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 4,335 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Abdominal wall malformations are a frequent category of congenital malformations in the last years in all Departments for Pediatric Surgery from our country. The authors are presenting a serie of 35 cases of newborns admitted and treated in the Pediatric Surgery and Orthopedics Department of the Clinical University Hospital No. 1 Craiova and in the same time they are evaluating in time the results after different surgical technics. From the point of view of the surgical techniques applied in the last 20 years the authors are communicating their results with the following procedures: Gross, Allen-Wren, Fufezan, and also their results with a nonsurgical procedure (Grob). The authors are manchenning that it is possible to combine (depending on the anatomo-clinical) the surgical approach with the non-operative management of the case. Clinical parameters such as sex, birthweight, immaturity, associated malformations, size of the abdominal deffect are the main element to predict the outcome of these patients and to choose the right management strategy, thus it is very important to investigate the pregnancy evolution and the birth period. The good results (82.9% surviving) are representing the prove of a good therapeutic decision being impossible to use synthetic materials for substitution (silicon, dacron, teflon, etc.) and in the same time an important guide for our future activity.
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PMID:[Follow-up evaluation of omphalocele treatment in children. The experience of the department of Pediatric Surgery and Orthopedics No.1 University Hospital Craiova]. 1273 Nov 53

Detection of neutrons, at high total efficiency, with greater resolution in kinetic energy, time and/or real-space position, is fundamental to the advance of subfields within nuclear medicine, high-energy physics, non-proliferation of special nuclear materials, astrophysics, structural biology and chemistry, magnetism and nuclear energy. Clever indirect-conversion geometries, interaction/transport calculations and modern processing methods for silicon and gallium arsenide allow for the realization of moderate- to high-efficiency neutron detectors as a result of low defect concentrations, tuned reaction product ranges, enhanced effective omnidirectional cross sections and reduced electron-hole pair recombination from more physically abrupt and electronically engineered interfaces. Conversely, semiconductors with high neutron cross sections and unique transduction mechanisms capable of achieving very high total efficiency are gaining greater recognition despite the relative immaturity of their growth, lithographic processing and electronic structure understanding. This review focuses on advances and challenges in charged-particle-based device geometries, materials and associated mechanisms for direct and indirect transduction of thermal to fast neutrons within the context of application. Calorimetry- and radioluminescence-based intermediate processes in the solid state are not included.
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PMID:The physics of solid-state neutron detector materials and geometries. 2140 41

Nanoscale specimens of semiconductor materials as diverse as silicon and diamond are now known to be deformable to large elastic strains without inelastic relaxation. These discoveries harbinger a new age of deep elastic strain engineering of the band structure and device performance of electronic materials. Many possibilities remain to be investigated as to what pure silicon can do as the most versatile electronic material and what an ultrawide bandgap material such as diamond, with many appealing functional figures of merit, can offer after overcoming its present commercial immaturity. Deep elastic strain engineering explores full six-dimensional space of admissible nonlinear elastic strain and its effects on physical properties. Here we present a general method that combines machine learning and ab initio calculations to guide strain engineering whereby material properties and performance could be designed. This method invokes recent advances in the field of artificial intelligence by utilizing a limited amount of ab initio data for the training of a surrogate model, predicting electronic bandgap within an accuracy of 8 meV. Our model is capable of discovering the indirect-to-direct bandgap transition and semiconductor-to-metal transition in silicon by scanning the entire strain space. It is also able to identify the most energy-efficient strain pathways that would transform diamond from an ultrawide-bandgap material to a smaller-bandgap semiconductor. A broad framework is presented to tailor any target figure of merit by recourse to deep elastic strain engineering and machine learning for a variety of applications in microelectronics, optoelectronics, photonics, and energy technologies.
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PMID:Deep elastic strain engineering of bandgap through machine learning. 3215 15