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A novel Eulerian-Lagrangian MPI parallelized solver is developed to resolve the dynamics of ellipsoidal fibers in the OpenFOAM platform. Due to the nonspherical shape of the ellipsoidal fibers and the dependence of the drag force on the orientation of the fiber, the solver solves the full conservation of linear and angular momentum equations, in addition to the time evolution equation for Euler's parameters, quaternions. To this end, a new parcel type is introduced to represent ellipsoidal fibers with several new properties, including Euler's parameters, angular velocity, and torque class. Finally, new member functions are defined to solve angular momentum and Euler's parameters time evolution equations. The solver is the first publicly available, robust and reliable computational framework for the numerical analysis of ellipsoidal fibers motion. It promotes the capability of the standard Lagrangian OpenFOAM solvers and libraries to capture the orientation and rotational dynamics of nonspherical particles. As validation cases, the solver was applied to four benchmarks: three-dimensional rotation of an ellipsoid in linear shear flow, two-dimensional rotation of a magnetic ellipsoid in linear shear flow subjected to a uniform magnetic field, motion of an ellipsoid in pipe flow, and ellipsoids deposition in three-dimensional bifurcation flow. Comparison of the results with analytical solutions, experimental data and in-silico results indicates close agreements and high accuracy of the developed numerical model for single- and multi-physics test cases.

This dataset comprises images of cotton leaves affected by various diseases, as well as healthy leaves. The data is meticulously organized into multiple folders, with each folder representing a specific disease or condition. The dataset includes images collected under diverse environmental conditions to enhance the robustness of machine learning models trained on it. The dataset is categorized as follows: Cotton_Healthy: Healthy cotton leaves without any visible signs of disease. Bacterial_Blight: Leaves showing symptoms of bacterial blight, characterized by dark, water-soaked spots that may enlarge over time. Fusarium_Wilt: Leaves affected by fusarium wilt, often exhibiting yellowing and wilting. Curl_Virus: Cotton leaves infected with curl virus, characterized by curling and deformation of leaves. The dataset is further stratified based on disease severity levels, including mild, moderate, severe, and critical stages. It is intended for training and validating machine learning models for automating disease detection and classification, enabling timely interventions in cotton farming.

Supplementary Materials Figures and Tables for the Study 'Risk Factors for Drug-Induced Severe Cutaneous Adverse Reactions: A Real-World Pharmacovigilance Analysis'

Structural mechanics is pivotal in comprehending how structures respond to external forces and imposed displacements. Typically, the analysis of structures is performed numerically using the direct stiffness method, which is an implementation of the finite element method. This method is commonly associated with the numerical solution of large systems of equations. However, the underlying theory can also be conveniently used to perform the analysis of structures either symbolically or in a hybrid symbolic-numerical fashion. This approach is useful to mitigate the computational burden as the obtained partial or full symbolic solution can be simplified and used to generate lean code for efficient simulations. Nonetheless, the symbolic direct stiffness method is also useful for model reduction purposes, as it allows the derivation of small-scale models that can be used for diminishing simulation time. Despite the mentioned advantages, symbolic computation carries intrinsically complex operations. In particular, the symbolic solution of large linear systems of equations is hard to compute, and it may not always be available due to software capabilities. This paper introduces a toolbox named TrussMe-Fem, whose implementation is based on the direct stiffness method. TrussMe-Fem leverages Maple®'s symbolic computation and Matlab®'s numerical capabilities for symbolic and hybrid symbolic-numerical analyses and solutions of structures. Efficient code generation is also possible by exploiting the simplification of the problem's expressions. The challenges posed by symbolic computation on the solution of large linear systems are addressed by introducing novel routines for the symbolic matrix factorization with the hierarchical representation of large expressions. For this purpose, the TrussMe-Fem toolbox optionally uses the Lem and Last Maple® packages, which are also available as open-source software.

It comprises structured data related to various aspects of prawn farming, including industry insights, production comparisons, AI-driven analyses, and potential applications of byproducts, such as cosmetics. It captures critical information to evaluate the sustainability, efficiency, and market potential of bio-based prawn farming practices. The data is organized across multiple sheets, focusing on key metrics, comparisons, and innovative approaches, providing a comprehensive foundation for research and development in the bio-based aquaculture industry.

The set of Maple routines that comprises the package Ndynamics has been improved. Apart one of the main motivations for its creation, namely, the routines to calculate the fractal dimension of boundaries (via box counting), the package deals with the numerical evolution of dynamical systems and provide flexible plotting of the results. The package also brings an initial conditions generator, a numerical solver manager, and a focusing set of routines that allow for better analysis of the graphical display of the results. Many new Maple-in-built numerical solvers are now programmed and available for the user of the package. The novelty that the package presented at the time of its release, an optional numerical interface, is maintained and updated.

The student data sheet contains 9000 records of students and twelve attributes. The course profile sheet contains 36 records and five attributes The course list sheet contains 37 records and three attributes The dataset is freely available for research purposes and for building and training AI and ML models. This dataset will only be used for the purpose of academic research.

The data used in the manuscript titled "Revealing Deep Heat Transfer Characteristics of the Yalu River Basin Using Joint Magnetotelluric and Gravity Inversion" is published here.

Mitochondrial RNA ligase 2 | KREL2; Leishmania donovani (HU3 strain)

Data and code used for our study entitled "Phytoplankton and periphyton nutrient limitation in Patagonian Andean Lakes" The metadata file explains the whole structure of this repository and how to obtain the results shown in the paper. For more information, contact Dr. Facundo Scordo (scordo@agro.uba.ar)