Technical University of Košice

Programmer attribution seeks to identify or verify the author of a source code artifact using stylistic, structural, or behavioural characteristics. This problem has been studied across software engineering, security, and digital forensics, resulting in a growing and methodologically diverse set of publications. This paper presents a systematic mapping study of programmer attribution research focused on source code analysis. From an initial set of 135 candidate publications, 47 studies published between 2012 and 2025 were selected through a structured screening process. The included works are analysed along several dimensions, including authorship tasks, feature categories, learning and modelling approaches, dataset sources, and evaluation practices. Based on this analysis, we derive a taxonomy that relates stylistic and behavioural feature types to commonly used machine learning techniques and provide a descriptive overview of publication trends, benchmarks, programming languages. A content-level analysis highlights the main thematic clusters in the field. The results indicate a strong focus on closed-world authorship attribution using stylometric features and a heavy reliance on a small number of benchmark datasets, while behavioural signals, authorship verification, and reproducibility remain less explored. The study consolidates existing research into a unified framework and outlines methodological gaps that can guide future work. This manuscript is currently under review. The present version is a preprint.

Biochar is widely recognized for its potential to enhance soil microbial activity and immobilize toxic heavy metals. However, its large-scale adoption is limited by inconsistent performance and high market costs (320 to 800 € m−3). Here, we present a cost-competitive strategy for the industrial-scale production of exfoliated biochar from phytowaste feedstock and its robust validation in soil–microbial systems. Exfoliated biochar was continuously manufactured at pilot-to-industrial scale and evaluated across multiple independent trials using representative agricultural soils. Its performance was systematically benchmarked against virgin biochar in terms of microbial metabolic activation and immobilization of environmentally relevant heavy metals (Cu2+, Cr6+, and As3+). Across all validation sets, exfoliated biochar consistently promoted higher microbial metabolic activity and superior metal adsorption efficiency, demonstrating both reproducibility and process robustness. These enhancements are attributed to increased surface area, optimized pore architecture, and improved accessibility of reactive functional groups introduced during exfoliation. Molecular dynamics simulations combined with radial distribution function analyses revealed distinct adsorption mechanisms, including preferential interactions of hydroxyl (–OH) groups with Cr6+ ions and pyridinic nitrogen sites with Cu2+ ions. Complementary spectroscopic analyses further identified aliphatic hydrocarbons, aromatic domains, aromatic C=C bonds, and hydrogen-bonded –OH groups as major contributors to adsorption performance. Overall, this study demonstrates that industrially scalable exfoliation, coupled with targeted structural and functional optimization, enables reproducible enhancement of biochar–microbe–metal interactions. This approach provides a robust, systems-oriented pathway for sustainable soil remediation and biomanufacturing-relevant environmental applications.

This paper presents the potential use of sunflower seed hulls (SSH) as a sustainable filler for poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) biocomposites. Ground SSH were incorporated into the PHBV matrix at loadings of 15, 30, and 45 wt% via extrusion and injection molding. The Fourier Transform Infrared Spectroscopy (FTIR) analysis indicated the presence of possible interactions between the filler and the matrix. Mechanical testing revealed a significant increase in stiffness, with the tensile modulus increasing from 2.6 GPa for pure PHBV to approximately 4.5 GPa for the composite containing 45 wt% SSH. However, the tensile strength decreased by approximately 10-40%, while elongation at break dropped to 1.0-1.5%, depending on the SSH dosage, respectively. The thermal analysis indicated that high filler contents suppress crystallization during cooling under laboratory conditions in Differential Scanning Calorimetry (DSC) analysis due to the confinement effect. The key practical advantage is the exceptional improvement in dimensional stability with a processing shrinkage reduction of approximately 80% in the thickness direction. Although water absorption increased with filler loading, biocomposites containing 15-30 wt% SSH exhibited the optimal balance of high stiffness, hardness, and dimensional accuracy. These properties make the developed material a promising option for the production of precise technical molded parts.

This paper investigates the joint impact of nodes mobility and imperfect successive interference cancellation (SIC) on the performance of a multi-tag ambient backscatter communication (AmBC) system over Nakagami-m fading channels. Specifically, the system comprises a mobile ambient RF source, K energy harvesting enabled mobile passive tags, and a moving reader. All wireless links are subject to time-selective fading, modeled using a first-order autoregressive process. To enhance the performance, a tag selection policy is employed to select the best tag among K candidates, while the reader utilizes both perfect SIC (pSIC) and imperfect SIC (ipSIC) techniques. Under this realistic setting, we derive closed-form analytical expressions for the outage probability (OP) and ergodic capacity in both pSIC and ipSIC scenarios. Furthermore, we present asymptotic OP analyses in the high signal-to-noise ratio (SNR) regime to extract key insights into the system’s diversity order. We also present the system throughput analysis under both pSIC and ipSIC cases. Several practical scenarios are also examined, including static nodes configuration and large time-varying errors, to characterize their effects on the system performance. We also analyze the influence of various system and channel parameters, nodes mobility, and the SIC control parameter on the system performance. Finally, simulation results are provided to validate the accuracy of the derived analytical expressions.

This article presents a scalable, data-driven formulation of city-wide Traffic Flow Optimization as a Quadratic Unconstrained Binary Optimization problem and evaluates its performance using quantum annealing and classical solvers on realistic urban networks. The framework builds a time-resolved congestion model from simulated mobility data by sampling vehicle trajectories at fixed intervals, identifying leader–follower interactions on shared road segments. In addition to congestion, the model incorporates route-duration penalties and an analytically derived penalty parameter that enforces one-hot route selection, ensuring feasible assignments while balancing network-wide congestion reduction and individual travel times. To mitigate the combinatorial growth of interactions in large-scale instances, the approach employs Leiden clustering to partition vehicles into dense communities that can be optimized independently. The resulting subproblems are solved using D-Wave’s LeapHybridBQMSampler, exact mixed-integer programming via Gurobi, and several classical metaheuristics and are evaluated on multiple city maps with up to 25,000 vehicles. Across large scenarios, the hybrid quantum-classical approach consistently produces feasible solutions within approximately 1% of Gurobi’s objective values, while maintaining stable runtimes. Both methods outperform shortest-route baselines, achieving reductions in the proposed congestion-cost objective of up to 24.4% for the hybrid solver and 29.4% for Gurobi. Finally, the study highlights the critical role of the underlying city map, showing that network structure directly influences interaction density, problem formulation, and the efficiency of embedding and solving on current quantum annealing hardware.