Rock mass quality prediction on tunnel faces with incomplete multi-source dataset via tree-augmented naive Bayesian network

Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.

Co-pyrolysis of Sewage Sludge with Paint Sludge: Kinetics and Thermodynamic Analysis via Iso-conversional Methods

This study explored the synergistic interaction of sewage sludge(SS)and automotive paint sludge(PS)during co-pyrolysis for the optimized treatment of sewage sludge in cement kiln systems,utilizing thermogravimetric analysis(TGA)and thermogravimetric-mass spectrometry(TGA-MS).The result reveals the coexisting synergistic and antagonistic effects in the co-pyrolysis of SS/PS.The synergistic effect arises from hydrogen free radicals in SS and catalytic components in PS,while the main source of the antagonistic effect is that,during the mechanical mixing process,the SS/PS is converted from the particulate form into a dough-like rubbery which contributes to the film-forming effect,hindering the volatilization of volatile components.SS/PS co-pyrolysis reduces the yielding of tar production while increasing coke and gas.This study will provide some in-depth insights into the co-pyrolysis of SS/PS,and offer theoretical support for the subsequent research on the collaborative disposal processes in cement kilns.

Optimization of chemistry and process parameters for control of intermetallic formation in Mg sludges

Intermetallic formation in sludge during magnesium(Mg)melting,holding and high pressure die casting practices is a very important issue.But,very often it is overlooked by academia,original equipment manufacturers(OEM),metal ingot producers and even die casters.The aim of this study was to minimize the intermetallic formation in Mg sludge via the optimization of the chemistry and process parameters.The Al8Mn5 intermetallic particles were identified by the microstructure analysis based on the Al and Mn ratio.The design of experiment(DOE)technique,Taguchi method,was employed to minimize the intermetallic formation in the sludge of Mg alloys with various chemical compositions of Al,Mn,Fe,and different process parameters,holding temperature and holding time.The sludge yield(SY)and intermetallic size(IS)was selected as two responses.The optimum combination of the levels in terms of minimizing the intermetallic formation were 9 wt.%Al,0.15 wt.%Mn,0.001 wt.%(10 ppm)Fe,690℃ for the holding temperature and holding at 30 mins for the holding time,respectively.The best combination for smallest intermetallic size were 9 wt.%Al,0.15 wt.%Mn,0.001 wt.%(10 ppm)Fe,630℃ for the holding temperature and holding at 60 mins for the holding time,respectively.Three groups of sludge factors,Chemical Sludge(CSF),Physical Sludge(PSF)and Comprehensive Sludge Factors(and CPSF)were established for prediction of sludge yields and intermetallic sizes in Al-containing Mg alloys.The CPSF with five independent variables including both chemical elements and process parameters gave high accuracy in prediction,as the prediction of the PSF with only the two processing parameters of the melt holding temperature and time showed a relatively large deviation from the experimental data.The Chemical Sludge Factor was primarily designed for small ingot producers and die casters with a limited melting and holding capacity,of which process parameters could be fixed easily.The Physical Sludge Factor could be used for mass production with a single type of Mg alloy,in which the chemistry fluctuation might be negligible.In large Mg casting suppliers with multiple melting and holding furnaces and a number of Mg alloys in production,the Comprehensive Sludge Factor should be implemented to diminish the sludge formation.

Influence of the Nature of the Incoming Sludge on the Performance of a Vertical Flow Reed Beds in Dakar-Senegal

This work investigates the influence of the type sludge on drainage, plant development, purification performances and biosolids quality. Drainage properties were measured through the frequency of clogging, the percentage of leachate recovered and the dryness of accumulated sludge. Plant development was measured through the density, the height and the stem diameter. Purification performance was evaluated from the reduction rate. Biosolids quality was measured after 3 months of maturation. The results show that the clogging frequencies were 9.5%;0% and 3.7%;the volume of leachate recovered was 42.2%;20.4% and 24.7% and, the dryness was 33.4%;61.1% and 52.4% for FS-ST, FS-STT and SS respectively. Plants densities were about, with densities 197.1, 171.3 and 178.3 plants/m2 in beds fed respectively with FS-ST, FS-STT and SS. Despite the high removal rates, the concentrations of pollutants in the leachates are above the Senegalese standard NS 05-061 for discharge into the environment. The biosolids are all mature with C/N and NH4+/NO3?ratios lower than 12 and 1 respectively. The biosolids are also rich in organic and mineral elements. The concentrations of Ascaris eggs are higher than the WHO recommendations. These biosolids should be stored for additional time or composted.

Resource utilization of hazardous gypsum sludge in oxidation smelting of lead concentrate

Gypsum sludge,a hazardous waste generated by the non-ferrous smelting industry,presents a significant challenge for disposal and utilization.To investigate the feasibility of substituting gypsum sludge for limestone as a flux for smelting,the effects of calcium sulfate(CaSO_(4))and smelting conditions on oxygen-rich smelting of lead concentrate were studied.The interaction between CaSO_(4)and sulfides facilitates the conversion of CaSO_(4)into CaO,which is crucial for slag formation.The order of the influence of sulfide minerals on the conversion of CaSO_(4)is pyrite>sphalerite>galena.When using gypsum sludge exclusively as the calcium source,under optimal conditions with a CaO/SiO_(2)mass ratio of 0.8,an FeO/SiO_(2)mass ratio of 1.2,a melting temperature of 1150℃,an oxygen flow rate of 1.3 L/min,the recovery rates of Pb and Zn in the lead-rich slag reached 85.01%and 95.69%,respectively,with a sulfur content of 2.65 wt%.The As content in the smelting slag obtained by reduction smelting was 0.02 wt%.Resource utilization of gypsum sludge in lead smelting is a feasible method.

Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

Synergetic Bioproduction of Short-Chain Fatty Acids from Waste Activated Sludge Intensified by the Combined Use of Potassium Ferrate and Biosurfactants

The synergetic effect and underlying mechanism of potassium ferrate(PF)with tea saponin(TS,a biosurfactant)in producing short chain fatty acids(SCFAs)from anaerobic fermentation of waste activated sludge(WAS)were explored in this work.Experimental results showed that 0.2 g PF(g TSS)^(-1)(total suspended solid)combined with 0.02 g TS(g TSS)^(-1) could further improve SCFAs’production,and the maximum SCFAs content reached 2008.7 mg COD L^(-1),which is 1.2 and 4.5 times higher than those with PF and TS individually added,respectively,and 5.3 times higher than that of blank WAS on Day 12.In the model substrates experiments,the degradation rates of bovine serum albumin and dextran with combination of PF and TS were 41.3%±0.1% and 48.5%±0.06%,respectively,on Day 3,which are lower than those in blank WAS(with degradation rates of 72.3%±0.5%and 90.3%±0.3%).It was revealed that the oxidative effect of PF and the solubilization of TS caused more organic matters to be dissolved out from WAS,providing a large number of biodegradable substances for subsequent SCFAs production.While WAS pretreated with the combination of PF and TS,the relative abundances of Firmicutes increased from 6.4%(blank)to 38.6%,and that of Proteobacteria decreased from 41.8%(blank)to 21.8%.The combination of PF and TS promoted the hydrolysis process of WAS by enriching Firmicutes,and then increased acetic acid production by inhibiting Proteobacteria that consumed SCFAs.Meanwhile,at the genus level,acidogenesis bacteria(e.g.,Proteiniclasticum and Petrimonas)were enriched whereas SCFAs consuming bacteria(e.g.,Dokdonella)were inhibited.

Simultaneous removal of sulfur dioxide and nitrogen oxide from flue gas by phosphorus sludge:The performance and absorption mechanism

Developing low-cost and green simultaneous desulfurization and denitrification technologies is of great significance for sulfur dioxide(SO_(2))and nitrogen oxide(NO_(x))emission control at low temperatures,especially for small and medium-sized coal-fired boilers and furnaces.Herein,phosphorus sludge,an industrial waste from the production process of yellow phosphorus,has been developed to simultaneously eliminate SO_(2)and NO_(x)from coal-fired flue gas.The key factors affecting the experimental results indicate that desulfurization and denitrification efficiency of over 95%can be achieved at a low temperature of 55℃.Further,the absorption mechanism was investigated by characterizing the solid and liquid phases of the phosphorus sludge during the absorption process.The efficient removal of SO_(2)is attributed to the abundance of iron(Fe^(3+))and manganese(Mn^(2+))in the absorbent.SO_(2)can be rapidly catalyzed and converted to SO_(4)^(2-)by them.The key to NOx removal is the oxidation of NO toward watersoluble high-valent nitrogen oxides by oxidizing reactive substances induced via yellow phosphorus,which are then absorbed by water and converted to NO_(3)^(-).Meanwhile,yellow phosphorus is oxidized to phosphoric acid(H_(3)PO_(4)).The spent absorption slurry can be reused through wet process phosphoric acid production,as it contains sulfuric acid(H_(2)SO_(4)),nitric acid(HNO_(3)),and H_(3)PO_(4).Accordingly,this is a technology with broad application prospects.

A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Uncovering the Challenges of Faecal Sludge Management in Benin’s Urban Hubs: The Cases of Abomey-Calavi and Natitingou

The issue of faecal sludge management in developing countries remains a significant challenge due to the indiscriminate discharge of sludge into the environment. This practice generates more environmental problems than it solves. This study aims to assess the management of household faecal sludge in the communes of Abomey-Calavi and Natitingou, Benin, in order to optimise the best storage conditions for subsequent more effective treatment. To this end, a sociological survey was conducted among households in Abomey-Calavi and Natitingou, as well as among manual emptiers. The results of our studies revealed that two types of latrines are used in households. 20.55% of households use traditional latrines, while 59.83% use ordinary latrines. 7.97% of households use both types of latrine. Moreover, the depth of the latrines varies from 2.5 m to 7 m in Abomey-Calavi and from 2.5 m to 8 m in Natitingou. Among households with a latrine, 28.26% empty their pit at least once, while 71.74% have never emptied it. The emptying cost varies between 35,000 FCFA and 90,000 FCFA. The mixing of faecal sludge with solid waste, including plastic materials, makes emptying difficult due to the obstruction of the vacuum pump. The results obtained will serve as a reliable database to facilitate decision-making in the context of faecal sludge management.

Co-pyrolysis of Sewage Sludge with Distillation Residue: Kinetics Analysis via Iso-conversional Methods

This study explored the synergistic interaction of sewage sludge(SS)and distillation residue(DR)during co-pyrolysis for the optimized treatment of sewage sludge in cement kiln systems,utilizing thermogravimetric analysis(TGA)and thermogravimetric analysis with mass spectrometry(TGA-MS).The results reveal the coexisting synergistic and antagonistic effects in the co-pyrolysis of SS/DR.The synergistic effect arises from hydrogen free radicals in SS and catalytic components in ash fractions,while the antagonistic effect is mainly due to the melting of DR on the surface of SS particles during pyrolysis and the reaction of SS ash with alkali metals to form inert substances.SS/DR co-pyrolysis reduces the yielding of coke and gas while increasing tar production.This study will promote the reduction,recycling,and harmless treatment of hazardous solid waste.

A Gasification Technology to Combine Oil Sludge with Coal-Water Slurry:CFD Analysis and Performance Determination

The development of more environment-friendly ways to dispose of oil sludge is currently regarded as a hot topic.In this context,gasification technologies are generally seen as a promising way to combine oil sludge with coal–water slurry(CWS)and generate resourceful fuel.In this study,a novel five-nozzle gasifier reactor was analyzed by means of a CFD(Computational fluid dynamic)method.Among several influential factors,special attention was paid to the height-to-diameter ratio of the gasifier and the mixing ratio of oil sludge,which are known to have a significant impact on the flow field,temperature distribution and gasifier performances.According to the numerical results,the optimal height-to-diameter ratio and oil mixing ratio are about 2.4:1 and 20%,respectively.Furthermore,the carbon conversion rate can become as high as 98.55%with the hydrolysis rate reaching a value of 53.88%.The consumption of raw coal and oxygen is generally reduced,while the effective gas production is increased to 50.93 mol/%.

Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things

The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.

Effect of Different Rates and Mixtures of Solid Household Waste and Faecal Sludge-Based Composts on Soil Fertility and Productivity of Sunflower (Helianthus annuus L.) in Dschang, West Cameroon

The unbalanced and inadequate use of fertilizers is one of the causes of soil degradation. Combined with the ever-increasing population, it is necessary to find sustainable agricultural production alternatives. The present work aims to determine the effect of different rates and mixtutes of organic amendments on soil fertility and the performance of Sunflower (Helianthus annuus L.). In the field, treatments consisted of solid household waste and faecal sludge in the ratios of 3/5 (V1), and a mixture of faecal sludge and household waste in the ratio of 3/5 with 900 worms (V2). At the end of the composting process, V1, V2 composts and the poultry manure (PM) were applied at rates of 4, 5 and 6 t∙ha−1 in a randomized complete block design with three replications. Soil samples were collected before and after the experiment and analyzed. The main results revealed that at the end of the composting process, there was a progressive improvement in the physico-chemical properties of V1 and V2 composts. In particular, the C/N ratio, phosphorus (P) and total nitrogen (TN) initially at 16.49 ± 0.42 (V1, V2), 21.06 ± 0.07 mg∙kg−1 (V1, V2), 0.76% ± 0.08% (V1, V2) respectively, increased after 60 days to 12.40 ± 0.41 (V1), 9.74 ± 0.28 (V2) for C/N, 21.94 ± 0.63 mg∙kg−1 (V1) and 22.04 ± 0.04 mg∙kg−1 (V2) for P, 0.96% ± 0.0% (V1) and 1.22 ± 0.04 (V2) for TN. The application of 6 t∙ha−1of PM had the greatest influence on the diameter and weight of the flower heads (27.16 ± 4.01 t∙ha−1 and 230.83 ± 2.64 t∙ha−1), while 4 t∙ha−1 of V2 gave the tallest sunflower plants (110.07 ± 73.28 cm) as well as the diameter at the crown (19.30 ± 9.07 cm). However, CEC was most influenced by 4 t∙ha−1 of V1, while 4 t∙ha−1 of PM had the greatest effect on organic carbon and phosphorus. However, 5 t∙ha−1 of PM showed the highest sunflower production and yield (1.67 ± 0.21 t∙ha−1). The combination with 900 earthworms is recommended for composting and 5 t∙ha−1 of PM is recommended to obtain a better sunflower production.

Efficient stabilization of dredged sludge with high water content using an improved bio-carbonation of reactive magnesia cement method

This study proposed an improved bio-carbonation of reactive magnesia cement(RMC)method for dredged sludge stabilization using the urea pre-hydrolysis strategy.Based on unconfined compression strength(UCS),pickling-drainage,and scanning electron microscopy(SEM)tests,the effects of prehydrolysis duration(T),urease activity(UA)and curing age(CA)on the mechanical properties and microstructural characteristics of bio-carbonized samples were systematically investigated and analyzed.The results demonstrated that the proposed method could significantly enhance urea hydrolysis and RMC bio-carbonation to achieve efficient stabilization of dredged sludge with 80%high water content.A significant strength increment of up to about 1063.36 kPa was obtained for the bio-carbonized samples after just 7 d of curing,which was 2.64 times higher than that of the 28-day cured ordinary Portland cement-reinforced samples.Both elevated T and UA could notably increase urea utilization ratio and carbonate ion yield,but the resulting surge in supersaturation also affected the precipitation patterns of hydrated magnesia carbonates(HMCs),which weakened the cementation effect of HMCs on soil particles and further inhibited strength enhancement of bio-carbonized samples.The optimum formula was determined to be the case of T?24 h and UA?10 U/mL for dredged sludge stabilization.A 7-day CA was enough for bio-carbonized samples to obtain stable strength,albeit slightly affected by UA.The benefits of high efficiency and water stability presented the potential of this method in achieving dredged sludge stabilization and resource utilization.This investigation provides informative ideas and valuable insights on implementing advanced bio-geotechnical techniques to achieve efficient stabilization of soft soil,such as dredged sludge.

Multi-Source Data Privacy Protection Method Based on Homomorphic Encryption and Blockchain

Multi-Source data plays an important role in the evolution of media convergence.Its fusion processing enables the further mining of data and utilization of data value and broadens the path for the sharing and dissemination of media data.However,it also faces serious problems in terms of protecting user and data privacy.Many privacy protectionmethods have been proposed to solve the problemof privacy leakage during the process of data sharing,but they suffer fromtwo flaws:1)the lack of algorithmic frameworks for specific scenarios such as dynamic datasets in the media domain;2)the inability to solve the problem of the high computational complexity of ciphertext in multi-source data privacy protection,resulting in long encryption and decryption times.In this paper,we propose a multi-source data privacy protection method based on homomorphic encryption and blockchain technology,which solves the privacy protection problem ofmulti-source heterogeneous data in the dissemination ofmedia and reduces ciphertext processing time.We deployed the proposedmethod on theHyperledger platformfor testing and compared it with the privacy protection schemes based on k-anonymity and differential privacy.The experimental results showthat the key generation,encryption,and decryption times of the proposedmethod are lower than those in data privacy protection methods based on k-anonymity technology and differential privacy technology.This significantly reduces the processing time ofmulti-source data,which gives it potential for use in many applications.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.