Search Results (11,627)

We construct and analyze a family of support-defined Brauer-type configurations canonically associated with the Boolean geometry underlying the Grassmann algebra. The construction is governed by an x-support map on monomial labels, which identifies the vertex set with the Boolean lattice $\mathcal{P}\left(\right[n\left]\right)$. This identification yields a Boolean support quiver isomorphic to the directed Hasse diagram of $\mathcal{P}\left(\right[n\left]\right)$, equivalently, to an oriented hypercube. We then equip the family with a canonical cyclic ordering at each vertex and obtain a genuine connected reduced Brauer configuration in the standard sense, together with its associated Brauer configuration algebra and its standard Brauer quiver. A ghost-variable mechanism is introduced to obtain a connected realization without altering any support-controlled invariants. We prove that polygon membership, valencies, multiplicities, Boolean stratification, and the support quiver are invariant under support-preserving ghost relabelings. We also give an explicit description of the standard Brauer quiver and show that it is different from the Boolean support quiver. On the algebraic side, we derive closed formulas for the center dimension, the algebra dimension, and the normalization constant of the induced weighted distribution. On the probabilistic side, we distinguish the vertex entropy from the layer entropy, establish an exact decomposition of the former by Hamming layers, and show that the layer distribution is asymptotically concentrated on the middle layers, while extremal vertices and any fixed maximal path contribute a negligible fraction of the total weight. As a consequence, the layer entropy satisfies a logarithmic asymptotic law. We also investigate geometric consequences of the Boolean model transported through the support identification. Coordinate projections produce a rigidity phenomenon for antipodal pairs, providing a combinatorial analogue of Greenberger–Horne–Zeilinger (GHZ)-type fragility, whereas the first Boolean layer exhibits a persistence property analogous to W-type robustness. Together, these results exhibit a concrete bridge between Grassmann combinatorics, Brauer configuration theory, hypercube geometry, and entropy asymptotics. Full article

Against the backdrop of global industrial sustainable transition and the advancement of UN Sustainable Development Goals (SDGs), higher education―a core carrier of sustainable human capital supply―plays a pivotal role in adjusting majors to meet labor market demands, resolving education–industry structural mismatch, and boosting regional sustainable development. From the perspective of “higher education supporting industrial sustainable transition,” this study explores how government Policy Mix Intensity enhances universities’ Major–Industry Alignment and its transmission mechanism, aiming to reveal higher education governance’s sustainable development path. Using panel data from 30 Chinese provinces (2012–2023), we constructed a PMI quantitative index and conducted empirical analysis via a two-way fixed-effects model. The results show the following: (1) high-intensity policy mixes significantly improve alignment, overcoming university organizational inertia and laying an institutional foundation for sustainable education–industry synergy; (2) Policy Mix Intensity acts through three pathways―optimizing capital allocation, deepening industry–education integration, and enhancing dynamic responsiveness―forming a “sustainable factor allocation—sustainable industry-education alignment” logic; (3) policy efficacy is more pronounced in highly marketized Eastern regions and via regulatory tools, reflecting the moderating effect of regional sustainable endowments and policy tool types. This study provides empirical evidence for the “policy mix intensity–sustainable efficacy” transformation mechanism, offers theoretical references and empirical insights from China for the global collaborative realization of SDG4, SDG8, and SDG9 through higher education policy optimization, and proposes that policy design should shift toward factor integration-based sustainable comprehensive governance. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature–like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature–like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩~r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

This study addresses the growing necessity to understand how artificial intelligence (AI) competency and soft skills jointly influence organizational innovation and performance in the era of digital transformation. Despite the rapid adoption of AI technologies across industries, organizations continue to face significant challenges in effectively integrating technical AI capabilities with essential human-centric soft skills such as communication, adaptability, and leadership. This gap often limits the realization of AI-driven value and sustainable competitive advantage. The primary challenge in this research area is the lack of comprehensive models that simultaneously examine AI competency and soft skills within a unified framework, particularly in emerging economies where digital maturity varies widely. Existing studies tend to focus either on technical competencies or behavioral factors in isolation, leading to fragmented insights. To address these challenges, this study proposes a novel integrated research model that examines the combined effects of AI competency and soft skills on innovation outcomes and organizational performance. The model is empirically validated using structural equation modeling (SEM), providing robust evidence of the interrelationships among key constructs. The findings reveal that both AI competency and soft skills significantly contribute to innovation capability, which in turn enhances organizational performance. The study offers important theoretical and practical implications by bridging the gap between technical and human dimensions of AI adoption, thereby providing a more holistic understanding of digital transformation success. Full article

Many tertiary mathematics departments are seeking to improve equity in their programs; however, they may struggle to translate these goals for equity into action. This longitudinal, qualitative study focuses on a Networked Improvement Community (NIC) within the mathematics department at a public, doctoral degree-granting university located in the Southeast United States. This NIC worked together for two years (Spring 2023 to Spring 2025) to become more reflective practitioners and critically transform the mathematics program at their institution. We used Cultural Historical Activity Theory (CHAT) to examine relationships between objects, tools, and outcomes for the NIC. Data included multiple interviews and journals from eleven (n = 11) participants, and was triangulated with observer field notes of monthly NIC meetings. Thematic analysis revealed three pathways that connected NIC members’ individual and collective goals (objects), NIC activities and resources (tools), and NIC members’ perspectives on teaching and students (outcomes). We found that sometimes objects, mediated by tools, led to aligned outcomes, but not always. Specific tools could lead the NIC to adopt a new and collective object (and outcome). In other cases, the lack of the right tool led to unrealized outcomes or even secondary outcomes within the NIC. Ultimately, the critical transformations that NIC members envisioned were not realized; however, the experience of examining student data and discussing with colleagues shaped their thinking about teaching and students in impactful ways that inform faculty development for institutional change efforts on a broader scale. Our findings highlight the importance of identifying the right tools to support critical transformation, including the value of examining data as a collaborative group. We also extend NIC scholarship by using second-generation CHAT to distinguish objects over time and specify pathway models linking tools to outcomes. Full article

Coal preparation plants pursue maximum economic benefit, yet product structure optimization under fluctuating coal quality and changing market demand is a coupled decision-making problem involving the organization of primary products such as lump clean coal, clean coal, raw fine coal, coal slime, and gangue, together with commercial coal blending and process-scheme selection. Conventional optimization methods that focus on a single stage are often insufficient to address such complex coordinated decisions. To this end, a GPSOM–WOA nested optimization model was developed to achieve the coordinated optimization of primary product separation, commercial coal blending, and process-scheme selection under the objective of economic benefit maximization. In the outer layer, where process-scheme selection and primary product structure adjustment involve both discrete decisions and continuous variables, a simplified Group-based Particle Swarm Optimization with Multiple Strategies (GPSOM) was employed to search the primary product structure parameters and generate engineering-feasible primary product balance tables. In the inner layer, where the commercial coal blending problem is subject to multiple constraints, including ash content, moisture, calorific value, and supply demand, the Whale Optimization Algorithm (WOA) was adopted to optimize blending ratios within a restricted feasible region. A piecewise penalty function was introduced for quality-limit violations to support profit-oriented constrained optimization. Subject to commercial coal quality constraints on ash content, moisture, and calorific value, a case study of a coal preparation plant in Inner Mongolia was conducted to compare product structures and economic benefits under different process conditions. The results show that the proposed model can realize the joint optimization of primary product structure and commercial coal blending, and can provide a quantitative basis for product structure optimization and process selection in coal preparation plants. Full article

Densely packed semiconducting carbon nanotube (CNT) arrays with well-controlled morphology are highly desirable for high-performance CNT-based electronics. Although dielectrophoresis (DEP) enables precise, efficient, and site-selective assembly, increasing array density often destabilizes process regulation and aggravates nanotube bundling because of the dynamic interplay among assembly conditions. Here, we introduce the effective deposition region (EDR) to reformulate DEP assembly into a framework that links DEP conditions and final arrays through an interpretable CNT deposition dynamic based on the effective DEP capture. Within this framework, experiments and modeling indicate a self-regulating, negative-feedback mechanism in which conductive CNT bridging reduces the gap voltage, contracts the EDR, and weakens sustained CNT-capture capability, thereby driving the assembly toward self-termination. By synergistically optimizing the applied voltage, electrode configuration, and CNT dispersion concentration to regulate EDR contraction, we obtained dense, bundle-suppressed CNT arrays with the number of nanotubes per unit width of approximately 140 tubes µm⁻¹. The formation of small bundles implies that further combination of EDR-regulated assembly with additional inter-tube interactions is required to realize dense, monolayer CNT arrays. This work provides a coherent mechanistic framework for understanding feedback-regulated DEP assembly and enables a practical approach for optimizing both densification and morphology control in CNT array assembly. Full article

To solve the problems of low seeding precision and the poor operational adaptability of traditional no-till seeders under dense planting mode, and meet the agronomic requirements for high maize yield, this study carried out optimization and experimental research on the no-till precision fertilizer-seed co-sowing system for maize with wide-narrow row dense planting, relying on the experimental base of the Science and Technology Courtyard for Super High-Yield Cropping Systems in Qihe, China Agricultural University. Through modular integration and the optimization of key components, precise row spacing adjustment and improved sowing depth consistency in complex plots were achieved. A tractor-implement integrated a kinematic model and a dynamic model of the seed metering tube, which were constructed to quantify the correlation between operational parameters and motion states, providing theoretical support for structural parameter optimization. Field tests showed that all operational quality indicators of the system met the local high-yield requirements for no-till dense planting; the comprehensive performance was optimal at a density of 75,000 plants·ha⁻¹, with the best seeding uniformity (coefficient of variation: 5.65%), seedling emergence and seedling uniformity, which is well adapted to the agronomic characteristics of the wheat–maize rotation areas in the Huang-Huai-Hai Plain. Subsequent optimization by reducing the operating speed and increasing the spring stiffness can further improve the operational quality, realize the deep integration of agronomy and agricultural machinery, provide agricultural machinery support for high-yield and high-quality maize cultivation, and is of great significance for improving agricultural production efficiency and resource utilization. Full article

With the advancement of digital hydropower stations, the requirements of real-time, high-precision industrial soft measurement of key power equipment operating status are attracting more and more attention. However, it is difficult to transfer energy to the monitoring sensor in strong electromagnetic environments. In this paper, a high-efficiency, high-power-density magnetic field energy harvester is proposed for monitoring sensors in hydropower stations, which captures the energy from the magnetic flux leakage of a hydroelectric generating set. Efficient magnetic energy capture is achieved by modeling material properties and optimizing the receiver’s magnetic core parameters via a Genetic Algorithm. The theoretical analysis of charging characteristics is given, and a Maximum Power Point Tracking (MPPT) control circuit is proposed, realizing high-efficiency energy conversion. Finally, an experimental planet is built. Under 70–130 Gs power-frequency magnetic fields, the system delivers 2.8–5.1 V open-circuit voltage, 66 mW maximum load power, and 6.5 mW/cm³ power density. Full article

A micro-cavity based on phase-change material is a very important strategy for the realization of tunable absorption and conversion of terahertz waves. In this work, a tunable terahertz metamaterial absorber based on the phase-change material germanium–antimony–tellurium (GST) is demonstrated. The device features a metal–insulator–metal triple-layer structure, where the dynamic switching of absorption characteristics is achieved via thermally controlled GST phase transition. In the amorphous state, the absorber exhibits a single absorption peak at 7.7 THz. Upon crystallization, the absorption switches to dual peaks at 5.1 THz and 8.3 THz, achieving near-perfect absorption in both states. Full-wave electromagnetic simulations and theoretical analysis based on a multiple-reflection interference model indicate that this performance tuning originates from the GST-phase-transition-induced change in the equivalent optical cavity length. This corresponds to a switch between two resonant modes: coupled inner–outer ring resonance and independent outer ring resonance. These results provide a foundation for developing dynamically tunable terahertz devices with promising applications in terahertz communications, imaging, and sensing. Full article

To address the problems of unreasonable task allocation and low target search efficiency in the collaborative search of multiple unmanned undersea vehicles (UUVs) in complex marine environments, this paper proposes a search-information-driven collaborative task planning method for multi-UUV systems, and constructs a systematic and integrated multi-UUV collaborative task planning framework. Considering the spatial characteristics of the complex underwater environment and sonar detection rules, an underwater task environment grid model and an active sonar instantaneous detection model are constructed as the environmental and detection foundation of the framework. Within the framework, the Gaussian Mixture Model (GMM) is adopted to realize dynamic division of task regions, and reasonable resource allocation among multiple UUVs is achieved by defining scientific area allocation indicators. A search information map consisting of target probability distribution and environmental uncertainty is established, and a receding horizon planning framework is introduced to balance short-term detection effectiveness and long-term search value. Furthermore, a motion-coded Grey Wolf Optimization (GWO) algorithm is proposed to generate continuous UUV paths, which avoids path discontinuity caused by discrete grids and ensures the convergence efficiency of the algorithm. Simulation results verify that compared with traditional methods, the proposed method improves the total probability benefit by 19.87% and the number of discovered targets by 18.29%, demonstrating better search performance and environmental adaptability. Full article

To address the problems of insufficient precision reserve, limited rotational speed, and excessive temperature rise in high-speed ultra-precision machine tool spindle bearings, the influence of frictional power loss on the thermo-mechanical behavior of the bearing system was investigated. Firstly, based on the analysis of the heat source of the bearing, the friction power consumption model of the bearing assembly is established, and the analysis of the bearing temperature field is realized by studying the heat energy transfer. Secondly, the test bench is built for experimental verification. Finally, through the study of thermal-mechanical coupling performance, the influence of different rotational speeds on bearing stress and life is analyzed. The results show that the friction power consumption generated by the spin sliding of the bearing rolling element accounts for the largest proportion, accounting for 31% of the total friction power consumption; the increase in bearing speed will increase the bearing temperature. At 55,000 r/min, the highest temperature at the rolling element is close to 75 °C, followed by the inner ring up to 68 °C, and the lowest outer ring temperature is 57 °C. The temperature has a great influence on the bearing performance. Under the same working conditions, the equivalent stress is increased by 21%, the contact pressure is increased by 25%, and the fatigue life of the bearing is reduced by 5.6%. Bearing performance is significantly affected by thermodynamic behavior. Full article

Softmax is a step in transformer computation during which the internal buffer size grows rapidly because of the use of the exponential function. Softmax is a fundamental yet computationally expensive operation in vision transformer attention, posing significant challenges for deployment on resource-constrained FPGAs (Field Programmable Gate Arrays). Computational precision demands grow at the softmax stage in the attention pipeline mainly because of the use of the exponential function in the softmax computation. This paper proposes a low-precision softmax approximation that combines a truncated Maclaurin-series exponential with input-range clamping to enable efficient hardware realization without sacrificing reconstruction quality. By bounding extreme attention scores that contribute negligibly to final outputs, the proposed method mitigates the instability of low-order polynomial approximations while preserving their hardware efficiency. The approach is first validated in software using SwinIR (Image restoration using the SWIN Transformer) super resolution to ensure reconstruction fidelity and is then analyzed for FPGA deployment. SWINIR is a multi-stage version of other transformers like Deit and Vit, making it a preferred option for testing the reconstruction fidelity of the change for transformers. Experimental results demonstrate that the proposed fourth-order clamped approximation achieves near-reference performance, incurring only 0.15 dB PSNR and 0.0059 SSIM degradation on SwinIR-M, while significantly reducing precision and memory requirements. For the large-sized SWINIR model (SWINIR-L), a PSNR increase with a less than 0.01 SSIM loss is observed, further highlighting the insignificance of extreme values as model size gets bigger. A Horner-form reformulation further improves hardware efficiency by limiting intermediate precision growth. Overall, this work presents a reconstruction-aware and hardware-friendly softmax reformulation that enables practical deployment of vision transformers on small FPGA platforms. This work also uses this contribution to improve the performance of the ViTA accelerator design. We also add bias initialization and a PE loop bound runtime variable to the existing ViTA accelerator design. Full article

The simulation of synthetic aperture radar (SAR) echo signals usually relies on complex hardware equipment and a large amount of scene data, which results in high costs and low efficiency. In order to simulate SAR echo signals of ship targets in the sea quickly and accurately in complex environments at a lower cost, this paper proposes a SAR echo simulation method based on model segmentation and electromagnetic scattering characteristic simulation. This method first implements the simulation of sea models under different sea conditions based on PM wave spectrum model and the Monte Carlo method, and segments them according to the requirements of simulation resolution. Then, it uses Python API 3.11 in Blender 4.5 to segment the ship model automatically and optimize the visible surface elements and mesh for each sub-model. Next, it uses Lua API in Feko to simulate the electromagnetic scattering characteristics of each sub-model of the sea and the ship target automatically, and obtains the required radar cross section (RCS) data of the ship target in the sea after processing. Finally, SAR echo simulation is realized through dual-channel technology. To further verify the simulation result, the chirp scaling (CS) algorithm is used for imaging processing. The results show that this method can realize SAR echo simulation of various ship targets under different sea conditions in a quick, accurate and cost-effective manner without the need for any hardware equipment. Full article

Unmanned Aerial Vehicles (UAVs) are complex nonlinear systems characterized by high dimensionality. They are prone to aerodynamic effects, structural dynamics, actuation constraints, and networked interactions, requiring advanced mathematical models and precise control. Their governing equations involve nonlinear rigid-body dynamics coupled with fluid and elasticity models, while modern architectures introduce redundancy that creates constrained mappings between generalized forces and actuator inputs. Coordinated UAV teams add another layer of mathematical structure through graph-based interaction models that determine consensus, formation keeping, and distributed stability. These characteristics give rise to several interconnected challenges. High-fidelity aerodynamic and aeroelastic solvers provide accurate results; however, these are computationally intensive, motivating the development of reduced-order models and data-driven approximations that preserve dominant physical behavior. Methods for quantifying uncertainty support robustness assessments by characterizing the effects of parametric variation and model form error. At the actuation level, control allocation problems rely on constrained linear algebra, convex optimization, and dynamic formulations to ensure feasible and stable realization of command forces and moments. In multi-agent systems, the spectral properties of adjacency and Laplacian matrices govern convergence and cooperative behavior. This article reviews the state of the art in these areas, highlights the mathematical foundations that relate them, and provides a coherent perspective on the methods that enable reliable modeling and control of modern UAV systems. Full article