Eficiencia energética en la programación de tareas con recursos restringidos

In the field of operations research, the set of scheduling problems of activities is considered as one of the most relevant ones due to its great applicability and complexity. Within the broad variety of problems in this set, it is remarkable the Resource-Constrained Project Scheduling Problem (RCPSP), since it is regarded as the most important-base problem in this area and it has been the object of study in countless research projects. Basically, this problem consists of a project split into sets of activities that are related to each other by means of precedence-constraints, and require an amount of each limited resource, to be performed. The objective, then, is to allocate in the most efficient way those resources to the activities in order to optimize a scoring function such as the makespan. Similar in importance is the multimodal-version of the RCPSP, called MRCPSP, in which for each activity there exists multiple execution modes that involve a different combination of limited resources, giving rise to a different execution time. In the literature, it has been addressed widely these two problems with both exact methods and approximation methods, being these latter the most successful. These research works have focused mainly on obtaining economic advantages such as costs and project time minimization. However, with the accelerating globalization and the fast countries' growing economies, the race for power resources have increased sharply. In fact, the importance of taking into account the energy consumption on modeling has become so important that it is now considered as important as other performance measures such as productivity and costs. Hence, the main goal of this Ph.D. dissertation is to develop a new RCPSP and MRCPSP approach based on the energetic efficiency, which is aimed at searching for sustainable solutions in terms of time and energy consumption. To this end, it has been proposed an extension of the RCPSP, named MRCPSP-ENERGY, which considers besides the traditional resources of the RCPSP, a variable energetic consumption that generates different execution modes for the activities. This proposal includes a new optimization criterion based on the energetic efficiency of a project, which considers simultaneously the minimization of both the total duration and the energy consumption of such project. Moreover, in order to assess the solution methods for the MRCPSP-ENERGY, the standard library mostly used for this purpose has been extended and a new one has been proposed, called PSPLIB-ENERGY. In order to solve the proposed problem, firstly, the most successful metaheuristics methods, which address the RCPSP, were analyzed. Secondly, it was shown that these methods lead to redundant solutions, hindering the search. Therefore, an evolutive method was proposed, whose main contribution is the development of a new mutation operator that reduces the number of redundant solutions. Similarly, in the multimodal case, it was determined that the most widespread searching methods are also focused on the activity list representation and therefore they yield redundant solutions. As a solution alternative for the MRCPSP-ENERGY, it was shown that such search can be carried out by focusing on the mode list representation, as different mode lists also reach diverse solutions, giving rise to a less number of redundant solutions. Keeping in mind this finds, it was proposed a new evolutive method for solving the MRCPSP-ENERGY, which unifies both searching methods such that the search is conducted with two optimization phases. Based on the obtained results given by the PSPLIB-ENERGY library, the proposed method proved to be able to reach highly efficient solutions.En la investigación operativa, el conjunto de problemas de secuenciación de actividades es considerado como uno de los más relevantes debido a su gran aplicabilidad y complejidad. Dentro de la amplia variedad de problemas en este conjunto, destaca el problema de programación de tareas con recursos restringidos (RCPSP por su sigla en inglés), pues es considerado como el problema base más importante en esta área y ha sido objeto de estudio de numerosas investigaciones. Básicamente, consiste de un proyecto subdividido en un conjunto de actividades que se encuentran relacionadas mediante restricciones de precedencia y requieren, para ser ejecutadas, una cantidad de cada tipo de recurso cuya disponibilidad máxima se encuentra limitada. El objetivo es asignar los recursos a las actividades de la manera más eficiente posible para optimizar una medida de desempeño, por ejemplo, la duración total del proyecto. Igualmente importante es la versión multi-modal del RCPSP, llamada MRCPSP, en la que para cada actividad existen múltiples modos de ejecución que involucran una combinación diferente de recursos limitados, dando origen a un tiempo de ejecución distinto. En la literatura se han abordado ampliamente estos dos problemas tanto con métodos exactos como de aproximación, siendo estos últimos los más exitosos. Estos trabajos se han centrado principalmente en la obtención de beneficios económicos, como la minimización de los costes o la obtención de la mínima duración del proyecto. Sin embargo, con la aceleración de la globalización y el rápido desarrollo de los países, la competencia por recursos energéticos ha aumentado drásticamente. Incluso, la importancia de tener en cuenta el consumo de energía en los modelos ha crecido de tal manera que, ahora es considerado con la misma relevancia que otras medidas de desempeño como la productividad y los costes. Así, el objetivo principal de esta tesis es desarrollar un nuevo enfoque del RCPSP y del MRCPSP, basado en la eficiencia energética, la cual busca soluciones sostenibles en términos de tiempo y de consumo energético. Para este fin, se ha propuesto una extensión del RCPSP denominada MRCPSP-ENERGY, la cual considera, además de los recursos tradicionales del RCPSP, un consumo de energía variable que da origen a distintos modos de ejecución de las actividades. Esta propuesta incluye un nuevo criterio de optimización basado en la eficiencia energética del proyecto, que tiene en cuenta de manera simultánea la minimización de la duración del proyecto y el consumo total de energía. Adicionalmente, con el objetivo de evaluar los métodos de solución para el MRCPSP-ENERGY, se ha ampliado la librería estándar de prueba más extendida para el RCPSP y se ha propuesto una nueva librería, denominada PSPLIB-ENERGY. Para encontrar solución al problema propuesto, primero se analizaron los mejores métodos metaheurísticos que abordan el RCPSP. Luego, se identificó que estos métodos conducen a soluciones redundantes, entorpeciendo la búsqueda. Por tanto, se propuso un método evolutivo cuya principal aportación es el desarrollo de un nuevo operador de mutación que disminuye la generación de soluciones redundantes. Similarmente, en el caso multi-modal se detectó que los principales métodos de búsqueda también se centran en la representación de lista de actividades y por tanto generan soluciones redundantes. Como alternativa de solución para el MRCPSP-ENERGY, se mostró que la búsqueda puede realizarse enfocándose en la lista de modos, ya que diferentes listas de modos también pueden alcanzar soluciones distintas, generando un menor número de soluciones redundantes. Teniendo en cuenta estos hallazgos, se propuso un nuevo método evolutivo para resolver el MRCPSP-ENERGY, que unifica ambos métodos de búsqueda para realizarla en dos fases de optimización. Basándose en los resultados obtenidos en la PSPLIB-ENERGY, se concluye que el mEn la investigació operativa, el conjunt de problemes de seqüenciació d'activitats és considerat com un dels més rellevants a causa de la seua gran aplicabilitat i complexitat. Dins de l'àmplia varietat de problemes en este conjunt, destaca el problema de programació de tasques amb recursos restringits (RCPSP per la seua sigla en anglés) , perquè és considerat com el problema base més important en esta àrea i ha sigut objecte d'estudi de nombroses investigacions. Bàsicament, consistix d'un projecte subdividit en un conjunt d'activitats que es troben relacionades per mitjà de restriccions de precedència i requerixen, per a ser executades, una quantitat de cada tipus de recurs la disponibilitat màxima de la qual es troba limitada. L'objectiu és assignar els recursos a les activitats de la manera més eficient possible per a optimitzar una mesura d'exercici, per exemple, la duració total del projecte. Igualment important és la versió multi- modal del RCPSP, crida MRCPSP, en la que per a cada activitat hi ha múltiples modes d'execució que involucren una combinació diferent de recursos limitats, donant origen a un temps d'execució distint. En la literatura s'han abordat àmpliament estos dos problemes tant amb mètodes exactes com d'aproximació, sent estos últims els més reeixits. Estos treballs s'han centrat principalment en l'obtenció de beneficis econòmics, com la minimització dels costos o l'obtenció de la mínima duració del projecte. No obstant això, amb l'acceleració de la globalització i el ràpid desenrotllament dels països, la competència per recursos energètics ha augmentat dràsticament. Inclús, la importància de tindre en compte el consum d'energia en els models ha crescut de tal manera que, ara és considerat amb la mateixa rellevància que altres mesures d'exercici com la productivitat i els costos. Així, l'objectiu principal d'esta tesi és desenrotllar un nou enfocament del RCPSP i del MRCPSP, basat en l'eficiència energètica, la qual busca solucions sostenibles en termes de temps i de consum energètic. Per a este fi, s'ha proposat una extensió del RCPSP denominada MRCPSP- ENERGY, la qual considera, a més dels recursos tradicionals del RCPSP, un consum d'energia variable que dóna origen a distints modes d'execució de les activitats. Esta proposta inclou un nou criteri d'optimització basat en l'eficiència energètica del projecte, que té en compte de manera simultània la minimització de la duració del projecte i el consum total d'energia. Addicionalment, amb l'objectiu d'avaluar els mètodes de solució per al MRCPSP-ENERGY, s'ha ampliat la llibreria estàndard de prova més estesa per al RCPSP i s'ha proposat una nova llibreria, denominada PSPLIB-ENERGY. Per a trobar solució al problema proposat, primer es van analitzar els millors mètodes metaheurísticos que aborden el RCPSP. Després, es va identificar que estos mètodes conduïxen a solucions redundants, entorpint la busca. Per tant, es va proposar un mètode evolutiu la principal aportació del qual és el desenrotllament d'un nou operador de mutació que disminuïx la generació de solucions redundants. Semblantment, en el cas multi- modal es va detectar que els principals mètodes de busca també se centren en la representació de llista d'activitats i per tant generen solucions redundants. Com a alternativa de solució per al MRCPSP-ENERGY, es va mostrar que la busca pot realitzar-se enfocant-se en la llista de modes, ja que diferents llistes de modes també poden aconseguir solucions distintes, generant un menor nombre de solucions redundants. Tenint en compte estes troballes, es va proposar un nou mètode evolutiu per a resoldre el MRCPSP-ENERGY, que unifica ambdós mètodes de busca per a realitzar-la en dos fases d'optimització. Basant-se en els resultats obtinguts en la PSPLIB-ENERGY, es conclou que el mètode proposat és capaç d'aconseguir solucions altament eficients.Morillo Torres, D. (2017). Eficiencia energética en la programación de tareas con recursos restringidos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90654TESI

A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem

[EN] This article focuses on obtaining sustainable and energy-efficient solutions for limited resource programming problems. To this end, a model for integrating makespan and energy consumption objectives in multi-mode resource-constrained project scheduling problems (MRCPSP-ENERGY) is proposed. In addition, a metaheuristic approach for the efficient resolution of these problems is developed. In order to assess the appropriateness of theses proposals, the well-known Project Scheduling Problem Library is extended (called PSPLIB-ENERGY) to include energy consumption to each Resource-Constrained Project Scheduling Problem instance through a realistic mathematical model. This extension provides an alternative to the current trend of numerous research works about optimization and the manufacturing field, which require the inclusion of components to reduce the environmental impact on the decision-making process. PSPLIB-ENERGY is available at http://gps.webs.upv.es/psplib-energy/.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Spanish Government under the research projects TIN2013-46511-C2-1 and TIN2016-80856-R.Morillo-Torres, D.; Barber, F.; Salido, MA. (2017). A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 1(1):1-13. https://doi.org/10.1177/0954405417711734S1131

Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem

[EN] This paper addresses an energy-based extension of the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP) called MRCPSP-ENERGY. This extension considers the energy consumption as an additional resource that leads to different execution modes (and durations) of the activities. Consequently, different schedules can be obtained. The objective is to maximize the efficiency of the project, which takes into account the minimization of both makespan and energy consumption. This is a well-known NP-hard problem, such that the application of metaheuristic techniques is necessary to address real-size problems in a reasonable time. This paper shows that the Activity List representation, commonly used in metaheuristics, can lead to obtaining many redundant solutions, that is, solutions that have different representations but are in fact the same. This is a serious disadvantage for a search procedure. We propose a genetic algorithm(GA) for solving the MRCPSP-ENERGY, trying to avoid redundant solutions by focusing the search on the execution modes, by using the Mode List representation. The proposed GA is evaluated on different instances of the PSPLIB-ENERGY library and compared to the results obtained by both exact methods and approximate methods reported in the literature. This library is an extension of the well-known PSPLIB library, which contains MRCPSP-ENERGY test cases.This paper has been partially supported by the Spanish Research Projects TIN2013-46511-C2-1-P and TIN2016-80856-R.Morillo-Torres, D.; Barber, F.; Salido, MA. (2017). Mode-Based versus Activity-Based Search for a Nonredundant Resolution of the Multimode Resource-Constrained Project Scheduling Problem. Mathematical Problems in Engineering. 2017:1-15. https://doi.org/10.1155/2017/4627856S1152017Mouzon, G., Yildirim, M. B., & Twomey, J. (2007). 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Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis

Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 stateof- the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression

Automatic Lung Segmentation in Chest X-Ray Images Using SAM With Prompts From YOLO

Despite the impressive performance of current deep learning models in the field of medical imaging, transferring the lung segmentation task in X-ray images to clinical practice is still a pending task. In this study, the performance of a fully automatic framework for lung field segmentation in chest X-ray images was evaluated. The framework is rooted in the combination of the Segment Anything Model (SAM) with prompt capabilities, and the You Only Look Once (YOLO) model to provide effective prompts. Transfer learning, loss functions, and several validation strategies were thoroughly assessed. This provided a complete benchmark that enabled future research studies to fairly compare new segmentation strategies. The results achieved demonstrated significant robustness and generalization capability against the variability in sensors, populations, disease manifestations, device processing, and imaging conditions. The proposed framework was computationally efficient, could address bias in training over multiple datasets, and had the potential to be applied across other domains and modalities.Consejería de Universidad, Investigación e Innovación de la Junta de Andalucía (ProyExcel_00942)15 páginas

Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images

After the 24 months embargo, this version of the article was accepted for publication, after peer review and does not reflect post-acceptance improvements, or any corrections. The published version is available online (2022-01-14) at: https://doi.org/10.1016/j.eswa.2021.116471.The quantification of biomarkers on immunohistochemistry breast cancer images is essential for defining appropriate therapy for breast cancer patients, as well as for extracting relevant information on disease prognosis. This is an arduous and time-consuming task that may introduce a bias in the results due to intra- and inter-observer variability which could be alleviated by making use of automatic quantification tools. However, this is not a simple processing task given the heterogeneity of breast tumors that results in non-uniformly distributed tumor cells exhibiting different staining colors and intensity, size, shape, and texture, of the nucleus, cytoplasm and membrane. In this research work we demonstrate the feasibility of using a deep learning-based instance segmentation architecture for the automatic quantification of both nuclear and membrane biomarkers applied to IHC-stained slides. We have solved the cumbersome task of training set generation with the design and implementation of a web platform, which has served as a hub for communication and feedback between researchers and pathologists as well as a system for the validation of the automatic image processing models. Through this tool, we have collected annotations over samples of HE, ER and Ki-67 (nuclear biomarkers) and HER2 (membrane biomarker) IHC-stained images. Using the same deep learning network architecture, we have trained two models, so-called nuclei- and membrane-aware segmentation models, which, once successfully validated, have revealed to be a promising method to segment nuclei instances in IHC-stained images. The quantification method proposed in this work has been integrated into the developed web platform and is currently being used as a decision support tool by pathologists

Cough Detection Using Acceleration Signals and Deep Learning Techniques

Cough is a frequent symptom in many common respiratory diseases and is considered a predictor of early exacerbation or even disease progression. Continuous cough monitoring offers valuable insights into treatment effectiveness, aiding healthcare providers in timely intervention to prevent exacerbations and hospitalizations. Objective cough monitoring methods have emerged as superior alternatives to subjective methods like questionnaires. In recent years, cough has been monitored using wearable devices equipped with microphones. However, the discrimination of cough sounds from background noise has been shown a particular challenge. This study aimed to demonstrate the effectiveness of single-axis acceleration signals combined with state-of-the-art deep learning (DL) algorithms to distinguish intentional coughing from sounds like speech, laugh, or throat noises. Various DL methods (recurrent, convolutional, and deep convolutional neural networks) combined with one- and two-dimensional time and time–frequency representations, such as the signal envelope, kurtogram, wavelet scalogram, mel, Bark, and the equivalent rectangular bandwidth spectrum (ERB) spectrograms, were employed to identify the most effective approach. The optimal strategy, which involved the SqueezeNet model in conjunction with wavelet scalograms, yielded an accuracy and precision of 92.21% and 95.59%, respectively. The proposed method demonstrated its potential for cough monitoring. Future research will focus on validating the system in spontaneous coughing of subjects with respiratory diseases under natural ambulatory conditions.Grant PID2021-126810OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU

PSPLIB-ENERGY: Una extension de la libreria PSPLIB para la evaluacion de la optimizacion energetica en el RCPSP

[EN] Scheduling problems is one of the core areas in the planning and development of any project, with a wide applicability to real-world situations. Due to the high complexity of these problems, the solving process is often based on metaheuristics techniques, so that the evaluation of these methods is empirical. Therefore benchmarks, which provide a set of test cases to assess the behavior of algorithms, are generated. This paper extends the PSPLIB library. This extension incorporates to each instance of RCPSP (Resource Constrained Project Scheduling Problem), a realistic mathematical model of energy consumption. This proposal provides an alternative to the current trend in the feld of optimization and manufacturing that requires the inclusion of components and methods that reduce the environmental impact in the process of decision making. Finally a new optimality criterion is proposed to compare dierent search techniques. The PSPLIB-ENERGY is available at http://gps.webs.upv.es/psplib-energy/[ES] Los problemas de scheduling constituyen una de las ´areas centrales en la planificaci´on y desarrollo de cualquier proyecto, con una gran aplicabilidad a situaciones del mundo real. Debido a la gran complejidad que habitualmente presentan estos problemas, su resoluci´on suele basarse en m´etodos metaheur´ısticos de optimizaci´on, de forma que la evaluaci´on de estos m´etodos es emp´ırica. Por esta raz´on se generan benchmarks, que proveen de un conjunto de casos de prueba que permiten evaluar el comportamiento de los algoritmos que se desarrollan. En este art´ıculo se extiende la librer´ıa PSPLIB. Esta extensi´on consiste en incorporar a cada instancia del RCPSP (Resource Constrained Project Scheduling Problem), un modelo matem´atico realista de consumo de energ´ıa. Esta propuesta brinda una alternativa a la tendencia actual en el campo de la optimizaci´on y la manufactura que demanda la inclusi´on de componentes y m´etodos que reduzcan el impacto ambiental en el proceso de toma de decisiones. Finalmente se propone un nuevo criterio de optimalidad para comparar las diferentes t´ecnicas de b´usqueda. La PSPLIB-ENERGY est´a disponible en http://gps.webs.upv.es/psplib-energy/.Este trabajo ha sido parcialmente financiado por el proyecto de Investigación TIN2013-46511-C2-1-P.Morillo Torres, D.; Barber Sanchís, F.; Salido Gregorio, MA. (2014). PSPLIB-ENERGY: Una extension de la libreria PSPLIB para la evaluacion de la optimizacion energetica en el RCPSP. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial. 17(54):35-48. https://doi.org/10.4114/intartif.vol17iss54pp48-61S3548175

Portable Oxygen Therapy: Is the 6-Minute Walking Test Overestimating the Actual Oxygen Needs?

The appropriate titration for the personalized oxygen needs of patients with chronic obstructive pulmonary disease (COPD) and severe hypoxemia is a determining factor in the success of long-term oxygen therapy. There are no standardized procedures to assist in determining the patient's needs during the physical activities of daily life. Despite that effort tests are a wide broad approach, further research concerning the development of protocols to titrate O-2 therapy is needed. The main objective of this study was to assess whether the level of oxygen titrated through the 6-minute walking test (6MWT) for patients with COPD and exertional hypoxemia is adequate to meet the patients' demand during their activities of daily living. Physiological and subjective variables were estimated for a study population during two walking tests: a 6MWT and a 20-minute walking circuit (20MWC), designed ad-hoc to reproduce daily physical activities more truthfully. The results indicate that in a significant proportion of patients, the 6MWT might not accurately predict their oxygen needs at a domiciliary environment. Therefore, the titration of the portable O-2 therapy could not be optimal in these cases, with the detrimental impact on the patient's health (hyperoxia episodes), the autonomy of the oxygen device, and the decrease of time out of the home

Cardiovascular-related proteomic changes in ECFCs exposed to the serum of COVID-19 patients

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection significantly affects the cardiovascular system, causing vascular damage and thromboembolic events in critical patients. Endothelial dysfunction represents one of the first steps in response to COVID-19 that might lead to cardiovascular complications and long-term sequelae. However, despite the enormous efforts in the last two years, the molecular mechanisms involved in such processes remain poorly understood. Herein, we analyzed the protein changes taking place in endothelial colony forming cells (ECFCs) after the incubation with the serum from individuals infected with COVID-19, whether asymptomatic or critical patients, by application of a label free-quantitative proteomics approach. Specifically, ECFCs from healthy individuals were incubated ex-vivo with the serum of either COVID-19 negative donors (PCR-/IgG-, n:8), COVID-19 asymptomatic donors at different infective stages (PCR+/ IgG-, n:8and PCR-/IgG+, n:8), or hospitalized critical COVID-19 patients (n:8), followed by proteomics analysis. In total, 590 proteins were differentially expressed in ECFCs in response to all infected serums. Predictive analysis highlighted several proteins like CAPN5, SURF4, LAMP2 or MT-ND1, as highly discriminating features between the groups compared. Protein changes correlated with viral infection, RNA metabolism or autophagy, among others. Remarkably, the angiogenic potential of ECFCs in response to the infected serums was impaired, and many of the protein alterations in response to the serum of critical patients were associated with cardiovascular-related pathologies.This study was supported by GLOBALCAJA-Ayuda COVID-19; and Fondo Supera COVID-19, funded by Banco Santander and CRUE universidades, Ref. IPSA-COVID-19, and the Institute of Health Carlos III, ISCIII (PI18-00427, PI20-00716), co-funded by European Regional Development “A way to make Europe”