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A partially degradable composite consisting of Ti-Zr-Cu-Pd-Sn metallic glass and Fe-Mg alloy for orthopedic applications

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Acta Biomaterialia 2025年 198卷 514-529页

作者： Du, Peng Zuo, Kun Yan, Rongqiang Li, Kun Chen, Shilu Yuan, Bo Zhang, Liang Xie, Guoqiang Shenzhen518055 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Shenzhen518055 China State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin150001 China School of Materials Science and Engineering Shandong University of Technology Zibo255000 China

Partially-degradable biomaterials refers to smart implants where biodegradable metals can gradually be replaced by newly growing bone or living tissues, and leave behind a porous inert metal skeleton that stably binds with the new bone tissue. In this research, a partially degradable composite was designed by integrating Ti-Zr-Cu-Pd-Sn metallic glass (MG) with designed Fe-Mg alloy using spark plasma sintering (SPS). The mechanical alloying technique successfully enabled the fusion of immiscible Fe and Mg, addressing the issues of Fe's slow degradation and Mg's rapid breakdown, while also minimizing potential fractures in the metal framework due to hydrogen gas evolution. The controlled degradation of Mg(Fe) promotes the formation of Ca-P compounds, enhancing the bioactivity of the Fe-Mg composite. This design endows the composite with plastic and ductile deformation under compression, providing a viable solution to the brittle fracture behaviour commonly associated with conventional bulk metallic glasses (BMGs). This advancement holds promise for aligning with the natural growth rate of human bone, further augmenting the bioactive properties and practical applications of the MG/Fe-Mg composite material. Statement of significance: In this research, a partially degradable composite was designed by integrating Ti-Zr-Cu-Pd-Sn metallic glass (MG) with designed Fe-Mg alloy using SPS. The Fe-Mg alloy act as temporary space holders can gradually being replaced by newly formed bone, thus establishing a dynamic equilibrium between the biodegradation of the bio-metals and the inward growth of new bone. The degradation of Mg(Fe) promotes the formation of Ca-P compounds, enhancing the bioactivity of the composite. This design endows the composite with plastic deformation under compression, providing a viable solution to the brittle fracture behavior of conventional MGs. This advancement holds promise for aligning with the natural growth rate of human bone, further augmenting the

关键词： Magnesium alloys

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LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation 31

LoRA-drop: Efficient LoRA Parameter Pruning based on Output ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhou, Hongyun Lu, Xiangyu Xu, Wang Zhu, Conghui Zhao, Tiejun Yang, Muyun Faculty of Computing Harbin Institute of Technology China Department of Computer Science & Technology Tsinghua University Beijing China

ISBN: (纸本)9798891761964

Low-Rank Adaptation (LoRA) is currently the most commonly used Parameter-efficient fine-tuning (PEFT) method. However, it still faces high computational and storage costs to models with billions of parameters. Most previous studies have tackled this issue by using pruning techniques. Nonetheless, these efforts only analyze LoRA parameter features to evaluate their importance, such as parameter count, size, and gradient. In fact, the output of LoRA directly impacts the fine-tuned model. Preliminary experiments indicate that a fraction of LoRA possesses significantly high output values, substantially influencing the layer output. Motivated by the observation, we propose LoRA-drop. Concretely, LoRA-drop evaluates the importance of LoRA based on the LoRA output. Then we retain LoRA for important layers and the other layers share the same LoRA. We conduct abundant experiments with models of different scales on NLU and NLG tasks. Results demonstrate that LoRA-drop can achieve performance comparable to full fine-tuning and LoRA while retaining 50% of the LoRA parameters on average. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and Networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-Communications Information Technology Center Nagoya University National Institute of Informatics(NII) Hohhot Minzu College Toyota Motor Corporation Department of Computer Science and Engineering Shanghai Jiaotong University

Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity of traditional communication methods. To tackle this, we propose a novel framework using semantic communications, through a region of interest semantic segmentation method, to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data. To solve the knowledge base inconsistencies inherent in semantic communications, we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases. This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management. Additionally, the implementation of blockchain sharding handles differentiated knowledge bases for various tasks, thus boosting overall blockchain efficiency. Experimental results show a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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Classifying the state of knowledge-based question answering: patterns, progress, and prospects

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International Journal of computers and Applications 2025年第1期47卷 93-105页

作者： Sharma, Sumit Jain, Sarika Tiwari, Mukesh Kumar lal, Sohan Department of Computer Science and Engineering Quantum University Roorkee Uttarakhand Roorkee India Department of Computer Applications National Institute of Technology Haryana Kurukshetra India

In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generation of QASs focuses on the types of inquiries that users typically enquire, the features of the data sources that are used, and the formats in which the right responses are produced. Many QASs have been developed since the 1960s when research on the subject first started. There are several cutting-edge frameworks for answering questions that can be used to categorize the QA system according to a number of criteria. The necessity for an extensive survey on QASs comes naturally in order to determine the future focus of research in this field. In this study, QASs are analyzed by the number of criteria used to categorize question-answering (QA) and provide the dimension using a variety of standards to cover all the QAS. We describe the current state of research in each QAS area and make recommendations for its future direction. Based on the dimension criteria outlined in the research methodology, research studies are chosen for additional review and comparison. Constructed some criteria to classify the QASs, we provide the classification and go over the benefits and drawbacks of each class's QAS, as well as the problems with the corresponding study. Results are detailed summarized and compared. As a result of this study, a general architecture of frameworks for QA over knowledge base (KB) is drawn in addition to the comparative analysis of systems, serving as a reference for academics working on related topics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Question answering

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Extended Multi-scale Feature Fusion and Balanced Generative Adversarial Network for Image Inpainting under Limited Data

Extended Multi-scale Feature Fusion and Balanced Generative ...

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2024 Workshop on Electronics Communication engineering, WECE 2024

作者： Xiao, Ting Sun, Minqian Chen, Haoyuan Li, Jiaqi Qiu, Yixing Wang, Zhe Lu, Dan Department of Computer Science and Engineering East China University of Science and Technology China School of Artificial Intelligence OPtics and ElectroNics Northwestern Polytechnical University China School of Computer Science and Technology Harbin Engineering University China

ISBN: (数字)9781510689145

ISBN: (纸本)9781510689138

Image inpainting has made remarkable progress in recent years due to the availability of sufficient training data. However, when the training data is insufficient, i.e., image inpainting in few-shot regimes, the performance of existing image inpainting methods degrades drastically. Two challenges impede image inpainting under few-shot regimes, adequate information and unstable training. To address the above issues, this paper proposes a new method for image inpainting under limited data. Specifically, an Extended Multi-scale Feature Fusion (EMFF) module is proposed to capture representative contextual information by progressively reducing the number of channels for features with increasing receptive fields, thereby utilizing effective information. Additionally, we leverage a multi-scale discriminator whose cross-layer features are mixed for supplementation to formulate a Balanced Generative Adversarial Network (BGAN) to stabilize the training dynamics. Comprehensive experiments demonstrate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Image fusion Image quality Feature fusion Education and training Image restoration Convolution Data modeling Image enhancement Gallium nitride Semantics

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Characterizing and Stage-Wise Differentiation of Coal Spontaneous Combustion in Deep Mines

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Energy engineering 2025年第4期122卷 1561-1580页

作者： Haitao Wang Pengxin Zhang Weihao Li Baogang Li Xianghui Xiong School of Resources and Engineering Department Heilongjiang University of TechnologyJixi158100China Intelligent Mine Interdisciplinary Research Institute Heilongjiang University of Science&TechnologyHarbin150022China

Deep mining,characterized by high stress,elevated geothermal gradients,and significant moisture content,significantly increases the risk of Coal Spontaneous Combustion(CSC),posing a major threat to mine *** study delves into the impact of these factors on the self-ignition properties of coal,leveraging data from four distinctmines inHeilongjiang Province,China:ShuangyashanDongrongNo.2 Mine,Hegang JundeCoal Mine,Qitaihe Longhu Coal Mine,and Jixi Ronghua *** have honed the theoretical framework to account for variations in gas content during *** investigation,conducted through programmed temperature rise experiments,scrutinized the generation and temperature-dependent evolution of gases,emphasizing individual indicators such as CO,O_(2),and CxHy,in addition to composite indicators like the ratio of change in CO to change in O_(2) concentration(∂C_(CO)/∂t:−∂C_(O_(2))/∂t)and the ratio of C2H4 to C_(2)H_(6).These insights have catalyzed the development of a CSC state energy level transition model and a precise method for phase-based quantification of combustion *** findings furnish a scientific foundation for the formulation of early warning and prevention strategies in deep mining settings.

关键词： Deep mine coal spontaneous combustion gas change rate index threshold

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High-Efficiency Asymmetrically Designed Three-Phase Virtual 48-pulse Power Supply for Electrolytic Hydrogen

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IEEE Transactions on Power Electronics 2025年第8期40卷 10335-10342页

作者： Tian, Hanlei Cui, Haoran Han, Wei Sathik M, Jagabar Liang, Guozhuang Chen, Maolin Mekhilef, Saad Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology (GZ) Guangzhou China Electrical Engineering School of Hebei University of Science and Technology Shijiazhuang China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India College of Electrical Engineering Sichuan University Chengdu China School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne VIC Australia

To address the low-voltage, high-current requirements in hydrogen production applications, a virtual 48-pulse three-phase rectifier is proposed, and achieves the equivalent performance of four parallel 12-pulse rectifiers (12-PR). The proposed approach builds upon the conventional 12-PR design and incorporates novel Asymmetric Current Injection Units (ACIU). The ACIU is installed on the DC side of the AC-DC converter, generating a specific current at the DC link to modulate and enhance the operating modes of the 12-PR. Consequently, the 12-pulse is extended to 48, resulting in an almost sinusoidal input current with a Total Harmonic Distortion (THD) of 3.2% and an output current with a ripple rate (RR) of 2.3%, thus significantly improving power quality. Furthermore, the asymmetric design of the current injection units eliminates the need for a phase-shifting transformer. The efficacy of the proposed rectifier for electrolytic hydrogen production is validated using a 2.2 kW experimental prototype. © 1986-2012 IEEE.

关键词： Voltage Hydrogen Rectifiers Production Windings Circuits Inductors Transformers Harmonic analysis Electrochemical processes

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The Future of Work: Robotic Process Automation and its Role in Shaping Tomorrow’s Business Landscape

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SN computer science 2025年第2期6卷 1-19页

作者： Bhardwaj, Vivek Yadav, Shveta Kaur, Navjeet Anand, Darpan School of Computer Science and Engineering Manipal University Jaipur Jaipur India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

In the new era of automation, Robotic Process Automation (RPA) has emerged as a powerful suite of tools for automating mundane, repetitive, rule-based, and structured tasks using software bots without disrupting existing infrastructure. Due to its significant potential, RPA has garnered considerable attention in the business world, being both easier and cheaper to implement while enhancing efficiency and productivity. This study begins by examining the characteristics of an RPA system and suitability of processes for RPA. This study delves into the role and impact of RPA in modern businesses, highlighting its transformative potential. It also explores the latest trends in RPA research, providing insights into cutting-edge developments and innovative applications. Furthermore, the paper addresses the challenges faced in implementing RPA and offers strategies for overcoming these obstacles, aiming to enhance the efficiency and effectiveness of RPA deployments in various business contexts. Lastly, RPA software bots were developed to quickly and accurately extract data from various invoices. The results show that, when implemented correctly, an RPA system is both easy to deploy and scalable, while maintaining high accuracy. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence Business processes Intelligent automation Robotic process automation (RPA) Software bots

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Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained Model for Acoustic Traffic Monitoring

Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained M...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Fan, Shitong Xiao, Feiyang Wang, Wenbo Qi, Shuhan Zhu, Qiaoxi Wang, Wenwu Guan, Jian Group of Intelligent Signal Processing Harbin Engineering University Harbin China Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Acoustics Lab University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350368741

Microphone array techniques are widely used in sound source localization and smart city acoustic-based traffic monitoring, but these applications face significant challenges due to the scarcity of labeled real-world traffic audio data and the complexity and diversity of application scenarios. The DCASE Challenge's Task 10 focuses on using multi-channel audio signals to count vehicles (cars or commercial vehicles) and identify their directions (left-to-right or vice versa). In this paper, we propose a graph-enhanced dual-stream feature fusion network (GEDF-Net) for acoustic traffic monitoring, which simultaneously considers vehicle type and direction to improve detection. We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance. A pre-trained model (PANNs) is used in the VTFE branch to mitigate data scarcity and enhance the type features, followed by a graph attention mechanism to exploit temporal relationships and highlight important audio events within these features. The frame-level fusion of direction and type features enables fine-grained feature representation, resulting in better detection performance. Experiments demonstrate the effectiveness of our proposed method. GEDF-Net is our submission that achieved 1st place in the DCASE 2024 Challenge Task 10. © 2025 IEEE.

关键词： Acoustic-based traffic monitoring feature fusion graph attention pre-trained model transfer learning

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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