Effect of Process Parameters on Laser Powder Bed Fusion of Al-Sn Miscibility Gap Alloy

Al-Sn binary system is a miscibility gap alloy consisting of an Al-rich phase and a Sn-rich phase. This system is traditionally applied in bearings and more recently found application as form-stable phase change material (PCM) exploiting solid-liquid phase transition of Sn. A careful choice of production process is required to avoid macro-segregation of the two phases, which have different densities and melting temperatures. In the present study, the additive manufacturing process known as laser powder bed fusion (LPBF) was applied to an Al-Sn alloy with 20% volume of Sn, as a rapid solidification process. The effect of process parameters on microstructure and hardness was evaluated. Moreover, feasibility and stability with thermal cycles of a lattice structure of the same alloy were experimentally investigated. An Al-Sn lattice structure could be used as container for a lower melting organic PCM (e.g., a paraffin or a fatty acid), providing high thermal diffusivity thanks to the metallic network and a "safety system" reducing thermal diffusivity if the system temperature overcomes Sn melting temperature. Even if focused on Al-Sn to be applied in thermal management systems, the study offers a contribution in view of the optimization of manufacturing processes locally involving high solidification rates and reheat cycles in other miscibility gap alloys (e.g., Fe-Cu) with similar thermal or structural applications

Tailoring parameter distributions to specific germplasm : impact on crop model-based ideotyping

Crop models are increasingly used to identify promising ideotypes for given environmental and management conditions. However, uncertainty must be properly managed to maximize the in vivo realizability of ideotypes. We focused on the impact of adopting germplasm-specific distributions while exploring potential combinations of traits. A field experiment was conducted on 43 Italian rice varieties representative of the Italian rice germplasm, where the following traits were measured: light extinction coefficient, radiation use efficiency, specific leaf area at emergence and tillering. Data were used to derive germplasm-specific distributions, which were used to re-run a previous modelling experiment aimed at identifying optimal combinations of plant trait values. The analysis, performed using the rice model WARM and sensitivity analysis techniques, was conducted under current conditions and climate change scenarios. Results revealed that the adoption of germplasm-specific distributions may markedly affect ideotyping, especially for the identification of most promising traits. A re-ranking of some of the most relevant parameters was observed (radiation use efficiency shifted from 4th to 1st), without clear relationships between changes in rankings and differences in distributions for single traits. Ideotype profiles (i.e., values of the ideotype traits) were instead more consistent, although differences in trait values were found

Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks

Explainability in Artificial Intelligence has been revived as a topic of active research by the need of conveying safety and trust to users in the `how' and `why' of automated decision-making. Whilst a plethora of approaches have been developed for post-hoc explainability, only a few focus on how to use domain knowledge, and how this influences the understandability of global explanations from the users' perspective. In this paper, we show how ontologies help the understandability of global post-hoc explanations, presented in the form of symbolic models. In particular, we build on Trepan, an algorithm that explains artificial neural networks by means of decision trees, and we extend it to include ontologies modeling domain knowledge in the process of generating explanations. We present the results of a user study that measures the understandability of decision trees using a syntactic complexity measure, and through time and accuracy of responses as well as reported user confidence and understandability. The user study considers domains where explanations are critical, namely, in finance and medicine. The results show that decision trees generated with our algorithm, taking into account domain knowledge, are more understandable than those generated by standard Trepan without the use of ontologies

Host-Based Treatments for Severe COVID-19

COVID-19 has been a global health problem since 2020. There are different spectrums of manifestation of this disease, ranging from asymptomatic to extremely severe forms requiring admission to intensive care units and life-support therapies, mainly due to severe pneumonia. The progressive understanding of this disease has allowed researchers and clinicians to implement different therapeutic alternatives, depending on both the severity of clinical involvement and the causative molecular mechanism that has been progressively explored. In this review, we analysed the main therapeutic options available to date based on modulating the host inflammatory response to SARS-CoV-2 infection in patients with severe and critical illness. Although current guidelines are moving toward a personalised treatment approach titrated on the timing of presentation, disease severity, and laboratory parameters, future research is needed to identify additional biomarkers that can anticipate the disease course and guide targeted interventions on an individual basis

Ce-exchange capacity of zeolite L in different cationic forms: a structural investigation

Cerium exchange by microporous materials, such as zeolites, has important applications in different fields, for example, rare earth element recovery from waste or catalytic processes. This work investigated the Ce-exchange capacity of zeolite L in three different cationic forms (the as-synthesized K form and Naand NH4-exchanged ones) from a highly concentrated solution. Chemical analyses and structural investigations allowed determination of the mechanisms involved in the exchanges and give new insights into the interactions occurring between the cations and the zeolite framework. Different cation sites are involved: (i) K present in the original LTL in the cancrinite cage (site KB) cannot be exchanged; (ii) the cations in KD (in the 12-membered ring channel) are always exchanged; while (iii) site KC (in the eight-membered ring channel) is involved only when K+ is substituted by NH4+, thus promoting a higher exchange rate for NH4+ -> K+ than for Na+ -> K+. In the Ce-exchanged samples, a new site occupied by Ce appears in the centre of the main channel, accompanied by an increase in the number of and a rearrangement of H2O molecules. In terms of Ce exchange, the three cationic forms behave similarly, from both the chemical and structural point of view (exchanged Ce ranges from 38 to 42% of the pristine cation amount). Beyond the intrinsic structural properties of the zeolite L framework, the Ce exchange seems thus also governed by the water coordination sphere of the cation. Complete Ce recovery from zeolite pores was achieved

Analysis and modeling of processes involved with salt tolerance and rice

Salinity is a worldwide problem for rice (Oryza sativa L.) cultivation, and a number of breeding programs targeting increased salt tolerance are ongoing. A new trait-based mathematical model for salt stress on rice was recently proposed, characterized by a high level of detail in the description of physiological mechanisms dealing with crop response to salinity. In this study, dedicated growth chamber experiments were performed where three rice cultivars with different degrees of tolerance were grown under different salinity levels. The aim was to improve the understanding of physiological mechanisms like Na+ uptake and sequestration in structural tissues, and to validate the model using new datasets where temporal dynamics in plant response to salt stress were analyzed. Model evaluation demonstrated strong agreement between measured and simulated dry weights of plant organs (e.g., R2 = 0.88-0.97 for aboveground biomass), [Na+] in plant tissues (R2 = 0.73-0.88), and green leaf area index (R2 = 0.71-0.99). These results demonstrate the reliability of the model and support its adoption within studies aimed at analyzing or predicting the response of different cultivars to temporal dynamics of Na+ concentration in soil and water

Trait-based model development to support breeding programs : A case study for salt tolerance and rice

Eco-physiological models are increasingly used to analyze G 7 E 7 M interactions to support breeding programs via the design of ideotypes for specific contexts. However, available crop models are only partly suitable for this purpose, since they often lack clear relationships between parameters and traits breeders are working on. Taking salt stress tolerance and rice as a case study, we propose a paradigm shift towards the building of ideotyping-specific models explicitly around traits involved in breeding programs. Salt tolerance is a complex trait relying on different physiological processes that can be alternatively selected to improve the overall crop tolerance. We developed a new model explicitly accounting for these traits and we evaluated its performance using data from growth chamber experiments (e.g., R2 ranged from 0.74 to 0.94 for the biomass of different plant organs). Using the model, we were able to show how an increase in the overall tolerance can derive from completely different physiological mechanisms according to soil/water salinity dynamics. The study demonstrated that a trait-based approach can increase the usefulness of mathematical models for supporting breeding programs

Inhaled corticosteroids reduce neutrophilic bronchial inflammation in patients with chronic obstructive pulmonary disease.

Abstract BACKGROUND: Airways inflammation is a feature of chronic obstructive pulmonary disease (COPD), but the role of corticosteroids in the management of clinically stable patients has yet to be established. A randomised controlled study was carried out to investigate the effect of high dose inhaled beclomethasone dipropionate (BDP) administered for two months to patients with stable, smoking related COPD. Sputum induction was used to evaluate bronchial inflammation response. METHODS: 34 patients (20 men and 14 women) were examined on three separate occasions. At the initial clinical assessment (visit 0), spirometry and blood gas analysis were performed. On visit 1 (within one week of visit 0) sputum induction was performed and each patient was randomised to receive either BDP 500 micrograms three times daily (treated group) or nothing (control group). After two months (visit 2), all patients underwent repeat clinical assessment, spirometry, and sputum induction. RESULTS: There were no differences in sputum cell counts between the groups at baseline. After two months of treatment, induced sputum samples from patients in the treated group showed a reduction in both neutrophils (-27%) and total cells (-42%) with respect to baseline, while the control group did not (neutrophils +9%, total cells +7%). Macrophages increased in the treated group but not in the control group. The mean final value of sputum neutrophils was 52% in the treated group and 73.3% in the control group (95% confidence interval (CI) -27.2 to -15.4). The mean final value of sputum macrophages was 35.8% in treated group and 19.3% in control group (95% CI 10.3 to 22.8). The differences between the treated and control groups for neutrophils (-21.3%), macrophages (+16.5%), and total cells (-65%) were significant. Spirometry and blood gas data did not change from baseline in either patient group. CONCLUSIONS: A two month course of treatment with high dose inhaled BDP reduces significantly neutrophil cell counts in patients with clinically stable, smoking related COPD. Further studies on the effectiveness of inhaled steroids in COPD are needed to confirm the clinical importance of this observation