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The latest technologies in natural language processing provide creative, knowledge retrieval, and question-answering technologies in the design of intelligent education, which can provide learners with personalized feedback and expert guidance. Entrepreneurship education aims to cultivate and develop the innovative thinking and entrepreneurial skills of students, making it a practical form of education. However, a knowledge retrieval and question-answering teaching assistant system for entrepreneurship education has not been proposed. This observation motivated us to develop a reading comprehension framework to address the challenges of domain-specific knowledge gaps and the weak comprehension of complex texts encountered by intelligent education models in practical applications. The proposed framework mainly includes: question understanding, relevant knowledge retrieval, mathematical calculation, and answer prediction. The techniques involved in the aforementioned modules mainly include text embedding, similarity retrieval, graph convolution, and long short-term memory network. By integrating this model into entrepreneurship courses, learners can participate in real-time discussions and receive immediate feedback, creating a more dynamic and interactive learning environment. To assess the effectiveness of the proposed model, this article conducts answer prediction on single-choice exercises related to entrepreneurship education courses. This study employs the potential of using a question-and-answer format to enhance intelligent entrepreneurship education, paving the way for a more effective and engaging online learning experience.

Abstract Load forecasting with distributed energy resources (DERs) behind‐the‐meter is more challenging owing to transformed data patterns. Traditional forecasting method which is only based on unmasked‐load could not suit the present limited masked‐load. To bridge the divergence between unmasked‐load and masked‐load, this article proposes a masked‐load forecasting (MLF) method based on transfer learning technique and Bayesian optimization, which is Maximum Mean Discrepancy‐Neural Network with Bayesian optimization (MMD‐NNb). At first, common feature vectors between unmasked‐load and masked‐load are extracted and an outcome predictor could be established based on feature vectors from historical unmasked‐load. The feature vectors from masked‐load could therefore accommodate to the outcome predictor, and the masked‐load could be forecast. Owing to the excessive hyperparameters involved in training, Bayesian optimization is adopted for hyperparameters fine‐tuning. MMD‐NNb was tested and compared with four related models. The improvements from MMD‐NNb were observed in all comparison scenarios. Also, MMD‐NNb was proved to have high resilience to the different DERs and not requiring additional DERs‐data.

Abstract This paper focuses on the online anti-epidemic resource allocation between demanders and suppliers considering the epidemic spatial spread. The spatial crowdsourcing with sharing platform is an effective way for anti-epidemic resource allocation, and a reasonable online matching strategy can improve the efficiency of resource utilization. The paper proposes online matching heuristic strategy (HSTRF-LRLUF strategy) and designs an online batch bilateral matching algorithm for anti-epidemic resources, which considers the impact of grid spatial aggregation and diffusion risk of emerging infectious diseases. The population distribution within grids and the commuting patterns between grids can provide decision support for selecting online matching strategies of anti-epidemic resources. A larger matching time window focusing on the spatial transmission risk (TR) of the epidemic can obtain better matching results. However, with a smaller matching time window, the decision makers can focus on spatial agglomeration risk (OR) or spatial diffusion risk (IR). The paper effectively combines the spatial crowdsourcing model with the anti-epidemic resource allocation to achieve the allocation of emergency resources to individuals. A combined anti-epidemic resource online matching heuristic strategies is designed from the spatial agglomeration risk and the spatial diffusion risk. Decision makers can dynamically adjust the online matching strategies of anti-epidemic resources by evaluating the spatial agglomeration risk, the spatial diffusion risk, and the overall spatial transmission risk based on the real-time spread of the epidemic and the supply of anti-epidemic resources.

Aiming at the problem that the number of spatial object catalogs is not high and the scheduling efficiency is low in traditional scheduling methods, a scheduling method using multi-rule scheduling strategy is proposed to maximize the number of object catalogs. This paper takes observation requirements and tracking arcs as inputs, forecasts the visibility of space target tracking arcs, integrates a variety of scheduling rules into the traditional scheduling process, and compares the scheduling results under two different scheduling strategies through simulation experiments with the number of multiple groups of different space targets as references. Experiments show that the same 5 monitoring devices perform the task of cataloging space objects. When the utilization rate of monitoring resources is close to 100%, the scheduling strategy based on multi-rules can achieve a maximum of 6 500 space objects stable cataloging, while the traditional scheduling strategy can only achieve a maximum of 3 200 space objects stable cataloging. Compared with the traditional scheduling strategy, the maximum number of catalogs is increased by 103%.

Introduction. The rational operation of the locomotive fleet is based on coordination of the locomotive turnover schedule with the train traffic schedule that assume unconditional fulfillment of standard transportation jobs and no less strict compliance with technical and technological standards for locomotive and train operations at locomotive turnover and re-coupling stations. Coordination is intended to set up balance of locomotives at traction interchange points. However, in the course of transportation, the intra-day dislocation of the operating fleet reaches significant unevenness and leads to imbalance. This requires a system for dispatching traction resources to their turnover and recoupling stations which will help to preserve traction balance at the interchange points to compensate for uneven intraday dislocation of the operating fleet, rationalise the locomotive fleet operations, and improve line capacity.Materials and methods. The balance of traction resources at locomotive turnover and re-coupling stations in intra-day intervals is achieved by interval regulation of traction resources.Results. The paper proposes an approach to establishing a balance of traction resources at current interlocking stations as the simplest type of turnover and recoupling stations (as a rule, they do not have additional adjacencies, using a one-to-one traction exchange). The approach applies the correlation between fluctuations in train traffic speeds and the amount of required traction reserves within daily three-hour intervals.Discussion and conclusion. This approach helps to improve the traction resource balancing technology at the current junction stations, and create a basis for building a mechanism for dispatching regulation reserves.

Abstract With the large-scale distributed energy resources (DERs) interfaced by power electronic converters connected to the power grid, the traditional synchronous generation is gradually replaced, and the inertia and primary frequency regulation capability of the power system is weakened, which seriously threatens the frequency security of the power grid. In view of the above problems, a hierarchical control strategy by coordinating the distributed wind turbine (WT) and photovoltaic (PV) is presented to improve the transient frequency (TF) and steady-state frequency (SSF). For the system control level, the optimal frequency regulation coefficients of multiple WTs and PVs clustering systems are determined by the optimal control principle with minimum frequency regulation energy, while considering the constraints of TF and SSF. For the WT/PV control level, the frequency regulation coefficients of WTs and PVs are solved by the model predictive controller (MPC) and alternating direction method of multipliers (ADMM), with the minimum control cost of WT and PV. It achieves the optimal allocation of frequency regulation power and improves the computing efficiency. Finally, the validity of the proposed method is tested in the modified IEEE 10-machine 39-bus system.

In the face of surging online education around the globe, it seems quite necessary and helpful for learners and teachers to have the plethora of online resources well sorted out beforehand. To some extent, the efficiency and accuracy of resource search and retrieval may determine the quality and influence of online education. In this research, based on the methodological framework of design science, the support vector machine (SVM) algorithm is chosen to optimise the design of an accurate resource classifier. The aim is to improve the unsatisfactory classification effect of traditional classification methods for online education resources, so that online learners can enjoy more accurate and convenient access to education resources they are seeking out of many more. For the purpose of performance evaluation, the proposed SVM-based classifier was compared with two other classification methods based on multiple neutral networks and deep learning respectively. Upon collection and pre-processing of online materials, the features of educational resources were extracted and output in the form of feature vectors. By calculating the similarity between the extracted feature vectors and the standard vectors of the set type, we obtained the classification results of online education resources for each of the three classifiers. It was found that, compared with those of the two traditional classification methods, the precision ratio and the recall ratio of the proposed classifier improved by 3.26% and 2.01% respectively. In the meantime, the proposed SVM-based classifier seems to more advantageous in performance balance with better F measurement.

This study aims to determine the effect of the Resource Based Learning (RBL) model in Ecosystem learning and its effect on biology learning outcomes at SMAN 3 Mataram. This type of research is research using a quantitative approach. The sample of this research was the students of class IX of SMAN 3 Mataram, which amounted to 32 students of X MIPA 1 for the control class and 32 students of X iMIPA i2 for the experimental class. The research data was obtained from a questionnaire on implementing the lesson plan and a learning outcome (post-test score) on ecosystem materials. The data analysis results show that the questionnaire score interval level for implementing the Resource Based Learning (RBL) model is 100% (very high). The conventional model shows the score interval that is implemented is 87.5%, and that is not implemented is 12.5%, and the level of validity of the questions in the cognitive domain, including the very valid criteria and the level of reliability of the questions in the cognitive domain, the criteria for reliable. The results of the data analysis of learning outcomes showed differences in the average post-test scores between the experimental class through the application of learning using the resource-based learning (RBL) learning model and the control class through the application of the conventional learning model. The post-test mean in the experimental class was 91.36, while the post-test mean in the control class was 71.69. The significance value of the count sample obtained is 0.532, the significance value obtained is greater than 0.05 Ho is rejected, and H1 is accepted, which means that the use of the Resource-based Learning (RBL) learning model is effective on student biology learning outcomes at SMAN 3 Mataram.

Abstract Background Artemisia selengensis, classified within the genus Artemisia of the Asteraceae family, is a perennial herb recognized for its dual utility in culinary and medicinal domains. There are few studies on the chloroplast genome of A. selengensis, and the phylogeographic classification is vague, which makes phylogenetic analysis and evolutionary studies very difficult. Results The chloroplast genomes of 10 A. selengensis in this study were highly conserved in terms of gene content, gene order, and gene intron number. The genome lengths ranged from 151,148 to 151,257 bp and were typical of a quadripartite structure with a total GC content of approximately 37.5%. The chloroplast genomes of all species encode 133 genes, including 88 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. Due to the contraction and expansion of the inverted repeats (IR), the overlap of ycf1 and ndhF genes occurred at the inverted repeats B (IRB) and short single copy sequence (SSC) boundaries. According to a codon use study, the frequent base in the chloroplast genome of A. selengensis’ third codon position was A/T. The number of SSR repeats was 42–44, most of which were single nucleotide A/T repeats. Sequence alignment analysis of the chloroplast genome showed that variable regions were mainly distributed in single copy regions, nucleotide diversity values of 0 to 0.009 were calculated by sliding window analysis, 8 mutation hotspot regions were detected, and coding regions were more conserved than non-coding regions. Analysis of non-synonymous substitution (Ka) and synonymous substitution (Ks) revealed that accD, rps12, petB, and atpF genes were affected by positive selection and no genes were affected by neutral selection. Based on the findings of the phylogenetic analysis, Artemisia selengensis was sister to the genus Artemisia Chrysanthemum and formed a monophyletic group with other Artemisia genera. Conclusions In this research, the present study systematically compared the chloroplast genomic features of A. selengensis and provided important information for the study of the chloroplast genome of A. selengensis and the evolutionary relationships among Asteraceae species.

The model of circular bipolar complex fuzzy (Cir-BCF) sets computed based on the membership function, non-membership function, and radius among both functions for each value of the universal set. The technique of the Cir-BCF set is the modified or extended form of fuzzy sets, complex fuzzy sets, bipolar fuzzy sets, bipolar complex fuzzy sets, and simple circular bipolar fuzzy sets to cope with uncertain and vague information. In this manuscript, we describe the novel technique of frank operational laws based on Cir-BCF values for frank t-norm and frank t-conorm. Further, we simplify the model of Cir-BCF frank power averaging (Cir-BCFFPA), Cir-BCF frank power weighted averaging (Cir-BCFFPWA), Cir-BCF frank power geometric (Cir-BCFFPG), Cir-BCF frank power weighted geometric (Cir-BCFFPWG) operators, and highlighted their valuable properties, called idempotency, monotonicity, and boundedness. Moreover, analysis of renewable energy resources is very awkward and complicated, but it is natural sources of energy that are refilled constantly or relativeness quickly on a human timescale. Further, solar energy, hydroelectric energy, geothermal energy, biomass energy, and wind energy are five major sources of energy, but it is complex to find which one is the best and which one is worst. For evaluating the above problems, we construct the technique of evaluation based on the distance from the average solution (EDAS) method under the presence of the Cir-BCF numbers (Cir-BCFNs). At the end, we arrange the comparison between proposed and existing techniques based on some numerical examples to describe the validity and proficiency of the initiated information.

With the continuous development of cloud computing and edge computing technologies, the education sector is gradually applying these technologies to enhance the management and utilization efficiency of teaching resources. Therefore, to address the issue of information overload mentioned above, it is necessary to establish a personalized recommendation system based on user needs, preferences, and other information, recommending products, information, and resources that may be of interest to users. This can not only save users search time, but also alleviate the problem of information overload to some extent. On this basis, this article discussed a new content oriented recommendation method: constructing a user interest feature vector resource association matching model, and analyzing it to achieve recommendation of similar resources. The experimental results showed that the MAE (Mean Absolute Error) value of personalized recommendation based on CF (Collaborative Filtering) algorithm was below 0.8, which was smaller than other algorithms, indicating high accuracy of recommendation based on CF algorithm. [ABSTRACT FROM AUTHOR]

With the popularization of intelligent devices and the development of internet technology, the Internet of Things technology is receiving increasing attention. However, how to effectively schedule and manage resources in the Internet of Things environment has become an increasingly important issue. In order to solve this problem, a cloud scheduling simulation system for IoT resources in a digital intelligent environment has emerged. This system utilizes cloud computing and IoT technology to simulate the behavior of intelligent devices and deep learning technology, achieving efficient resource scheduling. In addition, experimental data shows that the accuracy of the IoT resource cloud scheduling simulation system based on deep learning algorithm combined with digital intelligent environment is over 70%; the average task processing time is within 0.3 seconds; the average task response time is within 0.6 seconds. From this, it can be seen that combining deep learning algorithms with the system can quickly respond and solve problems, improving the reliability and stability of the system. The application range of this system is very wide, which can be used to optimize resource scheduling in many fields such as production, and can also be applied in fields such as smart home, smart medical, and intelligent transportation. With the continuous development of technology, this system would also be further improved and expanded to better serve people's lives and work. [ABSTRACT FROM AUTHOR]

Implementation of different strategies on the demand and supply side to deal with potential water scarcity is based on a comparison of future water demand and availability of water resources based on different scenarios of climate change and population growth. Especially, the Alpine region is characterised by many small and medium water supply systems (WSSs) having neither human resources nor time for advanced planning, requiring simple methods for estimating future development. Therefore, the aim of this work is to provide future projections of water demand, resource availability, and drinking water quality for an Alpine area based on simple approaches with minimal data requirements. As the results of the case study show, linear and polynomial regression with precipitation and temperature data can illustrate the temporal variation of system input and drinking water temperature with sufficient accuracy and is suitable for an estimation of future development. The groundwater modelling, however, requires the consideration of a non-linear term depending on the depth to obtain reasonable results. Due to the usage of open-access data and the easy approaches developed and applied, a good transferability to other case studies is expected which can provide stakeholders a first assessment of the future need for action. HIGHLIGHTS Simple regression analyses are used for future projections for water resources.; Precipitation and temperature are utilised as input parameters.; Water demand increases by 25–125% due to climate change and population growth.; Increased availability of open-access data can help future planning.;