The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of months.Although,regulation measures in China hav...The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of months.Although,regulation measures in China have decreased new cases by over 98%,this decrease is not the situation everywhere,and most of the countries still have been affected by it.The objective of this research work is to make a comparative analysis of the top 5 most populated countries namely United States,India,China,Pakistan and Indonesia,from 1st January 2020 to 31st July 2020.This research work also targets to predict an increase in the number of deaths and total infected cases in these five countries.In our research,the performance of the proposed framework is determined by using three Machine Learning(ML)regression algorithms namely Linear Regression(LR),Support Vector Regression(SVR),andRandom Forest(RF)Regression.The proposed model is also validated upon the infected and death cases of further dates.The performance of these three algorithms is compared using the RootMean Square Error(RMSE)metrics.Random Forest algorithm shows best performance as compared to other proposed algorithms,with the lowest RMSE value in the prediction of total infected and total deaths cases for all the top five most populated countries.展开更多
Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement e...Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement efficiency,owing to the single gas sensor without the identification ability of multiple gases.In this work,a novel gas-liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas-liquid separator under a large difference of partial pressure.The limit of detection(LOD)of the gas Raman spectrometer could arrive at about 14 μl·L^(-1)for N_(2)gas.Moreover,based on the continuous gas-liquid separation process,the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method(30 min).Effect of equilibrium time on gas-liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO_(2)>O_(2)>N_(2).In addition,the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed.The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power pla...As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.展开更多
BACKGROUND Early detection of precancerous lesions is of vital importance for reducing the incidence and mortality of upper gastrointestinal(UGI)tract cancer.However,traditional endoscopy has certain limitations in de...BACKGROUND Early detection of precancerous lesions is of vital importance for reducing the incidence and mortality of upper gastrointestinal(UGI)tract cancer.However,traditional endoscopy has certain limitations in detecting precancerous lesions.In contrast,real-time computer-aided detection(CAD)systems enhanced by artificial intelligence(AI)systems,although they may increase unnecessary medical procedures,can provide immediate feedback during examination,thereby improving the accuracy of lesion detection.This article aims to conduct a meta-analysis of the diagnostic performance of CAD systems in identifying precancerous lesions of UGI tract cancer during esophagogastroduodenoscopy(EGD),evaluate their potential clinical application value,and determine the direction for further research.AIM To investigate the improvement of the efficiency of EGD examination by the realtime AI-enabled real-time CAD system(AI-CAD)system.METHODS PubMed,EMBASE,Web of Science and Cochrane Library databases were searched by two independent reviewers to retrieve literature with per-patient analysis with a deadline up until April 2025.A meta-analysis was performed with R Studio software(R4.5.0).A random-effects model was used and subgroup analysis was carried out to identify possible sources of heterogeneity.RESULTS The initial search identified 802 articles.According to the inclusion criteria,2113 patients from 10 studies were included in this meta-analysis.The pooled accuracy difference,logarithmic difference of diagnostic odds ratios,sensitivity,specificity and the area under the summary receiver operating characteristic curve(area under the curve)of both AI group and endoscopist group for detecting precancerous lesion were 0.16(95%CI:0.12-0.20),-0.19(95%CI:-0.75-0.37),0.89(95%CI:0.85-0.92,AI group),0.67(95%CI:0.63-0.71,endoscopist group),0.89(95%CI:0.84-0.93,AI group),0.77(95%CI:0.70-0.83,endoscopist group),0.928(95%CI:0.841-0.948,AI group),0.722(95%CI:0.677-0.821,endoscopist group),respectively.CONCLUSION The present studies further provide evidence that the AI-CAD is a reliable endoscopic diagnostic tool that can be used to assist endoscopists in detection of precancerous lesions in the UGI tract.It may be introduced on a large scale for clinical application to enhance the accuracy of detecting precancerous lesions in the UGI tract.展开更多
Actuator dynamics introduce a synchronization disparity between commanded displacements transmitted to the actuator and the actual displacements generated by the actuator,thereby affecting its precision and potentiall...Actuator dynamics introduce a synchronization disparity between commanded displacements transmitted to the actuator and the actual displacements generated by the actuator,thereby affecting its precision and potentially leading to instability in real-time hybrid simulation(RTHS).This study aims to elucidate the relationship between calculated and measured displacements by analyzing their magnitude and phase in the frequency domain via transformations.The physical implications of these relationships are explored in the context of frequency domain evaluation indices(FEI),the transfer function of actuator dynamics,and delay compensation.Formulations for achieving perfect compensation of actuator dynamics are developed,and an enhanced compensation approach,termed improved windowed frequency domain evaluation index-based compensation(IWFEI),is introduced.The efficacy of IWFEI is assessed using a RTHS benchmark model,with perturbed simulations conducted to validate its robustness.Uncertainties inherent in actuator dynamics are represented as random variables in these simulations.Comparative analysis of the mean values and variances of evaluation criteria demonstrates that IWFEI enables more accurate and robust compensation.Furthermore,strong correlations observed among criteria in the time and frequency domains underscore the effectiveness of the proposed frequency domain-based compensation method in mitigating amplitude errors and phase delays in RTHS.展开更多
Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures.While earlier research has emphasized CPU utilization forecasti...Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures.While earlier research has emphasized CPU utilization forecasting,joint prediction of CPU and memory usage under real workload conditions remains underexplored.This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset.The framework combines Extreme Gradient Boosting(XGBoost)with a MultiOutputRegressor(MOR)to capture nonlinear interactions across multiple resource dimensions,supported by a leakage-safe imputation strategy that prevents bias frommissing values.Nested cross-validation was employed to ensure rigorous evaluation and reproducibility.Experiments demonstrated that memory usage can be predicted with higher accuracy and stability than processor usage.Residual error analysis revealed balanced error distributions and very low outlier rates,while regime-based evaluations confirmed robustness across both low and high utilization scenarios.Feature ablation consistently highlighted the central role of page cache memory,which significantly affected predictive performance for both CPU and RAM.Comparisons with baseline models such as linear regression and random forest further underscored the superiority of the proposed approach.To assess adaptability,an online prequential learning pipeline was deployed to simulate continuous operation.The system preserved offline accuracy while dynamically adapting to workload changes.It achieved stable performance with extremely low update latencies,confirming feasibility for deployment in environments where responsiveness and scalability are critical.Overall,the findings demonstrate that simultaneous modeling of CPU and RAM utilization enhances forecasting accuracy and provides actionable insights for cache management,workload scheduling,and dynamic resource allocation.By bridging offline evaluation with online adaptability,the proposed framework offers a practical solution for intelligent and sustainable cloud resource management.展开更多
In the food production sector,quickly identifying potential hazards is crucial due to the resilience of many pathogens,which could lead to wasted production results and,more severely,epidemic outbreaks.E.coli monitori...In the food production sector,quickly identifying potential hazards is crucial due to the resilience of many pathogens,which could lead to wasted production results and,more severely,epidemic outbreaks.E.coli monitoring is essential;however,traditional quality control methods in fish farming are often slow and intrusive,thus promoting an increase in fish stress and mortality rates.This paper presents an alternative method by utilizing a prototype inspired by polarized optical microscopy(POM),constructed with a Raspberry Pi microprocessor to assess pixel patterns and calculate analyte levels.展开更多
[Objective]To investigate the expression of zebrafish vascular endothelial growth factor-2（VEGFR-2） at different developmental stages.[Method]Total RNAs were extracted from 12,24,48,72 and 96 hpf stage zebrafish emb...[Objective]To investigate the expression of zebrafish vascular endothelial growth factor-2（VEGFR-2） at different developmental stages.[Method]Total RNAs were extracted from 12,24,48,72 and 96 hpf stage zebrafish embryos and larvae.Real-time quantitative RT-PCR was performed to examine the expression of VEGFR-2.The data were analyzed by 2^-△△Ct method.[Result]The expression level of VEGFR-2 gene increased gradually from 12 to 72 hpf,and subsequently decreased at 96 hpf.The expression level was lowest at 12 hpf,highest at 72 hpf,and had significant differences when compared with that of other developmental stages.[Conclusion]The expression level of VEGFR-2 increases gradually before blood vessel maturation and decreases as blood vessels mature.展开更多
The extremely high concentrations of PM2.5(particulate matter with an aerodynamic meter≤2.5 mm)during severe and persistent haze events in China have been closely related to the formation of secondary aerosols(SA).Ne...The extremely high concentrations of PM2.5(particulate matter with an aerodynamic meter≤2.5 mm)during severe and persistent haze events in China have been closely related to the formation of secondary aerosols(SA).New particle formation(NPF)is the critical initial step of SA formation.New particles are commonly formed from gas-phase precursors(e.g.,SO2,volatile organic compounds)via nucleation and initial growth,in which molecular clusters with a mobility diameter smaller than 3 nm(hereafter referred to nanoscale molecular clusters)will be involved throughout the whole process.Recently,significant breakthroughs have been obtained on NPF studies,which are mostly attributed to the technical development in the real-time analysis of size-resolved number concentration and chemical composition of nanoscale molecular clusters.Regarding the detection of size-resolved number concentrations of nanoscale molecular clusters,both methods and instruments have been well built up;practical application in laboratory-scale experiments and field measurements have also been successfully demonstrated.In contrast,real-time analysis of chemical composition of nanoscale molecular clusters has still encountered the great challenges caused by the complex organic compositions of the clusters,and improvement of present analytical strategies is urgently required.The better understanding in NPF will not only benefit the atmospheric modeling and climate predictions but also the source control of SA.展开更多
Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The re...Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The result is that knowledge gained can only benefit future responses rather than the response under investigation. This article argues that there is an opportunity to conduct analysis while the response is operational due to the increasing availability of information within hours and days of a disaster event. Hence, this article introduces a methodology for analyzing publicly communicated disaster response information in near-real-time. A classification scheme for the disaster information needs of the public has been developed to facilitate analysis and has led to the establishment of best observed practice standards for content and timeliness. By comparing the information shared with the public within days of a disaster to these standards,information gaps are revealed that can be investigated further. The result is identification of potential deficiencies in communicating critical disaster response information to the public at a time when they can still be corrected.展开更多
Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macro...Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macrophages have been poorly understood and largely overlooked. However, a recent study reported that border-associated macrophages participate in stroke-induced inflammation, although many details and the underlying mechanisms remain unclear. In this study, we performed a comprehensive single-cell analysis of mouse border-associated macrophages using sequencing data obtained from the Gene Expression Omnibus(GEO) database(GSE174574 and GSE225948). Differentially expressed genes were identified, and enrichment analysis was performed to identify the transcription profile of border-associated macrophages. CellChat analysis was conducted to determine the cell communication network of border-associated macrophages. Transcription factors were predicted using the ‘pySCENIC' tool. We found that, in response to hypoxia, borderassociated macrophages underwent dynamic transcriptional changes and participated in the regulation of inflammatory-related pathways. Notably, the tumor necrosis factor pathway was activated by border-associated macrophages following ischemic stroke. The pySCENIC analysis indicated that the activity of signal transducer and activator of transcription 3(Stat3) was obviously upregulated in stroke, suggesting that Stat3 inhibition may be a promising strategy for treating border-associated macrophages-induced neuroinflammation. Finally, we constructed an animal model to investigate the effects of border-associated macrophages depletion following a stroke. Treatment with liposomes containing clodronate significantly reduced infarct volume in the animals and improved neurological scores compared with untreated animals. Taken together, our results demonstrate comprehensive changes in border-associated macrophages following a stroke, providing a theoretical basis for targeting border-associated macrophages-induced neuroinflammation in stroke treatment.展开更多
Six new lanthanide complexes:[Ln(3,4-DEOBA)3(4,4'-DM-2,2'-bipy)]2·2C_(2)H_(5)OH,[Ln=Dy(1),Eu(2),Tb(3),Sm(4),Ho(5),Gd(6);3,4-DEOBA-=3,4-diethoxybenzoate,4,4'-DM-2,2'-bipy=4,4'-dimethyl-2,2'...Six new lanthanide complexes:[Ln(3,4-DEOBA)3(4,4'-DM-2,2'-bipy)]2·2C_(2)H_(5)OH,[Ln=Dy(1),Eu(2),Tb(3),Sm(4),Ho(5),Gd(6);3,4-DEOBA-=3,4-diethoxybenzoate,4,4'-DM-2,2'-bipy=4,4'-dimethyl-2,2'-bipyridine]were successfully synthesized by the volatilization of the solution at room temperature.The crystal structures of six complexes were determined by single-crystal X-ray diffraction technology.The results showed that the complexes all have a binuclear structure,and the structures contain free ethanol molecules.Moreover,the coordination number of the central metal of each structural unit is eight.Adjacent structural units interact with each other through hydrogen bonds and further expand to form 1D chain-like and 2D planar structures.After conducting a systematic study on the luminescence properties of complexes 1-4,their emission and excitation spectra were obtained.Experimental results indicated that the fluorescence lifetimes of complexes 2 and 3 were 0.807 and 0.845 ms,respectively.The emission spectral data of complexes 1-4 were imported into the CIE chromaticity coordinate system,and their corre sponding luminescent regions cover the yellow light,red light,green light,and orange-red light bands,respectively.Within the temperature range of 299.15-1300 K,the thermal decomposition processes of the six complexes were comprehensively analyzed by using TG-DSC/FTIR/MS technology.The hypothesis of the gradual loss of ligand groups during the decomposition process was verified by detecting the escaped gas,3D infrared spectroscopy,and ion fragment information detected by mass spectrometry.The specific decomposition path is as follows:firstly,free ethanol molecules and neutral ligands are removed,and finally,acidic ligands are released;the final product is the corresponding metal oxide.CCDC:2430420,1;2430422,2;2430419,3;2430424,4;2430421,5;2430423,6.展开更多
Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dua...Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.展开更多
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recogni...The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.展开更多
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ...Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.展开更多
Objective:To evaluate the global,regional,and national burden and determinants of Acute Respiratory Infections(ARIs)among children and adolescents from 1990 to 2021.Methods:We analysed ARI mortality and disability-adj...Objective:To evaluate the global,regional,and national burden and determinants of Acute Respiratory Infections(ARIs)among children and adolescents from 1990 to 2021.Methods:We analysed ARI mortality and disability-adjusted life years(DALYs),stratified by age,sex,and economic development level based on data retrieved from the Global Burden of Disease study 2021.Decomposition and frontier analyses were employed to identify key drivers of burden variation and visualize potential reductions based on development levels.Results:Between 1990 and 2021,the global burden of ARIs showed a significant decline in both achievable age-standardized DALYs rate and age-standardized mortality rates(EAPC=-3.87 and-3.81,respectively).Different age groups and sex witnessed different levels of ARI burden,males experienced heavier burden than females and the 0-4 years-old group experienced heavier burden than other study age groups.Most of the 204 countries and territories experienced a downward trend of ARI burden,with slight increases observed only in Lesotho and Dominica.A negative correlation was found between the Socio-demographic Index and ARI burden.Decomposition analysis indicated that the significant decreases in deaths and DALYs were primarily driven by epidemiological changes.Conclusions:The global burden of ARIs among children and adolescents has declined over the past three decades,but substantial regional disparities persist.Targeted public health strategies are needed to address the continued ARI burden in high-risk regions and vulnerable age groups.展开更多
The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial in...The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.展开更多
Red-fleshed fruits are valued for their vibrant color and high anthocyanin content.Pre-harvest fruit bagging enhances fruit peel pigmentation,but its effect on flesh coloration remains poorly characterized.This study ...Red-fleshed fruits are valued for their vibrant color and high anthocyanin content.Pre-harvest fruit bagging enhances fruit peel pigmentation,but its effect on flesh coloration remains poorly characterized.This study revealed that removing bags from‘Gengcunyangtao’red-fleshed peach fruits triggers the rapid and uniform accumulation of anthocyanins in the flesh,resulting in anthocyanin levels that exceed those in unbagged fruits.The exposure to light after bag removal triggered significant increases in anthocyanin levels within 24 h.This was accompanied by the rapid upregulation of light-responsive and flavonoid biosynthetic gene expression levels within 6 h.A metabolomic analysis indicated that anthocyanin precursors,especially p-coumaric acid,accumulated before bag removal,thereby increasing substrate availability for rapid anthocyanin synthesis.On the basis of a weighted gene co-expression network analysis,MYB transcription factors,anthocyanin transporters,glutathione S-transferase,and multidrug and toxic compound extrusion(MATE)were identified as key regulators that coordinate precursor storage along with light-induced transcriptional activation.Notably,PpMYB4 binds to the promoter of PpGSTF14 and activates its expression,thereby promoting anthocyanin accumulation.The study findings elucidated the temporal coordination of metabolic priming and light-responsive transcriptional regulation driving rapid anthocyanin biosynthesis,with possible implications for improving peach fruit flesh coloration.展开更多
文摘The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of months.Although,regulation measures in China have decreased new cases by over 98%,this decrease is not the situation everywhere,and most of the countries still have been affected by it.The objective of this research work is to make a comparative analysis of the top 5 most populated countries namely United States,India,China,Pakistan and Indonesia,from 1st January 2020 to 31st July 2020.This research work also targets to predict an increase in the number of deaths and total infected cases in these five countries.In our research,the performance of the proposed framework is determined by using three Machine Learning(ML)regression algorithms namely Linear Regression(LR),Support Vector Regression(SVR),andRandom Forest(RF)Regression.The proposed model is also validated upon the infected and death cases of further dates.The performance of these three algorithms is compared using the RootMean Square Error(RMSE)metrics.Random Forest algorithm shows best performance as compared to other proposed algorithms,with the lowest RMSE value in the prediction of total infected and total deaths cases for all the top five most populated countries.
基金the National Natural Science Foundation of China(52304236)the Natural Science Foundation of Shandong Province(ZR2021QE076)for the financial support to this research extracted from the project.
文摘Rapid and sensitive detection of dissolved gases in seawater is quite essential for the investigation of the global carbon cycle.Large quantities of in situ optical detection techniques showed restricted measurement efficiency,owing to the single gas sensor without the identification ability of multiple gases.In this work,a novel gas-liquid Raman detection method of monitoring the multi-component dissolved gases was proposed based on a continuous gas-liquid separator under a large difference of partial pressure.The limit of detection(LOD)of the gas Raman spectrometer could arrive at about 14 μl·L^(-1)for N_(2)gas.Moreover,based on the continuous gas-liquid separation process,the detection time of the dissolved gases could be largely decreased to about 200 s compared with that of the traditional detection method(30 min).Effect of equilibrium time on gas-liquid separation process indicated that the extracted efficiency and decay time of these dissolved gases was CO_(2)>O_(2)>N_(2).In addition,the analysis of the relationship between equilibrium time and flow speed indicated that the decay time decreased with the increase of the flow speed.The validation and application of the developed system presented its great potential for studying the components and spatiotemporal distribution of dissolved gases in seawater.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
文摘As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.
文摘BACKGROUND Early detection of precancerous lesions is of vital importance for reducing the incidence and mortality of upper gastrointestinal(UGI)tract cancer.However,traditional endoscopy has certain limitations in detecting precancerous lesions.In contrast,real-time computer-aided detection(CAD)systems enhanced by artificial intelligence(AI)systems,although they may increase unnecessary medical procedures,can provide immediate feedback during examination,thereby improving the accuracy of lesion detection.This article aims to conduct a meta-analysis of the diagnostic performance of CAD systems in identifying precancerous lesions of UGI tract cancer during esophagogastroduodenoscopy(EGD),evaluate their potential clinical application value,and determine the direction for further research.AIM To investigate the improvement of the efficiency of EGD examination by the realtime AI-enabled real-time CAD system(AI-CAD)system.METHODS PubMed,EMBASE,Web of Science and Cochrane Library databases were searched by two independent reviewers to retrieve literature with per-patient analysis with a deadline up until April 2025.A meta-analysis was performed with R Studio software(R4.5.0).A random-effects model was used and subgroup analysis was carried out to identify possible sources of heterogeneity.RESULTS The initial search identified 802 articles.According to the inclusion criteria,2113 patients from 10 studies were included in this meta-analysis.The pooled accuracy difference,logarithmic difference of diagnostic odds ratios,sensitivity,specificity and the area under the summary receiver operating characteristic curve(area under the curve)of both AI group and endoscopist group for detecting precancerous lesion were 0.16(95%CI:0.12-0.20),-0.19(95%CI:-0.75-0.37),0.89(95%CI:0.85-0.92,AI group),0.67(95%CI:0.63-0.71,endoscopist group),0.89(95%CI:0.84-0.93,AI group),0.77(95%CI:0.70-0.83,endoscopist group),0.928(95%CI:0.841-0.948,AI group),0.722(95%CI:0.677-0.821,endoscopist group),respectively.CONCLUSION The present studies further provide evidence that the AI-CAD is a reliable endoscopic diagnostic tool that can be used to assist endoscopists in detection of precancerous lesions in the UGI tract.It may be introduced on a large scale for clinical application to enhance the accuracy of detecting precancerous lesions in the UGI tract.
基金Ministry of Science and Technology of China under Grant No.2023YFC3804300National Science Foundation of China under Grant No.52178114。
文摘Actuator dynamics introduce a synchronization disparity between commanded displacements transmitted to the actuator and the actual displacements generated by the actuator,thereby affecting its precision and potentially leading to instability in real-time hybrid simulation(RTHS).This study aims to elucidate the relationship between calculated and measured displacements by analyzing their magnitude and phase in the frequency domain via transformations.The physical implications of these relationships are explored in the context of frequency domain evaluation indices(FEI),the transfer function of actuator dynamics,and delay compensation.Formulations for achieving perfect compensation of actuator dynamics are developed,and an enhanced compensation approach,termed improved windowed frequency domain evaluation index-based compensation(IWFEI),is introduced.The efficacy of IWFEI is assessed using a RTHS benchmark model,with perturbed simulations conducted to validate its robustness.Uncertainties inherent in actuator dynamics are represented as random variables in these simulations.Comparative analysis of the mean values and variances of evaluation criteria demonstrates that IWFEI enables more accurate and robust compensation.Furthermore,strong correlations observed among criteria in the time and frequency domains underscore the effectiveness of the proposed frequency domain-based compensation method in mitigating amplitude errors and phase delays in RTHS.
文摘Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures.While earlier research has emphasized CPU utilization forecasting,joint prediction of CPU and memory usage under real workload conditions remains underexplored.This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset.The framework combines Extreme Gradient Boosting(XGBoost)with a MultiOutputRegressor(MOR)to capture nonlinear interactions across multiple resource dimensions,supported by a leakage-safe imputation strategy that prevents bias frommissing values.Nested cross-validation was employed to ensure rigorous evaluation and reproducibility.Experiments demonstrated that memory usage can be predicted with higher accuracy and stability than processor usage.Residual error analysis revealed balanced error distributions and very low outlier rates,while regime-based evaluations confirmed robustness across both low and high utilization scenarios.Feature ablation consistently highlighted the central role of page cache memory,which significantly affected predictive performance for both CPU and RAM.Comparisons with baseline models such as linear regression and random forest further underscored the superiority of the proposed approach.To assess adaptability,an online prequential learning pipeline was deployed to simulate continuous operation.The system preserved offline accuracy while dynamically adapting to workload changes.It achieved stable performance with extremely low update latencies,confirming feasibility for deployment in environments where responsiveness and scalability are critical.Overall,the findings demonstrate that simultaneous modeling of CPU and RAM utilization enhances forecasting accuracy and provides actionable insights for cache management,workload scheduling,and dynamic resource allocation.By bridging offline evaluation with online adaptability,the proposed framework offers a practical solution for intelligent and sustainable cloud resource management.
基金European Commission(CZ.10.03.01/00/22-003/0000048)Fundacao para a Ciencia e a Tecnologia(PTDC/EEI-EEE/0415/2021),CICECO(UIDB/50011/2020,UIDP/50011/2020,LA/P/0006/2020)+1 种基金VSB-Technical University of Ostrava(SP2025/039)FCT/MCTES(UI/BD/153066/2022)。
文摘In the food production sector,quickly identifying potential hazards is crucial due to the resilience of many pathogens,which could lead to wasted production results and,more severely,epidemic outbreaks.E.coli monitoring is essential;however,traditional quality control methods in fish farming are often slow and intrusive,thus promoting an increase in fish stress and mortality rates.This paper presents an alternative method by utilizing a prototype inspired by polarized optical microscopy(POM),constructed with a Raspberry Pi microprocessor to assess pixel patterns and calculate analyte levels.
基金Supported by National Natural Science Foundation of Shandong Province (No. SY2008C179)~~
文摘[Objective]To investigate the expression of zebrafish vascular endothelial growth factor-2（VEGFR-2） at different developmental stages.[Method]Total RNAs were extracted from 12,24,48,72 and 96 hpf stage zebrafish embryos and larvae.Real-time quantitative RT-PCR was performed to examine the expression of VEGFR-2.The data were analyzed by 2^-△△Ct method.[Result]The expression level of VEGFR-2 gene increased gradually from 12 to 72 hpf,and subsequently decreased at 96 hpf.The expression level was lowest at 12 hpf,highest at 72 hpf,and had significant differences when compared with that of other developmental stages.[Conclusion]The expression level of VEGFR-2 increases gradually before blood vessel maturation and decreases as blood vessels mature.
基金supported by the National Natural Science Foundation of China(No.21107066)National Instrumentation Program(No.2011YQ170067)Young Teachers Program of Universities in Shanghai(2012).
文摘The extremely high concentrations of PM2.5(particulate matter with an aerodynamic meter≤2.5 mm)during severe and persistent haze events in China have been closely related to the formation of secondary aerosols(SA).New particle formation(NPF)is the critical initial step of SA formation.New particles are commonly formed from gas-phase precursors(e.g.,SO2,volatile organic compounds)via nucleation and initial growth,in which molecular clusters with a mobility diameter smaller than 3 nm(hereafter referred to nanoscale molecular clusters)will be involved throughout the whole process.Recently,significant breakthroughs have been obtained on NPF studies,which are mostly attributed to the technical development in the real-time analysis of size-resolved number concentration and chemical composition of nanoscale molecular clusters.Regarding the detection of size-resolved number concentrations of nanoscale molecular clusters,both methods and instruments have been well built up;practical application in laboratory-scale experiments and field measurements have also been successfully demonstrated.In contrast,real-time analysis of chemical composition of nanoscale molecular clusters has still encountered the great challenges caused by the complex organic compositions of the clusters,and improvement of present analytical strategies is urgently required.The better understanding in NPF will not only benefit the atmospheric modeling and climate predictions but also the source control of SA.
文摘Analysis of a disaster event can identify strengths and weaknesses of the response implemented by the disaster management system;however, analysis does not typically occur until after the response phase is over.The result is that knowledge gained can only benefit future responses rather than the response under investigation. This article argues that there is an opportunity to conduct analysis while the response is operational due to the increasing availability of information within hours and days of a disaster event. Hence, this article introduces a methodology for analyzing publicly communicated disaster response information in near-real-time. A classification scheme for the disaster information needs of the public has been developed to facilitate analysis and has led to the establishment of best observed practice standards for content and timeliness. By comparing the information shared with the public within days of a disaster to these standards,information gaps are revealed that can be investigated further. The result is identification of potential deficiencies in communicating critical disaster response information to the public at a time when they can still be corrected.
基金supported by Qingdao Key Medical and Health Discipline ProjectThe Intramural Research Program of the Affiliated Hospital of Qingdao University,No. 4910Qingdao West Coast New Area Science and Technology Project,No. 2020-55 (all to SW)。
文摘Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macrophages have been poorly understood and largely overlooked. However, a recent study reported that border-associated macrophages participate in stroke-induced inflammation, although many details and the underlying mechanisms remain unclear. In this study, we performed a comprehensive single-cell analysis of mouse border-associated macrophages using sequencing data obtained from the Gene Expression Omnibus(GEO) database(GSE174574 and GSE225948). Differentially expressed genes were identified, and enrichment analysis was performed to identify the transcription profile of border-associated macrophages. CellChat analysis was conducted to determine the cell communication network of border-associated macrophages. Transcription factors were predicted using the ‘pySCENIC' tool. We found that, in response to hypoxia, borderassociated macrophages underwent dynamic transcriptional changes and participated in the regulation of inflammatory-related pathways. Notably, the tumor necrosis factor pathway was activated by border-associated macrophages following ischemic stroke. The pySCENIC analysis indicated that the activity of signal transducer and activator of transcription 3(Stat3) was obviously upregulated in stroke, suggesting that Stat3 inhibition may be a promising strategy for treating border-associated macrophages-induced neuroinflammation. Finally, we constructed an animal model to investigate the effects of border-associated macrophages depletion following a stroke. Treatment with liposomes containing clodronate significantly reduced infarct volume in the animals and improved neurological scores compared with untreated animals. Taken together, our results demonstrate comprehensive changes in border-associated macrophages following a stroke, providing a theoretical basis for targeting border-associated macrophages-induced neuroinflammation in stroke treatment.
文摘Six new lanthanide complexes:[Ln(3,4-DEOBA)3(4,4'-DM-2,2'-bipy)]2·2C_(2)H_(5)OH,[Ln=Dy(1),Eu(2),Tb(3),Sm(4),Ho(5),Gd(6);3,4-DEOBA-=3,4-diethoxybenzoate,4,4'-DM-2,2'-bipy=4,4'-dimethyl-2,2'-bipyridine]were successfully synthesized by the volatilization of the solution at room temperature.The crystal structures of six complexes were determined by single-crystal X-ray diffraction technology.The results showed that the complexes all have a binuclear structure,and the structures contain free ethanol molecules.Moreover,the coordination number of the central metal of each structural unit is eight.Adjacent structural units interact with each other through hydrogen bonds and further expand to form 1D chain-like and 2D planar structures.After conducting a systematic study on the luminescence properties of complexes 1-4,their emission and excitation spectra were obtained.Experimental results indicated that the fluorescence lifetimes of complexes 2 and 3 were 0.807 and 0.845 ms,respectively.The emission spectral data of complexes 1-4 were imported into the CIE chromaticity coordinate system,and their corre sponding luminescent regions cover the yellow light,red light,green light,and orange-red light bands,respectively.Within the temperature range of 299.15-1300 K,the thermal decomposition processes of the six complexes were comprehensively analyzed by using TG-DSC/FTIR/MS technology.The hypothesis of the gradual loss of ligand groups during the decomposition process was verified by detecting the escaped gas,3D infrared spectroscopy,and ion fragment information detected by mass spectrometry.The specific decomposition path is as follows:firstly,free ethanol molecules and neutral ligands are removed,and finally,acidic ligands are released;the final product is the corresponding metal oxide.CCDC:2430420,1;2430422,2;2430419,3;2430424,4;2430421,5;2430423,6.
基金supported by the National Institute of Health(R37CA240806,U01CA288351,and R50CA283816)support from UCI Chao Family Comprehensive Cancer Center(P30CA062203).
文摘Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
文摘The Savitzky-Golay(SG)filter,which employs polynomial least-squares approximations to smooth data and estimate derivatives,is widely used for processing noisy data.However,noise suppression by the SG filter is recognized to be limited at data boundaries and high frequencies,which can significantly reduce the signal-to-noise ratio(SNR).To solve this problem,a novel method synergistically integrating Principal Component Analysis(PCA)with SG filtering is proposed in this paper.This approach avoids the is-sue of excessive smoothing associated with larger window sizes.The proposed PCA-SG filtering algorithm was applied to a CO gas sensing system based on Cavity Ring-Down Spectroscopy(CRDS).The perform-ance of the PCA-SG filtering algorithm is demonstrated through comparison with Moving Average Filtering(MAF),Wavelet Transformation(WT),Kalman Filtering(KF),and the SG filter.The results demonstrate that the proposed algorithm exhibits superior noise reduction capabilities compared to the other algorithms evaluated.The SNR of the ring-down signal was improved from 11.8612 dB to 29.0913 dB,and the stand-ard deviation of the extracted ring-down time constant was reduced from 0.037μs to 0.018μs.These results confirm that the proposed PCA-SG filtering algorithm effectively improves the smoothness of the ring-down curve data,demonstrating its feasibility.
基金the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.
文摘Objective:To evaluate the global,regional,and national burden and determinants of Acute Respiratory Infections(ARIs)among children and adolescents from 1990 to 2021.Methods:We analysed ARI mortality and disability-adjusted life years(DALYs),stratified by age,sex,and economic development level based on data retrieved from the Global Burden of Disease study 2021.Decomposition and frontier analyses were employed to identify key drivers of burden variation and visualize potential reductions based on development levels.Results:Between 1990 and 2021,the global burden of ARIs showed a significant decline in both achievable age-standardized DALYs rate and age-standardized mortality rates(EAPC=-3.87 and-3.81,respectively).Different age groups and sex witnessed different levels of ARI burden,males experienced heavier burden than females and the 0-4 years-old group experienced heavier burden than other study age groups.Most of the 204 countries and territories experienced a downward trend of ARI burden,with slight increases observed only in Lesotho and Dominica.A negative correlation was found between the Socio-demographic Index and ARI burden.Decomposition analysis indicated that the significant decreases in deaths and DALYs were primarily driven by epidemiological changes.Conclusions:The global burden of ARIs among children and adolescents has declined over the past three decades,but substantial regional disparities persist.Targeted public health strategies are needed to address the continued ARI burden in high-risk regions and vulnerable age groups.
基金support from the National Key Research and Development Program of China(No.2024YFB3713705)is acknowledgedWangzhong Mu would like to acknowledge the Strategic Mobility,Sweden(SSF,No.SM22-0039)+1 种基金the Swedish Foundation for International Cooperation in Research and Higher Education(STINT,No.IB2022-9228)the Jernkontoret(Sweden)for supporting this clean steel research.Gonghao Lian would like to acknowledge China Scholarship Council(CSC,No.202306080032).
文摘The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.
基金supported by the Key Scientific and Technological Grant of Zhejiang for Breeding New Agricultural Varieties(Grant No.2021C12066-4)Huzhou Agricultural Science and Technology Innovation Team Project(Grant No.2022HN01).
文摘Red-fleshed fruits are valued for their vibrant color and high anthocyanin content.Pre-harvest fruit bagging enhances fruit peel pigmentation,but its effect on flesh coloration remains poorly characterized.This study revealed that removing bags from‘Gengcunyangtao’red-fleshed peach fruits triggers the rapid and uniform accumulation of anthocyanins in the flesh,resulting in anthocyanin levels that exceed those in unbagged fruits.The exposure to light after bag removal triggered significant increases in anthocyanin levels within 24 h.This was accompanied by the rapid upregulation of light-responsive and flavonoid biosynthetic gene expression levels within 6 h.A metabolomic analysis indicated that anthocyanin precursors,especially p-coumaric acid,accumulated before bag removal,thereby increasing substrate availability for rapid anthocyanin synthesis.On the basis of a weighted gene co-expression network analysis,MYB transcription factors,anthocyanin transporters,glutathione S-transferase,and multidrug and toxic compound extrusion(MATE)were identified as key regulators that coordinate precursor storage along with light-induced transcriptional activation.Notably,PpMYB4 binds to the promoter of PpGSTF14 and activates its expression,thereby promoting anthocyanin accumulation.The study findings elucidated the temporal coordination of metabolic priming and light-responsive transcriptional regulation driving rapid anthocyanin biosynthesis,with possible implications for improving peach fruit flesh coloration.
