Neurodegenerative diseases, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), present a growing public health challenge globally. Recent advancements in neurotechnology and neuroengineering have significantly enhanced brain–computer interfaces, artificial intelligence, and organoid technologies, making them pivotal instruments for diagnosis, monitoring, disease modeling, treatment development, and rehabilitation of various diseases. Nonetheless, the majority of neural interface platforms focus on unidirectional control paradigms, neglecting the need for co-adaptive systems where both the human user and the interface continually learn and adapt. This selected review consolidates information from neuroscience, artificial intelligence, and organoid engineering to identify the conceptual underpinnings of co-adaptive and symbiotic human–machine interaction. We emphasize significant shortcomings in the advancement of long-term AI-facilitated co-adaptation, which permits individualized diagnostics and progression tracking in Alzheimer’s disease and Parkinson’s disease. We concentrate on incorporating deep learning for adaptive decoding, reinforcement learning for bidirectional feedback, and hybrid organoid–brain–computer interface platforms to mimic disease dynamics and expedite therapy discoveries. This study outlines the trends and limitations of the topics at hand, proposing a research framework for next-generation AI-enhanced neural interfaces targeting neurodegenerative diseases and neurological disorders that are both technologically sophisticated and clinically viable, while adhering to ethical standards.

Traffic congestion and inefficiencies in transportation networks pose significant challenges to road safety, travel times, and environmental sustainability. Traditional traffic management systems, typically reliant on sparse sensor data and rigid models, often fail to provide accurate, reliable, and user-friendly insights. This paper introduces a novel Physics-Informed Neural Network-Based Traffic State Estimator (PINN-TSE), framework that integrates the Aw-Rascle traffic flow model with advanced machine learning and natural language processing (NLP) techniques. By combining physics-informed modeling with data-driven learning, the framework ensures accurate and physically consistent predictions of traffic density and velocity. A multicomponent loss function balances data fidelity with physical constraints, while Large Language Models (LLMs) generate contextualized and interpretable traffic insights through a chat-based web interface. The system is designed to handle diverse user queries from precise spatio-temporal inputs to broad, general inquiries, making it highly adaptable for real-world deployment. Validated on real-world data from the US-101 highway, PINN-TSE demonstrated strong performance in capturing shockwave dynamics and transitions between traffic regimes. It achieved mean absolute errors (MAE) of 2.4 vehicles per mile (vpm) for density and 3.98 mph for velocity, representing improvements of 60% and 73%, respectively, over purely data-driven models. Furthermore, the shockwave speed error was reduced to 8%, significantly improving the reliability of traffic dynamic predictions. The system’s ability to provide actionable insights, such as identifying congestion hotspots and suggesting alternative routes, highlights its practical utility in real-world traffic management. This work makes three key contributions: (1) a robust PINN-TSE framework that embeds physical laws into neural networks, (2) an intuitive LLM-powered interface for real-time traffic interaction, and (3) a demonstration of its effectiveness in real-world settings. By bridging the gap between complex traffic data and human decision-making, this study advances the field of intelligent transportation systems, offering a transformative solution to safer, more efficient, and sustainable traffic management.

The growing demand for efficient energy management has become essential for achieving sustainable development across social, economic, and environmental sectors. Accurate energy demand forecasting plays a pivotal role in energy management. However, energy demand data present unique challenges due to their complex characteristics, such as multi-seasonality, hidden structures, long-range dependency, irregularities, volatilities, and nonlinear patterns, making energy demand forecasting challenging. We propose a hybrid dimension reduction deep learning algorithm, Temporal Variational Residual Network (TVRN), to address these challenges and enhance forecasting performance. This model integrates variational autoencoders (VAEs), Residual Neural Networks (ResNets), and Bidirectional Long Short-Term Memory (BiLSTM) networks. TVRN employs VAEs for dimensionality reduction and noise filtering, ResNets to capture local, mid-level, and global features while tackling gradient vanishing issues in deeper networks, and BiLSTM to leverage past and future contexts for dynamic and accurate predictions. The performance of the proposed model is evaluated using energy consumption data, showing a significant improvement over traditional deep learning and hybrid models. For hourly forecasting, TVRN reduces root mean square error and mean absolute error, ranging from 19% to 86% compared to other models. Similarly, for daily energy consumption forecasting, this method outperforms existing models with an improvement in root mean square error and mean absolute error ranging from 30% to 95%. The proposed model significantly enhances the accuracy of energy demand forecasting by effectively addressing the complexities of multi-seasonality, hidden structures, and nonlinearity.

The growing demand for efficient energy management has become essential for achieving sustainable development across social, economic, and environmental sectors. Accurate energy demand forecasting plays a pivotal role in energy management. However, energy demand data presents unique challenges due to its complex characteristics, such as multi-seasonality, hidden structures, long-range dependency, irregularities, volatilities, and nonlinear patterns, making energy demand forecasting challenging. We propose a hybrid dimension reduction deep learning algorithm, Temporal Variational Residual Network (TVRN), to address these challenges and enhance forecasting performance. This model integrates Variational Autoencoders (VAEs), Residual Neural Networks (ResNets), and Bidirectional Long Short-Term Memory Networks (BiLSTM). TVRN employs VAEs for dimensionality reduction and noise filtering, ResNets to capture local, mid-level, and global features while tackling gradient vanishing issues in deeper networks, and BiLSTM to leverage past and future contexts for dynamic and accurate predictions. The performance of the proposed model is evaluated using energy consumption data, showing a significant improvement over traditional deep learning and hybrid models. For hourly forecasting, TVRN reduces root mean square error and mean absolute error, ranging from 19\% to 86\% compared to other models. Similarly, for daily energy consumption forecasting, this method outperforms existing models with an improvement in root mean square error and mean absolute error ranging from 30\% to 95\%. The proposed model significantly enhances the accuracy of energy demand forecasting by effectively addressing the complexities of multi-seasonality, hidden structures, and nonlinearity.