Research on information construction of knowledge graph based on literature retrieval in english learning

Abstract This study aimed to explore the construction of an English language knowledge graph based on literature retrieval to support intelligent education. A questionnaire was administered to collect data on students’ experiences with traditional and technology-enhanced learning approaches. Literature was also retrieved and analyzed to populate the knowledge graph domains. The results showed that implementing a knowledge graph significantly improved learning personalization and fostered greater student engagement compared to conventional teaching methods. Real-time analytics and continuous feedback further optimized the learning process. Post-implementation assessments found notable gains in students’ academic performance and inclination toward English learning. The personalized, adaptive learning environment facilitated by the knowledge graph more effectively sustained interest and promoted achievement. In conclusion, knowledge graphs constructed through literature analysis hold promising potential for advancing English education when incorporated into intelligent tutoring systems. By mapping interconnections within the subject domain visually and computationally, they can power highly customized instruction tailored to individual needs

Applying Large Language Models to Power Systems: Potential Security Threats

Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency. However, this action may also incur potential security threats, which have not been fully recognized so far. To this end, this article analyzes potential threats incurred by applying LLMs to power systems, emphasizing the need for urgent research and development of countermeasures

Multi-objective optimization of energy consumption and surface quality in nanofluid SQCL assisted face milling

Considering the significance of improving the energy efficiency, surface quality and material removal quantity of machining processes, the present study is conducted in the form of an experimental investigation and a multi-objective optimization. The experiments were conducted by face milling AISI 1045 steel on a Computer Numerical Controlled (CNC) milling machine using a carbide cutting tool. The Cu-nano-fluid, dispersed in distilled water, was impinged in small quantity cooling lubrication (SQCL) spray applied to the cutting zone. The data of surface roughness and active cutting energy were measured while the material removal rate was calculated. A multi-objective optimization was performed by the integration of the Taguchi method, Grey Relational Analysis (GRA), and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The optimum results calculated were a cutting speed of 1200 rev/min, a feed rate of 320 mm/min, a depth of cut of 0.5 mm, and a width of cut of 15 mm. It was also endowed with a 20.7% reduction in energy consumption. Furthermore, the use of SQCL promoted sustainable manufacturing. The novelty of the work is in reducing energy consumption under nano fluid assisted machining while paying adequate attention to material removal quantity and the product’s surface quality

Blind equalization algorithm based on complex support vector regression

A new blind equalization algorithm for complex valued signals was proposed based on the framework of complex support vector regression(CSVR).In the proposed algorithm,the error function of multi-modulus algorithm (MMA) was substituted into CSVR to construct the cost function,and the regression relationship was established by widely linear estimation,and the equalizer coefficients were determined by the iterative re-weighted least square (IRWLS) method.Different from spliting the complex valued signals into real valued signals used in support vector regression,the Wirtinger’s calculus was used in complex support vector regression to analyze the complex signals directly in the complex regenerative kernel Hilbert space.Simulation experiments show that for QPSK modulated signals,compared with the blind equalization algorithm based on support vector regression,the equalization performance of the proposed algorithm is significantly improved in linear channel and nonlinear channel by choosing appropriate kernel function and iterative optimization method

3D channel modeling and space-time correlation analysis for V2V communications

To match complex wireless propagation scenarios,an improved 3D geometry-based stochastic model was proposed for vehicle to vehicle (V2V) communications channel.The exact relationship between the azimuth angle and elevation angle was taken into account and the corresponding space–time correlation function and space–Doppler power spectral density were derived,and the influence of important factors was analyzed.The observations and conclusions show that correlation characteristics is closely related to distribution of the scatterers and the angle of the antenna array under the non-isotropic scattering environment and is affected by the elevation angle of the antenna array under the isotropic scattering environment.And the space-time correlation characteristics in high vehicular traffic density is significantly lower than that in low vehicular traffic density.The corresponding simulation model is also derived by using a reasonable parameter calculation method.The simulation results validate the rationality of proposed model.It greatly improves analysis and simulation efficiency of V2V MIMO system

Simulation and evaluation of ecosystem service value along the Yellow River in Henan Province, China

The unprecedented growth in population and swift industrial advancements exert considerable strains on the ecosystem, particularly within medium-sized and large urban landscapes. The critical investigation into the intricate links between current and prospective land utilization, as well as the ecosystem service value (ESV), holds considerable empirical relevance for the calibration of land usage frameworks, thereby contributing to the sustainable evolution of extensive urban zones. Utilizing GlobeLand 30 data, the present research probes into the pattern of land transformation and the spatial-temporal dispersal of ESV in Henan’s Yellow River vicinity over a span from 2000 to 2020. For the enhancement of land usage alignment, a Markov-PLUS fusion model was devised to gauge three disparate ESV transition scenarios slated for 2030, namely, natural development scenario (NDS), cropland protection scenario (CPS), and ecological protection scenario (EPS). The principal determinants of land transformation within the 2000–2020 period were recognized as elevation, populace concentration, and atmospheric temperature. Amid the rapid accretion of construction land engulfing substantial cropland and grassland areas, there was an ESV diminution to the tune of 1.432 billion RMB between 2000 and 2020. The ESV’s high-value regions were discerned within relatively undisturbed ecosystem zones, with the lower-value sections identified in cropland and constructed areas, where human interventions exerted pronounced effects on the ecosystem. In accordance with the 2030 land usage simulations and analyses, in contrast to alternative scenarios, the EPS exhibited the least fluctuation in land type alterations in 2030, demonstrated the most pronounced escalation in cold spot concentration, and reached a peak agglomeration level. This underscores that the EPS not only offers a refinement in land utilization configuration but also mediates the equilibrium between economic and ecological considerations. The insights derived from this investigation afford innovative evaluative methods for spatial planning, ecological recompense, and sustainable land exploitation within large- and medium-scale urban domains

Gut Microbiota Is a Major Contributor to Adiposity in Pigs

Different breeds of pigs vary greatly in their propensity for adiposity. Gut microbiota is known to play an important role in modulating host physiology including fat metabolism. However, the relative contribution of gut microbiota to lipogenic characteristics of pigs remains elusive. In this study, we transplanted fecal microbiota of adult Jinhua and Landrace pigs, two breeds of pigs with distinct lipogenic phenotypes, to antibiotic-treated mice. Our results indicated that, 4 weeks after fecal transplantation, the mice receiving Jinhua pigs’ “obese” microbiota (JM) exhibited a different intestinal bacterial community structure from those receiving Landrace pigs’ “lean” microbiota (LM). Notably, an elevated ratio of Firmicutes to Bacteroidetes and a significant diminishment of Akkermansia were observed in JM mice relative to LM mice. Importantly, mouse recipients resembled their respective porcine donors in many of the lipogenic characteristics. Similar to Jinhua pig donors, JM mice had elevated lipid and triglyceride levels and the lipoprotein lipase activity in the liver. Enhanced expression of multiple key lipogenic genes and reduced angiopoietin-like 4 (Angptl4) mRNA expression were also observed in JM mice, relative to those in LM mice. These results collectively suggested that gut microbiota of Jinhua pigs is more capable of enhancing lipogenesis than that of Landrace pigs. Transferability of the lipogenic phenotype across species further indicated that gut microbiota plays a major role in contributing to adiposity in pigs. Manipulation of intestinal microbiota will, therefore, have a profound impact on altering host metabolism and adipogenesis, with an important implication in the treatment of human overweight and obesity

ElecBench: a Power Dispatch Evaluation Benchmark for Large Language Models

In response to the urgent demand for grid stability and the complex challenges posed by renewable energy integration and electricity market dynamics, the power sector increasingly seeks innovative technological solutions. In this context, large language models (LLMs) have become a key technology to improve efficiency and promote intelligent progress in the power sector with their excellent natural language processing, logical reasoning, and generalization capabilities. Despite their potential, the absence of a performance evaluation benchmark for LLM in the power sector has limited the effective application of these technologies. Addressing this gap, our study introduces ElecBench , an evaluation benchmark of LLMs within the power sector. ElecBench aims to overcome the shortcomings of existing evaluation benchmarks by providing comprehensive coverage of sector-specific scenarios, deepening the testing of professional knowledge, and enhancing decision-making precision. The framework categorizes scenarios into general knowledge and professional business, further divided into six core performance metrics: factuality, logicality, stability, security, fairness, and expressiveness, and is subdivided into 24 sub-metrics, offering profound insights into the capabilities and limitations of LLM applications in the power sector. To ensure transparency, we have made the complete test set public, evaluating the performance of eight LLMs across various scenarios and metrics. ElecBench aspires to serve as the standard benchmark for LLM applications in the power sector, supporting continuous updates of scenarios, metrics, and models to drive technological progress and application