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Measurement of atmospheric nanoparticles:Bridging the gap between gas-phase molecules and larger particles

Journal of Environmental Sciences 2023年第1期123卷 183-202页

作者： Chao Peng Chenjuan Deng Ting Lei Jun Zheng Jun Zhao Dongbin Wang Zhijun Wu Lin Wang Yan Chen Mingyuan Liu Jingkun Jiang Anpei Ye Maofa Ge Weigang Wang State Key Laboratory for Structural Chemistry of Unstable and Stable Species Beijing National Laboratory for Molecular Sciences(BNLMS)CAS Research/Education Center for Excellence in Molecular SciencesInstitute of ChemistryChinese Academy of SciencesBeijing 100190China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China School of Environment Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China School of Atmospheric Sciences Sun Yat-sen UniversityZhuhaiGuangdong 519082China State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science&EngineeringFudan UniversityShanghai 200433China Key Laboratory for the Physics and Chemistry of Nanodevices Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China University of Chinese Academy of Sciences Beijing 100049China

Atmospheric nanoparticles are crucial components contributing to fine particulate matter(PM_(2.5)),and therefore have significant effects on visibility,climate,and human *** to the unique role of atmospheric nanoparticles during the evolution process from gas-phase molecules to larger particles,a number of sophisticated experimental techniques have been developed and employed for online monitoring and characterization of the physical and chemical properties of atmospheric nanoparticles,helping us to better understand the formation and growth of new *** this paper,we firstly review these state-of-the-art techniques for investigating the formation and growth of atmospheric nanoparticles(e.g.,the gas-phase precursor species,molecular clusters,physicochemical properties,and chemical composition).Secondly,we present findings from recent field studies on the formation and growth of atmospheric nanoparticles,utilizing several advanced ***-more,perspectives are proposed for technique development and improvements in measuring atmospheric nanoparticles.

关键词： Nanoparticle Measurement techniques Size distributions Physical properties Chemical composition

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Exploring the non-Hermitian topology of exceptional points in lasers and fiber loops

Exploring the non-Hermitian topology of exceptional points i...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Schumer, Alexander Nasari, Hadiseh Liu, Yuzhou G.N. Lopez-Galmiche, Gisela Leshin, Jason Lopez-Aviles, Helena E. Ding, Lei Alahmadi, Yousef Hassan, Absar U. Zhong, Qi Rotter, Stefan LiKamWa, Patrick Christodoulides, Demetrios N. Khajavikhan, Mercedeh ViennaA-1040 Austria Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States CREOL The College of Optics & Photonics University of Central Florida OrlandoFL United States Center of Excellence for Telecomm Applications Joint Centers of Excellence Program King Abdul Aziz City for Science and Technology Riyadh11442 Saudi Arabia Department of Physics & Astronomy University of Southern California Los AngelesCA United States

ISBN: (纸本)9798350345995

When non-Hermitian eigenvalue surfaces form intertwined Riemann surfaces, the corresponding non-Hermitian singularities, also know as exceptional points (EPs), are located at the center of this specific topology. Various experimental platforms have recently been presented in which interesting new physics associated with these branch point singularities can be explored. In particular, it has been shown that dynamically encircling EPs may lead to a so-called chiral state transfer. The chirality in this context refers to the fact that in such an encircling protocol, the final state at the end of an EP-enclosing loop solely depends on the encircling direction (clockwise or counterclockwise), but not on the initial state. Such a chiral behavior is intrinsically linked to the state vector deviating from adiabatically following the system's eigenstates associated with the eigenvalue surfaces;instead, due to the non-Hermitian violation of the adiabatic theorem, non-adiabatic jumps let the system undergo transitions from lossy eigenstates to eigenstates with gain. Here, we present two experiments that implement concepts that go beyond this established paradigm. © 2023 IEEE.

关键词： Topology

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Environmental Considerations in the Ethics of AI Adoption in Healthcare: Striving for Sustainable and Responsible Practices 1st

Environmental Considerations in the Ethics of AI Adoption in...

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1st International Conference on Artificial Intelligence and its Applications, ICAIA 2023

作者： Lautre, Hitendra Kumar Gowrishankar, R. Prathibha, R.S. Pradeep, T. Parveen Akhter, A. Ravisankar, A. Sundramurthy, Venkatesa Prabhu Department of Chemistry BYOS SCIENTIFIC LAB Aman Nagar Chhattisgarh Mowa Raipur492007 India Coimbatore641402 India Department of Chemistry Amrita College of Engineering and Technology Erachakulam Nagercoil629004 India Department of Civil Engineering Kongu Engineering College Erode Tamilnadu Perundurai638060 India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai600119 India Erode Tamil Nadu Perineuria638057 India Center of Excellence for Bioprocess and Biotechnology Department of Chemical Engineering College of Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783031843969

Medical treatment may be expanded to disadvantaged areas, and gaps in healthcare provider availability can be closed using artificial intelligence (AI). Artificial intelligence (AI) can potentially improve healthcare, but it also poses risks to patients. Ethical concerns about AI in healthcare have been raised, but the essential thing to consider is how AI will affect the natural world, not software flaws. In many nations, the healthcare sector significantly contributes to greenhouse gas emissions. Ethical considerations must consider the potential for AI to harm the environment as it becomes increasingly integrated into medical practice. Therefore, this paper will begin with a general summary of healthcare's carbon footprint. Second, it will discuss five issues with using carbon estimates to tackle healthcare sustainability. By extracting normative conceptions from green bioethics, the objective of medicines and the ethics of biomedicines and this study will offer healthiness, justice, and conservation of resources as the criterion for sustainability Artificial Intelligence in fitness care. Finally, the essay concludes with a discussion of how health, justice, and resource conservation might shed light on the offered case studies of sustainable and unsustainable growth and the application of AI in health care. Ethical considerations surrounding the application of AI in healthcare should include the requirement for environmental sustainability to prevent the release of further damaging carbon emissions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Greenhouse gas emissions

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Secondary Frequency Control of Decoupled Trigonometric Saturated Droop Controller for Islanded Low-Voltage Mesh Microgrid with Linear Load

Secondary Frequency Control of Decoupled Trigonometric Satur...

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Power, Energy and Electrical engineering (CPEEE), International Conference on

作者： Mohammed Alghobari Abdullah Ali Alhussainy Sultan Alghamdi Muhyaddin Rawa Abdullah Abusorrah Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia

This paper presents a robust secondary frequency control integrated with decoupled trigonometric saturated droop controller (DTSDC). The primary frequency controller may induce an error in frequency regulation, which affects the load sharing accuracy among microgrid distributed generators (DGs). Therefore, a centralized secondary controller is designed to converge the frequency to its nominal value for grid-forming inverters. Using an advanced communication channel, the proposed primary and secondary controller can primarily restore the frequency during disturbances, such as DG on/off switching and loading system variation. The practicality and performance of the proposed controller have been demonstrated with MATLAB simulation environment on a mesh microgrid system consisting of four parallel DGs-based inverters. Simulation results show the proposed controller’s robustness in the microgrid frequency stability. Hence, it achieves accurate active power sharing with a linear load. Moreover, the impact of resistive and inductive line impedance is discussed in this study. A comparison between the microgrid centralized controller (MGCC) and the decoupled trigonometric saturated droop controller (DTSDC) only is conducted to show the robustness of the presented controller performance.

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Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II

arXiv

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arXiv 2024年

作者： Wu, Rixin Wang, Ran Hao, Jie Wu, Qiang Wang, Ping Niyato, Dusit The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing Jiangsu210038 China The Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University M3J 1P3 Canada The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learning (DRL) model to solve the entire multiobjective optimization problem. The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL, thereby mitigating the limitations of both approaches. Firstly, we design an MOVRPTW model to balance the minimization of travel cost and the maximization of customer satisfaction. Subsequently, we present a novel DRL framework that incorporates a transformer-based policy network. This network is composed of an encoder module, a weight embedding module where the weights of the objective functions are incorporated, and a decoder module. NSGA-II is then utilized to optimize the solutions generated by WADRL. Finally, extensive experimental results demonstrate that our method outperforms the existing and traditional methods. Due to the numerous constraints in VRPTW, generating initial solutions of the NSGA-II algorithm can be time-consuming. However, using solutions generated by the WADRL as initial solutions for NSGA-II significantly reduces the time required for generating initial solutions. Meanwhile, the NSGA-II algorithm can enhance the quality of solutions generated by WADRL, resulting in solutions with better scalability. Notably, the weight-aware strategy significantly reduces the training time of DRL while achieving better results, enabling a single DRL model to solve the entire multiobjective optimization problem. © 2024, CC BY.

关键词： Reinforcement learning

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A Hybrid CNN-GRU model for Session-based Recommender Systems

A Hybrid CNN-GRU model for Session-based Recommender Systems

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International Conference on computer and Communications (ICCC)

作者： Abebe Tamrat Tegene Qiao Liu Yanglei Gan Elias Jimale Melak Ayenew Habte Leka School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Center of Excellence of AI and Robotics Addis Ababa Science and Technology University Addis Ababa Ethiopia School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

In many e-commerce recommendation systems, user profiles are mostly not available. In such cases, the recommendation task considers user behaviors like click sessions or browsing history. Traditional recommender methods often rely on users’ past interactions for recommending user interests, which is difficult to apply to session-based recommender systems. However, these methods perform very poorly on this task. Recently, deep learning techniques such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) showed outstanding performance in session-based recommendation problems. In this paper, we propose a hybrid CNN and gated recurrent unit (GRU) model that exploits the advantages of both CNN and GRU in modeling the rigid order requirement for interactions inside sessions and modeling the sequential long-term dependencies in session data, respectively. Our proposed hybrid model has two architectures: the first part consists of the CNN layers that take input from Item ID to learn local features and relationships in sessions, and the second part is a GRU layer that takes the output from the CNN layer to learn the long-term dependencies in that session. This hybrid technique enables the architecture for modeling prominent features in the session to provide accurate recommendations. Extensive experiments over a real-world dataset demonstrate that the proposed hybrid model performs well and outperforms those GRU and CNN-based models in the state-of-the-art recommendation task on both metrics used.

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A Secure Framework for Authenticating and Controlling Access to IoT Devices Integrated with Blockchain

A Secure Framework for Authenticating and Controlling Access...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Tahir, Sibgha Tariq, Noshina Khan, Farrukh Aslam Khan, Hilmand Al-Muhtadi, Jalal Department of Avionics Engineering Air University Islamabad Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan Center of Excellence in Information Assurance King Saud University Riyadh Saudi Arabia Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (纸本)9798331521165

The Internet of Things (IoT) enables autonomous communication among devices, improving human life but posing security risks. The main challenges to IoT systems' security are unauthorized access, data tampering, and the inability of central access control mechanisms to cope with scalability and privacy. Therefore, robust authentication and access control mechanisms are crucial for secure data sharing across IoT devices. This paper combines Hyperledger Fabric (HLF) blockchain with IoT for enhanced access control to establish trust in authentication and identification of devices along with secure communication. Hyperledger components are implied for developing an Attribute-Based Access Control (ABAC) model, including certification authorities, endorsers, and membership service providers. The system is implemented using HLF source code, Docker images, and IBM Cloud Kubernetes services, with an interface via Node-Red for device registration, authentication, blockchain data storage, and ledger retrieval. Experimental results demonstrate effective attack mitigation and better efficiency in terms of time complexity and energy consumption. © 2025 IEEE.

关键词： Authentication

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A Big Data Analytics System for Predicting Suicidal Ideation in Real-Time Based on Social Media Streaming Data

arXiv

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arXiv 2024年

作者： Allayla, Mohamed A. Ayvaz, Serkan Dams and Water Resources Research Center University of Mosul Mosul Iraq Department of Computer Engineering Yildiz Technical University Istanbul Turkey Centre for Industrial Software University of Southern Denmark Sønderborg Denmark

Online social media platforms have recently become integral to our society and daily routines. Every day, users worldwide spend a couple of hours on such platforms, expressing their sentiments and emotional state and contacting each other. Analyzing such huge amounts of data from these platforms can provide a clear insight into public sentiments and help detect their mental status. The early identification of these health condition risks may assist in preventing or reducing the number of suicide ideation and potentially saving people’s lives. The traditional techniques have become ineffective in processing such streams and large-scale datasets. Therefore, the paper proposed a new methodology based on a big data architecture to predict suicidal ideation from social media content. The proposed approach provides a practical analysis of social media data in two phases: batch processing and real-time streaming prediction. The batch dataset was collected from the Reddit forum and used for model building and training, while streaming big data was extracted using Twitter streaming API and used for real-time prediction. After the raw data was preprocessed, the extracted features were fed to multiple Apache Spark ML classifiers: NB, LR, LinearSVC, DT, RF, and MLP. We conducted various experiments using various feature-extraction techniques with different testing scenarios. The experimental results of the batch processing phase showed that the features extracted of (Unigram + Bigram) + CV-IDF with MLP classifier provided high performance for classifying suicidal ideation, with an accuracy of 93.47%, and then applied for real-time streaming prediction phase. © 2024, CC BY-NC-SA.

关键词： Forecasting

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Towards Accurate Categorization of Network IP Traffic Using Deep Packet Inspection and Machine Learning

Towards Accurate Categorization of Network IP Traffic Using ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Waqar Ali Aziz Hassaan Khaliq Qureshi Adnan Iqbal Anwer Al-Dulaimi Saba Al–Rubaye Department of Computer Science University of Cyprus Nicosia Cyprus CYENS Center of Excellence Nicosia Cyprus School of Electrical Engineering and Computer Sciences NUST Islamabad Pakistan Department of Computer Science Namal Institute Mianwali Pakistan 5G Center of Excellence EXFO Montreal Canada School of Aerospace Transport and Manufacturing Cranfield University UK

Network traffic classification is crucial for optimal network resource management. Several network traffic classification methods have been proposed, e.g., Deep Packet Inspection (DPI), and machine learning-based network traffic classification. Each approach is generally efficient for a certain class of network traffic. However, there is no one-fit-all method, i.e., no method offers the best performance for all types of network traffic. In this paper, we propose a hybrid network traffic classification technique that uses a combination of DPI and machine learning to identify and classify the network traffic into different Quality of Service (QoS) classes. The traffic is first identified through the DPI module, and the unidentified traffic then goes through the machine learning module, offering a classification accuracy of more than 98%. The results are evaluated based on the combination of DPI and different machine learning methods, e.g. supervised and unsupervised learning algorithms.

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A 32-Channel Time-to-Digital Converter with 20-ps Resolution for ToF Applications 4

A 32-Channel Time-to-Digital Converter with 20-ps Resolution...

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4th IEEE International Conference on Circuits and Systems, ICCS 2022

作者： Ke, Qin Yu, Hang Li, Yan Ma, Siguang Xi, Daoming Chen, Rui Shenzhen University Shenzhen Laboratory of Ic Design for Iot College of Computer Science and Software Engineering Shenzhen China Physical Electronics Group of Jihua Laboratory Foshan China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Huazhong University of Science and Technology Department of Biomedical Engineering Wuhan China

ISBN: (数字)9781665460361

ISBN: (纸本)9781665460361

Single-photon avalanche diodes (SPAD) enable sensitive detection of single photons under ultra-weak light conditions. Combined with direct time-of-flight (ToF) measurements, SPAD sensors are widely used in 3D imaging systems. Time-to-Digital Converter (TDC) can accurately measure time intervals in picoseconds, which is widely used for direct TOF measurements. This paper proposes a 32-channel TDC of two-level architecture containing a delay locked loop (DLL) and a vernier delay line (VDL) loop to increase timing accuracy. Designed in a 180 nm CMOS process, the TDC occupies an area of 4.1 mm × 3.6 mm and achieves 20 ps resolution at 2 Msps measurement rate. © 2022 IEEE.

关键词： Particle beams

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