Driven by rapid advancements in interconnection, packaging, integration, and computing technologies, parallel and distributed systems have significantly evolved in recent years. These systems have become essential for addressing modern computational demands, offering enhanced processing power, scalability, and resource efficiency. This paper provides a comprehensive overview of parallel and distributed systems, exploring their interrelationships, their key distinctions, and the emerging trends shaping their evolution. We analyse four parallel computing paradigms—heterogeneous computing, quantum computing, neuromorphic computing, and optical computing—and examine emerging distributed systems such as blockchain, serverless computing, and cloud-native architectures. The associated challenges are highlighted, and potential future directions are outlined. This work serves as a valuable resource for researchers and practitioners aiming to stay informed about trends in parallel and distributed computing while understanding the challenges and future developments in the field.

In this paper, we propose and implement an EEG-based three-dimensional Convolutional Neural Network architecture, ‘AnxietyDecoder’, to predict anxious personality and decode its potential biomarkers from the participants. Since Goal-Conflict-Specific-Rhythmicity (GCSR) in the EEG is a sign of an anxiety-related system working, we first propose a two-dimensional Conflict-focused CNN (2-D CNN). It simulates the GCSR extraction process but with the advantages of automatic frequency band selection and functional contrast calculation optimization, thus providing more comprehensive trait anxiety predictions. Then, to generate more targeted hierarchical features from local spatio-temporal scale to global, we propose a three-dimensional Conflict-focused CNN (3-D CNN), which simultaneously integrates information in the temporal and brain-topology-related spatial dimensions. In addition, we embed Layer-wise Relevance Propagation (LRP) into our model to reveal the essential brain areas that are correlated to anxious personality. The experimental results show that the percentage variance accounted for by our three-dimensional Conflict-focused CNN is 33%, which is almost four times higher than the previous theoretically derived GCSR contrast (7%). Meanwhile, it also outperforms the 2-D model (26%) and the t-test difference between the 3-D and 2-D models is significant (t(4) = 5.4962, p = 0.0053). What’s more, the reverse engineering results provide an interpretable way to understand the prediction decision-making and participants’ anxiety personality. Our proposed AnxietyDecoder not only sets a new benchmark for EEG-based anxiety prediction but also reveals essential EEG components that contribute to the decision-making, and thus sheds some light on the anxiety biomarker research.

The basolateral amygdala (BLA) is crucial for the encoding and expression of fear memory, yet it remains unexplored how neural activity in this region is dynamically influenced by distributed circuits across the brain to facilitate expression of fear memory of different ages. Using longitudinal multisite electrophysiological recordings in male mice, we find that the recall of older contextual fear memory is accompanied by weaker, yet more rhythmic, BLA gamma activity which is distally entrained by theta oscillations in both the hippocampal CA1 and the anterior cingulate cortex. Computational modeling with Light Gradient Boosting Machine using extracted oscillatory features from these three regions, as well as with Transformer using raw local field potentials, accurately classified remote from recent memory recall primarily based on BLA gamma and CA1 theta. These results demonstrate in a non-biased manner that multi-regional control of BLA activity serves as reliable neural signatures for memory age-dependent recall mechanisms.

Driven by rapid advancements in interconnection, packaging, integration, and computing technologies, parallel and distributed systems have significantly evolved in recent years. These systems have become essential for addressing modern computational demands, offering enhanced processing power, scalability, and resource efficiency. This paper provides a comprehensive overview of parallel and distributed systems, exploring their interrelationships, key distinctions, and the emerging trends shaping their evolution. We analyze four parallel computing paradigms—heterogeneous computing, quantum computing, neuromorphic computing, and optical computing—and examine emerging distributed systems such as blockchain, serverless computing, and cloud-native architectures. The associated challenges are highlighted, and potential future directions are outlined. This work serves as a valuable resource for researchers and practitioners aiming to stay informed about trends in parallel and distributed computing while understanding the challenges and future developments in the field.