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
In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These j...In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These jamming signals severely degrade radar detection performance.Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems.To address this challenge,this article proposes a novel open-set compound jamming cognition(OSCJC)method.The proposed method employs a detection-classification dual-network architecture,which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments.To achieve unknown jamming detection,we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples.This strategy enables the network to acquire highly generalizable jamming features,thereby accurately localizing candidate regions for individual jamming components within compound jamming.Subsequently,we introduce contrastive learning to optimize the classification network,significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space.This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types.Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method.Compared with the state-of-the-art open-set recognition methods,the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.展开更多
Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emi...Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emissions are expected to simultaneously increase the probability of regional floods and droughts,threatening ecosystems within global terrestrial monsoon regions and the freshwater supply for billions of residents in these areas.In this study,the responses of GLMP to the evolution of ITC toward the carbon neutrality goal are assessed using multimodel outputs from a new model intercomparison project(CovidMIP).The results show that the Northern Hemisphere-Southern Hemisphere(NH-SH)asymmetry of GLMP in boreal summer weakens during the 2040s,as a persistent reduction in well-mixed greenhouse gas(WMGHG)emissions leads to a downward trend in the ITC after 2040.At the same time,the reduction in WMGHG emissions dampens the Eastern Hemisphere-Western Hemisphere(EH-WH)asymmetry of GLMP by inducing La Niña-like cooling and enhancing moisture transport to Inner America.The resulting increases in land monsoon precipitation(LMP)may alleviate drought under the global warming scenario by about 19%-25%and 7%-9%in the WH and SH monsoon regions,respectively.However,a persistent reduction in aerosol emissions in Asia will dominate the increases in LMP in this region until the mid-21st century,and these increases may be approximately 23%-60%of the growth under the global warming scenario.Our results highlight the different rates of response of aerosol and WMGHG concentrations to the carbon neutrality goal,leading to various changes in LMP at global and regional scales.展开更多
Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have n...Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have not been thoroughly investigated.In this study,multiple tracers(hydrochemistry,𝛿D,𝛿18O and 222Rn)were used to compare mechanisms of LGD in Daihai and Ulansuhai Lake in Inner Mongoli1,Northwest China.The hydrochemical types showed a trend from groundwater to lake water,indicating a hydraulic connection between them.In addition,the𝛿D and𝛿18O values of sediment pore water were between the groundwater and lake water,indicating the LGD processes.The radon mass balance model was used to estimate the average groundwater discharge rates of Daihai and Ulansuhai Lake,which were 2.79 mm/day and 3.02 mm/day,respectively.The total nitrogen(TN),total phosphorus(TP),and fluoride inputs associated with LGD in Daihai Lake accounted for 97.52%,96.59%,and 95.84%of the total inputs,respectively.In contrast,TN,TP and fluoride inputs in Ulansuhai Lake were 53.56%,40.98%,and 36.25%,respectively.This indicates that the pollutant inputs associated with LGD posed a potential threat to the ecological stability of Daihai and Ulansuhai Lake.By comparison,the differences of LGD process and associated pollutant flux were controlled by hydrogeological conditions,lakebed permeability and human activities.This study provides a reference for water resources management in Daihai and Ulansuhai Lake basins while improving the understanding of LGD in the Yellow River basin.展开更多
P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates...P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior performance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82%and 10.62%on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.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
The five-year survival rate of gastric cancer in China is close to those in European and North Americancountries but far lower than those in the Republic of Korea and Japan where national gastric cancerscreening syste...The five-year survival rate of gastric cancer in China is close to those in European and North Americancountries but far lower than those in the Republic of Korea and Japan where national gastric cancerscreening systems have been established.It is of great significance to build a high-quality gastric cancerscreening system adaptive to China's national conditions.Due to the large number of people at risk ofgastric cancer and the uneven distribution of medical resources,it is still difficult for China to carry out anationwide gastroscopy screening program for gastric cancer.Gastric oral contrast ultrasonography(OCUS)is a promising,non-invasive tool for initial gastric cancer screening,offering a painless,radiationfreealternative.Based on two 2020 OCUS consensuses,this document analyzes national gastric cancerscreening strategies and challenges to elaborate on the necessity,feasibility,and current problems ofpreliminary ultrasound screening in China.It details the key aspects of OCUS,including indications,contraindications,operator requirements,contrast agent standards,and essential scanning protocols.Italso introduces the standardized ultrasound sections and the Stomach Ultrasound Report and Data System(Su-RADS)and proposes the relevant consensus opinions.After several rounds of discussions and voting byexperts from multiple societies,a total of 17 consensus opinions have been formed on OCUS as a preliminary screening technique for gastric cancer,with the aim of standardizing the popularization ofOCUS.In addition,the consensus calls for conducting nationwide multicenter prospective studies toimprove the level of evidence and provide big data support for the construction of a preliminary gastriccancer ultrasound screening system that is in line with China's national conditions.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
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.
文摘In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These jamming signals severely degrade radar detection performance.Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems.To address this challenge,this article proposes a novel open-set compound jamming cognition(OSCJC)method.The proposed method employs a detection-classification dual-network architecture,which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments.To achieve unknown jamming detection,we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples.This strategy enables the network to acquire highly generalizable jamming features,thereby accurately localizing candidate regions for individual jamming components within compound jamming.Subsequently,we introduce contrastive learning to optimize the classification network,significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space.This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types.Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method.Compared with the state-of-the-art open-set recognition methods,the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.
基金funded by the National Natural Science Foundation of China(Grant No.42275039)the Meteorological Joint Fund by NSF and CMA(Grant No.U2342224)+1 种基金the National Key R&D Program of China(Grant No.2022YFC3701202)the S&T Development Fund of CAMS(Grant No.2024KJ019)。
文摘Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emissions are expected to simultaneously increase the probability of regional floods and droughts,threatening ecosystems within global terrestrial monsoon regions and the freshwater supply for billions of residents in these areas.In this study,the responses of GLMP to the evolution of ITC toward the carbon neutrality goal are assessed using multimodel outputs from a new model intercomparison project(CovidMIP).The results show that the Northern Hemisphere-Southern Hemisphere(NH-SH)asymmetry of GLMP in boreal summer weakens during the 2040s,as a persistent reduction in well-mixed greenhouse gas(WMGHG)emissions leads to a downward trend in the ITC after 2040.At the same time,the reduction in WMGHG emissions dampens the Eastern Hemisphere-Western Hemisphere(EH-WH)asymmetry of GLMP by inducing La Niña-like cooling and enhancing moisture transport to Inner America.The resulting increases in land monsoon precipitation(LMP)may alleviate drought under the global warming scenario by about 19%-25%and 7%-9%in the WH and SH monsoon regions,respectively.However,a persistent reduction in aerosol emissions in Asia will dominate the increases in LMP in this region until the mid-21st century,and these increases may be approximately 23%-60%of the growth under the global warming scenario.Our results highlight the different rates of response of aerosol and WMGHG concentrations to the carbon neutrality goal,leading to various changes in LMP at global and regional scales.
基金supported by the Natural Science Foundation of Inner Mongolia Autonomous Region of China(No.2023QN04011)the National Natural Science Foundation of China(Nos.42307092 and 52279067)+1 种基金Ordos Science and Technology Major Project(No.ZD20232303)Project of Key Laboratory of River and Lake in Inner Mongolia Autonomous Region(No.2022QZBZ0003).
文摘Lacustrine groundwater discharge(LGD)plays an important role in water resources management.Previous studies have focused on LGD process in a single lake,but the differences in LGD process within the same region have not been thoroughly investigated.In this study,multiple tracers(hydrochemistry,𝛿D,𝛿18O and 222Rn)were used to compare mechanisms of LGD in Daihai and Ulansuhai Lake in Inner Mongoli1,Northwest China.The hydrochemical types showed a trend from groundwater to lake water,indicating a hydraulic connection between them.In addition,the𝛿D and𝛿18O values of sediment pore water were between the groundwater and lake water,indicating the LGD processes.The radon mass balance model was used to estimate the average groundwater discharge rates of Daihai and Ulansuhai Lake,which were 2.79 mm/day and 3.02 mm/day,respectively.The total nitrogen(TN),total phosphorus(TP),and fluoride inputs associated with LGD in Daihai Lake accounted for 97.52%,96.59%,and 95.84%of the total inputs,respectively.In contrast,TN,TP and fluoride inputs in Ulansuhai Lake were 53.56%,40.98%,and 36.25%,respectively.This indicates that the pollutant inputs associated with LGD posed a potential threat to the ecological stability of Daihai and Ulansuhai Lake.By comparison,the differences of LGD process and associated pollutant flux were controlled by hydrogeological conditions,lakebed permeability and human activities.This study provides a reference for water resources management in Daihai and Ulansuhai Lake basins while improving the understanding of LGD in the Yellow River basin.
基金supported by the National Key Research and Development Program of China(Program No.:2022YFF1203003)the National Natural Science Foundation of China(Grant No.:82373791).
文摘P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of Pgp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior performance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82%and 10.62%on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.
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.
基金supported by the CAMS Innovation Fund for Medical Sciences(2023-I2M-C&T-B-016)lthe National High Level Hospital Clinical Research Funding(2022-PUMCH-C-048)l the Noncommunicable Chronic Diseases-National Science and Technology Major Project(2024ZD0520600).
文摘The five-year survival rate of gastric cancer in China is close to those in European and North Americancountries but far lower than those in the Republic of Korea and Japan where national gastric cancerscreening systems have been established.It is of great significance to build a high-quality gastric cancerscreening system adaptive to China's national conditions.Due to the large number of people at risk ofgastric cancer and the uneven distribution of medical resources,it is still difficult for China to carry out anationwide gastroscopy screening program for gastric cancer.Gastric oral contrast ultrasonography(OCUS)is a promising,non-invasive tool for initial gastric cancer screening,offering a painless,radiationfreealternative.Based on two 2020 OCUS consensuses,this document analyzes national gastric cancerscreening strategies and challenges to elaborate on the necessity,feasibility,and current problems ofpreliminary ultrasound screening in China.It details the key aspects of OCUS,including indications,contraindications,operator requirements,contrast agent standards,and essential scanning protocols.Italso introduces the standardized ultrasound sections and the Stomach Ultrasound Report and Data System(Su-RADS)and proposes the relevant consensus opinions.After several rounds of discussions and voting byexperts from multiple societies,a total of 17 consensus opinions have been formed on OCUS as a preliminary screening technique for gastric cancer,with the aim of standardizing the popularization ofOCUS.In addition,the consensus calls for conducting nationwide multicenter prospective studies toimprove the level of evidence and provide big data support for the construction of a preliminary gastriccancer ultrasound screening system that is in line with China's national conditions.
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.
