Gear Teeth Deflection Model for Spur Gears: Proposal of a 3D Nonlinear and Non-Hertzian Approach

In this paper, a three-dimensional model for the estimation of the deflections, load sharing attributes, and contact conditions will be presented for pairs of meshing teeth in a spur gear trans- mission. A nonlinear iterative approach based on a semi-analytical formulation for the deformation of the teeth under load will be employed to accurately determine the point of application of the load, its intensity, and the number of contacting pairs without a priori assumptions. At the end of this iterative cycle the obtained deflected shapes are then employed to compute the pressure distributions through a contact mechanics model with non-Hertzian features and a technique capable of obtaining correct results even at the free edges of the finite length contacting bodies. This approach is then applied to a test case with excellent agreement with its finite element counterpart. Finally, several results are shown to highlight the influence on the quasi-static behavior of spur gears of different kinds and amounts of flank and face-width profile modifications

Data-Aware Declarative Process Mining with SAT

Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Process models can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance checking or model enhancement. In Declarative Process Mining, process models are represented as sets of temporal constraints (instead of procedural descriptions where all control-flow details are explicitly modeled). An open research direction in Declarative Process Mining is whether multi-perspective specifications can be supported, i.e., specifications that not only describe the process behavior from the control-flow point of view, but also from other perspectives like data or time. In this paper, we address this question by considering SAT (Propositional Satisfiability Problem) as a solving technology for a number of classical problems in Declarative Process Mining, namely log generation, conformance checking and temporal query checking. To do so, we first express each problem as a suitable FO (First-Order) theory whose bounded models represent solutions to the problem, and then find a bounded model of such theory by compilation into SAT

a new fully three dimensional numerical model for ice dynamics

AbstractThe problem of describing ice dynamics has been faced by many researchers; in this paper a fully three-dimensional model for ice dynamics is presented and tested. Using an approach followed by other researchers, ice is considered a non-linear incompressible viscous fluid so that a fluid-dynamic approach can be used. The model is based on the full three-dimensional Stokes equations for the description of pressure and velocity fields, on the Saint-Venant equation for the description of the free-surface time evolution and on a constitutive law derived from Glen's law for the description of ice viscosity. The model computes the complete pressure field by considering both the hydrostatic and hydrodynamic pressure components; it is time-evolutive and uses high-order numerical approximation for equations and boundary conditions. Moreover it can deal with both constant and variable viscosity. Three theoretical tests and two applications to Priestley Glacier, Antarctica, are presented in order to evaluate the performance of the model and to investigate important phenomena of ice dynamics such as the influence of viscosity on pressure and velocity fields, basal sliding and flow over perturbed bedrocks. All these applications demonstrate the importance of treating the complete pressure and stress fields

Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis

Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools

Necrotizing pneumonia and sepsis due to Clostridium perfringens: a case report

Clostridia are uncommon causes of pleuropulmonary infection. Clostridial species infecting the pleuropulmonary structures characteristically cause a necrotizing pneumonia with involvement of the pleura. Most cases have iatrogenic causes usually due to invasive procedures into the pleural cavity, such as thoracentesis or thoracotomy, or penetrating chest injuries. Rarely clostridia pleuropulmonary infections are not related to these factors. The clinical course of pleuropulmonary clostridial infections can be very variable, but they may be rapid and fatal. We report a rare case of necrotizing pneumonia and sepsis due to Clostridium perfringens not related to iatrogenic causes or injuries in an 82 years old woman

The withdrawal from oncogenetic counselling and testing for hereditary and familial breast and ovarian cancer. A descriptive study of an Italian sample

<p>Abstract</p> <p>Background</p> <p>Oncogenetic counselling is seldom followed through, even when individuals are eligible according to the test criteria. The basic variables which influence the decision to undergo the genetic counselling process are: risk perception, expected benefit or limitations of genetic testing, general psychological distress or cancer-specific distress, lack of trust in one's emotional reactions when faced with negative events, expected level of family support and communications within the family. The aim of this study was to describe the psychosocial variables of an Italian sample that forgoes genetic counselling.</p> <p>Methods</p> <p>From May 2002 to December 2006 a psychological questionnaire was sent out to one hundred and six subjects, who freely requested a first genetic informative consultation, and never asked to have a second visit and the family tree drawn up in order to inquire about their eligibility for genetic testing. Statistical analysis was performed by Pearson chi-square test, t-test and Spearman RHO coefficient.</p> <p>Results</p> <p>The survey presents a lack of emotional cohesion and structured roles and rules within the family system and a positive correlation between the number of children, anxiety and risk perception. The main reasons for giving up on counselling were a sense that testing was a waste of time and the inability to emotionally handle the negative consequences of the test outcome. The subjects who maintained that test and an early diagnosis were a "waste of time" experienced more anxiety.</p> <p>Conclusion</p> <p>The study revealed the importance to ac knowledging the whole persona and their family system as well as provide information highlighting usefulness of early diagnosis.</p