In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes ...In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes an enhanced pavement crack detection model named Star-YOLO11.This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network.The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency.To enhance the accuracy of pavement crack detection and improve model efficiency,three key modifications to the YOLO11 architecture are proposed.Firstly,the original C3k2 backbone is replaced with a StarBlock-based structure,forming the Star-s50 feature extraction backbone network.This lightweight redesign reduces computational complexity while maintaining detection precision.Secondly,to address the inefficiency of the original Partial Self-attention(PSA)mechanism in capturing localized crack features,the convolutional prior-aware Channel Prior Convolutional Attention(CPCA)mechanism is integrated into the channel dimension,creating a hybrid CPC-C2PSA attention structure.Thirdly,the original neck structure is upgraded to a Star Multi-Branch Auxiliary Feature Pyramid Network(SMAFPN)based on the Multi-Branch Auxiliary Feature Pyramid Network architecture,which adaptively fuses high-level semantic and low-level spatial information through Star-s50 connections and C3k2 extraction blocks.Additionally,a composite dataset augmentation strategy combining traditional and advanced augmentation techniques is developed.This strategy is validated on a specialized pavement dataset containing five distinct crack categories for comprehensive training and evaluation.Experimental results indicate that the proposed Star-YOLO11 achieves an accuracy of 89.9%(3.5%higher than the baseline),a mean average precision(mAP)of 90.3%(+2.6%),and an F1-score of 85.8%(+0.5%),while reducing the model size by 18.8%and reaching a frame rate of 225.73 frames per second(FPS)for real-time detection.It shows potential for lightweight deployment in pavement crack detection tasks.展开更多
Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representati...Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.展开更多
Background:Neurological disorders(NDs),including ischemic stroke(IS),Parkinson’s disease(PD),and Alzheimer’s disease(AD),are major contributors to global morbidity and mortality.Boswellia extract has demonstrated ne...Background:Neurological disorders(NDs),including ischemic stroke(IS),Parkinson’s disease(PD),and Alzheimer’s disease(AD),are major contributors to global morbidity and mortality.Boswellia extract has demonstrated neuroprotective properties,yet a comprehensive systematic review assessing its efficacy remains absent.This study aims to evaluate the efficacy of Boswellia extract in treating NDs,with a particular focus on its effects in AD and its potential for long-term neurorestoration,thereby supporting further investigation into Boswellia’s therapeutic role in ND management.Methods:A systematic literature search was performed in PubMed,Web of Science,ScienceDirect,and Google Scholar for English-language studies published up to March 2024.Eighteen studies met the inclusion criteria and were included in the meta-analysis.The study protocol was registered on PROSPERO(CRD42024524386).Eligible studies involved rodent models of IS,PD,or AD with post-operative interventions using Boswellia extract.Data extraction focused on mechanisms of action,dosages,treatment durations,and therapeutic outcomes.Studies were excluded if they involved non-ND models,combined treatments,or had incomplete data.Two researchers independently conducted literature screening and data extraction.Statistical analyses were conducted using Stata(version 17)and RevMan(version 5.4),employing fixed or random-effects models based on heterogeneity assessments.Result s:Boswellia extract significantly improved the mean effect size for NDs(ES=1.28,95%CI(1.05,1.51),P<0.001).Specifically,it reduced cerebral infarct volume in IS(SMD=−2.87,95%CI(−3.42,−2.32))and enhanced behavioral outcomes in AD(SMD=3.26,95%CI(2.07,5.14))and PD(SMD=5.37,95%CI(3.93,6.80)).Subgroup analyses revealed that Boswellia extract exhibited superior efficacy in AD when administered orally and via intra-cerebroventricular injection.Long-term treatment with Boswellia extract suggested potential neurorestorative effects.Additionally,Boswellia extract was more effective than its monomeric constituents,highlighting its promising role in ND treatment.Conclusion:Boswellia extract demonstrates significant neuroprotective effects across various NDs,particularly in AD and in promoting long-term neurorestoration.These findings support the need for further research into Boswellia’s potential as a therapeutic agent in the management of neurological disorders.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Silver nanoparticles(Ag NPs)have attracted attention in the field of biomaterials due to their excellent antibacterial property.However,the reducing and stabilizing agents used for the chemical reduction of Ag NPs are...Silver nanoparticles(Ag NPs)have attracted attention in the field of biomaterials due to their excellent antibacterial property.However,the reducing and stabilizing agents used for the chemical reduction of Ag NPs are usually toxic and may cause water pollution.In this work,Ag NPs(31.2 nm in diameter)were prepared using the extract of straw,an agricultural waste,as the reducing and stabilizing agent.Experimental analysis revealed that the straw extract contained lignin,the structure of which possesses phenolic hydroxyl and methoxy groups that facilitate the reduction of silver salts into Ag NPs.The surfaces of Ag NPs were negatively charged due to the encapsulation of a thin layer of lignin molecules that prevented their aggregation.After the prepared Ag NPs were added to the precursor solution of acrylamide,free radical polymerization was triggered without the need for extra heating or light irradiation,resulting in the rapid formation of an Ag NP-polyacrylamide composite hydrogel.The inhibition zone test proved that the composite hydrogel possessed excellent antibacterial ability due to the presence of Ag NPs.The prepared hydrogel may have potential applications in the fabrication of biomedical materials,such as antibacterial dressings.展开更多
Solvent extraction is the main method used to separate and purify rare earth elements.In the process of rare earths extraction,emulsification often generated due to the instability of the aqueous and organic phases or...Solvent extraction is the main method used to separate and purify rare earth elements.In the process of rare earths extraction,emulsification often generated due to the instability of the aqueous and organic phases or improper operating conditions.Once emulsification occurs,it would not only lead to low rare earths recovery efficiency,small product quantities,high production costs and the losing of extractant and rare earth resources,but also result in serious environmental pollution.Therefore,it is very important to study the micro-mechanisms of emulsification and establish new methods to prevent emulsification at the source.In this paper,possible factors resulting in emulsification,such as the compositions and properties of the organic and aqueous phases,the operating conditions of the rare earths extraction are reviewed.The micro-mechanisms of emulsification are summarized basing on the microscopic structures in the bulk phase,aggregations of the extractants at the organic-aqueous interface,spectral characterizations and computational simulations.On this basis,new formation mechanisms are proposed for emulsification.Preliminary explorations are employed to verify the correctness of these new viewpoints.Finally,future directions for studies of the emulsification micro-mechanism are proposed.This study provides a theoretical basis for further understanding the micro-mechanisms of interfacial instability resulting in emulsification in the process of rare earths extraction.展开更多
采用顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)和液液萃取(liquid-liquid extraction,LLE)结合全二维气相色谱飞行时间质谱(comprehensive two-dimensional gas chromatography time of flight mass spectrometry...采用顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)和液液萃取(liquid-liquid extraction,LLE)结合全二维气相色谱飞行时间质谱(comprehensive two-dimensional gas chromatography time of flight mass spectrometry,GC×GC-TOF-MS)以及香气活度值(odour active value,OAV),对红星二锅头白酒的挥发性成分进行全面解析。研究发现,HS-SPME、LLE分别定性出928、802种挥发性化合物,共计定性出1304种挥发性化合物,共同定性出426种挥发性化合物。基于HS-SPME数据,通过香气数据库筛选出具有香气特征的382种香气化合物,其中酯类相对百分含量占比最高,其次是醇类、酸类和醛类。计算得到了49种香气化合物OAV>1,其中酯类(辛酸乙酯、异戊酸乙酯等)和萜烯类(β-大马酮)对白酒风味的贡献最大,醛类(异戊醛、己醛等)和含硫类(二甲基三硫)其次,醇类(1-辛烯-3-醇)和含氮类(2,3,5-三甲基吡嗪)也有一定风味贡献。该研究丰富了红星二锅头白酒的风味研究,也为下一步生产研究及调控提供了理论和数据支撑。展开更多
In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering...In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.展开更多
The use of fresh versus frozen spermatozoa in men with nonobstructive azoospermia (NOA) undergoingin vitro fertilization (IVF)has been a debated hot topic among reproductive specialists. Each approach presents distinc...The use of fresh versus frozen spermatozoa in men with nonobstructive azoospermia (NOA) undergoingin vitro fertilization (IVF)has been a debated hot topic among reproductive specialists. Each approach presents distinct advantages and disadvantages,with fresh sperm typically showing superior sperm quality, while frozen sperm offers logistical flexibility and a reliable backup forrepeated cycles. This review summarizes the latest advancements in sperm retrieval and cryopreservation techniques, providingpractitioners with a comprehensive analysis of each option’s strengths and limitations. Comparative studies indicate that, althoughfresh sperm often has better quality metrics, cryopreservation methods such as vitrification have significantly improved postthawoutcomes, making frozen sperm a viable choice in assisted reproductive technologies (ART). The findings show comparablerates for fertilization, implantation, clinical pregnancy, and live birth between fresh and frozen microdissection testicular spermextraction (micro-TESE) sperm in many cases, although patient-specific factors such as timing, cost-effectiveness, and proceduralconvenience should guide the final decision. Ultimately, the choice of using fresh or frozen sperm should align with the individualneeds and conditions of patients. This tailored approach, supported by the latest advancements, can optimize ART outcomes andprovide personalized reproductive care.展开更多
The therapeutic efficacy of traditional Chinese medicine has been widely acknowledged due to its extensive history of clinical effectiveness.However,the precise active components underlying each prescription remain in...The therapeutic efficacy of traditional Chinese medicine has been widely acknowledged due to its extensive history of clinical effectiveness.However,the precise active components underlying each prescription remain incompletely understood.Polysaccharides,as a major constituent of water decoctions—the most common preparation method for Chinese medicinals—may provide a crucial avenue for deepening our understanding of the efficacy principles of Chinese medicine and establishing a framework for its modern development.The structural complexity and diversity of Chinese herbal polysaccharides present significant challenges in their separation and analysis compared to small molecules.This paper aims to explore the potential of Chinese herbal polysaccharides efficiently by briefly summarizing recent advancements in polysaccharide chemical research,focusing on methods of acquisition,structure elucidation,and quality control.展开更多
The inhibitory properties of rapeseed cake meal extract(RCME)on the corrosion of cold rolled steel(CRS)in trichloroacetic acid(TCA)were systematically investigated using gravimetric,electrochemical,surface characteriz...The inhibitory properties of rapeseed cake meal extract(RCME)on the corrosion of cold rolled steel(CRS)in trichloroacetic acid(TCA)were systematically investigated using gravimetric,electrochemical,surface characterizations and theoretical calculations.The results demonstrate that RCME exhibits excellent inhibitory performance with a maximum inhibition efficiency of 92.7%for 100 mg L−1 RCME at 20℃.The adsorption of RCME obeys Langmuir isotherm at 20 and 30℃,Temkin isotherm at 40℃,and Freundlich isotherm at 50℃.RCME acts as a cathodic inhibitor.The charge transfer resistance is increased with the addition of RCME,while the double-layer capacitance decreases.SEM,AFM,CLSM,XPS,XRD and TOF-SIMS analyses confirm that the active components in RCME adsorb onto the surface of CRS,forming a protective film that effectively inhibits the corrosion of CRS by TCA.Along with the increase in the concentration of RCME,the surface tension of the inhibited solution gradually decreases,while the electrical conductivity increases before stabilizing.HPLC-MS and FTIR analyses reveal rutin,linolenic acid,linoleic acid and adenine are the effective substances in RCME.Quantum chemical(QC)calculations and molecular dynamic(MD)simulations indicate that the active centers of the effective inhibitor molecules are predominantly located on benzene rings,O-or N-containing heterocyclic rings,and functional groups such as C=O and C=C.Additionally,their main chains adsorb onto the Fe(001)surface in an approximately flat manner,involving both chemical and physical adsorption processes.展开更多
Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which ...Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.展开更多
As a typical bioflavonoid,diosmetin is desirable in the field of natural medicine,healthy food,and cosmetics by anti-cancer,antibacterial,antioxidant,estrogen-like and anti-inflammatory activities,and it comes from a ...As a typical bioflavonoid,diosmetin is desirable in the field of natural medicine,healthy food,and cosmetics by anti-cancer,antibacterial,antioxidant,estrogen-like and anti-inflammatory activities,and it comes from a wide range of sources in traditional Chinese medicine like spider fragrance,spearmint and chrysanthemum,as well as in Citrus fruit.However,traditional analytical methods such as silica gel column chromatography face multiple challenges in the selective extraction of diosmetin from biological materials and traditional Chinese medicinal materials.Therefore,it is urgent to develop a new type of absorbent with high efficiency,recyclability and good specificity to diosmetin.In this investigation,a magnetic surface molecularly imprinted polymer(labeled as Diosmetin/SMIPs)was synthesized employing magnetic nanoparticles as the carrier and 4-vinylpyridinyl(4-VP)as the functional monomer by surface imprinting technology.The functional monomer was screened by the binding energy(△E)between functional monomers and template molecules via computational simulation.The Diosmetin/SMIPs had a high level of specific recognition and adsorption capability towards diosmetin with a 20.25 mg g^(-1) adsorption capacity and an imprinting factor(IF)of 2.28.Additionally,it demonstrated excellent regeneration performance with 8 adsorption/desorption cycles.In addition,91.20%-94.16% of spiked diosmetin was recovered from the lemon peel samples.The strategy of constructing Diosmetin/SMIPs based on computational simulation can effectively enhance the specific adsorption performance of diosmetin.Meanwhile,Diosmetin/SMIPs synthesized by imprinting polymerization showed excellent anti-interference and reusability,and realized efficient targeted extraction of diosmetin from lemon peel samples.The results of this investigation provide a promising adsorbent for selective enrichment of diosmetin from Citrus fruit and complicated materials.展开更多
Identification of the most appropriate chemically extractable pool for evaluating Cd and Pb availability remains elusive,hindering accurate assessment on environmental risks and effectiveness of remediation strategies...Identification of the most appropriate chemically extractable pool for evaluating Cd and Pb availability remains elusive,hindering accurate assessment on environmental risks and effectiveness of remediation strategies.This study evaluated the feasibility of European Community Bureau of Reference(BCR)sequential extraction,Ca(NO_(3))_(2)extraction,and water extraction on assessing Cd and Pb availability in agricultural soil amended with slaked lime,magnesium hydroxide,corn stover biochar,and calcium dihydrogen phosphate.Moreover,the enriched isotope tracing technique(^(112)Cd and^(206)Pb)was employed to evaluate the aging process of newly introduced Cd and Pbwithin 56 days’incubation.Results demonstrated that extractable pools by BCR and Ca(NO_(3))_(2)extraction were little impacted by amendments and showed little correlation with soil pH.This is notable because soil pH is closely linked to metal availability,indicating these extraction methods may not adequately reflect metal availability.Conversely,water-soluble concentrations of Cd and Pb were markedly influenced by amendments and exhibited strong correlations with pH(Pearson’s r:-0.908 to-0.825,P<0.001),suggesting water extraction as a more sensitive approach.Furthermore,newly introduced metals underwent a more evident aging process as demonstrated by acid-soluble and water-soluble pools.Additionally,water-soluble concentrations of essential metals were impacted by soil amendments,raising caution on their potential effects on plant growth.These findings suggest water extraction as a promising and attractive method to evaluate Cd and Pb availability,which will help provide assessment guidance for environmental risks caused by heavy metals and develop efficient remediation strategies.展开更多
BACKGROUND This case report describes a protocol developed by Danaun Medical Clinic for the introduction of a pioneering intervention comprising intravenous human placen-tal extract(HPE)therapy to improve the liver fu...BACKGROUND This case report describes a protocol developed by Danaun Medical Clinic for the introduction of a pioneering intervention comprising intravenous human placen-tal extract(HPE)therapy to improve the liver function of patients with chronic liver disease(CLD).CASE SUMMARY This study involved data from patients whose chief complaint was reduced quality of life attributable to CLD.The new treatment approach resulted in improvements in the liver function and fatty liver of 30 patients with CLD.Im-provements were observed using abdominal ultrasonography.Unlike traditional methods,this protocol provided more sustainable and meaningful results.Treat-ment with 10 mL of HPE administered intravenously once or twice per week significantly improved liver function.The observed improvements in fatty liver and liver function suggest the utility of this approach for the management of patients with CLD.CONCLUSION This case series highlights the potential of innovative treatments for patients with CLD that could improve the quality of life of the patients.展开更多
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
Lonicera japonica(honeysuckle)is a traditional Chinese medicinal food,in which the main active ingredients are phenolic acids,polysaccharides,flavonoids,and volatile oils.They have various biological activities,includ...Lonicera japonica(honeysuckle)is a traditional Chinese medicinal food,in which the main active ingredients are phenolic acids,polysaccharides,flavonoids,and volatile oils.They have various biological activities,including antiviral,antibacterial,antioxidant,hypoglycemic and lipid-lowering,and anti-inflammatory effects.This review summarizes the health effects and pharmacodynamic mechanisms of L.japonica extracts and the major active ingredients in these extracts,and the structures,metabolic process in vivo,and biotransformation processes of these compounds.In addition,the current status of the development of L.japonica-related functional foods is summarized.The aim is to provide a theoretical basis and reference for the further development and use of the active ingredients in L.japonica as functional foods for disease prevention and treatment.展开更多
This research optimized the structure of lithium extraction solar ponds to enhance the crystallization rate and yield of Li_(2)CO_(3).Using the response surface methodology in Design-Expert 10.0.3,the authors conducte...This research optimized the structure of lithium extraction solar ponds to enhance the crystallization rate and yield of Li_(2)CO_(3).Using the response surface methodology in Design-Expert 10.0.3,the authors conducted experiments to investigate the influence of four factors related to solar pond structure on the crystallization of Li_(2)CO_(3) and their pairwise interactions.Computational Fluid Dynamics(CFD)simulations of the flow field within the solar pond were performed using COMSOL Multiphysics software to compare temperature distributions before and after optimization.The results indicate that the optimal structure for lithium extraction from the Zabuye Salt Lake solar ponds includes UCZ(Upper Convective Zone)thickness of 53.63 cm,an LCZ(Lower Convective Zone)direct heating temperature of 57.39℃,a CO32−concentration of 32.21 g/L,and an added soda ash concentration of 6.52 g/L.Following this optimized pathway,the Li_(2)CO_(3) precipitation increased by 7.34% compared to the initial solar pond process,with a 33.33% improvement in lithium carbonate crystallization rate.This study demonstrates the feasibility of optimizing lithium extraction solar pond structures,offering a new approach for constructing such ponds in salt lakes.It provides valuable guidance for the efficient extraction of lithium resources from carbonate-type salt lake brines.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present wi...Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.展开更多
基金funded by the Jiangxi SASAC Science and Technology Innovation Special Project and the Key Technology Research and Application Promotion of Highway Overload Digital Solution.
文摘In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes an enhanced pavement crack detection model named Star-YOLO11.This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network.The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency.To enhance the accuracy of pavement crack detection and improve model efficiency,three key modifications to the YOLO11 architecture are proposed.Firstly,the original C3k2 backbone is replaced with a StarBlock-based structure,forming the Star-s50 feature extraction backbone network.This lightweight redesign reduces computational complexity while maintaining detection precision.Secondly,to address the inefficiency of the original Partial Self-attention(PSA)mechanism in capturing localized crack features,the convolutional prior-aware Channel Prior Convolutional Attention(CPCA)mechanism is integrated into the channel dimension,creating a hybrid CPC-C2PSA attention structure.Thirdly,the original neck structure is upgraded to a Star Multi-Branch Auxiliary Feature Pyramid Network(SMAFPN)based on the Multi-Branch Auxiliary Feature Pyramid Network architecture,which adaptively fuses high-level semantic and low-level spatial information through Star-s50 connections and C3k2 extraction blocks.Additionally,a composite dataset augmentation strategy combining traditional and advanced augmentation techniques is developed.This strategy is validated on a specialized pavement dataset containing five distinct crack categories for comprehensive training and evaluation.Experimental results indicate that the proposed Star-YOLO11 achieves an accuracy of 89.9%(3.5%higher than the baseline),a mean average precision(mAP)of 90.3%(+2.6%),and an F1-score of 85.8%(+0.5%),while reducing the model size by 18.8%and reaching a frame rate of 225.73 frames per second(FPS)for real-time detection.It shows potential for lightweight deployment in pavement crack detection tasks.
文摘Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.
基金supported by the National Natural Science Foundation of China,specifically through grants(No.8227431382304947)Key Research and Development Project of Shaanxi Province(2023GHZD43).Peer re v iew information。
文摘Background:Neurological disorders(NDs),including ischemic stroke(IS),Parkinson’s disease(PD),and Alzheimer’s disease(AD),are major contributors to global morbidity and mortality.Boswellia extract has demonstrated neuroprotective properties,yet a comprehensive systematic review assessing its efficacy remains absent.This study aims to evaluate the efficacy of Boswellia extract in treating NDs,with a particular focus on its effects in AD and its potential for long-term neurorestoration,thereby supporting further investigation into Boswellia’s therapeutic role in ND management.Methods:A systematic literature search was performed in PubMed,Web of Science,ScienceDirect,and Google Scholar for English-language studies published up to March 2024.Eighteen studies met the inclusion criteria and were included in the meta-analysis.The study protocol was registered on PROSPERO(CRD42024524386).Eligible studies involved rodent models of IS,PD,or AD with post-operative interventions using Boswellia extract.Data extraction focused on mechanisms of action,dosages,treatment durations,and therapeutic outcomes.Studies were excluded if they involved non-ND models,combined treatments,or had incomplete data.Two researchers independently conducted literature screening and data extraction.Statistical analyses were conducted using Stata(version 17)and RevMan(version 5.4),employing fixed or random-effects models based on heterogeneity assessments.Result s:Boswellia extract significantly improved the mean effect size for NDs(ES=1.28,95%CI(1.05,1.51),P<0.001).Specifically,it reduced cerebral infarct volume in IS(SMD=−2.87,95%CI(−3.42,−2.32))and enhanced behavioral outcomes in AD(SMD=3.26,95%CI(2.07,5.14))and PD(SMD=5.37,95%CI(3.93,6.80)).Subgroup analyses revealed that Boswellia extract exhibited superior efficacy in AD when administered orally and via intra-cerebroventricular injection.Long-term treatment with Boswellia extract suggested potential neurorestorative effects.Additionally,Boswellia extract was more effective than its monomeric constituents,highlighting its promising role in ND treatment.Conclusion:Boswellia extract demonstrates significant neuroprotective effects across various NDs,particularly in AD and in promoting long-term neurorestoration.These findings support the need for further research into Boswellia’s potential as a therapeutic agent in the management of neurological disorders.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金financially supported by the National Natural Science Foundation of China(No.52203209)the State Key Laboratory of Solid Waste Reuse for Building Materials,China(No.SWR-2022-009)the Fundamental Research Funds for the Central Universities,China(No.FRF-IDRY22-012)。
文摘Silver nanoparticles(Ag NPs)have attracted attention in the field of biomaterials due to their excellent antibacterial property.However,the reducing and stabilizing agents used for the chemical reduction of Ag NPs are usually toxic and may cause water pollution.In this work,Ag NPs(31.2 nm in diameter)were prepared using the extract of straw,an agricultural waste,as the reducing and stabilizing agent.Experimental analysis revealed that the straw extract contained lignin,the structure of which possesses phenolic hydroxyl and methoxy groups that facilitate the reduction of silver salts into Ag NPs.The surfaces of Ag NPs were negatively charged due to the encapsulation of a thin layer of lignin molecules that prevented their aggregation.After the prepared Ag NPs were added to the precursor solution of acrylamide,free radical polymerization was triggered without the need for extra heating or light irradiation,resulting in the rapid formation of an Ag NP-polyacrylamide composite hydrogel.The inhibition zone test proved that the composite hydrogel possessed excellent antibacterial ability due to the presence of Ag NPs.The prepared hydrogel may have potential applications in the fabrication of biomedical materials,such as antibacterial dressings.
基金Project supported by the National Natural Science Foundation of China(52074031)the Key Research and Development Program of Shandong Province(ZR2021MB051,ZR2020ME256)the Open Project of Key Laboratory of Green Chemical Engineering Process of Ministry of Education(GCP202117)。
文摘Solvent extraction is the main method used to separate and purify rare earth elements.In the process of rare earths extraction,emulsification often generated due to the instability of the aqueous and organic phases or improper operating conditions.Once emulsification occurs,it would not only lead to low rare earths recovery efficiency,small product quantities,high production costs and the losing of extractant and rare earth resources,but also result in serious environmental pollution.Therefore,it is very important to study the micro-mechanisms of emulsification and establish new methods to prevent emulsification at the source.In this paper,possible factors resulting in emulsification,such as the compositions and properties of the organic and aqueous phases,the operating conditions of the rare earths extraction are reviewed.The micro-mechanisms of emulsification are summarized basing on the microscopic structures in the bulk phase,aggregations of the extractants at the organic-aqueous interface,spectral characterizations and computational simulations.On this basis,new formation mechanisms are proposed for emulsification.Preliminary explorations are employed to verify the correctness of these new viewpoints.Finally,future directions for studies of the emulsification micro-mechanism are proposed.This study provides a theoretical basis for further understanding the micro-mechanisms of interfacial instability resulting in emulsification in the process of rare earths extraction.
文摘采用顶空固相微萃取(headspace solid-phase microextraction,HS-SPME)和液液萃取(liquid-liquid extraction,LLE)结合全二维气相色谱飞行时间质谱(comprehensive two-dimensional gas chromatography time of flight mass spectrometry,GC×GC-TOF-MS)以及香气活度值(odour active value,OAV),对红星二锅头白酒的挥发性成分进行全面解析。研究发现,HS-SPME、LLE分别定性出928、802种挥发性化合物,共计定性出1304种挥发性化合物,共同定性出426种挥发性化合物。基于HS-SPME数据,通过香气数据库筛选出具有香气特征的382种香气化合物,其中酯类相对百分含量占比最高,其次是醇类、酸类和醛类。计算得到了49种香气化合物OAV>1,其中酯类(辛酸乙酯、异戊酸乙酯等)和萜烯类(β-大马酮)对白酒风味的贡献最大,醛类(异戊醛、己醛等)和含硫类(二甲基三硫)其次,醇类(1-辛烯-3-醇)和含氮类(2,3,5-三甲基吡嗪)也有一定风味贡献。该研究丰富了红星二锅头白酒的风味研究,也为下一步生产研究及调控提供了理论和数据支撑。
基金supported by the Science and Technology Development Plan Project of Jilin Provincial Department of Science and Technology (No.20220203112S)the Jilin Provincial Department of Education Science and Technology Research Project (No.JJKH20210039KJ)。
文摘In this study,eight different varieties of maize seeds were used as the research objects.Conduct 81 types of combined preprocessing on the original spectra.Through comparison,Savitzky-Golay(SG)-multivariate scattering correction(MSC)-maximum-minimum normalization(MN)was identified as the optimal preprocessing technique.The competitive adaptive reweighted sampling(CARS),successive projections algorithm(SPA),and their combined methods were employed to extract feature wavelengths.Classification models based on back propagation(BP),support vector machine(SVM),random forest(RF),and partial least squares(PLS)were established using full-band data and feature wavelengths.Among all models,the(CARS-SPA)-BP model achieved the highest accuracy rate of 98.44%.This study offers novel insights and methodologies for the rapid and accurate identification of corn seeds as well as other crop seeds.
文摘The use of fresh versus frozen spermatozoa in men with nonobstructive azoospermia (NOA) undergoingin vitro fertilization (IVF)has been a debated hot topic among reproductive specialists. Each approach presents distinct advantages and disadvantages,with fresh sperm typically showing superior sperm quality, while frozen sperm offers logistical flexibility and a reliable backup forrepeated cycles. This review summarizes the latest advancements in sperm retrieval and cryopreservation techniques, providingpractitioners with a comprehensive analysis of each option’s strengths and limitations. Comparative studies indicate that, althoughfresh sperm often has better quality metrics, cryopreservation methods such as vitrification have significantly improved postthawoutcomes, making frozen sperm a viable choice in assisted reproductive technologies (ART). The findings show comparablerates for fertilization, implantation, clinical pregnancy, and live birth between fresh and frozen microdissection testicular spermextraction (micro-TESE) sperm in many cases, although patient-specific factors such as timing, cost-effectiveness, and proceduralconvenience should guide the final decision. Ultimately, the choice of using fresh or frozen sperm should align with the individualneeds and conditions of patients. This tailored approach, supported by the latest advancements, can optimize ART outcomes andprovide personalized reproductive care.
基金supported by the Science and Technology Development Fund,Macao SAR (Nos.0075/2022/A and028/2022/ITP)the Zhuhai Science and Technology Plan Project in the Social Development Field (No.2220004000117)the University of Macao (Nos.MYRG-GRG2023-00082-ICMS-UMDF/CPG2024-00011-ICMS)。
文摘The therapeutic efficacy of traditional Chinese medicine has been widely acknowledged due to its extensive history of clinical effectiveness.However,the precise active components underlying each prescription remain incompletely understood.Polysaccharides,as a major constituent of water decoctions—the most common preparation method for Chinese medicinals—may provide a crucial avenue for deepening our understanding of the efficacy principles of Chinese medicine and establishing a framework for its modern development.The structural complexity and diversity of Chinese herbal polysaccharides present significant challenges in their separation and analysis compared to small molecules.This paper aims to explore the potential of Chinese herbal polysaccharides efficiently by briefly summarizing recent advancements in polysaccharide chemical research,focusing on methods of acquisition,structure elucidation,and quality control.
基金financially supported by the National Natural Science Foundation of China(No.52161016)Joint Key Project of Agricultural Fundamental Research in Yunnan Provinceg(No.202101BD070001-017)+3 种基金Yunnan Provincial Academician Workstation(No.202305AF150009)Yunnan Province Natural Science Key Foundation(No.202201AS070152)Special Project of“Top Young Talents”of Yunnan Ten Thousand Talents Plan(No.51900109)Special Project of"Leading Talents of Industrial Technology"of Yunnan Ten Thousand Talents Plan(No.80201408).
文摘The inhibitory properties of rapeseed cake meal extract(RCME)on the corrosion of cold rolled steel(CRS)in trichloroacetic acid(TCA)were systematically investigated using gravimetric,electrochemical,surface characterizations and theoretical calculations.The results demonstrate that RCME exhibits excellent inhibitory performance with a maximum inhibition efficiency of 92.7%for 100 mg L−1 RCME at 20℃.The adsorption of RCME obeys Langmuir isotherm at 20 and 30℃,Temkin isotherm at 40℃,and Freundlich isotherm at 50℃.RCME acts as a cathodic inhibitor.The charge transfer resistance is increased with the addition of RCME,while the double-layer capacitance decreases.SEM,AFM,CLSM,XPS,XRD and TOF-SIMS analyses confirm that the active components in RCME adsorb onto the surface of CRS,forming a protective film that effectively inhibits the corrosion of CRS by TCA.Along with the increase in the concentration of RCME,the surface tension of the inhibited solution gradually decreases,while the electrical conductivity increases before stabilizing.HPLC-MS and FTIR analyses reveal rutin,linolenic acid,linoleic acid and adenine are the effective substances in RCME.Quantum chemical(QC)calculations and molecular dynamic(MD)simulations indicate that the active centers of the effective inhibitor molecules are predominantly located on benzene rings,O-or N-containing heterocyclic rings,and functional groups such as C=O and C=C.Additionally,their main chains adsorb onto the Fe(001)surface in an approximately flat manner,involving both chemical and physical adsorption processes.
基金supported by the National Natural Science Foundation of China(No.52188102).
文摘Anomaly Detection (AD) has been extensively adopted in industrial settings to facilitate quality control of products. It is critical to industrial production, especially to areas such as aircraft manufacturing, which require strict part qualification rates. Although being more efficient and practical, few-shot AD has not been well explored. The existing AD methods only extract features in a single frequency while defects exist in multiple frequency domains. Moreover, current methods have not fully leveraged the few-shot support samples to extract input-related normal patterns. To address these issues, we propose an industrial few-shot AD method, Feature Extender for Anomaly Detection (FEAD), which extracts normal patterns in multiple frequency domains from few-shot samples under the guidance of the input sample. Firstly, to achieve better coverage of normal patterns in the input sample, we introduce a Sample-Conditioned Transformation Module (SCTM), which transforms support features under the guidance of the input sample to obtain extra normal patterns. Secondly, to effectively distinguish and localize anomaly patterns in multiple frequency domains, we devise an Adaptive Descriptor Construction Module (ADCM) to build and select pattern descriptors in a series of frequencies adaptively. Finally, an auxiliary task for SCTM is designed to ensure the diversity of transformations and include more normal patterns into support features. Extensive experiments on two widely used industrial AD datasets (MVTec-AD and VisA) demonstrate the effectiveness of the proposed FEAD.
基金supported by the National Natural Science Foundation of China(Nos.32301259,32101228,32271527 and 32371536)the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(Nos.2022C02023 and 2023C02015)+1 种基金the Research Foundation of Talented Scholars of Zhejiang A&F University(No.2021LFR058)the Dean-ship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through the project number“NBU-FPEJ-2024-177-01”.
文摘As a typical bioflavonoid,diosmetin is desirable in the field of natural medicine,healthy food,and cosmetics by anti-cancer,antibacterial,antioxidant,estrogen-like and anti-inflammatory activities,and it comes from a wide range of sources in traditional Chinese medicine like spider fragrance,spearmint and chrysanthemum,as well as in Citrus fruit.However,traditional analytical methods such as silica gel column chromatography face multiple challenges in the selective extraction of diosmetin from biological materials and traditional Chinese medicinal materials.Therefore,it is urgent to develop a new type of absorbent with high efficiency,recyclability and good specificity to diosmetin.In this investigation,a magnetic surface molecularly imprinted polymer(labeled as Diosmetin/SMIPs)was synthesized employing magnetic nanoparticles as the carrier and 4-vinylpyridinyl(4-VP)as the functional monomer by surface imprinting technology.The functional monomer was screened by the binding energy(△E)between functional monomers and template molecules via computational simulation.The Diosmetin/SMIPs had a high level of specific recognition and adsorption capability towards diosmetin with a 20.25 mg g^(-1) adsorption capacity and an imprinting factor(IF)of 2.28.Additionally,it demonstrated excellent regeneration performance with 8 adsorption/desorption cycles.In addition,91.20%-94.16% of spiked diosmetin was recovered from the lemon peel samples.The strategy of constructing Diosmetin/SMIPs based on computational simulation can effectively enhance the specific adsorption performance of diosmetin.Meanwhile,Diosmetin/SMIPs synthesized by imprinting polymerization showed excellent anti-interference and reusability,and realized efficient targeted extraction of diosmetin from lemon peel samples.The results of this investigation provide a promising adsorbent for selective enrichment of diosmetin from Citrus fruit and complicated materials.
基金supported by the National Natural Science Foundation of Shandong(No.ZR2020ZD20)the National Natural Science Foundation of China(No.22193051)+1 种基金the National Young Top-Notch Talents(No.W03070030)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.Y202011).
文摘Identification of the most appropriate chemically extractable pool for evaluating Cd and Pb availability remains elusive,hindering accurate assessment on environmental risks and effectiveness of remediation strategies.This study evaluated the feasibility of European Community Bureau of Reference(BCR)sequential extraction,Ca(NO_(3))_(2)extraction,and water extraction on assessing Cd and Pb availability in agricultural soil amended with slaked lime,magnesium hydroxide,corn stover biochar,and calcium dihydrogen phosphate.Moreover,the enriched isotope tracing technique(^(112)Cd and^(206)Pb)was employed to evaluate the aging process of newly introduced Cd and Pbwithin 56 days’incubation.Results demonstrated that extractable pools by BCR and Ca(NO_(3))_(2)extraction were little impacted by amendments and showed little correlation with soil pH.This is notable because soil pH is closely linked to metal availability,indicating these extraction methods may not adequately reflect metal availability.Conversely,water-soluble concentrations of Cd and Pb were markedly influenced by amendments and exhibited strong correlations with pH(Pearson’s r:-0.908 to-0.825,P<0.001),suggesting water extraction as a more sensitive approach.Furthermore,newly introduced metals underwent a more evident aging process as demonstrated by acid-soluble and water-soluble pools.Additionally,water-soluble concentrations of essential metals were impacted by soil amendments,raising caution on their potential effects on plant growth.These findings suggest water extraction as a promising and attractive method to evaluate Cd and Pb availability,which will help provide assessment guidance for environmental risks caused by heavy metals and develop efficient remediation strategies.
文摘BACKGROUND This case report describes a protocol developed by Danaun Medical Clinic for the introduction of a pioneering intervention comprising intravenous human placen-tal extract(HPE)therapy to improve the liver function of patients with chronic liver disease(CLD).CASE SUMMARY This study involved data from patients whose chief complaint was reduced quality of life attributable to CLD.The new treatment approach resulted in improvements in the liver function and fatty liver of 30 patients with CLD.Im-provements were observed using abdominal ultrasonography.Unlike traditional methods,this protocol provided more sustainable and meaningful results.Treat-ment with 10 mL of HPE administered intravenously once or twice per week significantly improved liver function.The observed improvements in fatty liver and liver function suggest the utility of this approach for the management of patients with CLD.CONCLUSION This case series highlights the potential of innovative treatments for patients with CLD that could improve the quality of life of the patients.
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
基金supports from the National Natural Science Foundation of China(82130112,U24A20789)Beijing Hospitals Authority Ascent Plan(DFL20190702)Youth Beijing Scholar(2022-051)。
文摘Lonicera japonica(honeysuckle)is a traditional Chinese medicinal food,in which the main active ingredients are phenolic acids,polysaccharides,flavonoids,and volatile oils.They have various biological activities,including antiviral,antibacterial,antioxidant,hypoglycemic and lipid-lowering,and anti-inflammatory effects.This review summarizes the health effects and pharmacodynamic mechanisms of L.japonica extracts and the major active ingredients in these extracts,and the structures,metabolic process in vivo,and biotransformation processes of these compounds.In addition,the current status of the development of L.japonica-related functional foods is summarized.The aim is to provide a theoretical basis and reference for the further development and use of the active ingredients in L.japonica as functional foods for disease prevention and treatment.
基金This study was supported by the National Natural Science Foundation of China(U20A20148)the Major Science and Technology Projects of the Xizang(Tibet)Autonomous Region(XZ202201ZD0004G and XZ202201ZD0004G01).
文摘This research optimized the structure of lithium extraction solar ponds to enhance the crystallization rate and yield of Li_(2)CO_(3).Using the response surface methodology in Design-Expert 10.0.3,the authors conducted experiments to investigate the influence of four factors related to solar pond structure on the crystallization of Li_(2)CO_(3) and their pairwise interactions.Computational Fluid Dynamics(CFD)simulations of the flow field within the solar pond were performed using COMSOL Multiphysics software to compare temperature distributions before and after optimization.The results indicate that the optimal structure for lithium extraction from the Zabuye Salt Lake solar ponds includes UCZ(Upper Convective Zone)thickness of 53.63 cm,an LCZ(Lower Convective Zone)direct heating temperature of 57.39℃,a CO32−concentration of 32.21 g/L,and an added soda ash concentration of 6.52 g/L.Following this optimized pathway,the Li_(2)CO_(3) precipitation increased by 7.34% compared to the initial solar pond process,with a 33.33% improvement in lithium carbonate crystallization rate.This study demonstrates the feasibility of optimizing lithium extraction solar pond structures,offering a new approach for constructing such ponds in salt lakes.It provides valuable guidance for the efficient extraction of lithium resources from carbonate-type salt lake brines.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金the“Intelligent Recognition Industry Service Center”as part of the Featured Areas Research Center Program under the Higher Education Sprout Project by the Ministry of Education(MOE)in Taiwan,and the National Science and Technology Council,Taiwan,under grants 113-2221-E-224-041 and 113-2622-E-224-002.Additionally,partial support was provided by Isuzu Optics Corporation.
文摘Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.
