Institute for Complex Systems

Recurrent neural networks with balanced excitation and inhibition exhibit irregular asynchronous dynamics, which is fundamental for cortical computations. Classical balance mechanisms require strong external input currents in order to sustain finite firing rates, thus raising concerns about their biological plausibility. Here, we investigate an alternative mechanism based on short-term synaptic depression (STD) acting on excitatory-excitatory synapses, which dynamically balances the network activity without the need of strong external driving. Using accurate numerical simulations and theoretical investigations we characterize the dynamics of a densely connected recurrent network made up of N rate-neuron models encompassing STD. Depending on the synaptic strength J0, the network exhibits two distinct regimes: at sufficiently small J0, it converges to a homogeneous fixed point, while for sufficiently large J0 Rate Chaos emerges. For finite networks, we observe a transition region at intermediate J0, where the system passes from the homogeneous fixed point to Rate Chaos following several different routes to chaos depending on the network realization. Furthermore, we show that the width of the transition region shrinks for increasing N and eventually vanishes in the thermodynamic limit ([Formula: see text]). The characterization of the Rate Chaos regime has been performed by means of Dynamical Mean Field (DMF) approaches. This analysis has revealed on one side that the novel balancing mechanism is able to sustain finite irregular activity even in the thermodynamic limit, and on the other side that balancing occurs via dynamic cancellation of the correlations in the synaptic input currents induced by the dense connectivity. Our findings show that STD provides an intrinsic self-regulating mechanism for balanced networks, sustaining irregular yet stable activity without the need of biologically unrealistic strong external currents. This work extends the balanced network paradigm, offering insights into how cortical circuits could maintain robust dynamics via synaptic adaptation.

Classical molecular dynamics simulations were carried out for liquid water using the TIP4P-2005 potential model along the 50 MPa isobar, covering a wide temperature range from ambient to near-critical conditions. Particular attention was given to the behavior of various local structural descriptors of liquid water, as well as to the corresponding dynamics and entropic quantities. The results obtained reveal the existence of two distinct structural transitions, located in the temperature range around 423.15 and 498.15K, respectively. The observed transitions have been characterized by local extrema and crossovers in many of the above-mentioned quantities. Significant changes in the hydrogen bond network were also observed across these transitions. These structural rearrangements are reflected in the calculated intermolecular vibrational and librational dynamics, as evidenced by clear modifications in the spectral densities of atomic velocity correlation functions and in the translational and rotational densities of states.

Traditional node-based dismantling strategies, which remove nodes along with their associated edges, tend to incur high costs. In contrast, edge-based strategies are more cost-effective but often suffer from low efficiency due to the large number of edges in most networks. To address these challenges, we propose a divide-and-conquer framework that reinterprets network-level dismantling as cluster-level dismantling. Specifically, we integrate community detection with explosive percolation to develop the Community-based Edge Percolation (CEP) algorithm, which targets critical edges whose removal effectively breaks the network into subcritical components, thereby optimizing dismantling efficiency while minimizing costs. Experiments on 38 synthetic networks derived from four different models, as well as on nine empirical networks, show that CEP consistently outperforms state-of-the-art (SOTA) algorithms across nearly all datasets, yielding improvements of up to 30.611 % in f c and 67.108 % in Schneider R . Further analysis indicates that the sets of removed edges identified by CEP have a low correlation with those identified by other benchmarks, underlining its novelty and superior capability in identifying critical edges. Overall, we propose a universal and efficient edge dismantling framework that exhibits substantial advantages in large-scale empirical networks, offering valuable insights into network robustness.

Ammonia (NH3) is considered a key fuel for future carbon-neutral energy systems. Understanding its combustion characteristics under intense turbulence with high Karlovitz (Ka) numbers is crucial for industrial applications. In this study, a three-dimensional quasi-direct numerical simulation (quasi-DNS) is performed on an experimental premixed ammonia jet flame at a Ka number of 1643. The simulation is conducted using the open-source computational platform DeepFlame, employing a minimum grid resolution of 56 μm and second-order spatial and temporal discretization schemes. The chemical system is modeled using a semi-detailed mechanism comprising 26 species and 100 elementary reactions. The simulation results show good agreement with laser-based diagnostic measurements. The general flame structure obtained from the simulation is consistent with fundamental combustion theory. In particular, turbulent eddies are observed to penetrate and broaden the reaction zone, with this effect intensifying downstream. Based on these results, turbulence–chemistry interactions and NO formation characteristics are systematically analyzed. For turbulence–chemistry interactions, the flame surface density transport theory is employed to clarify the roles of tangential strain rate, curvature stretch, and convection in governing the flame surface density distribution. Regarding NO formation, NO is primarily generated through the HNO and thermal pathways and consumed via the N2O and NHi pathways, leading to a net positive production on the product side of the flame. Furthermore, the influence of progress variable definitions is demonstrated in both turbulence–chemistry interactions and NO formation. It is found that the H2O-based progress variable captures more comprehensive information about the spatial and temporal distribution of chemical processes. These findings provide valuable insights into ammonia combustion under high-Ka conditions and contribute to the development of high-fidelity simulation databases essential for future modeling efforts.

Abstract Background: Human herpesvirus 6 (HHV-6) is a latent beta-herpesvirus with potential transfusion-transmissible relevance, particularly in immunocompromised recipients. Data on HHV-6 viremia among Sudanese blood donors are extremely limited. This study aimed to determine the molecular prevalence of HHV-6 DNA among blood donors in Kassala, Sudan. Methods: A descriptive cross-sectional study was conducted at Kassala Teaching Hospital. Whole-blood samples (n = 180) were screened for HHV-6 DNA using real-time PCR, followed by multiplex PCR for genotyping attempts. Fisher’s exact and Mann–Whitney U tests were used where applicable; however, statistical analyses were interpreted cautiously due to the small number of positive cases. Results: HHV-6 DNA was detected in 2.78% (5/180) of donors. No significant associations were found between HHV-6 positivity and age group, region, or ABO/Rh blood groups. Hematological parameters showed no significant differences between HHV-6–positive and negative donors. Genotyping was unsuccessful for all positive samples, likely due to low viral load (Ct values 38.7–39.9). Conclusion: HHV-6 viremia was infrequent among blood donors in Kassala. Given the very small number of positive cases and limited genotyping capacity, the findings should be considered preliminary. Larger multicenter studies incorporating more sensitive molecular methods are recommended to characterize the epidemiology of HHV-6 in Sudan.

Abstract Plasma pTau217 is a leading biomarker for Alzheimer’s disease (AD), but evidence from low- and middle-income countries (LMICs) remains limited. We evaluated its diagnostic performance in two memory-clinic cohorts from Brazil (Cog-Aging-Study, n = 353) and Argentina (GeNED.ar, n = 134), both genetically admixed. Using data-driven mixture modelling with single- and two-cut-off approaches, we assessed concordance with clinical diagnosis and, in Cog-Aging-Study-Brazil, CSF-defined amyloid pathology. In Cog-Aging-Study-Brazil, pTau217 and pTau217/Aβ42 with two-cut-off strategies accurately predicted amyloid positivity and showed high concordance with clinical AD diagnosis. In GeNED.ar-Argentina, both biomarkers similarly aligned with clinical diagnosis. APOE-ε4 and lower BMI or eGFR were associated with higher pTau217 in Cog-Aging-Study-Brazil, while education additionally influenced biomarker levels in GeNED.ar-Argentina. African ancestry modified APOE effect on CSF but not plasma biomarkers. These findings show that pTau217-based biomarkers show good performance in admixed South American populations and support their implementation in LMICs with limited access to CSF and PET.

This paper presents the development of a robust and intelligent multi-agent system (MAS) framework integrated with a microgrid (MG) in a co-simulation environment. The work begins with an overview of recent advances in MAS applications for MG control, cybersecurity, and transactive energy management, highlighting the rapid growth and significance of this research domain. The proposed MAS platform is designed to enable seamless MG operational mode transitions and includes detailed descriptions of agent platform initialisation, agent class modelling, logic definition, algorithm design, and communication mechanisms, alongside the comprehensive mathematical modelling of the MG. Multiple case studies are conducted to assess the system's performance and to verify seamless data exchange among distributed agents and between two distinct simulation platforms. The simulation results confirm that the proposed MAS-based solution effectively supports intelligent MG control across multiple operational scenarios, including grid-connected operation, islanding, load shedding, and grid reconnection. The findings demonstrate the framework's capability to enhance operational efficiency, resilience, and coordination within MG environments.

Capillary electrochromatography is a miniaturized technique offering some advantages over conventional ones in separation science. It combines the best features of capillary zone electrophoresis (high efficiency of separation) and liquid chromatography (high selectivity). Because of the low flow rates, minute volumes of mobile phases can be applied, and therefore, it can be considered a green analytical technique. Capillary electrochromatography has been successfully used to separate many classes of compounds, including peptides, proteins, drugs, antibiotics, pollutants, enantiomers, etc. In this overview chapter, the main features of this technique are summarized. Its potential for the separation of enantiomers of chiral compounds and the main chiral stationary phases employed are presented. Finally, some selected applications in this field, reported in the literature in the period 2020-2024, are presented.