This study uses advanced approaches on the enlarged BRATS dataset to increase brain magnetic resonance imaging (MRI) image reconstruction accuracy and reliability. This study addresses MRI image processing issues such as noise, artifacts, and high‐quality reconstruction. These traits are essential for brain tumor detection and analysis. This effort aims to establish a comprehensive image processing pipeline that standardizes MRI images, reduces noise, and improves clarity for better image reconstruction. ESORecon‐Net, which combines the echo state network (ESN) and osprey optimization algorithm (OSPREY), manages raw k‐space data cleverly and improves reconstruction. The model’s dual‐phase optimization ensures accuracy and efficiency in reconstructing high‐quality MRI images. The proposed ESORecon‐Net achieved a peak signal‐to‐noise ratio (PSNR) of 49.12 dB and a structural similarity index measure (SSIM) of 0.993, surpassing existing methods such as the fully sampled k‐space‐trained network (FS‐kNet) and motion‐informed deep learning network (MIDNet). These results confirm ESORecon‐Net’s effectiveness in enhancing brain MRI image reconstruction, improving both image quality and computational performance.

Open‐source libraries are indispensable for modern software development but can create substantial maintenance burdens when they become deprecated or unmaintained. Selecting an appropriate replacement among many candidates remains challenging, since methods relying only on historical mining or similarity metrics often miss subtle differences in meaning. We propose AMRerank, a novel framework that integrates multi‐agent qualitative analysis with a data‐driven, interpretable reranking model. AMRerank first deploys specialized agents to examine and classify semantic relationships between libraries, generating evidence‐backed labels and concise summaries. An interpretable reranking framework then fuses these qualitative signals with heuristic and semantic features to produce a fine‐grained, explainable ranking. Evaluated on the GT2014 benchmark against competitive baselines (LMG, MMR, MMRLC), AMRerank achieves Precision@1 of 0.899 and mean reciprocal rank (MRR) of 0.928. As our case studies show, the system provides actionable, human‐readable evidence that helps developers make more reliable migration choices.

The ability to comprehend complex viewpoints in text is critical for sentiment analysis (SA), particularly at the aspect level, yet existing models struggle with accurately identifying sentiment polarities and aspect‐specific expressions due to their reliance on large, manually annotated, domain‐specific datasets. To address these challenges, this paper introduces hybrid deep learning and RoBERTa‐based SA (HDR‐SA), a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs), bidirectional long short‐term memory (BiLSTM) networks, and the RoBERTa transformer model to perform comprehensive sentiment and aspect analysis. The proposed model begins with rigorous data preprocessing and normalization, utilizes Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment scoring, constructs embedding vectors via Word2Vec, and employs a CNN‐BiLSTM architecture enhanced by RoBERTa to capture both sequential and contextual embeddings for refined sentiment classification. The novelty of HDR‐SA lies in its hybrid integration of conventional natural language processing (NLP) techniques with deep learning and transformer‐based contextual understanding, enabling robust SA without the extensive need for domain‐specific annotated data. Evaluated on the large‐scale 515K Hotel Reviews dataset, HDR‐SA achieved an accuracy of 95.75%, a precision of 0.96, a recall of 0.97, and an F1‐score of 0.96, outperforming contemporary models such as target‐dependent LSTM (TD‐LSTM), ResNet‐SCSO, and CNN‐GA. These results demonstrate HDR‐SA’s effectiveness in aspect‐level SA and its scalability across diverse domains while reducing dependency on annotated resources.

Quantum computing is one of the research areas progressing rapidly toward practical deployment, yet the engineering of scalable and reliable quantum software remains underdeveloped. Current quantum software engineering (QSE) practices are largely tools‐driven and ad hoc that providing limited support for managing probabilistic execution, hybrid quantum–classical workflows, noise sensitivity, and hardware constraints. This study proposed a structured QSE lifecycle that integrates quantum‐specific characteristics with disciplined software engineering practices and principles. The proposed lifecycle organizes development into six phases, encompassing quantum requirements engineering, formal modeling, architecture and circuit design, hybrid integration, noise‐aware testing, and deployment with monitoring. Each phase is supported by explicit artifacts and quantitative criteria to enable systematic progression and iterative refinement. The QSE is validated through expert assessment and simulation‐based experimentation using representative variational quantum algorithms under the realistic noise conditions. The results show improved fidelity convergence, reduced resource overhead, enhanced development stability (DS), and more reliable validation compared with unstructured workflows, demonstrating the value of lifecycle‐driven engineering for quantum software systems.

Abstract The Internet of Things, as a highly integrated and comprehensive application of a new generation of information technology, has the characteristics of strong penetration, great driving effect and good comprehensive benefits. It is another promoter of the development of the information industry after the computer, the Internet and the mobile communication network. The Internet of Things enables an interconnected network of all ordinary objects that can perform independent functions. With the continuous improvement of the popularity of flute education, how to better promote students’ flute learning ability has become an important topic. The instrumental features are fresh and clear, and the tones are cool. The treble is lively and bright, and the bass is beautiful and melodious, which is widely used in wind bands, orchestras and military bands. Among them, continuously promoting the effective combination of flute teaching and aesthetic teaching has become an important development direction. Through the flute aesthetic teaching, not only can students truly perceive the beauty of music through imagination and association, but also can prevent students from being blindly immersed in flute skills learning. Therefore, this paper proposed an analysis of the enhancement of aesthetic perception in flute teaching by a smart music system based on the Internet of Things. The article mainly introduced the construction of the music system based on the Internet of Things, then analyzed the algorithm of music feature extraction, and finally took a middle school as the experimental object. Questionnaire design and teacher interviews were carried out for their flute teaching. The final experimental results showed that more than 68.7% of students believed that flute aesthetics teaching was included in flute teaching, which indicated that flute aesthetics teaching had been well promoted. It has also been recognized by the majority of students. 68% of the students said that teachers’ musical awareness was not valued, 28.1% said they did not pay attention to the development of music, and 40.6% said they never valued the cultivation of music. This also showed that teachers themselves had a certain lack of musical sense. Therefore, this paper suggested that teachers should actively drive students to improve their aesthetic perception of flute. This article is protected by copyright. All rights reserved.

Abstract With the development of information technology, deep learning optimization algorithm has a unique ability to perceive the surrounding environment, and has its appearance in many fields. Deep learning technology promotes the use of computer vision and enhances its ability to process images. The intelligent design decision in this paper has taken autopilot as an example. The autopilot technology is also budding with the rising of emerging technologies such as machine learning, neural language programming, computer vision, etc., and has been constantly improving. However, the use of deep learning optimization algorithm in the field of end‐to‐end automatic driving is still very limited. Therefore, the research of decision model based on deep learning algorithm has important significance and value. This paper has proposed a decision model based on deep learning algorithm. The model is used to study and predict driving behavior through confrontation training and is compared with other decision‐making models. The experimental results showed that the reward score of positive and negative feedback was the highest, which was 823 points, indicating that the learning ability of safe driving of the decision‐making model with positive feedback and negative feedback was stronger than that with only positive feedback or only negative feedback. The score of the decision model with only positive feedback was 812, which indicated that the learning and driving ability of the decision model was also strong in the case of only positive feedback. Moreover, the average decision satisfaction and recall rate of the decision model in this paper were better than other algorithms. The performance of artificial intelligence automatic driving decision model based on deep learning can be better. This article is protected by copyright. All rights reserved.

Abstract Physical education is a public subject education in higher vocational education and is responsible for improving the physical quality of all students in the school. Physical education resources are an important material guarantee for the development of sports. Among them, physical education resources are one of the basic conditions for the development of sports activities. Due to the increasing demand for sports and fitness from urban residents, the current location of resources is severely lacking. In this case, it is important to propose a physical education resource model. This thesis aims to study the physical education resource sharing model based on block‐chain can not only protect the intellectual property rights of resource providers, but also improve the quality of physical education resources, expand the scope of physical education resource sharing, and provide theoretical reference materials for realization. This article combines the Bloom filter mining algorithm in the block‐chain, and the space efficiency and time efficiency of the block‐chain are very high, and it has great security guarantee for the application in the construction of the physical education resource sharing model. The result of the empirical analysis is: the study of the physical education resource sharing model, using the block chain acquisition method. Compare and analyze the choice and effectiveness of the application mode of the physical education resources of the National Open University. The co‐construction and sharing of sports resources saves human resources and material resources for educational institutions, and fully reflects the talents of teachers. In 2009, statistics on the development of education in the country showed that there were 1,300 independent sports colleges and universities in the country, accounting for 62% of the total number of public universities in the country. In the popularization of higher education in our country, sports seem to account for half of the country. And it worked. Therefore, it is very necessary to realize the application of the block chain‐based resource acquisition method in the construction of the physical education resource sharing model. This article is protected by copyright. All rights reserved.

Abstract The scope and complexity of the issues faced in social production are expanding every day, affecting a variety of settings and industries, including government decision‐making, the control of unmanned equipment like robots, massive data processing and mining, and biomedical engineering. In order to ensure a significant rise in grain yield, it is imperative to maintain the availability and good quality of grain given the ongoing growth of the world's population. Optimizing rice breeding is essential for yield and high production. This paper optimizes the structure of convolutional neural network (CNN) by non‐numerical encoding (NNE‐CNN), and expounds the principle of algorithm realization, including initialization, encoding and population updating. After the image is segmented by significance detection, it is compared with the classical network structure and the convolution neural network structure optimized by intelligent algorithm on the public data set and the rice disease data set. The results show that the model has strong searching ability and can be used in the recognition of rice disease images, thus helping to realize the optimization of rice breeding. This article is protected by copyright. All rights reserved.

In software engineering, selecting the appropriate architectural style for software systems is risky and sensitive. The selection process is a multicriteria decision‐making (MCDM) problem. Consequently, selecting a suitable architecture is a key challenge in software development. This study presents an automated hybrid methodology based on the analytic hierarchy process (AHP) and fuzzy analytic hierarchy process (FAHP) to evaluate and suggest multiple architectural styles based on quality attributes (QAs) alone rather than relying on expert opinions. A Tera‐PROMISE dataset is presented to illustrate the proposed methodology and then compare the result of the methodology with expert judgments. Moreover, to support the proposed methodology, a case study is carried out to compare the proposed method to previous studies.