Rzeszów University of Technology, also known as Rzeszów Polytechnic (Polish: Politechnika Rzeszowska im. Ignacego Łukasiewicza, PRz) or Ignacy Łukasiewicz University of Technology, is a state-run institution of higher education in Rzeszów, Poland. According to international ranking by the Webometrics Ranking of World Universities published by the Spanish institute Consejo Superior de Investigaciones Científicas, the university takes 14th place among the technical universities within the country and, on the world scale, number 1,173 within all listed universities.Rzeszów Polytechnic began as a vocational school of engineering on the initiative of employees of the airplane factory PZL WS-2 (branch of PZL State Aviation Works) in 1951. It developed into a full-fledged university gradually between 1952 and 1974. Since 1976, it has operated the leading Aviation Training Centre on the outskirts of Rzeszow. The university is the only polytechnic in the country that awards degrees to civil aviation pilots.Students of the university have had multiple successes in various international competitions, including University Rover Challenge. In 2015 the university won the competition.
In the paper we introduce the concept of the Kuratowski distance between nonempty and bounded subsets of a Banach space. We show that the Kuratowski measure of noncompactness of a nonempty and bounded subset of a Banach space is equal to the Kuratowski distance of that subset to the family of all nonempty and relatively compact subsets of the mentioned Banach space.
With the growing share of distributed energy sources and energy storage, the problem of high-voltage line overloads is becoming one of the key challenges of modern power systems. This paper proposes a method to eliminate transmission line overloads by coordinating power changes among selected generation units, loads, and energy storage units, with network reconfiguration as needed. A key element of the proposed approach is identifying the controllable elements most strongly associated with overload patterns in post-contingency scenarios. These elements were identified using statistical correlation coefficients describing the relationship between line loading levels and the power generated, consumed, or stored at individual buses. Correlation analysis was used to determine a subset of candidate elements most relevant to the observed overload patterns, thereby reducing the dimension of the control problem. The proposed approach enables effective overload reduction while simultaneously reducing the problem's dimensionality. The results indicate that it can be a useful tool supporting network management under conditions of high generation and load variability.
Entropic uncertainty relations are universal quantifiers of fundamental uncertainties of quantum measurements and are widely discussed in the quantum metrology literature. Quantum memory is a phenomenon related to the specific type of quantum correlations that allows for reducing fundamental uncertainties of quantum measurements. In the present work, the modified concept of quantum memory for time-dependent problems is proposed. We compare the time-dependent formulation of quantum memory with the out-of-time-ordered correlator (OTOC). Quantum memory is a rigorous mathematical concept that requires demanding calculations. Thus, until now, quantum memory has been discussed mainly for simple model systems and stationary problems. In the present work, we demonstrate that quantum memory can also be studied for realistic and physically relevant systems, e.g., the atomic helical spin chain, as well as the emergence and propagation of quantum correlations in time. We found that quantum memory manifests faster oscillations in time than OTOC and does not equilibrate. Furthermore, an artificial neural network is trained and asked to predict results for OTOC and quantum memory. These results show that quantum memory is more sensitive than OTOC in terms of broken inversion symmetry and the nonreciprocal effect.
This paper evaluates the robustness and structural invariance of hybrid population-based metaheuristics under various objective space transformations. A light-weight plug-and-play hybridization operator is applied to nineteen state-of-the-art algorithms - including differential evolution, particle swarm optimization, and recent bio-inspired methods - without modifying their internal logic. Benchmarking on the CEC-2017 suite across four dimensions (10, 30, 50, 100) is performed under five transformation types: baseline, translation, scaling, rotation, and constant shift. Statistical comparisons based on Wilcoxon and Friedman tests, Bayesian dominance analysis, and convergence trajectory profiling consistently show that differential-based hybrids (e.g., hybrid improved multi-operator differential evolution, hybrid success-history adaptive differential evolution, hybrid double mutational salp swarm algorithm) maintain high accuracy, stability, and invariance across all tested deformations. In contrast, classical algorithms - especially particle swarm optimization- and Harris hawks optimization - based variants -exhibit significant performance degradation under non-separable or distorted landscapes. The findings confirm the superiority of adaptive, structurally resilient hybrids for real-world optimization tasks subject to domain-specific transformations. However, the evidence is based on deterministic, continuous, bound-constrained single-objective CEC-2017 functions. The experiments use a fixed budget and five predefined transformations. Therefore, the conclusions should be interpreted within this controlled setting and may not directly generalize to nonlinear constrained, noisy, dynamic, or multi-objective problems without dedicated protocols. To partially bridge to practice, we also include a constrained helical compression spring design case study evaluated with the same methodology.
CEC 2017 applies bias, shift and rotation simultaneously, obscuring their individual effects on algorithm behavior. We introduce a parameterized implementation controlling them independently while preserving the original functions and transformation data. It diagnoses hybrid Marine Predators Algorithm (hMPA), whose predicted-candidate mechanism relies on numerical objective values and coordinate-wise reconstruction. Deep Statistical Comparison (DSC) and extended DSC (eDSC) are adapted from algorithm-level comparison to configuration-level diagnosis. To our knowledge, this is the first exhaustive DSC and eDSC analysis of all eight bias-shift-rotation configurations of a parameterized CEC 2017 benchmark. We examine all 56 three-configuration subsets and comparisons with the untransformed control, using 29 functions, dimensions 10, 30, 50 and 100, 30 independent runs and a fixed budget of 10,000 objective-function evaluations per dimension. DSC and eDSC show statistically significant configuration differences at every dimension, but rankings vary across functions, precluding a consistent ordering. Most individual effect-size confidence intervals include zero. Among those excluding zero, comparisons favor MPA more often at dimension 10 and hMPA at dimensions 50 and 100. Shift generally worsens objective-value outcomes. Isolated rotation causes no systematic deterioration; isolated bias barely changes final-solution distributions. Combined shift and rotation differ most consistently from the control. Convergence plots show function- and dimension-dependent separation, plateaus and late improvement. Numerical findings are limited to hMPA, but the framework can be extended to other continuous optimizers and adapted to related CEC benchmarks, including CEC 2024 and CEC 2025.
