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Unsteady Flow Mechanism of Impeller Stall Inception in a High-Pressure ratio Centrifugal Compressor With Bleed Slots

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Journal of Turbomachinery 2025年第7期147卷 071013页

作者： Fujisawa, Nobumichi Yamamoto, Yuta Ohta, Yutaka Ogino, Atsushi Nakayama, ryo Kitamura, Eijiro Department of Applied Mechanics and Aerospace Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo169-8555 Japan Honda R&D Co. Ltd. 1-4-1 Chuo Wako-shi Saitama351-0113 Japan

The impeller stall inception in a high-pressure ratio centrifugal compressor with bleed slots was investigated by experiments and unsteady numerical analysis. This study reported that the flow instability of the internal flow was first initiated around the leading edge of the splitter blades and finally resulted in tip leakage flow instability at the leading edge of the main blades. The impeller stall consisted of a stall cell and rotated about 70% of the impeller rotational speed. Furthermore, a pressure disturbance occurs at the splitter blade leading edge before the main blade leading edge. The blockage within the passage of the suction side at the leading edge of the splitter blade was first increased because the leading-edge separation on the suction surface of the splitter blade was enlarged. The blockage within the passage of the pressure side was increased after that of the suction side stopped increasing. Therefore, the blockage development of both pressure and suction passage at the leading edge of the splitter blade was the main cause of the stall inception. Then, a longitudinal vortex was formed near the bleed slots and blocked the mainstream near tip side. Subsequently, the reverse flow region in the inducer was developed and extended to the impeller inlet. Finally, the flow angle to the main blade increased, the spillage occurred at a leading edge of a main blade, and the rotating stall was generated. The cause of impeller instability was the separation of the suction surface of the splitter blade and the formation of a stagnation region on the pressure surface at the splitter leading edge. Copyright © 2024 by ASME.

关键词： Computational fluid dynamics

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Energy-Efficient Cloud Computing Through reinforcement Learning-Based Workload Scheduling

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 645-656页

作者： Malipatil, Ashwini r Paramasivam, M.E. Gulyamova, dilfuza Saravanan, Aanandha ramesh, Janjhyam Venkata Naga Muniyandy, Elangovan Ghodhbani, refka Department of Computer Science and Engineering BNM Institute of Technology Bangalore India Department of ECE Sona College of Technology Salem India Computer Engineering Department University of Information Technologies in Tashkent Tashkent Uzbekistan Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

The basis for current digital infrastructure is cloud computing, which allows for scalable, on-demand computational resource access. data center power consumption, however, has skyrocketed because of demand increases, raising operating costs and their footprint. Traditional workload scheduling algorithms often assign performance and cost priority over energy efficiency. This paper proposes a workload scheduling method utilizing deep reinforcement learning (drL) that adjusts dynamically according to present cloud situations to ensure optimal energy efficiency without compromising performance. The proposed method utilizes deep Q-Networks (dQN) to perform feature engineering to identify key workload parameters such as execution time, CPU and memory consumption, and subsequently schedules tasks smartly based on these results. Based on evaluation output, the model brings down the latency to 15 ms and throughput up to 500 tasks/sec with 92% efficiency in load balancing, 95% resource usage, and 97% QoS. The proposed approach yields improved performance in terms of key parameters compared to conventional approaches such as round robin, FCFS, and heuristic methods. These findings show how reinforcement learning can significantly enhance the scalability, reliability, and sustainability of cloud environments. Future work will focus on enhancing fault tolerance, incorporating federated learning for decentralized optimization, and testing the model on real-world multi-cloud infrastructures. © (2025), (Science and Information Organization). All rights reserved.

关键词： deep reinforcement learning

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Multiscale Structure regulation Induced by Fluorine Coordination Enables High-Performance and durable PEMFC

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ACS Energy Letters 2025年 2743-2751页

作者： Bai, Jingsen Guan, Xin Qu, Hanshi Liang, Liang ru, Chunyu Gong, Xue Jin, Zhao Xiao, Meiling Liu, Changpeng Xing, Wei State Key Laboratory of Electroanalytic Chemistry Jilin Province Key Laboratory of Low Carbon Chemistry Power Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun 130022 China School of Applied Chemistry and Engineering University of Science and Technology of China Hefei 230026 China CIAC-HKUST Joint Laboratory for Hydrogen Energy Changchun 130022 China Powertrain Department General R&D Institute of China FAW Jilin Changchun 130011 China Engine Development Department General Institute of FAW Jilin Changchun 130011 China

The stability enhancement of metal-nitrogen-carbon (M-N-C) is often inevitably accompanied by a loss of activity. Additionally, during the long-term operation, the activity frequently declines due to water flooding effects, significantly reducing the lifetime of the membrane electrode assembly (MEA). Herein, fluorine (F)-doping was employed to bypass the activity-stability trade-off of Fe-N-C catalysts. F incorporation increases the metal dissolution energy and lowers the electron density of Fe-N₄ sites, improving both stability and catalytic activity. Besides, the hydrophobicity of F can improve water management performance within the MEA, markedly reducing oxygen transport resistance. As a result, the F-doped Fe-N-C cathode enables a high peak power density of 1.1 W cm^-2, far exceeding the Fe-N-C counterpart (0.79 W cm^-2). More importantly, 90% of the power density can be retained after 30000 cycles of accelerated stress testing, demonstrating huge application potential in fuel cells. © 2025 American Chemical Society.

关键词： Semiconductor doping

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Preliminary study on degradation mechanisms of plasma-treated dr1 by atomistic simulations

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Plasma Science and Technology 2025年第3期27卷 94-103页

作者： Namunakhon NABIYEVA Tohir AKrAMOV davronjon ABdUVOKHIdOV Yuantao ZHANG Maksudbek YUSUPOV Jamoliddin rAZZOKOV Institute of Fundamental and Applied Research National Research University TIIAMETashkent 100000Uzbekistan Laboratory of Experimental Biophysics Centre for Advanced TechnologiesTashkent 100174Uzbekistan Department of Theoretical Physics National University of Uzbekistan named after Mirzo UlugbekTashkent 100174Uzbekistan Institute of Nuclear Physics Academy of Sciences of UzbekistanTashkent 100214Uzbekistan R&D Center New Uzbekistan UniversityTashkent 100007Uzbekistan School of Electrical Engineering Shandong UniversityJinan 250061People’s Republic of China Department of Information Technologies Tashkent International University of EducationTashkent 100207Uzbekistan Kokand University Andijan Branch Andijan 170619Uzbekistan Department of Biotechnology Tashkent State Technical UniversityTashkent 100095Uzbekistan

Cold atmospheric plasma(CAP)has emerged as a promising technology for the degradation of organic dyes,but the underlying mechanisms at the molecular level remain poorly *** density-functional tight-binding(dFTB)-based quantum chemical molecular dynamics at 300 K,we have performed numerical simulations to investigate the degradation mechanism of disperse red 1(dr)interacting with CAP-generated oxygen *** hundred directdynamics trajectories were calculated for up to 100 ps simulation time,after which hydrogenabstraction,benzene ring-opening/expanding,formaldehyde formation and modification in the chromophoric azo group which can lead to color-losing were *** latter was obtained with yields of around 6%at the given *** findings not only enhance our understanding of CAP treatment processes but also have implications for the development of optimized purification systems for sustainable wastewater *** study underscores the utility of dFTB simulations in unraveling complex chemical processes and guiding the design of advanced treatment strategies in the context of CAP technology.

关键词： wastewater treatment disperse red 1 cold atmospheric plasma reactive oxygen species reactive molecular dynamics degradation mechanisms

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Safe reinforcement Learning on the Constraint Manifold: Theory and Applications

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IEEE Transactions on robotics 2025年 41卷 3442-3461页

作者： Liu, Puze Bou-Ammar, Haitham Peters, Jan Tateo, davide Technical University of Darmstadt Intelligent Autonomous Systems Group Germany German Research Center for AI Department of Systems AI for Robot Learning Germany Huawei R&D London United Kingdom Hessian Centre for Artificial Intelligence Centre of Cognitive Science Germany

Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. Most existing approaches rely on training in carefully calibrated simulators before being deployed on real robots, often without real-world fine-tuning. While effective in controlled settings, this framework falls short in applications where precise simulation is unavailable or the environment is too complex to model. Instead, On-robot Learning, which learns by interacting directly with the real world, offers a promising alternative. One major problem for on-robot reinforcement learning is ensuring safety, as uncontrolled exploration can cause catastrophic damage to the robot or the environment. Indeed, safety specifications, often represented as constraints, can be complex and non-linear, making safety challenging to guarantee in learning systems. In this paper, we show how we can impose complex safety constraints on learning-based robotics systems in a principled manner, both from theoretical and practical points of view. Our approach is based on the concept of the Constraint Manifold, representing the set of safe robot configurations. Exploiting differential geometry techniques, i.e., the tangent space, we can construct a safe action space, allowing learning agents to sample arbitrary actions while ensuring safety. We demonstrate the method's effectiveness in a real-world robot Air Hockey task, showing that our method can handle high-dimensional tasks with complex constraints. © 2004-2012 IEEE.

关键词： Geometry

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Hybrid Unified AI-Based Biometric Authentication: A Low-Power, High-Security Scheme

Hybrid Unified AI-Based Biometric Authentication: A Low-Powe...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Vaishnavi T Nalini N T. Archana M. Samayaraj Murali Kishanlal Srilla Jayasankar dontu rahul Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Saveetha Engineering College Chennai India University of Technology and Applied Science Ibri Oman

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Biometric options integrated with the surpassing conventional locks for the expedient advancement of smart home technology has revolutionized home security. The Hybrid Unified Multimodal Biometric Integration Algorithm (HUMBIA), is proposed in this paper, with integrated modules like voice, facial, and fingerprint recognition to progress admittance control. The proposed HUMBIA algorithm reduces False Acceptance rates (FAr) and False rejection rates (Frr) by incorporating robust refuge scheme with enhanced accurateness through the use of the ESP32 microcontroller and an amalgamation of biometric sense modality. The reliable performance in a variety of scenarios is analysed to ensure the scheme's dynamic adaptation by changing ecological environments. The evaluation with recent current techniques demonstrates the better precision and sensitivity of HUMBIA's authentication. Contemporary hardware integrated with AI driven processes is suggested in the proposed system for advanced security and user expediency. This research can mark a significant and trending progression in smart home-based security scheme.

关键词： Biometrics Sensitivity Face recognition Speech recognition Smart homes Fingerprint recognition Safety Security reliability Admittance control

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Modulating structural oxygen/crystallinity enables ambient cascade photo-upgrading of biomass saccharides to lactic acid

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Carbon Energy 2025年第3期7卷 91-109页

作者： Jinshu Huang Yan ding Jie Li Zhao Hu Shunmugavel Saravanamurugan Junqi Wang Yaqiong Su Song Yang Hu Li State Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide&Agricultural BioengineeringMinistry of EducationState-Local Joint Laboratory for Comprehensive Utilization of BiomassCenter for R&D of Fine ChemicalsGuizhou UniversityGuiyangGuizhouChina Laboratory of Bioproduct Chemistry Centre of Innovative and Applied Bioprocessing(CIAB)MohaliPunjabIndia Department of Earth and Environmental Science School of Human Settlements and Civil EngineeringXi'an Jiaotong UniversityXi'anShaanxiChina School of Chemistry Engineering Research Center of Energy Storage Materials and Devices of Ministry of EducationNational Innovation Platform(Center)for Industry-Education Integration of Energy Storage TechnologyXi'an Jiaotong UniversityXi'anChina

Photocatalytic transformation of biomass into biofuels and value-added chemicals is of great significance for carbon ***-free carbon nitride has extensive applications but with almost no absorption and utilization of near-infrared light,accounting for 50%of ***,a molten salt-assisted in-plane“stitching”and interlayer“cutting”protocol is developed for constructing a highly crystalline carbon nitride catalyst containing structural oxygen(HC-CN).HC-CN is highly efficient for the photothermal cascade transformation of biomass-derived glucose into lactic acid(LA)with an unprecedented yield(94.3%)at 25°C under full-spectrum light irradiation within 50 min,which is also applicable to quantitatively photo-upgrading various *** calculations expound that the light-induced glucose-to-catalyst charge transfer can activate the Cβ-H bond to promote the rate-determining step of intramolecular hydrogen shift in glucose-to-fructose ***,the introduced structural oxygen in HC-CN can not only facilitate the local electric field formation to achieve rapid charge transport/separation and regulate selective·O^(-)_(2)generation for oriented C3-C4 bond cleavage of fructose but also narrow the energy band gap to broaden the light absorption range of HC-CN,contributing to enhanced LA production without exogenous ***,HC-CN is highly recyclable and exhibits negligible environmental burden and low energy consumption,as disclosed by the life cycle *** construction of full-spectrum light adsorption and versatile reaction sites provides a reference for implementing multi-step biomass and organic conversion processes under mild conditions.

关键词： biomass valorization lactic acid life cycle assessment photocatalysis pyroelectric effect

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deep Learning for Ocular disease Classification: A Study on OdIr-5K dataset

Deep Learning for Ocular Disease Classification: A Study on ...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Suresh rasappan S. Ahamed Nishath Sathish Kumar Kumaravel regan Murugesan Kala raja Mohan Nagadevi Bala Nagaram Mathematics Department University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

This research investigates the utilization of deep learning methodologies for classifying ocular dis-eases using the OdIr-5K dataset. A tailored convolutional neural network (CNN) architecture is developed to categorize fundus images into five distinct classes: diabetes, glaucoma, cataract, age-related macular de-generation, and normal. Notably, experimental outcomes achieve a 96.32 % accuracy rate, underscoring the effi-cacy of deep learning in ocular disease diagnosis.

关键词： deep learning Glaucoma Technological innovation Accuracy Systematics Feature extraction Convolutional neural networks Medical diagnosis Optimization diseases

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Linearized Gait Phase Estimation Using BiLSTM

Linearized Gait Phase Estimation Using BiLSTM

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Yonghyun Lee dongbin Shin Younghoon Ji Hyeyoun Jang Changsoo Han Yeonjoon Lee Department Of Applied Artificial Intelligence (Major In Bio Artificial Intelligence) Hanyang University Seoul Korea R&D Center Hexar Humancare Ansan Korea Research&Development Hexar Humancare Ansan Korea CEO Hexar Humancare Ansan Korea Computer Science Hanyang University Ansan Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

This paper presents a novel deep learning-based approach to address time-delay challenges arising from nonlinear transformations in Gait Phase Estimation, a critical factor in analyzing human gait for lower limb exoskeleton robots. The proposed model leverages hip joint angles and angular velocities as feature data, organized using a sliding window technique, to predict the Linearized Gait Phase (LGP)‖a continuous repre-sentation of gait progression between Heel Strikes. Motivated by the need for real-time and precise gait phase estimation to enhance exoskeleton control, a Bidirectional LSTM network was employed to reduce delays and improve accuracy. The model's effectiveness was validated through comprehensive performance evaluations using metrics such as Mean Absolute Error (MAE), root Mean Square Error (rMSE), Mean Square Error (MSE), and r2 score, alongside a temporal analysis of delay reduction. The results demonstrated superior performance, with an MAE of 2.8±0.8%, rMSE of 3.9±1.2%, MSE of 0.1±0.1%, and an r2 score of 98.1±1.8%. Particularly, temporal analysis revealed a marked improvement, achieving an MAE of 0%, compared to 2.74±0.92% reported in prior studies. These findings underscore the proposed model's potential for real-time gait phase estimation, offering significant implications for enhancing the responsiveness and adaptability of lower limb exoskeleton robots.

关键词： Performance evaluation Limbs Phase estimation Exoskeletons Bidirectional long short term memory Predictive models real-time systems delays root mean square robots

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Facile design of label-free electrochemical immunosensor using AuNPs@ZIF-8@f-MWCNTs for CA-125 detection in ovarian cancer

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Electrochimica Acta 2025年 528卷

作者： runprapan, Nattharika Wang, Fu-Ming Yuan, Chiou-Chung Krathumkhet, Nattinee Yang, Zong-Yu Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei Taiwan Sustainable Energy Center National Taiwan University of Science and Technology Taipei Taiwan Department of Chemical Engineering Chung Yuan Christian University Taoyuan Taiwan R&D Center for Membrane Technology Chung Yuan Christian University Taoyuan Taiwan Department of Obstetrics and Gynecology Cheng Hsin General Hospital Taipei Taiwan

The early diagnosis of ovarian cancer (OC) is a critical factor in the effective treatment and improved patient outcomes. This study presents a facile design label-free electrochemical immunosensor, a biomarker associated with OC, for high-sensitivity detection of CA-125. Our approach, which involves an immobilization strategy utilizing nanocomposites of gold nanoparticles/zeolitic imidazolate framework-8/carboxylic acid-functionalized multi-walled carbon nanotubes (AuNPs@ZIF-8@f-MWCNTs), has significant potential for the early diagnosis of OC. The synergistic effect of AuNPs, ZIF-8, and f-MWCNTs within the nanocomposites enhances stability, electrical conductivity, and electrochemical signal while providing a large surface area with numerous binding sites for increased Ab loading. When used as electrode material for CA-125 detection, the AuNPs@ZIF-8@f-MWCNTs demonstrate a detection range of 10 to 10⁻⁶ µg/mL with a lower detection limit (LOd) of 1 pg/mL. Our proposed immunosensor shows promise in detecting CA-125 in human serum samples certified as healthy conditions (with a volume of CA-125 © 2025

关键词： Electrotherapeutics

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