arXiv subjects

Mohamed Kandil

Publications and source records attributed to Mohamed Kandil.

5 recordsLinked to original sources

arXiv2026

Optical poling of a quantum ferroelectric metal across the order-disorder phase transition

Electron-doped strontium titanate has emerged as a prototypical quantum ferroelectric metal. It provides a fertile ground to explore how ferroelectric instability intertwined with itinerant electrons creates quantum phenomena, including unconventional superconductivity. Despite extensive studies, the microscopic origin of the ferroelectric transition remains unsettled, with distinct interpretations based on displacive mechanism driven by soft mode and order-disorder alignment of local dipoles. In particular, the local dipoles form nanoscale, spatially heterogeneous clusters, termed polar nanoregions, posing a significant challenge for probing or manipulating them. Here, using rotational anisotropy second harmonic generation, a symmetry-resolved probe, we quantify the orientational statistics of polar nanoregions in dilute electron-doped Sr$_{0.95}$Ba$_{0.05}$Ti$_{1-x}$Nb$_x$O$_3$. By tracking the alignment and meltdown of polar nanoregions in thermal cycles, we unambiguously demonstrate the order-disorder nature of the ferroelectric transition. We further show that, above transition temperature, femtosecond optical fields enable deterministic control of otherwise disordered polar nanoregions, realizing reversible write and readout of polar textures on ultrafast timescales. Our findings provide new insight into ferroelectric instability and establish an all-optical route of controlling polar metal systems where conventional electrical approaches are not feasible.

cond-mat.mtrl-sci↗

arXiv2026

Co2SeO3Cl2: Studies of Emerging Magnetoelectric Coupling in a Polar, Buckled Honeycomb Material

The development of magnetoelectric materials requires chemical design strategies that integrate structural polarity with magnetic lattices capable of supporting competing spin interactions. Here, we demonstrate such an approach in the polar, buckled honeycomb magnet Co2SeO3Cl2. Magnetization and heat-capacity measurements reveal strong magnetic anisotropy and four successive magnetic transitions at 25.4, 16.8, 11, and 3 K. The recovered magnetic entropy through the ordering regime is only around half of the expected 2Rln(2), indicating persistent spin fluctuations. Second-harmonic generation measurements show three pronounced intensity anomalies at 11, 17, and 26 K that coincide with magnetic transitions while revealing that the crystallographic symmetry is preserved. Together, these results demonstrate that polar, buckled honeycomb magnets offer an unconventional phase space for coupling magnetic and electric dipoles in magnetoelectric materials.

cond-mat.mtrl-sci↗

arXiv2025

Leveraging Foundational Models and Simple Fusion for Multi-modal Physiological Signal Analysis

Physiological signals such as electrocardiograms (ECG) and electroencephalograms (EEG) provide complementary insights into human health and cognition, yet multi-modal integration is challenging due to limited multi-modal labeled data, and modality-specific differences . In this work, we adapt the CBraMod encoder for large-scale self-supervised ECG pretraining, introducing a dual-masking strategy to capture intra- and inter-lead dependencies. To overcome the above challenges, we utilize a pre-trained CBraMod encoder for EEG and pre-train a symmetric ECG encoder, equipping each modality with a rich foundational representation. These representations are then fused via simple embedding concatenation, allowing the classification head to learn cross-modal interactions, together enabling effective downstream learning despite limited multi-modal supervision. Evaluated on emotion recognition, our approach achieves near state-of-the-art performance, demonstrating that carefully designed physiological encoders, even with straightforward fusion, substantially improve downstream performance. These results highlight the potential of foundation-model approaches to harness the holistic nature of physiological signals, enabling scalable, label-efficient, and generalizable solutions for healthcare and affective computing.

cs.LG↗

arXiv2023

The Benefit of Noise-Injection for Dynamic Gray-Box Model Creation

Gray-box models offer significant benefit over black-box approaches for equipment emulator development for equipment since their integration of physics provides more confidence in the model outside of the training domain. However, challenges such as model nonlinearity, unmodeled dynamics, and local minima introduce uncertainties into grey-box creation that contemporary approaches have failed to overcome, leading to their under-performance compared with black-box models. This paper seeks to address these uncertainties by injecting noise into the training dataset. This noise injection enriches the dataset and provides a measure of robustness against such uncertainties. A dynamic model for a water-to-water heat exchanger has been used as a demonstration case for this approach and tested using a pair of real devices with live data streaming. Compared to the unprocessed signal data, the application of noise injection resulted in a significant reduction in modeling error (root mean square error), decreasing from 0.68 to 0.27°C. This improvement amounts to a 60% enhancement when assessed on the training set, and improvements of 50% and 45% when validated against the test and validation sets, respectively.

cs.LG↗

arXiv2022

Fault Detection for Non-Condensing Boilers using Simulated Building Automation System Sensor Data

Building performance has been shown to degrade significantly after commissioning, resulting in increased energy consumption and associated greenhouse gas emissions. Continuous Commissioning using existing sensor networks and IoT devices has the potential to minimize this waste by continually identifying system degradation and re-tuning control strategies to adapt to real building performance. Due to its significant contribution to greenhouse gas emissions, the performance of gas boiler systems for building heating is critical. A review of boiler performance studies has been used to develop a set of common faults and degraded performance conditions, which have been integrated into a MATLAB/Simulink emulator. This resulted in a labeled dataset with approximately 10,000 simulations of steady-state performance for each of 14 non-condensing boilers. The collected data is used for training and testing fault classification using K-nearest neighbour, Decision tree, Random Forest, and Support Vector Machines. The results show that the Support Vector Machines method gave the best prediction accuracy, consistently exceeding 90%, and generalization across multiple boilers is not possible due to low classification accuracy.

eess.SP↗