In this work, we address the problem of allocating a set of real-time tasks, which are represented as a directed acyclic graph (DAG), on a NoC-based manycore platform. We consider partitioned EDF scheduling and address the problem of subtasks-to-core and communication-to-network-components allocation by means of a simulated-annealing-like heuristic to explore the design space choices and derive a schedulable system. We propose an extention of the DAG task model to capture the platform features. We assign tasks to cores, handle memory copies to (and from) the main memory, and allocate inter-task communication to different virtual channels. We propose a novel deadline assignment technique that takes into account the specificities of the network on chip. We perform a large set of simulated scenarios to evaluate the effectiveness of our approaches.
This numerical study examines heat transfer phenomena, with particular focus on evaluating the cooling performance of embedded cavity technologies for electrical and electronic enclosure applications. The thermo-fluidic characteristics of laminar flow within a confined cavity bounded by isothermal heat sources were analyzed by experimentally measuring thermophysical of premixed binary fluids comprising water and ethylene glycol (EG) at concentrations ranging from 25% to 100%. The Navier-Stokes equations governing this steady-state open system are solved numerically using the finite volume method implemented in the commercial computational fluid dynamics (CFD) software Ansys Fluent v17. This computational approach enables precise determination of convective heat transfer characteristics during the cooling process. Key results are presented in terms of the mixture’s surface-averaged Nusselt number, total Nusselt number, and cooling efficiency within the cavity at various ethylene glycol concentrations (25-100%). Comparative analysis reveals that ethylene glycol coolant exhibits significantly superior cooling performance compared with pure water. This enhancement is demonstrated by progressive increases in both the total Nusselt number (from 11.5 to 14.8, 16.5, 21.8, and 35.9) and in cooling efficiency (from 87.4% to 92%, 95.2%, 97%, and 98.3%), corresponding to ethylene glycol concentrations of 25%, 50%, 75%, and 100%, respectively. Furthermore, enhanced thermo-fluidic behavior is consistently observed with increasing ethylene glycol content.
The intestinal microbiota plays a significant role in metabolic regulation, and Lactobacillus acidophilus has shown potential as an inhibitor of insulin resistance, a key factor in Type 2 diabetes mellitus (T2DM). This study investigates the modulation of L. acidophilus adhesion power (AP) and biofilm productivity (BP) under the influence of exopolysaccharides (EPS) extracted from Prevotella intermedia strains, specifically the high EPS-producing S4 strain, sourced from children's oral gums. EPS concentrations ranged from 0.79 ± 0.03 mg/mL to 0.65 ± 0.07 mg/mL, and their effects on L. acidophilus were assessed in vitro. The results revealed a clear correlation between EPS concentration and both AP and BP. At 50 µg/mL, the average AP and BP were 27.92 ± 1.7
Introduction. The exponential growth of waste electrical and electronic equipment (WEEE) requires efficient strategies for plastic waste management. Plastics, a major fraction of WEEE, represent both an environmental challenge due to low biodegradability and a valuable source of secondary raw materials. Problem. Tribo-aero-electrostatic separators with rotating disk electrodes offer a promising solution for fine plastic separation. However, their performance depends on multiple, nonlinear, and time-varying factors such as disk speed, voltage, and particle properties. These complex interactions make analytical modeling and stable process control difficult, limiting industrial implementation. The goal of this work is to develop a reliable dynamic model based on NARX neural networks capable of predicting the real-time evolution of key process variables such as recovered mass and particle charge. Methodology. The proposed NARX neural network learns temporal nonlinear relationships directly from experimental data, avoiding the need for explicit physical equations. Experiments were conducted on a synthetic 50:50 mixture of Acrylonitrile Butadiene Styrene (ABS) and Polystyrene (PS) particles (500-1000 μm) to assess model performance under varying disk speeds, voltages, and air flow rates. Results. The developed model accurately predicts the recovered mass and acquired charge of both ABS and PS over a wide range of operating conditions. The predictions show strong agreement with experimental measurements, maintaining low error levels even at parameter extremes. Scientific novelty. This work represents the first application of NARX neural networks to model the dynamic behavior of a two-rotating-disk tribo-aero-electrostatic separator. The approach captures essential time-dependent interactions that conventional static or analytical models fail to describe. Practical value. The NARX model exhibits high predictive accuracy and robustness across an extended operating domain (4–20 kV, 15–60 rpm, 7–9 m3/h), with errors limited to the 10–3 g and 10–3 µC ranges. These characteristics demonstrate its potential for real-time intelligent control and adaptive optimization of electrostatic separation processes in plastic waste recycling. References 39, tables 3, figures 9.
First-principles calculations were performed to investigate the structural, mechanical, electronic, transport, optical, and photocatalytic properties of Janus Cs2Li2X2 (X = S, Se, Te) monolayers. The pronounced Cs/Li ionic-size contrast induces structural asymmetry and intrinsic Janus polarity. Negative formation and cohesive energies, phonon spectra, and AIMD simulations at 300 K confirm thermodynamic, dynamical, and thermal stability. Elastic analysis reveals high Young's moduli of 256–286 N m−1 and progressive mechanical softening from sulfide to telluride. HSE06 calculations predict direct band gaps of 1.81–1.99 eV and favorable band-edge alignment for overall water splitting. Electron mobilities of 992–1220 cm2 V−1 s−1 substantially exceed hole mobilities of 114–176 cm2 V−1 s−1, indicating pronounced transport asymmetry. GW-BSE calculations reveal strong visible-light absorption, while predicted STH efficiencies of 13.29–26.77% demonstrate promising solar hydrogen production potential arising from favorable electronic, optical, and carrier-transport characteristics across the investigated Janus monolayers.
