Stream Model Research Articles

Non-equilibrium solute transport in dipole flow tracer test (DFTT) considering the mixing effect in the wellbore

Dipole flow tracer test (DFTT) has been proposed to estimate the vertical distribution of hydrogeological parameters by fitting the simulated and observed breakthrough curves (BTCs) in the extraction chamber of a vertical circulation well (VCW). Previous studies of DFTT models neglected the non-equilibrium sorption of tracer and the mixing process in the wellbore, which have been proved to strongly influence the estimation accuracy of the DFTT. In this study, we developed an improved DFTT model considering a two-site non-equilibrium sorption of the tracer as well as the impact of the mixing effects on the BTC behavior in both the extraction and injection chambers. The results indicate that when interpreting the DFTT with commonly-used organic tracers, an overestimation and underestimation occurs, especially when the models do not account for sorption and consider equilibrium sorption, respectively. In addition, the mixing effect was observed to affect the transport process as well as the BTC behaviors in the DFTT, leading to an overestimation of the dispersivity. The improved model proposed in this work was used to interpret the DFTT test conducted with Rhodamine WT (RWT) at the Lizzie Field Site, North Carolina. Results showed that the improved model had better performance in interpreting the DFTT compared to the stream tube model proposed by Sutton et al. (2000). This is especially when fluorescent dyes such as RWT or Rhodamine B are used as tracers. Furthermore, unlike previous models, the improved DFTT model is not limited as it is fit for tracers with various types of sorption properties. Meanwhile, the consideration for mixing effect in the wellbore, characterized by an ordinary differential equation, promotes the DFTT interpretation accuracy and also has great potential in the simulation of in situ remediation of subsurface contaminants through VCW, especially for in-well bioreactor technology.

On-the-fly energy release per fission model in STREAM with explicit neutron and photon heating

The on-the-fly energy release per fission (OTFK) model is implemented in STREAM to continuously update the Kappa values during the depletion calculation. The explicit neutron and photon energy distribution, which has not been considered in previous STREAM versions, is incorporated into the existing on-the-fly model. The impacts of the modified OTFK model with explicit neutron and photon heating in STREAM on the power distribution, fuel temperature, and other core parameters during depletion with feedback calculations are studied using several problems from the VERA benchmark suit. Overall, the explicit heating calculation provides a better power map for the feedback calculations particularly when strong gamma emitters are present. Generally, the fuel temperature decreases when neutron and photon heating is employed because fission neutrons and gamma rays are transported away from their points of generation. This energy release model in STREAM indicates that gamma energy accounts for approximately 9.5%–10% of the total energy released, and approximately 2.4%–2.6% of the total energy released will be deposited in the coolant for the VERA 5, NuScale, and Yonggwang Unit 3 2D cores.

Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed

Inclement weather and environmental factors impact traffic operations resulting in travel delays and a reduction in travel time reliability. Precipitation is an example of an environmental factor that affects travel conditions, including traffic speed. While Intelligent Transportation Systems services aim to proactively mitigate congestion on roadways, these services are often not sensitive to weather conditions. This paper investigates the application of high-resolution weather data in improving the performance of proactive transportation management models and proposes short-term speed prediction models that fuse real-time high-resolution weather surveillance radar data with traffic stream data to conduct spatial and temporal prediction of the speed of roadway segments. Extreme gradient boosting weather-aware speed prediction models were developed for a 7-km segment of Interstate 270 in St. Louis, MO, USA. The performance of the weather-aware models was compared with the performance of weather-insensitive speed prediction models that did not take precipitation into account. The results indicated that in the majority of instances, the weather-aware models outperformed the weather-insensitive models. The extreme gradient boosting models were compared with the K-nearest neighbors algorithm and feed-forward neural network models. The extreme gradient boosting model consistently outperformed the other two methods. In addition to speed prediction models, van Aerde speed-flow traffic stream models were developed for rain and no-rain conditions to study the impact of precipitation on the traffic stream across the corridor. Results indicated that the impact of precipitation is not identical across the corridor, which was mirrored in the results obtained from weather-aware speed prediction models.

3D‐Printed Stacked Ionic Assemblies for Iontronic Touch Sensors

AbstractSensing is the process of detecting and monitoring any physico‐chemical environmental parameters. Herein, new self‐powered iontronic sensors, which utilize touch‐induced ionic charge separation in ionically conductive hydrogels, are introduced for potential use in object mapping, recognition, and localization. This is accomplished using high‐resolution stereolithography (SLA) 3D printing of stacked ionic assemblies consisting of discrete compartments having different ion transport properties. The latter assemblies readily allow programming the output voltage magnitude and polarity by means of variations in ion type, charge density, and cross‐linking density within the iontronic device. Voltages of up to 70 mV are generated on application of compressive strains of as much as 50% (≈22.5 kPa), with the magnitude directly proportional to stress, and the polarity dependent on the sign of the mobile ion. As a proof‐of‐concept demonstration, the resulting touch sensors are integrated on the fingertip to enable the tactile feedback, mimicking the tactile perception of objects for recognition applications. In addition, it is proposed that streaming potential is the underlying mechanism behind the iontronic touch sensors. The electromechanical response is therein consistent with a streaming potential model.

Research on the commercial model and marketing strategy of Tik Tok live streaming

Tik Tok is now one of the most relied on platforms by all young people; even some elders and office workers will spare their leisure time to entertain themselves on Tik Tok. The Tik Tok platform has videos, just like elders can watch live sports and food on Tik Tok. But TikTok is not perfect. When you have a lot of different people on the platform, there’s always something to work on. Some problems have been on the platform for a long time, and there is no good way to improve them. Conducting customized experiments to study this topic, this paper collects basic Tik Tok user information and some live broadcast modes through questionnaire surveys or interviews to better understand Tik Tok’s business model and live broadcast strategy. After collecting enough materials, screening is carried out, and finally, the results and conclusions are drawn using materials, real data on the Internet, and literature. Hence, this article is to do some research on Tik Tok and some suggestions to write out, but also better service for users.

Directed energy deposition powder stream modeling using a Gaussian beam ray representation

The powder stream from a side feed nozzle, or part of the powder stream in some coaxial nozzles, in a directed energy deposition via powder feeding (DED-PF) machine, can be modeled using a particle velocity field that has a constant downward component and a linearly increasing outward component, in proportion to the powder stream's center line distance. However, when the powder stream is subject to a force field, it was found that the shape of the powder concentration function close to the center of the powder stream diverges considerably at high degrees of focusing. This discrepancy is reduced by modeling the powder stream based on the ray representation of a Gaussian beam. Experimental results from high-speed camera particle tracking and numerically extrapolating the trajectories to the nozzle exit suggests that the statistics of the powder stream correspond to this model. A novel method to compute the particle concentration along the stream using an optical system analog, with the focusing force field modeled as the transfer matrix of a graded refractive index (GRIN) lens, is also demonstrated. This method is orders of magnitude faster than the corresponding Lagrangian simulation.

A stochastic model for stop-and-go phenomenon in traffic oscillation: On the prospective of macro and micro traffic flow

To investigate the stop-and-go phenomenon triggered by car-following, a hybrid model of micro and macro is proposed. Firstly, with the stochastic nature of driving behavior, a Brownian noise is added into the velocity difference to give a modification to the car-following model. Then, to combine the macroscopic traffic flow characteristics, the traffic stream model is introduced, and the two models are fused in the context of high traffic flow. Next, the theoretical validity of the fusion model is verified by the existence and uniqueness of the solution. The coefficients of the hybrid model are extracted as the stop coefficient and go coefficient which reflect the micro driving behavior. The Maximum Likelihood Estimations of the two coefficients are also given, as well as the boundedness of traffic oscillation in stop-and-go. Finally, numerical simulations show that the model has good fitting and prediction ability for traffic flow in high flow period. Moreover, the greater the stop coefficient, the stronger the traffic flow oscillation intensity, the larger the upper boundedness of the oscillation, the more severe the stop-and-go.

Buffered Streaming Graph Partitioning

Partitioning graphs into blocks of roughly equal size is a widely used tool when processing large graphs. Currently, there is a gap observed in the space of available partitioning algorithms. On the one hand, there are streaming algorithms that have been adopted to partition massive graph data on small machines. In the streaming model, vertices arrive one at a time including their neighborhood, and then have to be assigned directly to a block. These algorithms can partition huge graphs quickly with little memory, but they produce partitions with low solution quality. On the other hand, there are offline (shared-memory) multilevel algorithms that produce partitions with high-quality but also need a machine with enough memory to partition huge networks. In this work, we make a first step to close this gap by presenting an algorithm that computes significantly improved partitions of huge graphs using a single machine with little memory in a streaming setting. First, we adopt the buffered streaming model which is a more reasonable approach in practice. In this model, a processing element can store a buffer of nodes alongside with their edges before making assignment decisions. When our algorithm receives a batch of nodes, we build a model graph that represents the nodes of the batch and the already present partition structure. This model enables us to apply multilevel algorithms and in turn, on cheap machines, compute much higher quality solutions of huge graphs than previously possible. To partition the model graph, we develop a multilevel algorithm that optimizes an objective function that has previously been shown to be effective for the streaming setting. Surprisingly, this also removes the dependency on the number of blocks k from the running time compared to the previous state-of-the-art. Overall, our algorithm computes, on average, 75.9% better solutions than Fennel [ 35 ] using a very small buffer size. In addition, for large values of k our algorithm becomes faster than Fennel .

To calculation of full elliptic integrals third sortsin applied researches

Introduction. When carrying out theoretical and applied researches on modeling of hydrogeophysical filtrational processes and other potential streams quite often there is a need of analytical determination of values of “impossible” elliptic integrals that is connected with labor-consuming mathematical calculations, cross nonlinear interpolating of these special tables and schedules, etc. Use at the solution of applied tasks for their definition of computer programs yields only discrete results in “figures” (but not in “symbols”) without a possibility of their subsequent transformations for receiving the common analytical decision in elementary functions.
 
 Materials and methods. Therefore, despite existence of various computing programs, analytical decisions are demanded so far as they have great opportunities for identification of internal physical relationships of cause and effect in solvable tasks and the subsequent their use in mathematical transformations.
 
 Results. In work estimated dependences in elementary functions by determination of values of full elliptic integral 3rd sorts with a sufficient accuracy (~ << 2–4 %) for carrying out applied (and theoretical) researches are offered. 
 
 Conclusions. L. Milne-Thomson is brought comparison of the received results with exact data of the Mathcad program for a wide interval of changes of parameter of integral and theoretical decisions.

Application of Spectral Analysis in Spinning Measurements

Abstract The presented analysis concerns the problem of irregularity in the mass distribution in linear textile products, its impact on the quality of flat products, and the methods of assessment. Based on the model of the ideal fiber stream, the purpose of the spectrogram has been justified, the characteristic form of the spectrogram for the yarn, roving, and sliver has been presented, and the impact of spinning machines on the form of the spectrogram has been described. The paper evaluates also the selected aspects of spectral analysis of the yarn and spinning semi-finished product irregularities.

Volunteer science data show degraded water quality disproportionately burdens areas of high poverty

Anthropogenic activity degrades stream water quality, especially in urban areas. Quantified connections between pollution sources, degree of water quality degradation, and the disproportionate impact of degradation on underserved communities are not yet fully explored. Here, the anthropogenic effects on water quality and the heterogeneous distribution of degraded streams were examined in the urban watershed of the Rouge River in metropolitan Detroit, Michigan. We used benthic macroinvertebrate data collected by volunteer scientists and aggregated into a Stream Quality Index (SQI) to define long-term water quality patterns. Spatial dependence of the data was assessed with spatial stream network models incorporating socio-economic and environmental predictors. The best model included poverty as an explanatory variable with a negative relationship with stream quality. SQI predictions under true watershed conditions revealed a 1% decrease in SQI with 1% increase in poverty. This work demonstrated the benefits of volunteer science and spatial modeling methods for urban stream modeling. Our finding of inequitably distributed water quality impairment in urban streams underscores the importance of focused restoration in economically oppressed urban areas.

Double-coupling learning for multi-task data stream classification

Data stream classification methods exploiting cohesion in a single data stream have demonstrated promising performance. However, scenarios involving multiple data streams are indeed common in practice. They involve several correlated data streams and can be viewed as multi-task data streams. Instead of considering them separately as individual data streams, it is beneficial to leverage the correlations among the multi-task data streams in data stream modeling. In this regard, a novel classification method called double-coupling support vector machines (DC-SVM) is proposed to classify multiple data streams simultaneously, where both the external correlations between multiple data streams and the internal relationships within each individual data stream are considered. Experimental results on synthetic and real-world multi-task data streams show that the proposed method outperforms traditional data stream classification methods.

Technology Adoption in the Digital Entertainment Industry during the COVID-19 Pandemic: An Extended UTAUT2 Model for Online Theater Streaming

While the digitalization of products and services in the entertainment industry has gained momentum in the last decades, online theater streaming is a relatively new phenomenon boosted by the COVID-19 restrictions, which created new market opportunities—and demand—for theaters’ online presence. This study investigates a new online platform providing theater streaming services in Hungary from a customer-centric, technology acceptance point of view. The survey-based study is designed to examine which factors of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model are—and were—relevant in the under-researched live performance art sector of the digital entertainment industry under the unprecedented, coercive conditions of pandemic lockdowns. The results of the partial least squares structural equation modeling (PLS-SEM) show that habit is the most influential factor of theater webcasting adoption (before hedonic motivations and price value), suggesting that the new habits formed during the COVID-19 lockdowns might serve as a basis of a sustainable digital business model for theatres in the post-pandemic era as well. The analysis also tested for potential generational differences between cohorts of users, finding no significant ones, which suggests that, under this specific set of social, technology and market conditions, all generations react similarly and are equally relevant for widening the customer base. Keeping in mind some limitations (self-reported and cross-sectional data), these empirical results can not only enrich the scientific body of knowledge but can also serve as the basis of future marketing and communication strategies developed by partitioners.

Galaxy and Mass Assembly (GAMA): probing galaxy-group correlations in redshift space with the halo streaming model

ABSTRACT We have studied the galaxy-group cross-correlations in redshift space for the Galaxy And Mass Assembly (GAMA) Survey. We use a set of mock GAMA galaxy and group catalogues to develop and test a novel ‘halo streaming’ model for redshift-space distortions. This treats 2-halo correlations via the streaming model, plus an empirical 1-halo term derived from the mocks, allowing accurate modelling into the non-linear regime. In order to probe the robustness of the growth rate inferred from redshift-space distortions, we divide galaxies by colour, and divide groups according to their total stellar mass, calibrated to total mass via gravitational lensing. We fit our model to correlation data, to obtain estimates of the perturbation growth rate, fσ8, validating parameter errors via the dispersion between different mock realizations. In both mocks and real data, we demonstrate that the results are closely consistent between different subsets of the group and galaxy populations, considering the use of correlation data down to some minimum projected radius, rmin. For the mock data, we can use the halo streaming model to below $r_{\rm min} = 5{\, h^{-1}\, \rm Mpc}$, finding that all subsets yield growth rates within about 3 per cent of each other, and consistent with the true value. For the actual GAMA data, the results are limited by cosmic variance: fσ8 = 0.29 ± 0.10 at an effective redshift of 0.20; but there is every reason to expect that this method will yield precise constraints from larger data sets of the same type, such as the Dark Energy Spectroscopic Instrument (DESI) bright galaxy survey.

ALGORITHMS FOR TRACKING OBJECTS ON A VIDEO STREAM

Algorithms for monitoring moving objects are of great interest in the field of computer vision. The paper presents the results of the development and research of image analysis algorithms for monitoring vehicles in a video stream. Currently, the monitoring of vehicles, especially in production areas, is performed manually by operators. Developing practical applications of video analytics will reduce operator workload, increase speed and accuracy of decision making, and automate the process of collecting and generating reporting statistics on plant transportation. Research was conducted and a video analysis system was developed that includes detection algorithms, classifications by vehicle type, color, and model, and vehicle tracking in the video stream. It should be noted that the architecture of existing solutions, as a rule, is monolithic and does not allow to refine and embed new unique modules to solve emerging problems. In modern conditions, in order to cover new requirements in the field of object recognition and detection in large vehicle flows, it is necessary to develop and implement new solutions. The results of the work of the algorithms on real images of existing datasets and the developed own dataset are presented. In this case, the objects are under different lighting conditions, in different angles, are rebuilt in the stream, there is an overlap of objects. The developed system has a microservice architecture, which allows to adapt the solutions to specific tasks.

Kingdon의 정책흐름모형을 활용한 「전라북도 학교자치조례」제정과정 분석

The purpose of this study is to analyze the dynamic enactment process of the 「Jeollabuk-do School Autonomy Ordinance」enacted in 2019 using Kingdon's policy stream model. The stream of policy issues includes indicators of the necessity of legal organization of school autonomous bodies, attempts to enact school autonomy ordinances by each city/provincial office of education, and feedback due to the limitations of the existing school autonomy operation. The stream of politics includes changes in the provincial atmosphere for school autonomy, the emergence and interest of a new administration, and the activities of interest groups in school autonomy ordinances. The stream of policy alternatives includes the proposal of Jeollabuk-do Office of Education, the proposal of Korean Teachers and Educational Workers' Union, the proposal of Korea Federation of Teacher's Associations, and the proposal of private school corporations and principals. These policy alternatives were influenced by policy communities such as teachers' organizations, teachers' unions, and school corporations. These three independent streams were combined as the change of administration became the trigger mechanism. In addition, as progressive policy entrepreneur such as the Jeollabuk-do Superintendent of Education is actively working to urge the enactment of the ordinance, the working-level council for promoting the school autonomy ordinance was held on March 26, 2018, combining these three streams to open a policy window. As a result, the Jeonlabuk-do School Autonomy Ordinance, which has been under legislative discussion for a long time, was finally enacted on February 1, 2019.

Construction of Intelligent Evaluation Platform Based on Random Matrix iWrite College English Writing

The advantages of iWrite English writing teaching and marking system 3.0 and the case study of English writing in higher education based on this system have a positive effect on forming a new model of English writing teaching in higher education under the background of Internet + and improving and solving the main problems of English writing teaching in higher education. Although the current intelligent marking systems have made a lot of achievements, they have not fundamentally solved the problem of the rationality of intelligent marking of subjective questions. In order to better perceive the sense of speech in English essays in depth and improve the rationality problem of intelligent marking, this study proposes a quantification of N-element sense of speech value based on correlation analysis and a scoring fitting algorithm for English essays based on rationality enhancement. The quantification of perceptual value is done by obtaining the N components of the composition and calculating their support in the corpus. In the daily teaching work, teachers should actively cooperate with the marking system to improve their own teaching level and explore ways and methods of students’ writing training, so as to cultivate more English talents. In addition, word features, sentence features, and chapter structure features were extracted from the essays to fit the English essay scores. Since not all students were able to complete the essays according to the requirements of the questions, a streaming scoring model was used to separate the normal essays from the low-scoring essays. The low-scoring essays were scored using the k-nearest neighbor algorithm, while the normal essays were fitted to the test takers’ scores using the support vector regression algorithm. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this study addresses this problem by using a rationality enhancement method, which gives a corresponding penalty factor according to the distribution of the data set.

A dynamic feature selection and intelligent model serving for hybrid batch-stream processing

The increasing complexity of real-world applications, especially those related to the Internet of Things and cloud computing, highlights the importance of using hybrid batch-stream processing techniques to analyze big data. Hybrid processing combines the accuracy of batch processing and the speed of stream processing. Among the important challenges of this approach are selecting relevant and diverse features to build the base models and intelligently choosing which of those models to use in computing the final results. We present the H-DIFS architecture, a dynamic and intelligent feature selection approach to address these two challenges of hybrid batch-stream processing for anomaly detection. The proposed architecture employs a dynamic feature selection method based on the genetic algorithm that is fully compatible with the nature of hybrid processing and dynamically changes the models’ features over time based on the input data stream. Additionally, two components, the model evaluator and intelligent model recommender, allow for offering various batch and stream models and selecting from them based on the defined policies. The experimental results, on the datasets with various feature set sizes, indicate that the proposed architecture increases the speed of hybrid processing and anomaly detection by eliminating irrelevant and redundant features. Moreover, it increases detection accuracy by selecting appropriate models and aggregating their results.

Human centred lean – introducing the people value stream

PurposeThe focus of lean has increasingly moved from a tools-based to a systems-based approach with particular emphasis on people. However, a conceptual framework for this is lacking. The people value stream concept provides a starting point for further discussion, research and practical application in this area.Design/methodology/approachIn this conceptual paper, the author draws on their extensive experience with lean through 30 years of researching, leading and consulting in lean transformations.FindingsThe people value stream concept is presented together with a series of theoretical, practical and social implications for its application. These include: a rethinking of the role of executives in lean organisations; the importance of people and their experience of work; how the issue of wasted human potential might be addressed; how lean can further evolve; how, in addition to products, people can be made the focus in lean; and how pull and flow can be applied to the people value stream, including what competencies, learning and development, behaviours, accountability, social support and mental and physical wellbeing, are required or should be provided to employees throughout their careers.Originality/valueThis paper widens the existing discussion of people within lean. To the best of the author’s knowledge, for the first time in an academic paper, it discusses this within a lean context by proposing a conceptual people value stream model.

The Netflix Original documentary, explained: global investment patterns in documentary films and series

ABSTRACT Subscription video-on-demand services have been increasingly moving away from licencing content to producing their own content. The ‘original’ label is applied to different types of productions for which streaming platforms own exclusive rights, usually worldwide and for specific periods. This has also been part of Netflix’s global strategy to attract new and existing subscribers. Research into the Netflix Original has become particularly relevant, due to its impact on audiovisual markets and the label’s opacity. This article focuses on Netflix investments in original documentaries, due to the genre’s growing popularity and the platform’s notable interest in documentary films and series. The analysis focuses on the company’s strategies and investment patterns in specific regions, languages and genres over time by mapping all documentary titles produced between 2012–2021 labelled as ‘Netflix Original’, resulting in a database of 479 titles. We found that investments in original documentaries have been growing over the years, and the large majority of these are commissions or exclusive titles. The transnational production and distribution of Netflix Original documentaries reflect wider changes in cultural trade brought on by the streaming model. However, the company’s investment strategy is also influenced by established market dynamics and financing practices in documentary production.