Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps i...Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.展开更多
The isolated fracture-vug systems controlled by small-scale strike-slip faults within ultra-deep carbonate rocks of the Tarim Basin exhibit significant exploration potential.The study employs a novel training set inco...The isolated fracture-vug systems controlled by small-scale strike-slip faults within ultra-deep carbonate rocks of the Tarim Basin exhibit significant exploration potential.The study employs a novel training set incorporating innovative fault labels to train a U-Net-structured CNN model,enabling effective identification of small-scale strike-slip faults through seismic data interpretation.Based on the CNN faults,we analyze the distribution patterns of small-scale strike-slip faults.The small-scale strike-slip faults can be categorized into NNW-trending and NE-trending groups with strike lengths ranging 200–5000 m.The development intensity of small-scale strike-slip faults in the Lower Yingshan Member notably exceeds that in the Upper Member.The Lower and Upper Yingshan members are two distinct mechanical layers with contrasting brittleness characteristics,separated by a low-brittleness layer.The superior brittleness of the Lower Yingshan Member enhances the development intensity of small-scale strike-slip faults compared to the upper member,while the low-brittleness layer exerts restrictive effects on vertical fault propagation.Fracture-vug systems formed by interactions of two or more small-scale strike-slip faults demonstrate larger sizes than those controlled by individual faults.All fracture-vug system sizes show positive correlations with the vertical extents of associated small-scale strike-slip faults,particularly intersection and approaching fracture-vug systems exhibit accelerated size increases proportional to the vertical extents.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as o...This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.展开更多
Malignant tumours always threaten human health.For tumour diagnosis,positron emission tomography(PET)is the most sensitive and advanced imaging technique by radiotracers,such as radioactive^(18)F,^(11)C,^(64)Cu,^(68)G...Malignant tumours always threaten human health.For tumour diagnosis,positron emission tomography(PET)is the most sensitive and advanced imaging technique by radiotracers,such as radioactive^(18)F,^(11)C,^(64)Cu,^(68)Ga,and^(89)Zr.Among the radiotracers,the radioactive^(18)F-labelled chemical agent as PET probes plays a predominant role in monitoring,detecting,treating,and predicting tumours due to its perfect half-life.In this paper,the^(18)F-labelled chemical materials as PET probes are systematically summarized.First,we introduce various radionuclides of PET and elaborate on the mechanism of PET imaging.It highlights the^(18)F-labelled chemical agents used as PET probes,including[^(18)F]-2-deoxy-2-[^(18)F]fluoro-D-glucose([^(18)F]-FDG),^(18)F-labelled amino acids,^(18)F-labelled nucleic acids,^(18)F-labelled receptors,^(18)F-labelled reporter genes,and^(18)F-labelled hypoxia agents.In addition,some PET probes with metal as a supplementary element are introduced briefly.Meanwhile,the^(18)F-labelled nanoparticles for the PET probe and the multi-modality imaging probe are summarized in detail.The approach and strategies for the fabrication of^(18)F-labelled PET probes are also described briefly.The future development of the PET probe is also prospected.The development and application of^(18)F-labelled PET probes will expand our knowledge and shed light on the diagnosis and theranostics of tumours.展开更多
Site-specific protein labeling plays important roles in drug discovery and illuminating biological processes at the molecular level.However,it is challenging to label proteins with high specificity while not affecting...Site-specific protein labeling plays important roles in drug discovery and illuminating biological processes at the molecular level.However,it is challenging to label proteins with high specificity while not affecting their structures and biochemical activities.Over the last few years,a variety of promising strategies have been devised that address these challenges including those that involve introduction of small-size peptide tags or unnatural amino acids(UAAs),chemical labeling of specific protein residues,and affinity-driven labeling.This review summarizes recent developments made in the area of site-specific protein labeling utilizing genetically encoding-and chemical-based methods,and discusses future issues that need to be addressed by researchers in this field.展开更多
The China National Institute of Standardization(CNIS)held the Academic Meeting on 20th Anniversary of China Energy Label in Beijing on June 27.The event took place during the 35th National Energy Conservation Publicit...The China National Institute of Standardization(CNIS)held the Academic Meeting on 20th Anniversary of China Energy Label in Beijing on June 27.The event took place during the 35th National Energy Conservation Publicity Week,which ran from June 23 to 29.展开更多
In 2012, Ponraj et al. defined a concept of k-product cordial labeling as follows: Let f be a map from V(G)to { 0,1,⋯,k−1 }where k is an integer, 1≤k≤| V(G) |. For each edge uvassign the label f(u)f(v)(modk). f is c...In 2012, Ponraj et al. defined a concept of k-product cordial labeling as follows: Let f be a map from V(G)to { 0,1,⋯,k−1 }where k is an integer, 1≤k≤| V(G) |. For each edge uvassign the label f(u)f(v)(modk). f is called a k-product cordial labeling if | vf(i)−vf(j) |≤1, and | ef(i)−ef(j) |≤1, i,j∈{ 0,1,⋯,k−1 }, where vf(x)and ef(x)denote the number of vertices and edges respectively labeled with x (x=0,1,⋯,k−1). Motivated by this concept, we further studied and established that several families of graphs admit k-product cordial labeling. In this paper, we show that the path graphs Pnadmit k-product cordial labeling.展开更多
The distance distributions between two site-specifically anchored spin labels in a protein,measured by pulsed electron-electron double resonance(PELDOR or DEER),provide rich sources of structural and conformational re...The distance distributions between two site-specifically anchored spin labels in a protein,measured by pulsed electron-electron double resonance(PELDOR or DEER),provide rich sources of structural and conformational restraints on the proteins or their complexes.The rigid connection of the nitroxide spin label to the protein improves the accuracy and precision of distance measurement.We report a new spin labelling approach by formation of thioester bond between nitroxide(NO)spin label,NOAI(NO spin labels activated by acetylimidazole),and a protein thiol,and this spin labeling method has demonstrated high performance in DEER distance measurement on proteins.The results showed that NOAI has shorter connection to the protein ligation site than 2,2,5,5-tetramethyl-pyrroline-1-oxyl methanethiosulfonate(MTSL)and 3-maleimido-proxyl(M-Prox)in the respective protein conjugate and produces narrower distance distributions for the tested proteins including ubiquitin(Ub),immunoglobulin-binding b1 domain of streptococcal protein G(GB1),and second mitochondria-derived activator of caspases(Smac).The NOAI protein conjugate connected by a thioester bond is resistant to reducing reagent and offers highfidelity DEER distance measurements in cell lysates.展开更多
This study summarizes the examination data of registration labels for ordinary cosmetics in Beijing from May 2021 to April 2024.It analyzes and categorizes the issues identified during label evaluations,explores the u...This study summarizes the examination data of registration labels for ordinary cosmetics in Beijing from May 2021 to April 2024.It analyzes and categorizes the issues identified during label evaluations,explores the underlying causes,and proposes regulatory countermeasures and recommendations for registrants,regulatory authorities,and social organizations.The objective is to offer practical insights and regulatory guidance to support the enhancement of cosmetic registration and regulatory standards.展开更多
Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance p...Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling.By integrating real-time images,electronic health records,and genomic information,the system enables personalized simulations for disease progression modeling,treatment response prediction,and preventive care strategies.In dysarthric speech,which is characterized by articulation imprecision,temporal misalignments,and phoneme distortions,existing models struggle to capture these irregularities.Traditional approaches,often relying solely on audio features,fail to address the full complexity of phoneme variations,leading to increased phoneme error rates(PER)and word error rates(WER).To overcome these challenges,we propose a novel multi-modal architecture that integrates both audio and articulatory data through a combination of Temporal Convolutional Networks(TCNs),Graph Convolutional Networks(GCNs),Transformer Encoders,and a cross-modal attention mechanism.The audio branch of the model utilizes TCNs and Transformer Encoders to capture both short-and long-term dependencies in the audio signal,while the articulatory branch leverages GCNs to model spatial relationships between articulators,such as the lips,jaw,and tongue,allowing the model to detect subtle articulatory imprecisions.A cross-modal attention mechanism fuses the encoded audio and articulatory features,enabling dynamic adjustment of the model’s focus depending on input quality,which significantly improves phoneme labeling accuracy.The proposed model consistently outperforms existing methods,achieving lower Phoneme Error Rates(PER),Word Error Rates(WER),and Articulatory Feature Misclassification Rates(AFMR).Specifically,across all datasets,the model achieves an average PER of 13.43%,an average WER of 21.67%,and an average AFMR of 12.73%.By capturing both the acoustic and articulatory intricacies of speech,this comprehensive approach not only improves phoneme labeling precision but also marks substantial progress in speech recognition technology for individuals with dysarthria.展开更多
Semantic segmentation is a core task in computer vision that allows AI models to interact and understand their surrounding environment. Similarly to how humans subconsciously segment scenes, this ability is crucial fo...Semantic segmentation is a core task in computer vision that allows AI models to interact and understand their surrounding environment. Similarly to how humans subconsciously segment scenes, this ability is crucial for scene understanding. However, a challenge many semantic learning models face is the lack of data. Existing video datasets are limited to short, low-resolution videos that are not representative of real-world examples. Thus, one of our key contributions is a customized semantic segmentation version of the Walking Tours Dataset that features hour-long, high-resolution, real-world data from tours of different cities. Additionally, we evaluate the performance of open-vocabulary, semantic model OpenSeeD on our own custom dataset and discuss future implications.展开更多
Sennoside A(SA),a typical prodrug,exerts its laxative effect only after its transformation into rheinanthrone catalyzed by gut microbial hydrolases and reductases.Hydrolases have been identified,but reductases remain ...Sennoside A(SA),a typical prodrug,exerts its laxative effect only after its transformation into rheinanthrone catalyzed by gut microbial hydrolases and reductases.Hydrolases have been identified,but reductases remain unknown.By linking a photoreactive group to the SA scaffold,we synthesized a photoaffinity probe to covalently label SA reductases and identified SA reductases using activity-based protein profiling(ABPP).From lysates of an active strain,Bifidobacterium pseudocatenulatum(B.pseudocatenulatum),397 proteins were enriched and subsequently identified using mass spectrometry(MS).Among these proteins,chromate reductase/nicotinamide adenine dinucleotide(NADH)phosphate(NADPH)-dependent flavin mononucleotide(FMN)reductase/oxygen-insensitive NADPH nitroreductase(nfrA)was identified as a potent SA reductase through further bioinformatic analysis and The Universal Protein Resource(UniProt)database screening.We also determined that recombinant nfrA could reduce SA.Our study contributes to further illuminating mechanisms of SA transformation to rheinanthrone and simultaneously offers an effective method to identify gut bacterial reductases.展开更多
The continuous mutation and rapid spread of the severe acute respiratory syndrome coronavirus 2(SARSCoV-2)have led to the ineffectiveness of many antiviral drugs targeting the original strain.To keep pace with the vi...The continuous mutation and rapid spread of the severe acute respiratory syndrome coronavirus 2(SARSCoV-2)have led to the ineffectiveness of many antiviral drugs targeting the original strain.To keep pace with the virus’evolutionary speed,there is a crucial need for the development of rapid,cost-effective,and efficient inhibitor screening methods.In this study,we created a novel approach based on fluorescence resonance energy transfer(FRET)technology for in vitro detection of inhibitors targeting the interaction between the SARS-CoV-2 spike protein RBD(s-RBD)and the virus receptor angiotensin-converting enzyme 2(ACE2).Utilizing crystallographic insights into the s-RBD/ACE2 interaction,we modified ACE2 by fusing SNAP tag to its N-terminus(resulting in SA740)and Halo tag to s-RBD’s C-terminus(producing R525H and R541H),thereby ensuring the proximity(<10 nm)of labeled FRET dyes.We found that relative to the R541H fusion protein,R525H exhibited higher FRET efficiency,which attributed to the shortened distance between FRET dyes due to the truncation of s-RBD.Utilizing the sensitive FRET effect between SA740 and R525H,we evaluated its efficacy in detecting inhibitors of SARS-CoV-2 entry in solution and live cells.Ultimately,this FRET-based detection method was demonstrated high sensitivity,rapidity,and simplicity in solution and held promise for high-throughput screening of SARS-CoV-2 inhibitors.展开更多
Since the 1970s,a series of international and national sources have supported the principle of accessibility,which slowly has become a statuary norm and a legislative obligation.Each country has implemented accessibil...Since the 1970s,a series of international and national sources have supported the principle of accessibility,which slowly has become a statuary norm and a legislative obligation.Each country has implemented accessibility through a singular policy.But in addition to the accessibility of a place or an activity,to inform about what is accessible is very important as well,and has not really taken off.Indeed,for disabled people,the difficulty lies not only with access to places and the use of resources,but also with the visibility of these resources.This means that information concerning accessibility has to be disclosed and provided effectively to disabled people,those involved with them and the relevant institutions.In different countries all over the world,many labels and pictograms have been created for this purpose and give information relating to accessibility.Using a socio-historical approach,we will present and analyze the different types of icons,symbols,pictograms and labels that have been put in place around the world and in France:what are they used for and for whom are they made?We will show that they are pointers which firstly reflect the diversity and range within the target group concerned by accessibility,and secondly the evolution of accessibility as a dynamic and ecological principle.展开更多
This paper is concerned with design-ing symbol labeling for a low-density parity-check(LDPC)-coded delayed bit-interleaved coded modu-lation(DBICM)scheme in a two-way relay channel(TWRC).We first present some properti...This paper is concerned with design-ing symbol labeling for a low-density parity-check(LDPC)-coded delayed bit-interleaved coded modu-lation(DBICM)scheme in a two-way relay channel(TWRC).We first present some properties of symbol labeling within a phase shift keying(PSK)modula-tion.These properties reduce the candidate labeling search space.Based on this search space,we take DBICM capacity as the cost function and propose a general method for optimizing symbol labeling by em-ploying the differential evolution algorithm.Numeri-cal results show that our labeling obtains a signal-to-noise ratio(SNR)gain up to 0.45 dB with respect to Gray labeling.展开更多
The accumulation of Cd by rice poses significant health risks.Foliar fertilizationwith Zn can reduce grain Cd contents in rice grown in Cd-contaminated soils.However,atmospheric deposition on leaves is another vector ...The accumulation of Cd by rice poses significant health risks.Foliar fertilizationwith Zn can reduce grain Cd contents in rice grown in Cd-contaminated soils.However,atmospheric deposition on leaves is another vector of Cd contamination,and it remains unclear how Zn application affects the allocation of such Cd.We conducted an experiment where the flag leaves of rice plants were treated with solutions with various Zn concentrations and a constant Cd concentration.The 111Cd stable isotope was used to trace the flux of foliarapplied Cd.Higher levels of foliar-applied Zn enhanced Cd efflux and grain allocation.This is attributed to limited sequestration of foliar-applied Cd in the leaf cell symplasm and increased Cd desorption from leaf cell walls when a high Zn^(2+)concentration occurs in the apoplast.Nonionic Zn oxide nanoparticlesmitigated these effects.Additionally,the expressions of OsLCT1 and OsZIP7 in flag leaves and OsHMA2 and OsZIP7 in the uppermost nodes were upregulated under high-Zn^(2+)treatment,which may facilitate Cd phloem loading and grain allocation.Caution is advised in using foliar Zn in areas with high atmospheric Cd due to potential grain-contamination risks.展开更多
Acute lung injury(ALI)is a serious clinical condition with a high mortality rate.Oxidative stress and inflammatory responses play pivotal roles in the pathogenesis of ALI.ONOO^(−)is a key mediator that exacerbates oxi...Acute lung injury(ALI)is a serious clinical condition with a high mortality rate.Oxidative stress and inflammatory responses play pivotal roles in the pathogenesis of ALI.ONOO^(−)is a key mediator that exacerbates oxidative damage and microvascular permeability in ALI.Accurate detection of ONOO^(−)would facilitate early diagnosis and intervention in ALI.Near-infrared fluorescence(NIRF)probes offer new solutions due to their sensitivity,depth of tissue penetration,and imaging capabilities.However,the developed ONOO^(−)fluorescent probes face problems such as interference from other reactive oxygen species and easy intracellular diffusion.To address these issues,we introduced an innovative self-immobilizing NIRF probe,DCI2F-OTf,which was capable of monitoring ONOO^(−)in vitro and in vivo.Importantly,leveraging the high reactivity of the methylene quinone(QM)intermediate,DCI2F-OTf was able to covalently label proteins in the presence of ONOO^(−),enabling in situ imaging.In mice models of ALI,DCI2F-OTf enabled real-time imaging of ONOO^(−)levels and found that ONOO^(−)was tightly correlated with the progression of ALI.Our findings demonstrated that DCI2F-OTf was a promising chemical tool for the detection of ONOO^(−),which could help to gain insight into the pathogenesis of ALI and monitor treatment efficacy.展开更多
Many real⁃world machine learning applications face the challenge of dealing with changing data over time,known as concept drift,and the issue of data indeterminacy,where all the true labels available are unrealistic.T...Many real⁃world machine learning applications face the challenge of dealing with changing data over time,known as concept drift,and the issue of data indeterminacy,where all the true labels available are unrealistic.This can lead to a decrease in the accuracy of the prediction models.The aim of this study is to introduce a new approach for detecting drift,which is based on neutrosophic set theory.This approach takes into account uncertainty in the prediction model and is able to handle indeterminate information,considering its impact on the models performance.The proposed method reads data into windows and calculates a set of values based on the concept of neutrosophic membership.These values are then used in the Neutrosophic Support Vector Machine(N⁃SVM).To address the issue of indeterminate true label data,the values issued by N⁃SVM are expressed as entropy and used as input for the ADWIN(Adaptive Windowing)change detector.When a drift is detected,the prediction model is retrained by including only the most recent instances with the original training data set.The proposed method gives promising results in terms of drift detection accuracy compared to the state of existing drift detection methods such as KSWIN,ADWIN,and DWM.展开更多
基金National Natural Science Foundation of China(No.42301518)Hubei Key Laboratory of Regional Development and Environmental Response(No.2023(A)002)Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources(Ministry of Education)(No.TDSYS202304).
文摘Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.
基金supported by the National Natural Science Foundation of China(No.U21B2062).
文摘The isolated fracture-vug systems controlled by small-scale strike-slip faults within ultra-deep carbonate rocks of the Tarim Basin exhibit significant exploration potential.The study employs a novel training set incorporating innovative fault labels to train a U-Net-structured CNN model,enabling effective identification of small-scale strike-slip faults through seismic data interpretation.Based on the CNN faults,we analyze the distribution patterns of small-scale strike-slip faults.The small-scale strike-slip faults can be categorized into NNW-trending and NE-trending groups with strike lengths ranging 200–5000 m.The development intensity of small-scale strike-slip faults in the Lower Yingshan Member notably exceeds that in the Upper Member.The Lower and Upper Yingshan members are two distinct mechanical layers with contrasting brittleness characteristics,separated by a low-brittleness layer.The superior brittleness of the Lower Yingshan Member enhances the development intensity of small-scale strike-slip faults compared to the upper member,while the low-brittleness layer exerts restrictive effects on vertical fault propagation.Fracture-vug systems formed by interactions of two or more small-scale strike-slip faults demonstrate larger sizes than those controlled by individual faults.All fracture-vug system sizes show positive correlations with the vertical extents of associated small-scale strike-slip faults,particularly intersection and approaching fracture-vug systems exhibit accelerated size increases proportional to the vertical extents.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金funded by Woosong University Academic Research 2024.
文摘This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel ViT.Leveraging awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and understanding.These techniques mitigated overfitting,stabilized training,and improved generalization capabilities.The LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,respectively.The findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature extraction.The additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial applications.For instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often misclassify.This study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are key.Future research may focus on enhancing LMViT’s computational efficiency for deployment in real-time quality control systems.
文摘Malignant tumours always threaten human health.For tumour diagnosis,positron emission tomography(PET)is the most sensitive and advanced imaging technique by radiotracers,such as radioactive^(18)F,^(11)C,^(64)Cu,^(68)Ga,and^(89)Zr.Among the radiotracers,the radioactive^(18)F-labelled chemical agent as PET probes plays a predominant role in monitoring,detecting,treating,and predicting tumours due to its perfect half-life.In this paper,the^(18)F-labelled chemical materials as PET probes are systematically summarized.First,we introduce various radionuclides of PET and elaborate on the mechanism of PET imaging.It highlights the^(18)F-labelled chemical agents used as PET probes,including[^(18)F]-2-deoxy-2-[^(18)F]fluoro-D-glucose([^(18)F]-FDG),^(18)F-labelled amino acids,^(18)F-labelled nucleic acids,^(18)F-labelled receptors,^(18)F-labelled reporter genes,and^(18)F-labelled hypoxia agents.In addition,some PET probes with metal as a supplementary element are introduced briefly.Meanwhile,the^(18)F-labelled nanoparticles for the PET probe and the multi-modality imaging probe are summarized in detail.The approach and strategies for the fabrication of^(18)F-labelled PET probes are also described briefly.The future development of the PET probe is also prospected.The development and application of^(18)F-labelled PET probes will expand our knowledge and shed light on the diagnosis and theranostics of tumours.
基金supported by the National Key R&D Program of China(No.2021YFC2103600)the National Natural Science Foundation of China(Nos.22278224,22478191)+1 种基金the Project of Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the State Key Laboratory of Materials Oriented Chemical Engineering(No.KL21-08)。
文摘Site-specific protein labeling plays important roles in drug discovery and illuminating biological processes at the molecular level.However,it is challenging to label proteins with high specificity while not affecting their structures and biochemical activities.Over the last few years,a variety of promising strategies have been devised that address these challenges including those that involve introduction of small-size peptide tags or unnatural amino acids(UAAs),chemical labeling of specific protein residues,and affinity-driven labeling.This review summarizes recent developments made in the area of site-specific protein labeling utilizing genetically encoding-and chemical-based methods,and discusses future issues that need to be addressed by researchers in this field.
文摘The China National Institute of Standardization(CNIS)held the Academic Meeting on 20th Anniversary of China Energy Label in Beijing on June 27.The event took place during the 35th National Energy Conservation Publicity Week,which ran from June 23 to 29.
文摘In 2012, Ponraj et al. defined a concept of k-product cordial labeling as follows: Let f be a map from V(G)to { 0,1,⋯,k−1 }where k is an integer, 1≤k≤| V(G) |. For each edge uvassign the label f(u)f(v)(modk). f is called a k-product cordial labeling if | vf(i)−vf(j) |≤1, and | ef(i)−ef(j) |≤1, i,j∈{ 0,1,⋯,k−1 }, where vf(x)and ef(x)denote the number of vertices and edges respectively labeled with x (x=0,1,⋯,k−1). Motivated by this concept, we further studied and established that several families of graphs admit k-product cordial labeling. In this paper, we show that the path graphs Pnadmit k-product cordial labeling.
基金supported by National Natural Science Foundation of China(22161142018,21991081,22177056,and 22174074)the Ministry of Science and Technology of China(2021YFA1600304).
文摘The distance distributions between two site-specifically anchored spin labels in a protein,measured by pulsed electron-electron double resonance(PELDOR or DEER),provide rich sources of structural and conformational restraints on the proteins or their complexes.The rigid connection of the nitroxide spin label to the protein improves the accuracy and precision of distance measurement.We report a new spin labelling approach by formation of thioester bond between nitroxide(NO)spin label,NOAI(NO spin labels activated by acetylimidazole),and a protein thiol,and this spin labeling method has demonstrated high performance in DEER distance measurement on proteins.The results showed that NOAI has shorter connection to the protein ligation site than 2,2,5,5-tetramethyl-pyrroline-1-oxyl methanethiosulfonate(MTSL)and 3-maleimido-proxyl(M-Prox)in the respective protein conjugate and produces narrower distance distributions for the tested proteins including ubiquitin(Ub),immunoglobulin-binding b1 domain of streptococcal protein G(GB1),and second mitochondria-derived activator of caspases(Smac).The NOAI protein conjugate connected by a thioester bond is resistant to reducing reagent and offers highfidelity DEER distance measurements in cell lysates.
文摘This study summarizes the examination data of registration labels for ordinary cosmetics in Beijing from May 2021 to April 2024.It analyzes and categorizes the issues identified during label evaluations,explores the underlying causes,and proposes regulatory countermeasures and recommendations for registrants,regulatory authorities,and social organizations.The objective is to offer practical insights and regulatory guidance to support the enhancement of cosmetic registration and regulatory standards.
基金funded by the Ongoing Research Funding program(ORF-2025-867),King Saud University,Riyadh,Saudi Arabia.
文摘Digital twin technology is revolutionizing personalized healthcare by creating dynamic virtual replicas of individual patients.This paper presents a novel multi-modal architecture leveraging digital twins to enhance precision in predictive diagnostics and treatment planning of phoneme labeling.By integrating real-time images,electronic health records,and genomic information,the system enables personalized simulations for disease progression modeling,treatment response prediction,and preventive care strategies.In dysarthric speech,which is characterized by articulation imprecision,temporal misalignments,and phoneme distortions,existing models struggle to capture these irregularities.Traditional approaches,often relying solely on audio features,fail to address the full complexity of phoneme variations,leading to increased phoneme error rates(PER)and word error rates(WER).To overcome these challenges,we propose a novel multi-modal architecture that integrates both audio and articulatory data through a combination of Temporal Convolutional Networks(TCNs),Graph Convolutional Networks(GCNs),Transformer Encoders,and a cross-modal attention mechanism.The audio branch of the model utilizes TCNs and Transformer Encoders to capture both short-and long-term dependencies in the audio signal,while the articulatory branch leverages GCNs to model spatial relationships between articulators,such as the lips,jaw,and tongue,allowing the model to detect subtle articulatory imprecisions.A cross-modal attention mechanism fuses the encoded audio and articulatory features,enabling dynamic adjustment of the model’s focus depending on input quality,which significantly improves phoneme labeling accuracy.The proposed model consistently outperforms existing methods,achieving lower Phoneme Error Rates(PER),Word Error Rates(WER),and Articulatory Feature Misclassification Rates(AFMR).Specifically,across all datasets,the model achieves an average PER of 13.43%,an average WER of 21.67%,and an average AFMR of 12.73%.By capturing both the acoustic and articulatory intricacies of speech,this comprehensive approach not only improves phoneme labeling precision but also marks substantial progress in speech recognition technology for individuals with dysarthria.
文摘Semantic segmentation is a core task in computer vision that allows AI models to interact and understand their surrounding environment. Similarly to how humans subconsciously segment scenes, this ability is crucial for scene understanding. However, a challenge many semantic learning models face is the lack of data. Existing video datasets are limited to short, low-resolution videos that are not representative of real-world examples. Thus, one of our key contributions is a customized semantic segmentation version of the Walking Tours Dataset that features hour-long, high-resolution, real-world data from tours of different cities. Additionally, we evaluate the performance of open-vocabulary, semantic model OpenSeeD on our own custom dataset and discuss future implications.
基金supported by the National Natural Science Foundation of China(Grant Nos.:U21A20407 and 81973467)Beijing Natural Science Foundation,China(Grant No.:7222276).
文摘Sennoside A(SA),a typical prodrug,exerts its laxative effect only after its transformation into rheinanthrone catalyzed by gut microbial hydrolases and reductases.Hydrolases have been identified,but reductases remain unknown.By linking a photoreactive group to the SA scaffold,we synthesized a photoaffinity probe to covalently label SA reductases and identified SA reductases using activity-based protein profiling(ABPP).From lysates of an active strain,Bifidobacterium pseudocatenulatum(B.pseudocatenulatum),397 proteins were enriched and subsequently identified using mass spectrometry(MS).Among these proteins,chromate reductase/nicotinamide adenine dinucleotide(NADH)phosphate(NADPH)-dependent flavin mononucleotide(FMN)reductase/oxygen-insensitive NADPH nitroreductase(nfrA)was identified as a potent SA reductase through further bioinformatic analysis and The Universal Protein Resource(UniProt)database screening.We also determined that recombinant nfrA could reduce SA.Our study contributes to further illuminating mechanisms of SA transformation to rheinanthrone and simultaneously offers an effective method to identify gut bacterial reductases.
基金supported by the National Natural Science Foundation of China(Nos.22225806,22378385,22078314,22278394)Dalian Institute of Chemical Physics(Nos.DICPI202142,DICPI202436,DMU-1&DICP UN202301).
文摘The continuous mutation and rapid spread of the severe acute respiratory syndrome coronavirus 2(SARSCoV-2)have led to the ineffectiveness of many antiviral drugs targeting the original strain.To keep pace with the virus’evolutionary speed,there is a crucial need for the development of rapid,cost-effective,and efficient inhibitor screening methods.In this study,we created a novel approach based on fluorescence resonance energy transfer(FRET)technology for in vitro detection of inhibitors targeting the interaction between the SARS-CoV-2 spike protein RBD(s-RBD)and the virus receptor angiotensin-converting enzyme 2(ACE2).Utilizing crystallographic insights into the s-RBD/ACE2 interaction,we modified ACE2 by fusing SNAP tag to its N-terminus(resulting in SA740)and Halo tag to s-RBD’s C-terminus(producing R525H and R541H),thereby ensuring the proximity(<10 nm)of labeled FRET dyes.We found that relative to the R541H fusion protein,R525H exhibited higher FRET efficiency,which attributed to the shortened distance between FRET dyes due to the truncation of s-RBD.Utilizing the sensitive FRET effect between SA740 and R525H,we evaluated its efficacy in detecting inhibitors of SARS-CoV-2 entry in solution and live cells.Ultimately,this FRET-based detection method was demonstrated high sensitivity,rapidity,and simplicity in solution and held promise for high-throughput screening of SARS-CoV-2 inhibitors.
文摘Since the 1970s,a series of international and national sources have supported the principle of accessibility,which slowly has become a statuary norm and a legislative obligation.Each country has implemented accessibility through a singular policy.But in addition to the accessibility of a place or an activity,to inform about what is accessible is very important as well,and has not really taken off.Indeed,for disabled people,the difficulty lies not only with access to places and the use of resources,but also with the visibility of these resources.This means that information concerning accessibility has to be disclosed and provided effectively to disabled people,those involved with them and the relevant institutions.In different countries all over the world,many labels and pictograms have been created for this purpose and give information relating to accessibility.Using a socio-historical approach,we will present and analyze the different types of icons,symbols,pictograms and labels that have been put in place around the world and in France:what are they used for and for whom are they made?We will show that they are pointers which firstly reflect the diversity and range within the target group concerned by accessibility,and secondly the evolution of accessibility as a dynamic and ecological principle.
文摘This paper is concerned with design-ing symbol labeling for a low-density parity-check(LDPC)-coded delayed bit-interleaved coded modu-lation(DBICM)scheme in a two-way relay channel(TWRC).We first present some properties of symbol labeling within a phase shift keying(PSK)modula-tion.These properties reduce the candidate labeling search space.Based on this search space,we take DBICM capacity as the cost function and propose a general method for optimizing symbol labeling by em-ploying the differential evolution algorithm.Numeri-cal results show that our labeling obtains a signal-to-noise ratio(SNR)gain up to 0.45 dB with respect to Gray labeling.
基金supported by Guangdong Basic and the Applied Basic Research Foundation(No.2022A1515011403)the Science and Technology Program of Guangdong Province(No.2022A0505090002)+1 种基金the National Natural Science Foundation of China(Nos.U23A2039 and 42007331)the Research Fund Program of Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology(No.2023B1212060016).
文摘The accumulation of Cd by rice poses significant health risks.Foliar fertilizationwith Zn can reduce grain Cd contents in rice grown in Cd-contaminated soils.However,atmospheric deposition on leaves is another vector of Cd contamination,and it remains unclear how Zn application affects the allocation of such Cd.We conducted an experiment where the flag leaves of rice plants were treated with solutions with various Zn concentrations and a constant Cd concentration.The 111Cd stable isotope was used to trace the flux of foliarapplied Cd.Higher levels of foliar-applied Zn enhanced Cd efflux and grain allocation.This is attributed to limited sequestration of foliar-applied Cd in the leaf cell symplasm and increased Cd desorption from leaf cell walls when a high Zn^(2+)concentration occurs in the apoplast.Nonionic Zn oxide nanoparticlesmitigated these effects.Additionally,the expressions of OsLCT1 and OsZIP7 in flag leaves and OsHMA2 and OsZIP7 in the uppermost nodes were upregulated under high-Zn^(2+)treatment,which may facilitate Cd phloem loading and grain allocation.Caution is advised in using foliar Zn in areas with high atmospheric Cd due to potential grain-contamination risks.
基金supported by the National Natural Science Foundation of China(Nos.22264013,21961010)Hainan Province Science and Technology Special Fund(Nos.ZDYF2021SHFZ219,ZDYF2022SHFZ037)+4 种基金Special Funds of S&T Cooperation and Exchange Projects of Shanxi Province(No.202204041101040)Natural Science Research Talent Project of Hainan Medical University(No.JBGS202101)Postgraduate Innovative Research Project of Hainan(No.Qhys2021-384)Hainan Province Clinical Medical Center(2021)Project for Functional Materials and Molecular Imaging Science Innovation Group of Hainan Medical University.
文摘Acute lung injury(ALI)is a serious clinical condition with a high mortality rate.Oxidative stress and inflammatory responses play pivotal roles in the pathogenesis of ALI.ONOO^(−)is a key mediator that exacerbates oxidative damage and microvascular permeability in ALI.Accurate detection of ONOO^(−)would facilitate early diagnosis and intervention in ALI.Near-infrared fluorescence(NIRF)probes offer new solutions due to their sensitivity,depth of tissue penetration,and imaging capabilities.However,the developed ONOO^(−)fluorescent probes face problems such as interference from other reactive oxygen species and easy intracellular diffusion.To address these issues,we introduced an innovative self-immobilizing NIRF probe,DCI2F-OTf,which was capable of monitoring ONOO^(−)in vitro and in vivo.Importantly,leveraging the high reactivity of the methylene quinone(QM)intermediate,DCI2F-OTf was able to covalently label proteins in the presence of ONOO^(−),enabling in situ imaging.In mice models of ALI,DCI2F-OTf enabled real-time imaging of ONOO^(−)levels and found that ONOO^(−)was tightly correlated with the progression of ALI.Our findings demonstrated that DCI2F-OTf was a promising chemical tool for the detection of ONOO^(−),which could help to gain insight into the pathogenesis of ALI and monitor treatment efficacy.
文摘Many real⁃world machine learning applications face the challenge of dealing with changing data over time,known as concept drift,and the issue of data indeterminacy,where all the true labels available are unrealistic.This can lead to a decrease in the accuracy of the prediction models.The aim of this study is to introduce a new approach for detecting drift,which is based on neutrosophic set theory.This approach takes into account uncertainty in the prediction model and is able to handle indeterminate information,considering its impact on the models performance.The proposed method reads data into windows and calculates a set of values based on the concept of neutrosophic membership.These values are then used in the Neutrosophic Support Vector Machine(N⁃SVM).To address the issue of indeterminate true label data,the values issued by N⁃SVM are expressed as entropy and used as input for the ADWIN(Adaptive Windowing)change detector.When a drift is detected,the prediction model is retrained by including only the most recent instances with the original training data set.The proposed method gives promising results in terms of drift detection accuracy compared to the state of existing drift detection methods such as KSWIN,ADWIN,and DWM.
