German University of Technology

Abstract: Beams commonly fail in shear due to diagonal tension, shear compression, or web shear, typically caused by insufficient reinforcement, overload, or material weaknesses. One solution is the use of fiber-reinforced polymer (FRP) wrapping, which enhances durability and strength. FRP, a composite of fibers and resin, exhibits superior mechanical properties such as impact resistance, stiffness, and corrosion resistance. This study investigates the effectiveness of Polypropylene Fiber Reinforced Polymer (PFRP) in strengthening reinforced concrete (RC) beams. Mechanical behaviour was assessed through compression, split tensile, and flexural tests. RC beams wrapped with PFRP were also analyzed in terms of shear strength using experimental and simulation methods. The results indicate higher strength in PFRP-wrapped beams compared to unwrapped ones.

The applications of nanofluids are widely beneficial in heat transmission and cooling systems. Nanofluid viscosity and thermal conductivity have a substantial effect on heat transfer applications and on devices such as solar and geothermal systems. Machine learning models enable faster, less expensive modeling of nanofluid thermophysical properties. These models are secure for future studies and in the development of nanotechnology. In this review, shape, size, temperature, and volume concentration are considered as inputs to develop several machine learning methods, such as artificial neural networks, support vector regression, decision trees, and random forests. These models were analyzed by comparing their R2 values, and the results indicated that machine learning-based models generally exhibited more reliable performance than the other approaches. The observation in this review was that thermal conductivity increases with temperature and volume fractions, whereas viscosity decreases with size, temperature, and volume fractions. To determine the optimal nanoparticle type, size, and concentration for specific applications such as data center cooling and high-heat-flux electronics, future research may employ ML-based optimization techniques.

Modern healthcare systems increasingly depend on wearable Internet of Medical Things (IoMT) devices for the continuous monitoring of patients’ physiological parameters. It remains challenging to differentiate between genuine physiological anomalies, sensor faults, and malicious cyber interference. In this work, we propose a hybrid fusion framework designed to attribute the most plausible source of an anomaly, thereby supporting more reliable clinical decisions. The proposed framework is developed and evaluated using two complementary datasets: CICIoMT2024 for modelling security threats and a large-scale intensive care cohort from MIMIC-IV for analysing key vital signs and bedside interventions. The core of the system combines a supervised XGBoost classifier for attack detection with an unsupervised LSTM autoencoder for identifying physiological and technical deviations. To improve clinical realism and avoid artefacts introduced by quantised or placeholder measurements, the physiological module incorporates quality-aware preprocessing and missingness indicators. The fusion decision policy is calibrated under prudent, safety-oriented constraints to limit false escalation. Rather than relying on fixed fusion weights, we train a lightweight fusion classifier that combines complementary evidence from the security and clinical modules, and we select class-specific probability thresholds on a dedicated calibration split. The security module achieves high cross-validated performance, while the clinical model captures abnormal physiological patterns at scale, including deviations consistent with both acute deterioration and data-quality faults. Explainability is provided through SHAP analysis for the security module and reconstruction-error attribution for physiological anomalies. The integrated fusion framework achieves a final accuracy of 99.76% under prudent calibration and a Matthews Correlation Coefficient (MCC) of 0.995, with an average end-to-end inference latency of 84.69 ms (p95 upper bound of 107.30 ms), supporting near real-time execution in edge-oriented settings. While performance is strong, clinical severity labels are operationalised through rule-based proxies, and cross-domain fusion relies on harmonised alignment assumptions. These aspects should be further evaluated using realistic fault traces and prospective IoMT data. Despite these limitations, the proposed framework offers a practical and explainable approach for IoMT-based patient monitoring.

The rapid adoption of electric vehicles (EVs) has heightened the need for advanced diagnostic frameworks to ensure the safety, reliability, and longevity of battery systems. Batteries, as the cornerstone of EV performance, are susceptible to capacity degradation, thermal instability, and anomalies that compromise efficiency and safety. Traditional diagnostic approaches, relying on physics-based models and heuristic thresholds, often fail to address the complexity and variability of real-world EV operations, necessitating scalable, efficient, and privacy-preserving solutions. This paper presents a Split Multi-Task Federated Learning (SMTFL) framework that leverages a Transformer-based architecture to address the dual challenges of battery health anomaly detection and capacity estimation. By combining the efficiency of split learning with the collaborative benefits of federated learning, the proposed system enables EVs to train and share partial model updates while retaining sensitive data locally. Experimental results on a recent large-scale EV dataset demonstrate the superior performance of the SMTFL framework, achieving an accuracy of 98.9% for battery health anomaly detection and a mean absolute error (MAE) of 0.32 Ah for capacity estimation, corresponding to a relative error of approximately 1.80%, significantly outperforming state-of-the-art approaches. By training a single model for both tasks and performing computations during charging sessions, the framework minimizes energy consumption and computational overhead, ensuring practicality for large-scale deployment in real-world EV systems.

This study aimed to examine the alignment of the newly introduced English language teaching syllabus, Team Together Oman, with the priorities and strategic directives outlined in Oman Vision 2040. It also aimed to explore English language teachers' perspectives on this alignment, highlighting both the syllabus's advantages and the challenges educators face in achieving coherence with Oman Vision 2040’s objectives. A descriptive research design was employed, incorporating content analysis to evaluate the Graded Readers used in the Team Together Oman textbook series. Two reviewers have analysed the content of the syllabus. Additionally, a questionnaire was utilized to gather data on teachers’ perceptions of the newly introduced textbooks. The findings indicate that Team Together Oman strongly reflects the goals of Oman Vision 2040 explicitly and implicitly. Regarding the questionnaire findings, participant teachers agree that Graded Readers effectively enhance students' reading skills (M=4.01), and they appreciate how the vocabulary used in the stories aligns with the vocabulary taught in the units, as seen in the high mean score (M=4.41). However, there are concerns regarding time constraints, with a relatively high standard deviation (1.68) indicating varied opinions about whether there is sufficient classroom time to teach the readings. Notably, teachers express strong agreement about the need for training programs to better align teaching practices with Oman Vision 2040, scoring the highest mean with the lowest standard deviation (M=4.51), reflecting a clear desire for professional development and support. It is recommended that further studies be conducted to investigate the alignment between the priorities and strategic directives of Oman Vision 2040 and the content of Team Together Oman textbooks across other grade levels.