Explore the vast range of titles available.

Monitoring what application or type of applications running on a computer or a cluster without violating the privacy of the users can be challenging, especially when we may not have operator access to these devices, or specialized software. Smart grids and Internet of things (IoT) devices can provide power consumption data of connected individual devices or groups. This research will attempt to provide insides on what applications are running based on the power consumption of the machines and clusters. It is therefore assumed that there is a correlation between electric power and what software application is running. Additionally, it is believed that it is possible to create power consumption profiles for various software applications and even normal and abnormal behavior (e.g., a virus). In order to achieve this, an experiment was organized for the purpose of collecting 48 h of continuous real power consumption data from two PCs that were part of a university computer lab. That included collecting data with a one-second sample period, during class as well as idle time from each machine and their cluster. During the second half of the recording period, one of the machines was infected with a custom-made virus, allowing comparison between power consumption data before and after infection. The data were analyzed using different approaches: descriptive analysis, F-Test of two samples of variance, two-way analysis of variance (ANOVA) and autoregressive integrated moving average (ARIMA). The results show that it is possible to detect what type of application is running and if an individual machine or its cluster are infected. Additionally, we can conclude if the lab is used or not, making this research an ideal management tool for administrators.

Algorithms for measuring semantic similarity between Gene Ontology (GO) terms has become a popular area of research in bioinformatics as it can help to detect functional associations between genes and potential impact to the health and well-being of humans, animals, and plants. While the focus of the research is on the design and improvement of GO semantic similarity algorithms, there is still a need for implementation of such algorithms before they can be used to solve actual biological problems. This can be challenging given that the potential users usually come from a biology background and they are not programmers. A number of implementations exist for some well-established algorithms but these implementations are not generic enough to support any algorithm other than the ones they are designed for. The aim of this paper is to shift the focus away from implementation, allowing researchers to focus on algorithm’s design and execution rather than implementation. This is achieved by an implementation approach capable of understanding and executing user defined GO semantic similarity algorithms. Questions and answers were used for the definition of the user defined algorithm. Additionally, this approach understands any direct acyclic digraph in an Open Biomedical Ontologies (OBO)-like format and its annotations. On the other hand, software developers of similar applications can also benefit by using this as a template for their applications.

This paper proposes a method combining fractional Fourier transform (FRFT) and a high peak-to-average power ratio suppression algorithm. Calculations show that as the order of the FRFT decreases, the peak-to-average power ratio of the signal gradually decreases; combined with the suppression algorithm can further reduce the peak-to-average power ratio of the system and solve the problem of the suppression algorithm affecting system performance. At the same time, this paper uses the 2012 World Bank survey data of Chinese manufacturing enterprises to study the influence of financial repression on the selection of financing channels of manufacturing enterprises. The research results show that financial repression has a significant impact on the choice of financing channels for manufacturing enterprises, which greatly increases the proportion of informal financial financing in the operating capital of enterprises and reduces the proportion of formal financial financing. Financial repression has led to an increase in the cost of formal financial financing, making enterprises choose informal financial channels for financing. Among them, large enterprises tend to choose commercial credit in informal finance, while small and medium-sized enterprises choose private credit It is possible to choose two financing channels, which reflects the ‘scale discrimination’ characteristics of financial repression.

In the selection of basketball players, the determination of the selection index system and the weight of each index is an important prerequisite for whether the selection is scientific or not. Only when the index system is determined and the importance of each index is sorted reasonably can it be guaranteed that the basketball tactics scoring work went smoothly. This research introduces a method of X fuzzy mathematics, called analytic hierarchy process, or AHP for short. The AHP method can be used in regional planning, resource allocation, program selection, policy analysis, conflict analysis, forecast estimation, decision research, etc. The AHP method schematises the thinking process of the human brain analysis program, which can simply, comprehensively, effectively and clearly deal with complex problems restricted by many factors; it is also a quantitative tool that can be used for the measurement of the sports evaluation system.

This article uses the calculus method and three-dimensional analysis system to photograph and analyse the action techniques of China's famous basketball players’ shooting training. Combining with the action technical parameters of outstanding male basketball players at home and abroad, this article analyses several main basketball players’ shooting training actions. Quantitative analysis of the calculus method was carried out in the technical link, and the problems existing in the basketball player's movement technology were found. Further, from the comparison of the physical fitness of excellent athletes, the reasons for the gap between the level of basketball in China and the world were discovered, and the physical quality of excellent male basketball players was established. Level evaluation models and standards provide a reliable guarantee for accurately grasping the development of athletes’ physical fitness, clarifying the status of each physical fitness in training, optimally controlling the basketball training process, and achieving scientific basketball training.

Aiming to solve the problems in the traditional multiple regression analysis model for predicting college sports performance based on the principles of econometrics, a predictive model that combines genetic algorithm (GA), college sports performance evaluation and regression analysis is proposed. GA is used to conduct dynamic and supervised optimisation evaluation of college sports performance; on this basis, combined with regression analysis and GA's global optimisation capabilities, a complex nonlinear relationship between student sports performance and influencing factors is established; the student's performance is calculated based on the college sports performance. The results show that the method has high prediction accuracy and good stability.

Based on the traditional form of the endogenous growth model, and for it to increase the micro-foundation that includes the homogeneous and representative bank resource allocation, this paper constructs an endogenous economic growth model that includes the investment structure of the residential sector and financial deepening. Using China’s prefecture-level data proves that due to the inherent difference between the central planner’s single equilibrium solution and the family’s decentralised equilibrium solution, when the residential sector’s preference for real estate investment causes the investment structure to deviate from the optimal level of society, the increase in the proportion of real estate investment The allocation efficiency of financial resources has a significant inhibitory effect and drags down the realisation of long-term potential economic growth. In the absence of a central planner in a market economy, increasing leverage may not mean financial deepening, but may reduce financial efficiency (FEt) and accumulate systemic financial risks.

The rapid growth of digital communication has led to a surge in spam messages, particularly through Short Message Service (SMS). These unsolicited messages pose risks such as phishing and malware, necessitating robust detection mechanisms. This study focuses on a comparative analysis of machine learning models for SMS spam detection, with a particular emphasis on a proposed SVM-DistilBERT model enhanced by a voting classifier. Using the UCI SMS Spam dataset, the models are evaluated based on recall, accuracy, precision, and Receiver Operating Characteristic Area Under the Curve (ROC AUC) scores to assess their effectiveness in correctly identifying spam messages. By leveraging Optuna for hyperparameter optimization, the proposed model achieves superior performance, with an accuracy of 99.6%, surpassing traditional methods like SVM with TF-IDF Bi-gram and AdaBoost, which achieved 98.03%. The study also examines the effects of lemmatization and synonym data augmentation, with lemmatization shown to improve spam detection by reducing feature space redundancy and enhancing semantic understanding. To ensure transparency in decision-making, Local Interpretable Model-Agnostic Explanations (LIME) is applied. The results demonstrate that the optimized SVM-DistilBERT with the voting classifier offers a robust and effective solution for SMS spam filtering.