Advanced Planning and Scheduling (APS) systems have become indispensable for modern manufacturing operations, enabling optimized resource allocation and production efficiency in increasingly complex and dynamic environments. While algorithms for solving abstracted scheduling problems have been extensively investigated, the critical prerequisite of specifying manufacturing requirements into formal constraints remains manual and labor-intensive. Although recent advances of generative models, particularly Large Language Models (LLMs), show promise in automating constraint specification from heterogeneous raw manufacturing data, their direct application faces challenges due to natural language ambiguity, non-deterministic outputs, and limited domain-specific knowledge. This paper presents a constraint-centric architecture that regulates LLMs to perform reliable automated constraint specification for production scheduling. The architecture defines a hierarchical structural space organized across three levels, implemented through domain-specific representation to ensure precision and reliability while maintaining flexibility. Furthermore, an automated production scenario adaptation algorithm is designed and deployed to efficiently customize the architecture for specific manufacturing configurations. Experimental results demonstrate that the proposed approach successfully balances the generative capabilities of LLMs with the reliability requirements of manufacturing systems, significantly outperforming pure LLM-based approaches in constraint specification tasks.

Self-driving laboratories have begun to replace human experimenters in performing single experimental skills or predetermined experimental protocols. However, as the pace of idea iteration in scientific research has been intensified by Artificial Intelligence, the demand for rapid design of new protocols for new discoveries become evident. Efforts to automate protocol design have been initiated, but the capabilities of knowledge-based machine designers, such as Large Language Models, have not been fully elicited, probably for the absence of a systematic representation of experimental knowledge, as opposed to isolated, flatten pieces of information. To tackle this issue, we propose a multi-faceted, multi-scale representation, where instance actions, generalized operations, and product flow models are hierarchically encapsulated using Domain-Specific Languages. We further develop a data-driven algorithm based on non-parametric modeling that autonomously customizes these representations for specific domains. The proposed representation is equipped with various machine designers to manage protocol design tasks, including planning, modification, and adjustment. The results demonstrate that the proposed method could effectively complement Large Language Models in the protocol design process, serving as an auxiliary module in the realm of machine-assisted scientific exploration.

Industrial designers have long sought a natural and intuitive way to achieve the targeted control of prototype models---using simple natural language instructions to configure and adjust the models seamlessly according to their intentions, without relying on complex modeling commands. While Large Language Models have shown promise in this area, their potential for controlling prototype models through language remains partially underutilized. This limitation stems from gaps between designers' languages and modeling languages, including mismatch in abstraction levels, fluctuation in semantic precision, and divergence in lexical scopes. To bridge these gaps, we propose an interface architecture that serves as a medium between the two languages. Grounded in design principles derived from a systematic investigation of fast prototyping practices, we devise the interface's operational mechanism and develop an algorithm for its automated domain specification. Both machine-based evaluations and human studies on fast prototyping across various product design domains demonstrate the interface's potential to function as an auxiliary module for Large Language Models, enabling precise and effective targeted control of prototype models.

This paper addresses the challenge of how enterprises can achieve optimal economic benefits under the green development model. To tackle this, the study introduces an innovative digital marketing prediction model based on neural networks, specifically utilizing convolutional neural networks (CNNs) to forecast the outcomes of digital marketing strategies within the context of green development. The model integrates linear regression techniques to predict the economic impact of digital marketing efforts on enterprise performance. By incorporating green development principles into the neural network framework, the research explores how sustainability-focused strategies can enhance both marketing effectiveness and profitability. Experimental results demonstrate that the proposed model increases enterprise sales performance by 27.8%, highlighting the substantial economic benefits of combining digital marketing with green development strategies. This paper contributes a novel approach to integrating AI-driven marketing prediction with sustainable business practices, offering valuable insights into the intersection of digital innovation and green growth.

Recent development in Artificial Intelligence (AI) models has propelled their application in scientific discovery, but the validation and exploration of these discoveries require subsequent empirical experimentation. The concept of self-driving laboratories promises to automate and thus boost the experimental process following AI-driven discoveries. However, the transition of experimental protocols, originally crafted for human comprehension, into formats interpretable by machines presents significant challenges, which, within the context of specific expert domain, encompass the necessity for structured as opposed to natural language, the imperative for explicit rather than tacit knowledge, and the preservation of causality and consistency throughout protocol steps. Presently, the task of protocol translation predominantly requires the manual and labor-intensive involvement of domain experts and information technology specialists, rendering the process time-intensive. To address these issues, we propose a framework that automates the protocol translation process through a three-stage workflow, which incrementally constructs Protocol Dependence Graphs (PDGs) that approach structured on the syntax level, completed on the semantics level, and linked on the execution level. Quantitative and qualitative evaluations have demonstrated its performance at par with that of human experts, underscoring its potential to significantly expedite and democratize the process of scientific discovery by elevating the automation capabilities within self-driving laboratories.

To obtain accurate classification results of polarimetric SAR images in different heterogeneity areas, a novel unsupervised classification method is proposed, which combines an advanced distribution with spatial contextual information based on stochastic expectation maximization (SEM) algorithm. Specifically, the probabilities of class membership are calculated by L distribution, and a neighborhood function is defined to describe spatial contextual information. Then the probabilities of class membership are altered by the predefined neighborhood function via probabilistic label relaxation (PLR) technique. Moreover, RADARSAT-2 and EMISAR data are used to verify the effectiveness of the proposed method. The experiment results show it can accurately classify different heterogeneity areas and obtain more consistent results compared with other algorithms.

Entropy, alpha, and anisotropy (H/α̅/A) of Cloude decomposition are effective in polarimetric SAR image understanding and geophysical information inversion. As an incoherent target decomposition, the inner sample covariance matrix estimation severely affects the estimated parameters. The contradiction between details preservation and accurate parameters estimation is still a challenge task. In this article, we propose adaptive coherency matrix estimation based on local heterogeneity coefficients, and utilize it to parameters estimation of Cloude decomposition. The results were evaluated with respect to details preservation and the accuracy of parameters estimation. The results of this work were analyzed by means of AIRSAR data.

In the process of BOF(basic oxygen furnace) steelmaking, in order to figure out and optimize the amount of alloy addition during deoxidizing and alloying of tapping, optimization model of this process is very indispensable. For this reason, multi-objective optimization model of alloy addition is developed. Firstly, the prediction model of ladle element content is established with Support Vector Machine. One objective function of multi- objective problem is the error between regulations and the result of the prediction model, the other objective function is the cost of alloy addition. And the alloy addition is optimized with modified dynamic neighborhood multi-objective Particle Swarm Optimization so as to compute the Pareto optimal solution set effectively. The final amount of alloy added is decided according to the practical need.