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Covalent Organic Framework Membranes with Spatially Aligned Ionic Sites Achieve Record Thermo-Osmotic Output Power Density

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advanced Energy Materials 2025年第0期

作者： Lai, Zhuozhi Su, Haitao Xian, Weipeng Guo, Qing Meng, Qing-Wei Wu, Di Wang, Sai Sun, Qi Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China Hangzhou Institute of Advanced Studies Zhejiang Normal University Hangzhou311231 China

The advancement of nanofluidic membranes is critical for mimicking bioelectrogenic ion-channel mechanisms and boosting output power density, essential for sustainable energy applications. The energy conversion efficiency of these devices significantly relies on the ion conductivity and permselectivity of the membranes. Membranes with aligned one-dimentional (1D) pores, high pore density, and organized dangling ionic groups are theorized to offer superior ion permeability and selectivity, yet these configurations remain significantly underexplored. Herein, the successful fabrication of oriented ionic covalent organic framework (COF) membranes is presented. These membranes exhibit precisely aligned cationic and anionic sites within their pore channels, achieved through post-synthetic modification using click chemistry, which shows high ion permselectivity and conductivity. When incorporated into full-cell thermo-osmotic generators, these membranes deliver an impressive output power density of 195 W m−‍2 under a 50-fold salinity gradient (NaCl: 0.01 m ‖ 0.5 m ‖ 0.01 m) along with a 35 K temperature differential. This power output substantially increases 2.41 times to 471 W m−2 when the salinity gradient is enhanced tenfold, surpassing the performance of existing nanofluidic membranes under similar conditions and thus offering a promising avenue for enhancing efficiency in energy and resource utilization. © 2025 Wiley-VCH GmbH.

关键词： Dialysis membranes

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Design and Analysis of Gudgeon Pin to Minimize Stress Concentration by Selecting Different Material Through FEA 5th

Design and Analysis of Gudgeon Pin to Minimize Stress Concen...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Kumaraswamy, J. Siva Ramakrishna, Ch. Ramasubramanian, S. Mishra, Rahul Sakhare, D.T. Karthik, K. Baskar, S. Pandey, Krishna Kant Department of Mechanical Engineering R. L. Jalappa Institute of Technology Karnataka Doddaballapur561203 India Department of Mechanical Engineering Vignan’s Institute of Information Technology Andhra Pradesh Visakhapatnam530049 India Department of Automobile Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai600117 India Department of Mechanical Engineering IES Institute of Technology & Management IES University Madhya Pradesh Bhopal462044 India Department of Chemistry Shivaji Art’s Commerce & Science College Kannad Maharashtra Aurangabad431103 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Mechatronics Engineering Manipal University Jaipur Rajasthan Jaipur303007 India

ISBN: (纸本)9789819767311

The gudgeon pin is one of the engine's most stressed components. Gudgeon pins are used in automotive engines to link the piston and connecting rod. Stress concentration is a critical concern in the design of mechanical components, as it can lead to premature fatigue failure and, consequently, catastrophic consequences. To mitigate this risk, engineers continually seek ways to optimize the design of components like the gudgeon pin, and one avenue of exploration is the judicious choice of materials. This study looks at the detailed design and analysis of existing gudgeon pins in order to detect the pin's stress concentration and then minimize it by making necessary changes to the pin's design and mounting. The current work involved constructing a gudgeon pin and then using finite element analysis (FEA) software to examine and minimize stresses on the pin. This comprehensive study delves into the intricate world of gudgeon pin design and analysis, with a specific emphasis on minimizing stress concentration by selecting different materials through FEA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stress concentration

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Biosynthesis, therapeutic characteristics, origin and strategies to improve the yield of vasicine in plants

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Vegetos 2025年 1-15页

作者： Padmanabhan, Deepthi Manimekalai, Ramaswamy Senthil-Nathan, Sengottayan Suganthi, Muthusamy Palanisamy, Senthilkumar Department of Genetic Engineering School of Bioengineering SRM Institute of Science and Technology Kattankulathur 603203 India Molecular Biology and Bioinformatics Sugarcane Breeding Institute Tamil Nadu Coimbatore 641 007 India Division of Biopesticides and Environmental Toxicology Sri Paramakalyani Centre for Excellence in Environmental Sciences Manonmaniam Sundaranar University Tamil Nadu Alwarkurichi India Department of Biotechnology Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai 600 117 India

From the year 2016 to 2019, a total of 196 botanical drugs were registered for clinical trials, out of which 81 drugs were in phase II or III laid an environment for further trials and marketing of the drugs. Vasicine, a pyrroquinazoline alkaloidal compound found in some medicinal plants such as Justicia adhatoda and Sida cordifolia to name a few. Due to its broad range of health-promoting properties, the compound has piqued the attention of nutritionists and medicinal chemists. The compound’s recent pharmacological applications include antiviral activity against SARS-Co-V2, antimicrobial activity against E. aerogenes, S. epidermidis and P. aeruginosa, cytotoxic effect against lung adenocarcinoma and free radical scavenging activity, implying a potential role as an antioxidant. Vasicine was also discovered to have a possible anti-inflammatory property in carrageenan-induced inflammation, as well as neuroprotective efficacy in in vitro experiments on neuroblastoma cells by blocking cholinesterases in Alzheimer’s disease and improving memory and cognition. The integration of a multi-omics approach to elucidate the biosynthetic pathway and key regulatory enzymes laid the basis for metabolically engineering the entire pathway in microbial systems, as well as polyculture of microbes for robust biosynthesis strategies to overcome the limitations of traditional monoculture techniques for bioactive production. Genome editing with CRISPR Cas technologies plays a major role in the transformation of vasicine metabolite engineering to alter the process of biosynthesis and increase large-scale production in plants. © The Author(s) under exclusive licence to Society for Plant Research 2025.

关键词： Medicinal plants Natural products Novel drugs Secondary metabolites

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Assessing the Impact of Physical Configuration and Lead Time on WRF Forecasting of an Extreme Wind Event

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Journal of Meteorological Research 2025年第1期39卷 154-171页

作者： Rocío OTERO Matías SUAREZ Edgardo PIEROBON Leandro MATURANO Ignacio MONTAMAT Juan Ezequiel SANCHEZ Lucia SANDALIO Andrés RODRIGUEZ Denis POFFO Hydrometeorology Laboratory Department of ExactPhysical and Natural SciencesNational University of CordobaCórdobaX5016GCAArgentina Institute for Advanced Studies in Engineering and Technology National Scientific and Technical Research CouncilCordobaX5016GCAArgentina Radar and Remote Sensing Laboratory Department of MathematicsAstronomyPhysics and Computer ScienceNational University of CordobaCordobaX5016HUAArgentina

The central region of Argentina is known to be a source of some most extreme weather events in the world,which are partially associated with the passage of cold fronts accompanied often by extreme wind *** may cause severe property damage and even loss of human ***,there is a lack of studies that evaluate the performance of the numerical weather prediction(NWP)models such as weather research and forecasting(WRF)model in anticipating this type of weather in the *** study compares the performance of the operational WRF in Argentina using four combinations of various planetary boundary layer(PBL)and microphysics parameterization schemes under varied lead times in predicting an extreme wind event(gusts>30 m s^(-1))in Central *** results demonstrate that the WRF model is capable of providing an acceptable prediction of wind speed during an extreme *** is found that no single combination outperforms the others,although there is a slight tendency for Combination A,which utilizes the Mellor-Yamada-Janjic(MYJ)parameterization for the PBL and the Eta similarity parameterization for the surface layer,to more accurately capture the extreme wind *** with wind gust observations at five weather stations,the wind gust parameterization predicted the intensity and occurrence time of the peak wind,with an acceptable bias(time of peak<±1 h).Analysis of grid configurations(resolutions of 4 vs 9/3/1 km)revealed that higher resolution does not imply an improvement in the wind gust forecast for this particular *** regard to lead time,a shorter lead time does not necessarily result in more accurate ***,it is beneficial to conduct multiple sensitivity runs in order to obtain and understand the dispersion of forecasted wind speeds.

关键词： extreme wind wind event weather research and forecasting(WRF)model wind gust forecast cold front lead time

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Experimental Investigation of Corrosion Effect of Automotive Fuel Tank for Biofuels 5th

Experimental Investigation of Corrosion Effect of Automotive...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Vinod Kumar, T. Chandrasekaran, M. Pugazhenthi, R. Sharma, Abhishek Yadav, Anil Singh Kumar, Rahul Kaliyaperumal, Gopal Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Tamil Nadu Chennai600117 India Department of Mechanical Engineering B I T Sindri Jharkhand Dhanbad828123 India Bihar Bakhtiyarpur Patna803212 India School of Mechanical Engineering Lovely Professional University Punjab Phagwara144001 India Department of Mechanical Engineering New Horizon College of Engineering Karnataka Bangalore560103 India

ISBN: (纸本)9789819767311

The present research work investigates the corrosion deformation of carbon steel materials at various temperatures such as 50, 60 °C, and ambient temperature. Stabling absorption tests were conducted in pure diesel, biodiesel in 50% diesel, and pure biodiesel for 720 h. After the tests were finished, the corrosion performance qualities were evaluated by using assessment weight loss methods to expose the metal surface. Microscopic and computational fluid dynamic oxidation analyzers were used to analyze biofuels of fuel's acidity and oxidation. Morphological tests were studied on the fuel tank surface by using SEM images and dispersive gauging. Results monitoring that the corrosion of automotive mild steel material increase of worm heating, the water content, and oxidation products are increased or decreased are evaluated with processing simulation with the experiment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Steel corrosion

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Nonlinear asymmetric thermomechanical buckling of shallow nanoscale arches having dissimilar end conditions embracing nonlocality and strain gradient size dependencies

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Defence technology 2025年 47卷 67-82页

作者： Sahmani, Saeid Kotrasova, Kamila Zareichian, Mona Sun, Jian Safaei, Babak Department of Civil Engineering School of Science and Technology The University of Georgia Tbilisi0171 Georgia Center for Advanced Materials and Structures School of Science and Technology The University of Georgia Tbilisi0171 Georgia Department of Mechanical Engineering College of Engineering 145 Anam-ro Seongbuk-gu Seoul 02841 Korea Republic of Department of Structural Mechanics Institute of Structural Engineering and Transportation Structures Faculty of Civil Engineering Technical University of Kosice Vysokoskolska 4 Kosice04200 Slovakia Yangzhou Polytechnic Institute Jiangsu Yangzhou225127 China Department of Mechanical Engineering Eastern Mediterranean University North Cyprus via Mersin 10 Famagusta Turkey UNESCO-UNISA Africa Chair in Nanosciences and Nanotechnology College of Graduate Studies University of South Africa Muckleneuk Ridge PO Box 392 Pretoria South Africa

The undercurrent research survey explores the roles of nonlocality and strain gradient size dependencies in nonlinear asymmetric buckling of shallow nanoscale arches having dissimilar end conditions through a numerical analysis. The arches, made from a functionally graded graphene nanofiller reinforced composite (FG-GNRC), are subjected to discretional radial concentrated loads along with converting of temperature. To account for the size dependencies, the exploration is carried out stemming from the nonlocal strain gradient theory (NSGT) in the sense of a quasi-2D parabolic shear flexible concept of curved beam. The material properties of the contemplated FG-GNRC sandwich are determined using the modified Halpin-Tsai micromechanics model. Subsequently, an extended isogeometric analysis (XIGA) is manipulated comprising insertion plus multiplication of knots to achieve the demanded lower continuity allocated to the integration between flexural and tangential reflexes. It is perceived that the both softening and stiffening concomitants assigned to the salient concentrated radial loads obtained by the developed NSGT-based XIGA diminish from the first upper limit to the second one, and then likewise from the first lower limit to the second one. Although, by becoming the upsurge in temperature higher, these softening and stiffening concomitants get more remarkable. © 2025 China Ordnance Society

关键词： Arches

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Improvement of Interference Fringes Analysis to Obtain Accurate Soret Coefficients

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Defect and Diffusion Forum 2025年 439卷 291-304页

作者： Horikoshi, Haruki Orikasa, Isamu Kataoka, Minami Inatomi, Yuko Suzuki, Shinsuke Department of Applied Mechanics and Aerospace Engineering Waseda University Tokyo169-8555 Japan Department of Materials Science Waseda University Tokyo169-8555 Japan Kagami Memorial Research Institute of Materials Science and Technology Waseda University Tokyo169-8555 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa252-5210 Japan Space and Astronautical Science The Graduate University for Advanced Studies SOKENDAI Kanagawa252-5210 Japan

In this study, we proposed a moved-view analysis, a method for obtaining the Soret coefficient ST by analyzing the interference fringe change during field-of-view movement in the steady state. This analysis was designed to solve the problem that the sign of the concentration gradient could also be reversed if the plot of the concentration distribution was only slightly shifted owing to the narrow field of view. The data obtained from the experiment conducted to measure ST at the International Space Station were analyzed using moved-view analysis. For the moved-view analysis, the linearity of the concentration distribution induced by the Soret effect is larger than that for the method without field-of-view movement, and a more reliable ST can be obtained. The analysis error that sometimes occurred when the laser wavelength switched led to the underestimation of the phase change Δ, resulting in generating the data with low linearity. This unreliable data should be removed. © 2025 Trans Tech Publications Ltd, All Rights Reserved.

关键词： Interferometers

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Securing Smart Grid IoT System using A Robust RNN based Cyberattack Detection Framework

Securing Smart Grid IoT System using A Robust RNN based Cybe...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Asha A Karthika S P.C. Saranya Deepak Arumugam St. Joseph’s Institute of Technology OMR Chennai KIT-Kalaignarkarunanidhi Institute of Technology Coimbatore School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Cybersecurity in smart grid IoT systems is now a concern of high priority, as the integration of connected devices increases, and these systems become more vulnerable to sophisticated cyberattacks. This paper proposes a new RNN+GRU-based cyberattack detection system to meet such security challenges in smart grid IoT networks. The proposed model is based on Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) to efficiently capture temporal and contextual patterns of the attack data for improving detection accuracy of different types of cyber-attacks. Comparing the performance of our proposed model with other models like Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM) network is shown. Experimental results show that the RNN+GRU-based model outperforms these approaches in some key performance metrics: accuracy, precision, recall, F1-score, and ROC-AUC, better than other compared methods by achieving superior attack detection rates. Moreover, correlation analysis is also included for both the existing DNN model and the proposed approach; thus, the effectiveness of the RNN+GRU model in adapting to various scenarios of cyberattacks is noted. In the final analysis, the RNN+GRU-based cyberattack detection system shows great promise in securing smart grid IoT networks and shows better performance compared to some of the existing techniques.

关键词： Support vector machines Radio frequency Adaptation models Analytical models Accuracy Data models Smart grids Convolutional neural networks Internet of Things Long short term memory

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models

Hyperspectral Images for Crop Prediction using Machine and D...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： R. Mahalakshmi K. Kumutha M. Sakthivanitha R. Bagavathi Lakshmi Mohamed Sirajudeen Sankar Padmanabhan Department of Computer Applications-PG Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Computer Applications-PG Vels Institute of Science Technology Advanced Studies Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India PG and Research Department of Computer Science Nilgiri College of Arts and Science The Nilgiris Tami Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data.

关键词： Support vector machines Radio frequency Deep learning Recurrent neural networks Accuracy Crops Spatial databases Convolutional neural networks Reliability Hyperspectral imaging

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Artificial Intelligence for Irrigation System Optimization: Leveraging Predictive Analytics to Enhance Water Management and Decrease Agricultural Resource Consumption

Artificial Intelligence for Irrigation System Optimization: ...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Pandi, V. Samuthira Kura, Prashanth Aruna, V. Kumar, K. Suresh Nirmala, D. Kumar, S. Rajesh Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India AVS Solutions Inc. 6010 W SPRING PKWY STE 339 PlanoTX7502 United States St.Joseph's Institute of Technology Department of Management Studies Tamil Nadu Chennai India Panimalar Engineering College Department of MBA Tamil Nadu Chennai India Rajalakshmi Engineering College Department of ECE Tamil Nadu Chennai India Tamil Nadu Chennai India

ISBN: (纸本)9798331512965

Irrigation system optimization is of utmost importance for sustainable agriculture in light of the growing worldwide water shortage caused by climate change and rising agricultural needs. In order to improve water management techniques and decrease agricultural resource use, this project investigates the use of AI and predictive analytics. An intelligent irrigation system that adapts water distribution in real-time according to predictive analytics is created in this study by combining state-of-the-art machine learning algorithms with real-time data from a variety of sources, such as weather predictions, soil moisture levels, and crop water needs. In order to reduce water waste and maximize irrigation efficiency, the suggested framework uses past data to train artificial intelligence models. Results from field studies on a variety of crops show that water efficiency has been improved, allowing for 30% reductions in irrigation water use without sacrificing or diminishing crop yields. Sustainable farming practices and better resource management are two additional benefits that farmers reap from using AI-driven insights to inform their decision-making. This research emphasizes the revolutionary power of AI to update irrigation methods, leading to more robust agricultural systems. This study highlights the significance of data-driven methods to water management in achieving food security and environmental sustainability through the integration of technology and agriculture. If we want to see optimal irrigation practices adopted all throughout the world, our next step is to improve the predictive models and make the framework function in more diverse agricultural settings. © 2025 IEEE.

关键词： Soil moisture

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