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Digital Biosensing at the Nanoscale:CMOS Nanocapacitor Arrays for Stochastic Biosensing, a Proof of Concept
Get PDFThis dissertation investigates CMOS-based nanocapacitor arrays (CMOS-NCAs) as an innovative platform for next-generation biosensing. By combining high-frequency impedance spectroscopy with large-scale arrays of nanoscale sensors, CMOS-NCAs offer unprecedented sensitivity and spatial resolution for probing dynamic biological processes at the nanoscale. The technology addresses major limitations in conventional biosensing, such as low sensitivity in complex media, difficulty detecting single particles or molecules, and reliance on bulky instrumentation.The research begins by validating the platform's sensitivity using self-assembled monolayers (SAMs), demonstrating its ability to detect molecular-level interfacial changes. It then explores real-time monitoring of supported lipid bilayer (SLB) formation via vesicle fusion, capturing key stages such as adsorption, spreading, and rupture. These findings show the platform’s strength in studying membrane assembly, dynamics, and protein-lipid interactions.The versatility of CMOS-NCAs is further demonstrated through digital detection of single-stranded DNA using a toehold-mediated strand displacement assay enhanced by nanoparticle tracking. This method achieves high specificity and signal clarity without the need for labels. The same principles are applied in a proof-of-concept for SARS-CoV-2 detection using aptamer-functionalized electrodes, enabling selective, label-free detection of viral particles in physiologically relevant environments.Additionally, CMOS-NCAs are shown to detect dielectric microparticles encapsulated within giant unilamellar vesicles (GUVs), providing a route for probing the contents of lipid vesicles. This opens new avenues for investigating organelles like mitochondria and vesicle-based biomarkers such as exosomes—relevant in both basic research and medical diagnostics.The dissertation also presents a robust experimental and analytical framework, including microfluidic integration, optimized chip architecture, and advanced signal processing for single-event detection and statistical analysis. Together, these features make CMOS-NCAs a powerful, scalable, and cost-effective tool for biosensing in clinical, biological, and environmental contexts. Looking ahead, potential enhancements include miniaturization, real-time data processing via machine learning, and broader application in personalized medicine and point-of-care diagnostics
Decentralized Autonomous Organization and AI Legal Personhood
No full textThis article argues that since a decentralized autonomous organization (DAO) can be granted legal personhood as a DAO LLC, the DAO LLC may be a precursor to granting legal personhood to an artificial intelligence (AI). The article first explores the concept of DAO and the legal personhood status of a DAO when it is registered as a DAO LLC. Under the DAO LLC laws of Wyoming, Tennessee, and Utah, a DAO is statutorily defined as a legal person or attains legal personhood by implication. As AI is increasingly used in DAOs, the article explores the possibility of extending the legal personhood status of a DAO to an AI. Irrespective of the development of artificial general intelligence (AGI), the type of autonomous AI the article envisions is one that fully controls a DAO, called an AI DAO. While an AI DAO has yet to be developed, the article explores technological developments at the convergence of AI and DAO that could lead to an AI DAO. The article discusses three arguments that could extend AI legal personhood from DAOs to an AI: (a) interpreting the statutory definition of a legal person to include an AI DAO, (b) treating the DAO as an indistinguishable legal entity as the AI that fully controls it, and (c) the AI’s exercise of the rights and duties of the DAO as creating legal personhood.<br/
A chance-constrained program for the allocation of nurses in acute home healthcare
Get PDFHome healthcare capacity is under great pressure due to demographic developments. Existing literature has exclusively focused on the planning, scheduling, and routing of non-acute care activities. However, similar to other healthcare settings, home healthcare also experiences acute care activities that disrupt operational performance. We study the planning and control of an acute care team for dealing with unplanned and urgent home healthcare activities. Particularly, we focus on determining the number of nurses per care level and their standby locations. The primary aim of this study is to introduce this novel problem, which we define as the acute care team location problem. We formulate this problem as a chance-constrained program. We solve the single location problem to optimality, and the multi-location problem with sample average approximation. The results show that our approach enables decision makers to optimally configure their acute care team, to respond quickly to acute care incidents. From a managerial perspective, our research provides a model that supports tactical capacity planning in HHC organisations and presents a benchmark for acute care management policies.</p
Optimizing Electric Vehicle Charging Through a Real-Time Control Mechanism
Get PDFThis paper explores an integrated approach combining real-time and predictive control mechanisms to manage electric vehicle charging in alignment with solar energy availability. By utilizing predictive control and a Real-Time Control Mechanism (RCM), the challenges posed by the variability of solar power and the increasing demand for EV charging are addressed. Through simulations and field tests, the proposed strategy demonstrates its ability to reduce peak grid loads, enhance self-sufficiency, and improve self-consumption, while maintaining user satisfaction. The findings indicate that integrating real-time adjustments with predictive EV charging scheduling can significantly contribute to a more stable and efficient grid.</p
A country-level comparison of present-day coastal vulnerability to coastal flooding and erosion
No full textTemplate-Assisted Growth of Cs<sub>x</sub>FA<sub>1-x</sub>PbI<sub>3</sub> with Pulsed Laser Deposition for Single Junction Perovskite Solar Cells
Get PDFCesium-formamidinium lead iodide (CsxFA1-xPbI3) perovskites are a promising methylammonium-free alternative for efficient single-junction solar cells. However, they have not been fully explored by vapor-phase deposition techniques. Herein, a template-assisted approach is demonstrated for the growth of CsxFA1-xPbI3 perovskite films using pulsed laser deposition (PLD) from a single-source target of mixed precursors. Implementing a lead iodide (PbI2) + CsxFA1-xPbI3 tailored template, phase-pure CsxFA1-xPbI3 films with uniform coverage on both planar and textured substrates are achieved. Compositional analysis via X-ray fluorescence confirms near-stoichiometric transfer of the inorganic cations (Cs/Pb), with identical Cs0.2FA0.8PbI3 composition and a bandgap of 1.58 eV achieved in templated and non-templated films. However, the presence of the template proves essential for attaining phase-pure films in the photoactive cubic (α-) phase. Proof-of-concept solar cells fabricated with templated-PLD α-CsxFA1-xPbI3 achieve an efficiency exceeding 12.9% on 0.1 cm2 area devices without the employment of passivation approaches. Additionally, increasing deposition rates does not alter the phase, morphology, or optoelectronic properties of the templated films on textured substrates, indicating the robustness of this methodology. The compositional control of PLD for Cs-FA-based perovskites is showcased, and template-assisted growth is demonstrated as a reliable pathway to high-quality reproducible perovskite films.</p
A data-driven approach to post-seismic landslide hazard assessment
Get PDFEarthquake-induced landslides cause a significant threat to communities living in earthquake-prone areas, as they potentially worsen the destructive impact of an earthquake event physically and socio-economically. This hazard emerges as an aftermath of strong ground motion in mountainous areas, which disturbs the stability of the hillslope material and reduces its shear strength, leading to failure. This earthquake legacy effect is often called shear-strength reduction (RSS). An understanding regarding this matter is important, as it can be used for an immediate post-seismic response and long-term mitigation strategies. However, incorporating RSS for post-seismic landslide predictions remains challenging due to the complex interactions between the hillslope and the ground shaking, making it hard to quantify the RSS degree. Applying the same RSS estimation method used for the 2008 Wenchuan earthquake to the 2023 Turkey earthquake, this study aims to estimate the RSS caused by the earthquake and incorporate it into the post-seismic landslide prediction model.The study uses a data-driven approach to develop the co-seismic landslides prediction model, utilizing the co-seismic landslide inventories and various predictor variables to see which variable most strongly contributes to the failure. The model was evaluated with random (RCV) and spatial cross-validation (SCV). Simulations will be conducted using a seismic hazard map as a ground-shaking predictor variable to estimate the spatial distribution of earthquake-induced landslides for future events.Preliminary results of the developed co-seismic landslide model showed that most of the morphometric variables significantly contributed to the failure, as well as the seismic factor, where only the sediment and metamorphic lithology gave a positive contribution to the failure. The Area Under the Curve (AUC) value from the RCV and SCV showed a strong correlation between observed and predicted landslide areas. The RSS will be integrated into the simulation output to evaluate its impact on the post-seismic landslide estimation, which is expected to provide valuable insight into the earthquake-induced landslide predictions
How to age right and care(fully) at home?:A protocol for a multistage comparative study of ageing in place and hospital at home care across three countries
Get PDFIntroduction Ageing right care(fully) is a transnational research study which explores and maps an understanding of the care pathways between ageing in place and hospital at home policy and practices for older adults in Israel, the Netherlands and Sweden. The countries are suited to be compared where they have growing, ageing populations, a focus on healthcare reform and several policies to reduce the cost of care for older populations. Ageing in place is a government-led policy that is often associated with choice; however, there is a recent debate about whether ageing in place is a universal desire for all older adults. Research shows that the care pathway between the hospital and the home, associated with ageing in place, can impact well-being, especially if the built, social and technological environments do not meet the healthcare needs and preferences of older adults. This is significant as new programmes for digital hospital at home innovations are being developed as part of a global transformation in healthcare systems. The aim of the study is to compare different approaches to ageing in place and hospital at home care in different regions. The multiapproach study explores the demographics, policy structure, decision-making process and the crucial role of the built, social and technological environments along the hospital to home care pathways of older adults.Methods and analysis The mixed-method, comparative study includes a new multienvironment theoretical contribution explored across a three-phase research method to understand the care pathways of older adults ageing in place receiving hospital at home care. The first phase compares each country’s population and policy structures relating to ageing in place, hospital discharge, home hospitalisation and at-home care for older adults. The second phase maps patient journeys of older adults living in each country through the perspective of the older adult, caregivers and care professionals. The third phase explores the synergies between the knowledge gained through phases 1 and 2—from a policy and a personal level—and mobilises the knowledge into policy recommendations and implementation guidelines.Ethics and dissemination The comparative study has been approved by the Sheba Medical Centre in Israel (SMC-1330-24), the Ethics Committee of Tel Aviv University (0009216-2), the Humanities and Social Science Ethics Committee at the University of Twente in the Netherlands (240040) and the Swedish Ethical Review Authority (Dnr 2024-07569-01). The results will be shared with end-users, including citizens, carers, healthcare policymakers, planners, architects and designers, through social media, publications, workshops and international conferences. This future-focused research approach will allow stakeholders to rethink and imagine ways that health and care systems can be personalised and responsive to the future needs of older adult populations
A Machine Learning Model Based on Radiomic Features as a Tool to Identify Active Giant Cell Arteritis on [<sup>18</sup>F]FDG-PET Images During Follow-Up
Get PDFObjective: To investigate the feasibility of a machine learning (ML) model based on radiomic features to identify active giant cell arteritis (GCA) in the aorta and differentiate it from atherosclerosis in follow-up [18F]FDG-PET/CT images for therapy monitoring. Methods: To train the ML model, 64 [18F]FDG-PET scans of 34 patients with proven GCA and 34 control subjects with type 2 diabetes mellitus were retrospectively included. The aorta was delineated into the ascending, arch, descending, and abdominal aorta. From each segment, 95 features were extracted. All segments were randomly split into a training/validation (n = 192; 80%) and test set (n = 46; 20%). In total, 441 ML models were trained, using combinations of seven feature selection methods, seven classifiers, and nine different numbers of features. The performance was assessed by area under the curve (AUC). The best performing ML model was compared to the clinical report of nuclear medicine physicians in 19 follow-up scans (7 active GCA, 12 inactive GCA). For explainability, an occlusion map was created to illustrate the important regions of the aorta for the decision of the ML model. Results: The ten-feature model with ANOVA as the feature selector and random forest classifier demonstrated the highest performance (AUC = 0.92 ± 0.01). Compared with the clinical report, this model showed a higher PPV (0.83 vs. 0.80), NPV (0.85 vs. 0.79), and accuracy (0.84 vs. 0.79) in the detection of active GCA in follow-up scans. Conclusions: The current radiomics ML model was able to identify active GCA and differentiate GCA from atherosclerosis in follow-up [18F]FDG-PET/CT scans. This demonstrates the potential of the ML model as a monitoring tool in challenging [18F]FDG-PET scans of GCA patients.</p
Correction to: Third-order geometric stiffness formulation for improved mesh convergence of thin and wide spatial beams in the generalized strain beam formulation: (Computational Mechanics, (2025), 75, 4, (1431-1447), 10.1007/s00466-024-02570-5)
Get PDFThe original version of the article has a error in the equation 70 (Derivative was missing). The corrected equation is given below: (Formula presented.) The uncorrected equation is given below: (Formula presented.) The original article has been corrected.</p