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predictive analysis of Water Wettability and Corrosion Resistance of Secondary AlSi10MnMg(Fe) Alloy Manufactured by Vacuum-Assisted High Pressure Die Casting

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INTERNATIONAL JOURNAL OF METALCASTING 2025年第1期19卷 75-85页

作者： Kordijazi, Amir Behera, Swaroop K. Jamet, Arthur Fernandez-Calvo, Ana Isabel Rohatgi, Pradeep Univ Southern Maine Dept Engn Gorham ME 04038 USA Univ Wisconsin Dept Mat Sci & Engn Milwaukee WI 53211 USA Univ Nantes Dept Mech Engn F-44035 Nantes France IK4 AZTERLAN Aliendalde Auzunea 6 Durango 48200 Spain Univ Wisconsin Dept Mat Sci & Engn Milwaukee WI 53211 USA

In the present study, a predictive analysis was performed to investigate the effect of droplet size, section size and type of the primary and secondary AlSi10MnMg alloys manufactured by vacuum-assisted high pressure die casting on wettability of the cast samples with water, since wettability influences corrosion resistance. Additionally, corrosion resistance of samples was studied using a linear polarization experiment. Contact angle (CA) measurements were performed on the specimens using a goniometer. An Artificial Neural Network was then developed to predict the contact angle values as a function of the predictor variables. The developed model was able to predict unseen CA values with excellent accuracy with the Pearson correlation coefficient of 0.96 between the predicted and observed CA. The modeling results show that the type of alloy (primary or secondary) is the most significant factor affecting CA, where almost 80% of CA variation is the result of changing the type of alloy. Confocal microscopy images demonstrate that this is attributed to the change in the heterogeneity of the surface, which affects contact angle values. The corrosion studies reveal that corrosion resistance is dependent on the type of alloy and surface roughness. The primary alloy possesses more corrosion resistance than the secondary alloy. This is due to the larger fraction of intermetallic compounds in the microstructure of the secondary alloy, which serve as galvanic sites in the corrosion reaction accelerating corrosion rate. Moreover, the non-uniformity induced by larger surface roughness is detrimental to the corrosion resistance of the samples. These results indicate that the data-driven approach used in this research is very promising not only to predict the performance, but also to optimize and design high-performance corrosion resistant surfaces of cast aluminum alloys.

关键词： high pressure die casting secondary alloy contact angle surface roughness corrosion predictive analysis artificial neural network

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predictive analysis of adsorption dynamics of textile dyes on date seed carbon (DSC)

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JOURNAL OF THE INDIAN CHEMICAL SOCIETY 2025年第2期102卷

作者： Boralkar, Tejas Mane, Satish Sawant, Manohar Dalvi, Vishwanath Inst Chem Technol Mumbai India

This research investigates the efficacy of date seed carbon (DSC), an eco-friendly and cost-effective adsorbent derived from agricultural waste, for the removal of textile dyes from wastewater. Textile effluents, which carry high pollutant loads, pose significant environmental threats. Batch adsorption experiments were conducted under varying conditions of dye concentration (50-150 mg/L), stirring speed (100-800 rpm), and adsorbent weight (0.5-1.5 g). The adsorption data were analysed using Freundlich and Langmuir isotherm models, with the maximum adsorption capacity observed at 72.5 mg/g for Reactive Green dye. The predictive model developed in this study determined a diffusion coefficient of 1.2 x 10-9 cm2/s and a mass transfer coefficient of 3.5 x 10- 4 cm/s under optimal conditions. The experimental results demonstrated dye removal efficiencies exceeding 90 % for concentrations up to 100 mg/L. These findings establish DSC as a promising candidate for efficient, sustainable, and industrial-scale textile effluent treatment. The presented study highlights the potential of DSC to advance eco-friendly wastewater management solutions and reduce environmental impacts.

关键词： Adsorption Date seed carbon Textile effluent predictive analysis Wastewater treatment Isotherm models Diffusion coefficient Mass transfer coefficient

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predictive analysis of Smart Grid Stability Using Diverse Machine Learning Algorithms 4

Predictive Analysis of Smart Grid Stability Using Diverse Ma...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Dipto, Durjoy Roy Shib, Sowrov Komar Rahman, Md Tanvir Shufian, Abu American International UniversityBangladesh Dept. of Electrical & Electronic Engg Dhaka Bangladesh

ISBN: (纸本)9798331530938

The increasing integration of diverse energy sources within smart grids has introduced new challenges for maintaining grid stability. Traditional stability evaluation techniques often fail to address modern power systems' energetic and nonlinear nature. The proposed study explores the application of machine learning algorithms, including Random Forests, XGBoost, Support Vector Machines (SVM), and Logistic Regression, to predict smart grid stability. A comprehensive dataset of 60,000 samples representing various grid scenarios was utilized, and the models were assessed using precision, accuracy, recall, and F1-score metrics. Among the tested models, XGBoost showed superior performance, with the highest accuracy of 97.93% across various conditions. This result underscores the potential of machine learning in enhancing grid reliability, reducing the risk of power outages, and supporting the integration of renewable energy sources. By advancing predictive analytics in smart grid systems, this approach contributes to developing more resilient and adaptive energy networks, essential for meeting the demands of a sustainable future. © 2025 IEEE.

关键词： intelligent controller machine learning predictive analysis Smart grid stability

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predictive analysis of Global Terrorist Attacks Using Lexical Patterns Across Multiple Datasets

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EXPERT SYSTEMS 2025年第1期42卷

作者： Atoum, Mohammed Salem Alarood, Ala Abdulsalam Alsolmi, Eesa Abdullah Obeidat, Areej Alazab, Moutaz Univ Jordan Dept Comp Sci Amman Jordan Univ Jeddah Coll Comp Sci & Engn Jeddah Saudi Arabia Al Balqa Appl Univ Fac Artificial Intelligence Dept Intelligent Syst Al Salt Jordan

Worldwide terrorist activities continue to pose a significant threat to global security and stability. The unpredictable nature of these acts necessitates advanced analytical approaches to enhance prevention and response strategies. This study examines undetectable word extensions across multiple datasets, using terrorism-related datasets as a case study. This research aims to overcome constraints in current predictive models associated with terrorist attack prediction. While many studies have used the GTD for predicting global terrorist attacks, this study expands beyond GTD by evaluating a corpus of terrorism incidents to enhance predictive analysis through lexical usage. The study employs several machine learning algorithms including Decision Tree (DT), Bootstrap Aggregating (BA), Random Forest (RF), Extra Trees (ET) and XGBoost (XG) algorithms for evaluation. Our approach integrates multiple datasets to reduce dependence on GTD alone. Findings indicate that RF performs best on the GTD database, with 90.20% accuracy in predicting worldwide terrorist attacks. DT achieves 90.40% accuracy when applied to the TF-IDF dataset. XG demonstrates superior performance across various aggregation settings and feature sets, achieving 95.77% accuracy in forecasting worldwide terrorist acts. XG's consistent and effective performance across various contexts highlights its versatility. Its high adaptability and robust performance position it as the preferred algorithm for conducting predictive research on global terrorist acts using the available datasets. Our research findings underscore the importance of incorporating diverse datasets to enhance understanding of terrorist activities and improve predictive capabilities.

关键词： machine learning multi-dataset integration predictive analysis terrorism terrorist organisation

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predictive analysis for race detection in software-defined networks

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Science China(Information Sciences) 2019年第6期62卷 34-53页

作者： Gongzheng LU Lei XU Yibiao YANG Baowen XU State Key Laboratory of Novel Software Technology Nanjing University School of Computer Engineering Suzhou Vocational University

Race condition remains one kind of the most common concurrency bugs in software-defined networks(SDNs). The race conditions can be exploited to lead to security and reliability risks. However,the race conditions are notoriously difficult to detect. The existing race detectors for SDNs have limited detection capability. They can only detect the races in the original traces(observed traces) and cause false negatives. In this study, we present a predictive analysis framework called SDN-predict for race detection in SDNs. By encoding the order between the specified network events in SDNs as constraint, we formulate race detection as a constraint solving problem. In addition to detecting the races in the original trace, our framework can also detect the races in the feasible traces got from reordering the events in the original trace while satisfying the consistency requirements of trace. Moreover, we formally prove that our predictive analysis framework is sound and can achieve the maximal possible detection capability for any sound dynamic race detector with respect to the same trace. We evaluate our framework on a set of traces collected from three SDN controllers(POX, Floodlight, ONOS), running 5 representative applications including reactive and proactive applications in large networks, on three different network topologies. These experiments show that our framework has higher race detection capability than exisiting SDN race detector-SDNRacer, and detects more 1173 races. These 1173 races were previously undetected and confirmed by checking the race graphs.

关键词： constraint solving predictive analysis race detection software-defined networks

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predictive analysis of the air quality indicators in the Yangtze River Delta in China: An application of a novel seasonal grey model

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 748卷 141428页

作者： Zhou, Weijie Wu, Xiaoli Ding, Song Cheng, Yuke Changzhou Univ Sch Econ Changzhou 213159 Jiangsu Peoples R China Changzhou Univ Coll Business Changzhou 213159 Jiangsu Peoples R China Zhejiang Univ Finance & Econ Sch Econ Hangzhou 310018 Peoples R China Zhejiang Univ Finance & Econ Ctr Reg Econ & Integrated Dev Hangzhou 310018 Peoples R China

Foreknowledge of the air quality indicators (i.e. AQI, PM2.5, PM10, SO2, CO, NO2, and O-3) provides decision-makers a possibility for building an early-warning system and tailoring related policies and plans accordingly so as to reduce the negative influences of these pollutants. However, accurate forecasts are hardly obtained because strong seasonal variations in meteorological circumstances can largely give rise to seasonal fluctuations in the time series of these indicators, which are difficult to be described and extracted by traditional forecasting tools. To address such issues, a seasonal nonlinear grey Bernoulli model is developed to provide skillful forecasts, which can effectively grasp the nonlinear and seasonal features. Subsequently, this paper elaborates on the model and method used for parameter estimations. For validation and verification purposes, operational seasonal forecasts of the air quality indicators in the four representative cities (Shanghai, Hangzhou, Nanjing, and Hefei) in the Yangtze River Delta are performed, in comparison with five prevalent forecasting tools including SFGM(1,1), SGM(1,1), LSSVM, SARIMA, and BPNN. Results show that the proposed model outperforms other competitors in improving the prediction accuracy of the seasonal air quality changes. Thus, the verified model is recommended to produce future estimations of the air quality indicators in the Yangtze River Delta from 2020 to 2021, revealing that Shanghai, Hangzhou, and Hefei will have better air quality than before, while Nanjing will be subjected to a poorer one. Eventually, some suggestions related to the prevention of atmospheric pollution are provided to further improve air quality. (c) 2020 Elsevier B.V. All rights reserved.

关键词： predictive analysis Air quality forecasts The Yangtze River Delta Seasonal grey model Environmental policy

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predictive analysis of Landmine Risk

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IEEE ACCESS 2019年 7卷 107259-107269页

作者： Rafique, Waqas Zheng, Dawei Barras, Jamie Joglekar, Sagar Kosmas, Panagiotis Kings Coll London Dept Informat London WC2R 2LS England

Demining is a highly impactful but complex problem which requires considerable resources and time. Land mine detection is the most hazardous and time consuming of the tasks in the demining pipeline. Currently, the risk of landmines being present in an area is estimated on the basis of non-technical surveys which are expensive and slow. This paper presents a novel spatial landmine risk prediction model to help and improve the allocation of resources in demining operations and even predict future areas of interest. Our approach is based on training predictive models on geographical and social development data for areas recorded to have been demined in the past. We then use this model to predict areas with high chance of mine presence in the vicinity of the demined area so as to progressively expand the area of operations. We explore weighted classification and biased scoring methods to improve the performance of our base logistic regression and support vector machine models. Refinement of conventional models allows us to tackle the problem of unbalanced datasets in our application. The resulting pipeline is then characterized in terms of various performance metrics. The results show that the pipeline has a potential to provide reliable predictive information based on historic demining data, which can help organizations plan their resource allocations in future demining operations.

关键词： Social implications of technology logistic regression support vector machine predictive analysis landmine risk

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predictive analysis of the scoliotic curve using a subject's 3D model

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE 2023年第8期237卷 1001-1007页

作者： Breskovic, Tomas Stefanovic, Branko Bednarcikova, Lucia Ferencik, Norbert Ondrejova, Bibiana Zivcak, Jozef Tech Univ Kosice Dept Mech Engn Kosice Slovakia Tech Univ Kosice Dept Mech Engn Letna 1-9 Kosice 04200 Slovakia

A predictive analysis of the conservative scoliosis treatment is necessary, in which a 3D model of an optimal treatment algorithm is a basic part in the design of a prosthetic corset. Since CAD technology has proven to be very useful in the field of prosthetics and orthotics, we used an open-source software to plan the correction of the scoliotic curve on a virtual model of the subject's torso. The shape of the scoliosis was simplified by means of a directional polygon, which was drawn in a reverse manner depending on the directional arcs of the scoliotic curve. The resulting scoliosis correction, simulated in a predictive analysis, was defined by changing the Cobb angle, eccentricity, and torso height. With the proposed low-cost method of predictive analysis, it is possible to help CPOs to a more accurate and effective design of orthoses and corrective aids and to comprehensively determine the entire treatment procedure.

关键词： Scoliosis predictive analysis simulation development 3D modeling

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predictive analysis and Simulation Uncertainty of a Distributed Hydrological Model

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WATER RESOURCES MANAGEMENT 2010年第12期24卷 2869-2880页

作者： Bahremand, Abdolreza De Smedt, Florimond Gorgan Univ Agr Sci & Nat Resources Dept Watershed Management Gorgan Iran Vrije Univ Brussel Dept Hydrol & Hydraul Engn B-1050 Brussels Belgium

Distributed hydrological models should pass through a careful calibration procedure backed by sensitivity, uncertainty and predictive analysis before they are utilized as a decision making aid in watershed management and scenario studies. This paper examines whether the uncertainty of the parameters of the spatially distributed hydrologic model WetSpa causes significant uncertainty in the model predictions. The WetSpa model is applied to the Torysa river basin, a rather large catchment located in Slovakia. Parameter estimation, sensitivity and predictive analysis of the model parameters are performed using a model-independent parameter estimator, PEST. It is found that the correction factor for measured evaporation data has the highest relative sensitivity. Parameter uncertainty and predictive analysis give an insight of a proper parameter set and parameter uncertainty intervals and prove that the parameter uncertainty of the model does not result in a significant level of predictive uncertainty.

关键词： WetSpa model PEST Sensitivity/uncertainty analysis predictive analysis Flow simulation

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predictive analysis visualization component in simulated data streams

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DISCOVER COMPUTING 2024年第1期27卷 12-12页

作者： Dudas, Adam Demian, Daniel Matej Bel Univ Fac Nat Sci Dept Comp Sci Tajovskeho 40 Banska Bystrica 97401 Slovakia

One of the most significant problems related to Big Data is their analysis with the use of various methods from the area of descriptive statistics or machine and deep learning. This process is interesting in both-static datasets containing various data sources which do not change over time, and dynamic datasets collected with the use of ambient data sources, which measure a number of attribute values over long periods. Since access to actual dynamic data systems is demanding, the focus of this work is put on the design and implementation of a framework usable in a simulation of data streams, their processing and subsequent dynamic predictive and visual analysis. The proposed system is experimentally verified in the context of a case study conducted on an environmental variable dataset, which was measured with the use of a real-life sensor network.

关键词： predictive analysis Simulation Data streams Big Data Visual analysis

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