Formal Verification of Temporal Properties for Parametrized Concurrent Programs and Concurrent Data Structures

Los tipos de datos concurrentes son implementaciones concurrentes de las abstracciones de datos clásicas, con la diferencia de que han sido específicamente diseñados para aprovechar el gran paralelismo disponible en las modernas arquitecturas multiprocesador y multinúcleo. La correcta manipulación de los tipos de datos concurrentes resulta esencial para demostrar la completa corrección de los sistemas de software que los utilizan. Una de las mayores dificultades a la hora de diseñar y verificar tipos de datos concurrentes surge de la necesidad de tener que razonar acerca de un número arbitrario de procesos que invocan estos tipos de datos de manera concurrente. Esto requiere considerar sistemas parametrizados. En este trabajo estudiamos la verificación formal de propiedades temporales de sistemas concurrentes parametrizados, poniendo especial énfasis en programas que manipulan estructuras de datos concurrentes. La principal dificultad a la hora de razonar acerca de sistemas concurrentes parametrizados proviene de la interacción entre el gran nivel de concurrencia que éstos poseen y la necesidad de razonar al mismo tiempo acerca de la memoria dinámica. La verificación de sistemas parametrizados resulta en sí un problema desafiante debido a que requiere razonar acerca de estructuras de datos complejas que son accedidas y modificadas por un numero ilimitado de procesos que manipulan de manera simultánea el contenido de la memoria dinámica empleando métodos de sincronización poco estructurados. En este trabajo, presentamos un marco formal basado en métodos deductivos capaz de ocuparse de la verificación de propiedades de safety y liveness de sistemas concurrentes parametrizados que manejan estructuras de datos complejas. Nuestro marco formal incluye reglas de prueba y técnicas especialmente adaptadas para sistemas parametrizados, las cuales trabajan en colaboración con procedimientos de decisión especialmente diseñados para analizar complejas estructuras de datos concurrentes. Un aspecto novedoso de nuestro marco formal es que efectúa una clara diferenciación entre el análisis del flujo de control del programa y el análisis de los datos que se manejan. El flujo de control del programa se analiza utilizando reglas de prueba y técnicas de verificación deductivas especialmente diseñadas para lidiar con sistemas parametrizados. Comenzando a partir de un programa concurrente y la especificación de una propiedad temporal, nuestras técnicas deductivas son capaces de generar un conjunto finito de condiciones de verificación cuya validez implican la satisfacción de dicha especificación temporal por parte de cualquier sistema, sin importar el número de procesos que formen parte del sistema. Las condiciones de verificación generadas se corresponden con los datos manipulados. Estudiamos el diseño de procedimientos de decisión especializados capaces de lidiar con estas condiciones de verificación de manera completamente automática. Investigamos teorías decidibles capaces de describir propiedades de tipos de datos complejos que manipulan punteros, tales como implementaciones imperativas de pilas, colas, listas y skiplists. Para cada una de estas teorías presentamos un procedimiento de decisión y una implementación práctica construida sobre SMT solvers. Estos procedimientos de decisión son finalmente utilizados para verificar de manera automática las condiciones de verificación generadas por nuestras técnicas de verificación parametrizada. Para concluir, demostramos como utilizando nuestro marco formal es posible probar no solo propiedades de safety sino además de liveness en algunas versiones de protocolos de exclusión mutua y programas que manipulan estructuras de datos concurrentes. El enfoque que presentamos en este trabajo resulta ser muy general y puede ser aplicado para verificar un amplio rango de tipos de datos concurrentes similares. Abstract Concurrent data types are concurrent implementations of classical data abstractions, specifically designed to exploit the great deal of parallelism available in modern multiprocessor and multi-core architectures. The correct manipulation of concurrent data types is essential for the overall correctness of the software system built using them. A major difficulty in designing and verifying concurrent data types arises by the need to reason about any number of threads invoking the data type simultaneously, which requires considering parametrized systems. In this work we study the formal verification of temporal properties of parametrized concurrent systems, with a special focus on programs that manipulate concurrent data structures. The main difficulty to reason about concurrent parametrized systems comes from the combination of their inherently high concurrency and the manipulation of dynamic memory. This parametrized verification problem is very challenging, because it requires to reason about complex concurrent data structures being accessed and modified by threads which simultaneously manipulate the heap using unstructured synchronization methods. In this work, we present a formal framework based on deductive methods which is capable of dealing with the verification of safety and liveness properties of concurrent parametrized systems that manipulate complex data structures. Our framework includes special proof rules and techniques adapted for parametrized systems which work in collaboration with specialized decision procedures for complex data structures. A novel aspect of our framework is that it cleanly differentiates the analysis of the program control flow from the analysis of the data being manipulated. The program control flow is analyzed using deductive proof rules and verification techniques specifically designed for coping with parametrized systems. Starting from a concurrent program and a temporal specification, our techniques generate a finite collection of verification conditions whose validity entails the satisfaction of the temporal specification by any client system, in spite of the number of threads. The verification conditions correspond to the data manipulation. We study the design of specialized decision procedures to deal with these verification conditions fully automatically. We investigate decidable theories capable of describing rich properties of complex pointer based data types such as stacks, queues, lists and skiplists. For each of these theories we present a decision procedure, and its practical implementation on top of existing SMT solvers. These decision procedures are ultimately used for automatically verifying the verification conditions generated by our specialized parametrized verification techniques. Finally, we show how using our framework it is possible to prove not only safety but also liveness properties of concurrent versions of some mutual exclusion protocols and programs that manipulate concurrent data structures. The approach we present in this work is very general, and can be applied to verify a wide range of similar concurrent data types.

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.

Safety study of transcranial static magnetic field stimulation (tSMS) of the human cortex

Transcranial static magnetic field stimulation (tSMS) in humans reduces cortical excitability. Objective: The objective of this study was to determine if prolonged tSMS (2 h) could be delivered safely in humans. Safety limits for this technique have not been described. Methods: tSMS was applied for 2 h with a cylindric magnet on the occiput of 17 healthy subjects. We assessed tSMS-related safety aspects at tissue level by measuring levels of neuron-specific enolase (NSE,a marker of neuronal damage) and S100 (a marker of glial reactivity and damage). We also included an evaluation of cognitive side effects by using a battery of visuomotor and cognitive tests. Results: tSMS did not induce any significant increase in NSE or S100. No cognitive alteration was detected. Conclusions: Our data indicate that the application of tSMS is safe in healthy human subjects, at least within these parameters

Intermediate hosts of Toxoplasma gondii and food safety:epidemiology, genetics and parasite survival in meat-producing animals in Italy

Toxoplasma gondii is a coccidian parasite with a global distribution. The definitive host is the cat (and other felids). All warm-blooded animals can act as intermediate hosts, including humans. Sexual reproduction (gametogony) takes place in the final host and oocysts are released in the environment, where they then sporulate to become infective. In intermediate hosts the cycle is extra-intestinal and results in the formation of tachyzoites and bradyzoites. Tachyzoites represent the invasive and proliferative stage and on entering a cell it multiplies asexually by endodyogeny. Bradyzoites within tissue cysts are the latent form. T. gondii is a food-borne parasite causing toxoplasmosis, which can occur in both animals and humans. Infection in humans is asymptomatic in more than 80% of cases in Europe and North-America. In the remaining cases patients present fever, cervical lymphadenopathy and other non-specific clinical signs. Nevertheless, toxoplasmosis is life threatening if it occurs in immunocompromised subjects. The main organs involved are brain (toxoplasmic encephalitis), heart (myocarditis), lungs (pulmonary toxoplasmosis), eyes, pancreas and parasite can be isolated from these tissues. Another aspect is congenital toxoplasmosis that may occur in pregnant women and the severity of the consequences depends on the stage of pregnancy when maternal infection occurs. Acute toxoplasmosis in developing foetuses may result in blindness, deformation, mental retardation or even death. The European Food Safety Authority (EFSA), in recent reports on zoonoses, highlighted that an increasing numbers of animals resulted infected with T. gondii in EU (reported by the European Member States for pigs, sheep, goats, hunted wild boar and hunted deer, in 2011 and 2012). In addition, high prevalence values have been detected in cats, cattle and dogs, as well as several other animal species, indicating the wide distribution of the parasite among different animal and wildlife species. The main route of transmission is consumption of food and water contaminated with sporulated oocysts. However, infection through the ingestion of meat contaminated with tissue cysts is frequent. Finally, although less frequent, other food products contaminated with tachyzoites such as milk, may also pose a risk. The importance of this parasite as a risk for human health was recently highlighted by EFSA’s opinion on modernization of meat inspection, where Toxoplasma gondii was identified as a relevant hazard to be addressed in revised meat inspection systems for pigs, sheep, goats, farmed wild boar and farmed deer (Call for proposals -GP/EFSA/BIOHAZ/2013/01). The risk of infection is more highly associated to animals reared outside, also in free-range or organic farms, where biohazard measure are less strict than in large scale, industrial farms. Here, animals are kept under strict biosecurity measures, including barriers, which inhibit access by cats, thus making soil contamination by oocysts nearly impossible. A growing demand by the consumer for organic products, coming from free-range livestock, in respect of animal-welfare, and the desire for the best quality of derived products, have all led to an increase in the farming of free-range animals. The risk of Toxoplasma gondii infection increases when animals have access to environment and the absence of data in Italy, together with need for in depth study of both the prevalence and genotypes of Toxoplasma gondii present in our country were the main reasons for the development of this thesis project. A total of 152 animals have been analyzed, including 21 free-range pigs (Suino Nero race), 24 transhumant Cornigliese sheep, 77 free-range chickens and 21 wild animals. Serology (on meat juice) and identification of T. gondii DNA through PCR was performed on all samples, except for wild animals (no serology). An in-vitro test was also applied with the aim to find an alternative and valid method to bioassay, actually the gold standard. Meat samples were digested and seeded onto Vero cells, checked every day and a RT-PCR protocol was used to determine an eventual increase in the amount of DNA, demonstrating the viability of the parasite. Several samples were alos genetically characterized using a PCR-RFLP protocol to define the major genotypes diffused in the geographical area studied. Within the context of a project promoted by Istituto Zooprofilattico of Pavia and Brescia (Italy), experimentally infected pigs were also analyzed. One of the aims was to verify if the production process of cured “Prosciutto di Parma” is able to kill the parasite. Our contribution included the digestion and seeding of homogenates on Vero cells and applying the Elisa test on meat juice. This thesis project has highlighted widespread diffusion of T. gondii in the geographical area taken into account. Pigs, sheep, chickens and wild animals showed high prevalence of infection. The data obtained with serology were 95.2%, 70.8%, 36.4%, respectively, indicating the spread of the parasite among numerous animal species. For wild animals, the average value of parasite infection determined through PCR was 44.8%. Meat juice serology appears to be a very useful, rapid and sensitive method for screening carcasses at slaughterhouse and for marketing “Toxo-free” meat. The results obtained on fresh pork meat (derived from experimentally infected pigs) before (on serum) and after (on meat juice) slaughter showed a good concordance. The free-range farming put in evidence a marked risk for meat-producing animals and as a consequence also for the consumer. Genotyping revealed the diffusion of Type-II and in a lower percentage of Type-III. In pigs is predominant the Type-II profile, while in wildlife is more diffused a Type-III and mixed profiles (mainly Type-II/III). The mixed genotypes (Type-II/III) could be explained by the presence of mixed infections. Free-range farming and the contact with wildlife could facilitate the spread of the parasite and the generation of new and atypical strains, with unknown consequences on human health. The curing process employed in this study appears to produce hams that do not pose a serious concern to human health and therefore could be marketed and consumed without significant health risk. Little is known about the diffusion and genotypes of T. gondii in wild animals; further studies on the way in which new and mixed genotypes may be introduced into the domestic cycle should be very interesting, also with the use of NGS techniques, more rapid and sensitive than PCR-RFLP. Furthermore wildlife can become a valuable indicator of environmental contamination with T. gondii oocysts. Other future perspectives regarding pigs include the expansion of the number of free-range animals and farms and for Cornigliese sheep the evaluation of other food products as raw milk and cheeses. It should be interesting to proceed with the validation of an ELISA test for infection in chickens, using both serum and meat juice on a larger number of animals and the same should be done also for wildlife (at the moment no ELISA tests are available and MAT is the reference method for them). Results related to Parma ham do not suggest a concerning risk for consumers. However, further studies are needed to complete the risk assessment and the analysis of other products cured using technological processes other than those investigated in the present study. For example, it could be interesting to analyze products such as salami, produced with pig meat all over the Italian country, with very different recipes, also in domestic and rural contexts, characterized by a very short period of curing (1 to 6 months). Toxoplasma gondii is one of the most diffuse food-borne parasites globally. Public health safety, improved animal production and protection of endangered livestock species are all important goals of research into reliable diagnostic tools for this infection. Future studies into the epidemiology, parasite survival and genotypes of T. gondii in meat producing animals should continue to be a research priority.

Behavior-Based Safety: Developing a Regional Guidance

A behavior-based safety program improves overall safety culture and safety performance of an organization. A solid behavior-based safety program is achieved by successfully implementing key components. Key components include management commitment to the process, an effective training program, a cohesive observation process and a successful data collecting and reporting system. This Applied Capstone project defines a regional approach for each of the key components of a behavior-based safety program. Recommendations resulting from the project provide the company studied guidance on developing a consistent behavior-based safety program.

Efficient Mitigation of Data and Control Flow Errors in Microprocessors

The use of microprocessor-based systems is gaining importance in application domains where safety is a must. For this reason, there is a growing concern about the mitigation of SEU and SET effects. This paper presents a new hybrid technique aimed to protect both the data and the control-flow of embedded applications running on microprocessors. On one hand, the approach is based on software redundancy techniques for correcting errors produced in the data. On the other hand, control-flow errors can be detected by reusing the on-chip debug interface, existing in most modern microprocessors. Experimental results show an important increase in the system reliability even superior to two orders of magnitude, in terms of mitigation of both SEUs and SETs. Furthermore, the overheads incurred by our technique can be perfectly assumable in low-cost systems.

Patient safety in the operating room and documentary quality related to infection and hospitalization

Objective: To describe the documentary quality of two records related to patient safety in the operating room and to identify differences between information related to infection and hospitalization. Methods: Comparative study based on two cross sections, conducted with 3,033 patients who had been hospitalized for more than 24 hours in an Orthopedics and Traumatology Center. Sociodemographic and clinical data, as well as information provided in forms were compared. Postoperative infection was identified as an adverse event. Results: There was a significant correlation between hospitalization days and the total number of diagnoses collected (Pearson=0.328; p<0.001). When diagnoses and infections were grouped together, a significant value was found between closed fractures and infection (p=0.001). Conclusion: Differences in the degree of completion were observed between the two records. There were no differences between adverse events.

Standardized reporting using CODES (Crash Outcome Data Evaluation System).

National Highway Traffic Safety Administration, Washington, D.C.

Expert systems for crash data collection.

Federal Highway Administration, Office of Safety and Traffic Operations, Washington, D.C.

Commercial vehicle safety -- technology and practice in Europe.

Federal Highway Administration, Office of Policy Planning, Office of International Program, Washington, D.C.

Ford Escort GL baseline weight data. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Development of tire intermix performance and inspection system. Volume III: laboratory tire test data. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Data for validation of crash victim simulator. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Test site instrumentation study. Volume II: Crash 1 and Crash 2 "raw" data. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Testing the validity of the lognormal probability model : computer analysis of carbon monoxide data from U.S. cities /

Mode of access: Internet.