Objective To predict the potential targets of Qingfu Juanbi Decoction(青附蠲痹汤,QFJBD)in treating rheumatoid arthritis(RA)using an improved Transformer model and investigate the network pharmacological mechanisms und...Objective To predict the potential targets of Qingfu Juanbi Decoction(青附蠲痹汤,QFJBD)in treating rheumatoid arthritis(RA)using an improved Transformer model and investigate the network pharmacological mechanisms underlying QFJBD’s therapeutic effects on RA.Methods First,a traditional Chinese medicine herb-target interaction(TCMHTI)model was constructed to predict herb-target interactions based on Transformer improvement.The per-formance of the TCMHTI model was evaluated against baseline models using three metrics:area under the receiver operating characteristic curve(AUC),precision-recall curve(PRC),and accuracy.Subsequently,a protein-protein interaction(PPI)network was built based on the predicted targets,with core targets identified as the top nine nodes ranked by degree val-ues.Gene Ontology(GO)functional and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses were performed using the targets predicted by TCMHTI and the targets identified through network pharmacology method for comparison.Then,the re-sults were compared.Finally,the core targets predicted by TCMHTI were validated through molecular docking and literature review.Results The TCMHTI model achieved an AUC of 0.883,PRC of 0.849,and accuracy of 0.818,predicting 49 potential targets for QFJBD in RA treatment.Nine core targets were identified:tumor necrosis factor(TNF)-α,interleukin(IL)-1β,IL-6,IL-10,IL-17A,cluster of differentia-tion 40(CD40),cytotoxic T-lymphocyte-associated protein 4(CTLA4),IL-4,and signal trans-ducer and activator of transcription 3(STAT3).The enrichment analysis demonstrated that the TCMHTI model predicted 49 targets and enriched more pathways directly associated with RA,whereas classical network pharmacology identified 64 targets but enriched pathways showing weaker relevance to RA.Molecular docking demonstrated that the active molecules in QFJBD exhibit favorable binding energy with RA targets,while literature research further revealed that QFJBD can treat RA through 9 core targets.Conclusion The TCMHTI model demonstrated greater accuracy than traditional network pharmacology methods,suggesting QFJBD exerts therapeutic effects on RA by regulating tar-gets like TNF-α,IL-1β,and IL-6,as well as multiple signaling pathways.This study provides a novel framework for bridging traditional herbal knowledge with precision medicine,offering actionable insights for developing targeted TCM therapies against diseases.展开更多
Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenari...Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenarios,including fluctuating noise levels and unpredictable environmental elements,these techniques do not fully resolve these challenges.We introduce an innovative strategy that builds upon Retinex theory and integrates a novel deep network architecture,merging the Convolutional Block Attention Module(CBAM)with the Transformer.Our model is capable of detecting more prominent features across both channel and spatial domains.We have conducted extensive experiments across several datasets,namely LOLv1,LOLv2-real,and LOLv2-sync.The results show that our approach surpasses other methods when evaluated against critical metrics such as Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM).Moreover,we have visually assessed images enhanced by various techniques and utilized visual metrics like LPIPS for comparison,and the experimental data clearly demonstrate that our approach excels visually over other methods as well.展开更多
Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning...Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels as distance increases. In illumination restoration, we used Unet++ with multi-level skip connections to better integrate semantic information at different scales. The designed Illumination Fusion Dual Self-Attention (IF-DSA) module embeds multi-scale dilated convolutions to achieve spatial self-attention. This module captures long-range spatial semantic relationships within acceptable computational complexity. Experimental results on the Low-Light (LOL) dataset show that Retexformer+ outperforms other State-Of-The-Art (SOTA) methods in both quantitative and qualitative evaluations, with the computational complexity increased to an acceptable 51.63 G FLOPS. On the LOL_v1 dataset, RetinexFormer+ shows an increase of 1.15 in Peak Signal-to-Noise Ratio (PSNR) and a decrease of 0.39 in Root Mean Square Error (RMSE). On the LOL_v2_real dataset, the PSNR increases by 0.42 and the RMSE decreases by 0.18. Experimental results on the Exdark dataset show that Retexformer+ can effectively enhance real-scene images and maintain their semantic information.展开更多
As antibiotic pollutants cannot be incompletely removed by conventional wastewater treatment plants,ultraviolet(UV)based advanced oxidation processes(AOPs)such as UV/persulfate(UV/PS)and UV/chlorine are increasingly c...As antibiotic pollutants cannot be incompletely removed by conventional wastewater treatment plants,ultraviolet(UV)based advanced oxidation processes(AOPs)such as UV/persulfate(UV/PS)and UV/chlorine are increasingly concerned for the effective removal of antibiotics from wastewaters.However,the specific mechanisms involving degradation kinetics and transformation mechanisms are not well elucidated.Here we report a detailed examination of SO_(4)•−/Cl•-mediated degradation kinetics,products,and toxicities of sulfathiazole(ST),sarafloxacin(SAR),and lomefloxacin(LOM)in the two processes.Both SO_(4)•−/Cl•-mediated transformation kinetics were found to be dependent on pH(P<0.05),which was attributed to the disparate reactivities of their individual dissociated forms.Based on competition kinetic experiments and matrix calculations,the cationic forms(H_(2)ST^(+),H_(2)SAR^(+),and H_(2)LOM^(+))were more highly reactive towards SO_(4)•−in most cases,while the neutral forms(e.g.,HSAR^(0)and HLOM^(0))reacted the fastest with Cl•for the most of the antibiotics tested.Based on the identification of 31 key intermediates using tandem mass spectrometry,these reactions generated different products,of which the majority still retained the core chemical structure of the parent compounds.The corresponding diverse transformation pathways were proposed,involving S−N breaking,hydroxylation,defluorination,and chlorination reactions.Furthermore,the toxicity changes of their reaction solutions as well as the toxicity of each intermediate were evaluated by the vibrio fischeri and ECOSAR model,respectively.Many primary by-products were proven to be more toxic than the parent chemicals,raising the wider issue of extended potency for these compounds with regards to their ecotoxicity.These results have implications for assessing the degradative fate and risk of these chemicals during the AOPs.展开更多
This study introduces an innovative computational framework leveraging the transformer architecture to address a critical challenge in chemical process engineering:predicting and optimizing light olefin yields in indu...This study introduces an innovative computational framework leveraging the transformer architecture to address a critical challenge in chemical process engineering:predicting and optimizing light olefin yields in industrial methanol-to-olefins(MTO)processes.Our approach integrates advanced machine learning techniques with chemical engineering principles to tackle the complexities of non-stationary,highly volatile production data in large-scale chemical manufacturing.The framework employs the maximal information coefficient(MIC)algorithm to analyze and select the significant variables from MTO process parameters,forming a robust dataset for model development.We implement a transformer-based time series forecasting model,enhanced through positional encoding and hyperparameter optimization,significantly improving predictive accuracy for ethylene and propylene yields.The model's interpretability is augmented by applying SHapley additive exPlanations(SHAP)to quantify and visualize the impact of reaction control variables on olefin yields,providing valuable insights for process optimization.Experimental results demonstrate that our model outperforms traditional statistical and machine learning methods in accuracy and interpretability,effectively handling nonlinear,non-stationary,highvolatility,and long-sequence data challenges in olefin yield prediction.This research contributes to chemical engineering by providing a novel computerized methodology for solving complex production optimization problems in the chemical industry,offering significant potential for enhancing decisionmaking in MTO system production control and fostering the intelligent transformation of manufacturing processes.展开更多
(Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4))_(100−x)Al_(x)(x=0,0.1,0.2,0.3,0.4 at.%)lightweight high-entropy alloys with different contents of Al were prepared via vacuum non-consumable arc melting method.Effects of adding varying...(Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4))_(100−x)Al_(x)(x=0,0.1,0.2,0.3,0.4 at.%)lightweight high-entropy alloys with different contents of Al were prepared via vacuum non-consumable arc melting method.Effects of adding varying Al contents on phase constitution,microstructure characteristics and mechanical properties of the lightweight alloys were studied.Results show that Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4)alloy is composed of body-centered cubic(BCC)phase and C15 Laves phase,while(Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4))_(100−x)Al_(x)lightweight high-entropy alloys by addition of Al are composed of BCC phase and C14 Laves phase.Addition of Al into Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4)lightweight high-entropy alloy can transform C15 Laves phase to C14 Laves phase.With further addition of Al,BCC phase of alloys is significantly refined,and the volume fraction of C14 Laves phase is raised obviously.Meanwhile,the dimension of BCC phase in the alloy by addition of 0.3 at.%Al is the most refined and that of Laves phase is also obviously refined.Adding Al to Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4)alloy can not only reduce the density of(Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4))_(100−x)Al_(x)alloy,but also improve strength of(Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4))_(100−x)Al_(x)alloy.As Al content increased from 0 to 0.4 at.%,the density of the alloy decreased from 6.22±0.875 to 5.79±0.679 g cm^(−3).Moreover,compressive strength of the alloy by 0.3 at.%Al addition is the highest to 1996.9 MPa,while fracture strain of the alloy is 16.82%.Strength improvement of alloys mainly results from microstructure refinement and precipitation of C14 Laves by Al addition into Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4)lightweight high-entropy alloy.展开更多
Purpose–This paper aims to provide a comprehensive analysis of the strategic adjustments in China’s transportation structure,with a particular focus on the pivotal role of railway freight and its integration into th...Purpose–This paper aims to provide a comprehensive analysis of the strategic adjustments in China’s transportation structure,with a particular focus on the pivotal role of railway freight and its integration into the modern logistics system.It seeks to address the need for a more nuanced understanding of the“road to rail”policy,emphasizing the importance of intermodal collaboration and service of fragmented market demands.Design/methodology/approach–The study employs a transport economics perspective to evaluate the achievements and shortcomings of China’s transportation structure optimization.It bases its assessment of the current state of railway freight logistics,multi-modal transportation and the broader implications for the transportation service market on data analysis.The methodology includes a review of existing policies,an examination of industry practices and a comparative analysis with global trends in railway logistics.Findings–The research underscores the importance of focusing on the development of non-bulk materials,noting the insufficiency in the development of China’s rail multi-modal transportation and highlighting the instructive value of successful cases in open-top container road-rail intermodal transportation.The study posits that the railway sector must enhance cooperation with other market entities,aligning with the lead enterprises in the logistics chain that are characterized by speed,high value and strong coordination capabilities,in order to better serve the transportation market.This approach moves away from a reliance on the railway’s own capabilities alone.Originality/value–This paper offers original insights into the transformation of railway freight in China,contributing to the body of knowledge on transportation economics and logistics.It provides valuable recommendations for policymakers and industry practitioners,emphasizing the strategic importance of railway logistics in the context of China’s economic development and intense competition in the supply chain.The value of the article lies in its comprehensive understanding of the complexities involved in the adjustment of transportation structures,providing direction for the market-oriented reform of China’s railway freight sector.展开更多
In this paper,we propose a new full-Newton step feasible interior-point algorithm for the special weighted linear complementarity problems.The proposed algorithm employs the technique of algebraic equivalent transform...In this paper,we propose a new full-Newton step feasible interior-point algorithm for the special weighted linear complementarity problems.The proposed algorithm employs the technique of algebraic equivalent transformation to derive the search direction.It is shown that the proximity measure reduces quadratically at each iteration.Moreover,the iteration bound of the algorithm is as good as the best-known polynomial complexity for these types of problems.Furthermore,numerical results are presented to show the efficiency of the proposed algorithm.展开更多
Recently,there has been a widespread application of deep learning in object detection with Synthetic Aperture Radar(SAR).The current algorithms based on Convolutional Neural Networks(CNN)often achieve good accuracy at...Recently,there has been a widespread application of deep learning in object detection with Synthetic Aperture Radar(SAR).The current algorithms based on Convolutional Neural Networks(CNN)often achieve good accuracy at the expense of more complex model structures and huge parameters,which poses a great challenge for real-time and accurate detection of multi-scale targets.To address these problems,we propose a lightweight real-time SAR ship object detector based on detection transformer(LSD-DETR)in this study.First,a lightweight backbone network LCNet containing a stem module and inverted residual structure is constructed to balance the inference speed and detection accuracy of model.Second,we design a transformer encoder with Cascaded Group Attention(CGA Encoder)to enrich the feature information of small targets in SAR images,which makes detection of small-sized ships more precise.Third,an efficient cross-scale feature fusion pyramid module(C3Het-FPN)is proposed through the lightweight units(C3Het)and the introduction of the weighted bidirectional feature pyramid(BiFPN)structure,which realizes the adaptive fusion of multi-scale features with fewer parameters.Ablation experiments and comparative experiments demonstrate the effectiveness of LSD-DETR.The model parameter of LSD-DETR is 8.8 M(only 20.6%of DETR),the model’s FPS reached 43.1,the average detection accuracy mAP50 on the SSDD and HRSID datasets reached 97.3%and 93.4%.Compared to advanced methods,the LSD-DETR can attain superior precision with fewer parameters,which enables accurate real-time object detection of multi-scale ships in SAR images.展开更多
基金General Program of the National Natural Science Foundation of China (82474352)Natural Science Foundation of Hunan Province (2023JJ60124)。
文摘Objective To predict the potential targets of Qingfu Juanbi Decoction(青附蠲痹汤,QFJBD)in treating rheumatoid arthritis(RA)using an improved Transformer model and investigate the network pharmacological mechanisms underlying QFJBD’s therapeutic effects on RA.Methods First,a traditional Chinese medicine herb-target interaction(TCMHTI)model was constructed to predict herb-target interactions based on Transformer improvement.The per-formance of the TCMHTI model was evaluated against baseline models using three metrics:area under the receiver operating characteristic curve(AUC),precision-recall curve(PRC),and accuracy.Subsequently,a protein-protein interaction(PPI)network was built based on the predicted targets,with core targets identified as the top nine nodes ranked by degree val-ues.Gene Ontology(GO)functional and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses were performed using the targets predicted by TCMHTI and the targets identified through network pharmacology method for comparison.Then,the re-sults were compared.Finally,the core targets predicted by TCMHTI were validated through molecular docking and literature review.Results The TCMHTI model achieved an AUC of 0.883,PRC of 0.849,and accuracy of 0.818,predicting 49 potential targets for QFJBD in RA treatment.Nine core targets were identified:tumor necrosis factor(TNF)-α,interleukin(IL)-1β,IL-6,IL-10,IL-17A,cluster of differentia-tion 40(CD40),cytotoxic T-lymphocyte-associated protein 4(CTLA4),IL-4,and signal trans-ducer and activator of transcription 3(STAT3).The enrichment analysis demonstrated that the TCMHTI model predicted 49 targets and enriched more pathways directly associated with RA,whereas classical network pharmacology identified 64 targets but enriched pathways showing weaker relevance to RA.Molecular docking demonstrated that the active molecules in QFJBD exhibit favorable binding energy with RA targets,while literature research further revealed that QFJBD can treat RA through 9 core targets.Conclusion The TCMHTI model demonstrated greater accuracy than traditional network pharmacology methods,suggesting QFJBD exerts therapeutic effects on RA by regulating tar-gets like TNF-α,IL-1β,and IL-6,as well as multiple signaling pathways.This study provides a novel framework for bridging traditional herbal knowledge with precision medicine,offering actionable insights for developing targeted TCM therapies against diseases.
文摘Recently,a multitude of techniques that fuse deep learning with Retinex theory have been utilized in the field of low-light image enhancement,yielding remarkable outcomes.Due to the intricate nature of imaging scenarios,including fluctuating noise levels and unpredictable environmental elements,these techniques do not fully resolve these challenges.We introduce an innovative strategy that builds upon Retinex theory and integrates a novel deep network architecture,merging the Convolutional Block Attention Module(CBAM)with the Transformer.Our model is capable of detecting more prominent features across both channel and spatial domains.We have conducted extensive experiments across several datasets,namely LOLv1,LOLv2-real,and LOLv2-sync.The results show that our approach surpasses other methods when evaluated against critical metrics such as Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM).Moreover,we have visually assessed images enhanced by various techniques and utilized visual metrics like LPIPS for comparison,and the experimental data clearly demonstrate that our approach excels visually over other methods as well.
基金supported by the Key Laboratory of Forensic Science and Technology at College of Sichuan Province(2023YB04).
文摘Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels as distance increases. In illumination restoration, we used Unet++ with multi-level skip connections to better integrate semantic information at different scales. The designed Illumination Fusion Dual Self-Attention (IF-DSA) module embeds multi-scale dilated convolutions to achieve spatial self-attention. This module captures long-range spatial semantic relationships within acceptable computational complexity. Experimental results on the Low-Light (LOL) dataset show that Retexformer+ outperforms other State-Of-The-Art (SOTA) methods in both quantitative and qualitative evaluations, with the computational complexity increased to an acceptable 51.63 G FLOPS. On the LOL_v1 dataset, RetinexFormer+ shows an increase of 1.15 in Peak Signal-to-Noise Ratio (PSNR) and a decrease of 0.39 in Root Mean Square Error (RMSE). On the LOL_v2_real dataset, the PSNR increases by 0.42 and the RMSE decreases by 0.18. Experimental results on the Exdark dataset show that Retexformer+ can effectively enhance real-scene images and maintain their semantic information.
基金supported by the Key Research and Development Program of Shaanxi Province(No.2024SF-YBXM-567)the National Natural Science Foundation of China(Nos.21976045,22076112)the China Scholarship Council(CSC)Scholarship(No.202308610123).
文摘As antibiotic pollutants cannot be incompletely removed by conventional wastewater treatment plants,ultraviolet(UV)based advanced oxidation processes(AOPs)such as UV/persulfate(UV/PS)and UV/chlorine are increasingly concerned for the effective removal of antibiotics from wastewaters.However,the specific mechanisms involving degradation kinetics and transformation mechanisms are not well elucidated.Here we report a detailed examination of SO_(4)•−/Cl•-mediated degradation kinetics,products,and toxicities of sulfathiazole(ST),sarafloxacin(SAR),and lomefloxacin(LOM)in the two processes.Both SO_(4)•−/Cl•-mediated transformation kinetics were found to be dependent on pH(P<0.05),which was attributed to the disparate reactivities of their individual dissociated forms.Based on competition kinetic experiments and matrix calculations,the cationic forms(H_(2)ST^(+),H_(2)SAR^(+),and H_(2)LOM^(+))were more highly reactive towards SO_(4)•−in most cases,while the neutral forms(e.g.,HSAR^(0)and HLOM^(0))reacted the fastest with Cl•for the most of the antibiotics tested.Based on the identification of 31 key intermediates using tandem mass spectrometry,these reactions generated different products,of which the majority still retained the core chemical structure of the parent compounds.The corresponding diverse transformation pathways were proposed,involving S−N breaking,hydroxylation,defluorination,and chlorination reactions.Furthermore,the toxicity changes of their reaction solutions as well as the toxicity of each intermediate were evaluated by the vibrio fischeri and ECOSAR model,respectively.Many primary by-products were proven to be more toxic than the parent chemicals,raising the wider issue of extended potency for these compounds with regards to their ecotoxicity.These results have implications for assessing the degradative fate and risk of these chemicals during the AOPs.
基金supported by the Humanities and Social Sciences Foundation of the Ministry of Education(22YJC910011)the China Postdoctoral Science Foundation(2023M733444)the Key Research and Development Program in Artificial Intelligence of Liaoning Province(2023JH26/10200012).
文摘This study introduces an innovative computational framework leveraging the transformer architecture to address a critical challenge in chemical process engineering:predicting and optimizing light olefin yields in industrial methanol-to-olefins(MTO)processes.Our approach integrates advanced machine learning techniques with chemical engineering principles to tackle the complexities of non-stationary,highly volatile production data in large-scale chemical manufacturing.The framework employs the maximal information coefficient(MIC)algorithm to analyze and select the significant variables from MTO process parameters,forming a robust dataset for model development.We implement a transformer-based time series forecasting model,enhanced through positional encoding and hyperparameter optimization,significantly improving predictive accuracy for ethylene and propylene yields.The model's interpretability is augmented by applying SHapley additive exPlanations(SHAP)to quantify and visualize the impact of reaction control variables on olefin yields,providing valuable insights for process optimization.Experimental results demonstrate that our model outperforms traditional statistical and machine learning methods in accuracy and interpretability,effectively handling nonlinear,non-stationary,highvolatility,and long-sequence data challenges in olefin yield prediction.This research contributes to chemical engineering by providing a novel computerized methodology for solving complex production optimization problems in the chemical industry,offering significant potential for enhancing decisionmaking in MTO system production control and fostering the intelligent transformation of manufacturing processes.
基金supported by National Natural Science Foundation of China(Grant No.52001114)Program for Science and Technology Innovation Talents in Universities of Henan Province(No.23HASTIT022 and 2021GGJS064)Scientific Research Fund of State Key Laboratory of Materials Processing and Die and Mould Technology(Grant No.P2023-005).
文摘(Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4))_(100−x)Al_(x)(x=0,0.1,0.2,0.3,0.4 at.%)lightweight high-entropy alloys with different contents of Al were prepared via vacuum non-consumable arc melting method.Effects of adding varying Al contents on phase constitution,microstructure characteristics and mechanical properties of the lightweight alloys were studied.Results show that Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4)alloy is composed of body-centered cubic(BCC)phase and C15 Laves phase,while(Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4))_(100−x)Al_(x)lightweight high-entropy alloys by addition of Al are composed of BCC phase and C14 Laves phase.Addition of Al into Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4)lightweight high-entropy alloy can transform C15 Laves phase to C14 Laves phase.With further addition of Al,BCC phase of alloys is significantly refined,and the volume fraction of C14 Laves phase is raised obviously.Meanwhile,the dimension of BCC phase in the alloy by addition of 0.3 at.%Al is the most refined and that of Laves phase is also obviously refined.Adding Al to Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4)alloy can not only reduce the density of(Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4))_(100−x)Al_(x)alloy,but also improve strength of(Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4))_(100−x)Al_(x)alloy.As Al content increased from 0 to 0.4 at.%,the density of the alloy decreased from 6.22±0.875 to 5.79±0.679 g cm^(−3).Moreover,compressive strength of the alloy by 0.3 at.%Al addition is the highest to 1996.9 MPa,while fracture strain of the alloy is 16.82%.Strength improvement of alloys mainly results from microstructure refinement and precipitation of C14 Laves by Al addition into Ti_(8)Zr_(6)Nb_(4)V_(5)Cr_(4)lightweight high-entropy alloy.
基金supported by the Yuxiu Innovation Project of NCUT(Grant No.2024NCUTYXCX211).
文摘Purpose–This paper aims to provide a comprehensive analysis of the strategic adjustments in China’s transportation structure,with a particular focus on the pivotal role of railway freight and its integration into the modern logistics system.It seeks to address the need for a more nuanced understanding of the“road to rail”policy,emphasizing the importance of intermodal collaboration and service of fragmented market demands.Design/methodology/approach–The study employs a transport economics perspective to evaluate the achievements and shortcomings of China’s transportation structure optimization.It bases its assessment of the current state of railway freight logistics,multi-modal transportation and the broader implications for the transportation service market on data analysis.The methodology includes a review of existing policies,an examination of industry practices and a comparative analysis with global trends in railway logistics.Findings–The research underscores the importance of focusing on the development of non-bulk materials,noting the insufficiency in the development of China’s rail multi-modal transportation and highlighting the instructive value of successful cases in open-top container road-rail intermodal transportation.The study posits that the railway sector must enhance cooperation with other market entities,aligning with the lead enterprises in the logistics chain that are characterized by speed,high value and strong coordination capabilities,in order to better serve the transportation market.This approach moves away from a reliance on the railway’s own capabilities alone.Originality/value–This paper offers original insights into the transformation of railway freight in China,contributing to the body of knowledge on transportation economics and logistics.It provides valuable recommendations for policymakers and industry practitioners,emphasizing the strategic importance of railway logistics in the context of China’s economic development and intense competition in the supply chain.The value of the article lies in its comprehensive understanding of the complexities involved in the adjustment of transportation structures,providing direction for the market-oriented reform of China’s railway freight sector.
基金Supported by the Optimisation Theory and Algorithm Research Team(Grant No.23kytdzd004)University Science Research Project of Anhui Province(Grant No.2024AH050631)the General Programs for Young Teacher Cultivation of Educational Commission of Anhui Province(Grant No.YQYB2023090).
文摘In this paper,we propose a new full-Newton step feasible interior-point algorithm for the special weighted linear complementarity problems.The proposed algorithm employs the technique of algebraic equivalent transformation to derive the search direction.It is shown that the proximity measure reduces quadratically at each iteration.Moreover,the iteration bound of the algorithm is as good as the best-known polynomial complexity for these types of problems.Furthermore,numerical results are presented to show the efficiency of the proposed algorithm.
基金National Nature Science Foundation of China(No.U24A20589)National Key Research and Development Program of China(No.2023YFB3905504)+1 种基金Innovation Team of the Ministry of Education of China(No.8091B042227)Innovation Group of Sichuan Natural Science Foundation(No.2023NSFSC1974).
文摘Recently,there has been a widespread application of deep learning in object detection with Synthetic Aperture Radar(SAR).The current algorithms based on Convolutional Neural Networks(CNN)often achieve good accuracy at the expense of more complex model structures and huge parameters,which poses a great challenge for real-time and accurate detection of multi-scale targets.To address these problems,we propose a lightweight real-time SAR ship object detector based on detection transformer(LSD-DETR)in this study.First,a lightweight backbone network LCNet containing a stem module and inverted residual structure is constructed to balance the inference speed and detection accuracy of model.Second,we design a transformer encoder with Cascaded Group Attention(CGA Encoder)to enrich the feature information of small targets in SAR images,which makes detection of small-sized ships more precise.Third,an efficient cross-scale feature fusion pyramid module(C3Het-FPN)is proposed through the lightweight units(C3Het)and the introduction of the weighted bidirectional feature pyramid(BiFPN)structure,which realizes the adaptive fusion of multi-scale features with fewer parameters.Ablation experiments and comparative experiments demonstrate the effectiveness of LSD-DETR.The model parameter of LSD-DETR is 8.8 M(only 20.6%of DETR),the model’s FPS reached 43.1,the average detection accuracy mAP50 on the SSDD and HRSID datasets reached 97.3%and 93.4%.Compared to advanced methods,the LSD-DETR can attain superior precision with fewer parameters,which enables accurate real-time object detection of multi-scale ships in SAR images.
