The purpose of this paper is to describe the design of a Hybrid Series Active Power Filter (HSeAPF) system to improve the quality of power on three-phase power distribution grids. The system controls are comprise of Pulse Width Modulation (PWM) based on the Synchronous Reference Frame (SRF) theory, and supported by Phase Locked Loop (PLL) for generating the switching pulses to control a Voltage Source Converter (VSC). The DC link voltage is controlled by Non-Linear Sliding Mode Control (SMC) for faster response and to ensure that it is maintained at a constant value. When this voltage is compared with Proportional Integral (PI), then the improvements made can be shown. The function of HSeAPF control is to eliminate voltage fluctuations, voltage swell/sag, and prevent voltage/current harmonics are produced by both non-linear loads and small inverters connected to the distribution network. A digital Phase Locked Loop that generates frequencies and an oscillating phase-locked output signal controls the voltage. The results from the simulation indicate that the HSeAPF can effectively suppress the dynamic and harmonic reactive power compensation system. Also, the distribution network has a low Total Harmonic Distortion (< 5%), demonstrating that the designed system is efficient, which is an essential requirement when it comes to the IEEE-519 and IEC 61,000–3-6 standards.

The generation of active power in renewable energy is dependent on several factors. These variables are related to the areas of weather, physical structure, control, and load behavior. Estimating the future value of the active power to be generated is difficult due to their unpredictable character. However, because of the higher precision required of the estimation, this problem becomes more complex if we examine a short-term temporal prediction. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms to perform the Short-term estimate. The environment, the operation, and the generated (normal or faulty) signal are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been realized to conduct an experiment. In addition, the LSTM and the GRU are compared to see how well they perform in this system. The proposed method's end findings outperform the current state-of-the-art.

The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by long-short-term memory and gated recurrent unit algorithms are used to perform the short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures.

Background: Mapping spatially coherent tissue domains from spatial transcriptomics data is a prerequisite for characterizing cell-type, developmental patterning, and disease-associated disruptions of tissue architecture. While graph neural network (GNN) methods have substantially improved domain identification over expression-only clustering, the effect of edge feature design (i.e. how spatial connections between tissue spots are represented) has never been systematically evaluated. All existing methods use a single scalar edge weight, leaving open the question of whether multi-dimensional edge features provide any benefit. Methods: We introduce RMGAT, a Relational Multi-Scale Graph Attention Network that applies multi-dimensional learned edge features to spatial transcriptomics, to the best of our knowledge for the first time. Each spatial edge is described by an 8-dimensional learned feature vector. We combine this with a three-component training objective (graph reconstruction, NT-Xent contrastive learning, and a self-expression decoder) and a V30 post-processing pipeline using consensus clustering, Hungarian label alignment, and spatial refinement. Results: Evaluated on the 12-section DLPFC (human dorsolateral prefrontal cortex) benchmark across 60 independent runs (5 seeds × 12 DLPFC sections), RMGAT achieves a mean adjusted Rand index (ARI) of 0.3422 ± 0.0402 (95% confidence interval [0.332, 0.353]), comparable to SpaGCN (~ 0.36). A systematic four-component ablation study with formal statistical testing identifies GAT attention as the only statistically confirmed essential contributor (ΔARI = − 0.028 when replaced with a graph convolutional network, GCN; p < 0.0001). Contrastive learning shows a directional benefit (ΔARI = − 0.017; p = 0.024 vs. scalar-weight baseline). The 8-dimensional EdgeMLP and self-expression decoder are both neutral (ΔARI ≈ 0.004; both p > 0.3), indicating neither contributes at PCA-50 input quality. Conclusions: The primary contribution of this work is a systematic ablation study providing evidence-based design guidance for spatial transcriptomics GNNs. At PCA-50 input quality, GAT attention is the statistically confirmed critical component; contrastive learning is beneficial ( p = 0.024); multi-dimensional edge features and additional decoder objectives do not improve performance under this input regime. For practitioners, these findings support using GAT attention ( p < 0.0001) and contrastive learning ( p = 0.024, non-significant after Bonferroni correction), and investing in node feature quality over edge feature complexity.

The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures.

Labelled EEG data for epileptic seizure detection are scarce, and clinical recordings routinely suffer from realistic signal degradation including electrode loss, baseline drift, and sampling jitter. We present a proof-of-concept study applying contrastive self-supervised learning (SSL) to EEG epileptic seizure detection, comparing a SimCLR-style InfoNCE encoder pre-trained on unlabelled EEG against a supervised-only baseline sharing the same backbone, and evaluating both through a framework encompassing discrimination, operating-point behaviour, calibration, and physiology-aligned robustness. Stratified random 60/20/20 splits were applied (the Kaggle CHB-MIT derivative lacks patient identifiers; subject leakage is acknowledged as a limitation). On the held-out test set (11,233 segments, 23 channels, 6.26 s at 256 Hz, 6.8:1 imbalance), the SSL model achieves AUROC 0.990 (95% CI: 0.985–0.994) versus 0.989 (0.983–0.994) for the baseline, with overlapping CIs confirming statistical comparability. At threshold 0.5, SSL achieves higher recall (0.951 [0.925, 0.975] versus 0.924 [0.891, 0.953]) at the cost of lower precision (0.669 [0.626, 0.713] versus 0.839 [0.799, 0.878]); non-overlapping CIs confirm the precision gap; the recall advantage has overlapping CIs; both carry alarm-fatigue implications. The baseline is better calibrated on both ECE (0.027 [0.021, 0.034] versus 0.066 [0.057, 0.075]) and Brier score. In robustness tests, SSL is more robust to sampling jitter (± 20%: 0.968 [0.960, 0.976] versus 0.960 [0.947, 0.970]) while the baseline is more robust to strong drift (amplitude 0.40: 0.970 [0.960, 0.979] versus 0.913 [0.894, 0.931]); the drift gap has non-overlapping CIs, the jitter gap marginally overlapping CIs; both trace to augmentation design. Findings inform SSL augmentation design for future EEG monitoring work.