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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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SMART COMMUNICATIONS AND NETWORKING FOR FUTURE DEEP-SPACE EXPLORATION

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China Communications 2020年第7期17卷 I0001-I0004页

作者： Qinyu Zhang Zhili Sun Tomaso de Cola Kanglian Zhao the EIE School the Institute for Communication Systems(ICS) Department of Electrical and Electronic EngineeringFaculty of Engineering and Physical SciencesUniversity of Surrey the German Aerospace Center(DLR) the Satellite and Space Communications(SSC)technical committee within ComSoc the Space-Internetworking Services(SIS)within the CCSDS standardization body the SatCom working group within the ETP Networld2020 platform School of Electronic Science and Engineering Nanjing University Institute of Space-Terrestrial Intelligent Networks Nanjing University

Since the first space probe flew to the Moon in 1959,over 200 deep-space exploration missions have already been carried out on the eight planets,various asteroids and comets in the solar *** of deepspace scientific ex... 详细信息

关键词： SPACE planet NETWORK

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PDADMAC/Alginate-Coated Gold Nanorod For Eradication of Staphylococcus Aureus Biofilms

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International journal of nanomedicine 2024年 19卷 3697-3714页

作者： Malarmugila Manimaran Yin Yin Teo James Chen Yong Kah Adilet Beishenaliev Yean Leng Loke Yiing Yee Foo Shiow-Fern Ng Chin Fei Chee Sek Peng Chin Farid Nazer Faruqu Chia-Yu Chang Misni Misran Lip Yong Chung Bey Fen Leo Shih-Hwa Chiou Chia-Ching Chang Sun Tee Tay Lik Voon Kiew Department of Pharmacology Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia. Department of Chemistry Faculty of Science Universiti Malaya Kuala Lumpur Malaysia. Department of Biomedical Engineering College of Design and Engineering National University of Singapore Singapore Singapore. Centre for Drug Delivery Technology and Vaccine Faculty of Pharmacy Universiti Kebangsaan Malaysia Kuala Lumpur Malaysia. Nanotechnology Catalysis Research Centre Universiti Malaya Kuala Lumpur Malaysia. Department of Pharmaceutical Chemistry Faculty of Pharmacy Universiti Malaya Kuala Lumpur Malaysia. Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Republic of China. Department of Molecular Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia. Department of Medical Research Taipei Veterans General Hospital Taipei Taiwan Republic of China. Institute of Pharmacology National Yang Ming Chiao Tung University Taipei Taiwan Republic of China. Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu Taiwan Republic of China. Center for Intelligent Drug Systems and Smart Bio-devices National Yang Ming Chiao Tung University Hsinchu Taiwan Republic of China. Institute of Physics Academia Sinica Nankang Taipei Taiwan Republic of China. Department of Medical Microbiology Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia.

Introduction:Over 75% of clinical microbiological infections are caused by bacterial biofilms that grow on wounds or implantable medical devices. This work describes the development of a new poly(diallyldimethylammonium chloride) (PDADMAC)/alginate-coated gold nanorod (GNR/Alg/PDADMAC) that effectively disintegrates the biofilms of (), a prominent pathogen responsible for hospital-acquired infections. Methods:GNR was synthesised via seed-mediated growth method, and the resulting nanoparticles were coated first with Alg and then PDADMAC. FTIR, zeta potential, transmission electron microscopy, and UV-Vis spectrophotometry analysis were performed to characterise the nanoparticles. The efficacy and speed of the non-coated GNR and GNR/Alg/PDADMAC in disintegrating -preformed biofilms, as well as their in vitro biocompatibility (L929 murine fibroblast) were then studied. Results:The synthesised GNR/Alg/PDADMAC (mean length: 55.71 ± 1.15 nm, mean width: 23.70 ± 1.13 nm, aspect ratio: 2.35) was biocompatible and potent in eradicating preformed biofilms of methicillin-resistant (MRSA) and methicillin-susceptible (MSSA) when compared to triclosan, an antiseptic used for disinfecting colonisation on abiotic surfaces in the hospital. The minimum biofilm eradication concentrations of GNR/Alg/PDADMAC (MBEC for MRSA biofilm = 0.029 nM; MBEC for MSSA biofilm = 0.032 nM) were significantly lower than those of triclosan (MBEC for MRSA biofilm = 10,784 nM; MBEC for MRSA biofilm 5967 nM). Moreover, GNR/Alg/PDADMAC was effective in eradicating 50% of MRSA and MSSA biofilms within 17 min when used at a low concentration (0.15 nM), similar to triclosan at a much higher concentration (50 µM). Disintegration of MRSA and MSSA biofilms was confirmed by field emission scanning electron microscopy and confocal laser scanning microscopy. Conclusion:These findings support the potential application of GNR/Alg/PDADMAC as an alternative agent to conventional antiseptics and antibiotics for the er

关键词： MRSA MSSA PDADMAC S. aureus biofilm gold nanorod

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Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation

Enhanced Subspace Distribution Matching for Fast Visual Doma...

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作者： Kang, Qi Yao, Siya Zhou, Mengchu Zhang, Kai Abusorrah, Abdullah Department of Control Science and Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Department of Electrical and Computer Engineering Faculty of Engineering and Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah21481 Saudi Arabia Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace. This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace. It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching. We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations. Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time. The results can be used in social cognition, person reidentification, and human-machine interactions. © 2014 IEEE.

关键词： Image classification

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Optical and photoluminescence modulation in monolayer molybdenum sulfide thin films via electrochemical deposition: Exploring the influence of deposition voltage and time

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Results in Physics 2024年 61卷

作者： Ajayeoba, Yetunde A. Adewinbi, Saheed A. Akinrinola, Olusola Akande, Akinlolu Pillai, Suresh C Awodugba, Ayodeji O. Department of Physics Faculty of Basic and Applied Sciences College of Science Engineering and Technology Osun State University P.M.B 4494 Osun State Osogbo Nigeria Department of Pure and Applied Physics Ladoke Akintola University of Technology Ogbomosho P.M.B 4000 Oyo State Ogbomosho Nigeria Nanotechnology and Bio-Engineering Research Group Department of Environmental Science Faculty of Science Atlantic Technological University ATU Sligo Ash Lane Sligo F91 YW50 Ireland Instituto de Fisica Facultad de Fisica Pontificia Universidad Catolica de Chile Santiago 7820436 Chile Centre for Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) Atlantic Technological University Ash Lane Ballytivnan Sligo F91 YW50 Ireland

Monolayer Molybdenum Disulfide (MoS2) exhibits a direct bandgap characterized by strong visible photoluminescence, high on/off ratio, high optical transparency, low dissipation rate, and light absorption in a wide ene... 详细信息

Monolayer Molybdenum Disulfide (MoS₂) exhibits a direct bandgap characterized by strong visible photoluminescence, high on/off ratio, high optical transparency, low dissipation rate, and light absorption in a wide energy spectrum. The influence of different deposition voltages and durations on the optical and photoluminescent characteristics was investigated using Raman spectroscopy, UV–visible spectrophotometry, and photoluminescence spectroscopy. All samples were analyzed for optical properties to confirm their suitability for optoelectronics. The energy band gap and Urbach energy (band tail width) of all films were 1.87 to 2.52 eV and 0.31 to 0.49 eV, respectively. The findings reveal a correlation between enhanced absorption properties and a decrease in the bandgap of the semiconducting film layers, implying the potential utility of the deposited films as efficient solar absorbers. Furthermore, the improved transmittance observed within the visible wavelength range suggests their applicability as effective window layers in thin-film photodiodes. It was established that varying the voltage and time of deposition alone can enhance the optoelectronic performance of the material in any device. © 2024 The Author(s)

关键词： 2D-MoS2 Deposition potential Deposition time Electrodeposition Optical properties Photoluminescence

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Two-stage distributionally robust optimization for joint system design and maintenance scheduling in high-consequence systems

A I I E Transactions

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A I I E Transactions 2024年第8期56卷

作者： Hanxiao Zhang Yan-Fu Li Min Xie Chen Zhang a Department of Systems Engineering City University of Hong Kong Hong Kong Chinab Centre for Intelligent Multidimensional Data Analysis Hong Kong Science Park Hong Kong China c Department of Industrial Engineering Tsinghua University Beijing China a Department of Systems Engineering City University of Hong Kong Hong Kong Chinad City University of Hong Kong Shenzhen Research Institute Shenzhen China

The failures of high-consequence systems can cause serious harm to humans, including loss of human health, life security, finance, and even social chaos. To protect high-consequence systems, both optimal system design and maintenance activities contribute to improving system reliability and social safety. The existing works generally optimize these two problems sequentially and assume that the degradation process of components is precisely known. However, sequential optimization often results in significant losses due to redundancies, and such a presumption usually cannot be guaranteed in practice, due to limited historical data or a lack of expert knowledge, referred to as epistemic uncertainty. To fill this gap, in this article, we consider an integrated optimization of system design and maintenance scheduling for multi-state high-consequence systems in which the component’s degradation is known with limited distributional information. To address this issue, we utilize the framework of distributionally robust optimization to provide a risk-averse decision to decision-makers even under the worst realizations of random parameters, and develop a two-stage integer distributionally robust model with moment-based ambiguity set to determine the system design and maintenance scheduling simultaneously. The proposed model can be converted to a tractable approximation as an integer linear stochastic programming problem. In order to solve large-scale problems, we develop a sample-based adaptive large neighborhood search algorithm to find the optimal system designs. In the numerical experiments, we present a case study on feedwater heating systems in nuclear power plants and demonstrate that an integrated optimization consideration creates significant benefits in profitability. We also present the out-of-sample performance of the distributionally robust design to avoid extreme risk.

关键词： Reliability optimization condition-based maintenance preventive maintenance uncertainty stochastic programming distributionally robust optimization

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UNPREDICTABILITY IN CONGESTION GAMES: MEMORY LOSS CAN PREVENT CHAOS

arXiv

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arXiv 2022年

作者： Bielawski, Jakub Chotibut, Thiparat Falniowski, Fryderyk Misiurewicz, Michal Piliouras, Georgios Department of Mathematics Cracow University of Economics Rakowicka 27 Kraków31-510 Poland Chula Intelligent and Complex Systems Department of Physics Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Mathematical Sciences Indiana University-Purdue University Indianapolis 402 N. Blackford Street IndianapolisIN46202 United States Engineering Systems and Design Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore

We study the dynamics of simple congestion games with two resources where a continuum of agents behaves according to a version of Experience-Weighted Attraction (EWA) algorithm. The dynamics is characterized by two parameters: the (population) intensity of choice a > 0 capturing the economic rationality of the total population of agents and a discount factor σ ∈ [0, 1] capturing a type of memory loss where past outcomes matter exponentially less than the recent ones. Finally, our system adds a third parameter b ∈ (0, 1), which captures the asymmetry of the cost functions of the two resources. It is the proportion of the agents using the first resource at Nash equilibrium, with b = 1/2 capturing a symmetric network. Within this simple framework, we show a plethora of bifurcation phenomena where behavioral dynamics destabilize from global convergence to equilibrium, to limit cycles or even (formally proven) chaos as a function of the parameters a, b and σ. Specifically, we show that for any discount factor σ the system will be destabilized for a sufficiently large intensity of choice a. Although for discount factor σ = 0 almost always (i.e., b 6= 1/2) the system will become chaotic, as σ increases the chaotic regime will give place to the attracting periodic orbit of period 2. Therefore, memory loss can simplify game dynamics and make the system predictable. We complement our theoretical analysis with simulations and several bifurcation diagrams that showcase the unyielding complexity of the population dynamics (e.g., attracting periodic orbits of different lengths) even in the simplest possible potential games. © 2022, CC BY.

关键词： Dynamics

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EnsembleFollower: A Hybrid Car-Following Framework Based on Hierarchical Planning and Reinforcement Learning

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IEEE Transactions on Vehicular Technology 2025年

作者： Han, Xu Chen, Xianda Zhu, Meixin Cai, Pinlong Zhou, Jianshan Chu, Xiaowen Data Science and Analytics Thrust Information Hub Guangzhou China Intelligent Transportation Thrust Systems Hub Guangzhou China Sensing and Computation for Ubiquitous Internet of Things Guangdong Provincial Key Lab of Integrated Communication China Southeast University School of Transportation Nanjing211189 China Shanghai Artificial Intelligence Laboratory Shanghai China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety Control State Key Laboratory of Intelligent Transportation Systems China Beihang University School of Transportation Science and Engineering Beijing100191 China

Car-following models have made significant contributions to our understanding of longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as they cannot fully capture the complexity inherent in car-following processes, or may struggle in unseen scenarios due to their reliance on confined driving skills present in training data. It is worth noting that each car-following model possesses its own strengths and weaknesses depending on specific driving scenarios. Therefore, we propose EnsembleFollower, a closed-loop hierarchical planning framework for achieving human-like autonomous car-following. The EnsembleFollower framework involves a high-level Reinforcement Learning-based agent responsible for judiciously managing multiple low-level models (such as intelligent Driver Model and Gipps model) according to the current state, either by selecting an appropriate car-following model to perform an action or by allocating different weights across all primitive models. Moreover, we propose a jerk-constrained kinematic model for more convincing microscopic traffic simulations. We evaluate the proposed method based on real-world driving data from the HighD dataset. The experimental results illustrate that EnsembleFollower yields an improved accuracy of human-like behavior and achieves effectiveness in combining hybrid models, demonstrating that our proposed framework can handle diverse driving conditions by leveraging the strengths of various car-following models. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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A Survey for Large Language Models in Biomedicine

arXiv

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arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

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Retrospective for the dynamic sensorium competition for predicting large-scale mouse primary visual cortex activity from videos 24

Retrospective for the dynamic sensorium competition for pred...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Polina Turishcheva Paul G. Fahey Michaela Vystrčilová Laura Hansel Rachel Froebe Kayla Ponder Yongrong Qiu Konstantin F. Willeke Mohammad Bashiri Ruslan Baikulov Yu Zhu Lei Ma Shan Yu Tiejun Huang Bryan M. Li Wolf De Wulf Nina Kudryashova Matthias H. Hennig Nathalie L. Rochefort Arno Onken Eric Wang Zhiwei Ding Andreas S. Tolias Fabian H. Sinz Alexander S. Ecker Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience & Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston Texas and Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine Stanford CA and Stanford Bio-X Stanford University Stanford CA and Wu Tsai Neurosciences Institute Stanford University Stanford CA Department of Neuroscience & Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston Texas Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany and Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine Stanford CA and Stanford Bio-X Stanford University Stanford CA and Wu Tsai Neurosciences Institute Stanford University Stanford CA Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany and International Max Planck Research School for Intelligent Systems Tübingen Germany and Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China and Beijing Academy of Artificial Intelligence China Beijing Academy of Artificial Intelligence China Institute of Automation Chinese Academy of Sciences China The Alan Turing Institute UK and School of Informatics University of Edinburgh UK School of Informatics University of Edinburgh UK Centre for Discovery Brain Sciences University of Edinburgh UK and Simons Initiative for the Developing Brain University of Edinburgh UK Department of Neuroscience & Center for Neuroscience and Artificial Intelligence Baylor College of Medicine Houston Texas and Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine Stanford CA and Stanford Bio-X Stanford University Stanford CA and Wu Tsai Neurosciences Institute Stanford University Stanf

ISBN: (纸本)9798331314385

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 Competition Track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***.

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