Lakeside Labs

Stable and resilient energy networks are of growing importance in the context of increasing complexity and decentralization in modern power systems. Initial research findings now indicate that self-organizing applications may offer promising opportunities for optimizing energy systems. The current work aims to investigate the concept of self-organized behavior, such as that observed in the plasmodial slime mold Physarum polycephalum [4], for its potential application in energy networks. Building on earlier investigations presented at ACSOS 2023 [7], this study presents initial results and application-oriented models that demonstrate the feasibility of such biologically inspired approaches. The overall research direction remains consistent since the first presentation of the project. The focus lies in analyzing both the advantages and limitations of slime moldinspired self-organizing systems in comparison to centralized control architectures. Particular attention will be given to identifying specific areas where self-organizing approaches can be applied effectively. Furthermore, the integration of biologically inspired methods with advanced technologies will be examined to improve system performance. The fundamental research questions of the planned dissertation are defined as follows:

This paper presents a novel data management approach for efficient data search and movement in a Distributed Knowledge Graph (DKG) within the Edge-Fog-Cloud continuum. The aim is to minimize data traffic and query time by relocating data closer to where it is needed. We propose a variation of the Ant Colony Optimization algorithm, using pheromone abstraction to identify optimal paths between network nodes. Once established, the algorithm moves data along these paths, evaluating the necessity of each relocation. We evaluate our approach through computer simulations, using query hit rates as the primary metric. We compare hit rates before and after data movement to assess algorithm efficiency. Our findings identify parameters that enhance search efficiency, achieving high hit rates while conserving resources. Future work will involve incorporating realistic network properties and exploring practical implementations.

Containerization has transformed application deployment across diverse environments in the edge-cloud computing continuum, providing lightweight, portable solutions that integrate seamlessly across various cloud-native environments. However, default scheduling often leads to resource underutilization due to overestimated requests. This results in inaccurate demand prediction and static allocation strategies. This paper enhances a bottom-up, self-organizing approach with an NNbased peer selection mechanism, enabling intelligent resource allocation while maintaining decentralization. The proposed NNbased approach enables a data-driven selection strategy that significantly improves resource utilization and system performance; a 21% improvement in resource utilization is reported in a high-traffic scenario compared to the predecessor framework. The findings contribute to the broader research community by demonstrating the potential of combining agent-based modeling with machine learning techniques, paving the way for more adaptive and efficient edge orchestration systems.

Across natural and human-made systems, transition points mark sudden changes of order and are thus key to understanding overarching system features. Motivated by recent experimental observations, we here uncover an intriguing class of transitions in coupled oscillators, extreme synchronization transitions, from asynchronous disordered states to synchronous states with almost completely ordered phases. Whereas such a transition appears like discontinuous or explosive phase transitions, it exhibits markedly distinct features. First, the transition occurs already in finite systems of N units and so constitutes an intriguing bifurcation of multi-dimensional systems rather than a genuine phase transition that emerges in the thermodynamic limit N → ∞ only. Second, the synchronization order parameter jumps from moderate values of the order of N−1/2 to values extremely close to 1, its theoretical maximum, immediately upon crossing a critical coupling strength. We analytically explain the mechanisms underlying such extreme transitions in coupled complexified Kuramoto oscillators. Extreme transitions may similarly occur across other systems of coupled oscillators as well as in certain percolation processes. In applications, their occurrence impacts our ability of ensuring or preventing strong forms of ordering, for instance in biological and engineered systems.

Collective migration of cancer cells is often interpreted using concepts derived from the physics of active matter, but the experimental evidence is mostly restricted to observations made in vitro. Here, we study collective invasion of metastatic cancer cells injected into the mouse deep dermis using intravital multiphoton microscopy combined with a skin window technique and three-dimensional quantitative image analysis. We observe a multicellular but low-cohesive migration mode characterized by rotational patterns which self-organize into antiparallel persistent tracks with orientational nematic order. We analyze the deformations induced by the cells in the extracellular matrix and find broadly distributed strain bands with a prevalence of compression. A model of active nematic hydrodynamics is able to describe several statistical features of the experimentally observed flow, suggesting that collective cancer cell invasion can be interpreted as a nematic active fluid in the turbulent regime. Our results help elucidate the migration patterns of cancer cells in vivo and provide quantitative guidance for the development of realistic in vitro and in silico models for collective cell migration.

Postdate pregnancy is characterized by a heightened risk for both maternal and perinatal complications. Owing to the risks, clinicians frequently turn to elective labor induction as a management strategy for postdate pregnancies. However, patients are increasingly informed and apprehensive about this approach and its associated risks. This has prompted a search for alternative management methods that may encourage spontaneous labor in pregnant women. One such approach is the use of fetal membrane sweeping, a method known to increase the likelihood of spontaneous labor onset. Yet, it remains unclear whether a single fetal membrane sweeping procedure can effectively reduce the need for elective labor induction in postdate pregnancies while minimizing risks to both the mother and fetus. The primary objective of this study was to assess the efficacy of a single fetal membrane sweeping procedure conducted between 38+0 and 40+6 weeks of gestation in reducing the rate of elective labor induction among postdate pregnancies at Central Hospital Benin City, Nigeria. Secondary objectives included evaluating the impact of membrane sweeping on maternal and perinatal outcomes. This open-label superiority randomized controlled study was carried out from June 2020 to March 2021, following ethical approval from the Hospital Management Board (HMB). One hundred and forty eligible participants, without contraindications to vaginal delivery, were randomly assigned to one of two groups. The first group received a single fetal membrane sweeping procedure between 38+0 and 40+6 weeks of gestation, while the control group underwent vaginal examination only to assess the Bishop score. Participants weremonitored until delivery.Data analysis was performed. Results were considered statistically significant at p < 0.05. The implementation of a single fetal membrane sweeping procedure effectively reduced the incidence of elective labor induction. Specifically, the membrane sweep group exhibited a significantly lower rate of elective labor induction compared to the control group (9.0% vs. 27.1%; p=0.0083). Moreover, a substantial proportion of the treatment group (91.4%) experienced spontaneous labor, while the control group reported a rate of 72.9%. Thedifference was statistically significant (p=0.0054). Notably, the control group exhibited a significantly longer mean time interval from recruitment to delivery (10.67±3.51 days) than the membrane sweeping group (3.64±4.123 days; p<0.05). Also, postdate women in the membrane sweep group were less likely to require cervical ripening with Foley's catheter than those in the control group (33.3% vs. 100%; RR: 0.33 (0.11-1.03); p=0.0057). Still, maternal satisfaction was significantly higher in the membrane-sweeping group (p<0.01). No significant differences were noted across the groups in maternal and neonatal outcomes. In low-risk term pregnancies, a single fetal membrane sweeping procedure is a superior alternative to no membrane sweeping in reducing the rate of elective labor induction for postdate pregnancies and in shortening the duration of term pregnancy.

Networks of spiking neurons constitute analog systems capable of effective and resilient computing. Recent work has shown that networks of symmetrically connected inhibitory neurons may implement basic computations such that they are resilient to system disruption. For instance, if the functionality of one neuron is lost (e.g., the neuron, along with its connections, is removed), the system may be robustly reconfigured by adapting only one global system parameter. How to effectively adapt network parameters to robustly perform a given computation is still unclear. Here, we present an analytical approach to derive such parameters. Specifically, we analyze k-winners-takes-all (k-WTA) computations, basic computational tasks of identifying the k largest signals from a total of N input signals from which one can construct any computation. We identify and characterize different dynamical regimes and provide analytical expressions for the transitions between different numbers k of winners as a function of both input and network parameters. Our results thereby provide analytical insights about the dynamics underlying k-winner-takes-all functionality as well as an effective way of designing spiking neural network computing systems implementing disruption-resilient dynamics.

Standard epidemic models exhibit one continuous, second order phase transition to macroscopic outbreaks. However, interventions to control outbreaks may fundamentally alter epidemic dynamics. Here we reveal how such interventions modify the type of phase transition. In particular, we uncover three distinct types of explosive phase transitions for epidemic dynamics with capacity-limited interventions. Depending on the capacity limit, interventions may (i) leave the standard second order phase transition unchanged but exponentially suppress the probability of large outbreaks, (ii) induce a first-order discontinuous transition to macroscopic outbreaks, or (iii) cause a secondary explosive yet continuous third-order transition. These insights highlight inherent limitations in predicting and containing epidemic outbreaks. More generally our study offers a cornerstone example of a third-order explosive phase transition in complex systems.