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Recent progress and perspectives in additive manufacturing of magnesium alloys

Journal of Magnesium and Alloys 2022年第6期10卷 1511-1541页

作者： Zhuoran Zeng Mojtaba Salehi Alexander Kopp Shiwei Xu Marco Esmaily Nick Birbilis College of Engineering and Computer Science Australian National UniversityACT.2601 Australia Additive Manufacturing Group Singapore Institute of Manufacturing Technology(SIMTech)Agency forScienceTechnology and Research(A∗STAR)73 Nanyang Drive637662 Singapore Meotec GmbH Philipsstr.8AachenGermany State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan UniversityChangsh1410082 China Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMA 02139United States of America Volvo Materials Technology Laboratory Volvo GroupLundbyGothenburg 41715Sweden

Magnesium alloys remain critical in the context of light-weighting and advanced devices. The increased utilisation of magnesium(Mg)each year reveals growing demand for its Mg-based alloys. Additive manufacturing(AM) provides the possibility to directly manufacture components in net-shape, providing new possibilities and applications for the use of Mg-alloys, and new prospects in the utilisation of novel physical structures made possible from ‘3D printing’. The review herein seeks to holistically explore the additive manufacturing of Mg-alloys to date, including a synopsis of processes used and properties measured(with a comparison to conventionally prepared Mg-alloys). The challenges and possibilities of AM Mg-alloys are critically elaborated for the field of mechanical metallurgy.

关键词： Magnesium Additive manufacturing Microstructure 3D printing Mechanical properties Electrochemical properties

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Continuous Bijection Supervised Pyramid Diffeomorphic Deformation for Learning Tooth Meshes From CBCT Images

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Zechu Peng, Weilong Wen, Jinyu Tang, Keke Fang, Meie Feng, David Dagan Li, Ping Guangzhou University Metaverse Research Institute School of Computer Science and Cyber Engineering Guangzhou511400 China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China The University of Sydney Biomedical and Multimedia Information Technology Research Group School of Computer Science SydneyNSW2006 Australia The Hong Kong Polytechnic University Department of Computing School of Design and Research Institute for Sports Science and Technology Hong Kong Hong Kong

Accurate and high-quality tooth mesh generation from cone-beam computerized tomography (CBCT) is an essential computer-aided technology for digital dentistry. However, existing segmentation-based methods require complicated post-processing and significant manual correction to generate regular tooth meshes. In this paper, we propose a method of continuous bijection supervised pyramid diffeomorphic deformation (PDD) for learning tooth meshes, which could be used to directly generate high-quality tooth meshes from CBCT Images. Overall, we adopt a classic two-stage framework. In the first stage, we devise an enhanced detector to accurately locate and crop every tooth. In the second stage, a PDD network is designed to deform a sphere mesh from low resolution to high one according to pyramid flows based on diffeomorphic mesh deformations, so that the generated mesh approximates the ground truth infinitely and efficiently. To achieve that, a novel continuous bijection distance loss on the diffeomorphic sphere is also designed to supervise the deformation learning, which overcomes the shortcoming of loss based on nearest-neighbour mapping and improves the fitting precision. Experiments show that our method outperforms the state-of-the-art methods in terms of both different evaluation metrics and the geometry quality of reconstructed tooth surfaces. © 1999-2012 IEEE.

关键词： Mapping

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Archer: adaptive memory compression with page-association-rule awareness for high-speed response of mobile devices 25

Archer: adaptive memory compression with page-association-ru...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Changlong Li Zongwei Zhu Chao Wang Fangming Liu Fei Xu Edwin H.-M. Sha Xuehai Zhou School of Computer Science and Technology East China Normal University and Jianghuai Advance Technology Center Hefei China and MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application School of Software Engineering University of Science and Technology of China Hefei China and Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Huazhong University of Science and Technology and Peng Cheng Laboratory School of Computer Science and Technology East China Normal University

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate associationrule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art.

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Short-term electricity demand forecasting via variational autoencoders and batch training-based bidirectional long short-term memory

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartC期52卷

作者： Moradzadeh, Arash Moayyed, Hamed Zare, Kazem Mohammadi-Ivatloo, Behnam [a]Faculty of Electrical and Computer Engineering University of Tabriz *** Tabriz Iran [b]GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (P. PORTO) P-4200-072 Porto Portugal [c]Information Technologies Application and Research Center İstanbul Ticaret University Istanbul Turkey

Electricity load forecasting is a key aspect for power producers to maximize their economic efficiency in deregulated markets. So far, many solutions have been employed to forecast the consumption load in power grids. However, most of these methods have suffered in modeling the time-series state of data and removing noise from real-world data. Thus, the forecasting results in most cases did not have acceptable accuracy due to the mentioned problems. In this paper, in order to short-term electricity load forecast in Tabriz, Iran, a hybrid technique based on deep learning applications called Variational Autoencoder Bidirectional Long Short-Term Memory (VAEBiLSTM) is presented. Pre-processing, noise cancellation, and time-series state modeling of the data are prominent features of the developed load forecasting model. In addition, in order to prevent overfitting problems in the process of training large amounts of data, the training process is developed in the form of batch training. Load forecasting is done using meteorological and environmental data of Tabriz city as well as historical information and days of the week as input variables. In the hybrid method structure, the Variational Autoencoders are applied to the data for data preprocessing and reconstruction. Then, the normalized, noise-free data is utilized as a dataset for training the Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed training method for BiLSTM is based on batch training. To present the effectiveness of the proposed technique in a comparative approach, the conventional LSTM and Support Vector Regression (SVR) algorithms are also applied to the data. Each network is trained with input data related to the years of 2017 and 2018 to predict the electricity load of the Tabriz city separately for each of the four seasons of the 2019 year. The forecasting results obtained from each method are evaluated by different statistical performance indicators. It can be seen that the proposed

关键词： Electricity demand Load forecasting Deep learning Variational Autoencoders (VAE) Bidirectional long short-term memory (BiLSTM)

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Understanding the predictability of path flow distribution in urban road networks using an information entropy approach

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Multimodal Transportation 2024年第2期3卷

作者： Guo, Bao Huang, Zhiren Zheng, Zhihao Zhang, Fan Wang, Pu School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan Province Central South University Changsha China Shandong Provincial Communications Planning and Design Institute Group Co. Ltd Jinan China Department of Computer Science Aalto University Helsinki Finland Department of Civil Engineering McGill University Montreal H3A 0C3 QC Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved. © 2024

关键词： Human mobility Information entropy Path flow distribution Predictability

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Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation Via Decoupling Optimization

SSRN

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SSRN 2023年

作者： Li, Mengtian Lin, Shaohui Wang, Zihan Shen, Yunhang Zhang, Baochang Ma, Lizhuang Visual Media Intelligent Processing Research Group Department of Film and Television Engineering Shanghai University Shanghai200072 China School of Computer Science and Technology East China Normal University Shanghai200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Youtu Lab Tencent China

Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding. However, the existing SSL-based methods suffer from severe training bias, mainly due to class imbalance and long-tail distributions of the point cloud data. As a result, they lead to a biased prediction for the tail class segmentation. In this paper, we introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively. In particular, we first employ two-round pseudo-label generation to select unlabeled points across head-to-tail classes. We further introduce multi-class imbalanced focus loss to adaptively pay more attention to feature learning across head-to-tail classes. We fix the backbone parameters after feature learning and retrain the classifier using ground-truth points to update its parameters. Ex-✩Fully documented templates are available in the elsarticle package on CTAN. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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EVALUATION OF PERFORMANCE OF A CI ENGINE FUELED WITH BIODIESEL PRODUCED FROM UNUSED ALGAE

Journal of Solid Waste Technology and Management

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Journal of Solid Waste Technology and Management 2023年第4期49卷 359-364页

作者： Karmakar, Rachan Kumar, Nitin Rajor, Anita Kundu, Krishnendu Kumar, Rajesh Gahlot, Dinesh Chaudhury, Bhaskerpratap Ragit, S.S. Kumar, Amit Gorai, Sanjoy Gangwar, Pavan SInghal, Rishabh Bhattacharya, Sourish Sharma, Rachna Savita Singh, Narpinder Kumar, Neeraj Graphic Era Deemed to be University Uttarakhand Dehradun India Dept. of Mechanical Engineering Roorkee Institute of Technology Uttarakhand Roorkee India School of Energy and Environment Thapar University Punjab Patiala India Dept. of Biofuel CSIR CMERI CoEFM Punjab Ludhiana India Dept. of Chemistry S.S.J. University Campus Almora Uttarakhand India Dept. of Physics Chandigarh Group of Colleges Punjab Jhanjheri India Dept. of Mechanical Engineering Guru Nanak Institute of Technology Maharashtra Nagpur India Dept. of Computer Application Quantum University Uttarakhand Roorkee India Dept. of Electronics and Communication Engineering IMS Engineering College U.P. Gaziabad India Dept. of Electrical Engineering COER University Uttarakhand Roorkee India Process Design and Engineering Cell Discipline CSIR Central Salt and Marine Chemicals Research Institute Gujrat Bhavnagar India Dept. of Applied Science Roorkee Institute of Technology Uttarakhand Roorkee India School of Agriculture Graphic Era Hill University Uttarakhand Dehradun India Dept. of Food Science and Technology Graphic Era Deemed to be University Uttarakhand Dehradun India

Abundant availability and easy culture process of algae make it a better resource than other vegetable crops for biodiesel production. The oil (with 21% FFA content) extracted from the unused, mixed culture of algae in this experiment was used to produce biodiesel by an ‘acid esterification followed by alkaline esterification’ procedure. After confirming the properties of the biodiesel to be within the limit of ASTM standard, three biodiesel blends (B10, B20 and B30) were used in an internal combustion engine (Four stroke single cylinder VCR engine) and the performance of the engine was observed at different engine loads (0%, 20%, 40%, 80%, 100%, 120%). The little higher brake specific fuel consumption (0.22kg/KWh, 0.25 kg/KWh, 0.26kg/KWh and 0.21kg/KWh respectively for B10, B20, B30 and petro-diesel at overload condition), lower brake power (3.41 kW, 3.37 kW, 3.25 kW, 3.39 kW for diesel, B10, B20 and B30 respectively for B10, B20, B30 and petro-diesel) and mechanical efficiency (63.34, 51.43%, 52.06% and 51.43% for petro-diesel, B10, B20 and B30 respectively) for biodiesel blends took place which might be the results of lower calorific value (40800 kJ/kg), higher density (875.27kg/m3) and viscosity (3.14 mm2/s) of the algal biodiesel than diesel. © 2023 Widener University School of Civil Engineering. All rights reserved.

关键词： Biodiesel

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Metal–organic frameworks modified electrode for H₂S detections in biological and pharmaceutical agents

MedComm - Biomaterials and Applications

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MedComm - Biomaterials and applications 2023年第3期2卷

作者： Durmus, Ceren Arul, Ponnusamy Alhaji, A. Shekhah, Osama Mani, Veerappan Eddaoudi, Mohamed Salama, Khaled Nabil Sensors Lab Advanced Membranes and Porous Materials Center Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Department of Chemical Engineering and Biotechnology Institute of Biochemical and Biomedical Engineering National Taipei University of Technology Taipei Taiwan Physical Sciences and Engineering Division Functional Materials Design Discovery and Development Research Group (FMD3) Advanced Membranes and Porous Materials Centre (AMPMC) King Abdullah University of Science and Technology (KAUST) Jeddah Saudi Arabia

The development of hydrogen sulfide (H2S) sensors is essential to address H2S-related pharmacology since slow-releasing H2S medications have been identified to be prospective options for cancer treatments. Here, we de... 详细信息

The development of hydrogen sulfide (H₂S) sensors is essential to address H₂S-related pharmacology since slow-releasing H₂S medications have been identified to be prospective options for cancer treatments. Here, we described an electrochemical sensor for highly selective and sensitive detection of aqueous H₂S, using a thin film of fumarate-based face-centered cubic (fcu)-based metal–organic frameworks (fum-fcu-MOF) modified on laser-scribed graphene (LSGE). The fum-fcu-MOF has shown a strong affinity and chemical stability to H₂S analysis. The electrochemical and H₂S catalytic properties were studied for fum-fcu-MOF/LSGE. An amperometry and differential pulse voltammetry techniques were demonstrated to validate the sensor. The resulting sensor delivered acceptable analytical parameters in terms of;detection limit (3.0 µM), dynamic range (10–500 µM), reproducibility, and stability (94.7%). The sensor's practical validity was demonstrated in bacterial cells and H₂S-releasing drug, where the sensor was able to monitor the continuous release of in-situ H₂S. The pharmacokinetics of a slow releasing H₂S donor is accessed at different time intervals and different concentration levels. Our research indicate that this fum-fcu-MOF based H₂S sensor holds potential in understanding pharmacokinetics of H₂S releasing drugs. © 2023 The Authors. MedComm – Biomaterials and applications published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).

关键词： H2S pharmacology hydrogen sulfide metal organic frameworks

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Chinese chestnut did not induce negative plant soil feedback during centuries of growth

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Science of the Total Environment 2025年 970卷 178883页

作者： Cheng, Xu Yan, Zhichun Li, Qian Schmitz, Lucas Yan, Jundi Ge, Yueyang Lan, Yanping Zhao, Yaceng Wang, Yiyang Li, Guangdong Liu, Yang Schneijderberg, Martinus Yang, Liu Bian, Huihui van Dijk, Aalt D.J. Qin, Ling Cao, Qingqin Bisseling, Ton Beijing Advanced Innovation Center for Tree Breeding by Molecular Design Beijing University of Agriculture Beinong Rd. #7 Beijing102206 China Laboratory of Molecular Biology Department of Plant Science Wageningen University Droevendaalsesteeg 1 Wageningen6708PB Netherlands Beijing Key Laboratory of New Technology and Agricultural Application College of Plant Science and Technology Beijing University of Agriculture Beinong Rd. #7 Beijing102206 China Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Key Laboratory of Synthetic Biology Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China Beijing Academy of Forestry and Pomology Sciences Beijing100093 China Bioinformatics Group Wageningen University and Research Wageningen Netherlands Swammerdam Institute for the Life Sciences University of Amsterdam Netherlands

Certain tree species can reach ages of centuries, whereas lifespan of species like apple are markedly shorter. The latter is caused by negative plant-soil feedback that results in microbiome changes. We hypothesized that tree species with a long lifespan will be able to avoid such negative feedback and their root-associated microbiomes will be similar in trees of different ages. To test this, we used Chinese chestnut (Castanea mollissima) trees, ranging from 8 to 830 years old from a Ming orchard at the Great Wall. Their root-associated microbiomes were analysed by using meta-amplicon sequencing analysis. Their root-associated bacterial microbiomes were rather similar although based on linear regression models we cannot exclude that age has a weak correlation with microbiome compositions. When chestnut seedlings were grown for 3 months in soil associated with young or old trees, the plants were healthy and their growth was similar. This strongly supported that negative feedback had not occurred. Pseudomonas OTU1, a member of the core microbiome and representing >50 % of the rhizosphere community, strongly inhibited growth of chestnut pathogens and stimulated plant growth. Such properties of the microbiome, in combination with a high number of resistance genes can contribute to longevity of chestnut. © 2025 The Authors

关键词： Plant diseases

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Energy Efficient User Clustering, Hybrid Precoding and Power Optimization in Terahertz MIMO-NOMA Systems

Energy Efficient User Clustering, Hybrid Precoding and Power...

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作者： Zhang, Haijun Zhang, Haisen Liu, Wei Long, Keping Dong, Jiangbo Leung, Victor C. M. Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing China China Mobile Group Design Institute Company Ltd Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multi-antenna also enable the network to carry more users and provide multiplexing gain. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO systems for the first time. And the original optimization problem is divided into user clustering, hybrid precoding and power optimization. Based on channel correlation characteristics, a fast convergence scheme for user clustering in THz-NOMA-MIMO system using enhanced K-means machine learning algorithm is proposed. Considering the power consumption and implementation complexity, the hybrid precoding scheme based on the sub-connection structure is adopted. Considering the fronthaul link capacity constraint, we design a distributed alternating direction method of multipliers (ADMM) algorithm for power allocation to maximize the EE of THz-NOMA cache-enabled system with imperfect successive interference cancellation (SIC). The simulation results show that the proposed user clustering scheme can achieve faster convergence and higher EE, the design of the hybrid precoding of the sub-connection structure can achieve lower power consumption and power optimization can achieve a higher EE for the THz cache-enabled network. © 1983-2012 IEEE.

关键词： Energy efficiency

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