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
Carbamazepine is an antiepileptic drug also used for neuropathic pain and mood stabilization.It is a strong enzyme inducer and autoinducer with multiple well-documented drug–drug interactions and adverse drug reactio...Carbamazepine is an antiepileptic drug also used for neuropathic pain and mood stabilization.It is a strong enzyme inducer and autoinducer with multiple well-documented drug–drug interactions and adverse drug reactions.Widely licensed and in use since the 1960s,carbamazepine has well-characterized pharmacological,pharmacogenetic,and safety profiles,and remains extensively used in neurology and psychiatry.In 2024,carbamazepine was recommended for inclusion in the World Health Organization list of essential medicines.Carbamazepine has a complex mode of action that includes neuronal stabilization,neuroprotection,neurotransmitter modulation,enhancement of autophagy,and anti-inflammatory effects.These make carbamazepine a good candidate for drug repurposing in oncology,genetic diseases,neurodegeneration,and systemic inflammation.Recent advances in precision medicine,genomics,and on/off-target drug repositioning have enabled the identification of new carbamazepinemolecular targets for novel applications in different therapeuticmodalities.This review highlights carbamazepine repurposing studies in cancers such as breast and colorectal,based on its mode of action.In addition,repurposing studies in genetic diseases such asmetaphyseal achondroplasia and Fragile-X,as well as in neurodegenerative conditions such as amyotrophic lateral sclerosis and Alzheimer's dementia,are discussed.The pharmacological mechanisms and drug repurposing pathways are critically summarized in order to provide insights into their therapeutic potential and proposed future directions.展开更多
目的通过观察20~30岁男性脂肪肝患者在接受运动减重计划后的体质量指数(body mass index,BMI)变化,绘制BMI变化轨迹,并分析不同BMI变化轨迹与胰岛素抵抗程度的关联。方法前瞻性选择2024年6月至2024年8月期间于解放军总医院京南医疗区永...目的通过观察20~30岁男性脂肪肝患者在接受运动减重计划后的体质量指数(body mass index,BMI)变化,绘制BMI变化轨迹,并分析不同BMI变化轨迹与胰岛素抵抗程度的关联。方法前瞻性选择2024年6月至2024年8月期间于解放军总医院京南医疗区永定路门诊部体检的148例20~30岁男性脂肪肝患者为研究对象。根据患者基线,以及运动后1、2及3个月的BMI变化资料,采用增长混合模型构建BMI变化轨迹模型;对不同BMI变化轨迹患者的胰岛素抵抗指数(homeostasis model assessment of insulin resistance index,HOMA-IR)水平进行重复测量资料的方差分析;采用多重线性回归分析BMI变化轨迹与HOMA-IR水平间的关联。结果增长混合模型结果表明,构建3个类别时,模型具有更接近1的熵值及更小的赤池信息准则值、贝叶斯信息准则值;3个类别组分别命名为缓慢减重组、常规减重组、快速减重组。不同BMI变化轨迹患者在不确定心理压力评分,睡眠质量评分,低密度脂蛋白、高密度脂蛋白、总胆固醇、甘油三酯、空腹血糖、空腹胰岛素、HOMA-IR水平,以及脂肪肝严重程度方面的差异均存在统计学意义(P<0.05)。重复测量资料的方差分析结果表明,时间效应、组间效应、交互效应均有统计学意义(F_(时间)=3.990、P时间=0.027;F_(组间)=8.880、P_(组间)<0.001;F_(交互)=5.046、P_(交互)=0.002),HOMA-IR水平随时间延长而呈升高趋势,且BMI变化轨迹不同则时间因素对HOMA-IR水平的影响不同。多重线性回归分析结果表明,常规减重组(β=−0.237,P=0.001)、快速减重组(β=−0.386,P<0.001)的HOMA-IR水平均低于缓慢减重组。结论20~30岁男性脂肪肝患者运动减重后的BMI变化轨迹与胰岛素抵抗存在关联。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
Silicon(Si)is a promising high-capacity anode in lithium-ion batteries but suffers from chronic chemical degradation and capacity fading during calendar aging,greatly hindering its automobile applications.Electrolyte ...Silicon(Si)is a promising high-capacity anode in lithium-ion batteries but suffers from chronic chemical degradation and capacity fading during calendar aging,greatly hindering its automobile applications.Electrolyte engineering currently relies on conventional evaluation criteria of reducing coulombic consumption,which implicitly presume its equivalence to irreversible capacity loss and complicates battery development.We introduce the detrimental ratioρto quantify the fraction of parasitic species that permanently degrades active material.This metric is independent and crucially complements total coulombic consumption for accurate performance evaluation.We systematically investigate multiple electrolyte formulations using high-precision leakage current measurements,open-circuit-voltage experiments,and post-mortem characterizations.Although some electrolytes exhibit similarly low coulombic consumption,they diverge significantly in capacity retention andρ.Especially,dimethyl-carbonate-based localized-high concentration electrolyte can synergically achieve low coulombic consumption and detrimental ratioρduring calendar aging,owing to its chemically inert and structurally resilient solidelectrolyte interface with minimal isolated Si material.By contrast,increasing fluoroethylene carbonate(FEC)additive content suppresses electrolyte breakdown but suffers aggravated chemical degradation of more LixSi isolation for irreversible capacity loss with a risingρ.This study critically reveals that the chemistry-characteristic detrimental ratioρestablishes physically informed performance evaluation to pave the way for accelerating battery development.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.
文摘Carbamazepine is an antiepileptic drug also used for neuropathic pain and mood stabilization.It is a strong enzyme inducer and autoinducer with multiple well-documented drug–drug interactions and adverse drug reactions.Widely licensed and in use since the 1960s,carbamazepine has well-characterized pharmacological,pharmacogenetic,and safety profiles,and remains extensively used in neurology and psychiatry.In 2024,carbamazepine was recommended for inclusion in the World Health Organization list of essential medicines.Carbamazepine has a complex mode of action that includes neuronal stabilization,neuroprotection,neurotransmitter modulation,enhancement of autophagy,and anti-inflammatory effects.These make carbamazepine a good candidate for drug repurposing in oncology,genetic diseases,neurodegeneration,and systemic inflammation.Recent advances in precision medicine,genomics,and on/off-target drug repositioning have enabled the identification of new carbamazepinemolecular targets for novel applications in different therapeuticmodalities.This review highlights carbamazepine repurposing studies in cancers such as breast and colorectal,based on its mode of action.In addition,repurposing studies in genetic diseases such asmetaphyseal achondroplasia and Fragile-X,as well as in neurodegenerative conditions such as amyotrophic lateral sclerosis and Alzheimer's dementia,are discussed.The pharmacological mechanisms and drug repurposing pathways are critically summarized in order to provide insights into their therapeutic potential and proposed future directions.
文摘目的通过观察20~30岁男性脂肪肝患者在接受运动减重计划后的体质量指数(body mass index,BMI)变化,绘制BMI变化轨迹,并分析不同BMI变化轨迹与胰岛素抵抗程度的关联。方法前瞻性选择2024年6月至2024年8月期间于解放军总医院京南医疗区永定路门诊部体检的148例20~30岁男性脂肪肝患者为研究对象。根据患者基线,以及运动后1、2及3个月的BMI变化资料,采用增长混合模型构建BMI变化轨迹模型;对不同BMI变化轨迹患者的胰岛素抵抗指数(homeostasis model assessment of insulin resistance index,HOMA-IR)水平进行重复测量资料的方差分析;采用多重线性回归分析BMI变化轨迹与HOMA-IR水平间的关联。结果增长混合模型结果表明,构建3个类别时,模型具有更接近1的熵值及更小的赤池信息准则值、贝叶斯信息准则值;3个类别组分别命名为缓慢减重组、常规减重组、快速减重组。不同BMI变化轨迹患者在不确定心理压力评分,睡眠质量评分,低密度脂蛋白、高密度脂蛋白、总胆固醇、甘油三酯、空腹血糖、空腹胰岛素、HOMA-IR水平,以及脂肪肝严重程度方面的差异均存在统计学意义(P<0.05)。重复测量资料的方差分析结果表明,时间效应、组间效应、交互效应均有统计学意义(F_(时间)=3.990、P时间=0.027;F_(组间)=8.880、P_(组间)<0.001;F_(交互)=5.046、P_(交互)=0.002),HOMA-IR水平随时间延长而呈升高趋势,且BMI变化轨迹不同则时间因素对HOMA-IR水平的影响不同。多重线性回归分析结果表明,常规减重组(β=−0.237,P=0.001)、快速减重组(β=−0.386,P<0.001)的HOMA-IR水平均低于缓慢减重组。结论20~30岁男性脂肪肝患者运动减重后的BMI变化轨迹与胰岛素抵抗存在关联。
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supported by the U.S.Department of Energy(DOE),Office of Energy Efficiency and Renewable Energy(EERE),Vehicle Technologies Office(VTO)under the Silicon Consortium Seedling project received by Z.H.Coperated for the DOE Office of Science by UChicago Argonne,LLC,under Contract DE-AC02-06CH11357+2 种基金Pacific Northwest National Laboratory(PNNL)was supported by the U.S.DOE,Office of Advanced Research Projects Agency-Energy(ARPA-E)under the EVs4ALL Program with the contract number DE-AC05-76RL01830operated by Battelle for the DOE under Contract DE-AC0576RL01830performed at the Oak Ridge National Laboratory(GMV)and supported by U.S.DOE’s VTO under the Silicon Consortium Program received by G.M.V.and directed by Carine Steinway,Nicolas Eidson Thomas,Thomas Do。
文摘Silicon(Si)is a promising high-capacity anode in lithium-ion batteries but suffers from chronic chemical degradation and capacity fading during calendar aging,greatly hindering its automobile applications.Electrolyte engineering currently relies on conventional evaluation criteria of reducing coulombic consumption,which implicitly presume its equivalence to irreversible capacity loss and complicates battery development.We introduce the detrimental ratioρto quantify the fraction of parasitic species that permanently degrades active material.This metric is independent and crucially complements total coulombic consumption for accurate performance evaluation.We systematically investigate multiple electrolyte formulations using high-precision leakage current measurements,open-circuit-voltage experiments,and post-mortem characterizations.Although some electrolytes exhibit similarly low coulombic consumption,they diverge significantly in capacity retention andρ.Especially,dimethyl-carbonate-based localized-high concentration electrolyte can synergically achieve low coulombic consumption and detrimental ratioρduring calendar aging,owing to its chemically inert and structurally resilient solidelectrolyte interface with minimal isolated Si material.By contrast,increasing fluoroethylene carbonate(FEC)additive content suppresses electrolyte breakdown but suffers aggravated chemical degradation of more LixSi isolation for irreversible capacity loss with a risingρ.This study critically reveals that the chemistry-characteristic detrimental ratioρestablishes physically informed performance evaluation to pave the way for accelerating battery development.
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.
