Technical University of Applied Sciences Würzburg-Schweinfurt

Abstract Purpose The lateral pressure of fresh concrete on formwork governs the structural safety and cost efficiency of formwork systems for concrete structures. While the behavior in vertical formworks has been extensively investigated, no systematic experimental data exist for inclined formworks, although they are increasingly applied in modern concrete construction. This study addresses the scientific question of the influence of inclination angle, concreting speed, concrete consistency, formwork width, reinforcement and vibration intensity on the fresh concrete pressure in inclined formwork systems. Methods A large-scale test program was carried out with 15 concreting tests, with vertical and inclined formwork systems being filled simultaneously. Pressure development was continuously monitored using both pressure sensors and anchor force measurements. The influence of fresh concrete properties, formwork width, casting rate, reinforcement and compaction methods was evaluated. In addition, a numerical model was developed to simulate the pressure distribution, calibrated and validated against the experimental data. Results The tests showed that inclined formwork exhibits significantly lower pressures than in vertical systems. These reductions were primarily attributed to increased friction effects and a more pronounced silo effect as well as a lower vibration influence. Systematic dependencies of concrete pressure were observed with respect to the angle of inclination, formwork thickness, concrete consistency, reinforcement, and vibration intensity. Numerical simulations reproduced these effects and provided recommendations for practical use. Conclusion This work provides the first comprehensive database on fresh concrete pressure on inclined formwork. The combined experimental–numerical approach enables more accurate design of formwork systems and offers guidance for future updates of international standards.

Conflicts in different regions of the world have had significant consequences on electric power supply appliances. This study was conducted in Ethiopia's Aksum town low-voltage (LV) electric supply lines using reliability indices, including SAIDI, SAIFI, CAIDI, and EENS, before, during, and after the post-period. Accordingly, the findings indicate that severe reliability problems occurred due to the conflict, which resulted in power interruptions (both forced and operational), infrastructure damage, the displacement of skilled engineers, and inadequate recovery efforts. The study introduced a mixed auditing mechanism, analyzed historical power outage data, and evaluated performance-based reliability indices. Results indicate that before the conflict relatively stable power supply, with manageable outage durations and energy losses. However, during the two-year conflict (2020-2022), the average SAIDI value peaked at 1,626.4h, the CAIDI flowed to 2,507.31h, and EENS intensified to 10,480.52 MWh, reflecting significant disruptions. Six months post-conflict, significant recovery efforts improved these metrics, though supply stability challenges persist. The resulting reliability of the post-conflict reliability indices exceeds international benchmarks, with SAIDI over 1-5h, SAIFI above 0.5, and CAIDI over 5h. Despite post-conflict improvements, high SAIFI values show supply instability. Strengthen infrastructure, improve maintenance, and enhance auditing for reliable power.

Machine learning as a service (MLaaS) enables scalable model deployment and inference on cloud servers. However, MLaaS exposes user queries and model parameters to servers. To guarantee confidentiality of queries and model parameters, multi-party computation (MPC) enables secure inference by distributing data and computations across multiple service providers. MPC eliminates single points of failure, mitigates provider breaches and ensures confidentiality beyond legal agreements. Beyond confidentiality of queries and parameters, the model itself can memorize and leak training data during inference. To mitigate privacy concerns, differential privacy (DP) provides a formal privacy guarantee for training data, which can be satisfied by injecting carefully calibrated noise into gradients during training. However, naive combinations of DP and MPC amplify accuracy loss due to DP noise and MPC approximations, and incur high computational and communication overhead due to cryptographic operations. We present SPRINT, the first scalable solution for efficient MPC inference on DP fine-tuned models with high accuracy. SPRINT fine-tunes public pre-trained models on private data using DP. It integrates DP-specific optimizations, e.g., parameter-efficient fine-tuning and noise-aware optimizers, with MPC optimizations, e.g., cleartext public parameters and efficient approximations of non-linear functions. We evaluate SPRINT on GLUE benchmark with RoBERTa, achieving up to 1.6x faster MPC inference than the state-of-the-art non-DP solution SHAFT, reducing communication by 1.6x. Notably, SPRINT maintains high accuracy during MPC inference, with <1 percentage point gap compared to cleartext accuracy.

Humanoid robots are a prominent subject of media portrayal and public discourse. While technological advancements promise increasingly human-like machines, it remains unclear whether such complexity is truly necessary or if alternative robotic solutions can fulfill the same roles more efficiently. This study examines the perceptions of international students from various academic backgrounds and nationalities on the necessity and future relevance of humanoid robots. We present quantitative insights derived from a study, investigating correlations between participants' backgrounds and their perception of humanoid robots. The findings suggest a nuanced perspective: while optimism exists regarding humanoid potential, many still consider simpler robotic solutions to be sufficient for most real-world applications.