A three-dimensional Lagrangian model capable of evaluating the influence of flow velocity, shear dispersion and turbulent diffusion on the transport and dispersal patterns of Asian carp eggs is presented. The model’s variables include not only biological behavior (growth rate, density changes) but also the physical characteristics of the flow field, such as mean velocities and eddy diffusivities.

Pronounced "effin' flow" because it's so badass, fnFlow is a Javascript control flow library heavily influenced by Caolan McMahon's async that encourages a proper functional design pattern.

🥤 MAKAHO (for MAnn-Kendall Analysis of Hydrological Observations) is an interactive cartographic visualization system that allows to calculate trends present in data from hydrometric stations with flows which are little influenced by human actions

3D printed hydroelectric power plant model with Pelton turbine is used for demonstration of physical laws in generating electrical energy using hydropower plants. By changing the height difference and changing the water flow, the user can directly influence the production of electricity. The BLDC motor is used as a generator and the LED strip as an indicator of produced power. The experimental system is a great tool to demonstrate basics of converting energy from potential energy -> kinetic -> mechanical -> electric.

This repository contains code for building and training Graph Neural Networks (GNNs) to predict the state of the electricity grid 24 hours ahead. The model leverages the power of graph-based deep learning to understand grid topology, temporal dependencies, and external factors influencing power flow and stability.

analyze the influence of heterogeneous platoons on the stability of traffic flow under different traffic condition

HTML5 Canvas circuitboard generator with the ability to influence the circuitboard flow by painting in the flow vectors.

Helper library for creating event listeners that influence a Symfony application flow based on annotations

1> Background information Bluebikes is Metro Boston’s public bike share program, with more than 1800 bikes at over 200 stations across Boston and nearby areas. The bikes sharing program launched in 2011. The program aimed for individuals to use it for short-term basis for a price. It allows individuals to borrow a bike from a dock station after using it, which makes it ideal for one-way trips. The City of Boston is committed to providing bike share as a part of the public transportation system. However, to build a transport system that encourages bicycling, it is important to build knowledge about the current bicycle flows, and what factors are involved in the decision-making of potential bicyclists when choosing whether to use the bicycle. It is logical to make hypotheses that age and gender, bicycle infrastructure, safety perception are possible determinants of bicycling. On the short-term perspective, it has been shown that weather plays an important role whether to choose the bicycle. 2> Data collection The Bluebikes collects and provides system data to the public. The datasets used in the project can be download through this link (https://www.bluebikes.com/system-data). Based on this time series dataset (start from 2017-01-01 00:00:00 to 2019-03-31 23:00:00), we could have the information includes: Trip duration, start time and data, stop time and data, start station name and id, end station name and id, bike id, user type (casual or subscribed), birth year, gender. Besides, any trips that were below 60 seconds in length is considered as potentially false starts, which is already removed in the datasets. The number of bicycles used during a particular time period, varies over time based on several factors, including the current weather conditions, time of the day, time of the year and the current interest of the biker to use the bicycle as a transport mode. The current interest is different between subscribed users and casual users, so we should analyze them separately. Factors such as season, day of a week, month, hour, and if a holiday can be extracted from the date and time column in the datasets. Since we would analyze the hourly bicycle rental flow, we need hourly weather conditions data from 2017-01-01 00:00:00 to 2019-03-31 23:00:00 to complete our regression model of prediction. The weather data used in the project is scrapped using python selenium from Logan airport station (42.38 °N, 71.04 °W) webpage (https://www.wunderground.com/history/daily/us/ma/boston/KBOS/date/2019-7-15) maintained by weather underground website. The hourly weather observations include time, temperature, dew point, humidity, wind, wind speed, wind gust, pressure, precipitation, precipitation accumulated, condition. 3> The problem The aims of the project are to gain insight of the factors that could give short-term perspective of bicycle flows in Boston. It also aimed to investigate the how busy each station is, the division of bicycle trip direction and duration of the usage of a busy station and the mean flows variation within a day or during that period. The addition to the factors included in the regression model, there also exist other factors than influence how the bicycle flows vary over longer periods time. For example, general tendency to use the bicycle. Therefore, there is potential to improve the regression model accuracy by incorporating a long-term trend estimate taken over the time series of bicycle usage. Then the result from the machine learning algorithm-based regression model should be compared with the time series forecasting-based models. 4> Possible solutions Data preprocessing/Exploration and variable selection: date approximation manipulation, correlation analysis among variables, merging data, scrubbing for duplicate data, verifying errors, interpolation for missing values, handling outliers and skewness, binning low frequent levels, encoding categorical variables. Data visualization: split number of bike usage by subscribed/casual to build time series; build heatmap to present how busy is each station and locate the busiest station in the busiest period of a busy day; using boxplot and histogram to check outliers and determine appropriate data transformation, using weather condition text to build word cloud. Time series trend curve estimates: two possible way we considered are fitting polynomials of various degrees to the data points in the time series or by using time series decomposition functions and forecast functions to extract and forecast. We would emphasize on the importance to generate trend curve estimates that do not follow the seasonal variations: the seasonal variations should be captured explicitly by the input weather related variables in the regression model. Prediction/regression/time series forecasting: It is possible to build up multilayer perceptron neural network regressor to build up models and give prediction based on all variables of data, time and weather. However, considering the interpretability of model, we prefer to build regression models based on machine learning algorithms (like random forest or SVM) respectively for subscribed/casual users. Then the regressor would be combined with trend curve extracted and forecasted by ARIMA, and then comparing with the result of time series forecasting by STL (Seasonal and Trend decomposition using Loess) with multiple seasonal periods and the result of TBATS (Trigonometric Seasonal, Box-Cox Transformation, ARMA residuals, Trend and Seasonality).

Many factors related to river runoff are vague, subjective and difficult to quantify. The fuzzy logic method is very useful for such problem solving approach such as small hydro power generation. The rule base and membership functions have a great influence on the performance and efficacy of the plant and also to optimize the small hydro power generation in the high altitude region particularly in India. The fuzzy linguistic variable performance can be easily characterized by common terms. The paper initially presents a new Fuzzy Logic Controller (FLC) method for safe reservoir control of dams through spillway gates. Finally it presents FLC method for turbine valve to control the water flow through turbine for hydro power generation. Thus it shows overall effective control and operation of the mechanical equipments in a hydro electric power generation project with FLC and its usefulness. The hardware of control system for fuzzifiers and defuzzifiers can be designed according to the need of system. All above proposed simplified models uses " Tabu Search Algorithm " , " Fuzzy Delphi Method " and " Mamdani Inference Method " to evaluate using manual " C.O.G. Defuzzification " and MATLAB FIS editor validation.

Provides for the flow of Elite:Dangerous faction influence data from providers (EDDN, Journal Log) to Google Sheets.

Quasi-geostropic code for a barotropic, 1-layer flow on a periodic domain under the influence of a mean homogeneous wind stress.

The Friction-Flow framework aims to analyze and track narrative field dynamics in complex social systems, emphasizing the evolution, interaction, and influence of stories.

Abstract— Directed links in social media may represent something from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links confirm the flow of data and therefore indicate a user's influence on others — an inspiration that is crucial in social science and infective agent selling. Throughout this paper, using a good deal of data collected from Twitter, we tend to gift an in-depth comparison of 3 measures of influence: indegree, retweets, and mentions. Supported these measures, we tend to investigate the dynamics of user influence across topics and time. We tend to create many fascinating observations. First, in style users World Health Organization have high indegree are not essentially prestigious in terms of spawning retweets or mentions. Second, most prestigious users will hold vital influence over a spread of topics. We tend to believe that these findings give new insights for infective agent selling and counsel that topological measures like indegree alone reveals very little or no concerning the influence of a user. These measures are terribly numerous. Some are supported easy metrics provided by the Twitter API, whereas others are supported complicated mathematical models.

This simulation models how social networks can influence the flow and reliability of information in inquiry. In the model, heterogeneous agents--driven, to varying degrees, by the competing priorities of accuracy and of conformity to one’s peers--share public opinions. Agents learn, via Bayesian conditionalization, both from private signal from nature, and from the public opinions of other agents.

This is our course homework. I create a physical simulation program to demonstrate the dynamics of human hair under the influence of air flow.

Understand current traffic patterns and challenges in the smart city. Analyze historical data to identify trends and factors influencing traffic flow. Assess the economic, environmental, and social impact. Recommend optimized traffic management and infrastructure planning. Improve smart city efficiency, sustainability, and livability.

OpenCCM is a CFD-based compartment modelling software package. It is primarily intended for convection dominated reactive flows which feature a weak or one-way coupling between the reactive species and the carrier fluid, i.e. the reaction does not substantially influence the fluid flow over the course of the simulation.

This project provides support for article ‘Research on spatial pattern evolution and influencing factors of tourism flow in Chengdu-Chongqing economic circle in China’, realizes online crawling and analysis of Ctrip 's travel diary, and completes the data preprocessing required for the research.

It is a fluid simulation program that visualizes the behavior of a fluid (such as smoke or water) in a 2D grid environment. The script uses numerical methods to simulate fluid dynamics, including diffusion, advection, and pressure projection, and it allows for interactive user input via mouse movements to dynamically influence the fluid flow.

One of the most prominent application fields is medical computer vision, or medical image processing, characterized by the extraction of information from image data to diagnose a patient. An example of this is detection of tumours, arteriosclerosis or other malign changes; measurements of organ dimensions, blood flow, etc. are another example. It also supports medical research by providing new information: e.g., about the structure of the brain, or about the quality of medical treatments. Applications of computer vision in the medical area also includes enhancement of images interpreted by humans—ultrasonic images or X-ray images for example—to reduce the influence of noise.

For this tutorial, we will build a model to predict the depth to groundwater of an aquifer located in Petrignano, Italy. The question we want to answer is What is the future depth to groundwater of a well belonging to the aquifier in Petrigrano over the next quarter? The wells field of the alluvial plain between Ospedalicchio di Bastia Umbra and Petrignano is fed by three underground aquifers separated by low permeability septa. The aquifer can be considered a water table groundwater and is also fed by the Chiascio river. The groundwater levels are influenced by the following parameters: rainfall, depth to groundwater, temperatures and drainage volumes, level of the Chiascio river. Indeed, both rainfall and temperature affect features like level, flow, depth to groundwater and hydrometry some time after it fell down.

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Custom CRM for a New Zealand influencer/talent agency — replaced Excel workflows with quotation builder and talent database. React, TypeScript, Supabase.

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Calculating network flow convergence for negating sybil influences

Analysis of the influence of traffic flow and power generation on air pollution

The flow would not be perceived clearly until we seeing ourselves under its influence.