Productivity Enhancement in Directed Energy Deposition: The Oscillating Scanning Strategy Approach

Directed Energy Deposition (DED) is an additive manufacturing process that enables the production of large metal components by melting the feedstock material while being deposited. An improvement of the production speed of this process would further increase its applicability in many industrial fields. The DED building rate is strictly related to the building parameters adopted, in particular to the laser spot diameter, which also affects the build accuracy and the surface quality of the components. The possibility of using a variable laser spot would result in a significant increase in the production rate in bulky zones, while also providing a good surface quality where needed. In the present work, an oscillating scanning strategy was used to create a large apparent laser spot (+ 170% of the nominal value) to produce 316L stainless steel samples via DED. The optimisation of the DED parameters with the oscillating strategy was performed using the single scan tracks (SSTs) approach. The morphologies of the SSTs obtained with different process parameters were assessed and the geometrical features related to the melt pools were analysed in order to select the most suitable X and Z displacements for the production of the cubic samples. The analyses of the cubes revealed that, if the correct overlap among nearby scans is selected, it is possible to obtain dense samples with all the oscillating diameters tested. Finally, comparing the building rate and powder efficiency values confirmed that this method can accelerate the building process and improve its overall performance

Iodine uptake and distribution in horticultural and fruit tree species.

Iodine is an essential microelement for humans and iodine deficiency disorder (IDD) is one of the most widespread nutrient-deficiency diseases in the world. Iodine biofortification of plants provides an attractive opportunity to increase iodine intake in humans and to prevent and control IDD. This study was conducted to investigate the iodine uptake and accumulation in edible portion of two fruit trees: plum and nectarine, and two horticultural crops: tomato and potato. Two type of iodine treatments (soil and foliar spray application), and, for fresh market tomato, two production systems (open field and greenhouse hydroponic culture) were tested. The distribution of iodine in potato stem and leaves, and in plum tree fruits, leaves, and branches was investigated. Iodine content of potato tubers after postharvest storage and processing (cooking), and iodine content of nectarine fruits after postharvest storage and processing (peeling) were also determined. Differences in iodine accumulation were observed among the four crops, between applications, and between production systems. In open field, the maximum iodine content ranged from 9.5 and 14.3 \u3bcg 100 g 121 for plum and nectarine fruit, to 89.4 and 144.0 \u3bcg 100 g 121 for potato tuber and tomato fruit, respectively. These results showed that nectarine and plum tree accumulated significantly lower amounts of iodine in their edible tissues, in comparison with potato and tomato. The experiments also indicated hydroponic culture as the most efficient system for iodine uptake in tomato, since its fresh fruits accumulated up to 2423 \u3bcg 100 g 121 of iodine. Iodine was stored mainly in the leaves, in all species investigated. Only a small portion of iodine was moved to plum tree branches and fruits, and to potato stems and tubers. No differences in iodine content after fruit peeling was observed. A significant increase in iodine content of potato was observed after baking, whereas a significant decrease was observed after boiling. We concluded that iodine biofortified fresh market tomato salad, both from field and hydroponics cultivation, and baked potatoes can be considered as potential functional foods for IDD prevention

Early Prediction of Response to Tyrosine Kinase Inhibitors by Quantification of EGFR Mutations in Plasma of NSCLC Patients.

IntroductionThe potential to accurately quantify epidermal growth factor receptor (EGFR) mutations in plasma from non–small-cell lung cancer patients would enable more rapid and more frequent analyses to assess disease status; however, the utility of such analyses for clinical purposes has only recently started to explore.MethodsPlasma samples were obtained from 69 patients with EGFR-mutated tumors and 21 negative control cases. EGFR mutations in plasma were analyzed by a standardized allele-specific polymerase chain reaction (PCR) test and ultra-deep next-generation sequencing (NGS). A semiquantitative index (SQI) was derived from dilutions of known EGFR mutation copy numbers. Clinical responses were evaluated by Response Evaluation Criteria in Solid Tumors 1.1 criteria and expressed as percent tumor shrinkage.ResultsThe sensitivity and specificity of the PCR test and NGS assay in plasma versus tissue were 72% versus 100% and 74% versus 100%, respectively. Quantitative indices by the PCR test and NGS were significantly correlated (p < 0.001). EGFR testing at baseline and serially at 4 to 60 days during tyrosine kinase inhibitor therapy revealed a progressive decrease in SQI, starting from day 4, in 95% of cases. The rate of SQI decrease correlated with percent tumor shrinkage at 2 months (p < 0.0001); at 14 days, it was more than 50% in 70% of patients (rapid responders). In two patients with slow response, an early increase in the circulating levels of the T790M mutation was observed. No early T790M mutations were seen in plasma samples of rapid responders.ConclusionsQuantification of EGFR mutations from plasma with a standardized PCR test is feasible. To our knowledge, this is the first study showing a strong correlation between the EGFR SQI in the first days of treatment and clinical response with relevant implications for patient management

Haemato-oncology and burnout: an Italian survey

This cross-sectional survey aimed to evaluate the prevalence of burnout and estimated psychiatric disorders among haemato-oncology healthcare professionals in Italy. The aspects of work that respondents perceive as stressful and satisfying have also been examined. The assessments were made using the Maslach Burnout Inventory (MBI), General Health Questionnaire and a study-specific questionnaire. Logistic regression models were applied to show associations between different sources of work-related stress and burnout. Three hundred and eighty-seven out of 440 (87.95%) returned their questionnaires. The scores on MBI subscales indicate a high level of emotional exhaustion in 32.2% of the physicians and 31.9% of the nurses; a high level of Depersonalisation in 29.8 and 23.6%, respectively; and a low level of personal accomplishment in 12.4 and 15.3% respectively. The estimated prevalence of psychiatric disorders was 36.4% in physicians and 28.8% in nurses. Statistical analysis confirmed age, sex, personal dissatisfaction, physical tiredness and working with demanding patients to be associated with burnout. In conclusion, haemato-oncology healthcare professionals report a level of burnout and estimated psychiatric morbidity comparable to other oncological areas. Knowledge of the mechanisms of burnout and preventing and dealing with them is therefore a fundamental requirement for the improvement of quality in health services and job satisfaction

A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels

Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e., widgets or swipeable carousels, each built with a specific criterion (e.g., most recent, TV series, etc.). Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. In this setting, the overall quality of the recommendations of a new algorithm cannot be assessed by measuring solely its individual recommendation quality. Rather, it should be evaluated in a context where other recommendation lists are already available, to account for how they complement each other. This is not considered by traditional offline evaluation protocols. Hence, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels. We report experiments on publicly available datasets on the movie domain and notice that under a carousel setting the ranking of the algorithms change. In particular, when a SLIM carousel is available, matrix factorization models tend to be preferred, while item-based models are penalized. We also propose to extend ranking metrics to the two-dimensional carousel layout in order to account for a known position bias, i.e., users will not explore the lists sequentially, but rather concentrate on the top-left corner of the screen

Measuring the ranking quality of recommendations in a two-dimensional carousel setting

Movie-on-demand and music streaming services usually provide the user with multiple recommendation lists, i.e., carousels, in a two-dimensional user interface, each generated according to different criteria (e.g., TV series, popular artists, etc.). In this two-dimensional setting it is not appropriate to use traditional ranking metrics designed for a single ranking list. It is well known that users do not explore a two-dimensional interface one row at a time, but rather focus their attention in a triangular area at the top-left corner. Furthermore, it is frequent for user interfaces to hide some items or lists due to space constraints, which can be shown by performing certain actions (i.e., click, swipe). In this paper we extend the widely used NDCG to a two-dimensional recommendation setting with a formulation that allows to account both the two-dimensional user exploration behaviour and interface-specific design. We also compare the proposed extension against single-list NDCG highlighting that they can lead to a different choice of the optimal algorithm in offline evaluation

Environmental, Social and Economic Resilience in Multi-Residential Buildings: Assessing SBToolCZ Rating System

Sustainable Building Tool for the Czech Republic (SBToolCZ) is the Czech national green building rating system that encourages the design of sustainable buildings by incentivising reductions in energy, water, and building materials consumption, as well as improving occupant health and community connections. In addition to reducing the overall environmental impacts, certified green buildings must also be resilient enough to withstand external stressors, most frequently the symptoms of climatic change that may arise throughout the building's lifetime. Therefore, a resilient building should be capable of adapting and remaining functional under the pressure of more frequent and severe challenges. The purpose of this study is to examine where the SBToolCZ certification system has inherent overlaps with the topics of resilience, considering the environmental, social and economic factors relevant to Central European contexts. This is accomplished by comparing the criteria of this certification system with the most accepted principles of resilient design that have emerged from the international resilience rating systems or guidelines. A number of synergistic opportunities, as well as improvements for better integrating resilient design into the SBToolCZ framework and, therefore, into green construction, are discussed to implement existing criteria or propose supplementary ones. A key component of implementing resilience for multi-residential buildings is the SBToolCZ Site category, which is key to addressing the unique regional needs of each project and should be integrated with resilience-enhancement indicators. Finally, climate projections should be used instead of historical climate data at an early design stage to improve the resilience of the building

Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels

It is common for video-on-demand and music streaming services to adopt a user interface composed of several recommendation lists, i.e., widgets or swipeable carousels, each generated according to a specific criterion or algorithm (e.g., most recent, top popular, recommended for you, editors' choice, etc.). Selecting the appropriate combination of carousel has significant impact on user satisfaction. A crucial aspect of this user interface is that to measure the relevance a new carousel for the user it is not sufficient to account solely for its individual quality. Instead, it should be considered that other carousels will already be present in the interface. This is not considered by traditional evaluation protocols for recommenders systems, in which each carousel is evaluated in isolation, regardless of (i) which other carousels are displayed to the user and (ii) the relative position of the carousel with respect to other carousels. Hence, we propose a two-dimensional evaluation protocol for a carousel setting that will measure the quality of a recommendation carousel based on how much it improves upon the quality of an already available set of carousels. Our evaluation protocol takes into account also the position bias, i.e., users do not explore the carousels sequentially, but rather concentrate on the top-left corner of the screen. We report experiments on the movie domain and notice that under a carousel setting the definition of which criteria has to be preferred to generate a list of recommended items changes with respect to what is commonly understood

Evaluating Recommendations in a User Interface With Multiple Carousels

Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e., widgets or swipeable carousels, each built with a specific criteria (e.g., most recent, TV series, etc.). Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. In this setting the overall quality of the recommendations of a new algorithm cannot be assessed by measuring solely its individual recommendation quality. Rather, it should be evaluated in a context where other recommendation lists are already available, to account for how they complement each other. The traditional offline evaluation protocol however does not take this into account. To address this limitation, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels. Our results indicate that under a carousel setting the ranking of the algorithms changes sometimes significantly. This work is an extended abstract of [1]