Nowadays, there is a plethora of computer data that needs to be compressed in order to save storage space, improve transmission, speed up sorting, etc. It is in this context that we propose to compare three different compression programs (winrar, winzip and a new prototype) in order to evaluate the characteristics of the prototype and classify it among these peers. This study could help us in the future to improve this prototype according to our needs. This ranking will therefore be based on the ease of use, operation, advantages and disadvantages of each candidate in order to facilitate the choice. For the sake of objectivity, this will be done without bias so as not to distort the final results.
Regarding the gas scheduling issue in the coal gas systems of steel enterprises, a gas scheduling optimization model is proposed based on the multi-input and multi-output characteristics of the gas system. This model combines knowledge-based and dynamic programming approaches, utilizing production experience to establish scheduling knowledge. The model calculates the scheduling results under two scenarios: non-optimized and optimized, across various time dimensions (1 minute, 15 minutes, 30 minutes) and different production conditions (with and without blast furnace maintenance). The results from simulation experiments with field data demonstrate the model's timeliness and practicality, providing a reasonable guide for developing gas system schemes. This model is particularly suitable for steel production enterprises with no gas holders and significant pipeline pressure fluctuations, offering a scientific and effective guidance method. This research can be applied to solve energy optimization problems in complex production systems and scenarios.
Quantum technology has become a strategic field that must be contested globally. On the basis of combing the current research situation at home and abroad, this paper analyzes the quantum science and technology plans of the United States, the European Union, Germany, the United Kingdom and other countries or regions from the aspects of “basic overview and implementation effect”, summarizes the main characteristics of these quantum science and technology plans, and proposes countermeasures and suggestions to promote China's quantum research on this basis, in order to provide empirical evidence for China's quantum planning and quantum technology breakthrough.
The learned index is a disruptive technique for big data management. The core idea of the learned index is to retrofit the data indexing in traditional index structures with machine learning (ML) models, providing fast data position prediction instead of slow index tree traversal. Yet, current learned indexes focus on fix-sized numeric data and all of them perform poorly for variable-length string data for two main reasons: 1) high computational overheads caused by lengthy string key prediction; 2) large ML model fitting errors due to variance in string prefixes. This paper introduces SLIN, a CPU-efficient, hybrid index for variable-length string data. SLIN features a hybrid tree and learned index structure that leverages the strength of each index to overcome the above-mentioned challenges. It proposes a string slice approach which converts variable-length strings into fix-sized integers, which facilitates using one-dimensional linear regression for key prediction to reduce computational overheads. Furthermore, it extracts the common prefix from string keys to decrease model fitting errors. Finally, we propose a strategy to adaptively choose the appropriate index to manage computational costs. Evaluations compare SLIN with state-of-the-art index structures. The experimental results show that SLIN achieves up to 2.47x higher throughputs and relatively small fitting errors.
The paper presents a comprehensive Natural Language Processing (NLP) evaluation framework for online public opinion analysis, addressing the limitations of singular NLP techniques. It highlights the significance of public opinion analysis in the era of big data and reviews commonly employed NLP methods. The proposed framework offers universality, comprehensiveness, and flexibility, aiding managers and users in better understanding product sentiments and purchasing behaviors for informed decision-making. The proposed NLP evaluation framework includes five stages, Data Collection, Qualitative Analysis, Sentiment Analysis, Semantic Relationship Mining, and Comprehensive Analysis. It involves techniques like Sentiment Analysis, Topic Modeling, and Co-Occurrence Networks to analyze sentiment and identify semantic relationships. Our case study on nursing care food market validates the framework's feasibility, proposing enhancements in taste, gifting nature, pricing, and product strategy. Future research will expand its applicability across domains and refine inter-stage connections while maintaining flexibility.
