Development of stable crops cultivars adapted to environmental constraints is very important for food security. Safflower, an oilseed crop which tolerates environmental abiotic stresses, is suitable for marginal lands...Development of stable crops cultivars adapted to environmental constraints is very important for food security. Safflower, an oilseed crop which tolerates environmental abiotic stresses, is suitable for marginal lands relatively dry and deprived from fertilizer inputs or irrigation. A set of Moroccan and introduced cultivars as well as international accessions were conducted at Oujda （Eastern of Morocco） during 2009-2010 for late and conventional sowing under two water regimes, in a field experiment using a completely randomized design, with three replications. The objective was to evaluate the effect of genotype and contrasting environment on safflower behavior and to select genotypes with large adaptation to the contrasted environmental conditions. Morphological, physiological and agronomic traits, as well as the stress susceptibility index （SSI）, were recorded in this study. Results showed significant effect of genotype, year （sowing time）, water regime and their interaction on most of the studied parameters. Late sowing and drought affected negatively all the parameters except seed oil which lightly increased under drought stress. Number of heads per plant （NHP） had the strongest association with seed yield under both drought and non-drought conditions, and hence could be taken as selection criterion for safflower seed yield improvement. Five accessions showed the highest overall mean seed yield （~ 1,000 kg/ha） and four accessions exhibited the highest overall mean seed oil content （〉 310 g/kg）. For late sowing, the accessions P1262421 and PI537604 produced the highest seed yield （〉 800 kg/ha） and the highest seed oil content （〉 290 g/kg）. For conventional sowing, the accessions PI250076 and PI250523 were the most performant, with a seed yield 〉 1,300 kg/ha and a seed oil content 〉 330 g/kg. Based on their mean productivity across environments, their SSI and their MDA, P1271073 and P1250076 could be selected and used as promising germplasm in safflower breeding program in Morocco as well as other dry areas throughout the world.展开更多
This paper studies the wall-bounded flow around a cylindrical at a high Reynolds numbers body in a determined computational domain, with simulations of the 3-D, turbulent concentric annulus flow in a straight pipe. Nu...This paper studies the wall-bounded flow around a cylindrical at a high Reynolds numbers body in a determined computational domain, with simulations of the 3-D, turbulent concentric annulus flow in a straight pipe. Numerical results show that a reversing zone, appearing as a tongue zone with nested velocities higher than the surrounding area, exists behind the cylindrical body. The annulus space is a region of high velocity and low pressure. The zero velocity, of combined the X- velocity and the Y- velocity, exists in the cross sections and no vortex shedding is formed behind the attaching cylinders. Among all investigated effecting factors, the diameters of the attaching and the main cylinders affect the wake feature behind the cylindrical body while the main cylinder length does not affect the distribution tendency of the flow field. The diameters of the main cylinder and the pipe affect the pressure values and the distribution tendencies on the main cylinder surface. Obviously, the increase of the pipe diameter reduces the drag coefficient of the cylindrical body and the increase of the diameter of the main cylinder increases the drag coefficient greatly. The numerical investigation of the concentric annulus flow provides foundations for further improvements of the intricate flow studies.展开更多
Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the pro...Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.展开更多
Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)t...Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
BACKGROUND Oil-based iodinated contrast media have excellent contrast properties and are widely used for hysterosalpingographic evaluation of female infertility.On abdominal radiography and computed tomography(CT)scan...BACKGROUND Oil-based iodinated contrast media have excellent contrast properties and are widely used for hysterosalpingographic evaluation of female infertility.On abdominal radiography and computed tomography(CT)scans,their radiodensity is similar to that of metallic objects,which can sometimes lead to diagnostic confusion in the postoperative settings.In this case,retained oil-based contrast medium was observed on an abdominal radiograph following a cesarean section,making it difficult to differentiate from an intraperitoneal foreign body from surgery.The patient was a 37-year-old pregnant woman who was referred to our hospital at 32 weeks and 1 day of pregnancy due to complete placenta previa for mana-gement of pregnancy and delivery.An elective cesarean section was performed at 37 weeks and 3 days.A plain abdominal radiograph taken immediately after surgery revealed a near-round,hyperdense,mass-like shadow with a regular margin in the pelvic cavity.An intraperitoneal foreign body was suspected;therefore,an abdominal CT scan was performed.The foreign body was located on the left side of the pouch of Douglas and had a CT value of 7000 Hounsfield units,similar to that of metals.The CT value strongly suggested the presence of an artificial object.However,further inquiries with the patient and her previous physician revealed a history of hysterosalpingography.Accordingly,retained oil-based iodinated contrast medium was suspected,and observation of the object’s course was adopted.CONCLUSION When intraperitoneal foreign bodies are suspected on postoperative radiographs,the possibility of oil-based iodinated contrast medium retention should be considered.展开更多
BACKGROUND Gastrointestinal dual-contrast ultrasonography(DCUS)is characterized by its high resolution,sensitivity,and specificity.AIM To determine the accuracy of DCUS in predicting lymph node metastasis in middle-ag...BACKGROUND Gastrointestinal dual-contrast ultrasonography(DCUS)is characterized by its high resolution,sensitivity,and specificity.AIM To determine the accuracy of DCUS in predicting lymph node metastasis in middle-aged and elderly patients with gastric cancer(GC).METHODS A total of 100 middle-aged and elderly patients with GC admitted to the Fourth Affiliated Hospital of Soochow University(Dushu Lake Hospital,Suzhou,China)between April 2022 and April 2024 were selected.The baseline data and lymph node metastasis status were collected.DCUS combined with intravenous contrast technology was used to calculate the enhancement time(ET),time to peak(TTP),and slope of the ascending branch wash-in rate(WIR).These indicators were used in assessing lymph node metastasis in patients with GC.RESULTS Among 100 middle-aged and elderly patients with GC,35(35.00%)had lymph node metastases.GC patients with lymph node metastasis had a higher propor-tion of stage II TNM classification and higher WIR values than those without lymph node metastasis.The ET and TTP values were lower in patients with lymph node metastases,and all differences were statistically significant(P<0.05).The area under the curve values for ET,TTP,WIR,and combined diagnosis of GC lymph node metastasis using DCUS were all>0.7.Optimal assessment was achieved when the cutoff values for ET,TTP,and WIR were set at 16.32 seconds,10.67 seconds,and 7.02,res-pectively.CONCLUSION DCUS-mediated assessment of ET,TTP,and WIR can effectively predict and evaluate lymph node metastasis status in patients with GC,with higher sensitivity when used in combination.展开更多
Patent foramen ovale(PFO)is a common congenital heart disorder associated with stroke,decompression sickness and migraine.Combining synchronized contrast transcranial Doppler with contrast transthoracic echocardiograp...Patent foramen ovale(PFO)is a common congenital heart disorder associated with stroke,decompression sickness and migraine.Combining synchronized contrast transcranial Doppler with contrast transthoracic echocardiography has important clinical significance and can improve the accuracy of detecting right-left shunts(RLSs)in patients with PFO.In this letter,regarding an original study presented by Yao et al,we present our insights and discuss how to better help clinicians evaluate changes in PFO-related RLS.展开更多
AIM:To evaluate the effects of polarized and nonpolarized sunglasses on visual functions,including distance and near visual acuity,phoria,stereopsis and contrast sensitivity across five spatial frequencies(1.5,3,6,12,...AIM:To evaluate the effects of polarized and nonpolarized sunglasses on visual functions,including distance and near visual acuity,phoria,stereopsis and contrast sensitivity across five spatial frequencies(1.5,3,6,12,18 cycles/degree).METHODS:A before-after study was conducted on 45 emmetropic students from Shahid Beheshti University of Medical Sciences.Visual acuity,contrast sensitivity,stereopsis and phoria were measured under three conditions:without sunglasses,with non-polarized sunglasses and with polarized sunglasses.Tests were conducted under controlled glare conditions to simulate outdoor environments.RESULTS:A total of 45 participants were evaluated,comprising 17 males(37.8%)and 28 females(62.2%).The mean age was 21.67±2.31y(range 18-27y).The mean of distance and near visual acuity in all three conditions were equal to 0.00 logMAR.Contrast sensitivity generally decreased slightly with the use of non-polarized sunglasses compared to the no-sunglasses condition.The mean stereopsis with polarized sunglasses was 101.33±56.139 arc sec,which was worse than the no-sunglasses condition(94.33±46.632 arc sec)and better than the non-polarized sunglasses condition(105.67±58.965 arc sec),although these changes were not significant.In the phoria parameter,distance phoria appeared more affected than near phoria.CONCLUSION:Polarized and non-polarized sunglasses do not significantly affect visual acuity,stereopsis,or phoria under controlled glare conditions.Slight changes in contrast sensitivity are noted,but they are not statistically significant.展开更多
AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited...AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.展开更多
Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often resu...Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often results in translated content that diverges significantly from the source text.This research proposes a refined Contrastive Decoding(CD)algorithm that dynamically adjusts weights of log probabilities from strong expert and weak amateur models to mitigate hallucinations in lowresource NMT and improve translation quality.Advanced large language NMT models,including ChatGLM and LLaMA,are fine-tuned and implemented for their superior contextual understanding and cross-lingual capabilities.The refined CD algorithm evaluates multiple candidate translations using BLEU score,semantic similarity,and Named Entity Recognition accuracy.Extensive experimental results show substantial improvements in translation quality and a significant reduction in hallucination rates.Fine-tuned models achieve higher evaluation metrics compared to baseline models and state-of-the-art models.An ablation study confirms the contributions of each methodological component and highlights the effectiveness of the refined CD algorithm and advanced models in mitigating hallucinations.Notably,the refined methodology increased the BLEU score by approximately 30%compared to baseline models.展开更多
Kounis syndrome(KS)is a rare but clinically significant condition characterized by the simultaneous occurrence of acute coronary syndrome(ACS)and allergic reactions,which can develop in patients with either normal or ...Kounis syndrome(KS)is a rare but clinically significant condition characterized by the simultaneous occurrence of acute coronary syndrome(ACS)and allergic reactions,which can develop in patients with either normal or diseased coronary arteries.[1,2]The condition is typically triggered by various allergens including medications(particularly contrast media),environmental factors,or food exposures,with symptom onset usually occurring within one hour of exposure.展开更多
Fisheye cameras offer a significantly larger field of view compared to conventional cameras,making them valuable tools in the field of computer vision.However,their unique optical characteristics often lead to image d...Fisheye cameras offer a significantly larger field of view compared to conventional cameras,making them valuable tools in the field of computer vision.However,their unique optical characteristics often lead to image distortions,which pose challenges for object detection tasks.To address this issue,we propose Yolo-CaSKA(Yolo with Contrastive Learning and Selective Kernel Attention),a novel training method that enhances object detection on fisheye camera images.The standard image and the corresponding distorted fisheye image pairs are used as positive samples,and the rest of the image pairs are used as negative samples,which are guided by contrastive learning to help the distorted images find the feature vectors of the corresponding normal images,to improve the detection accuracy.Additionally,we incorporate the Selective Kernel(SK)attention module to focus on regions prone to false detections,such as image edges and blind spots.Finally,the mAP_(50) on the augmented KITTI dataset is improved by 5.5% over the original Yolov8,while the mAP_(50) on the WoodScape dataset is improved by 2.6% compared to OmniDet.The results demonstrate the performance of our proposed model for object detection on fisheye images.展开更多
Sarcasm detection is a complex and challenging task,particularly in the context of Chinese social media,where it exhibits strong contextual dependencies and cultural specificity.To address the limitations of existing ...Sarcasm detection is a complex and challenging task,particularly in the context of Chinese social media,where it exhibits strong contextual dependencies and cultural specificity.To address the limitations of existing methods in capturing the implicit semantics and contextual associations in sarcastic expressions,this paper proposes an event-aware model for Chinese sarcasm detection,leveraging a multi-head attention(MHA)mechanism and contrastive learning(CL)strategies.The proposed model employs a dual-path Bidirectional Encoder Representations from Transformers(BERT)encoder to process comment text and event context separately and integrates an MHA mechanism to facilitate deep interactions between the two,thereby capturing multidimensional semantic associations.Additionally,a CL strategy is introduced to enhance feature representation capabilities,further improving the model’s performance in handling class imbalance and complex contextual scenarios.The model achieves state-of-the-art performance on the Chinese sarcasm dataset,with significant improvements in accuracy(79.55%),F1-score(84.22%),and an area under the curve(AUC,84.35%).展开更多
Endoscopic ultrasonography(EUS)is a valuable and widely used tool for evaluating pancreatic tumors.Accurate decision-making during EUS procedures,particularly for differentiating between benign and malignant lesions b...Endoscopic ultrasonography(EUS)is a valuable and widely used tool for evaluating pancreatic tumors.Accurate decision-making during EUS procedures,particularly for differentiating between benign and malignant lesions based on imaging characteristics and assessing the need for tissue sampling,is crucial.This review provides a comprehensive overview of pancreatic tumor features observed during EUS and highlights the key criteria for distinguishing between malignant and benign conditions.Additionally,we discuss the indications for fine-needle aspiration or biopsy to obtain histopathological and genetic confirmation.Improving our understanding of these critical aspects can help improve diagnostic accuracy and guide clinicians in determining the most appropriate management strategies for patients with pancreatic tumors.展开更多
Image classification is crucial for various applications,including digital construction,smart manu-facturing,and medical imaging.Focusing on the inadequate model generalization and data privacy concerns in few-shot im...Image classification is crucial for various applications,including digital construction,smart manu-facturing,and medical imaging.Focusing on the inadequate model generalization and data privacy concerns in few-shot image classification,in this paper,we propose a federated learning approach that incorporates privacy-preserving techniques.First,we utilize contrastive learning to train on local few-shot image data and apply various data augmentation methods to expand the sample size,thereby enhancing the model’s generalization capabilities in few-shot contexts.Second,we introduce local differential privacy techniques and weight pruning methods to safeguard model parameters,perturbing the transmitted parameters to ensure user data privacy.Finally,numerical simulations are conducted to demonstrate the effectiveness of our proposed method.The results indicate that our approach significantly enhances model generalization and test accuracy compared to several popular federated learning algorithms while maintaining data privacy,highlighting its effectiveness and practicality in addressing the challenges of model generalization and data privacy in few-shot image scenarios.展开更多
Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion...Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.展开更多
Conventional echocardiography can sometimes pose a challenge to diagnosis due to sub-optimal images.Ultrasound contrast agents(UCAs)have been shown to drastically enhance imaging quality,particularly depicting the lef...Conventional echocardiography can sometimes pose a challenge to diagnosis due to sub-optimal images.Ultrasound contrast agents(UCAs)have been shown to drastically enhance imaging quality,particularly depicting the left ventricular endocardial borders.Their use during echocardiography has become a valuable tool in non-invasive diagnostics.UCAs provide higher-quality images that may ultimately reduce the length of hospital stays and improve patient care.The higher cost associated with UCAs in many situations has been an impediment to frequent use.However,when used as an initial diagnostic test,UCA during rest echocardiogram is more cost-effective than the traditional diagnostic approach,which frequently includes multiple tests and imaging studies to make an accurate diagnosis.They can be easily performed across multiple patient settings and provide optimal images that allow clinicians to make sound medical decisions.This consequently allows for better diagnostic accuracies and improvement in patient care.展开更多
In computer vision applications like surveillance and remote sensing,to mention a few,deep learning has had considerable success.Medical imaging still faces a number of difficulties,including intra-class similarity,a ...In computer vision applications like surveillance and remote sensing,to mention a few,deep learning has had considerable success.Medical imaging still faces a number of difficulties,including intra-class similarity,a scarcity of training data,and poor contrast skin lesions,notably in the case of skin cancer.An optimisation-aided deep learningbased system is proposed for accurate multi-class skin lesion identification.The sequential procedures of the proposed system start with preprocessing and end with categorisation.The preprocessing step is where a hybrid contrast enhancement technique is initially proposed for lesion identification with healthy regions.Instead of flipping and rotating data,the outputs from the middle phases of the hybrid enhanced technique are employed for data augmentation in the next step.Next,two pre-trained deep learning models,MobileNetV2 and NasNet Mobile,are trained using deep transfer learning on the upgraded enriched dataset.Later,a dual-threshold serial approach is employed to obtain and combine the features of both models.The next step was the variance-controlled Marine Predator methodology,which the authors proposed as a superior optimisation method.The top features from the fused feature vector are classified using machine learning classifiers.The experimental strategy provided enhanced accuracy of 94.4%using the publicly available dataset HAM10000.Additionally,the proposed framework is evaluated compared to current approaches,with remarkable results.展开更多
文摘Development of stable crops cultivars adapted to environmental constraints is very important for food security. Safflower, an oilseed crop which tolerates environmental abiotic stresses, is suitable for marginal lands relatively dry and deprived from fertilizer inputs or irrigation. A set of Moroccan and introduced cultivars as well as international accessions were conducted at Oujda （Eastern of Morocco） during 2009-2010 for late and conventional sowing under two water regimes, in a field experiment using a completely randomized design, with three replications. The objective was to evaluate the effect of genotype and contrasting environment on safflower behavior and to select genotypes with large adaptation to the contrasted environmental conditions. Morphological, physiological and agronomic traits, as well as the stress susceptibility index （SSI）, were recorded in this study. Results showed significant effect of genotype, year （sowing time）, water regime and their interaction on most of the studied parameters. Late sowing and drought affected negatively all the parameters except seed oil which lightly increased under drought stress. Number of heads per plant （NHP） had the strongest association with seed yield under both drought and non-drought conditions, and hence could be taken as selection criterion for safflower seed yield improvement. Five accessions showed the highest overall mean seed yield （~ 1,000 kg/ha） and four accessions exhibited the highest overall mean seed oil content （〉 310 g/kg）. For late sowing, the accessions P1262421 and PI537604 produced the highest seed yield （〉 800 kg/ha） and the highest seed oil content （〉 290 g/kg）. For conventional sowing, the accessions PI250076 and PI250523 were the most performant, with a seed yield 〉 1,300 kg/ha and a seed oil content 〉 330 g/kg. Based on their mean productivity across environments, their SSI and their MDA, P1271073 and P1250076 could be selected and used as promising germplasm in safflower breeding program in Morocco as well as other dry areas throughout the world.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51179116,51109155)
文摘This paper studies the wall-bounded flow around a cylindrical at a high Reynolds numbers body in a determined computational domain, with simulations of the 3-D, turbulent concentric annulus flow in a straight pipe. Numerical results show that a reversing zone, appearing as a tongue zone with nested velocities higher than the surrounding area, exists behind the cylindrical body. The annulus space is a region of high velocity and low pressure. The zero velocity, of combined the X- velocity and the Y- velocity, exists in the cross sections and no vortex shedding is formed behind the attaching cylinders. Among all investigated effecting factors, the diameters of the attaching and the main cylinders affect the wake feature behind the cylindrical body while the main cylinder length does not affect the distribution tendency of the flow field. The diameters of the main cylinder and the pipe affect the pressure values and the distribution tendencies on the main cylinder surface. Obviously, the increase of the pipe diameter reduces the drag coefficient of the cylindrical body and the increase of the diameter of the main cylinder increases the drag coefficient greatly. The numerical investigation of the concentric annulus flow provides foundations for further improvements of the intricate flow studies.
基金supported by the Research Grant Fund from Kwangwoon University in 2023,the National Natural Science Foundation of China under Grant(62311540155)the Taishan Scholars Project Special Funds(tsqn202312035)the open research foundation of State Key Laboratory of Integrated Chips and Systems.
文摘Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.
基金supported by the Natural Science Foundation of China(No.41804112,author:Chengyun Song).
文摘Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
文摘BACKGROUND Oil-based iodinated contrast media have excellent contrast properties and are widely used for hysterosalpingographic evaluation of female infertility.On abdominal radiography and computed tomography(CT)scans,their radiodensity is similar to that of metallic objects,which can sometimes lead to diagnostic confusion in the postoperative settings.In this case,retained oil-based contrast medium was observed on an abdominal radiograph following a cesarean section,making it difficult to differentiate from an intraperitoneal foreign body from surgery.The patient was a 37-year-old pregnant woman who was referred to our hospital at 32 weeks and 1 day of pregnancy due to complete placenta previa for mana-gement of pregnancy and delivery.An elective cesarean section was performed at 37 weeks and 3 days.A plain abdominal radiograph taken immediately after surgery revealed a near-round,hyperdense,mass-like shadow with a regular margin in the pelvic cavity.An intraperitoneal foreign body was suspected;therefore,an abdominal CT scan was performed.The foreign body was located on the left side of the pouch of Douglas and had a CT value of 7000 Hounsfield units,similar to that of metals.The CT value strongly suggested the presence of an artificial object.However,further inquiries with the patient and her previous physician revealed a history of hysterosalpingography.Accordingly,retained oil-based iodinated contrast medium was suspected,and observation of the object’s course was adopted.CONCLUSION When intraperitoneal foreign bodies are suspected on postoperative radiographs,the possibility of oil-based iodinated contrast medium retention should be considered.
文摘BACKGROUND Gastrointestinal dual-contrast ultrasonography(DCUS)is characterized by its high resolution,sensitivity,and specificity.AIM To determine the accuracy of DCUS in predicting lymph node metastasis in middle-aged and elderly patients with gastric cancer(GC).METHODS A total of 100 middle-aged and elderly patients with GC admitted to the Fourth Affiliated Hospital of Soochow University(Dushu Lake Hospital,Suzhou,China)between April 2022 and April 2024 were selected.The baseline data and lymph node metastasis status were collected.DCUS combined with intravenous contrast technology was used to calculate the enhancement time(ET),time to peak(TTP),and slope of the ascending branch wash-in rate(WIR).These indicators were used in assessing lymph node metastasis in patients with GC.RESULTS Among 100 middle-aged and elderly patients with GC,35(35.00%)had lymph node metastases.GC patients with lymph node metastasis had a higher propor-tion of stage II TNM classification and higher WIR values than those without lymph node metastasis.The ET and TTP values were lower in patients with lymph node metastases,and all differences were statistically significant(P<0.05).The area under the curve values for ET,TTP,WIR,and combined diagnosis of GC lymph node metastasis using DCUS were all>0.7.Optimal assessment was achieved when the cutoff values for ET,TTP,and WIR were set at 16.32 seconds,10.67 seconds,and 7.02,res-pectively.CONCLUSION DCUS-mediated assessment of ET,TTP,and WIR can effectively predict and evaluate lymph node metastasis status in patients with GC,with higher sensitivity when used in combination.
文摘Patent foramen ovale(PFO)is a common congenital heart disorder associated with stroke,decompression sickness and migraine.Combining synchronized contrast transcranial Doppler with contrast transthoracic echocardiography has important clinical significance and can improve the accuracy of detecting right-left shunts(RLSs)in patients with PFO.In this letter,regarding an original study presented by Yao et al,we present our insights and discuss how to better help clinicians evaluate changes in PFO-related RLS.
文摘AIM:To evaluate the effects of polarized and nonpolarized sunglasses on visual functions,including distance and near visual acuity,phoria,stereopsis and contrast sensitivity across five spatial frequencies(1.5,3,6,12,18 cycles/degree).METHODS:A before-after study was conducted on 45 emmetropic students from Shahid Beheshti University of Medical Sciences.Visual acuity,contrast sensitivity,stereopsis and phoria were measured under three conditions:without sunglasses,with non-polarized sunglasses and with polarized sunglasses.Tests were conducted under controlled glare conditions to simulate outdoor environments.RESULTS:A total of 45 participants were evaluated,comprising 17 males(37.8%)and 28 females(62.2%).The mean age was 21.67±2.31y(range 18-27y).The mean of distance and near visual acuity in all three conditions were equal to 0.00 logMAR.Contrast sensitivity generally decreased slightly with the use of non-polarized sunglasses compared to the no-sunglasses condition.The mean stereopsis with polarized sunglasses was 101.33±56.139 arc sec,which was worse than the no-sunglasses condition(94.33±46.632 arc sec)and better than the non-polarized sunglasses condition(105.67±58.965 arc sec),although these changes were not significant.In the phoria parameter,distance phoria appeared more affected than near phoria.CONCLUSION:Polarized and non-polarized sunglasses do not significantly affect visual acuity,stereopsis,or phoria under controlled glare conditions.Slight changes in contrast sensitivity are noted,but they are not statistically significant.
文摘AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.
基金M.Faheem is supported by VTT Technical Research Center of Finland.
文摘Neural machine translation(NMT)has advanced with deep learning and large-scale multilingual models,yet translating lowresource languages often lacks sufficient training data and leads to hallucinations.This often results in translated content that diverges significantly from the source text.This research proposes a refined Contrastive Decoding(CD)algorithm that dynamically adjusts weights of log probabilities from strong expert and weak amateur models to mitigate hallucinations in lowresource NMT and improve translation quality.Advanced large language NMT models,including ChatGLM and LLaMA,are fine-tuned and implemented for their superior contextual understanding and cross-lingual capabilities.The refined CD algorithm evaluates multiple candidate translations using BLEU score,semantic similarity,and Named Entity Recognition accuracy.Extensive experimental results show substantial improvements in translation quality and a significant reduction in hallucination rates.Fine-tuned models achieve higher evaluation metrics compared to baseline models and state-of-the-art models.An ablation study confirms the contributions of each methodological component and highlights the effectiveness of the refined CD algorithm and advanced models in mitigating hallucinations.Notably,the refined methodology increased the BLEU score by approximately 30%compared to baseline models.
基金supported by the National Key Research and Development Program of China(No.2022YFB380-7300)the National Natural Science Foundation of China(No.12471455)+2 种基金the Clinical Cohort Construction Program of Peking University Third Hospital(BYSYDL2022005)the Key Clinical Projects of Peking University Third Hospital(BYSYZD2023006)the Innovation&Transfer Fund of Peking University Third Hospital(BYSYZHKC2023-109).
文摘Kounis syndrome(KS)is a rare but clinically significant condition characterized by the simultaneous occurrence of acute coronary syndrome(ACS)and allergic reactions,which can develop in patients with either normal or diseased coronary arteries.[1,2]The condition is typically triggered by various allergens including medications(particularly contrast media),environmental factors,or food exposures,with symptom onset usually occurring within one hour of exposure.
文摘Fisheye cameras offer a significantly larger field of view compared to conventional cameras,making them valuable tools in the field of computer vision.However,their unique optical characteristics often lead to image distortions,which pose challenges for object detection tasks.To address this issue,we propose Yolo-CaSKA(Yolo with Contrastive Learning and Selective Kernel Attention),a novel training method that enhances object detection on fisheye camera images.The standard image and the corresponding distorted fisheye image pairs are used as positive samples,and the rest of the image pairs are used as negative samples,which are guided by contrastive learning to help the distorted images find the feature vectors of the corresponding normal images,to improve the detection accuracy.Additionally,we incorporate the Selective Kernel(SK)attention module to focus on regions prone to false detections,such as image edges and blind spots.Finally,the mAP_(50) on the augmented KITTI dataset is improved by 5.5% over the original Yolov8,while the mAP_(50) on the WoodScape dataset is improved by 2.6% compared to OmniDet.The results demonstrate the performance of our proposed model for object detection on fisheye images.
基金granted by Qin Xin Talents Cultivation Program(No.QXTCP C202115),Beijing Information Science&Technology Universitythe Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Fund(No.GJJ-23),National Social Science Foundation,China(No.21BTQ079).
文摘Sarcasm detection is a complex and challenging task,particularly in the context of Chinese social media,where it exhibits strong contextual dependencies and cultural specificity.To address the limitations of existing methods in capturing the implicit semantics and contextual associations in sarcastic expressions,this paper proposes an event-aware model for Chinese sarcasm detection,leveraging a multi-head attention(MHA)mechanism and contrastive learning(CL)strategies.The proposed model employs a dual-path Bidirectional Encoder Representations from Transformers(BERT)encoder to process comment text and event context separately and integrates an MHA mechanism to facilitate deep interactions between the two,thereby capturing multidimensional semantic associations.Additionally,a CL strategy is introduced to enhance feature representation capabilities,further improving the model’s performance in handling class imbalance and complex contextual scenarios.The model achieves state-of-the-art performance on the Chinese sarcasm dataset,with significant improvements in accuracy(79.55%),F1-score(84.22%),and an area under the curve(AUC,84.35%).
文摘Endoscopic ultrasonography(EUS)is a valuable and widely used tool for evaluating pancreatic tumors.Accurate decision-making during EUS procedures,particularly for differentiating between benign and malignant lesions based on imaging characteristics and assessing the need for tissue sampling,is crucial.This review provides a comprehensive overview of pancreatic tumor features observed during EUS and highlights the key criteria for distinguishing between malignant and benign conditions.Additionally,we discuss the indications for fine-needle aspiration or biopsy to obtain histopathological and genetic confirmation.Improving our understanding of these critical aspects can help improve diagnostic accuracy and guide clinicians in determining the most appropriate management strategies for patients with pancreatic tumors.
基金supported by Suzhou Science and Technology Plan(Basic Research)Project under Grant SJC2023002Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant KYCX23_3322.
文摘Image classification is crucial for various applications,including digital construction,smart manu-facturing,and medical imaging.Focusing on the inadequate model generalization and data privacy concerns in few-shot image classification,in this paper,we propose a federated learning approach that incorporates privacy-preserving techniques.First,we utilize contrastive learning to train on local few-shot image data and apply various data augmentation methods to expand the sample size,thereby enhancing the model’s generalization capabilities in few-shot contexts.Second,we introduce local differential privacy techniques and weight pruning methods to safeguard model parameters,perturbing the transmitted parameters to ensure user data privacy.Finally,numerical simulations are conducted to demonstrate the effectiveness of our proposed method.The results indicate that our approach significantly enhances model generalization and test accuracy compared to several popular federated learning algorithms while maintaining data privacy,highlighting its effectiveness and practicality in addressing the challenges of model generalization and data privacy in few-shot image scenarios.
基金supported by the ScientificResearch and Innovation Team Program of Sichuan University of Science and Technology(No.SUSE652A006)Sichuan Key Provincial Research Base of Intelligent Tourism(ZHYJ22-03)In addition,it is also listed as a project of Sichuan Provincial Science and Technology Programme(2022YFG0028).
文摘Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.
文摘Conventional echocardiography can sometimes pose a challenge to diagnosis due to sub-optimal images.Ultrasound contrast agents(UCAs)have been shown to drastically enhance imaging quality,particularly depicting the left ventricular endocardial borders.Their use during echocardiography has become a valuable tool in non-invasive diagnostics.UCAs provide higher-quality images that may ultimately reduce the length of hospital stays and improve patient care.The higher cost associated with UCAs in many situations has been an impediment to frequent use.However,when used as an initial diagnostic test,UCA during rest echocardiogram is more cost-effective than the traditional diagnostic approach,which frequently includes multiple tests and imaging studies to make an accurate diagnosis.They can be easily performed across multiple patient settings and provide optimal images that allow clinicians to make sound medical decisions.This consequently allows for better diagnostic accuracies and improvement in patient care.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project,Grant/Award Number:PNURSP2023R333。
文摘In computer vision applications like surveillance and remote sensing,to mention a few,deep learning has had considerable success.Medical imaging still faces a number of difficulties,including intra-class similarity,a scarcity of training data,and poor contrast skin lesions,notably in the case of skin cancer.An optimisation-aided deep learningbased system is proposed for accurate multi-class skin lesion identification.The sequential procedures of the proposed system start with preprocessing and end with categorisation.The preprocessing step is where a hybrid contrast enhancement technique is initially proposed for lesion identification with healthy regions.Instead of flipping and rotating data,the outputs from the middle phases of the hybrid enhanced technique are employed for data augmentation in the next step.Next,two pre-trained deep learning models,MobileNetV2 and NasNet Mobile,are trained using deep transfer learning on the upgraded enriched dataset.Later,a dual-threshold serial approach is employed to obtain and combine the features of both models.The next step was the variance-controlled Marine Predator methodology,which the authors proposed as a superior optimisation method.The top features from the fused feature vector are classified using machine learning classifiers.The experimental strategy provided enhanced accuracy of 94.4%using the publicly available dataset HAM10000.Additionally,the proposed framework is evaluated compared to current approaches,with remarkable results.
