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Modeling of adsorptive removal of azithromycin from aquatic media by CoFe₂O₄_/NiO anchored microalgae-derived nitrogen-doped porous activated carbon adsorbent and colorimetric quantifying of azithromycin in pharmaceutical products

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Chemosphere 2023年第1期329卷 138635页

作者： Ameen, Fuad Mostafazadeh, Reza Hamidian, Yasamin Erk, Nevin Sanati, Afsaneh L. Karaman, Ceren Ayati, Ali Department of Botany and Microbiology College of Science King Saud University Riyadh 11451 Saudi Arabia Ankara University Faculty of Pharmacy Department of Analytical Chemistry Ankara 06560 Turkey Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Polo II Coimbra 3030-290 Portugal Akdeniz University Vocational School of Technical Sciences Department of Electricity and Energy Antalya 07070 Turkey ChemBio Cluster ITMO University Lomonosova Street 9 Saint Petersburg 191002 Russian Federation

Herein, it was aimed to optimize the removal process of Azithromycin (Azi) from the aquatic environment via CoFe2O4/NiO nanoparticles anchored onto the microalgae-derived nitrogen-doped porous activated carbon (N-PAC)... 详细信息

Herein, it was aimed to optimize the removal process of Azithromycin (Azi) from the aquatic environment via CoFe₂O₄_/NiO nanoparticles anchored onto the microalgae-derived nitrogen-doped porous activated carbon (N-PAC), besides developing a colorimetric method for the swift monitoring of Azi in pharmaceutical products. In this study, the Spirulina platensis (Sp) was used as a biomass resource for fabricating CoFe₂O₄/NiO@N-PAC adsorbent. The pores of N-PAC mainly entail mesoporous structures with a mean pore diameter of 21.546 nm and total cavity volume (V_total) of 0.033578 cm³. g⁻¹. The adsorption studies offered that 98.5% of Azi in aqueous media could remove by CoFe₂O₄/NiO@N-PAC. For the cyclic stability analysis, the adsorbent was separated magnetically and assessed at the end of five adsorption-desorption cycles with a negligible decrease in adsorption. The kinetic modeling revealed that the adsorption of Azi onto the CoFe₂O₄/NiO@N-PAC was well-fitted to the second-order reaction kinetics, and the highest adsorption capacity was found as 2000 mg. g⁻¹ at 25 °C based on the Langmuir adsorption isotherm model at 0.8 g. L⁻¹ adsorbent concentration. The Freundlich isotherm model had the best agreement with the experimental data. Thermodynamic modeling indicated the spontaneous and exothermic nature of the adsorption process. Moreover, the effects of pH, temperature, and operating time were also optimized in the colorimetric Azi detection. The blue ion-pair complexes between Azi and Coomassie Brilliant Blue G-250 (CBBG-250) reagent followed Beer's law at wavelengths of 640 nm in the concentration range of 1.0 μM to 1.0 mM with a 0.94 μM limit of detection (LOD). In addition, the selectivity of Azi determination was verified in presence of various species. Furthermore, the applicability of CBBG-250 dye for quantifying

关键词： Adsorption Adsorption modeling Azithromycin Biomass-derived activated carbon Spectrophotometry Spirulina platensis

A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh Graduate Degree Program of Robotics National Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering National Chiao Tung University The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan Department of Computer Science National Chiao Tung University Hsinchu Taiwan

Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholding, probabilistic submap building, and an NDT-based radar scan matching. The proposed RO has been tested on two public radar datasets: the Oxford Radar RobotCar dataset and the nuScenes dataset, which provide scanning and automotive radar data respectively. The results show that our approach surpasses state-of-the-art RO using either automotive or scanning radar by reducing translational error by 51% and 30%, respectively, and rotational error by 17% and 29%, respectively. Besides, we show that our RO achieves centimeter-level accuracy as lidar odometry, and automotive and scanning RO have similar accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

Creating Own AI Datasets from Different Language Sources Efficiently: Advantages, Disadvantages and Best Pracitces to Make Own Datasets

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Creating Own AI Datasets from Different Language Sources Eff...

International IEEE Conference and Workshop: Óbuda on electrical and Power engineering (CANDO-EPE)

作者： György Molnár József Cserkó Enikő Nagy Zoltán Balogh Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Széchenyi University Doctoral School of Multidisciplinary Engineering Sciences (MMTDI) Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Institute of Cyberphysical Systems Óbuda University Budapest Hungary Department of Informatics Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia

Artificial Intelligence (AI) has become a ubiquitous technology that has the potential to revolutionize many industries. However, AI requires access to vast amounts of data to achieve its full potential. By understanding these data sources and the best practices for working with them, we can unlock the full potential of AI and create groundbreaking solutions that benefit society as a whole. Data sources can be utilized to train AI algorithms, including structured and unstructured data, but finding relevant datasets is challenging. The main problem is the international sources. Most of the datasets are available only in English or other common languages. However these are good sources, but translating them is expensive and time-consuming. This paper shows how to generate own datasets from open-source data with translation.

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State-Based Transcription of Components of Carnatic Music

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State-Based Transcription of Components of Carnatic Music

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Venkata Subramanian Viraraghavan Arpan Pal Hema Murthy R Aravind Embedded Systems and Robotics TCS Research and Innovation Tata Consultancy Services Department of Computer Science and Engineering Indian Institute of Technology Madras Department of Electrical Engineering Indian Institute of Technology Madras

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Automatic Carnatic Music (CM) transcription is an open problem in need of a standardized descriptive notation. The level of detail needed in a descriptive transcription makes it tedious to obtain ground truth by manual means. In this paper, we propose a novel state-based representation of the pitch curve motivated by CM components called constant-pitch notes and stationary points. We also propose a novel transcription technique that uses the Viterbi algorithm to estimate the states and quantized pitch-values. The proposed technique adheres best to raga-notes compared to the existing critical-points technique and uniform quantization. In a listening test, clips synthesized from the proposed notation were rated significantly better (324 ratings, p <;0.001) than those from critical-points. Further, speed-halving based on state information best matches the actual, observed CM component-duration ratios without losing raga-characteristics. Thus, the proposed transcription can be corrected manually to obtain ground truth and can enhance learning tools.

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Multispectral Vineyard Segmentation: A Deep Learning Comparison Study

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arXiv 2021年

作者： Barros, Tiago Conde, P. Gonçalves, G. Premebida, C. Monteiro, M. Ferreira, C.S.S. Nunes, U.J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra Portugal University of Coimbra Institute for Systems Engineering and Computers at Coimbra Department of Mathematics Coimbra Portugal Research Centre for Natural Resources Environment and Society Polytechnic Institute of Coimbra Coimbra Agrarian Technical School Coimbra Portugal Bolin Centre for Climate Research Department of Physical Geography Stockholm University Stockholm Sweden Navarino Environmental Observatory Navarino Dunes Messinia Greece

Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available on https://***/Cybonic/***. © 2021, CC BY.

关键词： Precision agriculture

Continuous-time fitted value iteration for robust policies

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arXiv 2021年

作者： Lutter, Michael Belousov, Boris Mannor, Shie Fox, Dieter Garg, Animesh Peters, Jan Intelligent Autonomous Systems group Computer Science Department Technical University of Darmstadt Darmstadt Germany Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion – Israel Institute of Technology and a Distinguished Scientist at NVIDIA Tel Aviv Israel Allen School of Computer Science & Engineering University of Washington and Senior Director of Robotics Research NVIDIA Washington Seattle United States Computer Science Department University of Toronto NVIDIA Toronto Canada

Solving the Hamilton-Jacobi-Bellman equation is important in many domains including control, robotics and economics. Especially for continuous control, solving this differential equation and its extension the Hamilton-Jacobi-Isaacs equation, is important as it yields the optimal policy that achieves the maximum reward on a give task. In the case of the Hamilton-Jacobi-Isaacs equation, which includes an adversary controlling the environment and minimizing the reward, the obtained policy is also robust to perturbations of the dynamics. In this paper we propose continuous fitted value iteration (cFVI) and robust fitted value iteration (rFVI). These algorithms leverage the non-linear control-affine dynamics and separable state and action reward of many continuous control problems to derive the optimal policy and optimal adversary in closed form. This analytic expression simplifies the differential equations and enables us to solve for the optimal value function using value iteration for continuous actions and states as well as the adversarial case. Notably, the resulting algorithms do not require discretization of states or actions. We apply the resulting algorithms to the Furuta pendulum and cartpole. We show that both algorithms obtain the optimal policy. The robustness Sim2Real experiments on the physical systems show that the policies successfully achieve the task in the real-world. When changing the masses of the pendulum, we observe that robust value iteration is more robust compared to deep reinforcement learning algorithm and the non-robust version of the algorithm. Videos of the experiments are shown at https://***/view/rfvi Copyright © 2021, The Authors. All rights reserved.

关键词： Dynamic programming

Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines

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arXiv 2020年

作者： Bird, Jordan J. Faria, Diego R. Premebida, Cristiano Ekárt, Anikó Vogiatzis, George School of Engineering and Applied Science Aston University Birmingham United Kingdom Lab Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

The novelty of this study consists in a multi-modality approach to scene classification, where image and audio complement each other in a process of deep late fusion. The approach is demonstrated on a difficult classification problem, consisting of two synchronised and balanced datasets of 16,000 data objects, encompassing 4.4 hours of video of 8 environments with varying degrees of similarity. We first extract video frames and accompanying audio at one second intervals. The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89.27% and 93.72%, respectively. This is followed by late fusion of the two neural networks to enable a higher order function, leading to accuracy of 96.81% in this multi-modality classifier with synchronised video frames and audio clips. The tertiary neural network implemented for late fusion outperforms classical state-of-the-art classifiers by around 3% when the two primary networks are considered as feature generators. We show that situations where a single-modality may be confused by anomalous data points are now corrected through an emerging higher order integration. Prominent examples include a water feature in a city misclassified as a river by the audio classifier alone and a densely crowded street misclassified as a forest by the image classifier alone. Both are examples which are correctly classified by our multi-modality approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Grid to vehicle and vehicle to grid systems for large-scale penetration of renewable generation

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Grid to vehicle and vehicle to grid systems for large-scale ...

2019 ECEEE Summer Study on Energy Efficiency: Is Efficient Sufficient?

作者： Moura, Pedro S. Pires, António Delgado, Joaquim de Almeida, Aníbal T. Institute of Systems and Robotics Dep. of Electrical and Computer Engineering University of Coimbra Polo II Coimbra3030-290 Portugal Institute of Systems and Robotics Department of Electrical Engineering Escola Superior de Tecnologia de Viseu Campus Politecnico de Repeses Viseu3504-510 Portugal

ISBN: (纸本)9789198387841

The electric power system is quickly changing due to the growing penetration of intermittent and non-dispatchable renewable energy sources. Simultaneously, the demand should ideally be able to be adapted to the renewable generation availability, directly with demand response or indirectly using energy storage technologies. The transport sector with electric vehicles (EVs) is increasingly an important consumer of electricity, and as fleets increase, EVs can be used as controllable loads, charging in periods of high renewable generation or low prices, using the Grid to Vehicle (G2V) system. With adequate technology, in addition to absorbing power from the grid, EVs can also use some of their storage capacity to inject energy into the grid, in order to ensure the balance between the generation and demand, using the Vehicle to Grid (V2G) system. However, the additional charging cycles due to V2G will accelerate the degradation of batteries and therefore its associated cost must be taken into account. This paper discusses the technologies and methodologies for the implementation of Grid to Vehicle and Vehicle to Grid systems, as well as their potential benefits for the grid in a scenario with large-scale penetration of renewable generation. To assess such impact a case study for the Portuguese grid is presented, considering three scenarios of penetration of EVs in the Portuguese vehicle fleet (in which EVs constitute 10 %, 25 % or 50 % of the fleet). The obtained data allowed to assess the potential of V2G to transfer the generation surplus between hours of low and high demand. Additionally, the economic benefits from the grid and user point-of-views were assessed considering the degradation of the batteries (evaluated through a model developed in Simulink environment), associated with the use of V2G, and their replacement cost. The results show a high impact on the compensation of renewable generation surplus, as well as economic benefits from the grid point-of-view. Ev

关键词： Electric storage Electric vehicles Renewable energy Vehicle charging

Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network

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arXiv 2021年

作者： Wan, Jun Xi, Hui Zhou, Jie Lai, Zhihui Pedrycz, Witold Wang, Xu Sun, Hang School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China College of Computer Science and Software Engineering Shen zhen University Shenzhen518060 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Electrical & Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland College of Computer and Information Technology China Three Gorges University Yichang HuBei China

Current fully-supervised facial landmark detection methods have progressed rapidly and achieved remarkable performance. However, they still suffer when coping with faces under large poses and heavy occlusions for inaccurate facial shape constraints and insufficient labeled training samples. In this paper, we propose a semi-supervised framework, i.e., a Self-Calibrated Pose Attention Network (SCPAN) to achieve more robust and precise facial landmark detection in challenging scenarios. To be specific, a Boundary-Aware Landmark Intensity (BALI) field is proposed to model more effective facial shape constraints by fusing boundary and landmark intensity field information. Moreover, a Self-Calibrated Pose Attention (SCPA) model is designed to provide a self-learned objective function that enforces intermediate supervision without label information by introducing a self-calibrated mechanism and a pose attention mask. We show that by integrating the BALI fields and SCPA model into a novel self-calibrated pose attention network, more facial prior knowledge can be learned and the detection accuracy and robustness of our method for faces with large poses and heavy occlusions have been improved. The experimental results obtained for challenging benchmark datasets demonstrate that our approach outperforms state-of-the-art methods in the literature. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition