This study focuses on the production of sulfate-resistant Portland cement clinker using local raw materials found in the Aral Sea region of the Republic of Uzbekistan and on its properties. The obtained results highlight the technological, economic, and environmental advantages. The raw material mixtures consisted of limestone, barren sand, kaolin, and iron-containing industrial waste, resulting in a clinker with an optimized oxide composition (LSF = 0.82, SR = 2.15, AP = 0.94). X-ray phase analysis reveals a high content of alite (C3S = 38.6%) and belite (C2S = 41.0%) phases, which ensures high mechanical strength. Low tricalcium aluminate content (tricalcium aluminate < 5%) and sufficient ferrite content (C(4)AF = 16.2%) increased sulfate resistance and phase dominance. Sulfate resistance tests conducted in accordance with ASTM C1012/C1012M showed that sulfate-resistant Portland cement had the lowest swelling index among samples immersed in a 5% Na2SO4 solution, demonstrating its superior stability compared to conventional Portland cement and mixed cement types. The experimental results demonstrate the feasibility of producing high-quality sulfate-resistant Portland cement clinker using local raw materials and demonstrate its potential for use in the sulfate-rich conditions of the Aral Sea region. From an economic perspective, the use of local raw materials reduces production costs, the need for imports, and increases the competitiveness of the cement industry in our region. From an environmental perspective, the long service life of sulfate-resistant Portland cement and the ability to utilize industrial waste align with the principles of a "green economy" and promote waste reduction and sustainable growth.
In intelligent education, personalized mathematics question generation aims to produce mathematics questions that satisfy educational requirements while supporting adaptive assessment and learning. Existing LLM-based single-agent and multi-agent methods improve generation flexibility, but they still tend to rely on aggregated feedback or model randomness, making it difficult to jointly ensure dimension-wise objective alignment and controllable diversity. To address these challenges, we propose EduAgentQG, a multi-agent collaborative framework for personalized mathematics question generation with explicit diversity and objective-aware evaluation. EduAgentQG organizes question generation as a closed-loop process of planning, writing, evaluation, refinement, and checking: structured generation plans and multiple generation directions guide candidate generation, while fine-grained evaluation verifies logical correctness, solvability, and objective alignment in knowledge concepts, difficulty, grade level, and core competencies. We first construct a mathematics question generation benchmark containing 10,273 questions across Grades 1-9, covering 634 knowledge concepts, 16 core competencies, and three difficulty levels; for evaluation, it is organized into two subsets: MathChoice, with 489 educational objectives for multiple-choice question generation, and MathBlank, with 500 educational objectives for fill-in-the-blank question generation. Experiments using Gemini-2.5-Flash, GPT-4o-mini, and Qwen2.5-72B-Instruct show that EduAgentQG consistently outperforms COT, COTN, ReAct, and EQPR in diversity, Objective Consistency, and Win Rate. On Gemini-2.5-Flash, EduAgentQG improves Win Rate by 3.90 and 4.75 percentage points on MathChoice and MathBlank, respectively, over the strongest baseline, demonstrating its ability to generate diverse, solvable, and educationally aligned personalized mathematics questions with stronger controllability.
The dislocation behaviors in body-centered cubic (BCC) multi-principal element alloys (MPEAs) containing ordered phases remain incompletely understood. This study elucidates the dislocation-dominated deformation mechanism in the Al-12(HfNbTiZr)(88) with ordered phases on the scale of tens to one hundred nanometers. Advanced techniques, including HAADF-STEM, 3D APT, and DFT calculations, were employed to reveal the presence of substantial B2-type ordered phases coherently embedded in the BCC matrix. In-situ TEM observations combined with molecular dynamics simulations demonstrate that individual dislocations pair into superdislocations within the ordered phases. Interestingly, these superdislocations can readily enter and exit the ordered phases, without forming the pronounced pile-ups typically observed at ordered phase-matrix interfaces. Notably, these superdislocations frequently undergo cross-slip from the primary {110}< 111 > to the secondary {112}< 111 > slip system upon encountering pronounced local lattice friction. Such intriguing superdislocation behaviors enable the Al-12(HfNbTiZr)(88) alloy to achieve a good balance between strength and ductility, compared with the chemically disordered HfNbTiZr, short-range-ordered Al-8(HfNbTiZr)(92) (the size of the ordered phase < 1 nm), and intermetallic compound Al-16(HfNbTiZr)(84) (the size of the ordered phase > 500 nm). These findings deepen our understanding of dynamic dislocation slip behavior and elucidate the critical influence of nanoscale ordering on plastic deformation in BCC MPEAs.
We revisit Byzantine-robust distributed estimation for high-dimensional sparse linear models. By combining local ℓ1-regularized robust estimation with robust aggregation at the server, the resulting framework applies to pseudo-Huber regression, quantile regression, and sparse SVM. We show that the resulting estimators yield non-asymptotic guarantees and attain near-optimal statistical rates under mild conditions while remaining communication-efficient. Simulations confirm robust estimation, support recovery, and classification accuracy under various Byzantine attacks.
The integration of converter-interfaced generation introduces new transient stability challenges to modern power systems. Classical Lyapunov- and scalable passivity-based approaches typically rely on restrictive assumptions, and finding storage functions for large grids is generally considered intractable. Furthermore, most methods require an accurate grid dynamics model. To address these challenges, we propose a model-free, nonlinear, and dissipativity-based controller which, when applied to grid-connected virtual synchronous generators (VSGs), enhances power system transient stability. Using input-state data, we train neural networks to learn dissipativity-characterizing matrices that yield stabilizing controllers. Furthermore, we incorporate cost function shaping to improve the performance with respect to the user-specified objectives. Numerical results on a modified, all-VSG Kundur two-area power system validate the effectiveness of the proposed approach.
