We develop the theory of ħ-vertex algebras, algebraic structures closely related to vertex algebras but with a deformed translation covariance axiom. We establish their structure theory, including analogues of Goddard's Uniqueness Theorem, the Reconstruction Theorem, Borcherds Identity, and the OPE Expansion Formula, and introduce the associated notions of ħ-Lie conformal and ħ-Poisson vertex algebras. The formalism provides a natural and simplified construction of the Zhu algebra. The main application is to the chiralization of classical star-products: we show that every star-product on the symmetric algebra of a Lie algebra (or its central extensions) admits a chiralization, and we derive explicit formulae for these chiral star-products, including the Moyal–Weyl and Gutt star-products. Setting ħ = 0 recovers explicit deformation quantizations of a broad class of Poisson vertex algebras, including the classical limits of free-boson, βγ-system, affine, and Virasoro vertex algebras.

To enhance venous thromboembolism (VTE) prevention and management in county-level hospitals, we developed a VTE quality control platform leveraging clinical databases and Clinician Workstations. The platform is characterised by simplified construction, cost-effectiveness and a user-friendly interface enabling precise real-time monitoring of VTE prevention measures in hospitalised patients. Comparative analysis of pre-implementation and post implementation data revealed significant improvements in key metrics: VTE bleeding risk assessment rate (1.33% vs 7.43%, p<0.001), basic prevention compliance (20.87% vs 50.38%, p<0.001), mechanical prevention utilisation (24.60% vs 27.37%, p=0.002) and appropriate prevention rate (11.67% vs 53.11%, p<0.001). The platform effectively optimised VTE prevention practices, demonstrating ease of deployment, affordability and operational efficiency-making it a scalable and practical solution for county-level hospitals in China.

MoS2-supported multiepitope protein electrochemical biosensors: A simple and highly selective approach for the diagnosis of systemic mycoses.

Simplified metabolic pathway design for efficient production in halotolerant microbial systems.

In real-world online education environments, automated analysis of student learning engagement faces fundamental challenges. While existing models score well on specific test sets, they are often computationally sluggish and poorly adaptable when confronted with lengthy classroom videos and highly variable student behavioral habits. The core of the problem is that traditional models are prone to taking "shortcuts": mistaking students' innate habits for evidence of disengagement. Meanwhile, models like Transformers are constrained by their prohibitive computational costs. To solve these problems, this paper proposes a new framework, i.e., MC-MIFA, which combines efficient sequence modeling with causal reasoning methods. Specifically, in the feature extraction part, we designed a novel Interleaved MambaVision backbone. Through hierarchical processing and spatio-temporal compression, this network effectively captures long-range global information in videos while maintaining near-linear computational complexity. In the decision logic part, this framework does not stop at simple data fitting but constructs engagement analysis as a causally-motivated structured model. By introducing independence constraints and feature disentanglement techniques, we forcibly separate the core content representing engagement from the bias representing personal habits in the high-dimensional feature space. Then, using a counterfactual dynamic fusion mechanism, we intervene at the moment the model makes a decision, forcing the question: "If we disregard this student's specific sitting habit, is their true thinking state still focused?" Experimental results on multiple challenging datasets, including DAiSEE, UBFC-Engagement, and SEED-IV, prove that MC-MIFA is not only highly competitive in accuracy and mean absolute error but also shows minimal performance degradation when facing entirely new students.

The stiffened deep cement mixing (SDCM) pile, a composite structure comprising a reinforced core pile and cemented soil, has gained extensive application in geotechnical engineering due to its superior bearing capacity and simplified construction procedures. Although the construction of SDCM piles is more environmentally friendly and greener than that of bored piles, the generation of substantial waste slurry during construction processes remains a significant ecological concern. Therefore, this study utilizes the slurry discharged during construction to configure cemented soil to recycle the waste slurry and reduce the slurry pollution. A field static load test with core pile being a pipe pile was conducted to investigate the bearing performance and load transfer mechanism of the slurry-modified SDCM pile. Based on the test, a two-dimensional coupled discrete-element and finite-difference (DEM-FDM) model was developed considering three different core pile types (pipe pile, square pile, and nodular pile). By setting monitoring points in the DEM-FDM model, the relative displacement variation patterns of core pile–cemented soil interface with depth were obtained for different core pile types, and a principle of relative displacement superposition to explain these findings was proposed. Additionally, interface shear simulations under different vertical stresses were carried out, and the constitutive model of core pile–cemented soil interface skin friction–relative displacement was obtained to provide assistance for the bearing capacity calculation and design of SDCM piles.

The present study was a nonequivalent pretest-posttest control group design investigating the effects of corpus-based instruction on grammatical accuracy and written application of past simple forms of habitual expressions. An experimental group was exposed to two weeks of corpus-based instruction as compared to control group that had been exposed to a conventional grammar instruction. Quantitative and qualitative analysis of written tests, along with comparisons of Ethiopian Students Corpus to the British National Corpus was carried out. Common learner mistakes, including subject omission, misuse of used to, and overextension of would to single past occurrences were identified. The two groups showed improvement on the post-test, but the group taught through corpus-based instruction demonstrated significantly stronger improvement and a smaller number of errors: - overgeneralisation and subject-verb agreement issues were observed. This showed that the corpus-based instruction can be used to make accurate and contextually appropriate command of past simple habitual constructions in writing despite the small technological drawbacks.

We describe and discuss invertebrate trace fossils originating from a limited, less than 2 m-thick sequence of sandstone beds from the Emsian Klerf Formation of the Olkenbach Syncline (SE Eifel) in Germany. The trace fossil suites are characterized by vertical and horizontal dwelling or feeding burrows or trails with relatively simple constructions and show a low to moderate ichnodiversity (about 15 ichnotaxa), although some ichnotaxa are abundant. The following ichnogenera are recorded: ?Arenicolites (and possibly Skolithos), Conichnus, Gyrochorte, Helminthopsis, Lockeia, Nereites, Palaeophycus, Planolites, Psammichnites, Ptychoplasma, Rosselia, and Spirophyton. Lockeia alfbachensis isp. nov. is proposed for a resting trace probably produced by bivalves. The overall geological and sedimentological context, the presence of adjacent mudstones with non-fully marine biota, low amount of bioturbation, and a sporadically heterogeneous distribution of the trace fossils suggest a stressed environmental setting, possibly induced by reduced salinity and/or subjected to episodic erosion and deposition. We propose a marginal-marine, deltaic setting, probably a distal tidal flat, as sedimentary environment for the trace fossil-bearing bedset under consideration. urn:lsid:zoobank.org:pub:49D0E07E-622F-4DDF-AD30-C7C8DE67B8EE

Abstract In current academic research and engineering applications, ultra-wideband sound absorption has long been one of the most challenging directions in the field of acoustics. In response to the aforementioned issues, this paper proposes a double-layer perforated panel-based acoustic metamaterial with variable-scale cavities. First, a theoretical model of the sound absorption coefficient of the double-layer perforated plate-double-cavity (DPP-DC) unit is established using the transfer matrix method. A DPP-DC unit achieving perfect sound absorption at 468 Hz was investigated using the complex frequency plane analysis approach. Subsequently, relying on the theoretical model and the multi-unit composite local coupling effect, a multi-order resonant composite metamaterial was constructed by connecting four perfect absorption units in parallel. The results indicate that the coupling effect caused by the increased composite structure area undermines the perfect sound absorption performance of each unit. To address the issue of excessive loss, the influence of different geometric parameters on the sound absorption characteristics of the DPP-DC unit was investigated. To further broaden the effective sound absorption bandwidth, DPP-DC units were engineered to achieve under-loss sound absorption at nine discontinuous frequencies. The resulting NDPP-DC structure, formed by combining nine DPP-DC units, achieves a high sound absorption coefficient exceeding 0.80 over a wide frequency range from 484 to 732 Hz. The broadband sound absorption mechanism was analyzed based on a finite element model. Finally, samples were manufactured via 3D printing technology. The experimental results confirm that the NDPP-DC structure achieves a sound absorption coefficient exceeding 0.8 in the frequency range of 481–730 Hz, in broad agreement with the theoretical and simulation results. The proposed NDPP-DC structure features lightweight properties, high-efficiency sound absorption across a broad frequency range, and simple construction, providing a theoretical foundation and practical reference for mid-to-low frequency broadband noise control technology.

Cable dome structures are innovative architectural forms known for their architectural and structural advantages, making them popular in academic research and practical applications worldwide. Cable dome structures, both single-strut and multiple-strut types, are categorized into nine distinct types. This paper introduces a novel dome structure, the “sunflower three-strut cable dome,” which exhibits advantageous geometric features and mechanical properties when compared to existing cable dome types. It surpasses them in terms of topology, component and node count, structural mechanical attributes, and construction simplicity. Furthermore, no cable dome structure can exist without prestress, making the prestressed state a critical mechanical property. We establish general recursion formulas for internal forces in each component based on nodal equilibrium equations. Two practical design tables for the initial prestress state, tailored to various geometric parameters, are presented by these formulas. Subsequently, we analyze the distribution of prestressed states and tensioning characteristics both theoretically and experimentally. This study provides a theoretical foundation for designing and constructing the new multiple-strut cable dome.

Isomaltulose is produced via sucrose isomerase catalysis by Serratia plymuthica A30. The enzyme was expressed in Bacillus subtilis using surface display, in combination with Bacillus subtilis spore coat CotC. The promoter was further selected and optimized to determine PamyE as the most suitable promoter, while the spore coat protein assay reveals CotC as optimal. By scanning and analyzing the catalyst motifs with single-point mutation construction, a maximum isomaltulose yield of 27.21 mg/mL was recorded in the F181I-mutant enzyme. Another strain encoding the H363P-mutant reached a maximum yield of 20.84 mg/mL, while the kcat value also increased from 17.64 to 24.80. Structural analysis showed that the F181I-mutant had higher thermostability, whereas the H363P-mutant had increased kcat. Both mutants displayed a 5-fold increase in isomaltulose yield with relatively simple construction procedures, making them suitable for high-level isomaltulose production.

Enhancing NOx emission control and energy efficiency of H2/NH3 powered micro generator in a planar porous combustor.

Simple construction of starch-lipid-protein complexes for enhancing the encapsulation and delivery performance of mesalazine-loaded Pickering emulsions.

Induction motors are widely used in industries because of their high efficiency, reliability, and simple construction. They are commonly used in machines such as pumps, compressors, and conveyors. However, due to continuous operation and harsh working conditions, induction motors can develop different types of faults. These faults may be mechanical, such as bearing damage and misalignment, or electrical, such as unbalanced voltage and single phasing. If these faults are not detected early, they can lead to serious damage and unexpected system failure. In this project, motor fault detection is carried out using vibration analysis and current analysis. A vibration sensor measures the mechanical vibrations of the motor, while a current sensor monitors the electrical current drawn by the motor. The collected signals are processed and analyzed to identify abnormal conditions in the motor. This method helps in detecting faults at an early stage and improving the reliability of the motor system. This system helps in condition monitoring and predictive maintenance, which reduces maintenance cost and prevents unexpected breakdowns in industrial applications.

Given an algorithm that outputs the correct answer with bounded error, say 1 / 3 , it is sometimes desirable to reduce this error to some arbitrarily small &amp;#x03B5; – e.g., if one wants to call the algorithm many times as a subroutine. The usual method, for both quantum and randomized algorithms, is majority voting, which incurs a multiplicative overhead of O ( log &amp;#x2061; 1 &amp;#x03B5; ) from calling the algorithm this many times.Transducers are a recently introduced model of quantum computation, and it is possible to reduce the “error'' of a transducer arbitrarily with only constant overhead, using a construction analogous to majority voting called purification. Even error-free transducers map to bounded-error quantum algorithms, so this does not let you reduce algorithmic error for free, but it does allow bounded-error quantum algorithms to be composed without incurring log factors.In this paper, we present a new highly simplified purifier, that can be understood as a weighted walk on a line similar to a random walk interpretation of majority voting. Our purifier has much smaller space and time complexity than the previous one. Indeed, it only uses one additional counter, and only performs two increment and two decrement operations on each iteration. It also has quadratically better dependence on the soundness-completeness gap of the algorithm being purified. We prove that our purifier has optimal query complexity, even down to the constant, which matters when one composes quantum algorithms to super-constant depth.Purifiers can be seen as a way of turning a “Monte Carlo'' quantum algorithm into a “Las Vegas'' quantum algorithm – a process for which there is no classical analogue. Our simplified construction sheds light on this strange quantum phenomenon, and could have implications for the complexity of composed quantum algorithms.

The residual coal pillar and adjacent goafs in the upper coal seam have a superposition effect on the stress environment of the lower coal seam in close distance coal seams with downward mining. Therefore, it is essential to determine the appropriate location and cross section of the roadway. Through field measurement, theoretical analysis, and numerical simulation, the stability performance of the roadway was analyzed under the superposition effect of the residual coal pillar and adjacent goaf in the close distance coal seam at Meihuajing Coal Mine. Finally, the appropriate location and cross section of the roadway were determined. The results show that: (1) The 60m wide residual coal pillar in the upper coal seam can effectively bear the surrounding rock pressure and keep the roof structure stable. (2) The optimum position for the roadway in the lower coal seam should be located in the vertical stress decreasing area, with a horizontal distance of 10m between the roadway and the edge of the residual coal pillar. (3) The roadways with the straight wall semi-circular arch (SWSCA) cross section and the three-centered arch (TCA) cross section have the highest stability, while the one with the rectangular cross section (R) has the lowest stability of the surrounding rock, combining with the distributions of vertical stress and plastic zone. (4) Considering the superposition effect of the residual coal pillar and adjacent goaf, the roadway with SWSCA cross section is recommended for implementation in the lower coal seam because of its good stability and simple construction.

Synchronous reluctance motors (SynRMs) have gained renewed interest as economical, mag-net-free alternatives to permanent magnet and induction machines, particularly for small- and medium-power electric vehicle propulsion systems where cost, efficiency, and sustainability are critical. Their main advantages include simple construction, high reliability, and suitability for sustainable drive systems. However, SynRMs inherently suffer from torque ripple, acoustic noise, and limited power factor, mainly due to the anisotropic nature of their magnetic design. This study presents a fully integrated Multi-Objective Genetic Algorithm (MOGA)–Finite Ele-ment Method (FEM) framework, which simultaneously minimizes torque ripple and maximizes output power by systematically optimizing rotor flux barrier geometry, in contrast to traditional trial-and-error or single-objective optimization methods. The optimization process evaluates key rotor design parameters, including flux barrier angles, widths, and insulation ratios, while explor-ing a wide design space and corresponding objective function values. FEM analysis is employed at each optimization iteration to accurately assess electromagnetic performance and ensure con-vergence toward the optimal solution. The optimized rotor demonstrates a substantial perfor-mance improvement, with the average torque increasing from 13.15 Nm to 19 Nm and the output power rising from 4.13 kW to 6 kW. The power factor is improved from 0.85 to 0.9, indicating enhanced efficiency and reduced reactive current demand. Moreover, torque ripple is quantita-tively reduced from 8.95% to 8.5%, confirming the effectiveness of the proposed optimization strategy. The introduction of barrier fillets further decreases flux density in the tangential ribs, thereby reducing mechanical stress and core losses. These results confirm that systematic opti-mization of flux barrier geometry can significantly enhance SynRM performance without the use of permanent magnets or complex rotor structures, preserving simplicity and cost-effectiveness.

Two-dimensional (2D) metal halide perovskites have great potential for applications in photodetectors (PDs), but there are still many challenges in the simple and efficient construction of their heterojunctions and the realization of excellent self-powered functions. In this work, we employ a simple ion exchange strategy to directly grow a 2D PEA2PbI4 layer on the PEA2PbBr4 film using a simple and easily implemented solution method. By precisely controlling the concentration of the phenethylamine hydroiodide/isopropanol (PEAI/IPA) solution, high-quality PEA2PbBr4/PEA2PbI4 heterojunction films are obtained. Furthermore, the self-powered PDs assembled based on the 2D/2D PEA2PbBr4/PEA2PbI4 heterojunction exhibit outstanding self-powered photoelectric performance across the UV-visible spectrum, with a responsivity of 75.6 mA/W and a detectivity of 4.16 × 1011 Jones, respectively, which are more than five times that of the PEA2PbBr4 photodetector. In particular, this heterojunction photodetector exhibits excellent long-term self-powered photoelectric switching characteristics and environmental stability.

Mechanism and process optimization of a newly proposed carbonate and phosphate co-biomineralization approach for multiple heavy metal stabilization.

Predictive models play a pivotal role in industrial control systems, particularly demonstrating extensive and significant applications in data processing and data mining. Currently, both the gated recurrent unit (GRU) and the Transformer play a crucial role in building predictive models. However, most of the research methods still face the problems of imperfect data processing and simple model construction. Regarding data processing, the lack of redundant feature removal inevitably complicates the model's learning process. In terms of model construction, existing methods simply combine the two without deeper exploration and combination. To address these challenges, we present TG-FCM, a new prediction model in this study. Specifically, this paper develops a feature selection method using AGglomerative NESting (AGNES) and fuzzy c-mean (FCM), and constructs a Transformer prediction framework with memory function with the help of GRU. In the feature selection stage, the data features are first clustered using AGNES to obtain clustering results, which are then used as initial cluster centers for FCM. This combination method effectively alleviates the problem of FCM's dependence on initial values. In the prediction framework part, GRU is utilized to have the ability of storing historical data to provide memory function for Transformer. The specific situation is to add GRU-based storage memory unit and extraction memory unit to the encoder and decoder of Transformer respectively, so as to store the memory in the encoding process and extract the memory in the decoding process, thus realizing the memory function of Transformer. The experimental analysis of TG-FCM and five comparative models on eight real datasets shows that TG-FCM has high prediction performance and better generalization ability.