Deutsche Telekom (Austria)

Biometrics-based risk core recognition is identifying a person's unique finger pattern, such as texture and structures. Previous biometric validations have some limitations such as biological false silication paste in finger print forgery and low accuracy detection in reliable classification. The input dataset consists of multiple fingerprint biometric datasets, which are processed using image processing techniques. Initially, the BiHPF (Bilateral High-Pass Filters) pre-processing analyses to reduce intra-edge noise and improve image boundaries. In addition, wavelet analysis of time-domain data allows for multilabel data analysis, while smoothing enables the extraction of detailed point constructions in fingerprint data and texture descriptors in biometric images. The usage of LSTMs (Long Short-Term Memory) assistances to create compact feature representations, reducing the curse of dimensionality and improving classifier generalization. When combined with adversarial training paradigms, this approach creates a secure system capable of distinguishing between authentic and Generative Adversarial Network (GAN) generated biometric inputs. The experiment evaluated the proposed approach, resulting in an accuracy, precision, and recall of 97%. The outcome of this experiment is the ability to identify unique fingerprint patterns, which can improve the accuracy of secure p2p payments.

Artificial Intelligence (AI) enables machines to perform tasks that require human-like intelligence, including the prediction and detection of anomalies.The GRAPH Neural Networks (GNNs) can be successfully used in mobile payment systems to model user-user, device-user, merchant-user, and transaction relationships and detect fraud based on the pattern of relationships. Nevertheless, traditional approaches to AI face the challenges of massive imbalanced data, changing feature usefulness, multi-entity interactions, and real-time detection. To address these obstacles, we combine the best methods in our workflow. The proposed method, Edge-Level Real-Time Graph Neural Network Risk Scoring (ELRLVN2RS), allocates real-time risk ratings on transactions during the streaming mode, which allows the industry to provide high-risk activity alerts immediately. Additionally, synthetic fraud examples produced by SMOTE-GAN seem realistic and balance the PaySim data set, and they improve the acquisition of rare patterns of fraud. Furthermore, Meta-Learning of Feature Selection is a dynamic feature selection model that selects the most predictive features, adapting to the evolving fraud patterns as well as removing noise. Finally, Heterogeneous Graph Neural Networks (HGNNs) learn both local and global patterns of many entities, and this is because they capture the intricate interactions between them. The presented method AI-GNN framework that offers mobile payment fraud detection based on adaptive, heterogeneous, and real-time risks.

Creating trust in society for new technologies, such as a new types of powertrains, and making them marketable requires transparent, neutral, and independent technical verification. This is crucial for the acceptance and success of electrified vehicles in the used car markets. A key component of electric vehicles is the traction battery, whose current and future condition, particularly regarding aging, determines its residual value and safe operation. This review aims to identify and evaluate methods for predicting the lifetime of onboard traction batteries, focusing on their applicability in technical inspections. A systematic literature and patent review was conducted using targeted keywords, yielding 22 patents and 633 publications. From these, 150 distinct lifetime prediction methods were extracted and categorized into a four-level mind map. These methods are summarized, cited, and structured in detailed tables. The relationships between approaches are explained to clarify the current research landscape. Long Short-Term Memory, Convolutional Neural Networks, and Particle Filters were identified as the most frequently used techniques. However, no methods were found suitable for predicting the lifetime of traction batteries during technical vehicle inspections, which operate under short test durations, limited data access, and diverse real-world operating conditions. Most studies focused on cell-level testing and did not address complete battery systems in operational vehicles. This gap highlights the need for applied research and the development of practical methods to support battery assessment in real-world conditions. Advancing this field is essential to foster confidence in battery systems and enable a sustainable transition to electromobility.

The innovation of an intermodal bulk cargo transport service on the Danube addresses the gradual shift in the IWT market towards smaller quantities and more demand-oriented supplies to the customer. The design of the new transport service requires a decision on the precise service configuration with concept options between direct haulage, single-stop services and various liner services. For this purpose, agricultural production volumes and the infrastructural and nautical conditions along the Danube corridor have been used for the set-up of different service configuration variants. With the help of a dedicated logistics simulation study, the different service configurations have been examined and compared with one another with respect to a predetermined set of key performance indicators. The results of the study define the upscaling strategy following the initial deployment at a smaller scale, i. e., on a particular transport relation between a consignor and a consignee. The different extension options have been examined, compared with one another, and interpreted regarding their applicability. The results of the simulation study function as a guidance to decision-makers how the system behaves in different configurations.

ABSTRACT Maternal separation during early life is a well-established model for inducing neurodevelopmental impairments. Mechanical stimulation by passive movement, such as three-dimensional orbital shaking (3D-OS), has been suggested as a neuroprotective intervention in cell and tissue models, but its effects in neonatal animals remain unknown. We investigated whether 3D-OS applied during the postnatal period could attenuate neurogenesis impairments and cognitive deficits induced by maternal separation in rats. Eighteen newborn Sprague Dawley rats were assigned to control (C), maternal separation (MS), or maternal separation plus orbital shaking (MSOS) groups. Maternal separation was performed 3 h/day between postnatal days 2-21; MSOS additionally received 3D-OS stimulation (25 rpm, 3 h/day). Behavioural tests (Open Field, Novel Object Recognition, Morris Water Maze, Passive Avoidance), combined with RT-qPCR, Western blot, histology, and immunohistochemistry were conducted. MSOS rats displayed improved spatial learning in the early phases of the Morris Water Maze and recovered retention memory in Passive Avoidance compared to MS. In contrast, Open Field and Novel Object Recognition tests revealed no differences, and Western blot did not detect significant protein changes. Histological analysis showed reduced neuronal loss and vacuolisation in hippocampal and cortical regions of MSOS rats. RT-qPCR demonstrated reduced hippocampal FGF2 and FGFR1 expression in MS, partially restored by 3D-OS, while immunohistochemistry indicated increased FGFR2 and decreased FGFR1 in MSOS. Early-life 3D-OS administration alleviated maternal separation–induced cognitive decline and structural impairments, and enhanced hippocampal plasticity markers, providing preclinical evidence that moderate mechanical stimulation may support brain development and exert neuroprotective effects against early-life stress. Highlights Early-life 3D-OS mitigated maternal separation-induced deficits. Improved performance in Morris Water Maze and Passive Avoidance tasks. No significant effects observed in OFT, NOR, or Western blot. 3D-OS restored hippocampal FGF-2 and FGFR1 mRNA levels in stressed rats. Gentle mechanical stimulation may support early brain development.

Abstract This paper details a robust attitude and guidance control framework for the VS-50 suborbital launch vehicle, a Brazilian-German cooperative project. The strategy uses a state feedback architecture with Linear Matrix Inequality (LMI)-synthesized time-varying parameters to ensure stability and performance. A quadratic Lyapunov function addresses parametric variations, ensuring robustness against inherent dynamic uncertainties. This computationally efficient, mathematically grounded approach, unlike conventional gain-scheduling, bridges theoretical synthesis with practical implementation. Hardware-in-the-loop (HIL) simulations validate the controller, showing stable attitude regulation under disturbances and improved trajectory tracking with guidance control. Results confirm VS-50’s viability as a micro-satellite launch vehicle precursor and establish a scalable orbital framework. The integration of Lyapunov stability, LMI optimization, and HIL validation offers a replicable methodology for advanced aerospace systems, distinct from classical ad hoc strategies. Comprehensive supplementary material, including detailed mathematical models, control architectures, and HIL validation results, is available at [1].