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Prof. Dr. Stijn Luca
Ghent University

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0 Life Sciences
0 Probability
0 Quality Control
0 Statistics
0 Data Mining: classification and clustering

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Journal article
Published: 14 August 2021 in Foods
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Mangrove wetlands provide essential ecosystem services such as coastal protection and fisheries. Metal pollution due to industrial and agricultural activities represents an issue of growing concern for the Guayas River Basin and related mangroves in Ecuador. Fisheries and the related human consumption of mangrove crabs are in need of scientific support. In order to protect human health and aid river management, we analyzed several elements in the Guayas Estuary. Zn, Cu, Ni, Cr, As, Pb, Cd, and Hg accumulation were assessed in different compartments of the commercial red mangrove crab Ucides occidentalis (hepatopancreas, carapax, and white meat) and the environment (sediment, leaves, and water), sampled at fifteen sites over five stations. Consistent spatial distribution of metals in the Guayas estuary was found. Nickel levels in the sediment warn for ecological caution. The presence of As in the crabs generated potential concerns on the consumers’ health, and a maximum intake of eight crabs per month for adults is advised. The research outcomes are of global importance for at least nine Sustainable Development Goals (SDGs). The results presented can support raising awareness about the ongoing contamination of food and their related ecosystems and the corresponding consequences for environmental and human health worldwide.

ACS Style

Andrée De Cock; Niels De Troyer; Marie Anne Forio Eurie; Isabel Garcia Arevalo; Wout Van Echelpoel; Liesbeth Jacxsens; Stijn Luca; Gijs Du Laing; Filip Tack; Luis Dominguez Granda; Peter L. M. Goethals. From Mangrove to Fork: Metal Presence in the Guayas Estuary (Ecuador) and Commercial Mangrove Crabs. Foods 2021, 10, 1880 .

AMA Style

Andrée De Cock, Niels De Troyer, Marie Anne Forio Eurie, Isabel Garcia Arevalo, Wout Van Echelpoel, Liesbeth Jacxsens, Stijn Luca, Gijs Du Laing, Filip Tack, Luis Dominguez Granda, Peter L. M. Goethals. From Mangrove to Fork: Metal Presence in the Guayas Estuary (Ecuador) and Commercial Mangrove Crabs. Foods. 2021; 10 (8):1880.

Chicago/Turabian Style

Andrée De Cock; Niels De Troyer; Marie Anne Forio Eurie; Isabel Garcia Arevalo; Wout Van Echelpoel; Liesbeth Jacxsens; Stijn Luca; Gijs Du Laing; Filip Tack; Luis Dominguez Granda; Peter L. M. Goethals. 2021. "From Mangrove to Fork: Metal Presence in the Guayas Estuary (Ecuador) and Commercial Mangrove Crabs." Foods 10, no. 8: 1880.

Journal article
Published: 02 August 2021 in IEEE Access
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Recently proposed pre-trained language models can be easily fine-tuned to a wide range of downstream tasks. However, a large-scale labelled task-specific dataset is required for fine-tuning creating a bottleneck in the development process of machine learning applications. To foster a fast development by reducing manual labelling efforts, we propose a L abel- E fficient T raining S cheme (LETS). The proposed LETS consists of three elements: (i) task-specific pre-training to exploit unlabelled task-specific corpus data, (ii) label augmentation to maximise the utility of labelled data, and (iii) active learning to label data strategically. In this paper, we apply LETS to a novel aspect-based sentiment analysis (ABSA) use-case for analysing the reviews of the health-related program supporting people to improve their sleep quality. We validate the proposed LETS on a custom health-related program-reviews dataset and another ABSA benchmark dataset. Experimental results show that the LETS can reduce manual labelling efforts 2-3 times compared to labelling with random sampling on both datasets. The LETS also outperforms other state-of-the-art active learning methods. Furthermore, the experimental results show that LETS can contribute to better generalisability with both datasets compared to other methods thanks to the task-specific pre-training and the proposed label augmentation. We expect this work could contribute to the natural language processing (NLP) domain by addressing the issue of the high cost of manually labelling data. Also, our work could contribute to the healthcare domain by introducing a new potential application of NLP techniques.

ACS Style

Heereen Shim; Dietwig Lowet; Stijn Luca; Bart Vanrumste. LETS: A Label-Efficient Training Scheme for Aspect-Based Sentiment Analysis by Using a Pre-Trained Language Model. IEEE Access 2021, 9, 115563 -115578.

AMA Style

Heereen Shim, Dietwig Lowet, Stijn Luca, Bart Vanrumste. LETS: A Label-Efficient Training Scheme for Aspect-Based Sentiment Analysis by Using a Pre-Trained Language Model. IEEE Access. 2021; 9 ():115563-115578.

Chicago/Turabian Style

Heereen Shim; Dietwig Lowet; Stijn Luca; Bart Vanrumste. 2021. "LETS: A Label-Efficient Training Scheme for Aspect-Based Sentiment Analysis by Using a Pre-Trained Language Model." IEEE Access 9, no. : 115563-115578.

Journal article
Published: 12 April 2021 in Postharvest Biology and Technology
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Bell pepper (Capsicum annuum L.), with its wide array of colors and flavors, plays an important role in many different cuisines around the world. Yet once harvested, it is a highly perishable fruit and needs appropriate post-harvest handling. Recently, post-harvest internal rotting (IFR) by Fusarium lactis species complex isolates (FLASC), became an additional challenge to maintain shelf-life and quality of bell pepper fruit. Therefore, modified atmosphere packaging (MAP) was explored as a possible technique to postpone symptom development of infected bell peppers. Four artificially infected bell pepper cultivars with different susceptibility towards IFR were stored under MAP conditions for a maximum of 14 d at challenging conditions of 20 °C resembling unrefrigerated shelf life conditions. Each week, 5 fruit of each object were analyzed for IFR symptom development and additional physicochemical and quality parameters. For all cultivars, MAP packaged fruit showed less severe fungal proliferation compared to controls after 14 d. As total titratable acid (TA), total soluble solids (TSS) and vitamin C concentrations in fruit remained rather stable throughout the experiment, fungal development was likely to be postponed directly due to reduced oxygen levels in the pouches rather than a decreased host susceptibility by influencing fruit metabolism. Since no significant differences of disease development were observed between sensitive and less sensitive cultivars for both colors, sensitivity for IFR seems not likely to be caused by different post-harvest disease development patterns but rather by differences in the initial susceptibility for flower infection under normal growth conditions. Based on our results, MAP can indeed be considered a useful tool to ameliorate IFR development during post-harvest storage of bell pepper under conventional temperatures of 7−16 °C.

ACS Style

M. Frans; R. Aerts; N. Ceusters; S. Luca. Possibilities of modified atmosphere packaging to prevent the occurrence of internal fruit rot in bell pepper fruit (Capsicum annuum) caused by Fusarium spp. Postharvest Biology and Technology 2021, 178, 111545 .

AMA Style

M. Frans, R. Aerts, N. Ceusters, S. Luca. Possibilities of modified atmosphere packaging to prevent the occurrence of internal fruit rot in bell pepper fruit (Capsicum annuum) caused by Fusarium spp. Postharvest Biology and Technology. 2021; 178 ():111545.

Chicago/Turabian Style

M. Frans; R. Aerts; N. Ceusters; S. Luca. 2021. "Possibilities of modified atmosphere packaging to prevent the occurrence of internal fruit rot in bell pepper fruit (Capsicum annuum) caused by Fusarium spp." Postharvest Biology and Technology 178, no. : 111545.

Journal article
Published: 01 April 2021 in Journal of Marine Science and Engineering
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Oceanic islands harbor unique yet fragile marine ecosystems that require evidence-based environmental management. Among these islands, the Galapagos archipelago is well known for its fish diversity, but the factors that structure communities within and between its islands remain poorly understood. In this study, water quality, physical habitats and geographical distance were assessed as potential predictors for the diversity and structure of fish assemblages. Differences in the structure of fish assemblages of the two studied islands (Santa Cruz and Floreana) were most likely driven by temperature and nutrient concentrations. In the relatively highly populated island Santa Cruz, the structure of fish assemblages was more affected by water conditions than physical habitats while the contrary was true for the more pristine area of Floreana. A wide variety of species with different geographical origins were distributed over the different islands, which indicates that most fish species are able to reach the islands of the archipelago. However, temperature gradients and elevated nutrient levels cause large differences in the structure of local fish assemblages. In addition, in Santa Cruz nutrient concentrations were negatively correlated with α diversity. Since pollution is a clear pressure on the fish assemblages of oceanic islands, environmental management of the coastal areas is of paramount importance.

ACS Style

Stijn Bruneel; Wout Van Echelpoel; Long Ho; Heleen Raat; Amber Schoeters; Niels De Troyer; Ratha Sor; José Ponton-Cevallos; Ruth Vandeputte; Christine Van der Heyden; Nancy De Saeyer; Marie Forio; Rafael Bermudez; Luis Dominguez-Granda; Stijn Luca; Tom Moens; Peter Goethals. Assessing the Drivers behind the Structure and Diversity of Fish Assemblages Associated with Rocky Shores in the Galapagos Archipelago. Journal of Marine Science and Engineering 2021, 9, 375 .

AMA Style

Stijn Bruneel, Wout Van Echelpoel, Long Ho, Heleen Raat, Amber Schoeters, Niels De Troyer, Ratha Sor, José Ponton-Cevallos, Ruth Vandeputte, Christine Van der Heyden, Nancy De Saeyer, Marie Forio, Rafael Bermudez, Luis Dominguez-Granda, Stijn Luca, Tom Moens, Peter Goethals. Assessing the Drivers behind the Structure and Diversity of Fish Assemblages Associated with Rocky Shores in the Galapagos Archipelago. Journal of Marine Science and Engineering. 2021; 9 (4):375.

Chicago/Turabian Style

Stijn Bruneel; Wout Van Echelpoel; Long Ho; Heleen Raat; Amber Schoeters; Niels De Troyer; Ratha Sor; José Ponton-Cevallos; Ruth Vandeputte; Christine Van der Heyden; Nancy De Saeyer; Marie Forio; Rafael Bermudez; Luis Dominguez-Granda; Stijn Luca; Tom Moens; Peter Goethals. 2021. "Assessing the Drivers behind the Structure and Diversity of Fish Assemblages Associated with Rocky Shores in the Galapagos Archipelago." Journal of Marine Science and Engineering 9, no. 4: 375.

Journal article
Published: 18 November 2020 in Sensors
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In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.

ACS Style

Ahmed Youssef Ali Amer; Femke Wouters; Julie Vranken; Dianne De Korte-De Boer; Valérie Smit-Fun; Patrick Duflot; Marie-Hélène Beaupain; Pieter Vandervoort; Stijn Luca; Jean-Marie Aerts; Bart Vanrumste. Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology. Sensors 2020, 20, 6593 .

AMA Style

Ahmed Youssef Ali Amer, Femke Wouters, Julie Vranken, Dianne De Korte-De Boer, Valérie Smit-Fun, Patrick Duflot, Marie-Hélène Beaupain, Pieter Vandervoort, Stijn Luca, Jean-Marie Aerts, Bart Vanrumste. Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology. Sensors. 2020; 20 (22):6593.

Chicago/Turabian Style

Ahmed Youssef Ali Amer; Femke Wouters; Julie Vranken; Dianne De Korte-De Boer; Valérie Smit-Fun; Patrick Duflot; Marie-Hélène Beaupain; Pieter Vandervoort; Stijn Luca; Jean-Marie Aerts; Bart Vanrumste. 2020. "Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology." Sensors 20, no. 22: 6593.

Conference paper
Published: 29 March 2020 in Proceedings of the 35th Annual ACM Symposium on Applied Computing
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User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier. However, to train the deep neural networks model, a lot of labelled data is needed. To reduce manual data labelling, we propose a method which is a combination of data augmentation and pseudo-labelling: data augmentation is applied to labelled data to increase the size of the initial train set and then the trained model is used to annotate unlabelled data with pseudo-labels. The result shows that the model with the data augmentation achieves macro-averaged f1 score of 65.2% while using 4,300 training data, whereas the model without data augmentation achieves macro-averaged f1 score of 68.2% with around 14,000 training data. Furthermore, with the combination of pseudo-labelling, the model achieves macro-averaged f1 score of 62.7% with only using 1,400 training data with labels. In other words, with the proposed method we can reduce the amount of labelled data for training while achieving relatively good performance.

ACS Style

Heereen Shim; Stijn Luca; Dietwig Lowet; Bart Vanrumste. Data augmentation and semi-supervised learning for deep neural networks-based text classifier. Proceedings of the 35th Annual ACM Symposium on Applied Computing 2020, 1 .

AMA Style

Heereen Shim, Stijn Luca, Dietwig Lowet, Bart Vanrumste. Data augmentation and semi-supervised learning for deep neural networks-based text classifier. Proceedings of the 35th Annual ACM Symposium on Applied Computing. 2020; ():1.

Chicago/Turabian Style

Heereen Shim; Stijn Luca; Dietwig Lowet; Bart Vanrumste. 2020. "Data augmentation and semi-supervised learning for deep neural networks-based text classifier." Proceedings of the 35th Annual ACM Symposium on Applied Computing , no. : 1.

Journal article
Published: 08 January 2020 in IEEE Intelligent Systems
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The recognition of human physical activities and postures based on sensor data has received much research attention in several human health and biomedical engineering applications. In this study, the challenges of class-imbalance and ambiguity (or confusion) are discussed that frequently arise in data from human activity recognition (HAR) systems. In order to reduce the influence of imbalance and ambiguity in HAR problems, a novel hybrid localised learning approach of K-nearest neighbours least-squares support vector machine (KNN-LS-SVM) is proposed. The classifier is applied to different synthetic and real-world datasets where imbalance and ambiguity are present. In this study, it is novel to apply a hybrid localised learning algorithm to the HAR problem. When compared to different global and local approaches, higher classification performances could be obtained by using the proposed localised learning approach. Furthermore, the computational effort could be reduced in an online learning mode.

ACS Style

Ahmed Youssef Ali Amer; Jean-Marie Aerts; Bart Vanrumste; Stijn Luca. A Localized Learning Approach Applied to Human Activity Recognition. IEEE Intelligent Systems 2020, 36, 58 -71.

AMA Style

Ahmed Youssef Ali Amer, Jean-Marie Aerts, Bart Vanrumste, Stijn Luca. A Localized Learning Approach Applied to Human Activity Recognition. IEEE Intelligent Systems. 2020; 36 (3):58-71.

Chicago/Turabian Style

Ahmed Youssef Ali Amer; Jean-Marie Aerts; Bart Vanrumste; Stijn Luca. 2020. "A Localized Learning Approach Applied to Human Activity Recognition." IEEE Intelligent Systems 36, no. 3: 58-71.

Journal article
Published: 27 August 2019 in Applied Sciences
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Mortality prediction for intensive care unit (ICU) patients is a challenging problem that requires extracting discriminative and informative features. This study presents a proof of concept for exploring features that can provide clinical insight. Through a feature engineering approach, it is attempted to improve ICU mortality prediction in field conditions with low frequently measured data (i.e., hourly to bi-hourly). Features are explored by investigating the vital signs measurements of ICU patients, labelled with mortality or survival at discharge. The vital signs of interest in this study are heart and respiration rate, oxygen saturation and blood pressure. The latter comprises systolic, diastolic and mean arterial pressure. In the feature exploration process, it is aimed to extract simple and interpretable features that can provide clinical insight. For this purpose, a classifier is required that maximises the margin between the two classes (i.e., survival and mortality) with minimum tolerance to misclassification errors. Moreover, it preferably has to provide a linear decision surface in the original feature space without mapping to an unlimited dimensionality feature space. Therefore, a linear hard margin support vector machine (SVM) classifier is suggested. The extracted features are grouped in three categories: statistical, dynamic and physiological. Each category plays an important role in enhancing classification error performance. After extracting several features within the three categories, a manual feature fine-tuning is applied to consider only the most efficient features. The final classification, considering mortality as the positive class, resulted in an accuracy of 91.56 % , sensitivity of 90.59 % , precision of 86.52 % and F 1 -score of 88.50 % . The obtained results show that the proposed feature engineering approach and the extracted features are valid to be considered and further enhanced for the mortality prediction purpose. Moreover, the proposed feature engineering approach moved the modelling methodology from black-box modelling to grey-box modelling in combination with the powerful classifier of SVMs.

ACS Style

Ahmed Y. A. Amer; Julie Vranken; Femke Wouters; Dieter Mesotten; Pieter Vandervoort; Valerie Storms; Stijn Luca; Bart Vanrumste; Jean-Marie Aerts. Feature Engineering for ICU Mortality Prediction Based on Hourly to Bi-Hourly Measurements. Applied Sciences 2019, 9, 3525 .

AMA Style

Ahmed Y. A. Amer, Julie Vranken, Femke Wouters, Dieter Mesotten, Pieter Vandervoort, Valerie Storms, Stijn Luca, Bart Vanrumste, Jean-Marie Aerts. Feature Engineering for ICU Mortality Prediction Based on Hourly to Bi-Hourly Measurements. Applied Sciences. 2019; 9 (17):3525.

Chicago/Turabian Style

Ahmed Y. A. Amer; Julie Vranken; Femke Wouters; Dieter Mesotten; Pieter Vandervoort; Valerie Storms; Stijn Luca; Bart Vanrumste; Jean-Marie Aerts. 2019. "Feature Engineering for ICU Mortality Prediction Based on Hourly to Bi-Hourly Measurements." Applied Sciences 9, no. 17: 3525.

Articles
Published: 26 August 2019 in Quality Engineering
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Acceptance sampling plans are used to determine whether production lots can be accepted or rejected. Existing tools only provide a limited functionality for the two-point design and the risk analysis of such plans. In this article, a web-based tool is presented to study single- and double-stage sampling plans. In contrast to existing solutions, the tool is an interactive applet that is freely available. Analytic properties are derived to support the development of search strategies for the design of double-stage sampling plans that are more efficient and accurate in comparison with existing routines. Several case studies are presented.

ACS Style

Stijn Luca; Johan Vandercappellen; Johan Claes. A web-based tool to design and analyze single- and double-stage acceptance sampling plans. Quality Engineering 2019, 32, 58 -74.

AMA Style

Stijn Luca, Johan Vandercappellen, Johan Claes. A web-based tool to design and analyze single- and double-stage acceptance sampling plans. Quality Engineering. 2019; 32 (1):58-74.

Chicago/Turabian Style

Stijn Luca; Johan Vandercappellen; Johan Claes. 2019. "A web-based tool to design and analyze single- and double-stage acceptance sampling plans." Quality Engineering 32, no. 1: 58-74.

Conference paper
Published: 01 July 2019 in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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Excessive or inadequate Gestational Weight Gain (GWG) is considered to not only put the mothers, but also the infants at increased risks with a number of adverse outcomes. In this paper, we use self-reported weight measurements from the early days of pregnancy to predict and classify the end-of-pregnancy weight gain into an underweight, normal or obese category in accordance with the Institute of Medicine recommended guidelines. Self-reported weight measurements suffer from issues such as lack of enough data and non-uniformity. We propose and compare two novel parametric and non-parametric approaches that utilise self-training data along with population data to tackle limited data availability. We, dynamically find the subset of closest time series from the population weight-gain data to a given subject. Then, a non-parametric Gaussian Process (GP) regression model, learnt on the selected subset is used to forecast the self-reported weight measurements of given subject. Our novel approach produces mean absolute error (MAE) of 2.572 kgs in forecasting end-of-pregnancy weight gain and achieves weight-category-classification accuracy of 63.75% mid-way through the pregnancy, whereas a state-of-the-art approach is only 53.75% accurate and produces high MAE of 16.22 kgs. Our method ensures reliable prediction of the end-of-pregnancy weight gain using few data points and can assist in early intervention that can prevent gaining or losing excessive weight during pregnancy.

ACS Style

Chetanya Puri; Gerben Kooijman; Felipe Masculo; Shannon Van Sambeek; Sebastiaan Den Boer; Stijn Luca; Bart Vanrumste. PREgDICT : Early prediction of gestational weight gain for pregnancy care. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019, 4274 -4278.

AMA Style

Chetanya Puri, Gerben Kooijman, Felipe Masculo, Shannon Van Sambeek, Sebastiaan Den Boer, Stijn Luca, Bart Vanrumste. PREgDICT : Early prediction of gestational weight gain for pregnancy care. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019; ():4274-4278.

Chicago/Turabian Style

Chetanya Puri; Gerben Kooijman; Felipe Masculo; Shannon Van Sambeek; Sebastiaan Den Boer; Stijn Luca; Bart Vanrumste. 2019. "PREgDICT : Early prediction of gestational weight gain for pregnancy care." 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) , no. : 4274-4278.

Articles
Published: 19 May 2019 in Journal of Microencapsulation
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Microencapsulation is almost exclusively performed in batch processes. With today’s chemistry increasingly performed in flow reactors, this work aims to realise a continuous reactor setup for the encapsulation of an ester with a polyuria (PU) shell. The generation of an emulsion template is performed in a recirculation loop driven by a pump and equipped with static mixers, screen type and Kenics®. Calorimetric measurements are performed to characterise the energy dissipation rate inside the loop. The curing step is performed in a coiled tube reactor with two geometric configurations. Number based capsule size distributions are derived from micrograph analysis. Results indicate that the recycle pump is the main contributor to determine the capsule size distribution. A continuous setup is achieved for PU microcapsules containing hexyl acetate with a production rate of 198 g/h dry capsules, and a mean capsule diameter of 13.3 µm with a core content of 54 wt%. Subject classification codes: include these here if the journal requires them

ACS Style

Sven R. L. Gobert; Marleen Segers; Stijn Luca; Roberto F. A. Teixeira; Simon Kuhn; Leen Braeken; Leen C. J. Thomassen. Development of a continuous reactor for emulsion-based microencapsulation of hexyl acetate with a polyuria shell. Journal of Microencapsulation 2019, 36, 371 -384.

AMA Style

Sven R. L. Gobert, Marleen Segers, Stijn Luca, Roberto F. A. Teixeira, Simon Kuhn, Leen Braeken, Leen C. J. Thomassen. Development of a continuous reactor for emulsion-based microencapsulation of hexyl acetate with a polyuria shell. Journal of Microencapsulation. 2019; 36 (4):371-384.

Chicago/Turabian Style

Sven R. L. Gobert; Marleen Segers; Stijn Luca; Roberto F. A. Teixeira; Simon Kuhn; Leen Braeken; Leen C. J. Thomassen. 2019. "Development of a continuous reactor for emulsion-based microencapsulation of hexyl acetate with a polyuria shell." Journal of Microencapsulation 36, no. 4: 371-384.

Journal article
Published: 11 April 2019 in Journal Of Experimental Botany
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Crassulacean acid metabolism (CAM) is a major adaptation of photosynthesis that involves temporally separated phases of CO2 fixation and accumulation of organic acids at night, followed by decarboxylation and refixation of CO2 by the classical C3 pathway during the day. Transitory reserves such as soluble sugars or starch are degraded at night to provide the phosphoenolpyruvate (PEP) and energy needed for initial carboxylation by PEP carboxylase. The primary photosynthetic pathways in CAM species are well known, but their integration with other pathways of central C metabolism during different phases of the diel light-dark cycle is poorly understood. Gas exchange was measured in leaves of the CAM orchid Phalaenopsis 'Edessa' and leaves were sampled every 2 h during a complete 12-h light-12-h dark cycle for metabolite analysis. A hierarchical agglomerative clustering approach was employed to explore the diel dynamics and relationships of metabolites in this CAM species, and compare these with those in model C3 species. High levels of 3-phosphoglycerate (3PGA) in the light activated ADP-glucose pyrophosphorylase, thereby enhancing production of ADP-glucose, the substrate for starch synthesis. Trehalose 6-phosphate (T6P), a sugar signalling metabolite, was also correlated with ADP-glucose, 3PGA and PEP, but not sucrose, over the diel cycle. Whether or not this indicates a different function of T6P in CAM plants is discussed. T6P levels were low at night, suggesting that starch degradation is regulated primarily by circadian clock-dependent mechanisms. During the lag in starch degradation at dusk, carbon and energy could be supplied by rapid consumption of a large pool of aconitate that accumulates in the light. Our study showed similarities in the diel dynamics and relationships between many photosynthetic metabolites in CAM and C3 plants, but also revealed some major differences reflecting the specialized metabolic fluxes in CAM plants, especially during light-dark transitions and at night.

ACS Style

Nathalie Ceusters; Stijn Luca; Regina Feil; Johan E Claes; John E Lunn; Wim Van Den Ende; Johan Ceusters. Hierarchical clustering reveals unique features in the diel dynamics of metabolites in the CAM orchid Phalaenopsis. Journal Of Experimental Botany 2019, 70, 3269 -3281.

AMA Style

Nathalie Ceusters, Stijn Luca, Regina Feil, Johan E Claes, John E Lunn, Wim Van Den Ende, Johan Ceusters. Hierarchical clustering reveals unique features in the diel dynamics of metabolites in the CAM orchid Phalaenopsis. Journal Of Experimental Botany. 2019; 70 (12):3269-3281.

Chicago/Turabian Style

Nathalie Ceusters; Stijn Luca; Regina Feil; Johan E Claes; John E Lunn; Wim Van Den Ende; Johan Ceusters. 2019. "Hierarchical clustering reveals unique features in the diel dynamics of metabolites in the CAM orchid Phalaenopsis." Journal Of Experimental Botany 70, no. 12: 3269-3281.

Journal article
Published: 01 September 2018 in Computational Statistics & Data Analysis
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Novelty detection is a particular example of pattern recognition identifying patterns that departure from some model of “normal behaviour”. The classification of point patterns is considered that are defined as sets of NN observations of a multivariate random variable XX and where the value NN follows a discrete stochastic distribution. The use of point process models is introduced that allow us to describe the length NN as well as the geometrical configuration in data space of such patterns. It is shown that such infinite dimensional study can be translated into a one-dimensional study that is analytically tractable for a multivariate Gaussian distribution. Moreover, for other multivariate distributions, an analytic approximation is obtained, by the use of extreme value theory, to model point patterns that occur in low-density regions as defined by XX. The proposed models are demonstrated on synthetic and real-world data sets.

ACS Style

Stijn E. Luca; Marco A.F. Pimentel; Peter J. Watkinson; David A. Clifton. Point process models for novelty detection on spatial point patterns and their extremes. Computational Statistics & Data Analysis 2018, 125, 86 -103.

AMA Style

Stijn E. Luca, Marco A.F. Pimentel, Peter J. Watkinson, David A. Clifton. Point process models for novelty detection on spatial point patterns and their extremes. Computational Statistics & Data Analysis. 2018; 125 ():86-103.

Chicago/Turabian Style

Stijn E. Luca; Marco A.F. Pimentel; Peter J. Watkinson; David A. Clifton. 2018. "Point process models for novelty detection on spatial point patterns and their extremes." Computational Statistics & Data Analysis 125, no. : 86-103.

Original article
Published: 19 March 2018 in Journal of Plant Diseases and Protection
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Internal fruit rot in bell pepper is an important fungal disease which results in mycelium growth and/or necrosis on the ovarium and fruit flesh. It is mainly caused by members of the Fusarium lactis species complex and emerged as a major threat for bell pepper production worldwide. Infection already starts during anthesis, but the symptoms are only visible later on in the production chain. An accurate prediction of the disease incidence in the greenhouse based on environmental parameters is an important step towards a sustainable disease control. Based on a large dataset (2011–2016), a binomial, logistic regression model was developed. This model enables an accurate prediction of internal fruit rot occurrence based on simple and robust input parameters such as temperature and relative humidity during anthesis. Spore density was included as a simplified, practical parameter describing the presence or absence of internal fruit rot 1 week earlier. The obtained model was validated with an independent dataset of five different commercial bell pepper greenhouses. The chance of internal fruit rot infection increased with temperature and relative humidity. Once a greenhouse is infected, only lower temperatures can reduce future risks. However, the chance of the disease to occur remains very high. This prediction model offers a strong instrument for growers to optimize greenhouse climate conditions to restrain internal fruit rot incidence. In addition, the model can be used to apply accurate biological or chemical treatments to achieve a more sustainable greenhouse control. A guideline table for climate adjustment is presented.

ACS Style

M. Frans; R. Moerkens; S. Van Gool; C. Sauviller; S. Van Laethem; S. Luca; R. Aerts; J. Ceusters. Modelling greenhouse climate factors to constrain internal fruit rot (Fusarium spp.) in bell pepper. Journal of Plant Diseases and Protection 2018, 125, 425 -432.

AMA Style

M. Frans, R. Moerkens, S. Van Gool, C. Sauviller, S. Van Laethem, S. Luca, R. Aerts, J. Ceusters. Modelling greenhouse climate factors to constrain internal fruit rot (Fusarium spp.) in bell pepper. Journal of Plant Diseases and Protection. 2018; 125 (4):425-432.

Chicago/Turabian Style

M. Frans; R. Moerkens; S. Van Gool; C. Sauviller; S. Van Laethem; S. Luca; R. Aerts; J. Ceusters. 2018. "Modelling greenhouse climate factors to constrain internal fruit rot (Fusarium spp.) in bell pepper." Journal of Plant Diseases and Protection 125, no. 4: 425-432.

Original articles
Published: 13 September 2017 in Journal of Applied Statistics
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The purpose of acceptance sampling is to develop decision rules to accept or reject production lots based on sample data. When testing is destructive or expensive, dependent sampling procedures cumulate results from several preceding lots. This chaining of past lot results reduces the required size of the samples. A large part of these procedures only chain past lot results when defects are found in the current sample. However, such selective use of past lot results only achieves a limited reduction of sample sizes. In this article, a modified approach for chaining past lot results is proposed that is less selective in its use of quality history and, as a result, requires a smaller sample size than the one required for commonly used dependent sampling procedures, such as multiple dependent sampling plans and chain sampling plans of Dodge. The proposed plans are applicable for inspection by attributes and inspection by variables. Several properties of their operating characteristic-curves are derived, and search procedures are given to select such modified chain sampling plans by using the two-point method.

ACS Style

Stijn Luca. Modified chain sampling plans for lot inspection by variables and attributes. Journal of Applied Statistics 2017, 45, 1447 -1464.

AMA Style

Stijn Luca. Modified chain sampling plans for lot inspection by variables and attributes. Journal of Applied Statistics. 2017; 45 (8):1447-1464.

Chicago/Turabian Style

Stijn Luca. 2017. "Modified chain sampling plans for lot inspection by variables and attributes." Journal of Applied Statistics 45, no. 8: 1447-1464.

Journal article
Published: 01 August 2017 in Innovative Food Science & Emerging Technologies
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Specific processing steps after industrial rearing of insects for food and feed, being starvation and rinsing, are assumed to have an impact on their microbial quality. The aim of this study was to assess the effect on the microbiota of starvation (24 or 48 h, 10 or 30 °C) and rinsing (1 min using tap water) at the end of the rearing period of yellow mealworm larvae (Tenebrio molitor). Microbial numbers were determined using plate counts and the microbial community composition using metagenetic analyses. Total viable counts ranged from 7.7 to 8.4 log cfu/g for all treatments. Starvation did not evoke prominent shifts in the bacterial community, which was predominated by Proteobacteria and Firmicutes. No bacterial food pathogens were detected using metagenetics. Our data suggest that the processing steps under study do not contribute to a better microbial quality of fresh mealworm larvae.

ACS Style

E. Wynants; Sam Crauwels; B. Lievens; Stijn Luca; Johan Claes; A. Borremans; L. Bruyninckx; Leen Van Campenhout. Effect of post-harvest starvation and rinsing on the microbial numbers and the bacterial community composition of mealworm larvae ( Tenebrio molitor ). Innovative Food Science & Emerging Technologies 2017, 42, 8 -15.

AMA Style

E. Wynants, Sam Crauwels, B. Lievens, Stijn Luca, Johan Claes, A. Borremans, L. Bruyninckx, Leen Van Campenhout. Effect of post-harvest starvation and rinsing on the microbial numbers and the bacterial community composition of mealworm larvae ( Tenebrio molitor ). Innovative Food Science & Emerging Technologies. 2017; 42 ():8-15.

Chicago/Turabian Style

E. Wynants; Sam Crauwels; B. Lievens; Stijn Luca; Johan Claes; A. Borremans; L. Bruyninckx; Leen Van Campenhout. 2017. "Effect of post-harvest starvation and rinsing on the microbial numbers and the bacterial community composition of mealworm larvae ( Tenebrio molitor )." Innovative Food Science & Emerging Technologies 42, no. : 8-15.

Book chapter
Published: 28 October 2016 in Machine Learning for Healthcare Technologies
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Chapter Contents: 13.1 Introduction and overview 13.2 Supervised classification 13.2.1 Gaussian mixture models for classification 13.2.2 Support Vector Machines 13.2.3 Classification of activities of daily living 13.3 Novelty detection 13.3.1 One-class support vector machines 13.3.2 Extreme value theory 13.3.3 Epileptic seizure detection 13.4 Conclusion References Decision support systems for home monitoring applications: Classification of activities of daily living and epileptic seizures, Page 1 of 2

ACS Style

Stijn Luca Luca; Lode Vuegen Vuegen; Hugo Van Hamme Van Hamme; Peter Karsmakers Karsmakers; Bart Vanrumste Vanrumste. Decision support systems for home monitoring applications: Classification of activities of daily living and epileptic seizures. Machine Learning for Healthcare Technologies 2016, 271 -291.

AMA Style

Stijn Luca Luca, Lode Vuegen Vuegen, Hugo Van Hamme Van Hamme, Peter Karsmakers Karsmakers, Bart Vanrumste Vanrumste. Decision support systems for home monitoring applications: Classification of activities of daily living and epileptic seizures. Machine Learning for Healthcare Technologies. 2016; ():271-291.

Chicago/Turabian Style

Stijn Luca Luca; Lode Vuegen Vuegen; Hugo Van Hamme Van Hamme; Peter Karsmakers Karsmakers; Bart Vanrumste Vanrumste. 2016. "Decision support systems for home monitoring applications: Classification of activities of daily living and epileptic seizures." Machine Learning for Healthcare Technologies , no. : 271-291.

Journal article
Published: 20 February 2016 in BMC Medical Research Methodology
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As gait speed and transfer times are considered to be an important measure of functional ability in older adults, several systems are currently being researched to measure this parameter in the home environment of older adults. The data resulting from these systems, however, still needs to be reviewed by healthcare workers which is a time-consuming process. This paper presents a system that employs statistical process control techniques (SPC) to automatically detect both positive and negative trends in transfer times. Several SPC techniques, Tabular cumulative sum (CUSUM) chart, Standardized CUSUM and Exponentially Weighted Moving Average (EWMA) chart were evaluated. The best performing method was further optimized for the desired application. After this, it was validated on both simulated data and real-life data. The best performing method was the Exponentially Weighted Moving Average control chart with the use of rational subgroups and a reinitialization after three alarm days. The results from the simulated data showed that positive and negative trends are detected within 14 days after the start of the trend when a trend is 28 days long. When the transition period is shorter, the number of days before an alert is triggered also diminishes. If for instance an abrupt change is present in the transfer time an alert is triggered within two days after this change. On average, only one false alarm is triggered every five weeks. The results from the real-life dataset confirm those of the simulated dataset. The system presented in this paper is able to detect both positive and negative trends in the transfer times of older adults, therefore automatically triggering an alarm when changes in transfer times occur. These changes can be gradual as well as abrupt.

ACS Style

Greet Baldewijns; Stijn Luca; Bart Vanrumste; Tom Croonenborghs. Developing a system that can automatically detect health changes using transfer times of older adults. BMC Medical Research Methodology 2016, 16, 23 .

AMA Style

Greet Baldewijns, Stijn Luca, Bart Vanrumste, Tom Croonenborghs. Developing a system that can automatically detect health changes using transfer times of older adults. BMC Medical Research Methodology. 2016; 16 (1):23.

Chicago/Turabian Style

Greet Baldewijns; Stijn Luca; Bart Vanrumste; Tom Croonenborghs. 2016. "Developing a system that can automatically detect health changes using transfer times of older adults." BMC Medical Research Methodology 16, no. 1: 23.

Conference
Published: 01 August 2015 in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.

ACS Style

Greet Baldewijns; Stijn Luca; William Nagels; Bart Vanrumste; Tom Croonenborghs. Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015, 2015, 5046 -5049.

AMA Style

Greet Baldewijns, Stijn Luca, William Nagels, Bart Vanrumste, Tom Croonenborghs. Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015; 2015 ():5046-5049.

Chicago/Turabian Style

Greet Baldewijns; Stijn Luca; William Nagels; Bart Vanrumste; Tom Croonenborghs. 2015. "Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly." 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015, no. : 5046-5049.

Journal article
Published: 13 April 2015 in Journal of Texture Studies
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The Foodtexture Puff Device (FPD) is a noncontact rheological measurement device, which applies an air pulse on the sample and measures the subsequent deformation of the sample surface with a laser distance sensor. The deformation behavior is considered as a measure for the rheological properties of the sample. The applicability of this device was studied for use on viscous food products with a broad range of rheological characteristics. In this study, sugar and fat-based systems with a viscosity range of respectively 0.001–6.1 Pa.s and 0.01–5.9 Pa.s were tested. Comparison of the FPD with classical rheological analyses showed that the maximum deformation created by the FPD is strongly correlated to the viscosity. Hence, the FPD is well suited for measurements on sugar-based and fat-based systems. It is capable of providing accurate, noncontact, fast, easy and nondestructive rheological measurements on food products.

ACS Style

Sofie Morren; Tim Van Dyck; Frank Mathijs; Stijn Luca; Ruth Cardinaels; Paula Moldenaers; Bart De Ketelaere; Johan Claes. Applicability of the Foodtexture Puff Device for Rheological Characterization of Viscous Food Products. Journal of Texture Studies 2015, 46, 94 -104.

AMA Style

Sofie Morren, Tim Van Dyck, Frank Mathijs, Stijn Luca, Ruth Cardinaels, Paula Moldenaers, Bart De Ketelaere, Johan Claes. Applicability of the Foodtexture Puff Device for Rheological Characterization of Viscous Food Products. Journal of Texture Studies. 2015; 46 (2):94-104.

Chicago/Turabian Style

Sofie Morren; Tim Van Dyck; Frank Mathijs; Stijn Luca; Ruth Cardinaels; Paula Moldenaers; Bart De Ketelaere; Johan Claes. 2015. "Applicability of the Foodtexture Puff Device for Rheological Characterization of Viscous Food Products." Journal of Texture Studies 46, no. 2: 94-104.