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Towards an understanding of wildfire risk at the wildland urban interface as a socio-ecological system
No full textWildfires increasingly affect urban development, especially at the wildland urban interface (WUI), where human activity and natural systems meet. Urban expansion exposes more residents to fire hazards and wildlands to urban-caused ignitions. This study examined WUI dynamics in Cape Town South Africa, at two scales: citywide fire and landcover patterns (1990–2019) and in depth the informal settlement of Imizamo Yethu (IY), situated next to the Table Mountain National park home to fire-dependent fynbos.Findings show urban expansion has reduced annual burned area, reflecting a suppression-first approach that conflicts with environmental policy aimed at conserving fynbos. Reduced fire frequency has led to fuel accumulation, contributing to large, destructive fires such as those in Table Mountain National Park in 2021 and 2025.The study investigated residents’ motivations for living in IY. Local leaders were interviewed, and residents surveyed using a framework combining Turner’s (1968) informal settlement resident typology, Protection Motivation Theory, and place attachment. The investigation revealed both Turner’s classical resident types and new subtypes with distinct reasons for staying or leaving. After a fire in August 2021, a follow-up survey found that residents who lost homes felt more vulnerable but were less likely to engage in risk-reduction actions immediately after the event, highlighting timing as crucial for interventions.An agent-based model (ABM) was then developed in NetLogo to simulate the effects of five fire management strategies on settlement and wildland outcomes. Results showed that controlled burning combined with suppression provided the best outcomes for both residents and fynbos.This research contributes multiple frameworks for fire-landcover and policy change analysis and micro-level residential decision-making including ABM modelling. It demonstrates that WUI fire risk management involves unavoidable trade-offs. The findings provide evidence to guide integrated fire management balancing human safety, ecological needs, and political realities
Inline calibration of spatial light modulators in nonlinear microscopy
No full textWe present a method for calibrating the response of a phase-only spatial light modulator in nonlinear microscopy. Our method uses the microscope image itself as a calibration measurement and requires no additional hardware components. It is adapted to the nonlinear signals encountered in multi-photon excitation fluorescence microscopes and works well even under low-light conditions and with strong photobleaching.</p
Quantifying the likelihood of learning collusive strategy equilibria
No full textWe develop a method for quantifying the likelihood of observing collusive strategies among provably convergent decentralized multiagent reinforcement learning algorithms in a pricing setting. This is necessary to accurately assess the threat that colluding algorithms pose for society. The tools are, however, more generally applicable. Specifically, we obtain conditions for the weak acyclicity of families of two-player, symmetric Markov games in which best responses are unique. In this case, the individual best-response graphs (a concept we introduce in the article) belong to the class of functional relations. Using the structural properties of this class of graphs, we provide conditions on the individual best-response graphs for the game being weakly acyclic. In addition, we characterize the stationary distribution of the best-response strategy adjustment process in such games. Using these results, we show that Decentralized Q-learning is provably convergent in three two-player, two-action games with a memory of one period, analyze its probability of converging to different equilibria, and interpret the results in the context of algorithmic collusion
Fair voltage regulation and energy management in smart distribution grids
No full textThe energy transition toward decentralised, renewable-based generation is reshaping electricity distribution networks. In particular, the rapid growth of photovoltaic (PV) systems introduces new operational challenges such as overvoltage, voltage unbalance, and power quality (PQ) degradation. These problems are amplified by the intermittent nature of renewable generation and the limited hosting capacity of low-voltage (LV) grids. Conventional grid operation methods, designed for unidirectional power flows, are no longer sufficient. At the same time, most existing control approaches focus solely on technical feasibility, often overlooking fairness in how grid resources and constraints are shared among prosumers.This thesis develops fairness-incorporated control strategies to maintain grid operation standards while ensuring equity in LV grids. It begins by outlining the technical and socio-economic implications of high PV penetration and then introduces new approaches for fair voltage regulation and unbalance reduction. Two active power curtailment (APC) methods are proposed to distribute curtailment equitably among prosumers, with one designed for computational efficiency in real-time applications. While fairness entails higher total power curtailment compared to traditional droop control, it prevents disproportionate burdens on individual users.To address both unbalance and overvoltage, reactive power control (RPC) and neutral current compensation (NCC) are integrated with fairness-driven APC into a global control algorithm. This approach reduces neutral currents, improves grid balance, and lowers curtailment needs, and demonstrates the alternate voltage unbalance factor (AVUF) as a more accurate metric for unbalance in LV grids.The thesis further introduces proportional voltage fairness (PVF), a scalable fairness metric that accounts for voltage sensitivity across diverse network configurations. PVF-based voltage regulation enables targeted curtailment, reduces overall power losses, and provides distribution system operators (DSOs) with a transparent mechanism to make a trade-off between fairness and efficiency.Finally, a data-driven energy management system (EMS) is presented to coordinate demand, storage, and generation in real time. By combining Bayesian inference-based decision trees with a hierarchical control framework, the EMS achieves predictive and interpretable control, balancing local prosumer needs with system-wide requirements
Global 10 year ecological momentary assessment and mobile sensing study on tinnitus and environmental sounds
No full textIn most tinnitus patients, tinnitus can be masked by external sounds. However, evidence for the efficacy of sound-based treatments is scarce. To elucidate the effect of sounds on tinnitus under real-world conditions, we collected data through the TrackYourTinnitus mobile platform over a ten-year period using Ecological Momentary Assessment and Mobile Crowdsensing. Using this dataset, we analyzed 67,442 samples from 572 users. Depending on the effect of environmental sounds on tinnitus, we identified three groups (T-, T+, T0) using Growth Mixture Modeling (GMM). Moreover, we compared these groups with respect to demographic, clinical, and user characteristics. We found that external sound reduces tinnitus (T-) in about 20% of users, increases tinnitus (T+) in about 5%, and leaves tinnitus unaffected (T0) in about 75%. The three groups differed significantly with respect to age and hearing problems, suggesting that the effect of sound on tinnitus is a relevant criterion for clinical subtyping.</p
Immunostimulatory effects of IL-12 targeted pH-responsive nanoparticles in macrophage-enriched 3D immuno-spheroids in vitro model
No full textMetastatic colorectal cancer (CRC) has the dismal 5-year survival rate of only 14%, and immunotherapies fail to improve the patient outcome. One reason for the poor response rate is the slightly acidic (~ 6.5) immunosuppressive microenvironment. Interleukin 12 (IL-12) is a highly potent pro-inflammatory cytokine that can stimulate tumor immune cells and reverse immunosuppression by inducing interferon gamma (IFN-γ) expression. However, its clinical applications are hindered by systemic side effects. In this study, we developed pH-responsive polymeric nanoparticles (NPs) encapsulating IL-12 to enhance its therapeutic efficacy into the tumor microenvironment (TME). IL-12-loaded pH-responsive NPs induced antitumoral pro-inflammatory response in macrophages at pH ~ 6.5, determined by increased IFN-γ levels and nitric oxide (NO) release, without affecting metabolic activity. In contrast, IL-12-loaded pH non-responsive PLGA NPs showed much lower macrophage activation. To validate the specificity and efficacy in a complex immune-rich microenvironment, we developed a novel CRC 3D immuno-spheroid by incorporating human monocyte-derived macrophages with tumor cells in collagen, mimicking CRC spatial organization and extracellular matrix. The interaction of IL-12 pH-responsive NPs induced macrophage polarization, by providing a reduction of M2-like markers (CD14 + CD163+) while increasing pro-inflammatory M1-like counterparts (CD14 + CD86+). Moreover, IL-12 pH-responsive NPs increased IFN-γ levels and reduced anti-inflammatory IL-10 secretion. Overall, this study provides two major findings (1) a pH-responsive NP system to effectively deliver IL-12 to the TME and reprogram local macrophages into pro-inflammatory phenotype; (2) a macrophage-enriched human 3D immuno-spheroid in vitro system as a tool to test the effectivity of immunomodulatory NPs.</p
Learning hemodynamic scalar fields on coronary artery meshes:A benchmark of geometric deep learning models
No full textCoronary artery disease involves the narrowing of coronary vessels due to atherosclerosis and is currently the leading cause of death worldwide. The gold standard for its diagnosis is the fractional flow reserve (FFR) examination, which measures the trans-stenotic pressure ratio during maximal vasodilation. However, the invasiveness and cost of this procedure have prompted the development of computer-based virtual FFR (vFFR) computation, which simulates coronary flow using computational fluid dynamics (CFD) techniques. Geometric deep learning algorithms have recently shown the capability to learn features on meshes, including applications in cardiovascular research. In this work, we aim to conduct a comprehensive empirical analysis of different backends for predicting vFFR fields in coronary arteries, serving as surrogates for CFD simulations. We evaluate six different backends and compare their performance in learning hemodynamics on meshes using CFD solutions as ground truth. This study is divided into two main parts: i) First, we use a dataset of 1,500 synthetic bifurcations of the left coronary artery. Each model is trained to predict various pressure-related fields, from which the vFFR field is reconstructed. We compare the models’ performance when different learning variables are used during training. ii) Second, we use a dataset of 427 patient-specific CFD simulations from a previous study by our group. Here, we repeat the experiments conducted on the synthetic dataset, focusing on the learning variable that yielded the best performance in the synthetic dataset. Most backends achieved very good performance on the synthetic dataset, particularly when learning the pressure drop over the manifold. For other network output variables (e.g., pressure and the vFFR field), transformer-based backends outperformed all other architectures. When trained on patient-specific data, transformer-based architectures were the only ones to achieve strong performance, both in terms of average per-point error and in accurately predicting vFFR in stenotic lesions. Our findings indicate that various geometric deep learning backends can serve as effective CFD surrogates for problems involving simple geometries. However, for tasks involving datasets with complex and heterogeneous topologies, transformer-based networks are the optimal choice. Additionally, pressure drop emerged as the optimal network output for learning pressure-related fields.</p
Kunststoffe der Zukunft? Interdisziplinäre Analyse biobasierter und biologisch abbaubarer Polymere:Fortschritte in der Chemie, gesellschaftliche Perspektiven und ökologische Auswirkungen
No full textDie globalen Bestrebungen zur Reduktion von Plastikmüll und der Verschmutzung durch Mikroplastik haben in den letzten Jahren zugenommen. Dies hat auch die Forschung, Entwicklung und vermehrte Anwendung biologisch abbaubarer und biobasierter Polymere (BBP) vorangetrieben. BBPs gelten als vielversprechende Alternativen zu herkömmlichen Kunststoffen und besitzen das Potenzial, die Umweltbelastung durch persistente Kunststoffabfälle zu verringern. Dieser Artikel präsentiert unsere aktualisierte Einschätzung der Auswirkungen von BBPs, fünf Jahre nach Veröffentlichung unseres vorherigen Beitrags. Wir stellen die jüngsten Fortschritte vor, insbesondere im Hinblick auf die Untersuchung einer größeren Vielfalt an „feedstock“, neuartiger chemischer Modifikationen und Funktionalitäten. Lebenszyklusanalysen („Life-Cycle-Assessments“) zeigen, dass die Nachhaltigkeit von BBPs von vielen Faktoren abhängt, darunter die Auswahl des Ausgangsmaterials, die Produktionseffizienz und das Abfallmanagement. Darüber hinaus hat die Einführung von BBPs in verschiedene Alltagsprodukte auch die Verbraucherwahrnehmung, die Marktdynamik und die regulatorischen Rahmenbedingungen beeinflusst. Obwohl BBPs in bestimmten Anwendungen Umweltvorteile bieten, werfen sie auch Bedenken hinsichtlich ihrer biologischen Abbaubarkeit unter verschiedenen Umweltbedingungen, der potenziellen Erzeugung von Mikroplastik und der Auswirkungen auf die Bodengesundheit auf. Wir unterstreichen die Notwendigkeit eines zirkulären Ansatzes, der den gesamten Lebenszyklus des Polymers berücksichtigt, von der Beschaffung, Modifizierung und Verwendung des Ausgangsmaterials bis hin zu den Entsorgungsoptionen. Interdisziplinäre Forschung, kollaborative Initiativen und eine fundierte Politikgestaltung sind entscheidend, um das volle Potenzial von BBPs auszuschöpfen und ihren Beitrag zur Kreislaufwirtschaft und einer nachhaltigeren Zukunft zu nutzen
Complex All-comers and Patients with Diabetes and Prediabetes Treated with Xience Sierra Everolimus-eluting Stents:COASTLINE High-Risk
Get PDFAims PCI is frequently performed in patients with high-risk (HR) of adverse clinical events. Therefore, the COASTLINE HR analysis aimed to assess the safety and efficacy of Xience Sierra everolimus-eluting stents (EES) in patients with diabetes, prediabetes, or other criteria of increased cardiovascular risk. Methods and results This is the primary analysis of COASTLINE, an investigator-initiated, prospective, multicenter registry in all-comers treated with Xience Sierra EES. After enrolment, HR patients were identified according to prespecified criteria, and per protocol compared with HR patients treated in two randomized all-comer trials with Resolute-type zotarolimus-eluting stents (ZES). Primary endpoint at 1-year follow-up is target vessel failure (TVF), a composite of cardiac death, target vessel myocardial infarction (TVMI), or target vessel revascularization. Of 1768 all-comers, treated with Xience Sierra EES and enrolled in this registry, 1317 (74.5%) were HR patients. Clinical outcome of these patients was compared with the control group, consisting of 1682 HR patients treated with Resolute-type ZES. At 1-year follow-up, no significant difference was observed in TVF between-stents (EES: 4.9% vs. ZES: 6.0%, adjusted hazard ratio 0.78, 95% confidence interval (CI) 0.57-1.06, P = 0.12). Furthermore, a significantly lower rate of secondary endpoint TVMI was observed in Xience Sierra EES patients (1.4 vs. 2.5%, adjusted hazard ratio 0.50, 95% CI 0.28-0.87, P = 0.014), driven by periprocedural myocardial infarction. Conclusion In patients with diabetes, prediabetes, or other HR criteria, Xience Sierra EES showed safety and efficacy, comparable to Resolute-type ZES, including Resolute Onyx. The significant difference in TVMI was driven by periprocedural events, as a landmark analysis at 7 days found no between-stent differences. Registration https://clinicaltrials.gov/study/NCT04475380</p
Personalized Prediction of Total Knee Arthroplasty Mechanics Based on Sparse Input Data—Model Validation Using In Vivo Force Data
Get PDFBackground/Objectives: Computational models are increasingly used in orthopedic research, such as in the context of total knee arthroplasty (TKA). However, the models’ actual integration in clinical practice is far from routine. Major limitations include the amount of input data, effort, and time required for personalization and simulation. In this paper, we present and validate a patient-specific multi-body musculoskeletal TKA model based on sparse input data to address these limitations. Methods: The simulation model was individualized based on the patients’ bone and knee implant 3D geometries, predicted bony landmarks, and soft tissue attachments using annotated statistical shape models, a statistical squat motion pattern, and a statistically based load case. For the validation, we used publicly accessible in vivo knee contact forces during squatting from four patients of the Grand Challenge Competitions (GCCs). Results: The prediction accuracy was quantified using several error metrics, including the root mean square error (RSME). For GCC3 and GCC5, both the range and trend of the mean in vivo contact forces were well matched by the simulation (RMSE lateral: 8.2–26.1% of body weight (BW); RMSE medial: 15.9–42.7 %BW). In contrast, there were relevant deviations between the experiment and simulation in the trend of contact forces for patient GCC2, as well as in the range of medial contact forces for patient GCC6 (RMSE medial: 52.6 %BW). The model setup time was at the magnitude of 15 min per patient, and the simulation was completed in less than 4 min. Conclusions: When comparing our results with the literature, we found similar accuracy to state-of-the-art models in predicting knee contact forces. While remaining deviations between in vivo and simulation data still warrant investigation and evaluation for clinical significance, the model has already successfully addressed important limitations of these previous models, which represent significant barriers to clinical application.</p