With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Alth...With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.展开更多
Unmanned Aerial Vehicles(UAVs)coupled with deep learning such as Convolutional Neural Networks(CNNs)have been widely applied across numerous domains,including agriculture,smart city monitoring,and fire rescue operatio...Unmanned Aerial Vehicles(UAVs)coupled with deep learning such as Convolutional Neural Networks(CNNs)have been widely applied across numerous domains,including agriculture,smart city monitoring,and fire rescue operations,owing to their malleability and versatility.However,the computation-intensive and latency-sensitive natures of CNNs present a formidable obstacle to their deployment on resource-constrained UAVs.Some early studies have explored a hybrid approach that dynamically switches between lightweight and complex models to balance accuracy and latency.However,they often overlook scenarios involving multiple concurrent CNN streams,where competition for resources between streams can substantially impact latency and overall system performance.In this paper,we first investigate the deployment of both lightweight and complex models for multiple CNN streams in UAV swarm.Specifically,we formulate an optimization problem to minimize the total latency across multiple CNN streams,under the constraints on UAV memory and the accuracy requirement of each stream.To address this problem,we propose an algorithm called Adaptive Model Switching of collaborative inference for MultiCNN streams(AMSM)to identify the inference strategy with a low latency.Simulation results demonstrate that the proposed AMSM algorithm consistently achieves the lowest latency while meeting the accuracy requirements compared to benchmark algorithms.展开更多
The proliferation of streaming service system in various application areas gains increasing importance and also poses more challenges in the research of streaming service system. In this paper, we propose a novel dyna...The proliferation of streaming service system in various application areas gains increasing importance and also poses more challenges in the research of streaming service system. In this paper, we propose a novel dynamic model composed of a set of differential equations to describe the evolution of streaming service systems. And in the model, we focus on how the policies for admission control and peer selection influence on the system. We first introduce a flexible abstraction of streaming service systems. The abstraction is generally enough to capture the essences of streaming service systems with different structures, physical characteristics,software protocols and client behaviors. Then, by analyzing the state which is defined as the number of requests, a novel dynamic model is developed in microscopic scale to characterize the behaviors of streaming service systems. The model proposed in this paper demonstrates the interactions between clients and servers and also between different servers. The interactions are primarily influenced by the admission control policy and peer selection policy. Finally, some experiments are designed to verify the validation and reasonability of the model.展开更多
Droplet size distributions have been investigated with a two-probe system above a rotatingstream tray of 300 mm diameter.The measured distributions are found to follow the upper limitedlognormal distribution with thre...Droplet size distributions have been investigated with a two-probe system above a rotatingstream tray of 300 mm diameter.The measured distributions are found to follow the upper limitedlognormal distribution with three parameters dependent primarily on gas hole F-factor.A probabilitymethod is used to describe the initial state of the droplet population above the tray,and a model fordroplet motion is presented.The results computed with model agree well with experimental data.展开更多
The present paper contributes to the modeling of unsteady flow analysis of vertical axis wind turbine (VAWT). Double multiple streamtube (DSMT) model was applied for the performance prediction of straight bladed fixed...The present paper contributes to the modeling of unsteady flow analysis of vertical axis wind turbine (VAWT). Double multiple streamtube (DSMT) model was applied for the performance prediction of straight bladed fixed pitch VAWT using NACA0018 airfoil at low wind speed. A moving mesh technique was used to investigate two-dimensional unsteady flow around the same VAWT model with NACA0018 airfoil modified to be flexible at 150 from the main blade axis of the turbine at the trailing edge located about 70 % of the blade chord length using fluent solving Reynolds average Navier-strokes equation. The results obtained from DMST model and the simulation results were then compared. The result shows that the CFD simulation with airfoil modified has shown better performance at low tip speed ratios for the modeled turbine.展开更多
To complete the previously information issued on the feasibility study and some technical challenges identi-fied from the application of bacterial technology, this study presents another characteristics of numerical o...To complete the previously information issued on the feasibility study and some technical challenges identi-fied from the application of bacterial technology, this study presents another characteristics of numerical output as the bacterial growth is now also limited to ammonium nitrogen and water temperature. Based on the results obtained, it is found that the degradation of readily biodegradable COD will be much slower be-cause of lower bacterial growth. At certain period, the COD concentration will increase and be plotted higher later on compared to the model which is limited only to substrate and oxygen. Besides the ammonium nitro-gen, other parameters i.e. particulate products from COD decay and particulate degradable organic nitrogen will also increase soon after certain time. Considering the increase of ammonium nitrogen as it is also con-verted to nitrate nitrogen, it can be predicted that some algae may show up during the treatment processes. When the model is simulated under different water temperature, slower biodegradation process is presented at lower water temperature. Because the bacteria grow better at higher water temperature, more oxygen is then required. Finally, from this study, it is also identified that the artificial mixing and addition of oxygen at initial stage of treatment will considerably influence the restoration.展开更多
Modeling stream flow forms a basis upon which policy makers, watershed planners and managers make ap- propriate decisions consistent with sustainable management of land and water resources in the watershed. The aim of...Modeling stream flow forms a basis upon which policy makers, watershed planners and managers make ap- propriate decisions consistent with sustainable management of land and water resources in the watershed. The aim of this research is to provide a preliminary assessment of the performance of a complex watershed model in predicting stream flow on the Naro Moru river catchment in Ewaso Ng’iro river basin, Kenya. The research involved model input data preparation, model set up and test running, sensitivity analysis and cali- bration of the Soil Water Assessment Tool (SWAT) model. Preliminary evaluation of the model performance involved the use of known quantitative evaluation statistics that included correlation coefficient, Nash Sut- cliffe efficiency (NSE), Deviation Volume (Dv) and a graphical technique for comparing observed and simu- lated flows. Initial model runs yielded poor daily flow simulations compared to monthly simulations. Poor daily simulation was attributed to differences in the timing of observed and simulated hydrographs. The model was calibrated for a three year period followed by a three year validation period based on monthly flows. Calibration results indicated an acceptable, but modest, agreement between observed and simulated monthly stream flows with a correlation coefficient (r) of about 0.7, NSE = 5%, and Dv= 61.7%. After vali- dation, the model performance was satisfactory with the coefficient of determination (R2 ≈ 0.6), Nash-Sut- cliffe efficiency (NSE) of 0.51 and a deviation volume (Dv) value of 24.7%. The modest model performance was associated with input data deficiencies and model limitations. Even then, the results indicate that the model can possibly be adapted to the local conditions in the catchment for which it is being applied but with improvements involving better parameter calibration techniques, and collection of better quality data. Such a study may be used to predict the effect of climate change on river flows as well as the effect of land use changes on the hydrologic response of a catchment.展开更多
Stream networks are considered important units in many environmental decision making processes. The extraction of streams using digital elevation models (DEMs) presents many advantages. However it is very sensitive to...Stream networks are considered important units in many environmental decision making processes. The extraction of streams using digital elevation models (DEMs) presents many advantages. However it is very sensitive to the uncertainty of the elevation datasets used. The main aim of this paper is to implement geostatistical simulations and assess the propagated uncertainty and map the error of location streams. First, point sampled elevations are used to fit a variogram model. Next two hundred DEM realizations are generated using conditional sequential Gaussian simulation;the stream network map is extracted for each of these realizations, and the collection of stream networks is analyzed to quantify the error propagation. At each grid cell, the probability of the occurrence of a stream and the propagated error are estimated. The more probable stream network are delineated and compared with the digital stream network derived from topographic map. The method is illustrated using a small dataset (8742 sampled elevations) for Anaguid Saharan platform. All computations are run in two free softwares: R and SAGA. R is used to fit variogram and to run sequential Gaussian simulation. SAGA is used to extract streams via RSAGA library.展开更多
Algebraic attack was applied to attack Filter-Combintr model keystreamgenerators. We proposed the technique of function composition to improve the model, and the improvedmodel can resist the algebraic attack. A new cr...Algebraic attack was applied to attack Filter-Combintr model keystreamgenerators. We proposed the technique of function composition to improve the model, and the improvedmodel can resist the algebraic attack. A new criterion for designing Filter-Combiner model was alsoproposed: the total length I. of Linear Finite State Machines used in the model should be largeenough and the degree d of Filter-Combiner function should be approximate [L/2].展开更多
Ngwerere and Kanakatampa Streams are the main tributaries of the Chongwe River. The Ngwerere stream originates from the city of Lusaka and meanders through Lusaka City and Chongwe Town for an approximate distance of 4...Ngwerere and Kanakatampa Streams are the main tributaries of the Chongwe River. The Ngwerere stream originates from the city of Lusaka and meanders through Lusaka City and Chongwe Town for an approximate distance of 41 km before joining into the upper part of Chongwe River. The Kanakatampa Stream is a tributary of the Chongwe River. It meanders from the Kanakatampa Area for approximately 52 km before discharging into the middle of the upper part of the Chongwe River. The Chongwe River Catchment which is a sub-catchment of the Zambezi Basin drew the attention of researchers and policymakers when the Chongwe River started drying up in the dry seasons causing a water crisis particularly in the downstream regions of the middle catchment. Therefore, it is important from the water resources management perspective, to assess the contribution of tributaries into the flows of the Chongwe River. Ngwerere and Kanakatampa streams are socially, economically, and environmentally important streams for the city of Lusaka and surrounding area. This study, therefore, concentrated on evaluating the flow contribution of the two streams to the Chongwe River using the Water Evaluation And Planning (WEAP) tool. The streamflow data (1970-2010) recorded at the Chongwe Great East Road Bridge gauging station were used in the WEAP embedded Parameter ESTimation (PEST) auto-calibration tool to calibrate (1970-1999) and validate (2000-2010) the model. The monthly streamflow model calibration and validation results were assessed using the correlation coefficient (CC), Coefficient of determination (R<sup>2</sup>), Nash-Sutcliffe Coefficient of Efficiency (NSE), and Percent bias (PBIAS). The model performance results achieved were PBIAS of 1.24%, CC = 0.81, R<sup>2</sup> = 0.66 and NSE = 0.62 during the calibration period and a positive PBIAS of 2.94%, CC = 0.81, R<sup>2</sup> = 0.67 and NSE = 0.62 during the validation period. The median of the flows (Q<sub>50</sub>) was obtained from the historical flow duration curves (FDCs) generated in averaged intervals of 10-year from 1970 to 2019. The results showed that on average, the Ngwerere and Kanakatampa Streams contribute 52.8% and 29.6% respectively, to the flow of the Chongwe River in the upper and middle Catchment. The results also showed that the contribution of the Ngwerere and Kanakatampa Streams to the Chongwe River discharge has been reducing historically at a rate of 0.65% per decade and 1.35% per decade respectively over a period of 50 years (1970-2019). Suggestions for sustainable management of the tributaries such as the Ngwerere and Kanakatampa Streams were provided in this study.展开更多
Water temperature is a key physical habitat determinant in lotic ecosystems as it influences many physical, chemical and biological properties of rivers. Hence, a good understanding of the thermal regime of rivers is ...Water temperature is a key physical habitat determinant in lotic ecosystems as it influences many physical, chemical and biological properties of rivers. Hence, a good understanding of the thermal regime of rivers is essential for effective management of water and fisheries resources. This study deals with the modeling of hourly stream water temperature using the equilibrium temperature model. This water temperature model was applied on two thermally different watercourses, namely, the Little Southwest Miramichi River (LSWM) and Catamaran Brook (CatBk;New Brunswick). The equilibrium temperature model is a simplified version of a deterministic model. As such, in the equilibrium temperature model the total heat flux at the surface is assumed proportional to the difference between the water temperature and an equilibrium temperature. In the present study, the equilibrium temperature was assumed to vary linearly with hourly air temperature. This study showed that there was a good relationship between the equilibrium and air temperature at the hourly time scale. The root-mean-square error (RMSE) obtained with the hourly equilibrium temperature model was similar to results reported in previous studies with values of 1.05°C (CatBk) and 1.36°C (LSWM). The model’s performance was best in late summer and autumn when water levels were low. In contrast, the presence of snowmelt in the spring resulted in poorer performances. This study also showed good results in estimating the daily mean (Tmean) and maximum (Tmax) water temperatures from the predicted hourly water temperatures, which were often required in fishery management.展开更多
The SWAT model was used to predict total phosphorus (TP) loadings for a 1555-ha karst watershed—Chapel Branch Creek (CBC)—which drains to a lake via a reservoir-like embayment (R-E). The model was first tested for m...The SWAT model was used to predict total phosphorus (TP) loadings for a 1555-ha karst watershed—Chapel Branch Creek (CBC)—which drains to a lake via a reservoir-like embayment (R-E). The model was first tested for monthly streamflow predictions from tributaries draining three potential source areas as well as the downstream R-E, followed by TP loadings using data collected March 2007-October 2009. Source areas included 1) a golf course that received applied wastewater, 2) urban areas, highway, and some agricultural lands, and 3) a cave spring draining a second golf course along with agricultural and forested areas, including a substantial contribution of subsurface water via karst connectivity. SWAT predictions of mean monthly TP loadings at the first two source outlets were deemed reasonable. However, the predictions at the cave spring outlet were somewhat poorer, likely due to diffuse variable groundwater flow from an unknown drainage area larger than the actual surface watershed, for which monthly subsurface flow was represented as a point source during simulations. Further testing of the SWAT model to predict monthly TP loadings at the R-E, modeled as a completely mixed system, resulted in their over-predictions most of the months, except when high lake water levels occurred. The mean monthly and annual flows were calibrated to acceptable limits with the exception of flow over-prediction when lake levels were low and surface water from tributaries disappeared into karst connections. The discrepancy in TP load predictions was attributed primarily to the use of limited monthly TP data collected during baseflow in the embayment. However, for the 22-month period, over-prediction of mean monthly TP load (34.6 kg/mo) by 13% compared to measured load (30.6 kg/mo) in the embayment was deemed acceptable. Simulated results showed a 42% reduction in TP load due to settling in the embayment.展开更多
Aquatic habitat assessments encompass large and small wadeable streams which vary from many meters wide to ephemeral. Differences in stream sizes within or across watersheds, however, may lead to incompatibility of da...Aquatic habitat assessments encompass large and small wadeable streams which vary from many meters wide to ephemeral. Differences in stream sizes within or across watersheds, however, may lead to incompatibility of data at varying spatial scales. Specifically, issues caused by moving between scales on large and small streams are not typically addressed by many forms of statistical analysis, making the comparison of large (>30 m wetted width) and small stream (<10 m wetted width) habitat assessments difficult. Geographically weighted regression (GWR) may provide avenues for efficiency and needed insight into stream habitat data by addressing issues caused by moving between scales. This study examined the ability of GWR to consistently model stream substrate on both large and small wadeable streams at an equivalent resolution. We performed GWR on two groups of 60 randomly selected substrate patches from large and small streams and used depth measurements to model substrate. Our large and small stream substrate models responded equally well to GWR. Results showed no statistically significant difference between GWR R<sup>2 </sup>values of large and small stream streams. Results also provided a much needed method for comparison of large and small wadeable streams. Our results have merit for aquatic resource managers, because they demonstrate ability to spatially model and compare substrate on large and small streams. Using depth to guide substrate modeling by geographically weighted regression has a variety of applications which may help manage, monitor stream health, and interpret substrate change over time.展开更多
The purpose of this paper was to implement ＂Soil and Water Assessment Tool （SWAT）＂ model and Geographic Information System （GIS） to evaluate the impact of land use changes on stream discharge in Nghinh Tuong wat...The purpose of this paper was to implement ＂Soil and Water Assessment Tool （SWAT）＂ model and Geographic Information System （GIS） to evaluate the impact of land use changes on stream discharge in Nghinh Tuong watershed （a brand of Cau River） in Northern Vietnam. The watershed was coverd by 56% forestry land, 30% agricultural land and the remain 14% for others. Stream discharge observed data from 2002 to 2007 were used for calibration period and from 2008 to 2012 for validation period. The result showed that two coefficients （NSE and PBIAS） to evaluate model performance were 0.76 and 6.54% for calibration period and 0.87 and 4.74% for validation period, respectively. Stream discharge strongly depends not only on quantity of precipitation but also on land use change. Through the scenario 1, agricultural land （corn, orchard and tea） increases 9,782.67 ha （2.45%）, meanwhile forest （forest-mixed） decreases 1,091.77 ha （2.75%） as compared to baseline scenario. Additionally, precipitation increases 3.74% in mean wet season, but decreases 0.5% in mean dry season with respect to baseline period. SWAT model was able to simulate stream discharge and sediment yield for Nghinh Tuong watershed successfully not only for baseline scenario but also for scenario 1. In brief, SWAT proves its ability in simulation stream discharge in subwatershed level. It is a useful tool to assist water quantity and quality management process in Nghinh Tuong watershed. This work one more time indicated that SWAT is useful tool for resources and environment management.展开更多
In this study, we performed a conceptual modeling on solute transport based on theoretical stream tube model (STM) with various travel time distributions assuming a pure convective flow through each tube in order to i...In this study, we performed a conceptual modeling on solute transport based on theoretical stream tube model (STM) with various travel time distributions assuming a pure convective flow through each tube in order to investigate how the lengths and distributions of solute travel time through STM affect the breakthrough curves at the end mixing surface. The conceptual modeling revealed that 1) the shape of breakthrough curve (BTC) at the mixing surface was determined by not only input travel time distributions but also solute injection mode such as sampling time and pulse lengths;2) the increase of pulse length resulted in the linear increase of the first time moment (mean travel time) and quadratic increase of the second time moment (variance of travel time) leading to more spreading of solute, however, the second time moment was not affected by travel time distributions and 3) for a given input distributions the increase in travel distance resulted in more dispersion with the quadratic increase of travel time variance. This indicates that stream tube model obeying strictly pure convective flow follows the concept of convective-lognormal transport (CLT) model regardless the input travel time distributions.展开更多
The performance and annual energy output have to be predicted to maximize the economic benefits from a wind turbine. Mathematically predicting the performance of Darrieus type lift based turbines are challenging due t...The performance and annual energy output have to be predicted to maximize the economic benefits from a wind turbine. Mathematically predicting the performance of Darrieus type lift based turbines are challenging due to the inconsistent angle of attack, blade wake interaction and local induced velocities giving rise to complex flow physics. A reliable and validated mathematical model is therefore essential to optimize the various design parameters prior to manufacture. The objective of the current study is to evaluate widely employed aerodynamic models based on their prediction accuracy, limitations, and computational requirements. Double multiple stream tube models have been discussed in detail and the predictions are experimentally validated through the wind tunnel test of three-bladed H-Darrieus rotor in terms of torque and power coefficient. The possible sources for the deviation between the predicted and measured values have been discussed and concluded with potential solutions.展开更多
In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreami...In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
Hydropower gains increasing importance as a steerable and controllable power source in a renewable energy mix and deregulated markets. Although hydropower produces fossil-free energy, it has a significant impact on th...Hydropower gains increasing importance as a steerable and controllable power source in a renewable energy mix and deregulated markets. Although hydropower produces fossil-free energy, it has a significant impact on the local environment. This review investigates the effects of flow alterations by hydropower on the downstream river system and the possibilities to integrate these effects into hydraulic modeling. The results show that various effects of flow regulation on the ecosystem, but also social and economic effects on related communities were observed in the last decades. The application of hydraulic models for investigations of ecological effects is common. Especially hydraulic effects and effects on fish were extensively modeled with the help of hydraulic 1D- and 2D-simulations. Current applications to investigate social and economic effects integrated into hydraulic modeling are meanwhile limited. Approaches to realizing this integration are presented. Further research on the economic valuation of ecosystems and integration of social and economic effects to hydraulic models is necessary to develop holistic tools to support decision-making on sustainable hydropower.展开更多
A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR...A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR） modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.展开更多
基金funded by the Joint Project of Industry-University-Research of Jiangsu Province(Grant:BY20231146).
文摘With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.
基金supported by the National Natural Science Foundation of China(No.61931011)the Jiangsu Provincial Key Research and Development Program,China(No.BE2021013-4)the Fundamental Research Project in University Characteristic Disciplines,China(No.ILF240071A24)。
文摘Unmanned Aerial Vehicles(UAVs)coupled with deep learning such as Convolutional Neural Networks(CNNs)have been widely applied across numerous domains,including agriculture,smart city monitoring,and fire rescue operations,owing to their malleability and versatility.However,the computation-intensive and latency-sensitive natures of CNNs present a formidable obstacle to their deployment on resource-constrained UAVs.Some early studies have explored a hybrid approach that dynamically switches between lightweight and complex models to balance accuracy and latency.However,they often overlook scenarios involving multiple concurrent CNN streams,where competition for resources between streams can substantially impact latency and overall system performance.In this paper,we first investigate the deployment of both lightweight and complex models for multiple CNN streams in UAV swarm.Specifically,we formulate an optimization problem to minimize the total latency across multiple CNN streams,under the constraints on UAV memory and the accuracy requirement of each stream.To address this problem,we propose an algorithm called Adaptive Model Switching of collaborative inference for MultiCNN streams(AMSM)to identify the inference strategy with a low latency.Simulation results demonstrate that the proposed AMSM algorithm consistently achieves the lowest latency while meeting the accuracy requirements compared to benchmark algorithms.
基金supported by National Natural Science Foundationof China(Nos.61174124 and 61233003)Research Fund for DoctoralProgram of Higher Education of China(No.20123402110029)Natural Science Research Program of the Anhui High Education Bureau of China(No.KJ2012A286)
文摘The proliferation of streaming service system in various application areas gains increasing importance and also poses more challenges in the research of streaming service system. In this paper, we propose a novel dynamic model composed of a set of differential equations to describe the evolution of streaming service systems. And in the model, we focus on how the policies for admission control and peer selection influence on the system. We first introduce a flexible abstraction of streaming service systems. The abstraction is generally enough to capture the essences of streaming service systems with different structures, physical characteristics,software protocols and client behaviors. Then, by analyzing the state which is defined as the number of requests, a novel dynamic model is developed in microscopic scale to characterize the behaviors of streaming service systems. The model proposed in this paper demonstrates the interactions between clients and servers and also between different servers. The interactions are primarily influenced by the admission control policy and peer selection policy. Finally, some experiments are designed to verify the validation and reasonability of the model.
基金Supported by the National and Zhejiang Provincial Natural Science Foundations.
文摘Droplet size distributions have been investigated with a two-probe system above a rotatingstream tray of 300 mm diameter.The measured distributions are found to follow the upper limitedlognormal distribution with three parameters dependent primarily on gas hole F-factor.A probabilitymethod is used to describe the initial state of the droplet population above the tray,and a model fordroplet motion is presented.The results computed with model agree well with experimental data.
文摘The present paper contributes to the modeling of unsteady flow analysis of vertical axis wind turbine (VAWT). Double multiple streamtube (DSMT) model was applied for the performance prediction of straight bladed fixed pitch VAWT using NACA0018 airfoil at low wind speed. A moving mesh technique was used to investigate two-dimensional unsteady flow around the same VAWT model with NACA0018 airfoil modified to be flexible at 150 from the main blade axis of the turbine at the trailing edge located about 70 % of the blade chord length using fluent solving Reynolds average Navier-strokes equation. The results obtained from DMST model and the simulation results were then compared. The result shows that the CFD simulation with airfoil modified has shown better performance at low tip speed ratios for the modeled turbine.
文摘To complete the previously information issued on the feasibility study and some technical challenges identi-fied from the application of bacterial technology, this study presents another characteristics of numerical output as the bacterial growth is now also limited to ammonium nitrogen and water temperature. Based on the results obtained, it is found that the degradation of readily biodegradable COD will be much slower be-cause of lower bacterial growth. At certain period, the COD concentration will increase and be plotted higher later on compared to the model which is limited only to substrate and oxygen. Besides the ammonium nitro-gen, other parameters i.e. particulate products from COD decay and particulate degradable organic nitrogen will also increase soon after certain time. Considering the increase of ammonium nitrogen as it is also con-verted to nitrate nitrogen, it can be predicted that some algae may show up during the treatment processes. When the model is simulated under different water temperature, slower biodegradation process is presented at lower water temperature. Because the bacteria grow better at higher water temperature, more oxygen is then required. Finally, from this study, it is also identified that the artificial mixing and addition of oxygen at initial stage of treatment will considerably influence the restoration.
文摘Modeling stream flow forms a basis upon which policy makers, watershed planners and managers make ap- propriate decisions consistent with sustainable management of land and water resources in the watershed. The aim of this research is to provide a preliminary assessment of the performance of a complex watershed model in predicting stream flow on the Naro Moru river catchment in Ewaso Ng’iro river basin, Kenya. The research involved model input data preparation, model set up and test running, sensitivity analysis and cali- bration of the Soil Water Assessment Tool (SWAT) model. Preliminary evaluation of the model performance involved the use of known quantitative evaluation statistics that included correlation coefficient, Nash Sut- cliffe efficiency (NSE), Deviation Volume (Dv) and a graphical technique for comparing observed and simu- lated flows. Initial model runs yielded poor daily flow simulations compared to monthly simulations. Poor daily simulation was attributed to differences in the timing of observed and simulated hydrographs. The model was calibrated for a three year period followed by a three year validation period based on monthly flows. Calibration results indicated an acceptable, but modest, agreement between observed and simulated monthly stream flows with a correlation coefficient (r) of about 0.7, NSE = 5%, and Dv= 61.7%. After vali- dation, the model performance was satisfactory with the coefficient of determination (R2 ≈ 0.6), Nash-Sut- cliffe efficiency (NSE) of 0.51 and a deviation volume (Dv) value of 24.7%. The modest model performance was associated with input data deficiencies and model limitations. Even then, the results indicate that the model can possibly be adapted to the local conditions in the catchment for which it is being applied but with improvements involving better parameter calibration techniques, and collection of better quality data. Such a study may be used to predict the effect of climate change on river flows as well as the effect of land use changes on the hydrologic response of a catchment.
文摘Stream networks are considered important units in many environmental decision making processes. The extraction of streams using digital elevation models (DEMs) presents many advantages. However it is very sensitive to the uncertainty of the elevation datasets used. The main aim of this paper is to implement geostatistical simulations and assess the propagated uncertainty and map the error of location streams. First, point sampled elevations are used to fit a variogram model. Next two hundred DEM realizations are generated using conditional sequential Gaussian simulation;the stream network map is extracted for each of these realizations, and the collection of stream networks is analyzed to quantify the error propagation. At each grid cell, the probability of the occurrence of a stream and the propagated error are estimated. The more probable stream network are delineated and compared with the digital stream network derived from topographic map. The method is illustrated using a small dataset (8742 sampled elevations) for Anaguid Saharan platform. All computations are run in two free softwares: R and SAGA. R is used to fit variogram and to run sequential Gaussian simulation. SAGA is used to extract streams via RSAGA library.
文摘Algebraic attack was applied to attack Filter-Combintr model keystreamgenerators. We proposed the technique of function composition to improve the model, and the improvedmodel can resist the algebraic attack. A new criterion for designing Filter-Combiner model was alsoproposed: the total length I. of Linear Finite State Machines used in the model should be largeenough and the degree d of Filter-Combiner function should be approximate [L/2].
文摘Ngwerere and Kanakatampa Streams are the main tributaries of the Chongwe River. The Ngwerere stream originates from the city of Lusaka and meanders through Lusaka City and Chongwe Town for an approximate distance of 41 km before joining into the upper part of Chongwe River. The Kanakatampa Stream is a tributary of the Chongwe River. It meanders from the Kanakatampa Area for approximately 52 km before discharging into the middle of the upper part of the Chongwe River. The Chongwe River Catchment which is a sub-catchment of the Zambezi Basin drew the attention of researchers and policymakers when the Chongwe River started drying up in the dry seasons causing a water crisis particularly in the downstream regions of the middle catchment. Therefore, it is important from the water resources management perspective, to assess the contribution of tributaries into the flows of the Chongwe River. Ngwerere and Kanakatampa streams are socially, economically, and environmentally important streams for the city of Lusaka and surrounding area. This study, therefore, concentrated on evaluating the flow contribution of the two streams to the Chongwe River using the Water Evaluation And Planning (WEAP) tool. The streamflow data (1970-2010) recorded at the Chongwe Great East Road Bridge gauging station were used in the WEAP embedded Parameter ESTimation (PEST) auto-calibration tool to calibrate (1970-1999) and validate (2000-2010) the model. The monthly streamflow model calibration and validation results were assessed using the correlation coefficient (CC), Coefficient of determination (R<sup>2</sup>), Nash-Sutcliffe Coefficient of Efficiency (NSE), and Percent bias (PBIAS). The model performance results achieved were PBIAS of 1.24%, CC = 0.81, R<sup>2</sup> = 0.66 and NSE = 0.62 during the calibration period and a positive PBIAS of 2.94%, CC = 0.81, R<sup>2</sup> = 0.67 and NSE = 0.62 during the validation period. The median of the flows (Q<sub>50</sub>) was obtained from the historical flow duration curves (FDCs) generated in averaged intervals of 10-year from 1970 to 2019. The results showed that on average, the Ngwerere and Kanakatampa Streams contribute 52.8% and 29.6% respectively, to the flow of the Chongwe River in the upper and middle Catchment. The results also showed that the contribution of the Ngwerere and Kanakatampa Streams to the Chongwe River discharge has been reducing historically at a rate of 0.65% per decade and 1.35% per decade respectively over a period of 50 years (1970-2019). Suggestions for sustainable management of the tributaries such as the Ngwerere and Kanakatampa Streams were provided in this study.
文摘Water temperature is a key physical habitat determinant in lotic ecosystems as it influences many physical, chemical and biological properties of rivers. Hence, a good understanding of the thermal regime of rivers is essential for effective management of water and fisheries resources. This study deals with the modeling of hourly stream water temperature using the equilibrium temperature model. This water temperature model was applied on two thermally different watercourses, namely, the Little Southwest Miramichi River (LSWM) and Catamaran Brook (CatBk;New Brunswick). The equilibrium temperature model is a simplified version of a deterministic model. As such, in the equilibrium temperature model the total heat flux at the surface is assumed proportional to the difference between the water temperature and an equilibrium temperature. In the present study, the equilibrium temperature was assumed to vary linearly with hourly air temperature. This study showed that there was a good relationship between the equilibrium and air temperature at the hourly time scale. The root-mean-square error (RMSE) obtained with the hourly equilibrium temperature model was similar to results reported in previous studies with values of 1.05°C (CatBk) and 1.36°C (LSWM). The model’s performance was best in late summer and autumn when water levels were low. In contrast, the presence of snowmelt in the spring resulted in poorer performances. This study also showed good results in estimating the daily mean (Tmean) and maximum (Tmax) water temperatures from the predicted hourly water temperatures, which were often required in fishery management.
文摘The SWAT model was used to predict total phosphorus (TP) loadings for a 1555-ha karst watershed—Chapel Branch Creek (CBC)—which drains to a lake via a reservoir-like embayment (R-E). The model was first tested for monthly streamflow predictions from tributaries draining three potential source areas as well as the downstream R-E, followed by TP loadings using data collected March 2007-October 2009. Source areas included 1) a golf course that received applied wastewater, 2) urban areas, highway, and some agricultural lands, and 3) a cave spring draining a second golf course along with agricultural and forested areas, including a substantial contribution of subsurface water via karst connectivity. SWAT predictions of mean monthly TP loadings at the first two source outlets were deemed reasonable. However, the predictions at the cave spring outlet were somewhat poorer, likely due to diffuse variable groundwater flow from an unknown drainage area larger than the actual surface watershed, for which monthly subsurface flow was represented as a point source during simulations. Further testing of the SWAT model to predict monthly TP loadings at the R-E, modeled as a completely mixed system, resulted in their over-predictions most of the months, except when high lake water levels occurred. The mean monthly and annual flows were calibrated to acceptable limits with the exception of flow over-prediction when lake levels were low and surface water from tributaries disappeared into karst connections. The discrepancy in TP load predictions was attributed primarily to the use of limited monthly TP data collected during baseflow in the embayment. However, for the 22-month period, over-prediction of mean monthly TP load (34.6 kg/mo) by 13% compared to measured load (30.6 kg/mo) in the embayment was deemed acceptable. Simulated results showed a 42% reduction in TP load due to settling in the embayment.
文摘Aquatic habitat assessments encompass large and small wadeable streams which vary from many meters wide to ephemeral. Differences in stream sizes within or across watersheds, however, may lead to incompatibility of data at varying spatial scales. Specifically, issues caused by moving between scales on large and small streams are not typically addressed by many forms of statistical analysis, making the comparison of large (>30 m wetted width) and small stream (<10 m wetted width) habitat assessments difficult. Geographically weighted regression (GWR) may provide avenues for efficiency and needed insight into stream habitat data by addressing issues caused by moving between scales. This study examined the ability of GWR to consistently model stream substrate on both large and small wadeable streams at an equivalent resolution. We performed GWR on two groups of 60 randomly selected substrate patches from large and small streams and used depth measurements to model substrate. Our large and small stream substrate models responded equally well to GWR. Results showed no statistically significant difference between GWR R<sup>2 </sup>values of large and small stream streams. Results also provided a much needed method for comparison of large and small wadeable streams. Our results have merit for aquatic resource managers, because they demonstrate ability to spatially model and compare substrate on large and small streams. Using depth to guide substrate modeling by geographically weighted regression has a variety of applications which may help manage, monitor stream health, and interpret substrate change over time.
文摘The purpose of this paper was to implement ＂Soil and Water Assessment Tool （SWAT）＂ model and Geographic Information System （GIS） to evaluate the impact of land use changes on stream discharge in Nghinh Tuong watershed （a brand of Cau River） in Northern Vietnam. The watershed was coverd by 56% forestry land, 30% agricultural land and the remain 14% for others. Stream discharge observed data from 2002 to 2007 were used for calibration period and from 2008 to 2012 for validation period. The result showed that two coefficients （NSE and PBIAS） to evaluate model performance were 0.76 and 6.54% for calibration period and 0.87 and 4.74% for validation period, respectively. Stream discharge strongly depends not only on quantity of precipitation but also on land use change. Through the scenario 1, agricultural land （corn, orchard and tea） increases 9,782.67 ha （2.45%）, meanwhile forest （forest-mixed） decreases 1,091.77 ha （2.75%） as compared to baseline scenario. Additionally, precipitation increases 3.74% in mean wet season, but decreases 0.5% in mean dry season with respect to baseline period. SWAT model was able to simulate stream discharge and sediment yield for Nghinh Tuong watershed successfully not only for baseline scenario but also for scenario 1. In brief, SWAT proves its ability in simulation stream discharge in subwatershed level. It is a useful tool to assist water quantity and quality management process in Nghinh Tuong watershed. This work one more time indicated that SWAT is useful tool for resources and environment management.
文摘In this study, we performed a conceptual modeling on solute transport based on theoretical stream tube model (STM) with various travel time distributions assuming a pure convective flow through each tube in order to investigate how the lengths and distributions of solute travel time through STM affect the breakthrough curves at the end mixing surface. The conceptual modeling revealed that 1) the shape of breakthrough curve (BTC) at the mixing surface was determined by not only input travel time distributions but also solute injection mode such as sampling time and pulse lengths;2) the increase of pulse length resulted in the linear increase of the first time moment (mean travel time) and quadratic increase of the second time moment (variance of travel time) leading to more spreading of solute, however, the second time moment was not affected by travel time distributions and 3) for a given input distributions the increase in travel distance resulted in more dispersion with the quadratic increase of travel time variance. This indicates that stream tube model obeying strictly pure convective flow follows the concept of convective-lognormal transport (CLT) model regardless the input travel time distributions.
文摘The performance and annual energy output have to be predicted to maximize the economic benefits from a wind turbine. Mathematically predicting the performance of Darrieus type lift based turbines are challenging due to the inconsistent angle of attack, blade wake interaction and local induced velocities giving rise to complex flow physics. A reliable and validated mathematical model is therefore essential to optimize the various design parameters prior to manufacture. The objective of the current study is to evaluate widely employed aerodynamic models based on their prediction accuracy, limitations, and computational requirements. Double multiple stream tube models have been discussed in detail and the predictions are experimentally validated through the wind tunnel test of three-bladed H-Darrieus rotor in terms of torque and power coefficient. The possible sources for the deviation between the predicted and measured values have been discussed and concluded with potential solutions.
文摘In recent years,with the rapid development and popularization of Internet information technology,many new media platforms have risen rapidly,and major e-commerce companies have begun to explore the mode of livestreaming.Especially during the COVID-19 pandemic,due to the lockdown,live-streaming has become an important means of economic development in many places.Owing to its remarkable characteristics of timeliness,entertainment,and interactivity,it has become the latest and trendiest sales mode of e-commerce channels,reflecting huge economic potential and commercial value.This article analyzes two models and their characteristics of live-streaming sales from a practical perspective.Based on this,it outlines consumer purchasing decisions and the factors that affect consumer purchasing decisions under the live-streaming sales model.Finally,it discusses targeted suggestions for using the live-streaming sales model to expand the consumer market,hoping to promote the healthy and steady development of the live-streaming sales industry.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
文摘Hydropower gains increasing importance as a steerable and controllable power source in a renewable energy mix and deregulated markets. Although hydropower produces fossil-free energy, it has a significant impact on the local environment. This review investigates the effects of flow alterations by hydropower on the downstream river system and the possibilities to integrate these effects into hydraulic modeling. The results show that various effects of flow regulation on the ecosystem, but also social and economic effects on related communities were observed in the last decades. The application of hydraulic models for investigations of ecological effects is common. Especially hydraulic effects and effects on fish were extensively modeled with the help of hydraulic 1D- and 2D-simulations. Current applications to investigate social and economic effects integrated into hydraulic modeling are meanwhile limited. Approaches to realizing this integration are presented. Further research on the economic valuation of ecosystems and integration of social and economic effects to hydraulic models is necessary to develop holistic tools to support decision-making on sustainable hydropower.
基金The National Natural Science Foundation of China(No.60673060)the Natural Science Foundation of Jiangsu Province(No.BK2005047)
文摘A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive （AR） modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters,namely,amplitude,phase,damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model,the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.
