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Chiroptical sensing for remote chiral amines via a C-H activation reaction

CHINESE CHEMICAL LETTERS 2025年第5期36卷

作者： Li, Xin Lu, Jia-Min Li, Bo Zhao, Chen Yang, Bei-Bei Li, Li Chinese Acad Med Sci & Peking Union Med Coll State Key Lab Digest Hlth Beijing Key Lab Act Subst Discovery & Druggabil Ev Inst Mat Med Beijing 100050 Peoples R China

An electronic circular dichroism (ECD)-based chiroptical sensing method has been developed for beta- and gamma-chiral primary amines via a C-H activation reaction. With the addition of Pd(OAc)2, the flexible remote chiral primary amine fragment in the bidentate ligand intermediate was fixed to form a cyclopalladium complex, producing an intense ECD response. The correlation between the sign of Cotton effects and the absolute configuration of substrates was proposed, together with theoretical verification using time- dependent density functional theory (TDDFT). Chiroptical sensing of an important drug raw material was performed to provide rapid and accurate information on the absolute optical purity. This work introduces an alternative perspective of C-H activation reaction as well as a feasible chiroptical sensing method of remote chiral amines. (c) 2025 Published by Elsevier B.v. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.

关键词： Electronic circular dichroism (ECD) remote chiral amines C-H activation Cyclo-palladium Absolute optical purity

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Dynamic Loss Decay Based Robust Oriented Object Detection on remote sensing Images with Noisy Labels 27th

Dynamic Loss Decay Based Robust Oriented Object Detection on...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Liu, Guozhang Liu, Ting Yuan, Mengke Pang, Tao Yang, Guangxing Fu, Hao Wang, Tao Liao, Tongkui Piesat Information Technology Beijing China

ISBN: (纸本)9783031781094

The ambiguous appearance, tiny scale, and fine-grained classes of objects in remote sensing imagery inevitably lead to the noisy annotations in category labels of detection dataset. However, the effects and treatments of the label noises are underexplored in modern oriented remote sensing object detectors. To address this issue, we propose a robust oriented remote sensing object detection method through dynamic loss decay (DLD) mechanism, inspired by the two phase "early-learning" and "memorization" learning dynamics of deep neural networks on clean and noisy samples. To be specific, we first observe the end point of early learning phase termed as EL, after which the models begin to memorize the false labels that significantly degrade the detection accuracy. Secondly, under the guidance of the training indicator, the losses of each sample are ranked in descending order, and we adaptively decay the losses of the top K largest ones (bad samples) in the following epochs. Because these large losses are of high confidence to be calculated with wrong labels. Experimental results show that the method achieves excellent noise resistance performance tested on multiple public datasets such as HRSC2016 and DOTA-v1.0/v2.0 with synthetic category label noise. Our solution also has won the 2st place in the "fine-grained object detection based on sub-meter remote sensing imagery" track with noisy labels of 2023 National Big Data and computing Intelligence Challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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Building extraction based on multi-feature iterative method from remote sensing images 6

Building extraction based on multi-feature iterative method ...

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6th International conference on Geoscience and remote sensing Mapping, GRSM 2024

作者： Ma, Haoxuan Wu, Yongchuang Yang, Hui Wu, Yanlan School of Artificial Intelligence Anhui University Anhui Hefei230601 China Institutes of Physical Science and Information Technology Anhui University Anhui Hefei230601 China

ISBN: (纸本)9781510688018

Building extraction methods based on deep learning have technical characteristics such as high extraction accuracy and fast processing speed. However, when using high-resolution remote sensing images, traditional methods often struggle to extract occluded buildings. To address this problem, this paper introduces the Multi-Feature Iterative Model (MFIM), a multi-scale feature iterative model that extracts features at different scales and fuses them in a compressed manner to obtain a structure that preserves global features without losing local details. In order to evaluate the performance of the method, we conducted a comparative analysis on the WHU dataset, using U-Net, DeepLab v3+, and HR-Net as benchmark models. The experimental results indicate that the MFIM method achieves IoU, F1, Recall, and Precision scores of 89.85%, 94.74%, 94.86%, and 94.81%, respectively. This method significantly improves the accuracy of building extraction, and its effectiveness in complex scenes makes it valuable for building extraction tasks. © 2025 SPIE.

关键词： Semantic Segmentation

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Research on remote Monitoring System for Corrosion of Buried Pipelines Based on the Internet of Things (IoT)

Research on Remote Monitoring System for Corrosion of Buried...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Ren, Tianyi Tang, Yu Feng, Meixing Huang, Lihua China University of Petroleum-Beijing at Karamay Karamay China

ISBN: (纸本)9798331504205

To address the issues of high cost, poor timeliness, and significant limitations in existing pipeline corrosion monitoring methods, this paper designs a remote buried pipeline corrosion monitoring system based on the four-layer architecture of the Internet of Things (IoT). The system consists of a cathodic protection potential collector, an alarm device, and a remote monitoring system, enabling real-time monitoring of pipeline corrosion and the transmission of alarm information. At the core of the system is the Siemens S7-200 SMART series PLC controller, equipped with an analog extension module for real-time data acquisition. Using a serial communication protocol, real-time data is uploaded to a cloud platform with a visual interface via the AMX-IOT-M301 IoT data acquisition gateway module, achieving dynamic data storage and regulation. The system was applied to a self-built experimental platform, where its communication stability, data accuracy, and Android client performance were tested. The real-time monitored data included a natural potential of 1.53v, an on-potential of 0.09v, and an off-potential of 0.33v. The communication success rate of the system exceeded 97%, and the data transmission error was within ±l %. All other system functions operated normally. The test results indicate that the system performs stably and reliably, achieving real-time remote monitoring of pipeline corrosion and providing a technical reference for future development of such monitoring systems. © 2025 IEEE.

关键词： Pipeline corrosion

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6G Mobile Network Latency Mobile Edge computing Cloud Architecture 1

6G Mobile Network Latency Mobile Edge Computing Cloud Archit...

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1st International conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Naval, Preeti Pushparajesh, v. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Ramnagar District Karnataka India

ISBN: (纸本)9798331518578

Communication refers to the process by which one individual conveys information to another. Telecommunication and the instruments are made available by technological gadgets, which is now feasible. 5G mobile network standards are being used at present. It is thought that 6th-generation mobile network standards will address several user needs. 6G will be universally available by 2030. In a cloud computing setup, a specific hardware or geographic location is not required. The main aim of this method is to offer computing resources whenever they are needed. By fusing the two, mobile cloud computing enables more flexible and personalized solutions. Machine learning is finding its way into a wide variety of devices. With the use of AI., mobile network services (MNS) may improve the reliability and personalization of user experiences in areas like fraud detection, mobile transaction security, network operation monitoring, and management. A world without traffic accidents, high-tec, specialized healthcare, and criminal activity would be a reality if 6G mobile network services integrated cloud computing with artificial intelligence. The most important requirements for 6G standards are high data rate connection per user and the ability to manage huge record numbers. Research and development are underway for the next-generation mobile network. There are several intriguing aspects of this age. The most pressing problem that requires solving using suitable forensic methods is security. high-performance computers are needed to improve customer service. Two situations are created by combining technological, organizational, and cloud-hosted application-based research approaches in a high-performance computing setting: monitoring performance via remote desktop connection and real-time stream processing. This article seeks to narrow the designated worldwide spectators with a wide variety of empirical chances to allocate resources for trustworthy and defensible on-demand services. ©

关键词： Mobile edge computing

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A Novel Radiometric Scene Generator for Reconfigurable Microwave remote sensing

A Novel Radiometric Scene Generator for Reconfigurable Micro...

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2025 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2025

作者： Thomas, Renish Reising, Steven C. Blackwell, William Chandrasekar, v. Abedin, Malisa Mohammad, Zayed Farzana, Sharmin Milstein, Adam Pieper, Michael Leslie, vincent Microwave Systems Laboratory Colorado State University Fort CollinsCO80523 United States MIT Lincoln Laboratory LexingtonMA02421 United States

ISBN: (纸本)9781946815200

There is growing interest within the Earth remote sensing community in developing reconfigurable microwave sensors capable of dynamically adjusting their spectral, radiometric, spatial, and viewing angle properties in real-time. These sensors would adapt to targets of interest within their field of regard, enabling more efficient use of resources. For example, in areas with high spatial frequency, such as tropical storms, the sensor can be configured to observe the region at high spatial resolution, while in calmer weather, where spatial frequency is lower, the sensor can conserve resources by reducing resolution. This adaptive approach enhances the overall performance and efficiency of the sensor. © 2025 USNC-URSI.

关键词： Radiometry

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Research on the Application of remote Health Monitoring System Based on Intelligent sensing in Home-Based Elderly Care

Research on the Application of Remote Health Monitoring Syst...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Li, Miao Jiang, Rui Changchun Humanities and Sciences College Faculty of Social Welfare Dept. of Social Welfare Jilin Changchun130117 China Changchun Humanities and Sciences College Office of Academic Research Jilin Changchun130117 China

ISBN: (纸本)9798331504205

This paper designs a real-time stream processing algorithm driven by intelligent sensing to meet the high requirements of real-time and accuracy in home-based elderly care scenarios. The system collects physiological and behavioral data by integrating multimodal sensors (such as heart rate, blood oxygen, motion sensors, etc.), and uses distributed stream processing architecture to realize real-time analysis and anomaly detection of data. Then an improved dynamic threshold adaptive algorithm is proposed, which significantly improves the detection accuracy of health events while reducing the false alarm rate. The performance and reliability of the system are verified based on real health data sets in model simulation. The experimental results show that the designed system achieves an accuracy of 98.5% in abnormal event detection, and the processing delay is controlled within 200 milliseconds, which is more than 30% better than the traditional algorithm. In addition, the system has good scalability and stability in multi-task concurrent scenarios. This remote health monitoring system can effectively improve the health management level of home-based elderly care and provide personalized and efficient health protection for the elderly. © 2025 IEEE.

关键词： Electronic health record

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Classification of vehicle Classes at Toll Gates Using YOLOv9 and Resnet50

Classification of Vehicle Classes at Toll Gates Using YOLOv9...

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2025 International conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Febriyansah, Abdul Aziz Kosala, Gamma School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Indonesia's current toll road payment system uses Automatic vehicle Classification (AvC) technology, which relies on various sensors such as induction sensors, LiDAR, and radar. This approach incurs high operational costs. For this reason, more efficiency is required. Toll roads are vital infrastructure supporting vehicle mobility in Indonesia, and operational efficiency directly impacts overall performance. The existing AvC technology entails high costs and often faces technical challenges, necessitating a solution that enhances efficiency in terms of operational costs, accuracy, and system maintenance. This research combines the You Only Look Once (YOLO) method for detecting vehicle axles, represented by tires, and Residual Network (ResNet) for classifying vehicle categories based on YOLO's detection results. The dataset comprises 1,196 vehicle images across five distinct classes. The proposed system aims to improve accuracy, cost, and system maintenance efficiency. The findings indicate that the system achieves a classification accuracy of 97%, providing a practical solution that contributes to the advancement of toll gate vehicle classification technology with lower operational costs and higher accuracy. © 2025 IEEE.

关键词： remote sensing

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Lightweight remote sensing Target Detection Algorithm Based on Improved YOLOv5 16th

Lightweight Remote Sensing Target Detection Algorithm Based ...

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16th International conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Wang, Chaomin Dong, Fang Liang, Sheng Yu, Wanxin Wu, Kaixian Yu, Long Wenfeng, Wang School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Jiangxi Province Key Laboratory of Smart Water Conservancy Nanchang Institute of Technology Nanchang330099 China School of Statistics and Data Science Jiangxi University of Finance and Economics Nanchang330013 China

ISBN: (纸本)9789819615346

Aiming at the problems of large memory space occupied by remote sensing image detection models in complex scenes, high computational complexity, and detection speed that is difficult to meet the real-time target detection requirements, a model compression algorithm based on dynamic detection head and channel pruning is proposed. Based on YOLOv5s, the head of the original YOLOv5 is replaced by the DyHead detection head, which improves the detection performance of the model by combining and unifying the three attention mechanisms of scale, space and task. Secondly, the Focaler-IoU loss function is introduced to further improve the predictive regression ability of the model. Finally, the channel pruning algorithm is used to eliminate the redundant parameters in the network to further compress the parameters and computation of the model, and the accuracy of the pruned model is improved by model fine-tuning. The experimental results show that there is a large improvement on the DOTAv1.0 dataset, and the validity is further verified on the RSOD dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Jurassic

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Palladium Electrode-Based vertical Junctionless FET for high-performance Hydrogen Gas sensing 3rd

Palladium Electrode-Based Vertical Junctionless FET for High...

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3rd International conference on Intelligent computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Raj, Abhishek Swain, Sukanta Kumar Kumar, Rishu Sharma, Shashi Kant Department of Electronics and Communication Engineering Indian Institute of Information Technology Jharkhand Ranchi835217 India

ISBN: (纸本)9789819784639

In this research article, a vertical Junctionless Field Effect Transistor (v-JLFET) is designed for Hydrogen (H2) gas sensing. This device utilizes a palladium (Pd) electrode which is a catalytic metal for sensing of hydrogen gas. When the H2 gas meets catalytic Pd electrode, it creates pressure, causing a shift in the Pd gate workfunction. This change in workfunction is employed to detect the occurrence of H2 gas. Several electrical characteristics such as current through drain (IDS), transconductance (gm), drain conductance (gd), surface potential (Φs), and electron concentration were studied to sense the existence of H2 gas. Additionally, a sensitivity analysis is carried out to assess the effectiveness of the device in terms of threshold voltage (vth) with varying pressure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensitivity analysis

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