Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,...Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,and interference from contamination.To address these challenges,this paper proposes the Real-time Cable Defect Detection Network(RC2DNet),which achieves an optimal balance between detection accuracy and computational efficiency.Unlike conventional approaches,RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids,multi-level feature fusion,and an adaptive weighting mechanism.Additionally,a boundary feature enhancement module is designed,incorporating boundary-aware convolution,a novel boundary attention mechanism,and an improved loss function to significantly enhance boundary localization accuracy.Experimental results demonstrate that RC2DNet outperforms state-of-the-art methods in precision,recall,F1-score,mean Intersection over Union(mIoU),and frame rate,enabling real-time and highly accurate cable defect detection in complex backgrounds.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
AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longit...AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.展开更多
AIM： To rapidly quantify hepatitis B virus （HBV） DNA by real-time PCR using efficient TaqMan probe and extraction methods of virus DNA. METHODS： Three standards were prepared by cloning PCR products which targeted...AIM： To rapidly quantify hepatitis B virus （HBV） DNA by real-time PCR using efficient TaqMan probe and extraction methods of virus DNA. METHODS： Three standards were prepared by cloning PCR products which targeted S, C and X region of HBV genome into pGEM-T vector respectively. A pair of primers and matched TaqMan probe were selected by comparing the copy number and the Ct values of HBV serum samples derived from the three different standard curves using certain serum DNA. Then the efficiency of six HBV DNA extraction methods including guanidinium isothiocyanate, proteinase K, NaI, NaOH lysis, alkaline lysis and simple boiling was analyzed in sample A, B and C by real-time PCR. Meanwhile, 8 clinical HBV serum samples were quantified. RESULTS： The copy number of the same HBV serum sample originated from the standard curve of S, C and X regions was 5.7 × 10^4/mL, 6.3 × 10^2/mL and 1.6 × 10^3/ mL respectively. The relative Ct value was 26.6, 31.8 and 29.5 respectively. Therefore, primers and matched probe from S region were chosen for further optimization of six extraction methods. The copy number of HBV serum samples A, B and C was 3.49 × 10^9/mL, 2.08 × 10^6/mL and 4.40 × 10^7/mL respectively, the relative Ct value was 19.9, 30 and 26.2 in the method of NaOH lysis, which was the efficientest among six methods. Simple boiling showed a slightly lower efficiency than NaOH lysis. Guanidinium isothiocyanate, proteinase K and NaI displayed that the copy number of HBV serum sample A, B and C was around 10^S/mL, meanwhile the Ct value was about 30. Alkaline failed to quantify the copy number of three HBV serum samples. Standard deviation （SD） and coefficient variation （CV） were very low in all 8 clinical HBV serum samples, showing that quantification of HBV DNA in triplicate was reliable and accurate. CONCLUSION： Real-time PCR based on optimized primers and TaqMan probe from S region in combination with NaOH lysis is a simple, rapid and accurate method for quantification of HBV serum DNA.展开更多
An effective approach is presented to extract welds from real-time radiographs, Firstly an algorithm based on an adaptive bidirectional threshold was proposed to segment the gradient image into ternary image, and then...An effective approach is presented to extract welds from real-time radiographs, Firstly an algorithm based on an adaptive bidirectional threshold was proposed to segment the gradient image into ternary image, and then the bidirectional accumulator Hough Transform was developed to extract weld edges from the ternary image. Different values of the coefficient proposed in the threshold algorithm were tested, and the proposed approach was applied to extract welds from real-time radiographic images of different types of welds with defects. Results show that the proposed method is adaptive and effective to extract welds from real-time radiographs of linear welds.展开更多
Violence recognition is crucial because of its applications in activities related to security and law enforcement.Existing semi-automated systems have issues such as tedious manual surveillances,which causes human err...Violence recognition is crucial because of its applications in activities related to security and law enforcement.Existing semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less effective.Several approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based methods.Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods.However,the their performance must be improved.This study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video data.In the proposed framework,the keyframe extraction technique eliminates duplicate consecutive frames.This keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate frames.For feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the architecture.For empirical analysis,four widely used standard datasets are used with diverse activities.The results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.展开更多
An algorithm applied to a real-time extraction image of vehicle is introduced. The algorithm include an image processing with a binarzation method, image grab for a vehicle with high speed, character isolator one by o...An algorithm applied to a real-time extraction image of vehicle is introduced. The algorithm include an image processing with a binarzation method, image grab for a vehicle with high speed, character isolator one by one, and neural network algorithm. The techniques include vehicles sensing, image garb control, vehicle license location, lighting and optic character recognition. The system is much more robust and faster than the traditional thresholding method.展开更多
Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed vi...Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed video with interframe motion vectors for speed, density and flow detection, has been proposed for ex-traction of traffic information under fixed camera setting and well-defined environment. The motion vectors arefirst separated from the compressed video streams, and then filtered to eliminate incorrect and noisy vectors u-sing the well-defined environmental knowledge. By applying the projective transform and using the filtered mo-tion vectors, speed can be calculated from motion vector statistics, density can be estimated using the motionvector occupancy, and flow can be detected using the combination of speed and density. The embodiment of aprototype system for sky camera traffic monitoring using the MPEG video has been implemented, and experi-mental results proved the effectiveness of the method proposed.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
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
The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for th...The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for the market service for green energy consumers.This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and termfrequency-inverse document frequency(TF-IDF)algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market.First,the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete,interval,and relational features.A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts.Then,TF-IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters.Finally,the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China.It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages,whose averaged values are 25.64%,50%,39.66%,and 24.89%in four different clusters,respectively.Additionally,power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.展开更多
Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatical...Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatically mine available SFO resources.An essential aspect of constructing SFO-KG is the extraction of Chinese entity relations.Unfortunately,there is currently no publicly available Chinese SFO entity Relation Extraction(RE)dataset.Moreover,publicly available SFO text data contain numerous NA(representing for“No Answer”)relation category sentences that resemble other relation sentences and pose challenges in accurate classification,resulting in low recall and precision for the NA relation category in entity RE.Consequently,this issue adversely affects both the accuracy of constructing the knowledge graph and the efficiency of RE processes.To address these challenges,this paper proposes a method for extracting Chinese SFO text entity relations based on dynamic integrated learning.This method includes the construction of a manually annotated Chinese SFO entity RE dataset and a classifier combining features of SFO resource data.The proposed approach combines integrated learning and pre-training models,specifically utilizing Bidirectional Encoder Representation from Transformers(BERT).In addition,it incorporates one-class classification,attention mechanisms,and dynamic feedback mechanisms to improve the performance of the RE model.Experimental results show that the proposed method outperforms the traditional methods in terms of F1 value when extracting entity relations from both balanced and long-tailed datasets.展开更多
Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the con...Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.展开更多
Background:Ampelopsis grossedentata,vine tea,which is the tea alternative beverages in China.In vine tea processing,a large amount of broken tea is produced,which has low commercial value.Methods:This study investigat...Background:Ampelopsis grossedentata,vine tea,which is the tea alternative beverages in China.In vine tea processing,a large amount of broken tea is produced,which has low commercial value.Methods:This study investigates the influence of different extraction methods(room temperature water extraction,boiling water extraction,ultrasonic-assisted room temperature water extraction,and ultrasonic-assisted boiling water extraction,referred to as room temperature water extraction(RE),boiling water extraction(BE),ultrasonic assistance at room temperature water extraction(URE),and ultrasonic assistance in boiling water extraction(UBE))on the yield,dihydromyricetin(DMY)content,free amino acid composition,volatile aroma components,and antioxidant properties of vine tea extracts.Results:A notable influence of extraction temperature on the yield of vine tea extracts(P<0.05),with BE yielding the highest at 43.13±0.26%,higher than that of RE(34.29±0.81%).Ultrasound-assisted extraction significantly increased the DMY content of the extracts(P<0.05),whereas DMY content in the RE extracts was 59.94±1.70%,that of URE reached 66.14±2.78%.Analysis revealed 17 amino acids,with L-serine and aspartic acid being the most abundant in the extracts,nevertheless ultrasound-assisted extraction reduced total free amino acid content.Gas chromatography-mass spectrometry analysis demonstrated an increase in the diversity and quantity of compounds in the vine tea water extracts obtained through ultrasonic-assisted extraction.Specifically,69 and 68 volatile compounds were found in URE and UBE extracts,which were higher than the number found in RE and BE extracts.In vitro,antioxidant activity assessments revealed varying antioxidant capacities among different extraction methods,with RE exhibiting the highest DPPH scavenging rate,URE leading in ABTS•+free radical scavenging,and BE demonstrating superior ferric ion reducing antioxidant activity.Conclusion:The findings suggest that extraction methods significantly influence the chemical composition and antioxidant properties of vine tea extracts.Ultrasonic-assisted extraction proved instrumental in elevating the DMY content in vine tea extracts,thereby enriching its flavor profile while maintaining its antioxidant properties.展开更多
The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for he...The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.展开更多
Semantic segmentation in street scenes is a crucial technology for autonomous driving to analyze the surrounding environment.In street scenes,issues such as high image resolution caused by a large viewpoints and diffe...Semantic segmentation in street scenes is a crucial technology for autonomous driving to analyze the surrounding environment.In street scenes,issues such as high image resolution caused by a large viewpoints and differences in object scales lead to a decline in real-time performance and difficulties in multi-scale feature extraction.To address this,we propose a bilateral-branch real-time semantic segmentationmethod based on semantic information distillation(BSDNet)for street scene images.The BSDNet consists of a Feature Conversion Convolutional Block(FCB),a Semantic Information Distillation Module(SIDM),and a Deep Aggregation Atrous Convolution Pyramid Pooling(DASP).FCB reduces the semantic gap between the backbone and the semantic branch.SIDM extracts high-quality semantic information fromthe Transformer branch to reduce computational costs.DASP aggregates information lost in atrous convolutions,effectively capturingmulti-scale objects.Extensive experiments conducted on Cityscapes,CamVid,and ADE20K,achieving an accuracy of 81.7% Mean Intersection over Union(mIoU)at 70.6 Frames Per Second(FPS)on Cityscapes,demonstrate that our method achieves a better balance between accuracy and inference speed.展开更多
This study aimed to investigate the effect of ultrasound-assisted alkaline extraction(UAE)(at 20 kHz and different powers of 0,200,300,400,500 and 600 W for 10 min)on the yield,structure and emulsifying properties of ...This study aimed to investigate the effect of ultrasound-assisted alkaline extraction(UAE)(at 20 kHz and different powers of 0,200,300,400,500 and 600 W for 10 min)on the yield,structure and emulsifying properties of chickpea protein isolate(CPI).Compared with the non-ultrasound group,ultrasound treatment at 400 W resulted in the largest increase in CPI yield,and both the particle size and turbidity decreased with increasing ultrasound power from 0 to 400 W.The scanning electron microscope results showed a uniform structural distribution of CPI.Moreover,itsα-helix content increased,β-sheet content decreased,and total sulfhydryl group content and endogenous fluorescence intensity rose,illustrating that UAE changed the secondary and tertiary structure of CPI.At 400 W,the solubility of the emulsion increased to 63.18%,and the best emulsifying properties were obtained;the emulsifying activity index(EAI)and emulsifying stability index(ESI)increased by 85.42%and 46.78%,respectively.Furthermore,the emulsion droplets formed were smaller and more uniform.In conclusion,proper UAE power conditions increased the extraction yield and protein content of CPI,and effectively improved its structure and emulsifying characteristics.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
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
基金supported by the National Natural Science Foundation of China under Grant 62306128the Basic Science Research Project of Jiangsu Provincial Department of Education under Grant 23KJD520003the Leading Innovation Project of Changzhou Science and Technology Bureau under Grant CQ20230072.
文摘Real-time detection of surface defects on cables is crucial for ensuring the safe operation of power systems.However,existing methods struggle with small target sizes,complex backgrounds,low-quality image acquisition,and interference from contamination.To address these challenges,this paper proposes the Real-time Cable Defect Detection Network(RC2DNet),which achieves an optimal balance between detection accuracy and computational efficiency.Unlike conventional approaches,RC2DNet introduces a small object feature extraction module that enhances the semantic representation of small targets through feature pyramids,multi-level feature fusion,and an adaptive weighting mechanism.Additionally,a boundary feature enhancement module is designed,incorporating boundary-aware convolution,a novel boundary attention mechanism,and an improved loss function to significantly enhance boundary localization accuracy.Experimental results demonstrate that RC2DNet outperforms state-of-the-art methods in precision,recall,F1-score,mean Intersection over Union(mIoU),and frame rate,enabling real-time and highly accurate cable defect detection in complex backgrounds.
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.
文摘AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.
基金Supported by the National Natural Science Foundation of China(No. 30371328), the Key Project of Natural Science Foundationof Shandong Province (No. Z2002C01), and the Key Project ofShandong Academy of Medical Sciences (No. 2005007)
文摘AIM： To rapidly quantify hepatitis B virus （HBV） DNA by real-time PCR using efficient TaqMan probe and extraction methods of virus DNA. METHODS： Three standards were prepared by cloning PCR products which targeted S, C and X region of HBV genome into pGEM-T vector respectively. A pair of primers and matched TaqMan probe were selected by comparing the copy number and the Ct values of HBV serum samples derived from the three different standard curves using certain serum DNA. Then the efficiency of six HBV DNA extraction methods including guanidinium isothiocyanate, proteinase K, NaI, NaOH lysis, alkaline lysis and simple boiling was analyzed in sample A, B and C by real-time PCR. Meanwhile, 8 clinical HBV serum samples were quantified. RESULTS： The copy number of the same HBV serum sample originated from the standard curve of S, C and X regions was 5.7 × 10^4/mL, 6.3 × 10^2/mL and 1.6 × 10^3/ mL respectively. The relative Ct value was 26.6, 31.8 and 29.5 respectively. Therefore, primers and matched probe from S region were chosen for further optimization of six extraction methods. The copy number of HBV serum samples A, B and C was 3.49 × 10^9/mL, 2.08 × 10^6/mL and 4.40 × 10^7/mL respectively, the relative Ct value was 19.9, 30 and 26.2 in the method of NaOH lysis, which was the efficientest among six methods. Simple boiling showed a slightly lower efficiency than NaOH lysis. Guanidinium isothiocyanate, proteinase K and NaI displayed that the copy number of HBV serum sample A, B and C was around 10^S/mL, meanwhile the Ct value was about 30. Alkaline failed to quantify the copy number of three HBV serum samples. Standard deviation （SD） and coefficient variation （CV） were very low in all 8 clinical HBV serum samples, showing that quantification of HBV DNA in triplicate was reliable and accurate. CONCLUSION： Real-time PCR based on optimized primers and TaqMan probe from S region in combination with NaOH lysis is a simple, rapid and accurate method for quantification of HBV serum DNA.
文摘An effective approach is presented to extract welds from real-time radiographs, Firstly an algorithm based on an adaptive bidirectional threshold was proposed to segment the gradient image into ternary image, and then the bidirectional accumulator Hough Transform was developed to extract weld edges from the ternary image. Different values of the coefficient proposed in the threshold algorithm were tested, and the proposed approach was applied to extract welds from real-time radiographic images of different types of welds with defects. Results show that the proposed method is adaptive and effective to extract welds from real-time radiographs of linear welds.
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2018R1D1A1B07042967)the Soonchunhyang University Research Fund.
文摘Violence recognition is crucial because of its applications in activities related to security and law enforcement.Existing semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less effective.Several approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based methods.Previous studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other methods.However,the their performance must be improved.This study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video data.In the proposed framework,the keyframe extraction technique eliminates duplicate consecutive frames.This keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate frames.For feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the architecture.For empirical analysis,four widely used standard datasets are used with diverse activities.The results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.
基金Supported by the Emphases Science and Technology Tackle Key Problem of Wuhan!(98320 1005)
文摘An algorithm applied to a real-time extraction image of vehicle is introduced. The algorithm include an image processing with a binarzation method, image grab for a vehicle with high speed, character isolator one by one, and neural network algorithm. The techniques include vehicles sensing, image garb control, vehicle license location, lighting and optic character recognition. The system is much more robust and faster than the traditional thresholding method.
文摘Extraction of traffic information from image or video sequence is a hot research topic in intelligenttransportation system and computer vision. A real-time traffic information extraction method based on com-pressed video with interframe motion vectors for speed, density and flow detection, has been proposed for ex-traction of traffic information under fixed camera setting and well-defined environment. The motion vectors arefirst separated from the compressed video streams, and then filtered to eliminate incorrect and noisy vectors u-sing the well-defined environmental knowledge. By applying the projective transform and using the filtered mo-tion vectors, speed can be calculated from motion vector statistics, density can be estimated using the motionvector occupancy, and flow can be detected using the combination of speed and density. The embodiment of aprototype system for sky camera traffic monitoring using the MPEG video has been implemented, and experi-mental results proved the effectiveness of the method proposed.
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.
基金support by the Science and Technology Project of Guangdong Power Exchange Center Co.,Ltd.(No.GDKJXM20222599)National Natural Science Foundation of China(No.52207104)Natural Science Foundation of Guangdong Province(No.2024A1515010426).
文摘The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for the market service for green energy consumers.This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and termfrequency-inverse document frequency(TF-IDF)algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market.First,the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete,interval,and relational features.A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts.Then,TF-IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters.Finally,the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China.It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages,whose averaged values are 25.64%,50%,39.66%,and 24.89%in four different clusters,respectively.Additionally,power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.
文摘Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatically mine available SFO resources.An essential aspect of constructing SFO-KG is the extraction of Chinese entity relations.Unfortunately,there is currently no publicly available Chinese SFO entity Relation Extraction(RE)dataset.Moreover,publicly available SFO text data contain numerous NA(representing for“No Answer”)relation category sentences that resemble other relation sentences and pose challenges in accurate classification,resulting in low recall and precision for the NA relation category in entity RE.Consequently,this issue adversely affects both the accuracy of constructing the knowledge graph and the efficiency of RE processes.To address these challenges,this paper proposes a method for extracting Chinese SFO text entity relations based on dynamic integrated learning.This method includes the construction of a manually annotated Chinese SFO entity RE dataset and a classifier combining features of SFO resource data.The proposed approach combines integrated learning and pre-training models,specifically utilizing Bidirectional Encoder Representation from Transformers(BERT).In addition,it incorporates one-class classification,attention mechanisms,and dynamic feedback mechanisms to improve the performance of the RE model.Experimental results show that the proposed method outperforms the traditional methods in terms of F1 value when extracting entity relations from both balanced and long-tailed datasets.
基金supported by the National Natural Science Foundation of China(62222212).
文摘Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.
基金supported by the Key Research and Development Program of Hunan Province of China(No.2022NK2036)Xiangxi Prefecture Science and Technology Plan Project"School-Local Integration"Special Project(No.2022001)the scientific research project of Hunan Provincial Department of Education(No.22B0520).
文摘Background:Ampelopsis grossedentata,vine tea,which is the tea alternative beverages in China.In vine tea processing,a large amount of broken tea is produced,which has low commercial value.Methods:This study investigates the influence of different extraction methods(room temperature water extraction,boiling water extraction,ultrasonic-assisted room temperature water extraction,and ultrasonic-assisted boiling water extraction,referred to as room temperature water extraction(RE),boiling water extraction(BE),ultrasonic assistance at room temperature water extraction(URE),and ultrasonic assistance in boiling water extraction(UBE))on the yield,dihydromyricetin(DMY)content,free amino acid composition,volatile aroma components,and antioxidant properties of vine tea extracts.Results:A notable influence of extraction temperature on the yield of vine tea extracts(P<0.05),with BE yielding the highest at 43.13±0.26%,higher than that of RE(34.29±0.81%).Ultrasound-assisted extraction significantly increased the DMY content of the extracts(P<0.05),whereas DMY content in the RE extracts was 59.94±1.70%,that of URE reached 66.14±2.78%.Analysis revealed 17 amino acids,with L-serine and aspartic acid being the most abundant in the extracts,nevertheless ultrasound-assisted extraction reduced total free amino acid content.Gas chromatography-mass spectrometry analysis demonstrated an increase in the diversity and quantity of compounds in the vine tea water extracts obtained through ultrasonic-assisted extraction.Specifically,69 and 68 volatile compounds were found in URE and UBE extracts,which were higher than the number found in RE and BE extracts.In vitro,antioxidant activity assessments revealed varying antioxidant capacities among different extraction methods,with RE exhibiting the highest DPPH scavenging rate,URE leading in ABTS•+free radical scavenging,and BE demonstrating superior ferric ion reducing antioxidant activity.Conclusion:The findings suggest that extraction methods significantly influence the chemical composition and antioxidant properties of vine tea extracts.Ultrasonic-assisted extraction proved instrumental in elevating the DMY content in vine tea extracts,thereby enriching its flavor profile while maintaining its antioxidant properties.
基金funded by the ICT Division of theMinistry of Posts,Telecommunications,and Information Technology of Bangladesh under Grant Number 56.00.0000.052.33.005.21-7(Tracking No.22FS15306)support from the University of Rajshahi.
文摘The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.
基金supported in part by the National Natural Science Foundation of China[Grant number 62471075]the Major Science and Technology Project Grant of the Chongqing Municipal Education Commission[Grant number KJZD-M202301901]Graduate Innovation Fund of Chongqing[gzlcx20253235].
文摘Semantic segmentation in street scenes is a crucial technology for autonomous driving to analyze the surrounding environment.In street scenes,issues such as high image resolution caused by a large viewpoints and differences in object scales lead to a decline in real-time performance and difficulties in multi-scale feature extraction.To address this,we propose a bilateral-branch real-time semantic segmentationmethod based on semantic information distillation(BSDNet)for street scene images.The BSDNet consists of a Feature Conversion Convolutional Block(FCB),a Semantic Information Distillation Module(SIDM),and a Deep Aggregation Atrous Convolution Pyramid Pooling(DASP).FCB reduces the semantic gap between the backbone and the semantic branch.SIDM extracts high-quality semantic information fromthe Transformer branch to reduce computational costs.DASP aggregates information lost in atrous convolutions,effectively capturingmulti-scale objects.Extensive experiments conducted on Cityscapes,CamVid,and ADE20K,achieving an accuracy of 81.7% Mean Intersection over Union(mIoU)at 70.6 Frames Per Second(FPS)on Cityscapes,demonstrate that our method achieves a better balance between accuracy and inference speed.
文摘This study aimed to investigate the effect of ultrasound-assisted alkaline extraction(UAE)(at 20 kHz and different powers of 0,200,300,400,500 and 600 W for 10 min)on the yield,structure and emulsifying properties of chickpea protein isolate(CPI).Compared with the non-ultrasound group,ultrasound treatment at 400 W resulted in the largest increase in CPI yield,and both the particle size and turbidity decreased with increasing ultrasound power from 0 to 400 W.The scanning electron microscope results showed a uniform structural distribution of CPI.Moreover,itsα-helix content increased,β-sheet content decreased,and total sulfhydryl group content and endogenous fluorescence intensity rose,illustrating that UAE changed the secondary and tertiary structure of CPI.At 400 W,the solubility of the emulsion increased to 63.18%,and the best emulsifying properties were obtained;the emulsifying activity index(EAI)and emulsifying stability index(ESI)increased by 85.42%and 46.78%,respectively.Furthermore,the emulsion droplets formed were smaller and more uniform.In conclusion,proper UAE power conditions increased the extraction yield and protein content of CPI,and effectively improved its structure and emulsifying characteristics.
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.
