Liturgia y nueva evangelización.

La notable fuerza evangelizadora de la liturgia se aprecia tanto en el anuncio del primer kerygma, como después en el momento de la educación en la fe ya poseída. La pastoral litúrgica se siente vigorosamente interpelada por el funcionamiento efectivo de los signos propios de la función litúrgica de modo que adquieran su plena eficacia con vistas al anuncio y a la comunión. Una liturgia a la vez seria, simple y hermosa, en la riqueza de sus diversos códigos de comunicación, transmite el misterio al mismo tiempo que sigue siendo comprensible, capaz de narrar la alianza perenne de Dios con los hombres

Investigation on the effect of the gas-to-metal ratio on powder properties and PBF-LB/M processability

Metal powders for the laser powder bed fusion process are usually produced via gas atomization. However, due to the tight particle size distribution required for this application, the yield of the atomization process is low, resulting in a high-powder cost. In this work, atomization process parameters were varied to increase the gas-to-metal ratio to reduce the particle size distribution produced, and therefore increase the yield of the process. As a result, eight powders were produced starting from scrap AISI 136L material at different gas-to-metal ratio values, and the atomization process yield was successfully increased by 50%. First, the eight powders were characterized in terms of powder size, shape distributions, and flowability. Later, all powders were used to produce tensile specimens. The powders produced at higher yield exhibited a larger number of fine particles but slightly lower circularity, particularly in the coarse fraction. Furthermore, powders produced at a high gas-to-metal ratio demonstrated enhanced flowing properties and higher packing density. Consequently, these powders exhibited superior tensile performance, with ultimate tensile strength (UTS) ranging from 651 to 673 MPa and elongation values between 63 and 66%

Ocular Refraction at Birth and Its Development During the First Year of Life in a Large Cohort of Babies in a Single Center in Northern Italy

The purpose of this study was to investigate refraction at birth and during the first year of life in a large cohort of babies born in a single center in Northern Italy. We also aimed to analyze refractive errors in relation to the gestational age at birth. An observational ophthalmological assessment was performed within 24 h of birth on 12,427 newborns. Refraction was examined using streak retinoscopy after the administration of tropicamide (1%). Values in the range of between +0.50 ≤ D ≤ +4.00 were defined as physiological refraction at birth. Newborns with refraction values outside of the physiological range were followed up during the first year of life. Comparative analyses were conducted in a subgroup of babies with known gestational ages. The following distribution of refraction at birth was recorded: 88.03% of the babies had physiological refraction, 5.03% had moderate hyperopia, 2.14% had severe hyperopia, 3.4%, had emmetropia, 0.45%, had myopia, 0.94% had astigmatism, and 0.01% had anisometropia. By the end of the first year of life, we observed reductions in hyperopia and astigmatism, and stabilization of myopia. Preterm babies had a four-fold higher risk of congenital myopia and a three-fold higher risk of congenital emmetropia as compared to term babies. Refraction profiles obtained at birth changed during the first year of life, leading to a normalization of the refraction values. Gestational age at birth affected the incidence of refractive errors and amblyopia

Improvement of surface flatness in high precision milling

The use of high precision micro components has increased in various industrial fields in recent years. Repeatable techniques are needed to face very tight tolerances and make micro fabrication processes industrially feasible against current micro machining limitation. Improving surface flatness in high precision milling is the main target of the present research. Critical issues such as machining strategy, spindle thermal transient management and tool wear compensation were considered for machining operations on a representative part

A techno-economic approach for decision-making in metal additive manufacturing: metal extrusion versus single and multiple laser powder bed fusion

This work presents a decision-making methodology that allows the merging of quantitative and qualitative decision variables for selecting the optimal metal Additive Manufacturing (AM) technology. The approach is applied on two competing technologies in the field of metal AM industry, i.e., the metal extrusion AM process (metal FFF) and the Laser Powder Bed Fusion process (LPBF) with single and multiple lasers, which represent the benchmark solution currently on the market. A comprehensive techno-economical comparison is presented where the two processes are analysed in terms of process capabilities (quality, easiness of use, setup time, range of possible materials, etc.) and costs, considering two different production scenarios and different parts’ geometries. In the first scenario, the AM system is assumed to be dedicated to one single part production while in this second scenario, the AM system is assumed to be saturated, as devoted to producing a wide mix of part types. For each scenario, two different part types made of 17–4 PH stainless steel are considered as a reference to investigate the effect of shape complexity, part size and production times to select the best technology when metal FFF and LPBF must be considered. The first part type refers to an extrusion die, to represent typical shapes of interest in the tooling industry, while the second part type is an impeller which can be used in many different industrial sectors, ranging from oil and gas to aerospace. In order to include quantitative and qualitative criteria, a decision-making model based on Analytic Hierarchy Process (AHP) is proposed as the enabler tool for decision making. The proposed approach allows to determine the most effective solution depending on the different production configurations and part types and can be used as a guideline and extended to include other technologies in the field of metal AM. On the other side, the critical discussion of the criteria selected, and the results achieved allow to highlight the pros and cons of the competing technologies, thus defining the existing limits to define directions for future research

Analysis of lexical semantic changes in corpora with the diachronic engine

With the growing availability of digitized diachronic corpora, the need for tools capable of taking into account the diachronic component of corpora becomes ever more pressing. Recent works on diachronic embeddings show that computational approaches to the diachronic analysis of language seem to be promising, but they are not user friendly for people without a technical background. This paper presents the Diachronic Engine, a system for the diachronic analysis of corpora lexical features. Diachronic Engine computes word frequency, concordances and collocations taking into account the temporal dimension. It is also able to compute temporal word embeddings and time-series that can be exploited for lexical semantic change detection

A study of Machine Learning models for Clinical Coding of Medical Reports at CodiEsp 2020

The task of identifying one or more diseases associated with a patient’s clinical condition is often very complex, even for doctors and specialists. This process is usually time-consuming and has to take into account different aspects of what has occurred, including symptoms elicited and previous healthcare situations. The medical diagnosis is often provided to patients in the form of written paper without any correlation with a national or international standard. Even if the WHO (World Health Organization) released the ICD10 international glossary of diseases, almost no doctor has enough time to manually associate the patient’s clinical history with international codes. The CodiEsp task at CLEF 2020 addressed this issue by proposing the development of an automatic system to deal with this task. Our solution investigated different machine learning strategies in order to identify an approach to face that challenge. The main outcomes of the experiments showed that a strategy based on BERT for pre-filtering and one based on BiLSTMCNN-SelfAttention for classification provide valuable results. We carried out several experiments on a subset of the training set for tuning the final model submitted to the challenge. In particular, we analyzed the impact of the algorithm, the input encoding strategy, and the thresholds for multi-label classification. A set of experiments has been carried out also during a post hoc analysis. The experiments confirmed that the strategy submitted to the CodiEsp task is the best performing one among those evaluated, and it allowed us to obtain a final mean average error value on the test set equal to 0.202. To support future developments of the proposed approach and the replicability of the experiments we decided to make the source code publicly accessible

Microscale Analysis of Spacecraft Heat Shields

Imagine entering Earths atmosphere after returning from the outer solar system. A heat shield less than 2 inches thick protects you from temperatures up to 2,900 Celsius (5,252 Fahrenheit). Such conditions were experienced by NASAs Stardust capsule during reentry in 2006. The only materials capable of providing the necessary protection are composites with complex microstructures. Evaluating these materials is difficult, requiring precise knowledge of their properties. To this end, NASA scientists are developing research codes to compute material properties and simulate ablation at the microscale using agency supercomputers. Utilizing these tools, along with experiments, researchers are working to push the limits of spaceflight, allowing for greater flexibility in future space missions

An investigation on the impact of natural language on conversational recommendations

In this paper, we investigate the combination of Virtual Assistants and Conversational Recommender Systems (CoRSs) by designing and implementing a framework named ConveRSE, for building chatbots that can recommend items from different domains and interact with the user through natural language. An user experiment was carried out to understand how natural language influences both the cost of interaction and recommendation accuracy of a CoRS. Experimental results show that natural language can indeed improve user experience, but some critical aspects of the interaction should be mitigated appropriately