Computation

So far, synchronization tasks designed in complex systems are working at the level of their constituting elements. The proposed pilot study explores synchronization at the level of emergents. The main objective of this research is threefold: (i) to demonstrate that synchronizing emergent structures rise from a continuous interaction of micro-processes that are operating above a constituting matrix (medium) using a simple microevolution rule and a special type of extended neighborhood; (ii) to provide a specific example of a long-range emergent coordination that is generated within a short-range communicating medium; and (iii) to demonstrate a possibility of synchronization at the level of emergents. Those goals are achieved by the utilization of emergent information processing that uses uniform changes of all updated elements of the matrix, a fixed microevolution rule, and a fixed neighborhood. A fast solution to a computational problem of solving long-range synchronization within massively parallel and short-range communicating environments is presented. It is named the semi-two-dimensional synchronization problem (S2D-SP). The proposed emergent solution of S2D-SP differs from all previous manually designed solutions to other synchronization tasks. The number of steps t, which is necessary to synchronize an emergent array, is substantially lower than the width w of the synchronized emergent array. In addition to the pure theoretical importance, the solution demonstrates the usefulness of the concept of extended neighborhoods in search of not only biologically relevant computational models of self-organization and emergence. The presented solution of the S2D-SP problem uncovers a novel approach to the design of artificial, self-organizing emergent systems and possibly paves a path towards understanding natural computation. The software GoL-N24 v1.4 used to generate all results is publicly available.

This study addresses a limitation in classical models of nanofluid flow, namely, their inability to adequately capture microscale effects associated with fluid-element microrotation and local microstructural dynamics. Specifically, the effects of nanoparticle geometry and microrotation on the hydrodynamic and thermal behavior of copper (Cu)–water nanofluids flowing over a stretching surface under a transverse magnetic field are investigated. Four nanoparticle morphologies: bricks, cylinders, platelets, and blades, are considered, with water (Pr = 6.2) as the base fluid. Appropriate similarity transformations are employed to reduce the governing nonlinear partial differential equations (PDEs) to a system of ordinary differential equations (ODEs), which is solved numerically using the Runge–Kutta–Fehlberg (RKF) integration scheme. A comprehensive parametric analysis is conducted to examine the effects of the governing physical parameters on the velocity, microrotation, and temperature distributions. The results reveal that increasing the thermal radiation parameter enhances the temperature distribution for all four nanoparticle morphologies. Furthermore, comparative analysis indicates that water-based nanofluids exhibit a more pronounced temperature gradient than kerosene-based fluids under identical physical conditions. These findings highlight the importance of nanoparticle morphology and microscale rotational effects in modeling the flow and heat transfer characteristics of nanofluids.

Large Language Models (LLMs) can translate natural-language instructions into code, but when the input is unstructured clinical protocol text, the generated code may omit doses, monitoring intervals, or safety thresholds that the text states. Such omissions matter because they can propagate into software that supports clinical monitoring. We propose SPE-ECG (Structured Prompt Engineering for Enhanced Code Generation), a transparent, rule-based pipeline that converts unstructured domain text into a JSON prompt using text cleaning, part-of-speech tagging, bigram matching, and hand-built domain dictionaries, without fine-tuning any model. In a case study of 43 rheumatoid arthritis DMARD monitoring protocols, we compared raw and structured prompts on three LLMs (GPT-3.5, LLaMA 2, and Gemma), scoring the generated Python code using version 3.15.1 with BLEU against the raw-prompt code of the other two models. Mean BLEU rose for all three models (relative gains of 55.1%, 32.5%, and 19.9%); the paired differences were significant for GPT-3.5 and LLaMA 2 but not, after correction for multiple comparisons, for Gemma. These results suggest that lightweight input structuring can change how closely different models’ outputs agree, and that it could serve as a low-cost, auditable preparation step for protocol-to-code tools built on free or open models. BLEU does not measure correctness, no ablation or state-of-the-art baseline was run, and clinical validation is required before any use.