City of Muskogee Animal Control Supervisor Phil Blair is known for his devotion to animals,but his quick thinking this week took that commitment to a whole new level-saving the life of an unborn baby deer after a trag...City of Muskogee Animal Control Supervisor Phil Blair is known for his devotion to animals,but his quick thinking this week took that commitment to a whole new level-saving the life of an unborn baby deer after a tragic accident.Blair was called to the scene of a deer struck by a car.Upon arrival,he found the mother deer had suffered four broken legs and sadly had to be put down to prevent further suffering.展开更多
This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional(1D)barcodes and Quick Response(QR)codes,addressing critical challenges in logistics ope...This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional(1D)barcodes and Quick Response(QR)codes,addressing critical challenges in logistics operations.The proposed solution leverages an efficient Pix2Pix-based framework,a type of conditional Generative Adversarial Network(GAN)optimized for image-to-image translation tasks,enabling the recovery of degraded barcodes and QR codes with minimal computational overhead.A core contribution of this work is the development of a synthetic dataset that simulates realistic damage scenarios frequently encountered in logistics environments,such as low contrast,misalignment,physical wear,and environmental interference.By training on this diverse and realistic dataset,the model demonstrates exceptional performance in restoring readability and decoding accuracy.The lightweight architecture,featuring a U-Net-based encoder-decoder with separable convolutions,ensures computational efficiency,making the approach suitable for real-time deployment on embedded and resource-constrained devices commonly used in logistics systems.Experimental results reveal significant improvements:QR code decoding ratios increased from 14%to 99%on training data and from 15%to 68%on validation data,while 1D barcode decoding ratios improved from 7%to 73%on training data and from 9%to 44%on validation data.By providing a robust,resource-efficient solution for restoring damaged barcodes and QR codes,this study offers practical advancements for enhancing the reliability of automated scanning systems in logistics operations,particularly under challenging conditions.展开更多
In today's fast-paced modern life, whether for fitness training, outdoor adventures, or daily commutes, we all yearn for quick-dry apparel that can rapidly wick away moisture and keep our bodies dry and comfortabl...In today's fast-paced modern life, whether for fitness training, outdoor adventures, or daily commutes, we all yearn for quick-dry apparel that can rapidly wick away moisture and keep our bodies dry and comfortable. As a standout in functional textiles, quick-dry fabrics are becoming the top choice for more and more people, thanks to their exceptional moisture-wicking performance and rapid drying capabilities.展开更多
Ammonia is a key industry raw material for fertilizers and the electro-reduction of N_(2)(NRR)can be served as a promising method.It is urgently needed to discover advanced catalysts while the lack of design principle...Ammonia is a key industry raw material for fertilizers and the electro-reduction of N_(2)(NRR)can be served as a promising method.It is urgently needed to discover advanced catalysts while the lack of design principles still hinders the high-throughput screen of efficient candidates.Herein,we have provided an up-to-date review of NRR catalysts mainly on theoretical works and highlighted the latest achievements on descriptors,which can be served as valid guidance of optimal catalysts.The descriptors are classified with adsorption energy and the corresponding derived ones,which can screen the NRR catalysts from various aspects.Finally,the challenges and opportunities in the descriptor field are presented.展开更多
QR codes are widely used in applications such as information sharing,advertising,and digital payments.However,their growing adoption has made them attractive targets for malicious activities,including malware distribu...QR codes are widely used in applications such as information sharing,advertising,and digital payments.However,their growing adoption has made them attractive targets for malicious activities,including malware distribution and phishing attacks.Traditional detection approaches rely on URL analysis or image-based feature extraction,whichmay introduce significant computational overhead and limit real-time applicability,and their performance often depends on the quality of extracted features.Previous studies in malicious detection do not fully focus on QR code securitywhen combining convolutional neural networks(CNNs)with recurrent neural networks(RNNs).This research proposes a deep learning model that integrates AlexNet for feature extraction,principal component analysis(PCA)for dimensionality reduction,and RNNs to detect malicious activity in QR code images.The proposed model achieves both efficiency and accuracy by transforming image data into a compact one-dimensional sequence.Experimental results,including five-fold cross-validation,demonstrate that the model using gated recurrent units(GRU)achieved an accuracy of 99.81%on the first dataset and 99.59%in the second dataset with a computation time of only 7.433 ms per sample.A real-time prototype was also developed to demonstrate deployment feasibility.These results highlight the potential of the proposed approach for practical,real-time QR code threat detection.展开更多
As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The ...As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.展开更多
Thermochemical conversions are pathways for biomass utilization to produce various value-added energy and chemical products. For the development of novel thermochemical conversion technologies, an accurate understandi...Thermochemical conversions are pathways for biomass utilization to produce various value-added energy and chemical products. For the development of novel thermochemical conversion technologies, an accurate understanding of the reaction performance and kinetics is essential. Given the diversity of the thermal analysis techniques, it is necessary to understand the features and limitations of the reactors, ensuring that the selected thermal analysis reactor meets the specific need for reaction characterization. This paper provides a critical overview of the thermal analysis reactors based on the following perspectives: 1) gas flow conditions in the reactor, 2) particle’s external and internal heat and mass transfer limitations, 3) heating rate, 4) temperature distribution, 5) nascent char production and reaction, 6) liquid feeding and atomization, 7) simultaneous sampling and analyzing of bed materials, and 8) reacting atmosphere change. Finally, prospects and future research directions in the development of analysis techniques are proposed.展开更多
The urgent need to mitigate climate change impacts and achieve net zero emissions has led to extensive research on carbon dioxide(CO_(2))-capture technologies.This study focuses on the kinetics of CO_(2)capture using ...The urgent need to mitigate climate change impacts and achieve net zero emissions has led to extensive research on carbon dioxide(CO_(2))-capture technologies.This study focuses on the kinetics of CO_(2)capture using solid adsorbents specifically through thermal gravimetric analysis(TGA).The research explores the principles behind TGA and its application in analyzing adsorbent performance and the significance of kinetics in optimizing CO_(2)-capture processes.Solid adsorbents have gained significant attention due to their potential for efficient and cost-effective CO_(2)capture.Therefore,three different types of adsorbents,namely calcium-,tin-,and zirconium-based ones(quicklime:CaO,potassium stannate:K_(2)SnO_(3),and sodium zirconate:Na_(2)ZrO_(3)),in adsorbing high-temperature carbon dioxide were investigated;their quality and performance by various factors such as price,stability,non-toxicity,and efficiency are different.The diffusion models and geometrical contraction models were the best-fitted models to explain the kinetic of these solid adsorbents for high-temperature CO_(2)sorption;it means the morphology is important for solid adsorbent performance.The minimum energy needed to start a reaction for K_(2)SnO_(3),Na_(2)ZrO_(3),and CaO,is 73.55,84.33,and 86.23 kJ·mol^(-1),respectively;with the lowest value being for potassium stannate.The high-temperature CO_(2)adsorption performance of various solid adsorbents in regard with the rate of reaction followed the order of K_(2)SnO_(3)>CaO>>Na_(2)ZrO_(3),based on experiments and kinetic studies.展开更多
This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi...This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.展开更多
As new COVID-19 strains surface and proliferate over the world,the pandemic is still evolving[1].The FLiRT variants are one such family of variants that have recently drawn notice.They are a family of Omicron sub-vari...As new COVID-19 strains surface and proliferate over the world,the pandemic is still evolving[1].The FLiRT variants are one such family of variants that have recently drawn notice.They are a family of Omicron sub-variants quickly becoming dominant.These FLiRT variants include KP.2 and KP.1.1.Due to certain changes in the virus's spike proteins,these variants have been dubbed"FLiRT."展开更多
The origin of Old Beijing hot pot dates back to the Yuan Dynasty.It is said that Kublai Khan,during his expedition,craved his hometown cuisine.The chef had a clever idea:He cut lamb into slices,put them in a pot for a...The origin of Old Beijing hot pot dates back to the Yuan Dynasty.It is said that Kublai Khan,during his expedition,craved his hometown cuisine.The chef had a clever idea:He cut lamb into slices,put them in a pot for a quick rinse,and sprinkled some salt.Kublai Khan found it very delicious.This way of eating quickly spread,and the history of hot pot has been around for 700 years now.展开更多
This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison ...This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases.展开更多
文摘City of Muskogee Animal Control Supervisor Phil Blair is known for his devotion to animals,but his quick thinking this week took that commitment to a whole new level-saving the life of an unborn baby deer after a tragic accident.Blair was called to the scene of a deer struck by a car.Upon arrival,he found the mother deer had suffered four broken legs and sadly had to be put down to prevent further suffering.
基金supported by the Scientific and Technological Research Council of Turkey(TÜB˙ITAK)through the Industrial R&D Projects Grant Program(TEYDEB)under Project No.3211077(grant recipient:Metin Kahraman)。
文摘This study introduces a lightweight deep learning model and a novel synthetic dataset designed to restore damaged one-dimensional(1D)barcodes and Quick Response(QR)codes,addressing critical challenges in logistics operations.The proposed solution leverages an efficient Pix2Pix-based framework,a type of conditional Generative Adversarial Network(GAN)optimized for image-to-image translation tasks,enabling the recovery of degraded barcodes and QR codes with minimal computational overhead.A core contribution of this work is the development of a synthetic dataset that simulates realistic damage scenarios frequently encountered in logistics environments,such as low contrast,misalignment,physical wear,and environmental interference.By training on this diverse and realistic dataset,the model demonstrates exceptional performance in restoring readability and decoding accuracy.The lightweight architecture,featuring a U-Net-based encoder-decoder with separable convolutions,ensures computational efficiency,making the approach suitable for real-time deployment on embedded and resource-constrained devices commonly used in logistics systems.Experimental results reveal significant improvements:QR code decoding ratios increased from 14%to 99%on training data and from 15%to 68%on validation data,while 1D barcode decoding ratios improved from 7%to 73%on training data and from 9%to 44%on validation data.By providing a robust,resource-efficient solution for restoring damaged barcodes and QR codes,this study offers practical advancements for enhancing the reliability of automated scanning systems in logistics operations,particularly under challenging conditions.
文摘In today's fast-paced modern life, whether for fitness training, outdoor adventures, or daily commutes, we all yearn for quick-dry apparel that can rapidly wick away moisture and keep our bodies dry and comfortable. As a standout in functional textiles, quick-dry fabrics are becoming the top choice for more and more people, thanks to their exceptional moisture-wicking performance and rapid drying capabilities.
基金supported by the National Natural Science Foundation of China(No.21603109)the Henan Joint Fund of the National Natural Science Foundation of China(No.U1404216)+2 种基金the Special Fund of Tianshui Normal University,China(No.CXJ2020-08)the Scientific Research Program Funded by Shaanxi Provincial Education Department(No.20JK0676)supported by Natural Science Basic Research Program of Shanxi(Nos.2022JQ-108,2022JQ-096).
文摘Ammonia is a key industry raw material for fertilizers and the electro-reduction of N_(2)(NRR)can be served as a promising method.It is urgently needed to discover advanced catalysts while the lack of design principles still hinders the high-throughput screen of efficient candidates.Herein,we have provided an up-to-date review of NRR catalysts mainly on theoretical works and highlighted the latest achievements on descriptors,which can be served as valid guidance of optimal catalysts.The descriptors are classified with adsorption energy and the corresponding derived ones,which can screen the NRR catalysts from various aspects.Finally,the challenges and opportunities in the descriptor field are presented.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University Jeddah,under grant no.(GPIP:1168-611-2024)The authors acknowledge the DSR for financial and technical support.
文摘QR codes are widely used in applications such as information sharing,advertising,and digital payments.However,their growing adoption has made them attractive targets for malicious activities,including malware distribution and phishing attacks.Traditional detection approaches rely on URL analysis or image-based feature extraction,whichmay introduce significant computational overhead and limit real-time applicability,and their performance often depends on the quality of extracted features.Previous studies in malicious detection do not fully focus on QR code securitywhen combining convolutional neural networks(CNNs)with recurrent neural networks(RNNs).This research proposes a deep learning model that integrates AlexNet for feature extraction,principal component analysis(PCA)for dimensionality reduction,and RNNs to detect malicious activity in QR code images.The proposed model achieves both efficiency and accuracy by transforming image data into a compact one-dimensional sequence.Experimental results,including five-fold cross-validation,demonstrate that the model using gated recurrent units(GRU)achieved an accuracy of 99.81%on the first dataset and 99.59%in the second dataset with a computation time of only 7.433 ms per sample.A real-time prototype was also developed to demonstrate deployment feasibility.These results highlight the potential of the proposed approach for practical,real-time QR code threat detection.
基金supported by the Meteorological Soft Science Project(Grant No.2023ZZXM29)the Natural Science Fund Project of Tianjin,China(Grant No.21JCYBJC00740)the Key Research and Development-Social Development Program of Jiangsu Province,China(Grant No.BE2021685).
文摘As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.
基金supported by the National Natural Science Foundation of China(U1908201,U1903130)the Ministry of Science and Technology of the People’s Republic of China(2020YFC1909300)the Natural Science Foundation of Liaoning Province of China(2021-NLTS-12-09).
文摘Thermochemical conversions are pathways for biomass utilization to produce various value-added energy and chemical products. For the development of novel thermochemical conversion technologies, an accurate understanding of the reaction performance and kinetics is essential. Given the diversity of the thermal analysis techniques, it is necessary to understand the features and limitations of the reactors, ensuring that the selected thermal analysis reactor meets the specific need for reaction characterization. This paper provides a critical overview of the thermal analysis reactors based on the following perspectives: 1) gas flow conditions in the reactor, 2) particle’s external and internal heat and mass transfer limitations, 3) heating rate, 4) temperature distribution, 5) nascent char production and reaction, 6) liquid feeding and atomization, 7) simultaneous sampling and analyzing of bed materials, and 8) reacting atmosphere change. Finally, prospects and future research directions in the development of analysis techniques are proposed.
文摘The urgent need to mitigate climate change impacts and achieve net zero emissions has led to extensive research on carbon dioxide(CO_(2))-capture technologies.This study focuses on the kinetics of CO_(2)capture using solid adsorbents specifically through thermal gravimetric analysis(TGA).The research explores the principles behind TGA and its application in analyzing adsorbent performance and the significance of kinetics in optimizing CO_(2)-capture processes.Solid adsorbents have gained significant attention due to their potential for efficient and cost-effective CO_(2)capture.Therefore,three different types of adsorbents,namely calcium-,tin-,and zirconium-based ones(quicklime:CaO,potassium stannate:K_(2)SnO_(3),and sodium zirconate:Na_(2)ZrO_(3)),in adsorbing high-temperature carbon dioxide were investigated;their quality and performance by various factors such as price,stability,non-toxicity,and efficiency are different.The diffusion models and geometrical contraction models were the best-fitted models to explain the kinetic of these solid adsorbents for high-temperature CO_(2)sorption;it means the morphology is important for solid adsorbent performance.The minimum energy needed to start a reaction for K_(2)SnO_(3),Na_(2)ZrO_(3),and CaO,is 73.55,84.33,and 86.23 kJ·mol^(-1),respectively;with the lowest value being for potassium stannate.The high-temperature CO_(2)adsorption performance of various solid adsorbents in regard with the rate of reaction followed the order of K_(2)SnO_(3)>CaO>>Na_(2)ZrO_(3),based on experiments and kinetic studies.
基金the National Natural Science Foundation of China(Grant No.42274119)the Liaoning Revitalization Talents Program(Grant No.XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(Grant No.2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.
文摘As new COVID-19 strains surface and proliferate over the world,the pandemic is still evolving[1].The FLiRT variants are one such family of variants that have recently drawn notice.They are a family of Omicron sub-variants quickly becoming dominant.These FLiRT variants include KP.2 and KP.1.1.Due to certain changes in the virus's spike proteins,these variants have been dubbed"FLiRT."
文摘The origin of Old Beijing hot pot dates back to the Yuan Dynasty.It is said that Kublai Khan,during his expedition,craved his hometown cuisine.The chef had a clever idea:He cut lamb into slices,put them in a pot for a quick rinse,and sprinkled some salt.Kublai Khan found it very delicious.This way of eating quickly spread,and the history of hot pot has been around for 700 years now.
文摘This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases.
