The leaf epidermis of Japanese honeysuckle （Lonicera japonica Thunb.） and Wild Honeysuckle （Lonicera confusa） in the genus of Flos Lonicerae were mainly observed by scanning electron microscopes （SEM） to study t...The leaf epidermis of Japanese honeysuckle （Lonicera japonica Thunb.） and Wild Honeysuckle （Lonicera confusa） in the genus of Flos Lonicerae were mainly observed by scanning electron microscopes （SEM） to study the characteristics of stomata, trichomes and dermal cell, etc.. The results showed that stoma exists only on the lower epidermis and its distribution is irregular, and leaf epidermis consist of epidermis cells, stoma complexes and bushy trichomes including glandular hair and non-glandular hair. On the upper epidermis, anticlinal wall caves in sinuous groove to countercheck the transpiration. Evidences from leaf morphological structures serve as another proof on drought-resistant mechanisms. Some strumaes distributing regularly are hypothesized as oxalic calcium on the lower epidermis under laser scanning confocal microscopy （LSCM） with Fluo-3/AM, which can increase their endurance to drought stress. Therefore, the above characteristics of Flos Lonicerae can reduce the loss of water and make Japanese honeysuckle and Wild Honeysuckle adapt to the droughty environment at Karst area in southwest China. However, there is some difference of the two species. From the SEM （Scanning Electron Microscopy） result, it is shown that on the upper epidermis, some glandular hair regularly present along the midrib of Japanese honeysuckle, but Wild Honeysuckle has no glandular hair on the upper epidermis, which can verify the relationships of Flos Lonicerae species and provide the significance for classification of Flos Lonicerae.展开更多
Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable t...Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights.The probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision boundary.However,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation.To tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during training.Specifi-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example.Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label.CL-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight.Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.展开更多
CHINESE are well known for their desire to learn English and practice on unsuspecting foreigners, with levels of spoken and written English improving remark- ably across the country in recent years. One stand out, how...CHINESE are well known for their desire to learn English and practice on unsuspecting foreigners, with levels of spoken and written English improving remark- ably across the country in recent years. One stand out, however, has not kept pace with the levels of people＇s English. Every foreigner who has traveled or lived in China will have a story to tell about an English- language sign that has been lost in translation.展开更多
The term“nipple confusion”accurately describes the confusion newborns experience between their mother’s nipple and an artificial nipple during feeding.Specifically,it refers to the feeding habits infants develop ba...The term“nipple confusion”accurately describes the confusion newborns experience between their mother’s nipple and an artificial nipple during feeding.Specifically,it refers to the feeding habits infants develop based on their initial feeding experiences after birth.Infants accustomed to the maternal nipple often resist bottle-feeding;conversely,those accustomed to bottle-feeding may reject the maternal nipple.This confusion is particularly common among infants receiving mixed feeding.展开更多
Nipple confusion,a term that accurately describes the confusion that occurs between the mother’s nipple and the artificial teat during feeding in newborns.Specifically,it refers to the fact that babies develop specif...Nipple confusion,a term that accurately describes the confusion that occurs between the mother’s nipple and the artificial teat during feeding in newborns.Specifically,it refers to the fact that babies develop specific breastfeeding habits after birth,based on their initial feeding experience.For babies who are accustomed to their mother’s nipple,they tend to show resistance to bottle feeding;on the contrary,those who have adapted to bottle feeding may refuse to accept their mother’s nipple.This confusion is particularly common among mixed-feeding babies.展开更多
Recently,Joe,a foreign English teacher in China,said to me,"I feel dazed and disoriented;my students here are unusually quiet. I can’t tell if they understand my lessons." His plight is not surprising.Many ...Recently,Joe,a foreign English teacher in China,said to me,"I feel dazed and disoriented;my students here are unusually quiet. I can’t tell if they understand my lessons." His plight is not surprising.Many foreign ESL(English as a second language)instructors often feel confused when they first step into a Chinese classroom.Chinese ESL students are different because their展开更多
confucius is one of the rare men who impacted human history by dint of his personal and intellectual gifts. So much legend and tradition whirls around this great figure that it becomes hard to clearly understand the m...confucius is one of the rare men who impacted human history by dint of his personal and intellectual gifts. So much legend and tradition whirls around this great figure that it becomes hard to clearly understand the man and his teachings.展开更多
Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is graduall...Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.展开更多
Rockbursts have become a significant hazard in underground mining,underscoring the need for a robust early warning model to ensure safety management.This study presents a novel approach for rockburst prediction,integr...Rockbursts have become a significant hazard in underground mining,underscoring the need for a robust early warning model to ensure safety management.This study presents a novel approach for rockburst prediction,integrating the Mann-Kendall trend test(MKT)and multi-indices fusion to enable real-time and quantitative assessment of rockburst hazards.The methodology employed in this study involves the development of a comprehensive precursory index library for rockbursts.The MKT is then applied to analyze the real-time trend of each index,with adherence to rockburst characterization laws serving as the warning criterion.By employing a confusion matrix,the warning effectiveness of each index is assessed,enabling index preference determination.Ultimately,the integrated rockburst hazard index Q is derived through data fusion.The results demonstrate that the proposed model achieves a warning effectiveness of 0.563 for Q,surpassing the performance of any individual index.Moreover,the model’s adaptability and scalability are enhanced through periodic updates driven by actual field monitoring data,making it suitable for complex underground working environments.By providing an efficient and accurate basis for decision-making,the proposed model holds great potential for the prevention and control of rockbursts.It offers a valuable tool for enhancing safety measures in underground mining operations.展开更多
Confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,lev...Confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,leveraging deep learning methodologies.Despite garnering increasing attention in computer vision,the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures.As of now,there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks.To fill this gap,our study presents a pioneering review that covers both themodels and the publicly available benchmark datasets,while also identifying potential directions for future research in this field.The current dataset primarily focuses on single confusing object detection at the image level,with some studies extending to video-level data.We conduct an in-depth analysis of deep learning architectures,revealing that the current state-of-the-art(SOTA)COD methods demonstrate promising performance in single object detection.We also compile and provide detailed descriptions ofwidely used datasets relevant to these detection tasks.Our endeavor extends to discussing the limitations observed in current methodologies,alongside proposed solutions aimed at enhancing detection accuracy.Additionally,we deliberate on relevant applications and outline future research trajectories,aiming to catalyze advancements in the field of glass,mirror,and camouflaged object detection.展开更多
With the rapid development of digital information technology,images are increasingly used in various fields.To ensure the security of image data,prevent unauthorized tampering and leakage,maintain personal privacy,and...With the rapid development of digital information technology,images are increasingly used in various fields.To ensure the security of image data,prevent unauthorized tampering and leakage,maintain personal privacy,and protect intellectual property rights,this study proposes an innovative color image encryption algorithm.Initially,the Mersenne Twister algorithm is utilized to generate high-quality pseudo-random numbers,establishing a robust basis for subsequent operations.Subsequently,two distinct chaotic systems,the autonomous non-Hamiltonian chaotic system and the tentlogistic-cosine chaotic mapping,are employed to produce chaotic random sequences.These chaotic sequences are used to control the encoding and decoding process of the DNA,effectively scrambling the image pixels.Furthermore,the complexity of the encryption process is enhanced through improved Joseph block scrambling.Thorough experimental verification,research,and analysis,the average value of the information entropy test data reaches as high as 7.999.Additionally,the average value of the number of pixels change rate(NPCR)test data is 99.6101%,which closely approaches the ideal value of 99.6094%.This algorithm not only guarantees image quality but also substantially raises the difficulty of decryption.展开更多
Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is desi...Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.展开更多
[Objectives]This study aimed to investigate the incidence and risk factors associated with SSD in patients following cardiac surgery.[Methods]A total of 378 patients who underwent cardiac surgery in Taihe Hospital wer...[Objectives]This study aimed to investigate the incidence and risk factors associated with SSD in patients following cardiac surgery.[Methods]A total of 378 patients who underwent cardiac surgery in Taihe Hospital were recruited and screened.Diagnosis of delirium was made using evaluation methods and DSM-5 criteria.SSD was defined as the presence of one or more core features of delirium without meeting the full diagnostic criteria.Statistical analysis included independent samples t-test for group comparisons and binary logistic regression analysis to identify independent risk factors for SSD after cardiac surgery.[Results]Among the 378 subjects,112(29.63%)had SSD,28(7.41%)had delirium,and the remaining 238 patients(62.96%)did not present with delirium.Univariate analysis revealed that age,APACHE II score,duration of aortic clamping,length of ICU stay,duration of sedation use,and daily sleep time were significant risk factors for the occurrence of SSD(P<0.05).Logistic regression analysis identified age>70 years old,APACHE II score>20 points,length of ICU stay>5 d,and duration of sedation use>24 h as independent risk factors for SSD after cardiac surgery(P<0.05).A functional model was fitted based on the analysis results of the binary logistic regression model,yielding the equation logit P=1.472X_(1)+2.213X_(2)+3.028X_(3)+1.306X_(4).[Conclusions]Comprehensive clinical assessment is crucial for patients undergoing cardiac surgery,and appropriate preventive measures should be taken for patients with identified risk factors.Close monitoring of the patient s consciousness should be implemented postoperatively,and timely interventions should be conducted.Further research should focus on model validation and optimization.展开更多
With western Jilin Province as the study region, spectral characteristics and texture features of remote sensing images were taken as the classification basis to construct a Decision Tree Model and extract information...With western Jilin Province as the study region, spectral characteristics and texture features of remote sensing images were taken as the classification basis to construct a Decision Tree Model and extract information about settlements in western Jilin Province, and the manually-extracted information about settlements in western Jilin Province was evaluated by confusion matrix. The results showed that Decision Tree Model was convenient for extracting settlements information by integrating spectral and texture features, and the accuracy of such a method was higher than that of the traditional Maximum Liklihood Method, in addition, calculation methods of extracting settlements information by this mean were concluded.展开更多
Naipaul＇s novel A bend in the river illustrates the social confusion and people＇s destitution and wasting time in African countries after their national independence. An important reason is the president＇s self-pub...Naipaul＇s novel A bend in the river illustrates the social confusion and people＇s destitution and wasting time in African countries after their national independence. An important reason is the president＇s self-publicity, tyrannical hess and destructiveness. Whereas the author＇s subjective suspicion is visible in democratic development of African countries. Indeed, in a country development, it is quite necessary to depend on its citizen, observing public feelings; controlling a state with science; benefiting citizen; utilizing resources; constructing homestead, intending creation with a harmonious and stable way.展开更多
1.文学常识1.Modernism-It is a label loosely applied to theworks of certain writers of the late nineteenthand early twentieth centuries who investigatedthe structure and texture of literature andchallenged its CONVENTI...1.文学常识1.Modernism-It is a label loosely applied to theworks of certain writers of the late nineteenthand early twentieth centuries who investigatedthe structure and texture of literature andchallenged its CONVENTIONS.The years from1910 to 1930 are often called the Era of Moder-nism,for there seems to have been in both展开更多
The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas...The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis.In the past,the airlines could not obtain deviations autonomously.At present,a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations.However,it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks.To obtain monitoring autonomy of each aero-engine model,it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models.This paper adopts the Residual-Back Propagation Neural Network(Res-BPNN)to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy(MK-MMD)adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space(RKHS)for discrepancy measurement.To further reduce the distribution discrepancy of each aero-engine model,the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible,and then the learned features can be confused.Through the above methods,domain-invariant features can be extracted,and the optimal adaptation effect can be achieved.Finally,the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.展开更多
This study aimed to assess the potential of in-situ measured soil and vegetation characteristics in landslide susceptibility analyses.First,data for eight independent variables,i.e.,soil moisture content,soil organic ...This study aimed to assess the potential of in-situ measured soil and vegetation characteristics in landslide susceptibility analyses.First,data for eight independent variables,i.e.,soil moisture content,soil organic content,compaction of soil(soil toughness),plant root strength,crop biomass,tree diameter at knee height,Shannon Wiener Index(SWI)for trees and herbs was assembled from field tests at two historic landslide locations:Aranayaka and Kurukudegama,Sri Lanka.An economical,finer resolution database was obtained as the field tests were not cost-prohibitive.The logistic regression(LR)analysis showed that soil moisture content,compaction of soil,SWI for trees and herbs were statistically significant at P<0.05.The variance inflation factors(VIFs)were computed to test for multicollinearity.VIF values(<2)confirmed the absence of multicollinearity between four independent variables in the LR model.Receiver Operating Characteristics(ROC)curve and Confusion Metrix(CM)methods were used to validate the model.In ROC analysis,areas under the curve of Success Rate Curve and Prediction Rate Curve were 84.5% and 96.6%,respectively,demonstrating the model’s excellent compatibility and predictability.According to the CM,the model demonstrated a 79.6% accuracy,63.6% precision,100% recall,and a F-measure of 77.8%.The model coefficients revealed that the vegetation cover has a more significant contribution to landslide susceptibility than soil characteristics.Finally,the susceptibility map,which was then classified as low,medium,and highly susceptible areas based on the natural breaks(Jenks)method,was generated using geographical information systems(GIS)techniques.All the historic landslide locations fell into the high susceptibility areas.Thus,validation of the model and inspection of the susceptibility map indicated that the in-situ soil and vegetation characteristics used in the model could be employed to demarcate historical landslide patches and identify landslide susceptible locations with high confidence.展开更多
Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them....Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.展开更多
Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as i...Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.展开更多
基金This study was supported by the Ministry of Sciences and Technology of China (No.2005DIB3J067)the National Science Foundation of China (No.40572107, No.40231008, No.40672165 and No.30600074)+2 种基金the Chongqing Science & Technology Commission (No.2005AB7006)the Open Fund and Key Subject of Physical Geog-raphy, Southwest Normal University of China (No.250-411110)the Open Fund of Key Laboratory of Chinese Academy of Geological Sci-ences (No.KL05-20).
文摘The leaf epidermis of Japanese honeysuckle （Lonicera japonica Thunb.） and Wild Honeysuckle （Lonicera confusa） in the genus of Flos Lonicerae were mainly observed by scanning electron microscopes （SEM） to study the characteristics of stomata, trichomes and dermal cell, etc.. The results showed that stoma exists only on the lower epidermis and its distribution is irregular, and leaf epidermis consist of epidermis cells, stoma complexes and bushy trichomes including glandular hair and non-glandular hair. On the upper epidermis, anticlinal wall caves in sinuous groove to countercheck the transpiration. Evidences from leaf morphological structures serve as another proof on drought-resistant mechanisms. Some strumaes distributing regularly are hypothesized as oxalic calcium on the lower epidermis under laser scanning confocal microscopy （LSCM） with Fluo-3/AM, which can increase their endurance to drought stress. Therefore, the above characteristics of Flos Lonicerae can reduce the loss of water and make Japanese honeysuckle and Wild Honeysuckle adapt to the droughty environment at Karst area in southwest China. However, there is some difference of the two species. From the SEM （Scanning Electron Microscopy） result, it is shown that on the upper epidermis, some glandular hair regularly present along the midrib of Japanese honeysuckle, but Wild Honeysuckle has no glandular hair on the upper epidermis, which can verify the relationships of Flos Lonicerae species and provide the significance for classification of Flos Lonicerae.
基金supported by the National Natural Science Foundation of China (No.62072127,No.62002076,No.61906049)Natural Science Foundation of Guangdong Province (No.2023A1515011774,No.2020A1515010423)+3 种基金Project 6142111180404 supported by CNKLSTISS,Science and Technology Program of Guangzhou,China (No.202002030131)Guangdong basic and applied basic research fund joint fund Youth Fund (No.2019A1515110213)Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No.MJUKF-IPIC202101)Scientific research project for Guangzhou University (No.RP2022003).
文摘Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights.The probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision boundary.However,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation.To tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during training.Specifi-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example.Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label.CL-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight.Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.
文摘CHINESE are well known for their desire to learn English and practice on unsuspecting foreigners, with levels of spoken and written English improving remark- ably across the country in recent years. One stand out, however, has not kept pace with the levels of people＇s English. Every foreigner who has traveled or lived in China will have a story to tell about an English- language sign that has been lost in translation.
文摘The term“nipple confusion”accurately describes the confusion newborns experience between their mother’s nipple and an artificial nipple during feeding.Specifically,it refers to the feeding habits infants develop based on their initial feeding experiences after birth.Infants accustomed to the maternal nipple often resist bottle-feeding;conversely,those accustomed to bottle-feeding may reject the maternal nipple.This confusion is particularly common among infants receiving mixed feeding.
文摘Nipple confusion,a term that accurately describes the confusion that occurs between the mother’s nipple and the artificial teat during feeding in newborns.Specifically,it refers to the fact that babies develop specific breastfeeding habits after birth,based on their initial feeding experience.For babies who are accustomed to their mother’s nipple,they tend to show resistance to bottle feeding;on the contrary,those who have adapted to bottle feeding may refuse to accept their mother’s nipple.This confusion is particularly common among mixed-feeding babies.
文摘Recently,Joe,a foreign English teacher in China,said to me,"I feel dazed and disoriented;my students here are unusually quiet. I can’t tell if they understand my lessons." His plight is not surprising.Many foreign ESL(English as a second language)instructors often feel confused when they first step into a Chinese classroom.Chinese ESL students are different because their
文摘confucius is one of the rare men who impacted human history by dint of his personal and intellectual gifts. So much legend and tradition whirls around this great figure that it becomes hard to clearly understand the man and his teachings.
基金Project(52161135301)supported by the International Cooperation and Exchange of the National Natural Science Foundation of ChinaProject(202306370296)supported by China Scholarship Council。
文摘Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.
基金The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China(Grant Nos.52011530037 and 51904019)the Fundamental Research Funds for the Central Universities and the Youth Teacher International Exchange&Growth Program(Grant No.QNXM20210004).We also greatly appreciate the assistance provided by Kuangou coal mine,China Energy Group Xinjiang Energy Co.,Ltd.
文摘Rockbursts have become a significant hazard in underground mining,underscoring the need for a robust early warning model to ensure safety management.This study presents a novel approach for rockburst prediction,integrating the Mann-Kendall trend test(MKT)and multi-indices fusion to enable real-time and quantitative assessment of rockburst hazards.The methodology employed in this study involves the development of a comprehensive precursory index library for rockbursts.The MKT is then applied to analyze the real-time trend of each index,with adherence to rockburst characterization laws serving as the warning criterion.By employing a confusion matrix,the warning effectiveness of each index is assessed,enabling index preference determination.Ultimately,the integrated rockburst hazard index Q is derived through data fusion.The results demonstrate that the proposed model achieves a warning effectiveness of 0.563 for Q,surpassing the performance of any individual index.Moreover,the model’s adaptability and scalability are enhanced through periodic updates driven by actual field monitoring data,making it suitable for complex underground working environments.By providing an efficient and accurate basis for decision-making,the proposed model holds great potential for the prevention and control of rockbursts.It offers a valuable tool for enhancing safety measures in underground mining operations.
基金supported by the NationalNatural Science Foundation of China Nos.62302167,U23A20343Shanghai Sailing Program(23YF1410500)Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(23CGA34).
文摘Confusing object detection(COD),such as glass,mirrors,and camouflaged objects,represents a burgeoning visual detection task centered on pinpointing and distinguishing concealed targets within intricate backgrounds,leveraging deep learning methodologies.Despite garnering increasing attention in computer vision,the focus of most existing works leans toward formulating task-specific solutions rather than delving into in-depth analyses of methodological structures.As of now,there is a notable absence of a comprehensive systematic review that focuses on recently proposed deep learning-based models for these specific tasks.To fill this gap,our study presents a pioneering review that covers both themodels and the publicly available benchmark datasets,while also identifying potential directions for future research in this field.The current dataset primarily focuses on single confusing object detection at the image level,with some studies extending to video-level data.We conduct an in-depth analysis of deep learning architectures,revealing that the current state-of-the-art(SOTA)COD methods demonstrate promising performance in single object detection.We also compile and provide detailed descriptions ofwidely used datasets relevant to these detection tasks.Our endeavor extends to discussing the limitations observed in current methodologies,alongside proposed solutions aimed at enhancing detection accuracy.Additionally,we deliberate on relevant applications and outline future research trajectories,aiming to catalyze advancements in the field of glass,mirror,and camouflaged object detection.
基金supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202208)the Natural Science Foundation of Chongqing(Grant No.CSTB2023NSCQLZX0139)the National Natural Science Foundation of China(Grant No.61772295).
文摘With the rapid development of digital information technology,images are increasingly used in various fields.To ensure the security of image data,prevent unauthorized tampering and leakage,maintain personal privacy,and protect intellectual property rights,this study proposes an innovative color image encryption algorithm.Initially,the Mersenne Twister algorithm is utilized to generate high-quality pseudo-random numbers,establishing a robust basis for subsequent operations.Subsequently,two distinct chaotic systems,the autonomous non-Hamiltonian chaotic system and the tentlogistic-cosine chaotic mapping,are employed to produce chaotic random sequences.These chaotic sequences are used to control the encoding and decoding process of the DNA,effectively scrambling the image pixels.Furthermore,the complexity of the encryption process is enhanced through improved Joseph block scrambling.Thorough experimental verification,research,and analysis,the average value of the information entropy test data reaches as high as 7.999.Additionally,the average value of the number of pixels change rate(NPCR)test data is 99.6101%,which closely approaches the ideal value of 99.6094%.This algorithm not only guarantees image quality but also substantially raises the difficulty of decryption.
基金Project supported by the National Natural Science Foundation of China(Grant No.62061014)the Natural Science Foundation of Liaoning province of China(Grant No.2020-MS-274).
文摘Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.
基金Supported by Philosophy and Social Science Research Project of Hubei Education Department in 2022(22D092)Guiding Scientific Research Project of Shiyan Science and Technology Bureau in 2022(22Y34).
文摘[Objectives]This study aimed to investigate the incidence and risk factors associated with SSD in patients following cardiac surgery.[Methods]A total of 378 patients who underwent cardiac surgery in Taihe Hospital were recruited and screened.Diagnosis of delirium was made using evaluation methods and DSM-5 criteria.SSD was defined as the presence of one or more core features of delirium without meeting the full diagnostic criteria.Statistical analysis included independent samples t-test for group comparisons and binary logistic regression analysis to identify independent risk factors for SSD after cardiac surgery.[Results]Among the 378 subjects,112(29.63%)had SSD,28(7.41%)had delirium,and the remaining 238 patients(62.96%)did not present with delirium.Univariate analysis revealed that age,APACHE II score,duration of aortic clamping,length of ICU stay,duration of sedation use,and daily sleep time were significant risk factors for the occurrence of SSD(P<0.05).Logistic regression analysis identified age>70 years old,APACHE II score>20 points,length of ICU stay>5 d,and duration of sedation use>24 h as independent risk factors for SSD after cardiac surgery(P<0.05).A functional model was fitted based on the analysis results of the binary logistic regression model,yielding the equation logit P=1.472X_(1)+2.213X_(2)+3.028X_(3)+1.306X_(4).[Conclusions]Comprehensive clinical assessment is crucial for patients undergoing cardiac surgery,and appropriate preventive measures should be taken for patients with identified risk factors.Close monitoring of the patient s consciousness should be implemented postoperatively,and timely interventions should be conducted.Further research should focus on model validation and optimization.
基金Supported by Financial Support of China Geological Survey(1212010916048)the Fundamental Research Funds for the Central Universities(200903046)~~
文摘With western Jilin Province as the study region, spectral characteristics and texture features of remote sensing images were taken as the classification basis to construct a Decision Tree Model and extract information about settlements in western Jilin Province, and the manually-extracted information about settlements in western Jilin Province was evaluated by confusion matrix. The results showed that Decision Tree Model was convenient for extracting settlements information by integrating spectral and texture features, and the accuracy of such a method was higher than that of the traditional Maximum Liklihood Method, in addition, calculation methods of extracting settlements information by this mean were concluded.
文摘Naipaul＇s novel A bend in the river illustrates the social confusion and people＇s destitution and wasting time in African countries after their national independence. An important reason is the president＇s self-publicity, tyrannical hess and destructiveness. Whereas the author＇s subjective suspicion is visible in democratic development of African countries. Indeed, in a country development, it is quite necessary to depend on its citizen, observing public feelings; controlling a state with science; benefiting citizen; utilizing resources; constructing homestead, intending creation with a harmonious and stable way.
文摘1.文学常识1.Modernism-It is a label loosely applied to theworks of certain writers of the late nineteenthand early twentieth centuries who investigatedthe structure and texture of literature andchallenged its CONVENTIONS.The years from1910 to 1930 are often called the Era of Moder-nism,for there seems to have been in both
基金supported by the Shandong Provincial Natural Science Foundation(No.ZR2019MEE096)the Key National Natural Science Foundation of China(No.U1733201)。
文摘The variations in gas path parameter deviations can fully reflect the healthy state of aeroengine gas path components and units;therefore,airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis.In the past,the airlines could not obtain deviations autonomously.At present,a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations.However,it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks.To obtain monitoring autonomy of each aero-engine model,it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models.This paper adopts the Residual-Back Propagation Neural Network(Res-BPNN)to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy(MK-MMD)adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space(RKHS)for discrepancy measurement.To further reduce the distribution discrepancy of each aero-engine model,the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible,and then the learned features can be confused.Through the above methods,domain-invariant features can be extracted,and the optimal adaptation effect can be achieved.Finally,the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.
基金funded by the National Research Council,Sri Lanka[NRC 17-066]。
文摘This study aimed to assess the potential of in-situ measured soil and vegetation characteristics in landslide susceptibility analyses.First,data for eight independent variables,i.e.,soil moisture content,soil organic content,compaction of soil(soil toughness),plant root strength,crop biomass,tree diameter at knee height,Shannon Wiener Index(SWI)for trees and herbs was assembled from field tests at two historic landslide locations:Aranayaka and Kurukudegama,Sri Lanka.An economical,finer resolution database was obtained as the field tests were not cost-prohibitive.The logistic regression(LR)analysis showed that soil moisture content,compaction of soil,SWI for trees and herbs were statistically significant at P<0.05.The variance inflation factors(VIFs)were computed to test for multicollinearity.VIF values(<2)confirmed the absence of multicollinearity between four independent variables in the LR model.Receiver Operating Characteristics(ROC)curve and Confusion Metrix(CM)methods were used to validate the model.In ROC analysis,areas under the curve of Success Rate Curve and Prediction Rate Curve were 84.5% and 96.6%,respectively,demonstrating the model’s excellent compatibility and predictability.According to the CM,the model demonstrated a 79.6% accuracy,63.6% precision,100% recall,and a F-measure of 77.8%.The model coefficients revealed that the vegetation cover has a more significant contribution to landslide susceptibility than soil characteristics.Finally,the susceptibility map,which was then classified as low,medium,and highly susceptible areas based on the natural breaks(Jenks)method,was generated using geographical information systems(GIS)techniques.All the historic landslide locations fell into the high susceptibility areas.Thus,validation of the model and inspection of the susceptibility map indicated that the in-situ soil and vegetation characteristics used in the model could be employed to demarcate historical landslide patches and identify landslide susceptible locations with high confidence.
基金supported by the National Natural Science Foundation of China(Grant Nos.61203094 and 61305042)the Natural Science Foundation of the United States(Grant Nos.CNS-1253424 and ECCS-1202225)+3 种基金the Science and Technology Foundation of Henan Province,China(Grant No.152102210048)the Foundation and Frontier Project of Henan Province,China(Grant No.162300410196)the Natural Science Foundation of Educational Committee of Henan Province,China(Grant No.14A413015)the Research Foundation of Henan University,China(Grant No.xxjc20140006)
文摘Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.
基金This work was supported in part by Shandong Provincial Natural Science Foundation(ZR2019PF007)the National Key Research and Development Plan of China(2018YFB0803504)+2 种基金Basic Scientific Research Operating Expenses of Shandong University(2018ZQXM004)Guangdong Province Key Research and Development Plan(2019B010137004)the National Natural Science Foundation of China(U20B2046).
文摘Electronic voting has partially solved the problems of poor anonymity and low efficiency associated with traditional voting.However,the difficulties it introduces into the supervision of the vote counting,as well as its need for a concurrent guaranteed trusted third party,should not be overlooked.With the advent of blockchain technology in recent years,its features such as decentralization,anonymity,and non-tampering have made it a good candidate in solving the problems that electronic voting faces.In this study,we propose a multi-candidate voting model based on the blockchain technology.With the introduction of an asymmetric encryption and an anonymity-preserving voting algorithm,votes can be counted without relying on a third party,and the voting results can be displayed in real time in a manner that satisfies various levels of voting security and privacy requirements.Experimental results show that the proposed model solves the aforementioned problems of electronic voting without significant negative impact from an increasing number of voters or candidates.
