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Hybrid membrane processes for micropollutant removal from wastewater
Get PDFThe quality of available water is at risk due to the growing issue of organic micropollutants (OMPs) present in surface waters. These OMPs can be pharmaceuticals, personal care products, per- and polyfluoroalkayl substances (PFAS), industrial chemicals, or pesticides. One pathway for OMPs to enter surface water is through wastewater, since typical wastewater treatment plants (WWTPs) are not designed to remove such compounds. A possible solution to remove OMPs from wastewater is to add additional treatment steps to conventional WWTPs. When nanofiltration (NF) or Reverse Osmosis (RO) membranes are used, the OMPs can be retained by the membrane. This concentrate requires further treatment. This work focuses on treating the concentrate by recirculating it to conventional biological treatment. The goal of Chapter 2 is to investigate which type of dense membrane has the highest potential for practical application. A model shows that the combination of conventional treatment, membrane filtration and concentrate recirculation can substantially improve the overall removal of OMPs with low bioremoval and high membrane retention. The effects of scale, recovery, flux, crossflow velocity and staging are investigated in detail for the dNF40 hollow fiber nanofiltration (HFNF) membrane in Chapter 3, with a focus on their effect on retention of ions and OMPs and calculated energy consumption. In Chapter 4, the dNF40 membrane is used on pilot scale to investigate the effect of concentrate recirculation on the total treatment system. The combination of biological treatment, the HFNF membrane and concentrate recirculation can increase overall removal compared to only biological treatment for several compounds. The primary focus of Chapter 5 is to achieve a balance between enhancing the transmittance of the membrane permeate, thereby reducing the energy consumption of a subsequent oxidation step, and minimizing the energy required for membrane filtration
A review of compression therapy, effect, characteristics, and theories for chronic venous insufficiency
Get PDFIntroductionCompression therapy is an efficient technique in managing Chronic Venous Insufficiency (CVI) to help blood return. Despite its widespread clinical use, biomechanical properties, and mathematical models remain underexplored in a cohesive framework that offers evidence of how compression therapy works.ObjectivesThis review aims to guide the development of next-generation compression systems, by synthesizing insights from underlying compression mechanisms and biomechanical theories.MethodsA literature review was conducted using Scopus, search terms including CVI, compression, etc., encompassing articles from 1990 to 2025 with inclusion and exclusion criteria focused on three domains: biomechanical tissue properties, compression levels to treat CVI, theoretical models relating interface pressure to functional compression.ResultsForty studies met the criteria: 28 addressed effects and characteristics, and 12 proposed theoretical models. Applied compression to the tissue induces an increase in overall stiffness and reduces venous diameters to restore impaired venous valves. Different parameter combinations, which depend on clinical stages, affected tissue (superficial or deep vein), and body positions (standing or supine), were identified as design requirements, including pressure type (static or dynamic), pressure value, and distribution (graduated or progressive). One theoretical model highlights interface pressure modeling in the design process.ConclusionPersonalized therapy for optimal management of CVI is challenging in current solutions. The dynamic compression systems hold promise for accommodating individual variability in muscle stiffness—a critical factor influencing the transmission of external pressure to venous veins. Such systems can modulate pressure in real-time, ensuring optimal pressure under various body positions and adapting to symptom progression over extended periods.<br/
The quantum valley Hall effect in twisted bilayer silicene and germanene
Get PDFWe show that twisted bilayers of silicene or germanene can be utilized for a novel transistor concept that relies on the quantum valley Hall effect. The application of an electric field normal to the twisted bilayer allows to tailor the size of the bandgap in AB- and BA-stacked domains of the twisted bilayer. In contrast to twisted bilayer graphene, the AB and BA bandgaps in twisted bilayer silicene and germanene are not inverted for small electric fields. However, above a critical electric field, the bandgaps invert, giving rise to a two-dimensional triangular network of topologically protected channels. The possibility to controllably switch these topologically protected states on and off using an electric field, combined with its inherent robustness against defects and impurities, establishes a foundation for a new type of transistor with an exceptional resilience.</p
Building Bridges:Empirical Philosophy (of Technology) and Research through Design
No full textPhilosophy is marked by an absence of a (scientific) method, which leads to the challenge of how to integrate Philosophy into interdisciplinary research project. Research through Design (RtD) creates an opening for philosophical investigations, which are more than the application of philosophical insights to a given case. Rather, by taking design research seriously as a way of knowing the world, Designer and Philosophers can make use of their joint interest in “matters of choice, with things that could be other than they are” (Buchanan, 1995). Thus, Philosophers have an opportunity to gain empirical insights which fit their interests, while Designers can benefit from the alternative conceptional frameworks offered in Philosophy. In this respect, the suggested approach goes beyond other forms of Applied and Application-oriented Philosophy (including Value-Sensitive Design), which often remain at the level of Research for Design
Lost in Models? Structuring Managerial Decision Support in Process Mining with Multi-criteria Decision Making
Get PDFProcess mining is increasingly adopted in modern organizations, producing numerous process models that, while valuable, can lead to model overload and decision-making complexity. This paper explores a multi-criteria decision-making (MCDM) approach to evaluate and prioritize process models by incorporating both quantitative metrics (e.g., fitness, precision) and qualitative factors (e.g., cultural fit). An illustrative logistics example demonstrates how MCDM, specifically the Analytic Hierarchy Process (AHP), facilitates trade-off analysis and promotes alignment with managerial objectives. Initial insights suggest that the MCDM approach enhances context-sensitive decision-making, as selected models address both operational metrics and broader managerial needs. While this study is an early-stage exploration, it provides an initial foundation for deeper exploration of MCDM-driven strategies to enhance the role of process mining in complex organizational settings
Trajectories of prolonged grief disorder severity after loss during the COVID-19 pandemic
Get PDFDue to the presumed high risk for prolonged grief disorder (PGD) in people bereaved during the COVID-19 pandemic, it is crucial to examine how grief develops over time in this population. This is the first study examining longitudinal symptom-profiles of PGD severity in people bereaved during the pandemic. We aimed to identify latent trajectories of DSM-5-TR PGD severity and predictors thereof in Dutch adults bereaved during the pandemic. Latent class growth modeling was employed to identify differential trajectories of PGD severity in 266 people bereaved on average 115 days prior when entering the study. Participants completed a PGD measure online (using the Traumatic Grief Inventory-Self Report-Plus) at three time-intervals six months apart. Associations between class-membership and socio-demographic, loss-related, health-related, and trauma-related characteristics were examined using multinomial logistic regression. Three trajectories were identified: Low/decreasing PGD symptoms (74%), Mild/stable PGD symptoms (18%), and High/decreasing PGD trajectory (8%). Closer kinship to the deceased, poorer self-rated health status, and having a mental disorder increased the likelihood of belonging to the Mild/stable PGD symptoms and High/decreasing PGD trajectories. This study provides insights in longitudinal PGD symptom-profiles in people bereaved during the pandemic. We found that PGD severity either remained low/mild or eventually decreased over time.</p
A Comprehensive Open-Source Toolbox for Analyzing and Improving Orientation Estimation From Inertial Sensors for Biomechanical Applications
Get PDFDrift remains a significant challenge in using inertial measurement units (IMUs) for human movement analysis. Drift reduction in orientation estimation is of particular interest, since inaccuracies in these estimates will negatively affect estimated linear kinematics (e.g., position). To address this, we developed an open-source toolbox designed to investigate the effects of signal characteristics, sampling frequencies, and integration orders on orientation estimation accuracy. The toolbox uses Taylor series approximations to estimate the change in orientation from angular velocity and contains two pipelines: a reference-based (RB) pipeline that compares the estimated orientation against a known ground-truth orientation, and a reference-free (RF) pipeline that does not rely on a ground-truth orientation. We demonstrate the toolbox’s capabilities with three case studies. These case studies are also used to investigate the effects of signal characteristics, sampling frequency, and integration order on orientation estimation accuracy. Results show improved orientation estimation for slower movements, higher sampling frequencies, and higher integration orders. Additionally, through the case studies, we highlight how the toolbox can guide decisions on sampling frequencies and data processing strategies for specific application scenarios.</p
InTreeger:An End-to-End Framework for Integer-Only Decision Tree Inference
Get PDFInteger quantization has emerged as a critical technique to facilitate deployment on resource-constrained devices. Although they do reduce the complexity of the learning models, their inference performance is often prone to quantization-induced errors. To this end, we introduce InTreeger: an end-to-end framework that takes a training dataset as input, and outputs an architecture-agnostic integer-only C implementation of tree-based machine learning model, without loss of precision. This framework enables anyone, even those without prior experience in machine learning, to generate a highly optimized integer-only classification model that can run on any hardware simply by providing an input dataset and target variable. We evaluated our generated implementations across three different architectures (ARM, x86, and RISC-V), resulting in significant improvements in inference latency. In addition, we show the energy efficiency compared to typical decision tree implementations that rely on floating-point arithmetic. The results underscore the advantages of integer-only inference, making it particularly suitable for energy- and area-constrained devices such as embedded systems and edge computing platforms, while also enabling the execution of decision trees on existing ultra-low power devices
Spin-dependent phenomena in superconductor hybrid structures
Get PDFThis thesis describes the interplay of the superconducting proximity effect of conventional and unconventional superconductors with materials that are magnetic or have spin-orbit coupling. The different Chapters in this thesis predict that this interplay yields several interesting emergent phenomena, such anomalous currents and superconducting diode effects. The three main achievements obtained within this thesis are the following. A formalism is developed to derive the transport equations and corresponding boundary conditions for a large range of materials through the construction of the quasiclassical action, called the nonlinear sigma model (NLSM), based on phenomenological symmetry considerations. Using this approach the theory of transport in the presence of a broad range of magnetic and spin-orbit interactions is developed. This theory is used for the prediction of several effects that can be used to characterize materials in which these phenomena are present. The second main achievement is the characterization of gyrotropy on multiple scales, which explains that non-reciprocal transport may appear in junctions in which none of the materials contains microscopic gyrotropy. To this end we have shown that superconducting diode effects exist even in cubic normal metals with a nonzero spin-Hall angle, when in contact with a ferromagnetic insulator on one side and a superconductor on another side. The third main achievement is the development of the theory of noise in junctions with unconventional superconductors. This theory is subsequently used to show that noise is a suitable tool for the clarification of the pairing symmetry of unconventional superconductors, because surface Andreev bound states yield distinctive signatures in the differential Fano factor, the ratio of the differential noise power and the conductance
A Deep Learning Solution for Phase Screen Estimation in SAR Tomography
No full textMultibaseline and tomographic synthetic aperture radar (SAR) data are often affected by phase distortions known as phase screens. These distortions stem either from atmospheric effects or residual errors in platform motion. Calibrating and compensating for the phase screen is crucial to prevent spreading and defocusing in multidimensional tomographic imaging. Given the growing interest in artificial intelligence and deep learning, we aim to utilize their potential to develop a phase calibration process for SAR tomographic data. Our proposed framework is based upon a convolutional neural network (CNN) and generates training patches directly from the tomographic images under consideration, without relying on external references or resources. Once trained, the network effectively estimates phase distortions across the entire image; these are then used to calibrate the tomographic data. Experimental results from AfriSAR and UAVSAR tomographic datasets are included to showcase the effectiveness of the proposed solutio