R2M Solution (Italy)

The acceleration of digital transformation is achieved by the integration of cloud workflows, business operations, and data analytics by SAP platforms. On the other hand, the increasing prevalence of cloud-based platforms and open API ecosystems has made it more difficult to maintain data security. Applications provided by third parties, remote access points, and interactions between platforms have all contributed to an increase in the number of data breaches, insider threats, and illicit activities. A comprehensive cybersecurity architecture that can easily link to SAP systems is designed in this study. The purpose of this architecture is to safeguard cloud-based operations, API communications, and sensitive company data during the process of digital business development. According to the findings of the studies, SAP ought to use encryption, restricted access, and intelligent threat detection in order to safely protect its digital infrastructure. The protection of SAP application programming interfaces (APIs), which enable SAP applications to transfer data to other systems in a secure and legal manner, is the primary emphasis of this initiative. Atypical data transmission and access trends are investigated in this study, together with identity system management and real-time monitoring of these activities. Through consolidation of SAP cybersecurity, businesses have the potential to become more robust and adaptable. When it comes to digital businesses, it is essential to keep trust and compliance intact. A further potential application of blockchain technology is to make SAP operations more auditable and transparent. Immutable ledger technology protects data over geographically distributed networks. This strategy makes it harder for dishonest people to change transaction records without detection. With this technology and AI-driven insights, SAP users may anticipate and automatically resolve difficulties. The investigation shows that digital organizations cannot afford to add cybersecurity as a duty; it must be built into SAP systems. Organizations may protect sensitive data and ensure smooth digital transformation initiatives by using adaptable, intelligent, and transparent security solutions.

Abstract This work presents a novel end-to-end approach to early defect detection in thermo-insulating panels, widely used in the construction sector. Unlike existing studies that focus on isolated aspects, our contribution combines: (i) a composable and interoperable software platform designed to support data augmentation, segmentation, and classification; (ii) a multi-technique synthetic image generation strategy that overcomes the limitations of scarce and unbalanced datasets; and (iii) a hybrid segmentation and classification pipeline that extends traditional U-Net architectures with post-segmentation polygonal analysis for robust defect identification. The proposed solution is validated on the GAMHE 5.0 Industry 5.0 pilot line, demonstrating not only improvements in detection accuracy and control time but also its feasibility in human-in-the-loop, near-production environments, a gap rarely addressed in the literature. These results confirm that the approach goes beyond a mere consolidation of existing methods, providing instead a scalable, human-centric, and sustainable framework for Zero-Defect Manufacturing.

We have reviewed the technological, regulatory, standardization and industrial trends for wireless power transmission (WPT). On the technology front, it appears likely that high-power WPT will be integrated into major markets—such as robotics and electric vehicles—within the next decade. Long-di stance WPT could soon be coupled with sensors in smart factories. Once such technologies permeate daily life, electromagnetic-safety engineering will become the bottleneck. Only the ad vent of safe, high-power WPT will enable this future-oriented technology to be deployed across diverse industrial sectors.

The need to protect the climate is enshrined in a number of international agreements and national targets. This requires insulation systems in energy technology to be reconsidered. The replacement of previous insulation materials, such as SF6, with climate-neutral insulation materials requires a lower field strength than before. The brazing seams of the climate-neutral vacuum circuit breaker are critical areas for the external insulation system. In order to minimise the local field strength an industrially manufactured vacuum interrupter is equipped with field control elements on the outside. Different variants of field control elements were investigated with lightning impulse voltage to determine the statistical withstand voltage. The results show, that field control elements increase the withstand voltage of vacuum interrupter up to 26 % and so effectively reduce the maximum electric field strength. There is an indication that the flashover process can be influenced by the design of the field control elements. Due to the effective reduction of the field strength the use of climate-neutral insulation materials is feasible.

With the increasing demand for high-performance materials in energy, healthcare, and electronics, the discovery of novel nanomaterials has become a critical area of research. Traditional methods for predicting material properties, such as Density Functional Theory (DFT) and classical machine learning (ML), often struggle with scalability and computational efficiency when dealing with complex, high-dimensional quantum systems. This study explores the potential of Quantum Machine Learning (QML) as a transformative tool for accelerating nanomaterial property prediction. By leveraging quantum computing frameworks like IBM Qi skit and PennyLane, quantum algorithms such as Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) were applied to a real-world dataset containing 486 nanomaterial entries. Comparative analysis using performance metrics like MAE, RMSE, and R2 demonstrated that quantum models outperformed classical ones in both accuracy and adaptability to data complexity. Power BI was used for advanced visualization to interpret feature relationships, entropy levels, and prediction patterns across models. Key findings suggest that QML provides a promising alternative to conventional methods by offering faster, more accurate predictions, particularly for datasets with high entropy. While challenges remain—such as limited dataset scope and hardware access—this study lays the groundwork for broader adoption of QML in computational material science and calls for future research involving real quantum hardware and larger, more diverse datasets.

<div class="section abstract"><div class="htmlview paragraph">While hybrid electric powertrains are the standard for passenger cars, the application to motorcycles is almost nil. The reason is the increase in weight, cost and overall dimensions, which can compromise the layout and dynamics of the motorcycle. A viable path is to replace the standard internal combustion engine with a much smaller and lighter unit, which leaves room for the installation of the electric components. The 2-Stroke (2S) cycle technology, thanks to double cycle frequency and inherent simplicity, can be the key to reduce engine dimensions, weight and cost, while keeping high power outputs.</div><div class="htmlview paragraph">The HybridTec project, discussed in this paper, aims to develop a compact and lightweight V-90° two-cylinder 2S engine, coupled to an electric motor installed downstream of the gearbox (P3 configuration). The total installed power should be about 110 kW.</div><div class="htmlview paragraph">The engine features loop-scavenging, actuated by a crankshaft-driven supercharger, while an exhaust rotary valve and electronic fuel injection minimize fuel short-circuit over a broad operating range. Moreover, the control of air and residuals trapped within the cylinder is made by a bypass valve downstream of the supercharger. A 4-Stroke-like forced lubrication system is also adopted.</div><div class="htmlview paragraph">Another advantage of this engine design is the possibility to dilute the fresh charge with exhaust gas, at any operating condition: a proper percentage of residuals helps to cut NO<sub>x</sub> emissions without the need for complex and expensive after-treatment systems.</div><div class="htmlview paragraph">A single-cylinder prototype of the engine was preliminarily built and tested, the initial design being carried out with the support of a 1D model, which was then calibrated based on a preliminary configuration of the engine.</div><div class="htmlview paragraph">According to the 1D simulation results, the engine is able to meet the performance targets and shows good potential at part-load operation, but engine injection system and control strategy need further optimization, which will be supported by future detailed 3D-CFD analysis.</div></div>

Single-cell DNA methylation has a significant role in the regulation of gene expression as well as cellular differentiation. Nonetheless, despite sparse data and the high dimensions of prediction changing to single-cell opportunity, a significant challenge has been to predict methylation states exquisitely by focusing on single-cell sequencing tools along with the intrinsic noise of such technologies. In this instance, we introduce DEEPCPG a deep learning model tailor-made to predict the state of single-cell DNA methylation. DEEPCPG combines the object of convolutional and recurrent neural network to store local and long-range dependencies in the pattern of methylations. To increase the predictive accuracy of proposed framework, this strategy is a hybrid feature encoding approach, which integrates sequence context, CpG density, and a set of epigenetic signals adjacent to the predicted CpG motifs. DEEPCPG outperforms both traditional machine learning and already known deep learning models in benchmark single-cell methylome datasets in terms of precision, recall, and F1-score. The interpreted executable module that marks key CpG sites affecting prediction, which aids in creating biological insight. The outcomes show that DEEPCPG can be an effective tool in single-cell epigenomics, which will facilitate the enhancement of the knowledge about cell heterogeneity and epigenetic control. But on the other hand, it is not clear whether the model can be currently applied in large-scale studies due to its reliance on high-quality training data and computational resources at present. The next steps involve incorporating multi-omics findings and streamlining models to the point of being able to work on ultra-large single-cell datasets.