The current state of research on software-defined control systems was reviewed， and the role and development of control systems throughout the industrial revolutions were analyzed. The intelligent development direction for software-defined control systems was proposed. The case study of a software-defined end-edge-cloud collaborative PID（proportional-integral-derivative） tuning intelligence system was presented， which demonstrates that the tight conjoining and coordination between industrial artificial intelligence， industrial Internet， and other new-generation information technologies with software‐defined control systems has opened up a new way for the development of software-defined intelligent control systems. Finally， the principal research directions for software-defined intelligent control systems were pointed out by considering the challenges faced by software-defined control systems and those specific to their intelligent transformation.

Energy routers （ERs） are one of the core components of the energy Internet for achieving multi-port energy conversion and active energy flow control. This paper classified ERs into three categories： electrical ERs， information ERs， and multi-energy ERs. Based on the differences between these categories， the research on ERs is divided into four aspects： electrical conversion， focusing on topology and control of multi-port electrical conversion； energy routing control， primarily concerned with the regulation of power flow between ports of ERs； information processing and optimal control， emphasizing the acquisition and transmission of information and optimizing energy flow； and multi-energy coordination， with multi-energy comprehensive utilization as the main goal. Based on these four research aspects， this paper explored topology， control， communication， and multi-energy optimization of ERs， as well as the interrelationships between different aspects.

As a key development direction integrating new-generation information technology with advanced manufacturing techniques， industrial intelligence leverages intelligent， digital， and automated methods to significantly enhance industrial production efficiency and optimize the prediction and maintenance management of industrial equipment. This paper focuses on the intelligentization of industrial equipment， with the goal of ensuring the efficient and stable operation of power transformers within power systems. A digital twin model for transformer inter-turn short circuit faults is constructed based on electromagnetic field equations and equivalent circuit models. The model analyzes the symmetry of the transformer in both normal and fault conditions from an electromagnetic field perspective， thereby integrating digital twin technology with fault diagnosis. Furthermore， through in-depth analysis of the virtual model of the transformer， the location of faults is accurately identified， ensuring the safe operation of the transformer， improving its reliability and efficiency， and advancing the intelligentization and modernization of the entire power system.

An optimal control strategy considering the state of charge （SoC） of energy storage is proposed for an isolated DC microgrid for hydrogen production system composed of renewable energy， electrolytic cell， and energy storage equipment. Firstly， the characteristics of hydrogen production efficiency of alkaline electrolyzers are analyzed， and an optimal control method for adaptive adjustment of hydrogen production efficiency with bus voltage change is proposed. By coordinating with the energy storage system， the hydrogen production efficiency is kept within a high range. When the SoC of energy storage violates the upper and lower limits， a communication-independent SoC active recovery control strategy is designed to ensure the safe operation of the energy storage system. Secondly， a power coordinated control strategy considering extreme conditions is designed to ensure the stable operation of the DC microgrid through flexible switching between various operating modes. Finally， the effectiveness of the proposed control strategy is verified by MATLAB/Simulink simulation platform.

To address the issue of high data dependency and insufficient accuracy in mixed gas identification for traditional semiconductor gas sensors， a ConvGRUAttention network model that integrates gated recurrent units （GRU）， convolutional layers， and attention mechanism is proposed. Empirical wavelet transform （EWT） is employed to convert raw signals into the time-frequency domain and perform multi-scale decomposition， which suppresses noise， reduces data dependency， and enhances the model’s robustness. The model extracts local dynamic features through convolutional layers， captures long-term dependencies using GRU， and optimizes feature weights across multi-scale signals via the attention mechanism， thereby improving feature extraction and generalization capabilities. Experimental results demonstrate 100% accuracy in qualitative identification and a root mean square error （RMSE） of 3.3×10⁻⁶ in quantitative detection. Compared with the traditional methods， the detection accuracy for mixed gases is significantly improved.

The cable tunnel is closed and narrow， with repetitively laid cable racks and similar scene textures， which is a degraded scenario. To address this environment， a visual-inertial SLAM （simultaneous localization and mapping） algorithm based on point-line feature fusion is proposed. The algorithm improves the high-dimensional line features through length suppression and short line fitting to make it more effective in describing the structural features of tunnel scene. In addition， for the problem of loop closure detection failure due to feature similarity in cable tunnels， ArUco markers with efficient recognition and accurate pose estimation are introduced to limit the loop closure area， and the optimal loop closure frames are selected using the minimized pose transformation to improve detection accuracy and localization precision. Finally， dataset collection and experimental validation were conducted in actual cable tunnels. The results show that the absolute trajectory accuracy of the algorithm is improved by 69.73% on average relative to VINS–Mono（visual intertial system-Mono）， which meets the application requirements of cable tunnel inspection.

In recent years， compounded crises such as geopolitical conflicts （e.g.， the Russia-Ukraine conflict） and technological containment （e.g.， the China-U.S. trade friction） have continuously exerted a profound impact worldwide， revealing the vulnerabilities of global supply chains. Enhancing the supply chain resilience has become a critical strategy to ensure the sustainable development of countries around the world， especially China， and it serves as a vital foundation for making China strong in manufacturing. On this basis， existing research on supply chain resilience was comprehensively reviewed， with particular focus on its origins， conceptual definitions， and driving factors. The evolution of the research was systematically analyzed， and prospective research directions were explored from four dimensions： collaborative optimization， resource allocation， dynamic response， and risk management. The findings aim to provide theoretical support and decision reference for enhancing supply chain resilience both globally and within China.

The output performance of assembly lines is not only affected by machine unreliability and limited buffer capacity but also constrained by line-side buffers. The analytical modeling and performance evaluation of multi-stage assembly lines with line-side buffers were investigated. Firstly， for the single-stage assembly lines， the steady-state probability distribution of system states was derived based on Markov chains. Secondly， for the two-stage assembly lines， each single-stage subsystem was modeled as a machine with one operational state and one failure state. A performance evaluation model was then established using Markov chains， and closed-form expressions for performance indicators were obtained. Thirdly， for the multi-stage assembly lines， an aggregation method was proposed to approximate the performance indicators. Furthermore， the accuracy of the performance evaluation method was validated through numerical experiments. Finally， utilizing the proposed method， numerical experiments were conducted to examine system properties， such as reversibility and monotonicity in the multi-stage assembly lines.

Based on an analysis of the extensive and profound impact of the development of artificial intelligence on manufacturing technology， it is expounded that intelligent manufacturing is the main direction for building a strong manufacturing country， and its important roles are discussed in the construction of a new industrialization system. Through an introduction to intelligent manufacturing empowerment technology， it is claimed that intelligent manufacturing technology by the deep integration of intelligent technology and advanced manufacturing technology will drive the progress of the new round of industrial revolution and industrial transfor-mation. Subsequently， typical research results and application examples of intelligent manufacturing are used to clarify the essential differences and engineering applications between intelligent manufacturing and traditional automation. Finally， it is pointed out that the advanced production mode of the deep integration of new generation information technology and advanced manufacturing technology demonstrates a new form of future manufacturing industry.

High-performance rolling bearings， as essential components of major equipment， are increasingly in demand for intelligent capabilities in fields such as wind power， engineering machinery， and rail transportation. Firstly，the technical characteristics of smart bearings are analyzed， and the related research progress and development trends at home and abroad are summarized. Then， the system composition， working principle and key technologies of the smart bearing embedded perception microsystem are mainly discussed， including integrated functional-structural design， sensing mechanisms and digital sensing technology， precision manufacturing and assembly processes， as well as performance testing and experimental evaluation. Finally， the future development trends and application potential of smart bearing technology are expounded and predicted， providing theoretical and practical guidance for technological innovation and industrialization in related fields.

For the Weibull distribution parameter estimation， a pseudo-estimator of scale parameters is constructed， and the estimated parameter values can be obtained by finding the extreme point of relevant variables based on the principle that the right location parameter and shape parameter minimize the diversity of the scale parameter estimates associated with individual sample values. Essentially， parameter estimation extracts（overall）information based on specific patterns reflected by a set of data with uncertainty （random variable samples）. However， the pattern is statistical in nature rather than deterministic. In terms of the occurrence of extreme points in the related functions， the exact value of the estimater does not necessarily occur at the extreme point in a deterministic extreme point. It is shown that there is typically a deviation between the point where the exact parameter is located and the theoretical extreme point， and the accuracy and robustness of the parameter estimation method can be greatly improved by introducing an offset value in the minimum value criterion （modifying “the first derivative being equal to zero” to “the first derivative being equal to a value greater than zero”）. A large number of parameter estimation cases show that the range of the estimated value of the Weibull location parameter （true value is 1 000） is narrowed from 0~1 500 to 500~1 550 by taking an offset value of 0.1.

With the advancement of the digital information era， the digital transformation of blast furnaces has begun. Steel enterprises have applied intelligent closed-loop control， digital twins， and AI-based predictive models to develop intelligent systems for smart blast furnace operation， blast furnace condition assessment， and quality optimization. Research on digital blast furnaces primarily focuses on variable prediction， state diagnosis， and blast furnace condition optimization， with these domains evolving from traditional approaches toward complex optimization modeling， multidimensional comprehensive evaluation， and multi-objective collaborative optimization， respectively. However， current predictive models require enhanced online self-updating and integration of data and mechanisms； evaluation systems need to emphasize multidimensional and fine-grained diagnostics， and blast furnace condition optimization has to overcome single-indicator limitations by focusing on low-risk， low-cost， and multi-objective coupled strategies. According to the actual needs of the blast furnace site， a physical system of blast furnace information was developed， where data， mechanisms， and experience were reasonably matched and called upon to form an integrated technology encompassing data governance， rule mining， intelligent prediction， comprehensive evaluation， multi-objective optimization， and decision feedback， which was identified as one of the key directions for future development of digital blast furnace ironmaking.

Large-sized high-performance metal products serve as the foundation and precursor for the development of high-end equipment manufacturing industries. However， traditional preparation methods for large-sized casting billets in China commonly suffer from casting defects such as inclusions， segregation， coarse grains， and cracks， leading to issues like low metallurgical quality and poor yield. These problems render the billets unable to meet the processing and manufacturing requirements of large-sized high-end metal products. Therefore， developing new metallurgical quality control technologies for large-sized casting billets and overcoming challenges in achieving purification， homogenization， grain refinement structure， and low-stress casting are effective pathways to break through these bottlenecks. After over two decades of collaborative research， industry， and academia cooperation and practical application， by utilizing the unique effects of electromagnetic fields during large-sized billet preparation， Northeastern University has precisely controlled the solidification behavior during the preparation process and accomplished a full-chain innovation in electromagnetic control technologies.It has established an electromagnetic control theory for large-sized metal billet preparation and developed core electromagnetic control technologies and equipment. These advancements have successfully produced a series of large-sized and high-quality steel and magnesium alloy， as well as aluminum alloy billets， driving progress in the preparation technologies of high-end materials.

Hydrogen-based shaft furnace process can significantly reduce CO₂ emission， which is an effective way for low-carbon and green development of iron and steel industry. In this study， the reaction mechanism of H₂ and CO with Fe₂O₃ in the hydrogen-based shaft furnace reduction process was investigated in depth based on density functional theory（DFT）. The results show that the most stable adsorption configuration of H₂ molecule has an adsorption energy of -1.65 eV and the CO molecule has an adsorption energy of -2.10 eV， which is favorable for the adsorption of CO molecule. The energy barrier of H₂ molecule for the reaction is 0.64 eV， and CO molecule has an energy barrier of 1.40 eV， which is favorable for the reaction of H₂ molecule with Fe₂O₃ in the kinetic. Increasing temperature is unfavorable for the adsorption of gas molecules， but favoring the kinetics of reduction reaction. And increasing temperature can compensate for the thermodynamic disadvantage of the adsorption and reaction of H₂ molecules. The operating pressure should be increased， while the reduction temperature can be increased appropriately to accelerate the reaction rate， but the adsorption efficiency should be ensured for hydrogen-rich or pure hydrogen shaft furnace.

In response to the problem of insufficient automation and intelligence in the microseismic monitoring and early warning for rock bursts during tunnelling boring machine （TBM） excavation of deep metal mines， research on multi-dimensional parameter recognition of drilling holes based on deep machine vision DPED-AT method was conducted； automatic disassembly and assembly device for microseismic sensors was developed， and the decision-making system was designed， achieving automatic disassembly and assembly of microseismic sensors during TBM excavation. Microseismic intelligent frequency conversion acquisition technology was developed， realizing continuous and high-fidelity acquisition of rock rupture information during the rock burst incubation process. An improved neural network algorithm for identifying and picking up rupture signals was proposed， as well as a three-dimensional characterization algorithm for the probability field of microseismic sources induced by rock bursts incubation. Intelligent interpretation and refined early warning of rock burst incubation information during TBM excavation were preliminarily realized， and an intelligent monitoring and early warning technology system for rock burst that integrated intelligent drilling hole recognition， automatic sensor disassembly and assembly， and intelligent signal acquisition and interpretation was ultimately established. The application in Ruihai Gold Mine shows that it has achieved automatic microseismic monitoring， interpretation， and early warning of rock burst， providing strong support for less manned and unmanned TBM excavation in deep metal mines.

In order to improve the accuracy of mine slope displacement prediction， a mine slope displacement prediction model based on an improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN）， least squares fitting method and long short-term memory network integrated into the attention mechanism was proposed. Firstly， considering the temporal and nonlinear characteristics of the mining slope displacement monitoring data， an improved mode decomposition method was employed to perform the time-frequency decomposition of cumulative displacement， resulting in trend， periodic， and random components， thereby effectively reducing the data complexity. Secondly， to predict the trend component， a cubic polynomial regression prediction model was developed by using the least squares fitting method. To predict the periodic component， an attention mechanism was introduced to distinguish the importance of displacement data at different times， which effectively captured the internal dependencies within the long-term displacement sequences. Finally， the predicted results of the trend and periodic components were integrated to obtain the cumulative displacement of the mining slope. Taking the slope of Julong Copper Mine in Xizang as an example， the performance of the proposed method was tested. The results demonstrate that the proposed slope displacement prediction model achieves a root mean square error （RMSE） of 5.99 mm and a mean absolute percentage error （MAPE） of 5.94%. The RMSE and MAPE decrease by 51.30% and 55.17% compared with the traditional LSTM model， respectively. These results highlight the significant improvement in prediction accuracy achieved by the proposed method.