Google Colab Research Articles

Breast Thermograms Analysis using Deep Neural Network

Breast cancer is one of the leading causes of mortality among women globally. Early identification of this kind of cancer is important for a successful treatment outcome. A variety of screening techniques can be used to identify breast cancer. Thermography is possible for early diagnosis that uses thermal cameras with great resolution and sensitivity. The goal is to develop a system that automatically captures and classifies thermographic imaging of the breast as normal or abnormal. In this method,the detection and classification of breast cancer from thermography images usingadeeplearning-based convolution neural network using the VGG-19 algorithm. Breast cancer detection is performed using CNN with the help of Google collaboratory (Google Colab). Finally, as the result of experimental studies, the major focus is on the performance accuracy of the train and test dataset, and the graph is plotted between the training and validation accuracy.The VGG-19 network achieved the highesttestperformanceof 99.80% in breast cancer detection and to classify cancer using the DMR Mastology Research dataset.

HCIU: Hybrid clustered inception‐based UNET for the automatic segmentation of organs at risk in thoracic computed tomography images

AbstractAutomatic segmentation is the most effective and fast method to plan radiotherapy treatment in thoracic computed tomography (CT), as manual segmentation takes time and produces variability in the results. This article proposes a hybrid HCIU method to segment the thoracic organs at risk (OAR) using prior localization techniques. The dataset of 40 Patients containing 7420 slices of thoracic CT has been used to perform the experiment on the Google Colab Pro+ framework. HCIU constitutes a hybrid deep learning model for features extraction, a K‐mean algorithm to form the clusters based on the extracted features, and the ensemble UNET InceptionV3 model trained on the data of each cluster for the segmentation of OAR. With HCIU, we achieved an average dice score of 92.05%, 97.39%, 95.75%, and 96.45% for the esophagus, heart, trachea, and aorta. The average sensitivity and specificity values achieved were 98.38% and 97.43% on the test dataset. Also, statistical tests have been applied to each organ's pixel values for analyzing the model's segmented results. A hybrid method has been proposed to improve the localization in thoracic OAR segmentation in CT. The proposed method achieved precise accuracy in segmenting the four organs compared with the other state‐of‐the‐art methods.

A Comparative Study of Automated Machine Learning Platforms for Exercise Anthropometry-Based Typology Analysis: Performance Evaluation of AWS SageMaker, GCP VertexAI, and MS Azure.

The increasing prevalence of machine learning (ML) and automated machine learning (AutoML) applications across diverse industries necessitates rigorous comparative evaluations of their predictive accuracies under various computational environments. The purpose of this research was to compare and analyze the predictive accuracy of several machine learning algorithms, including RNNs, LSTMs, GRUs, XGBoost, and LightGBM, when implemented on different platforms such as Google Colab Pro, AWS SageMaker, GCP Vertex AI, and MS Azure. The predictive performance of each model within its respective environment was assessed using performance metrics such as accuracy, precision, recall, F1-score, and log loss. All algorithms were trained on the same dataset and implemented on their specified platforms to ensure consistent comparisons. The dataset used in this study comprised fitness images, encompassing 41 exercise types and totaling 6 million samples. These images were acquired from AI-hub, and joint coordinate values (x, y, z) were extracted utilizing the Mediapipe library. The extracted values were then stored in a CSV format. Among the ML algorithms, LSTM demonstrated the highest performance, achieving an accuracy of 73.75%, precision of 74.55%, recall of 73.68%, F1-score of 73.11%, and a log loss of 0.71. Conversely, among the AutoML algorithms, XGBoost performed exceptionally well on AWS SageMaker, boasting an accuracy of 99.6%, precision of 99.8%, recall of 99.2%, F1-score of 99.5%, and a log loss of 0.014. On the other hand, LightGBM exhibited the poorest performance on MS Azure, achieving an accuracy of 84.2%, precision of 82.2%, recall of 81.8%, F1-score of 81.5%, and a log loss of 1.176. The unnamed algorithm implemented on GCP Vertex AI showcased relatively favorable results, with an accuracy of 89.9%, precision of 94.2%, recall of 88.4%, F1-score of 91.2%, and a log loss of 0.268. Despite LightGBM's lackluster performance on MS Azure, the GRU implemented in Google Colab Pro displayed encouraging results, yielding an accuracy of 88.2%, precision of 88.5%, recall of 88.1%, F1-score of 88.4%, and a log loss of 0.44. Overall, this study revealed significant variations in performance across different algorithms and platforms. Particularly, AWS SageMaker's implementation of XGBoost outperformed other configurations, highlighting the importance of carefully considering the choice of algorithm and computational environment in predictive tasks. To gain a comprehensive understanding of the factors contributing to these performance discrepancies, further investigations are recommended.

The new histological system for the diagnosis of adrenocortical cancer.

Adrenocortical cancer (ACC) is a rare malignant tumor that originates in the adrenal cortex. Despite extensive molecular-genetic, pathomorphological, and clinical research, assessing the malignant potential of adrenal neoplasms in clinical practice remains a daunting task in histological diagnosis. Although the Weiss score is the most prevalent method for diagnosing ACC, its limitations necessitate additional algorithms for specific histological variants. Unequal diagnostic value, subjectivity in evaluation, and interpretation challenges contribute to a gray zone where the reliable assessment of a tumor's malignant potential is unattainable. In this study, we introduce a universal mathematical model for the differential diagnosis of all morphological types of ACC in adults. This model was developed by analyzing a retrospective sample of data from 143 patients who underwent histological and immunohistochemical examinations of surgically removed adrenal neoplasms. Statistical analysis was carried out on Python 3.1 in the Google Colab environment. The cutting point was chosen according to Youden's index. Scikit-learn 1.0.2 was used for building the multidimensional model for Python. Logistical regression analysis was executed with L1-regularization, which is an effective method for extracting the most significant features of the model. The new system we have developed is a diagnostically meaningful set of indicators that takes into account a smaller number of criteria from the currently used Weiss scale. To validate the obtained model, we divided the initial sample set into training and test sets in a 9:1 ratio, respectively. The diagnostic algorithm is highly accurate [overall accuracy 100% (95% CI: 96%-100%)]. Our method involves determining eight diagnostically significant indicators that enable the calculation of ACC development probability using specified formulas. This approach may potentially enhance diagnostic precision and facilitate improved clinical outcomes in ACC management.

Improved YOLO-v5 model for boosting face mask recognition accuracy on heterogeneous IoT computing platforms

The World Health Organization (WHO) claimed that wearing masks can effectively reduce the spread of COVID-19. In this paper, we devise novel YOLO-v5s detectors for improving the accuracy of recognizing face masks under heterogeneous IoT computing platforms. First, a module of coordinate attention is merged with the original YOLO-v5s backbone to enhance the model's attention to key information and filter out redundant information. Second, the original feature pyramid network (FPN) in the neck network is replaced with the bidirectional FPN for fast multiscale feature fusion. Third, the block of adaptive spatial feature fusion is embedded into the head layer to further solve the problem of feature inconsistency with different scales. Last, the SCYLLA-IoU metric serves as the new bounding box loss function to accelerate the model convergence. The training results show that applying the improved YOLO-v5s model to the AIZOO dataset yields an improvement in recognition accuracy of +2.2% and that the improved model has lower false detections and better detection of dense crowds. In addition, we propose a new evaluation standard of the scale limit factor to measure the detection performance of the model with different shooting distances. The proposed model is deployed on three heterogeneous IoT platforms, the Google Colab cloud, a personal computing terminal, and an Nvidia Jetson Nano edge computing device, to validate the inference feasibility, effectiveness, and robustness.

Heat Transfer in a Silo Containing Rice: Solution by the Explicit Method of the Finite Differences with Approximations of Order 2 and 4 for a One-Dimensional Transient Model

Rice is one of the most consumed cereals in the world, and the cultivation of this crop has significant relevance in the southern region of Brazil. When subjected to inadequate conditions of temperature and humidity, rice becomes susceptible to attacks from pests and fungi and, therefore, care in the storage process is of paramount importance, since this is largely responsible for the quality of the harvest. Such care allows the food to arrive without harm to the consumer. In this sense, the mathematical modeling, among numerous possibilities, allows for evaluating the internal temperature of a silo and, through this, taking preventive measures so that the grains maintain their quality. The objective of this work is to model the heat transfer process in a silo prototype containing rice in husk through the explicit finite difference method for a one-dimensional and transient model considering two approaches centered on the spatial derivative: error of order 2 and 4. In addition, the thermal diffusivity of the grain with average value was analyzed. The results obtained by the solutions were analyzed through graphs and statistical indexes comparing with the experimental data of the literature, and the computer simulation was performed through the Google Colab platform. The chosen methodology proved effective for the work, and the predicted temperatures for the approximations of order 2 and 4 denote similarities both graphically and in the precision of the statistical indexes.

Applying Artificial Intelligence to Predict Complications After Endovascular Aneurysm Repair.

Objective: Complications after Endovascular Aneurysm Repair (EVAR) can be fatal. Patient follow-up for surveillance imaging is becoming more challenging as fewer patients are seen, particularly after the first year. The aim of this study was to develop an artificial intelligence model to predict the complication probability of individual patients to better identify those needing more intensive post-operative surveillance. Methods: Pre-operative CTA 3D reconstruction images of AAA from 273 patients who underwent EVAR from 2011-2020 were collected. Of these, 48 patients had post-operative complications including endoleak, AAA rupture, graft limb occlusion, renal artery occlusion, and neck dilation. A deep convolutional neural network model (VascAI©) was developed which utilized pre-operative 3D CT images to predict risk of complications after EVAR. The model was built with TensorFlow software and run on the Google Colab Platform. An initial training subset of 40 randomly selected patients with complications and 189 without were used to train the AI model while the remaining 8 positive and 36 negative cases tested its performance and prediction accuracy. Data down-sampling was used to alleviate data imbalance and data augmentation methodology to further boost model performance. Results: Successful training was completed on the 229 cases in the training set and then applied to predict the complication probability of each individual in the held-out performance testing cases. The model provided a complication sensitivity of 100% and identified all the patients who later developed complications after EVAR. Of 36 patients without complications, 16 (44%) were falsely predicted to develop complications. The results therefore demonstrated excellent sensitivity for identifying patients who would benefit from more stringent surveillance and decrease the frequency of surveillance in 56% of patients unlike to develop complications. Conclusion: AI models can be developed to predict the risk of post-operative complications with high accuracy. Compared to existing methods, the model developed in this study did not require any expert-annotated data but only the AAA CTA images as inputs. This model can play an assistive role in identifying patients at high risk for post-EVAR complications and the need for greater compliance in surveillance.

Self-detection based fault diagnosis for wireless sensor networks

Wireless sensor network (WSN) is a collection of low-power and low-cost sensor nodes (SNs) deployed stochastically in the monitoring area to support various applications. The random occurrence of faulty sensor nodes affects the quality of service in WSN. Most of the existing fault detection algorithms are based on statistics, thresholds, majority voting, hypothetical tests, comparison, or machine learning and depend on the neighbor's sensing information to detect the fault status of SNs. Hence, these fault detection algorithms suffer from low detection accuracy (DA) and a high false alarm rate (FAR). Moreover, existing fault detection techniques are not scalable for large-scale WSNs, as they suffer from high energy overhead and detection latency. Furthermore, existing fault diagnosis algorithms burden an SN's resources while estimating the fault status of the WSN. This paper proposes a deep belief network (DBN) based self-detection algorithm to address these issues. The performance of the proposed DBN based self-detection algorithm has been evaluated using google colab, and MATLAB. The simulation results of the proposed algorithm has been compared with the existing fault detection algorithms. Simulation results show that the proposed algorithm performs better than the existing algorithms in terms of DA, FAR, and false positive rate. The performance of the proposed algorithm has also been evaluated in other network parameters such as energy consumption, and fault detection latency.

Deep learning-based vehicle detection from orthophoto and spatial accuracy analysis

Deep Learning algorithms are used by many different disciplines for various purposes, thanks to their ever-developing data processing skills. Convolutional neural network (CNN) are generally developed and used for this integration purpose. On the other hand, the widespread usage of Unmanned Aerial Vehicles (UAV) enables the collection of aerial photographs for Photogrammetric studies. In this study, these two fields were brought together and it was aimed to find the equivalents of the objects detected from the UAV images using deep learning in the global coordinate system and to evaluate their accuracy over these values. For these reasons, v3 and v4 versions of the YOLO algorithm, which prioritizes detecting the midpoint of the detected object, were trained in Google Colab’s virtual machine environment using the prepared data set. The coordinate values read from the orthophoto and the coordinate values of the midpoints of the objects, which were derived according to the estimations made by the YOLO-v3 and YOLOV4-CSP models, were compared and their spatial accuracy was calculated. Accuracy of 16.8 cm was obtained with the YOLO-v3 and 15.5 cm with the YOLOv4-CSP. In addition, the mAP value was obtained as 80% for YOLOv3 and 87% for YOLOv4-CSP. F1-score is 80% for YOLOv3 and 85% for YOLOv4-CSP.

Forced innovation: leveraging text data to analyse firms’ response to COVID-19

Purpose The purpose of this study is to explore and present a clear overview of innovation topics during the first year of the COVID-19 pandemic, and then organise these topics into various analyses. Design/methodology/approach The authors use multiple language analysis methods, such as text mining and latent Dirichlet allocation topic modelling, to address the research questions. A total of 440 news articles are analysed using Python and Google Colaboratory tools. Findings The analysis identified 20 innovation topics, highlighted sector-specific analyses and proposed phases of innovation. The authors suggest that each sector develops unique patterns and forms of innovation for long-term benefits and further research. This study expands upon existing literature on innovation and crisis at a theoretical level by incorporating an actor as the agency. Research limitations/implications Based on the findings, the authors conclude that the COVID-19 pandemic has prompted businesses to adopt dynamic capabilities. Furthermore, the authors provide several strategic recommendations for addressing the pandemic in the developing context. The study discusses the roles of policymakers, business practitioners and academia in this context as well. Originality/value Very few studies specifically explore and identify forced innovation topics in emerging countries during the pandemic. There has been no review of forced innovations implemented in Indonesia using news media as a source. Additionally, this study presents the trajectory of innovation during the time of crises.

Classification of Traffic Congestion in Indonesia Using the Naive Bayes Classification Method

The purpose of this research is to analyze the accuracy of congestion data using Google Colab in detecting congestion by the province in Indonesia the author tries to test strategies for dealing with congestion in the Indonesian region by utilizing the Naïve Bayes method. In this journal, apply with Google Collab . This research uses data that comes from crawling data on Twitter. Using the Naive Bayes method to find the shortest route is efficient and not congested. Implementation of online school transportation using the naive Bayes method in minimizing travel costs to pick up students can reduce traffic jams, reduce accidents, reduce student tardiness, and minimize travel costs. The Naive Bayes method can be used to identify relevant information about traffic jams in Indonesia through Twitter data with a good degree of accuracy. These results can assist decision-making and strategic planning in overcoming the problem of traffic congestion in Indonesia. Therefore, this research implies that it can help improve the accuracy of traffic congestion data in Indonesia. By using Google Colab, more advanced analysis methods and machine learning algorithms can be applied to process the existing traffic data. Additionally, utilizing Google Colab allows for fast and efficient data processing.

Shopee on Twitter: Uncovering Digital Marketing Strategies through Network Analysis

This qualitative descriptive study aims to map the interactions and structures among Shopee consumers on Twitter, utilizing the Social Network Analysis (SNA) method. Secondary data from Twitter were collected, and data analysis was facilitated using various tools, including Google Colab, Dictionary, Wordij, and Gephi. The network properties, such as nodes, edges, average degree, diameter, and average path length, were calculated for different promotional content keywords. The findings revealed that the flash sale keyword in 2020 exhibited a significant number of nodes and interactions, indicating extensive discussions on social media. This research offers valuable insights for digital marketing strategies within the Shopee marketplace, based on the observed patterns and implications derived from the SNA results.
 Highlights:
 
 
 In-depth analysis: This research provides an in-depth analysis of consumer interactions on Twitter within the context of Shopee, shedding light on the dynamics and structures of these interactions.
 
 
 Valuable insights: The study offers valuable insights into digital marketing strategies for the Shopee marketplace by examining the patterns and trends identified through social network analysis.
 
 
 Implications for promotion: By mapping the interactions and structures among Shopee consumers on Twitter, this research helps identify effective promotional strategies, such as utilizing flash sales, to engage with users on social media platforms.
 
 
 Keywords: Shopee, Twitter, Consumer Interactions, Digital Marketing Strategies, Social Network Analysis.

Variation of Mass Value for Spring Damper System in Suspension Using Python Simulation in Google Colab

An analysis has been carried out for the Spring Damper System using the Python programming language on Google Colab. The first stage that is carried out before the simulation is to determine the differential equation based on Newton's II law equation. The Python programming language was chosen because only needs to run in a browser, users can monitor the training process (or even coding) via a smartphone browser if the smartphone is connected to the same Google Drive. The simulation is carried out by varying the mass value from 5kg - 50kg with a mass increase range of 5kg. This is done to determine the effect of mass on changes in position and velocity/speed. Based on the simulation results, the greater the mass value given will affect the amplitude value, the position graph will increase while the velocity graph will decrease, and the time needed for both amplitudes to stabilize will increase.

Search-Based Regular Expression Inference on a GPU

Regular expression inference (REI) is a supervised machine learning and program synthesis problem that takes a cost metric for regular expressions, and positive and negative examples of strings as input. It outputs a regular expression that is precise (i.e., accepts all positive and rejects all negative examples), and minimal w.r.t. to the cost metric. We present a novel algorithm for REI over arbitrary alphabets that is enumerative and trades off time for space. Our main algorithmic idea is to implement the search space of regular expressions succinctly as a contiguous matrix of bitvectors. Collectively, the bitvectors represent, as characteristic sequences, all sub-languages of the infix-closure of the union of positive and negative examples. Mathematically, this is a semiring of (a variant of) formal power series. Infix-closure enables bottom-up compositional construction of larger from smaller regular expressions using the operations of our semiring. This minimises data movement and data-dependent branching, hence maximises data-parallelism. In addition, the infix-closure remains unchanged during the search, hence search can be staged: first pre-compute various expensive operations, and then run the compute intensive search process. We provide two C++ implementations, one for general purpose CPUs and one for Nvidia GPUs (using CUDA). We benchmark both on Google Colab Pro: the GPU implementation is on average over 1000x faster than the CPU implementation on the hardest benchmarks.

Solution Structure Ensembles of the Open and Closed Forms of the ∼130 kDa Enzyme I via AlphaFold Modeling, Coarse Grained Simulations, and NMR.

Large-scale interdomain rearrangements are essential to protein function, governing the activity of large enzymes and molecular machineries. Yet, obtaining an atomic-resolution understanding of how the relative domain positioning is affected by external stimuli is a hard task in modern structural biology. Here, we show that combining structural modeling by AlphaFold2 with coarse-grained molecular dynamics simulations and NMR residual dipolar coupling data is sufficient to characterize the spatial domain organization of bacterial enzyme I (EI), a ∼130 kDa multidomain oligomeric protein that undergoes large-scale conformational changes during its catalytic cycle. In particular, we solve conformational ensembles for EI at two different experimental temperatures and demonstrate that a lower temperature favors sampling of the catalytically competent closed state of the enzyme. These results suggest a role for conformational entropy in the activation of EI and demonstrate the ability of our protocol to detect and characterize the effect of external stimuli (such as mutations, ligand binding, and post-translational modifications) on the interdomain organization of multidomain proteins. We expect the ensemble refinement protocol described here to be easily transferrable to the investigation of the structure and dynamics of other uncharted multidomain systems and have assembled a Google Colab page (https://potoyangroup.github.io/Seq2Ensemble/) to facilitate implementation of the presented methodology elsewhere.

A Novel Fake-News Dataset and Detection System to Mitigate Cyber War with Emphasis on Nigerian News Events

Fake-news refers to a cyber-weapon launched through the social media, as, its consequence can result to the breakdown of law and order in the society both physically and on the cyber-social-space. In Nigeria, there is currently no established law that guides the use of social media. Therefore, the rate at which fake-news propagates is alarming. This paper presents a new dataset, with focus on Nigeria’s trending news such as EndSARS and Herdsmen attacks, which was further used to simulate Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) machine learning models to detect fake-news. The data were extracted from twitter using twitter Application Package Interface (API) and from facebook using a scraping tool. The dataset was encoded using Unicode escape function in python to make all characters accessible by the algorithm and tokenised using Global Vectors for Word Representation. The dataset was used to train CNN and RNN models built in python on google colab platform to detect fake-news using accuracy, sensitivity, recall and F1 score as evaluation metrics. Results showed that RNN performed better in terms of accuracy and precision, at 82.34% and 93.19% compared to 81.96% and 79.65% for CNN, F1 scores are approximately the same for both models and CNN performed better than RNN in terms of recall at 98.03% to 50.61% for RNN.

End-to-end AI framework for interpretable prediction of molecular and crystal properties

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab.

Measurement of vitamin K and its derivatives in human plasma through enzyme-assisted liquid chromatography-tandem mass spectrometry assay.

Vitamin K is an essential lipophilic vitamin that acts as a coenzyme in several metabolic pathways. Accurate measurement of apolar metabolites transported by lipoproteins in serum matrices requires high-recovery extractions of vitamin K and its derivatives following standardized protocols. Conventionally developed methods in this field have predominantly employed solid-phase extraction for the measurement of vitamin K and its derivatives. In this study, our objective was to develop an enzyme-assisted extraction method for the precise measurement of vitamin K and its derivatives. Our methodology involved mixing 450 µL of serum samples with 50 µL of an internal standard and 50 µL of a lipase enzyme solution. Following vortexing, the mixture was incubated at 37°C for 15 min to activate the enzymes. The enzyme reaction was subsequently quenched with a mixture of 250 µL of methanol and 1 mL of hexane, followed by centrifugation at 12,000 g for 5 min. The upper phase was collected, concentrated using a concentrator device, and dissolved in a 100 µL solution of methanol/acetone/isopropanol (7:1:1, v/v/v) for analysis. Spectrum analysis was performed using the open-source MZmine 3 software, and a reference interval was established using the Python programming language on the Google Colab platform. The developed method for measuring vitamin K and its derivatives exhibited limit of detection and limit of quantitation values of 0.005 and 0.01 ng/mL, respectively. In conclusion, our study presents a precise and reliable method for the measurement of vitamin K and its derivatives using enzyme-assisted extraction.

THE IMPLEMENTATION OF PANDAS PROFILING AS A TOOL FOR ANALYZING MECHANICAL PROPERTIES DATA OF NICKEL-BASED SUPERALLOYS BASED ON ALLOY CHEMICAL COMPOSITION

The purpose of this study is to evaluate the mechanical properties of nickel-based superalloys with variations in alloy chemical compositions using the Exploratory Data Analysis (EDA) method with the assistance of the pandas profiling library on Google Colab. In this study, data from 312 tensile tests of nickel-based superalloys were used as research samples, with alloy chemical compositions including carbon (C), manganese (Mn), silicon (Si), chromium (Cr), nickel (Ni), molybdenum (Mo), vanadium (V), nitrogen (N), niobium (Nb), cobalt (Co), tungsten (W), aluminum (Al), and titanium (Ti), as well as mechanical properties such as yield strength (YS), tensile strength (TS), and elongation (EL). The methodology used in this study was the EDA method with the assistance of the pandas profiling library on Google Colab, which enables the automatic creation of a dataset report, presenting information on various aspects such as data structure, descriptive statistics, correlation, distribution, and missing values. The results show that yield strength has a fairly high correlation with titanium (0.51), medium correlations with nickel (0.25), vanadium (0.2), and cobalt (0.2). Tensile strength in nickel-based superalloys has a fairly high correlation with yield strength (0.88), carbon (0.49), and cobalt (0.55), and medium correlations with titanium (0.25) and vanadium (0.25). Elongation in nickel-based superalloys has a negative and fairly high correlation with tensile strength (-0.62) and yield strength (-0.58). Some warnings for missing data and zero values in some variables were identified. These results indicate that the pandas profiling library can be used as a tool to analyze the data of mechanical properties of nickel-based superalloys quickly and easily, and provide clear information on data patterns, data structure, and correlation among data.

A comparison of zero- and minimal-intelligence agendas in majority-rule voting models

Emergent behavior in repeated collective decisions of minimally intelligent agents—who at each step in time invoke majority rule to choose between a status quo and a random challenge—can manifest through the long-term stationary probability distributions of a Markov chain. We use this known technique to compare two kinds of voting agendas: a zero-intelligence agenda that chooses the challenger uniformly at random and a minimally intelligent agenda that chooses the challenger from the union of the status quo and the set of winning challengers. We use Google Co-Lab’s GPU accelerated computing environment to compute stationary distributions for some simple examples from spatial-voting and budget-allocation scenarios. We find that the voting model using the zero-intelligence agenda converges more slowly, but in some cases to better outcomes.