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The pioneering objective of a permanent outpost on the lunar surface requires the development of technologies for in situ resource utilisation. Consolidation of lunar soil into products is imperative to avoid unaffordable transportation burdens. The paper addresses the feasibility of the Local Microwave Heating effect as a promising technology for sintering lunar regolith based on microwave radiation, with a simple solid-state generator requiring a relatively modest amount of energy. Experiments were conducted using terrestrial simulants with physical and chemical properties similar to those of lunar regolith. Laboratory equipment with a purpose-built microwave actuator was designed and manufactured with "Design for Embarking" in mind. Experiments have demonstrated the feasibility of the proposed technology with a reasonable degree of confidence. Solid artefacts were obtained with proper setting of microwave parameters and appropriate scanning strategy, proving the feasibility of regolith sintering with a localised microwave source A roadmap for future development of the technology has been established. • Preliminary study on Localized Microwave Sintering of Lunar Regolith: Highlights. • Localized Microwave Heating is effective to consolidate lunar regolith simulants. • A solid-state generator with an open-end antenna is a promising lightweight low-energy system for ISRU. • Microwave heating of regolith is feasible regardless of the simulant's ilmenite content. • The technique can be used to produce 3D parts using an additive approach. [ABSTRACT FROM AUTHOR]

The present investigation elaborates on the level of game theory application for the applied and theoretical analyses of climate change and development studies. This editorial shows that the common good character of global climate alongside the increasing internalization of environmental externalities through national regulations and international environmental treaties jointly result in the increasing congruence between the context of climate change problems and the game theoretical method. Furthermore, the adoption of the Paris Accord by the overwhelming majority of developing countries as well as the disproportionate vulnerability of the Global South have led to an increasing shift in focus with regard to international development cooperation, from poverty alleviation and economic growth to green growth and circular economy solutions, within developing countries. The underutilization of game theory in the context of development studies is not satisfactory. This paper underlines the importance of implementing an impetus to researchers for scholarly discussions and applications of game theory in a discourse on the following topics: 1. economic growth; 2. climate change mitigation and adaptation; and 3. a broader socioeconomic development. [ABSTRACT FROM AUTHOR]

Commons governance theory is central to identifying and managing conflicts arising from natural and cultural resources traps. Scholars – using game theory and economic analysis of law – have proposed alternative models, consisting of a set of mitigated scenarios, multiple players, and new equilibria in commons governance. Likewise, novel legal innovations of the commons have also been designed. Reinterpreting the commons in light of political economy, ecology, and pluralistic approaches, a critical review of existing scholarship, economic analysis of law, and case study investigations are performed. Examining an array of views – including governance of water in Ecuador, Bolivia, and Italy – a research and policy agenda is put forward to offer original interpretations and novel holistic perspectives. Germane environmental policy implications deriving from SDGs, resilient governance, and polycentric perspectives are thus extrapolated. Finally, pluralistic frameworks drafted by mitigation and adaptation are measured by improved sustainable development performance in commons, resource, and water governance. [ABSTRACT FROM AUTHOR]

Since December 2019, the novel coronavirus disease (COVID-19) has had a considerable impact on the health and socio-economic fabric of Italy. The effective reproduction number Rt is one of the most representative indicators of the contagion status as it reports the number of new infections caused by an infected subject in a partially immunized population. The task of predicting Rt values forward in time is challenging and, historically, it has been addressed by exploiting compartmental models or statistical frameworks. The present study proposes an Artificial Neural Networks-based approach to predict the Rt temporal trend at a daily resolution. For each Italian region and autonomous province, 21 daily COVID-19 indicators were exploited for the 7-day ahead prediction of the Rt trend by means of different neural network architectures, i.e., Feed Forward, Mono-Dimensional Convolutional, and Long Short-Term Memory. Focusing on Lombardy, which is one of the most affected regions, the predictions proved to be very accurate, with a minimum Root Mean Squared Error (RMSE) ranging from 0.035 at day t + 1 to 0.106 at day t + 7. Overall, the results show that it is possible to obtain accurate forecasts in Italy at a daily temporal resolution instead of the weekly resolution characterizing the official Rt data. [ABSTRACT FROM AUTHOR]

Environmental, energy, business and development dynamics have received strong research interest. However, previous studies have mainly explored these topics separately. Conversely, we study these aspects jointly, constructing a composite indicator and assessing resulting impact on several SDGs domains. The aim of the analysis is to discover the importance of latent aspects in explaning the environmental, energy and business efficiency for specific countries. Estimating this multidimensional efficiency is relevant for the identification of the conditions for the achievement of CO 2 containment and sustainable development progress. We make use of the World Bank's World Development Indicators (WB WDI) for selected emerging countries from the Asia-Pacific region ranging from lower-middle-income to upper-middle-income economies, following the WB Group country classifications by income level. We build a composite indicator for eliciting diverse dimensions related to the environment, energy policy and economic factors. The first steps of the indicators analysis are achieved by the combination of the DEA and Tobit analyses. Therefore, we examine the environmental, energy and business efficiency according to the economic development stage of the analysed countries, making use of an entropy index. The index' robustness is tested at both stages – first, exploring the Tobit analysis, therefore, by inspecting an additional Asia-Pacific country having a different income with respect to the benchmark. The results suggest the creation of country clusters. Above all, China outstands in terms of environmental efficiency and R&D, as a result of a broader energy development strategic plan in the last few decades. • Environmental, energy, business and development dynamics are analysed jointly for the first time • A composite indicator on selected SDGs components is built • The World Bank's World Development Indicators are used for Asia-Pacific emerging countries • DEA, Tobit, entropy and robustness analyses explore the effect of environmental efficiency on CO 2 emissions • We observe different clusters for all sub-regions for the evaluation of efficiency and R&D intensity [ABSTRACT FROM AUTHOR]

The ongoing invasion of Russia of Ukraine and energy crises have sparked concern about economic and geopolitical stability all over the world. In Europe, the war has destabilized and endangered the energy cooperation and transition between European countries within and outside of the EU. This emergency has shown once more the importance of energy resilience policies to offset the vulnerability of energy systems and energy insecurity at the national and regional levels. Consilience has been reached on the necessity of enhancing EU energy security as an adaptation strategy. This work contributes to the existing scholarship on renewable energy transition and citizens' perception, focusing on European Union member states. Key characteristics of the renewable energy transition in the EU prior to the energy crisis and the war in Ukraine are examined. To this end, we analyze selected economic, environmental, social, policy and political variables on energy sorting from the Eurobarometer studying European citizens' perceptions. The exercise makes use of spatially-clustered regression to explore spatial heterogeneity and to elicit determinant information on specific regional groups. We learn that southern Europeans attribute less importance to energy infrastructure to facilitate the renewable energy transition and repute the EU solidity not a requirement for energy security access. Conversely, northern European citizens tend not to associate the responsibility of the EU in guiding competitiveness and policy toward green energy sources transformation. Robustness tests confirm our hypothesis. Regardless of regional differences, the EU energy and ecological transition will thrive with industrial and political cohesion. This process will pass through increased trust in institutions and dedicated energy policy action which will smooth the risks and disruptions coming from current and future shocks. • Eurobarometer variables European citizens' perceptions of renewable energy transition are analyzed. • Spatial heterogeneity statistics builds regional clusters southern Europeans ex-ante analysis of the Ukraine war and energy crisis. • Southern Europeans attribute less importance to energy infrastructure for renewable energy transition • Southern Europeans repute EU solidity not a requirement for energy security access. • Northern European citizens do not associate EU competitiveness with green energy sources transformation. [ABSTRACT FROM AUTHOR]

The present paper reviews the green hydrogen uptake policies in the United States and the European Union. While the EU has a rather broad public support strategy that envisages the entire hydrogen value chain, the U.S. policies are more focused on hydrogen research hubs and subsidies having electric vehicle applications. From the experience of the solar and wind power rollout of the past three decades, we know that the subsidization schemes of the end product play an important role in the transition to renewables. These kinds of subsidization schemes are still lacking for clean hydrogen both in the EU and the U.S. This paper provides a brief overview of selected subsidization schemes from other renewables markets, which are then applied to define an exemplary subsidization path to empower large-scale growth in a market for green hydrogen. Here is proposed a long-term subsidization scheme – deemed as an adequate strategy for the expansion of clean hydrogen in an electricity mix. The suggested exemplary subsidization path recommends shifting from feed-in tariffs to direct marketing with market premiums once a certain threshold is reached, while both can be flanked by the issuance of a Guarantee of Origin for green hydrogen so that there is one market for hydrogen and another market for the certificates. • -A long-term subsidization scheme for the rollout of green hydrogen in the electricity mixes is proposed. • -An exemplary subsidization path to empower large-scale growth in a market for green hydrogen is drafted. • -U.S. and EU are used as case studies to capture lessons for subsidization of green hydrogen economy uptake. • -The U.S. has more centralized green hydrogen rollout policies, whereas the EU has a wider policy toolkit. • -Shifting from FIT to direct marketing with market premiums is key once a certain pre-defined threshold is reached. [ABSTRACT FROM AUTHOR]

Seasonal hunger is the most common food insecurity experience for millions of small dryland farmers. This study tests the relationships between food insecurity, farm forests, and biomass poverty using a longitudinal dataset from the Amhara region of Ethiopia. These data form part of the Ethiopia Socioeconomic Survey, which collected panel data over three survey rounds from 530 households between 2011 and 2016. This dataset represents a collection of unique socioeconomic, wellbeing, and micro-land use measures, including farm forests. Hierarchical mixed effect regression models assessed the relationship between food insecurity and farm forests as well as the conditional effects of biomass poverty among the poorest farmers and women-headed households. Over a six-year study period, farmers reported increased stress from smaller land holdings, higher prices, and climate-related shocks. A clear trend towards spontaneous dispersed afforestation is observed by both researchers and satellite remote sensing. Model results indicate, dedicating approximately 10% of farm area to forest reduces months of food insecurity by half. The greatest reductions in food insecurity from farm forests are reported by ultra-poor and crop residue-burning households, suggesting that biomass poverty may be a major constraint to resilient food security on these farms. This research provides novel quantitative evidence of induced intensification and food security impacts of farm management preserving and building stores of biomass value as green assets. The results reported here have important implications for nature-based solutions as a major strategy to achieve sustainable development in some contexts. [ABSTRACT FROM AUTHOR]

Energy recovery is a viable option for waste management to integrate prevention, reuse and recycling when those strategies are not feasible. As such, efficient management of energy from waste can facilitate the circular economy (CE) transition. Efficient waste and energy management can assist sustainable development by curbing environmental degradation and resource depletion, improving energy security and health and safety conditions and creating new jobs and income. European countries have put substantial efforts and investments to improve their management efficiency of energy from waste and align to CE. To this end, this study develops a robust composite indicator to clarify and quantify this nexus, calculating European countries' achievements and filling a literature gap. A set of statistical techniques is employed to analyze a large panel dataset of 17 countries and 14 energy efficiency variables for 15 years. One may observe that Denmark, the Netherlands, Austria, the UK and Germany get positive outcomes. Conversely, the remaining 12 European countries get negative performances at the global level. Results vary across the different sub-indexes. 5 sensitivity and robustness analyses corroborate the results and confirm the hypothesis. The findings can assist the CE transition and may be used for research, management and policy purposes. This work's rationale. [Display omitted] [ABSTRACT FROM AUTHOR]

The present study investigates the nexus between electrification of the mountainous rural areas and selected indicators of socioeconomic development, such as household income, learning hours, and fuel choices in cooking behavior. The empirical investigation is based on the field survey in the recently electrified Yapshor and non-electrified Deh villages of the Rushan district of the Gorno-Badakhshan Autonomous Region of Tajikistan. The study reveals a strong positive impact of electrification of these villages on the level of household income and daily learning hours of school-age children. Getting electricity access leads to a 34–48% increase in household income and to 71–95 min of additional daily learning hours for school-age children. Furthermore, electricity access also strongly contributes to the substitution of firewood, coal, or animal manure with clean electricity for cooking. Due to individual taste preferences there is, however, still a substantial percentage of village dwellers who have not shifted to clean electricity for cooking. [ABSTRACT FROM AUTHOR]

Circular economy proposes paradigm shifts from linear production patterns. Entrepreneurs can highly benefit from fresh models driven by industrial symbiosis. In this setting, renewable energy is a prime asset for fostering circular economy and sustainable development. Equity crowdfunding can assist this transition, proposing an array of possibilities for supporting green entrepreneurship. The inquiry at hand is the first exercise linking circular economy, renewable energy and equity crowdfunding. This work tests whether the deal value of an equity-based crowdfunding campaign is higher in countries with renewable energy orientation. It is made use of probit model estimations. One may observe that being a firm from a country consuming large proportions of renewable energy is positively associated with higher crowdfunding performance. Conversely, when a company is located in a country which has large shares/ratios of renewable electricity production, it usually gets modest results when compared to its international competitors. Lower deal values are also attributed to firms situated in countries relying on biomass use. The solidity of the analyses is ensured by robustness tests. The outcomes may elicit policy and research formulation on environmental sustainability and business success at micro/macro levels – of primary importance to illuminate energy, circular economy and ecological transitions. Renewable energy-Equity crowdfunding-Circular economy interplay. [Display omitted] • Circular economy, renewable energy and equity crowdfunding are analysed for the first time. • The deal value of an equity-based crowdfunding campaign is higher in countries with renewable energy orientation. • Countries consuming large proportions of renewable energy are positively associated with a higher crowdfunding performance. • Companies from a country which has large shares of renewable electricity production, get modest results. • Lower deal values are attributed to firms situated in countries relying on biomass use. [ABSTRACT FROM AUTHOR]

Back in December 2019, the novel coronavirus disease 2019 (Covid-19) started rapidly spreading worldwide, especially in Italy that was among the most affected countries. The geographical distribution of air pollution and Covid-19 mortality in Italy suggested atmospheric pollution as a worsening factor of severe Covid-19 health outcomes. The present nationwide ecological study focused on all 107 Italian territorial areas, aiming to assess the potential association between Particulate Matter concentration, less than 2.5 μm in diameter (exposure), and Covid-19 mortality rate (outcome) throughout 2020, by looking at 28 potential confounders. A potential positive association between exposure and outcome was observed when performing a multivariate regression analysis with a Negative Binomial model, suggesting that an increase of 1 μg/m3 in the exposure is associated with an increase of 9.0% (95% CI: 6.5%–11.6%) in the average Covid-19 mortality rate, conditional on all 28 potential confounders. A sensitivity analysis, based on the E-value, shows that a hypothetical unmeasured confounder would have to be associated with both PM 2.5 concentration and Covid-19 mortality rate by a rate ratio of at least 1.40-fold each to explain away the exposure-outcome association, conditional on all 28 covariates included in the main analysis model. Moreover, the Observed Covariate E-value (OCE) was reported to provide a contextualization of the E-value on the observed covariates included in the study. The OCE sensitivity analysis shows that a set of unknown confounders similar in size and magnitude to the set of the considered climatic factors could potentially explain away the estimated exposure-outcome association. Consequently, the role of climatic factors in the Covid-19 pandemic is worth of further investigation. [ABSTRACT FROM AUTHOR]

The long-accepted thermoelectric model for electro-discharge machining is being brought into question. Several experimental facts prompt the proposal of a new theory based on the effect of gap pollution on the ignition of discharges. The first experimental proof comes from the recently reported observation of debris chains and clusters. In this view, each step of the process depends on the previous ones through a deterministic relation, even if the overall evolution is unpredictable. The paper establishes mathematical grounds for the abovementioned intuitions by setting up and solving a recursive equation for the machining energy employed at each discharge event. By means of numerical and algebraic tools, the above equation is studied and shows a chaotic evolution similar to that of the logistic map. Results reconcile the apparent paradox between deterministic nature and stochastic localization of the discharges and introduce a description of the chaotic dynamics of electro-discharge processes. [ABSTRACT FROM AUTHOR]

Abstract: Electro-discharge (ED) processes depend on the contemporaneous effect of many factors, which complicates process control/predictability and induced many authors in the last 60 years to work on explicative models. Studies split into two main approaches: theoretical and empirical. Theoretical works are based on the thermo-electrical theory and try to describe process phenomena by a physical model, with unavoidable assumptions and simplifications that cause a move away from veracity. On the contrary, experimentalists establish empirical models based on statistical analysis of results and optimization methods, but the findings are limited to domain-specific applications. In addition, numerous papers focus on single-spark analysis, failing in considering the interaction between successive discharges, or of transient phenomena as the presence of bubbles and debris in the gap. At present the scientific debate involves the ignition model, with two different points of view regarding the discharge-driving phenomenon: the debris bridge effect (pollutants in the dielectric drive the performances), and the dielectric strength effect (properties of the dielectric drive the performances). The paper addresses this dispute by investigating the debris formed during small-hole ED drilling of a 72wt% Al2O3–28wt% TiC composite. Particles are found to hollow out and pack within the gap, joining by necks. The first experimental evidence is given of the presence of chains and clusters of debris, towards a new model for electro-discharge processes that goes beyond the thermoelectric theory. [Copyright &y& Elsevier]

Cobalt-base alloys are generally adopted in applications that require wear, corrosion, and heat resistance. In particular, cobalt-chromium-molybdenum (Co-Cr-Mo) alloy is currently being used in the aerospace and medical field. Both applications require product personalization and take advantage by parts manufacturing through additive technologies, such as direct metal laser sintering (DMLS). This technology still has limitations for the manufacture of small cavities, such as holes with diameters below 0.6 mm and high aspect ratio, which require drilling of the additive manufactured part. This research aims at investigating the effects of electrodischarge drilling (EDD) on surface and subsurface features of CoCrMo samples built by DMLS. High aspect ratio holes are produced with diameter of 0.6 mm and depth 15 times higher. Due to the absence of physical contact between tool and workpiece in EDD, machining forces are negligible and microfeatures can be produced with a high accuracy irrespective of the material hardness and strength. In this study, peak current, pulse-on-time, and duty ratio are adopted as control factors using the Taguchi method. Material removal rate, Electrode wear rate (EW), and surface roughness are measured as responses. S/ N ratios and analysis of variance analysis lead to identify the significant process parameters and the optimal combination level of machining parameters. From the responses can be determined which ensures high productivity and smooth surface finish, even if tool wear is medium high. All the analyses are supported by the study of material removal mechanisms and surface morphology in the microscale, to account for the macroscopic trends. [ABSTRACT FROM AUTHOR]

Resilience is a hot topic within the international development agenda and Sustainable Development Goals. Nowadays, resilience plays a crucial role in improving the quality of life of vulnerable categories and is designed as a major strategy to face the diverse dimensions and dynamics of vulnerability. Energy results among the most relevant fields of applications of resilience policies, especially when it comes to electricity. As a multidimensional concept, energy resilience policies must relate to the dimensions of sustainability – here considered as the interplay between the economic, social, environmental and governance dimensions. Due to the complexity of the phenomenon, energy resilience can be effectively outlined making use of composite indicators techniques. This paper presents the following new results: i) energy resilience is defined and ranked, strengthening a composite indicator for both OECD and non-OECD countries; the determinants of energy resilience are investigated; ii) an innovation on the construction of the World Bank's Regulatory Indicators on Sustainable Energy is operated. Our exercise makes use of an approach based on interval data to assess the sensitivity of the measure from different specifications. For the same scope, the robustness of the ranks obtained is analyzed through an uncertainty analysis. These choices aim to enhance the soundness and the validity of the composite indicator. The methodology provides a more reliable baseline to validate the results and the conceptual assumptions undertaken. It is found that, according to the diverse theoretical frameworks and methodologies applied, some countries vary considerably both in the pillars that aggregate the variables and within the minima, the centers, and the maxima of the intervals. • Energy and resilience are compelling development policy, regulatory, and scientific quests. • Energy resilience is measured by an interval data composite indicator. • Interval data is a robust method for measuring energy resilience. • GDP is related to energy resilience. • Results vary with respect to linear methods in that they allow to better represent uncertainty. [ABSTRACT FROM AUTHOR]

Vulnerability has garnered an increasing attention from academia, international community and industry. Nonetheless, formal definition, mainstreaming, and measurement of vulnerability are still flawed in the economic literature. Energy vulnerability, intended as the exposure of an energy system to adverse events and change, often overlaps with other energy policy concepts such as resilience, security, poverty, justice, and sustainability. This paper improves understanding of vulnerability in economics, energy, and sustainability studies by: i) constructing a dataset on energy vulnerability made of 180.000 observations; ii) formally defining energy vulnerability, while considering the regulatory framework and development agenda; iii) building a composite indicator on energy vulnerability; iv) analyzing and ranking the energy vulnerability of a vast number of OECD and non-OECD countries; v) testing for robustness checks. The analysis suggests that GDP is not necessarily a leading driver for energy vulnerability, whilst resource embedment is, since fossil and renewable energy producers are less vulnerable. Eventually, the paper validates that green countries are less vulnerable, differently from cold, heavily-industrialized, and highly-consuming countries. • GDP is not a strong driver for energy vulnerability. • Green, scarcely consuming, warm, non-industrial countries perform better. • Resource endowment and electricity access matter. • Fossil and renewable energy exporters display a lower vulnerability. • Rankings for the single energy vulnerability pillars vary widely. [ABSTRACT FROM AUTHOR]

Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data. [ABSTRACT FROM AUTHOR]

The growing interest in advancing microfluidic devices for manipulating fluids within micrometer-scale channels has prompted a shift in manufacturing practices, moving from single-component production to medium-size batches. This transition arises due to the impracticality of lab-scale manufacturing methods in accommodating the increased demand. This experimental study focuses on the design of master benchmarks 1–5, taking into consideration critical parameters such as rib width, height, and the relative width-to-height ratio. Notably, benchmarks 4 and 5 featured ribs that were strategically connected to the inlet, outlet, and reaction chamber of the master, enhancing their utility for subsequent replica production. Vat photopolymerization was employed for the fabrication of benchmarks 1–5, while replicas of benchmarks 4 and 5 were generated through polydimethylsiloxane casting. Dimensional investigations of the ribs and channels in both the master benchmarks and replicas were conducted using an optical technique validated through readability analysis based on the Michelson global contrast index. The primary goal was to evaluate the potential applicability of vat photopolymerization technology for efficiently producing microfluidic devices through a streamlined production process. Results indicate that the combination of vat photopolymerization followed by replication is well suited for achieving a minimum rib size of 25 µm in width and an aspect ratio of 1:12 for the master benchmark. [ABSTRACT FROM AUTHOR]

This work introduces a revised version of the Food Sustainability Index , proposed by the Economist Intelligence Unit and the Barilla Center for Food and Nutrition in 2016. Our Adjusted Food Sustainability Index features two important advantages: 1) it employs the Mazziotta-Pareto method to compute weights, hence granting an objective aggregation criterion and 2) it does not take policy variables into account, thus focusing on the status quo . The policy variables are aggregated into the Policy Index , measuring the quality of the food sustainability policies. We compute the two indices for 25 countries worldwide, then we use the Data Envelopment Analysis to evaluate policy efficiency. Our results show that country-level variation in policy efficiency is wide and policies affect food sustainability significantly, especially when they target nutritional challenges. [ABSTRACT FROM AUTHOR]

The attachment or entrapment of microbial cells and enzymes are promising solutions for various industrial applications. When the traps are beads, they are dispersed in a fluidized bed in a vessel where a pump guarantees fresh liquid inflow and waste outflow without washing out the cells. Scientific papers report numerous types of cell entrapment, but most of their applications remain at the laboratory level. In the present research, rigid polymer beads were manufactured by two different additive manufacturing (AM) techniques in order to verify the economy, reusability, and stability of the traps, with a view toward a straightforward industrial application. The proposed solutions allowed for overcoming some of the drawbacks of traditional manufacturing solutions, such as the limited mechanical stability of gel traps, and they guaranteed the possibility of producing parts of constant quality with purposely designed exchange surfaces, which are unfeasible when using conventional processes. AM proved to be a viable manufacturing solution for beads with complex shapes of two different size ranges. A deep insight into the production and characteristics of beads manufactured by AM is provided. The paper provides biotechnologists with a manufacturing perspective, and the results can be directly applied to transit from the laboratory to the industrial scale. [ABSTRACT FROM AUTHOR]

Electro-discharge machining (EDM) of thin deep features widens processing opportunities of difficult-to-cut materials, such as Ni-based alloys, towards attractive industrial applications. Conversely, the complex interaction between electrical, thermal, and chemical phenomena in EDM thwarts process modeling and prediction. As a matter of fact, recent experimental discoveries encourage setting forth a new theory of discharge ignition in the gap, based on the role of debris, which lays foundations for a recursive mathematical model showing a chaotic evolution. The paper reports on electro-discharge drilling of small deep holes in Inconel 718. Process performances are measured with varying electrode size and geometry, attesting a pivotal effect of gap pollution on productivity and on the onset of a secondary detrimental removal of material by intergranular corrosion. All findings support the emerging model for discharge ignition via debris bridges, and, most notably, debris chains are documented for the first time in the field of metals, second only to a previous case for a ceramic composite. On the whole, the paper provides conclusive validation of the role of debris in the ignition of discharges. [ABSTRACT FROM AUTHOR]

Food system resilience has multiple dimensions. We draw on food system and resilience concepts and review resilience framings of different communities. We present four questions to frame food system resilience (Resilience of what? Resilience to what? Resilience from whose perspective? Resilience for how long?) and three approaches to enhancing resilience (robustness, recovery, and reorientation—the three "Rs"). We focus on enhancing resilience of food system outcomes and argue this will require food system actors adapting their activities, noting that activities do not change spontaneously but in response to a change in drivers: an opportunity or a threat. However, operationalizing resilience enhancement involves normative choices and will result in decisions having to be negotiated about trade-offs among food system outcomes for different stakeholders. New approaches to including different food system actors' perceptions and goals are needed to build food systems that are better positioned to address challenges of the future. [ABSTRACT FROM AUTHOR]

One of the most important open challenges in climate science is downscaling. It is a procedure that allows making predictions at local scales, starting from climatic field information available at large scale. Recent advances in deep learning provide new insights and modeling solutions to tackle downscaling-related tasks by automatically learning the coarse-to-fine grained resolution mapping. In particular, deep learning models designed for super-resolution problems in computer vision can be exploited because of the similarity between images and climatic fields maps. For this reason, a new architecture tailored for statistical downscaling (SD), named MSG-GAN-SD, has been developed, allowing interpretability and good stability during training, due to multi-scale gradient information. The proposed architecture, based on a Generative Adversarial Network (GAN), was applied to downscale ERA-Interim 2-m temperature fields, from 83.25 to 13.87 km resolution, covering the EURO-CORDEX domain within the 1979--2018 period. The training process involves seasonal and monthly dataset arrangements, in addition to different training strategies, leading to several models. Furthermore, a model selection framework is introduced in order to mathematically select the best models during the training. The selected models were then tested on the 2015-2018 period using several metrics to identify the best training strategy and dataset arrangement, which finally produced several evaluation maps. This work is the first attempt to use the MSG-GAN architecture for statistical downscaling. The achieved results demonstrate that the models trained on seasonal datasets performed better than those trained on monthly datasets. This study presents an accurate and cost-effective solution that is able to perform downscaling of 2 m temperature climatic maps. [ABSTRACT FROM AUTHOR]

Laser Powder Bed Fusion (LPBF) technology was used to produce samples based on the Ti–6Al–4V alloy for biomedical applications. Solid-state phase transformations induced by thermal treatments were studied by neutron diffraction (ND), X-ray diffraction (XRD), scanning transmission electron microscopy (STEM) and energy-dispersive spectroscopy (EDS). Although, ND analysis is rather uncommon in such studies, this technique allowed evidencing the presence of retained β in α' martensite of the as-produced (#AP) sample. The retained β was not detectable by XRD analysis, nor by STEM observations. Martensite contains a high number of defects, mainly dislocations, that anneal during the thermal treatment. Element diffusion and partitioning are the main mechanisms in the α ↔ β transformation that causes lattice expansion during heating and determines the final shape and size of phases. The retained β phase plays a key role in the α' → β transformation kinetics. [ABSTRACT FROM AUTHOR]

Metal additive manufacturing is now taking the lead over traditional manufacturing techniques in applications such as aerospace and biomedicine, which are characterized by low production volumes and high levels of customization. While fulfilling these requirements is the strength of metal additive manufacturing, respecting the tight tolerances typical of the mentioned applications is a harder task to accomplish. Powder bed fusion (PBF) is a class of additive manufacturing in which layers of metal powder are fused on top of each other by a high-energy beam (laser or electron beam) according to a computer-aided design (CAD) model. The quality of raw powders for PBF affects the mechanical properties of additively manufactured parts strongly, and therefore it is crucial to avoid the presence of any source of contamination, particularly cross-contamination. In this study, the identification and quantification of cross-contamination in powders of Ti-6Al-4V and maraging steel was performed using scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) techniques. Experimental results showed an overall good reliability of the developed method, opening the way for applications in machine learning environments. [ABSTRACT FROM AUTHOR]

We present three-dimensional radiation-hydrodynamical simulations of the impact of stellar winds, photoelectric heating, photodissociating and photoionizing radiation, and supernovae on the chemical composition and star formation in a stratified disc model. This is followed by a sink-based model for star clusters with populations of individual massive stars. Stellar winds and ionizing radiation regulate the star formation rate at a factor of ~10 below the simulation with only supernova feedback due to their immediate impact on the ambient interstellar medium after star formation. Ionizing radiation (with winds and supernovae) significantly reduces the ambient densities for most supernova explosions to ρ < 10-25 g cm-3, compared to 10-23g cm-3 for the model with only winds and supernovae. Radiation from massive stars reduces the amount of molecular hydrogen and increases the neutral hydrogen mass and volume filling fraction. Only this model results in a molecular gas depletion time-scale of 2 Gyr and shows the best agreement with observations. In the radiative models, the Hα emission is dominated by radiative recombination as opposed to collisional excitation (the dominant emission in non-radiative models), which only contributes ~1-10 per cent to the total Hα emission. Individual massive stars (M ≥ 30M☉) with short lifetimes are responsible for significant fluctuations in the Hα luminosities. The corresponding inferred star formation rates can underestimate the true instantaneous star formation rate by a factor of ~10. [ABSTRACT FROM AUTHOR]

We present three-dimensional radiation-hydrodynamical simulations of the impact of stellar winds, photoelectric heating, photodissociating and photoionizing radiation, and supernovae on the chemical composition and star formation in a stratified disc model. This is followed by a sink-based model for star clusters with populations of individual massive stars. Stellar winds and ionizing radiation regulate the star formation rate at a factor of ∼10 below the simulation with only supernova feedback due to their immediate impact on the ambient interstellar medium after star formation. Ionizing radiation (with winds and supernovae) significantly reduces the ambient densities for most supernova explosions to ρ < 10-25 g cm-3, compared to 10-23g cm-3 for the model with only winds and supernovae. Radiation from massive stars reduces the amount of molecular hydrogen and increases the neutral hydrogen mass and volume filling fraction. Only this model results in a molecular gas depletion time-scale of 2 Gyr and shows the best agreement with observations. In the radiative models, the Hα emission is dominated by radiative recombination as opposed to collisional excitation (the dominant emission in non-radiative models), which only contributes ∼1-10 per cent to the total Hα emission. Individual massive stars (M ≥ 30M☉) with short lifetimes are responsible for significant fluctuations in the Hα luminosities. The corresponding inferred star formation rates can underestimate the true instantaneous star formation rate by a factor of ∼10. [ABSTRACT FROM AUTHOR]

The SILCC project (SImulating the Life-Cycle of molecular Clouds) aims at a more selfconsistent understanding of the interstellar medium (ISM) on small scales and its link to galaxy evolution. We present three-dimensional (magneto)hydrodynamic simulations of the ISM in a vertically stratified box including self-gravity, an external potential due to the stellar component of the galactic disc, and stellar feedback in the form of an interstellar radiation field and supernovae (SNe). The cooling of the gas is based on a chemical network that follows the abundances of H+, H, H2, C+, and CO and takes shielding into account consistently. We vary the SN feedback by comparing different SN rates, clustering and different positioning, in particular SNe in density peaks and at random positions, which has a major impact on the dynamics. Only for random SN positions the energy is injected in sufficiently low-density environments to reduce energy losses and enhance the effective kinetic coupling of the SNe with the gas. This leads to more realistic velocity dispersions (sHI 0.8s300-8000K ~ 10-20 km s-1, sHa 0.6s8000-3 105 K ~ 20-30 km s-1), and strong outflows with mass loading factors (ratio of outflow to star formation rate) of up to 10 even for solar neighbourhood conditions. Clustered SNe abet the onset of outflows compared to individual SNe but do not influence the net outflow rate. The outflows do not contain any molecular gas and are mainly composed of atomic hydrogen. The bulk of the outflowing mass is dense (~ 10-25-10-24 g cm-3) and slow (v ~ 20-40 km s-1) but there is a high-velocity tail of up to v ~ 500 km s-1 with ~ 10-28-10-27 g cm-3. [ABSTRACT FROM AUTHOR]

Highlights: [•] Racecar exhaust collector successfully built in Inconel by laser consolidation. [•] Failure modes and microstructure are related to the construction plan. [•] LC build strategy is as critical to ensure fatigue resistance as component design. [ABSTRACT FROM AUTHOR]

Ceramic matrix composites (CMCs) can be attractive for structural applications, but their machining by conventional methods is expensive and often critical. Complex geometries on advanced ceramics require contactless processes, such as electrodischarge machining or drilling (EDD). These proved to be viable for CMCs with electrical resistivity below a critical value in the range of 1-3 Ω m. The condition is complied with by many CMCs: an example is alumina with titanium carbide. Material removal of ceramics by electrical discharges is a complex process involving different mechanisms, depending on the process setup. The paper describes an experimental study on EDD of 0.4-mm diameter holes with an aspect ratio of 20 in AlO-TiC, using copper electrodes. Peak current ( I), pulse-on time ( t), and pulse-off time ( t) are varied as independent variables. Four performance indicators are measured: material removal rate, electrode wear rate, overcut, and surface roughness ( R, S). Empirical models are proposed to describe the effect of process parameters on the output indicators. The analysis is supported by the observation of the surface and subsurface morphology, with the aim of investigating the material removal mechanisms and attaining a full comprehension of macroscopic results. It is found that removal mostly occurs by melting and evaporation and that surface morphology is determined by two phenomena ruled by pulse power. A process description is proposed, built around power as the ruling factor. [ABSTRACT FROM AUTHOR]

Association panels represent a useful tool for quantitative trait loci (QTL) mapping and pre-breeding. In this study, we report on the development of a European and Mediterranean chickpea association panel as a useful tool for gene discovery and breeding. Chickpea (Cicer arietinum L.) is one of the most important food legumes worldwide and a key crop in the Mediterranean environments. The selection of genotypes followed criteria aimed to build a set of materials representative of the genetic diversity of chickpea germplasm focusing on the European and Mediterranean environments, which have largely been ignored to date. This tool can help breeders to develop novel varieties adapted to European and Mediterranean agro-ecosystems. Initially, 1931 chickpea accessions were phenotypically evaluated in a field trial in central Italy. From these, an association panel composed by 480 genotypes derived from single-seed descent was identified and phenotypically evaluated. Current and future phenotypic data combined with the genotypic characterization of the association panel will allow to dissect the genetic architecture of important adaptive and quality traits and accelerate breeding. This information can be used to predict phenotypes of unexploited chickpea genetic resources available in genebanks for breeding. [ABSTRACT FROM AUTHOR]

The development of novel Ti-based amorphous or β-phase nanostructured metallic materials could have significant benefits for implant applications, due to improved corrosion and mechanical characteristics (lower Young's modulus, better wear performance, improved fracture toughness) in comparison to the standardized α+β titanium alloys. Moreover, the devitrification phenomenon, occurring during heating, could contribute to lower input power during additive manufacturing technologies. Ti-based alloy ribbons were obtained by melt-spinning, considering the ultra-fast cooling rates this method can provide. The titanium alloys contain in various proportions Zr, Nb, and Si (Ti60Zr10Si15Nb15, Ti64Zr10Si15Nb11, Ti56Zr10Si15Nb19) in various proportions. These elements were chosen due to their reported biological safety, as in the case of Zr and Nb, and the metallic glass-forming ability and biocompatibility of Si. The morphology and chemical composition were analyzed by scanning electron microscopy and energy-dispersive X-ray spectroscopy, while the structural features (crystallinity, phase attribution after devitrification (after heat treatment)) were assessed by X-ray diffraction. Some of the mechanical properties (hardness, Young's modulus) were assessed by instrumented indentation. The thermal stability and crystallization temperatures were measured by differential thermal analysis. High-intensity exothermal peaks were observed during heating of melt-spun ribbons. The corrosion behavior was assessed by electrocorrosion tests. The results show the potential of these alloys to be used as materials for biomedical applications. [ABSTRACT FROM AUTHOR]