Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanal...Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanalysis database(ERA5)is used.Seeing calculated from ERA5 is compared consistently with the Differential Image Motion Monitor seeing at the height of 12 m.Results show that seeing decays exponentially with height at the Muztagh-Ata site.Seeing decays the fastest in fall in 2021 and most slowly with height in summer.The seeing condition is better in fall than in summer.The median value of seeing at 12 m is 0.89 arcsec,the maximum value is1.21 arcsec in August and the minimum is 0.66 arcsec in October.The median value of seeing at 12 m is 0.72arcsec in the nighttime and 1.08 arcsec in the daytime.Seeing is a combination of annual and about biannual variations with the same phase as temperature and wind speed indicating that seeing variation with time is influenced by temperature and wind speed.The Richardson number Ri is used to analyze the atmospheric stability and the variations of seeing are consistent with Ri between layers.These quantitative results can provide an important reference for a telescopic observation strategy.展开更多
Peanut allergy is majorly related to severe food induced allergic reactions.Several food including cow's milk,hen's eggs,soy,wheat,peanuts,tree nuts(walnuts,hazelnuts,almonds,cashews,pecans and pistachios),fis...Peanut allergy is majorly related to severe food induced allergic reactions.Several food including cow's milk,hen's eggs,soy,wheat,peanuts,tree nuts(walnuts,hazelnuts,almonds,cashews,pecans and pistachios),fish and shellfish are responsible for more than 90%of food allergies.Here,we provide promising insights using a large-scale data-driven analysis,comparing the mechanistic feature and biological relevance of different ingredients presents in peanuts,tree nuts(walnuts,almonds,cashews,pecans and pistachios)and soybean.Additionally,we have analysed the chemical compositions of peanuts in different processed form raw,boiled and dry-roasted.Using the data-driven approach we are able to generate new hypotheses to explain why nuclear receptors like the peroxisome proliferator-activated receptors(PPARs)and its isoform and their interaction with dietary lipids may have significant effect on allergic response.The results obtained from this study will direct future experimeantal and clinical studies to understand the role of dietary lipids and PPARisoforms to exert pro-inflammatory or anti-inflammatory functions on cells of the innate immunity and influence antigen presentation to the cells of the adaptive immunity.展开更多
Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision...Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.展开更多
This research paper compares Excel and R language for data analysis and concludes that R language is more suitable for complex data analysis tasks.R language’s open-source nature makes it accessible to everyone,and i...This research paper compares Excel and R language for data analysis and concludes that R language is more suitable for complex data analysis tasks.R language’s open-source nature makes it accessible to everyone,and its powerful data management and analysis tools make it suitable for handling complex data analysis tasks.It is also highly customizable,allowing users to create custom functions and packages to meet their specific needs.Additionally,R language provides high reproducibility,making it easy to replicate and verify research results,and it has excellent collaboration capabilities,enabling multiple users to work on the same project simultaneously.These advantages make R language a more suitable choice for complex data analysis tasks,particularly in scientific research and business applications.The findings of this study will help people understand that R is not just a language that can handle more data than Excel and demonstrate that r is essential to the field of data analysis.At the same time,it will also help users and organizations make informed decisions regarding their data analysis needs and software preferences.展开更多
Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive test.It can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire body.Therefore,ECG has been wi...Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive test.It can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire body.Therefore,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric recognition.In recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis techniques.Here we systematically summarize and analyze the ECGbased automatic analysis methods and applications.Specifically,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes.Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications.Finally,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.展开更多
The application of single-cell RNA sequencing(scRNA-seq)in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategie...The application of single-cell RNA sequencing(scRNA-seq)in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies.With the expansion of capacity for high-throughput scRNA-seq,including clinical samples,the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field.Here,we review the workflow for typical scRNA-seq data analysis,covering raw data processing and quality control,basic data analysis applicable for almost all scRNA-seq data sets,and advanced data analysis that should be tailored to specific scientific questions.While summarizing the current methods for each analysis step,we also provide an online repository of software and wrapped-up scripts to support the implementation.Recommendations and caveats are pointed out for some specific analysis tasks and approaches.We hope this resource will be helpful to researchers engaging with scRNA-seq,in particular for emerging clinical applications.展开更多
In the nonparametric data envelopment analysis literature,scale elasticity is evaluated in two alternative ways:using either the technical efficiency model or the cost efficiency model.This evaluation becomes problema...In the nonparametric data envelopment analysis literature,scale elasticity is evaluated in two alternative ways:using either the technical efficiency model or the cost efficiency model.This evaluation becomes problematic in several situations,for example(a)when input proportions change in the long run,(b)when inputs are heterogeneous,and(c)when firms face ex-ante price uncertainty in making their production decisions.To address these situations,a scale elasticity evaluation was performed using a value-based cost efficiency model.However,this alternative value-based scale elasticity evaluation is sensitive to the uncertainty and variability underlying input and output data.Therefore,in this study,we introduce a stochastic cost-efficiency model based on chance-constrained programming to develop a value-based measure of the scale elasticity of firms facing data uncertainty.An illustrative empirical application to the Indian banking industry comprising 71 banks for eight years(1998–2005)was made to compare inferences about their efficiency and scale properties.The key findings are as follows:First,both the deterministic model and our proposed stochastic model yield distinctly different results concerning the efficiency and scale elasticity scores at various tolerance levels of chance constraints.However,both models yield the same results at a tolerance level of 0.5,implying that the deterministic model is a special case of the stochastic model in that it reveals the same efficiency and returns to scale characterizations of banks.Second,the stochastic model generates higher efficiency scores for inefficient banks than its deterministic counterpart.Third,public banks exhibit higher efficiency than private and foreign banks.Finally,public and old private banks mostly exhibit either decreasing or constant returns to scale,whereas foreign and new private banks experience either increasing or decreasing returns to scale.Although the application of our proposed stochastic model is illustrative,it can be potentially applied to all firms in the information and distribution-intensive industry with high fixed costs,which have ample potential for reaping scale and scope benefits.展开更多
As COVID-19 poses a major threat to people’s health and economy,there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently.In non-stationary time series forecasting jobs,ther...As COVID-19 poses a major threat to people’s health and economy,there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently.In non-stationary time series forecasting jobs,there is frequently a hysteresis in the anticipated values relative to the real values.The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network(MDTCNet)for COVID-19 prediction to address this problem.In particular,it is possible to record the deep features and temporal dependencies in uncertain time series,and the features may then be combined using a feature fusion network and a multilayer perceptron.Last but not least,the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty,realizing the short-term and long-term prediction of COVID-19 daily confirmed cases,and verifying the effectiveness and accuracy of the suggested prediction method,as well as reducing the hysteresis of the prediction results.展开更多
Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in ...Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in the received VLF signal.This study uses the VLF signal received in Qingdao City,Shandong Province,from the Russian Alpha navigation system to explore the multimode interference problem of VLF signal propagation.The characteristics of the effect of multimode interference phenomena on the phase are analyzed according to the variation of the phase of the VLF signal.However,the phase of VLF signals will also be affected by the X-ray and energetic particles that are released during the eruption of solar flares,therefore the two phenomena are studied in this work.It is concluded that the X-ray will not affect the phase of VLF signals at night,but the energetic particles will affect the phase change,and the influence of energetic particles should be excluded in the study of multimode interference phenomena.Using VLF signals for navigation positioning in degraded or unavailable GPS conditions is of great practical significance for VLF navigation systems as it can avoid the influence of multimode interference and improve positioning accuracy.展开更多
Human living would be impossible without air quality. Consistent advancements in practically every aspect of contemporary human life have harmed air quality. Everyday industrial, transportation, and home activities tu...Human living would be impossible without air quality. Consistent advancements in practically every aspect of contemporary human life have harmed air quality. Everyday industrial, transportation, and home activities turn up dangerous contaminants in our surroundings. This study investigated two years’ worth of air quality and outlier detection data from two Indian cities. Studies on air pollution have used numerous types of methodologies, with various gases being seen as a vector whose components include gas concentration values for each observation per-formed. We use curves to represent the monthly average of daily gas emissions in our technique. The approach, which is based on functional depth, was used to find outliers in the city of Delhi and Kolkata’s gas emissions, and the outcomes were compared to those from the traditional method. In the evaluation and comparison of these models’ performances, the functional approach model studied well.展开更多
Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometri...Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.展开更多
This article presents a comprehensive analysis of the current state of research on the English translation of Lu You’s poetry, utilizing a data sample comprising research papers published in the CNKI Full-text Databa...This article presents a comprehensive analysis of the current state of research on the English translation of Lu You’s poetry, utilizing a data sample comprising research papers published in the CNKI Full-text Database from 2001 to 2022. Employing rigorous longitudinal statistical methods, the study examines the progress achieved over the past two decades. Notably, domestic researchers have displayed considerable interest in the study of Lu You’s English translation works since 2001. The research on the English translation of Lu You’s poetry reveals a diverse range of perspectives, indicating a rich body of scholarship. However, several challenges persist, including insufficient research, limited translation coverage, and a noticeable focus on specific poems such as “Phoenix Hairpin” in the realm of English translation research. Consequently, there is ample room for improvement in the quality of research output on the English translation of Lu You’s poems, as well as its recognition within the academic community. Building on these findings, it is argued that future investigations pertaining to the English translation of Lu You’s poetry should transcend the boundaries of textual analysis and encompass broader theoretical perspectives and research methodologies. By undertaking this shift, scholars will develop a more profound comprehension of Lu You’s poetic works and make substantive contributions to the field of translation studies. Thus, this article aims to bridge the gap between past research endeavors and future possibilities, serving as a guide and inspiration for scholars to embark on a more nuanced and enriching exploration of Lu You’s poetry as well as other Chinese literature classics.展开更多
To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRD...To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRDP can perform operations such as baseband data unpacking,channel separation,coherent dedispersion,Stokes detection,phase and folding period prediction,and folding integration in GPU clusters.We tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes,and the J0332+5434 pulsar baseband data generated by the self-developed backend of the Nan Shan Radio Telescope.We obtained the pulse profiles of each baseband data.Through experimental analysis,we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy(DSPSR),which verified the effectiveness of the PSRDP algorithm.Furthermore,using the same baseband data,we compared the processing speed of PSRDP with DSPSR,and the results showed that PSRDP was not slower than DSPSR in terms of speed.The theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT(Qi Tai radio Telescope)ultra-wide bandwidth pulsar baseband data.展开更多
Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for rep...Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.展开更多
Highly turbulent water flows,often encountered near human constructions like bridge piers,spillways,and weirs,display intricate dynamics characterized by the formation of eddies and vortices.These formations,varying i...Highly turbulent water flows,often encountered near human constructions like bridge piers,spillways,and weirs,display intricate dynamics characterized by the formation of eddies and vortices.These formations,varying in sizes and lifespans,significantly influence the distribution of fluid velocities within the flow.Subsequently,the rapid velocity fluctuations in highly turbulent flows lead to elevated shear and normal stress levels.For this reason,to meticulously study these dynamics,more often than not,physical modeling is employed for studying the impact of turbulent flows on the stability and longevity of nearby structures.Despite the effectiveness of physical modeling,various monitoring challenges arise,including flow disruption,the necessity for concurrent gauging at multiple locations,and the duration of measurements.Addressing these challenges,image velocimetry emerges as an ideal method in fluid mechanics,particularly for studying turbulent flows.To account for measurement duration,a probabilistic approach utilizing a probability density function(PDF)is suggested to mitigate uncertainty in estimated average and maximum values.However,it becomes evident that deriving the PDF is not straightforward for all turbulence-induced stresses.In response,this study proposes a novel approach by combining image velocimetry with a stochastic model to provide a generic yet accurate description of flow dynamics in such applications.This integration enables an approach based on the probability of failure,facilitating a more comprehensive analysis of turbulent flows.Such an approach is essential for estimating both short-and long-term stresses on hydraulic constructions under assessment.展开更多
Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for rese...Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.展开更多
Android smartphones largely dominate the smartphone market. For this reason, it is very important to examine these smartphones in terms of digital forensics since they are often used as evidence in trials. It is possi...Android smartphones largely dominate the smartphone market. For this reason, it is very important to examine these smartphones in terms of digital forensics since they are often used as evidence in trials. It is possible to acquire a physical or logical image of these devices. Acquiring physical and logical images has advantages and disadvantages compared to each other. Creating the logical image is done at the file system level. Analysis can be made on this logical image. Both logical image acquisition and analysis of the image can be done by software tools. In this study, the differences between logical image and physical image acquisition in Android smartphones, their advantages and disadvantages compared to each other, the difficulties that may be encountered in obtaining physical images, which type of image contributes to obtaining more useful and effective data, which one should be preferred for different conditions, and the benefits of having root authority are discussed. The practice of getting the logical image of the Android smartphones and making an analysis on the image is also included. Although root privileges are not required for logical image acquisition, it has been observed that very limited data will be obtained with the logical image created without root privileges. Nevertheless, logical image acquisition has advantages too against physical image acquisition.展开更多
To enhance the safety of road traffic operations,this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics.Initially,the process involved data cleaning,trans...To enhance the safety of road traffic operations,this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics.Initially,the process involved data cleaning,transformation,and normalization.Subsequently,various classification models were constructed,including logistic regression,k-nearest neighbors,gradient boosting,decision trees,AdaBoost,and extra trees models.Evaluation metrics such as accuracy,precision,recall,F1 score,and Hamming loss were employed.Upon analysis,the passive-aggressive classifier model exhibited superior comprehensive indices compared to other models.Based on the model’s output results,an in-depth examination of the factors influencing traffic accidents was conducted.Additionally,measures and suggestions aimed at reducing the incidence of severe traffic accidents were presented.These findings served as a valuable reference for mitigating the occurrence of traffic accidents.展开更多
This paper presents a comprehensive analysis of global human trafficking trends over a twenty-year period, leveraging a robust dataset from the Counter Trafficking Data Collaborative (CTDC). The study unfolds in a sys...This paper presents a comprehensive analysis of global human trafficking trends over a twenty-year period, leveraging a robust dataset from the Counter Trafficking Data Collaborative (CTDC). The study unfolds in a systematic manner, beginning with a detailed data collection phase, where ethical and legal standards for data usage and privacy are strictly observed. Following collection, the data undergoes a rigorous preprocessing stage, involving cleaning, integration, transformation, and normalization to ensure accuracy and consistency for analysis. The analytical phase employs time-series analysis to delineate historical trends and utilizes predictive modeling to forecast future trajectories of human trafficking using the advanced analytical capabilities of Power BI. A comparative analysis across regions—Africa, the Americas, Asia, and Europe—is conducted to identify and visualize the distribution of human trafficking, dissecting the data by victim demographics, types of exploitation, and duration of victimization. The findings of this study not only offer a descriptive and predictive outlook on trafficking patterns but also provide insights into the regional nuances that influence these trends. The article underscores the prevalence and persistence of human trafficking, identifies factors contributing to its evolution, and discusses the implications for policy and law enforcement. By integrating a methodological approach with quantitative analysis, this research contributes to the strategic planning and resource allocation for combating human trafficking. It highlights the necessity for continued research and international cooperation to effectively address and mitigate this global issue. The implications of this research are significant, offering actionable insights for policymakers, law enforcement, and advocates in the ongoing battle against human trafficking.展开更多
In this work,the Slacks-Based Measure(SBM)model within Data Envelopment Analysis was employed to establish a set of indicators for evaluating the energy efficiency of manufacturing workshops.The energy efficiency of 1...In this work,the Slacks-Based Measure(SBM)model within Data Envelopment Analysis was employed to establish a set of indicators for evaluating the energy efficiency of manufacturing workshops.The energy efficiency of 12 Company CW’s manufacturing workshops from 2016 to 2022 was assessed.The findings indicated that aside from a few workshops operating at the production frontier,the rest exhibit significant fluctuations in energy efficiency and generally low energy efficiency.Subsequently,a combined GRA-Tobit analysis model was introduced to identify factors influencing the energy efficiency of Company CW’s manufacturing workshops.Regression analysis revealed that technological investments,employee quality,workshop production scale,investment in clean energy,and the level of pollution control all significantly impact the energy efficiency of Company CW’s manufacturing workshops.By evaluating the energy efficiency of Company CW’s manufacturing workshops and studying their influencing factors,this research aids company managers in understanding the energy efficiency of the manufacturing process.It optimizes the combination of various production elements,thereby offering effective guidance for improving the energy efficiency issues of the company’s manufacturing workshops,which can contribute to enhancing the corporation’s overall energy efficiency.展开更多
基金funded by the National Natural Science Foundation of China(NSFC)the Chinese Academy of Sciences(CAS)(grant No.U2031209)the National Natural Science Foundation of China(NSFC,grant Nos.11872128,42174192,and 91952111)。
文摘Seeing is an important index to evaluate the quality of an astronomical site.To estimate seeing at the Muztagh-Ata site with height and time quantitatively,the European Centre for Medium-Range Weather Forecasts reanalysis database(ERA5)is used.Seeing calculated from ERA5 is compared consistently with the Differential Image Motion Monitor seeing at the height of 12 m.Results show that seeing decays exponentially with height at the Muztagh-Ata site.Seeing decays the fastest in fall in 2021 and most slowly with height in summer.The seeing condition is better in fall than in summer.The median value of seeing at 12 m is 0.89 arcsec,the maximum value is1.21 arcsec in August and the minimum is 0.66 arcsec in October.The median value of seeing at 12 m is 0.72arcsec in the nighttime and 1.08 arcsec in the daytime.Seeing is a combination of annual and about biannual variations with the same phase as temperature and wind speed indicating that seeing variation with time is influenced by temperature and wind speed.The Richardson number Ri is used to analyze the atmospheric stability and the variations of seeing are consistent with Ri between layers.These quantitative results can provide an important reference for a telescopic observation strategy.
文摘Peanut allergy is majorly related to severe food induced allergic reactions.Several food including cow's milk,hen's eggs,soy,wheat,peanuts,tree nuts(walnuts,hazelnuts,almonds,cashews,pecans and pistachios),fish and shellfish are responsible for more than 90%of food allergies.Here,we provide promising insights using a large-scale data-driven analysis,comparing the mechanistic feature and biological relevance of different ingredients presents in peanuts,tree nuts(walnuts,almonds,cashews,pecans and pistachios)and soybean.Additionally,we have analysed the chemical compositions of peanuts in different processed form raw,boiled and dry-roasted.Using the data-driven approach we are able to generate new hypotheses to explain why nuclear receptors like the peroxisome proliferator-activated receptors(PPARs)and its isoform and their interaction with dietary lipids may have significant effect on allergic response.The results obtained from this study will direct future experimeantal and clinical studies to understand the role of dietary lipids and PPARisoforms to exert pro-inflammatory or anti-inflammatory functions on cells of the innate immunity and influence antigen presentation to the cells of the adaptive immunity.
文摘Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.
文摘This research paper compares Excel and R language for data analysis and concludes that R language is more suitable for complex data analysis tasks.R language’s open-source nature makes it accessible to everyone,and its powerful data management and analysis tools make it suitable for handling complex data analysis tasks.It is also highly customizable,allowing users to create custom functions and packages to meet their specific needs.Additionally,R language provides high reproducibility,making it easy to replicate and verify research results,and it has excellent collaboration capabilities,enabling multiple users to work on the same project simultaneously.These advantages make R language a more suitable choice for complex data analysis tasks,particularly in scientific research and business applications.The findings of this study will help people understand that R is not just a language that can handle more data than Excel and demonstrate that r is essential to the field of data analysis.At the same time,it will also help users and organizations make informed decisions regarding their data analysis needs and software preferences.
基金Supported by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization(U1909208)the Science and Technology Major Project of Changsha(kh2202004)the Changsha Municipal Natural Science Foundation(kq2202106).
文摘Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive test.It can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire body.Therefore,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric recognition.In recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis techniques.Here we systematically summarize and analyze the ECGbased automatic analysis methods and applications.Specifically,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes.Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications.Finally,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.
基金suppor ted by the National Key Research and Development Program of China (2022YFC2702502)the National Natural Science Foundation of China (32170742, 31970646, and 32060152)+7 种基金the Start Fund for Specially Appointed Professor of Jiangsu ProvinceHainan Province Science and Technology Special Fund (ZDYF2021SHFZ051)the Natural Science Foundation of Hainan Province (820MS053)the Start Fund for High-level Talents of Nanjing Medical University (NMUR2020009)the Marshal Initiative Funding of Hainan Medical University (JBGS202103)the Hainan Province Clinical Medical Center (QWYH202175)the Bioinformatics for Major Diseases Science Innovation Group of Hainan Medical Universitythe Shenzhen Science and Technology Program (JCYJ20210324140407021)
文摘The application of single-cell RNA sequencing(scRNA-seq)in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies.With the expansion of capacity for high-throughput scRNA-seq,including clinical samples,the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field.Here,we review the workflow for typical scRNA-seq data analysis,covering raw data processing and quality control,basic data analysis applicable for almost all scRNA-seq data sets,and advanced data analysis that should be tailored to specific scientific questions.While summarizing the current methods for each analysis step,we also provide an online repository of software and wrapped-up scripts to support the implementation.Recommendations and caveats are pointed out for some specific analysis tasks and approaches.We hope this resource will be helpful to researchers engaging with scRNA-seq,in particular for emerging clinical applications.
文摘In the nonparametric data envelopment analysis literature,scale elasticity is evaluated in two alternative ways:using either the technical efficiency model or the cost efficiency model.This evaluation becomes problematic in several situations,for example(a)when input proportions change in the long run,(b)when inputs are heterogeneous,and(c)when firms face ex-ante price uncertainty in making their production decisions.To address these situations,a scale elasticity evaluation was performed using a value-based cost efficiency model.However,this alternative value-based scale elasticity evaluation is sensitive to the uncertainty and variability underlying input and output data.Therefore,in this study,we introduce a stochastic cost-efficiency model based on chance-constrained programming to develop a value-based measure of the scale elasticity of firms facing data uncertainty.An illustrative empirical application to the Indian banking industry comprising 71 banks for eight years(1998–2005)was made to compare inferences about their efficiency and scale properties.The key findings are as follows:First,both the deterministic model and our proposed stochastic model yield distinctly different results concerning the efficiency and scale elasticity scores at various tolerance levels of chance constraints.However,both models yield the same results at a tolerance level of 0.5,implying that the deterministic model is a special case of the stochastic model in that it reveals the same efficiency and returns to scale characterizations of banks.Second,the stochastic model generates higher efficiency scores for inefficient banks than its deterministic counterpart.Third,public banks exhibit higher efficiency than private and foreign banks.Finally,public and old private banks mostly exhibit either decreasing or constant returns to scale,whereas foreign and new private banks experience either increasing or decreasing returns to scale.Although the application of our proposed stochastic model is illustrative,it can be potentially applied to all firms in the information and distribution-intensive industry with high fixed costs,which have ample potential for reaping scale and scope benefits.
基金supported by the major scientific and technological research project of Chongqing Education Commission(KJZD-M202000802)The first batch of Industrial and Informatization Key Special Fund Support Projects in Chongqing in 2022(2022000537).
文摘As COVID-19 poses a major threat to people’s health and economy,there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently.In non-stationary time series forecasting jobs,there is frequently a hysteresis in the anticipated values relative to the real values.The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network(MDTCNet)for COVID-19 prediction to address this problem.In particular,it is possible to record the deep features and temporal dependencies in uncertain time series,and the features may then be combined using a feature fusion network and a multilayer perceptron.Last but not least,the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty,realizing the short-term and long-term prediction of COVID-19 daily confirmed cases,and verifying the effectiveness and accuracy of the suggested prediction method,as well as reducing the hysteresis of the prediction results.
基金supported by the National Natural Science Foundation of China(U1704134)。
文摘Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in the received VLF signal.This study uses the VLF signal received in Qingdao City,Shandong Province,from the Russian Alpha navigation system to explore the multimode interference problem of VLF signal propagation.The characteristics of the effect of multimode interference phenomena on the phase are analyzed according to the variation of the phase of the VLF signal.However,the phase of VLF signals will also be affected by the X-ray and energetic particles that are released during the eruption of solar flares,therefore the two phenomena are studied in this work.It is concluded that the X-ray will not affect the phase of VLF signals at night,but the energetic particles will affect the phase change,and the influence of energetic particles should be excluded in the study of multimode interference phenomena.Using VLF signals for navigation positioning in degraded or unavailable GPS conditions is of great practical significance for VLF navigation systems as it can avoid the influence of multimode interference and improve positioning accuracy.
文摘Human living would be impossible without air quality. Consistent advancements in practically every aspect of contemporary human life have harmed air quality. Everyday industrial, transportation, and home activities turn up dangerous contaminants in our surroundings. This study investigated two years’ worth of air quality and outlier detection data from two Indian cities. Studies on air pollution have used numerous types of methodologies, with various gases being seen as a vector whose components include gas concentration values for each observation per-formed. We use curves to represent the monthly average of daily gas emissions in our technique. The approach, which is based on functional depth, was used to find outliers in the city of Delhi and Kolkata’s gas emissions, and the outcomes were compared to those from the traditional method. In the evaluation and comparison of these models’ performances, the functional approach model studied well.
基金funded by the National Natural Science Foundation of China(NSFC,Nos.12373086 and 12303082)CAS“Light of West China”Program+2 种基金Yunnan Revitalization Talent Support Program in Yunnan ProvinceNational Key R&D Program of ChinaGravitational Wave Detection Project No.2022YFC2203800。
文摘Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.
文摘This article presents a comprehensive analysis of the current state of research on the English translation of Lu You’s poetry, utilizing a data sample comprising research papers published in the CNKI Full-text Database from 2001 to 2022. Employing rigorous longitudinal statistical methods, the study examines the progress achieved over the past two decades. Notably, domestic researchers have displayed considerable interest in the study of Lu You’s English translation works since 2001. The research on the English translation of Lu You’s poetry reveals a diverse range of perspectives, indicating a rich body of scholarship. However, several challenges persist, including insufficient research, limited translation coverage, and a noticeable focus on specific poems such as “Phoenix Hairpin” in the realm of English translation research. Consequently, there is ample room for improvement in the quality of research output on the English translation of Lu You’s poems, as well as its recognition within the academic community. Building on these findings, it is argued that future investigations pertaining to the English translation of Lu You’s poetry should transcend the boundaries of textual analysis and encompass broader theoretical perspectives and research methodologies. By undertaking this shift, scholars will develop a more profound comprehension of Lu You’s poetic works and make substantive contributions to the field of translation studies. Thus, this article aims to bridge the gap between past research endeavors and future possibilities, serving as a guide and inspiration for scholars to embark on a more nuanced and enriching exploration of Lu You’s poetry as well as other Chinese literature classics.
基金supported by the National Key R&D Program of China Nos.2021YFC2203502 and 2022YFF0711502the National Natural Science Foundation of China(NSFC)(12173077 and 12003062)+5 种基金the Tianshan Innovation Team Plan of Xinjiang Uygur Autonomous Region(2022D14020)the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095)the Scientific Instrument Developing Project of the Chinese Academy of Sciences,grant No.PTYQ2022YZZD01China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing technology.PSRDP can perform operations such as baseband data unpacking,channel separation,coherent dedispersion,Stokes detection,phase and folding period prediction,and folding integration in GPU clusters.We tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes,and the J0332+5434 pulsar baseband data generated by the self-developed backend of the Nan Shan Radio Telescope.We obtained the pulse profiles of each baseband data.Through experimental analysis,we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy(DSPSR),which verified the effectiveness of the PSRDP algorithm.Furthermore,using the same baseband data,we compared the processing speed of PSRDP with DSPSR,and the results showed that PSRDP was not slower than DSPSR in terms of speed.The theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT(Qi Tai radio Telescope)ultra-wide bandwidth pulsar baseband data.
文摘Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.
文摘Highly turbulent water flows,often encountered near human constructions like bridge piers,spillways,and weirs,display intricate dynamics characterized by the formation of eddies and vortices.These formations,varying in sizes and lifespans,significantly influence the distribution of fluid velocities within the flow.Subsequently,the rapid velocity fluctuations in highly turbulent flows lead to elevated shear and normal stress levels.For this reason,to meticulously study these dynamics,more often than not,physical modeling is employed for studying the impact of turbulent flows on the stability and longevity of nearby structures.Despite the effectiveness of physical modeling,various monitoring challenges arise,including flow disruption,the necessity for concurrent gauging at multiple locations,and the duration of measurements.Addressing these challenges,image velocimetry emerges as an ideal method in fluid mechanics,particularly for studying turbulent flows.To account for measurement duration,a probabilistic approach utilizing a probability density function(PDF)is suggested to mitigate uncertainty in estimated average and maximum values.However,it becomes evident that deriving the PDF is not straightforward for all turbulence-induced stresses.In response,this study proposes a novel approach by combining image velocimetry with a stochastic model to provide a generic yet accurate description of flow dynamics in such applications.This integration enables an approach based on the probability of failure,facilitating a more comprehensive analysis of turbulent flows.Such an approach is essential for estimating both short-and long-term stresses on hydraulic constructions under assessment.
文摘Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.
文摘Android smartphones largely dominate the smartphone market. For this reason, it is very important to examine these smartphones in terms of digital forensics since they are often used as evidence in trials. It is possible to acquire a physical or logical image of these devices. Acquiring physical and logical images has advantages and disadvantages compared to each other. Creating the logical image is done at the file system level. Analysis can be made on this logical image. Both logical image acquisition and analysis of the image can be done by software tools. In this study, the differences between logical image and physical image acquisition in Android smartphones, their advantages and disadvantages compared to each other, the difficulties that may be encountered in obtaining physical images, which type of image contributes to obtaining more useful and effective data, which one should be preferred for different conditions, and the benefits of having root authority are discussed. The practice of getting the logical image of the Android smartphones and making an analysis on the image is also included. Although root privileges are not required for logical image acquisition, it has been observed that very limited data will be obtained with the logical image created without root privileges. Nevertheless, logical image acquisition has advantages too against physical image acquisition.
文摘To enhance the safety of road traffic operations,this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics.Initially,the process involved data cleaning,transformation,and normalization.Subsequently,various classification models were constructed,including logistic regression,k-nearest neighbors,gradient boosting,decision trees,AdaBoost,and extra trees models.Evaluation metrics such as accuracy,precision,recall,F1 score,and Hamming loss were employed.Upon analysis,the passive-aggressive classifier model exhibited superior comprehensive indices compared to other models.Based on the model’s output results,an in-depth examination of the factors influencing traffic accidents was conducted.Additionally,measures and suggestions aimed at reducing the incidence of severe traffic accidents were presented.These findings served as a valuable reference for mitigating the occurrence of traffic accidents.
文摘This paper presents a comprehensive analysis of global human trafficking trends over a twenty-year period, leveraging a robust dataset from the Counter Trafficking Data Collaborative (CTDC). The study unfolds in a systematic manner, beginning with a detailed data collection phase, where ethical and legal standards for data usage and privacy are strictly observed. Following collection, the data undergoes a rigorous preprocessing stage, involving cleaning, integration, transformation, and normalization to ensure accuracy and consistency for analysis. The analytical phase employs time-series analysis to delineate historical trends and utilizes predictive modeling to forecast future trajectories of human trafficking using the advanced analytical capabilities of Power BI. A comparative analysis across regions—Africa, the Americas, Asia, and Europe—is conducted to identify and visualize the distribution of human trafficking, dissecting the data by victim demographics, types of exploitation, and duration of victimization. The findings of this study not only offer a descriptive and predictive outlook on trafficking patterns but also provide insights into the regional nuances that influence these trends. The article underscores the prevalence and persistence of human trafficking, identifies factors contributing to its evolution, and discusses the implications for policy and law enforcement. By integrating a methodological approach with quantitative analysis, this research contributes to the strategic planning and resource allocation for combating human trafficking. It highlights the necessity for continued research and international cooperation to effectively address and mitigate this global issue. The implications of this research are significant, offering actionable insights for policymakers, law enforcement, and advocates in the ongoing battle against human trafficking.
文摘In this work,the Slacks-Based Measure(SBM)model within Data Envelopment Analysis was employed to establish a set of indicators for evaluating the energy efficiency of manufacturing workshops.The energy efficiency of 12 Company CW’s manufacturing workshops from 2016 to 2022 was assessed.The findings indicated that aside from a few workshops operating at the production frontier,the rest exhibit significant fluctuations in energy efficiency and generally low energy efficiency.Subsequently,a combined GRA-Tobit analysis model was introduced to identify factors influencing the energy efficiency of Company CW’s manufacturing workshops.Regression analysis revealed that technological investments,employee quality,workshop production scale,investment in clean energy,and the level of pollution control all significantly impact the energy efficiency of Company CW’s manufacturing workshops.By evaluating the energy efficiency of Company CW’s manufacturing workshops and studying their influencing factors,this research aids company managers in understanding the energy efficiency of the manufacturing process.It optimizes the combination of various production elements,thereby offering effective guidance for improving the energy efficiency issues of the company’s manufacturing workshops,which can contribute to enhancing the corporation’s overall energy efficiency.