The aim of this study was to investigate the amount of heterosis and performance of faba bean synthetic cultivars compared to line cultivars under semi-arid conditions. Five inbred lines in at least S6 generation were...The aim of this study was to investigate the amount of heterosis and performance of faba bean synthetic cultivars compared to line cultivars under semi-arid conditions. Five inbred lines in at least S6 generation were developed and used to develop F1s hybrid (in all possible combination excluding reciprocal), lines mixtures (Syn-0) and synthetic generations of Syn-1. Evaluation of the entries showed the lines to have high general and specific combining ability, high yield and high average degree of cross-fertilization (0.36);hetrosis relative to mid-parent for yield was 67%. Lines mixture from four inbred lines (Hudeiba/93, Bassabier, Ed-Damar and Shabah) gave the highest yield of 3.40 t/ha for Syn-0 and 3.96 t/ha for Syn-1. Compared to the average yield t/ha of the pure stand of the four lines (3.11 t/ha), the increase in yield of was 9% in sy-0 and 27% in Syn-1. Compared to the individual yield t/ha of the pure stand of the lines, the performance of Syn-0 surpassed that of the individual pure stands of the linesby 14% for Hudiaba/93 and Bassabier and 4% for Ed-Damar and 7% for Shabah, whereas the increase in performance of Syn-1 compared to pure stand of the lines was 32%, 25% and 21%, respectively. The results confirm the previous knowledge on yield increase with successive syn-generations in faba bean due to the effects of heterogeneity and heterozygosity. Such results could be used as a base for an effective breeding program for improvement of yield of faba bean grown under the semi-arid zones.展开更多
Since 2018,under the guidance of the China Nonwovens&Industrial Textiles Association,with the support of the majority of member companies in the industry,the Geosynthetics Branch of China Nonwovens&Industrial ...Since 2018,under the guidance of the China Nonwovens&Industrial Textiles Association,with the support of the majority of member companies in the industry,the Geosynthetics Branch of China Nonwovens&Industrial Textiles Association has made some achievements in promoting the development of the industry and promoting the progress of enterprises.展开更多
On 22 November,the 3rd EurAsian Geosynthetics Symposium(EAGS-2024)was held in Tai'an City,Shandong Province.Liu Fengmei,Vice Mayor of Tai'an People's Government,introduced that Tai'an's geosyntheti...On 22 November,the 3rd EurAsian Geosynthetics Symposium(EAGS-2024)was held in Tai'an City,Shandong Province.Liu Fengmei,Vice Mayor of Tai'an People's Government,introduced that Tai'an's geosynthetics industry has had production capacity in the 1980s,and the production products involve geogrid,geotextile,geomembrane,geonet and so on.Geosynthetics has become a beautiful industrial business card of Tai'an.展开更多
Geosynthetics,as an important category of industrial textiles,are widely used in water conservancy,highways,railways,ports,construction,etc.,and play an important role in national infrastructure construction and the d...Geosynthetics,as an important category of industrial textiles,are widely used in water conservancy,highways,railways,ports,construction,etc.,and play an important role in national infrastructure construction and the development of the national economy.“Industrial textiles,owing to high technology,wide application,strong driving effect,and large market potential,are becoming an important driving force for economic growth and transformation of China’s textile industry in the new era.”Said Mr.Li Lingshen,Vice President of China National Textile and Apparel Council&President of China Nonwovens&Industrial Textiles Association(CNITA).Moreover,as Mr.Pierre Wiertz,General Manager of EDANA,once stated that since the Chinese Belt and Road Initiative was launched,geotextiles are even more topical and promising as a market segment.It is believed that the global geosynthetics industry will usher in a new round of development opportunities.展开更多
Given the broad applicability of carbazole structural moieties in materials science and medicinal chemistry,significant efforts have been devoted to developing efficient synthetic catalytic methodologies to access thi...Given the broad applicability of carbazole structural moieties in materials science and medicinal chemistry,significant efforts have been devoted to developing efficient synthetic catalytic methodologies to access this valuable scaffold.Catalyzed direct Csp^(2)-H functionalization provides an effective and costefficient approach to synthesizing carbazoles from simple and readily available starting materials,ensuring a promising path characterized by excellent atom and step economy.This review highlights the substantial progress made in the last 10 years in advancing catalytic Csp^(2)-H functionalization techniques for synthesizing carbazoles.展开更多
Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propo...Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propose a synthetic speech detection model called TFTransformer,which integrates both local and global features to enhance detection capabilities by effectively modeling local and global dependencies.Structurally,the model is divided into two main components:a front-end and a back-end.The front-end of the model uses a combination of SincLayer and two-dimensional(2D)convolution to extract high-level feature maps(HFM)containing local dependency of the input speech signals.The back-end uses time-frequency Transformer module to process these feature maps and further capture global dependency.Furthermore,we propose TFTransformer-SE,which incorporates a channel attention mechanism within the 2D convolutional blocks.This enhancement aims to more effectively capture local dependencies,thereby improving the model’s performance.The experiments were conducted on the ASVspoof 2021 LA dataset,and the results showed that the model achieved an equal error rate(EER)of 3.37%without data augmentation.Additionally,we evaluated the model using the ASVspoof 2019 LA dataset,achieving an EER of 0.84%,also without data augmentation.This demonstrates that combining local and global dependencies in the time-frequency domain can significantly improve detection accuracy.展开更多
Medicinal plants serve as valuable sources of bioactive compounds with critical applications across pharmaceutical,agricultural,and industrial sectors.Compared to chemical synthesis and plant extraction,synthetic biol...Medicinal plants serve as valuable sources of bioactive compounds with critical applications across pharmaceutical,agricultural,and industrial sectors.Compared to chemical synthesis and plant extraction,synthetic biology offers a green,efficient,and sustainable alternative for producing bioactive compounds,which represents a state of art technology.However,this technology still faces several challenges,including overly long metabolic pathways,inadequate catalytic efficiency of key enzymes in the pathway,and incompatibility between gene elements and host cells,leading to low yields of target bioactive compounds.The development and application of regulatory tools in synthetic biology hold great promise for overcoming these obstacles.This review first summarizes the classification and biosynthesis of bioactive compounds based on structural types.Subsequently,recent advancements are outlined in regulation tools and their application in the heterologous production of bioactive compounds.This review aims to establish a foundation for the efficient production of bioactive compounds based on microbial cell factories.This not only has significant practical implications for reducing the resource consumption and environmental impact of traditional production methods,but also highlights the central role of synthetic biology in promoting the sustainable production of bioactive compounds derived from medicinal plants.展开更多
A trace analytical method based on solid-phase extraction gas chromatography-tandem mass spectrometry(SPE–GC–MS/MS)was developed for the rapid detection of 256 semi-volatile organic compounds(SVOCs),including 25 pol...A trace analytical method based on solid-phase extraction gas chromatography-tandem mass spectrometry(SPE–GC–MS/MS)was developed for the rapid detection of 256 semi-volatile organic compounds(SVOCs),including 25 polycyclic aromatic hydrocarbons(PAHs),70 polychlorinated biphenyls(PCBs),123 pesticides,20 phthalate esters(PAEs),4 organophosphate esters(OPEs),9 synthetic musks(SMs),and 5 UV filters(UVs)in water.No-tably,this method provided a decent linearity of calibration standards(R^(2)>0.999),excellent method limits of quantification(MLOQs)(0.12–11.41 ng/L),satisfactory matrix spiking recovery rates(60.4%–126%),and high precision(intra-day relative standard deviations(RSDs):1.0%–10.0%,inter-day RSDs:3.0%–15.0%,and inter-week RSDs:3.4%–15.7%),making it suitable for trace-level studies.Statistical analysis revealed that SVOCs with higher volatility exhibited enhanced recovery rates.Validation of the methodology involved analyzing SVOCs in real spring water and river water samples.Twenty-seven SVOCs were detected in spring water and 58 in river water,with an average concentration of 631.73 and 16,095 ng/L,respectively.Among the detected SVOCs,PAEs constituted the predominant proportion.This study underscored the presence of SVOCs contamination specifi-cally within the spring water,although SVOCs concentrations in river water were significantly greater than those found in spring water.In summary,this sensitive method based on SPE–GC–MS/MS was successfully developed and validated for the rapid analysis of a diverse array of 256 SVOCs at trace levels in water,including not only the traditional highly valued PAHs,PCBs,pesticides,and PAEs,but also the emerging OPEs,UVs,and SMs.展开更多
Driven by rapid global population growth and evolving consumer demands for high-quality nutrition and enhanced sensory enjoyment,the food industry is advancing towards healthier,more efficient,more sustainable,and mor...Driven by rapid global population growth and evolving consumer demands for high-quality nutrition and enhanced sensory enjoyment,the food industry is advancing towards healthier,more efficient,more sustainable,and more personalized targets.Notably,this transformation is being accelerated by the convergence of advanced biotechnology(BT)and information technology(IT)in food production systems.This review examines pivotal technologies shaping future food systems,highlighting two biotechnological frontiers and two artificial intelligence-driven domains.Specifically,evolutionary engineering enhances microbial resilience and production efficiency while synthetic biology expands the diversity of food sources and enables personalized food ingredient design,both of which advance sustainable food production.Meanwhile,artificial intelligence promotes food flavor innovation by integrating existing knowledge in molecular structure,flavoromics and consumer preferences.Furthermore,insights into gut microbiota,along with the development of omics techniques and wearable biosensors are unlocking attainable solutions for precision nutrition.Collectively,the trilateral convergence of BT,IT and food technology,embodying a harmonious balance,is reshaping the paradigm of future food manufacturing.展开更多
Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and ...Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and artifacts.To address this challenge,this study leverages Denoising Diffusion Probabilistic Models(DDPMs)to generate high-quality synthetic crack images,enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation.The proposed framework involves a two-stage pipeline:first,DDPMs are used to synthesize high-fidelity crack images that capture fine structural details.Second,these generated samples are combined with real data to train segmentation networks,thereby improving accuracy and robustness in crack detection.Compared with GAN-based approaches,DDPM achieved the best fidelity,with the highest Structural Similarity Index(SSIM)(0.302)and lowest Learned Perceptual Image Patch Similarity(LPIPS)(0.461),producing artifact-free images that preserve fine crack details.To validate its effectiveness,six segmentation models were tested,among which LinkNet consistently achieved the best performance,excelling in both region-level accuracy and structural continuity.Incorporating DDPM-augmented data further enhanced segmentation outcomes,increasing F1 scores by up to 1.1%and IoU by 1.7%,while also improving boundary alignment and skeleton continuity compared with models trained on real images alone.Experiments with varying augmentation ratios showed consistent improvements,with F1 rising from 0.946(no augmentation)to 0.957 and IoU from 0.897 to 0.913 at the highest ratio.These findings demonstrate the effectiveness of diffusion-based augmentation for complex crack detection in structural health monitoring.展开更多
Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows rais...Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.展开更多
Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observat...Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.展开更多
α-Trifluoromethyl ketones are a class of useful compounds with versatile applications.Their synthetic application via the transformation of the C—F bonds is of particular interest by allowing the synthesis of organi...α-Trifluoromethyl ketones are a class of useful compounds with versatile applications.Their synthetic application via the transformation of the C—F bonds is of particular interest by allowing the synthesis of organic compounds with diverse structures.Herein,the advances in the research areas ofα-trifluoromethyl ketone synthesis and their defluorination reactions are reviewed.Discussion on the mechanisms of the typical reactions has also been provided,in hope of affording some guides to the chemistry ofα-trifluoromethyl ketones in the synthetic methods toward themselves and their derivatives.展开更多
With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electro...With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electronic instruments.Therefore,the design and preparation of electromagnetic absorbing composites represent an efficient approach to mitigate the current hazards of electromagnetic radiation.However,traditional electromagnetic absorbers are difficult to satisfy the demands of actual utilization in the face of new challenges,and emerging absorbents have garnered increasing attention due to their structure and performance-based advantages.In this review,several emerging composites of Mxene-based,biochar-based,chiral,and heat-resisting are discussed in detail,including their synthetic strategy,structural superiority and regulation method,and final optimization of electromagnetic absorption ca-pacity.These insights provide a comprehensive reference for the future development of new-generation electromagnetic-wave absorption composites.Moreover,the potential development directions of these emerging absorbers have been proposed as well.展开更多
Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike sc...Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike scatterometers and al-timeters,space-borne SAR offers high-resolution images of the ocean,regardless of weather conditions or time of day.SAR imagery provides rich information about the sea surface,capturing complicated dynamic processes in the upper layers of the ocean,particular-ly in relation to tropical cyclones.Over the past four decades,the advantages of SAR have been increasingly recognized,leading to notable marine applications,especially in the development of algorithms for retrieving wind and wave data from SAR images.This study reviews the history,progress,and future outlook of SAR-based monitoring of sea surface wind and waves.In particular,the ap-plicability of various SAR wind and wave algorithms is systematically investigated,with a particular focus on their performance un-der extreme sea conditions.展开更多
Epigenetics-mediated breeding(epibreeding)involves engineering crop traits and stress responses through the targeted manipulation of key epigenetic features to enhance agricultural productivity.While conventional bree...Epigenetics-mediated breeding(epibreeding)involves engineering crop traits and stress responses through the targeted manipulation of key epigenetic features to enhance agricultural productivity.While conventional breeding methods raise concerns about reduced genetic diversity,epibreeding propels crop improvement through epigenetic variations that regulate gene expression,ultimately impacting crop yield.Epigenetic regulation in crops encompasses various modes,including histone modification,DNA modification,RNA modification,non-coding RNA,and chromatin remodeling.This review summarizes the epigenetic mechanisms underlying major agronomic traits in maize and identifies candidate epigenetic landmarks in the maize breeding process.We propose a valuable strategy for improving maize yield through epibreeding,combining CRISPR/Cas-based epigenome editing technology and Synthetic Epigenetics(SynEpi).Finally,we discuss the challenges and opportunities associated with maize trait improvement through epibreeding.展开更多
This study examines the evolving use of synthetic chemicals in intensive agriculture over the past decade.It highlights the negative impacts of chemical inputs on soil health and ecosystem integrity and recommends kno...This study examines the evolving use of synthetic chemicals in intensive agriculture over the past decade.It highlights the negative impacts of chemical inputs on soil health and ecosystem integrity and recommends knowledge-sharing platforms,soil protection laws,and collaborative efforts between regulatory agencies and agricultural experts.The study emphasizes the need for a balanced approach that includes natural methods alongside synthetic chemicals,particularly herbicides.Ten years ago,farmers primarily used urea,DAP,and potassium for nutrients.However,increased awareness,market forces,and government subsidies have led to a significant rise in herbicide use as a cost-effective weed management strategy.Over the past decade,synthetic fertilizer use for cotton cultivation has increased by 80%,leading to deteriorating soil quality.Paddy cultivation has decreased by 23%,while cotton cultivation has increased by 20.4%due to higher economic incentives.Currently,89.1%of farmers use herbicides,compared to 97.2%who did not a decade ago.Insecticide use has also surged,with 97.8%of farmers applying 1.5 liters or more per acre.The excessive use of chemicals threatens soil fertility and disrupts the ecosystem’s balance.This article explores the reasons behind the adoption of chemical-intensive farming practices and offers insights into farmers’decision-making processes.The careful use of synthetic chemicals is essential to safeguard soil health and maintain ecological balance.展开更多
Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices....Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices.By integrating advanced genetic tools,computational modeling,and systems biology,researchers can precisely modify plant genomes to enhance traits such as yield,stress tolerance,and nutrient use efficiency.The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges.Here,we highlight recent advancements and applications of plant synthetic biology in agriculture,focusing on key areas such as photosynthetic efficiency,nitrogen fixation,drought tolerance,pathogen resistance,nutrient use efficiency,biofortification,climate resilience,microbiology engineering,synthetic plant genomes,and the integration of artificial intelligence with synthetic biology.These innovations aim to maximize resource use efficiency,reduce reliance on external inputs,and mitigate environmental impacts associated with conventional agricultural practices.Despite challenges related to regulatory approval and public acceptance,the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems,contributing to global food security and environmental sustainability.Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.展开更多
Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples w...Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples were collected from 10 provinces in China,and six SPAs(three parent SPAs and their three transformation products)were analyzed.The concentrations of6SPAs(the sum of six target compounds)ranged from 15.4 to 3210 ng/g(geometric mean(GM):169 ng/g).The highest concentration of6SPAswas found in Sichuan Province(GM:349 ng/g),which was approximately 4 times higher than that in Hubei Province(81.6 ng/g)(p<0.05).The concentrations of butylated hydroxytoluene(BHT),2,2'-methylene bis(4-methyl-6–tert-butylphenol)(AO2246),2,6-di–tert–butyl–1,4-benzoquinone(BHT-Q),2,6-di–tert–butyl–4-(hydroxymethyl)phenol(BHT-OH),and ∑_(p)-SPAs were substantially higher in dust from urban areas than rural areas(p<0.05).AO2246 concentration in dust from homes(GM:0.400 ng/g)was about 4 times higher than that in workplaces(0.116 ng/g)(p<0.01).Significantly higherp-SPAs concentrations were found in dust from homes(GM:17.5 ng/g)than workplaces(11.4 ng/g)(p<0.01).The estimated daily intakes(EDIs)of ∑_(6)SPAs exposed through dust ingestion were 0.582,0.342,0.197,0.076,and 0.080 ng/kg bw/day in different age groups,and exposed through dermal contact was 0.358,0.252,0.174,0.167,and 0.177 ng/kg bw/day.EDIs showed that the exposure risks of SPAs decreased with age.This is the first work to determine SPAs in dust from10 provinces in China and investigate the spatial distribution of SPAs in those regions.展开更多
文摘The aim of this study was to investigate the amount of heterosis and performance of faba bean synthetic cultivars compared to line cultivars under semi-arid conditions. Five inbred lines in at least S6 generation were developed and used to develop F1s hybrid (in all possible combination excluding reciprocal), lines mixtures (Syn-0) and synthetic generations of Syn-1. Evaluation of the entries showed the lines to have high general and specific combining ability, high yield and high average degree of cross-fertilization (0.36);hetrosis relative to mid-parent for yield was 67%. Lines mixture from four inbred lines (Hudeiba/93, Bassabier, Ed-Damar and Shabah) gave the highest yield of 3.40 t/ha for Syn-0 and 3.96 t/ha for Syn-1. Compared to the average yield t/ha of the pure stand of the four lines (3.11 t/ha), the increase in yield of was 9% in sy-0 and 27% in Syn-1. Compared to the individual yield t/ha of the pure stand of the lines, the performance of Syn-0 surpassed that of the individual pure stands of the linesby 14% for Hudiaba/93 and Bassabier and 4% for Ed-Damar and 7% for Shabah, whereas the increase in performance of Syn-1 compared to pure stand of the lines was 32%, 25% and 21%, respectively. The results confirm the previous knowledge on yield increase with successive syn-generations in faba bean due to the effects of heterogeneity and heterozygosity. Such results could be used as a base for an effective breeding program for improvement of yield of faba bean grown under the semi-arid zones.
文摘Since 2018,under the guidance of the China Nonwovens&Industrial Textiles Association,with the support of the majority of member companies in the industry,the Geosynthetics Branch of China Nonwovens&Industrial Textiles Association has made some achievements in promoting the development of the industry and promoting the progress of enterprises.
文摘On 22 November,the 3rd EurAsian Geosynthetics Symposium(EAGS-2024)was held in Tai'an City,Shandong Province.Liu Fengmei,Vice Mayor of Tai'an People's Government,introduced that Tai'an's geosynthetics industry has had production capacity in the 1980s,and the production products involve geogrid,geotextile,geomembrane,geonet and so on.Geosynthetics has become a beautiful industrial business card of Tai'an.
文摘Geosynthetics,as an important category of industrial textiles,are widely used in water conservancy,highways,railways,ports,construction,etc.,and play an important role in national infrastructure construction and the development of the national economy.“Industrial textiles,owing to high technology,wide application,strong driving effect,and large market potential,are becoming an important driving force for economic growth and transformation of China’s textile industry in the new era.”Said Mr.Li Lingshen,Vice President of China National Textile and Apparel Council&President of China Nonwovens&Industrial Textiles Association(CNITA).Moreover,as Mr.Pierre Wiertz,General Manager of EDANA,once stated that since the Chinese Belt and Road Initiative was launched,geotextiles are even more topical and promising as a market segment.It is believed that the global geosynthetics industry will usher in a new round of development opportunities.
基金support and funding by the European Union-Next Generation EU under the Italian Ministry of University and Research (MUR) National Innovation Ecosystem (No.ECS00000041-VITALITY and also “Ecosistema TECH4YOU-(Spoke 3-Goal 3.5)MUR is thanked for PRIN-PNRR 2022 project "P2022XKWH7-Circular Waste+3 种基金The University of Perugia is acknowledged for financial support to the university project “Fondo Ricerca di Ateneo,edizione 2022”The National Ph D program in Catalysis coordinated by the University of Perugia is also thankedthe financial supports of key research and development and technology transfer projects of Inner Mongolia Autonomous Region (No.2025KJHZ0008)major special projects of science and technology of Ordos (No.2022EEDSKJZDZX003)。
文摘Given the broad applicability of carbazole structural moieties in materials science and medicinal chemistry,significant efforts have been devoted to developing efficient synthetic catalytic methodologies to access this valuable scaffold.Catalyzed direct Csp^(2)-H functionalization provides an effective and costefficient approach to synthesizing carbazoles from simple and readily available starting materials,ensuring a promising path characterized by excellent atom and step economy.This review highlights the substantial progress made in the last 10 years in advancing catalytic Csp^(2)-H functionalization techniques for synthesizing carbazoles.
基金supported by project ZR2022MF330 supported by Shandong Provincial Natural Science Foundationthe National Natural Science Foundation of China under Grant No.61701286.
文摘Synthetic speech detection is an essential task in the field of voice security,aimed at identifying deceptive voice attacks generated by text-to-speech(TTS)systems or voice conversion(VC)systems.In this paper,we propose a synthetic speech detection model called TFTransformer,which integrates both local and global features to enhance detection capabilities by effectively modeling local and global dependencies.Structurally,the model is divided into two main components:a front-end and a back-end.The front-end of the model uses a combination of SincLayer and two-dimensional(2D)convolution to extract high-level feature maps(HFM)containing local dependency of the input speech signals.The back-end uses time-frequency Transformer module to process these feature maps and further capture global dependency.Furthermore,we propose TFTransformer-SE,which incorporates a channel attention mechanism within the 2D convolutional blocks.This enhancement aims to more effectively capture local dependencies,thereby improving the model’s performance.The experiments were conducted on the ASVspoof 2021 LA dataset,and the results showed that the model achieved an equal error rate(EER)of 3.37%without data augmentation.Additionally,we evaluated the model using the ASVspoof 2019 LA dataset,achieving an EER of 0.84%,also without data augmentation.This demonstrates that combining local and global dependencies in the time-frequency domain can significantly improve detection accuracy.
基金financial support from National Natural Science Foundation of China(No.32401215 to HS No.2247081930 to HYJ)the non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(No.2023-I2M-3-015)State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs(No.20240104).
文摘Medicinal plants serve as valuable sources of bioactive compounds with critical applications across pharmaceutical,agricultural,and industrial sectors.Compared to chemical synthesis and plant extraction,synthetic biology offers a green,efficient,and sustainable alternative for producing bioactive compounds,which represents a state of art technology.However,this technology still faces several challenges,including overly long metabolic pathways,inadequate catalytic efficiency of key enzymes in the pathway,and incompatibility between gene elements and host cells,leading to low yields of target bioactive compounds.The development and application of regulatory tools in synthetic biology hold great promise for overcoming these obstacles.This review first summarizes the classification and biosynthesis of bioactive compounds based on structural types.Subsequently,recent advancements are outlined in regulation tools and their application in the heterologous production of bioactive compounds.This review aims to establish a foundation for the efficient production of bioactive compounds based on microbial cell factories.This not only has significant practical implications for reducing the resource consumption and environmental impact of traditional production methods,but also highlights the central role of synthetic biology in promoting the sustainable production of bioactive compounds derived from medicinal plants.
基金supported by the National Natural Science Foundation of China(No.51939009)Shenzhen Science and Technology Program(Nos.JCYJ20241202125905008 and GXWD20201231165807007-20200810165349001).
文摘A trace analytical method based on solid-phase extraction gas chromatography-tandem mass spectrometry(SPE–GC–MS/MS)was developed for the rapid detection of 256 semi-volatile organic compounds(SVOCs),including 25 polycyclic aromatic hydrocarbons(PAHs),70 polychlorinated biphenyls(PCBs),123 pesticides,20 phthalate esters(PAEs),4 organophosphate esters(OPEs),9 synthetic musks(SMs),and 5 UV filters(UVs)in water.No-tably,this method provided a decent linearity of calibration standards(R^(2)>0.999),excellent method limits of quantification(MLOQs)(0.12–11.41 ng/L),satisfactory matrix spiking recovery rates(60.4%–126%),and high precision(intra-day relative standard deviations(RSDs):1.0%–10.0%,inter-day RSDs:3.0%–15.0%,and inter-week RSDs:3.4%–15.7%),making it suitable for trace-level studies.Statistical analysis revealed that SVOCs with higher volatility exhibited enhanced recovery rates.Validation of the methodology involved analyzing SVOCs in real spring water and river water samples.Twenty-seven SVOCs were detected in spring water and 58 in river water,with an average concentration of 631.73 and 16,095 ng/L,respectively.Among the detected SVOCs,PAEs constituted the predominant proportion.This study underscored the presence of SVOCs contamination specifi-cally within the spring water,although SVOCs concentrations in river water were significantly greater than those found in spring water.In summary,this sensitive method based on SPE–GC–MS/MS was successfully developed and validated for the rapid analysis of a diverse array of 256 SVOCs at trace levels in water,including not only the traditional highly valued PAHs,PCBs,pesticides,and PAEs,but also the emerging OPEs,UVs,and SMs.
基金financially supported by National Natural Science Foundation(32302265).
文摘Driven by rapid global population growth and evolving consumer demands for high-quality nutrition and enhanced sensory enjoyment,the food industry is advancing towards healthier,more efficient,more sustainable,and more personalized targets.Notably,this transformation is being accelerated by the convergence of advanced biotechnology(BT)and information technology(IT)in food production systems.This review examines pivotal technologies shaping future food systems,highlighting two biotechnological frontiers and two artificial intelligence-driven domains.Specifically,evolutionary engineering enhances microbial resilience and production efficiency while synthetic biology expands the diversity of food sources and enables personalized food ingredient design,both of which advance sustainable food production.Meanwhile,artificial intelligence promotes food flavor innovation by integrating existing knowledge in molecular structure,flavoromics and consumer preferences.Furthermore,insights into gut microbiota,along with the development of omics techniques and wearable biosensors are unlocking attainable solutions for precision nutrition.Collectively,the trilateral convergence of BT,IT and food technology,embodying a harmonious balance,is reshaping the paradigm of future food manufacturing.
基金the National Natural Science Foundation of China(Grant No.:52508343)the Fundamental Research Funds for the Central Universities(Grant No.:B250201004).
文摘Crack detection accuracy in computer vision is often constrained by limited annotated datasets.Although Generative Adversarial Networks(GANs)have been applied for data augmentation,they frequently introduce blurs and artifacts.To address this challenge,this study leverages Denoising Diffusion Probabilistic Models(DDPMs)to generate high-quality synthetic crack images,enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation.The proposed framework involves a two-stage pipeline:first,DDPMs are used to synthesize high-fidelity crack images that capture fine structural details.Second,these generated samples are combined with real data to train segmentation networks,thereby improving accuracy and robustness in crack detection.Compared with GAN-based approaches,DDPM achieved the best fidelity,with the highest Structural Similarity Index(SSIM)(0.302)and lowest Learned Perceptual Image Patch Similarity(LPIPS)(0.461),producing artifact-free images that preserve fine crack details.To validate its effectiveness,six segmentation models were tested,among which LinkNet consistently achieved the best performance,excelling in both region-level accuracy and structural continuity.Incorporating DDPM-augmented data further enhanced segmentation outcomes,increasing F1 scores by up to 1.1%and IoU by 1.7%,while also improving boundary alignment and skeleton continuity compared with models trained on real images alone.Experiments with varying augmentation ratios showed consistent improvements,with F1 rising from 0.946(no augmentation)to 0.957 and IoU from 0.897 to 0.913 at the highest ratio.These findings demonstrate the effectiveness of diffusion-based augmentation for complex crack detection in structural health monitoring.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
基金Supported by the National Natural Science Foundation of China(Nos.T2261149752,41976163,42476172)。
文摘Bathymetric measurement of shallow water is of fundamental importance to coastal environment research and resource management.However,there are still great challenges in estimating water depth using satellite observations in turbid coastal waters.In this paper,we developed a physicsenhanced deep neural network to estimate bathymetry of highly turbid waters of the Changjiang(Yangtze)River estuary from dual-polarized synthetic aperture radar(SAR)images.Sentinel-1A/B SAR images with a spatial resolution of 20 m×22 m were collected and matched with water depth data from nautical charts during 2017-2023.For the input parameters of the model,in addition to the normalized radar backscatter cross section(NRCS)at single polarization and incidence angle,the impacts of both polarimetric characteristics and physical environmental factors on model performance were discussed in detail.Results of feature importance analysis and sensitivity experiments indicate that the polarization ratio and NRCS after removing the influence of background sea surface wind field make significant contributions to the bathymetry retrieval model.The root mean square error(RMSE)of SAR derived water depth decreases from 1.44 to 0.78 m within 0-30-m depth,and the mean relative error(MRE)is reduced from 15.6%to 8.6%.Compared with other machine learning models such as ResNet,XGBoost,and Random Forest,the MRE is reduced by 3.9%,5.7%,and 7.4%,respectively.The spatial distribution of SAR derived water depth also exhibits a high degree of consistency with observations,demonstrating the great potential of the model in estimating the depth of turbid shallow waters.
文摘α-Trifluoromethyl ketones are a class of useful compounds with versatile applications.Their synthetic application via the transformation of the C—F bonds is of particular interest by allowing the synthesis of organic compounds with diverse structures.Herein,the advances in the research areas ofα-trifluoromethyl ketone synthesis and their defluorination reactions are reviewed.Discussion on the mechanisms of the typical reactions has also been provided,in hope of affording some guides to the chemistry ofα-trifluoromethyl ketones in the synthetic methods toward themselves and their derivatives.
基金supported by the Surface Project of Local De-velopment in Science and Technology Guided by Central Govern-ment(No.2021ZYD0041)the National Natural Science Founda-tion of China(Nos.52377026 and 52301192)+3 种基金the Natural Science Foundation of Shandong Province(No.ZR2019YQ24)the Taishan Scholars and Young Experts Program of Shandong Province(No.tsqn202103057)the Special Financial of Shandong Province(Struc-tural Design of High-efficiency Electromagnetic Wave-absorbing Composite Materials and Construction of Shandong Provincial Tal-ent Teams)the“Sanqin Scholars”Innovation Teams Project of Shaanxi Province(Clean Energy Materials and High-Performance Devices Innovation Team of Shaanxi Dongling Smelting Co.,Ltd.).
文摘With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electronic instruments.Therefore,the design and preparation of electromagnetic absorbing composites represent an efficient approach to mitigate the current hazards of electromagnetic radiation.However,traditional electromagnetic absorbers are difficult to satisfy the demands of actual utilization in the face of new challenges,and emerging absorbents have garnered increasing attention due to their structure and performance-based advantages.In this review,several emerging composites of Mxene-based,biochar-based,chiral,and heat-resisting are discussed in detail,including their synthetic strategy,structural superiority and regulation method,and final optimization of electromagnetic absorption ca-pacity.These insights provide a comprehensive reference for the future development of new-generation electromagnetic-wave absorption composites.Moreover,the potential development directions of these emerging absorbers have been proposed as well.
基金supported by the National Nat-ural Science Foundation of China(No.42376174)the Natural Science Foundation of Shanghai(No.23ZR 1426900).
文摘Synthetic aperture radar(SAR)aboard SEASAT was first launched in 1978.At the beginning of the 21st century,the Chinese remote sensing community recognized the urgent need to develop domestic SAR capabilities.Unlike scatterometers and al-timeters,space-borne SAR offers high-resolution images of the ocean,regardless of weather conditions or time of day.SAR imagery provides rich information about the sea surface,capturing complicated dynamic processes in the upper layers of the ocean,particular-ly in relation to tropical cyclones.Over the past four decades,the advantages of SAR have been increasingly recognized,leading to notable marine applications,especially in the development of algorithms for retrieving wind and wave data from SAR images.This study reviews the history,progress,and future outlook of SAR-based monitoring of sea surface wind and waves.In particular,the ap-plicability of various SAR wind and wave algorithms is systematically investigated,with a particular focus on their performance un-der extreme sea conditions.
基金supported by funding from the National Key R&D Program of China(2023ZD0407304)the Sci-Tech Innovation 2030 Agenda(2022ZD0115703)Fundamental Research Funds for Central Non-Profit of Chinese Academy of Agricultural Sciences(Y2023PT20).
文摘Epigenetics-mediated breeding(epibreeding)involves engineering crop traits and stress responses through the targeted manipulation of key epigenetic features to enhance agricultural productivity.While conventional breeding methods raise concerns about reduced genetic diversity,epibreeding propels crop improvement through epigenetic variations that regulate gene expression,ultimately impacting crop yield.Epigenetic regulation in crops encompasses various modes,including histone modification,DNA modification,RNA modification,non-coding RNA,and chromatin remodeling.This review summarizes the epigenetic mechanisms underlying major agronomic traits in maize and identifies candidate epigenetic landmarks in the maize breeding process.We propose a valuable strategy for improving maize yield through epibreeding,combining CRISPR/Cas-based epigenome editing technology and Synthetic Epigenetics(SynEpi).Finally,we discuss the challenges and opportunities associated with maize trait improvement through epibreeding.
文摘This study examines the evolving use of synthetic chemicals in intensive agriculture over the past decade.It highlights the negative impacts of chemical inputs on soil health and ecosystem integrity and recommends knowledge-sharing platforms,soil protection laws,and collaborative efforts between regulatory agencies and agricultural experts.The study emphasizes the need for a balanced approach that includes natural methods alongside synthetic chemicals,particularly herbicides.Ten years ago,farmers primarily used urea,DAP,and potassium for nutrients.However,increased awareness,market forces,and government subsidies have led to a significant rise in herbicide use as a cost-effective weed management strategy.Over the past decade,synthetic fertilizer use for cotton cultivation has increased by 80%,leading to deteriorating soil quality.Paddy cultivation has decreased by 23%,while cotton cultivation has increased by 20.4%due to higher economic incentives.Currently,89.1%of farmers use herbicides,compared to 97.2%who did not a decade ago.Insecticide use has also surged,with 97.8%of farmers applying 1.5 liters or more per acre.The excessive use of chemicals threatens soil fertility and disrupts the ecosystem’s balance.This article explores the reasons behind the adoption of chemical-intensive farming practices and offers insights into farmers’decision-making processes.The careful use of synthetic chemicals is essential to safeguard soil health and maintain ecological balance.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Category B,XDB1090000).
文摘Plant synthetic biology has emerged as a transformative field in agriculture,offering innovative solutions to enhance food security,provide resilience to climate change,and transition to sustainable farming practices.By integrating advanced genetic tools,computational modeling,and systems biology,researchers can precisely modify plant genomes to enhance traits such as yield,stress tolerance,and nutrient use efficiency.The ability to design plants with specific characteristics tailored to diverse environmental conditions and agricultural needs holds great potential to address global food security challenges.Here,we highlight recent advancements and applications of plant synthetic biology in agriculture,focusing on key areas such as photosynthetic efficiency,nitrogen fixation,drought tolerance,pathogen resistance,nutrient use efficiency,biofortification,climate resilience,microbiology engineering,synthetic plant genomes,and the integration of artificial intelligence with synthetic biology.These innovations aim to maximize resource use efficiency,reduce reliance on external inputs,and mitigate environmental impacts associated with conventional agricultural practices.Despite challenges related to regulatory approval and public acceptance,the integration of synthetic biology in agriculture holds immense promise for creating more resilient and sustainable agricultural systems,contributing to global food security and environmental sustainability.Rigorous multi-field testing of these approaches will undoubtedly be required to ensure reproducibility.
基金supported by the National Key Research and Development Program of China(No.2023YFC3706602)the National Natural Science Foundation of China(Nos.22225605 and 22193051)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0750200).
文摘Synthetic phenolic antioxidants(SPAs)are widely used in diverse industries due to their exceptional antioxidant characteristics.However,human exposure to SPAs may cause health problems.In this study,226 dust samples were collected from 10 provinces in China,and six SPAs(three parent SPAs and their three transformation products)were analyzed.The concentrations of6SPAs(the sum of six target compounds)ranged from 15.4 to 3210 ng/g(geometric mean(GM):169 ng/g).The highest concentration of6SPAswas found in Sichuan Province(GM:349 ng/g),which was approximately 4 times higher than that in Hubei Province(81.6 ng/g)(p<0.05).The concentrations of butylated hydroxytoluene(BHT),2,2'-methylene bis(4-methyl-6–tert-butylphenol)(AO2246),2,6-di–tert–butyl–1,4-benzoquinone(BHT-Q),2,6-di–tert–butyl–4-(hydroxymethyl)phenol(BHT-OH),and ∑_(p)-SPAs were substantially higher in dust from urban areas than rural areas(p<0.05).AO2246 concentration in dust from homes(GM:0.400 ng/g)was about 4 times higher than that in workplaces(0.116 ng/g)(p<0.01).Significantly higherp-SPAs concentrations were found in dust from homes(GM:17.5 ng/g)than workplaces(11.4 ng/g)(p<0.01).The estimated daily intakes(EDIs)of ∑_(6)SPAs exposed through dust ingestion were 0.582,0.342,0.197,0.076,and 0.080 ng/kg bw/day in different age groups,and exposed through dermal contact was 0.358,0.252,0.174,0.167,and 0.177 ng/kg bw/day.EDIs showed that the exposure risks of SPAs decreased with age.This is the first work to determine SPAs in dust from10 provinces in China and investigate the spatial distribution of SPAs in those regions.
