Do we need a “new” international convention that helps to avoid trafficking in organs? Some “criminal (and civil) law aspects” - Convention on Human Rights and Biomedicine – updated or outdated?

Abstract: § 1 «Do we need a “new” international convention that helps to avoid trafficking in organs? Some criminal (and civil) law aspects”» - «Convention on Human Rights and Biomedicine – updated or outdated?». § 2 Some important connections: on the one hand, between the 1997 Council of Europe Convention on Human Rights and Biomedicine; the 2002 Additional Protocol to the Convention on Human Rights and Biomedicine concerning Transplantation of Organs and Tissues of Human Origin; and, on the other hand, the problem of trafficking in organs, tissues and cells and trafficking in human beings for the purpose of the removal organs. Some connections. § 3 The «international undisputed principle». § 4 Trafficking in organs, tissues and cells; and trafficking in human beings for the purpose of the removal organs. Criminal Law and Civil Law. § 5 Promote organ donation. § 6 The necessity to collect reliable data on both trafficking cases. § 7 The necessity for an internationally agreed definition of trafficking in OTC: Convention on Human Rights and Biomedicine – updated or outdated? § 8 The (inter)national and (il)legal organ («tissue and cell») trade: some cases and some conclusions. § 9 Do we need a new international convention to prevent trafficking in organs, tissues and cells (OTC)? § 10 Of course we need a «new» international convention to prevent trafficking in organs, tissues and cells (OTC). § 11 At the present moment, we do not need a «new» international convention to prevent trafficking in human beings for the purpose of the removal organs. § 12 The Portuguese case. § 13 «Final conclusions.» § Resumo: § 1 «Precisamos de uma "nova" convenção internacional que ajude a evitar o tráfico de órgãos? Alguns aspectos de lei criminal (e civil)» - «Convenção sobre Direitos Humanos e Biomedicina - Actualizada ou desactualizada?». § 2 Algumas conexões importantes: por um lado, entre a Convenção do Conselho da Europa de 1997 sobre Direitos Humanos e Biomedicina; o Protocolo Adicional de 2002 à Convenção sobre os Direitos do Homem e da Biomedicina relativo ao transplante de órgãos e tecidos de origem humana, e, por outro lado, o problema do tráfico de órgãos, tecidos e células e tráfico de seres humanos para fins de remoção dos órgãos. § 3 O «indiscutível princípio internacional». § 4 O Tráfico de órgãos, tecidos e células; e o tráfico de seres humanos para fins de remoção dos órgãos. Direito Penal e Direito Civil. § 5 Promover a doação de órgãos. § 6 A necessidade de colectar dados fidedignos sobre os dois casos de tráfico. § 7 A necessidade de uma definição internacionalmente acordada de tráfico de OTC: Convenção sobre Direitos Humanos e Biomedicina - actualizada ou desactualizada? § 8 A (inter)nacional e (il)legal comercialização de órgãos («de tecidos e de células»): alguns casos e algumas conclusões. § 9 Será que precisamos de uma nova convenção internacional para prevenir o tráfico de órgãos, tecidos e células (OTC)? § 10 É claro que precisamos de uma «nova» convenção internacional para prevenir o tráfico de órgãos, tecidos e células (OTC). § 11 No presente momento, não precisamos de uma «nova» convenção internacional para impedir o tráfico de seres humanos para fins de remoção dos órgãos. § 12 O caso Português. § 13 «As conclusões finais.»

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.