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The standard hydrodynamic Drude model with hard-wall boundary conditions can give accurate quantitative predictions for the optical response of noble-metal nanoparticles. However, it is less accurate for other metallic nanosystems, where surface effects due to electron density spill-out in free space cannot be neglected. Here we address the fundamental question whether the description of surface effects in plasmonics necessarily requires a fully quantum-mechanical ab initio approach. We present a self-consistent hydrodynamic model (SC-HDM), where both the ground state and the excited state properties of an inhomogeneous electron gas can be determined. With this method we are able to explain the size-dependent surface resonance shifts of Na and Ag nanowires and nanospheres. The results we obtain are in good agreement with experiments and more advanced quantum methods. The SC-HDM gives accurate results with modest computational effort, and can be applied to arbitrary nanoplasmonic systems of much larger sizes than accessible with ab initio methods. Recent experiments with plasmonic nanostructures have found phenomena that cannot be explained classically, necessitating new theoretical models. Toscano et al . present a self-consistent hydrodynamic theory that describes both the nonlocal response and the electronic spill-out for noble and simple metals.

Chronological age represents the main factor in donor selection criteria for organ transplantation, however aging is very heterogeneous. Defining the biological aging of individual organs may contribute to supporting this process. In this study we examined the biological age of the heart [right (RA)/left atrium (LA)] and peripheral blood leucocytes in the same subject, and compared these to assess whether blood mirrors cardiac biological aging. Biological aging was studied in 35 donors (0.4–72 years) by exploring mitotic and non-mitotic pathways, using telomere length (TL) and age-dependent methylation changes in certain CpG loci (DNAmAge). Heart non-mitotic DNAmAge was strongly younger than that of both blood (− 10 years, p  < 0.0001) and chronological age (− 12 years, p  < 0.0001). Instead, heart and blood mitotic age (TL) were similar, and there was no difference in DNAmAge and TL between RA and LA. DNAmAge negatively correlated with TL in heart and blood ( p  ≤ 0.01). Finally, blood and heart TL ( p  < 0.01) and DNAmAge ( p  < 0.0001) were correlated. Therefore, blood can be a proxy indicator of heart biological age. While future investigation on post-transplant graft performance in relation to biological aging is still needed, our study could contribute to opening up novel basic and clinical research platforms in the field of organ transplantation.

The fossil fuel society is facing environmental, socio-economic, and geopolitical issues [...]

The goal of this work was to test a patented pruning harvester and a mobile pelleting system specifically designed for the vineyard agripellet chain. Biomass was characterized before and after storage and after the pelleting stage. The performance, the fuel consumption, and the work quality of the harvester were assessed together with the productivity and the power consumption of the mobile pelleting system. Production costs of pellet were estimated for the whole logistic chain, considering two scenarios: Storage and pelleting directly at the farm site or at a dedicated location at variable distance from the fields. For comparison, the direct production of chips without pelleting was considered. Results indicate that harvester performance was quite good and comparable with commercial solutions; the chips produced exhibited excellent storage performance, allowing direct pelleting without forced drying; the pellet quality was good comparable with that produced from forestry biomass. From an economic point of view, in-field pelleting was the most cost-effective solution, with a good margin of profit up to 57€ t−1; on the other hand, when transport to an intermediate storage center is necessary, profit margin reduces gradually and fades off at an average 50 km distance from the fields.

Predictive Maintenance (PdM) is a prominent strategy comprising all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the main challenges of PdM is to design and develop an embedded smart system to monitor and predict the health status of the machine. In this work, we use a data-driven approach based on machine learning applied to woodworking industrial machines for a major woodworking Italian corporation. Predicted failures probabilities are calculated through tree-based classification models (Gradient Boosting, Random Forest and Extreme Gradient Boosting) and calculated as the temporal evolution of event data. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime (RUL) of woodworking machines. The effectiveness of the proposed method is showed by testing an independent sample of additional woodworking machines without presenting machine down. The Gradient Boosting model achieved accuracy, recall, and precision of 98.9%, 99.6%, and 99.1%. Our predictive maintenance approach deployed on a Big Data framework allows screening simultaneously multiple connected machines by learning from terabytes of log data. The target prediction provides salient information which can be adopted within the maintenance management practice.

Biological treatments focused on stabilizing and detoxifying olive mill wastewater facilitate agronomic reuse for irrigation and fertilization. Anaerobic digestion is particularly attractive in view of energy recovery, but is severely hampered by the microbial toxicity of olive mill wastewater. In this work, the addition of biochar to the digestion mixture was studied to improve the stability and efficiency of the anaerobic process. Kinetics and yields of biogas production were evaluated in batch digestion tests with biochar concentrations ranging from 0 to 45 g L−1. The addition of biochar reduced sensibly the lag phase for methanogenesis and increased the maximum rate of biogas generation. Final yields of hydrogen and methane were not affected. Upon addition of biochar, soluble COD removal increased from 66% up to 84%, and phenolics removal increased from 50% up to 95%. Digestate phytotoxicity, as measured by seed germination tests, was reduced compared to raw wastewater. Addition of biochar further reduced phytotoxicity and, furthermore, a stimulatory effect was observed for a twenty-fold dilution. In conclusion, biochar addition enhances the anaerobic digestion of olive mill wastewaters by effectively reducing methanogenesis inhibition and digestate phytotoxicity, thus improving energy and biomass recovery.

Phasing out fossil fuels to renewables is currently a global priority due to the climate change threat. Advocacy for biomass use as an energy source requires assessing the quality biomass and ecological impacts of bioenergy supply chains. This study evaluated the quality of biomass residues from orchards and silviculture transported from different Northern and Central Italy locations and the carbon footprint of a biomass power plant. The total greenhouse emissions were calculated based on primary data for 2017 according to the ISO/TS 14067. All the residue samples showed their suitability for biofuel use. Ash content was relatively low on average (3–5% d.m.), except for grapevine residues (18% d.m.). The lower heating value was within the expected range of 15–21 MJ kg−1 for plant species. The average GHG emission from the power plant was 17.4 g CO2 eq./MJ of electrical energy, with the energy conversion (38%) and transportation of biomass (34%) phases being the main impact contributors. For this study, impacts of residual agricultural residue were about half that of residues from forest management, mainly due to chipping and greater transport distance. Results show that sourcing residual biomass materials for electricity generation close to power plants significantly reduce GHG emissions compared to conventional fossil fuels.

The combustion efficiency of wood pellets is partly affected by their average length. The ISO 17829 standard defines the methodology for assessing the average length of sample pellets, but the method does not always lead to representative data. Furthermore, a standard analysis is time-consuming as it requires manual measurement of the pellets using a caliper. This paper, whilst evaluating the effect of pellet length on combustion efficiency, proposes a pending-patented dimensional image processing method (DIP) for assessing pellet length. DIP allows the dimensional data of grouped and stacked pellets to be obtained by exploiting the shadows produced by pellets when exposed to a light source, assuming that different-sized pellets produce different shadows. Thus, the proposed method allows for the extraction of dimensional information from non-distinct objects, overcoming the reliance of classical image processing methods on object distance for effective segmentation. Combustion tests, carried out using pellets varying only in length, confirmed the influence of length on combustion efficiency. Shorter pellets, compared to longer ones, significantly reduced CO emissions by up to 94% (mg/MJ). However, they exhibited a higher fuel mass consumption rate (kg/h), with an increase of up to 22.8% compared to the longest sample. In addition, longer pellets produced fewer but larger shadows than shorter ones. Further studies are needed to correlate the number and size of shadows with samples’ average length so that DIP could be implemented in stoves and programmed to communicate with the control unit and automatically optimize the setting in order to improve combustion efficiency.

The dark fermentation of lignocellulose hydrolysates is a promising process for the production of hydrogen from renewable sources. Nevertheless, hydrogen yields are often lower than those obtained from other carbohydrate sources due to the presence of microbial growth inhibitors in lignocellulose hydrolysates. In this study, a microbial consortium for the production of hydrogen by dark fermentation has been obtained from a wild methanogenic sludge by means of thermal treatments. The consortium has been initially acclimated to a glucose-based medium and then used as inoculum for the fermentation of Arundo donax hydrolysates. Hydrogen yields obtained from fermentation of A. donax hydrolysates were lower than those obtained from glucose fermentation using the same inoculum (0.30 ± 0.05 versus 1.11 ± 0.06 mol of H2 per mol of glucose equivalents). The hydrogen-producing bacteria belonged mainly to the Enterobacteriaceae family in cultures growing on glucose and to Clostridium in those growing on A. donax hydrolysate. In the latter cultures, Lactobacillus outcompeted Enterobacteriaceae, although Clostridium also increased. Lactobacillus outgrowth could account for the lower yields observed in cultures growing on A. donax hydrolysate.

The Donation-after-Circulatory-Death (DCD) heart transplantation program increases donor pool but resulting in more serious ischemic-related myocardial injury (IRI), leading to higher incidence of primary graft dysfunction (PGD). Ex-vivo machine perfusion (EVMP) for DCD heart is being considered a useful aid in improving grafts number and quality assessment, aiming to better outcomes. In this review we will analyze the role of EVMP techniques in the context of DCD with special attention to their clinical aims and results and future perspectives. A review of available clinical and pre-clinical studies involving EVMP with DCD donation model was performed. Thirty-four original articles about preclinical studies were found. First studies were designed to evaluate graft function in DCD hearts after EVMP, while recent research focus on possible therapies that could be associated with EVMP. Twenty-one original articles about clinical studies were found with the Organ-Care-System (TransMedics) as MP used. Outcomes, such as survival rates or rejection episodes, are comparable to outcomes from donation-after-brain-death. EVMP in the setting of DCD heart transplantation can be a valid tool for organ preservation and transport. The role of pre-clinical research will be crucial to reduce IRI, achieve organ reconditioning and reduce incidence of PGD.