El presente trabajo de Investigación tiene como objetivo mejorar la Seguridad de las Edificaciones, en el presente caso de estudio del Distrito de Miraflores, el cual podría ser aplicable en todos los gobiernos locales al ser un tema de ámbito nacional. Las deficiencias de la seguridad de edificaciones se deben a la inadecuada supervisión e inspecciones de los profesionales (en especial el ámbito municipal – ente rector) que tienen a su cargo velar por la correcta ejecución de las obras. A fin de garantizar que los proyectos de edificación cumplan con los requisitos de seguridad requeridos, la Municipalidad debe cumplir con la Ley 29090, referida a las Supervisiones de Obra, convocando a los Profesionales Calificados por los Colegios Profesionales correspondientes por cada especialidad. El personal municipal debe estar capacitado para realizar una revisión de la presentación de expedientes previo a la recepción, verificar los requisitos mínimos que debe contener para un edificio de determinada altura, área y uso, tanto en planos como la documentación técnica necesaria, siguiendo las pautas establecidas por la Ley 29090 y su reglamento, así como las ordenanzas municipales. Como resultado se podrá garantizar obras ejecutadas de acuerdo a los Proyectos Aprobados de Licencia, con las conformidades necesarias solicitar las certificaciones de funcionamiento, ya que contarían con la opinión favorable de todas las especialidades. Con el ingreso correcto de los expedientes al inicio de cada procedimiento no se tendrían retrasos para las gestiones de cada proceso de licencia. / The objective of this research work is to improve Building Safety, in this specific case of study in the District of Miraflores, but it could be applicable in all local governments as it is a national issue. The deficiencies in the security of buildings are due to inadequate supervision and inspections by professionals (especially the municipal area - governing body) who are in charge of ensuring the proper execution of the works. In order to guarantee that the building projects comply with the mandatory safety requirements, the Municipality must comply with Law 29090, referred to the Works Supervisions, calling the Professionals Qualified by the corresponding Professional Associations for each specialty. The municipal staff must be able to carry out an examination of the presentation of files prior to the reception as such, verify the minimum requirements that a building of a certain height, area and use must possess, both in plans as well as in the technical documentation required, following the guidelines established by Law 29090 and its regulations, as well as municipal ordinances. As a result, works executed in accordance with their Planning Permits can be guaranteed, with the necessary conformities when requesting the operating certifications, since they would have the favorable opinion of all the specialists. With the correct submission of the files at the start of each procedure there would be no delays in the management of each permission process. / Trabajo de investigación

Sudden cardiac arrest continues to be a major cause of firefighter deaths during training due to a lack of individual firefighter adherence to National Fire Protection Association (NFPA) standards. These standards identify requirements for fire departments to create and maintain fitness programs. Existing research has not identified any relationships between training fatalities and individual firefighter adherence to NFPA 1583, Standard on Health-Related Fitness Programs for Fire Department Members. Using self-determination theory as the foundation, the purpose of this cross-sectional correlation study was to investigate whether individual firefighter adherence to NFPA 1583 has a measurable effect on training fatalities. Survey data were collected from 441 paid firefighters from 7 fire departments located in a rural county in a southern U.S. state. Data were analyzed using multiple linear regression. Results indicated that adherence to NFPA 1583 has a statistically significant relationship with reduced firefighter training fatalities (p = .000). Recommendations include examining adherence policies to all elements of the NFPA 1583 standard, not just chapters 5 through 8 in the publication. These include chapter 1 administration, chapter 2 referenced publications, chapter 3 definitions, and chapter 4 program organization specifications. The study results may be used by fire department training divisions to improve the health and safety of firefighters.

The purpose of this research was to perform a post-use evaluation of retired firefighter turnout gear. Garments were categorized as 10-12 years old, 13-17 years old and 18-21 years old. Inspection and test procedures required by National Fire Protection Association (NFPA) 1851 and NFPA 1971 were followed to determine if the current recommended 10 year wear life (retirement age) is appropriate. Testing included a visual inspection (closure system functionality, light evaluation, leakage evaluation and flashlight test) and performance properties (Thermal Protective Performance (TPP), flammability, breaking strength, tear strength, seam strength and water penetration) completed on 108 garments. Ten-year retirement, care, and use were criteria evaluated. The results confirm the flashlight test allows the firefighter to effectively evaluate trim reflectance on their turnout gear according to NFPA 1851. The leakage tests were not replicated by the water penetration tests. Based on the results of this study, the advanced visual inspection and light evaluation confirm the recommended retirement age of a garment that was at least ten years from manufacture date.

One of the activities conducted by state fire supervision authorities is the investigation of the cause of fires (ZPP) and one of its areas is explosions. These cases are requiring investigators to have very specific knowledge. The intention of the MV-GŘ HZS ČR is to create updated methodological material for ZPP, which will include a section devoted to the investigation of explosions. A major source of information for the creation of this material was the American guide NFPA 921. My goal was to collect Czech and foreign material dealing with different aspects of explosions, to compare this information with the content of NFPA 921 and determine the usability of these sources for the planned methodological manual. The research question was whether current conditions for ZPP and its procedures are adequate for HZS ČR investigators in the case of explosions. I also processed data on explosions using the Incident Statistics/Response Reports (SSU/ZOZ) program. Processed data created a picture of the development of these cases in ČR. The following findings emerged from that process: the number of cases has continued to increase and the dominant area where explosions occur in, is housing. In all areas the dominant cause is human negligence. The most frequent types of explosions are gas explosions. Explosions of flammable liquid vapour dominate the number of deaths as a direct consequence. When comparing the information in Czech material with NFPA 921, discrepancies were found in the values of the properties in the tables. With the modification the information in NFPA 921 is usable for the Czech manual. When confronted with statistical outputs, the shortcomings of the topics in NFPA 921 emerge. The investigative procedures are focus largely on explosions of considerable scope. These cases form a tiny percentage in ČR and their application is limited. There is a lack of concrete information on their most common causes. Thus NFPA 921, together with Czech material, is primarily of use as a general overview. In view of these facts, I have concluded that the information base and conditions required for ZPP are not adequate for HZS ČR investigators. The text presented in the results section of this work is designed to fill this gap of ZPP and can be used as the basis for the creation of the planned methodological manual.

No description available.

Hazard communication is an essential part of a comprehensive safety plan, especially for those facilities that contain reactive chemicals. There are a variety of means of communicating a chemical hazard, but one of the most prevalent in the United States is the Instability Rating found in the NFPA 704 standard. While the NFPA 704 identifies hazards associated with exothermically decomposing compounds, it neglects compounds that decompose endothermicly to form large quantities of gas. Such compounds have been known to cause accidents due to pressure buildup, such as in the BP Amoco Polymers explosion in 2001. In this work, twenty-five compounds were examined via an APTAC to determine their pressure and temperature profiles. These profiles were then used to determine the amount of gas generated, the gas generation rate, the gas generation product, the onset temperature, and the instantaneous power density. These properties were analyzed to determine those that best represented the instability hazard of the chemical. Ultimately, the molar gas generation rate and onset temperature were chosen to rate the selected chemicals, and new cut-offs were established to divide the chemicals into revised instability groupings. Compounds that did not decompose in the temperature range examined were given the rating of zero. Compounds with low onset temperatures and high gas generation rates were assigned the rating of 4, while chemicals with high T_onset and low dn/dt_maxn were assigned a value of 1. Chemicals with high onset temperatures and high gas generation rates were grouped into rating 3. Group 2 included low onset temperature compounds with low gas generation rates. The cut-offs used to define these regions were 130 degrees C for the onset temperature and 0.01 (1/min) for the gas generation rate. The ratings were found to be comparable to the current NFPA system, but improved upon it by providing a valid rating (group 1) for the chemicals that endothermically generated gas. Detailed plots of the data are provided as well as suggestions for future work.

O presente trabalho propõe o uso de parâmetros de textura extraídos computacionalmente de IRM como biomarcadores de imagem na predição de recorrência tumoral em pacientes de adenomas pituitários clinicamente não funcionantes (APNF). Para isso, esse estudo analisou imagens de RM de 15 pacientes de APNF retrospectivamente separados em dois grupos: O grupo de pacientes recorrentes, definido por 7 pacientes que exibiram recorrência tumoral em um período de 4, 640 +- 0, 653 anos (média +- erro padrão) de acompanhamento clínico após a primeira abordagem cirúrgica; e o grupo de pacientes estáveis, formado por 8 pacientes com lesões consideradas estáveis em um período de 4,512 +- 0, 536 anos. Uma máscara de segmentação tridimensional da lesão tumoral foi construída manualmente por um especialista sobre a imagem 3D T1-W DCE pré-operatória para cada paciente. Em seguida, essa segmentação e a própria imagem de ressonância foram usadas para extrair 48 características numéricas de textura. Adicionalmente, 4 características clínicas foram consideradas no estudo: a imunohistoquímica, invasividade, idade na primeira cirurgia e sexo, totalizando 52 características. Cada uma destas 52 características fora testada através de testes estatísticos convencionais univariados para ver se existia evidencias do poder discriminatório dessas características para diferenciar esses dois grupos de pacientes. Mais adiante, diferentes subconjuntos dessas características foram usados para construir modelos de predição baseados na teoria de aprendizagem de máquinas (usando os algoritmos k-nearest neighbor (kNN), decision tree (DTC), e random forest (RFC)) para investigar um modelo de classificação capaz de identificar os pacientes que experimentariam recorrência tumoral após a primeira cirurgia. 9 características de textura foram consideradas individualmente significantes (p < 0, 05) na diferenciação dos grupos de paciente recorrente e estável. Afirmando esses achados, a análise com a curva ROC para cada uma das 9 características exibiu medidas de AUC de 0,803 a 0,857 significando uma boa performance de classificação. A idade, imunoistoquímica, invasividade e sexo não mostraram evidencias de associação com recorrência tumoral. As melhores performances com algoritmos de classificação foram com kNN e RFC, ambos atingiram uma especificidade de 1,000 conservando alta acurácia (0,933) e obtendo 0,991 na análise com a curva ROC, o que caracteriza uma performance de classificação quase perfeita. DTC não mostrou nenhuma melhora se comparado com os resultados das classificações univariadas. Esses resultados permitem concluir que parâmetros de textura são úteis na predição de recorrência tumoral após a primeira cirurgia em pacientes de APNF. E que os valores de predição dessas características podem ser observados por testes estatísticos univariados convencionais e por análises multivariadas através de algoritmos baseados em aprendizagem de máquinas / The present work proposes the usage of texture features computationally extracted from MRI as imaging biomarkers in the prediction of tumor recurrence in patients with non-functioning pituitary adenomas (NFPA). With this purpose, this study analyzed MR images from 15 patients of NFPA retrospectively separated in two groups: the recurrent patient group, formed by seven (7) patients who exhibited tumor recurrence in a period of 4,640 +- 0,653 years (mean +- standard error) of follow-up period after the first surgical approach; and the stable patient group formed by eight (8) patients with lesions considered stable in a period of 4,512 +- 0,536 years. A three-dimensional segmentation mask of the tumor lesion was manually performed by a specialist over preoperative 3D T1-W DCE MR image for each patient. Next, this segmentation and the preoperative MRI itself were used to extract 48 numerical textural features. Additionally, 4 clinical features were considered in the study: immunohistochemistry, invasiveness, age at first surgery, and gender, totalizing 52 features. Each one of those 52 features were tested through conventional univariate statistical tests to see if there were evidence of their discrimination power to differentiate these two patient groups. Moreover, different subsets of those features were used to build machine learning prediction models (using k-nearest neighbor (kNN), decision tree (DTC), and random forest (RFC) algorithms) to investigate a classification model capable of identifying the patients that would experience tumor recurrence after the first surgery. 9 quantitative textural features were found to be individually significant (p < 0,05) in the differentiation of recurrent and stable patient group. Affirming these findings, the ROC curve analysis for each one of those 9 features exhibited an AUC score from 0.803 to 0.857 meaning a good classification performance. Age, immunohistochemistry and invasiveness status, and gender did not show evidence of association with tumor recurrence. The best performances with classification algorithms were obtained with kNN and RFC, both reached specificity of 1.000 conserving high accuracy (0.933) and scoring 0.991 in ROC curve analysis, what characterizes an almost perfect classification performance. DTC did not show any improvement compared to the univariate classification results. These findings allow to conclude that textural features are useful in the prediction of tumor recurrence after first surgery in NFPA patients. And that the prediction value of those features can be observed with both conventional univariate statistical tests and multivariate analyses through machine learning algorithms

O presente trabalho propõe o uso de parâmetros de textura extraídos computacionalmente de IRM como biomarcadores de imagem na predição de recorrência tumoral em pacientes de adenomas pituitários clinicamente não funcionantes (APNF). Para isso, esse estudo analisou imagens de RM de 15 pacientes de APNF retrospectivamente separados em dois grupos: O grupo de pacientes recorrentes, definido por 7 pacientes que exibiram recorrência tumoral em um período de 4, 640 +- 0, 653 anos (média +- erro padrão) de acompanhamento clínico após a primeira abordagem cirúrgica; e o grupo de pacientes estáveis, formado por 8 pacientes com lesões consideradas estáveis em um período de 4,512 +- 0, 536 anos. Uma máscara de segmentação tridimensional da lesão tumoral foi construída manualmente por um especialista sobre a imagem 3D T1-W DCE pré-operatória para cada paciente. Em seguida, essa segmentação e a própria imagem de ressonância foram usadas para extrair 48 características numéricas de textura. Adicionalmente, 4 características clínicas foram consideradas no estudo: a imunohistoquímica, invasividade, idade na primeira cirurgia e sexo, totalizando 52 características. Cada uma destas 52 características fora testada através de testes estatísticos convencionais univariados para ver se existia evidencias do poder discriminatório dessas características para diferenciar esses dois grupos de pacientes. Mais adiante, diferentes subconjuntos dessas características foram usados para construir modelos de predição baseados na teoria de aprendizagem de máquinas (usando os algoritmos k-nearest neighbor (kNN), decision tree (DTC), e random forest (RFC)) para investigar um modelo de classificação capaz de identificar os pacientes que experimentariam recorrência tumoral após a primeira cirurgia. 9 características de textura foram consideradas individualmente significantes (p < 0, 05) na diferenciação dos grupos de paciente recorrente e estável. Afirmando esses achados, a análise com a curva ROC para cada uma das 9 características exibiu medidas de AUC de 0,803 a 0,857 significando uma boa performance de classificação. A idade, imunoistoquímica, invasividade e sexo não mostraram evidencias de associação com recorrência tumoral. As melhores performances com algoritmos de classificação foram com kNN e RFC, ambos atingiram uma especificidade de 1,000 conservando alta acurácia (0,933) e obtendo 0,991 na análise com a curva ROC, o que caracteriza uma performance de classificação quase perfeita. DTC não mostrou nenhuma melhora se comparado com os resultados das classificações univariadas. Esses resultados permitem concluir que parâmetros de textura são úteis na predição de recorrência tumoral após a primeira cirurgia em pacientes de APNF. E que os valores de predição dessas características podem ser observados por testes estatísticos univariados convencionais e por análises multivariadas através de algoritmos baseados em aprendizagem de máquinas / The present work proposes the usage of texture features computationally extracted from MRI as imaging biomarkers in the prediction of tumor recurrence in patients with non-functioning pituitary adenomas (NFPA). With this purpose, this study analyzed MR images from 15 patients of NFPA retrospectively separated in two groups: the recurrent patient group, formed by seven (7) patients who exhibited tumor recurrence in a period of 4,640 +- 0,653 years (mean +- standard error) of follow-up period after the first surgical approach; and the stable patient group formed by eight (8) patients with lesions considered stable in a period of 4,512 +- 0,536 years. A three-dimensional segmentation mask of the tumor lesion was manually performed by a specialist over preoperative 3D T1-W DCE MR image for each patient. Next, this segmentation and the preoperative MRI itself were used to extract 48 numerical textural features. Additionally, 4 clinical features were considered in the study: immunohistochemistry, invasiveness, age at first surgery, and gender, totalizing 52 features. Each one of those 52 features were tested through conventional univariate statistical tests to see if there were evidence of their discrimination power to differentiate these two patient groups. Moreover, different subsets of those features were used to build machine learning prediction models (using k-nearest neighbor (kNN), decision tree (DTC), and random forest (RFC) algorithms) to investigate a classification model capable of identifying the patients that would experience tumor recurrence after the first surgery. 9 quantitative textural features were found to be individually significant (p < 0,05) in the differentiation of recurrent and stable patient group. Affirming these findings, the ROC curve analysis for each one of those 9 features exhibited an AUC score from 0.803 to 0.857 meaning a good classification performance. Age, immunohistochemistry and invasiveness status, and gender did not show evidence of association with tumor recurrence. The best performances with classification algorithms were obtained with kNN and RFC, both reached specificity of 1.000 conserving high accuracy (0.933) and scoring 0.991 in ROC curve analysis, what characterizes an almost perfect classification performance. DTC did not show any improvement compared to the univariate classification results. These findings allow to conclude that textural features are useful in the prediction of tumor recurrence after first surgery in NFPA patients. And that the prediction value of those features can be observed with both conventional univariate statistical tests and multivariate analyses through machine learning algorithms

No description available.

For gaseous fire extinguishing systems, the maximum percent of agent in pipe, i.e., pipe volume vs. agent liquid volume should be determined for proper system design and performance by confirming the maximum length of pipe run in which their flow calculation methods can predict the discharge pressures and agent concentration. It is the purpose of this paper to determine the ability and limitations of the NFPA 12 flow calculation methodology to identify the maximum percent of agent in pipe by conducting full scale low-pressure CO2 system discharge tests. A total of twenty low-pressure CO2 system discharge tests were conducted under different conditions. If all the measured pressures at the three node points of pipe runs and the measured CO2 concentrations in the test enclosures did not deviate from the predicted values of computerized flow calculations by more than ¡¾10 percent, the tests were judged to be acceptable. In the test results, the low-pressure CO2 system with a pipe run exceeding 492 ft (150 m) was not likely to achieve the concentration required for fire extinguishment within the determined discharge time although the pipe network was installed in compliance with the calculations based on the pressure drop equation in NFPA 12.