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Primary data were collected during 2025 from poultry farmers in Salah al-Din Governorate. Data were collected in three stages during (2025), with each stage covering a complete production cycle. A purposive sample of (105) officially registered poultry farmers was selected to ensure the availability of accurate, in-depth, and relevant management information

The sample data and plotting routines for the new horizontal transport scheme in TIEGCM

In this article, we hypothesize that droplet transport in air-assisted spraying systems is related to the different diameter-dependent regime change, when aerodynamic drag, particle inertia, and canopy resistance interact. We also hypothesize that this porous canopy substantially affects how droplets fly and how they are made, so how they dispersed, transported and deposited. To verify this hypothesis, a 3D transient CFD model has been developed based on dynamic mesh strategy and a Lagrangian Discrete Phase model (DPM). The model is an air-assisted sprayer, based on a real fruit orchard setup with open-field and porous canopy for example. The canopy was modeled as porous to account for vegetation resistance and droplet injection was performed with air-blast atomization, and droplet movement dynamics, for example with their diameter distribution, velocity and turbulence was studied. Diameter and movement regimes of a droplet. The thin droplets (d < 80 µm) have strong air coupling and a high transport potential and drive the spray drift. Intermediate droplets, with 80-120 µm (Dv50) in diameter represent a critical phase in its transport. Droplets with large diameters are influenced more by inertia and present a good quality of deposition in the canopy. The porous canopy greatly reduces the flow of air and distorts turbulence patterns thus leading to reduced transport of droplets and hence reduced deposition efficiency. The effect of porous canopy is even more pronounced for intermediate droplet sizes which is where canopy resistance reduces acceleration and interception of jet-driven droplets. In general, our results show that droplet transport and deposition depend upon the aerodynamic composition of the droplets and canopy interaction. These results are useful for the development of spray application plans to control droplet distribution and airflow during orchard applications to ensure that the number of droplets does not increase while still delivering high deposition and drift results.

We undertake a comprehensive examination of the complex interplay between deoxyribonucleic acid (DNA), nanotechnology, and the origin of life, critically engaging with prevailing abiogenetic models. We advance the hypothesis that DNA functions at the quantum scale or exhibits quantum-mechanical characteristics, demonstrating a level of structural stability and informational complexity that challenges the assumptions underpinning theories of spontaneous molecular evolution. Central to the critique is the recognition of the indispensable role of enzymatic machinery in DNA replication—enzymes that, paradoxically, require DNA for their synthesis—thereby presenting a classic instantiation of the "chicken-and-egg" paradox. We further interrogate the significance of molecular chirality and evaluate the environmental prerequisites for biogenesis, contending that early Earth conditions were inherently unfavorable for the natural formation of either DNA or RNA. By synthesizing insights from molecular biology, quantum physics, and information theory, this analysis supports alternative frameworks. Ultimately, we call for a fundamental reassessment of evolutionary mechanisms and reposition DNA not merely as a passive genetic substrate, but as an advanced, self-organizing system for information storage and processing—one that challenges conventional biological paradigms. We propose a Mathematical proof utilizing Minimal Genome formation and the Universe's limit of Genetic generative capacity.

The dataset comprises time-series measurements acquired from an inertial measurement unit installed aboard a PZL-104 Wilga aircraft. The recorded signals include linear accelerations along three orthogonal axes, denoted as ax, ay and az, expressed in accordance with the conventional aviation reference frame. Additionally, the dataset provides orientation information in the form of Euler angles—pitch, roll, and yaw—estimated using the Madgwick algorithm sensor fusion algorithm. Each observation is annotated with a categorical variable (class) representing the phase of flight, defined as follows: 0 — engine start; 1 — takeoff; 2 — cruise and maneuvering flight; 3 — approach; 4 — landing. The data were collected during operational flights involving takeoff and landing procedures conducted on an airfield with an unpaved (grass) runway surface.

The dataset comprises time-series measurements acquired from an inertial measurement unit installed aboard a PZL-110 Koliber aircraft. The recorded signals include linear accelerations along three orthogonal axes, denoted as ax, ay and az, expressed in accordance with the conventional aviation reference frame. Additionally, the dataset provides orientation information in the form of Euler angles—pitch, roll, and yaw—estimated using the Madgwick algorithm sensor fusion algorithm. Each observation is annotated with a categorical variable (class) representing the phase of flight, defined as follows: 0 — engine start; 1 — takeoff; 2 — cruise and maneuvering flight; 3 — approach; 4 — landing. The data were collected during operational flights involving takeoff and landing procedures conducted on an airfield with an unpaved (grass) runway surface.

This dataset accompanies the study “Optimizing Timing of Autologous Skin Cell Suspension after Enzymatic Debridement of Porcine Burns: Evaluation with Digital Image Speckle Correlation (DISC)” and includes histological images, DISC-derived displacement maps, and quantitative force-propagation ratio (R) measurements from a porcine deep partial-thickness burn model. The working hypothesis is that successful wound healing is associated with restoration of biomechanical force transmission across the wound bed, and that this recovery can be quantified noninvasively using DISC. We further hypothesize that autologous skin cell suspension (ASCS) accelerates this biomechanical restoration compared with standard treatment, while a 24-hour delay in application does not significantly alter long-term outcomes when residual enzymatic activity is minimized through adequate rinsing. Histological data consist of H&E-stained sections collected longitudinally (days 7, 14, 21, and 28), providing information on re-epithelialization, collagen deposition, vascularization, and inflammatory response. These data represent localized structural snapshots of wound healing. In parallel, DISC measurements were obtained by applying a standardized mechanical indentation (~4 N) adjacent to the wound and capturing images before and at peak deformation. Using optical flow analysis, pixel-wise displacement fields and corresponding heatmaps were generated, providing spatially resolved maps of tissue deformation across the wound surface. A quantitative metric, the force-propagation ratio (R), was derived as the fraction of wound area exhibiting displacement above a threshold (1/e of peak displacement), normalized to uninjured skin (100%). Lower R values indicate poor mechanical connectivity and stiffness, whereas higher values reflect restoration of tissue continuity and compliance. Notably, DISC measurements parallel histological remodeling while providing a noninvasive, full-field assessment that captures spatial heterogeneity and functional recovery not accessible through biopsy-based methods. Conventional clinical and histological scoring systems show higher variability and reduced sensitivity to these changes.

Supplemental Data

Introduction The Chili Leaf Disease Dataset contains 1544 images of chili leaves, captured using smartphone cameras in agricultural fields across Bangladesh. The images are divided into four classes: Anthracnose, Cercospora Leaf Spot, Leaf Curl Disease, and Healthy Leaves. This dataset is designed to assist in the development of machine learning models for automated disease detection in chili plants, supporting agricultural innovation and sustainable farming practices. Dataset Overview o Number of Images: 1544 o Classes: 4 — Anthracnose, Cercospora Leaf Spot, Leaf Curl Disease, Healthy Leaves o Image Sources: Captured using smartphones with varying camera specifications: Redmi 12 (50 MP), Redmi 13 (108 MP), and Tecno Spark 8 Pro (48 MP) o Geographical Region: Bangladesh The dataset includes images with varying lighting, backgrounds, and angles to ensure diversity in field conditions. This allows the dataset to reflect real-world conditions that farmers might encounter when diagnosing. Chili Leaf Diseases Anthracnose: A fungal disease caused by Colletotrichum species, leading to dark, sunken lesions on leaves and fruits. Cercospora Leaf Spot: Caused by Cercospora capsici, this disease results in dark, circular spots on leaves, causing premature leaf drop. Leaf Curl Disease: Caused by viruses like ChiVMV and ToLCV, this disease causes leaves to curl, leading to stunted growth and reduced yield. Healthy Leaves: Includes disease-free chili leaves, serving as a baseline for comparison with diseased leaves. Data Collection The images were captured across various chili fields in Bangladesh using the smartphones listed above. These smartphones were chosen for their accessibility and image quality, reflecting conditions under which farmers typically use smartphones for agricultural tasks. Images were taken from various angles and distances, with different lighting conditions, to simulate real-world scenarios. Use Cases Mobile Applications for Farmers: Develop smartphone apps enabling farmers to take pictures of their plants and receive instant diagnoses on disease presence. Precision Agriculture: Assist farmers by providing early disease detection, reducing pesticide use, and improving crop management. Agricultural Research: Support studies in plant pathology and machine learning for improved disease diagnosis and management systems. Conclusion The dataset is publicly available through Mendeley Data and comes in four different folders class-wise containing the raw JPG images and corresponding CSV metadata files.This dataset provides a valuable resource for developing automated systems that assist farmers in Bangladesh and other regions with disease detection and crop management. By leveraging machine learning, this dataset helps reduce reliance on manual inspection, improves crop health monitoring, and supports more sustainable agricultural practices.

This repository contains the database of suicide mortality rates (Colombia 2010–2022), in addition to the R code corresponding to the intervention analysis for the overall series.