This paper investigates iterative learning control for stochastic differential systems of fractional order in the Hilfer sense. Unlike existing studies that treat either fractional dynamics or stochastic effects separately, we develop an integrated framework that combines Hilfer fractional derivatives, Brownian perturbations, and a proportional-fractional integral learning law. The proposed approach captures both the memory effects and random uncertainties inherent in complex systems. As a case study, we apply the method to a gantry robot equipped with a flexible arm. Numerical simulations show that the Hilfer derivative significantly improves tracking accuracy and convergence speed compared to integer-order models, highlighting the potential of the proposed strategy for robotic applications under uncertainty.
Motivation: Proportional Venn diagrams provide a compact representation of the relationships between sets. Each relationship is represented with a region whose area reflects the number of elements shared by a given combination of sets. This means that the number of regions grows exponentially with the number of sets, which is why proportional Venn diagrams with more than five sets are cumbersome to interpret and seldom used. However, Venn diagrams with a large number of sets may still be legible if enough regions are empty and do not need to be represented.Results: Here, we present nVenn2, the second version of the nVenn algorithm, to create quasi-proportional Venn diagrams. This new version uses a different, more flexible approach which includes steps to minimize the complexity of the diagram. Thus, computation time for nVenn2 mainly grows with the number of non-empty diagram regions, rather than with the number of sets. This property allows users to create interpretable quasi-proportional Venn diagrams with large numbers of sets.Availability and implementation: The nVenn2 algorithm is freely available as an executable program, as a web page, as an R package (nVennR2) and as a Python package (nVennPy). All interfaces allow users to edit the appearance of the resulting diagram.
This study investigates potential alloy chemistries for the thixoforming of Mg–Al-Sr alloys in the Mg-rich region, using CALPHAD-based thermodynamic calculations to address the challenges of designing new thixoformable alloys experimentally. Given the extensive alloy space in the Mg–Al–Sr system, thermodynamic calculations were employed to model the partial isothermal section phase diagram of the Mg–Al–Sr ternary system at 450 °C. This model was validated by comparing its predictions with literature and experimental data, showing good agreement. The thermodynamically calculated Fraction Liquid vs. Temperature (fL vs. T) relationship for three alloys, Mg–4Sr–3Al, Mg–4Sr–6Al, and Mg–4Sr–9Al, was validated experimentally, with the curves closely following the Scheil–Gulliver solidification path. By varying Al and Sr concentrations systematically, the thixoforming capabilities of these alloys were assessed using parameters such as solidification interval, fraction liquid sensitivity, highest inflection point, and hot tearing susceptibility. Alloys with compositions Mg–7Al–6Sr, Mg–8Al–5Sr, and Mg–9Al–5Sr met all thixoforming design criteria. The study also distinguishes between solidification range and hot tearing susceptibility. It was found that high solidification ranges do not necessarily correlate with reduced hot tearing tendency; conversely, some alloys with shorter freezing ranges are more prone to hot tearing.
The Sustainability Nexus Analytics, Informatics, and Data (AID) Programme of the United Nations University (UNU), aims to provide information, data, computational, and analytical tools to support the sustainable management and long-term security of natural resources using a nexus approach. This paper introduces the Soil Health Module of the Sustainability Nexus AID Programme. Healthy soil is crucial for life on Earth, and it is essential for ecosystem services and functioning, access to clean water, socioeconomic structure, biodiversity, and food security for the growing population of the world. Healthy soils contribute to mitigating the effects of climate change and reduce the consequences of extreme events such as flooding and drought. Healthy soils influence the hydrologic cycle by regulating transpiration, water infiltration, and soil water evaporation affecting land–atmosphere interactions. The Soil Health Module of the UNU Sustainability Nexus AID Programme aims to evolve into the ultimate focal point, supporting a diverse array of stakeholders with state-of-the-art data and tools that are essential for soil health monitoring and projection. This paper discusses the importance of adopting a nexus approach for ensuring soil health, explores the AID tools currently at our disposal for quantifying and predicting soil health, and concludes with recommendations for future effort and direction within the Sustainability Nexus AID Programme concerning soil health.
This research explores the integration of smart technologies in the tourism sector, emphasizing their potential to enhance sustainability while enriching tourist experiences. By examining the role of technologies such as IoT systems, data analytics, and mobile applications, the study highlights their ability to optimize resource management, reduce environmental impact, and support conservation efforts. IoT systems in hotels can monitor and control energy use, reducing waste and carbon emissions. Additionally, mobile applications can inform tourists about sustainable practices, guiding them toward eco-friendly activities. The literature identifies that smart technologies can significantly lower the ecological footprint of tourism while enhancing economic and social benefits for local communities. This study provides valuable insights for policymakers and tourism stakeholders, advocating for the strategic integration of technology to align with sustainability goals and promote environmental preservation, cultural heritage, and inclusive growth.
