Abstract: As AI technologies increasingly play a crucial role in organisational decision-making, ethical frameworks and governance guidelines have been developed to ensure accountability, transparency, and responsible use. However, these governance structures primarily assume that organisations have the formal capacity to oversee AI, without examining whether such capacity is actually present. Empirical evidence on how organisations truly govern AI—and where responsibility is fundamentally lacking—remains scarce. This paper offers an initial empirical delineation of formal AI governance responsibilities across diverse sectors and regions. It employs survey data from 351 organisations to investigate the existence of positions such as Chief Artificial Intelligence Officer (CAIO), AI Ethics Officer, Responsible AI Lead, Algorithmic Auditor, and AI Governance Committees. Furthermore, it analyses variations in these jobs across industries and geographies, as well as their structural characteristics, such as seniority, reporting relationships, authority, and available resources. The research reveals prevalent profiles of governance maturity. The findings indicate that formal roles for AI governance are not consistently implemented and, when they do exist, often lack the necessary authority, resources, and integration at a senior institutional level. Executive-level leadership roles and specialised audit functions are rare, and many organisations operate without any formal AI governance roles despite using AI technologies. The study outlines four profiles of governance maturity: Governance Absence, Symbolic Governance, Operational Governance, and Institutionalised Governance, highlighting that mature governance is often more the exception than the norm. By empirically assessing the presence or absence of AI governance, this research presents an absence-based viewpoint on AI ethics. It indicates that ethical concerns often arise from inadequacies in governance design rather than from flaws in existing frameworks. These results establish a foundational empirical baseline for subsequent studies on how various AI governance models influence compliance, trust, and ethical risks.

Abstract: Soil is among the environmental compartments increasingly affected by microplastics (MPs) contamination, mainly coming from industrial activities, agricultural practices, atmospheric or waterborne transport, and improper waste disposal. Despite the increasing attention to the fate of MPs in soil over the last years, research in this area is still limited compared to aquatic ecosystems. The introduction of MPs into the soil environment can modify both the soil properties and the interactions among soil components, plants and microorganisms, thus affecting also the mobility and availability of other contaminants, such as potentially toxic elements (PTEs). This review critically examines the complex dynamics between MPs and PTEs in the soil ecosystem, in particular in agricultural soils, and the conditions under which MPs can act as a source or a sink of PTEs. Indeed, microplastics can adsorb or complex PTEs on their surfaces, thus reducing their mobility and availability, or release/mobilize PTEs after their degradation or as micro/nano-vectors of PTEs. Understanding such mechanisms is relevant to evaluating the environmental risks associated with the co-presence of MPs and PTEs in soil, a situation very likely to occur in most contaminated sites as well as in soils strongly affected by anthropogenic activities.

Abstract: Background: Supported standing is commonly prescribed for children and adolescents with cerebral palsy (CP) to support musculoskeletal health and participation. However, objective data on plantar loading under different positioning conditions are limited, particularly in individuals with severe motor impairment (GMFCS IV–V). This study quantified plantar loading as an operational measure of foot weight-bearing during supported standing across combinations of verticalization angle and hip/knee flexion. Methods: Twenty-six children and adolescents with CP (GMFCS IV–V; 6–17 years) were assessed using a standardized back-supported standing system. Plantar loading was measured with two calibrated force plates at six verticalization angles (0°, 30°, 45°, 60°, 75°, 90°) combined with four hip/knee flexion angles (0°, 15°, 30°, 45°). Loading was expressed as percentage of body weight (% BW). Effects were analyzed using repeated-measures analysis of variance. Results: Plantar loading increased progressively with increasing verticalization angles across all hip/knee flexion conditions. Clinically relevant loading levels (>70% BW) were achieved at a verticalization angle of 60° in most flexion conditions. Maximum plantar loading was observed at 90° verticalization combined with 30° hip/knee flexion (96.4% BW). At 90° verticalization, plantar loading remained substantial even with 45° hip/knee flexion (81.4% BW). Increasing hip/knee flexion did not result in a linear reduction in plantar loading; a significant decrease was observed only at 45° flexion. Conclusion: Verticalization angle is the primary determinant of plantar loading during supported standing in children and adolescents with severe CP. Clinically meaningful plantar loading – and thus effective foot weight-bearing – can be achieved at moderate verticalization angles despite hip and knee flexion, supporting flexible positioning strategies.

Abstract: Background/Objectives: Intraoperative methadone has emerged as a significant pharmacological strategy in cardiac surgery to improve postoperative analgesic outcomes and reduce the reliance on rescue short action opioids. This review aims to synthesize evidence regarding the safety and efficacy of intravenous methadone compared to other strategies for postoperative pain control in adult and pediatric cardiac surgeries. Methods: This scoping review relied on electronic searches in PubMed, Web of Science, Cochrane Library, and EMBASE up to January 2026. From 199 articles retrieved, 41 were included, focusing on analgesic efficacy, safety, pharmacokinetic variations during cardiopulmonary bypass (CPB), and cost-effectiveness. Results: The implementation of methadone results in a 30% to 70% reduction in postoperative opioid requirements. Patients experience significantly lower pain scores from 24 to 72 hours and a 20% to 30% improvement in satisfaction regarding their recovery. In pediatric populations (neonates and children), the use of methadone leads to a significant reduction in opioid needs and a high rate of extubation in the operating room. Pharmacokinetically, a 48% drop in methadone concentration occurs during CPB due to hemodilution and sequestration. Safety data confirms that intraoperative use does not prolong mechanical ventilation; however, doses exceeding 0.25 mg/kg are linked to an increased incidence of delirium. Economically, optimized recovery generates savings of up to $6,355 per patient within seven days. Conclusions: Intraoperative methadone is a robust and safe analgesic option for cardiac surgery, including pediatric cases. It offers profound opioid-sparing effects and significant cost reductions, if dosages are carefully managed to minimize delirium risks.

Abstract: Building on a previously proposed coupling between the Maxwell tensor and the Riemann‑Christoffel curvature tensor [Ann. Phys. 465, 169661 (2024)],[i] this manuscript develops the conceptual and foundational implications of that framework, with particular emphasis on source self‑consistency and the unification of electromagnetic and gravitational source terms. The theory eliminates the need for externally introducing charge and mass and instead defines these quantities self-consistently in terms of the theory’s fundamental fields. By construction, all solutions of the theory satisfy the equations of Classical Electrodynamics identically. This approach highlights a conceptual ambiguity in the merger of Classical Electrodynamics and General Relativity, where the standard merger can admit multiple, potentially inequivalent local definitions of mass density. Here, by enforcing source self-consistency, the geometric framework provides a unified and intrinsic foundation for both charge and mass, ensuring compatibility across all equations of motion. Notably, the global symmetries of the theory lead to the emergence of antimatter and dictate its behavior in electromagnetic and gravitational fields, in agreement with classical expectations. Despite the nontrivial integrability conditions imposed by the geometric coupling, exact particle‑like and radiative solutions are presented which illustrate the unification of electromagnetic and gravitational phenomena and demonstrate the constraints imposed by the geometric structure. The results suggest new directions for foundational research, emphasizing the role of internal logical consistency in classical field theory and its potential implications for outstanding problems such as the equivalence principle and the nature of dark matter.

Abstract: This study proposes a unified physical framework integrating conservation-based spatial foundations with discrete spatial quantum mechanics. By leveraging spatial quantum's localized splitting, adjacent capture, and density gradient effects, we develop a coherent explanation for the microscopic origins of gravity, cosmic expansion, dark matter, dark energy, and vacuum energy divergence. The theoretical mechanism posits that the total spatial volume remains strictly conserved, with space composed of indivisible fundamental units called spatial quantum. To maintain energy, momentum, and angular momentum conservation, bound matter continuously undergoes virtual particle processes—quantum information exchanges that require spatial quantum as the minimal physical degree of freedom, leading to their gradual increase over time. Gravity emerges as a geometric dynamics effect driven by spatial quantum density gradients, while cosmic expansion manifests as the continuous fragmentation of this conservation-based foundation into quantum units, observable through the light-cone causality structure. This model serves as a microscopic extension and refinement of general relativity, effectively addressing black hole singularities and Big Bang singularities. Without introducing dark matter particles, dark energy scalar fields, or additional gravitational corrections, it provides a self-consistent explanation for observed phenomena including galactic rotation curves, gravitational lensing, bullet clusters, and super-diffuse galaxies, while mitigating vacuum energy density divergence-induced "vacuum catastrophe" issues. The theory satisfies Lorentz covariance and local causality, featuring a relatively closed underlying structure with minimal assumptions, offering a potential pathway toward constructing a complete, singularity-free unified description of gravity and cosmology.

Abstract: The Theory of Inventive Problem Solving (TRIZ) has long been a cornerstone for systematic innovation in engineering domains, including chemical and materials science. This paper proposes a novel framework that integrates TRIZ principles with large language models (LLMs) to emulate researcher-like reasoning in atomistic materials science. By structuring LLM prompts around TRIZ tools—such as patterns of evolution, contradiction matrices, and inventive principles—we enable models to identify problems, frame contradictions, and generate inventive solutions for challenges like data scarcity, poor interpretability, and unphysical predictions in quantum-chemical simulations and machine learning (ML) models. Drawing on recent artificial intelligence-TRIZ hybrids, like AutoTRIZ and TRIZ-GPT (generative pre-trained transformer), we demonstrate applications in molecular design, such as resolving contradictions in shape-memory polymers. This approach not only amplifies current trends in physics-informed ML and generative design but also democratizes advanced problem-solving, accelerating discoveries toward ideality

Abstract: La Paz Bay (BLP), Baja California Sur, Mexico, is one of the country's most important destinations for fishing tournament, with recreational fishing considered a significant tourist activity in terms of ecosystem services and the economic benefits it provides to participants. The purpose of this study was to estimate the demand for sport fishing tournaments and the monetary benefit of this activity through the willingness to pay (WTP) for access to fishing tournaments by anglers and based on it make some proposal of tourism promotion and recreational fisheries management. A total of 184 surveys were conducted at tournaments held in 2022 and 2023; the collected data were used to apply the individual travel cost method. The data enabled the description of the profile of anglers participating in fishing tournaments in BLP. The demand function for fishing tournaments was estimated, which includes seven determinants. With this information, the individual DAP per angler was estimated at USD 608.63, and the recreational economic value of fishing tournaments in La Paz is estimated at USD 1.08 million. Strategies for conserving species reserved for sport fishing and promoting tourism are discussed, which could help improve tournament activity and promote the rational use of natural resources.

Abstract: Strategic decision-making (SDM) has traditionally been viewed as a human activity based on judgment, experience, and negotiation among senior managers. These decisions are limited by attention constraints, incomplete information, and bounded rationality. Today, many firms embed artificial intelligence (AI) and algorithmic decision-making systems into strategic processes. In some cases, algorithms do more than support managers. They filter options, rank priorities, and strongly shape final decisions. This article asks when SDM remains meaningfully human and when it becomes effectively algorithmic in algorithmically mediated enterprises. The study uses a theory-building integrative review of 62 contributions from strategy, information systems, behavioural research, and governance. It compares human and algorithmic decision-making across five dimensions: interpretive authority, search structure, time orientation, accountability, and scalability. Based on this analysis, it develops a framework of human–AI decision structures. The framework identifies three main forms: human-dominant, sequential hybrid (AI-to-human or human-to-AI), and aggregated human–AI governance structures. Each form affects not only decision accuracy but also power, learning, agency, and accountability. The key challenge is not to defend purely human strategy. It is to design governance systems where decision rights, oversight, and contestability remain strong when algorithms act as active decision participants.

Abstract: We demonstrate deterministic localization of cryptographic hash preimages within specific layers of deep neural networks trained on information-geometric principles. Using a modified Spin-Glass architecture, MD5 and SHA-256 password preimages are consistently identified in layers ES15-ES20 with >90% accuracy for passwords and >85% for hash values. Analysis reveals linear scaling where longer passwords occupy proportionally expanded layer space, with systematic replication in higher-dimensional layers showing exact topological correspondence.Critically, independent network runs with fresh initialization maintain 41.8% information persistence across 11 trials using unique hash strings and binary representations. Layer-to-layer correlations exhibit non-linear temporal coupling, violating fundamental assumptions of both relativistic causality and quantum mechanical information constraints. Pearson correlations between corresponding layers across independent runs approach ±1.0, indicating information preservation through mechanisms inconsistent with substrate-dependent encoding.These findings suggest the cryptographic "one-way property" represents a geometric barrier in information space rather than mathematical irreversibility. Hash function security may be perspectival accessible through dimensional navigation within neural manifolds that preserve topological invariants across initialization states. Results challenge conventional cryptographic assumptions and necessitate reconceptualization of information persistence independent of physical substrates.

Abstract: A simple, rapid, and cost-effective method for the determination of BaP in edible oil was developed and validated. Nickel oxide deposited silica (SiO2@NiO) prepared by depositing nickel oxide onto silica using liquid phase deposition method was employed as solid-phase extraction (SPE) adsorbent for the extraction of benzo[a]pyrene (BaP) in edible oil followed by high performance liquid chromatography-diode array detector (HPLC-DAD) analysis. The edible oil was diluted with n-hexane and then directly loaded to SiO2@NiO for SPE. The n-hexane was also used to clean the fat-soluble interference in the edible oil, while BaP was selectively captured due to the electron donor-acceptor interaction with SiO2@NiO. The extraction conditions such as amount of sorbent, volume of washing solvent, type and volume of desorption solvent were optimized. The method demonstrated good linearity over the range of 6-1875 ng/g with the limit of detection of 1.3 ng/g, the spiked recoveries in the range of 97.4-105.1 %, and the relative standard deviation (RSD) less than 3.0 %. The method was applied for the analysis of BaP in 12 actual oil samples and the results showed that unrefined oil and high-temperature frying oil were at risk of BaP exceeding the acceptable level.

Abstract: The rapid growth of medical imaging data has intensified the need for advanced computational tools to support clinical decision-making. However, centralized approaches to artificial intelligence development raise significant challenges related to privacy, regulation, and generalizability. This paper introduces FedIHRAS (Federated Intelligent Humanized Radiology Analysis System), a privacy-preserving federated learning framework that enables multi-institutional collaboration for chest X-ray analysis. FedIHRAS integrates pathology classification, visual explainability, anatomical segmentation, and automated clinical report generation into a unified system that incorporates adaptive aggregation strategies, heterogeneity, and non-IID distributions. The framework employs multi-layered differential privacy mechanisms and a secure communication infrastructure to ensure compliance with strict healthcare data protection standards. Experimental validation across four large-scale chest radiograph datasets (approximately 874k images) demonstrates that FedIHRAS retains 98.8\% of the diagnostic accuracy of a centralized model (mean AUC-ROC = 0.911 vs. 0.922) and achieves superior generalization to unseen institutions (94.2\% retention). Explainability and interpretability were preserved at near-centralized levels, with expert radiologists rating 94.6\% of attention maps as clinically reliable. Moreover, privacy robustness tests confirm strong resistance against inference and reconstruction attacks. FedIHRAS reduces barriers to collaborative research and mitigates algorithmic bias, ultimately offering a scalable and equitable solution for radiological analysis in real-world healthcare systems.

Abstract: Seasonal precipitation forecasting remains challenging in regions with complex topography and high climatic variability, such as the state of Minas Gerais, Brazil. This study evaluates the performance of an Artificial Intelligence (AI)–based ensemble approach for seasonal precipitation prediction during 2024 and compares its results with those obtained from the NCEP Climate Forecast System version 2 (NCEP-CFSv2), a model from the North American Multi-Model Ensemble (NMME). The AI model was trained using high-resolution precipitation data from the MERGE-CPTEC dataset and applied to generate seasonal forecasts. Model performance was assessed using Root Mean Square Error (RMSE), Mean Square Error (MSE), and Relative Error (RE). Observed seasonal precipitation anomalies for 2024 were also examined to contextualize forecast skill under different climatic conditions. The results show that the AI-based forecasts consistently outperform the NCEP-CFSv2 from NMME across all seasons, exhibiting lower error metrics and improved representation of spatial precipitation patterns. The highest forecast skill was observed during winter (JJA), when atmospheric conditions are more stable and precipitation variability is low. During the wet seasons (DJF and SON), despite increased convective activity and spatial heterogeneity, the AI model maintained greater spatial coherence and closer agreement with observations than the dynamical forecasts. Overall, the findings demonstrate that AI-based approaches represent a promising and computationally efficient complementary tool for regional-scale seasonal precipitation forecasting, particularly in climatically heterogeneous regions.

Abstract: Background/Objectives: Irritable bowel syndrome (IBS) is a prevalent gastrointestinal disorder with implications for individual quality of life and society. Patients with IBS suffer a variety of symptoms but have few treatment options. The level of satisfaction with IBS treatment is low, stressing the need to expand the IBS treatment toolbox. The aim of this study is to describe drivers and barriers to the implementation of time-restricted eating TRE as a treatment alternative for patients with IBS. Methods: A convenience sample of 14 informants was drawn from a pool of 97 successful participants in an eight-week 16:8 TRE intervention. The informants participated in audio-recorded semi-structured in-depth interviews. Recordings were processed by a computer language model and interview transcripts were generated automatically. The transcripts were proofread, structured and analysed with a reflexive inductive thematic analysis approach. Results: The analysis generated six main themes consisting of 18 sub-themes in total. One main theme describes drivers of implementation concerning domains such as motivation, supporting factors, mentality, behaviour and determinants of sustainability. The results from this study are largely coherent with the findings from earlier feasibility studies conducted on other populations, but several key differences related to population characteristics emerged. Conclusions: Overall, the analysis suggests that TRE can be a feasible treatment option for IBS, but successful implementation is dependent on individual ability, external support and symptom relief.

Abstract: We present the Hvala algorithm, an ensemble approximation method for the Minimum Vertex Cover problem that combines graph reduction techniques, optimal solving on degree-1 graphs, and complementary heuristics (local-ratio, maximum-degree greedy, minimum-to-minimum). The algorithm processes connected components independently and selects the minimum-cardinality solution among five candidates for each component. \textbf{Empirical Performance:} Across 233+ diverse instances from four independent experimental studies---including DIMACS benchmarks, real-world networks (up to 262,111 vertices), NPBench hard instances, and AI-validated stress tests---the algorithm achieves approximation ratios consistently in the range 1.001--1.071, with no observed instance exceeding 1.071. \textbf{Theoretical Analysis:} We prove optimality on specific graph classes: paths and trees (via Min-to-Min), complete graphs and regular graphs (via maximum-degree greedy), skewed bipartite graphs (via reduction-based projection), and hub-heavy graphs (via reduction). We demonstrate structural complementarity: pathological worst-cases for each heuristic are precisely where another heuristic achieves optimality, suggesting the ensemble's minimum-selection strategy should maintain approximation ratios well below $\sqrt{2} \approx 1.414$ across diverse graph families. \textbf{Open Question:} Whether this ensemble approach provably achieves $\rho < \sqrt{2}$ for \textit{all possible graphs}---including adversarially constructed instances---remains an important theoretical challenge. Such a complete proof would imply P = NP under the Strong Exponential Time Hypothesis (SETH), representing one of the most significant breakthroughs in mathematics and computer science. We present strong empirical evidence and theoretical analysis on identified graph classes while maintaining intellectual honesty about the gap between scenario-based analysis and complete worst-case proof. The algorithm operates in $\mathcal{O}(m \log n)$ time with $\mathcal{O}(m)$ space and is publicly available via PyPI as the Hvala package.

Abstract: Diffuse large B-cell lymphoma (DLBCL) is molecularly heterogeneous; genotype-directed first-line therapy may improve outcomes. We conducted a single-center, prospective, non-randomized interventional study evaluating a molecularly adapted R-CHOP-X strategy with two-year follow-up. Between February 2023 and the data cut-off (September 16, 2025), 43 adults with newly diagnosed DLBCL (excluding high-grade B-cell lymphoma, primary immune-privileged, and primary mediastinal large B-cell lymphomas) underwent tumor genotyping using LymphGen after targeted sequencing: an initial cohort had Sanger sequencing of a 19-gene panel (n = 35) and a second cohort an expanded 60-gene panel (n = 8). All patients received one cycle of R-CHOP followed by five cycles of R-CHOP-X, with the additional agent (vorinostat, acalabrutinib, decitabine, or lenalidomide) selected according to molecular subtype. Response assessment followed Lugano criteria; adverse events were recorded per NCI CTCAE v5.0. The overall response rate was 100% (n = 43); complete response among patients completing therapy (n = 35) was 100%. At two years, overall survival was 92% (95% CI 83%–100%) and progression-free survival was 94% (95% CI 86%–100%); two early relapses occurred. These findings indicate that molecularly adapted R-CHOP-X is feasible and associated with high response rates and favorable two-year survival and warrant validation in larger randomized clinical trials.

Abstract: Following the evolution from theoretical foundations to advanced deep learning algorithms, a coherent overview of reinforcement learning (RL) is proposed in this tutorial. We begin with the mathematical formalization of sequential decision-making via Markov decision processes (MDPs). In MDP, Bellman equation and Bellman optimality equation play important roles, they provide for policy evaluation and the fundamental condition for optimal behavior, respectively. The movement from these equations to practical algorithms is explored, starting with model-based dynamic programming and progressing to model-free temporal-difference (TD) learning. As a pivotal model-free algorithm, Q-learning directly implements the Bellman optimality equation through sampling. To handle high-dimensional state spaces, function approximation and deep reinforcement learning emerge, exemplified by Deep Q-Networks (DQN). Thereafter, actor-critic methods address the challenge of continuous action spaces. As a typical actor-critic scheme, the deep deterministic policy gradient (DDPG) algorithm is illustrated in detail on how it adapts the principles of optimality to continuous control by maintaining separate actor and critic networks. Finally, the tutorial concludes with a unified perspective, observing the development of RL as a logical progression from defining optimality conditions to developing scalable solution algorithms. Furthermore, future directions are summarized.

Abstract: This qualitative research examines expert advice on interventions for populations facing social vulnerability. Based on semi-structured interviews with professionals in psy-chosocial support, health, education, human geography and public policy, the study employs reflexive thematic analysis to detect common themes in how vulnerability is perceived and managed in practice. The results identify three interconnected interpre-tive clusters: first, viewing vulnerability as a product of structural factors, highlighting issues like institutional fragmentation, bureaucratic obstacles, and policy inconsisten-cies rather than individual shortcomings; second, emphasizing relational and recogni-tion processes, such as trust, active listening, and respect for personal journeys as key to meaningful engagement; and third, focusing on mediation and empowerment tactics, including institutional mediation, critical education, and digital literacy, to improve access and agency without shifting responsibility to individuals. Overall, the findings suggest that effective intervention demands integrated strategies that address struc-tural conditions, relational factors, and empowerment methods. By consolidating expert insights, the study offers empirically based guidance for practice and service organiza-tion, emphasizing the need for structurally aware, relationally grounded, and con-text-sensitive responses to current vulnerabilities.

Abstract: Background: Polysaccharide-based dynamic hydrogels are promising for wound management due to their biocompatibility, injectability, and tunable biofunctionality. The integration of therapeutic gasotransmitter donors offers a strategy to modulate the wound microenvironment. Objectives: This study aimed to develop an injectable, self-healing carbohydrate hydrogel capable of sustained hydrogen sulfide (H₂S) release for burn wound therapy, and to evaluate its physicochemical properties, in vivo efficacy, and mechanism of action. Methods: A dynamic hydrogel (ACMOD) was fabricated via Schiff-base crosslinking between oxidized dextran (OD) and carboxymethyl chitosan (CMCS), incorporating the H₂S donor ADT-OH. Rheological and recovery tests characterized its mechanical and self-healing properties. Efficacy and mechanisms were assessed in a rat full-thickness burn model, analyzing wound closure, histology, oxidative stress, macrophage polarization, angiogenesis, and collagen deposition. Results: ACMOD exhibited shear-thinning, rapid self-healing, and strong tissue adherence. Sustained H₂S release from ACMOD significantly accelerated wound closure and improved tissue regeneration compared to controls. Mechanistically, H₂S attenuated oxidative stress, promoted a pro-regenerative M2 macrophage phenotype, enhanced angiogenesis via VEGF upregulation, and fostered organized collagen deposition and extracellular matrix remodeling. Conclusions: This work demonstrates a versatile, carbohydrate-based dynamic hydrogel platform that synergizes polymer network dynamics with bioactive H₂S delivery to effectively promote burn wound healing. The findings underscore the potential of polysaccharide hydrogels with integrated gasotransmitter release for regenerative therapy and biomaterials applications.

Abstract: The construction of partial minimum spanning trees being NP-hard, several heuristic algorithms have already been formulated. Many of these heuristics (such as Kruskal's) use shortest paths to connect the components of the tree. In this work, we present an approximate construction algorithm for the minimum Steiner tree (the optimal tree for diffusion multicast). This construction is based on graph-related structures more advantageous than shortest paths. The algorithm uses connections like simple Steiner trees if necessary. These simple trees can be represented by hyperedges. A hyper metric closure can also be used.