Broccoli has been consumed around the world in various ways; either raw, blanched, frozen, dehydrated or fermented; however, functional foods and nutraceuticals are currently being designed and marketed from broccoli, through the extraction of compounds such as sulforaphane, which according to several studies and depending on its bioavailability has a protective effect on some types of cancer. Likewise, several food technologies are reported to seek to offer innovative foods to increasingly careful and critical consumers, ensuring that they retain their nutritional and sensory attributes even after processing and that they are also safe. In this sense, studies on the effect of processing on compounds of interest to health are of great relevance. Therefore, this article presents an overview on the study of traditionally consumed broccoli and the design of new products from the use of agro-industrial residues that, due to their high content of fiber and fitochemical compounds, can benefit the quality of life of the human population.

Banana (Musa spp.) productivity is limited by sodic soils, which impairs root growth and nutrient uptake. Analyzing root traits under stress conditions can aid in identifying tolerant genotypes. This study investigates the root morphological traits of banana cultivars under sodic soil stress conditions using Rhizovision software. The pot culture experiment was laid out in a Completely Randomized Design (CRD) under open field conditions, with treatments comprising the following varieties: Poovan (AAB), Udhayam (ABB), Karpooravalli (ABB), CO 3 (ABB), Kaveri Saba (ABB), Kaveri Kalki (ABB), Kaveri Haritha (ABB), Monthan (ABB), Nendran (AAB), and Rasthali (AAB), each replicated thrice. Parameters such as the number of roots, root tips, diameter, surface area, perimeter, and volume were assessed to evaluate the performance of different cultivars. The findings reveal that Karpooravali and Udhayam cultivars exhibited superior performance in terms of root morphology compared to other cultivars under sodic soil stress. These cultivars displayed increased root proliferation, elongation, and surface area, indicating their resilience to sodic soil stress. The utilization of Rhizovision software facilitated precise measurement and analysis of root traits, providing valuable insights into the adaptation mechanisms of banana cultivars to adverse soil conditions.

This study uses the UTAUT2 (Unified theory of acceptance and use of technology) model as well as adding other factors such as Platform Usability, User Autonomy to determine the behavioral intention and behavior of online shoppers using e-commerce applications (ECAs) in Vietnam. Using the analysis results from structural equation modeling, it was shown that Social Influence, Use Proficiency, Hedonic Motivation, User Skill, Effort Expectancy positively affect Behavioral Intention. At the same time, Behavioral Intention is negatively affected by Performance Expectancy. Behavioral Intention and Facilitating Conditions are two factors that positively affect Use Behavior. Besides, User Autonomy negatively affects Use Behavior. The research results are an important basis for ECAs providers, managers and stakeholders to apply in assessing the behavioral intentions and behaviors of online shopping customers using ECAs in Vietnam to promote the use of ECAs in online shopping.

Research indicates a strong correlation between sociodemographic factors and success in learning to read. This study examines the sociodemographic characteristics of 1131 preschool and 1st-grade children in Portuguese public schools and explores the relationship between these characteristics and key competencies for reading acquisition. The collection included a sociodemographic questionnaire and pre-reading skills, such as letter-sound knowledge. To assess the relationship between the sociodemographic variables and the letter-sound knowledge, inter-subjects (parametric and non-parametric) difference tests were conducted, as well as correlation analyses. To understand whether letter-sound knowledge is predicted by sociodemographic variables, a multiple linear regression analysis was performed using the Enter method. The results suggest that the mother’s education is the variable that most strongly contributes to success in reading acquisition. Socioeconomic status and the type of school also play a role in reading achievement. Identifying the sociodemographic factors that most strongly correlate with reading acquisition success is crucial for a more accurate identification of at-risk children and to provide targeted support/inclusion in reading skills promotion projects.

In response to the prevailing energy crisis, this research focuses on elevating the potential of lithium niobate (LN) thin films for advanced optoelectronic applications. Employing electron beam evaporation, films undergo precise annealing (700°C to 1100°C), revealing a structural evolution through X-ray diffraction—crystallite sizes transition from 69.34 nm (unannealed) to 47.90 nm (1100°C). Scanning electron microscopy captures the transformation from coarse grains to photonic crystal clusters, while energy dispersion X-ray analysis discloses LN's composition (97.27 wt.% oxygen, 2.73 wt.% niobium). Rutherford backscattering spectroscopy illustrates surface damage post-Helium ion implantation, proportionate to depth. UV-VIS spectrophotometry highlights a significant blue shift in the optical band gap (3.70 eV to 2.52 eV), with further reduction at 700°C (2.48 eV) and a climactic shift at 1100°C (2.68 eV). This study not only addresses the pressing energy crisis but also emphasizes the indispensable role of lithium niobate in shaping the future of optoelectronics. It provides insights into tailoring LN properties for sustainable advancements in optoelectronic devices, marking a crucial chapter in our collective journey towards energy resilience. The urgency of innovation in the face of global challenges is underscored, marking a crucial chapter in our collective journey towards energy resilience.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.