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Decoding the Biomimetic Mineralization of Metal-Organic Frameworks in Water

ACS Nano 2024年第36期18卷 25170-25182页

作者： Lin, Shang-Wei Lam, Phuc Khanh Wu, Chin-Teng Su, Kuan-Hsuan Sung, Chi-Fang Huang, Sen-Ruo Chang, Je-Wei Shih, Orion Yeh, Yi-Qi Vo, Trung Hieu Tsao, Heng-Kwong Hsieh, Haw-Ting Jeng, U-Ser Shieh, Fa-Kuen Yang, Hsiao-Ching Department of Chemistry Fu Jen Catholic University New Taipei City24205 Taiwan Department of Chemistry National Central University Taoyuan32001 Taiwan National Synchrotron Radiation Research Center Hsinchu Science Park Hsinchu300092 Taiwan Department of Chemical and Materials Engineering National Central University Taoyuan32001 Taiwan Department of Electrical Engineering and Computer Sciences University of California at Berkeley CA94720 United States Department of Chemical Engineering College of Semiconductor Research National Tsing Hua University Hsinchu300044 Taiwan

This study unveils the "green" metal-organic framework (MOF) structuring mechanism by decoding proton transfer in water during ZIF-8 synthesis. Combining in situ small- to wide-angle X-ray scattering, multiscale simulations, and quantum calculations, we reveal that the ZIF-8 early-stage nucleation and crystallization process in aqueous solution unfolds in three distinct stages. In stage I, imidazole ligands replace water in zinc-water cages, triggering an "acidity flip" that promotes proton transfer. This leads to the assembly of structures from single zinc ions to 3D amorphous cluster nuclei. In stage II, amorphous nuclei undergo a critical transformation, evolving into crystalline nuclei and subsequently forming mesoscale-ordered structures and crystallites. The process proceeds until the amorphous precursors are completely consumed, with the transformation kinetics governed by an energy barrier that determines the rate-limiting step. In stage III, stable crystallite nanoparticles form in solution, characterized by a temperature-dependent thermal equilibrium of molecular interactions at the crystal-solution interface. Beyond these core advancements, we explore the influence of encapsulated pepsin and nonencapsulated lysozyme on ZIF-8 formation, finding that their amino acid proton transfer capacity and concentration influence the resulting biomolecule-MOF composite’s shape and encapsulation efficiency. The findings contribute to understanding the molecular mechanisms behind biomimetic mineralization and have potential implications for engineering proteins within amorphous MOF nuclei as protein embryo growth sites. © 2024 American Chemical Society.

关键词： X ray scattering

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Enhancing Drone Video Analytics Security Management using an AERPAW Testbed

Enhancing Drone Video Analytics Security Management using an...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Alicia Esquivel Morel Zack Murry Kevin Kostage Chengyi Qu Prasad Calyam Department of Electrical Engineering and Computer Science University of Missouri Columbia USA Department of Computing and Software Engineering Florida Gulf Coast University Florida USA

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Drone-based applications involving real-time video analytics are emerging to serve diverse situational awareness use cases ranging from precision agriculture to disaster response. Hence, there is a need to study robust security measures to ensure the integrity of information and safeguard communication, as well as data transmission in drone video analytics. In this paper, we investigate methods to enhance the security management of drone video analytics in terms of reliability and integrity within realistic settings using testbed resources in the NSF-supported AERPAW infrastructure. Specifically, we study security mechanisms to model and detect threats such as Replay, Packet Injection, and Physical Capture attacks caused by situations in dynamic and potentially adversarial network environments. In addition, we generate balanced datasets through Generative Adversarial Networks (GAN) to address challenges posed by unbalanced datasets that are common when applying machine learning models for attack detection impacting drone video analytics traffic. Our experimental environment in AERPAW involves a setup for secure communication through a MAVLink-based (open-standard) drone communication protocol that uses continuous authentication via digital signatures. Our experiment results compare the efficiency gains achieved through secure MAVLink-based communication with unsecured counterparts, examining factors such as packet encryption, digital signatures, and nonces. Further, our results provide valuable insights into the adaptability of security mechanisms for drone video analytics within realistic environments.

关键词： Protocols Visual analytics Security management Authentication Machine learning Generative adversarial networks Encryption

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Leveraging Machine Learning to Identify Synergistic Drug Combinations for Effective Cancer Treatment 7

Leveraging Machine Learning to Identify Synergistic Drug Com...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Sujatha, P. Saravanan, K. Sohail, Mohammed Ali Basi Reddy, A. Dixit, Rohit R. Krishnaiah, Nallam Amrita Vishwa Vidyapeetham Department of AI/ML Guntur Amaravati India R.M.K Engineering College Department of Information Technology Tamil Nadu Tiruvallur District India Jazan University College of Computer Science & Information Technology Department of Computer & Network Engineering Jazan Saudi Arabia Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Siemens Healthineers NH03755 United States St.Martin's Engineering College Department of Ai&ml Secunderabad Telangana Dhulapally500100 India

ISBN: (纸本)9798350371406

The potential of drug combinations to treat and overcome medication resistance complex genetic diseases is evident. Synergistic drug combinations offer a promising way to enhance drug therapy efficacy and reduce the required medication dosage. However, developing effective combination medication therapies with synergistic effects has been challenging, despite numerous ongoing clinical investigations. Current models and approaches to detect medication synergy outlined in the literature lack the expected consistency in outcomes. to better comprehend the impact of particular medication combinations, it is essential to be familiar with the vocabulary used to describe synergy. In this study, a combinational drug screen is utilized to identify useful features for locating synergistic or efficient drug combinations. The feature selection algorithm (Boruta) helps select the most relevant features, and machine learning models are then trained using the selected feature dataset. Performance assessment metrics like sensitivity, accuracy, and specificity are used to compare the trained models, and the Random Forest model stands out for its significantly better performance compared to other models. © 2023 IEEE.

关键词： Drug dosage

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Quantifying the Impact of Incentives on Service Availability at the Extreme Edge

Quantifying the Impact of Incentives on Service Availability...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Sherif B. Azmy MHD Saria Allahham Nizar Zorba Hossam S. Hassanein Department of Electrical and Computer Engineering Queen’s University Kingston Canada School of Computing Queen’s University Kingston Canada Electrical Engineering Department Qatar University Doha Qatar

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Edge computing seeks to optimize service provision over enterprise-owned infrastructure near the end-user at the network’s edge. However, it misses out on the opportunity to utilize user-owned hardware at the extreme edge of the network as workers in a sharing economy. In this work, we build upon the existing Incentive Vacation Queueing (IVQ) model and develop the Virtual Kiosk Model (VKM) to analyze service availability and the dynamics of multiple workers’ participation in the provision of a service on the extreme edge. We formulate an optimization problem to minimize total cost of incentive payments while maintaining service availability under temporal constraints. We propose the Model-based Incentive Strategy at the Edge (MISE) algorithm to iteratively adjust incentives in real-time. MISE is compared against traditional numerical optimizers and a baseline naive approach that greedily focuses on minimizing incentives. Our findings demonstrate that MISE ensures sustained service availability without overburdening the workers at a cost acceptable to the service provider, striking a crucial balance in the management of extreme edge computing resources.

关键词： Analytical models Costs Computational modeling Heuristic algorithms Sharing economy Real-time systems Numerical models Queueing analysis Optimization Edge computing

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Optimal Dispatch Strategy for Roof Top Solar PV-Based EV Charging Stations 10

Optimal Dispatch Strategy for Roof Top Solar PV-Based EV Cha...

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10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Mishra, Rishabh Husain, Mohammed Aslam Pandey, Yudhishthir Khan, Asif Agrawal, Alka Mishra, Shivam Rajkiya Engineering College Department of Electrical Engineering Ambedkar Nagar UP India Integral University Department of Computer Application UP Lucknow India Babasaheb Bhimrao Ambedkar University Department of Information Technology UP Lucknow India

ISBN: (纸本)9798331511890

Electric vehicles (EV) are exciting alternatives to conventional vehicles (CV) which have zero carbon emission during operation. The electric vehicle's ability is to reduce total climate effect & pollutant emission significantly. In This paper present a comparative analysis study of an optimization dispatch strategy for electric vehicles (ODSEV) charging station by using general algebraic modelling system (GAMS) to solve optimization dispatch equation. In the current distributed electricity generation scenario, a reliable, economical energy distribution plan is required. The problem of pollutant emissions from fossil fuels and their finite supply have forced researchers to concentrate on the future of renewable energy production. The output of this paper is to optimization dispatch strategy for electric vehicles (ODSEV) which includes four thermal & one solar power plant. This paper aims to introduce a cost-effective and environmentally conscious algorithm that integrates both thermal and solar power plants. Additionally, the algorithm accounts for the uncertainties associated with varying climatic conditions, ensuring a more practical and adaptable solution. © 2025 IEEE.

关键词： Solar fuels

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Design of a Standalone Hybrid Renewable Energy System - A Hospital in Lafayette, Indiana, USA

Design of a Standalone Hybrid Renewable Energy System - A Ho...

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Communication, computing and Industry 4.0 (C2I4), International Conference on

作者： Samir Thapa Arash Asrari Ehsan Naderi Poria Fajri Department of Electrical and Computer Engineering Purdue University Northwest Hammond IN USA Department of Electrical Engineering College of Engineering and Computer Science Arkansas State University Jonesboro AR USA Department of Electrical and Biomedical Engineering University of Nevada Reno NV USA

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

This paper presents the design of a standalone hybrid renewable energy system (HRES) for a hospital in Lafayette, Indiana, USA, to enhance energy reliability and reduce costs. The proposed system integrates photovoltaic (PV) panels, wind turbines, batteries, and a backup diesel generator. Using Hybrid Optimization Model for Electric Renewables (HOMER) software, the system is optimized for cost-effectiveness and sustainability, while electrical Transient Analyzer Program (ETAP) software is employed to ensure a safe operation under varying conditions. Results verify that the developed HRES meets the hospital’s energy needs, significantly reduces operational costs, and minimizes carbon emissions, offering a sustainable model for healthcare facilities.

关键词： Renewable energy sources Costs Hospitals Reliability engineering Generators Software reliability Wind turbines Transient analysis Sustainable development Load modeling

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Extreme Edge computing Challenges on the Edge-Cloud Continuum

Extreme Edge Computing Challenges on the Edge-Cloud Continuu...

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Canadian Conference on electrical and computer engineering (CCECE)

作者： Sherif B. Azmy Rawan F. El-Khatib Nizar Zorba Hossam S. Hassanein Department of Electrical and Computer Engineering Queen’s University Kingston Canada Electrical Engineering Department Qatar University Doha Qatar School of Computing Queen’s University Kingston Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

The evolution of AI and LLMs is transforming service provision, revealing the limitations of traditional cloud computing in latency and privacy. Edge computing overcomes such limitations by moving resources closer to users, but often overlooks user-owned devices. This work examines the Edge-Cloud continuum, including the role of Extreme Edge computing (XEC), which extends computational reach to user-owned devices. We discuss how this continuum can lead to reduced latency, enhanced privacy, and improved energy efficiency, while noting the challenges posed by heterogeneous and unpredictable user-owned resources.

关键词： Privacy Electric potential Cloud computing Costs Navigation Dynamic scheduling Energy efficiency

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Advances and Open Challenges in Federated Foundation Models

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IEEE Communications Surveys and Tutorials 2025年

作者： Ren, Chao Yu, Han Peng, Hongyi Tang, Xiaoli Zhao, Bo Yi, Liping Tan, Alysa Ziying Gao, Yulan Li, Anran Li, Xiaoxiao Li, Zengxiang Yang, Qiang School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Sweden College of Computing and Data Science Nanyang Technological University Singapore College of Computer Nankai University China School of Medicine Yale University United States Digital Research Institute of Enn Group Langfang China Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong WeBank Shenzhen China

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabilities, while addressing concerns of privacy, data decentralization and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of FMs. A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. Key challenges, including how to enable FL to deal with high complexity of computational demands, privacy considerations, contribution evaluation, and communication efficiency, are thoroughly discussed. Moreover, this paper explores the intricate challenges of communication, scalability and security inherent in training/fine-tuning FMs via FL. It highlights the potential of quantum computing to revolutionize the processes of training, inference, optimization and security. This survey also introduces the implementation requirement of FedFM and some practical FedFM applications. It highlights lessons learned with a clear understanding of our findings for FedFM. Finally, this survey not only provides insights into the current state and challenges of FedFM, but also offers a blueprint for future research directions, emphasizing the need for developing trustworthy solutions. It serves as a foundational guide for researchers and practitioners interested in contributing to this interdisciplinary and rapidly advancing field. © 2025 IEEE.

关键词： Federated learning

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Smart Waste Collecting Robot Integration With IoT and Machine Learning 2

Smart Waste Collecting Robot Integration With IoT and Machin...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Srinivasan, S. Jambulingam, Umamagewaran Purushothaman, K.E. Jegan Amarnath, J. Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu India College of Computer Science King Khalid University Saudi Arabia Department of Computer Science and Engineering Kammavari Sangham Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9798350305418

Non-biodegradable waste collecting from a wide area is a time-consuming process. The study aims to create an intelligent robot that can independently collect waste utilizing innovative technology. The study uses the Internet of Things (IoT) to connect and manage the various robot parts. A Raspberry Pi organizes many functions as the system's central processing unit. Real-time visual data, essential for waste detection and identification, is captured by a camera module. Ultrasonic sensors identify obstacles and measure distances so the robot can navigate its environment. The sensor helps the robot avoid collisions and navigate the predetermined space effectively. The robot's robotic arm, which enables it to pick up and collect waste materials physically, is one of its primary features. The Raspberry Pi controls the arm, allowing for accurate and synchronized motions for waste collection. They use machine learning methods to facilitate waste identification and categorization, especially a CNN model. To reliably identify various forms of waste, the CNN model is trained using a collection of waste images. These technologies may be combined to enable the automated waste-collected robot to function independently while effectively locating and collecting rubbish. By seamlessly integrating IoT, machine learning, and robots, this solution helps to save human labor and enhance waste management procedures. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Environmental sustainability Image processing Internet of Things (IoT) Waste Collecting Robot

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Metamaterial-Enabled Ultrawideband mmWave Antenna-in-Package Using Heterogeneously-Integrated Silicon IPD and HDI-PCB for B5G/ 6G Applications

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IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2024年第1期14卷 7-18页

作者： Khiabani, Neda Chiang, Ching-Wen Liu, Nai-Chen Chen, Pai-Yen Kuan, Yen-Cheng Wu, Chung-Tse Michael Rutgers University Department of Electrical and Computer Engineering New BrunswickNJ08854 United States Nokia Bell Labs Murray HillNJ07974 United States National Yang Ming Chiao Tung University International College of Semiconductor Technology Hsinchu30010 Taiwan Industrial Technology Research Institute Information and Communications Research Laboratories Hsinchu County310401 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan MediaTek Inc. Hsinchu30078 Taiwan University of Illinois at Chicago Department of Electrical and Computer Engineering ChicagoIL60607 United States

This work presents the design, creation, and testing of ultrawideband millimeter-wave (mmWave) antennas with a tightly coupled array (TCA) configuration. These antennas are made using metamaterial (MTM) designs and advanced high-density interconnect (HDI) antenna-in-package (AiP) technologies, ideal for beyond-5G (B5G) and 6G networks. The main elements of the MTM antenna array are constructed with silicon-based integrated passive device (IPD) technology and are flip-chip bonded to a multi-layered HDI-PCB that includes a resistive frequency selective surface (FSS). These array antennas are differentially fed through a coax-via system. The study presents two types of 5\times 5 finite arrays: a metal-insulator-metal (MIM) capacitor-based MTM bowtie array with a differential Voltage Standing Wave Ratio (VSWR) \le3.5 , operating from 16.2 to 100 GHz (excluding 18.26-18.68 GHz and 60.8-61.13 GHz), and an interdigital capacitor-based MTM bowtie array functioning from 18.85 to 100 GHz (excluding 41.52-42.25 GHz). Experimental validation of these prototypes confirms their performance, aligning well with simulated results in terms of bandwidth and radiation characteristics. © 2011 IEEE.

关键词： Frequency selective surfaces

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