Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor

Wind energy evaluation is an important goal in the conversion of energy systems to more environmentally friendly solutions. In this paper, we present a novel approach to wind speed spatial estimation on the isle of Sicily (Italy): an incremental self-organizing neural network (Generalized Mapping Regressor - GMR) is coupled with exploratory data analysis techniques in order to obtain a map of the spatial distribution of the average wind speed over the entire region. First, the topographic surface of the island was modelled using two different neural techniques and by exploiting the information extracted from a digital elevation model of the region. Then, GMR was used for automatic modelling of the terrain roughness. Afterwards, a statistical analysis of the wind data allowed for the estimation of the parameters of the Weibull wind probability distribution function. In the last sections of the paper, the expected values of the Weibull distributions were regionalized using the GMR neural networ

Potential of hydrophobic paper-based sorptive phase prepared by in-situ thermal imidization for the extraction of methadone from oral fluid samples

Paper-based sorptive phases (PSPs) are functional planar materials with a demonstrated potential in analytical sample preparation. This article describes the synthesis of a polyimide coated paper by an in-situ imidization at a high temperature. Polyimides (PI) are synthesized in two subsequent steps where a hydrophilic polymer, in this case, poly(amic acid) (PAA), is formed as an intermediate product. PAA is finally transformed into hydrophobic PI by thermal curing at 180 °C. The synthesis of PI-paper takes advantage of this two-step procedure. In the first stage, a segment of filter paper is immersed into an aqueous PAA solution. After the solvent evaporation, the paper is heated at 180 °C for 1 h inducing the formation of the hydrophobic PI over the cellulose fibers. Infrared spectroscopy has been used to characterize the synthesized materials by defining a coverage factor F. The hydrophobicity of the materials has been studied using an aqueous methylene blue solution as a marker. To fully demonstrate the usefulness of the material in the sample preparation field, the extraction of methadone from oral fluid (OF) samples has been considered as a model analytical problem. The main variables affecting the synthesis (PAA concentration on the precursor solution and number of dips) and the extraction (elution and extraction times) have been fully evaluated. Working under the optimum conditions, a limit of quantification of 9 µg/L, intraday and interday precision better than 14.6%, and accuracy in the range of 87–108% were obtained

Green analytical chemistry (GAC) applications in sample preparation for the analysis of anthocyanins in products and by-products from plant sources

Agri-food industry manufacturing is an important source of environmental pollution and eutrophication, both intrinsically and due to the generation of significant amount of by-products. For this reason, green chemistry is currently at the forefront of efforts to make all steps of agri-food workflows more sustainable and environmentally friendly and to reduce their carbon footprint. Green analytical chemistry (GAC) is an integral part of these efforts, although it has been largely neglected until now, due to the fact that analytical procedures are mainly limited to quality control in this field, and thus produce just a small fraction of the overall environmental burden of agri-food processes. In this mini-review, the most recent developments of green analytical methods are described, relative to their applications for anthocyanin determination in agri-food products and by-products. Anthocyanins have been chosen as they are among the most valuable secondary plant metabolites, with a wide range of possible applications exploiting their preservative, antioxidant and coloring properties. Non-separative and separative analytical meth- ods are included in this mini-review. The former are mainly spectrometric in nature, and usually mostly allow to detect and/or quantify groups or classes of molecules. However, they also provide very high throughput and the greatest chance to develop low-energy, low-solvent consumption procedures, even to the point of enabling direct determinations in solid samples as such. On the other hand, separative methods provide far greater selectivity and far wider applicability, but at the price of higher energy and resource consumption and usually lower throughput

Generative adversarial networks review in earthquake-related engineering fields

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions

Microhabitat selection of a Sicilian subterranean woodlouse and its implications for cave management

Human activities in subterranean environments can affect different ecosystem components, including the resident fauna. Subterranean terrestrial invertebrates are particularly sensitive to environmental change, especially microclimatic variations. For instance, microclimate modifications caused by the visitors may directly affect local fauna in caves opened to the public. However, since numerous factors act synergistically in modulating the distribution and abundance of subterranean species, it remains challenging to differentiate the impact of human intervention from that of other factors. Therefore, evidence of the impact of tourism on cave invertebrate fauna remains scarce. Over a year and with approximately two visits a month, we investigated the effects of the presence of visitors on the subterranean endemic woodlouse Armadillidium lagrecai in the strict natural reserve of Monello Cave (Sicily, Italy). We found that natural microclimatic fluctuations, and not direct human disturbance, were the main factors driving the distribution of A. lagrecai. Specifically, A. lagrecai select for more climatically stable areas of the cave, where the temperature was constantly warm and the relative humidity close to saturation. We also observed a significant temporal effect, with a greater abundance of A. lagrecai in summer and a gradual decrease during the winter months. The number of visitors in the Monello cave had no effect on the abundance and distribution of A. lagrecai. However, considering the high sensitivity of the species to microclimatic variations, it seems likely that a significant increase in the number of visitors to the cave could indirectly affect this species by altering local microclimate. Constant monitoring of the environmental parameters within the cave is therefore recommended.Peer reviewe

Improving quality in school canteens: “Un fiore in mensa” (A Flower in canteen) project from ATS Val Padana (Italy)

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New detailed characterization of the residual luminescence emitted by the GAGG:Ce scintillator crystals for the HERMES Pathfinder mission

The HERMES (High Energy Rapid Modular Ensemble of Satellites) Pathfinder mission aims to develop a constellation of nanosatellites to study astronomical transient sources, such as gamma-ray bursts, in the X and soft $\gamma$ energy range, exploiting a novel inorganic scintillator. This study presents the results obtained describing, with an empirical model, the unusually intense and long-lasting residual emission of the GAGG:Ce scintillating crystal after irradiating it with high energy protons (70 MeV) and ultraviolet light ($\sim$ 300 nm). From the model so derived, the consequences of this residual luminescence for the detector performance in operational conditions has been analyzed. It was demonstrated that the current generated by the residual emission peaks at 1-2 pA, thus ascertaining the complete compatibility of this detector with the HERMES Pathfinder nanosatellites

A system capable of dampening roll and producing electricity installed on the hull of a fishing vessel

The necessity to improve the security of fishing’s crew during their work can create a favourable condition to produce electrical energy. This solution to problem can do with a system that balance the forces induct from the sea to the fishing vessel. This device in this action generates electrical energy

Life during COVID-19 lockdown in Italy: the influence of cognitive state on psychosocial, behavioral and lifestyle profiles of older adults.

Few studies have examined lockdown effects on the way of living and well-being of older adults stratified by cognitive state. Since cognitive deficits are common in this population, we investigated how cognition influenced their understanding of the pandemic, socio-behavioral responses and lifestyle adaptations during lockdown, and how these factors affected their mood or memory.Telephone-based survey involving 204 older adults ≥65 y/o (median: 82) with previous assessments of cognitive state: 164 normal-old (NOLD), 24 mild-neurocognitive disorder (mild-NCD), 18 mild-moderate dementia. A structured questionnaire was developed to assess psychological and socio-behavioral variables. Logistic regression was used to ascertain their effects on mood and memory.With increasing cognitive deficits, understanding of the pandemic and the ability to follow lockdown policies, adapt to lifestyle changes, and maintain remote interactions decreased. Participants with dementia were more depressed; NOLDs remained physically and mentally active but were more bored and anxious. Sleeping and health problems independently increased the likelihood of depression (OR: 2.29; CI: 1.06-4.93;NOLD and mild-NCD groups showed similar mood-behavioral profiles suggesting better tolerance of lockdown. Those with dementia were unable to adapt and suffered from depression and cognitive complaints. To counteract lockdown effects, physical and mental activities and digital literacy should be encouraged

Targeted quantitative metabolic profiling of brain-derived cell cultures by semi-automated MEPS and LC-MS/MS

The accurate characterisation of metabolic profiles is an important prerequisite to determine the rate and the efficiency of the metabolic pathways taking place in the cells. Changes in the balance of metabolites involved in vital processes such as glycolysis, tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS), as well as in the biochemical pathways related to amino acids, lipids, nucleotides, and their precursors reflect the physiological condition of the cells and may contribute to the development of various human diseases. The feasible and reliable measurement of a wide array of metabolites and biomarkers possesses great potential to elucidate physiological and pathological mechanisms, aid preclinical drug development and highlight potential therapeutic targets. An effective, straightforward, sensitive, and selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach was developed for the simultaneous quali-quantitative analysis of 41 compounds in both cell pellet and cell growth medium obtained from brain-derived cell cultures. Sample pretreatment miniaturisation was achieved thanks to the development and optimisation of an original extraction/purification approach based on digitally programmed microextraction by packed sorbent (eVol®-MEPS). MEPS allows satisfactory and reproducible clean-up and preconcentration of both low-volume homogenate cell pellet lysate and cell growth medium with advantages including, but not limited to, minimal sample handling and method sustainability in terms of sample, solvents, and energy consumption. The MEPS-LC-MS/MS method showed good sensitivity, selectivity, linearity, and precision. As a proof of concept, the developed method was successfully applied to the analysis of both cell pellet and cell growth medium obtained from a line of mouse immortalised oligodendrocyte precursor cells (OPCs; Oli-neu cell line), leading to the unambiguous determination of all the considered target analytes. This method is thus expected to be suitable for targeted, quantitative metabolic profiling in most brain cell models, thus allowing accurate investigations on the biochemical pathways that can be altered in central nervous system (CNS) neuropathologies, including e.g., mitochondrial respiration and glycolysis, or use of specific nutrients for growth and proliferation, or lipid, amino acid and nucleotide metabolism