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Journal of Environmental Sciences 2024年第2期136卷 313-324页

作者： Saibal Ghosh Sandip Mondal Jajati Mandal Abhishek Mukherjee Pradip Bhattacharyya Agricultural and Ecological research Unit Indian Statistical InstituteGiridihJharkhand 815301India School of Science Engineering&EnvironmentUniversity of SalfordManchester M54WTUK

Heavy metals from mica waste not only deteriorate the soil quality but also results in the uptake of metals in the crop.The present investigation was conducted to evaluate the effects of different fractions of metals on the uptake in rice,soil microbial and biochemical properties in mica waste-contaminated soils of Jharkhand,India.From each active mine,soil samples were randomly collected at distances of100 m(zone 3).Sequential metal extraction was used to determine the fractions of different metals(nickel(Ni),cadmium(Cd),chromium(Cr)and lead(Pb))including water-soluble(Ws)and exchangeable metals(Ex),carbonate-bound metals(CBD),Fe/Mn oxide(OXD)bound metals,organically bound metals(ORG),and residues(RS).The Ni,Cr,Cd and Pb in rice grain were 0.83±0.41,0.41±0.19,0.21±0.14 and 0.17±0.08 mg/kg respectively.From the variable importance plot of the random forest(RF)algorithm,the Ws fraction of Ni,Cr and Cd and Ex fraction of Pb was the most important predictor for rice grain metal content.Further,the partial dependence plots(PDP)give us an insight into the role of the two most important metal fractions on rice grain metal content.The microbial and enzyme activity was significantly and negatively correlated with Ws and Ex metal fractions,indicating that water-soluble and exchangeable fractions exert a strong inhibitory effect on the soil microbiological parameters and enzyme activities.

关键词： Metal fractions Microbial parameters Mica waste soil Soil enzymes Rice Random forest

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Inference for high-dimensional streamed longitudinal data

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Acta Mathematica Sinica,English Series 2024年

作者： Senyuan ZHENG Ling ZHOU Center of statistical research and School of Statistics Southwestern University of Finance and Economics

With the advent of modern devices, such as smartphones and wearable devices, high-dimensional data are collected on many participants for a period of time or even in perpetuity. For this type of data,dependencies between and within data batches exist because data are collected from the same individual over time. Under the framework of streamed data, individual historical data are not available due to the storage and computation burden. It is urgent to develop computationally efficient methods with statistical guarantees to analyze high-dimensional streamed data and make reliable inferences in practice. In addition, the homogeneity assumption on the model parameters may not be valid in practice over time. To address the above issues, in this paper, we develop a new renewable debiased-lasso inference method for high-dimensional streamed data allowing dependences between and within data batches to exist and model parameters to gradually change. We establish the large sample properties of the proposed estimators, including consistency and asymptotic normality. The numerical results, including simulations and real data analysis, show the superior performance of the proposed method.

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Exploring Long-Memory Process in the Prediction of Interval-Valued Financial Time Series and Its Application

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Journal of Systems Science & Complexity 2024年第2期37卷 759-775页

作者： SHEN Tingting TAO Zhifu CHEN Huayou School of Economics Anhui UniversityHefei 230601China Center for Financial and statistical research Anhui UniversityHefei 230061China Stony Brook Institute at Anhui University Anhui UniversityHefei 230601China Center for Applied Mathematics Anhui UniversityHefei 230601China

Long-memory process has been widely studied in classical financial time series analysis,which has merely been reported in the field of interval-valued financial time series.The aim of this paper is to explore long-memory process in the prediction of interval-valued time series(IvTS).To model the long-memory process,two novel interval-valued time series prediction models named as interval-valued vector autoregressive fractionally integrated moving average(IV-VARFIMA)and ARFIMAX-FIGARCH were established.In the developed long-memory pattern,both of the short term and long-term influences contained in IvTS can be included.As an application of the proposed models,interval-valued form of WTI crude oil futures price series is predicted.Compared to current IvTS prediction models,IV-VARFIMA and ARFIMAX-FIGARCH can provide better in-sample and out-of-sample forecasts.

关键词： ARFIMAX-FIGARCH interval-valued time series IV-VARFIMA long-memory process WTI crude oil futures price

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Dynamical behaviors of a constant prey refuge ratio-dependent prey-predator model with Allee and fear effects

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International Journal of Biomathematics 2024年第1期17卷 227-250页

作者： Soumitra Pal Pijush Panday Nikhil Pal A.K.Misra Joydev Chattopadhyays Department of Mathematics Institute of Science Banaras Hindu University Varanasi 221005India Department of Mathematical and statistical Sciences University of Alberta EdmontonAB T6G 2G1Canada Department of Mathematics Visva-Bharati Santiniketan 731235India Agricultural and Ecological research Unit Indian statistical Institute 203 B.T.RoadKolkata 700108India

In this paper,we consider a nonlinear ratio-dependent prey-predator model with constant prey refuge in the prey population.Both Allee and fear phenomena are incorporated explicitly in the growth rate of the prey population.The qualitative behaviors of the proposed model are investigated around the equilibrium points in detail.Hopf bifurcation including its direction and stability for the model is also studied.We observe that fear of predation risk can have both stabilizing and destabilizing effects and induces bubbling phenomenon in the system.It is also observed that for a fixed strength of fear,an increase in the Allee parameter makes the system unstable,whereas an increase in prey refuge drives the system toward stability.However,higher values of both the Allee and prey refuge parameters have negative impacts and the populations go to extinction.Further,we explore the variation of densities of the populations in different bi-parameter spaces,where the coexistence equilibrium point remains stable.Numerical simulations are carried out to explore the dynamical behaviors of the system with the help of MATLAB software.

关键词： Predator-prey system Allee effect fear effect prey refuge bifurcation population density

A Proposed Approach for Measuring Maturity Level of Software Delivery

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Journal of Software Engineering and Applications 2024年第5期17卷 228-245页

作者： Osama Hassan Mansour Atef Raslan Nagy Ramadan Faculty of Postgraduate Studies for statistical research Cairo University Cairo Egypt

Software delivery is vital for modern organizations, driving innovation and competitiveness. Measuring an organization’s maturity in software delivery is crucial for efficiency and quality. The Capability Maturity Model (CMM) framework provides a roadmap for improvement but assessing an organization’s CMM Level is challenging. This paper offers a quantitative approach tailored to the CMM framework, using Goal-Question-Metric (GQM) frame-works for each key process area (KPA). These frameworks include metrics and questions to compute maturity scores effectively. The study also refines practices into questions for a thorough assessment. The result is an Analysis Matrix that calculates weighted scores and an overall maturity score. This approach helps organizations assess and enhance their software delivery processes systematically, aiming for improved practices and growth.

关键词： CMM CMMI Software Delivery Maturity KPAs GQM Practices

Variational Bayesian Tensor Quantile Regression

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Acta Mathematica Sinica,English Series 2024年

作者： Yunzhi JIN Yanqing ZHANG Yunnan Key Laboratory of statistical Modeling and Data Analysis Southwest United Graduate School Yunnan University Yunnan Key Laboratory of statistical Modeling and Data Analysis Yunnan University

Quantile regression is widely used in variable relationship research for statistical learning.Traditional quantile regression model is based on vector-valued covariates and can be efficiently estimated via traditional estimation methods. However, many modern applications involve tensor data with the intrinsic tensor structure. Traditional quantile regression can deal with tensor regression issues well. To this end, we consider a tensor quantile regression with tensor-valued covariates and develop a novel variational Bayesian estimation approach to make estimation and prediction based on the asymmetric Laplace model and the CANDECOMP/PARAFAC decomposition of tensor coefficients. To incorporate the sparsity of tensor coefficients, we consider the multiway shrinkage priors for marginal factor vectors of tensor coefficients. The key idea of the proposed method is to efficiently combine the prior structural information of tensor and utilize the matricization of tensor decomposition to simplify the complexity of tensor coefficient estimation. The coordinate ascent algorithm is employed to optimize variational lower bound. Simulation studies and a real example show the numerical performances of the proposed method.

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Enable Excel-Based Basic Cybersecurity Features for End Users by Using Python-Excel Integration

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Journal of Software Engineering and Applications 2024年第6期17卷 522-529页

作者： Mohamed Breik Osama Magdy Essam Amin Tarek Aly Mervat Gheith Software Engineering Department Faculty of Graduate Studies for Statistical Research Cairo University Cairo Egypt

In the digital age, the global character of the Internet has significantly improved our daily lives by providing access to large amounts of knowledge and allowing for seamless connections. However, this enormously interconnected world is not without its risks. Malicious URLs are a powerful menace, masquerading as legitimate links while holding the intent to hack computer systems or steal sensitive personal information. As the sophistication and frequency of cyberattacks increase, identifying bad URLs has emerged as a critical aspect of cybersecurity. This study presents a new approach that enables the average end-user to check URL safety using Microsoft Excel. Using the powerful VirusTotal API for URL inspections, this study creates an Excel add-in that integrates Python and Excel to deliver a seamless, user-friendly interface. Furthermore, the study improves Excel’s capabilities by allowing users to encrypt and decrypt text communications directly in the spreadsheet. Users may easily encrypt their conversations by simply typing a key and the required text into predefined cells, enhancing their personal cybersecurity with a layer of cryptographic secrecy. This strategy democratizes access to advanced cybersecurity solutions, making attentive digital integrity a feature rather than a daunting burden.

关键词： Python End-User Approach Excel Excel Add-In Cybersecurity URL Check API Virustotal API Encryption Decryption Vigenère Cipher Python-Excel Integration

Enhancing Data Analysis and Automation: Integrating Python with Microsoft Excel for Non-Programmers

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Journal of Software Engineering and Applications 2024年第6期17卷 530-540页

作者： Osama Magdy Ali Mohamed Breik Tarek Aly Atef Tayh Nour El-Din Raslan Mervat Gheith Software Engineering Department Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt

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.

关键词： Python End-User Approach Microsoft Excel Data Analysis Integration Spreadsheet Programming Data Visualization

Feedback of Efficient Shock Acceleration on Magnetic-field Structure Inside Young Type Ia Supernova Remnants

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research in Astronomy and Astrophysics 2024年第6期24卷 236-246页

作者： Jun-Yu Shen Bi-Wen Bao Li Zhang Department of Astronomy Key Laboratory of Astroparticle Physics of Yunnan Province Yunnan University Key Laboratory of statistical Modeling and Data Analysis of Yunnan Province Yunnan University

Using an effective adiabatic index γeffto mimic the feedback of efficient shock acceleration, we simulate the temporal evolution of a young type Ia supernova remnant （SNR） with two different background magnetic field（BMF） topologies:a uniform and a turbulent BMF. The density distribution and magnetic-field characteristics of our benchmark SNR are studied with two-dimensional cylindrical magnetohydrodynamic simulations. When γeffis considered, we find that:（1） the two-shock structure shrinks and the downstream magnetic-field orientation is dominated by the Rayleigh–Taylor instability structures;（2） there exists more quasi-radial magnetic fields inside the shocked region; and （3） inside the intershock region, both the quasi-radial magnetic energy density and the total magnetic energy density are enhanced:in the radial direction, with γeff=1.1, they are amplified about 10–26 times more than those with γeff=5/3. While in the angular direction, the total magnetic energy densities could be amplified about 350 times more than those with γeff=5/3, and there are more grid cells within the intershock region where the magnetic energy density is amplified by a factor greater than 100.

关键词： ISM: magnetic fields ISM: supernova remnants magnetohydrodynamics (MHD) methods: numerical

An Improved Enterprise Resource Planning System Using Machine Learning Techniques

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Journal of Software Engineering and Applications 2024年第5期17卷 203-213页

作者： Ahmed Youssri Zakaria Elsayed Abdelbadea Atef Raslan Tarek Ali Mervat Gheith Al-Sayed Khater Essam A. Amin Department of Software Engineering Faculty of Graduate Studies for Statistical Research (FGSSR) Cairo University Giza Egypt

Traditional Enterprise Resource Planning (ERP) systems with relational databases take weeks to deliver predictable insights instantly. The most accurate information is provided to companies to make the best decisions through advanced analytics that examine the past and the future and capture information about the present. Integrating machine learning (ML) into financial ERP systems offers several benefits, including increased accuracy, efficiency, and cost savings. Also, ERP systems are crucial in overseeing different aspects of Human Capital Management (HCM) in organizations. The performance of the staff draws the interest of the management. In particular, to guarantee that the proper employees are assigned to the convenient task at the suitable moment, train and qualify them, and build evaluation systems to follow up their performance and an attempt to maintain the potential talents of workers. Also, predicting employee salaries correctly is necessary for the efficient distribution of resources, retaining talent, and ensuring the success of the organization as a whole. Conventional ERP system salary forecasting methods typically use static reports that only show the system’s current state, without analyzing employee data or providing recommendations. We designed and enforced a prototype to define to apply ML algorithms on Oracle EBS data to enhance employee evaluation using real-time data directly from the ERP system. Based on measurements of accuracy, the Random Forest algorithm enhanced the performance of this system. This model offers an accuracy of 90% on the balanced dataset.

关键词： ERP HCM Machine Learning Employee Performance Pythonista Pythoneer