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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Colloidal synthesis of lead chalcogenide/lead chalcohalide core/shell nanostructures and structural evolution

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Journal of Semiconductors 2025年第4期46卷 38-44页

作者： Yang Liu Kunyuan Lu Yujie Zhu Xudong Hu Yusheng Li Guozheng Shi Xingyu Zhou Lin Yuan Xiang Sun Xiaobo Ding Irfan Ullah Muhammad Qing Shen Zeke Liu Wanli Ma Institute of Functional Nano&Soft Materials(FUNSOM) Joint International Research Laboratory of Carbon-Based Functional Materials and DevicesSoochow UniversitySuzhou 215123China Jiangsu Key Laboratory of Advanced Negative Carbon Technologies Soochow UniversitySuzhou 215123China Faculty of Informatics and Engineering University of Electro-CommunicationsTokyo 182−8585Japan

Lead chalcohalides(PbYX,X=Cl,Br,I;Y=S,Se)is an extension of the classic Pb chalcogenides(PbY).Constructing the heterogeneous integration with PbYX and PbY material systems makes it possible to achieve significantly improved optoelectronic *** this work,we studied the effect of introducing halogen precursors on the structure of classical PbS nanocrystals(NCs)during the synthesis process and realized the preparation of PbS/Pb_(3)S_(2)X_(2) core/shell structure for the first *** core/shell structure can effectively improve their optical ***,our approach enables the synthesis of Pb_(3)S_(2)Br_(2) that had not yet been *** results not only provide valuable insights into the heterogeneous integration of PbYX and PbY materials to elevate material properties but also provide an effective method for further expanding the preparation of PbYX material systems.

关键词： lead chalcohalides lead chalcogenides PbS nanocrystal core/shell structure Pb_(3)S_(2)X_(2)nanocrystal

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Wetland bird utilisation of ephemerally flooded rice paddies in late winter snowmelt season in central Japan

Watershed Ecology and the Environment

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Watershed Ecology and the Environment 2025年 7卷 178-186页

作者： Hisano, Masumi Deguchi, Shota Sannoh, Keinosuke Motomura, Ken Lin, Da-Li of Advanced Science and Engineering Hiroshima University 1-5-1 Kagamiyama Higashihiiroshima 739-8529 Japan of Informatics Kyoto University Yoshida-honmachi Sakyo Kyoto 606-8501 Japan of Agricultural and Life Sciences The University of Tokyo 1-1-1 Yayoi Tokyo Bunkyo 113-8657 Japan Fukui City Museum of Natural History 147 Asuwakami-cho Fukui 918-8006 Japan Nihonkai Eco Engineering Technologies 735-10 Kizu Toyama Takaoka 933-0857 Japan Board of Education Nakano City Hall 1-3-19 Miyoshi-cho Nakano Nagano 383-8614 Japan Taiwan Biodiversity Research Institute Ministry of Agriculture 1 Ming-Sheng East Road Jiji Nantou 552 Taiwan

This study highlights the importance of rice paddies in snow-rich regions of central Japan as habitat for wetland bird species during snow-melting late winter season. During the study period, we recorded seven wetland species, including waders, egrets, and waterfowls. The abundance and richness of these birds were not significantly associated with the patch size of rice paddies. However, bird abundance presented a marginally positive association with the coverage of open water bodies within the landscape, while species richness showed a marginal relationship with the distance to the nearest water body. Our findings suggest that snowmelt-created wetlands may provide functions as critical stopover sites for migratory birds along the East Asian-Australian Flyway, and that enhancing water features in agricultural landscapes may yield greater conservation benefits than simply modifying the patch size of agricultural wetlands. © 2025 The Authors

关键词： Agricultural landscape Artificial wetland Avifauna Community ecology Open water Wader Waterfowl

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Optimizing droplet coalescence dynamics in microchannels: A comprehensive study using response surface methodology and machine learning algorithms

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Heliyon 2025年第1期11卷 e41510页

作者： Javadpour, Seyed Morteza Kadivar, Erfan Zarneh, Zienab Heidary Kadivar, Ebrahim Gheibi, Mohammad Department of Mechanical Engineering University of Gonabad Gonabad Iran Department of Physics Shiraz University of Technology Shiraz 71555-313 Iran Institute of Ship Technology Ocean Engineering and Transport Systems University of Duisburg-Essen Duisburg 47057 Germany Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec 46117 Czech Republic Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Liberec Czech Republic Faculty of Mechanical Engineering Tarbiat Modares University Tehran 141171-3116 Iran

Droplet coalescence in microchannels is a complex phenomenon influenced by various parameters such as droplet size, velocity, liquid surface tension, and droplet-droplet spacing. In this study, we thoroughly investigate the impact of these control parameters on droplet coalescence dynamics within a sudden expansion microchannel using two distinct numerical methods. Initially, we employ the boundary element method to solve the Brinkman integral equation, providing detailed insights into the underlying physics of droplet coalescence. Furthermore, we integrate Response Surface Methodology (RSM) to effectively optimize droplet coalescence dynamics, harnessing the power of machine learning algorithms. Our results showcase the efficacy of these computational techniques in enhancing experimental efficiency. Through rigorous evaluation utilizing Regression Coefficient and Mean Absolute Error metrics, we ascertain the accuracy of our estimations. Our findings highlight the significant influence of key parameters, specifically the non-dimensional initial distance of the droplets (D), viscosity ratio (μ), Capillary number (Ca), and width (w), as identified by the non-dimensional final droplet-droplet spacing (DD), velocity of the first droplet (V_FD), and velocity of the second droplet (V_BD), respectively. This comprehensive approach provides valuable insights into droplet coalescence phenomena and offers a robust framework for optimizing microfluidic systems. The most influential parameters on DD are the values of A_d and D, while viscosity has the lowest influence on DD. The most influential parameters on droplet velocity are viscosity and channel width, whereas the initial distance and Ca have the least influence on droplet velocity. The comparison of different machine learning algorithms indicates that the best ones for predicting DD, V_FD, and V_BD are function, SMOreg, Lazy-IBK, and Meta-Bagging, respectively. © 2025 The

关键词： Coalescence Droplet Machine learning Microchannel Optimization

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Intelligent chlorination in water distribution networks: Integrating genetic algorithm and EPANET fusion

Kuwait Journal of Science

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Kuwait Journal of Science 2025年第3期52卷

作者： Chahkandi, Benyamin Montazeri Namin, Masoud Gheibi, Mohammad Dhoska, Klodian Moezzi, Reza Fathollahi-Fard, Amir M. School of Civil Engineering University of Tehran Tehran 14174-66191 Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Studentská 1402/2 Liberec 461 17 Czech Republic Faculty of Mechanical Engineering Polytechnic University of Tirana Tirana Albania Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Liberec 461 17 Czech Republic Association of Talent Under Liberty in Technology (TULTECH) Tallinn 10615 Estonia Institute of Forestry and Engineering Estonian University of Life Sciences Tartu 51006 Estonia Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa 999078 Macao Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 Gdansk 80-233 Poland

The primary goal of chlorination, a chemical treatment process employed in water distribution networks (WDNs), is to safeguard public health by disinfecting potable water. This research delves into the hydraulic and qualitative analysis of WDNs in the Golestan district of Ahwaz city, Iran, utilizing a demand-based methodology integrating genetic algorithm (GA) and Environmental Protection Agency Network Evaluation Tool (EPANET) simulation software. The investigation incorporates varied concentrations of booster chlorination, and a re-qualitative analysis is subsequently performed. Three distinct scenarios, each employing one, two, and three booster pumps, are conceptualized. A quality performance indicator is developed to enhance comprehension and facilitate a comprehensive water assessment. To optimize the quality performance index (QPI), the GA is employed with an objective function, i.e., determining the most effective placement of chlorine injection pumps. The results reveal that the network performance index achieved percentages of 78 %, 78 %, and 80 % across the three scenarios. Moreover, the water quality in the second and third scenarios attains notably high acceptability levels, highlighting the efficacy of the proposed chlorination strategy. © 2025 The Authors

关键词： Biological water quality Chlorination EPANET modeling Genetic algorithm Smart system Water distribution network

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An epidemiological model of SIR in a nanotechnological innovation environment

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Heliyon 2025年第3期11卷 e42309页

作者： Svoboda, David Havelka, Ondřej Holendová, Julie Kraft, Jiří Faculty of Economics Technical University of Liberec Voroněžská 13 Liberec 460 01 Czech Republic Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Studentská 1402/2 Liberec 461 17 Czech Republic Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Studentská 1402/2 Liberec 461 17 Czech Republic Zittau Görlitz University of Applied Sciences Faculty of Business Administration and Industrial Engineering Theodor-Körner-Allee 16 Zittau 02763 Germany

Today, when nanotechnological innovation, in particular, faces stringent regulations, the question arises concerning a tool that can quantify individual interventions and thus complement current knowledge in the diffusion theory of innovation. This paper examines the complex nature of innovation diffusion in a rapidly evolving technological environment. The research presents current knowledge in the field linking diffusion of innovation theory and the basic epidemiological model of SIR (Susceptible, Infected, Recovered). Epidemiological models, originally developed to study the spread of infectious diseases, offer intriguing parallels to innovation diffusion due to shared characteristics in propagation dynamics. Integrating the SIR epidemiological model into the current theoretical framework allows the SIR model to be considered as a tool capable of filling current gaps in the literature. Nanotechnological innovations are chosen because of their significant impact on society, which faces unique market entry challenges. Within the framework of high interdisciplinarity, nanotechnologies, like viruses, tend to 'mutate' into different industries where their 'infectivity' varies. The case of nanotechnology serves to illustrate the usefulness of the proposed model and shows how factors that influence the spread of viruses can similarly affect the adoption of technological innovations. Similar characteristics in the propagation framework between innovations and viruses can serve as one of many arguments for the use of the SIR model in this field. Using an integrative review, aspects that have the potential to add to the SIR model in the current literature are identified. By combining epidemiological findings with innovation theory, the paper contributes to a richer and more integrated understanding of the phenomena of diffusion of nanotechnological innovations. The motivation is to open a debate regarding the ability of the epidemiological model of SIR to reveal the impact

关键词： Bass model Diffusion of innovation Nanotechnology SIR model

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Real-time traffic classification based on cosine similarity using sub-application vectors

Real-time traffic classification based on cosine similarity ...

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4th International Workshop on Traffic Monitoring and Analysis, TMA 2012

作者： Beşiktaş, Cihangir Mantar, Haci Ali Department of Computer Engineering Gebze Institute of Technology Turkey Center of Research for Advanced Technologies of Informatics and Information Security Tubitak Bilgem Turkey

ISBN: (纸本)9783642285332

Internet traffic classification has a critical role on network monitoring, quality of service, intrusion detection, network security and trend analysis. The conventional port-based method is ineffective due to dynamic port usage and masquerading techniques. Besides, payload-based method suffers from heavy load and encryption. Due to these facts, machine learning based statistical approaches have become the new trend for the network measurement community. In this short paper, we propose a new statistical approach based on DBSCAN clustering and weighted cosine similarity. Our experimental test results show that the proposed approach achieves very high accuracy. © 2012 Springer-Verlag.

关键词： Intrusion detection

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Metrics-based analysis of thread behavior using an aspect-oriented programming approach

Metrics-based analysis of thread behavior using an aspect-or...

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IEEE International Conference on Computer Science and Automation engineering

作者： Alan, Oral Erdoǧan, Nadia Information Technologies Institute Research Center for Advanced Technologies on Informatics and Information Security Kocaeli Turkey Computer Engineering Department Faculty of Electrical and Electronic Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781424487257

Understanding thread behavior makes multi-threaded programming easier. Thread behavior can be observed by calculating metrics that are driven from thread state changes and time durations spent in those states. This paper presents the design and implementation of a new thread profiler that builds on aspect-oriented programming (AOP) approach and its use on multi-threaded applications. The new profiler differs from existing profilers in that it adopts a metrics based approach, calculating certain metrics for threads profiled. Also presented is a method to retrieve exact time information at the machine level from the JVM context. The paper concludes with a description of results obtained for thread profiling and metric calculations and a comparison of the overhead the profiler introduces with that of other profilers. © 2011 IEEE.

关键词： Aspect oriented programming

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DCT and DWT based image compression in remote sensing images

DCT and DWT based image compression in remote sensing images

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Antennas and Propagation Society International Symposium

作者： I. Hacihaliloglu M. Karta Advanced Technologies in Engineering Informatics Institute Istanbul Turkey

With the improvement of synthetic aperture radar technology, larger areas are being imaged and the resolution of the images has increased. Larger images have to be transmitted and stored. Due to the limited storage and downlink capacity on the airplane or satellite, the volume of data must be reduced. This makes compression of SAR images with minimal loss of information important. This study aims to compare the best-known compression techniques, namely discrete cosine transform and discrete wavelet transform. It investigates RADARSAT and SPOT images of different regions of different characteristics. The regions investigated are sea areas, forest areas, and residential and industrial areas which define different patterns of urban land use. The studies showed that compression ratios changed according to the pixel classification. The second purpose of this study is to compare the two compression algorithms. The DWT based algorithm gave the minimum mean square error compared to the DCT based compression algorithm. The results changed according to the quantization process and the transform-coding algorithm.

关键词： Discrete cosine transforms Discrete wavelet transforms Image coding Remote sensing Compression algorithms Image resolution Downlink Airplanes Satellites Mean square error methods

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A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale

Artificial Intelligence in Geosciences

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Artificial Intelligence in Geosciences 2025年第1期6卷

作者： Khadija Meghraoui Teeradaj Racharak Kenza Ait El Kadi Saloua Bensiali Imane Sebari Research Unit of Geospatial Technologies for A Smart Decision. IAV Hassan II Rabat 10101 Morocco Advanced Institute of So-Go-Chi (Convergence Knowledge) Informatics Tohoku University Miyagi Japan School of Information Science Japan Advanced Institute of Science and Technology Ishikawa Japan School of Geomatics and Surveying Engineering. IAV Hassan II Rabat 10101 Morocco Department of Applied Statistics and Computer Science. IAV Hassan II Rabat 10101 Morocco

Crop-yield is a crucial metric in agriculture, essential for effective sector management and improving the overall production process. This indicator is heavily influenced by numerous environmental factors, particularly those related to soil and climate, which present a challenging task due to the complex interactions involved. In this paper, we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction. This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods, specifically representation learning techniques, along with predictions derived from remote sensing imagery. We tested our proposed methodology on a public dataset centered on corn, aiming to predict crop-yield. Our developed smart model achieved promising results in terms of crop-yield prediction, with a root mean squared error (RMSE) of 1.72, outperforming the baseline models. The ontology-based approach achieved an RMSE of 1.73, while the remote sensing-based method yielded an RMSE of 1.77. This confirms the superior performance of our proposed approach over those using single modalities. This integrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence (AI) represents a significant advancement in agricultural applications. It is particularly effective for crop-yield prediction at the field scale, thus facilitating more informed decision-making in advanced agricultural practices. Additionally, it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.

关键词： Crop-yield prediction Neuro-symbolic AI Ontology Ontology embedding Satellite imagery Machine learning

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