Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July...Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July 23 of 2014, this article adopted Pearson correlation coefficient method to determine the relevance among each pollutant of these cities with the help of SPSS. The results showed that such three leading indexes as SO2, PM10 and PM2.5 had strong correlation in Beijing, Tianjin and main cities of Hebei. Finally, some suggestions and preventive measures for the cooperative governance of air pollution in Beijing-Tianjin-Hebei Region were put forward, hoping this can help them.展开更多
Ten upland cotton strains exhibiting 3 fiber quality traits and 8 yield traits, were grown for two years in an investigation of the correlation between grey relational analysis （GRA） and genetic identity in heterosi...Ten upland cotton strains exhibiting 3 fiber quality traits and 8 yield traits, were grown for two years in an investigation of the correlation between grey relational analysis （GRA） and genetic identity in heterosis of cot- ton hybrid. The aim was to establish the optimal approach for heterosis prediction and parent selection. Plant traits data were collected and analyzed for GRA. In addition, 72 simple sequence repeat （SSR） markers were examined and 148 polymorphisms were detected. Correlation analysis of GRA, genetic identity, Ft fiber quality and yield heterosis was conducted. Significant differences were observed between the two analytic methods, whereas compa- rable predictions were given for yield heterosis. GRA for yield exhibited slightly higher correlation than genetic identity analysis, with a correlation coefficient of 0.49. GRA and genetic analysis exhibited overlapping yet dis- tinct advantages in heterosis prediction. Therefore, these analytical methods should be integrated to achieve the op- timal heterosis prediction and parent selection.展开更多
The concept of crisis evolution is still not fully understood, despite over 40 years of research into investigations in the field of crisis and insolvency prediction. This is due to the fact that the financial situati...The concept of crisis evolution is still not fully understood, despite over 40 years of research into investigations in the field of crisis and insolvency prediction. This is due to the fact that the financial situation of a firm changes within an unobservable life cycle continuum, comprising different economic states which are not in fact properly defined. The aim of this study was to contribute towards a better understanding of the differences between solvent and insolvent finns for the periods of one and two years prior to insolvency respectively. Through the application of correlation and factor analysis, an attempt was made to detect behavioral pattems in accounting ratios, which can in turn explain differences and similarities between the two groups of finns. The results of this study show that although accounting ratios from two consecutive years had low correlations for both groups of finns, they were much higher for insolvent firms. This provides evidence that the economic and financial situation of insolvent firms is much more dependent on its history when compared to solvent firms. Moreover, there is evidence to suggest that the change of the economic and fmancial situation of insolvent firms within the life cycle continuum tends to follow a predetermined path, in contrast to the more random nature of a solvent firm＇s behavior. Additionally, the results showed that the factor loadings for solvent and insolvent finns differ for both observation periods, indicating that there are different underlying factors affecting the final outcomes for the two groups of firms. This is mainly attributable to disturbances in the scaling factors of total assets for both observation periods, as well as the disappearing size factor for the pre-distress year for insolvent firms, based on factor analysis.展开更多
BACKGROUND Anxiety,depression,and other negative emotions are common among patients with chronic renal failure(CRF).Analyzing the factors related to negative emotions is necessary to provide targeted nursing care.AIM ...BACKGROUND Anxiety,depression,and other negative emotions are common among patients with chronic renal failure(CRF).Analyzing the factors related to negative emotions is necessary to provide targeted nursing care.AIM To explore the correlations among life satisfaction,pleasure levels,and negative emotions in patients with CRF.METHODS One hundred patients with CRF who received therapy at the First Affiliated Hospital of Jinzhou Medical University between December 2022 and February 2025 were included.The Depression,Anxiety,and Stress Scale(DASS-21),Satisfaction with Life Scale(SWLS),and Temporal Experience of Pleasure Scale(TEPS)were used to evaluate negative emotions,life satisfaction,and pleasure level,respectively.Pearson’s correlation coefficient analyzed the correlation between life satisfaction,pleasure level,and negative emotions.Linear regression analysis identified the factors affecting negative emotions.RESULTS The average DASS-21 score among patients with CRF was 51.90±2.30,with subscale scores of 17.90±1.50 for depression,18.53±1.18 for anxiety,and 15.47±2.36 for stress,all significantly higher than the domestic norm(P<0.05).The average SWLS score was 22.17±4.90.Correlation analysis revealed a negative correlation between the SWLS and total DASS-21 scores(P<0.05),but not with the individual depression,anxiety,or stress dimensions.The average TEPS score was 67.80±8.34.TEPS scores were negatively correlated with the DASS-21 score and the stress dimension(P<0.05),but not with depression or anxiety.Linear regression analysis showed that TEPS scores significantly influenced DASS-21 scores(P<0.05).CONCLUSION Patients with CRF experience high levels of negative emotions,which are negatively correlated with life satisfaction and pleasure.Furthermore,pleasure level had an impact on negative emotions.展开更多
The Haoping 40 m radio telescope at the National Time Service Center,Chinese Academy of Sciences was built in 2014 and is primarily used to observe navigation satellites and pulsars.Since the first successful very lon...The Haoping 40 m radio telescope at the National Time Service Center,Chinese Academy of Sciences was built in 2014 and is primarily used to observe navigation satellites and pulsars.Since the first successful very long baseline interferometry(VLBI)observation of L-band radio source fringes in 2022,ten observations have been made so far.The stations involved in the observations include the Haoping 40 m radio telescope(Haoping),the Tianma 65 m radio telescope(Tianma),the Nanshan 26 m radio telescope(Urumqi),the Guizhou 500 m radio telescope(FAST),the Jilin 13 m radio telescope(Jilin),the Effelsberg 100 m radio telescope(Effelsberg),the Onsala 25 m radio telescope(Onsala),and the Chiang Mai 40 m radio telescope(Chiang Mai).This paper presents details on the specifications of the Haoping 40 m radio telescope,as well as the design of the VLBI experiment,the observation process,and the data processing.We also discuss the analysis of the fringe results involving the Haoping 40 m radio telescope,using Distributed FX Correlator to obtain excellent results.We confirm that the telescope is capable of participating in VLBI observations and performing specific data processing tasks.It can therefore play a greater role in future VLBI observations.展开更多
Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with ...Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with Tajikistan representing a typical example of such disparities.Based on 81 SDG indicators for Tajikistan from 2001 to 2023,this study applied a three-level coupling network framework:at the microscale,it identified synergies and trade-offs between indicators;at the mesoscale,it examined the strength and direction of linkages within four SDG-related components(society,finance,governance,and environment);and at the global level,it focused on the overall SDG interlinkages.Spearman’s rank correlation,sliding window method,and topological properties were employed to analyze the coupling dynamics of SDGs.Results showed that over 70.00%of associations in the global SDG network were of medium-to-low intensity,alongside extremely strong ones(|r|value approached 1.00,where r is the correlation coefficient).SDG interactions were generally limited,with stable local synergy clusters in core livelihood sectors.Network modularity fluctuated,reflecting a cycle of differentiation,integration,and fragmentation,while coupling efficiency varied with the external environment.Each component exhibited distinct functional characteristics.The social component maintained high connectivity through the“poverty alleviation-education-healthcare”loop.The environmental component shifted toward coordinated eco-economic governance.The governance-related component broke interdepartmental barriers,while the financial component showed weak links between resource-based indicators and consumption/employment indicators.Tajikistan’s SDG coupling evolved through three phases:survival-oriented(2001–2012),policy integration(2013–2018),and shock adaptation(2019–2023).These phases were driven by policy changes,resource industries,governance optimization,and external factors.This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.展开更多
Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infec...Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infection,hematoma,and bleeding,with incidence rates of 3–4%.Previous studies have examined individual risk factors separately,but integrated predictive models are lacking.We compared the predictive performance and interpretability of artificial neural network(ANN)and logistic regression models to evaluate their respective strengths in clinical risk assessment.Methods:This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device(CIED)implantation in Taiwan between 2017 and 2018.To address class imbalance and enhance model training,the dataset was augmented to 540 records using the Synthetic Minority Oversampling Technique(SMOTE).A total of 13 clinical risk factors were evaluated(e.g.,age,body mass index(BMI),platelet count,left ventricular ejection fraction(LVEF),prothrombin time/international normalized ratio(PT/INR),hemoglobin(Hb),comorbidities,and antithrombotic use).Results:The most influential risk factors identified by the ANN model were platelet count,PT/INR,LVEF,Hb,and age.In the logistic regression analysis,reduced LVEF,lower hemoglobin levels,prolonged PT/INR,and lower BMI were significantly associated with an increased risk of complications.ANN model achieved a higher area under the curve(AUC=0.952)compared to the logistic regression model(AUC=0.802),indicating superior predictive performance.Additionally,the overall model quality was also higher for the ANN model(0.93)than for logistic regression(0.76).Conclusions:This study demonstrates that ANN models can effectively predict complications associated CIED procedures and identify critical preoperative risk factors.These findings support the use of ANN-based models for individualized risk stratification,enhancing procedural safety,improving patient outcomes,and potentially reducing healthcare costs associated with postoperative complications.展开更多
Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery...Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery of high-entropy alloys(HEAs)comprising multi-principal elements.Owing to the four“core-effects”,these alloys exhibit exceptional properties including better structural stability,high strength and ductility,improved fatigue/fracture toughness,high corrosion and oxidation resistance,superconductiv-ity,magnetic properties,and good thermal properties.Different synthesis routes have been designed and used to meet the properties of interest for particular applications with varying dimensions.How-ever,HEAs are providing new opportunities and challenges for computational modelling of the complex structure-property correlations and in predictions of phase stability necessary for optimum performance of the alloy.Several attempts have been made to understand these alloys by empirical and computa-tional models,and data-driven approaches to accelerate the materials discovery with a desired set of properties.The present review discusses advances and inferences from simulations and models spanning multiple length and time scales explaining a comprehensive set of structure-properties relations.Addi-tionally,the role of machine learning approaches is also reviewed,underscoring the transformative role of computational modelling in unravelling the multifaceted properties and applications of HEAs,and the scope for future efforts in this direction.展开更多
Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the m...Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.展开更多
Objectives:This systematic review and meta-analysis aimed to identify and synthesize the factors correlated with posttraumatic growth(PTG)in patients with colorectal cancer(CRC).Methods:PubMed,Web of Science,Embase,Ps...Objectives:This systematic review and meta-analysis aimed to identify and synthesize the factors correlated with posttraumatic growth(PTG)in patients with colorectal cancer(CRC).Methods:PubMed,Web of Science,Embase,PsycINFO,CINAHL,Cochrane Library,China National Knowledge Infrastructure(CNKI),Wanfang database,China Science and Technology Journal Database(VIP)and SinoMed were searched for studies that reported data on the correlated factors associated with PTG in patients with CRC from inception to September 3,2024.The methodological quality of the included studies was assessed via the Agency for Healthcare Research and Quality(AHRQ)methodology checklist and the Newcastle-Ottawa Scale(NOS).Pearson correlation coefficient(r)was utilized to indicate effect size.Meta-analysis was conducted in R Studio.Results:Thirty-one eligible studies encompassing 6,400 participants were included in this review.Correlated factors were identified to be significantly associated with PTG in patients with CRC including demographic factors:residential area(r=0.13),marital status(r=0.10),employment status(r=0.18),education level(r=0.19),income level(r=0.16);disease-related factors:time since surgery(r=0.17),stoma-related complications(r=0.14),health-promoting behavior(r=0.46),and sexual function(r=0.17);psychosocial factors:confrontation coping(r=0.68),avoidance coping(r=-0.65),deliberate rumination(r=0.56),social support(r=0.47),family function(r=0.50),resilience(r=0.53),selfefficacy(r=0.91),self-compassion(r=-0.32),psychosocial adjustment(r=0.39),gratitude(r=0.45),stigma(r=-0.65),self-perceived burden(r=-0.31),fear of cancer recurrence(r=-0.45);and quality of life(r=0.32).Conclusions:This meta-analysis identified 23 factors associated with PTG in CRC patients.Medical workers can combine those relevant factors from the perspective of positive psychology,further explore the occurrence and development mechanism of PTG,and establish targeted interventions to promote PTG.展开更多
BACKGROUND Resilience is an individual’s ability and psychological rebound capacity to adapt well after experiencing adversity,trauma,etc.Patients with strong resilience can face illnesses actively.AIM To determine t...BACKGROUND Resilience is an individual’s ability and psychological rebound capacity to adapt well after experiencing adversity,trauma,etc.Patients with strong resilience can face illnesses actively.AIM To determine the association of resilience with coping styles and quality of life in patients with malignancies.METHODS This study included patients with malignant tumors who were hospitalized at Fuyang Hospital Affiliated to Anhui Medical University from March 2022 to March 2024.The Connor-Davidson Resilience Scale,Medical Coping Modes Questionnaire,Social Support Rating Scale,and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 were utilized to assess patients’resilience,coping styles,social support,and quality of life,respectively.Pearson correlation analysis was conducted to assess the correlations.RESULTS A total of 175 patients with malignant tumors demonstrated no marked difference in terms of age,education level,employment status,monthly household income,and disease staging(P<0.05).Further,patients with malignancies demonstrated scores of 17.49±1.20,17.27±1.46,and 11.19±1.29 points in terms of coping styles in confrontation,avoidance,and resignation dimensions,respectively.Subjective support,objective support,and support utilization scores in terms of social support were 10.67±1.80,11.26±2.08,and 9.24±1.14 points,respectively.The total resilience score and tenacity,self-improvement,and optimism dimension scores were positively correlatedwith the confrontation coping style score,whereas the total resilience score and tenacity and self-improvementscores were negatively associated with avoidance and resignation coping style scores(P<0.05).The total resiliencescore and the tenacity dimension score were positively associated with physical,role,cognitive,emotional,andsocial functions,as well as global health status(P<0.05),and were inversely related to fatigue,insomnia,andeconomic difficulties(P<0.05).CONCLUSIONThe resilience of patients with malignancies is positively associated with the confrontation dimension in the copingstyle,the total and various social support domain scores,and the overall quality of life.Clinical medical staff needto pay attention to the effect of medical coping styles and social support on the resilience level of patients withmalignancies to further improve their quality of life.展开更多
Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(...Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(MLT)and Abu Simbel(ABS)Egyptian geomagnetic observatories were processed and found to be of good quality.In this study,Egyptian observatory data were tested during both quiet and disturbed events and compared with data from INTERMAGNET observatories worldwide at different latitudes and within a narrow range of longitudes in both hemispheres.This study investigated the relationships between magnetic field components from Egyptian observatories and those from INTERMAGNET observatories using graphical representations of the X components;Pearson’s correlation for the X,Y,Z,and F components;cross-correlation for the X component;and wavelet coherence for the F component.The results of this study showed a high correlation between Egyptian observatories and all utilized INTERMAGNET stations,except those located at high latitudes,during both quiet and disturbed events.Additionally,the study confirmed the observed consistency between Egyptian observatories and selected INTERMAGNET stations.Therefore,Egyptian observatories can feasibly fill the gap in the Middle East and North Africa.展开更多
The Fushan Depression is one of the petroliferous depressions in the Beibuwan Basin,South China Sea.Previous studies have preliminarily explored the origin and source of crude oils in some areas of this depression.Nev...The Fushan Depression is one of the petroliferous depressions in the Beibuwan Basin,South China Sea.Previous studies have preliminarily explored the origin and source of crude oils in some areas of this depression.Nevertheless,no systematic investigations on the classification and origin of oils and hy-drocarbon migration processes have been made for the entire petroleum system in this depression,which has significantly hindered the hydrocarbon exploration in the region.A total of 32 mudstone and 58 oil samples from the Fushan Depression were analyzed to definite the detailed oil-source correlation within the sequence and sedimentary framework.The organic matter of third member of Paleogene Liushagang Formation(Els(3))source rocks,both deltaic and lacustrine mudstone,are algal-dominated with high abundance of C_(23)tricyclic terpane and C_(30)4-methylsteranes.The deltaic source rocks occur-ring in the first member(Els_(1))and second member(Els_(2))of the Paleogene Liushagang Formation are characterized by high abundance of C_(19+20)tricyclic terpane and oleanane,reflecting a more terrestrial plants contribution.While lacustrine source rocks of Els_(1)and Els_(2)display the reduced input of terrige-nous organic matter with relatively low abundance of C 19+20 tricyclic terpane and oleanane.Three types of oils were identified by their biomarker compositions in this study.Most of the oils discovered in the Huachang and Bailian Els_(1)reservoir belong to group A and were derived from lacustrine source rocks of Els_(1)and Els_(2).Group B oils are found within the Els_(1)and Els_(2)reservoirs,showing a close relation to the deltaic source rocks of Els_(1)and Els_(2),respectively.Group C oils,occurring in the Els3 reservoirs,have a good affinity with the Els3 source rocks.The spatial distribution and accumulation of different groups of oils are mainly controlled by the sedimentary facies and specific structural conditions.The Els_(2)reservoir in the Yong'an area belonging to Group B oil,are adjacent to the source kitchen and could be considered as the favorable exploration area in the future.展开更多
Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence o...Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction,contributing to better forecasting methods.The study analyzes data from two solar panels,aSiMicro03036 and aSiTandem72-46,over 7,14,17,21,28,and 38 days,with each dataset comprising five independent and one dependent parameter,and split 80–20 for training and testing.Results indicate that Random Forest consistently outperforms other models,achieving the highest correlation coefficient of 0.9822 and the lowest Mean Absolute Error(MAE)of 2.0544 on the aSiTandem72-46 panel with 21 days of data.For the aSiMicro03036 panel,the best MAE of 4.2978 was reached using the k-Nearest Neighbor(k-NN)algorithm,which was set up as instance-based k-Nearest neighbors(IBk)in Weka after being trained on 17 days of data.Regression performance for most models(excluding IBk)stabilizes at 14 days or more.Compared to the 7-day dataset,increasing to 21 days reduced the MAE by around 20%and improved correlation coefficients by around 2.1%,highlighting the value of moderate dataset expansion.These findings suggest that datasets spanning 17 to 21 days,with 80%used for training,can significantly enhance the predictive accuracy of solar power generation models.展开更多
Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimila...Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging.This is because there is limited knowledge about the cross-component background error covariance(BEC)between LST and atmospheric state variables.This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables,and whether this relationship varies spatially and temporally.To this end,the BEC coupled with atmospheric variables and LST was constructed(LST-BEC),and its characteristics were analyzed based on the 2023 mei-yu season.The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height,and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature.The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background,and also have obvious diurnal variations.These results provide valuable information for strongly coupled land-atmosphere assimilation.展开更多
The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A da...The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.展开更多
Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains...Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains a significant challenge.To enhance the accuracy(ACC)of bird audio detection(BAD)and reduce both false negatives and false positives,this study proposes a BAD method based on a Dual-Feature Enhancement Fusion Model(DFEFM).This method incorporates per-channel energy normalization(PCEN)to suppress noise in the input audio and utilizes mel-frequency cepstral coefficients(MFCC)and frequency correlation matrices(FCM)as input features.It achieves deep feature-level fusion of MFCC and FCM on the channel dimension through two independent multi-layer convolutional network branches,and further integrates Spatial and Channel Synergistic Attention(SCSA)and Multi-Head Attention(MHA)modules to enhance the fusion effect of the aforementioned two deep features.Experimental results on the DCASE2018 BAD dataset show that our proposed method achieved an ACC of 91.4%and an AUC value of 0.963,with false negative and false positive rates of 11.36%and 7.40%,respectively,surpassing existing methods.The method also demonstrated detection ACC above 92%and AUC values above 0.987 on datasets from three sites of different natural scenes in Beijing.Testing on the NVIDIA Jetson Nano indicated that the method achieved an ACC of 89.48%when processing an average of 10 s of audio,with a response time of only 0.557 s,showing excellent processing efficiency.This study provides an effective method for filtering non-bird vocalization audio in bird vocalization monitoring devices,which helps to save edge storage and information transmission costs,and has significant application value for wild bird monitoring and ecological research.展开更多
The effects of plasma screening on the ^(1)P^(o) resonance states of H-and He below the n=3 and n=4 thresholds of the respective subsystemsare investigated using the stabilization method and correlated exponential wav...The effects of plasma screening on the ^(1)P^(o) resonance states of H-and He below the n=3 and n=4 thresholds of the respective subsystemsare investigated using the stabilization method and correlated exponential wave functions.Two plasma mediums,namely,the Debye plasma and quantum plasma environments are considered.The screened Coulomb potential(SCP)obtained from Debye-Hückel model is used to represent Debye plasma environments and the exponential cosine screened Coulomb potential(ECSCP)obtained from a modified Debye-Hückel model is used to represent quantum plasma environments.The resonance parameters(resonance positions and widths)are presented in terms of the screening parameters.展开更多
Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithm...Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.展开更多
文摘Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July 23 of 2014, this article adopted Pearson correlation coefficient method to determine the relevance among each pollutant of these cities with the help of SPSS. The results showed that such three leading indexes as SO2, PM10 and PM2.5 had strong correlation in Beijing, Tianjin and main cities of Hebei. Finally, some suggestions and preventive measures for the cooperative governance of air pollution in Beijing-Tianjin-Hebei Region were put forward, hoping this can help them.
基金Hi-tech Research and Development Program of China(No.2012AA101108-02)National Science and Technology Pillar Program(No.2011BAD35B05)Modern Agro-industry Technology Research System(No.CARS-18-05)
文摘Ten upland cotton strains exhibiting 3 fiber quality traits and 8 yield traits, were grown for two years in an investigation of the correlation between grey relational analysis （GRA） and genetic identity in heterosis of cot- ton hybrid. The aim was to establish the optimal approach for heterosis prediction and parent selection. Plant traits data were collected and analyzed for GRA. In addition, 72 simple sequence repeat （SSR） markers were examined and 148 polymorphisms were detected. Correlation analysis of GRA, genetic identity, Ft fiber quality and yield heterosis was conducted. Significant differences were observed between the two analytic methods, whereas compa- rable predictions were given for yield heterosis. GRA for yield exhibited slightly higher correlation than genetic identity analysis, with a correlation coefficient of 0.49. GRA and genetic analysis exhibited overlapping yet dis- tinct advantages in heterosis prediction. Therefore, these analytical methods should be integrated to achieve the op- timal heterosis prediction and parent selection.
文摘The concept of crisis evolution is still not fully understood, despite over 40 years of research into investigations in the field of crisis and insolvency prediction. This is due to the fact that the financial situation of a firm changes within an unobservable life cycle continuum, comprising different economic states which are not in fact properly defined. The aim of this study was to contribute towards a better understanding of the differences between solvent and insolvent finns for the periods of one and two years prior to insolvency respectively. Through the application of correlation and factor analysis, an attempt was made to detect behavioral pattems in accounting ratios, which can in turn explain differences and similarities between the two groups of finns. The results of this study show that although accounting ratios from two consecutive years had low correlations for both groups of finns, they were much higher for insolvent firms. This provides evidence that the economic and financial situation of insolvent firms is much more dependent on its history when compared to solvent firms. Moreover, there is evidence to suggest that the change of the economic and fmancial situation of insolvent firms within the life cycle continuum tends to follow a predetermined path, in contrast to the more random nature of a solvent firm＇s behavior. Additionally, the results showed that the factor loadings for solvent and insolvent finns differ for both observation periods, indicating that there are different underlying factors affecting the final outcomes for the two groups of firms. This is mainly attributable to disturbances in the scaling factors of total assets for both observation periods, as well as the disappearing size factor for the pre-distress year for insolvent firms, based on factor analysis.
文摘BACKGROUND Anxiety,depression,and other negative emotions are common among patients with chronic renal failure(CRF).Analyzing the factors related to negative emotions is necessary to provide targeted nursing care.AIM To explore the correlations among life satisfaction,pleasure levels,and negative emotions in patients with CRF.METHODS One hundred patients with CRF who received therapy at the First Affiliated Hospital of Jinzhou Medical University between December 2022 and February 2025 were included.The Depression,Anxiety,and Stress Scale(DASS-21),Satisfaction with Life Scale(SWLS),and Temporal Experience of Pleasure Scale(TEPS)were used to evaluate negative emotions,life satisfaction,and pleasure level,respectively.Pearson’s correlation coefficient analyzed the correlation between life satisfaction,pleasure level,and negative emotions.Linear regression analysis identified the factors affecting negative emotions.RESULTS The average DASS-21 score among patients with CRF was 51.90±2.30,with subscale scores of 17.90±1.50 for depression,18.53±1.18 for anxiety,and 15.47±2.36 for stress,all significantly higher than the domestic norm(P<0.05).The average SWLS score was 22.17±4.90.Correlation analysis revealed a negative correlation between the SWLS and total DASS-21 scores(P<0.05),but not with the individual depression,anxiety,or stress dimensions.The average TEPS score was 67.80±8.34.TEPS scores were negatively correlated with the DASS-21 score and the stress dimension(P<0.05),but not with depression or anxiety.Linear regression analysis showed that TEPS scores significantly influenced DASS-21 scores(P<0.05).CONCLUSION Patients with CRF experience high levels of negative emotions,which are negatively correlated with life satisfaction and pleasure.Furthermore,pleasure level had an impact on negative emotions.
基金supported by the National Science and Technology Major Project(E152KJ1201)the Natural Science Basic Research Program of Shaanxi(2024JC-YBQN-0036)+1 种基金the National Natural Science Foundation of China(42030105 and 11973046)the National SKA Program of China(2020SKA0120200).
文摘The Haoping 40 m radio telescope at the National Time Service Center,Chinese Academy of Sciences was built in 2014 and is primarily used to observe navigation satellites and pulsars.Since the first successful very long baseline interferometry(VLBI)observation of L-band radio source fringes in 2022,ten observations have been made so far.The stations involved in the observations include the Haoping 40 m radio telescope(Haoping),the Tianma 65 m radio telescope(Tianma),the Nanshan 26 m radio telescope(Urumqi),the Guizhou 500 m radio telescope(FAST),the Jilin 13 m radio telescope(Jilin),the Effelsberg 100 m radio telescope(Effelsberg),the Onsala 25 m radio telescope(Onsala),and the Chiang Mai 40 m radio telescope(Chiang Mai).This paper presents details on the specifications of the Haoping 40 m radio telescope,as well as the design of the VLBI experiment,the observation process,and the data processing.We also discuss the analysis of the fringe results involving the Haoping 40 m radio telescope,using Distributed FX Correlator to obtain excellent results.We confirm that the telescope is capable of participating in VLBI observations and performing specific data processing tasks.It can therefore play a greater role in future VLBI observations.
文摘Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with Tajikistan representing a typical example of such disparities.Based on 81 SDG indicators for Tajikistan from 2001 to 2023,this study applied a three-level coupling network framework:at the microscale,it identified synergies and trade-offs between indicators;at the mesoscale,it examined the strength and direction of linkages within four SDG-related components(society,finance,governance,and environment);and at the global level,it focused on the overall SDG interlinkages.Spearman’s rank correlation,sliding window method,and topological properties were employed to analyze the coupling dynamics of SDGs.Results showed that over 70.00%of associations in the global SDG network were of medium-to-low intensity,alongside extremely strong ones(|r|value approached 1.00,where r is the correlation coefficient).SDG interactions were generally limited,with stable local synergy clusters in core livelihood sectors.Network modularity fluctuated,reflecting a cycle of differentiation,integration,and fragmentation,while coupling efficiency varied with the external environment.Each component exhibited distinct functional characteristics.The social component maintained high connectivity through the“poverty alleviation-education-healthcare”loop.The environmental component shifted toward coordinated eco-economic governance.The governance-related component broke interdepartmental barriers,while the financial component showed weak links between resource-based indicators and consumption/employment indicators.Tajikistan’s SDG coupling evolved through three phases:survival-oriented(2001–2012),policy integration(2013–2018),and shock adaptation(2019–2023).These phases were driven by policy changes,resource industries,governance optimization,and external factors.This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.
文摘Background:Cardiac implantable electronic devices(CIEDs)are essential for preventing sudden cardiac death in patients with cardiovascular diseases,but implantation procedures carry risks of complications such as infection,hematoma,and bleeding,with incidence rates of 3–4%.Previous studies have examined individual risk factors separately,but integrated predictive models are lacking.We compared the predictive performance and interpretability of artificial neural network(ANN)and logistic regression models to evaluate their respective strengths in clinical risk assessment.Methods:This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device(CIED)implantation in Taiwan between 2017 and 2018.To address class imbalance and enhance model training,the dataset was augmented to 540 records using the Synthetic Minority Oversampling Technique(SMOTE).A total of 13 clinical risk factors were evaluated(e.g.,age,body mass index(BMI),platelet count,left ventricular ejection fraction(LVEF),prothrombin time/international normalized ratio(PT/INR),hemoglobin(Hb),comorbidities,and antithrombotic use).Results:The most influential risk factors identified by the ANN model were platelet count,PT/INR,LVEF,Hb,and age.In the logistic regression analysis,reduced LVEF,lower hemoglobin levels,prolonged PT/INR,and lower BMI were significantly associated with an increased risk of complications.ANN model achieved a higher area under the curve(AUC=0.952)compared to the logistic regression model(AUC=0.802),indicating superior predictive performance.Additionally,the overall model quality was also higher for the ANN model(0.93)than for logistic regression(0.76).Conclusions:This study demonstrates that ANN models can effectively predict complications associated CIED procedures and identify critical preoperative risk factors.These findings support the use of ANN-based models for individualized risk stratification,enhancing procedural safety,improving patient outcomes,and potentially reducing healthcare costs associated with postoperative complications.
基金the Science and Engineering Re-search Board(SERB),India for providing the financial assistance to support this work(Project No.SRG/2020/002449).
文摘Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery of high-entropy alloys(HEAs)comprising multi-principal elements.Owing to the four“core-effects”,these alloys exhibit exceptional properties including better structural stability,high strength and ductility,improved fatigue/fracture toughness,high corrosion and oxidation resistance,superconductiv-ity,magnetic properties,and good thermal properties.Different synthesis routes have been designed and used to meet the properties of interest for particular applications with varying dimensions.How-ever,HEAs are providing new opportunities and challenges for computational modelling of the complex structure-property correlations and in predictions of phase stability necessary for optimum performance of the alloy.Several attempts have been made to understand these alloys by empirical and computa-tional models,and data-driven approaches to accelerate the materials discovery with a desired set of properties.The present review discusses advances and inferences from simulations and models spanning multiple length and time scales explaining a comprehensive set of structure-properties relations.Addi-tionally,the role of machine learning approaches is also reviewed,underscoring the transformative role of computational modelling in unravelling the multifaceted properties and applications of HEAs,and the scope for future efforts in this direction.
文摘Ten physical and environmental variables collected from an on-the-go soil sensor at two field sites (MF3E and MF11S) in Mississippi, USA, were analyzed to assess soil variability and the interrelationships among the measurements. At MF3E, moderate variability was observed in apparent electrical conductivity shallow (ECas), slope, and ECa ratio measurements, with coefficients of variation ranging from 20% to 27%. In contrast, MF11S exhibited higher variability, particularly in ECas and ECad (deep) measurements, which exceeded 30% in their coefficient of variation values, indicating significant differences in soil composition and moisture content. Correlation analysis revealed strong positive relationships between the near-infrared-to-red ratio and red reflectance (r = 0.897***) soil values at MF3E. MF11S demonstrated a strong negative correlation between ECas and ECad readings with the x-coordinate (r ***). Scatter plots and fitted models illustrated the complexity of relationships, with many showing nonlinear trends. These findings emphasize the need for continuous monitoring and advanced modeling to understand the dynamic nature of soil properties and their implications for agricultural practices. Future research should explore the underlying mechanisms driving variability in the soil characteristics to enhance soil management strategies at the study sites.
基金supported by the‘Double First-Class’Construction Specialized Discipline Project at Zhejiang University(No HL2024012).
文摘Objectives:This systematic review and meta-analysis aimed to identify and synthesize the factors correlated with posttraumatic growth(PTG)in patients with colorectal cancer(CRC).Methods:PubMed,Web of Science,Embase,PsycINFO,CINAHL,Cochrane Library,China National Knowledge Infrastructure(CNKI),Wanfang database,China Science and Technology Journal Database(VIP)and SinoMed were searched for studies that reported data on the correlated factors associated with PTG in patients with CRC from inception to September 3,2024.The methodological quality of the included studies was assessed via the Agency for Healthcare Research and Quality(AHRQ)methodology checklist and the Newcastle-Ottawa Scale(NOS).Pearson correlation coefficient(r)was utilized to indicate effect size.Meta-analysis was conducted in R Studio.Results:Thirty-one eligible studies encompassing 6,400 participants were included in this review.Correlated factors were identified to be significantly associated with PTG in patients with CRC including demographic factors:residential area(r=0.13),marital status(r=0.10),employment status(r=0.18),education level(r=0.19),income level(r=0.16);disease-related factors:time since surgery(r=0.17),stoma-related complications(r=0.14),health-promoting behavior(r=0.46),and sexual function(r=0.17);psychosocial factors:confrontation coping(r=0.68),avoidance coping(r=-0.65),deliberate rumination(r=0.56),social support(r=0.47),family function(r=0.50),resilience(r=0.53),selfefficacy(r=0.91),self-compassion(r=-0.32),psychosocial adjustment(r=0.39),gratitude(r=0.45),stigma(r=-0.65),self-perceived burden(r=-0.31),fear of cancer recurrence(r=-0.45);and quality of life(r=0.32).Conclusions:This meta-analysis identified 23 factors associated with PTG in CRC patients.Medical workers can combine those relevant factors from the perspective of positive psychology,further explore the occurrence and development mechanism of PTG,and establish targeted interventions to promote PTG.
文摘BACKGROUND Resilience is an individual’s ability and psychological rebound capacity to adapt well after experiencing adversity,trauma,etc.Patients with strong resilience can face illnesses actively.AIM To determine the association of resilience with coping styles and quality of life in patients with malignancies.METHODS This study included patients with malignant tumors who were hospitalized at Fuyang Hospital Affiliated to Anhui Medical University from March 2022 to March 2024.The Connor-Davidson Resilience Scale,Medical Coping Modes Questionnaire,Social Support Rating Scale,and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 were utilized to assess patients’resilience,coping styles,social support,and quality of life,respectively.Pearson correlation analysis was conducted to assess the correlations.RESULTS A total of 175 patients with malignant tumors demonstrated no marked difference in terms of age,education level,employment status,monthly household income,and disease staging(P<0.05).Further,patients with malignancies demonstrated scores of 17.49±1.20,17.27±1.46,and 11.19±1.29 points in terms of coping styles in confrontation,avoidance,and resignation dimensions,respectively.Subjective support,objective support,and support utilization scores in terms of social support were 10.67±1.80,11.26±2.08,and 9.24±1.14 points,respectively.The total resilience score and tenacity,self-improvement,and optimism dimension scores were positively correlatedwith the confrontation coping style score,whereas the total resilience score and tenacity and self-improvementscores were negatively associated with avoidance and resignation coping style scores(P<0.05).The total resiliencescore and the tenacity dimension score were positively associated with physical,role,cognitive,emotional,andsocial functions,as well as global health status(P<0.05),and were inversely related to fatigue,insomnia,andeconomic difficulties(P<0.05).CONCLUSIONThe resilience of patients with malignancies is positively associated with the confrontation dimension in the copingstyle,the total and various social support domain scores,and the overall quality of life.Clinical medical staff needto pay attention to the effect of medical coping styles and social support on the resilience level of patients withmalignancies to further improve their quality of life.
文摘Geomagnetic observatory data are crucial for all branches of geophysics because they can contribute to earthquake research by detecting anomalies in the Earth’s magnetic field.Recently,data records from the Misallat(MLT)and Abu Simbel(ABS)Egyptian geomagnetic observatories were processed and found to be of good quality.In this study,Egyptian observatory data were tested during both quiet and disturbed events and compared with data from INTERMAGNET observatories worldwide at different latitudes and within a narrow range of longitudes in both hemispheres.This study investigated the relationships between magnetic field components from Egyptian observatories and those from INTERMAGNET observatories using graphical representations of the X components;Pearson’s correlation for the X,Y,Z,and F components;cross-correlation for the X component;and wavelet coherence for the F component.The results of this study showed a high correlation between Egyptian observatories and all utilized INTERMAGNET stations,except those located at high latitudes,during both quiet and disturbed events.Additionally,the study confirmed the observed consistency between Egyptian observatories and selected INTERMAGNET stations.Therefore,Egyptian observatories can feasibly fill the gap in the Middle East and North Africa.
基金funded by the South Oil Exploration and Development Company of PetroChina(2021-HNYJ-010).
文摘The Fushan Depression is one of the petroliferous depressions in the Beibuwan Basin,South China Sea.Previous studies have preliminarily explored the origin and source of crude oils in some areas of this depression.Nevertheless,no systematic investigations on the classification and origin of oils and hy-drocarbon migration processes have been made for the entire petroleum system in this depression,which has significantly hindered the hydrocarbon exploration in the region.A total of 32 mudstone and 58 oil samples from the Fushan Depression were analyzed to definite the detailed oil-source correlation within the sequence and sedimentary framework.The organic matter of third member of Paleogene Liushagang Formation(Els(3))source rocks,both deltaic and lacustrine mudstone,are algal-dominated with high abundance of C_(23)tricyclic terpane and C_(30)4-methylsteranes.The deltaic source rocks occur-ring in the first member(Els_(1))and second member(Els_(2))of the Paleogene Liushagang Formation are characterized by high abundance of C_(19+20)tricyclic terpane and oleanane,reflecting a more terrestrial plants contribution.While lacustrine source rocks of Els_(1)and Els_(2)display the reduced input of terrige-nous organic matter with relatively low abundance of C 19+20 tricyclic terpane and oleanane.Three types of oils were identified by their biomarker compositions in this study.Most of the oils discovered in the Huachang and Bailian Els_(1)reservoir belong to group A and were derived from lacustrine source rocks of Els_(1)and Els_(2).Group B oils are found within the Els_(1)and Els_(2)reservoirs,showing a close relation to the deltaic source rocks of Els_(1)and Els_(2),respectively.Group C oils,occurring in the Els3 reservoirs,have a good affinity with the Els3 source rocks.The spatial distribution and accumulation of different groups of oils are mainly controlled by the sedimentary facies and specific structural conditions.The Els_(2)reservoir in the Yong'an area belonging to Group B oil,are adjacent to the source kitchen and could be considered as the favorable exploration area in the future.
文摘Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction,contributing to better forecasting methods.The study analyzes data from two solar panels,aSiMicro03036 and aSiTandem72-46,over 7,14,17,21,28,and 38 days,with each dataset comprising five independent and one dependent parameter,and split 80–20 for training and testing.Results indicate that Random Forest consistently outperforms other models,achieving the highest correlation coefficient of 0.9822 and the lowest Mean Absolute Error(MAE)of 2.0544 on the aSiTandem72-46 panel with 21 days of data.For the aSiMicro03036 panel,the best MAE of 4.2978 was reached using the k-Nearest Neighbor(k-NN)algorithm,which was set up as instance-based k-Nearest neighbors(IBk)in Weka after being trained on 17 days of data.Regression performance for most models(excluding IBk)stabilizes at 14 days or more.Compared to the 7-day dataset,increasing to 21 days reduced the MAE by around 20%and improved correlation coefficients by around 2.1%,highlighting the value of moderate dataset expansion.These findings suggest that datasets spanning 17 to 21 days,with 80%used for training,can significantly enhance the predictive accuracy of solar power generation models.
基金sponsored by the National Natural Science Foundation of China[grant number U2442218]。
文摘Land surface temperature(LST)is the key variable in land-atmosphere interaction,having an important impact on weather and climate forecasting.However,achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging.This is because there is limited knowledge about the cross-component background error covariance(BEC)between LST and atmospheric state variables.This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables,and whether this relationship varies spatially and temporally.To this end,the BEC coupled with atmospheric variables and LST was constructed(LST-BEC),and its characteristics were analyzed based on the 2023 mei-yu season.The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height,and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature.The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background,and also have obvious diurnal variations.These results provide valuable information for strongly coupled land-atmosphere assimilation.
基金supported by the Basic Ability Improvement Project of Young and Middle-Aged Teachers in Colleges and Universities of Guangxi(2022KY1922,2021KY1938).
文摘The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.
基金supported by the Beijing Natural Science Foundation(5252014)the National Natural Science Foundation of China(62303063)。
文摘Passive acoustic monitoring(PAM)technology is increasingly becoming one of the mainstream methods for bird monitoring.However,detecting bird audio within complex natural acoustic environments using PAM devices remains a significant challenge.To enhance the accuracy(ACC)of bird audio detection(BAD)and reduce both false negatives and false positives,this study proposes a BAD method based on a Dual-Feature Enhancement Fusion Model(DFEFM).This method incorporates per-channel energy normalization(PCEN)to suppress noise in the input audio and utilizes mel-frequency cepstral coefficients(MFCC)and frequency correlation matrices(FCM)as input features.It achieves deep feature-level fusion of MFCC and FCM on the channel dimension through two independent multi-layer convolutional network branches,and further integrates Spatial and Channel Synergistic Attention(SCSA)and Multi-Head Attention(MHA)modules to enhance the fusion effect of the aforementioned two deep features.Experimental results on the DCASE2018 BAD dataset show that our proposed method achieved an ACC of 91.4%and an AUC value of 0.963,with false negative and false positive rates of 11.36%and 7.40%,respectively,surpassing existing methods.The method also demonstrated detection ACC above 92%and AUC values above 0.987 on datasets from three sites of different natural scenes in Beijing.Testing on the NVIDIA Jetson Nano indicated that the method achieved an ACC of 89.48%when processing an average of 10 s of audio,with a response time of only 0.557 s,showing excellent processing efficiency.This study provides an effective method for filtering non-bird vocalization audio in bird vocalization monitoring devices,which helps to save edge storage and information transmission costs,and has significant application value for wild bird monitoring and ecological research.
基金Supported by the Natural Science Foundation of Heilongjiang Province(LH2024A025)。
文摘The effects of plasma screening on the ^(1)P^(o) resonance states of H-and He below the n=3 and n=4 thresholds of the respective subsystemsare investigated using the stabilization method and correlated exponential wave functions.Two plasma mediums,namely,the Debye plasma and quantum plasma environments are considered.The screened Coulomb potential(SCP)obtained from Debye-Hückel model is used to represent Debye plasma environments and the exponential cosine screened Coulomb potential(ECSCP)obtained from a modified Debye-Hückel model is used to represent quantum plasma environments.The resonance parameters(resonance positions and widths)are presented in terms of the screening parameters.
基金supported in part by the National Natural Science Foundation of China (No. U23B2011)。
文摘Video snapshot compressive imaging(Video SCI) modulates scenes using various encoding masks and captures compressed measurements with a low-speed camera during a single exposure. Subsequently, reconstruction algorithms restore image sequences of dynamic scenes, offering advantages such as reduced bandwidth and storage space requirements. The temporal correlation in video data is crucial for Video SCI, as it leverages the temporal relationships among frames to enhance the efficiency and quality of reconstruction algorithms, particularly for fast-moving objects.This paper discretizes video frames to create image datasets with the same data volume but differing temporal correlations. We utilized the state-of-the-art(SOTA) reconstruction framework, EfficientSCI++, to train various compressed reconstruction models with these differing temporal correlations. Evaluating the reconstruction results from these models, our simulation experiments confirm that a reduction in temporal correlation leads to decreased reconstruction accuracy. Additionally, we simulated the reconstruction outcomes of datasets devoid of temporal correlation, illustrating that models trained on non-temporal data affect the temporal feature extraction capabilities of transformers, resulting in negligible impacts on the evaluation of reconstruction results for non-temporal correlation test datasets.
