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This dataset contains acoustic data acquired from a brushed permanent magnet DC motor with a detached permanent magnet and from the same motor after permanent magnet re-bonding. Acoustic recordings were acquired with an Android smartphone positioned approximately 1 m from the motor during steady-state no-load operation. Measurements cover two motor conditions, five rotational speeds (100, 200, 500, 750, and 1000 rpm), and two rotation directions, giving 20 operating conditions. Supplementary low-rate AV-160B vibration tables and vibrometer plot images are included as descriptive supporting files. ## Contents - `MobilePhoneRecordings/`: original smartphone acoustic acquisition files retained as M4A source recordings, organized by motor condition. - `TrimmedWav/`: decoded and trimmed smartphone acoustic recordings in WAV format, organized by motor condition. - `VibrationMeasurementTables/`: supplementary AV-160B vibrometer table exports in XLS format, organized by motor condition. - `VibrationPlotImages/`: supplementary AV-160B vibrometer chart images in JPG format, organized by motor condition. - `DocumentationImages/`: JPG photographs documenting the investigated motor, stator inner surface, and motor nameplate. - `MetadataFiles/metadata.csv`: one row per motor condition, rotational speed, and rotation direction, with direct links to the corresponding M4A, WAV, XLS, and JPG files. - `MetadataFiles/file_manifest.csv`: minimal secondary file list for provenance and file-format checking. ## File Counts - Acoustic M4A source files: 20 - Acoustic WAV working-copy files: 20 - Vibration XLS files: 20 - Vibration plot JPG files: 20 - Documentation JPG files: 3 - Operating-condition rows in `MetadataFiles/metadata.csv`: 20 ## Notes All filenames are in English and use condition, rotational speed, and rotation-direction tokens where applicable. Most users should start with `MetadataFiles/metadata.csv`, which links each operating condition to its corresponding acoustic M4A source file, acoustic WAV working copy, vibration XLS file, and vibration plot image. The M4A files in `MobilePhoneRecordings/` are the smartphone acoustic source recordings. The WAV files in `TrimmedWav/` are decoded working copies with matching basenames. The first 2.0 s of each M4A recording were removed before writing the WAV file to exclude leading silence and possible microphone-handling artifacts introduced when the smartphone recording was started. No additional duration clipping was applied. The vibration XLS files contain low-rate readings, typically 10-12 readings per measured quantity for each operating condition. They are included as descriptive supplementary files rather than as a primary vibration machine-learning dataset.

"This dataset contains survey responses gathered from female micro-entrepreneurs in Natore District, Bangladesh, regarding their adoption of social media marketing and its impact on sustainable business growth. It includes structured questionnaire responses analyzed through PLS-SEM/Statistical tools."

Supplementary Information: Additional details on supplementary results. Supplemental Table I: Distribution of specific virus detection types between the tofacitinib and control groups. STROBE Statement.

This record contains the reproducibility package for a frozen, zero-training external evaluation of the released Ribeiro et al. six-output 12-lead deep neural network's atrial-fibrillation output in PTB-XL version 1.0.3 and the Chapman-Shaoxing/Ningbo ECG database version 1.0.0. The package contains analysis and inference scripts, tests, a frozen protocol and design lock, software requirements, derived cohort and label audits, complete prediction tables, bootstrap and statistical outputs, figure source data, publication figures, tables, and manifests. No PhysioNet ECG waveforms, waveform arrays, WFDB headers, released model binaries, inference batch caches, credentials, direct identifiers, or local-machine metadata are redistributed. Record-level tables retain only public de-identified source identifiers and fields needed for provenance, patient-cluster resampling, subgroup analyses, and the prespecified first-ECG sensitivity analysis. Users must preserve de-identification and comply with the cited upstream terms.

Public transportation routing optimization plays a vital role in reducing operational costs and energy consumption, especially with the rise of electric vehicles. However, this problem is inherently NP-hard, making it computationally challenging for traditional optimization methods to solve efficiently, particularly for large-scale scenarios. In this paper, to address this gap, we propose a novel metaheuristic-inspired approach called the Divine Religion Algorithm (DRA). This algorithm draws on societal relations among followers, missionaries, and leaders within a political-ideological framework, offering an innovative evolutionary strategy for optimization. Recognizing the potential for further enhancements, we extended this approach to a more powerful version called DRA-II. This extended version introduces a dynamic reward operator that incentivizes high-performing followers, a penalty operator that discourages underperformers, and a follower migration operator that promotes progressive transformations within the solution units, fostering a competitive and adaptive environment. Our methodology is evaluated within the context of the Electric Vehicle Routing Problem (EVRP), a critical and representative challenge in sustainable transportation planning. The comparative analysis is conducted on medium and large-scale intelligent transportation scenarios, assessing performance through metrics such as "best cost," "average cost," and "standard deviation." Evaluated against Harmony Search, Genetic Algorithm, and Imperialist Competitive Algorithm across 35 instances, DRA-II achieved superior results in small-to-medium-scale scenarios (14.7% cost reduction) with execution times close to GA. Statistical tests (ANOVA, Friedman) confirmed robustness (p < 0.05). DRA-II’s scalability and low standard deviation (Fig. 9) make it viable for real-world logistics. This work advances metaheuristic methods for sustainable transportation.

Data and code for "Disentangling climate sensitivity from management and place in Amazonian açaí production: an explainable machine-learning attribution with leakage-safe validation". This dataset supports a climate attribution and sensitivity study of açaí (Euterpe oleracea Mart.) production in the Eastern Amazon. It provides the assembled municipal panel, the climate-projection results, and the full analysis code needed to reproduce the study, which is framed as attribution rather than yield prediction. The production panel combines Brazilian official statistics from IBGE/SIDRA: extractive fruit output (PEVS, table 289; 1986 to 2024) and planted cultivation (PAM, table 5457; 2015 to 2024), covering 466 municipalities and 9,094 municipality-year records. A modeling panel pairs these targets with climate predictors aggregated into four phenological windows relative to the December harvest reference (October to December, July to September, April to June, and January to March), using variables of vapour-pressure deficit, mean, maximum and minimum temperature, relative humidity, solar radiation, wind speed, and precipitation. Municipality centroid coordinates are included for the 359 extractive municipalities. The projection file reports the ceteris paribus water-channel response of extractive production under a five-model NEX-GDDP-CMIP6 ensemble (ACCESS-CM2, EC-Earth3, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM) for SSP2-4.5 and SSP5-8.5 at the 2041 to 2060 and 2061 to 2080 horizons. The raw sources are public: production from IBGE/SIDRA; climate predictors from TerraClimate, CHIRPS and NASA POWER; and climate projections from NASA NEX-GDDP-CMIP6, accessed through the NASA NCCS THREDDS NetcdfSubset Service. This record redistributes the derived panel and results together with the code so the analysis can be reproduced. The code (Python) covers phenological-window feature construction, baseline models, the nested variance decomposition that isolates the partial contribution of climate against a climate-free control, blocked cross-validation (leave-one-year-out, spatial KMeans blocks, and leave-one-state-out), SHAP attribution, and the CMIP6 extraction and projection pipeline. Data files are released under CC BY 4.0 and code under the MIT License; the underlying public datasets remain under the terms of their original providers.

The Electric Vehicle Routing Problem (EVRP) is a central challenge in intelligent transportation, given its NP-hard nature and the substantial computational burden of exact approaches. To address this problem, researchers have increasingly turned to heuristic and metaheuristic methods to accelerate solution discovery. In this paper, we tackle EVRP with a novel hybrid metaheuristic: the Hybrid Divine Religions Algorithm Improved by Differential Evolution (HDRADE). The framework of HDRADE combines the Divine Religion Algorithm (DRA), a concept inspired by human behavioral tendencies and social dynamics, with the robust search operators of Differential Evolution (DE). To further enhance performance, we introduce a blacklist mechanism within DRA: penalized individuals are removed from subsequent steps (selection, mutation, and crossover), which helps mitigate bias and promotes diversity. The extended DRA is then integrated with DE’s mutation and crossover operators to yield an adaptive and balanced search process. The empirical results across more than 35 test cases show that HDRADE outperforms Harmony Search (HS), the Grasshopper Optimization Algorithm (GOA), and Harris Hawks Optimization (HHO) on both small- and large-scale instances, achieving an average cost reduction of 18.7% and faster run times than GOA. In addition to improving optimization performance, the proposed HDRADE supports sustainable logistics and eco-efficient transportation by reducing energy consumption, minimizing charging-related costs, and promoting intelligent green routing decisions in electric vehicle networks. Overall, HDRADE demonstrates a compelling balance among solution quality, convergence speed, robustness, and sustainability-oriented performance, making it a promising candidate for solving complex EVRP instances in smart transportation environments.

This dataset contains processed optical-flow descriptors, machine-learning inputs, statistical outputs, and reproducibility materials supporting a comparative evaluation of RAFT, FastFlowNet, and Farnebäck for image-based river surface-velocity estimation using the open River Dart dataset. For each optical-flow method, separate calibration and independent temporal-validation descriptor tables are provided. The common descriptors used for Random Forest regression include mean optical-flow magnitude, standard deviation of magnitude, 75th and 99th percentiles of magnitude, and mean horizontal and vertical flow components. The repository also includes temporal cross-validation definitions, model-selection outputs, out-of-fold and independent validation predictions, performance metrics, bootstrap confidence intervals, pairwise statistical comparisons, permutation feature importance, feature-ablation results, velocity-range analyses, computational-demand summaries, and code to reproduce the downstream analysis and manuscript figures. The calibration set contains 4,241 observations and the independent validation set 5,910 observations. Validation data were not used for hyperparameter selection. Raw River Dart videos are not redistributed. They remain available from the original Newcastle University dataset by Perks, DOI: 10.25405/data.ncl.19762027. This repository begins at the processed descriptor stage; the complete video-level optical-flow extraction implementation is outside its reproducibility boundary.

This dataset contains Python simulation source code and raw simulation output data for the aqueous‑dispersed micro‑nano swarm distributed fault‑tolerant computing architecture. The code implements distributed swarm unit dynamic separation, merging, fault‑tolerance logic and 1‑D projection‑based greedy grouping algorithm. Includes runnable scripts, parameter configuration files and raw output records from simulation experiments. Users can reproduce the simulation results described in the corresponding SSRN preprint paper.

THE CORE PHILOSOPHY Two principles recur across every domain in this registry. They are the reason a rice paddy and a lunar transit corridor belong in the same document. The Off-Vehicle Optimization Principle Let the road carry the engine. A rocket carries its own propellant. A habitat lifts its own shielding. An excavator carries the full weight of its mission in one irreplaceable body. In each case the mobile element is taxed with transporting the machinery of its own movement — and that tax, compounded, is what has kept space at the price of nations and heavy transport at the price of oil. The principle inverts this: shift energy, computation, and kinetic mass off the individual unit and onto permanent, passive, or shared structural grids. The vehicle becomes light because the infrastructure became capable. Its sharpest formulation appears in the Celestial LifeStar paper: the heavy infrastructure belongs on the route, not in the vehicle. Applied to launch it produces the Celestial Lattice; applied to ground transit, solar rail; applied to a household robot, a companion that never needs charging; applied to a phone in a disaster, a beacon that needs no network. The Amplified Upcycling Economy The surplus is already here. Roughly a hundred thousand shipping containers sit idle in the world's ports. Highways carry millions of linear kilometres of sun-facing guardrail. Railways hold continuous cleared corridors across every continent. Deserts hold the silicate from which glass and photovoltaic substrate can be melted directly. These are not waste streams awaiting disposal. They are pre-financed, pre-sited, pre-permitted foundations awaiting a second function. It is cheaper to give an asset a second job than to build a first one. A Third Pattern Across several papers a third principle emerges without having been stated: infrastructure that manufactures its successor. The solar planting train builds the corridor it travels along. The Glass City bootstrap sends mobile units that build fixed glassworks that build cities. The LifeStar seeds packets that improve the road for the journeys that follow. Each is a system whose first unit is small enough to deliver and whose every subsequent unit is made locally. A Fourth: The Honest Limit The strongest documents here share a habit that is rare in speculative engineering. AetherShield declines refrigeration because the physics will not support the claim. Chandler Forest states that a crown fire will overrun it. The Solar Korsi opens by listing what it cannot do. The SEIR physiology paper predicts a null result and points to the literature confirming it. Young Initiative names its own missing computational study. Stating the failure condition is not a weakness in these designs. It is the mechanism by which they can be tested, and therefore the mechanism by which they could ever be built