Abstract

Abstract
Switched reluctance generators (SRG) are one of the machines with huge potential in wind power generation due to their reliability and robust design. However, the inherent characteristics of SRGs lead to significant challenges in achieving high efficiency and low output current and torque ripple simultaneously. The performance of SRGs is hindered by conflicting requirements. To address these issues, this chapter presents an optimization control strategy aimed at improving the static performance of SRGs. The chapter discusses the application of the Particle Swarm Optimization (PSO) technique to optimize the commutation angles, specifically the turn-on (?on) and turn-off (?off) angles, for an 8/6 SRG. The proposed strategy consists of two main steps. First, a Maximum Power Point Tracking (MPPT) algorithm is implemented to maximize power output at varying rotor speeds, combined with a direct power control method to regulate the power generated by the SRG. Second, a multi-objective function is developed to evaluate the SRG performance, considering key factors such as power output, output current ripple, and torque ripple. The simulation results indicate that implementing optimized turn-on and turn-off angles leads to a reduction in torque ripple from -1.78 Nm using the conventional technique to -0.66 Nm with the proposed method, corresponding to an impressive 63% decrease. Furthermore, the optimization strategy effectively maximizes the efficiency of the system employing an MPPT approach, ensuring optimal energy conversion under varying operating conditions. Future research directions include experimental validation of the proposed control system on real hardware to assess its practical feasibility and performance under real-world operating conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract

Abstract
Understanding how structural change drives long-run growth requires jointly considering the dynamics of productive and scientific specialisations, and science-industry alignment. This paper develops and tests a unified framework that integrates evolutionary, structuralist, complexity, and innovation-systems perspectives to assess how productive and scientific specialisations, science-industry alignment, diversification, and global value chain integration shape economic performance. To operationalize this framework, we construct new indicators, including a Science-Industry Matching (SIM) index, measures of dynamic entry and relatedness density, and specialisation-based diversity indices, and apply them to a panel of up to 142 countries over 2000-2018/2023. Estimation relies on country fixed effects with Driscoll-Kraay standard errors to address heteroskedasticity, autocorrelation, and cross-sectional dependence. The results reveal that persistent specialisation in high- and medium-high-tech industries fosters growth, while low-tech dependence constrains it. Scientific specialisation in enabling fields such as mathematics, physics, chemistry, and energy/environmental sciences supports growth, but excessive concentration risks lock-in. Science-industry alignment enhances growth in advanced economies with strong absorptive capacity but penalises weaker systems. Industrial diversification often dilutes resources, whereas scientific diversification consistently promotes growth by broadening the knowledge base for recombination. Finally, integration into global value chains is growth-enhancing in developing economies, while advanced economies can sustain higher domestic value added without significant penalties.

Abstract

Abstract
In recent years, the research community, but also the general public, has raised serious questions about the reproducibility and replicability of scientific work. Since many studies include some kind of computational work, these issues are also a technological challenge, not only in computer science, but also in most research domains. Computational replicability and reproducibility are not easy to achieve due to the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment via the same frameworks, code, programming languages, dependencies, and so on. We propose a framework, known as SciRep, that supports the configuration, execution, and packaging of computational experiments by defining their code, data, programming languages, dependencies, databases, and commands to be executed. After the initial configuration, the experiments can be executed any number of times, always producing exactly the same results. Our approach allows the creation of a reproducibility package for experiments from multiple scientific fields, from medicine to computer science, which can be re-executed on any computer. The produced package acts as a capsule, holding absolutely everything necessary to re-execute the experiment. To evaluate our framework, we compare it with three state-of-the-art tools and use it to reproduce 18 experiments extracted from published scientific articles. With our approach, we were able to execute 16 (89%) of those experiments, while the others reached only 61%, thus showing that our approach is effective. Moreover, all the experiments that were executed produced the results presented in the original publication. Thus, SciRep was able to reproduce 100% of the experiments it could run. © 2025 The Authors