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The landscape of modern computing systems is shifting towards architectures built by combining available services under the “everything as a service” paradigm. These architectures are deployed on distributed cloud-edge infrastructures, aiming to provide innovative services to a wide range of users. However, it is crucial for these systems to address environmental sustainability concerns. This poses challenges in operating such systems in open, dynamic, and uncertain environments while minimizing their energy consumption. To tackle these challenges, we propose a decentralized service assembly approach that ensures the assembly is energetically self-sustainable by relying on locally harvested and stored energy. In our contribution, we introduce a general service selection template that enables the derivation of different selection policies. These policies guide the construction and maintenance of the service assembly. To evaluate their effectiveness in meeting the sustainability requirements, we conduct a comprehensive set of simulation experiments, providing valuable insights.

Abstract Factoring large integers represents a crucial challenge for computational mathematics and global cryptographic security. Although Shor's algorithm paved the way for a quantum revolution, its large-scale implementation remains theoretical, leaving a gap that requires innovative solutions. In this study, conducted in collaboration between Intesa Sanpaolo and Data Reply, we propose a critical review and reformulation of the cell method, demonstrating how unconventional approaches can redefine current limitations. Our contribution is based on two strategic axes. The first challenges current benchmarks for a full QUBO formulation in the literature with a 23-bit number and demonstrate how quantum-inspired techniques can anticipate the future of factorization. The second focuses on optimizing the method on CPU architecture using an innovative filtration procedure, capable of drastically reducing execution times and memory footprint. This combination allowed us to factor an 80-bit number, reaching a really promising result for this approach and ease outperform hybrid quantum results. The implications of these results are profound: not only they redefine the prospects of factorization in post-quantum contexts, but they also open up new scenarios for cryptographic resilience and complexity theory, marking a decisive step towards the next generation of short- to mid-term algorithms.

The recent JASN article by Binois et al.1 offers important insights into the prognostic value of kidney histopathology in scleroderma renal crisis. Through histologic clustering and outcome analysis, the study highlights marked heterogeneity and identifies acute arteriolar thrombotic microangiopathy and onion skinning as predictors of kidney failure.1 Interestingly, chronic changes such as glomerulosclerosis and interstitial fibrosis were linked to better preserved kidney function, suggesting a potential adaptive or protective role. A key area of ongoing uncertainty concerns the role of angiotensin-converting enzyme (ACE) inhibitors when administered before the onset of scleroderma renal crisis. Although ACE inhibitors are well established as a cornerstone of management of scleroderma renal crisis due to their proven efficacy in controlling malignant hypertension and limiting further vascular injury, their prophylactic use in patients with systemic sclerosis remains a matter of substantial controversy. Several observational studies have paradoxically linked prior ACE inhibitor use to a higher risk of scleroderma renal crisis.2,3 However, a critical limitation of these studies lies in the lack of accompanying kidney histopathologic data, which impedes a mechanistic interpretation of this apparent contradiction. This study offers a valuable opportunity to address a critical gap in our understanding of the effect of prior ACE inhibitor use in scleroderma renal crisis. Notably, approximately 30% of patients in the cohort had been treated with ACE inhibitors before the onset of scleroderma renal crisis. This observation prompts several important considerations, including the possibility that prior exposure to ACE inhibitors may have been associated with less severe acute histologic lesions.4 A focused subgroup analysis comparing histopathologic features and kidney outcomes between patients with and without prior ACE inhibitor use could provide meaningful insights. Such data may help determine whether ACE inhibitors exert a protective or deleterious effect when administered prophylactically in patients with systemic sclerosis. The apparent discrepancy between the established therapeutic efficacy of ACE inhibitors in the treatment of scleroderma renal crisis and their potentially adverse effects when used prophylactically remains insufficiently understood from a pathophysiologic perspective. To clarify this issue, future studies should investigate the interactions between ACE inhibitors and the progressive vascular injury that characterizes systemic sclerosis. This will require the integration of clinical, hemodynamic, and immunologic data, including complement activation, as well as histopathologic findings to achieve a more comprehensive understanding of pathogenesis of scleroderma renal crisis.

While the O-RAN Alliance is gaining momentum in democratizing RAN innovation through multi-vendor interoperability, there remains a gap in empirical performance evaluation of O-RAN applications deployed with future-proof security measures on carrier-grade components. Furthermore, how implementing an advanced security measure impacts the overall O-RAN performance is largely understudied in true cloud-native deployments. This paper presents a comprehensive integration of an O-RAN-specific testbed that utilizes a quantum-safe security technology, an industry-leading O-RAN test emulator, and a carrier-grade Near-Real-Time (near-RT) RIC platform hosting our custom AI-driven Traffic Steering (TS) xApp. Our threefold contribution includes developing an innovative TS xApp for efficient RAN load-balancing on the fly, empirically measuring RAN key performance indicators (KPIs), and assessing the security solution's impact on overall system performance. Results demonstrate that our TS xApp enhances the downlink throughput by 3.88% and improves the median user QoS scores by 2.52%. In particular, the quantum-safe security measure implemented to secure the O-RAN E2 interface in the multi-vendor deployment, adds only 0.818 milliseconds of average latency which remains well below one second, the maximum acceptable threshold for near-RT RIC control loop response time.

Gaussian Boson Sampling (GBS) is a recently developed paradigm of quantum computing consisting of sending a Gaussian state through a linear interferometer and then counting the number of photons in each output mode. When the system encodes a symmetric matrix, GBS can be viewed as a tool to sample subgraphs: the most sampled are those with a large number of perfect matchings, and thus are the densest ones. This property has been the foundation of the novel clustering approach we propose in this work, called GBS-based clustering, which relies solely on GBS, without the need of classical algorithms. The GBS-based clustering has been tested on several datasets and benchmarked with two well-known classical clustering algorithms. Results obtained by using a GBS simulator show that on average, our approach outperforms the two classical algorithms in two out of the three chosen metrics, proposing itself as a viable quantum-classical hybrid clustering option.

Breast cancer incidence and screening participation exhibit an unequal distribution in the population. This study aims to investigate the impact of socioeconomic position (SEP) on three breast screening indicators (participation, recall, and cancer detection rates) among women aged 50–69 in the city of Turin between 2010 and 2019. The study also aims to determine whether contextual factors (deprivation index) or individual factors (educational level) have a greater influence. The data used in this study are sourced from the Turin Breast Screening Program (TBSP) and the Turin Longitudinal Study (TLS). To test the hypothesis and account for the hierarchical structure of the data, multilevel models were used. Both contextual and individual SEP were found to be associated with screening participation. Participation increased with higher levels of deprivation (odds ratio for most deprived: 1.13; 95% CI 1.11–1.16) and decreased with higher educational levels (OR for low educated: 1.37; 95% CI 1.34–1.40). Contextual SEP did not show any association with recall or cancer detection rates, but individual SEP had an impact. Women with lower educational levels had a statistically significant 19% lower odds of being recalled and a statistically significant 20% lower odds of being diagnosed with cancer. Additionally, immigrant women were less likely to participate in screening, be recalled, or receive a cancer diagnosis. Educational level consistently influenced the analyzed screening indicators, while contextual deprivation appeared to have less importance. It is likely that women living in less deprived areas and with higher education have greater access to opportunistic screening.

It's no secret that threat actors are enthusiastically adopting generative AI systems to help them launch more-effective cyber attacks. Fortunately, it's possible to fight AI with AI. There are new tools becoming available to help organisations protect their data and networks. But you need to know where to use them, when to use them and which are the most appropriate tools for each task.

Sap exudation is the process whereby trees such as sugar (Acer saccharum Marsh.) and red maple (Acer rubrum L.) generate unusually high positive stem pressure in response to repeated cycles of freeze and thaw. This elevated xylem pressure permits the sap to be harvested over a period of several weeks and hence is a major factor in the viability of the maple syrup industry. The extensive literature on sap exudation documents competing hypotheses regarding the physical and biological mechanisms that drive positive pressure generation in maple, but to date, relatively little effort has been expended on devising mathematical models for the exudation process. In this paper, we utilize an existing model of Graf et al. (J Roy Soc Interface 12:20150665, 2015) that describes heat and mass transport within the multiphase gas–liquid–ice mixture in the porous xylem tissue. The model captures the inherent multiscale nature of xylem transport by including phase change and osmotic transport in wood cells on the microscale, which is coupled to heat transport through the tree stem on the macroscale. A parametric study based on simulations with synthetic temperature data identifies the model parameters that have greatest impact on stem pressure build-up. Measured daily temperature fluctuations are then used as model inputs and the resulting simulated pressures are compared directly with experimental measurements taken from mature red and sugar maple stems during the sap harvest season. The results demonstrate that our multiscale freeze–thaw model reproduces realistic exudation behavior, thereby providing novel insights into the specific physical mechanisms that dominate positive pressure generation in maple trees.

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