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- PublicationOptimization of stir bar sorptive extraction (SBSE) and multi-stir bar sorptive extraction (mSBSE) to improve must volatile compounds extraction(Sociedade Brasileira de Ciência e Tecnologia de Alimentos, 2022-12-30) Marín-San Román, Sandra; Carot Sierra, José Miguel; Sáenz de Urturi, Itziar; Rubio-Bretón, Pilar; Pérez-Álvarez, Eva P.; Garde-Cerdán, Teresa; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Gobierno de La Rioja; Agencia Estatal de Investigación; Ministerio de Ciencia, Innovación y Universidades[EN] The aromatic compounds present in grapes are found in very low concentrations, since, for their determination, a previous step of selective extraction is necessary. In this work we optimize and compare, for the first time, the techniques of extraction by stir bar sorption (SBSE) and multi-stir bar sportive extraction (mSBSE), with the aim of analyzing the must volatile composition. For this purpose, two randomized factorial designs were carried out in which the following factors and levels were combined and optimized: for SBSE, extraction mode (headspace (HS), direct immersion (DI), and both at the same time), extraction speed (500/1000 rpm), extraction time (1/3/ 6 h), extraction temperature (20/40/60 degrees C) and NaCl addition (with and without NaCl, and sequential); and for mSBSE: extraction speed (500/1000 rpm), extraction time (1/3/6 h), extraction temperature (20/40/60 degrees C), and NaCl addition (with and without). The results showed that SBSE technique provided a higher extraction of volatile compounds than mSBSE. After performing principal component analysis (PCA) and analysis of variance (ANOVA) multifactorial, it was concluded that the best conditions for SBSE were: HS, 500 rpm, 6 h, 60 degrees C and adding NaCl (sequential); and for mSBSE were: 500 rpm, 6 h, 60 degrees C and without NaCl.
- PublicationAre Water User Associations Prepared for a Second-Generation Modernization? The Case of the Valencian Community (Spain)(MDPI AG, 2020-08) González Pavón, César; Arviza Valverde, Jaime; Balbastre Peralta, Iban; Carot Sierra, José Miguel; Palau Salvador, Guillermo; Dpto. de Física Aplicada; Escuela Técnica Superior de Ingeniería de Telecomunicación; Instituto de Gestión de la Innovación y del Conocimiento; Dpto. de Ingeniería Rural y Agroalimentaria; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Centro Valenciano de Estudios sobre el Riego; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural[EN] This work focuses on the situation of the technological transition to new technologies in drip irrigation in the Valencian Community (Spain). The study covers the last decade with data from interviews to managers of Irrigation Communities in 2010 and 2018. We analyze the main technological problems in seven topics: (i) Catchment & Pumping; (ii) Storage & Regulation; (iii) Treatment & Filtering; (iv) Transport & Distribution; (v) Maneuver, Regulation & Protection; (vi) Automation; (vii) Theft and Vandalism. We also have researched the influence of the performance of the Automation system, the presence of a technician in the Irrigation Community and the use of sensors or climatic data. Results show that problems related to technological maintenance of filtering systems or automation are very common and important and they are more important in large Irrigation Communities. We have also observed that mostly large ICs are using sensors or climatic data for their irrigation schedule. We can conclude that their current situation is focused in the daily maintenance of technological problems, inherited from the first modernization processes at the beginning of 21st century. Hence, they are far away from a second stage of modernization or the smart irrigation pushed by the new advances on technology.
- PublicationA Model for Developing an Academic Activity Index for Higher Education Instructors Based on Composite Indicators(SAGE Publications, 2022-07) Bas, María del Carmen; Carot Sierra, José Miguel; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio[EN] The assessment of the teacher performance is a subject of criticism due to the lack of a well-establish methodology. This study develops an overall score to measure the dimensions that encompass the academic activities. To that end, a Benefit-of-the-doubt model is proposed. The advantage of this technique is the flexibility in the weights, so that the model selects for each teacher the most favourable set of weights. Furthermore, the paper proposes the barycentric coordinate system as a method to classify the teachers in clusters depending on their contribution to the dimensions. A specific pie chart has been proposed as an efficient way to report the contribution of the teachers to the dimensions and the overall teacher performance.
- PublicationSensitivity Analysis: A Necessary Ingredient for Measuring the Quality of a Teaching Activity Index(Springer Verlag, 2016-03-18) Bas Cerdá, María del Carmen; Tarantola, Stefano; Carot Sierra, José Miguel; Conchado Peiró, Andrea; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Escuela Técnica Superior de Ingeniería Informática; Generalitat ValencianaIn recent years, and following the introduction of the European Higher Education Area, universities have developed measurement mechanisms to ensure improvement in the quality of their teaching and teaching staff. One of the measurement tools increasingly used in Higher Education to implement continuous improvement policies for university teaching is composite indicators, which are a mathematical aggregation of a selected set of suitably weighted indicators. Composite indicator building should be accompanied by sensitivity analysis to ensure good practice. However, this is rarely done. Sensitivity analysis helps to improve the understanding and, ultimately, the soundness of the composite. In most cases, sensitivity analysis shows that the weights assigned to indicators do not reflect the actual importance of those indicators in the aggregation to the composite because of the heteroskedasticity of, and correlation between the underlying indicators. This paper proposes a composite indicator for the teaching activity of academic staff in a Spanish university. As we shall see in the paper, the desired weights stated by developers rarely represent the effective importance of the components. Hence, we propose sensitivity analysis as a necessary tool for readjusting weights in order to achieve the desired level of importance for each component indicator.
- PublicationA Quantitative Model of the City in 15 Minutes for Decision-making(2024) Carot Sierra, José Miguel; Villalba Ortiz, Aida; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio[EN] Addressing challenges like urban sprawl, pollution, and heat islands is imperative in contemporary urban contexts. Understanding urban mechanisms and prioritising proximity and sustainable mobility is crucial for meeting citizens' needs. This study presents a quantitative model with 21 composite indicators, aligning with the 15 -minute city theory's dimensions (Education, Health and Social Welfare, Leisure and Culture, and Supply) to measure resource accessibility. Focused on Valencia, the analysis of its 70 neighbourhoods reveals significant disparities in indicators, mainly due to geographical distribution. Peripheries consistently score lower, while city centres and high -status neighbourhoods score higher. It underscores the importance of targeted interventions for equitable access to resources across urban areas.
- PublicationComparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images(MDPI AG, 2022-08) Veiga-Canuto, Diana; Cerdà-Alberich, Leonor; Sangüesa Nebot, Cinta; Martínez de las Heras, Blanca; Pötschger, Ulrique; Gabelloni, Michela; Carot Sierra, José Miguel; Taschner-Mandl, Sabine; Düster, Vanessa; Cañete, Adela; Ladenstein, Ruth; Neri, Emanuele; Marti-Bonmati, Luis; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; European Commission[EN] Simple Summary Tumor segmentation is a key step in oncologic imaging processing and is a time-consuming process usually performed manually by radiologists. To facilitate it, there is growing interest in applying deep-learning segmentation algorithms. Thus, we explore the variability between two observers performing manual segmentation and use the state-of-the-art deep learning architecture nnU-Net to develop a model to detect and segment neuroblastic tumors on MR images. We were able to show that the variability between nnU-Net and manual segmentation is similar to the inter-observer variability in manual segmentation. Furthermore, we compared the time needed to manually segment the tumors from scratch with the time required for the automatic model to segment the same cases, with posterior human validation with manual adjustment when needed. Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (+/- 0.032 IQR). The median DSC for the automatic tool was 0.965 (+/- 0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%.
- PublicationA dynamic supply chain BSC-based methodology to improve operations efficiency(Elsevier, 2020-11) Rodríguez Rodríguez, Raúl; Alfaro Saiz, Juan José; Carot Sierra, José Miguel; Dpto. de Organización de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Centro de Investigación en Gestión e Ingeniería de Producción; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio[EN] This paper presents how to objectively set up the process for activation of the future action plans from a supply chain Balanced Scorecard (BSC), aligning such an activation process to reach the main objectives and being able to save resources. Additionally, it also shows how to define supply chain business scenarios based on the future expected values of the Key Performance Indicators (KPI). Once the future business scenario has been chosen, the KPI values associated with this scenario will become the new KPI values of the BSC in order to align efforts and save resources Further, the strategic objectives associated with the KPIs may also be extensively retuned, thus redefining their target values. From the application carried out, there were four KPIs whose values needed, in absolute terms, to be increased and four other KPIs to be decreased. Additionally, the associated strategic objectives were retuned; for example, the value of the strategic objective "To reduce product development costs" was initially set in the BSC at 10 % but it was deduced to 8% as a consequence of the application of the proposal. As a result, this methodology has aligned all the future efforts of a whole supply chain in order to reach one point on a plane, which is a combination of interrelated supply chain KPI.
- PublicationIndividualized diagnosis of psychosis based on machine learning from functional magnetic resonance data using an emotional auditory paradigm(Oxford University Press, 2019-04) SANJUÁN ARIAS, JULIO; Castro Bleda, María José; España Boquera, Salvador; Garcia-Marti, G.; Carot Sierra, José Miguel; Corripio, I.; Soldevila-Matias, P.; MARTÍ-BONMATÍ, LUIS; Rubio, J.M.; Crespo-Facorro, B.; Dpto. de Sistemas Informáticos y Computación; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Escuela Técnica Superior de Ingeniería Informática; Instituto Universitario Valenciano de Investigación en Inteligencia Artificial
- PublicationComputer learner corpora: analysing interlanguage errors in synchronous and asynchronous communication(University of Hawai‘i, 2013-06) Mac Donald, Penny; García Carbonell, Amparo; Carot Sierra, José Miguel; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; Universitat Politècnica de València[EN] This study focuses on the computer-aided analysis of interlanguage errors made by the participants in the telematic simulation IDEELS (Intercultural Dynamics in European Education through on-Line Simulation). The synchronous and asynchronous communication analysed was part of the MiLC Corpus, a multilingual learner corpus of texts written by language learners from different language backgrounds. The main research questions centred on the differences in the amount and types of errors found in both the synchronous and asynchronous modes of communication, and whether different L1 groups committed certain errors more than their counterparts from other mother tongue backgrounds. As we hypothesised, more errors were found in the synchronous mode of communication than in the asynchronous; however, when examining the exact types of errors, some categories were more frequent in the synchronous mode (the formal and grammatical errors, among others), while in the asynchronous, errors of style and lexis occurred more frequently. A analysis of the data revealed that the frequency of error types varied with each different L1 group participating in the simulation, this same analysis also showed that highly relevant associations could be established the participants’ L1 and specific error types.
- PublicationIndependent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images(MDPI AG, 2023-03) Veiga-Canuto, Diana; Cerdá-Alberich, Leonor; Jimenez-Pastor, Ana; Carot Sierra, José Miguel; Gomis-Maya, Armando; Sangüesa Nebot, Cinta; Fernandez-Patón, Matías; Martinez de las Heras, Blanca; Taschner-Mandl, Sabine; Düster, Vanessa; Pötschger, Ulrike; Simon, Thorsten; Neri, Emanuele; Alberich-Bayarri, Angel; Cañete, Adela; Hero, Barbara; Ladenstein, Ruth; Martí-Bonmatí, Luis; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Escuela Técnica Superior de Ingeniería Industrial; Centro de Gestión de la Calidad y del Cambio; European Commission[EN] Tumor segmentation is a key step in oncologic imaging processing. We have recently developed a model to detect and segment neuroblastic tumors on MR images based on deep learning architecture nnU-Net. In this work, we performed an independent validation of the automatic segmentation tool with a large heterogeneous dataset. We reviewed the automatic segmentations and manually edited them when necessary. We were able to show that the automatic network was able to locate and segment the primary tumor on the T2 weighted images in the majority of cases, with an extremely high agreement between the automatic tool and the manually edited masks. The time needed for manual adjustment was very low. Objectives. To externally validate and assess the accuracy of a previously trained fully automatic nnU-Net CNN algorithm to identify and segment primary neuroblastoma tumors in MR images in a large children cohort. Methods. An international multicenter, multivendor imaging repository of patients with neuroblastic tumors was used to validate the performance of a trained Machine Learning (ML) tool to identify and delineate primary neuroblastoma tumors. The dataset was heterogeneous and completely independent from the one used to train and tune the model, consisting of 300 children with neuroblastic tumors having 535 MR T2-weighted sequences (486 sequences at diagnosis and 49 after finalization of the first phase of chemotherapy). The automatic segmentation algorithm was based on a nnU-Net architecture developed within the PRIMAGE project. For comparison, the segmentation masks were manually edited by an expert radiologist, and the time for the manual editing was recorded. Different overlaps and spatial metrics were calculated to compare both masks. Results. The median Dice Similarity Coefficient (DSC) was high 0.997; 0.944-1.000 (median; Q1-Q3). In 18 MR sequences (6%), the net was not able neither to identify nor segment the tumor. No differences were found regarding the MR magnetic field, type of T2 sequence, or tumor location. No significant differences in the performance of the net were found in patients with an MR performed after chemotherapy. The time for visual inspection of the generated masks was 7.9 +/- 7.5 (mean +/- Standard Deviation (SD)) seconds. Those cases where manual editing was needed (136 masks) required 124 +/- 120 s. Conclusions. The automatic CNN was able to locate and segment the primary tumor on the T2-weighted images in 94% of cases. There was an extremely high agreement between the automatic tool and the manually edited masks. This is the first study to validate an automatic segmentation model for neuroblastic tumor identification and segmentation with body MR images. The semi-automatic approach with minor manual editing of the deep learning segmentation increases the radiologist's confidence in the solution with a minor workload for the radiologist.