[cerebral Hemometabolism: Variability In The Acute Phase Of Traumatic Coma].

to evaluate the interrelationships between cerebral and systemic hemometabolic alterations in patients with severe traumatic brain injury managed according to a standardized therapeutic protocol. prospective, interventional study in patients with traumatic coma. a general Intensive Care Unit in a teaching hospital. twenty-seven patients (21M e 6F), aging 14 - 58 years, with severe acute brain trauma, presenting with three to eight points on the Glasgow Coma Scale, were prospectively evaluated according to a cumulative protocol for the management of acute intracranial hypertension, where intracranial pressure (ICP) and cerebral extraction of oxygen (CEO2) were routinely measured. Hemometabolic interrelationships involving mean arterial pressure (MAP), ICP, arterial carbon dioxide tension (PaCO2), CEO2, cerebral perfusion pressure (CPP) and systemic extraction of oxygen (SEO2) were analyzed. routine therapeutic procedures. no correlation was found between CEO2 and CPP (r = -0.07; p = 0.41). There was a significant negative correlation between PaCO2 and CEO2 (r = -0.24; p = 0.005) and a positive correlation between SEO2 and CEO2 (r = 0.24; p = 0.01). The mortality rate in this group of patients was 25.9% (7/27). 1) CPP and CEO2 are unrelated; 2) CEO2 and PaCO2 are closely related; 3) during optimized hyperventilation, CEO2 and SEO2 are coupled.58877-8

A buckling instability prediction model for the reliable design of sheet metal panels based on an artificial intelligent self-learning algorithm

Sheets’ buckling instability, also known as oil canning, is an issue that characterizes the resistance to denting in thin metal panels. The oil canning phenomenon is characterized by a depression in the metal sheet, caused by a local buckling, which is a critical design issue for aesthetic parts, such as automotive outer panels. Predicting the buckling instability during the design stage is not straightforward since the shape of the component might change several times before the part is sent to production and can actually be tested. To overcome this issue, this research presents a robust prediction model based on the convolutional neural network (CNN) to estimate the buckling instability of automotive sheet metal panels, based on the major, minor, and Gaussian surface curvatures. The training dataset for the CNN model was generated by implementing finite element analysis (FEA) of the outer panels of various commercial vehicles, for a total of twenty panels, and by considering different indentation locations on each panel. From the implemented simulation models the load-stroke curves were exported and utilized to determine the presence, or absence, of buckling instability and to determine its magnitude. Moreover, from the computer aided design (CAD) files of the relevant panels, the three considered curvatures on the tested indentation points were acquired as well. All the positions considered in the FEA analyses were backed up by industrial experiments on the relevant panels in their assembled position, allowing to validate their reliability. The combined correlation of curvatures and load-displacement curves allowed correlating the geometrical features that create the conditions for buckling instability to arise and was utilized to train the CNN algorithm, defined considering 13 convolution layers and 5 pooling layers. The trained CNN model was applied to another automotive frame, not used in the training process, and the prediction results were compared with experimental indentation tests. The overall accuracy of the CNN model was calculated to be 90.1%, representing the reliability of the proposed algorithm of predicting the severity of the buckling instability for automotive sheet metal panels

Stable forming conditions and geometrical expansion of L-shape rings in ring rolling process

Based on previous research results concerning the radial-axial ring rolling process of flat rings, this paper details an innovative approach for the determination of the stable forming conditions to successfully simulate the radial ring rolling process of L-shape profiled rings. In addition to that, an analytical model for the estimation of the geometrical expansion of L-shape rings from its initial flat ring preform is proposed and validated by comparing its results with those of numerical simulations. By utilizing the proposed approach, steady forming conditions could be achieved, granting a uniform expansion of the ring throughout the process for all of the six tested cases of rings having the final outer diameter of the flange ranging from 545mm and 1440mm. The validation of the proposed approach allowed concluding that the geometrical expansion of the ring, as estimated by the proposed analytical model, is in good agreement with the results of the numerical simulation, with a maximum error of 2.18%, in the estimation of the ring wall diameter, 1.42% of the ring flange diameter and 1.87% for the estimation of the inner diameter of the ring, respectively

Slip line model for forces estimation in the radial-axial ring rolling process

In the research presented in this paper, a slip line-based model is proposed for the estimation of both radial and axial force in the radial-axial ring rolling (RARR) process. Based on the shape of the contact arcs between ring and tools in the two deformation gaps present in the ring rolling process, a recursive algorithm for the calculation of the two slip line fields starting from the two pairs of opposite tools is derived and implemented in a commercial spreadsheet software (MS Excel). By considering the stress boundary conditions applied to the portion of material undergoing the deformation, both for the radial and axial deformation gaps, the pressure factors those make the two slip line fields starting from the two opposite tools to intersect are calculated and utilized for the estimation of radial and axial forces, for each round of the process. The developed model has been validated by cross-comparing its results with those of laboratory experiment and numerical simulation. For the validation study case, the average deviations, in comparison to the experimental results, are calculated in 1.86% and 4.55% for the slip line force model whereas in 6.86% and 0.88% for the numerical simulation, for the radial and axial forces respectively. The proposed slip line model has been also utilized for the estimation of radial and axial forming forces of nine different study cases of flat rings having the outer diameter ranging from 800 mm to 2000 mm, observing a maximum deviation, in comparison to the relevant FEM simulation, of 4.92% (radial force) and 5.88% (axial force). The developed slip line force model allows estimating almost in real time and with a reasonable accuracy the process forces and, for this reason, it may be of interest for both industrial and academic researchers dealing with the set-up and control of the radial-axial ring rolling process

Contact geometry estimation and precise radial force prediction for the radial-axial ring rolling process

In the present research work, the modular parametric design plug-in Grasshopper, available in Rhinoceros 5, is utilized as a pre-processor for the estimation of the projection of the contact length between ring and tools in the radial-axial ring rolling process. The estimated lengths, for each round of the process, are then used in a slip line based force model for the precise estimation of the radial forming force. The proposed method allows reducing the inaccuracies of the traditional approaches since it supersedes the concept of common thickness draft on both mandrel and main roll side, allowing a more precise estimation of the projection of the contact arc between ring and tools, considered to have a unique value on both mandrel side and main roll side. The fulfillment of this last assumption ensures the forming force to have the same value regardless it is calculated on the mandrel side or on the main roll side. The model has been validated by cross-comparing the analytical results with those of laboratory experiment and finite element simulation. The developed analytical model has been also applied to three different study cases where the previous literature models for the calculation of the projection of the contact arc have shown inaccuracies, demonstrating that the proposed approach can overcome these limitations. The positive cross comparisons among laboratory experiment, FEM simulations, and analytical estimations prove the reliability of the proposed approach, as well as its good integration with authors\u2019 previous analytical algorithms

A Quantitative Enzyme-linked Immunosorbent Assay (elisa) For The Immunodiagnosis Of Neurocysticercosis Using A Purified Fraction From Taenia Solium Cysticerci.

A quantitative enzyme-linked immunosorbent assay (ELISA) for the immunodiagnosis of neurocysticercosis is described. The ELISA was standardized using a purified Taenia solium cysticerci fraction (PCF) obtained by ion exchange chromatography. The ELISA using PCF (PCF-ELISA) and a qualitative ELISA using a whole extract from T. solium cysticerci (WEC-ELISA) were used to screen for cysticercus-specific IgG antibodies in cerebrospinal fluid (CSF) samples from 57 patients with neurocysticercosis and 50 patients with other neurologic disorders. The sensitivity of both assays was 95%, whereas the specificities of PCF-ELISA and WEC-ELISA were 100% and 92%, respectively. The excellent sensitivity and specificity of the PCF-ELISA make this assay a potentially useful tool in screening for antibodies against T. solium cysticerci.3787-9