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Impact of CR Forbush Decreases on Upper Atmospheric Pressure and Temperature in Saudi Arabia

Atmospheric and Climate sciences 2025年第1期15卷 200-217页

作者： Maghrabi Abdullrahman Alghamdi Rasha Aldosari Abdulah Almutairy Mohammed National centre for Climate Change King Abdulaziz City for Science and Technology Riyadh Saudi Arabia Astronomy and Space science Department Faculty of Science King Abdulaziz University Jeddah Saudi Arabia

The objective of this research is to investigate the effects of cosmic ray Forbush Decreases (FDs) exceeding 7% in magnitude, occurring between 1985 and 2016, on upper atmospheric pressure and temperature at Abha and Tabouk. Employing the super epoch analysis method, the study concentrated on altitudes of 5 km and 10 km, uncovering significant variations. Seasonal and synoptic-scale variations were considered and excluded when necessary across eight 9-day periods. Both locations showed considerable fluctuations in pressure and temperature before and after the events. At 5 km altitude (21 events), Abha experienced more pressure increases both before (9 vs. 7) and after (12 vs. 11) the events compared to Tabouk. For temperature, Abha recorded more increases before the events (5 vs. 1), while Tabouk showed more decreases (19 vs. 15). Post-event, Tabouk had more temperature increases (13 vs. 10). At 10 km altitude (20 events), both regions experienced more decreases than increases in pressure and temperature before the events and more increases afterward. Notably, Abha experienced more pressure increases both 4 days before (9 vs. 7) and after the events (12 vs. 11) than Tabouk. For temperature, Abha recorded more increases before the events (5 vs. 1), while Tabouk showed more decreases (19 vs. 15). Post-event, Tabouk had more temperature increases (13 vs. 10). These findings underscore both similarities and differences in atmospheric responses to FDs between Abha and Tabouk. Both locations exhibited cooling trends before and warming trends after the events, with Tabouk demonstrating a more pronounced warming trend post-event. These results enhance our understanding of the atmospheric dynamics linked to FDs and assist in predicting weather patterns associated with these phenomena.

关键词： Forbush Decrease Upper Air Temperature Saudi Arabia Climate Change Solar Activity

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The upper tropospheric-lower stratospheric ozone drives summer precipitation and wildfire changes in West Siberia

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science Bulletin 2025年第2期70卷 255-262页

作者： Fei Xie Yan Xia Wenshou Tian Martyn P.Chipperfield Wuhu Feng Ke Gui Yingli Niu Na Liu Yuanyuan Han School of Systems science Beijing Normal UniversityBeijing 100875China Key Laboratory for Semi-Arid Climate Change of the Ministry of Education College of Atmospheric SciencesLanzhou UniversityLanzhou 730000China School of Earth and Environment University of LeedsLeedsLS29JTUK National centre for Earth Observation University of LeedsLeedsLS29JTUK National centre for Atmospheric science University of LeedsLeedsLS29PHUK State Key Laboratory of Severe Weather&Key Laboratory of Atmospheric Chemistry of CMA Chinese Academy of Meteorological SciencesBeijing 100081China Faculty of Geographical science Beijing Normal UniversityBeijing 100875China Key Laboratory of Textile Chemical Engineering Auxiliaries School of Environmental and Chemical EngineeringXi’an Polytechnic UniversityXi’an 710048China

Siberian wildfires are pivotal in determining the carbon cycle and climate dynamics,exerting a profound impact on the ecosystems of the entire Arctic *** the past few decades,variations in summer precipitation in West Siberia have significantly influenced wildfire *** study analyzed precipitation trends in West Siberia from 1982 to 2021 using observations and transient simulations,uncovering a strong correlation between precipitation variability and ozone concentrations in the upper troposphere-lower stratosphere(UTLS).Heightened UTLS ozone levels warm the upper atmosphere over West Siberia during *** warming modifies the regional polar jet stream,intensifying its southern branch and weakening the northern one,leading to a southward shift in the jet ***,cyclonic circulation anomalies emerge in the upper troposphere,characterized by a barotropic structure with unusual upward movements around 60°*** upward motion triggers corresponding anomalies in zonal winds in the lower troposphere,fostering a low-pressure system at the *** atmospheric shift results in an influx of warm,moist air from the south and cold,dry air from the north into Siberia,enhancing cloud formation and ***,our analysis suggests that the rise in summer precipitation in West Siberia between 1993 and 2010 is linked to increased UTLS ozone concentrations during this ***,the decline in UTLS ozone since 2010 may increase the risk of wildfires by suppressing *** findings underscore the pivotal role of stratospheric chemistry in shaping the regional climate and wildfire behavior.?2024 science China *** by Elsevier *** science China *** rights are reserved,including those for text and data mining,AI training,and similar technologies.

关键词： Upper troposphere-lower stratosphere Ozone Radiative effect Precipitation Siberia

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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Finite-time H∞ control and energy cost optimization for nonlinear delayed systems through switching analysis and interval matrix method

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science China(Information sciences) 2025年

作者： Guici CHEN Huimin ZENG Song ZHU Shiping WEN Junhao HU Hubei Province Key Laboratory of System science in Metallurgical Process Wuhan University of Science and Technology School of Mathematics China University of Mining and Technology centre for Artificial Intelligence University of Technology Sydney School of Mathematics and Statistics South-Central Minzu University

Stability, as one of the most important research directions,has caught the attention of many scholars[1–3]. Simultaneously, in domains such as vessel control and vehicle systems,external disturbances are unavoidable. The research challenge thus emerges:“How can one attain the desired system performance in a finite time while controlling the impact of external disturbances on output variables?”. Finite-time H∞control （H∞FTC） is a salient solution to this problem.

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5DGWO-GAN:A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT Systems

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Computers, Materials & Continua 2025年第1期82卷 881-911页

作者： Sarvenaz Sadat Khatami Mehrdad Shoeibi Anita Ershadi Oskouei Diego Martín Maral Keramat Dashliboroun Department of data science Engineering University of HoustonHoustonTX 77204USA The WPI Business School Worcester Polytechnic InstituteWorcesterMA 01609USA School of Systems and Enterprises Stevens Institute of TechnologyHobokenNJ 07030USA Department of Computer science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Ernest G.Welch School of Art&Design Georgia State UniversityAtlantaGA 30303USA

The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control *** the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are *** detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate *** paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT ***,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss *** address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN *** 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local *** proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model *** generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded *** evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and *** conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false *** 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence *** proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and

关键词： Internet of things intrusion detection generative adversarial networks five-dimensional binary gray wolf optimizer deep learning

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Recent developments in MQL machining of aeronautical materials:A comparative review

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Chinese Journal of Aeronautics 2025年第1期38卷 43-69页

作者： Syed Hammad ALI Yu YAO Bangfu WU Biao ZHAO Wenfeng DING Muhammad JAMIL Ahmar KHAN Asra BAIG Qi LIU Dongdong XU National Key Laboratory of science and Technology on Helicopter Transmission Nanjing University of Aeronautics and AstronauticsNanjing 210016China Department of Mechanical Engineering NED University of Engineering and TechnologyKarachi 75270Pakistan centre for Precision Manufacturing University of StrathclydeGlasgow G11XQUK School of Mechanical Engineering Tongji UniversityShanghai 200092China

Minimum quantity lubrication(MQL),as a new sustainable and eco-friendly alternative cooling/lubrication technology that addresses the limitations of dry and wet machining,utilizes a small amount of lubricant or coolant to reduce friction,tool wear,and heat during cutting *** technique has witnessed significant developments in recent years,such as combining MQL with other sustainable techniques to achieve optimum results,using biodegradable lubricants,and innovations in nozzle designs and delivery *** review presents an in-depth analysis of machining characteristics(e.g.,cutting forces,temperature,tool wear,chip morphology and surface integrity,etc.)and sustainability characteristics(e.g.,energy consumption,carbon emissions,processing time,machining cost,etc.)of conventional MQL and hybrid MQL techniques like cryogenic MQL,Ranque-Hilsch vortex tube MQL,nanofluids MQL,hybrid nanofluid MQL and ultrasonic vibration assisted MQL in machining of aeronautical ***,the latest research and developments are analyzed and summarized in the field of MQL,and provide a detailed comparison between each technique,considering advantages,challenges,and limitations in practical *** addition,this review serves as a valuable source for researchers and engineers to optimize machining processes while minimizing environmental impact and operational ***,the potential future aspects of MQL for research and industrial execution are discussed.

关键词： Aerospace materials Minimum Quantity Lubrication(MQL) Cryogenic Nanofluid Grinding Milling Sustainability

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Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices

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Journal of Energy Chemistry 2025年第2期101卷 298-323页

作者： Bonghyun Jo Wenning Chen Hyun Suk Jung School of Advanced Materials science and Engineering Sungkyunkwan University SKKU Institute of Energy science and Technology (SIEST) Sungkyunkwan University Department of Future Energy Engineering Sungkyunkwan University

Perovskite solar cells(PSCs) have developed rapidly,positioning them as potential candidates for nextgeneration renewable energy ***,conventional trial-and-error approaches and the vast compositional parameter space continue to pose challenges in the pursuit of exceptional performance and high stability of perovskite-based *** increasing demand for novel materials in optoelectronic devices and establishment of substantial databases has enabled data-driven machinelearning(ML) approaches to swiftly advance in the materials *** review succinctly outlines the fundamental ML procedures,techniques,and recent breakthroughs,particularly in predicting the physical characteristics of perovskite ***,it highlights research endeavors aimed at optimizing and screening materials to enhance the efficiency and stability of ***,this review highlights recent efforts in using characterization data for ML,exploring their correlations with material properties and device performance,which are actively being researched,but they have yet to receive significant ***,we provide future perspectives,such as leveraging Large Language Models(LLMs) and text-mining,to expedite the discovery of novel perovskite materials and expand their utilization across various optoelectronic fields.

关键词： Characterization data-driven machine learning Perovskite materials Perovskite solar cell

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A Generative Model-Based Network Framework for Ecological data Reconstruction

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Computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen School of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China School of Computer science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

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Statistical characteristics and classification of ionospheric mid-latitude trough as revealed by the observations of DMSP-F18

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Earth and Planetary Physics 2025年第1期9卷 137-147页

作者： Yi Liu JiuHou Lei FuQing Huang Su Zhou School of Earth and Space sciences University of Science and Technology of ChinaHefei 230026China School of science Guiyang UniversityGuiyang 550005China

Statistical characteristics and the classification of the topside ionospheric mid-latitude trough are systemically analyzed,using observations from the Defense Meteorological Satellite Program F18(DMSP-F18)*** data was obtained at an altitude of around 860 km in near polar orbit,throughout *** study identified the auroral boundary based on the in-situ electron density and electron spectrum,allowing us to precisely determine the location of the mid-latitude *** differs from most previous works,which only use Total Electron Content(TEC)or in-situ electron *** our study,the troughs exhibited a higher occurrence rate in local winter than in summer,and extended to lower latitudes with increasing geomagnetic *** was found that the ionospheric mid-latitude trough,which is associated with temperature changes or enhanced ion drift,exhibited distinct ***,the ionospheric mid-latitude troughs related to electron temperature(Te)peak were located more equatorward of auroral oval boundary in winter than in *** ionospheric mid-latitude troughs related to Te-maximum were less frequently observed at 60−70°S magnetic latitude and 90−240°E ***,the troughs related to ion temperature(Ti)maximums were observed at relatively higher latitudes,occurring more frequently in *** addition,the troughs related to ion velocity(Vi)maximums could be observed in all *** troughs with the maximum-Ti and maximum-Vi were located closer to the equatorward boundary of the auroral oval at the nightside,and in both *** implies that enhanced ion drift velocity contributes to increased collisional frictional heating and enhanced ion temperatures,resulting in a density depletion within the trough region.

关键词： topside ionosphere mid-latitude trough ionospheric main trough auroral oval boundary temperature maximum

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Vehicle and onboard UAV collaborative delivery route planning:considering energy function with wind and payload

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Journal of Systems Engineering and Electronics 2025年第01期 194-208页

作者： GUO Jingfeng SONG Rui HE Shiwei Key Laboratory of Transport Industry of Big data Application Technologies for Comprehensive Transport Beijing Jiaotong University

The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means. Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery *** collaboration, along with energy consumption with payload and wind conditions play important roles in delivery route planning. This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV) and emphasizes the consideration of real-world scenarios, focusing on time collaboration and energy consumption with wind and payload. To address this, a mixed integer linear programming(MILP) model is formulated to minimize the energy consumption costs of vehicle and UAV. Furthermore, an adaptive large neighborhood search(ALNS) algorithm is applied to identify high-quality solutions efficiently. The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.

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