The new Italian SIDAPA Baseline Series for patch testing (2023): an update according to the new regulatory pathway for contact allergens

Allergic contact dermatitis (ACD) is a common inflammatory skin disease caused by delayed hypersensitivity to chemical and biotic contact allergens. ACD significantly affects the patients' quality of life negatively impacting both occupational and non-occupational settings. Patch testing is the gold standard diagnostic in vivo test to precise the ACD etiology and to correctly perform prevention. According to the Italian Medicines Agency (AIFA) legislative decree no. 178 of 29th May 1991, allergens are defined as medicines and therefore they are subject to strict regulation. In 2017, AIFA (decree no. 2130/2017) started a procedure to regulate contact allergens on the Italian market and actually the contact allergens temporarily authorized are reported in AIFA decree no. 98/2022, valid until November 2023. The availability on the market of contact allergens to diagnose ACD and continuous updating on the basis of new epidemiological trends are mandatory, jointly with the continuous update of the baseline and integrative series for patch testing. For this reason, the scientific community represented in Italy by the Skin Allergies Study Group of SIDeMaST (Italian Society of Dermatology and Venereology) and SIDAPA (Italian Society of Allergological, Occupational and Environmental Dermatology) are constantly working, in close relationship with the European scientific communities with large expertise in this important sector of the modern Dermatology. Herein, we report the setting up of regulatory legislation by AIFA and the new Italian Adult Baseline Series for patch testing

Decision Agriculture

In this chapter, the latest developments in the field of decision agriculture are discussed. The practice of management zones in digital agriculture is described for efficient and smart faming. Accordingly, the methodology for delineating management zones is presented. Modeling of decision support systems is explained along with discussion of the issues and challenges in this area. Moreover, the precision agriculture technology is also considered. Moreover, the chapter surveys the state of the decision agriculture technologies in the countries such as Bulgaria, Denmark, France, Israel, Malaysia, Pakistan, United Kingdom, Ukraine, and Sweden. Finally, different field factors such as GPS accuracy and crop growth are also analyzed

Using Images Generated by Sentinel-2 Satellite Optical Sensor for Burned Area Mapping

Remote Sensing is identified as an effective and efficient tool for monitoring fire events, and for quantifying fire effects on environment. Satellite images are used both to identify active fires and to analyse their effects, as well as to define burnt areas and map the severity of fires. Fires modify the structure and the reflectance of vegetation as well as the soil properties within the burned area; the produced changes are detectable in the visible, infrared and microwave parts of the electromagnetic spectrum. One of the most useful approach is based on classification of images using the spectral properties of burnt residues. This paper aims to use data obtained from optical sensor mounted on Sentinel-2 platform for mapping areas damaged by fire in a precise and rapid way. Sentinel-2 offers multispectral medium and high spatial resolution images with 13 spectral bands and about 5-day temporal resolution. Two images concerning the same scene in Campania Region but acquired on different dates are considered: pre-fire and post-fire. For each image, the Normalized Burned Ratio Index (NBR) is calculated, which allows to identify the areas affected by the fire and the relative degree of severity. Using change detection techniques, burned map can be identified. The evaluation of the accuracy is carried out using some indexes widespread in remote sensing literature, such as User’s Accuracy, Producer’s Accuracy, Overall Accuracy and Kappa coefficient. The values obtained in the confusion matrix showed the high quality of the developed method based on the use of the NBR index

Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 Imagery

The monitoring of burned areas can easily be performed using satellite multispectral images: several indices are available in the literature for highlighting the differences between healthy vegetation areas and burned areas, in consideration of their different signatures. However, these indices may have limitations determined, for example, by the presence of clouds or water bodies that produce false alarms. To avoid these inaccuracies and optimize the results, this work proposes a new index for detecting burned areas named Normalized Burn Ratio Plus (NBR+), based on the involvement of Senti-nel-2 bands. The efficiency of this index is verified by comparing it with five other existing indices, all applied on an area with a surface of about 500 km2 and covering the north-eastern part of Sicily (Italy). To achieve this aim, both a uni-temporal approach (single date image) and a bi-temporal approach (two date images) are adopted. The maximum likelihood classifier (MLC) is applied to each resulting index map to define the threshold separating burned pixels from non-burned ones. To evaluate the efficiency of the indices, confusion matrices are constructed and compared with each other. The NBR+ shows excellent results, especially because it excludes a large part of the areas incorrectly classified as burned by other indices, despite being clouds or water bodies

A comparison of different algorithms for the delineation of management zones

One approach to the application of site-specific techniques and technologies in precision agriculture is to subdivide a field into a few contiguous homogenous zones, often referred to as management zones (MZs). Delineating MZs can be based on some sort of clustering, however there is no widely accepted method. The application of fuzzy set theory to clustering has enabled researchers to account better for the continuous variation in natural phenomena. Moreover, the methods based on non-parametric density estimation can detect clusters of unequal size and dispersion. The objectives of this paper were to: (1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with potential yield. One hundred georeferenced point measurements of soil and crop properties were obtained from a 12 ha field cropped with durum wheat for two seasons. The trial was carried out at the experimental farm of CRA-CER in Foggia (Italy). All variables were interpolated on a 1 9 1 m grid using the geostatistical techniques of kriging and cokriging. The techniques compared to identify MZs were: (1) the ISODATA method, (2) the fuzzy c-means algorithm and (3) a nonparametric density algorithm. The ISODATA method, which was the simplest, subdivided the field into three distinct classes of suitable size for uniform management, whereas the other two methods created two classes. The non-parametric density algorithm characterized the edge properties between adjacent clusters more efficiently than the fuzzy method. The clusters from the non-parametric density algorithm and yield maps for three seasons (2005–2006, 2006–2007 and 2007–2008) were compared and agreement measures were computed. The kappa coefficients for the three seasons were negative or small positive values which indicate only slight agreement. These results illustrate the importance of temporal variation in spatial variation of yield in rainfed conditions, which limits the use of the MZ approac