA methylome(whole genome DNA methylation sequencing)and transcriptome(RNA sequencing)of milk somatic cells from cows with naturally occurring S.uberis subclinical mastitis and healthy control cows(n=3/group)were studied.Results:Globally,the DNA methylation levels of CpG sites were low in the promoters and first exons but high in inner exons and introns.The DNA methylation levels at the promoter,first exon and first intron regions were nega‑tively correlated with the expression level of genes at a whole‑genome‑wide scale.In general,DNA methylation level was lower in S.uberis‑positive group(SUG)than in the control group(CTG).A total of 174,342 differentially methylated cytosines(DMCs)(FDR<0.05)were identified between SUG and CTG,including 132,237,7412 and 34,693 DMCs in the context of CpG,CHG and CHH(H=A or T or C),respectively.Besides,101,612 methylation haplotype blocks(MHBs)were identified,including 451 MHBs that were significantly different(dMHB)between the two groups.A total of 2130 differentially expressed(DE)genes(1378 with up‑regulated and 752 with down‑regulated expression)were found in SUG.Integration of methylome and transcriptome data with MethGET program revealed 1623 genes with signifi‑cant changes in their methylation levels and/or gene expression changes(MetGDE genes,MethGET P‑value<0.001).Functional enrichment of genes harboring≥15 DMCs,DE genes and MetGDE genes suggest significant involvement of DNA methylation changes in the regulation of the host immune response to S.uberis infection,especially cytokine activities.Furthermore,discriminant correlation analysis with DIABLO method identified 26 candidate biomarkers,including 6 DE genes,15 CpG‑DMCs and 5 dMHBs that discriminated between SUG and CTG.Conclusion:The integration of methylome and transcriptome of milk somatic cells suggests the possible involve‑ment of DNA methylation changes in the regulation of the host immune response to subclinical mastitis due to S.uberis.The presented genetic and epigenetic biomarkers could contribute to the design of management strategies of subclinical mastitis and breeding for mastitis resistance.

Conceptual design of a Cs2LiLaBr6 scintillator‑based neutron total cross section spectrometer on the back‑n beam line at CSNS

To reduce the experimental uncertainty in the 235 U resonance energy region and improve the detection efficiency for neutron total cross section measurements compared with those obtained with the neutron total cross section spectrometer(NTOX), a dedicated lithium-containing scintillation detector has been developed on the Back-n beam line at the China Spallation Neutron Source. The Fast Scintillator-based Neutron Total Cross Section(FAST) spectrometer has been designed based on a Cs2Li La Br6(CLLB) scintillator considering the γ-ray flash and neutron environment on the Back-n beam line. The response of the CLLB scintillator to neutrons and γ-rays was evaluated with different 6Li/7 Li abundance ratios using Geant4. The neutron-γdiscrimination performance of the CLLB has been simulated considering different scintillation parameters, physical designs,and light readout modes. A cubic 6Li-enriched( > 90%) CLLB scintillator, which has a thickness of 4-9 mm and side length of no less than 50 mm to cover the Φ 50 mm neutron beam at the spectrometer position, has been proposed coupling to a side readout SiPM array to construct the FAST spectrometer. The developed simulation techniques for neutron-γ discrimination performance could provide technical support for other neutron-induced reaction measurements on the Back-n beam line.

Egg recognition and nestling discrimination in the Crested Myna(Acridotheres cristatellus):Size matters

Most studies exploring abilities of hosts to detect brood parasitism are based on detecting colour and/or pattern differences among parasitic and host eggs or nestlings,while only few were focused on size differences.True recognition and recognition by discordancy are used to explain cognitive mechanisms of host egg recognition;however,only a few studies have found that hosts use recognition by discordancy.This study investigated:1)whether egg and nestling recognitions in the Crested Myna(Acridotheres cristatellus) are based on size cues;2)whether the egg cognitive mechanism is recognition by discordancy based on size cues;and 3) whether the longer the experiment time,the higher the egg recognition rate.Our results showed that the Crested Myna uses egg or nestling size as a recognition cue while the egg and nestling colour and patterning are not associated with egg or nestling rejection,thus the cognitive mechanism of egg recognition in the Crested Myna is recognition by discordancy based on egg size cues.Furthermore,there is a rejection delay in time of egg rejection behaviour of the Crested Myna.Therefore,we suggest that the periodicity of egg rejection experiments could be appropriately extended,especially for species with relatively low egg recognition ability.

Copy-Move Geometric Tampering Estimation Through Enhanced SIFT Detector Method

Digital picture forgery detection has recently become a popular and sig-nificant topic in image processing.Due to advancements in image processing and the availability of sophisticated software,picture fabrication may hide evidence and hinder the detection of such criminal cases.The practice of modifying origi-nal photographic images to generate a forged image is known as digital image forging.A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move forgery.In order to make the forgeries real and inconspicuous,geometric or post-processing techniques are frequently performed on tampered regions during the tampering process.In Copy-Move forgery detection,the high similarity between the tampered regions and the source regions has become crucial evidence.The most frequent way for detecting copy-move forgeries is to partition the images into overlapping square blocks and utilize Discrete cosine transform(DCT)com-ponents as block representations.Due to the high dimensionality of the feature space,Gaussian Radial basis function(RBF)kernel based Principal component analysis(PCA)is used to minimize the dimensionality of the feature vector repre-sentation,which improves feature matching efficiency.In this paper,we propose to use a novel enhanced Scale-invariant feature transform(SIFT)detector method called as RootSIFT,combined with the similarity measures to mark the tampered areas in the image.The proposed method outperforms existing state-of-the-art methods in terms of matching time complexity,detection reliability,and forgery location accuracy,according to the experimental results.The F1 score of the proposed method is 92.3%while the literature methods are around 90%on an average.

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.