Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex ...

Researchers have developed a novel generative AI model, called Collaborative Competitive Agents (CCA), that significantly ...

Learn the Ralph cycle for smarter AI work with Claude Code, using a bash script, a task plan, and staying within the 30–60% ...

ABSTRACT: In this paper, variational iteration method and He-Laplace method are used to solve the nonlinear ordinary and partial differential equations. Laplace transformation with the homotopy ...

Introduction: Optimizing fracturing parameters under multi-factor, complex conditions remains challenging in low-permeability reservoirs. Methods: We extract stage-aware construction-curve features, ...

In order to accurately describe the impact of the volatility and randomness of renewable energy output power on the operation of industrial park microgrids, a data-driven robust optimization method ...

Traditional approaches to analytical method optimization (e.g., univariate and “guess-and-check”) can be time-consuming, costly, and often fail to identify true optima within the parameter space.

Conclusions: Combining multiple methods guided by the ISR framework and elements of the Design Thinking Process can help researchers and developers leverage the strengths of mixed-method iterative ...

As large language models (LLMs) scale in size and capability, the choice of pretraining data remains a critical determinant of downstream performance. Most LLMs are trained on large, web-scale ...

ABSTRACT: The alternating direction method of multipliers (ADMM) and its symmetric version are efficient for minimizing two-block separable problems with linear constraints. However, both ADMM and ...