The Ontology for Conceptual Characterization of Ontologies

Ontologies as computational artifacts have been seen as a solution to FAIRness due to their characteristics, applications, and semantic competencies. Conceptualizations of complex and vast domains can be fragmented in different ways and can compose what is known as ontology networks. Thus, the ontologies produced can relate to each other in many different ways, making the ontological artifacts themselves subject to FAIRness. The problem is that in the Ontology Engineering Process, stakeholders take different perspectives of the conceptualizations, and this causes ontologies to have biases that are sometimes more ontological and sometimes more related to the domain. Besides, usually, Ontology Engineers provide well-grounded reference ontologies, but rarely are they implemented. At the same time, Domain Specialists produce operational ontologies storing large amounts of valid data but with naive ontological support or even without any. We address this problem of lack of consensual conceptualization by proposing a reference conceptual model (O4OA) that considers ontological-related and domain-related perspectives, knowledge, and commitment necessary to facilitate the process of Ontological Analysis, including the analysis of ontologies composing an ontology network. Indeed, O4OA is a (meta)ontology grounded in the Unified Foundational Ontology (UFO) and supported by well-known ontological classification standards, guides, and FAIR principles. We demonstrate how this approach can suitably promote conceptual clarification and terminological harmonization in this area through our framework proposal and its case studies.</p

Strategies for detecting cardiac sources of embolism after ischemic stroke

Detecting the probable cause of ischemic stroke is one of the main goals of the diagnostic evaluation of ischemic stroke patients. Various causes of stroke require specific treatment changes that can lower the risk of recurrent stroke. However, after the first in-hospital analysis, no cause of stroke is detected in about 25% of patients. Because several cardiac diseases can be a cause of ischemic stroke, this group of patients undergoes additional cardiac evaluation. This thesis aims to evaluate the use of routine cardiac investigations for the detection of major cardiac sources of embolism (CSE) in patients with ischemic stroke or TIA of undetermined cause, and to find possibilities for improvement of the current strategy. Part one focuses on the detection of atrial fibrillation (AF). We show that the use of an automated detection algorithm for AF during in-hospital heart rhythm monitoring improves AF detection in stroke patients. After hospital discharge, ambulatory heart rhythm monitoring further increases the rate of AF detection. In addition, longer monitoring duration leads to higher detection rates. These results support extending the current guideline-recommended monitoring period of three days to at least seven days. Part two concentrates on the detection of structural CSEs with transthoracic echocardiography (TTE). Currently, in the Netherlands, the availability of TTE is limited due to shortage of experienced echocardiographers. When combining data from our systematic review and meta-analysis, retrospective single-center study, and prospective multicenter study, we can conclude that routine TTE in patients with ischemic stroke or TIA of undetermined cause infrequently detects a major CSE, in about 1% of patients. The majority of these patients also had major abnormalities on their electrocardiogram (ECG). Results of a subsequent cost-effectiveness analysis show that the strategy of only selecting patients with such ECG-abnormalities for TTE is cost-effective compared to performing TTE in all patients with ischemic stroke or no patients. Thus, a strategy that only selects patients with major ECG-abnormalities to undergo TTE would therefore be more efficient, reduce the amount of unnecessary TTE examinations and consequently reduce pressure on the Dutch healthcare system.<br/

Local energy management:A base model for the optimization of virtual economic units

As non-renewable resources are limited and the overall CO2 emissions need to be reduced drastically, there is an increasing need in making the energy supply more sustainable. This leads to a needed change in the traditional energy system, in which decentralized local energy resources must be better integrated. In order to face the challenges for a future energy management, the consideration of the domestic level is gaining more attention. In this paper, we aim to get insights into the potential value of local cooperations and focus on economic and control issues as to whether and how the domestic level can participate in upcoming solutions. We introduce a possible setup for a virtual economic unit representing a hybrid cooperation of domestic energy participants, whereby the objective of this cooperation is to realize the maximum possible savings for the community with specified individual contracts between the participants and assuming that participants continue to have additional contracts with their energy service provider. We propose a deterministic linear optimization model for determining optimal energy load profiles of the participants with the external suppliers and energy exchange between participants in a virtual economic unit. To efficiently solve this model and get an exact assignment of supply and demand, we present a maximum saving flow algorithm taking into account the underlying bipartite structure of this problem. The solution achieved is specified as a peer-to-peer allocation between the participants involved and provides insights into the aspects that determine the concrete assignment. It also has the advantage of leading to a robust solution within the collaboration case studies for a basic set-up demonstrate the impact of this approach on the economic potential of aggregating local generation and demand at the same time.</p

Spectral estimation model for linear displacement and vibration monitoring with GBSAR system

In recent years, there has been a growing interest in the development of ground-based Synthetic Aperture Radars (GBSAR) for the purpose of monitoring structural displacements. GBSAR offers high-resolution monitoring over a wide area and can capture data every few minutes. However, compact high-frequency multiple input multiple output (MIMO) radars have emerged as an alternative for monitoring sub-second displacements, such as structural vibrations. MIMO radar has sub-second acquisition interval. However, it has limited cross-range resolution compared to GBSAR, and interference between antennas and presence of multiple scatterers in the scene can cause strong sidelobes in the processed data. On the other hand, GBSAR utilizes a long synthetic aperture to achieve high cross-range resolution. However, due to its longer data acquisition time compared to MIMO radar, conventional methods are insufficient for detecting scatterers’ sub-second displacements that occur during the data acquisition process. This study proposes a method to effectively monitor sub-second or sub-minute displacements using GBSAR signals. The proposed method enhances the conventional radar interferometric processes by employing spectral estimation, allowing for multi-dimensional detection of targets’ azimuth angle, linear displacement, and vibrational characteristics. Consequently, this method improves both the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals. The paper presents the theoretical details and mathematical formulations of the proposed method for both MIMO radar and GBSAR imaging modes. To evaluate the effectiveness of the proposed method, numerical simulations and real experiments are conducted. The experimental results validate the capability of the proposed method in both GBSAR and MIMO configuration modes for high-resolution monitoring of fast linear displacements and vibrations. The results exhibit promising signal-to-noise ratio (SNR) and peak-to-sidelobe ratio (PSLR) values

UAVPal:A New Dataset for Semantic Segmentation in Complex Urban Landscape with Efficient Multiscale Segmentation

Semantic segmentation has recently emerged as a prominent area of interest in Earth observation. Several semantic segmentation datasets already exist, facilitating comparisons among different methods in complex urban scenes. However, most open high-resolution urban datasets are geographically skewed toward Europe and North America, while coverage of Southeast Asia is very limited. The considerable variation in city designs worldwide presents an obstacle to the applicability of computer vision models, especially when the training dataset lacks significant diversity. On the other hand, naively applying computationally expensive models leads to inefficacies and sometimes poor performance. To tackle the lack of data diversity, we introduce a new UAVPal dataset of complex urban scenes from the city of Bhopal, India. We complement this by introducing a novel dense predictor head and demonstrate that a well-designed head can efficiently take advantage of the multiscale features to enhance the benefits of a strong feature extractor backbone. We design our segmentation head to learn the importance of features at various scales for each individual class and refine the final dense prediction accordingly. We tested our proposed head with a state-of-the-art backbone on multiple UAV datasets and a high-resolution satellite image dataset for LULC classification. We observed improved intersection over union (IoU) in various classes and up to 2$\%$ better mean IoU. Apart from the performance improvements, we also observed nearly 50$\%$ reduction in computing operations required when using the proposed head compared to the traditional segmentation head.</p

Tunable Hybrid-Integrated Diode Laser at 637 nm

Photonic quantum technologies, such as quantum-key distribution and photonic quantum processing, are currently undergoing a transition from research labs to industrial applications [1]. Upscaling of such systems calls for on-chip laser sources. In particular, many applications require lasers in the visible range, e.g., for addressing particular atomic and ionic transitions, quantum dots or nitrogen vacancy (NV) centers. Specifically, narrow linewidths and wide tunabilty are required for addressing quantum emitters. Scaling and industrial applications also demand mode stability and robustness, such as for portability.</p

Modelling and experiments of dry ice sublimation in an insulation box

A crucial aspect of the cold chain involves maintaining temperature-sensitive products at the desired temperature during transportation to guarantee the safety and quality of perishable products and fulfil regulatory requirements set by various governing bodies. Dry ice is used as a cold agent for transporting temperature-sensitive products packed inside an insulation container. Since dry ice is commercially available in multiple forms and shapes, it is essential to predict the life of various types of dry ice inside an insulation container. In this work, the sublimation process of three types of dry ice, namely, snow, pellets, and slices, placed inside an insulation container made of expanded polystyrene is experimentally and numerically investigated. It is shown that the mass of dry ice inside the insulation container, irrespective of its form, decreases linearly with time for most of the sublimation process except towards the end of sublimation

Improving the energy yield of plasma-based NOX synthesis with in situ adsorption

Plasma-based NOX synthesis from air is a promising option to electrify nitrogen fixation. However, the energy efficiency of direct plasma-based NOX synthesis in a plasma reactor is severely limited by NOX decomposition in the plasma phase. In situ NOX adsorption on MgO improves the NOX energy yield in a dielectric barrier discharge (DBD) plasma reactor by a factor of 15

Valuable bioproducts from microalgae:A superstructure optimization approach

A superstructure to produce added-value products (pigment, omega-3, glycerol, biodiesel, biogas, and fertilizers) from three species of microalgae (Chlorella vulgaris, Haematococcus pluvialis, Nannochloropsis spp.) is developed in this study. The superstructure is converted into a mixed-integer nonlinear programming (MINLP) model. A block integration approach is used to drastically decrease the CPU times by reducing the number of variables, parameters, and constraints. The model is solved with Baron/AOA in AIMMS software, and the most promising production pathway is identified. For all three biorefineries (cultivating different microalgae), the most promising production pathways (in terms of cost-effectiveness) remain consistent. These pathways involve an open pond, sedimentation and flotation, flocculation without any dryer, sonication, organic solvent pigment extraction, n-butanol solvent lipid extraction, lipid production, and anaerobic digestion. Changing technologies of dewatering stages (flocculation to centrifugation and filter press) proposes the second and third cost-effective production pathways. The most profitable biorefinery cultivates Haematococcus pluvialis, with annual profits of 62 $/kg of microalgae. A high amount of valuable pigment produced by Haematococcus pluvialis leads to 22 times higher profits than Chlorella vulgaris and 47 times higher than Nannochloropsis spp. The Haematococcus pluvialis biorefinery produces approximately 500 *103Kg of pigment bioproducts from 24 *106Kg biomass by using 200 *106Kg wastewater and 164 *106Kg of carbon dioxide, annually. Ultimately, a sensitivity analysis is executed to confirm how the production of pigment, the price of this bioproduct, and day/ night ratio affect the profitability of microalgae biorefineries.</p

TransFusion: Multi-modal Fusion Network for Semantic Segmentation

The complementary properties of 2D color images and 3D point clouds can potentially improve semantic segmentation compared to using uni-modal data. Multi-modal data fusion is however challenging due to the heterogeneity, dimensionality of the data, the difficulty of aligning different modalities to the same reference frame, and the presence of modality-specific bias. In this regard, we propose a new model, TransFusion, for semantic segmentation that fuses images directly with point clouds without the need for lossy pre-processing of the point clouds. TransFusion outperforms the baseline FCN model that uses images with depth maps. Compared to the baseline, our method improved mIoU by 4% and 2% for the Vaihingen and Potsdam datasets. We demonstrate the capability of our proposed model to adequately learn the spatial and structural information resulting in better inference.</p