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Understanding air pollution dynamics of Antalya Manavgat forest fires: a WRF-Chem analysis

ENvIRONMENTAL MONITORING AND ASSESSMENT 2025年第4期197卷 1-29页

作者： Kara, Yigitalp Yavuz, veli Toros, Huseyin Istanbul Tech Univ Fac Aeronaut & Astronaut Dept Meteorol Engn Istanbul Turkiye Univ Samsun Fac Aeronaut & Astronaut Dept Meteorol Engn 19 Mayis Samsun Turkiye

This study investigates the air pollution dynamics of the Antalya Manavgat forest fires in Turkey, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem v4.4.2) and Fire INventory from NCAR (FINN v2.5) boundary fire emission data for the period between 28 July 2021 and 6 August 2021. It focuses on the synoptic and microscale atmospheric conditions during the fire event. The research also delves into the micrometeorological factors, particularly examining the Foehn effect's influence in intensifying the initial fire conditions through 3D trajectory modeling and surface chart analysis. The study assesses the performance of the WRF-Chem model in predicting aerosol optical depth (AOD), particulate matter (PM10), and various meteorological variables. This assessment utilized diverse re-sampled and interpolated satellite products, including MODIS MAIAC and Sentinel-5P NRTI AER AI. The Manavgat air quality and meteorological station, situated near the fire, proved highly effective in modeling atmospheric parameters, with strong correlations in AOD (0.93), AAI (0.81), and ground-level PM10 (0.82), indicating accurate predictions of particulate pollution levels. The station was also ranked as the second most accurate in modeling overall meteorological conditions. The complex meteorological conditions led to significant discrepancies between ground-measured PM and AOD. The study attempts to elucidate these differences using 3D plume modeling and cross-sectional analysis. Notably, a low-pressure system was identified as a key factor in the vertical expansion of the fire plume, enabling it to reach heights of up to six kilometers above sea level (ASL). Lastly, satellite imagery from August 6 and 7, 2021, reveals persistent aerosol levels in eastern Antalya, driven by atmospheric transport of smoke and particulate matter from local and distant fires, including remnants from the extinguished Manavgat forest fire, as well as fires in Mu & gbreve;la and

关键词： Forest fire WRF-Chem Aerosol optical depth Fire emission data Foehn effect 3D plume modeling

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v2v 3D MIMO channel modeling and statistical characteristics analysis based on urban street scenarios 16

V2V 3D MIMO channel modeling and statistical characteristics...

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16th International conference on Signal Processing Systems, ICSPS 2024

作者： Qiu, Tian Rui, Yibin Qian, Feng Xie, Renhong Li, Peng School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9781510689251

In order to accurately depict v2v MIMO channel characteristics in urban street scenes, a new 3D random channel model based on geometry is proposed, and the simulation model is derived. The model comprehensively considers the distribution of dynamic and static effective scatterers in the environment, effectively extends the geometry-based vehicle-to-vehicle channel modeling method, and fully reflects the influence of different scatterers distribution, antenna array and vehicle flow density on the channel statistical characteristics in the actual scene. The simulation results verify the correctness of the channel model, which can be used in the design and performance analysis of vehicle-connected wireless communication system. © 2025 SPIE.

关键词： Digital elevation model

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A Pure CMOS Stack Electrostatic Micromirror Featuring Simplified Fabrication and Stress-Adjusted modeling 38

A Pure CMOS Stack Electrostatic Micromirror Featuring Simpli...

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38th IEEE International conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Chen, Wenhao Tavakkoli, Hadi Zhao, Bin Zhang, Maojie Piyawattanametha, Wibool Lee, Yi-Kuen Hong Kong University of Science and Technology Hong Kong CanSemi Technology Co. Ltd. China King Mongkut's Institute of Technology Ladkrabang Thailand Michigan State University United States

ISBN: (纸本)9798331508890

We report a pure CMOS stack-made comb-actuated electrostatic micromirror for non-resonant scanning using a standard 0.18 μm 1-polysilicon 6-metal CMOS foundry process without complex post-processing steps. The staggered vertical combs are formed by different metal layers to provide a nonlinear electrostatic torque, enabling the device to achieve a 12.4° optical scan angle under 70v with a mirror size of 0.4×0.4 mm2. Considering CMOS process-induced stress, a modified theoretical model of the comb actuators agrees with experimental data, which gives a guideline for the design optimization of non-resonant CMOS-MEMS mirrors. This innovative integration of comb-drive actuator and mirror structure within the limited thickness of the standard CMOS stack (11 μm) and improved manufacturability boost a new generation of compact, high-performance optical MEMS devices. © 2025 IEEE.

关键词： CMOS integrated circuits

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Employability Prediction Based on Student performance Using CNN Model

Employability Prediction Based on Student Performance Using ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： M T vasumathi v Asha Arpana Prasad Bhavya M Chaithra v Bhanurekha N Department of MCA New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Unemployment among graduates is a persistent issue, largely because traditional academic evaluations don't always reflect the skills employers seek. This study explores a new approach to predict employability based on students' exam performance using Convolutional Neural Networks (CNNs). We utilized the “Student performance” dataset, which includes a variety of academic, demographic, and socioeconomic factors. By comparing CNNs with other machine learning models such as SvM, Decision Trees, and XGBoost, we observed that SvM obtained an accuracy of 94.33%, whereas Decision Trees and XGBoost obtained 96.00% and 97.00%, respectively. CNN obtained the highest accuracy of 99.50%. CNN is sometimes misclassified but its strength lies in unearthing complex patterns in student data. This research underscores how advanced machine learning models, especially CNNs, can enhance employability predictions, enabling educational institutions to refine their curricula and better prepare students for the evolving job market.

关键词： Support vector machines Accuracy Computational modeling Machine learning Predictive models Data models Decision trees Convolutional neural networks Unemployment Tuning

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Predictive Analytics for Freelancer Compensation: A modeling Approach

Predictive Analytics for Freelancer Compensation: A Modeling...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International conference on

作者： Kandavel N Leena H Swetha v R. Gowri Zahid Hussain J Yogapriya M Department of CSE Chennai Institute of Technology Chennai Tamilnadu Department of CSE-AIML Chennai Institute of Technology Chennai Tamilnadu

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

This study focussed on freelancers, particularly in the delivery sector, where delivery-based wages are common. This analysis interprets and provides an accurate amount of wages for the freelancers and embeds mathematical modeling for calculating hourly wages with Non-linear transformations, hybrid modeling for parameters and representing graphical methods, and comprising the data of the real-time scenario in the prevailing job market. The Embedding Machine Learning model features a Random Forest Algorithm which predicts the hourly wages of each employee and controls overfitting. The proposed solution provides an efficient wage prediction and obtains a high performance. Furthermore, providing the model, is user-friendly, which allows employees to input their performance metrics, and obtain real-time predictions of hourly wages. Real-time tracking tools help ensure hourly work, providing actionable knowledge for workers and businesses and data-driven insights to enhance worker experiences. Furthermore, the bridge gap between the freelancers and the job market includes, providing hourly wages for freelancers in the on-demand service gig economy, providing advanced search functionality using Artificial Intelligence in the Machine learning model, Time tracking, embarking comprehensive portfolios, and profiles will enhance visibility and trust between freelancers and employers. The intercession aims to develop a gig economy ecosystem, bridging existing gaps and strengthening long-term sustainability in the freelancer’s community. This research concludes that providing per hourly wages, for each freelancer and increasing the sustainable gig economy in the community.

关键词： Analytical models Machine learning algorithms Accuracy Companies Predictive models Prediction algorithms Real-time systems Data models Reliability Random forests

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Implementation of Deep Learning Algorithm to Estimate the State of Charge (SOC) of Lithium Ion Battery

Implementation of Deep Learning Algorithm to Estimate the St...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International conference on

作者： M. Sasikumar v. Indumathi R. Gopalakrishnan P.S. Sudhakar S. Sridharnish v. Sitheshwaran Mechatronics Engineering K.S.Rangasamy College of Technology (Autonomous) Tiruchengode India Electrical and Electronics Engineering K.S.Rangasamy College of Technology (Autonomous) Tiruchengode India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

In electric vehicles (Evs), the need for superior battery management systems (BMS) to confirm efficiency, maximum performance of lithium-ion batteries (LIBs) and safety is increasing. Accurate evaluation of state of charge (SOC) is essential to avoid overcharge and over discharge, which can degrade the battery health and performance. Conventional techniques for estimating SOC often struggle with the nonlinearities and temporal complexities of LIB dynamics, make necessary adoption of advanced machine learning techniques. To enhance the SOC estimation, this analysis suggests a combined Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model to ensure the accuracy under dynamic operating conditions. CNNs are adapt at extracting spatial features from battery data, while GRUs excel in modeling sequential dependencies. The combined CNN-GRU architecture leverages the complementary strengths of both models, providing robust predictions that address the limitations of standalone frameworks. The performance of this model is measured using dataset based on the widely used model like Long Short-Term Memory (LSTM) in data network. The CNN-GRU model performs far better than the LSTM model, based on the experimental data, with lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The CNN-GRU's capability to record both local as well as global temporal patterns results in the reduced prediction deviations, particularly during rapid SOC transitions. Analysis of training and validation losses further highlights the CNN-GRU's generalization capability, with stable convergence and minimal overfitting. This analysis underscores the potential of hybrid architectures in advancing intelligent BMS design. By integrating efficient feature extraction and sequence modeling, the CNN-GRU framework ensures accurate and robust SOC estimation and real-time performance.

关键词： Lithium-ion batteries Deep learning Adaptation models Accuracy Feature extraction Data models State of charge Convolutional neural networks vehicle dynamics Long short term memory

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performance Analysis of Triple Hop RF-RIS Convergent with FSO and UWOC System

Performance Analysis of Triple Hop RF-RIS Convergent with FS...

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International conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

作者： Sheeba A Dharshini v M Suvetha P Kavitha Kamatchi Suveetha Dhanaselvam P Karthikeyan B Angayarkanni v Prabu Krishnan Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

This paper proposes a hybrid communication system for integration within the space-air-ground-sea network (SAGSIN) architecture, tailored for 6G Internet of Things (6GIoT) applications. The system employs a triple-hop link combining Reconfigurable Intelligent Surface (RF-RIS), Free Space Optics (FSO), and Underwater Wireless optical Communication (UWOC), facilitating communication between a base station, a lighthouse, a ship, and Underwater Aerial vehicles (UWAvs). The architecture, using differential phaseshift keying, is ideal for Underwater optical Wireless Sensor Networks (UOWSNs) and Internet of Underwater Things (IoUT). performance analysis models RF-RIS with Nakagamim distribution, the FSO link with the Malaga distribution, and the UWOC link using the Exponential Generalized Gamma (EGG) distribution. The system's end-to-end cumulative distribution function (CDF) is evaluated via the Meijer-G function, while outage probability is examined under varying conditions, including atmospheric turbulence, fading parameters, and salinity levels. This system shows promise for coastal environments with changing weather conditions like rain, haze, and fog.

关键词： 6G mobile communication Wireless communication Fading channels Wireless sensor networks Atmospheric modeling Reconfigurable intelligent surfaces Probability Power system reliability performance analysis Meteorology

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Assessment of CNN Models for optical Character Recognition of Modi Lipi Script

Assessment of CNN Models for Optical Character Recognition o...

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Smart Systems for applications in Electrical Sciences (ICSSES), International conference on

作者： Mangesh Ingavale Jayamala Kumar Patil Department of Technology Shivaji University Kolhapur India Bharati Vidyapeeth's College of Engg. Kolhapur India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The preservation of ancient scripts like Modi Lipi, historically used for writing languages such as Marathi, is crucial for cultural heritage. This study assesses the effectiveness and performance of standard Convolutional Neural Network (CNN) models in the optical Character Recognition (OCR) of Modi Lipi script. Nine prominent CNN architectures—vGG16, vGG19, ResNet-50, ResNet-101, DenseNet121, Inceptionv3, MobileNet-v3, EfficientNet-B0, and EfficientNet-B1—were evaluated on a comprehensive dataset comprising 575,920 images of numerals, vowels, and consonants. performance metrics including accuracy, sensitivity, specificity, and F1 score are analyzed across three classification tasks. Advanced models like EfficientNet-B1 and DenseNet121 demonstrated superior performance due to their innovative architectures capable of capturing complex script patterns. The findings highlight that while deeper and more complex models offer higher accuracy, they require more computational resources. This assessment underscores the viability of standard CNN models for Modi Lipi OCR and provides insights for future developments in script digitization efforts.

关键词： Deep learning Accuracy Sensitivity Computational modeling optical character recognition Computer architecture Feature extraction Convolutional neural networks Character recognition Standards

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A Pure CMOS Stack Electrostatic Micromirror Featuring Simplified Fabrication and Stress-Adjusted modeling

A Pure CMOS Stack Electrostatic Micromirror Featuring Simpli...

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IEEE International conference on Micro Electro Mechanical Systems

作者： Wenhao Chen Hadi Tavakkoli Bin Zhao Maojie Zhang Wibool Piyawattanametha Yi-Kuen Lee Hong Kong University of Science and Technology Hong Kong SAR CanSemi Technology Co. Ltd. China King Mongkut's Institute of Technology Ladkrabang Thailand Michigan State University USA

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

We report a pure CMOS stack-made comb-actuated electrostatic micromirror for non-resonant scanning using a standard 0.18 μm 1-polysilicon 6-metal CMOS foundry process without complex post-processing steps. The staggered vertical combs are formed by different metal layers to provide a nonlinear electrostatic torque, enabling the device to achieve a 12.4° optical scan angle under 70v with a mirror size of 0.4×0.4 mm 2 . Considering CMOS process-induced stress, a modified theoretical model of the comb actuators agrees with experimental data, which gives a guideline for the design optimization of non-resonant CMOS-MEMS mirrors. This innovative integration of comb-drive actuator and mirror structure within the limited thickness of the standard CMOS stack (11 μm) and improved manufacturability boost a new generation of compact, high-performance optical MEMS devices.

关键词： Semiconductor device modeling Micromechanical devices Electrodes optical device fabrication Foundries Adaptive optics Electrostatics Micromirrors Stress Standards

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Micro- and Macro-Mechanical Behavior of Inter- and Intra-Laminar Hybrid Composite Laminates with Natural and Synthetic Fibers: RvE modeling and Classical Laminate Theory

Micro- and Macro-Mechanical Behavior of Inter- and Intra-Lam...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Nabil Zerouali Abdellah Zamma Salah Nissabouri Hassan Samri Department of Mechanical Engineering ENSET-Mohammedia Hassan II University of Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

This study investigates the micromechanical and macromechanical behavior of hybrid composite laminates composed of flax and E-glass fibers in an epoxy matrix. At the microscale, effective mechanical properties are determined using Representative volume Element (RvE) modeling with Periodic Boundary Conditions (PBC), validated against analytical models such as the Modified Halpin-Tsai and the rule of mixtures. At the macroscale, the laminate stiffness is evaluated using Classical Laminate Theory (CLT). Two hybridization configurations with symmetrical ply stacking sequences are analyzed: inter-laminar, where flax and E-glass fibers are distributed in separate plies, and intra-laminar, where both fiber types are combined within each ply. Both configurations maintain a total fiber volume fraction (v f ) of 0.5. Results show that the intra-laminar hybrid provides more balanced stiffness, whereas the inter-laminar configuration increases longitudinal stiffness but reduces transverse stiffness while enhancing shear properties. These findings provide insights into the influence of hybridization configurations on composite laminate performance.

关键词： Solid modeling Analytical models Three-dimensional displays Stacking Synthetic fibers Boundary conditions optical fiber theory Mechanical factors Laminates Microstructure

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