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Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Dopamine MoS_2 Biosensor Multiwalled carbon nanotubes Machine learning Complex mixture

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Invisible Warning Line: Efficient and Generic Regulation for Anonymous Cryptocurrencies

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2022-2036页

作者： Gao, Rui Wan, Zhiguo Wang, Huaqun Luo, Shaoteng Nanjing University of Posts and Telecommunications College of Computer Jiangsu Cryptographic Technology Engineering Research Center Nanjing210023 China Zhejiang Lab Hangzhou310000 China UCAS School of Software Engineering Hangzhou Institute for Advanced Study Hangzhou310024 China

Decentralized finance based on blockchain has experienced rapid development. To safeguard the privacy of participants, decentralized anonymous payment (DAP) systems such as ZCash and Zether have emerged. These systems employ cryptographic techniques to conceal the trader addresses and payment amounts. However, this anonymity presents challenges in terms of regulation. To address this issue, we propose the Walsh-DAP (WDAP) scheme, an efficient and generic regulation scheme for decentralized anonymous payments that strikes a balance between regulation and privacy preservation. Our scheme introduces two regulation policies: first, users who have exceeded their spending limits within a certain period will be identified during the regulation process;second, the supervisor possesses the capability to trace any anonymous transaction. To implement regulation effectively, we have designed an innovative commitment scheme, Walsh commitment, which leverages the orthogonal properties of Walsh codes to achieve the features of aggregatability and extractability. The supervisor in WDAP only needs to deal with the aggregation result of the Walsh commitments instead of the huge amount of raw transactions information, which greatly increases the efficiency. In a DAP system with 256 users, 10 transactions per second and 30 days as a regulation period, we reduced the communication cost for regulation from 14 GB to 94.20 KB, and the computing cost from 1.6 × 105 s to 2.17s. Both improvement is of over five orders of magnitude. We formally discussed the security of the whole system, and verified its feasibility and practicability in the ZCash system. © 2004-2012 IEEE.

关键词： Decentralized finance

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From image classification to segmentation: a comprehensive empirical review

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Iran Journal of computer Science 2025年 1-31页

作者： Ruksana Jim, Jamin Rahman Hossain, Md Jonair Das, Aritra Kabir, Md Mohsin Mridha, M.F. Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Science and Engineering American International University-Bangladesh Dhaka 1229 Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207 Bangladesh Department of Computer Science and Engineering Bangladesh Open University Gazipur 1700 Bangladesh

With the development of the machine learning age, deep learning techniques for computer vision tasks use real-world data to analyze problems across various fields. In particular, it has achieved significant success in image classification, segmentation, and detection on different datasets. As the area of deep learning expands, annotated datasets can be challenging owing to various factors such as the labeling process or difficulty in correctly classifying data, even for experts. Therefore, an extensive survey of machine learning techniques is introduced. Through empirical analysis, several deep learning architectures were applied to benchmark datasets, such as ImageNet, CIFAR-10, COCO, and MNIST. This study includes Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and their variants in these datasets. Additionally, this work reviews the experimental results from modern studies and explains the methodological nuances and comparative performance. In addition to examining the relevance of model performance to preprocessing techniques, various evaluation metrics and promising challenges with future directions for new frontiers are also discussed. Subsequently, this research investigation addresses challenges such as dataset characteristics, model complexity, and computational efficiency and provides insights into the practical implementation of deep learning solutions for real-world applications. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： CIFAR-10 Classification COCO Convolutional neural networks Deep learning Detection ImageNET MNIST Segmentation

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Theoretical analysis and design of a dual-wavelength and selectable all-optical broadband QDs semiconductor optical amplifier (QDs-SOA) with inhomogeneous broadening

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Optical and Quantum Electronics 2025年第2期57卷 1-26页

作者： Eyvazi, Mahdiyeh Yadipour, Reza Rostami, Ali Rostami, Parisa Mirtagioglu, Hamid Faculty of Electrical and Computer Engineering University of Tabriz Tabriz*** Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz*** Iran SP-EPT Lab ASEPE Company Industrial Park of Advanced Technologies Tabriz Iran Faculty of Medicine Tabriz University of Medical Science Tabriz Iran Department of Statistics Faculty of Science and Literature University of Bitlis Eren Bitlis Turkey

This study delves into the significant role played by Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs) in meeting the ever-growing bandwidth demands. QD-SOAs offer a unique blend of cost-effectiveness, integration capabilities, wide bandwidth, rapid responsiveness, robust power output, stability, and spectral adaptability, driving notable advancements in optical communication systems. In this work, we introduce a novel two-wavelength amplifier structure based on Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs) that utilizes quantum dots of different sizes to achieve efficient amplification at specific mid-infrared wavelengths. This innovative approach, which incorporates quantum dots with varying sizes, enables enhanced performance by optimizing the amplification process for each specific wavelength. Furthermore, this work demonstrates the use of tailored optical pumping mechanisms that enhance the carrier recovery process and reduce carrier relaxation times in the active region. This novel optical pumping technique leads to a significant increase in the efficiency and speed of the amplifier, distinguishing this study from others in the field. Simulation results provide detailed insights into the complex interplay between carrier interactions and gain spectra, offering a comprehensive understanding of QD-SOA operational dynamics. The proposed Quantum Dot Semiconductor Optical Amplifier (QD-SOA) achieves significant advancements in optical communication, offering maximum amplification rates of 26.9 and 19.9 times for QD1 and QD2, respectively, and bandwidths of 12 THz and 15 THz. The study highlights the role of quantum dot size, homogeneous and inhomogeneous broadening, and optical pumping in enhancing gain and performance, demonstrating the potential of QD-SOAs for high-gain, wide-bandwidth applications in photonic systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semiconductor quantum dots

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InGaZnO4 and In2O3 Mobility Trend: The Role of Structural Disorder from Amorphous to Crystalline States

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ACS Materials Letters 2025年 2024-2030页

作者： Jang, Hyeongjun Kim, Taehyun Lee, Wonsok Cho, Minhee Ha, Daewon Alam, Muhammad A. Ye, Peide D. Jeong, Changwook Graduate School of Semiconductor Materials and Devices Engineering Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Advanced Device Research Lab Semiconductor R&D Center Samsung Electronics Suwon16677 Korea Republic of School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

This study investigates the mobility trends of indium-gallium-zinc oxide (IGZO) thin-film transistors over a wide range of fabrication conditions, from amorphous to crystalline phases. By utilizing machine learning potential (MLP) to generate 70 distinct IGZO structures, we analyze electron mobility while taking into account structural disorder and electron scattering mechanisms. Our findings reveal that mobility peaks at the transition from amorphous to nanocrystalline phases, and then, as more ordered, crystalline structures emerge, the mobility decreases sharply due to the distortion of polyhedron of indium-oxygen in well-ordered regions. These results offer critical insights into optimizing the fabrication conditions for high-performance IGZO devices by identifying the ideal structural phase for achieving maximum mobility. © 2025 American Chemical Society.

关键词： Electron scattering

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Dynamically-Tunable Narrowband Color Sensor Compatible with CMOS Technology

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IEEE Sensors Journal 2025年第11期25卷 19117-19124页

作者： Asl, Akbar Beheshti Ahmadi, Hodjat Rostami, Ali Faculty of Electrical and Computer Engineering Tabriz Iran Industrial Park of Advanced Technologies SP-EPT Lab. ASEPE Company Tabriz Iran

Color sensors have broad applications in spectroscopy, medical imaging, and CMOS-based cameras. One of the fundamental challenges in this field is implementing a dynamically tunable filter capable of covering the visible spectrum;hence, designing a tunable filter with high spectral resolution and a narrow bandwidth frequency response is essential. In this work, a multilayer color sensor based on Fabry-Pérot resonance is designed, offering the capability for static and dynamic tuning of the transmitted wavelength and the ability to detect different colors in an integrated device, including a filter and detector. The filter comprises four parts: blue, green, yellow-orange, and red. The detector is a simple PN photodiode. The dynamic adjustment is performed via the voltage applied to the filter. In addition to the dynamic tunability, a key characteristic of this structure is its bandwidth, which remains consistently below 9, 8.5, 8, and 11.5 nm for blue, green, yellow-orange, and red regions, respectively. Notably, the materials used in this structure have been selected to be compatible with CMOS technology. Another feature of this sensor is its 50 nm spectral shift for each specified spectrum, achieved with a 15-volt bias. Furthermore, this device presents minuscule crosstalk (below 5%) compared to other devices. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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Fratricide! Hijacking in Android Multi-Window

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 3075-3092页

作者： Guo, Chenkai Wang, Tianhong Wang, Qianlu Dong, Naipeng Luo, Xiangyang Liu, Zheli Nankai University College of Cyber Science Tianjin300350 China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Anhui 230007 China Haihe Lab of ITAI Tianjin300350 China Nankai University College of Computer Science Tianjin300350 China University of Queensland School of Information Technology and Electrical Engineering Saint LuciaQLD4072 Australia State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

Android’s multi-window solutions allow several apps to coexist on one screen, providing powerful functionalities and appealing visuals for modern mobile devices. However, this opens the door for potential malware hijacking attacks. In our study, we unveiled Android’s insufficient security measures against malware activity in multi-window modes, and identified strategies on how to bypass these measures. We first introduced long-term monitoring methods through background services or malicious sub-window activities, and then designed and implemented 7 multi-window hijacking strategies to achieve multi-window hijacking in the three mainstream multi-window modes. To evaluate the effectiveness and impact of the attacks, we ran the hijacking in different system versions supporting multi-window solutions, and simulated the hijacking on 262 popular apps. All of the apps suffer from at least two types of multi-window hijacking attacks, and more than 65% of them can be attacked by all the 7 multi-window hijacking attacks. Furthermore, we designed and distributed a questionnaire to gather user opinions towards these attacks. 85% of respondents believe that the multi-window hijacking is highly covert and difficult to defend against. Of significance, we received acknowledgement and award fund from development teams of impactful apps. © 2004-2012 IEEE.

关键词： Android malware

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Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15200-15214页

作者： Song, Yaoxian Sun, Penglei Jin, Piaopiao Ren, Yi Zheng, Yu Li, Zhixu Chu, Xiaowen Zhang, Yue Li, Tiefeng Gu, Jason Zhejiang University Center for X-Mechanics School of Aeronautics and Astronautics Zhejiang Hangzhou China Information Hub Data Science and Analytics Thrust Guangdong China Huawei Technologies Advanced Manufacturing Lab Guangdong Shenzhen China UBTECH Robotics Inc Research Institute Guangdong Shenzhen China Renmin University of China School of Information School of Smart Governance Beijing China Westlake University Institute of Advanced Technology Westlake Institute for Advanced Study School of Engineering Zhejiang Hangzhou China Dalhousie University Department of Electrical and Computer Engineering HalifaxB3H 4R2 Canada

Robotic grasping is a fundamental ability for a robot to interact with the environment. Current methods focus on how to obtain a stable and reliable grasping pose in object level, while little work has been studied on part (shape)-wise grasping which is related to fine-grained grasping and robotic affordance. Parts can be seen as atomic elements to compose an object, which contains rich semantic knowledge and a strong correlation with affordance. However, lacking a large part-wise 3D robotic dataset limits the development of part representation learning and downstream applications. In this paper, we propose a new large Language-guided SHape grAsPing datasEt (named LangSHAPE) to promote 3D part-level affordance and grasping ability learning. From the perspective of robotic cognition, we design a two-stage fine-grained robotic grasping framework (named LangPartGPD), including a novel 3D part language grounding model and a partaware grasp pose detection model, in which explicit language input from human or large language models (LLMs) could guide a robot to generate part-level 6-DoF grasping pose with textual explanation. Our method combines the advantages of humanrobot collaboration and LLMs’ planning ability using explicit language as a symbolic intermediate. To evaluate the effectiveness of our proposed method, we perform 3D part grounding and fine-grained grasp detection experiments on both simulation and physical robot settings, following language instructions across different degrees of textual complexity. Results show our method achieves competitive performance in 3D geometry fine-grained grounding, object affordance inference, and 3D part-aware grasping tasks. © 2004-2012 IEEE.

关键词： Visual languages

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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