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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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AI-Driven Smart Health Monitoring System Using Wearable IoT Devices and Predictive Analytics 2

AI-Driven Smart Health Monitoring System Using Wearable IoT ...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Prashanthi, Bh. Sharma, Ashish Mahima, M. Navya, K. Al-Fatlawy, Ramy Riad Chidambaranathan, Senthilnathan GRIET Bachupally Department of CSE Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India CMR Institute of Technology Department of ECE Telangana Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Virtusa Corporation Department of Architecture and Design NJ United States

ISBN: (纸本)9798350368109

The demonstrative realisation of this research is the smart health monitoring system, proposed and designed to employ wearable IoT technology and predictive analysis for constant health check. The system is intended to gather the physiological data;for example, pulse, blood pressure, and oxygen saturation and others via wearables and process it using sophisticated AI techniques and approaches, including deep learning based on CNN and LSTM. According to the systems, there are predictions that need to be offered by means of the programs and health risks that should be anticipated for timely intervention in order to increase chances for the patients' recovery. Although the system is designed for home use, the writers applied a cloud-based solution that provides scalability and real-time data processing;data is securely transferred from the wearable devices to the cloud through Bluetooth and Wi-Fi. This physiological data is augmented by the other public datasets available in the healthcare domain such that the AI learning algorithms can learn from an extensive health record. From the above research, it manifests how the system was able to predict serious health precursors including;heart attack and respiratory failure with LSTM models having prediction rate of more than 96%. It also delivers on-time alarms to the clinical practitioners, with average response time of less than three seconds for critical states. A set of surveys concerning the usability and comfort of the wearable devices among the patients has been conducted and the results show high satisfaction levels reported by the patients about the accuracy as well as simplicity of the system. Despite the introduced effort of the system, there exist significant concerns involving data protection, compatibility, and AI ethic;therefore, the future study should overcome such shortcomings. With these advancements of an artificial intelligence driven health monitoring system, healthcare systems would shift to being mor

关键词： Blood pressure

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Physics inspired compact modelling of BiFeO3 based memristors for hardware security applications

arXiv

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arXiv 2022年

作者： Yarragolla, Sahitya Du, Nan Hemke, Torben Zhao, Xianyue Chen, Ziang Polian, Ilia Mussenbrock, Thomas Chair of Applied Electrodynamics and Plasma Technology Ruhr University Bochum Germany Germany Germany Institute of Computer Science and Computer Engineering University of Stuttgart Stuttgart Germany

With the advent of the Internet of Things, nanoelectronic devices or memristors have been the subject of significant interest for use as new hardware security primitives. Among the several available memristors, BiFeO3 (BFO)-based electroforming-free memristors have attracted considerable attention due to their excellent properties, such as long retention time, self-rectification, intrinsic stochasticity, and fast switching. They have been actively investigated for use in physical unclonable function (PUF) key storage modules, artificial synapses in neural networks, nonvolatile resistive switches, and reconfigurable logic applications. In this work, we present a physics-inspired 1D compact model of a BFO memristor to understand its implementation for such applications (mainly PUFs) and perform circuit simulations. The resistive switching based on electric field-driven vacancy migration and intrinsic stochastic behaviour of the BFO memristor are modelled using the cloud-in-a-cell scheme. The experimental current-voltage characteristics of the BFO memristor are successfully reproduced. The response of the BFO memristor to changes in electrical properties, environmental properties (such as temperature) and stress are analyzed and consistent with experimental results. Copyright © 2022, The Authors. All rights reserved.

关键词： Memristors

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Machine Learning Model for Predicting Failures in the Context of Autonomous Vehicles 7

Machine Learning Model for Predicting Failures in the Contex...

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7th International Conference on System Reliability and Safety, ICSRS 2023

作者： Krini, Abderrahim Ouertani, Aymen Börcsök, Habil. Josef Robert Bosch GmbH Engineering of Reliability and Quality Functional Safety Schwäbisch Gmünd Germany Institute of Functional Safety & Cyber Security Baden-Wuerttemberg Cooperative State University Lörrach Germany University of Kassel Computer Architecture und System Programming Kassel Germany

ISBN: (纸本)9798350306057

In general, failure data is obtained in the automotive industry during the warranty period. If these contain the expression of a service life characteristic for each failure, statements can be made about the reliability and availability of the systems. For this purpose, estimation methods are used to adapt empirical lifetime distributions to theoretical lifetime distributions. By means of the distribution characteristics, a prognosis of the reliability and availability to be expected is also possible beyond the observation time. In addition to the data basis to be investigated, which is available completely in test bench and experimental trials or re-censored in the field, various methods can be used for estimation. In the past, several methods have been used, such as the estimators according to Eckel [1], Kaplan-Meier [2] or the estimators according to Pauli [3]. In general, a field failure is subject to several stresses, which can be described by expressions of several lifetime characteristics. For this application case, which occurs in the automotive industry, the known estimation methods, [1]-[3], cannot be used. In this paper, the necessity of multidimensional estimation methods will be introduced first. Then, a new estimation method for multidimensional metrics is presented. In a further step, mathematical proof is given that the new method can provide realistic results. Finally, two example data sets from bench testing and field are presented. Furthermore, some recommendations for the use of the new method are concretized in this context. © 2023 IEEE.

关键词： Automotive industry

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The relationship between TSH levels, maternal characteristics and racial group of the aneuploidy screening

The relationship between TSH levels, maternal characteristic...

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2022 Innovations in Intelligent Systems and Applications Conference, ASYU 2022

作者： Soylemez, Ummu Gulsum Celebiler, Hande Kaymakcalan Harkonen, Juho Bakir-Gungor, Burcu Mus Alparslan University Faculty of Engineering and Architecture Department of Software Engineering Mus Turkey School of Medicine Istanbul Bilim University Department of Pediatrics Istanbul Turkey Sociology European University Institute Florence Italy School of Engineering Abdullah Gul University Department of Computer Engineering Kayseri Turkey

ISBN: (数字)9781665488945

ISBN: (纸本)9781665488945

First-trimester maternal screening is a widely used test for detecting fetal aneuploidies and neural tube defects for over two decades. Human chorionic gonadotropin hormone (beta-hCG) and pregnancy-associated plasma protein A (PAPP-A) are two serum biomarkers that are analyzed in this screening. The thyroid hormone is a critical hormone for normal pregnancy and fetal development. During the first half of pregnancy, placental and fetal development depends on the thyroid hormone levels in the mother. Therefore, thyroid abnormalities in the mother can result in unfavorable pregnancy outcomes such as intrauterine growth restriction, miscarriage, hypertensive disorders, premature birth, and an increase in the risk of low IQ in the newborn. In this study, we analyzed the first-trimester screening data collected from 410 pregnant women who were seen at Yale university Hospital Prenatal Unit;and checked for possible correlations of TSH levels with maternal characteristics, racial group PAPP-A MoM levels. © 2022 IEEE.

关键词： Obstetrics

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Statistical static timing analysis for circuit aging prediction

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The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 14-23页

作者： Duan Shengyu Zhai Dongyao Lu Yue Shanghai Engineering Research Center of Intelligent Computing System Shanghai UniversityShanghai 200444China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit *** results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic *** amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design *** this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI *** consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability *** method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.

关键词： bias temperature instability(BTI) reliability prediction statistical static timing analysis(SSTA)

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Algorithm-Oriented Qubit Mapping for Variational Quantum Algorithms

arXiv

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arXiv 2023年

作者： Ji, Yanjun Chen, Xi Polian, Ilia Ban, Yue Institute of Computer Architecture and Computer Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70569 Germany Cantoblanco MadridE-28049 Spain Departamento de Física Universidad Carlos III de Madrid Avda. de la Universidad 30 Leganés28911 Spain Derio48160 Spain

Quantum algorithms implemented on near-term devices require qubit mapping due to noise and limited qubit connectivity. In this paper we propose a strategy called algorithm-oriented qubit mapping (AOQMAP) that aims to bridge the gap between exact and scalable mapping methods by utilizing the inherent structure of algorithms. While exact methods provide optimal solutions, they become intractable for large circuits. Scalable methods, like SWAP networks, offer fast solutions but lack optimality. AOQMAP bridges this gap by leveraging algorithmic features and their association with specific device substructures to achieve optimal and scalable solutions. The proposed strategy follows a two stage approach. First, it maps circuits to subtopologies to meet connectivity constraints. Second, it identifies the optimal qubits for execution using a cost function. Notably, AOQMAP provides both scalable and optimal solutions for variational quantum algorithms with fully connected two qubit interactions on common subtopologies including linear, T-, and H-shaped, minimizing circuit depth. Benchmarking experiments conducted on IBM quantum devices demonstrate significant reductions in gate count and circuit depth compared to Qiskit, Tket, and SWAP network. Specifically, AOQMAP achieves up to an 82% reduction in circuit depth and an average 138% increase in success probability. This scalable and algorithm-specific approach holds the potential to optimize a wider range of quantum algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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MuDSE: GA-ILP-based Framework for Automated Deployment of Multiple DNNs on Heterogeneous Mixed-Criticality Systems

MuDSE: GA-ILP-based Framework for Automated Deployment of Mu...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Alexander Hoffman Ala Fnayou Fedor Smirnov Daniel Mueller-Gritschneder Ulf Schlichtmann Chair of Electronic Design Automation Technical University of Munich Munich Germany Robert Bosch GmbH Stuttgart Germany Institute of Computer Engineering TU Wien Vienna Austria

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

In modern edge computing, deploying multi-deep neural network (DNN) applications is essential for addressing complex tasks such as visual classification, object tracking, and navigation. The intricate nature of these Machine Learning (ML) applications, coupled with their soft and hard real-time performance requirements, underscores the necessity for automated optimisation of both scheduling and mapping configurations on computationally robust heterogeneous embedded systems. This paper introduces a novel approach for the automated mapping and scheduling of multiple DNNs onto heterogeneous hardware platforms. Our methodology leverages an integer linear program (ILP) scheduler formulation that accommodates soft and hard real-time constraints. This is complemented by a mapping generation process that employs (a) an advanced ILP formulation and (b) a Genetic Algorithm (GA) designed to identify optimised solutions for large-scale mappings. The GA is mainly utilised when the expansive design space renders the ILP formulation impractical in terms of computational solving time. We rigorously test and evaluate our framework using scaling input model configurations and a real-world mixed-model scenario. The results demonstrate that our hybrid optimisation solution, which integrates Prepositional Satisfiability Problem (SAT) decoding, the NSGA-II GA, and ILP, significantly enhances scalability. This improvement is vital for efficiently deploying complex systems, marking a substantial advancement in the embedded ML field.

关键词： Visualization Processor scheduling Scalability Real-time systems Hardware Object tracking Task analysis

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TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracy

arXiv

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arXiv 2025年

作者： Valverde, Juan Miguel Koga, Motoya Otsuka, Nijihiko Dahl, Anders Bjorholm Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark A.I. Virtanen Institute University of Eastern Finland Finland Department of Architecture Faculty of Engineering Sojo University Japan

We present TopoMortar, a brick wall dataset that is the first dataset specifically designed to evaluate topology-focused image segmentation methods, such as topology loss functions. TopoMortar enables to investigate in two ways whether methods incorporate prior topological knowledge. First, by eliminating challenges seen in real-world data, such as small training set, noisy labels, and out-of-distribution test-set images, that, as we show, impact the effectiveness of topology loss functions. Second, by allowing to assess in the same dataset (TopoMortar) topology accuracy across dataset challenges, isolating dataset-related effects from the effect of incorporating prior topological knowledge. In these two experiments, it is deliberately difficult to improve topology accuracy without actually leveraging topology information, thus, permitting to attribute an improvement in topology accuracy to the incorporation of prior topological knowledge. To this end, TopoMortar includes three types of labels (accurate, pseudo-labels, noisy labels), two fixed training sets (large and small), and in-distribution and out-of-distribution test-set images. We compared eight loss functions on TopoMortar, and we found that clDice generally achieved the most topologically accurate segmentations, Skeleton Recall loss performed best particularly with noisy labels, and the relative advantageousness of the other loss functions depended on the experimental setting. Additionally, we show that simple methods, such as data augmentation and self-distillation, can elevate Cross entropy Dice loss to surpass most topology loss functions, and that those simple methods can enhance topology loss functions as well. clDice and Skeleton Recall loss, both skeletonization-based loss functions, were also the fastest to train, making this type of loss function a promising research direction. TopoMortar and our code can be found at https://***/jmlipman/TopoMortar. © 2025, CC BY.

关键词： Musculoskeletal system

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Enhanced Lung Cancer Detection Using CNNs for Accurate and Efficient CT Image Classification 1

Enhanced Lung Cancer Detection Using CNNs for Accurate and E...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Bdair, Mohammad Ruby, E D Kanmani Juyal, Prachi Taseen, Rakheeba Abirami, A. Soujanya, A. School of Architecture Computing & Engineering University of East London London United Kingdom Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of ECE Tamil Nadu Chennai India Uttarakhand Dehradun India School of computer science and Engineering Presidency university Bangalore India Sona College of Technology Department of Mathematics Tamil Nadu Salem India Ibrahimpatnam India

ISBN: (纸本)9798350353648

Lung cancer must be detected as early as possible, but it may be difficult with existing methods since they often depend on human judgment and outdated image processing. These techniques take a lot of time and are prone to errors, which produces uneven outcomes. The research provides a unique deep learning-based method for classifying lung nodules from CT images using convolutional neural networks (CNNs). By automating feature extraction and categorization, the suggested technique greatly increases diagnostic precision. The result shows superior performance over existing systems is demonstrated by evaluation measures such as AUC-ROC, accuracy, sensitivity, and specificity. With a training duration of 12 hours, an AUC-ROC of 0.95, accuracy of 92%, sensitivity of 94%, specificity of 90%, and inference speed of 0.5 seconds per scan, the results demonstrate notable improvements in diagnostic efficiency and accuracy. Deep learning has the potential to revolutionize the diagnosis of lung cancer and enhance patient care, as demonstrated by the study. © 2024 IEEE.

关键词： Lung cancer

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