Sant'Elia's Last Project

Based on what were thought to be the only two representations of Sant'Elia's design for a military cemetery at Monfalcone, scholars believed until recently that the design was only partially executed. Two newly unearthed photographs instead affirm that the execution of the project followed Sant'Elia's plan, and they provide much fuller documentation of the project itself. The new documents also call into question the historiographic treatment of Sant'Elia in which he has chiefly been regarded a precursor of subsequent architects.     

GFR equilibrium cycle analysis with the EQL3D procedure

Advanced fast reactors of the fourth generation should be capable to breed their own fuel from ²³⁸U feed and to recycle the actinides from their own spent fuel. This recycling or virtually the closure of fuel cycle can converge to an equilibrium fuel cycle and has impact on the safety-related parameters. The goals of this study are: (i) to apply an equilibrium cycle procedure EQL3D to the Gas cooled Fast Reactor (GFR), (ii) to simulate and confirm the GFR neutronics capability for closed fuel cycle, and (iii) to evaluate the safety-related parameters of the equilibrium cycle.Equilibrium cycle method for considering the homogeneous recycling of actinides is a known approach. However, in EQL3D the equilibrium method is newly applied for hexagonal-z 3D core geometry and 33 energy-groups neutron-flux calculation. This geometry enables to characterize the equilibrium cycle for complex reloading patterns within a multi-batch cycle.Two GFR geometries were studied, the first based on an international neutronics benchmark with a simple set-up and the second based on more advanced core design. For the advanced design, three reloading patterns within a multi-batch cycle with four different feeds were compared.The GFR neutronics capability for closed cycle was proved. The negative impact of the fuel cycle closure on safety-related parameters was confirmed and quantified. The GFR core with closed fuel cycle could serve after prospective optimization as a sustainable and clean energy source.     

Gatekeeping African studies: what does “editormetrics” indicate about journal governance?

The purpose of the study was to examine the productivity of faculty in social work doctoral programs. This study builds on previous investigations on the scholarship of social work faculty using the h-Index (i.e., citation analysis). This study examined the scholarly productivity of the full population (N?=?1699) of tenure-track faculty in all 76 United States social work doctoral programs by analyzing the h-Index scores of each program. Information on funding sources, regional location, year of establishment, and faculty demographics was collected to better understand why faculty and programs differ in their h-Index. A hierarchical regression analysis was used in creating a predictive model. The final model explained 51% of the variance in h-Index scores (R²?=?.51). Academic rank was the strongest predictor of school h-Index. Each school’s faculty size, gender proportion, region, college age, and auspice also contributed to the predictive power of the model. The proportion of senior faculty (Associate Professors and Full Professors) and college age were the strongest predictors based on standardized regression coefficients. The finding that academic rank contributed the most variance to the regression model provides empirical support to the long-argued importance of publication in career advancement. The overall results of the model confirm that institutional factors such as faculty size, region, and auspice do have unique effects on research productivity even after accounting for individual level differences in faculty across diverse social work programs.     

Weighted partition consensus via kernels

The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable information that can improve the quality of the combination process. Besides, we propose a new similarity measure between partitions proving that it is a kernel function. A new consensus function is introduced using this similarity measure and based on the idea of finding the median partition. Related to this consensus function, some theoretical results that endorse the suitability of our methods are proven. Finally, we conduct a numerical experimentation to show the behavior of our method on several databases by making a comparison with simple clustering algorithms as well as to other cluster ensemble methods.     

Posidonia oceanica genetic and biometry mapping through high-resolution satellite spectral vegetation indices and sea-truth calibration

In the framework of Posidonia oceanica (PO) preservation activities, a small-scale restoration pilot project was implemented in 2005 at a Santa Marinella site to replace the loss of this important species of seagrass in this zone of the central Tyrrhenian coast via an innovative transplantation approach. In this context, taking into account the recent advances in the fields of high-resolution (HR) satellite/airborne remote-sensing and genetics laboratory analysis techniques, we propose this integrated methodology for monitoring changes in transplanted meadows in regard to perspective to provide support in the assessment of the entire local PO and seagrass population dynamic. According to specific information requirements in terms of radiometric and spectral/spatial resolution, the multispectral data currently available from the QuickBird polar satellite’s four-band (red, green, blue visible and near-infrared) HR sensor were exploited for methodology implementation using a practical ‘image-based’ approach to account for atmospheric and water column turbidity typical of this mid-coastal Mediterranean region. First, the extents and types of seagrass cover were suitably mapped, and then also the distributions of specific vegetation parameters related to PO dynamics and health were assessed by exploiting the remotely sensed satellite-derived radiance signals and point sea-truth calibration measurements of the bio-genetic parameters. In particular, we implemented maps of leaf area index, genetic similarity, and density Giraud indices corresponding to distributions of PO patches using multivariate and data-mining models (artificial neural network) based on appropriately preprocessed radiometric and auxiliary (bathymetry) input variables.     

Analysis of laminar-to-turbulent transition for isothermal gas flows in microchannels

In this work the laminar-to-turbulent transition in microchannels of circular cross-section is studied experimentally. In order to single out the effects of relative roughness, compressibility and channel length-to-diameter ratio on the Reynolds number at which transition occurs, experimental runs have been carried out on circular microchannels in fused silica—smooth for all purposes—and in stainless steel (which possess a high surface roughness), with a diameter between 125 and 180 μm and a length of 5–50 cm through which nitrogen flows. For each tube the friction factor has been computed. The values of the critical Reynolds number have been determined plotting the Poiseuille number (i.e., the product of the friction factor, f, times the Reynols number, Re) as a function of the average Mach number between inlet and outlet. The transitional regime was found to start no earlier than at values of the Reynolds number around 1,800–2,000. It has been observed that surface roughness has no effect on the hydraulic resistance in the laminar region for a relative roughness lower than 4.4%, and that friction factor obeys the Poiseuille law, if it is correctly computed taking compressibility into account. It is found that recent correlations for the prediction of the critical Reynolds number in microchannels that link the relative roughness of the microtubes to the critical Reynolds number do not agree with the present results.     

Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap

The well-known bounds on the generalizationability of learning machines, based on the Vapnik–Chernovenkis (VC) dimension,are very loose when applied to Support Vector Machines (SVMs).In this work we evaluate the validity of the assumption that these bounds are,nevertheless, good indicators of the generalization ability of SVMs.We show that this assumption is, in general, true and assessits correctness, in a statistical sense, on several pattern recognition benchmarks throughthe use of the bootstrap technique.     

Droughts Prediction: a Methodology Based on Climate Seasonal Forecasts

This study proposes a methodology for the drought assessment based on the seasonal forecasts. These are climate predictions of atmospheric variables, such as precipitation, temperature, wind speed, for upcoming season, up to 7 months. In regions particularly vulnerable to droughts and to changes in climate, such as the Mediterranean areas, predictions of precipitation with months in advance are crucial for understanding the possible shifts, for example, in water resource availability. Over Europe, practical applications of seasonal forecasts are still rare, because of the uncertainties of their skills; however, the predictability varies depending on the season and area of application. In this study, we describe a methodology which integrates, through a statistical approach, seasonal forecast and reanalysis data to assess the climate state, i.e. drought or not, of a region for predefined periods in the next future, at monthly scale. Additionally, the skill of the forecasts and the reliability of the released climate state assessment are estimated in terms of the false rate, i.e. the probability of missing alerts or false alarms. The methodology has been first built for a case study in Zakynthos (Greece) and then validated for a case study in Sicily (Italy). The selected locations represent two areas of the Mediterranean region often suffering from drought and water shortage situations. Results showed promising findings, with satisfying matching between predictions and observations, and false rates ranging from 1 to 50%, depending on the selected forecast period.

     

The role of settling velocity formulation in the determination of gully pot trapping efficiency: comparison between analytical and experimental data

Roadside gully pots are the connecting points between surface runoff and the underground drainage network; therefore they can be considered as the most superficial component of urban drainage systems. Gully pots are supposed to trap particulate matter washed off the catchment surface, but also to collect and convey stormwater into the network. The continuous accumulation of particulate matter results in a progressive loss of the gully pot hydraulic conveyance, thereby increasing the probability of urban flooding during rainstorm events. This study has therefore the objective to determine which variables influence the gully pot capability of retaining solids (efficiency), both experimentally and analytically. Several laboratory tests have been performed on a simple plastic gully pot, with different inflow rates and using both mono and hetero-disperse particle samples. Particular attention has been given to the influence exerted by the way particle settling velocity is expressed: efficiency has been analytically determined by means of multiple settling velocity formulas proposed by various authors and eventually compared to experimental data. Results deriving from the adoption of each single settling velocity formula have been extensively analysed, showing fairly different outcomes.     

Annealing and crystallization kinetics of poly(lactic acid) pieces obtained by additive manufacturing

In this study, we investigated the influence of isothermal treatment of poly(lactic acid) (PLA) 3D printed samples at different crystallization temperatures. In this case, we analyzed the effect of each crystallization temperature on spherulites formation in printed PLA, affecting the final mechanical properties of pieces. For such, the thermomechanical properties, morphological structure, and crystallization kinetics were analyzed before and after thermal treatment. The 3D printed samples were heat treated at 80°C, 90°C, 100°C, 110°C, and 119°C. With annealing, we observed an improvement in the mechanical PLA properties; however, the exothermic crystallization peak was different for the samples. Pieces before annealing were found to have a low crystallinity index (Ic) of 2%–7%, and the pieces after annealing presented a considerable Ic (27%–34%). Annealing temperatures of 100°C, 110°C, and 119°C produced the fastest crystallization kinetics, while annealing temperatures of 80°C and 90°C resulted in the lowest crystallization kinetics for complete crystallization. After annealing, improvement in the flexural strength (34%–47%) and Young's modulus (26%–51%) for all annealed pieces occurred. The appropriate condition was observed at 100°C, which was the onset temperature of crystallization, owing to the combination of the shorter time of crystallization with the increased mechanical properties.