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A methodological framework for estimating ambient PM_(2.5)particulate matter concentrations in the UK

Journal of Environmental sciences 2025年第4期150卷 676-691页

作者： David Galán-Madruga Parya Broomandi Alfrendo Satyanaga Ali Jahanbakhshi Mehdi Bagheri Aram Fathian Rasoul Sarvestan J.Cárdenas-Escudero J.O.Cáceres Prashant Kumar Jong Ryeol Kim Department of Atmospheric Pollution National Centre for Environment HealthHealth Institute Carlos III.Ctra.Majadahonda a Pozuelo km 2.2Madrid 28220Spain Department of Civil and Environmental Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan Department of Electrical and Computer Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan School of Architecture Building and Civil EngineeringLoughborough UniversityLeicestershire LE113TUUnited Kingdom Neotectonics and Natural Hazards Institute RWTH Aachen UniversityAachen 52056Germany UNESCO Chair on Coastal Geo-Hazard Analysis Research Institute for Earth SciencesTehran 13185-1494Iran Water SedimentHazardsand Earth-surface Dynamics(waterSHED)LabDepartment of GeoscienceUniversity of CalgaryCalgary Alberta T2N 1N4Canada Department of Climatology Faculty of Geography and Environmental SciencesHakim Sabzevari UniversitySabzevar ***Iran Laser Chemistry Research Group Department of Analytical ChemistryFaculty of ChemistryComplutense University of MadridPlaza de Ciencias 1Madrid 28040Spain Analytical Chemistry Department FCNETUniversity of PanamaUniversity CityUniversity MailPanama 4Panama City 3366Panama Global Centre for Clean Air Research(GCARE) School of SustainabilityCivil and Environmental EngineeringFaculty of Engineering and Physical SciencesUniversity of SurreyGuildford GU27XHUnited Kingdom Institute for Sustainability University of SurreyGuildford GU27XHUnited Kingdom

Scientific evidence sustains PM_(2.5)particles’inhalation may generate harmful impacts on human beings’health;therefore,theirmonitoring in ambient air is of paramount relevance in terms of public *** to the limited number of fixed stations within the air qualitymonitoring networks,development ofmethodological frameworks tomodel ambient air PM_(2.5)particles is primordial to providing additional information on PM_(2.5)exposure and its *** this sense,this work aims to offer a global easily-applicable tool to estimate ambient air PM_(2.5)as a function of meteorological conditions using a multivariate *** PM_(2.5)data measured by 84 fixed monitoring stations and meteorological data from ERA5(ECMWF Reanalysis v5)reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested *** from January 2017 to December 2020 were employed to build amathematical expression that related the dependent variable(PM_(2.5))to predictor ones(sea-level pressure,planetary boundary layer height,temperature,precipitation,wind direction and speed),while 2021 data tested the *** indicators evidenced a good performance of model(maximum values of RMSE,MAE and MAPE:1.80μg/m^(3),3.24μg/m^(3),and 20.63%,respectively),compiling the current legislation’s requirements for modelling ambient air PM_(2.5)concentrations.A retrospective analysis of meteorological features allowed estimating ambient air PM_(2.5)concentrations from 2000 to *** highest PM_(2.5)concentrations relapsed in theMid-and Southlands,while Northlands sustained the lowest concentrations.

关键词： Air quality PM_(2.5)particles Meteorological variables Prediction model Long-term trend

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A New IoT-Based Smart Greenhouse for Cultivation of Water Convolvulus

A New IoT-Based Smart Greenhouse for Cultivation of Water Co...

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International electrical engineering Congress (iEECON)

作者： Prayoot Akkaraekthalin Tanee Somwong Nonchanutt Chudpooti Nuttawut Hirun Suwaluck. Meesomklin Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronic and Telecommunications Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electronics Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents a new IOT system for monitoring and controlling plant cultivation in greenhouse system. The system aims to regulate and monitor the environmental conditions suitable for plant cultivation in real-time. The system utilizes sensors to measure environmental conditions, such as soil moisture sensor, light intensity sensor, temperature sensor, and air humidity sensor. The Node MCU ESP32 is used for processing and controlling these operations. The system is designed to send notifications through Line application and allows farmers to monitor the system's performance and plant growth in real-time via a smartphone application. It can utilize the Blynk IoT application on a smartphone for remotely monitoring and controlling the environmental conditions and provides historical data. This experiment demonstrates that the designed system can successfully cultivate plants and promote better growth compared to traditional cultivation methods with a 13.38% improvement. Additionally, it provides convenience and ease of management for plant cultivation.

关键词： Temperature sensors Temperature measurement Smart agriculture System performance Green products Sensor systems Real-time systems Sensors Internet of Things Monitoring

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Gain-analytical equations generalized for FOPID controllers — An application with DC–DC power converters

e-Prime - Advances in Electrical Engineering, Electronics an...

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e-Prime - Advances in electrical engineering, Electronics and Energy 2025年 12卷

作者： Pereira, Luís Felipe da S.C. Volpato, Anderson S. Batista, Edson Antonio de Brito, Moacyr A.G. Godoy, Ruben B. Pinto, João O.P. Tolbert, Leon M. Faculty of Engineering Architecture and Urban Planning and Geography Federal University of Mato Grosso do Sul University City Campo Grande 79070-900 Brazil Oak Ridge National Laboratory Oak Ridge TN United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States

This study presents algebraic formulations for determining the gains of a fractional-order PID (FOPID) regulator, with attention to gain crossover frequency, phase margin, and robustness against gain variations (iso-damping). The differential evolution (DE) algorithm is employed for optimal tuning of fractional-order values, aligned with FOPID gain parameters. FPGA-in-the-loop simulations for Buck and DC–DC Boost converters are conducted to validate the DE-tuned FOPID controllers, optimized using the L² norm and compared to MATLAB-Simulink simulations. The approach introduces a key innovation: disaggregating the FOPID equation for memory efficiency. Simulations and experiments demonstrate the controller's precision in setpoint tracking and resilience to load and input voltage variations in Buck and DC–DC Boost converters, with effective damping of load disturbances. A test bench validated the proposed method, which was also compared to conventional PID, Type 3, and PIDD2-IMC controllers. While the proposed FOPID controller performed better in the presented results, a conclusive comparison could not be drawn due to unequal tuning efforts. Future work will focus on a more thorough comparison of controllers. © 2025

关键词： Buck converter DC–DC boost converter Differential evolution FPGA-in-the-loop Fractional-order PID Oustaloup filter

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2025年 14784 LNCS卷 v页

作者： Bai, Guangdong Ishikawa, Fuyuki Aït-Ameur, Yamine Papadopoulos, George A. School of Electrical Engineering and Computer Science Faculty of Engineering Architecture and Information Technology University of Queensland BrisbaneQLD Australia Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Chiyoda-ku Japan IRIT University of Toulouse Toulouse France Department of Computer Science University of Cyprus Nicosia Cyprus

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Reducing energy consumption by load aggregation with an optimized dynamic live migration of virtual machines

Reducing energy consumption by load aggregation with an opti...

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5th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2010

作者： Versick, Daniel Tavangarian, Djamshid University of Rostock Faculty of Electrical Engineering and Computer Science Department of Computer Architecture Rostock Germany

ISBN: (纸本)9780769542379

Energy consumption of data centers has been in-creasing continuously during the last years due to rising demands of computational power especially in current Grid- and Cloud-Computing systems. One promising approach of reducing this energy consumption is the consolidation of servers by virtualization. Many low loaded computer systems are virtualized and run on few physical servers for reducing the number of energy-consuming computers. At present this consolidation is usually done statically, thus, the administrator of a data center manually migrates many virtual machines with low load onto one physical server which may lead to overloading when the workload is rising unexpectedly. Dynamic server migration that adapts the number of running physical machines to the current workload overcomes these problems. Physical machines can be highly loaded and in case of further rising load virtual machines are migrated to other physical server systems that have been switched on. Such dynamic load aggregation approaches are rarely used and typically only consider few criteria for migration. This paper presents a classification of migration criteria for live migration of virtual machines in load aggregation environments and proposes an algorithm for combining many different kinds of migration criteria to a clustering-based metric. Thus, the novel load aggregation algorithm optimizes energy consumption as well as other migration criteria like runtime performance of applications. © 2010 IEEE.

关键词： Energy efficiency

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High level structural description of streaming applications

High level structural description of streaming applications

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22nd International Conference on Field Programmable Logic and Applications, FPL 2012

作者： Niedermeier, Anja Kuper, Jan Smit, Gerard J.M. Faculty of Electrical Engineering Mathematics and Computer Science Computer Architecture Embedded Systems University of Twente Netherlands

ISBN: (纸本)9781467322560

Motivated by the need to describe the structure of streaming applications on a high level of abstraction, we designed a compiler prototype based on the functional programming language Haskell, which by itself already has a notion of structure. We showed how the proposed language was implemented and also how it can be used to describe the structure of a streaming application and how this description can be used to map the application to a hardware architecture. © 2012 IEEE.

关键词： High level languages

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The smart miniFab: An industrial IoT demonstrator anywhere at any time 4th

The smart miniFab: An industrial IoT demonstrator anywhere a...

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4th International KES conference on Smart Education and Smart e-Learning, SEEL 2017

作者： Schubert, Tobias Völker, Benjamin Pfeifer, Marc Becker, Bernd Chair of Computer Architecture Faculty of Engineering Institute of Computer Science Albert-Ludwigs-University Freiburg Freiburg79110 Germany

ISBN: (纸本)9783319594507

The fourth industrial revolution is predicted to be one major topic in the upcoming decades, affecting nearly every industrial facility. The key enabler for the successful integration of corresponding developments are well-trained engineers with elaborated knowledge in the area of the Industrial Internet of Things (IIoT). This paper introduces a concept enabling engineering students to gain the required skills for the upcoming challenges connected with the fourth industrial revolution. Therefore, the Smart MiniFab was developed - a demonstrator of an industrial facility primed for the implementation and the analysis of different IIoT concepts on a small scale. Due to the modular structure of the system, students can use the demonstrator to programmatically implement the functionality of an entire facility from scratch or to test only certain new concepts. The hardware can be programmed via remote access from anywhere at any time, while live feedback is provided through a webcam and different other communication possibilities directly within the remote access application. While the Smart MiniFab was already the HW environment of different student projects, a new lab course is currently under development which enables the access to the Smart MiniFab for a broad number of students. © Springer International Publishing AG 2018.

关键词： Students

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GAIA: A Global, Multi-modal, Multi-scale Vision-Language Dataset for Remote Sensing Image Analysis

arXiv

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arXiv 2025年

作者： Zavras, Angelos Michail, Dimitrios Zhu, Xiao Xiang Demir, Begüm Papoutsis, Ioannis Orion Lab School of Rural Surveying and Geoinformatics Engineering National Technical University of Athens Athens15772 Greece Institute of Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens Athens11810 Greece Department of Informatics and Telematics Harokopio University of Athens Athens17676 Greece Chair of Data Science in Earth Observation Technical University of Munich Munich80333 Germany Munich Center for Machine Learning Munich80333 Germany Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Berlin10623 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10623 Germany

The continuous operation of Earth-orbiting satellites generates vast and ever-growing archives of Remote Sensing (RS) images. Natural language presents an intuitive interface for accessing, querying, and interpreting the data from such archives. However, existing Vision-Language Models (VLMs) are predominantly trained on web-scraped, noisy image-text data, exhibiting limited exposure to the specialized domain of RS. This deficiency results in poor performance on RS-specific tasks, as commonly used datasets often lack detailed, scientifically accurate textual descriptions and instead emphasize solely on attributes like date and location. To bridge this critical gap, we introduce GAIA, a novel dataset designed for multi-scale, multi-sensor, and multi-modal RS image analysis. GAIA comprises of 205,150 meticulously curated RS image-text pairs, representing a diverse range of RS modalities associated to different spatial resolutions. Unlike existing vision-language datasets in RS, GAIA specifically focuses on capturing a diverse range of RS applications, providing unique information about environmental changes, natural disasters, and various other dynamic phenomena. The dataset provides a spatially and temporally balanced distribution, spanning across the globe, covering the last 25 years with a balanced temporal distribution of observations. GAIA’s construction involved a two-stage process: (1) targeted web-scraping of images and accompanying text from reputable RS-related sources, and (2) generation of five high-quality, scientifically grounded synthetic captions for each image using carefully crafted prompts that leverage the advanced vision-language capabilities of GPT-4o. We also release an automated processing framework developed for this purpose, enabling the broader research community to generate captions for RS images using the web-crawled image-text data. Our extensive experiments, including fine-tuning of CLIP and BLIP2 models, demonstrate that GAIA significan

关键词： Visual languages

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Population Size in Differential Evolution 1

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7th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2019, and 5th International Conference on Fuzzy and Neural Computing, FANCCO 2019

作者： Alić, Amina Berkovič, Klemen Bošković, Borko Brest, Janez Faculty of Electrical Engineering and Computer Science Computer Architecture and Languages Laboratory Institute of Computer Science University of Maribor Maribor2000 Slovenia

ISBN: (数字)9783030378387

ISBN: (纸本)9783030378370

In this paper we examined how the population size affects the performance of the differential evolution algorithm. First, we tested the original differential evolution algorithm, and then the improved self-adaptive differential evolution algorithm, on ten benchmark functions, that have been proposed for the CEC 2019 competition. We used six different population sizes. Afterwards, we tested the newly created algorithm with population reinitialization. The results show that the population size affects the algorithm’s efficiency, and that we need to tune it to obtain the best results. In the paper, we demonstrate that the newly created algorithm with reinitialization gives better, or at least comparable, results than the two algorithms without reinitialization. © Springer Nature Switzerland AG 2020.

关键词： Population statistics

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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

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5th IEEE Middle East and Africa Conference on Biomedical engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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