WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This was done using two different approaches: one based on expert opinions, and one based on statistical learning. This GDR submission includes the datasets used to produce the statistical learning-based weights. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. The drawback is that, to apply these types of approaches, a dataset is needed. Therefore, we attempted to build comprehensive standardized datasets mapping anomalies in each exploration dataset to each component of each play. This data was gathered through a literature review focused on magmatic hydrothermal plays along with well-characterized areas where superhot or supercritical conditions are thought to exist. Datasets were assembled for all three play types, but the hydrothermal dataset is the least complete due to its relatively low priority. For each known or assumed resource, the dataset states what anomaly in each exploration dataset is associated with each component of the system. The data is only a semi-quantitative, where values are either high, medium, or low, relative to background levels. In addition, the dataset has significant gaps, as not every possible exploration dataset has been collected and analyzed at every known or suspected geothermal resource area, in the context of all possible play types. The following training sites were used to assemble this dataset: - Conventional magmatic hydrothermal: Akutan (from AK PFA), Oregon Cascades PFA, Glass Buttes OR, Mauna Kea (from HI PFA), Lanai (from HI PFA), Mt St Helens Shear Zone (from WA PFA), Wind River Valley (From WA PFA), Mount Baker (from WA PFA). - Superhot EGS: Newberry (EGS demonstration project), Coso (EGS demonstration project), Geysers (EGS demonstration project), Eastern Snake River Plain (EGS demonstration project), Utah FORGE, Larderello, Kakkonda, Taupo Volcanic Zone, Acoculco, Krafla. - Supercritical: Coso, Geysers, Salton Sea, Larderello, Los Humeros, Taupo Volcanic Zone, Krafla, Reyjanes, Hengill. **Disclaimer: Treat the supercritical fluid anomalies with skepticism. They are based on assumptions due to the general lack of confirmed supercritical fluid encounters and samples at the sites included in this dataset, at the time of assembling the dataset. The main assumption was that the supercritical fluid in a given geothermal system has shared properties with the hydrothermal fluid, which may not be the case in reality. Once the datasets were assembled, principal component analysis (PCA) was applied to each. PCA is an unsupervised statistical learning technique, meaning that labels are not required on the data, that summarized the directions of variance in the data. This approach was chosen because our labels are not certain, i.e., we do not know with 100% confidence that superhot resources exist at all the assumed positive areas. We also do not have data for any known non-geothermal areas, meaning that it would be challenging to apply a supervised learning technique. In order to generate weights from the PCA, an analysis of the PCA loading values was conducted. PCA loading values represent how much a feature is contributing to each principal component, and therefore the overall variance in the data.

Libraries Import:

Importing necessary libraries such as pandas, seaborn, matplotlib, scikit-learn's KMeans, and warnings. Data Loading and Exploration:

Reading a dataset named "Mall_Customers.csv" into a pandas DataFrame (df). Displaying the first few rows of the dataset using df.head(). Conducting univariate analysis by calculating descriptive statistics with df.describe(). Univariate Analysis:

Visualizing the distribution of the 'Annual Income (k$)' column using sns.distplot. Looping through selected columns ('Age', 'Annual Income (k$)', 'Spending Score (1-100)') and plotting individual distribution plots. Bivariate Analysis:

Creating a scatter plot for 'Annual Income (k$)' vs 'Spending Score (1-100)' using sns.scatterplot. Generating a pair plot for selected columns with gender differentiation using sns.pairplot. Gender-Based Analysis:

Grouping the data by 'Gender' and calculating the mean for selected columns. Computing the correlation matrix for the grouped data and visualizing it using a heatmap. Univariate Clustering:

Applying KMeans clustering with 3 clusters based on 'Annual Income (k$)' and adding the 'Income Cluster' column to the DataFrame. Plotting the elbow method to determine the optimal number of clusters. Bivariate Clustering:

Applying KMeans clustering with 5 clusters based on 'Annual Income (k$)' and 'Spending Score (1-100)' and adding the 'Spending and Income Cluster' column. Plotting the elbow method for bivariate clustering and visualizing the cluster centers on a scatter plot. Displaying a normalized cross-tabulation between 'Spending and Income Cluster' and 'Gender'. Multivariate Clustering:

Performing multivariate clustering by creating dummy variables, scaling selected columns, and applying KMeans clustering. Plotting the elbow method for multivariate clustering. Result Saving:

Saving the modified DataFrame with cluster information to a CSV file named "Result.csv". Saving the multivariate clustering plot as an image file ("Multivariate_figure.png").

AQQAD, ABDELRAHIM (2023), “insurance_claims ”, Mendeley Data, V2, doi: 10.17632/992mh7dk9y.2

https://data.mendeley.com/datasets/992mh7dk9y/2

Latest version Version 2 Published: 22 Aug 2023 DOI: 10.17632/992mh7dk9y.2

Data Acquisition: - Obtain the dataset titled "Insurance_claims" from the following Mendeley repository: https://https://data.mendeley.com/drafts/992mh7dk9y - Download and store the dataset locally for easy access during subsequent steps.

Data Loading & Initial Exploration: - Use Python's Pandas library to load the dataset into a DataFrame. python Code used:

Load the Dataset File

insurance_df = pd.read_csv('insurance_claims.csv')

• Inspect the initial rows, data types, and summary statistics to get an understanding of the dataset's structure.

Data Cleaning & Pre-processing: - Handle missing values, if any. Strategies may include imputation or deletion based on the nature of the missing data. - Identify and handle outliers. In this research, particularly, outliers in the 'umbrella_limit' column were addressed. - Normalize or standardize features if necessary.

Exploratory Data Analysis (EDA): - Utilize visualization libraries such as Matplotlib and Seaborn in Python for graphical exploration. - Examine distributions, correlations, and patterns in the data, especially between features and the target variable 'fraud_reported'. - Identify features that exhibit distinct patterns for fraudulent and non-fraudulent claims.

Feature Engineering & Selection: - Create or transform existing features to improve model performance. - Use techniques like Recursive Feature Elimination (RFECV) to identify and retain only the most informative features.

Modeling: - Split the dataset into training and test sets to ensure the model's generalizability. - Implement machine learning algorithms such as Support Vector Machine, RandomForest, and Voting Classifier using libraries like Scikit-learn. - Handle class imbalance issues using methods like Synthetic Minority Over-sampling Technique (SMOTE).

Model Evaluation: - Evaluate the performance of each model using metrics like precision, recall, F1-score, ROC-AUC score, and confusion matrix. - Fine-tune the models based on the results. Hyperparameter tuning can be performed using techniques like Grid Search or Random Search.

Model Interpretation: - Use methods like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to interpret and understand the predictions made by the model.

Deployment & Prediction: - Utilize the best-performing model to make predictions on unseen data. - If the intention is to deploy the model in a real-world scenario, convert the trained model into a format suitable for deployment (e.g., using libraries like joblib or pickle).

Software & Tools: - Programming Language: Python (version: GoogleColab) - Libraries: Pandas, Numpy, Matplotlib, Seaborn, Scikit-learn, Imbalanced-learn, LIME, and SHAP. - Environment: Jupyter Notebook or any Python IDE.

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This was done using two different approaches: one based on expert opinions, and one based on statistical learning. This GDR submission includes the datasets used to produce the statistical learning-based weights. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. The drawback is that, to apply these types of approaches, a dataset is needed. Therefore, we attempted to build comprehensive standardized datasets mapping anomalies in each exploration dataset to each component of each play. This data was gathered through a literature review focused on magmatic hydrothermal plays along with well-characterized areas where superhot or supercritical conditions are thought to exist. Datasets were assembled for all three play types, but the hydrothermal datasetmore » is the least complete due to its relatively low priority. For each known or assumed resource, the dataset states what anomaly in each exploration dataset is associated with each component of the system. The data is only a semi-quantitative, where values are either high, medium, or low, relative to background levels. In addition, the dataset has significant gaps, as not every possible exploration dataset has been collected and analyzed at every known or suspected geothermal resource area, in the context of all possible play types. The following training sites were used to assemble this dataset: - Conventional magmatic hydrothermal: Akutan (from AK PFA), Oregon Cascades PFA, Glass Buttes OR, Mauna Kea (from HI PFA), Lanai (from HI PFA), Mt St Helens Shear Zone (from WA PFA), Wind River Valley (From WA PFA), Mount Baker (from WA PFA). - Superhot EGS: Newberry (EGS demonstration project), Coso (EGS demonstration project), Geysers (EGS demonstration project), Eastern Snake River Plain (EGS demonstration project), Utah FORGE, Larderello, Kakkonda, Taupo Volcanic Zone, Acoculco, Krafla. - Supercritical: Coso, Geysers, Salton Sea, Larderello, Los Humeros, Taupo Volcanic Zone, Krafla, Reyjanes, Hengill. **Disclaimer: Treat the supercritical fluid anomalies with skepticism. They are based on assumptions due to the general lack of confirmed supercritical fluid encounters and samples at the sites included in this dataset, at the time of assembling the dataset. The main assumption was that the supercritical fluid in a given geothermal system has shared properties with the hydrothermal fluid, which may not be the case in reality. Once the datasets were assembled, principal component analysis (PCA) was applied to each. PCA is an unsupervised statistical learning technique, meaning that labels are not required on the data, that summarized the directions of variance in the data. This approach was chosen because our labels are not certain, i.e., we do not know with 100% confidence that superhot resources exist at all the assumed positive areas. We also do not have data for any known non-geothermal areas, meaning that it would be challenging to apply a supervised learning technique. In order to generate weights from the PCA, an analysis of the PCA loading values was conducted. PCA loading values represent how much a feature is contributing to each principal component, and therefore the overall variance in the data.« less

Astronauts lose considerable bone mass during long duration spaceflight. These losses are one of the major concerns for proposed exploration class missions to the Moon, Mars, and Near Earth Objects due to the increase in fracture risk associated with reduced bone strength. These losses are seen even with the intervention of current exercise countermeasures. Although it is possible that the newest exercise machine, the Advanced Resistive Exercise Device (ARED), will be more effective in preventing bone and muscle losses, its size may be prohibitive in bringing it on interplanetary missions. In order for astronauts to be able to perform successful exploration tasks, they need to arrive at their destinations healthy and capable of doing work. Because of this, new countermeasures will need to be developed to prevent musculoskeletal deconditioning.

The goal of this proposal is to meet this need by producing a wearable countermeasure suit. The suits primary goal will be to impose static loading, similar to that produced by gravity, on the user. In addition, incorporating a dynamic loading component, such as a form of vibration, may enhance the effectiveness of the suit. Finally, integrating the suit with existing countermeasures could serve to improve the overall efficacy of the entire countermeasure program. While astronauts currently exercise for around 2 hours, this suit could be worn for a longer period of time, including while the astronauts are performing other tasks. The suits will also be lightweight and easily packable, which is a major consideration for space exploration missions.

The research objectives of this proposal are as follows: To produce a comprehensive model of suit-body interactions to aid in suit design To investigate the integration of the suit with existing countermeasures To investigate forms of dynamic loading, and their effects on subject comfort and performance To build and characterize prototype countermeasure suits The model from aim 1 will be created using Matlab and body modeling software, and will be used to inform suit design. It will be used to compute the effects of integrating the suit with existing countermeasures on overall suit characteristics. Dynamic loading mechanisms will be evaluated on their loading qualities and influence on subject comfort and performance. After the suits are constructed, their loading and comfort traits will be characterized.

The Gravity Loading Countermeasure Skinsuit will reduce the musculoskeletal deconditioning seen during long duration spaceflight, allowing for a more robust and effective exploration program. The technologies developed in this proposal will also have applications in the medical field, for treating bed rest patients and in healing musculoskeletal injuries.

Ethical clearance reference number: refer to the uploaded document Ethics Certificate.pdf.

General (0)

0 - Built diagrams and figures.pdf: diagrams and figures used for the thesis

Analysis of country data (1)

0 - Country selection.xlsx: In this analysis the sub-Saharan country (Niger) is selected based on the kWh per capita data obtained from sources such as the United Nations and the World Bank. Other data used from these sources includes household size and electricity access. Some household data was projected using linear regression. Sample sizes VS error margins were also analyzed for the selection of a smaller area within the country.

Smart metering experiment (2)

The figures (PNG, JPG, PDF) include:

    - The experiment components and assembly
    - The use of device (meter and modem) softwar tools to program and analyse data
    - Phasor and meter detail
    - Extracted reports and graphs from the MDMS

The datasets (CSV, XLSX) include:

    - Energy load profile and register data recorded by the smart meter and collected by both meter configuration and MDM applications.
    - Data collected also includes events, alarm and QoS data.

Data applicability to SEAP (3)

3 - Energy data and SEAP.pdf: as part of the Smart Metering VS SEAP framework analysis, a comparison between SEAP's data requirements, the applicable energy data to those requirements, the benefits, and the calculation of indicators where applicable. 3 - SEAP indicators.xlsx: as part of the Smart Metering VS SEAP framework analysis, the applicable calculation of indicators for SEAP's data requirements.

Load prediction by machine learning (4)

The coding (IPYNB, PY, HTML, ZIP) shows the preparation and exploration of the energy data to train the machine learning model. The datasets (CSV, XLSX), sequentially named, are part of the process of extracting, transforming and loading the data into a machine learning algorithm, identifying the best regression model based on metrics, and predicting the data.

HRES analysis and optimization (5)

The figures (PNG, JPG, PDF) include:

    - Household load, based on the energy data from the smart metering experiment and the machine learning exercise
    - Pre-defined/synthetic load, provided by the software when no external data (household load) is available, and
    - The HRES designed
    - Application-generated reports with the results of the analysis, for both best case HRES and fully renewable scenarios.

The datasets (XLSX) include the 12-month input load for the simulation, and the input/output analysis and calculations. 5 - Gorou_Niger_20220529_v3.homer: software (Homer Pro) file with the simulated HRES

· Conferences (6)

6 – IEEE_MISTA_2022_paper_51.pdf: paper (research in progress) presented at the IEEE MISTA 2022 conference, occurred in March-2022, and published in the respective proceeding, 6 - IEEE_MISTA_2022_proceeding.pdf. 6 - ITAS_2023.pdf: paper (final research) recently presented at the ITAS 2023 conference in Doha, Qatar, in March-2023. 6 - Smart Energy Seminar 2023.pptx: PowerPoint slide version of the paper, recently presented at the Smart Energy Seminar held at CPUT, in March-2023.

Snapshot img

Marine Loading Arms Market Size 2025-2029

The marine loading arms market size is valued to increase by USD 82 million, at a CAGR of 4.1% from 2024 to 2029. New oil and gas exploration policies will drive the marine loading arms market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 43% growth during the forecast period.
By Application - Crude oil segment was valued at USD 130.10 million in 2023
By Type - Manual marine loading arms segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 35.06 million
Market Future Opportunities: USD 82.00 million
CAGR from 2024 to 2029 : 4.1%

Market Summary

The market is a critical component of the global oil and gas industry, facilitating the transfer of petroleum products from vessels to onshore storage and distribution facilities. A single data point illustrates the market's significance: it was valued at USD 3.5 billion in 2020. Advancements in technology have significantly influenced the market's evolution. The advent of motion-recognizing marine loading arms has streamlined the loading process, enhancing efficiency and safety. However, the high cost associated with these advanced systems presents a challenge for market growth. Despite this hurdle, the market continues to adapt and innovate.
Manufacturers are exploring cost-effective solutions, such as modular and lightweight designs, to make marine loading arms more accessible to a wider range of customers. Furthermore, the integration of automation and remote monitoring systems is expected to drive market expansion. The market's future direction lies in enhancing operational efficiency, ensuring safety, and reducing environmental impact. As the industry navigates the complexities of new exploration policies and evolving market dynamics, marine loading arms will remain a vital link in the oil and gas supply chain.

What will be the Size of the Marine Loading Arms Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Marine Loading Arms Market Segmented?

The marine loading arms industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Crude oil
  LG
  IG


Type

  Manual marine loading arms
  Hydraulic marine loading arms


Method

  Top loading
  Bottom Loading


Material

  Carbon steel
  Stainless steel
  Aluminum
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The crude oil segment is estimated to witness significant growth during the forecast period.

The market is undergoing continuous evolution, with the crude oil segment experiencing significant growth due to the increasing demand for efficient and safe transfer systems in the oil and gas industry. Marine loading arms play a crucial role in the loading and unloading of crude oil from tankers to storage facilities or pipelines, ensuring minimal spillage and enhanced safety. The global trade of crude oil, particularly from regions like the Middle East and Asia-Pacific, has necessitated the adoption of advanced marine loading arms. These innovations include material selection criteria that prioritize vapor recovery systems, automated loading systems, and seal technology.

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The Crude oil segment was valued at USD 130.10 million in 2019 and showed a gradual increase during the forecast period.

Rotary joints, product compatibility testing, high-pressure loading arms, safety interlocks systems, and operational efficiency metrics are becoming increasingly important. Furthermore, leak detection systems, environmental impact assessment, balancing systems, corrosion protection methods, emergency shutdown systems, and vessel compatibility are all critical considerations. The integration of subsea and offshore loading arms, remote control systems, fluid transfer optimization, cryogenic loading arms, throughput optimization, flexible joints, hydraulic power units, and loading arm integrity is also transforming the market.

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Regional Analysis

APAC is estimated to contribute 43% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How Marine Loading Arms Market Demand is Rising in APAC Request Free Sample

The market in APAC is experiencing notable growth due to the expanding application sectors, including crude oil imports, refined products exports, and the IGs in

This repository contains the code and data supporting the results presented in Chapter 4 of the dissertation "Multi-Fidelity Probabilistic Design Framework for Early-Stage Design of Novel Vessels" and the paper "Multi-fidelity design framework to support early-stage design exploration of the AXE frigates: the vertical bending moment case". The research explores the potential of harnessing multi-fidelity models for early-stage predictions of wave-induced loads, with a specific focus on wave-induced vertical bending moments. The assessed models include the application of both linear and nonlinear Gaussian processes and compositional kernels to improve predictions of wave-induced loads, specifically focusing on wave-induced vertical bending moments. The case study focuses on the early-stage exploration of the AXE frigates. Multi-fidelity models were constructed using both frequency- and time-domain methods to evaluate the vertical bending moments experienced by the hull.

The data include: (1) the parametric model developed in Rhino and Grasshopper used to generate the hull mesh, (2) the simulation data, (3) the data associated with the analyzed cases, and (4)the Python scripts can be found in this gitlab repository. The analysis solvers used to calculate the vertical bending moments for calculating the vertical bending moments are not included in this repository.

This end-to-end machine learning pipeline addresses the problem of credit card fraud detection. The pipeline consists of several key steps: Problem Statement: The notebook begins by outlining the importance of detecting credit card fraud for financial institutions and customers. It frames the task as a binary classification problem. Data Loading and Exploration: The pipeline uses a credit card transaction dataset, likely from Kaggle. It explores the data structure, checks for missing values, and examines the class distribution of fraudulent vs. non-fraudulent transactions. Data Preprocessing: The features are separated from the target variable. The data is split into training and testing sets. Feature scaling is applied using StandardScaler. SMOTE (Synthetic Minority Over-sampling Technique) is used to address class imbalance in the training data. Model Training: Two models are trained: Logistic Regression and Random Forest Classifier. Both models are fitted on the resampled training data. Model Evaluation: The models are evaluated using various metrics including accuracy, precision, recall, F1-score, and ROC AUC score. Confusion matrices are plotted for both models to visualize their performance. For the Random Forest model, feature importance is calculated and visualized. Discussion and Recommendations: The notebook concludes with a discussion on the strengths and limitations of the approach. It provides business recommendations based on the model results. The importance of model explainability is addressed, suggesting the use of SHAP values for more detailed interpretations. This pipeline demonstrates a comprehensive approach to fraud detection, from data preprocessing to model evaluation and business recommendations. It addresses common challenges in fraud detection such as class imbalance and the need for interpretable results

The datasets and code used in this study are publicly available.Datasets:The datasets used in this study have been divided into training, testing, and validation sets.Code:This code repository includes Python scripts that replicate the experimental setup described in the paper. The code is organized into the following modules:Data Preprocessing: This module contains code for loading, cleaning, and transforming the datasets.Model Training: This module includes code for training various named entity recognition models using pre-trained language models. The code also includes implementations of the ablation experiments and data augmentation techniques described in the paper.Evaluation: This module contains code for evaluating the performance of the trained models on the held-out data sets.The data and code are provided to facilitate reproducibility and further research on named entity recognition in the Chinese language.Clarification: None of the authors are affiliated with Tsinghua University.

Project Documentation: Predicting S&P 500 Price Problem Statement: The goal of this project is to develop a machine learning model that can predict the future price of the S&P 500 index based on historical data and relevant features. By accurately predicting the price movements, we aim to assist investors and financial professionals in making informed decisions and managing their portfolios effectively. Dataset Description: The dataset used for this project contains historical data of the S&P 500 index, along with several other features such as dividends, earnings, consumer price index (CPI), interest rates, and more. The dataset spans a certain time period and includes daily values of these variables. Steps Taken: 1. Data Preparation and Exploration: • Loaded the dataset and performed initial exploration. • Checked for missing values and handled them if any. • Explored the statistical summary and distributions of the variables. • Conducted correlation analysis to identify potential features for prediction. 2. Data Visualization and Analysis: • Plotted time series graphs to visualize the S&P 500 index and other variables over time. • Examined the trends, seasonality, and residual behavior of the time series using decomposition techniques. • Analyzed the relationships between the S&P 500 index and other features using scatter plots and correlation matrices. 3. Feature Engineering and Selection: • Selected relevant features based on correlation analysis and domain knowledge. • Explored feature importance using tree-based models and selected informative features. • Prepared the final feature set for model training. 4. Model Training and Evaluation: • Split the dataset into training and testing sets. • Selected a regression model (Linear Regression) for price prediction. • Trained the model using the training set. • Evaluated the model's performance using mean squared error (MSE) and R-squared (R^2) metrics on both training and testing sets. 5. Prediction and Interpretation: • Obtained predictions for future S&P 500 prices using the trained model. • Interpreted the predicted prices in the context of the current market conditions and the percentage change from the current price. Limitations and Future Improvements: • The predictive performance of the model is based on the available features and historical data, and it may not capture all the complexities and factors influencing the S&P 500 index. • The model's accuracy and reliability are subject to the quality and representativeness of the training data. • The model assumes that the historical patterns and relationships observed in the data will continue in the future, which may not always hold true. • Future improvements could include incorporating additional relevant features, exploring different regression algorithms, and considering more sophisticated techniques such as time series forecasting models.

This project performs a Bayesian hierarchical analysis to investigate the factors influencing energy cost burden across different ZIP codes and years. Using panel data from multiple Excel files spanning several years (2012-2022), the project aims to model the relationship between energy cost burden and various predictors including tax_returns, uptake (presumably related to program participation or energy efficiency measures), and percent_white.

The core of the analysis involves:

Data Loading and Preprocessing: Combining data from multiple years, handling missing values, and standardizing predictor variables. Hierarchical Modeling: Building a Bayesian hierarchical model using PyMC that accounts for variation across both ZIP codes and years through the use of random effects. Inference: Performing inference using both variational inference (ADVI) and Markov Chain Monte Carlo (MCMC) methods, specifically the No-U-Turn Sampler (NUTS), to estimate the posterior distributions of the model parameters. Diagnostics and Comparison: Analyzing the convergence diagnostics (R-hat, ESS, divergences) for the MCMC samples and comparing the results obtained from ADVI and NUTS to understand the reliability of the different inference methods for this model and dataset. Exploratory Analysis: Including steps for basic data exploration such as summary statistics, correlation analysis, and time trends of key variables. The project highlights the importance of using robust MCMC methods like NUTS for complex models, especially when simpler approximations like ADVI might yield conflicting conclusions, and includes steps to improve sampler performance and assess convergence.

The critical influence of solvent effects on proline-catalyzed aldol reactions has been extensively described. Herein, we apply multivariate regression strategies to probe the influence of different solvents on an aldol reaction catalyzed by proline modified at its secondary sphere with boronic acids. In this system, both in situ binding of the boronic acid to proline and the outcome of the aldol reaction are impacted by the solvent-controlled microenvironment. Thus, with the aim of uncovering mechanistic insight and an ancillary aim of identifying methodological improvements, we designed a set of experiments, spanning 15 boronic acids in five different solvents. Based on hypothesized intermediates or interactions that could be responsible for the selectivity in these reactions, we proposed several structural configurations for the library of boronic acids. Subsequently, we compared the statistical models correlating the outcome of the reaction in different solvents with molecular descriptors produced for each of these proposed configurations. The models allude to the importance of different interactions in controlling selectivity in each of the studied solvents. As a proof-of-concept for the practicality of our approach, the models in chloroform ultimately led to lowering the ketone loading to only two equivalents while retaining excellent yield and enantio- and diastereo-selectivity.

QLKNN11D training set

This dataset contains a large-scale run of ~1 billion flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at https://gitlab.com/qualikiz-group/QuaLiKiz/. This dataset was generated with the 'QLKNN11D-hyper' tag of QuaLiKiz, equivalent to 2.8.1 apart from the negative magnetic shear filter being disabled. See https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/QLKNN11D-hyper for the in-repository tag.

The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See https://doi.org/10.1063/1.5134126 for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on arXiv https://arxiv.org/abs/1911.05617 and the older dataset on Zenodo https://doi.org/10.5281/zenodo.3497066. For an application example, see Van Mulders et al 2021 https://doi.org/10.1088/1741-4326/ac0d12, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. For any learned surrogates developed for QLKNN11D, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended.

Related repositories:

General QuaLiKiz documentation https://qualikiz.com

QuaLiKiz/QLKNN input/output variables naming scheme https://qualikiz.com/QuaLiKiz/Input-and-output-variables

Training, plotting, filtering, and auxiliary tools https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop

QuaLiKiz related tools https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools

FORTRAN QLKNN implementation with wrapper for Python and MATLAB https://gitlab.com/qualikiz-group/QLKNN-fortran

Weights and biases of 'hyperrectangle style' QLKNN https://gitlab.com/qualikiz-group/qlknn-hype

Data exploration

The data is provided in 43 netCDF files. We advise opening single datasets using xarray or multiple datasets out-of-core using dask. For reference, we give the load times and sizes of a single variable that just depends on the scan size dimx below. This was tested single-core on a Intel Xeon 8160 CPU at 2.1 GHz and 192 GB of DDR4 RAM. Note that during loading, more memory is needed than the final number.

Timing of dataset loading


    Amount of datasets
    Final in-RAM memory (GiB)

Loading time single var (M:SS)

    1
    10.3
    0:09


    5
    43.9
    1:00


    10
    63.2
    2:01


    16
    98.0
    3:25


    17
    Out Of Memory
    x:xx

Full dataset

The full dataset of QuaLiKiz in-and-output data is available on request. Note that this is 2.2 TiB of netCDF files!

Key Features

How to use this dataset

1. Data Access: Retrieve the dataset from the provided source or API to access FDA records of product recalls in the United States.

2. Data Exploration: Thoroughly explore the dataset by loading it into your preferred data analysis tool. Familiarize yourself with the columns and their meanings.

3. Filter and Sort: Tailor your analysis by filtering and sorting the data as per your research needs. For example, filter by "product_type" or sort by "report_date" for specific insights.

4. Recall Analysis: Examine the "reason_for_recall" column to understand the reasons behind product recalls. This is crucial for assessing common issues in recalled products.

5. Visualization: Create visualizations, such as graphs and charts, to convey your findings effectively. These can help in identifying trends and patterns in the recall data.

If you find this dataset useful, give it an upvote – it's a small gesture that goes a long way! Thanks for your support. 😄

TEXT PREPROCESSING PIPELINE :

DATASET INFORMATION: Total samples: 16,000 Input shape (X): (16000, 19) Labels shape (y): (16000,)

PREPROCESSING STEPS COMPLETED: 1. Data Loading & Exploration 2. Text Cleaning & Standardization (URLs, HTML, contractions) 3. Punctuation Removal 4. Lowercasing 5. Tokenization 6. Stopword Removal 7. Part-of-Speech (POS) Tagging 8. Lemmatization (using POS tags for accuracy) 9. Vocabulary Building 10. Sequence Padding (uniform length) 11. Embedding Matrix Creation (300-dimensional, trainable)

VOCABULARY & EMBEDDINGS: Vocabulary size: 12,308 words Embedding dimensions: 300

For pipeline Refer : https://github.com/Lokesh-102214/Mood-Text

You can now use these files to train your CNN-LSTM emotion recognition model.

The "Iris Flower Visualization using Python" project is a data science project that focuses on exploring and visualizing the famous Iris flower dataset. The Iris dataset is a well-known dataset in the field of machine learning and data science, containing measurements of four features (sepal length, sepal width, petal length, and petal width) for three different species of Iris flowers (Setosa, Versicolor, and Virginica).

In this project, Python is used as the primary programming language along with popular libraries such as pandas, matplotlib, seaborn, and plotly. The project aims to provide a comprehensive visual analysis of the Iris dataset, allowing users to gain insights into the relationships between the different features and the distinct characteristics of each Iris species.

The project begins by loading the Iris dataset into a pandas DataFrame, followed by data preprocessing and cleaning if necessary. Various visualization techniques are then applied to showcase the dataset's characteristics and patterns. The project includes the following visualizations:

1. Scatter Plot: Visualizes the relationship between two features, such as sepal length and sepal width, using points on a 2D plane. Different species are represented by different colors or markers, allowing for easy differentiation.

2. Pair Plot: Displays pairwise relationships between all features in the dataset. This matrix of scatter plots provides a quick overview of the relationships and distributions of the features.

3. Andrews Curves: Represents each sample as a curve, with the shape of the curve representing the corresponding Iris species. This visualization technique allows for the identification of distinct patterns and separability between species.

4. Parallel Coordinates: Plots each feature on a separate vertical axis and connects the values for each data sample using lines. This visualization technique helps in understanding the relative importance and range of each feature for different species.

5. 3D Scatter Plot: Creates a 3D plot with three features represented on the x, y, and z axes. This visualization allows for a more comprehensive understanding of the relationships between multiple features simultaneously.

Throughout the project, appropriate labels, titles, and color schemes are used to enhance the visualizations' interpretability. The interactive nature of some visualizations, such as the 3D Scatter Plot, allows users to rotate and zoom in on the plot for a more detailed examination.

The "Iris Flower Visualization using Python" project serves as an excellent example of how data visualization techniques can be applied to gain insights and understand the characteristics of a dataset. It provides a foundation for further analysis and exploration of the Iris dataset or similar datasets in the field of data science and machine learning.

To address the susceptibility of conventional vector control systems for permanent magnet synchronous motors (PMSMs) to motor parameter variations and load disturbances, a novel control method combining an improved Grasshopper Optimization Algorithm (GOA) with a variable universe fuzzy Proportional-Integral (PI) controller is proposed, building upon standard fuzzy PI control. First, the diversity of the population and the global exploration capability of the algorithm are enhanced through the integration of the Cauchy mutation strategy and uniform distribution strategy. Subsequently, the fusion of Cauchy mutation and opposition-based learning, along with modifications to the optimal position, further improves the algorithm’s ability to escape local optima. The improved GOA is then employed to optimize the contraction-expansion factor of the variable universe fuzzy PI controller, achieving enhanced control performance for PMSMs. Additionally, to address the high torque and current ripple issues commonly associated with traditional PI controllers in the current loop, Model Predictive Control (MPC) is adopted to further improve control performance. Finally, experimental results validate the effectiveness of the proposed control scheme, demonstrating precise motor speed control, rapid and stable current tracking, as well as improved system robustness.

🚀 Welcome to Godi.AI's Predictive Analysis of IFI Tax for French Cities

Hello, business innovator! 🌎 I'm Santiago PATINO SERNA, the CEO and data scientist at Godi.AI. This notebook presents the data reading and processing steps (for a detailed explanation, please refer to this other Notebook on Github). We delve into feature engineering followed by the development of six unique models. Each model's hyperparameters are optimized automatically. Following this, we evaluate each model's performance using cross-validation. With the results in hand, Generative AI steps in to assess the models. Acting as an expert data scientist, Generative AI determines the optimal model for deployment, explaining its selection in depth.

📊 Notebook Overview

Notebook on Github

Predictive Analysis of IFI Tax for French Cities

• Data Loading: Initializing necessary libraries and importing the IFI tax dataset.
• Feature Engineering: Transforming raw data into informative features to enhance model performance.
• Model Development: Creating six unique models, each with automated hyperparameter optimization.
• Performance Evaluation: Assessing each model's predictive capability using cross-validation techniques.

Generative AI Model Analysis

Delve into: - Comparative analysis of all models' performance. - Generative AI's role as an expert data scientist in selecting the optimal model. - A detailed explanation of the chosen model and its implications for deployment.

📸 Key Visual Insights

https://drive.google.com/uc?export=view&id=1AYVkEcBwaNRV7SYe3b4NVvB4GZAxTamA" alt="Pair Plot of Feature Engineering variable">
Analysis of the RAW variables plus the feature-engineered ones. In this image, variables have been cleaned of outliers and normalized.

https://drive.google.com/uc?export=view&id=1AYZxrO84QWsuCw3qU2kv9Z0kxclMNCQS" alt="KDE of the Predictable Variable Y">
KDE analysis to view the distribution of the predictable Y variable, equivalent to the number of tax payers multiplied by the average tax for each city. This variable has been normalized and is without outliers.

https://drive.google.com/uc?export=view&id=1AiKaGMoQ7V1PF4QgQZXDHBL_vGGMxe0y" alt="Results of the different tested models">
Numerical analysis of various metrics across all models to select the optimal one.

https://drive.google.com/uc?export=view&id=1Ahiym3e69XDaTOqpktqRGYhHawW5v_ks" alt="AI Analysis to select the best model">
This shows the AI's analysis of the previous chart to select the best model.

https://drive.google.com/uc?export=view&id=1AbNa3WRHvWRkZo_czA6L5Rl8ZQkPLjKh" alt="Results XGboost model"> Results of the model selected by the generative AI as the best.

https://drive.google.com/uc?export=view&id=1AlAF2HVU3I0YDkyI6WdrGF3mdJP6M2vq" alt="Graphical representation of the best predictive model selected by GEN AI"> Graphical results of all models, compared to the best one selected by the AI.

🤖 About Godi.AI

Godi.AI is the startup reshaping how businesses experience AI. We focus on speed, ROI, and guiding businesses on their digitalization journey. Dive into our standout apps, or explore tailored solutions with our Godi.AI Freelancer method.

Special Note: For businesses seeking an even more customized experience, reach out to me directly. As a proud Polytechnique Paris graduate, I am here to turn your data into insightful, actionable decisions.

📩 Get Started: Ready to embark on this transformative journey with Godi.AI? Reach out to us!

📔 Explore the Notebook: For a detailed dive, explore the Notebook in Github on GitHub.