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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Radha Mahendran Ramanakar Reddy Danda Asma Anjum Rashmi Abhay Kamde Bhagyashree Shailesh Firake Jayshree Suhas Wasnik Department of Bioinformatics Vels Institute of Science Technology and Advanced Studies Chennai India Architect Hyderabad Telangana India Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India Department of Electrical Engineering K J Somaiya Polytechnic Mumbai Maharashtra India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Sustainable agriculture requires new strategies to tackle difficulties such as rising food demand, environmental pressures, and resource constraints. This study introduces a sophisticated approach that combines artificial neural networks (ANNs) with genetic advancements to enhance crop characteristics. The system attains enhanced accuracy (98.1%), F1 score (0.97), and reduced RMSE (0.012) by utilising high-dimensional genetic, phenotypic, and environmental data, surpassing baseline models. The technique integrates ANN predictions with gene-editing frameworks, guaranteeing accurate interventions and effective trait optimisation. The results demonstrate considerable improvements in forecast accuracy and resource efficiency compared to current techniques, creating a scalable, data-driven framework to promote sustainable agriculture practices worldwide.

关键词： Precision agriculture Technological innovation Adaptation models Accuracy Visual analytics Crops Artificial neural networks Genetics Sustainable development Optimization

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Mahendran, Radha Danda, Ramanakar Reddy Anjum, Asma Kamde, Rashmi Abhay Firake, Bhagyashree Shailesh Wasnik, Jayshree Suhas Vels Institute of Science Technology and Advanced Studies Department of Bioinformatics Chennai India Architect Telangana Hyderabad India Hkbk College of Engineering Visvesvaraya Technological University Department of Computer Science and Engineering Bangalore India K J Somaiya Polytechnic Department of Electrical Engineering Maharashtra Mumbai India

ISBN: (纸本)9798331521394

关键词： Precision agriculture

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Functional Unit: A New Perspective on Materials Science Research Paradigms

arXiv

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arXiv 2025年

作者： Ye, Caichao Feng, Tao Liu, Weishu Zhang, Wenqing Department of Materials Science and Engineering Institute of Innovative Materials Southern University of Science and Technology Shenzhen518055 China Academy for Advanced Interdisciplinary Studies Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and Technology Shenzhen518055 China

New materials have long marked the civilization level, serving as an impetus for technological progress and societal transformation. The classic structure-property correlations were key of materials science and engineering. However, the knowledge of materials faces significant challenges in adapting to exclusively data-driven approaches for new material discovery. This perspective introduces the concepts of functional units (FUs) to fill the gap in understanding of material structure-property correlations and knowledge inheritance as the "composition-microstructure" paradigm transitions to a data-driven AI paradigm transitions. Firstly, we provide a bird’s-eye view of the research paradigm evolution from early "process-structure-properties-performance" to contemporary data-driven AI new trend. Next, we highlight recent advancements in the characterization of functional units across diverse material systems, emphasizing their critical role in multiscale material design. Finally, we discuss the integration of functional units into the new AI-driven paradigm of materials science, addressing both opportunities and challenges in computational materials innovation. Copyright © 2025, The Authors. All rights reserved.

关键词： Materials properties

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Enhancing Employee Retention with AI: Predictive Analytics and Decision Support Systems

Enhancing Employee Retention with AI: Predictive Analytics a...

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Automation and Computation (AUTOCOM), International Conference on

作者： J Bamini A. Govindarajan Bala Murugan D Sudeshna Seal Jason Elroy Martis S. Meenakshi Director-Management Studies Sri Ramakrishna College of Arts & Science Coimbatore India MEASI Institute of Management RoyapattahChennai India School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamilnadu India OmDayal Group of Institutions Howrah West Bengal India Department of ISE NMAM Institute of Technology Nitte Deemed to be University Mangaluru Karnataka India Department of Science and Humanities R.M.K. Engineering College Kavarapettai Tamilnadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Maintaining a competitive advantage over their respective rivals depends on companies giving PADS (Predictive Analytics and Decision Support Systems) top priority. Conversely, experts in human resources often find it difficult to predict, from the current data, the expectations of employees. This work targets an original PADSS idea, Deep Decision Tree (DDT), with an aim of extracting already existing data. The authors of this work used the DDT method since it is efficient in handling big datasets. By means of an analysis of a range of data including publicly employed individuals, companies, and products that make use of technology, this paper aims to show how DDT could be able to better fulfill the employment requirements of the future. When compared to other approaches regarded to be quite conventional, the DDT method provides better accuracy and scalability. Professionals in the field of human resources can then use this data to make well-informed decisions about the configuration of digital workforce. This work significantly advances the field by laying a fresh basis for PADSS by means of advanced machine learning techniques.

关键词： Decision support systems Accuracy Automation Scalability Employment Machine learning Companies Decision trees Data mining Predictive analytics

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Evaluating environmental impacts and techno-economic feasibility of an integrated and novel wastewater and sludge treatment system for circular economy objectives

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Computers and Industrial engineering 2025年 204卷

作者： Ahmad, Shamraiz Daddi, Tiberio Novi, Alessio Marrucci, Luca Sant'Anna School of Advanced Studies Institute of Management Piazza Martiri della Libertà 33 Pisa56127 Italy Ergo S.r.l. Via Mario Giuntini 25/29 PI Cascina56127 Italy School of Mechanical and Manufacturing Engineering National University of Sciences and Technology Islamabad44000 Pakistan Golisano Institute for Sustainability Rochester Institute of Technology RochesterNY14623 United States

Wastewater and sewage sludge present major global challenges today, necessitating innovative approaches and systems, especially those aligned with circular economy principles. In order to identify the most sustainable and circular solutions, there is an urgent need for research that investigates such novel systems and technologies, especially the use of hydrothermal carbonization (HTC) in wastewater and sludge treatment processes. Currently, no study has analyzed the environmental impacts and techno-economic feasibility of such an integrated system at a large scale within municipal and a centralized setting. Thus, this study aims to fill this gap by examining a HTC-based circular model through a life cycle assessment to quantify the environmental impacts and employing a life cycle costing approach for economic evaluation. We compare a baseline scenario with a circular HTC-integrated scenario using three alternative strategies that vary by mud/sludge age of 10, 20, and 40 days. Overall, the results revealed that the waterlines generated more environmental impacts than the sludgelines, primarily due to high energy and chemical consumption. The most significant impact categories identified were climate change, marine eutrophication, use of fossil resources and acidification. The model with a 10-day sludge age emerged as the optimal strategy for the circular HTC-integrated scenario. In the baseline scenario, the largest cost was related to sludge disposal. In contrast, the HTC-based solution significantly reduced both disposal and energy costs, while also showing potential for revenue generation by selling recovered hydrochar. This novel system has the potential to save three million euros annually, with a payback time of less than three years. In addition, the cost of environmental externalities was reduced from €0.073/m3 in the baseline scenario to €0.061/m3 for the innovative HTC-integrated system. © 2025 Elsevier Ltd

关键词： Circular economy

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Improvement of Lattice Parameter Accuracy in Single Crystal XRD Based on a Laser-Induced X-Ray Source

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高压物理学报 2025年第4期39卷 9-15页

作者： LIU Jin WANG Qiannan LI Jiangtao National Key Laboratory of Shock Wave and Detonation Physics Institute of Fluid PhysicsChina Academy of Engineering PhysicsMianyang 621999SichuanChina Pico Electron Microscopy Center Innovation Institute for Ocean Materials Characterization TechnologyCenter for Advanced Studies in Precision InstrumentHainan UniversityHaikou 570228HainanChina State Key Laboratory for Environment-Friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010SichuanChina

The lattice parameter,measured with sufficient accuracy,can be utilized to evaluate the quality of single crystals and to determine the equation of state for *** propose an iterative method for obtaining more precise lattice parameters using the interaction points for the pseudo-Kossel pattern obtained from laser-induced X-ray diffraction(XRD).This method has been validated by the analysis of an XRD experiment conducted on iron single ***,the method was used to calculate the compression ratio and rotated angle of an LiF sample under high pressure *** technique provides a robust tool for in-situ characterization of structural changes in single crystals under extreme *** has significant implications for studying the equation of state and phase transitions.

关键词： lattice parameter measurement accuracy single crystal X-ray diffraction iterative algorithm high pressure ratio of compression

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Implementing and Managing Green IT Strategies to Enhance Corporate Sustainability

Implementing and Managing Green IT Strategies to Enhance Cor...

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： K. Sudharson N. Vijayakumar L. Anitha V. Subhashini G. Madhumita A. SureshKumar Department of AIML R.M.D. Engineering College Chennai India Department of Commerce B.S.Abdur Rahman Crescent Institute of Science & Technology Chennai India Department of MBA JNN Institute of Engineering Chennai India Department of Accounting and Finance Dwaraka Doss Goverdhan DossVaishnav College Chennai India School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS Chennai India Department of MBA VelTech HighTech Dr. Rangarajan Dr. Sankunthala Engineering College

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Importance of Green IT practices is that it improves corporate sustainability through conserving the natural environment and minimizing the use of resource. This paper focuses on next generation IT management for power-optimized hardware, virtualization, cloud computing, and intelligent system through SITOF. Some of the key technologies are: ARM-based low power servers, vSphere-server visualization, Docker- Containerization, Solar/wind hybrid power. It shows that the main benefits of the IT of smart buildings are a 35% saving in energy and 40% reduction in carbons, as well as a quarter decrease in operation costs when compared to traditional IT practices. Moreover, data analytic improve performance measurement and management and hence contributing to an improvement in sustainability issues. These results also show that SITOF is feasible to assist firms in attaining corporate sustainability objectives while embracing the best practice of technology management.

关键词： Measurement technology management Smart buildings Green products Hybrid power systems Servers Sustainable development Virtualization Intelligent systems Next generation networking

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Amelioration of Mechanical Properties of Rolled Mg-4.5Al-2.5Zn Alloy by Cryogenic Cycling Treatment

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Acta Metallurgica Sinica (English Letters) 2025年 1-17页

作者： Pang, Haoran Lu, Liwei Yang, Gongji Wang, Xiaojun Wang, Wen Zhang, Hua Wu, Yujuan Sanya Institute of Hunan University of Science and Technology Sanya China National Key Laboratory of Precision Hot Processing of Metal Harbin Institute of Technology Harbin China Hunan Rongtuo New Material Research Co. Ltd. Xiangtan China School of Metallurgical Engineering National and Local Joint Engineering Research Center for Functional Materials Processing Xi’an University of Architecture and Technology Xi’an China Institute for Advanced Studies in Precision Materials Yantai University Yantai China National Engineering Research Center of Light Alloy Net Forming and State Key Laboratory of Metal Matrix Composites Shanghai Jiao Tong University Shanghai China

In this study, cryogenic cycling treatment was used to process the hot-rolled Mg-4.5Al-2.5Zn alloy sheets to research the influence on mechanical properties and microstructure. Optical microscopy, electron back-scatter diffraction and transmission electron microscopy were applied to characterize the microstructures and analyze the mechanisms. The consequences indicate that the cryogenic cycling treatment has significantly influence on improving the mechanical properties. With the cycle of cryogenic cycling treatment increasing to 5 cycles, the sample processed by 3 cycles presents the highest ductility (~ 18.6%), while the 4-cycle one shows the highest strength (~ 311.8 MPa). The improvement can be attributed to fine grains, introduced high-density dislocation, 9.8%-fraction low-angle grain boundaries (LAGBs), the precipitation of Mg17Al12 phase and the texture with the intensity of 17.5. Although the average grain sizes of the samples processed by cryogenic cycling treatment have no obvious difference, internal stress variations induced by cryogenic cycling treatment significantly influence LAGBs, the basal texture evolution, and the prismatic < a > slip, pyramidal < c > slip and pyramidal < c + a > slip activation.

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Optimized Alkaline Synthesis of Hydrotalcite-like CO2 Adsorbents from Blast Furnace Slag and the Influence of Interlayered Silicon on Structure and Adsorption Performance

SSRN

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SSRN 2025年

作者： Hua, Han Chen, Hao Su, Yiping Yan, Feng Shen, Xuehua Quan, Zewei Zhang, Zuotai School of Environmental Science and Engineering Key Laboratory of Municipal Solid Waste Recycling Technology and Management of Shenzhen City Southern University of Science and Technology Shenzhen518055 China College of Materials Science and Engineering Xi’an University of Architecture and Technology Xi’an710055 China Department of Chemistry Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen518055 China Guizhou Green Industry Technology Institute Guiyang550014 China

Synthesizing hydrotalcite-like (HT) CO2 adsorbents from blast furnace slag (BFS) is regarded as a promising strategy for valorizing industrial waste while mitigating CO2 emissions in steel industry. However, conventional methods via acid extraction face challenges due to their high reagent consumption and energy-intensive processes. This study proposes an economical alkaline hydrothermal synthesis method for direct conversion of wet-milled BFS slurry into HT-like adsorbents. Synthesized BFS-HT achieves a total CO2 adsorption capacity of 0.27 mmol CO2/g BFS-LDO after calcination at 400 ℃ and subsequent CO2 capture at 200 ℃, representing 68.8% increase compared to unmilled group. HT phases synthesized in Si-rich solutions exhibit a consistent crystalline structure with chemical-sourced layered double hydroxides (syn-LDH). A hydrothermal post-treatment in deionized water partially removes interlayer Si, further increasing CO2 uptake to 0.30 mmol CO2/g BFS-LDO27Al nuclear magnetic resonance (NMR) analysis identifies Al coordination changes during calcination of HT under the impact of interlayer Si by forming Al-O-Si bond. Density functional theory (DFT) modeling reveals the strong interfacial interactions between defective CaO surfaces and silica nanoparticles, potentially inhibiting subsequent carbonation or rehydration reactions. Overall, alkaline-synthesized BFS-HT achieves 24% of the CO2 sorption capacity of syn-LDH, with much lower consumption of acid/alkaline reagents and shorter hydrothermal treatment times compared to conventional acid methods. These results underscore its promising potential for carbon neutralization in the steel industry. © 2025, The Authors. All rights reserved.

关键词： Blast furnaces

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A Web 3.0 Integrated Blockchain Enabled Access System Augmented by Meta-Heuristic Cognitive Learning Framework for Mitigating Threats in IoT Enabled Consumer Electronic Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Aruna, Etikala Sahayadhas, Arun Kalpana, Ponugoti Khan, Surbhi B. Quasim, Tabrez Almusharrf, Ahlam VELS Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Pallavaram Chennai India AVN Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad501510 India University of Salford School of Science Engineering and Environment SalfordM5 4WT United Kingdom Chandigarh University University Centre for Research and Development Mohali India Chitkara University Centre for Research Impact and Outcome India University of Bisha College of Computing and Information Technology Department of Computer Science and Artificial Intelligence Bisha Saudi Arabia King Khalid Univeersity Saudi Arabia

Consumer Electronic Devices have become an open network model because of the infusion of the Internet of Things (IoT) and other communication technologies such as 5G/6G. Though these devices have provided the high-end sophistication even to common person, but it has proved its darker side by triggering more security breaches and privacy problems. Hence, securing and authenticating these Internet enabled consumer devices has become a probable issue to be solved for safer and secured communication. Therefore, this paper presents a novel fusion of Web 3.0-based Blockchain (WBC) and Deep learning (DL) technique for securing consumer electronic devices in an IoT ecosystem. The proposed framework k(MTD-BCAM) is devised into two components: Multiple-Threat Detection(MTD) and Access Management Mechanism(AMM). In the first component, a DL model is applied for threat detection, whereas WBC is meant for an efficient authentication process. Furthermore, a novel residual fast-gated recurrent neural network is proposed. To reduce the complexity, the komodo Mlipir optimization (KMO) approach is used to tune the hyper-parameters of the network. The comprehensive experimental outcome study of the proposed approach employs NSL-KDD datasets in which the distinct metrics of both DL and Blockchain (BC) are measured and analyzed. Results demonstrated the superior accuracy of the model by achieving 99.78% with less computational time and higher transaction speed. Additionally, the statistical validation and security strength of the model are also analyzed and examined with the varied state-of-art models. © 2025 IEEE. All rights reserved.

关键词： Recurrent neural networks

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