Poznan Supercomputing and Networking Center

Abstract The transportation problem considered in this work consists of minimizing the sum of penalties for tardiness in the delivery of the product from the base to clients. We propose an innovative method for constructing hybrid algorithms using quantum annealing that guarantees the optimality of the solution. The hybrid algorithm alternately performs calculations on a classical computer equipped with a silicon CPU and a quantum annealer with a QPU processor. The entire method is based on the branch and bound scheme. The lower and upper bounds are calculated on the QPU, while the CPU is responsible for organizing the calculations, recalling the solution tree. The problem under consideration is of great practical importance because it is a component of many complex issues related to solving the traveling repairmen problem, which are very difficult or even impossible to solve today using classical methods. Undoubtedly, an important application of quantum computers in the future is NP‐hard optimization tasks related to transportation scheduling and logistics.

Data discovery, processing and access APIs play an important role in the integration and harmonization of Digital Twins' implementations. Within the Iliad project's System of Systems (SoS) federated approach to the distributed platform architecture for digital twins operating oceanographic data, APIs have been applied. By enabling interoperability between diverse sources—legacy sensors, citizen science projects, third-party data hubs, and models—these APIs ensure standardized access to distributed datasets without requiring a centralized repository. A key focus of this paper is the implementation of flexible, open API architectures that align metadata, standardize access protocols, and facilitate data discovery across the system of systems (SoS). The adoption of cloud-native formats, rich web services, and streaming mechanisms enhances accessibility while supporting both existing and emerging technologies. The paper explores efforts around standards development organizations to refine API frameworks into knowledge services, ensuring they remain adaptable to evolving requirements. These advancements are instrumental in enabling high-resolution modeling, large-scale simulations, and leveraging potential of the AI applications within the environmental Digital Twins ecosystem.

Established in 2012 by members of the Food and Agriculture Organisation of the United Nations, the Global Soil Partnership (GSP) is a global network of stakeholders promoting sound land and soil management practices towards a sustainable world food system. However, soil survey largely remains a local or regional activity, bound to heterogeneous methods and conventions. Recognising the relevance of global and trans-national policies towards sustainable land management practices, the GSP elected data harmonisation and exchange as one of its key lines of action. Building upon international standards and previous work towards a global soil data ontology, an improved domain model was eventually developed within the GSP, the basis for a Global Soil Information System (GloSIS). This work also identified the Semantic Web as a possible avenue to operationalise the domain model. This article presents the GloSIS web ontology, an implementation of the GloSIS domain model with the Web Ontology Language (OWL). Thoroughly employing a host of Semantic Web standards (Sensor, Observation, Sample, and Actuator ontology (SOSA), Simple Knowledge Organisation System (SKOS), GeoSPARQL, QUDT), GloSIS lays out not only a soil data ontology but also an extensive set of ready-to-use code-lists for soil description and physico-chemical analysis. Various examples are provided on the provision and use of GloSIS-compliant linked data, showcasing the contribution of this ontology to the discovery, exploration, integration and access of soil data.

BackgroundThe number of thyroid cancer diagnoses has been increasing for several decades, with a significant part of cases being detected incidentally (thyroid incidentaloma, TI) by imaging studies performed for reasons other than thyroid disease, including PET/CT with [18F]FDG. The chacteristics of the detected TI cannot be determined solely on the basis of conventional parameters used in everyday clinical practice, such as SUVmax. In recent years, there has been a growing interest in radiomics, which is a quantitative method of analyzing radiological images based on the analysis of image texture. Textural analysis may be helpful, as it allows to characterize features invisible to the physician with the naked eye.ResultsOf the 54 patients who presented focal [18F]FDG-avid TI and had subsequent fine needle aspiration biopsy, 4 patients were excluded from the analysis due to the unavailability of the final diagnostic information. Hence, in the final analysis, data from 50 patients were used (39 females and 11 males) with a mean age of 58.5 ± 11.26. Of these 50 patients, 11 (22.0%) [18F]FDG-avid nodules were diagnosed as malignant. The performance of the XGBoost model in assessing [18F]FDG-avid TI was similar (0.846 [confidence interval, CI, 95% 0.737–0.956]) to SUVmax (0.797 [CI 95%: 0.622–0.973]; p = 0.60).ConclusionsWith an AI-based algorithm using radiomics features it is possible to detect the malignancy of thyroid nodule. However, no statistically significant differences were observed between the AI and radiomics approach, and when using a conventional measure, i.e., SUVmax.

Background: Cardiovascular diseases have long been studied to identify their causal factors and counteract them effectively. Atherosclerosis, an inflammatory process of the blood vessel wall, is a common cardiovascular disease. Among the many well-known risk factors, hypercholesterolemia is undoubtedly a significant condition for atherosclerotic plaque formation and is linked to atherosclerosis on many levels, i.e. cell interactions, cytokines levels, diet, and lifestyle. Current studies suggest that controlling balance between proinflammatory (M1) and anti-inflammatory (M2) types of macrophages may be used for patient condition improvement and necrotic core reduction. Methods: This study considered the effects of hypercholesterolemia on the population dynamics of macrophages (M0, M1, M2, foam cells) in atherosclerotic plaque. A mathematical model using a matrix approach to population dynamics was proposed and tested in various scenarios. In order to check model sensitivity and variability associated with error propagation, the uncertainty analysis was performed based on the Monte Carlo approach. Results: Simulations of macrophage population dynamics provided the assessment of necrotic core development and plaque instability. Excess lipid levels emerged as the most critical factor for necrotic core development. However, plaque growth can be significantly slowed if macrophages and foam cells can maintain proper lipid levels. This balance may be disrupted by proinflammatory lipids that eventually will increase plaque size, what is also reflected by M1/M2 dynamics. Conclusion: Hypercholesterolemia accelerates atherosclerosis development, leading to earlier cardiovascular incidents. In silico results suggest that reducing lipid intake and portion of proinflammatory lipids is crucial to slowing plaque development and reducing rupture risk, all of which requires preserving fragile M1/M2 balance. Targeting the inflammatory microenvironment and macrophage polarization represents a promising approach for atherosclerosis management.

The complexity of data in smart cities creates challenges for developers and hinders cohesive understanding of diverse datasets. These critical data sets are often underutilized due to opaque organization and accessibility. In our research, we use Large Language Models (LLMs) as "data custodians" to improve data navigability and usability in smart city platforms. We evaluated the ability of LLMs to generate accurate data descriptions, identify feature names, and discern relationships from limited raw data, demonstrating their proficiency with minimal input

Poznan Supercomputing and Networking Center (PSNC) developed an ambisonic installation and workflow as part of audio-visual 8K VR 360° immersive media experiments. This work aimed to investigate the quality of performance of the PSNC setup through both subjective tests as well as simulations providing objective parameters of interaural characteristics in a real-life scenario of PSNC studio. For the objective part, an algorithm for angle estimation has been proposed and computations were performed.

In this paper we investigate the interoperability of two different designs of repeater laser stations in inhomogeneous optical frequency transfer link. Such stations are used to regenerate weak optical signals and are key components of any long-distance link. We propose to use an acousto-optic modulator with appropriately selected shift to bridge the frequency gap between the two repeater laser stations. Experimental investigation showed that such a solution works correctly and does not degrade the transfer performance.