Brain-computer interfaces (BCIs) have received considerable attention in gaming, enabling innovative interactions with digital environments. Visual Evoked Potentials (VEPs)—robust, noninvasive neural responses to visual stimuli—offer high information transfer rates, making them particularly promising. This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, examines VEP-based BCIs in gaming. We searched the Web of Science and Google Scholar, identifying 16 347 studies from the past decade, with 46 selected for in-depth analysis after rigorous screening. The review explores VEP response modeling, electroencephalography (EEG) signal acquisition and processing, stimulation paradigms, and their gaming applications. These systems enhance accessibility for players with physical or cognitive impairments, support adaptive difficulty scaling, personalize gameplay, aid neurorehabilitation, and enable multiplayer interactions. However, challenges remain, including technical limitations, complex data interpretation, user adaptability, and ergonomic issues. Advances in signal processing, personalized calibration, and hybrid multimodal approaches could improve usability. Future research should focus on integrating VEP-based BCIs with emerging technologies, optimizing user comfort, and developing adaptive interaction models to enhance immersion and accessibility. By addressing these challenges and utilizing neuroscience and computational advancements, VEP-based BCIs promise to transform gaming into a more inclusive and immersive experience for diverse users.

Brain-Computer Interfaces (BCIs) leverage brain signals to facilitate communication and control, particularly benefiting individuals with motor impairments. Motor imagery (MI)-based BCIs, utilizing non-invasive electroencephalography (EEG), face challenges due to high signal variability, noise, and class overlap. Deep learning architectures, such as CNNs and LSTMs, have improved EEG classification but still struggle to fully capture discriminative features for overlapping motor imagery classes. This study introduces a hybrid deep neural architecture that integrates Convolutional Neural Networks, Long Short-Term Memory networks, and a novel SVM-enhanced attention mechanism. The proposed method embeds the margin maximization objective of Support Vector Machines directly into the self-attention computation to improve interclass separability during feature learning. We evaluate our model on four benchmark datasets: Physionet, Weibo, BCI Competition IV 2a, and 2b, using a Leave-One-Subject-Out (LOSO) protocol to ensure robustness and generalizability. Results demonstrate consistent improvements in classification accuracy, F1-score, and sensitivity compared to conventional attention mechanisms and baseline CNN-LSTM models. Additionally, the model significantly reduces computational cost, supporting real-time BCI applications. Our findings highlight the potential of SVM-enhanced attention to improve EEG decoding performance by enforcing feature relevance and geometric class separability simultaneously.

We developed a comprehensive multiplexed set of primers adapted for the Oxford Nanopore Rapid Barcoding library kit that allows universal SARS-CoV-2 genome sequencing. This primer set is designed to set up any variants of the primers pool for whole-genome sequencing of SARS-CoV-2 using single- or double-tiled amplicons from 1.2 to 4.8 kb with the Oxford Nanopore. This multiplexed set of primers is also applicable for tasks like targeted SARS-CoV-2 genome sequencing. We proposed here an optimized protocol to synthesize cDNA using Maxima H Minus Reverse Transcriptase with a set of SARS-CoV-2 specific primers, which has high yields of cDNA template for RNA and is capable of long-length cDNA synthesis from a wide range of RNA amounts and quality. The proposed protocol allows whole-genome sequencing of the SARS-CoV-2 virus with tiled amplicons up to 4.8 kb on low-titer virus samples and even where RNA degradation has occurred. This protocol reduces the time and cost from RNA to genome sequence compared to the Midnight multiplex PCR method for SARS-CoV-2 genome sequencing using the Oxford Nanopore.

Background: Coordinating differential-drive mobile robots for landmark coverage is challenging due to non-holonomic dynamics, clutter, and sparse rewards. Standard multi-agent RL pipelines often show unstable learning and inconsistent completion in this setting. Methodology: We adopt a centralized-training, decentralized-execution actor-critic without inter-agent commu nication. Our replay-centric design combines a tagged buffer that up-samples goal-reaching transitions and an offline replay initialization that seeds early learning with curated trajectories. Dynamic task assignment uses the Hungarian algorithm during training and evaluation, and we benchmark against uniform replay and established variants. Results: In a cluttered six-robot arena, the approach improves training stability relative to uniform replay. Convergence is faster and requires fewer updates to reach consistent success. Coverage efficiency increases as landmarks are reached earlier across runs. Collisions per episode decrease without adding communication or architectural changes. Multi seed evaluations show gains that persist with narrow confidence intervals. Train-evaluation gaps shrink on unseen maps, indicating improved generalization. Ablations attribute complementary ben efits to the tagged and offline components. Performance remains competitive with prioritized and hindsight replay baselines under matched budgets. Computational overhead is small because sam pling logic changes while network sizes do not. Conclusions: Focus ing on replay design substantially stabilizes multi-agent learning for differential-drive coordination. The pipeline integrates cleanly with standard CTDE implementations and supports practical deployment in coverage tasks.

Maintaining a specific geometric pattern is essential in various applications where groups of autonomous robots must follow a given path. Proper organization of the geometric pattern can lead to several benefits such as cost reduction, increased system reliability, and efficiency while providing a reconfigurable and flexible structure of the system. Military missions and traffic systems are examples where maintaining certain geometric patterns are widely used. However, little is known about how to develop an effective algorithm that guarantees collision avoidance and obstacle avoidance while maintaining the geometric pattern. This paper presents an algorithm for movement with a certain geometric structure of a group of autonomous mobile robots that maintains the required geometric pattern and ensures the avoidance of collisions and obstacles. The proposed algorithm is behavior-based and utilizes a set of rules that allow the robots to navigate around obstacles and avoid collisions. The algorithm's performance is demonstrated through simulations in a variety of scenarios with different numbers of robots and geometric patterns. The algorithm proposed in this paper provides an effective solution for controlling a group of autonomous mobile robots to maintain a certain geometric pattern. The proposed algorithm has the potential to be utilized in numerous applications where multiple robots must work together to achieve a common goal while maintaining a specific formation. The use of behavior-based approach and obstacle avoidance rules ensures that the robots avoid collisions and obstacles while maintaining the required pattern

Software-defined networking (SDN) requires adaptive control strategies to handle dynamic traffic conditions and heterogeneous network environments. Reinforcement learning (RL) has emerged as a promising solution, yet deep RL methods often face instability, non-stationarity, and reproducibility challenges that limit practical deployment. To address these issues, a kernel-based RL framework is introduced, embedding transition dynamics into reproducing kernel Hilbert spaces (RKHS) and combining kernel ridge regression with policy iteration. This approach enables stable value estimation, enhanced sample efficiency, and interpretability, making it suitable for large-scale and evolving SDN scenarios. Experimental evaluation demonstrates consistent convergence and robustness under traffic variability, with cumulative rewards exceeding those of baseline deep RL methods by more than 22%. The findings highlight the potential of kernel-embedded RL as a practical and theoretically grounded solution for adaptive SDN management and contribute to the broader development of intelligent systems in complex environments.