The Internet of Things (IoT) demands scalable, secure, and feeless distributed ledger technologies (DLTs) to enable seamless machine-to-machine transactions. The IOTA DLT was developed to fulfill this vision through its feeless Directed Acyclic Graph (DAG) named the Tangle, whose announced upgrade to IOTA 2.0 promised feeless microtransactions and coordinator-free (Coordicide) decentralization via a Nakamoto Consensus mechanism and a Mana anti-spam system. However, its delayed decentralization and scalability limitations hindered ecosystem growth and practical IoT adoption, leading to a new ledger architecture named IOTA Rebased. This paper critically analyzes this architectural pivot and its implications for IoT applications, contrasting the abandoned IOTA 2.0 protocol—a leaderless, feeless DAG designed for the IoT—with the adoption of a Move Virtual Machine-based, object-oriented ledger secured by a Delegated Proof-of-Stake consensus via the Mysticeti protocol in IOTA Rebased. We evaluate IOTA Rebased trade-offs: enhanced programmability and speed versus compromised IoT suitability due to fees, and explore mitigation strategies such as sponsored transactions, lightweight clients, and hierarchical tiered transaction architecture to align IOTA Rebased with IoT environments where microtransactions are prevalent. A use case analysis is provided for the integration of IOTA Rebased in IoT scenarios. This study underscores the tension between technological innovation and decentralization, offering insights for balancing scalability with the unique demands of the IoT.

Rising electricity demand and the need to reduce pollutant emissions highlight the importance of renewable energy, especially solar power. While most studies on photovoltaic (PV) integration focus on developed countries, least developed and developing countries such as the Democratic Republic of Congo (DRC) face particular challenges due to fragile grid infrastructure. This work evaluates the technical and operational impacts of PV integration into the western grid of the DRC using DIgSILENT PowerFactory 2021 SP2 simulations. It examines penetration levels from 10% to 50% based on a 2012 MW baseline, and evaluates power losses, short-circuit ratios (SCRs), grid stability, harmonic distortions, and voltage oscillations. Results reveal that moderate penetration levels (10–20%) reduce active power losses by 25% while maintaining stability. However, above 30% penetration, critical challenges arise, including a drop of the SCR below the minimum recommended value of 3, prolonged voltage oscillations, and increased harmonic distortions, resulting from the reduced overall inertia of the grid following the increase in PV power from inverters without inertia. These findings emphasize the need for targeted solutions like Battery Energy Storage Systems (BESSs), Static Synchronous Compensators (STATCOMs), and harmonic filters. This work provides foundational insights for PV integration in fragile grids of LDCs and developing countries.

Sentiment analysis is vital for evaluating user feedback in drug reviews because understanding patient experiences leads to more personalized treatment recommendations by providing insights into the real-world effectiveness and tolerability of medications, which are often overlooked in clinical trials. This study evaluates the effectiveness of word-level and sentence-level embeddings for feature extraction in sentiment analysis. These embeddings are used in sequential models (Bi-LSTM, CNN) and non-sequential models (Random Forest, DNN, ExtraTreesClassifier). The Random Forest model with LLM2Vec achieves the best performance, with 0.93 accuracy, F1-scores of 0.95 (positive) and 0.88 (negative), and precision scores of 0.93 (positive) and 0.94 (negative). This approach detects subtle negative feedback often missed by standard models. To capture social consensus in patient feedback, we introduce Adaptive Confidence-Weighted Scoring. This method leverages social validation as an implicit confidence signal, enabling sentiment scores to reflect both individual experiences and community agreement. It enhances trust and interpretability while standardizing sentiment polarity on a $[-5, +5]$ scale. Clinically validated drug-related information, scraped from trusted sources, is encoded using the Llama-3.2-3B-Instruct model to extract context-aware representations. Features derived from this external medical knowledge improve semantic understanding, ensure grounding and safety, and enhance robustness even when reviews are sparse or noisy. The final vectors are employed within a cosine similarity framework to recommend relevant drugs, aligning recommendations with user satisfaction. This work demonstrates how LLM-based feature engineering can advance clinically valid, patient-aligned healthcare recommender systems informed by real-world feedback.

Introduction: Motor Imagery (MI) Electroencephalography (EEG)-based control in online Brain-Computer Interfaces requires decisions to be made within short temporal windows. However, the majority of published Deep Learning (DL) EEG decoders are developed and validated offline on public datasets using longer window lengths, leaving their real-time applicability unclear. Methods: To address this gap, we evaluate 10 representative DL decoders, including convolutional neural networks (CNNs), filter-bank CNNs, temporal convolutional networks (TCNs), and attention- and Transformer-based hybrids-under a soft real-time protocol using 2-s windows. We quantify performance using accuracy, sensitivity, precision, miss-as-neutral rate (MANR), false-alarm rate (FAR), information-transfer rate (ITR), and workload. To relate decoder behavior to physiological markers, we examine lateralization indices, mu-band power at C3 vs. C4, and topographical contrasts between MI and neutral conditions. Results: Results show shifts in performance ranking between offline and online BCI settings, along with a pronounced increase in inter-subject variability. Best online means were FBLight ConvNet 71.7% (±2.1) and EEG-TCNet 70.0% (±5.3), with attention/Transformer designs less stable. Errors were mainly Left-Right swaps while Neutral was comparatively stable. Lateralization indices/topomaps revealed subject-specific μ/β patterns consistent with class-wise precision/sensitivity. Discussion: Compact spectro-temporal CNN backbones combined with lightweight temporal context (such as TCNs or dilated convolutions) deliver more stable performance under short-time windows, whereas deeper attention and Transformer architectures are more susceptible to variation across subjects and sessions. This study establishes a reproducible benchmark and provides actionable guidance for designing and calibrating online-first EEG decoders that remain robust under real-world, short-time constraints.

Electroencephalography (EEG) data acquisition process in Brain-Computer Interfaces (BCIs) is inevitably affected by uncertainty which introduces variability in the data. This variability, often over-looked, affects the training and testing of neural network (NN) models. This study evaluates the impact of systematic bias (±2%) plus aleatoric uncertainty (2–5% random Gaussian perturbations) on the classification confidence of the EEGNet model for four class-Motor Imagery (MI) tasks using the BCI Competition IV 2a dataset. Through two Monte Carlo simulations with 100 iterations each, perturbed datasets were generated to mimic real-world EEG acquisition uncertainties. Softmax outputs served to analyze overlap in predicted probabilities and quantify model confidence in classification decisions. We introduce robust evaluation metrics, including the proportion of area under the curve (AUC) of probability density functions (PDFs) ≥ 70% accuracy, overlap coefficients, and percentile-based thresholding, which provide a more comprehensive assessment of model performance, capturing not only accuracy but also confidence and ambiguity in predictions. Results show that the robustness of EEGNet in the face of realistic measurement uncertainties is prone to inter-subject variability, with the model achieving higher confidence for Subject 1 (average 90.52%) compared to Subject 2 (62.96%). EEGNet demonstrates resilience to directional calibration shifts in the data, with model confidence varying by 0.22% for Subject 1 and by 6.24% for Subject 2, showing that aleatoric precision errors dominate over small systematic shifts. Our approach provides a rigorous framework for quantifying the impact of measurement variability on EEG-based BCI classification, thereby enhancing reliability and generalizability in practical BCI deployments.

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is widely recognized as an effective multi-criteria decision-making algorithm for handover management in terrestrial cellular networks, especially in scenarios involving dynamic and multi-faceted criteria. While TOPSIS is widely adopted in terrestrial cellular networks for handover management, its application in satellite networks, particularly in Low Earth Orbit (LEO) constellations, remains limited and underexplored. In this work, the performance of three TOPSIS algorithms is evaluated for handover management in LEO satellite networks, where efficient handover management is crucial due to rapid changes in satellite positions and network conditions. Sensitivity analysis is conducted on Standard Deviation TOPSIS (SD-TOPSIS), Entropy-TOPSIS, and Importance-TOPSIS in the context of LEO satellite networks, assessing their responsiveness to small variations in key performance metrics such as upload speed, download speed, ping, and packet loss. This study uses real-world data from “Starlink-on-the-road-Dataset”. Results show that SD-TOPSIS effectively optimizes handover management in dynamic LEO satellite networks thanks to its lower standard deviation scores and reduced score variation rate, thus demonstrating superior stability and lower sensitivity to small variations in performance metrics values compared to both Entropy-TOPSIS and Importance-TOPSIS. This ensures more consistent decision-making, avoidance of unnecessary handovers, and enhanced robustness in rapidly-changing network conditions, making it particularly suitable for real-time services that require stable, low-latency, and reliable connectivity.