Scan to BIM for 3D reconstruction of the papal basilica of saint Francis in Assisi In Italy

The historical building heritage, present in the most of Italian cities centres, is, as part of the construction sector, a working potential, but unfortunately it requires planning of more complex and problematic interventions. However, policies to support on the existing interventions, together with a growing sensitivity for the recovery of assets, determine the need to implement specific studies and to analyse the specific problems of each site. The purpose of this paper is to illustrate the methodology and the results obtained from integrated laser scanning activity in order to have precious architectural information useful not only from the cultural heritage point of view but also to construct more operative and powerful tools, such as BIM (Building Information Modelling) aimed to the management of this cultural heritage. The Papal Basilica and the Sacred Convent of Saint Francis in Assisi in Italy are, in fact, characterized by unique and complex peculiarities, which require a detailed knowledge of the sites themselves to ensure visitor’s security and safety. For such a project, we have to take in account all the people and personnel normally present in the site, visitors with disabilities and finally the needs for cultural heritage preservation and protection. This aim can be reached using integrated systems and new technologies, such as Internet of Everything (IoE), capable of connecting people, things (smart sensors, devices and actuators; mobile terminals; wearable devices; etc.), data/information/knowledge and processes to reach the desired goals. The IoE system must implement and support an Integrated Multidisciplinary Model for Security and Safety Management (IMMSSM) for the specific context, using a multidisciplinary approach

The Influence of Higher Education on Promotional Outcomes in the New Jersey State Police

This study examined the strength of four predictor variables (i.e., level of education, seniority, gender and race) found in the archival data provided by the New Jersey State Police to predict the likelihood of promotional outcomes for five separate and distinct participant groups (i.e., Sergeant, Sergeant First Class, Lieutenant, Captain, and Major). Five separate participant group analyses were conducted using binary logistic regression modelling. The participant data examined in this study, which represents a total population sample, pertained to 3,515 enlisted members of the New Jersey State Police considered for promotion during one, or both, of the promotional events held on September 14, 2012 and October 25, 2011 to one of the aforementioned ranks. For each participant group, with the exception of the Promotion to Major participant group, the results of this study revealed education, when controlling for other predictor variables in the binary logistic regression model, to be the strongest predictor of promotional outcomes, while seniority was the second strongest predictor of promotional outcomes. Gender and race were not statistically significant. As a result, the null hypotheses for these participant groups were rejected. The null hypothesis for the Promotion to Major group was retained due to the statistical insignificance of the chi square statistic and all four predictor variables in the binary logistic regression model

The Influence of Higher Education on Promotional Outcomes in the New Jersey State Police

This study examined the strength of four predictor variables (i.e., level of education, seniority, gender and race) found in the archival data provided by the New Jersey State Police to predict the likelihood of promotional outcomes for five separate and distinct participant groups (i.e., Sergeant, Sergeant First Class, Lieutenant, Captain, and Major). Five separate participant group analyses were conducted using binary logistic regression modelling. The participant data examined in this study, which represents a total population sample, pertained to 3,515 enlisted members of the New Jersey State Police considered for promotion during one, or both, of the promotional events held on September 14, 2012 and October 25, 2011 to one of the aforementioned ranks. For each participant group, with the exception of the Promotion to Major participant group, the results of this study revealed education, when controlling for other predictor variables in the binary logistic regression model, to be the strongest predictor of promotional outcomes, while seniority was the second strongest predictor of promotional outcomes. Gender and race were not statistically significant. As a result, the null hypotheses for these participant groups were rejected. The null hypothesis for the Promotion to Major group was retained due to the statistical insignificance of the chi square statistic and all four predictor variables in the binary logistic regression model

Low Velocity Granular Drag in Reduced Gravity

We probe the dependence of the low velocity drag force in granular materials on the effective gravitational acceleration (geff) through studies of spherical granular materials saturated within fluids of varying density. We vary geff by a factor of 20, and we find that the granular drag is proportional to geff, i.e., that the granular drag follows the expected relation Fprobe = {\eta} {\rho}grain geff dprobe hprobe^2 for the drag force, Fprobe on a vertical cylinder with depth of insertion, hprobe, diameter dprobe, moving through grains of density {\rho}grain, and where {\eta} is a dimensionless constant. This dimensionless constant shows no systematic variation over four orders of magnitude in effective grain weight, demonstrating that the relation holds over that entire range to within the precision of our data

Bell inequalities from variable elimination methods

Tight Bell inequalities are facets of Pitowsky's correlation polytope and are usually obtained from its extreme points by solving the hull problem. Here we present an alternative method based on a combination of algebraic results on extensions of measures and variable elimination methods, e.g., the Fourier-Motzkin method. Our method is shown to overcome some of the computational difficulties associated with the hull problem in some non-trivial cases. Moreover, it provides an explanation for the arising of only a finite number of families of Bell inequalities in measurement scenarios where one experimenter can choose between an arbitrary number of different measurements

Fault detection, diagnosis, and prognosis of a process operating under time-varying conditions

In the industrial panorama, many processes operate under time-varying conditions. Adapt¬ing high-performance diagnostic techniques under these relatively more complex situations is ur¬gently needed to mitigate the risk of false alarms. Attention is being paid to fault anticipation, requiring an in-depth study of prediction techniques. Predicting remaining life before the occurrence of faults allows for a comprehensive maintenance management protocol and facilitates the wear management of the machine, avoiding faults that could permanently compromise the integrity of such machinery. This study focuses on canonical variate analysis for fault detection in processes operating under time-varying conditions and on its contribution to the diagnostic and prognostic analysis, the latter of which was performed with machine learning techniques. The approach was validated on actual datasets from a granulator operating in the pharmaceutical sector

Low Molecular Weight Hyaluronic Acid (500–730 Kda) Injections in Tendinopathies—A Narrative Review

Tendinopathies are common causes of pain and disability in general population and athletes. Conservative treatment is largely preferred, and eccentric exercise or other modalities of therapeutic exercises are recommended. However, this approach requests several weeks of consecutive treatment and could be discouraging. In the last years, injections of different formulations were evaluated to accelerate functional recovery in combination with usual therapy. Hyaluronic acid (HA) preparations were proposed, in particular LMW-HA (500-730 kDa) for its unique molecular characteristics in favored extracellular matrix homeostasis and tenocyte viability. The purpose of our review is to evaluate the state-of-the-art about the role of 500-730 kDa in tendinopathies considering both preclinical and clinical findings and encourage further research on this emerging topic

The role of hyaluronic acid injection for the treatment of tendinopathy

Hyaluronic acid has carved out an essential, though sometimes discussed, role in the treatment of joint degenerative pathology. Recent studies, first in vitro, then preclin-ical, have paved the way for use in tendon pathology. Clinical experience to date has shown extremely encouraging results in different tendinopathy frameworks such as tenosynovitis, insertional tendinopathies and tendon mid-portion

Fault diagnosis of a granulator operating under time-varying conditions using canonical variate analysis

Granulators play a key role in many pharmaceutical processes because they are involved in the production of tablets and capsule dosage forms. Considering the characteristics of the production processes in which a granulator is involved, proper maintenance of the latter is relevant for plant safety. During the operational phase, there is a high risk of explosion, pollution, and contamination. The nature of this process also requires an in-depth examination of the time-dependence of the process variables. This study proposes the application of canonical variate analysis (CVA) to perform fault detection in a granulation process that operates under time-varying conditions. Beyond this, a different approach to the management of process non-linearities is proposed. The novelty of the study is in the application of CVA in this kind of process, because it is possible to state that the actual literature on the theme shows some limitations of CVA in such processes. The aim was to increase the applicability of CVA in variable contexts, with simple management of non-linearities. The results, considering process data from a pharmaceutical granulator, showed that the proposed approach could detect faults and manage non-linearities, exhibiting future scenarios for more performing and automatic monitoring techniques of time-varying processes