Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-...Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.展开更多
Altermagnetism,a recently identified class of collinear magnetism,combines key features of antiferromagnets and ferromagnets.Despite having zero net magnetization,altermagnetic materials exhibit anomalous transport ef...Altermagnetism,a recently identified class of collinear magnetism,combines key features of antiferromagnets and ferromagnets.Despite having zero net magnetization,altermagnetic materials exhibit anomalous transport effects,including the anomalous Hall,Nernst,and thermal Hall effects,as well as magneto-optical Kerr and Faraday effects.These phenomena,previously thought unique to ferromagnets,are dictated by symmetry,as confirmed by density functional theory(DFT)calculations.However,an effective model-based approach to verify these symmetry constraints remains unavailable.In this Letter,we construct a k·ρ model for d-wave altermagnets CuX_(2)(X=F,Cl)using spin space group representations and apply it to calculate the anomalous Hall effect.The symmetry-imposed transport properties predicted by the model are in agreement with the DFT results,providing a foundation for further investigation into symmetry-restricted transport phenomena in altermagnetic materials.展开更多
Microfinance institutions in Kenya play a unique role in promoting financial inclusion,loans,and savings provision,especially to low-income individuals and small-scale entrepreneurs.However,despite their benefits,most...Microfinance institutions in Kenya play a unique role in promoting financial inclusion,loans,and savings provision,especially to low-income individuals and small-scale entrepreneurs.However,despite their benefits,most of their products and programs in Machakos County have been reducing due to re-payment challenges,threatening their financial ability to extend further credit.This could be attributed to ineffective credit scoring models which are not able to establish the nuanced non-linear repayment behavior and patterns of the loan applicants.The research objective was to enhance credit risk scoring for microfinance institutions in Machakos County using supervised machine learning algorithms.The study adopted a mixed research design under supervised machine learning approach.It randomly sampled 6771 loan application ac-count records and repayment history.Rstudio and Python programming lan-guages were deployed for data pre-processing and analysis.Logistic regression algorithm,XG Boosting and the random forest ensemble method were used.Metric evaluations used included the performance accuracy,Area under the Curve and F1-Score.Based on the study findings:XG Boosting was the best performer with 83.3%accuracy and 0.202 Brier score.Development of legal framework to govern ethical and open use of machine learning assessment was recommended.A similar research but using different machine learning al-gorithms,locations,and institutions,to ascertain the validity,reliability and the generalizability of the study findings was recommended for further re-search.展开更多
Vascular wilt caused by Fusarium oxysporum f.sp.batatas(Fob)is a devastating disease threatening global sweet potato production.To elucidate Fob’s pathogenicitymechanisms and informeffective control strategies,we gen...Vascular wilt caused by Fusarium oxysporum f.sp.batatas(Fob)is a devastating disease threatening global sweet potato production.To elucidate Fob’s pathogenicitymechanisms and informeffective control strategies,we generated a green fluorescent protein(GFP)-tagged Fob strain to track infection dynamics in sweet potato susceptible cultivar Xinzhonghua and resistant cultivar Xiangshu75-55,respectively.Through cytological observation,we found in the susceptible Xinzhonghua,Fob predominantly colonized stem villi,injured root growth points,and directly invaded vascular bundles through stemwounds.Spore germination peaked at 2-3 h post-inoculation(hpi),followed by cyclical mycelial expansion and sporulation within vascular tissues with sustaining infection.In contrast,the resistant Xiangshu75-55 exhibited strong suppression of Fob:spores rarely germinated in vascular bundles or on trichomes by 3 hpi,and mature hyphae were absent in stems at 24 hpi.Quantitative reverse transcription PCR(qRT-PCR)confirmed significantly higher Fob biomass in Xinzhonghua than in Xiangshu75-55 by 16 hpi.Additionally,transcriptional profiling revealed distinct pathogen-host interactions during the compatible and incompatible reactions.In Xinzhonghua,Fob virulence genes FobPGX1,FobICL1,FobCTF2,FobFUB5 and FobFUB6 were upregulated within 16 hpi.Conversely,host defense genes IbMAPKK9,IbWRKY61,IbWRKY75,IbSWEET10,IbBBX24 and IbPIF4 were activated in Xiangshu75-55 during the same period.This study provides spatiotemporal cytological and molecular insights into Fob pathogenicity and host resistance,offering a foundation for early disease detection and improved Fusarium wilt management in sweet potato.展开更多
This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolu...This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).展开更多
Wheat powdery mildew caused by Blumeria graminis f.sp.tritici(Bgt)is an important disease worldwide.Detection of latent infection of leaves by the pathogen in late autumn is valuable for estimating the inoculum potent...Wheat powdery mildew caused by Blumeria graminis f.sp.tritici(Bgt)is an important disease worldwide.Detection of latent infection of leaves by the pathogen in late autumn is valuable for estimating the inoculum potential to assess disease risks in the spring.We developed a new tool for rapid detection and quantification of latent infection of seedlings by the pathogen.The method was based on recombinase polymerase amplification(RPA)coupled with an end-point detection via lateral flow device(LFD).The limit of detection is 100 agμL^(-1)of Bgt DNA,without noticeable interference from either other common wheat pathogens or wheat material(Triticum aestivum).It was evaluated on wheat seedlings for this accuracy and sensitivity in detecting latent infection of Bgt.We further extended this RPALFD assay to estimate the level of latent infection by Bgt based on imaging analysis.There was a strong correlation between the image-based and real-time PCR assay estimates of Bgt DNA.The present results suggested that this new tool can provide rapid and accurate quantification of Bgt in latently infected leaves and can be further development as an on-site monitoring tool.展开更多
Fusarium wilt, caused by Fusarium oxyporum f. sp. cubense(Foc), is the most serious disease affecting banana production.To clarify the distribution of the Foc races in Fujian Province of China, 79 soil samples were co...Fusarium wilt, caused by Fusarium oxyporum f. sp. cubense(Foc), is the most serious disease affecting banana production.To clarify the distribution of the Foc races in Fujian Province of China, 79 soil samples were collected from four regions of Zhangzhou City, the primary banana production area in Fujian. We isolated and identified 12 Foc strains based on internal transcribed spacer(ITS) sequence analysis, PCR amplification by using Foc-specific primers and pathogenicity assays.Our analysis indicated that 11 isolates belong to Foc race 1, and 1 isolate belongs to the Foc tropical species race 4(TR4).Although TR4 has previously been reported to occur in primary banana-producing provinces, such as Hainan, Guangxi,and Guangdong of China, this is the first report of TR4 isolated from the soil in Fujian Province. Monitoring the presence of Foc, in particular, the TR4 strains in the soil, is the basic strategy to prevent and control Fusarium wilt.展开更多
The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To ...The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.展开更多
基金supported by the National Natural Science Foundation of China(62272078)Chongqing Natural Science Foundation(CSTB2023NSCQ-LZX0069)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202300210)
文摘Dear Editor,This letter presents a novel latent factorization model for high dimensional and incomplete (HDI) tensor, namely the neural Tucker factorization (Neu Tuc F), which is a generic neural network-based latent-factorization-of-tensors model under the Tucker decomposition framework.
基金supported by the National Natural Science Foundation of China(Grant No.12274117)the Natural Science Foundation of Henan(Grant No.242300421214)+4 种基金the Program for Innovative Research Team(in Science and Technology)in the University of Henan Province(Grant No.24IRTSTHN025)the Open Fund of Guangdong Provincial Key Laboratory of Nanophotonic Manipulation(No.202502)Guangdong S&T Program(No.2023B1212010008)the High-Performance Computing Center of Henan Normal Universitysupported by the U.S.DOE,Office of Science(Grant No.DE-FG02-05ER46237)。
文摘Altermagnetism,a recently identified class of collinear magnetism,combines key features of antiferromagnets and ferromagnets.Despite having zero net magnetization,altermagnetic materials exhibit anomalous transport effects,including the anomalous Hall,Nernst,and thermal Hall effects,as well as magneto-optical Kerr and Faraday effects.These phenomena,previously thought unique to ferromagnets,are dictated by symmetry,as confirmed by density functional theory(DFT)calculations.However,an effective model-based approach to verify these symmetry constraints remains unavailable.In this Letter,we construct a k·ρ model for d-wave altermagnets CuX_(2)(X=F,Cl)using spin space group representations and apply it to calculate the anomalous Hall effect.The symmetry-imposed transport properties predicted by the model are in agreement with the DFT results,providing a foundation for further investigation into symmetry-restricted transport phenomena in altermagnetic materials.
文摘Microfinance institutions in Kenya play a unique role in promoting financial inclusion,loans,and savings provision,especially to low-income individuals and small-scale entrepreneurs.However,despite their benefits,most of their products and programs in Machakos County have been reducing due to re-payment challenges,threatening their financial ability to extend further credit.This could be attributed to ineffective credit scoring models which are not able to establish the nuanced non-linear repayment behavior and patterns of the loan applicants.The research objective was to enhance credit risk scoring for microfinance institutions in Machakos County using supervised machine learning algorithms.The study adopted a mixed research design under supervised machine learning approach.It randomly sampled 6771 loan application ac-count records and repayment history.Rstudio and Python programming lan-guages were deployed for data pre-processing and analysis.Logistic regression algorithm,XG Boosting and the random forest ensemble method were used.Metric evaluations used included the performance accuracy,Area under the Curve and F1-Score.Based on the study findings:XG Boosting was the best performer with 83.3%accuracy and 0.202 Brier score.Development of legal framework to govern ethical and open use of machine learning assessment was recommended.A similar research but using different machine learning al-gorithms,locations,and institutions,to ascertain the validity,reliability and the generalizability of the study findings was recommended for further re-search.
基金supported by the following grants,Earmarked fund for CARS-10-Sweet potato,High-quality development of agriculture“5511”collaborative innovation project(XTCXGC2021005)Natural Science Foundation of Fujian province(2021J01495)+1 种基金Basic Scientific Research Special Project for Fujian Provincial Public Research Institutes(2021R1031008)Science and Technology Innovation Team of Fujian Academy of Agricultural Sciences(CXTD2021012-1).
文摘Vascular wilt caused by Fusarium oxysporum f.sp.batatas(Fob)is a devastating disease threatening global sweet potato production.To elucidate Fob’s pathogenicitymechanisms and informeffective control strategies,we generated a green fluorescent protein(GFP)-tagged Fob strain to track infection dynamics in sweet potato susceptible cultivar Xinzhonghua and resistant cultivar Xiangshu75-55,respectively.Through cytological observation,we found in the susceptible Xinzhonghua,Fob predominantly colonized stem villi,injured root growth points,and directly invaded vascular bundles through stemwounds.Spore germination peaked at 2-3 h post-inoculation(hpi),followed by cyclical mycelial expansion and sporulation within vascular tissues with sustaining infection.In contrast,the resistant Xiangshu75-55 exhibited strong suppression of Fob:spores rarely germinated in vascular bundles or on trichomes by 3 hpi,and mature hyphae were absent in stems at 24 hpi.Quantitative reverse transcription PCR(qRT-PCR)confirmed significantly higher Fob biomass in Xinzhonghua than in Xiangshu75-55 by 16 hpi.Additionally,transcriptional profiling revealed distinct pathogen-host interactions during the compatible and incompatible reactions.In Xinzhonghua,Fob virulence genes FobPGX1,FobICL1,FobCTF2,FobFUB5 and FobFUB6 were upregulated within 16 hpi.Conversely,host defense genes IbMAPKK9,IbWRKY61,IbWRKY75,IbSWEET10,IbBBX24 and IbPIF4 were activated in Xiangshu75-55 during the same period.This study provides spatiotemporal cytological and molecular insights into Fob pathogenicity and host resistance,offering a foundation for early disease detection and improved Fusarium wilt management in sweet potato.
基金supported by the Shanxi Agricultural University Science and Technology Innovation Enhancement Project。
文摘This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS).
基金supported by the funding from the National Natural Science Foundation of China(32072359)。
文摘Wheat powdery mildew caused by Blumeria graminis f.sp.tritici(Bgt)is an important disease worldwide.Detection of latent infection of leaves by the pathogen in late autumn is valuable for estimating the inoculum potential to assess disease risks in the spring.We developed a new tool for rapid detection and quantification of latent infection of seedlings by the pathogen.The method was based on recombinase polymerase amplification(RPA)coupled with an end-point detection via lateral flow device(LFD).The limit of detection is 100 agμL^(-1)of Bgt DNA,without noticeable interference from either other common wheat pathogens or wheat material(Triticum aestivum).It was evaluated on wheat seedlings for this accuracy and sensitivity in detecting latent infection of Bgt.We further extended this RPALFD assay to estimate the level of latent infection by Bgt based on imaging analysis.There was a strong correlation between the image-based and real-time PCR assay estimates of Bgt DNA.The present results suggested that this new tool can provide rapid and accurate quantification of Bgt in latently infected leaves and can be further development as an on-site monitoring tool.
基金supported by the National Natural Science Foundation of China (31601583)the Natural Science Foundation of Fujian Province, China (2016J01113)+1 种基金the Young Teacher Education Research Project of Fujian Province, China (JAT160178)the Fujian Agriculture and Forestry University Outstanding Youth Scientific Research Project, China (xjq201625)
文摘Fusarium wilt, caused by Fusarium oxyporum f. sp. cubense(Foc), is the most serious disease affecting banana production.To clarify the distribution of the Foc races in Fujian Province of China, 79 soil samples were collected from four regions of Zhangzhou City, the primary banana production area in Fujian. We isolated and identified 12 Foc strains based on internal transcribed spacer(ITS) sequence analysis, PCR amplification by using Foc-specific primers and pathogenicity assays.Our analysis indicated that 11 isolates belong to Foc race 1, and 1 isolate belongs to the Foc tropical species race 4(TR4).Although TR4 has previously been reported to occur in primary banana-producing provinces, such as Hainan, Guangxi,and Guangdong of China, this is the first report of TR4 isolated from the soil in Fujian Province. Monitoring the presence of Foc, in particular, the TR4 strains in the soil, is the basic strategy to prevent and control Fusarium wilt.
文摘The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.
