In recent years, deep learning (DL) has garnered significant attention for its successful applications across various domains in solving complex problems. This interest has spurred the development of numerous neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and the more recently introduced Transformers. The choice of architecture depends on the data characteristics and the specific task at hand. In the 1D domain, one-dimensional CNNs (1D CNNs) are widely used, particularly for tasks involving the classification and recognition of 1D signals. While there are many applications of 1D CNNs in the literature, the technical details of their training are often not thoroughly explained, posing challenges for those developing new libraries in languages other than those supported by available open-source solutions. This paper offers a comprehensive, step-by-step tutorial on deriving feedforward and backpropagation equations for 1D CNNs, applicable to both regression and classification tasks. By linking neural networks with linear algebra, statistics, and optimization, this tutorial aims to clarify concepts related to 1D CNNs, making it a valuable resource for those interested in developing new libraries beyond existing ones.

Here, the potential of laser-induced breakdown spectroscopy (LIBS) in grading calcareous rocks for the lime industry was investigated. In particular, we developed a system equipped with non-intensified detectors operating in scanning mode, defined a suitable data acquisition protocol, and implemented quantitative data processing using both partial least squares regression (PLS-R) and a multilayer perceptron (MLP) neural network. Tests were carried out on 32 samples collected in various limestone quarries, which were preliminarily analyzed using traditional laboratory X-ray fluorescence (XRF); then, they were divided into two groups for calibration and validation. Particular attention was dedicated to the development of LIBS methodology providing a reliable basis for precise material grading. The congruence of the results achieved demonstrates the capability of the present approach to precisely quantify major and minor geochemical components of calcareous rocks, thus disclosing a concrete application perspective within the lime industry production chain.

Terahertz (THz) technology has proven to be a powerful tool for investigating a wide range of physical, chemical, and biological phenomena, as extensively demonstrated by years of research and innovation. Today, THz time-domain spectroscopy has become a trusted technique for the characterization of the dielectric properties of materials, while advances in tomographic imaging have propelled THz waves from research laboratories to practical real-world applications. However, THz spectroscopy and imaging generate increasingly complex and high-dimensional data, posing challenges for traditional signal processing techniques, which often struggle to extract meaningful insights efficiently. Machine learning (ML) offers a transformative solution by extending classical signal processing capabilities to address broader challenges. The integration of ML into THz spectroscopy and imaging drives significant progress in areas such as data pre-processing, feature extraction, pattern recognition, and both qualitative and quantitative analyses. This review explores the application of these cutting-edge algorithms in THz technology from the past five years in non-destructive testing. It provides an up-to-date overview of the current technological landscape and challenges, providing insights and opportunities to propel further advancements in scientific research and commercial innovation.

Understanding the relation between the susceptibility of different leafy greens to human pathogen contamination and leaf traits can contribute to increase the food safety of the fresh vegetable industry. The aim of this research was to evaluate the susceptibility to E. coli ATCC 35218 attachment in 30 accessions of baby leaves, and to identify leaf traits potentially involved in the contamination. The accessions were surface inoculated with a bacterial suspension containing 1 × 10⁷ cells/mL and the attachment was measured 1.5 h after inoculation. Significant differences in attachment were detected between the accessions for p ≤ 0.05. The three most and the three least susceptible accessions were selected and characterized for leaf micro-morphological traits (stomata density and size, surface roughness) and water content. Scanning electron microscopy was used to analyse the stomatal parameters. Roughness was measured by an innovative portable 3D digital microscope. No significant correlation between the attachment of E. coli ATCC 35218 and stomatal parameters was detected, while the attachment was positively correlated with roughness and water content. The E. coli ATCC 35218 population in surface-inoculated leaves was also measured after a UV treatment, which was found to be less effective in reducing bacterial contamination in the rougher leaves. This result suggested that roughness offers UV protection, further highlighting its impact on the microbiological safety of baby leafy greens.