Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.

A PCR-Based Method for Detecting Pathogen of Rice False Smut

Rice false smut is a destructive disease that affects rice grain badly.The disease seriously affects the yield and quality of rice in Heilongjiang Province.In this paper,a pair of specific primers was designed to detect the false smut pathogen rapidly and efficiently.The results showed that the pair of primers had strong specificity for false smut pathogen.In addition,the sensitivity of this primer to the genomic DNA of rice false smut pathogen in PCR reaction was 1 pg.By using these primers,the rice false smut pathogen could be detected within 48 h after inoculation,and a PCR reaction system with good specificity and high sensitivity was established.

Water Extract of Rice False Smut Balls Activates Nrf2/HO-1 and Apoptosis Pathways,Causing Liver Injury

Ustiloxins are vital cyclopeptide mycotoxins originally isolated from rice false smut balls that form in rice spikelets infected by the fungal pathogen Ustilaginoidea virens.The toxicity of the water extract of rice false smut balls(RBWE) remains to be investigated.Studies have shown that RBWE may be toxic to animals,but toxicological evidence is still lacking.In this study,we found that the IC50 values of RBWE to BNL CL.2 cells at 24 and 48 h were 40.02 and 30.11 μg/m L,respectively,with positive correlations with dose toxicity and time toxicity.After treatment with RBWE,the number of BNL CL.2 cells decreased significantly,and the morphology of BNL CL.2 cells showed atrophy and wall detachment.RBWE induced DNA presynthesis phase arrest of BNL CL.2 cells,increased the proportion of apoptotic cells and inhibited cell proliferation.RBWE up-regulated reactive oxygen species(ROS) levels and lowered mitochondrial membrane potentials.Additionally,Western blot and q RT-PCR results suggested that RBWE exerted the above effects by promoting the Nrf2/HO-1 and caspase-induced apoptosis pathways in vitro and in vivo.The contents of alanine aminotransferase,aspartate aminotransferase,alkaline phosphatase,and total bile acids in the serum of mice from Institute of Cancer were significantly up-regulated by RBWE.At the same time,RBWE can lead to increases in ROS and malondialdehyde contents,decreases in contents of oxidized glutathione,glutathione and reduced glutathione,as well as decrease in catalase and superoxide dismutase activities in mouse liver tissues,demonstrating that oxidative stress occurred in mice.Moreover,liver damage was further detected by haematoxylin-eosin staining and electron microscopy to verify the damage to the mice caused by RBWE.In general,RBWE may cause hepatotoxicity in vivo and in vitro via the apoptosis pathway,which provides a reference for hepatotoxicity and its mechanism of action.

Residual-Based False Data Injection Attacks Against Multi-Sensor Estimation Systems

This paper investigates the security issue of multisensor remote estimation systems.An optimal stealthy false data injection(FDI)attack scheme based on historical and current residuals,which only tampers with the measurement residuals of partial sensors due to limited attack resources,is proposed to maximally degrade system estimation performance.The attack stealthiness condition is given,and then the estimation error covariance in compromised state is derived to quantify the system performance under attack.The optimal attack strategy is obtained by solving several convex optimization problems which maximize the trace of the compromised estimation error covariance subject to the stealthiness condition.Moreover,due to the constraint of attack resources,the selection principle of the attacked sensor is provided to determine which sensor is attacked so as to hold the most impact on system performance.Finally,simulation results are presented to verify the theoretical analysis.

Kinematic Control of Serial Manipulators Under False Data Injection Attack

With advanced communication technologies,cyberphysical systems such as networked industrial control systems can be monitored and controlled by a remote control center via communication networks.While lots of benefits can be achieved with such a configuration,it also brings the concern of cyber attacks to the industrial control systems,such as networked manipulators that are widely adopted in industrial automation.For such systems,a false data injection attack on a control-center-to-manipulator(CC-M)communication channel is undesirable,and has negative effects on the manufacture quality.In this paper,we propose a resilient remote kinematic control method for serial manipulators undergoing a false data injection attack by leveraging the kinematic model.Theoretical analysis shows that the proposed method can guarantee asymptotic convergence of the regulation error to zero in the presence of a type of false data injection attack.The efficacy of the proposed method is validated via simulations.

Rice False Smut Fungus, <i>Ustilaginoidea virens</i>, Inhibits Pollen Germination and Degrades the Integuments of Rice Ovule

Ustilaginoidea virens is a flower-infecting fungus that forms false smut balls in rice panicle. Rice false smut has long been considered a minor disease, but recently it occurred frequently and emerged as a major disease in rice production. In vitro co-cultivation of U. virens strain with young rice panicles showed that U. virens enters inside of spikelets from the apex and then grows downward to infect floral organs. In response to U. virens infection, rice host exhibits elevated ROS accumulation and enhanced callose deposition. The secreted compounds of U. virens can suppress rice pollen germination. Examination of sectioning slides of freshly collected smut balls demonstrated that both pistil and stamens of rice flower are infected by U. virens, hyphae degraded the contents of the pollen cells, and also invaded the filaments. In addition, U. virens entered rice ovary through the thin-walled papillary cells of the stigma, then decomposed the integuments and infected the ovary. The invaded pathogen could not penetrate the epidermis and other layers of the ovary. Transverse section of the pedicel just below the smut balls showed that there were no fungal hyphae observed in the vascular bundles of the pedicel, implicating that U. virens is not a systemic flower-infecting fungus.

Effect of False Windows on Light Exposure and Sleep Quality in Hospitalized Patients

Purpose: We aimed to investigate the effects of installing false windows next to hospital beds without windows on the amount of light received by patients and their sleep quality. Methods: The study included patients admitted to the Department of Neurology at our hospital between September 2020 and August 2021. An Actigraph device was fitted to patients’ wrist and their beds to measure the amount of light received and sleep quality. Patients were divided into three groups: bed with a window, aisle bed with a false window, and aisle bed without a window. Mean sleep efficiency (%), mean steps (per day), and the amount of light (lux) received by the patients and beds were measured. Results: Valid data were obtained for 48 participants (median age, 66.5 years). There were 23 patients in beds with a window, 13 patients in aisle beds without a false window, and 12 in aisle beds with a false window. No statistically significant differences were found in terms of mean sleep efficiency, number of steps taken, and the amount of light received by the patients (P > 0.05);however, difference in the mean amount of light received by the beds at the location of the bed was statistically significant (P Conclusion: The amount of light that the patient receives is not necessarily affected by the location of the bed or the presence of a false window.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

False positive detection of serum cryptococcal antigens due to insufficient sample dilution:A case series

At present,with the development of technology,the detection of cryptococcal antigen(CRAG)plays an increasingly important role in the diagnosis of cryptococcosis.However,the three major CRAG detection technologies,latex agglutination test(LA),lateral flow assay(LFA)and Enzyme-linked Immunosorbent Assay,have certain limitations.Although these techniques do not often lead to false-positive results,once this result occurs in a particular group of patients(such as human immunodeficiency virus patients),it might lead to severe consequences.

Survey and examination of the potential alternative hosts of Villosiclava virens, the pathogen of rice false smut, in China

Rice false smut is caused by ascomycete Villosiclava virens, whose potential alternative hosts have been assumed previ- ously. Here its potential alternative hosts were surveyed and identified from 2008 to 2013 in the main rice-growing regions in China. Two common weeds in paddy fields, Digitaria sanguinalis Scop. and Echinochloa crusgalli （L.） Beauv., were found to present the similar symptoms to smut diseases in a few individuals in 2012 and 2013 in Zhejiang and Sichuan provinces of China, respectively. After the examinations of the spore morphology, their infection and extension mode in hosts, pathogen cell wall components, and molecular identification, the two pathogens were identified to be the Basidiomycetes, Ustilago syntherismae and Ustilago trichophora, respectively. So far there has been no alternative host of V. virens to be identified in China. These suggest that the alternative hosts of V. virens, if they do exist, are not possible to play an important role in the pathogen life cycle and the disease epidemics.

毛竹ICE基因家族的全基因组鉴定及低温胁迫下的表达模式分析

【目的】对毛竹Phyllostachys edulis ICE基因家族进行鉴定及分析,找出响应毛竹抗寒关键家族成员,研究毛竹ICE基因的生物学功能、响应低温胁迫的分子机制及遗传转化,为提高毛竹抗寒性奠定理论基础。【方法】利用生物信息学方法分析毛竹ICE基因家族成员,并对4、0、−2℃低温处理0(对照)、0.5、1.0、24.0、48.0 h的毛竹生理指标和ICE基因的表达模式进行分析。【结果】共鉴定了4个毛竹ICE基因。保守结构域和多重序列比对分析表明:PeICE基因结构高度相似。系统发育关系及启动子顺式作用元件分析显示:PeICE基因与水稻Oryza sativa亲缘关系更近,同时存在大量与非生物胁迫相关的顺式作用元件。活性氧自由基(ROS)染色发现随着处理时间增长,ROS染色逐渐加深,但是其0℃处理24.0 h、−2℃处理1.0 h后染色逐渐减弱。脯氨酸(Pro)质量摩尔浓度、超氧化物歧化酶(SOD)活性显示:4和0℃条件下,Pro质量摩尔浓度和SOD活性整体增加,但−2℃时低于对照。过氧化物酶(POD)活性显示:在3个低温处理下均增加。ICE基因表达模式分析发现:4、0℃处理时PeICE表达量整体增加,且都以PeICE3增量最明显;而−2℃处理下PeICE整体表达量水平低于对照。【结论】随着温度降低和处理时间增强,毛竹受到的损伤不断增强,其内酶活系统以及ICE基因积极响应低温胁迫,其中,PeICE3对低温胁迫最为敏感,但在−2℃时,ICE基因表达量并未增加,推测该基因家族响应了寒冷胁迫而非冷冻胁迫。

SCADA Data-Based Support Vector Machine for False Alarm Identification for Wind Turbine Management

Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)devices.These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers.Cyber-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)attacks.In this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their damage.The recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying cyber-attacks.The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT environment.The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment.To accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square test.To detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this study.Finally,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency.The proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct aspects.The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

Joint Probability Analysis and Prediction of Sea Ice Conditions in Liaodong Bay

Sea ice conditions in Liaodong Bay of China are often described by sea ice grades,which classify annual sea ice conditions based on the annual maximum sea ice thickness(AM-SIT)and annual maximum floating ice extent(AM-FIE).The joint probability distribution of AM-SIT and AM-FIE was established on the basis of their data pairs from 1949/1950 to 2019/2020 in Liaodong Bay.The joint intensity index of the sea ice condition in the current year is calculated,and the joint classification criteria of the sea ice grades in past years are established on the basis of the joint intensity index series.A comparison of the joint criteria with the 1973 and 2022 criteria revealed that the joint criteria of the sea ice grade match well,and the joint intensity index can be used to quantify the sea ice condition over the years.A time series analysis of the sea ice grades and the joint intensity index sequences based on the joint criteria are then performed.Results show a decreasing trend of the sea ice condition from 1949/1950 to 2019/2020,a mutation in 1990/1991,and a period of approximately 91 years of the sea ice condition.In addition,the Gray-Markov model(GMM)is applied to predict the joint sea ice grade and the joint intensity index of the sea ice condition series in future years,and the error between the results and the actual sea ice condition in 2020/2021 is small.

Spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice in multiple dimensions during 1979 to 2020

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change.The rapid changes in Arctic sea ice have been widely concerned.However,the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series.In this study,the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data.The results indicated that Arctic sea ice was declining at a remarkably high rate of–5.4×10^(4) km^(2)/a in sea ice area(SIA)and–2.2 cm/a in sea ice thickness(SIT)during 1979 to 2020,and the reduction of SIA and SIT was the largest in summer and the smallest in winter.Spatially,compared with other sub-regions,SIA showed a sharper declining trend in the Barents Sea,Kara Sea,and East Siberian Sea,while SIT presented a larger downward trend in the northern Canadian Archipelago,northern Greenland,and the East Siberian Sea.Regarding to the seasonal trend of sea ice on sub-region scale,the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons,especially in summer and winter,i.e.,the sub-regions linked to the open ocean exhibited a higher decline rate in winter;however,the other sub-regions blocked by the coastlines presented a greater decline rate in summer.For SIT,the sub-regions such as the Beaufort Sea,East Siberian Sea,Chukchi Sea,Central Arctic,and Canadian Archipelago always showed a higher downward rate in all seasons.Furthermore,a striking freeze-thaw asymmetry of Arctic sea ice was also detected.Comparing sea ice changes in different dimensions,sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension.The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period,and the rate of sea ice melting was 0.3×10^(4) km^(2)/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions,respectively.Moreover,there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004,respectively,in the horizontal/vertical dimension.

Preface to the Special Topic on Ocean, Sea Ice and Northern Hemisphere Climate:In Remembrance of Professor Yongqi GAO's Key Contributions

This special issue commemorates the life work of Prof. Yongqi GAO who passed away in July 2021, his time cut short by illness. He had many great achievements, but still much more to contribute. The seven articles in this special issue are from research areas where he contributed, and they illustrate how his close colleagues are continuing his work.

Interaction Between Ustilaginoidea virens and Rice and Its Sustainable Control

Ustilaginoidea virens is a common rice pathogen that can easily lead to a decline in rice quality and the production of toxins pose potential risks to human health.In this review,we present a comprehensive literature review of research since the discovery of rice false smut.We provide a comprehensive and,at times,critical overview of the main results and findings from related research,and propose future research directions.Firstly,we delve into the interaction between U.virens and rice,including the regulation of transcription factors,the process of U.virens infecting rice panicles,and the plant immune response caused by rice infection.Following that,we discuss the identification and characterization of mycotoxins produced by the pathogenic fungus,as well as strategies for disease management.We emphasize the importance of comprehensive agricultural prevention and control methods for the sustainable management of U.virens.This knowledge will update our understanding of the interaction between U.virens and rice plants,offering a valuable perspective for those interested in U.virens.

An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

Sea ice surface roughness(SIR)affects the energy transfer between the atmosphere and the ocean,and it is also an important indicator for sea ice characteristics.To obtain a small-scale SIR with high spatial resolution,a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar(SAR)images,utilizing an ensemble learning method.Firstly,the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice,including the scale and direction of ice patterns.Secondly,a model is developed using the Adaboost Regression model to establish a relationship among SIR,radar backscatter and the spatial information of sea ice.The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper(ATM)in the summer Beaufort Sea.The determination of coefficient,mean absolute error,root-mean-square error and mean absolute percentage error of the testing data are 0.91,1.71 cm,2.82 cm,and 36.37%,respectively,which are reasonable.Moreover,K-fold cross-validation and learning curves are analyzed,which also demonstrate the method’s applicability in retrieving SIR from SAR images.

Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario

The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.