Cloud environments are essential for modern computing,but are increasingly vulnerable to Side-Channel Attacks(SCAs),which exploit indirect information to compromise sensitive data.To address this critical challenge,we...Cloud environments are essential for modern computing,but are increasingly vulnerable to Side-Channel Attacks(SCAs),which exploit indirect information to compromise sensitive data.To address this critical challenge,we propose SecureCons Framework(SCF),a novel consensus-based cryptographic framework designed to enhance resilience against SCAs in cloud environments.SCF integrates a dual-layer approach combining lightweight cryptographic algorithms with a blockchain-inspired consensus mechanism to secure data exchanges and thwart potential side-channel exploits.The framework includes adaptive anomaly detection models,cryptographic obfuscation techniques,and real-time monitoring to identify and mitigate vulnerabilities proactively.Experimental evaluations demonstrate the framework's robustness,achieving over 95%resilience against advanced SCAs with minimal computational overhead.SCF provides a scalable,secure,and efficient solution,setting a new benchmark for side-channel attack mitigation in cloud ecosystems.展开更多
Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods.Conventional approaches rely on visual examination by experts to d...The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods.Conventional approaches rely on visual examination by experts to detect infected palm leaves,which is time intensive and susceptible to mistakes.This study proposes an automated leaf classification system that uses deep learning algorithms to identify and categorize diseases in date palm tree leaves with high precision and dependability.The system leverages pretrained convolutional neural network architectures(InceptionV3,DenseNet,and MobileNet)to extract and examine leaf characteristics for classification purposes.A publicly accessible dataset comprising multiple classes of diseased and healthy date palm leaf samples was used for the training and assessment.Data augmentation techniques were implemented to enhance the dataset and improve model resilience.In addition,Synthetic Minority Oversampling Technique(SMOTE)was applied to address class imbalance and further improve the classification performance.The system was trained and evaluated using this dataset,and two of the models,DenseNet and MobileNet,achieved classification accuracies greater than 95%.MobileNetV2 emerged as the top-performing model among those assessed,achieving an overall accuracy of 96.99%and macro-average F1-score of 0.97.All nine categories of date palm leaf conditions were consistently and accurately identified,showing exceptional precision and dependability.Comparative experiments were conducted to assess the performance of the Convolutional Neural Network(CNN)architectures and demonstrate their potential for scalable and automated disease detection.This system has the potential to serve as a valuable agricultural tool for assisting in disease management and monitoring date palm cultivation.展开更多
Tidal creeks are the main channels of land-sea ecosystem interactions,and their high dynamics are an important factor affecting the hydrological connectivity of tidal flats.Taking the Yellow River Delta as the researc...Tidal creeks are the main channels of land-sea ecosystem interactions,and their high dynamics are an important factor affecting the hydrological connectivity of tidal flats.Taking the Yellow River Delta as the research area,we selected remote sensing images obtained during five periods from 1998 to 2018 as the data sources.Based on the spatial analysis function in GIS,the typical morphological characteristics of tidal creeks,such as the level,length,density,curvature,bifurcation ratio,and overmarsh path length(OPL),were extracted to characterize the degree of development of the tidal creeks in the Yellow River Delta wetlands.The spatio-temporal evolution of the tidal creeks was studied,and the development process and the characteristics of the tidal creeks during the different stages of development were investigated.The results revealed that(1)The number,density,and bifurcation ratio of tidal creeks exhibit an increasing trend,but the growth of the trend is slowing.The number of tidal creeks increased by 44.9%from the initial stage of the Yellow River diversion to the late stage of the wetland restoration,but it only increased by 26.2%from the late stage of the wetland restoration to the slow expansion of the Spartina alterniflora.(2)The curvature of the tidal creeks on the landward side is greater than that on the seaward side.(3)The development degree of tidal creek has spatial heterogenetiy,which is AreaⅢ>AreaⅡ>AreaⅠ.(4)The drainage efficiency is significantly correlated with the tidal creak density and bifurcation ratio.Based on the analysis of the various morphological parameters and the drainage efficiency,it was found that after the rapid change in the tidal creek system in the early stage,the tidal creeks entered a state of slow change,and the development state of the tidal creeks tends to be in dynamic balance.The results of this study are expected to provide scientific support for the sustainable development and utilization of coastal tidal flats.展开更多
Freeze-thaw erosion is the third largest soil erosion type after water erosion and wind erosion. Restricted by many factors, few researches on freeze-thaw erosion have so far been done at home and abroad, especially t...Freeze-thaw erosion is the third largest soil erosion type after water erosion and wind erosion. Restricted by many factors, few researches on freeze-thaw erosion have so far been done at home and abroad, especially those on the assessment method of freeze-thaw erosion. Based on the comprehensive analysis of impact factors of free-thaw erosion, this paper chooses six indexes, including the annual temperature range, annual precipitation, slope, aspect, vegetation and soil, to build the model for relative classification of freeze-thaw erosion using weighted and additive methods, and realizes the relative classification of the freeze-thaw erosion in Tibet with the support of GIS software. Then a synthetic assessment of freeze-thaw erosion in Tibet has been carried out according to the relative classification result. The result shows that the distribution of freeze-thaw eroded area is very extensive in Tibet, accounting for 55.3% of the total local land area; the spatial differentiation of freeze-thaw erosion with different intensities is obvious; and the difference in distribution among different regions is also obvious.展开更多
The Cloud system shows its growing functionalities in various industrial applications.The safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential ele...The Cloud system shows its growing functionalities in various industrial applications.The safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential element to fulfill security.Recently,Machine Learning(ML)approaches have been used for the construction of intellectual IDS.Most IDS are based on ML techniques either as unsupervised or supervised.In supervised learning,NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns.Similarly,the unsupervised model fails to provide a satisfactory outcome.Hence,to boost the functionality of unsupervised learning,an effectual auto-encoder is applied for feature selection to select good features.Finally,the Naïve Bayes classifier is used for classification purposes.This approach exposes the finest generalization ability to train the data.The unlabelled data is also used for adoption towards data analysis.Here,redundant and noisy samples over the dataset are eliminated.To validate the robustness and efficiency of NIDS,the anticipated model is tested over the NSL-KDD dataset.The experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%,which is higher compared to J48,AB tree,Random Forest(RF),Regression Tree(RT),Multi-Layer Perceptrons(MLP),Support Vector Machine(SVM),and Fuzzy.Similarly,False Alarm Rate(FAR)and True Positive Rate(TPR)of Naive Bayes(NB)is 0.3 and 0.99,respectively.When compared to prevailing techniques,the anticipated approach also delivers promising outcomes.展开更多
Introduction: The foreign bodies of the oropharynx are mainly encountered in children. They rarely raise diagnostic problem, but remain a haunting of the CCF and ENT surgeon. Objective: We report a special case of for...Introduction: The foreign bodies of the oropharynx are mainly encountered in children. They rarely raise diagnostic problem, but remain a haunting of the CCF and ENT surgeon. Objective: We report a special case of foreign body entering the oropharynx and measuring 15 cm of long in a boy of 7 years, in order to discuss the diagnostic and therapeutic approach. Observation: A 7 year old male student has been received in emergency for accidental trauma of the oropharynx by a particular object, a pencil. The diagnosis has been essentially clinical. The exhibition of the oropharynx using the open mouth of Boyles Davis has obviously shown the foreign body penetrating the right anterior pillar crossing the parapharyngeal spaces till the right posterolateral prevertebral space. The extraction of the foreign body was done by endoscopic route under general anesthesia. As a remarkable fact, it was the gumming end that was penetrating. The postoperative course was uneventful. Conclusion: This type of foreign body of oropharynx constitutes a medical and surgical emergency. From an easy and positive diagnosis, these foreign bodies especially raise a problem of lesion diagnostic and therapeutic approach. Prevention through education and awareness of all the actors (students, children, parents) remain the pledge of their control.展开更多
This year marks the 45th anniversary of Malaysia-China diplomatic relations. In the early 15th Century, Zheng He, a Ming Dynasty maritime explorer, made seven voyages to the Western Seas and passed through the Malacca...This year marks the 45th anniversary of Malaysia-China diplomatic relations. In the early 15th Century, Zheng He, a Ming Dynasty maritime explorer, made seven voyages to the Western Seas and passed through the Malacca Strait more than once, resulting in many tales of trade and cultural exchanges between the peoples of China and Malaysia. Over the past 40 years since the beginning of China’s reform and openingup, Chinese Malaysians have invested in businesses and made donations to schools in China, laying a solid foundation for the further development of Malaysia- China relations.展开更多
文摘Cloud environments are essential for modern computing,but are increasingly vulnerable to Side-Channel Attacks(SCAs),which exploit indirect information to compromise sensitive data.To address this critical challenge,we propose SecureCons Framework(SCF),a novel consensus-based cryptographic framework designed to enhance resilience against SCAs in cloud environments.SCF integrates a dual-layer approach combining lightweight cryptographic algorithms with a blockchain-inspired consensus mechanism to secure data exchanges and thwart potential side-channel exploits.The framework includes adaptive anomaly detection models,cryptographic obfuscation techniques,and real-time monitoring to identify and mitigate vulnerabilities proactively.Experimental evaluations demonstrate the framework's robustness,achieving over 95%resilience against advanced SCAs with minimal computational overhead.SCF provides a scalable,secure,and efficient solution,setting a new benchmark for side-channel attack mitigation in cloud ecosystems.
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
基金funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R821),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods.Conventional approaches rely on visual examination by experts to detect infected palm leaves,which is time intensive and susceptible to mistakes.This study proposes an automated leaf classification system that uses deep learning algorithms to identify and categorize diseases in date palm tree leaves with high precision and dependability.The system leverages pretrained convolutional neural network architectures(InceptionV3,DenseNet,and MobileNet)to extract and examine leaf characteristics for classification purposes.A publicly accessible dataset comprising multiple classes of diseased and healthy date palm leaf samples was used for the training and assessment.Data augmentation techniques were implemented to enhance the dataset and improve model resilience.In addition,Synthetic Minority Oversampling Technique(SMOTE)was applied to address class imbalance and further improve the classification performance.The system was trained and evaluated using this dataset,and two of the models,DenseNet and MobileNet,achieved classification accuracies greater than 95%.MobileNetV2 emerged as the top-performing model among those assessed,achieving an overall accuracy of 96.99%and macro-average F1-score of 0.97.All nine categories of date palm leaf conditions were consistently and accurately identified,showing exceptional precision and dependability.Comparative experiments were conducted to assess the performance of the Convolutional Neural Network(CNN)architectures and demonstrate their potential for scalable and automated disease detection.This system has the potential to serve as a valuable agricultural tool for assisting in disease management and monitoring date palm cultivation.
基金National Key R&D Program of China,No.2017YFC0505903National Natural Science Foundation of China,No.41971381。
文摘Tidal creeks are the main channels of land-sea ecosystem interactions,and their high dynamics are an important factor affecting the hydrological connectivity of tidal flats.Taking the Yellow River Delta as the research area,we selected remote sensing images obtained during five periods from 1998 to 2018 as the data sources.Based on the spatial analysis function in GIS,the typical morphological characteristics of tidal creeks,such as the level,length,density,curvature,bifurcation ratio,and overmarsh path length(OPL),were extracted to characterize the degree of development of the tidal creeks in the Yellow River Delta wetlands.The spatio-temporal evolution of the tidal creeks was studied,and the development process and the characteristics of the tidal creeks during the different stages of development were investigated.The results revealed that(1)The number,density,and bifurcation ratio of tidal creeks exhibit an increasing trend,but the growth of the trend is slowing.The number of tidal creeks increased by 44.9%from the initial stage of the Yellow River diversion to the late stage of the wetland restoration,but it only increased by 26.2%from the late stage of the wetland restoration to the slow expansion of the Spartina alterniflora.(2)The curvature of the tidal creeks on the landward side is greater than that on the seaward side.(3)The development degree of tidal creek has spatial heterogenetiy,which is AreaⅢ>AreaⅡ>AreaⅠ.(4)The drainage efficiency is significantly correlated with the tidal creak density and bifurcation ratio.Based on the analysis of the various morphological parameters and the drainage efficiency,it was found that after the rapid change in the tidal creek system in the early stage,the tidal creeks entered a state of slow change,and the development state of the tidal creeks tends to be in dynamic balance.The results of this study are expected to provide scientific support for the sustainable development and utilization of coastal tidal flats.
基金Key Basic Research Project of China, No.2004CCA03600
文摘Freeze-thaw erosion is the third largest soil erosion type after water erosion and wind erosion. Restricted by many factors, few researches on freeze-thaw erosion have so far been done at home and abroad, especially those on the assessment method of freeze-thaw erosion. Based on the comprehensive analysis of impact factors of free-thaw erosion, this paper chooses six indexes, including the annual temperature range, annual precipitation, slope, aspect, vegetation and soil, to build the model for relative classification of freeze-thaw erosion using weighted and additive methods, and realizes the relative classification of the freeze-thaw erosion in Tibet with the support of GIS software. Then a synthetic assessment of freeze-thaw erosion in Tibet has been carried out according to the relative classification result. The result shows that the distribution of freeze-thaw eroded area is very extensive in Tibet, accounting for 55.3% of the total local land area; the spatial differentiation of freeze-thaw erosion with different intensities is obvious; and the difference in distribution among different regions is also obvious.
文摘The Cloud system shows its growing functionalities in various industrial applications.The safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential element to fulfill security.Recently,Machine Learning(ML)approaches have been used for the construction of intellectual IDS.Most IDS are based on ML techniques either as unsupervised or supervised.In supervised learning,NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns.Similarly,the unsupervised model fails to provide a satisfactory outcome.Hence,to boost the functionality of unsupervised learning,an effectual auto-encoder is applied for feature selection to select good features.Finally,the Naïve Bayes classifier is used for classification purposes.This approach exposes the finest generalization ability to train the data.The unlabelled data is also used for adoption towards data analysis.Here,redundant and noisy samples over the dataset are eliminated.To validate the robustness and efficiency of NIDS,the anticipated model is tested over the NSL-KDD dataset.The experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%,which is higher compared to J48,AB tree,Random Forest(RF),Regression Tree(RT),Multi-Layer Perceptrons(MLP),Support Vector Machine(SVM),and Fuzzy.Similarly,False Alarm Rate(FAR)and True Positive Rate(TPR)of Naive Bayes(NB)is 0.3 and 0.99,respectively.When compared to prevailing techniques,the anticipated approach also delivers promising outcomes.
文摘Introduction: The foreign bodies of the oropharynx are mainly encountered in children. They rarely raise diagnostic problem, but remain a haunting of the CCF and ENT surgeon. Objective: We report a special case of foreign body entering the oropharynx and measuring 15 cm of long in a boy of 7 years, in order to discuss the diagnostic and therapeutic approach. Observation: A 7 year old male student has been received in emergency for accidental trauma of the oropharynx by a particular object, a pencil. The diagnosis has been essentially clinical. The exhibition of the oropharynx using the open mouth of Boyles Davis has obviously shown the foreign body penetrating the right anterior pillar crossing the parapharyngeal spaces till the right posterolateral prevertebral space. The extraction of the foreign body was done by endoscopic route under general anesthesia. As a remarkable fact, it was the gumming end that was penetrating. The postoperative course was uneventful. Conclusion: This type of foreign body of oropharynx constitutes a medical and surgical emergency. From an easy and positive diagnosis, these foreign bodies especially raise a problem of lesion diagnostic and therapeutic approach. Prevention through education and awareness of all the actors (students, children, parents) remain the pledge of their control.
文摘This year marks the 45th anniversary of Malaysia-China diplomatic relations. In the early 15th Century, Zheng He, a Ming Dynasty maritime explorer, made seven voyages to the Western Seas and passed through the Malacca Strait more than once, resulting in many tales of trade and cultural exchanges between the peoples of China and Malaysia. Over the past 40 years since the beginning of China’s reform and openingup, Chinese Malaysians have invested in businesses and made donations to schools in China, laying a solid foundation for the further development of Malaysia- China relations.
