Global trends in health, climate, and population growth drive the demand for high-nutrient plants like spinach, which thrive under controlled conditions with minimal resources. Despite technological advances in agriculture, current systems often rely on traditional methods and need robust computational models for precise plant growth forecasting. Optimizing vegetable growth using advanced agricultural and computational techniques, addressing challenges in food security, and obtaining efficient resource utilization within urban agriculture systems are open problems for humanity. Considering the above, this paper presents an enclosed agriculture system for growth and modeling spinach of the Viroflay (Spinacia oleracea L.) species. It encompasses a methodology combining data science, machine learning, and mathematical modeling. The growth system was built using LED lighting, automated irrigation, temperature control with fans, and sensors to monitor environmental variables. Data were collected over 60 days, recording temperature, humidity, substrate moisture, and light spectra information. The experimental results demonstrate the effectiveness of polynomial regression models in predicting spinach growth patterns. The best-fitting polynomial models for leaf length achieved a minimum Mean Squared Error (MSE) of 0.158, while the highest MSE observed was 1.2153, highlighting variability across different leaf pairs. Leaf width models exhibited improved predictability, with MSE values ranging from 0.0741 to 0.822. Similarly, leaf stem length models showed high accuracy, with the lowest MSE recorded at 0.0312 and the highest at 0.3907.

This work investigates the changes in the optical response of photonic crystals based on porous silicon (PSi) as a function of temperature. Using the transfer matrix method in combination with thermo-optical properties, we numerically calculate the optical response of two types of photonic crystals: Distributed Bragg Reflectors (DBRs) and Fabry–Perot microcavities (FPMs). The results reveal that the photonic bandgap shifts with increasing temperature and pressure, with the defect mode in the microcavity notably shifting to longer wavelengths as the temperature rises. Additionally, we explore the transformation of PSi into porous quartz (PQz) via thermal oxidation, which preserves the porosity and multilayer structure, while altering the chemical composition. This results in geometrically identical photonic systems with distinct chemical properties, offering enhanced stability. Our simulations show that PSi structures exhibit a redshift in the photonic bandgap due to thermal expansion, while PQz structures remain optically stable even at elevated temperatures. This work highlights the potential of PQz as a robust material for high-temperature photonic applications, with tunable optical properties and stable performance under extreme conditions. The findings emphasize the feasibility of using porous-silicon-based photonic crystals for advanced optical devices in harsh environments.

This work proposes an optimization tool based on genetic algorithms for the inverse design of photonic crystals. Based on target reflectance, the algorithm generates a population of chromosomes where the genes represent the thickness of a layer of a photonic crystal. Each layer is independent of another. Therefore, the sequence obtained is a disordered configuration. In the genetic algorithm, two dielectric materials are first selected to generate the population. Throughout the simulation, the chromosomes are evaluated, crossed over, and mutated to find the best-fitted one based on an error function. The target reflectance was a perfect mirror in the visible region. As a result, it was found that obtaining photonic crystal configurations with a specific stop band with disordered arrangements is possible. The genetic information of the best-fitted individuals (layer sequence, optical response, and error) is stored in an h5 format. This method of generating artificial one-dimensional photonic crystal data can be used to train a neural network for solving the problem of the inverse design of any crystal with a specific optical response.

This study evaluates the cosmetic degradation of polyethylene (PE) and polypropylene (PP) automotive components under four exposure scenarios—no exposure, outdoor exposure with and without glass shielding, and accelerated UV chamber weathering (ASTM G154)—through the evolution of visible (VIS) reflectance. Thirty-two samples (16 PE, 16 PP) were monitored over five time points; surface reflectance was recorded at 21 wavelengths and summarized into seven VIS bands, and hardness (Shore D) was measured pre/post-exposure. Repeated-measures univariate and multivariate analyses consistently revealed significant effects of Condition, Time, and their interaction on reflectance, with initial-reflectance adjustment improving inference stability across bands. PE exhibited more gradual and coherent reflectance decay, whereas PP showed greater band-to-band variability—most notably under UV chamber exposure. Additionally, hardness decreased in most exposed groups, aligning with optical changes. To place spectral trajectories in a kinetic context, a family of exponential models with small-sample information criterion selection was fitted, yielding $\eta \left(t\right)$—a dimensionless degradation efficiency summarizing spectral change. The contribution of this work is a multi-statistical framework—combining VIS-band-aware summaries with covariate-adjusted univariate/multivariate testing—that supports comparisons across materials and exposure conditions, underscoring the practical value of UV chamber protocols as surrogates for outdoor weathering. In sum, the study demonstrates the effectiveness of multivariate and covariate-adjusted models in quantifying optical degradation of polyolefins, offering pragmatic guidance for assessing mid- to long-term performance in automotive applications.

This paper presents a novel non-invasive methodology to study the cutaneous wound healing process in rabbits. Spectral images were used to establish a quantitative evaluation of the healing process time-line dynamics. Measurements were conducted by image analysis of the wounds of an experimental population with two different systems: an acousto-optical acquiring/processing system permitted the experimental quantitative study of different stages naturally occurring during the healing process time-line. Honey and saline solution application were used in a subgroup of the population under study as examples of treatments that could be employed to purportedly accelerate cutaneous tissue restoration, and their efficacy was evaluated. The methodology described on this paper allows for a non-invasive and effective way to quantitatively study/monitor the wound healing processes in terms of spectral changes of the wound area. Finally, it was found that this methodology allows quantitative parametrization of measurements to be used as accurate testing grounds for new wound healing assisting clinical treatments.

A harmonically driven oscillatory fully developed laminar flow through a rectangular cross-sectioned duct is analytically and experimentally studied. Experimental data is gathered at low cost with a relatively easy to implement, non-commercial, do-it-yourself (DIY) particle image velocimetry (PIV) system, which is proposed here as a flexible pedagogic resource for use in physics/engineering fluid mechanics undergraduate or graduate level education. As a DIY system, its components can be generic and/or open source based, making it useful in developing countries as well. An analytical model was developed, solved, and compared to the experimental data while the key features of the oscillatory flow were recognised and reciprocally represented by theory and experiments. This validated the proposed system as a low-cost alternative to the commercially available systems and it also confirmed it as a viable pedagogical resource. Consequently, the activities described could be straightforwardly implemented into undergraduate/graduate fluid mechanics pedagogical contexts, such as lecture aids, course projects, and laboratory practices. The proposed DIY-PIV system constitutes a teaching with technology pedagogical resource on fluid mechanics physics/engineering education that is also able to function as a limited/auxiliary or even state of the art research tool, according to the budget and the objectives.