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Tide gauge records are amongst the oldest and most numerous oceanographic records available. They provide critical information to the study of changes in tidal signals, storminess, storm surge, and mean sea level. Many years, likely thousands of years cumulatively, of paper data remain undigitized across the globe. Traditional manual methods of digitization are slow. Here we test whether an AI-powered optical character recognition (OCR) system can rapidly and accurately digitize images of paper tide records. About 21,000 hourly records were processed in 2-3 hours per model year, approximately 5-10 times faster than typical human-based efforts. The observed error rate of 1 in 129 points was slightly larger than, but comparable to, human error rates of roughly 1 in 160, based on previous digitization efforts. Image quality (primarily focus) was found to be an important determinant of processing accuracy. Our approach greatly expands the potential throughput of oceanographic digitization efforts, likely removes one of the major impediments to digitization and reanalysis of historical records, and thus our understanding of long-term changes caused by natural and anthropogenic processes.

This replication folder contains the relevant programs and calculations/data used in the paper mentioned above. These codes and calculations/data were used to produce the paper's figures and tables. One may use them to replicate the paper's findings. MATLAB 2025a or higher (with Econometrics and Optimization toolboxes) is required to run these codes. This paper develops a micro-founded monetary model in which a profit-maximizing monopolist payment network endogenously determines transaction fees alongside agents' portfolio choices between cash and e-money, which is more secure than cash. There are two equilibrium regimes with e-money: a mixed equilibrium, where both monies circulate, and a pure e-money equilibrium. Threat of entry from a competing network constrains the monopolist's fee, significantly shrinking the monetary policies and parameter values that support the pure e-money equilibrium. Calibrated to 2024 U.S. data, the model yields a non-trivial monetary policy pass-through: a one percentage point increase in the nominal interest rate raises the equilibrium transaction fee by 0.13 - 0.35 percentage points. Despite competitive pressure from a potential entrant, e-money equilibria consistently deliver welfare losses relative to pure cash, driven by a holdup problem and the distortionary proportional fee. Replicators are requested to read the README file first. Data sharing: No primary data was used. Secondary data is public; however, for replicators' convenience, it is included in the relevant folders/subfolders (described below) of the replication folder. Analysis data: All analysis data has been provided in the replication package. Calculation of average transaction fee for card payments and transaction fee for Bankgirot is included in relevant spreadsheets as explained below. Non-public data: No non-public data has been used. Helcim's data is from its website (open to the public). However, screenshots have been provided in the relevant folder. eMarketer data is from the company's website (open to the public). Code sharing: All codes used in this project are available in the replication folder. All programs are cleaned, well-commented, and well-formatted. They should produce the exact same figures, tables, or calibration as in the paper if executed on MATLAB 2025a or higher according to the instructions provided in the ## Calibration and List of Figures, Tables and Programs section of the README file.

This dataset contains indoor plant leaf images developed for research on automated plant disease classification and data augmentation. The dataset was created as part of the study “Deep Learning-Driven Leaf Disease Classification in Indoor Plants with YOLOv11: Stable Diffusion Augmentation Approach,” presented at the 2025 28th International Conference on Computer and Information Technology (ICCIT). The original dataset consists of 787 high-resolution images covering nine classes across three commonly cultivated indoor plant species: Money Plant, Snake Plant, and Spider Plant. The classes include Money Plant Bacterial Wilt Disease, Money Plant Healthy, Money Plant Manganese Toxicity, Snake Plant Anthracnose, Snake Plant Healthy, Snake Plant Leaf Withering, Spider Plant Fungal Leaf Spot, Spider Plant Healthy, and Spider Plant Leaf Tip Necrosis. Images were manually collected from several plant nurseries in Sylhet City, Bangladesh, under natural lighting using three different smartphones. The dataset is provided in three separate collections: 1. 01_raw_dataset_787_images.zip - The original dataset containing 787 manually collected images. 2. 02_traditional_augmented_dataset.zip - Images generated using traditional geometric and photometric augmentation with the Albumentations library, including horizontal flipping, random 90-degree rotation, brightness and contrast adjustment, color jitter, and Gaussian blur. This process expanded the dataset from 787 to 4,722 images. 3. 03_diffusion_augmented_dataset.zip - Synthetic image variants generated using Stable Diffusion v1.5 in image-to-image mode. Class-specific text prompts were used with the original images as inputs. The generation process used a denoising strength of 0.1, guidance scale of 7.5, and 30 inference steps. Generated images were manually reviewed for diagnostic relevance and visual realism. During preprocessing, images were quality-checked, converted to JPEG, resized to 224 × 224 pixels, and manually cropped to isolate the leaf region and reduce irrelevant background information. This dataset is intended for research in indoor plant disease classification, computer vision, deep learning, image augmentation, synthetic data generation, and plant health monitoring. Associated publication: Nishad Mahmud Opu, Md Saiful Islam, Sayed Hanzala Abdullah, and Ruma Akter, “Deep Learning-Driven Leaf Disease Classification in Indoor Plants with YOLOv11: Stable Diffusion Augmentation Approach,” 2025 28th International Conference on Computer and Information Technology (ICCIT), pp. 443-448, 2025. DOI: 10.1109/ICCIT68739.2025.11491324. Researchers using this dataset are encouraged to cite the associated publication.

mpxv inflammation

This dataset includes all the supplementary files for the paper titled "The entertainment gap: a bibliometric mapping of research in haptics and digital games"

• Perceived value carries almost all antecedent effects on BIS adoption in SMEs. • Relative advantage and owner-manager support drive perceived value most strongly. • Competitive pressure is insignificant on its own but works inside configurations. • Owner-manager innovativeness strengthens the value-to-adoption conversion. • fsQCA reveals four equifinal recipes for high adoption in a conflict-affected region.

MeDAl is a corpus of PhD dissertations from the University of Medicine, Tirana, built to support Albanian medical natural language processing (NLP), a severely under-resourced setting. This repository holds two releases: v2/ — MeDAl 2.0 (current release): 454 dissertations (2010–2024), 44 medical specializations, a 286-document bilingual Albanian-English subset, structured metadata, and two full technical-validation experiments (machine translation and text classification). v1/ — MeDAl 1.0 (2024 pilot): the original 323-document monolingual pilot corpus, kept for provenance.

This dataset is the replication package for the paper "The Expenditure Elasticity for Food and Calories: Evidence from Unconditional Cash Transfers in Kenya", in the Journal of Development Economics. The analysis pools three survey rounds from two unconditional cash transfer experiments in Kenya. The package contains datasets and Stata and R codes that reproduce the paper's tables and figures.

Real-Time Operation of the Proposed R2R DT-Based Anomaly Detection System

The input data