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Mountain areas have been the testbed for sustainable development models focused on balancing their vulner-ability and the value of their natural, cultural, and social resources. In these areas, the continuous adaptative interaction between bio-geophysical and socio-cultural processes assembles Socio-Ecological Systems (SES) characterized by a great diversity of ecosystems and land uses, which provide substantial support for the live-lihoods of mountain communities and essential ecosystem services for uplands and lowlands. In this paper, we take value chains to be the operative units of analysis to examine human-natural systems interactions in mountain areas. Value chains mobilize resources and connect actors beyond territorial boundaries and economic sectors to generate economic, environmental, and social values. Strategies for local development of these areas should direct attention to value generation activities and require a systemic, integrated and assets-based approach which explores the potential synergies emerging from the coordination of the diversity of local spec-ificities and considers the opportunities and threats emerging from external sub-systems. This paper proposes a novel comparative framework to characterize value chains contribution to resilient and sustainable development of SESs in mountain areas. This framework is meant for researchers and policy analysts to identify the role value chains might accomplish for a better balance between natural resource conservation and sustainable socio-economic development in European (remote) rural areas. Two mountain food value chains are used to illus-trate the framework and test its efficacy. The cases depict two value chain configurations which result in different social, economic and environmental outcomes for the sustainable development of the SES.

Telemedicine may be of benefit in patients with heart failure (HF), although virtual visits are often carried out on an ad hoc basis. Herein, we discuss a structured protocol that defines all the steps needed for virtual visits, LEAP Virtual Visit, which includes guides and support kits for clinicians and patients.A working group of clinicians was established to develop a protocol for virtual visits. The virtual model for monitoring HF patients was based on measurement and sizing of patient flows, type of services, and critical organizational points. The process of virtual visits was broken down to identify the specific activities, players, roles, and support tools needed for implementation in daily practice.In the protocol developed, sections were included on patient eligibility, exams, supportive tools, and practical organizational aspects. IT requirements were also considered and the most widely used teleconference platforms were compared according to features that were considered to be priority for virtual visits. Protocols and support kits were developed, including guides for clinicians and patients, two questionnaires to assess the level of satisfaction, patient diary, disease awareness brochure, and template to monitor key performance indicators. The phases include an enrolment phase, document sharing phase, pre-evaluation phase, the virtual visit itself, and the post-visit phase. The protocol for the virtual visit was divided into 8 sequential steps to be carried out between the clinic and patient/caregiver.The protocol described can be expected to have a number of benefits for the patient, clinic, and healthcare structure.

To provide an overview of existing literature on pathogenetic and clinical aspects of cardiac and vascular involvement in eosinophilic granulomatosis with polyangiitis (EGPA).In EGPA, cardiac and vascular involvement are more common than previously thought. However, no international recommendations on the topic are available yet. Herein, we summarize the existing evidence on the topic and propose a diagnostic approach for cardiac involvement in EGPA. The prevalence of cardiovascular involvement in patients with EGPA varies greatly among published studies, ranging between 3.1-18.7% for occlusive arterial disease, 5.8-30% for venous thrombosis and 17-92% for heart involvement. Cardiac involvement in EGPA is associated with high mortality even though manifestations are heterogeneous. In principle, every anatomical structure of the heart can be involved, and EGPA-related heart disease may be completely asymptomatic at first. A careful diagnostic work-up for early detection and prompt treatment initiation is therefore required. While cardiac manifestations are more common in anti-neutrophil cytoplasmic antibodies (ANCA)-negative patients, arterial and venous thrombotic events are not linked to ANCA status but correlate closely with disease activity and accumulate at disease onset. Thrombotic events (mainly venous) are considerably more frequent in EGPA than in the general population contributing substantially to morbidity and highlighting the importance of developing specific prevention strategies for patients who are diagnosed with EGPA.

Systemic vasculitis are rare heterogeneous disorders potentially involving any organ and system with a relevant burden of mortality and comorbidity.As in the previous annual reviews of this series, in this review we will provide a critical digest of the most recent literature regarding pathophysiology, clinical manifestations, diagnostic tools and treatment options in small- and large-vessel vasculitis.

Our economy drives on reactive nitrogen (Nr); while Nr emissions to the environment surpass the planetary boundary. Increasingly, it is advocated to recover Nr contained in waste streams and to reuse it ‘directly’ in the agri-food chain. Alternatively, Nr in waste streams may be removed as N2 and refixed via the Haber-Bosch process in an ‘indirect’ reuse loop. As a systematic sustainability analysis of ‘direct’ Nr reuse and its comparison to the ‘indirect’ reuse loop is lacking, this structured review aimed to analyze literature determining the environmental and economic sustainability of Nr recovery technologies. Bibliometric records were queried from 2000 to 2020 using Boolean search strings, and manual text coding. In total, 63 studies were selected for the review. Results suggest that ‘direct’ Nr reuse using Nr recovery technologies is the preferred paradigm as the majority of studies concluded that it is sustainable or that it can be sustainable depending on technological assumptions and other scenario variables. Only 17 studies compared the ‘direct’ with the ‘indirect’ Nr reuse route, therefore a system perspective in Nr recovery sustainability assessments should be more widely adopted. Furthermore, Nr reuse should also be analyzed in the context of a ‘new Nr economy’ that relies on decentralized Nr production from renewable energy. It is also recommended that on-par technology readiness level comparisons should be carried out, making use of technology development and technology learning methodologies. Finally, by-products of Nr recovery are important to be accounted for as they are reducing the environmental burdens through avoided impacts.

The Ricardian model is a widely used approach based on cross-sectional regression analysis to estimate climate change impacts on agricultural productivity. Up until now, researchers have focused on the impacts of gradual changes in temperature and precipitation, even though climate change is known to encompass also changes in the severity and frequency of extreme weather events. This research investigates the impact of heatwaves on European agriculture, additional to the impact of average climate change. Using a dataset of more than 60,000 European farms, the study examines whether adding a measure for heatwaves to the Ricardian model influences its results. We find that heatwaves have a minor impact on agricultural productivity and that this impact is moderated by average temperature. In colder regions, farm productivity increases with the number of heatwave days. For warmer regions, land values decrease with heatwave frequency. Despite the moderating effect, the marginal effect of heatwave frequency, i.e. the percentage change in agricultural land values caused by one more heatwave day per year, is small in comparison to the effect of average temperature increases. Non-marginal effects are found to be relevant, but only in the case of increased heatwave frequency. According to our results, farms are not expected to suffer more from extreme weather than from mean climate change, as was claimed by several previous studies.

Purpose Italian expenditure for vitamin D greatly increased in the last few years, reaching €314 million ($376.8 million) in 2019. In Italy, the main cause of the increase in public spending for vitamin D is the marketing of high-cost medicines. At national and regional levels, some interventions have been performed to reduce expenditure, but spending has continued to increase. The aim of this work is to propose a new saving strategy determined by an analysis of a significant sample of the market. Methods Data on the use of vitamin D formulations, including data for the different active substances that represent its pharmaceutical analogue and composition of groups of equivalence, were extrapolated from the Italian Medicines Agency transparency lists and from the Farmadati database. Data on pharmaceutical expenditure were obtained from the Data Warehouse of Liguria Region; the composition of this expenditure was analyzed in detail, focusing on the characteristics of the pharmaceutical preparations and their cost (price per defined daily dose). Findings Vitamin D expenditure paralleled that of cholecalciferol, the most used active ingredient, which in Liguria increased from €643,352 ($772,022.4) in 2010 to €8,006,574 ($9,607,888.8) in 2019 (increase of 1144%). Spending focused on high-cost formulations, exceeding 90% of total cholecalciferol cost in 2019. We simulated a possible optimization of the expense for cholecalciferol by applying a revised price to all the cholecalciferol consumptions in high-cost products because these formulations do not have an added therapeutic value, finding that the saving would be at least 60%. National data on the detailed expenditure composition for vitamin D are not available, but we found a strong resemblance between total cholecalciferol expenditure time series in Italy and the Liguria Region. Implications The expense of cholecalciferol and consequently the expense of vitamin D could be optimized by modifying the reimbursement of high-cost formulations. At a national level, savings should be proportional to that estimated for the Liguria Region. On the basis of the 2019 data, Italian savings with respect to total cholecalciferol expenditure should be €170.65 million ($204.78 million); per capita cholecalciferol expenditure would shift from €4.66 ($5.59) to €1.84 ($2.21).

We present a solution for layer inspection based on digital imaging and machine learning (ML) suitable for application to in-process monitoring of fused filament fabrication. Top-down images of the layer are captured in-process via a digital camera, decomposed into patches representing specific types of topographic patterns, and processed through a binary classifier, trained to recognize acceptable and out-of-control states in relation to the presence/absence of topographic defects. Classifiers implementing different types of ML technologies (support vector machines on dense image features, convolutional neural networks of different depths, and convolutional autoencoder) are investigated and compared in terms of performance at detecting layer defects. The generalizability of the approach to different part geometries is also discussed. A prototype implementation is illustrated through application to selected test parts. Research achievements as well as open challenges are highlighted.

In this work an approach to investigate measurement uncertainty in coordinate metrology is presented, based on fitting Gaussian random fields to high-density point clouds produced by measurement repeats. The fitted field delivers a depiction of the spatial distribution of random measurement error over a part geometry, and can incorporate local bias information through further measurement or with the use of an external model to obtain a complete, spatial uncertainty map. The statistical model also allows the application of Monte Carlo simulation to investigate how error propagates through the data processing pipeline ultimately affecting the determination of features of size and the verification of conformance to specifications. The proposed approach is validated through application to simulated test cases involving known measurement error, and then applied to a real part, measured with optical and contact technologies. The results indicate the usefulness of the approach to estimate measurement uncertainty and to investigate performance and behaviour of measurement solutions applied to the inspection and verification of industrial parts. The approach paves the way for the implementation of automated measurement systems capable of self-assessment of measurement performance.

ObjectivesIn patients with systemic autoimmune rheumatic disorders (SARDs), vaccination with SARS-CoV-2 mRNA vaccines has been proposed. The aim of this study is to evaluate the immune response elicited by vaccination with mRNA vaccine, testing IgM, IgA and IgG antibodies to SARS-CoV-2 receptor-binding domain (RBD) and measuring neutralising antibodies.MethodsIgG, IgM and IgA anti-RBD antibodies were measured in 101 patients with SARDs. Antibodies inhibiting the interaction between RBD and ACE2 were evaluated. Antibody avidity was tested in a chaotropic ELISA using urea. Twenty-one healthcare workers vaccinated with mRNA vaccine served as control group.ResultsAnti-RBD IgG and IgA were produced after the first dose (69% and 64% of the patients) and after the boost (93% and 83%). Antibodies inhibiting the interaction of RBD with ACE2 were detectable in 40% of the patients after the first dose and 87% after boost, compared with 100% in healthy controls (pConclusionsThese data show that double-dose vaccination induced in patients with SARDs anti-RBD IgG and IgA antibodies in amounts not significantly different from controls, and, most interestingly, characterised by high avidity and endowed with neutralising activity.

Environmental effects and natural resources depletion associated with agriculture production affect the agriculture response to climate change. Traditional cross-sectional climate response models ignore this requirement. This research estimates the impact of climate on European agriculture using a continental scale Ricardian analysis. We correct farm income by accounting for resources (energy, fertilisers, pesticides, and water) use intensity and calculate the sustainable value for a sample of 9497 specialized field crop farms. Compared with the traditional Ricardian method, the marginal effects of temperature remain positive (but less positive) in Northern countries, while it leads to less damages in Southern countries when net revenue and farms' sustainable values are used as dependent variables. Accounting for the environmental effects and depletion of natural capital improves the ability of the Ricardian method to estimate agriculture climate response functions in the long run.

A simulation model is presented in this work, capable to predict temperature and velocity of the extrudate as well as the compressive force acting on the filament in a fused filament fabrication (FFF) machine operating with a Bowden extruder. The simulation model is developed for future incorporation into a larger on-machine apparatus designed for process monitoring and adaptive control and destined to the fabrication of high value-added parts. The simulation model captures mechanical aspects of the extruder (including slippage at the drive gear, filament compression into the hot end), as well as temperature and pressure changes within the extrusion chamber, where the filament is liquefied and transformed into the extrudate. The model is implemented so that it can react to changes in the part program as it is being executed, offering predictions on the extrusion process that can be compared with sensor measurements. The simulation model is implemented in prototype form and calibrated on a multi-sensor FFF machine working with a PLA filament within a range of predefined operating conditions (heating temperatures and print feed rates). Load cells, high-speed optical imaging and infra-red thermal imaging of the extrudate mounted on the FFF machine are used to verify the predictions of the simulation model.

A solution for in-process monitoring of part warpage in fused filament fabrication is presented, based on real-time measurement of the upwards-oriented repulsive force acting on the extruder during deposition. The force signal is processed with the help of simulation software implementing a mechanical model which incorporates the stiffness of the machine and the compliance of the part as it is fabricated. The model acts as a digital twin, where compliance is constantly updated while part geometry evolves through the creation of new layers. The incorporation of stiffness/compliance simulation helps the monitoring system in the interpretation of the force signal, leading to a better isolation of warpage events. The proposed monitoring solution is validated through the implementation of a sensorised hardware prototype and the execution of experiments involving the fabrication of test parts. The results indicate warpage detection performance with 92.9% accuracy, 91.5% specificity and 95.7% sensitivity, and demonstrate that it is possible to use a relatively affordable, quick and non-destructive sensing solution (that is, measuring the repulsive force acting on the extruder) as an effective means to detect part warpage. However, the developed solution highlights the challenge of developing accurate digital twins for the prediction of part compliance, essential for the correct interpretation of the force signal.

In the past decade, to improve crop production and productivity, Ethiopia has embarked on an ambitious irrigation farming expansion program and has introduced new large- and small-scale irrigation initiatives. However, in Ethiopia, poverty remains a challenge, and crop productivity per unit area of land is very low. Literature on the technical efficiency (TE) of large-scale and small-scale irrigation user farmers as compared to the non-user farmers in Ethiopia is also limited. Investigating smallholder farmers’ technical efficiency (TE) level and its principal determinants is very important to increase crop production and productivity and to improve smallholder farmers’ livelihood and food security. Using 1026 household-level cross-section data, this study adopts a technology flexible stochastic frontier approach to examine agricultural TE of large-scale irrigation users, small-scale irrigation users and non-user farmers in Ethiopia. The results indicate that, due to poor extension services and old-style agronomic practices, the mean TE of farmers is very low (44.33%), implying that there is a wider room for increasing crop production in the study areas through increasing the TE of smallholder farmers without additional investment in novel agricultural technologies. Results also show that large-scale irrigation user farmers (21.05%) are less technically efficient than small-scale irrigation user farmers (60.29%). However, improving irrigation infrastructure shifts the frontier up and has a positive impact on smallholder farmers’ output.

Purpose This paper aims to illustrate the integration of multiple heterogeneous sensors into a fused filament fabrication (FFF) system and the implementation of multi-sensor data fusion technologies to support the development of a “smart” machine capable of monitoring the manufacturing process and part quality as it is being built. Design/methodology/approach Starting from off-the-shelf FFF components, the paper discusses the issues related to how the machine architecture and the FFF process itself must be redesigned to accommodate heterogeneous sensors and how data from such sensors can be integrated. The usefulness of the approach is discussed through illustration of detectable, example defects. Findings Through aggregation of heterogeneous in-process data, a smart FFF system developed upon the architectural choices discussed in this work has the potential to recognise a number of process-related issues leading to defective parts. Research limitations/implications Although the implementation is specific to a type of FFF hardware and type of processed material, the conclusions are of general validity for material extrusion processes of polymers. Practical implications Effective in-process sensing enables timely detection of process or part quality issues, thus allowing for early process termination or application of corrective actions, leading to significant savings for high value-added parts. Originality/value While most current literature on FFF process monitoring has focused on monitoring selected process variables, in this work a wider perspective is gained by aggregation of heterogeneous sensors, with particular focus on achieving co-localisation in space and time of the sensor data acquired within the same fabrication process. This allows for the detection of issues that no sensor alone could reliably detect.

Cost simulations provide a strong tool to render the production of microalgae economically viable. This study evaluated the unexplored effect of harvesting time and the corresponding microalgal biomass composition on the overall production cost, under both continuous light and light/dark regime using techno-economic analysis (TEA). At the same time, the TEA gives evidence that a novel product “proteinaceous salt” from Dunaliella microalgae production is a promising high-value product for commercialization with profitability. The optimum production scenario is to employ natural light/dark regime and harvest microalgal biomass around late exponential phase, obtaining the minimum production cost of 11 €/kg and a profitable minimum selling price (MSP) of 14.4 €/kg for the “proteinaceous salt”. For further optimization of the production, increasing microalgal biomass concentration is the most effective way to reduce the total production cost and increase the profits of microalgae products.

The relationship between irrigation water availability and crop revenue is multifaceted. However, most of the previous studies focused only on the direct effect of irrigation water on crop revenue or considered that the indirect effect passes only through the farmers’ improved farm inputs usage. Nevertheless, unlike previous studies, this study argues that a one-sided argument that irrigation water directly causes high crop revenue or indirectly affects crop revenue only via the farmers’ improved farm inputs usage is incomplete, as irrigation water not only directly contributes to crop revenue but also indirectly conduces to crop revenue via both the type of crops produced and the farmers’ improved farm inputs usage. Considering the previous studies’ limitations, this study investigates pathways how small-scale irrigation water affects crop revenue and identifies challenges of small-scale irrigation farming in Fogera district, Ethiopia. Results endorsed that irrigation water has both direct and indirect effects on crop revenue. The indirect effect is 67 percent of the total effect and it is mediated by both the type of crops produced and farmers’ improved farm inputs usage. The result also indicated that irrigation user farmers have a higher income, more livestock assets and resources and better food, housing, and cloths than the non-users. Moreover, challenges related to agricultural output and input market were identified as the most severe problem followed by crop disease. The findings of our study suggest that to utilize the benefits of irrigation water properly, it is crucial to encourage farmers to use more improved farm inputs and to shift from staple to cash crop production. Moreover, farmers are frequently exposed to cheating by illegal brokers in the output market, therefore it is also important to increase farmers’ accessibility to output and input markets, the quality of improved farm inputs, and the bargaining power of farmers with market information.

Photomodelling is an innovative, economical and fast technique based on the same principles of photogrammetry, which leads to the creation of 3-dimensional models starting from the simple acquisition of photographs. The aim of this paper is to define performances and metrological characteristics of this new technique and to understand the full potential offered by point cloud processing software. The analytical comparison considers a structured light 3D scanning system Creaform Go Scan 50 with metrological certification as a reference, in order to verify the accuracy and precision of photomodelling, using a modified function of the ICP algorithm and spatial, volumetric and superficial comparison criteria.

The quality of dental arch impression has a substantial role in the precision of the intervention. It is traditionally acquired with resins that solidify when in contact with the air. Compared to that method, digital impression gives great advantages and, together with three-dimensional (3D) digitization devices, allows a simplification of the digital impression process. The growing adoption of such systems by a large number of dental clinics determines the need for an in-depth evaluation of the accuracy and the precision of the different systems. The aim of this work is to define a methodology for the evaluation of the accuracy and precision of 3D intraoral and desktop scanning systems, by using volumetric and linear methods. The replica of a tooth was realized with zirconium, afterward, high-accuracy point clouds of the master model were acquired by a coordinate measurement machine (CMM). In this way, the dimensions of the replica were accurately known. An intraoral scanner (I) and three desktops (D1, D2, D3) were then used to scan the replica. The geometry resulting from the CMM was compared with the ones derived from the scanners, using two different commercial programs (Geomagic and 3-Matic) and a custom-developed algorithm (MATLAB). Geomagic showed the mean values to be in a range from 0.0286 mm (D1) to 0.1654 mm (I), while 3-Matic showed mean values from &minus, 0.0396 mm (D1) to 0.1303 mm (I). MATLAB results ranged from 0.00014 mm (D1) to 0.00049 mm (D2). The probability distributions of the volumetric error of the measurements obtained with the different scanners allow a direct comparison of their performances. For the results given by our study, the volumetric approach that we adopted appears to be an excellent system of analysis.

Wastewater reuse provides valuable solutions to solve the societal challenges of decreasing availability and limiting access to secure water resources. The present study quantifies the environmental performance of nectarine orchards irrigation using treated municipal wastewater (TMW) and surface water using a unique dataset based on field experimental data. Climate change, toxicity (for human and freshwater), eutrophication (marine and freshwater) and acidification impacts were analysed using the impact assessment method suggested by the International Reference Life Cycle Data System (ILCD). The water footprint associated to the life cycles of each system has been estimated using the Available WAter REmaining (AWARE) method. Monte Carlo simulation was used to assess data uncertainty. The irrigation of nectarine orchards using TMW performs better than the irrigation using surface water for eutrophication impact categories. Compared with surface water resources, the potential impacts of TMW reuse in agriculture on climate change and toxicity are affected by the wastewater treatment phase (WWT). Only eutrophication and acidification burdens are generated by in-field substitution of surface water with TMW. Considering human and ecosystem water demand, the irrigation with TMW increases water consumption of 19.12 m3 per kg of nectarine produced. Whereas, it shows a positive contribution to water stress (−0.19 m3) if only human water demand is considered. This study provides important results that allow for a better understanding of the potential environmental consequences of TMW reuse in agriculture. It suggests that embracing the type of WWTs, the replacement of fertilizers, the effects on water scarcity and ecosystem quality might be useful to redefine water reuse regulations and increase public acceptance for the reuse of TMW in agriculture. Moreover, this study reveals the need for developing consensus and standardized guidance for life cycle analysis of water reuse applications.

Surface topography modifications due to wear or other factors are usually investigated by visual and microscopic inspection, and – when quantitative assessment is required – through the computation of surface texture parameters. However, the current state-of-the-art areal topography measuring instruments produce detailed, areal reconstructions of surface topography which, in principle, may allow accurate comparison of the individual topographic formations before and after the modification event. The main obstacle to such an approach is registration, i.e. being able to accurately relocate the two topography datasets (measured before and after modification) in the same coordinate system. The challenge is related to the measurements being performed in independent coordinate systems, and on a surface which, having undergone modifications, may not feature easily-identifiable landmarks suitable for alignment. In this work, an algorithmic registration solution is proposed, based on the automated identification and alignment of matching topographic features. A shape descriptor (adapted from the scale invariant feature transform) is used to identify landmarks. Pairs of matching landmarks are identified by similarity of shape descriptor values. Registration is implemented by resolving the absolute orientation problem to align matched landmarks. The registration method is validated and discussed through application to simulated and real topographies selected as test cases.

Despite great advances, experiments concerning the response of ecosystems to climate change still face considerable challenges, including the high complexity of climate change in terms of environmental variables, constraints in the number and amplitude of climate treatment levels, and the limited scope of responses and interactions covered. Drawing on the expertise of researchers from a variety of disciplines, this Perspective outlines how computational and technological advances can help in designing experiments that can contribute to overcoming these challenges, and also outlines a first application of such an experimental design. We thank the Flemish government (through Hercules Stichting big infrastructure and the Fund for Scientific Research Flanders project G0H4117N) and LSM (Limburg Sterk Merk, project 271) for providing funds to build the UHasselt Ecotron; Hasselt University for both funding and policy support (project BOF12BR01 and Methusalem project 08M03VGRJ); and the ecotron research committee for comments on the experimental design. We also thank Regional Landscape Kempen and Maasland for its collaboration and support. N.W., S.L., A.N. and I.V. are funded by Research Foundation-Flanders (FWO).

Production of struvite (MgNH4PO4·6H2O) from waste streams is increasingly implemented to recover phosphorus (P), which is listed as a critical raw material in the European Union (EU). To facilitate EU-wide trade of P-containing secondary raw materials such as struvite, the EU issued a revised fertilizer regulation in 2019. A comprehensive overview of the supply of struvite and its quality is presently missing. This study aimed: i) to determine the current EU struvite production volumes, ii) to evaluate all legislated physicochemical characteristics and pathogen content of European struvite against newly set regulatory limits, and iii) to compare not-regulated struvite characteristics. It is estimated that in 2020, between 990 and 1250 ton P are recovered as struvite in the EU. Struvite from 24 European production plants, accounting for 30% of the 80 struvite installations worldwide was sampled. Three samples failed the physicochemical legal limits; one had a P content of 90% DW. All samples showed a heavy metal content below the legal limits. Microbiological analyses indicated that struvite may exceed certain legal limits. Differences in morphology and particle size distribution were observed for struvite sourced from digestate (rod shaped; transparent; 82 mass% 2 mm). A uniform soil-plant P-availability pattern of 3.5–6.5 mg P/L soil/d over a 28 days sampling period was observed. No differences for plant biomass yield were observed. In conclusion, the results highlight the suitability of most struvite to enter the EU fertilizer market.

On-machine, optical imaging of the layer is bound to play an important role in the development of future in-process monitoring solutions for fused filament fabrication. In addition to detecting layer internal defects, layer imaging offers the possibility to retrieve the outer contours of a layer and thus three-dimensionally reconstruct the side walls of a part by vertically stacking contours belonging to sequential layers. The current open challenges for layer imaging relate to the compromise between imaging resolution and extension of covered area, and to the complexity of image interpretation, exacerbated by optically difficult materials, complex filament patterns and non-uniform environmental conditions. In this work we propose an original optical imaging solution for layer contour identification and stacking. Our solution overcomes the current challenges related to range/resolution and image interpretation by making use of digital twins, i.e. geometric simulations of the layer, based on the current manufacturing process parameters. Thanks to the use of digital twins, our vision system is able to: use high-resolution imaging only where it is needed, by following the predicted positions of the layer contours while acquiring a sequence of images; algorithmically interpret each image by concentrating only on interesting regions as suggested by the simulated prediction, thus reducing the risk for false positives; and finally monitor contour quality by performing a more accurate comparison between the measurement and a reference contour obtained by a simulation which replicates the actual fingerprint of the process, as opposed to simply relying on the edge obtained by slicing the CAD model. The in-process monitoring capability of the computer vision system is demonstrated through the fabrication of acceptable and defected instances of a test geometry. Fabrication issues are effectively and efficiently detected by the system and alarms are triggered that can be used to command early termination of the build process or to initiate corrective actions.

Several indicators and methods are already applied for sustainability assessment in agriculture. The links between sustainability indicators, agricultural management and policies are not well explained. The aim of this study is to combine biophysical and monetary sustainability assessment tools to support agriculture policy decision-making. Three methodological steps are considered: i) the environmental impacts of farms are assessed using terrestrial acidification, freshwater eutrophication, soil and freshwater ecotoxicity as well as natural land transformation; ii) the most relevant indicators of agriculture damages on ecosystems quality are aggregated into an index; iii) the farms' index scores are integrated with farm assets, land and labor, into the Sustainable Value approach (SVA), as indicator of natural resources used by farms. As a case study, the methodology was applied to arable farms with and without animal husbandry of the "Alta Murgia" National Park. The crop farms, in our sample, have a higher sustainable value using their economic and environmental resources. Mixed farms need to improve their resources use efficiency. Although crop farms have lower land-use efficiency compared to mixed farms, our results suggest supporting, by means of policy options, the specialized crop farms that, on average, perform better in terms of ecosystems quality preservation. Finally, we find that Life Cycle Assessment (LCA) to soundly measure the environmental impacts clearly enriches the SVA. (C) 2016 Elsevier Ltd. All rights reserved.

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Damping of vibrating systems converts the vibrational energy into other forms, such as heat and sound radiation. Heating of the material is often assumed to be one of the biggest drains of energy; however, it is very hard to experimentally identify how much of the damped energy is converted to heat. This manuscript introduces the damping heat coefficient and via high-speed cameras (optical and thermal) identifies that, for the selected material, approximatelly 30% of the damped energy is converted to heat.

This study evaluates the environmental profile of a real biomass-based hydrogen production small-scale (1 MWth) system composed of catalytic candle indirectly heated steam gasifier coupled with zinc oxide (ZnO) guard bed, water gas shift (WGS) and pressure swing absorber (PSA) reactors. Environmental performance from cradle-to-gate was investigated by life cycle assessment (LCA) methodology. Biomass production shows high influence over all impact categories. In the syngas production process, the main impacts observed are global warming potential (GWP) and acidification potential (AP). Flue gas emission from gasifier burner has the largest proportion of total GWP. The residual off gas use in internal combustion engine (ICE) leads to important environmental savings for all categories. Hydrogen renewability score is computed as 90% due to over 100% decline in non-renewable energy demand. Sensitivity analysis shows that increase in hydrogen production efficiency does not necessarily result in decrease in environmental impacts. In addition, economic allocation of environmental charges increases all impact categories, especially AP and photochemical oxidation (POFP). This research has been supported by the following projects: the Italian national Ricerca di Sistema research project HBF2.0 funded by Italian Ministry for Economic Developments and European project UNIfHY (UNIque gasifier for HYdrogen production) funded by FP7 European Commission Programme. This research has been supported by the following projects: HBF2.0 funded by Italian Ministry of Economic Development within the Italian National System Research, in the Programme called Ricerca di Sistema, and the European project UNIfHY (UNIque gasifier for HYdrogen production) funded by FP7 EuropeanCommissionProgramme. Within these projects we thank especially Andrea Di Carlo of University of L'Aquila, for the plant flowsheet and part of the plant data and Donatella Barisano of ENEA for the data and the picture of the UNIFHY 1000 kWth plant.

Agricultural adaptation to climate change is indispensable. Unfortunately, most climate response modeling methods accounting for adaptation are based on economic modelling that assumes simple farm profit-maximization and autonomous farm adaptation. This makes adaptation look like something unconditional , explaining why agricultural policy down-sized the attention for adaptation. This is incorrect as adaptation is facing numerous barriers such as low levels of adaptive capacity. This paper therefore captures and quantifies the impact of adaptive capacity explicitly in economic cross-sectional models, showing that those methods can be more policy-oriented. It shows that higher levels of adaptive capacity lead to more positive climate responses. Acknowledgement : This paper was supported by the Horizon 2020 project SUFISA (Grant Agreement No. 635577).

Photomodelling is a new and fast solution for 3D modelling, based on the same principles of photogrammetry. The comparison between photomodelling and the metrological technique of structured light 3D scanning, provided by the Creaform Go Scan 50 with metrological certification, is the aim of this paper, defining performances and verifying the potential of this innovative, simple and economical technique.

Organic municipal solid waste (OMSW) as a feedstock for energy recovery and material recycling offers the potential to reduce environmental impacts from energy production while displacing emission intensive waste management strategies such as landfills. This paper quantifies the environmental impact of anaerobic digestion of local, residual biomass. A life-cycle assessment was jointly performed for two scenarios for the biological treatment of local organic municipal solid waste and pig manure in the Netherlands. Scenario 1 was a separate treatment using anaerobic digestion, and Scenario 2 was a bio-refinery system that integrates anaerobic digestion of organic, municipal solid waste, and co digestion of pig manure and other organic co-substrates \. For both scenarios, electricity and heat are generated using a combined heat and power engine. The bio-refinery system (Scenario 2) contribution to climate change resulted in 0.16 Mt CO2 eq./yr, which is lower than the 0.17 Mt CO2 eq./yr of Scenario 1. Both scenarios are found to be beneficial with regard to resource depletion and human toxicity. The integration of organic waste and manure anaerobic digestion has no effect on acidification and terrestrial eutrophication impact categories, resulting in 43.59 AE eq. and 86.33 AE eq. for Scenario 1 and 43.58 AE eq. and 86.30 AE eq. for Scenario 2. Moreover, Scenario 2 yields 18% lower emissions than those from natural gas derived electricity in the Netherlands. The biorefinery system represents an opportunity to improve organic waste-management strategies, at the same time as reducing the environmental impact from energy production and the costs for surplus manure disposal by producing high-quality commodities that can be traded on the market. (C) 2017 Elsevier Ltd. All rights reserved.