Coronavirus disease 2019 (COVID-19) is a highly communicable viral infection caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), which has spread rapidly throughout the world. From a computer science point of view, research efforts have focused on the use of approaches such as machine learning and curve fitting to predict or simulate disease behavior. However, the mathematical characterization of the spread of COVID-19 is a topic that has not yet been explored by these techniques. In this work, we propose a novel metaheuristic framework called META-COVID19, which merges the Generalized Boltzmann distribution and the family of Jacobi polynomials to automatically characterize the COVID-19 spread without prior knowledge of the data and without involving a human expert. In general terms, the algorithm receives as input a time series of daily reported cases and the output is a polynomial mathematical model. Our framework only needs a single parameter, which is the number of Jacobi polynomials to analyze during the iterative process, and it is capable of proposing polynomials whose adjustment error is close to 1E-3. Finally, we show the applicability of the polynomial models found by META-COVID19, through a theoretical mathematical analysis in order to know attributes of the spread of COVID-19 in different periods of time, allowing to generate better strategies to face it in the future.

Edible coatings are widely used to modulate oil uptake and moisture in fried foods. In this study, we evaluated a microfluid-assisted flow-blurring spray against conventional application by dipping/spraying, focusing on the coating efficiency and preliminary implications for sustainable process. This study combines benchtop experiments with a near-nozzle numerical analysis where the gas–liquid interface and primary breakup are modeled using the Volume of Fluid (VOF) approach implemented in OpenFOAM, configured for a flow-blurring geometry to generate whey protein isolate (WPI) coatings. Viscosity, density, solid content, and contact angle were validated experimentally and used in the simulation setup. An image-based droplet pipeline quantified spray characteristics, yielding a volumetric median diameter D₅₀ = 83.69 µm and confirming process uniformity. Contact angles showed marked substrate dependence: hydrophilic surfaces, 68°–85°; hydrophobic surfaces, 95°–110°. For turkey sausages, sessile-drop contact angles were not determinable (N.D.) due to wicking/roughness; wettability was therefore assessed on smooth surrogates and via performance metrics. Fit-for-purpose simulation procedures are outlined. Microfluidic application (WPI-McF) lowered oil uptake versus uncoated controls. Together, robust modeling, targeted image analytics, and high-precision microfluidics enable rational tuning of coating microstructure and barrier performance, offering a scalable pathway to reduce lipid content and enhance fried food quality.

Achieving the highest levels of repeatability and precision, especially in robot manipulators applied in automation manufacturing, is a practical pose-recognition problem in robotics. Deviations from nominal robot geometry could produce substantial errors at the end effector, which can be more than 0.5 inches for a 6 ft robot arm. In this research, a pose-recognition system is developed for estimating the position of each robot joint and end-effector pose using image processing. To generate the joint angle, the system is developed via the modeling of a pose obtained by combining a convolutional neural network (CNN) and a multi-layer perceptron network (MLP). The CNN categorizes the input image generated by a remote monocular camera and generates a classification probability vector. The MLP generates a multiple linear regression model based on the probability vector generated by a CNN and describes the values of each joint angle. The proposed model is compared with the P-n-Perspective problem-solving method, which is based on marker tracking using ArUco markers and the encoder values. The system was verified using a robot manipulator with four degrees of freedom. Additionally, the proposed method exhibits superior performance in terms of joint-by-joint error, with an absolute error that is three units less than that of the computer vision method. Furthermore, when evaluating the end-effector pose, the proposed method showed a lower average standard deviation of 9mm compared with the computer vision method, which had a standard deviation of 13 mm.

Measurement of a pose for a robotic arm is fu-damentental for its calibration and performance measurements along several tasks. The most known methods usually involve using sensors like Laser Trackers, Coordinate Measuring Machines, etc. To simplify these measurements there are another options using novel computer vision techniques which use 2D images and their implementation to recreate environments or objects. In this research article, the implementation of a method that is suitable alternative for calibrating camera using a single image and we can estimate robot end efector position. Robot repeatability was also measured using the proposed camera calibration algorithm.

Edible coatings are widely studied as food-compatible formulations for reducing moisture loss, oxidation, microbial spoilage, oil uptake, and quality deterioration. Their translation from laboratory formulation to industrial use, however, depends not only on film-forming composition but also on controlled deposition, retained dose, surface coverage, drying history, defect formation, hygienic operation, and reproducible performance on heterogeneous food surfaces. An OpenAlex-supported evidence-map audit (2014–2026) was used to separate direct food-coating validation from adjacent engineering models. This review reframes edible coatings as engineered deposited interfaces and proposes a claim-controlled, evidence-tiered framework linking food-grade biopolymer fluids, processability, atomization, droplet impact, wet-film evolution, dry-film structure, image-based metrology, multiphase modeling, and food-performance endpoints. This review outlines the prerequisites for future digital-twin-ready edible coating workflows by linking functional biopolymer fluids, deposition technologies, droplet physics, intelligent image metrology, Computational Fluid Dynamics (CFD), Volume of Fluid (VOF), uncertainty reporting, food-performance endpoints, safety, Life-Cycle Assessment (LCA), Techno-Economic Analysis (TEA) and patent-aware innovation. Digital twins are treated as a future integration target that depends on validated inputs, standardized reporting, deposition metrology and food-specific model validation. The central argument is that progress in edible coatings requires fewer isolated formulation claims and stronger validated links between deposited-interface properties and food-relevant function. A minimum reporting checklist is proposed to support reproducible comparison of deposition routes, coating structures, and translation potential.

Microfluidic technologies are increasingly used as upstream structuring tools in the development of edible films and coatings. This review examines how flow-focusing, T-junction, co-flow, step-emulsification, and related microfluidic platforms generate emulsions, double emulsions, hydrogel microparticles, and multicompartment carriers that are relevant to coating design. Rather than treating microfluidics as an end in itself, the review evaluates how microfluidically generated structures influence carrier protection, matrix compatibility, controlled release, and the final functionality of edible films and coatings. Particular attention is paid to whether carrier architecture survives matrix incorporation, deposition, drying, and end use, because these downstream steps determine whether structural advantages translate into barrier, mechanical, optical, and preservation-related performance. The review also discusses the principal factors that currently limit industrial implementation, including throughput, fouling, sanitation, thermoplastic manufacturing, bonding, and integration into hygienic food-processing environments. Overall, microfluidics offers a highly controlled route for mechanism-oriented formulation development, but its practical value for edible coatings depends on whether this structural control can be translated into robust, scalable, and food-compliant manufacturing routes.