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Advancements in characterization Techniques, empirical Models, and Artificial intelligence for comprehensive understanding of heavy metal adsorption on sewage sludge biochar

Waste Management Bulletin

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Waste management Bulletin 2025年第1期3卷 193-206页

作者： Shanmughan, Bhavana Nighojkar, Amrita Kandsubramanian, Balasubramanian Nano Surface Texturing Lab Department of Metallurgical and Materials Engineering Defence Institute of Advanced Technology (DU) Pune India Management Analytics and AI Lab Department of Technology Management Defence Institute of Advanced Technology (DU) Pune India

Heavy metal-containing industrial effluents, such as Pb2+, Cd2+, and Cu2+, pose serious threats to the environment and public health since they are not biodegradable and can accumulate over time. Biochar, particularly... 详细信息

Heavy metal-containing industrial effluents, such as Pb²⁺, Cd²⁺, and Cu²⁺, pose serious threats to the environment and public health since they are not biodegradable and can accumulate over time. Biochar, particularly sewage sludge-derived biochar (SSBC), has arisen as a promising and cost-effective material for heavy metal removal from wastewater due to its high adsorption capacity, large surface area, and rich porous structure. This review explores the use of SSBC for the adsorption of heavy metals, highlighting the impact of pyrolysis temperature on its surface properties, such as specific surface area and functional groups. Characterization techniques, including SEM, FTIR, XRD, XPS, AES, GC–MS, ICP, and ESR, are employed to analyze the chemical and structural properties of SSBC, providing insights into the changes that enhance its adsorption performance. Additionally, Artificial Neural Network (ANN) models are utilized to portend the adsorption efficiency of SSBC, offering a quantitative understanding of the relationship between heavy metal removal efficiency and biochar properties. This review emphasizes the importance of pyrolysis in optimizing SSBC for wastewater treatment and demonstrates how advanced characterization techniques and predictive models can guide the progress of more efficient biochar-based adsorbents for environmental remediation. The results highlight the promising role of SSBC in providing a sustainable remedy for heavy metal contamination in industrial wastewater. © 2025 The Authors

关键词： Artificial Neural Networks Auger Pyrolysis SEM Wastewater

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Artificial Intelligence in Maritime Anomaly Detection: A Decadal Bibliometric Analysis (2014–2024)

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Journal of The Institution of Engineers (India): Series C 2025年第2期106卷 665-689页

作者： Thakur, Aman Singh Alex, T. Lawrence Nighojkar, Amrita Department of Mechanical Engineering National Institute of Technology Raipur Raipur India R & D Management and AI Analytics Research Group Defence Institute of Advanced Technology Pune India

This investigation builds upon scholarly research on AI-integrated anomaly detection in maritime safety operations from 2014 to 2024. The bibliometric analysis analyzes key Countries, Journals, Research themes, Keyword frequency, Co-citation analysis, Impactful authors, and significant collaborations among researchers and relevant institutes for Maritime Anomaly Detection Research. Our findings highlight substantial contributors, including China, France, Italy, and the USA, emphasizing essential collaboration networks and influential journals such as IEEE Transactions on Intelligent Transportation Systems and the International Journal of Approximate Reasoning. We scrutinize the application of various AI and machine learning models, such as LSTM, CNN, and SVM, across different anomaly types. These models are frequently implemented to detect environmental, navigation, cybersecurity, sensor, operational, communication, and behavioural anomalies. For navigation anomalies like unexpected course changes and speed variations, LSTM is utilized due to its capability to handle sequential data. CNN is employed for spatial-temporal pattern recognition in navigation routes, while SVM is applied to classify navigation patterns and detect deviations. Similarly, LSTM effectively identifies security anomalies, including unauthorized boardings and suspicious activities, through sequence modelling, and CNN proves helpful for spatial analysis. This study provides comprehensive insights into the current state of maritime anomaly detection research. It proposes future research areas, emphasizing model accuracy and efficiency enhancements, developing hybrid models, and addressing emerging challenges. Our analysis aims to guide researchers and practitioners by identifying critical areas for advancement and fostering potential collaborative efforts in this field. © The Institution of Engineers (India) 2025.

关键词： Deep learning

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A new optimal allocation of DGs in distribution networks by using coot bird optimization method

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Energy Informatics 2023年第1期6卷 30页

作者： Memarzadeh, Gholamreza Arabzadeh, Mohammadreza Keynia, Farshid Department of Power and Control Engineering Graduate University of Advanced Technology Kerman Iran Department of Energy Management and Optimization Institute of Science and High Technology and Environmental Sciences Graduate University of Advanced Technology Kerman Iran

Energy is one of the most important topics in the world today and is considered as one of the most effective factors for the development of countries. Due to the limitation of non-renewable energy sources and undesirable effects of consuming these resources on the environment, the strategy of countries has changed towards the use of renewable energy. Renewable energy sources do not decrease over time and operate independently of price fluctuations and are more available, thus being able to play a greater role in modern power systems. Therefore, the optimal location and use of these resources will have an impact on modifying the parameters of the power grid. In this paper an analytical approach for optimal placement and sizing of distributed generation (DG) in power distribution networks to minimize the power loss, bus voltage limits, DG capacity limits, current limits, and DG penetration limit. In the first step, determines the DG capacity causing maximum benefit at different buses, and then selects the best location for DG placement which corresponds to highest benefit in the buses. This method is applicable for sizing and siting of single as well as multiple DG units. The coot bird optimization method (CBOM) is proposed for solving optimal placement, size, and power factor (PF) of DG in distribution network. The suggested method is tested on the IEEE 33-bus, 69-bus, Distribution Networks. The proposed CBOM method has good performance to find optimal placement, size, and PF of DG and it can be applied for various distribution system. © 2023, Springer Nature Switzerland AG.

关键词： Birds

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Bifunctionally Hydrophobic MOF-Supported Platinum Catalyst for the Removal of Ultralow Concentration Hydrogen Isotope

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Energy & Environmental Materials 2025年第2期8卷 140-150页

作者： Huiryung Heo Jeong-un Jang Euna Jeong Hyung-Ju Kim Young Jin Kim Chan Woo Park Jungseob So Dong-Yeun Koh Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Korea Decommissioning Technology Division Korea Atomic Energy Research InstituteDaejeon 34057Korea Department of Chemical Engineering and Applied Chemistry Chungnam National UniversityDaejeon 34134Korea Department of Environmental Engineering Kyungpook National UniversityDaegu 41566Korea CO_(2)&Energy Research Center Korea Research Institute of Chemical Technology(KRICT)Daejeon 34114Korea Saudi Aramco-KAIST CO_(2)Management Center Daejeon 34141Korea

Water often presents significant challenges in catalysts by deactivating active sites,poisoning the reaction,and even degrading composite *** challenges are amplified when the water participates as a reactant and is fed as a liquid phase,such as trickle bed-type reactors in a hydrogen-water isotope exchange(HIE)*** key balance in such multiphase reactions is the precise control of catalyst design to repel bulk liquid water while diffusing water ***,a platinum-incorporated metal-organic framework(MIL-101)based bifunctional hydrophobic catalyst functionalized with long alkyl chains(C_(12),dodecylamine)and further manufactured with poly(vinylidene fluoride),Pt@MIL-101-12/PVDF,has been developed which can show dramatically improved catalytic activity under multi-phase reactions involving hydrogen gas and liquid ***@MIL-101-12/PVDF demonstrates enhanced macroscopic water-blocking properties,with a notable reduction of over 65%in water adsorption capacity and newly introduced liquid water *** exhibiting a negligible increase in mass transfer resistance,i.e.,bifunctional *** catalytic activity,evaluated via HIE reaction,and its durability underscore the impact of bifunctional *** situ DRIFTS analysis elucidates water adsorption/desorption dynamics within the catalyst composite,highlighting reinforced water diffusion at the microscopic level,affirming the catalyst's bifunctionality in different length *** demonstrated radiation resistance,Pt@MIL-101-12/PVDF emerges as a promising candidate for isotope exchange reactions.

关键词： bifunctional hydrophobiccatalyst hydrogen-waterisotopeexchange hydrophobic modification metal-organic framework tritiumremoval

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Facial Landmark-Based Human Emotion Recognition Technique for Oriented Viewpoints in the Presence of Facial Attributes

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SN Computer Science 2023年第3期4卷 273页

作者： Sharma, Utkarsh Faisal, Kazi Newaj Sharma, Rishi Raj Arya, K.V. Department of Electronics Engineering Defence Institute of Advanced Technology Pune 411025 India ABV Indian Institute of Information Technology and Management Gwalior 474015 India

With the expansion of machine learning and deep learning technology, facial expression recognition methods have become more accurate and precise. However, in a real-case scenario, the presence of facial attributes, weakly posed expressions and variation in viewpoint can significantly reduce the performance of those systems designed only for frontal faces without facial attributes. A facial landmark distance-based model is proposed in this paper to explore a new method that can effectively recognize emotions in oriented faces with facial attributes. The proposed model computes distance-based features utilizing the inter-spaces between facial landmarks generated by a face mesh algorithm from pre-processed images. These features are normalized and ranked to find the optimal features for classifying emotions. The experimental results exhibit that the proposed model can effectively classify different emotions in the IIITM Face dataset with an overall accuracy of 61% using the SVM classifier (vertically oriented with different facial attributes). The model also classifies emotions posed in front, up, and down orientations with 70%, 58%, and 55% accuracy, respectively. The efficacy test of the model on laterally oriented faces from the KDEF database results in an overall accuracy of 80%. Comparison with the existing CNN and facial landmark-based method reveals that the proposed model exhibits an improved recognition rate for the oriented viewpoints with facial attributes. Considering the results of the proposed model, it is apparent that vertical viewpoints, forcing level of expression, and facial attributes can limit the performance of emotion recognition algorithm in realistic situations. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Emotion recognition Facial attributes Facial expression recognition (FER) Facial landmarks Vertical orientation

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Design of a machine learning to classify health beverages preferences for elderly people: an empirical study during COVID-19 in Thailand

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International Journal of Industrial and Systems engineering 2022年第3期42卷 319-337页

作者： Kengpol, Athakorn Klunngien, Jakkarin Advanced Industrial Engineering Management Systems Research Center Department of Industrial Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Thailand

This research designed a decision support system based upon a machine learning (DSS-ML) model for classifying health beverage preferences for elderly people. A neural network was designed involving training using particle swarm optimisation (PSO) in comparison with two ML models: logistic regression (LR) and a neural network (NN). The DSS-ML model was able to classify accurately and autonomously the preference complexities associated with the health beverage preferences for elderly people in accordance with the WHO’s recommendation. In terms of contribution, the results demonstrated that NN training with PSO resulted in a higher ability to classify the preferences for health beverages than for the two ML models. Furthermore, NN training with PSO achieved faster convergence than NN. The benefits of this research can be separated into two parts. First, manufacturers can introduce beverages that satisfy elderly people’s preferences. Second, elderly people can be made aware of appropriate health beverages. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： COVID-19

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Multi-objective optimization method for cross-workshop linkage production of partially flexible free-forging with forward single-machine scheduling

Multi-objective optimization method for cross-workshop linka...

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作者： Cheng, Qiang Kan, Hongmei Ren, Jiaxiang Liu, Zhifeng Zhang, Yueze Cheng, Chenyang Institute of Advanced Manufacturing and Intelligent Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Advanced Manufacturing Technology Beijing University of Technology Beijing100124 China Department of Industrial Engineering and Management National Taipei University of Technology Taipei10608 Taiwan

Forging is an important sector in China's machinery manufacturing industry. To complete the processing of forgings, it is often necessary to go through multiple processes, which are commonly performed by different workshops. Due to the complexity of cross-workshop production, there are few studies on cross-workshop scheduling in the forging industry. Therefore, in order to realize resource sharing and collaborative production between multiple workshops, and improve the overall production efficiency and resource utilization rate, it is very important to optimize the scheduling of linked cross-workshop production. In this paper, a new cross-workshop partial flexible hammer forging scheduling model (CSPFH-FSM) is established to solve the scheduling problem of linked cross-workshop production with production time and energy consumption serving as the overall optimization goals in the whole partially flexible free forging production line (P3FPL). A single-machine forward-prediction variable genetic operator NGSA-II algorithm (SPVGO-NGSA II) is proposed to solve the multi-objective optimization problem of partially flexible production, in which the variable genetic operator is added to the effective coding, and the search strategy is dynamically adjusted to avoid reaching locally optimal solutions. Due to the interference of maintenance and the insufficient utilization of energy after forging, a fixed maintenance disturbance and a residual temperature utilization strategy are added to the scheduling process. Finally, the optimization obtained using the proposed variable and traditional fixed genetic operators are compared for different orders, and the algorithm proposed in this paper is compared with the typical multi-objective optimization algorithms. The results validate the effectiveness of the proposed algorithm, and provide a basic scheme for the linked scheduling of the whole production line in practical applications. © 2024 Elsevier Ltd

关键词： Energy-efficient scheduling Linkage production Multi-objective optimization Partial flexibility Temperature and disturbance constraints

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A Comprehensive Study of Scene Text Recognition in Scene Text Image Super-Resolution with Parametric Frameworks

A Comprehensive Study of Scene Text Recognition in Scene Tex...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Viriyavisuthisakul, Supatta Sanguansat, Parinya Yamasaki, Toshihiko Japan Advanced Institute of Information Technology School of Information Science Nomi Japan Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand The University of Tokyo Department of Information and Communication Engineering Tokyo Japan

ISBN: (纸本)9798350324136

Scene Text Recognition (STR) is a technique to detect and recognize text in images. Predicting text in real-world scene images is challenging due to various uncontrollable environmental factors. State-of-the-art text detection and recognition models leverage deep learning and Transformer architectures, consequently achieving impressive accuracy on benchmark datasets. However, challenges persist in accurately processing text within real-world images, often due to unseen data or limited datasets. Both the limitations of STR and the quality of scene text images are crucial factors. Recently, a parametric weight and multiple parametric regularizations were proposed to improve the quality of real-world scene text images. Different from previous surveys in this area, this study has three main objectives. Firstly, to confirm the performance of parametric methods, the text recognition accuracy between with and without methods is compared by using different STR methods. Second, to make the comprehensive experiments, the outcomes of each STR method are compared to show their prediction performances. Third, several existing challenges and research directions are discussed. © 2024 IEEE.

关键词： Scene Text Detection Scene Text Recognition Super Resolution Wild images

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Comparative study on electroless composite coatings of textured and untextured Al-substrates

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Materials Today: Proceedings 2023年 80卷 233-240页

作者： Singh, Garima Mohanty, Shalini Kumar Singh, Rabesh Rai Dixit, Amit Kumar Sharma, Anuj Centre for Advanced Studies Uttar Pradesh Lucknow 226031 India Department of Mechanical and Industrial Engineering Tallinn University of Technology Tallinn 12616 Estonia Dhanbad 826004 India Department of Mechanical Engineering S R institute of management and technology Bakshi Ka Talab U.P. Lucknow 226201 India

In the present work, electroless composite coating of Ni-P-MoS2 is deposited on EDM textured Al-alloy substrate. A comparative study is carried out to analyse the effect of textures (prepared through electrical discharge machining) on the coated surface properties. The coatings were analysed on account of their surface morphology through Field Emission Scanning Electron Microscopy (FESEM), composition using Electron Dispersion Spectroscopy (EDS), surface roughness and topography by means of optical profilometer. Results indicate that the composite coatings produced on the textured surfaces have better deposition as compared to non-textured surfaces. The transfer of different elements i.e., Ni, P, Mo, and S on both the coated surfaces was confirmed from the EDS plots while the profilometer images showed the areal surface roughness parameters. © 2022

关键词： Scanning electron microscopy

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