• WMCs are important brain mechanism in hypertension-related cognitive decline. • Periventricular or long-range tracts of WM were susceptible areas. • The effects of WMCs on different cognitive domains have spatial characteristic. • Meta-analyses confirmed the hypertension-WMCs-cognition link.

Introduction: Urban spatial planning is critical for the development of sustainable and livable cities. However, traditional planning methods often face challenges in handling complex planning scenarios and large-scale data.Methods: This paper introduces UrbanGenoGAN, a novel algorithm that integrates generative adversarial networks (GANs), genetic optimization algorithms (GOAs), and geographic information system (GIS) to address these challenges. Leveraging the generative power of GANs, the optimization capabilities of genetic algorithms, and the spatial analysis capabilities of GIS, UrbanGenoGAN is designed to generate optimized urban plans that cater to various urban planning challenges. Our methodology details the algorithm’s design and integration of its components, data collection and preprocessing, and the training and implementation processes.Results: Through rigorous evaluation metrics, comparative analysis with existing methodologies, and case studies, the proposed algorithm demonstrates significant improvement in urban planning outcomes. The research also explores the technical and practical considerations for implementing UrbanGenoGAN, including scalability, computational efficiency, data privacy, and ethical considerations.Discussion: The findings suggest that the integration of advanced machine learning and optimization techniques with spatial analysis offers a promising approach to enhancing decision-making in urban spatial planning. This work contributes to the growing field of AI applications in urban planning and paves the way for more efficient and sustainable urban development.

In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model’s prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R² value of 0.97 for unknown conditions.

Facial hallucination (FH) aims to construct super-resolution (SR) face images based on the corresponding low-resolution (LR) thumbnails. The desired results of FH require correct identity and photorealistic image quality, however, developing a method that satisfies both requirements remains challenging. Although the lifelike faces predicted by some existing methods, such as PULSE, are impressive, the loss of original identity is unacceptable. In view of the deficiency of PULSE, in this letter we incorporate a conditional constraint to its unsupervised pixel recovery process. Specifically, we embed a PULSE generator within the Laplacian pyramid framework and install a corresponding pre-trained conditional adversarial net (CGAN) at each pyramid level to predict the difference between the reconstructed SR image obtained by PULSE and the ground truth. The difference is added to the reconstructed SR image at each level to obtain a new SR image that is closer to the ground truth, such a process is repeated across the subsequent levels until a full-resolution version is yielded. Extensive experiments have demonstrated that our architecture can obtain a realistic high-resolution (HR) face with a correct identity, which is not possible obtained by other FH methods before.