On the occasion of the additional issue of the CONTAINERIZATION, I’d like to express my sincere appreciation to the readers and authors both at home and abroad on behalf of the CONTAINERIZATION. The container transpo...On the occasion of the additional issue of the CONTAINERIZATION, I’d like to express my sincere appreciation to the readers and authors both at home and abroad on behalf of the CONTAINERIZATION. The container transport in China is booming with leaps and bounds and the CONTAINERIZATION is becoming prosperous with each passing day. With the concern, support and help of the Ministry of Communications, Shanghai News Publication展开更多
Background: While causing a financial and economic loss in many countries in a few months, COVID 19 was of great impact on public health. The Iraqi government took several measures since the first Iraqi case was disco...Background: While causing a financial and economic loss in many countries in a few months, COVID 19 was of great impact on public health. The Iraqi government took several measures since the first Iraqi case was discovered in February 2020. One of the fundamental measures to control this pandemic is social isolation, which depends greatly on people’s awareness to decrease the incidence of COVID-19. Objectives: To study the COVID-19-effect on dentists’ social life and their future plan, and to find the relation between that effect and some dentists’ demographic variables. Methods: A cross-sectional study was conducted from 2nd January to 7th May 2021, by an electronic version of the questionnaire through Google form. With facilitating orders delivered to all the specialist dental centers for adults of Al-Resafa health directorate;any dentists working in specialist dental centers for adults of Al-Resafa health directorate, and accept to participate in this study. Results: A total of 10 specialist dental centers for adults of the Al-Resafa health directorate represent 566/628 dentists enrolled in this study with a response rate of 88.99%. Most of them aged less than 30 years old (62.2%), female (60.6%), married status (50.4%), had 1 - 3 children (71.91%), Rotator (51.1%), with less than five years of experience (61.3%). Conclusion: COVID-19 affected so many aspects of health workers’ lives including dentists. About two-thirds of dentists had negative feelings, of which almost all had anxiety. Younger ages, females, married and having children are factors that contribute to the affection of dental social lives more than other categories.展开更多
Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified...Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.展开更多
Unmanned combat aerial vehicles require lightweight,stealth-capable exhaust systems.However,traditional metallic nozzles increase radar detectability and reduce range,while advanced composites offer high performance b...Unmanned combat aerial vehicles require lightweight,stealth-capable exhaust systems.However,traditional metallic nozzles increase radar detectability and reduce range,while advanced composites offer high performance but are expensive.Therefore,to improve the operational range and survivability of unmanned combat aerial vehicles,a lightweight,high-temperature-resistant,oxidation-resistant,and low-observable composite exhaust nozzle is developed to replace conventional metallic straight-type nozzles.The nozzle features a double serpentine shape to reduce radar and infrared signatures and is manufactured as a monolithic structure using the filament winding process,accommodating the complex geometry and large size(length:1.8 m,width:0.8 m).The exhaust nozzle consists of a ceramic matrix composite made of silicon carbide fibers and a silicon oxycarbide matrix,which absorbs and scatters radio frequency signals while withstanding prolonged exposure to high-temperature(700℃)oxidizing environments typical of engine exhaust gases.The polysiloxane resin used to produce the silicon oxycarbide matrix poses significant challenges owing to its low tackiness and high viscosity variations depending on the presence of nanoparticles,making filament winding difficult.These challenges are addressed by optimizing resin viscosity and winding pattern design.As a result,the tensile strength of the composite specimens fabricated with the optimized viscosity increases by 228.03% before pyrolysis and 97.68%after pyrolysis,compared with that of the non-optimized specimens.In addition,the density and tensile strength of the composite processed via three cycles of polymer infiltration and pyrolysis increased by 13.08% and 80.37%,respectively,compared to those of the non-densified composite.High-temperature oxidation and flame tests demonstrate exceptional thermal and oxidative stability.Furthermore,when compared with carbon fiber-reinforced ceramic matrix composites,the developed composite exhibits a permittivity at least two levels lower and a reflection loss below7 dB within the frequency range of 9.3-10.9 GHz,underscoring its superior electromagnetic stealth performance.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
Neuronal plasticity,the brain's ability to adapt structurally and functionally,is essential for learning,memory,and recovery from injuries.In neurodegenerative diseases such as Alzheimer's disease and Parkinso...Neuronal plasticity,the brain's ability to adapt structurally and functionally,is essential for learning,memory,and recovery from injuries.In neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease,this plasticity is disrupted,leading to cognitive and motor deficits.This review explores the mechanisms of neuronal plasticity and its effect on Alzheimer's disease and Parkinson's disease.Alzheimer's disease features amyloid-beta plaques and tau tangles that impair synaptic function,while Parkinson's disease involves the loss of dopaminergic neurons affecting motor control.Enhancing neuronal plasticity offers therapeutic potential for these diseases.A systematic literature review was conducted using databases such as PubMed,Scopus,and Google Scholar,focusing on studies of neuronal plasticity in Alzheimer's disease and Parkinson's disease.Data synthesis identified key themes such as synaptic mechanisms,neurogenesis,and therapeutic strategies,linking molecular insights to clinical applications.Results highlight that targeting synaptic plasticity mechanisms,such as long-term potentiation and long-term depression,shows promise.Neurotrophic factors,advanced imaging techniques,and molecular tools(e.g.,clustered regularly interspaced short palindromic repeats and optogenetics)are crucial in understanding and enhancing plasticity.Current therapies,including dopamine replacement,deep brain stimulation,and lifestyle interventions,demonstrate the potential to alleviate symptoms and improve outcomes.In conclusion,enhancing neuronal plasticity through targeted therapies holds significant promise for treating neurodegenerative diseases.Future research should integrate multidisciplinary approaches to fully harness the therapeutic potential of neuronal plasticity in Alzheimer's disease and Parkinson's disease.展开更多
Physician well-being is vital to delivering high-quality emergency care.A supported and healthy emergency medicine workforce leads to better patient outcomes,fewer medical errors,and greater job satisfaction and staff...Physician well-being is vital to delivering high-quality emergency care.A supported and healthy emergency medicine workforce leads to better patient outcomes,fewer medical errors,and greater job satisfaction and staff retention.[1,2]Emergency physicians(EPs)face unique pressures,including shift work,high patient volumes and acuities,overcrowding,and systemic inefficiencies that escalate their risk of burnout.As a result,EPs have reported the highest rates of burnout among physician specialties.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
文摘On the occasion of the additional issue of the CONTAINERIZATION, I’d like to express my sincere appreciation to the readers and authors both at home and abroad on behalf of the CONTAINERIZATION. The container transport in China is booming with leaps and bounds and the CONTAINERIZATION is becoming prosperous with each passing day. With the concern, support and help of the Ministry of Communications, Shanghai News Publication
文摘Background: While causing a financial and economic loss in many countries in a few months, COVID 19 was of great impact on public health. The Iraqi government took several measures since the first Iraqi case was discovered in February 2020. One of the fundamental measures to control this pandemic is social isolation, which depends greatly on people’s awareness to decrease the incidence of COVID-19. Objectives: To study the COVID-19-effect on dentists’ social life and their future plan, and to find the relation between that effect and some dentists’ demographic variables. Methods: A cross-sectional study was conducted from 2nd January to 7th May 2021, by an electronic version of the questionnaire through Google form. With facilitating orders delivered to all the specialist dental centers for adults of Al-Resafa health directorate;any dentists working in specialist dental centers for adults of Al-Resafa health directorate, and accept to participate in this study. Results: A total of 10 specialist dental centers for adults of the Al-Resafa health directorate represent 566/628 dentists enrolled in this study with a response rate of 88.99%. Most of them aged less than 30 years old (62.2%), female (60.6%), married status (50.4%), had 1 - 3 children (71.91%), Rotator (51.1%), with less than five years of experience (61.3%). Conclusion: COVID-19 affected so many aspects of health workers’ lives including dentists. About two-thirds of dentists had negative feelings, of which almost all had anxiety. Younger ages, females, married and having children are factors that contribute to the affection of dental social lives more than other categories.
文摘Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.
基金supported by the Agency for Defense Development Grant Funded by the Korean Government(Grant No.912822501).
文摘Unmanned combat aerial vehicles require lightweight,stealth-capable exhaust systems.However,traditional metallic nozzles increase radar detectability and reduce range,while advanced composites offer high performance but are expensive.Therefore,to improve the operational range and survivability of unmanned combat aerial vehicles,a lightweight,high-temperature-resistant,oxidation-resistant,and low-observable composite exhaust nozzle is developed to replace conventional metallic straight-type nozzles.The nozzle features a double serpentine shape to reduce radar and infrared signatures and is manufactured as a monolithic structure using the filament winding process,accommodating the complex geometry and large size(length:1.8 m,width:0.8 m).The exhaust nozzle consists of a ceramic matrix composite made of silicon carbide fibers and a silicon oxycarbide matrix,which absorbs and scatters radio frequency signals while withstanding prolonged exposure to high-temperature(700℃)oxidizing environments typical of engine exhaust gases.The polysiloxane resin used to produce the silicon oxycarbide matrix poses significant challenges owing to its low tackiness and high viscosity variations depending on the presence of nanoparticles,making filament winding difficult.These challenges are addressed by optimizing resin viscosity and winding pattern design.As a result,the tensile strength of the composite specimens fabricated with the optimized viscosity increases by 228.03% before pyrolysis and 97.68%after pyrolysis,compared with that of the non-optimized specimens.In addition,the density and tensile strength of the composite processed via three cycles of polymer infiltration and pyrolysis increased by 13.08% and 80.37%,respectively,compared to those of the non-densified composite.High-temperature oxidation and flame tests demonstrate exceptional thermal and oxidative stability.Furthermore,when compared with carbon fiber-reinforced ceramic matrix composites,the developed composite exhibits a permittivity at least two levels lower and a reflection loss below7 dB within the frequency range of 9.3-10.9 GHz,underscoring its superior electromagnetic stealth performance.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
基金financially supported by King Abdulaziz University,Deanship of Scientific Research(DSR)。
文摘Neuronal plasticity,the brain's ability to adapt structurally and functionally,is essential for learning,memory,and recovery from injuries.In neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease,this plasticity is disrupted,leading to cognitive and motor deficits.This review explores the mechanisms of neuronal plasticity and its effect on Alzheimer's disease and Parkinson's disease.Alzheimer's disease features amyloid-beta plaques and tau tangles that impair synaptic function,while Parkinson's disease involves the loss of dopaminergic neurons affecting motor control.Enhancing neuronal plasticity offers therapeutic potential for these diseases.A systematic literature review was conducted using databases such as PubMed,Scopus,and Google Scholar,focusing on studies of neuronal plasticity in Alzheimer's disease and Parkinson's disease.Data synthesis identified key themes such as synaptic mechanisms,neurogenesis,and therapeutic strategies,linking molecular insights to clinical applications.Results highlight that targeting synaptic plasticity mechanisms,such as long-term potentiation and long-term depression,shows promise.Neurotrophic factors,advanced imaging techniques,and molecular tools(e.g.,clustered regularly interspaced short palindromic repeats and optogenetics)are crucial in understanding and enhancing plasticity.Current therapies,including dopamine replacement,deep brain stimulation,and lifestyle interventions,demonstrate the potential to alleviate symptoms and improve outcomes.In conclusion,enhancing neuronal plasticity through targeted therapies holds significant promise for treating neurodegenerative diseases.Future research should integrate multidisciplinary approaches to fully harness the therapeutic potential of neuronal plasticity in Alzheimer's disease and Parkinson's disease.
文摘Physician well-being is vital to delivering high-quality emergency care.A supported and healthy emergency medicine workforce leads to better patient outcomes,fewer medical errors,and greater job satisfaction and staff retention.[1,2]Emergency physicians(EPs)face unique pressures,including shift work,high patient volumes and acuities,overcrowding,and systemic inefficiencies that escalate their risk of burnout.As a result,EPs have reported the highest rates of burnout among physician specialties.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
