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
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
With the legislative development,the organic and inorganic composition separation has become the primary requirement for sewer sediment disposal,however the relevant technology has been rarely reported and the driving...With the legislative development,the organic and inorganic composition separation has become the primary requirement for sewer sediment disposal,however the relevant technology has been rarely reported and the driving mechanism was still unclear.In this study,direct disintegration of biopolymers and indirect broken of connection point were investigated on the hydrolysis and component separation.Three typical sewer sediment treatment approaches,i.e.,alkaline,thermal and cation exchange treatments were proposed,which represented the hydrolysis-driving forces of chemical hydrolysis,physical hydrolysis and innovative cation bridging break-age.The results showed that the organic and inorganic separation rates of sewer sediment driven by alkaline,thermal and cation exchange treatments reached 21.26%,23.80%,and 19.56%-48.0%,respectively,compared to 4.43%in control.The secondary structure of proteins was disrupted,transitioning from𝛼α-helix to𝛽β-turn and random coil.Meanwhile,much biopolymers were released from solid to the liquid phase.From thermody-namic perspective,sewer sediment deposition was controlled by short-range interfacial interactions described by extended Derjaguin-Landau-Verwey-Overbeek theory.Additionally,the separation of organic and inorganic components was positively correlated with the thermodynamic parameters(Corr=0.87),highlighted the robust-ness of various driving forces.And the flocculation energy barriers were 2.40(alkaline),1.60 times(thermal),and 4.02–4.97 times(cation exchange)compared to control group.The findings revealed the contrition differ-ence of direct disintegration of gelatinous biopolymers and indirect breakage of composition connection sites in sediment composition separation,filling the critical gaps in understanding the specific mechanisms of sediment biopolymer disintegration and intermolecular connection breakage.展开更多
Two-dimensional(2D)materials show great potential as novel membrane materials due to their atomic thickness and periodic pore structure.Currently,free-standing membranes based on 2D materials open up new avenues for u...Two-dimensional(2D)materials show great potential as novel membrane materials due to their atomic thickness and periodic pore structure.Currently,free-standing membranes based on 2D materials open up new avenues for ultra-fast and highly selective separation.With the absence of porous substrates,free-standing membranes offer shortened transport paths for efficient mass transfer.The interfacial defects between the substrate and selective layer are eliminated to alleviate the internal membrane fouling,enabling the intact structure for precise separation.Hence,this review aims to outline the superiority of 2D material-based free-standing membranes for selective separation applications.Free-standing 2D material membranes composed of the most representative graphenebased materials,MXene,covalent organic framework(COF),metal organic framework(MOF),and hydrogen-bonded organic framework(HOF)are summarized with the discussion on the influence of substrate on their structural properties.The separation performance enhancement strategies in regard to the 2D material,membrane structure,and mechanical properties are examined.Finally,we propose several critical challenges and perspectives in terms of pore size control,mechanical strength improvement,understanding the underlying mass transfer mechanism,issues related to membrane fabrication optimization,scale production,and separation application versatility.This review will provide researchers with practical guidelines for advancing free-standing 2D material membranes for future selective separation applications.展开更多
The conceptions of the knowledge screen generated by S-rough sets are given: f- screen and - screen , and then puts forward - filter theorem, - filter theorem of knowledge. At last, the applications of knowledge separ...The conceptions of the knowledge screen generated by S-rough sets are given: f- screen and - screen , and then puts forward - filter theorem, - filter theorem of knowledge. At last, the applications of knowledge separation are given according to - screen and - screen.展开更多
Mineral fulvic acid(MFA)was used as an eco-friendly pyrite depressant to recover chalcopyrite by flotation with the use of the butyl xanthate as a collector.Flotation experiments showed that MFA produced a stronger in...Mineral fulvic acid(MFA)was used as an eco-friendly pyrite depressant to recover chalcopyrite by flotation with the use of the butyl xanthate as a collector.Flotation experiments showed that MFA produced a stronger inhibition effect on pyrite than on chalcopyrite.The separation of chalcopyrite from pyrite was realized by introducing 150 mg/L MFA at a pulp pH of approximately 8.0.The copper grade,copper recovery,and separation efficiency were 28.03%,84.79%,and 71.66%,respectively.Surface adsorption tests,zeta potential determinations,and localized electrochemical impedance spectroscopy tests showed that more MFA adsorbed on pyrite than on chalcopyrite,which weakened the subsequent interactions between pyrite and the collector.Atomic force microscope imaging further confirmed the adsorption of MFA on pyrite,and X-ray photoelectron spectroscopy results indicated that hydrophilic Fe-based species on the pyrite surfaces increased after exposure of pyrite to MFA,thereby decreasing the floatability of pyrite.展开更多
Melamine sponge is a major concern for oil-water separation due to its lightweight,high porosity(>99%),cost-effectiveness,impressive mechanical properties,and chemical/thermal stability.However,its amphiphilic natu...Melamine sponge is a major concern for oil-water separation due to its lightweight,high porosity(>99%),cost-effectiveness,impressive mechanical properties,and chemical/thermal stability.However,its amphiphilic nature hinders selective oil absorption in water.Recent strategies to enhance hydrophobicity are reviewed,including synthetic methods and materials,with comprehensive explanations of the mechanisms driven by surface energy and roughness.Key performance indicators for MS in oil-water separation,including adsorption capacity,wettability,stability,emulsion separation,reversible wettability switching,flame retardancy,mechanical properties,and recyclability,are thoroughly discussed.In conclusion,this review provides insights into the future potential and direction of functional melamine sponges in oil-water separation.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
BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on ...BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on the knowledge,attitudes,and preventive practices regarding breast cancer and the associated factors among females in Wollo,Ethiopia,remain limited.AIM To assess the impact of family history(FH)of breast disease on knowledge,attitudes,and breast cancer preventive practices among reproductive-age females.METHODS A community-based cross-sectional study was conducted in May and June 2022 in Northeast Ethiopia and involved 143 reproductive-age females with FH of breast diseases and 209 without such a history.We selected participants using the systematic random sampling technique.We analyzed the data using Statistical Package for Social Science version 25 software,and logistic regression analysis was employed to determine odds ratios for variable associations,with statistical significance set at P<0.05.RESULTS Among participants with FH of breast diseases,the levels of knowledge,attitudes,and preventive practices were found to be 83.9%[95%confidence interval(CI):77.9-89.9],49.0%(95%CI:40.8-57.1),and 74.1%(95%CI:66.9-81.3),respectively.In contrast,among those without FH of breast diseases,these levels were significantly decreased to 10.5%(95%CI:6.4-14.7),32.1%(95%CI:25.7-38.4),and 16.7%(95%CI:11.7-21.8),respectively.This study also indicated that knowledge,attitudes,and preventive practices related to breast cancer are significantly higher among participants with FH of breast diseases compared to those without HF breast diseases.CONCLUSION Educational status,monthly income,and community health insurance were identified as significant factors associated with the levels of knowledge,attitudes,and preventive practices regarding breast cancer among reproductive-age females.展开更多
Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate...Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.展开更多
Comprehensive utilization of phosphogypsum(PG)has attracted much attention,especially for the recovery of rare earth elements(REEs)and gypsum due to the issues of stockpile,environmental pollution,and waste of associa...Comprehensive utilization of phosphogypsum(PG)has attracted much attention,especially for the recovery of rare earth elements(REEs)and gypsum due to the issues of stockpile,environmental pollution,and waste of associated resources.Traditional utilization methods suffered the issues of low REEs leaching efficiency,huge amount of CaSO_(4)saturated wastewater and high recovery cost.To solve these issues,this study investigated the occurrence of REEs in PG and the leaching of REEs.The results show that REEs in PG are in the forms of(1)REEs mineral inclusions,(2)REEs isomorphous substitution of Ca^(2+)in gypsum lattice,(3)dispersed soluble REEs salts.Acid leaching results demonstrate that(1)the dissolution of gypsum matrix is the control factor of REEs leaching;(2)H_(2)SO_(4)is a promising leachant considering the recycle of leachate;(3)the gypsum matrix suffers a recrystallization during the acid leaching and releases the soluble REEs from PG to aqueous solution.For the recovery of the undissolved REEs mineral inclusions,wet sieving concentrated 37.1 wt%of the REEs in a 10.7 wt%mass,increasing REEs content from 309 to 1071 ppm.Finally,a green process combining gravity separation and hydrometallurgy is proposed.This process owns the merits of wastewater free,considerable REEs recovery(about 10%increase compared with traditional processes),excellent gypsum purification(>95 wt%CaSO_(4)·2H_(2)O,with<0.06 wt%of soluble P_(2)O_(5) and<0.015 wt%of soluble F)and reagent saving(about 2/3less reagent consumption than non-cyclical leaching).展开更多
Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challeng...Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.展开更多
Instrument separation is a critical complication during root canal therapy,impacting treatment success and long-term tooth preservation.The etiology of instrument separation is multifactorial,involving the intricate a...Instrument separation is a critical complication during root canal therapy,impacting treatment success and long-term tooth preservation.The etiology of instrument separation is multifactorial,involving the intricate anatomy of the root canal system,instrument-related factors,and instrumentation techniques.Instrument separation can hinder thorough cleaning,shaping,and obturation of the root canal,posing challenges to successful treatment outcomes.Although retrieval of separated instrument is often feasible,it carries risks including perforation,excessive removal of tooth structure and root fractures.Effective management of separated instruments requires a comprehensive understanding of the contributing factors,meticulous preoperative assessment,and precise evaluation of the retrieval difficulty.The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes.The current manuscript provides a framework for understanding the causes,risk factors,and clinical management principles of instrument separation.By integrating effective strategies,endodontists can enhance decision-making,improve endodontic treatment success and ensure the preservation of natural dentition.展开更多
Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have in...Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific accuracy.To address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model co-evolution.First,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge prompting.Second,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge injection.Third,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual learning.We illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various domains.We conclude by outlining future pathways for further advancement and applications.展开更多
In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by pred...In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.展开更多
Copper and cobalt were recovered from SICOMINES mining waste rock in the Democratic Republic of Congo.The process mineralogy of the samples was analyzed using scanning electron microscopy and energy dispersive spectro...Copper and cobalt were recovered from SICOMINES mining waste rock in the Democratic Republic of Congo.The process mineralogy of the samples was analyzed using scanning electron microscopy and energy dispersive spectroscopy.The results showed that copper minerals exhibited various forms and uneven particle sizes,while cobalt existed in the form of highly dispersed asbolane,and large amounts of easily slimed gangue minerals were filled in the samples,making it difficult to separate copper and cobalt minerals.The particle size range plays a decisive role in selecting the separation method for the copper−cobalt ore.Gravity separation was suitable for particles ranging from 43 to 246μm,while flotation was more effective for particles below 43μm.After ore grinding and particle size classification,applying a combined gravity separation(shaking table)−flotation method yielded concentrated minerals with a copper recovery of 72.83%and a cobalt recovery of 31.13%.展开更多
The research,fabrication and development of piezoelectric nanofibrous materials offer effective solutions to the challenges related to energy consumption and non-renewable resources.However,enhancing their electrical ...The research,fabrication and development of piezoelectric nanofibrous materials offer effective solutions to the challenges related to energy consumption and non-renewable resources.However,enhancing their electrical output still remains a significant challenge.Here,a strategy of inducing constrained phase separation on single nanofibers via shear force was proposed.Employing electrospinning technology,a polyacrylonitrile/polyvinylidene difluoride(PAN/PVDF)nanofibrous membrane was fabricated in one step,which enabled simultaneous piezoelectric and triboelectric conversion within a single-layer membrane.Each nanofiber contained independent components of PAN and PVDF and exhibited a rough surface.The abundant frictional contact points formed between these heterogeneous components contributed to an enhanced endogenous triboelectric output,showcasing an excellent synergistic effect of piezoelectric and triboelectric response in the nanofibrous membrane.Additionally,the component mass ratio influenced the microstructure,piezoelectric conformation and piezoelectric performance of the PAN/PVDF nanofibrous membranes.Through comprehensive performance comparison,the optimal mass ratio of PAN to PVDF was determined to be 9∶1.The piezoelectric devices made of the optimal PAN/PVDF nanofibrous membranes with rough nanofiber surfaces generated an output voltage of 20 V,which was about 1.8 times that of the smooth one at the same component mass ratio.The strategy of constrained phase separation on the surface of individual nanofibers provides a new approach to enhance the output performance of single-layer piezoelectric nanofibrous materials.展开更多
Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI pre...Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.展开更多
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 State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
基金supported by Shaanxi Key Research and Development Program(No.2024SF-YBXM-546)the National Natural Science Foundation of China(No.52470161)the State Key Laboratory of Pollution Control and Resource Reuse Foundation(No.PCRRF21007).
文摘With the legislative development,the organic and inorganic composition separation has become the primary requirement for sewer sediment disposal,however the relevant technology has been rarely reported and the driving mechanism was still unclear.In this study,direct disintegration of biopolymers and indirect broken of connection point were investigated on the hydrolysis and component separation.Three typical sewer sediment treatment approaches,i.e.,alkaline,thermal and cation exchange treatments were proposed,which represented the hydrolysis-driving forces of chemical hydrolysis,physical hydrolysis and innovative cation bridging break-age.The results showed that the organic and inorganic separation rates of sewer sediment driven by alkaline,thermal and cation exchange treatments reached 21.26%,23.80%,and 19.56%-48.0%,respectively,compared to 4.43%in control.The secondary structure of proteins was disrupted,transitioning from𝛼α-helix to𝛽β-turn and random coil.Meanwhile,much biopolymers were released from solid to the liquid phase.From thermody-namic perspective,sewer sediment deposition was controlled by short-range interfacial interactions described by extended Derjaguin-Landau-Verwey-Overbeek theory.Additionally,the separation of organic and inorganic components was positively correlated with the thermodynamic parameters(Corr=0.87),highlighted the robust-ness of various driving forces.And the flocculation energy barriers were 2.40(alkaline),1.60 times(thermal),and 4.02–4.97 times(cation exchange)compared to control group.The findings revealed the contrition differ-ence of direct disintegration of gelatinous biopolymers and indirect breakage of composition connection sites in sediment composition separation,filling the critical gaps in understanding the specific mechanisms of sediment biopolymer disintegration and intermolecular connection breakage.
基金granted by Shandong Provincial Natural Science Foundation,China(No.ZR2023QB170)Guangxi First class Disciplines(Agricultural Resources and Environment),Open Project of State Key Laboratory of Urban Water Resource and Environment,Harbin Institute of Technology(No.ES202428)+3 种基金Shandong Excellent Young Scientists Fund Program(Overseas)(No.2024HWYQ-051)the National Natural Science Fund of China(No.22506033)Young Elite Scientists Sponsorship Program by CASTYoung Taishan Scholars Program of Shandong Province.
文摘Two-dimensional(2D)materials show great potential as novel membrane materials due to their atomic thickness and periodic pore structure.Currently,free-standing membranes based on 2D materials open up new avenues for ultra-fast and highly selective separation.With the absence of porous substrates,free-standing membranes offer shortened transport paths for efficient mass transfer.The interfacial defects between the substrate and selective layer are eliminated to alleviate the internal membrane fouling,enabling the intact structure for precise separation.Hence,this review aims to outline the superiority of 2D material-based free-standing membranes for selective separation applications.Free-standing 2D material membranes composed of the most representative graphenebased materials,MXene,covalent organic framework(COF),metal organic framework(MOF),and hydrogen-bonded organic framework(HOF)are summarized with the discussion on the influence of substrate on their structural properties.The separation performance enhancement strategies in regard to the 2D material,membrane structure,and mechanical properties are examined.Finally,we propose several critical challenges and perspectives in terms of pore size control,mechanical strength improvement,understanding the underlying mass transfer mechanism,issues related to membrane fabrication optimization,scale production,and separation application versatility.This review will provide researchers with practical guidelines for advancing free-standing 2D material membranes for future selective separation applications.
文摘The conceptions of the knowledge screen generated by S-rough sets are given: f- screen and - screen , and then puts forward - filter theorem, - filter theorem of knowledge. At last, the applications of knowledge separation are given according to - screen and - screen.
基金supported by Fundamental Research Projects of Yunnan Province,China(Nos.202101BE070001-009,202301AU070189).
文摘Mineral fulvic acid(MFA)was used as an eco-friendly pyrite depressant to recover chalcopyrite by flotation with the use of the butyl xanthate as a collector.Flotation experiments showed that MFA produced a stronger inhibition effect on pyrite than on chalcopyrite.The separation of chalcopyrite from pyrite was realized by introducing 150 mg/L MFA at a pulp pH of approximately 8.0.The copper grade,copper recovery,and separation efficiency were 28.03%,84.79%,and 71.66%,respectively.Surface adsorption tests,zeta potential determinations,and localized electrochemical impedance spectroscopy tests showed that more MFA adsorbed on pyrite than on chalcopyrite,which weakened the subsequent interactions between pyrite and the collector.Atomic force microscope imaging further confirmed the adsorption of MFA on pyrite,and X-ray photoelectron spectroscopy results indicated that hydrophilic Fe-based species on the pyrite surfaces increased after exposure of pyrite to MFA,thereby decreasing the floatability of pyrite.
基金supported by the National Natural Science Foundation of China(Nos.52372093 and 52102145)the Key R&D Program of Shaanxi Province(Nos.2023GXLH-045 and 2022SF-168)+4 种基金the Xi’an Programs for Science and Technology Plan(Nos.2020KJRC0090 and 21XJZZ0045)the Opening Project of Shanxi Key Laboratory of Advanced Manufacturing Technology(No.XJZZ202001)the Xi’an Municipal Bureau of Science and Technology(No.21XJZZ0054)the Open Foundation of Key Laboratory of Auxiliary Chemistry and Technology for Chemical Industry,Ministry of Education,Shaanxi University of Science and Technology(No.KFKT2021-01)the Shaanxi Collaborative Innovation Center of Industrial Auxiliary Chemistry and Technology,Shaanxi University of Science and Technology(No.KFKT2021-01).
文摘Melamine sponge is a major concern for oil-water separation due to its lightweight,high porosity(>99%),cost-effectiveness,impressive mechanical properties,and chemical/thermal stability.However,its amphiphilic nature hinders selective oil absorption in water.Recent strategies to enhance hydrophobicity are reviewed,including synthetic methods and materials,with comprehensive explanations of the mechanisms driven by surface energy and roughness.Key performance indicators for MS in oil-water separation,including adsorption capacity,wettability,stability,emulsion separation,reversible wettability switching,flame retardancy,mechanical properties,and recyclability,are thoroughly discussed.In conclusion,this review provides insights into the future potential and direction of functional melamine sponges in oil-water separation.
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.
文摘BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on the knowledge,attitudes,and preventive practices regarding breast cancer and the associated factors among females in Wollo,Ethiopia,remain limited.AIM To assess the impact of family history(FH)of breast disease on knowledge,attitudes,and breast cancer preventive practices among reproductive-age females.METHODS A community-based cross-sectional study was conducted in May and June 2022 in Northeast Ethiopia and involved 143 reproductive-age females with FH of breast diseases and 209 without such a history.We selected participants using the systematic random sampling technique.We analyzed the data using Statistical Package for Social Science version 25 software,and logistic regression analysis was employed to determine odds ratios for variable associations,with statistical significance set at P<0.05.RESULTS Among participants with FH of breast diseases,the levels of knowledge,attitudes,and preventive practices were found to be 83.9%[95%confidence interval(CI):77.9-89.9],49.0%(95%CI:40.8-57.1),and 74.1%(95%CI:66.9-81.3),respectively.In contrast,among those without FH of breast diseases,these levels were significantly decreased to 10.5%(95%CI:6.4-14.7),32.1%(95%CI:25.7-38.4),and 16.7%(95%CI:11.7-21.8),respectively.This study also indicated that knowledge,attitudes,and preventive practices related to breast cancer are significantly higher among participants with FH of breast diseases compared to those without HF breast diseases.CONCLUSION Educational status,monthly income,and community health insurance were identified as significant factors associated with the levels of knowledge,attitudes,and preventive practices regarding breast cancer among reproductive-age females.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDB0740000National Key Research and Development Program of China,No.2022YFB3904200,No.2022YFF0711601+1 种基金Key Project of Innovation LREIS,No.PI009National Natural Science Foundation of China,No.42471503。
文摘Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.
基金Project supported by the National Natural Science Foundation of China(52104354)the National Natural Science Foundation of China(51674036)+1 种基金Joint Fund for Nuclear Technology Innovation Sponsored by the National Natural Science Foundation of Chinathe China National Nuclear Corporation(U2067201)。
文摘Comprehensive utilization of phosphogypsum(PG)has attracted much attention,especially for the recovery of rare earth elements(REEs)and gypsum due to the issues of stockpile,environmental pollution,and waste of associated resources.Traditional utilization methods suffered the issues of low REEs leaching efficiency,huge amount of CaSO_(4)saturated wastewater and high recovery cost.To solve these issues,this study investigated the occurrence of REEs in PG and the leaching of REEs.The results show that REEs in PG are in the forms of(1)REEs mineral inclusions,(2)REEs isomorphous substitution of Ca^(2+)in gypsum lattice,(3)dispersed soluble REEs salts.Acid leaching results demonstrate that(1)the dissolution of gypsum matrix is the control factor of REEs leaching;(2)H_(2)SO_(4)is a promising leachant considering the recycle of leachate;(3)the gypsum matrix suffers a recrystallization during the acid leaching and releases the soluble REEs from PG to aqueous solution.For the recovery of the undissolved REEs mineral inclusions,wet sieving concentrated 37.1 wt%of the REEs in a 10.7 wt%mass,increasing REEs content from 309 to 1071 ppm.Finally,a green process combining gravity separation and hydrometallurgy is proposed.This process owns the merits of wastewater free,considerable REEs recovery(about 10%increase compared with traditional processes),excellent gypsum purification(>95 wt%CaSO_(4)·2H_(2)O,with<0.06 wt%of soluble P_(2)O_(5) and<0.015 wt%of soluble F)and reagent saving(about 2/3less reagent consumption than non-cyclical leaching).
基金supported by Natural Science Foundation of Sichuan,China(Grant No.:2024ZDZX0019).
文摘Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.
文摘Instrument separation is a critical complication during root canal therapy,impacting treatment success and long-term tooth preservation.The etiology of instrument separation is multifactorial,involving the intricate anatomy of the root canal system,instrument-related factors,and instrumentation techniques.Instrument separation can hinder thorough cleaning,shaping,and obturation of the root canal,posing challenges to successful treatment outcomes.Although retrieval of separated instrument is often feasible,it carries risks including perforation,excessive removal of tooth structure and root fractures.Effective management of separated instruments requires a comprehensive understanding of the contributing factors,meticulous preoperative assessment,and precise evaluation of the retrieval difficulty.The application of appropriate retrieval techniques is essential to minimize complications and optimize clinical outcomes.The current manuscript provides a framework for understanding the causes,risk factors,and clinical management principles of instrument separation.By integrating effective strategies,endodontists can enhance decision-making,improve endodontic treatment success and ensure the preservation of natural dentition.
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
基金supported in part by National Natural Science Foundation of China(62441605)。
文摘Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific accuracy.To address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model co-evolution.First,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge prompting.Second,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge injection.Third,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual learning.We illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various domains.We conclude by outlining future pathways for further advancement and applications.
基金supported by National Natural Science Foundation of China(Nos.62266054 and 62166050)Key Program of Fundamental Research Project of Yunnan Science and Technology Plan(No.202201AS070021)+2 种基金Yunnan Fundamental Research Projects(No.202401AT070122)Yunnan International Joint Research and Development Center of China-Laos-Thailand Educational Digitalization(No.202203AP140006)Scientific Research Foundation of Yunnan Provincial Department of Education(No.2024Y159).
文摘In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.
基金National Key Research and Development Program of China(No.2020YFC1909202)Major Science and Technology Program of Yunnan Province,China(No.202202AB080012)for financial support。
文摘Copper and cobalt were recovered from SICOMINES mining waste rock in the Democratic Republic of Congo.The process mineralogy of the samples was analyzed using scanning electron microscopy and energy dispersive spectroscopy.The results showed that copper minerals exhibited various forms and uneven particle sizes,while cobalt existed in the form of highly dispersed asbolane,and large amounts of easily slimed gangue minerals were filled in the samples,making it difficult to separate copper and cobalt minerals.The particle size range plays a decisive role in selecting the separation method for the copper−cobalt ore.Gravity separation was suitable for particles ranging from 43 to 246μm,while flotation was more effective for particles below 43μm.After ore grinding and particle size classification,applying a combined gravity separation(shaking table)−flotation method yielded concentrated minerals with a copper recovery of 72.83%and a cobalt recovery of 31.13%.
基金National Natural Science Foundation of China(No.52373281)National Energy-Saving and Low-Carbon Materials Production and Application Demonstration Platform Program,China(No.TC220H06N)。
文摘The research,fabrication and development of piezoelectric nanofibrous materials offer effective solutions to the challenges related to energy consumption and non-renewable resources.However,enhancing their electrical output still remains a significant challenge.Here,a strategy of inducing constrained phase separation on single nanofibers via shear force was proposed.Employing electrospinning technology,a polyacrylonitrile/polyvinylidene difluoride(PAN/PVDF)nanofibrous membrane was fabricated in one step,which enabled simultaneous piezoelectric and triboelectric conversion within a single-layer membrane.Each nanofiber contained independent components of PAN and PVDF and exhibited a rough surface.The abundant frictional contact points formed between these heterogeneous components contributed to an enhanced endogenous triboelectric output,showcasing an excellent synergistic effect of piezoelectric and triboelectric response in the nanofibrous membrane.Additionally,the component mass ratio influenced the microstructure,piezoelectric conformation and piezoelectric performance of the PAN/PVDF nanofibrous membranes.Through comprehensive performance comparison,the optimal mass ratio of PAN to PVDF was determined to be 9∶1.The piezoelectric devices made of the optimal PAN/PVDF nanofibrous membranes with rough nanofiber surfaces generated an output voltage of 20 V,which was about 1.8 times that of the smooth one at the same component mass ratio.The strategy of constrained phase separation on the surface of individual nanofibers provides a new approach to enhance the output performance of single-layer piezoelectric nanofibrous materials.
基金supported by the National Natural Science Foundation of China(Nos.82173746 and U23A20530)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission)。
文摘Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.
