- ItemOpen AccessDetecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features(MDPI, 2022) Tena del Pozo, Alberto; Clarià Sancho, Francisco; Solsona Tehàs, Francesc; Povedano, MònicaThe term “bulbar involvement” is employed in ALS to refer to deterioration of motor neurons within the corticobulbar area of the brainstem, which results in speech and swallowing dysfunctions. One of the primary symptoms is a deterioration of the voice. Early detection is crucial for improving the quality of life and lifespan of ALS patients suffering from bulbar involvement. The main objective, and the principal contribution, of this research, was to design a new methodology, based on the phonatory-subsystem and time-frequency characteristics for detecting bulbar involvement automatically. This study focused on providing a set of 50 phonatory-subsystem and time-frequency features to detect this deficiency in males and females through the utterance of the five Spanish vowels. Multivariant Analysis of Variance was then used to select the statistically significant features, and the most common supervised classifications models were analyzed. A set of statistically significant features was obtained for males and females to capture this dysfunction. To date, the accuracy obtained (98.01% for females and 96.10% for males employing a random forest) outperformed the models in the literature. Adding time-frequency features to more classical phonatory-subsystem features increases the prediction capabilities of the machine-learning models for detecting bulbar involvement. Studying men and women separately gives greater success. The proposed method can be deployed in any kind of recording device (i.e., smartphone).
- ItemOpen AccessManaging quality, supplier selection, and cold-storage contracts in agrifood supply chain through stochastic optimization(Wiley, 2021-10) Mateo Fornés, Jordi; Soto Silva, Wladimir Eduardo; González Araya, Marcela Cecilia; Pla Aragonés, Lluís Miquel; Solsona Tehàs, FrancescThe quality of such processed agrifood products as dehydrated apple is related to the quality and variety offresh harvested products and connected with wastage reduction throughout the agrifood supply chain. Forthis purpose, cold-storage management is important to avoid or mitigate the quality decay of fresh prod-ucts stored in refrigerated systems. This paper explores the benefits of a two-stage stochastic programmingmodel for product quality through the selection of producers and the management of cold storage to miti-gate deterioration and guarantee the maintenance of quality. A case study with real data from an agribusinesscompany is presented in the case study to illustrate and assess the suitability of the stochastic approach. Un-certainty regarding the conversion rate of fresh apples into the final dehydrated product and the purchasecost of the apples in the system are represented through scenarios generated from historical data. Recourseactions include the purchase of additional fruit and renting of additional cold stores to meet the demand.Based on the different scenarios, the value of the stochastic solution shows that modeling and solving theproposed stochastic model minimizes costs by an average of around 6.4%. In addition, the expected valueof perfect information demonstrates that using a proactive strategy could reduce costs by up to 9%. Theseresults ensure the applicability of this model in practice before and during the harvesting season for planningand replanning as uncertainty is revealed under a rolling horizon.
- ItemOpen AccessSmart contract formation enabling energy-as-a-service in a virtual power plant(John Wiley & Sons Ltd., 2021-10-22) Mishra, Sambeet; Crasta, Cletus John; Bordin, Chiara; Mateo Fornés, JordiEnergy as a service (EaaS) is an emerging business model that enables the otherwise passive energy consumers to play an active role and participate in the energy utility services. This platform is formed through smart contracts registering peer-to-peer (P2P) transactions of energy through price and quantity. Many industries, including finance, have already leveraged smart contracts to introduce digital currencies. At this time, the utility industry is faced with the challenge of how to structure smart contract formation in a local energy market. Specifically, they are faced with the challenge of maintaining a balance between energy generation and demand while enabling traceability, security, and unbiased peer-to-peer energy transactions, especially within a virtual power plant. This article aims at addressing the aforementioned challenges. In particular, this article investigates how to structure the microgrids in a local energy market, and how to ensure balance and resiliency with incomplete information. Taking various generation asset dimensions and demand profiles into account, simulations are performed. A novel evolutionary computing strategy to structure the simulation is proposed. A comparison is made among random order, random selection, profit-based ranking, and evolutionary strategy for coordinating the contract formation. The discussions draw attention to each method's advantages and disadvantages in terms of their value as a strategy for forming smart contracts in a local energy market.
- ItemOpen AccessAccurate consistency-based MSA reducing the memory footprint(Elsevier, 2021) Lladós Segura, Jordi; Cores Prado, Fernando; Guirado Fernández, Fernando; Lérida Monsó, Josep LluísBackground and Objective: The emergence of Next-Generation sequencing has created a push for faster and more accurate multiple sequence alignment tools. The growing number of sequences and their longer sizes, which require the use of increased system resources and produce less accurate results, are heavily challenging to these applications. Consistency-based methods have the most intensive CPU and memory usage requirements. We hypothesize that library reductions can enhance the scalability and performance of consistency-based multiple sequence alignment tools; however, we have previously shown a noticeable impact on the accuracy when extreme reductions were performed. Methods: In this study, we propose Matrix-Based T-Coffee, a consistency-based method that uses library reductions in conjunction with a complementary objective function. The proposed method, implemented in T-Coffee, can mitigate the accuracy loss caused by low memory resources. Results: The use of a complementary objective function with a library reduction of 30% improved the accuracy of T-Coffee. Interestingly, 50% library reduction achieved lower execution times and better overall scalability. Conclusions: Matrix-Based T-Coffee benefits from accurate alignments while achieving better scalability. This leads to a reduction in memory footprint and execution time. In addition, these enhancements could be applied to other aligners based on consistency.
- ItemOpen AccessTControl: A mobile app to follow up tobacco-quitting patients(Elsevier, 2017-04) Pifarré Montalà, Marc; Carrera Peruga, Adrián; Vilaplana Mayoral, Jordi; Cuadrado, Josep; Solsona, Sara; Abella i Pons, Francesc; Solsona Tehàs, Francesc; Alves, RuiBackground and Objective Tobacco smoking is a major risk factor for a wide range of respiratory and circulatory diseases in active and passive smokers. Well-designed campaigns are raising awareness to the problem and an increasing number of smokers seeks medical assistance to quit their habit. In this context, there is the need to develop mHealth Apps that assist and manage large smoke quitting programs in efficient and economic ways. Objectives Our main objective is to develop an efficient and free mHealth app that facilitates the management of, and assistance to, people who want to quit smoking. As secondary objectives, our research also aims at estimating the economic effect of deploying that App in the public health system. Methods Using JAVA and XML we develop and deploy a new free mHealth App for Android, called TControl (Tobacco-quitting Control). We deploy the App at the Tobacco Unit of the Santa Maria Hospital in Lleida and determine its stability by following the crashes of the App. We also use a survey to test usability of the app and differences in aptitude for using the App in a sample of 31 patients. Finally, we use mathematical models to estimate the economic effect of deploying TControl in the Catalan public health system. Results TControl keeps track of the smoke-quitting users, tracking their status, interpreting it, and offering advice and psychological support messages. The App also provides a bidirectional communication channel between patients and clinicians via mobile text messages. Additionally, registered patients have the option to interchange experiences with each other by chat. The App was found to be stable and to have high performances during startup and message sending. Our results suggest that age and gender have no statistically significant effect on patient aptitude for using TControl. Finally, we estimate that TControl could reduce costs for the Catalan public health system (CPHS) by up to € 400M in 10 years. Conclusions TControl is a stable and well behaved App, typically operating near optimal performance. It can be used independent of age and gender, and its wide implementation could decrease costs for the public health system.