6. Motif Code Theory by William Terry (22279591)

“…<p dir="ltr">The Motif Code Theory (MCT) simulation code, mct_unified_code.py, is a Python 3.9 script that models the universe as a time-dependent directed multigraph G(t) = (V(t), E(t)) with N=10^7 vertices (representing quantum fields/particles) and edges (interactions). …”

Globus Compute: Federated FaaS for Integrated Research Solutions by eRNZ Admin (6438486)

“…HPC enables researchers to perform simulations, modeling, and analysis, which are critical to predicting outcomes, guiding experiments, and developing new technologies [1]. …”

BGC-Prophet by Haohong Zhang (17911673)

“…/output/ --name split --threads 10</code></pre><p dir="ltr"><b>4. Gene Prediction</b></p><p dir="ltr">Detect BGC genes using a trained model:</p><pre><code>bgc_prophet predict --datasetPath .…”

Hierarchical Deep Learning Framework for Automated Marine Vegetation and Fauna Analysis Using ROV Video Data by Bjørn Christian Weinbach (16918707)

“…</li><li>confidence: Confidence score for the prediction.</li><li>predicted_species: Predicted species label.…”

IGD-cyberbullying-detection-AI by Bryan James (19921044)

“…</li></ul></li><li><b>Internet Gaming Disorder Prediction</b>:</li><li><ul><li>Open the <code>Gamestudy.ipynb</code> notebook and run the cells to analyze IGD data using models like LSTM and CNN to detect patterns in gaming behavior.…”

Code by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr"><b>Prediction and Design of 5′ UTRs</b></p><p dir="ltr">We developed a convolutional neural network (CNN) model to predict 5′ UTRs. …”

Core data by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr"><b>Prediction and Design of 5′ UTRs</b></p><p dir="ltr">We developed a convolutional neural network (CNN) model to predict 5′ UTRs. …”

Supplementary Material for review——Revealing the co-occurrence patterns of the group emotions from social media data by Yang Hua (21399140)

“…</p><p dir="ltr">The main.py file can be run directly to automate the computation and output of the model.</p><p dir="ltr">表格部分</p><p dir="ltr">1.Table 3：Model accuracy assessment</p><p dir="ltr">脚本路径：’code/bert.py’</p><p dir="ltr">输入数据：’data/wh_data_cleaned.csv’</p><p dir="ltr">输出位置：’data/emotion_prediction_wh.csv’</p><p dir="ltr">说明：Output Precision, Recall, F1 for each emotion, and calculate the weighted average of Precision, Recall, F1</p><p dir="ltr">2.Table 4： Examples of different types of emotional structures</p><p dir="ltr">脚本路径：’code/countnum.py’</p><p dir="ltr">输入数据：’data/emotion_prediction_wh.csv’</p><p dir="ltr">输出位置：’data/emotion_prediction_wh.csv’</p><p dir="ltr">说明：Determine whether an emotion is of a single type, a dominant subsidiary type, or one of the composite types by using emotion probabilities and entropy values</p><p dir="ltr">3.Table 5-6： Examples of different types of emotional structures</p><p dir="ltr">①脚本路径：’code/lat_lon.py’</p><p dir="ltr">输入数据：’data/emotion_prediction_wh.csv’</p><p dir="ltr">输出位置：’result/bert/wh/128/grid_lat_lon.csv’</p><p dir="ltr">说明：The study area can be gridded by running the file.…”

AGU24 - EP11D-1300 - Revisiting Megacusp Embayment Occurrence in Monterey Bay and Beyond: High Spatiotemporal Resolution Satellite Imagery Provides New Insight into the Wave Condit... by Kelby Kramer (20706767)

“…Previous studies using both site observation and numerical models have yielded rough characterization of the wave conditions necessary for MCE formation, including wave energy and direction. …”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Reproducibility Framework Container Orchestration # Kubernetes deployment manifest for reproducible environment apiVersion: apps/v1 kind: Deployment metadata: name: energy-benchmark-pod spec: replicas: 1 selector: matchLabels: app: benchmark-runner template: metadata: labels: app: benchmark-runner spec: nodeSelector: hardware.profile: "high-performance" containers: - name: benchmark-container image: albumforge/energy-benchmark:v2.1.3 resources: requests: cpu: "8000m" memory: "16Gi" nvidia.com/gpu: 1 limits: cpu: "16000m" memory: "32Gi" env: - name: MEASUREMENT_PRECISION value: "high" - name: POWER_SAMPLING_RATE value: "1000" # 1kHz sampling Dependency Management FROM ubuntu:22.04-cuda11.8-devel RUN apt-get update && apt-get install -y \ perf-tools \ powertop \ intel-gpu-tools \ nvidia-smi \ cpupower \ msr-tools \ && rm -rf /var/lib/apt/lists/* COPY requirements.txt /opt/ RUN pip install -r /opt/requirements.txt Usage Examples and API Documentation Python Data Analysis Interface import pandas as pd import numpy as np from scipy import stats import matplotlib.pyplot as plt import seaborn as sns # Load dataset with optimized dtypes for memory efficiency df = pd.read_csv('ecological_benchmark_dataset.csv', dtype={'hardware_config': 'category', 'test_type': 'category'}) # Compute energy efficiency metrics df['energy_per_photo'] = df['energy_consumption_kwh'] / df['photo_count'] df['co2_per_gigabyte'] = df['co2_equivalent_g'] / df['total_volume_gb'] # Statistical analysis with confidence intervals local_energy = df[df['test_type'] == 'local_processing']['energy_consumption_kwh'] cloud_energy = df[df['test_type'] == 'cloud_processing']['energy_consumption_kwh'] t_stat, p_value = stats.ttest_ind(local_energy, cloud_energy) effect_size = (cloud_energy.mean() - local_energy.mean()) / np.sqrt((cloud_energy.var() + local_energy.var()) / 2) print(f"Statistical significance: p = {p_value:.2e}") print(f"Cohen's d effect size: {effect_size:.3f}") R Statistical Computing Environment library(tidyverse) library(lme4) # Linear mixed-effects models library(ggplot2) library(corrplot) # Load and preprocess data df <- read_csv("ecological_benchmark_dataset.csv") %>% mutate( test_type = factor(test_type), hardware_config = factor(hardware_config), log_energy = log(energy_consumption_kwh), efficiency_ratio = energy_consumption_kwh / processing_time_sec ) # Mixed-effects regression model accounting for hardware heterogeneity model <- lmer(log_energy ~ test_type + log(photo_count) + (1|hardware_config), data = df) # Extract model coefficients with confidence intervals summary(model) confint(model, method = "Wald") Advanced Analytics and Machine Learning Integration Predictive Modeling Framework from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.model_selection import cross_val_score, GridSearchCV from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.metrics import mean_absolute_error, r2_score # Feature engineering pipeline def create_feature_matrix(df): features = df[['photo_count', 'avg_file_size_mb', 'total_volume_gb']].copy() # Polynomial features for capturing non-linear relationships features['photo_count_squared'] = features['photo_count'] ** 2 features['size_volume_interaction'] = features['avg_file_size_mb'] * features['total_volume_gb'] # Hardware configuration encoding le = LabelEncoder() features['hardware_encoded'] = le.fit_transform(df['hardware_config']) return features # Energy consumption prediction model X = create_feature_matrix(df) y = df['energy_consumption_kwh'] # Hyperparameter optimization param_grid = { 'n_estimators': [100, 200, 500], 'max_depth': [10, 20, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4] } rf_model = RandomForestRegressor(random_state=42) grid_search = GridSearchCV(rf_model, param_grid, cv=5, scoring='neg_mean_absolute_error') grid_search.fit(X, y) print(f"Best cross-validation score: {-grid_search.best_score_:.6f}") print(f"Optimal hyperparameters: {grid_search.best_params_}") Carbon Footprint Calculation Methodology Emission Factor Coefficients Carbon intensity calculations employ region-specific emission factors from the International Energy Agency (IEA) database: EMISSION_FACTORS = { 'EU_AVERAGE': 0.276, # kg CO₂/kWh (European Union average 2024) 'FRANCE': 0.057, # kg CO₂/kWh (Nuclear-dominant grid) 'GERMANY': 0.485, # kg CO₂/kWh (Coal transition period) 'NORWAY': 0.013, # kg CO₂/kWh (Hydroelectric dominant) 'GLOBAL_AVERAGE': 0.475 # kg CO₂/kWh (Global weighted average) } def calculate_carbon_footprint(energy_kwh: float, region: str = 'EU_AVERAGE') -> float: """ Calculate CO₂ equivalent emissions using lifecycle assessment methodology Args: energy_kwh: Energy consumption in kilowatt-hours region: Geographic region for emission factor selection Returns: CO₂ equivalent emissions in grams """ emission_factor = EMISSION_FACTORS.get(region, EMISSION_FACTORS['GLOBAL_AVERAGE']) co2_kg = energy_kwh * emission_factor return co2_kg * 1000 # Convert to grams Citation and Attribution This dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0) license. …”

Nucleotide analogue tolerant synthetic RdRp mutant construct for Surveillance and Therapeutic Resistance Monitoring in SARS-CoV-2 by Tahir Bhatti (20961974)

“…</p><p dir="ltr">Using the output of this locally trained AI model a synthetic RdRp region was generated incorporating those predicted mutations and was inserted into the consensus backbone.…”

Dataset for:Exploring the Pharmacological Properties and Mechanism of Action of Lithocarpus litseifolius (Hance) Chun. in Treating Diabetic Neuropathy Based on SwissADME, Network P... by Xiaoyong Wang (22361497)

“…</p><p dir="ltr">Targets/</p><p dir="ltr">– 1,346 unique SwissTargetPrediction hits (Homo sapiens, probability > 0) for all compounds.…”

Mean Annual Habitat Quality and Its Driving Variables in China (1990–2018) by ChenXi Zhu (21374876)

“…</p><p dir="ltr">(HQ: Habitat Quality; CZ: Climate Zone; FFI: Forest Fragmentation Index; GPP: Gross Primary Productivity; Light: Nighttime Lights; PRE: Mean Annual Precipitation Sum; ASP: Aspect; RAD: Solar Radiation; SLOPE: Slope; TEMP: Mean Annual Temperature; SM: Soil Moisture)</p><p dir="ltr"><br>A Python script used for modeling habitat quality, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), and implementation of four machine learning models to predict habitat quality.…”

Landscape Change Monitoring System (LCMS) Conterminous United States Cause of Change (Image Service) by U.S. Forest Service (17476914)

“…Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. …”

<b>Historical Nifty 50 Constituent Weights (Rolling 20-Year Window)</b> by Sukrit Bera (22314922)

“…</li><li>Building features for quantitative models that aim to predict market movements.</li><li>Backtesting investment strategies benchmarked against the Nifty 50.…”

OVEP code by Yuanfang Guan (22258765)

“…</p><p><br></p><p><br></p><p>---</p><p><br></p><p dir="ltr">## Directory structure (top-level)</p><p><br></p><p dir="ltr">- `rate_code/` </p><p>---</p><p><br></p><p dir="ltr">## Key scripts </p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation/calculate_predicted_snp_by_category_maskfunctional.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category maskfunctional.py</p><p dir="ltr">- `rate_code/revision/JARVIS/modules/jarvis/deep_learn_raw_seq/for_prediction_train_nn_model.py` </p><p dir="ltr"> Arg 'input_features' may take 1 of 3 possible values: - stuctured: using only structured features as input - sequence: using only sequence features as input - both: using both structured and sequence features as inputs</p><p dir="ltr">- `rate_code/revision/JARVIS/modules/jarvis/variant_classification/plot_gb_feature_importance.py` </p><p dir="ltr"> Script related to: plot gb feature importance.py</p><p dir="ltr">- `rate_code/external_test/calculate_AUC_dataset1_masksnp.py` </p><p dir="ltr"> Script related to: calculate AUC dataset1 masksnp.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_masklabel/calculate_predicted_snp_by_category_new.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category new.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_masklabel/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_maskfunctional_maskrepeat/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation/calculate_predicted_snp_by_category_nolabel.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category nolabel.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/exon_analysis_masklabel/calculate_predicted_snp_by_exon_mask.py` </p><p dir="ltr"> Script related to: calculate predicted snp by exon mask.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/category_annotation_calibration_maskfunctional/calculate_predicted_snp_by_category.py` </p><p dir="ltr"> Script related to: calculate predicted snp by category.py</p><p dir="ltr">- `rate_code/post_analysis_maskmissing/exon_analysis/calculate_predicted_snp_by_exon_mask.py` </p><p dir="ltr"> Script related to: calculate predicted snp by exon mask.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintest_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintest_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyintest_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyintest_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/plot/plot_indel_snp_auc.py` </p><p dir="ltr"> !…”