Electrochemical impedance spectroscopy (EIS) is an important electrochemical technique that is used to detect changes and ongoing processes in a given material. The main challenge of EIS is interpreting the collected measurements, which can be performed in several ways. This article focuses on the electrical equivalent circuit (EEC) approach and uses grammatical evolution to automatically construct an EEC that produces an AC response that corresponds to one obtained by the measured electrochemical process(es). For fitting purposes, synthetic measurements and data from measurements in a realistic environment were used. In order to be able to faithfully fit realistic data from measurements, a new circuit element (ZARC) had to be implemented and integrated into the SPICE simulator, which was used for evaluating EECs. Not only is the presented approach able to automatically (i.e., with almost no user input) produce a more than satisfactory EEC for each of the datasets, but it also can also generate completely new EEC configurations. These new configurations may help researchers to find some new, previously overlooked ongoing electrochemical processes.

Analog circuit design requires large amounts of human knowledge. A special case of circuit design is the synthesis of robust and failure-resilient electronics. Evolutionary algorithms can aid designers in exploring topologies with new properties. Here, we show how to encode a circuit topology with an upper-triangular incident matrix and use the NSGA-II algorithm to find computational circuits that are robust to component failure. Techniques for robustness evaluation and evolutionary algorithm guidances are described. As a result, we evolve square root and natural logarithm computational circuits that are robust to high-impedance or short-circuit malfunction of an arbitrary rectifying diode. We confirm the simulation results by hardware circuit implementation and measurements. We think that our research will inspire further searches for failure-resilient topologies.

Light emitting diodes (LEDs) have experienced rapid technological development in the past decade, making them a winning alternative to conventional light sources in many applications. LED arrays allow precise control of the desired irradiance profile in a target area by adjusting the position and output power of individual LEDs. However, despite increased efficiency, many LEDs still transform a large proportion of the input electrical power into heat, requiring an efficient cooling system. This paper presents a modular LED array light source mounted on a water-cooled aluminum plate. Novel electronic LED driver modules, connected via a serial communication bus in a daisy-chain topology, were developed with the ability to set the operating current of individual LEDs. A modular layout of cooling and mounting system and LED driver modules, as well as a specialized design for the LED soldering footprint, was able to house a variety of common commercial LEDs, enabling easy adjustment of the lighting system to the required application and size of the irradiated area. In a prototype of one plate containing 10 LEDs, individual LED radiance was optimized for a better irradiance homogeneity in the target area. Array characterization showed a low standard deviation of the irradiance of 1.8% and a good fit between measured and calculated irradiance. A test of the array at elevated temperatures showed moderate LED radiance degradation and a wavelength shift of the measured spectra after extended use.

Although proposed more than half a century ago, the Nelder–Mead simplex search algorithm is still widely used. Four numeric constants define the operations and behavior of the algorithm. The algorithm with the original constant values performs fine on most low-dimensional, but poorly on high-dimensional, problems. Therefore, to improve its behavior in high dimensions, several adaptive schemas setting the constants according to the problem dimension were proposed in the past. In this work, we present a novel adaptive schema obtained by a meta-optimization procedure. We describe a schema candidate with eight parameters subject to meta-optimization and define an objective function evaluating the candidate’s performance. The schema is optimized on up to 100-dimensional problems using the Parallel Simulated Annealing with Differential Evolution global method. The obtained global minimum represents the proposed schema. We compare the performance of the optimized schema with the existing adaptive schemas. The data profiles on the Gao–Han modified quadratic, Moré–Garbow–Hilstrom, and CUTEr (Constrained and Unconstrained Testing Environment, revisited) benchmark problem sets show that the obtained schema outperforms the existing adaptive schemas in terms of accuracy and convergence speed.

Genetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly utilizing tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve ’real’ Turing-like imperative programs (as contrasted with functional programming) from the ground up. Existing research focuses mainly on specific special cases where the structure of the solution is partly known. This paper explores the potential of integrating tree and linear GP paradigms to develop an encoding scheme that universally supports genetic operators without constraints and consistently generates syntactically correct Python programs from scratch. By blending the symmetrical structure of tree-based representations with the inherent asymmetry of linear sequences, we created a versatile environment for program evolution. Our approach was rigorously tested on 35 problems characterized by varying Halstead complexity metrics, to delineate the approach’s boundaries. While expected brute-force program solutions were observed, our method yielded more sophisticated strategies, such as optimizing a program by restricting the division trials to the values up to the square root of the number when counting its proper divisors. Despite the recent groundbreaking advancements in large language models, we assert that the GP field warrants continued research. GP embodies a fundamentally different computational paradigm, crucial for advancing our understanding of natural evolutionary processes.

This study investigates the impact of introducing a mandatory practical programming exam on student learning outcomes in introductory programming courses. To facilitate structured coding practice and scalable automated feedback, we developed Programmers’ Interactive Virtual Onboarding (PIVO), a novel Automated Programming Assessment System (APAS). Traditional programming curricula often prioritize theoretical concepts, limiting practical coding opportunities and immediate feedback, resulting in poor skill retention and proficiency. By integrating mandatory practical assessments together with voluntary, self-driven programming tasks through PIVO, we aimed to enhance student engagement, programming proficiency, and overall academic performance. Results show a substantial reduction in failure rates following the introduction of the practical exam, and statistical analyses revealed moderate correlation between students’ voluntary engagement in non-mandatory coding exercises and their performance in both theoretical and practical examinations. These findings indicate an association among engagement in structured, automated practice assessments, algorithmic thinking, and problem-solving capabilities.