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Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory condition characterized by inflammation and narrowing of the airways, leading to symptoms such as shortness of breath, coughing, and chest tightness. Treatment typically involves lifestyle adjustments, medication, and pulmonary rehabilitation to improve lung function and quality of life. This study presents a model examining COPD patient behavior within cohort-based strategies, focusing on how environmental factors impact vital signs across the entire cohort. We developed a comprehensive virtual clinical trial model that encompasses study protocol design, participant recruitment, virtual data collection, outcome analysis, and conclusions. This includes remote symptom monitoring, virtual healthcare consultations, treatment adherence assessments, and research data collection. Additionally, we explore the influence of external variables such as environmental conditions, comorbidities, and lifestyle factors on chronic disease symptoms and disease stability. We used an Agent-Based Model(ABM) to incorporate these factors to assess COPD progression and treatment efficacy. Individual agents represent COPD patients, each characterized by attributes such as age, smoking history, lung function, comorbidities, and treatment plans.

Tools to identify problems and improve MPI applications performance running on an HPC system require information from the application and the system. In this work we will focus on defining a methodology to analyze how memory usage affects an MPI application performance running on this HPC system. This methodology will obtain valid and comparable data based on different memory access patterns that will allow defining key performance values used to characterize the HPC system behavior in front of these access patterns and to characterize the Application Signature behavior, (PAS2P Signature that obtains the representative phases of the MPI application) in front of these same access patterns. With these key performance values, we will be able to detect memory access application problems, suggest improvements and define a mapping policy for this application in this HPC system to improve its performance and to determine limits to these improvements.

As optimization problems become more complicated and extensive, parameterization becomes complex, resulting in a difficult task requiring significant amounts of time and resources. In this paper we propose a heuristic search algorithm we call MCSA (Montecarlo-Clustering Search Algorithm), which is based on Montecarlo sampling, and a clustering strategy involving two techniques. Our objective is to apply MCSA, an inherently stochastic method, to address optimization problems. To assess its performance, we conducted an evaluation using classical benchmark optimization functions. Additionally, we leveraged the CEC2017 benchmark suite to comprehensively evaluate the algorithm, highlighting the pivotal role of the Exploration stage in our methodology. Subsequently, we extended our methodology to tackle a practical combinatorial problem, the Knapsack problem. This NP-Hard problem holds significant real-world applications in resource allocation, scheduling, planning, logistics, and more. Our contributions lie in parameterizing the Knapsack Problem to align with MCSA’s parameters for reference indicators adjustment and achieving high-quality solutions, surpassing 90% in comparison to exhaustive methods such as branch and bound.

In this paper, a new method is presented to study the impacts of telemedicine on the performance of an emergency department in Spain. Spain's Demographics indicate that this country is experiencing population aging, resulting in overcrowding of emergency departments and significant demand on the healthcare system. However, it has been reported that most patients visiting emergency departments are not in an urgent clinical condition, thus they causing hospital overcrowding, high medical expenses, delays in clinical service delivery and low service efficiency for urgent patients who truly need emergency care. Telemedicine and e-health are considered as solutions for remote delivery of health services to care seekers in order to decrease hospital visits for patients who are in less of an emergency condition. In this study, by using detailed computational modeling and clinical data, we have investigated the impacts of telemedicine on the performance of an emergency department through estimations of Length of Stay as a quantitative index for evaluation of quality of service in the emergency department. Specifically, an agent-based modeling and simulation system was developed and used to study the behavior of the emergency department by taking detailed modeling parameters, including varying the number of non-urgent arrivals as a result of telemedicine, into account as inputs of the model. The inputs were provided through collection and analysis of clinical data that enabled us to predict how telemedicine changes emergency department visits. Our results indicated that emergency departments would experience decreases equal to 41.14% in total Length of Stay if eliminating all non-urgent visits and decreases of up to 10.48% if restricting the non-urgent visits. The developed computational tool in this study and the corresponding results obtained can provide decision makers and health care providers with objective information on the impacts of e-health services on the efficiency of emergency department and they can have also implications for care delivery, optimizing resources, planning, and improving the quality of care.

The combination of the progressive growth of an aging population, increased life expectancy and a greater number of chronic diseases all contribute significantly to the growing demand for emergency medical care, and thus, causing saturation in Emergency Departments (EDs). This saturation is usually due to the admission of non-urgent patients, who constitute a high percentage of patients in an ED. The Agent-based Model (ABM) is one of the most important tools that helps to study complex systems and explores the emergent behavior of this type of department. Its simulation more accurately reflects the complexity of the operation of real systems. Our proposal is the design of an ABM to schedule the access of these non-critical patients into an ED, which can be useful for the service management dealing with the actual growing demand for emergency care. We suppose that a relocation of these non-critical patients within the expected input pattern, provided initially by historical records, enables a reduction in waiting time for all patients, and therefore, it will lead to an improvement in the quality of service. It would also allow us to avoid long waiting times. This research offers the availability of relevant knowledge for Emergency Department managers in order to help them make decisions to improve the quality of the service, in anticipation of the expected growing demand of the service in the very near future.

Progress in the parallel programming field has allowed scientific applications to be developed with more complexity and accuracy. However, such precision requires greater computational power in order to be executed. How- ever, updating the local systems could be considered an expensive decision. For this reason, cloud computing is emerging as a commercial infrastructure that allows us to eliminate maintaining the computing hardware. For this reason, cloud is promising to be a computing alternative to clusters, grids and supercomputing for executing these applications. In this sense, this work is focused on describing the manner of migrating our prediction tool PAS2P (parallel application signature for performance prediction), and how we have to analyze our method for executing SPMD ap- plications efficiently on these cloud environments. In both cases, cloud could be considered a huge challenge due to the environment virtualization and the communication heterogeneities, which can seriously affect the application performance. However, our experimental evaluations make it clear that our prediction tool can predict with an error rate lower than 6,46%, considering that the signature for prediction represents a small portion of the execution time. On the other hand, analyzing the application parameters over the cloud computing allows us to find through an analytical model, which is the ideal number of virtual cores needed to obtain the maximum speedup under a defined efficiency. In this case the error rate was lower that 9% for the application tested.

Over the last decade, the computing clusters have been updated in order to satisfy the increasing demand of greater computational power for running applications. However, this increasing is transformed in more system en- ergy consumption, which results in financial, environmental and in some cases with social consequences. Hence, the ideal is to achieve an scenario that allows the system admin- istrator to find a trade-off between time and energy-efficiency for parallel algorithms on virtualized environments. The main objective of this work is based on developing an analytical model to predict the energy consumption and energy delay product (EDP) for SPMD applications on virtual environments. The SPMD applications selected are designed through a message passing interface (MPI) library with high communication volumes, which can generate im- balance issues that affect seriously the execution time and also the energy-efficiency. Our method is composed by four phases (characterization, tile distribution model, mapping and scheduling). This method has been validated using scientific applications and we observe that the minimum Energy and EDP values are located close to the values calculated with our analytical model with an error rate between 4% and 9%.

Cloud Computing offers the possibility of computing resources, allowing remote access to software, storage and data processing through the Internet. Infrastructures as a Service (IaaS), it is a flexible space which can be used as an experimental environment, in which experiments can be carried out similar to a real environment, such as in a cluster can be carried out. Before making installations and changes in a production cluster or select resource in the cloud, it is important to analyze the impact of this change. For this reason we propose using the cloud to carry out the study of previous viability. In this paper, we observe the viability of using the cloud to analyze the behavior of the Checkpoint as one of the Fault Tolerance strategies, establishing the differences that exist in the information generated in a real environment (cluster) and a virtual environment (cloud). The results obtained show that due to the variability of the cloud, the impact on the benefits cannot be analyzed. However, the cloud is suitable for extracting the spatial and temporal behavior pattern of the checkpoint, which helps to characterize it and this will help us to know the right configuration and the development of methodologies and tools that simulate and predict the execution of the checkpoint in a real environment.

Introduction: In intensive and intermediate care units, patient data consists of lower frequency asynchronous clinical data and higher frequency synchronous physiological data streams generated by medical equipment. Traditionally, nurses manually fill out forms, recording clinical data, and physiological data observed in each equipment at hourly intervals. At the end of the day, the physicians exhaustively analyse the data of the forms and give nurses the treatment indications. This methodology can produce delayed and inaccurate diagnosis, and is prone to human errors due to the large volume of data to analyse. We propose a cost-effective system for automatic and real-time analysis of patients data that increase the quality of care and quantity of treated patients. The system improves the horizontal integration between nurses and physicians, and gives medical staff the ability to plan and begin treatments sooner or potentially stop the progression of a condition. The treatement costs are reduced and the number of patients are increased by reducing the length of stay. Furthermore, the system can be made more intelligent by extracting new medical knowledge from historical patients data. Significant technical challenges are related to data extraction from medical equipment, and the development of the real-time big data analysis infrastructure. Methods: Our system is based on clinical rules. Each rule defines the conditions, relating parameters and values, which must be met to generate an alert indicating posible risk (current or future) in the patient’s health. Each patient can be associated with a particular set of rules, created from knowledge of experts or clinical guidelines. Our system acquire data from medical equipment and the electronic health record system. The clinical rules associated to each patient are continuously evaluated, and an alert is issue when a rule match. Historical patient data from multiple hospitals are used to create new clinical rules. Results: We have implemented a prototype at Hospital Francisco Lopez Lima. It includes: 1) a validated embedded device that extracts the electrocardiogram signal from an analog output of a medical monitor, 2) a real-time big data infrastructure capable of processing a complete set of clinical rules of different types in time and form, using Apache Kafka, Flink and Cassandra. Discussions: Unlike other approaches, our findings suggest that it is possible to develop the system using mature Free Software products. They offer a high level of security for sensitive data and at a lower cost. Furthermore, when possible, interfacing with analog outputs of medical equipment produces portable and cost-effective solutions, avoiding the use of expensive additional devices provided by manufacturers. Conclusions: A system for real-time analysis of high frequency physiological data is possible with the current technology. This tool can improve the delivery of integrated intensive health care performance. Our system tries to be a low cost solution to be adopted by a greater number of hospitals, benefiting more people. We find that the techniques and technologies used grant solid foundations for the construction of a reliable, secure and cost-effective system, able to scale and support an increasing number of patients and captured data. This research has been supported under contract TIN2017-84875-P, (AEI/FEDER,UE).

Introducción: Tradicionalmente la fisioterapia, ha precisado de la interacción presencial del fisioterapeuta y el paciente. Sin embargo existen tratamientos, como el que hemos desarrollado, donde podemos contar con el paciente, empoderándolo, para que sea responsable y pueda trabajar de forma autónoma en su tratamiento, bajo la supervisión de un profesional que vigilará que el cumplimiento y la evolución sean las adecuadas. Método: Se propone un método de trabajo, en este caso específico de la fisioterapia del suelo pélvico, pero adaptable a otros tratamientos donde sea posible la “actividad guiada del paciente en el tratamiento”, que combina la experiencia profesional de los fisioterapeutas y su experiencia en el uso de la tecnología, para conseguir la máxima eficiencia clínica, optimizando a la vez el tiempo, la comodidad y la inversión económica, para mejorar la calidad de vida de nuestros pacientes. Se ha diseñado un modelo de telemedicina para evaluar, controlar y tratar la incontinencia urinaria, garantizando, además de la calidad del tratamiento, la privacidad y confidencialidad de las pacientes. Se evalúan las pacientes mediante la cumplimentación de una historia clínica, los cuestionarios relacionados con la patología y una medición de presión intravaginal mediante un biofeedback. Posteriormente, se realiza una primera visita virtual por videoconferencia. En ella se establecen los objetivos de tratamiento y se realiza la formación teórica y práctica de las pacientes: aprendizaje de los ejercicios y uso de los dispositivos. El tratamiento de fisioterapia se realiza complementando dos procedimientos distintos: 1) Clases grupales (máximo 6 pacientes), mediante videoconferencia, para realizar gimnasia abdominal Hipopresiva; 2) Un plan de ejercicios personalizado de entrenamiento de la musculatura del suelo pélvico (Kegel), que realizará con ayuda de un biofeedback. Las pacientes recibirán una evaluación periódica de su fisioterapeuta y ambos podrán seguir y consultar la evolución durante el proceso. Las pacientes podrán ponerse en contacto con su fisioterapeuta, en cualquier momento, y concertar una consulta por videoconferencia si fuera necesario. Resultados: En una prueba piloto, se ha observado que, utilizando este método, la adherencia de las pacientes al tratamiento es superior y las pacientes se sienten más involucradas en el proceso, lo que se traduce en una mejora clínica frente al “método tradicional”. Discusión: El uso de la telemedicina no es simplemente trasladar el tratamiento presencial a una sesión de videoconferencia, sino reinventar la forma como se plantean los tratamientos y adaptarlos de manera que puedan ser realizados mediante la telemedicina. Esto hace necesaria una formación básica para el paciente en el uso de las distintas herramientas, así como una capacidad de adaptación del fisioterapeuta al uso de la tecnología a su alcance. Conclusiones: Los resultados obtenidos en la prueba piloto sugieren que, el uso de la telemedicina puede contribuir, de forma altamente positiva, en el tratamiento de la incontinencia urinaria. Lecciones aprendidas: La respuesta a este modelo de tratamiento ha sido mucho más positiva de lo que se esperaba. Será necesario perfilar y mejorar aspectos metodológicos, sobre todo en la evaluación de las pacientes, lo que permitirá una mejor personalización del tratamiento.

In recent years, the use of public cloud platforms as infrastructure has been gaining popularity in many scientific areas and High Performance Computing (HPC) is no exception. These kinds of platforms can be used by system administrators as Test-Bed systems for evaluating and detecting performance inefficiencies in the I/O subsystem, and for taking decisions about the configuration parameters that have influence on the performance of an application, without compromising the performance of the production HPC system. In this paper, we propose a methodology to evaluate parallel applications by using virtual clusters as a test system. Our experimental validation indicates that virtual clusters are a quick and easy solution for system administrators, for analyzing the impact of the I/O system on the I/O kernels of the parallel applications and for taking performance decisions in a controlled environment.

Cloud computing can offer us different distance services over the internet. We propose an online application model for using health care systems that works by using cloud computing. It can provide a higher quality of services remotely and along with that, it decreases the cost of chronic patients. This model is composed of two sub-model, each of which uses a different service. One of these is software as a service (SaaS), which is user related, and the other one is Platform as a service (PaaS), that is engineer related. Doctors classify the chronic diseases into different stages according to their symptoms. As the clinical data has a non-numeric value, we use the fuzzy logic system in the Paas model to design this online application model. Based on this classification, patients can receive the proper recommendation through smart devices (SaaS model).

Even though the cloud platform promises to be reliable, several availability incidents prove that it is not. How can we be sure that a parallel application finishes it´s execution even if a site is affected by a failure? This paper presents H-RADIC, an approach based on RADIC architecture, that executes parallel applications protected by RADIC in at least 3 different virtual clusters or sites. The execution state of each site is saved periodically in another site and it is recovered in case of failure. The paper details the configuration of the architecture and the experiment´s results using 3 clusters running NAS parallel applications protected with DMTCP, a very well-known distributed multi-threaded checkpoint tool. Our experiments show that by adding a cluster protector it will be possible to implement the next level in the hierarchy, where the first level in the RADIC hierarchy works as an observer at a site level. In adition, the experiments showed that the protection implementation is out of the critical path of the application and it depends on the utilized resources.

Currently, there is an increasing interest about the cloud platform by the High Performance Computing (HPC) community, and the Parallel I/O for High Performance Systems is not an exception. In cloud platforms, the user takes into account not only the execution time but also the cost, because the cost can be one of the most important issue. In this paper, we propose a methodology to quickly evaluate the performance and cost of Virtual Clusters for parallel scientific application that uses parallel I/O. From the parallel application I/O model automatically extracted with our tool PAS2P-IO, we obtain the I/O requirements and then the user can select the Virtual Cluster that meets the application requirements. The application I/O model does not depend on the underlying I/O system. One of the main benefits of applying our methodology is that it is not necessary to execute the application to select the Virtual Cluster on cloud. Finally, costs and performance-cost ratio for the Virtual Clusters are provided to facilitate the decision making on the selection of resources on a cloud platform.

Transient faults are becoming a critical concern among current trends of design of general-purpose multiprocessors. Because of their capability to corrupt programs outputs, their impact gains importance when considering long duration, parallel scientific applications, due to the high cost of re-launching execution from the beginning in case of incorrect results. This paper introduces SMCV tool which improves reliability for high-performance systems. SMCV replicates application processes and validates the contents of the messages to be sent, preventing the propagation of errors to other processes and restricting detection latency and notification. To assess its utility, the overhead of SMCV tool is evaluated with three computationally-intensive, representative parallel scientific applications. The obtained results demonstrate the efficiency of SMCV tool to detect transient faults occurrences.

Extracting the performance characteristics of the different I/O configurations of the I/O system. These activities are independent. The characterization of application is done off-line and the application I/O model can be applied to analyze different target systems. The application I/O model is defined by three characteristics: metadata, spatial global pattern and temporal global pattern. The I/O model of the application is expressed by I/O phases, where an I/O phase is a repetitive sequence of same pattern on a file for a number of processes of the parallel application. A phase will be significant depending on data transferred that represent its weight.

Fault tolerance has become an important issue for parallel applications in the last few years. The parallel systems' users want them to be reliable considering two main dimensions, availability and data consistency. Availability can be provided with solutions such as RADIC, a fault tolerant architecture with different protection levels, offering high availability with transparency, decentralization, flexibility and scalability for message-passing systems. Transient faults may cause an application running in a computer system to be removed from execution, however the biggest risk of transient faults is to provoke undetected data corruption that changes the final result of the application without anyone knowing. To evaluate the effects of transient faults in the robustness of applications and validate new fault detection mechanism and strategies, we have developed a full-system simulation fault injection environment

The challenge of improving the performance of current processors is achieved by increasing the integration scale. This carries a growing vulnerability to transient faults, which increase their impact on multicore clusters running large scientific parallel applications. The requirement for enhancing the reliability of these systems, coupled with the high cost of rerunning the application from the beginning, create the motivation for having specific software strategies for the target systems. This paper introduces SMCV, which is a fully distributed technique that provides fault detection for message-passing parallel applications, by validating the contents of the messages to be sent, preventing the transmission of errors to other processes and leveraging the intrinsic hardware redundancy of the multicore. SMCV achieves a wide robustness against transient faults with a reduced overhead, and accomplishes a trade-off between moderate detection latency and low additional workload.

Continuous IC process enhancements make possible to integrate on a single chip the re-sources required for simultaneously executing multiple control flows or threads, exploiting different levels of thread-level parallelism: application-, function-, and loop-level. Scalable simultaneous multi-threading combines static and dynamic mechanisms to assemble a complexity-effective design that provides high instruction per cycle rates without sacrificing cycle time nor single-thread performance. This paper addresses the design of the fetch unit for a high-performance, scalable, simultaneous multithreaded processor. We present the detailed microarchitecture of a clustered and reconfigurable fetch unit based on an existing single-thread fetch unit. In order to minimize the occurrence of fetch hazards, the fetch unit dynamically adapts to the available thread-level parallelism and to the fetch characteristics of the active threads, working as a single shared unit or as two separate clusters. It combines static and dynamic methods in a complexity-efficient way. The design is supported by a simulation- based analysis of different instruction cache and branch target buffer configurations on the context of a multithreaded execution workload. Average reductions on the miss rates between 30% and 60% and peak reductions greater than 200% are obtained.