Time Characteristics Research Articles

DEVELOPMENT OF A HYBRID NETWORK CONGESTION MONITORING SYSTEM

Constantly growing requirements for the information systems service quality require monitoring of network congestion. The experimental results proved that when using hybrid networks, it is difficult to estimate the network load based on the average time of information delivery due to the presence of a significant scatter of time data. A mathematical model of information transmission in wireless computer networks in the form of a queuing system has been developed. The system identifies the main elements: a data transmission channel, a data frame and a device that transmits data. Analysis of transmission standards allows us to identify the following main transmission modes for modeling: one frame successful transmission; transmitting a frame with an error and repeating the transmission (possibly several times); frame transmission using performance-enhancing technology (Bursting technology is used as an example). Events associated with the transmission of information over the wireless network of one device are considered as monitoring system states under consideration. Packet exchange process was considered to be a homogeneous Markov process with discrete states, continuous time and constant intensities. A mathematical model has been developed based on the state graph of the device-frame-transmission channel system, which allows one to determine the timing characteristics for the previously identified modes. The methods for assessing network congestion based on statistical information during data transmission is proposed. Delivery times are broken down into ranges derived from the simulation. The proportion of packets whose delivery time lies within the selected test range is estimated. To evaluate the results, experimental studies were carried out on a fragment of the hybrid network. Delivery time results for each experiment were divided into four ranges: corresponding to accelerated delivery of fragments, successful delivery on the first, second and third attempts, respectively. Results analysis showed that it is preferable to use the fourth range to assess network congestion. Approximation reliability in this case is 0.85, the correlation coefficient is 0.92. The proposed methodology allows us to estimate network congestion based on testing data with a fairly high degree of reliability.

Breakdown calculation of zoning tests by positive lightnings

To simulate a natural lightning strike, the breakdown should occur within 1–3 μs in an aircraft lightning zoning test. However, the breakdown voltage–time characteristic of the combined gap is hard to derive because of the complex breakdown process. In this paper, a series of positive lightning impulse discharge tests in a rod electrode–floating aircraft model–plate combined gap were conducted. The leader propagation processes in both sub-gaps were observed using a high-speed camera. The potential of the floating aircraft model was analyzed. We propose a potential calculation model for the floating aircraft during the leader propagation phase. After fitting test data, an empirical formula for the connected streamer plasma was given. Taking that model into account, the calculation result of the average breakdown time of the combined gap has an error of <5.7%.

Evaluation of self-compacting geopolymer concrete properties containing different water glass solutions

This research investigates how water glass solutions (Ms 1.99 and Ms 2.92) impact the standard consistency, setting time, rheological properties, and mechanical and microstructural characteristics of self-compacting geopolymer concrete (SCGC) with various amounts of fly ash, ground granulated blast furnace slag (GGBFS), and rice husk ash (RHA). Six Self compacting geopolymer concrete (SCGC) samples were prepared from each of the two glass water solutions. Mechanical properties, such as compressive, split tensile, and flexural properties, were evaluated for the blends. Compared to Ms 1.99, concrete using water glass solution Ms 2.92 had a standard consistency of 49%, and the setting time was increased from 1100 to 1250 min. The Ms. 2.92 combine improves workability. Mechanical properties revealed that 28-day-cured concrete with Ms 2.92 water glass solution had the highest compressive strength of 55.6 MPa and achieved the highest split tensile and flexural strengths, 6.67 MPa and 10 MPa, respectively. XRD, SEM, EDX, and FTIR are used to investigate SCGC's microstructures.

Implementation of a Digital Model of Thermal Characteristics Based on the Temperature Field

Introduction. Computer modeling allows engineers to make valid design decisions by accurately assessing the thermal characteristics of design objects. The implementation of digital twin technology in the process of designing technical facilities is the current direction of scientific research and development. To do this, it is necessary to develop computer models whose accuracy meets the requirements for digital twins. However, the scientific literature does not widely present the results of research aimed at implementing digital twin technology in the design process. The general issues related to the use of digital twins in various industries are mainly considered. Therefore, the objective of this study was the development of a digital model and a comparative analysis of the accuracy of calculations of thermal characteristics of the design object.Materials and Methods. The main tool for conducting the research was the methodology proposed by the authors for developing a computer model of thermal characteristics for the implementation of digital twin technology. The numerical solution was implemented through constructing a thermal model for calculating the temperature field based on the finite element method in the ANSYS engineering analysis system from ANSYS, Inc. (USA). For the analytical solution, a computer model of thermal characteristics developed on the basis of the state-space method, implemented in the ANSYS Twin Builder module, was used. The state-space model was matched to the behavior of the original thermal model through approximating the transfer function to the stepwise response of the thermal load using the time domain vector approximation method. Verification of the constructed analytical model was carried out in the engineering calculation system MATLAB from the MathWorks company (USA). The research was carried out for a 400V machine model manufactured by NPO “Stankostroenie” LLC, Sterlitamak (Russia).Results. The developed digital model makes it possible to calculate the thermal characteristics of the design object with high accuracy. The results of the comparative analysis showed a high degree of correspondence between the values of thermal characteristics obtained using the proposed digital model and the results of numerical simulation. The maximum error in calculating thermal characteristics did not exceed 0.1ºC.Discussion and Conclusion. Computer modeling that combines numerical calculation methods and a scientific approach based on digital twin technology, provides obtaining the result as close as possible to the results of experiments. The digital model proposed in the study is an effective solution, since it provides performing calculations to evaluate thermal characteristics in real time, which is one of the most important requirements for the implementation of digital twin technology.

Modern approaches to the use of Berberis Thunbergii cultivars for sustainable landscape design

Purpose. For the purposes of sustainable landscape design of the urban space, to substantiate new modern nature-oriented solutions for the use of dwarf cultivars of the Berberis Thunberg species. Methods. Visual assessment, computer modeling and visualization. Results. One of the aspects of realizing the goals of sustainable development, namely Sustainable development of cities and communities, is the rationalization of living space in cities. An integral part of this process is the aesthetic arrangement of territories, which ensures sustainable landscape design. The design of flower arrangements, flower beds can and should change, be updated, absorb new things. One of the ways of realizing such a context when creating permanent flower beds is the use of metal frame structures, which solve the task of creating a contrast with the colors of decorative plant compositions. The choice of the color of the metal frame should be subject to the thematic and landscape context. In order to give graphicness to decorative plant compositions from cultivars of Berberis thunbergii species, the expediency of horizontal contouring of the plant ornament with metal structures with a width of contour lines of 3–5% of the linear dimensions of the structure is shown. Modeling of structures was carried out using the program Realtime Landscaping Architect 2023.02, Trial Free Version. The use of the color solution, shape, texture of the modeled structures, the use of contour lighting of the decorative-but-foliage installation is substantiated. Cultivars of Berberis Thunberg are given, which are appropriate for use, the life span of which can reach 50 years under the conditions of performing the necessary minimum care. Conclusions. The creation of permanent, aesthetically expressive flower beds and flower beds involves not only the use of environmentally friendly materials, but also the choice of local or adaptive decorative cultivars that provide all-season decorativeness, the absence of the need for annual renewal of plant material, artificial watering, and the creation of a habitat for pollinators and birds The use of cultivars of the Berberis Thunberg species contributes to the creation of stable flower beds that are able to preserve their decorative characteristics for a long time. The most harmoniously proposed compositions will be revealed in the presence of dynamic visual points, a deep perspective and a panoramic view.

Formal timing analysis of gate-level digital circuits using model checking

Due to the continuous reduction in the transistors sizing ruled by the Moore’s law, digital devices have become smaller, and more complex resulting in an enormous rise in the delay variations. Therefore, there is a dire need of precise and rigorous timing analysis to overcome anomalies during the timing analysis. Timings of digital circuits can be verified using various simulation or static timing analysis (STA) based tools but they provide estimated results due to their inherent in-exhaustive nature or report timing paths corresponding to non-existent functional paths, respectively. Formal verification provides complete and sound analysis results and has widely been used for the functional verification of digital circuits but its application in the timing analysis domain is somewhat limited. We present a generic framework to perform formal timing analysis of digital circuits with the help of Uppaal model-checker. The given digital circuit along with its timing parameters in the form of state transition diagram are modeled using timed automata in the Uppaal model checker. Timing delays are calculated from corresponding technology parameters, and Quartus Prime Pro is used to obtain the information about the circuits’ paths. In order to make the analysis scalable, we also propose a novel path partitioning technique and compare its results with complete path analysis and traditional STA. The formal model is verified with the help of properties to assess the timing characteristics, like time period of a clock, critical path, and propagation delay of the considered circuit. Modeling and verification of ISCAS-85 and ISCAS-89 benchmark circuits is presented for illustration purposes.

Predicting the dynamics of heart rate variability in patients with type 1 diabetes against improving glycemic control

The aim of the study: to analyze the effect of improvement of glycemic control on heart rate variability changes in patients with type 1 diabetes against the background of insulin therapy correction and to predict this dynamics based on the parameters of continuous glucose monitoring. We examined 49 patients with the level of glycosylated hemoglobin ≥7% and <10% without late diabetic complications. The average age – 32.0 (21.5; 38.0) years, the average disease duration – 9.5 (5.0; 17.5) years. The study was based on the simultaneous registration of heart rate variability and continuous glucose monitoring before and 3 months after correction of insulin therapy. Logistic regression analysis and ROC-analysis were used to predict the changes. After 3 months, the patients had significant decrease in the level of glycosylated hemoglobin, glycose variability, and a decrease in the frequency of hypoglycemic episodes. Heart rate variability increased in 73.5% of persons. The presence of hypoglycemia, standard deviation of blood glucose levels and glomerular filtration rate after treatment turned out to be prognostic factors for the predicting improvement in heart rate variability (the proportion of correct prediction of the patient's actual belonging to one or another prognostic group was 76.39%). Based on the calculation of the theoretical values of the positive result probability using the logistic equation, a detailed scale for predicting changes in heart rhythm variability for type 1 diabetes patients was proposed: up to 0.07 – a low probability of a positive result; 0.07-0.29 – the probability of a positive result is below average; 0.29-0.51 – a moderate probability of a positive result; 0.51-0.90 – high probability of a positive result; more than 0.90 – a very high probability of a positive result. We found that improvement of glycemic control leads to an increase in both frequency and time characteristics of heart rate variability. The increase in the likelihood of improvement of heart rate variability in patients with type 1 diabetes was more likely to be associated with reduced glycose variability and fewer hypoglycemic episodes. We developed a predictive mathematical model of heart rate variability based on the continuous glucose monitoring parameters for type 1 diabetes with sensitivity of 88.0% and specificity 68.18%, AUC 0.739 (p=0.001).

Preferences and welfare impacts of new metro serving airport access

The construction of a new rail infrastructureto connectairportswith cities plays a vital role in enhancing the accessibility of airport ground access and mitigating negative externalities resulting fromautomobiles. To identify air travelers’ preferences for airport ground access by considering a new metro line in Taiwan, we develop nested logit models, which combine revealed and stated preference data. The results reveal significant effects of travel time, travel cost, and individual characteristics; the metro and taxis are highly substituted for airport access. Consumer surplus or accessibility is not equally distributed across air travelers. Travelers who are female, young adults, lower income, and without vehicles in the household obtain considerable consumer benefits from using the new metro. Policy incentives to time and cost have a small impact on the increase in metro share, but consumer surplus surges considerably.

Fast Prediction of Airfoil Aerodynamic Characteristics Based on a Combined Autoencoder

Aircraft airfoils are classified into two main categories: symmetrical and asymmetrical. Both types of airfoils have a significant impact on the flight performance and safety of the aircraft. The fast prediction of the aerodynamic coefficients and pressure distributions of airfoils is crucial for the design of aircraft. The traditional wind tunnel test and CFD methods have the disadvantages of high test cost and high time consumption. To solve these problems, a combined autoencoder (CAE) network is proposed in this paper, which can achieve the fast prediction of airfoil aerodynamic coefficients and pressure distributions. The network consists of an airfoil shape autoencoder (AE) network and a multilayer perceptron (MLP) network. Firstly, an autoencoder network reflecting the characteristics of the airfoil shape is established, and the effects of different latent variables on the performance of the autoencoder network are investigated. Then, the latent variables obtained from the autoencoder are concatenated with the inflow conditions such as the Reynolds number and the angle of attack to be used as inputs to the MLP network, and the aerodynamic coefficients of different airfoils in different inflow conditions are predicted. The effects of various latent variable inputs, as well as the direct input of the airfoil shape into the MLP network, on the prediction performance of aerodynamic coefficients are compared and analyzed. The optimal aerodynamic coefficient prediction network is then obtained. Finally, the CAE network is also applied to predict the pressure distributions of different airfoils in different inflow conditions and the effects of different latent variables and input conditions on the prediction performance of the pressure distributions are analyzed and compared with the advantages and disadvantages of the CAE network and the conditional variational autoencoder (CVAE) network. The results demonstrate that the proposed method is capable of accurately predicting aerodynamic characteristics in a shorter time, offering a valuable reference for the fast and efficient design of aircraft airfoils.

Simplified calculation method of non-Gaussian wind pressure peak factor for large-span spatial roof structures

Based on the wind tunnel test data of a long-span spatial roof structure, the correlation between higher-order statistics and peak factors was obtained according to the time and frequency domain characteristics of wind pressure-time history, as well as the coincidence condition between wind pressure power spectra of measurement points and wind speed power spectra. A simplified peak factor calculation formula was proposed based on the moment-based Hermite polynomial model. The Gaussian part of the peak factor was calculated through the wind speed spectrum based on the zero-crossing theory, and the parameters related to higher-order statistics were considered as the non-Gaussian part of the peak factor. The simplified method was verified through the instantaneous extreme value method based on a coal shed structure with available long-term wind pressure-time history. It revealed that the results of the proposed simplified method correlate well with that of the instantaneous extreme value method, indicating its accuracy and potential value for engineering practice.

Investigation into the Time-Dependent Characteristics of Stress and Deformation of Weak Surrounding Rock and Lining Structure in Operational Tunnels: Model Test

During the long-term operation of tunnels, surrounding rock undergoes creep effects under environmental loads, resulting in changes in the aging evolution model of stress and deformation in surrounding rock and lining, which affects the long-term operational safety of the tunnel. Therefore, using the model test device for time-dependent characteristics of stress and deformation of weak surrounding rock and lining structure in operational tunnels, taking into account the influence of tunnel burial depth and lateral pressure coefficient of surrounding rock, a model test on time-dependent characteristics of stress and deformation in weak surrounding rock and lining structure was conducted, and the stress and deformation time-varying curves at key locations of surrounding rock and lining were obtained. The time characteristics of surrounding rock stress, the contact force between surrounding rock and lining, internal force, and displacement of lining structure were analyzed. Research findings indicate that the stress of surrounding rock, the internal force and displacement of lining structure, and the contact force between surrounding rock and lining all increase and tend to be stable over time under constant load. This implies that the stress and deformation of the surrounding rock and lining structure exhibit time-dependent changes. With changes in burial depth and lateral pressure coefficient, significant variations are observed in the various indicators of stress and deformation in the surrounding rock and lining structure, indicating both time-dependent and long-term characteristics in terms of stress and deformation. The research results provide basic data support for the study of the time-dependent characteristics of stress and deformation between weak surrounding rock and lining structures in operational tunnels and can provide theoretical and technical guidance for the long-term service status discrimination and disaster prevention and control of operational tunnels.

STFT-TCAN: A TCN-attention based multivariate time series anomaly detection architecture with time-frequency analysis for cyber-industrial systems

Networks and industrial systems play a pivotal role in modern society, and their security has garnered increasing attention. Anomalies within industrial equipment may propagate through fault transmission, leading to a cascade of failures. Additionally, cyberattacks on equipment can result in significant losses. Therefore, in the realm of industrial and cyberspace domains, an effective multivariate time series anomaly detection system for monitoring equipment is instrumental in ensuring the healthy operation of the machinery. Nevertheless, detecting anomalies in numerous time series remains challenging, stemming from the absence of anomaly labels and the complexity of the data patterns. Existing algorithms predominantly concentrate on modeling within the time domain, falling short in fully leveraging the informative features present in frequency domain data, resulting in diminished detection performance. This paper introduces STFT-TCAN, a model for anomaly detection in time series that seamlessly integrates information from both time and frequency domains for extracting data features. Sliding windows and the Short Time Fourier Transform (STFT) are utilized to construct a frequency matrix, effectively amalgamating the characteristics of both time and frequency domains within the time series. Furthermore, the model employs Temporal Convolutional Networks (TCN) and Transformer attention mechanisms (which combined to form the TCAN module) to capture the features of multivariate time series, thereby resulting in heightened detection accuracy. The proposed model undergoes validation on six publicly available datasets, showcasing the superior performance of the STFT-TCAN model in comparison to current baseline methods. It adeptly extracts features from both frequency and time domains in sequential data, thereby achieving state-of-the-art performance in tasks related to anomaly detection in multivariate time series.

Study on hormone induction of the male parent and embryonic development, gonad differentiation, and growth of “All-female No.1” Culteralburnus

Female Culter alburnus was faster in growth rate than males. In this study, the gynogenetic G2 and the pseudo-male G2' were used as the female and male parents, respectively, to breed a new national variety “All-female No.1” C. alburnus (AFC). Hormone induction, embryonic development, gonadal differentiation, and growth of AFC were studied. The results showed induction with low concentrations of 17α-methyltestosterone in a indoor-net cage culture was not effective. Under the stimulation of 17α-methyltestosterone, some gonads had a tendency to transform into testis, but not completely. There were three types of gonads in 5-month-old and four types of gonads in 12-month-old fishes, however, they all differentiated into ovaries in 15-month-old fishes. Testosterone propionate and high concentrations of 17α-methyltestosterone in pond culture induction had a good effect resulting in ①a functional pseudo-male with normal testis development that could successfully extrude semen during the breeding period, ②a pseudo-male with normal testis development, but could not extrude semen, and ③the appearance of intersexual glands. The second experiment revealed that with common fish, all-female fish embryo had normal embryonic development. The development time and morphological characteristics of each stage were similar, but the development of the all-female embryo was slightly slower than the common embryos. The gonad differentiation of the all-female embryo were normal and none differentiated into testis, which indicated that all-female could ensure the female sex without affecting the normal gonad differentiation. The correlation between body weight, length, and month-age of all-female and common fish was strong. The all-female had faster growth rate and more uniform growth specification than the common fish. Therefore, the use of testosterone propionate and high concentrations of 17α-methyltestosterone in pond culture induction could avoid complete degeneration of gonads into ovaries. The all-female embryo had the advantages of normal embryonic development and gonadal differentiation, faster growth, and uniform growth specification.

A novel method combining deep learning with the Kennard-Stone algorithm for training dataset selection for image-based rice seed variety identification.

Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial for both trading and planting. This study collected RGB images of 20 hybrid rice seed varieties. An enhanced deep super-resolution network (EDSR) was employed to enhance image resolution, and a variety classification model utilizing the high-resolution dataset demonstrated superior performance to that of the model using the low-resolution dataset. A novel training sample selection methodology was introduced integrating deep learning with the Kennard-Stone (KS) algorithm. Convolutional neural networks (CNN) and autoencoders served as supervised and unsupervised feature extractors, respectively. The extracted feature vectors were subsequently processed by the KS algorithm to select training samples. The proposed methodologies exhibited superior performance over the random selection approach in rice variety classification, with an approximately 10.08% improvement in overall classification accuracy. Furthermore, the impact of noise on the proposed methodology was investigated by introducing noise to the images, and the proposed methodologies maintained superior performance relative to the random selection approach on the noisy image dataset. The experimental results indicate that both supervised and unsupervised learning models performed effectively as feature extractors, and the deep learning framework significantly influenced the selection of training set samples. This study presents a novel approach for training sample selection in classification tasks and suggests the potential for extending the proposed method to image datasets and other types of datasets. Further exploration of this potential is warranted. © 2024 Society of Chemical Industry.

Physicochemical and sensory properties of vegetarian pasta produced with pea (Pisum sativum) protein powder

Dry legumes are frequently preferred as a protein source in plant-based diets. In this study, the production of "pea pasta" with the addition of pea protein powder was aimed. Moisture, ash, protein, weight loss, volume, and cooking time characteristics of the pasta were examined. Additionally, the sensory properties of the produced pea pasta and its effect on individuals' satiety were investigated, and the results were evaluated. In all sensory analyses, the pasta sample containing 10% pea protein powder was the most preferred among 8 different criteria. It was observed that the satiety lasted longer when consumers consumed pasta containing 10% pea protein powder compared to the control pasta consumption.

Pore structure characterization and reservoir quality prediction in deep and ultra-deep tight sandstones by integrating image and NMR logs

Pore structure and fracture determine reservoir quality in deep and ultra-deep tight sandstones, however, the limited availability of core data makes it challenging to describe the complexity of pore-throat structure and characterize the wide range of fractures. To fill this gap, a comprehensive analysis was conducted to evaluate pore structure and fractures from multiple perspectives. Integration of core, thin sections, scanning electron microscopy (SEM), high pressure mercury injection (HPMI) and nuclear magnetic resonance (NMR) tests are used to evaluate pore structure and characterize fracture of deep and ultra-deep tight sandstones in the Cretaceous Bashijiqike Formation in the Kuqa Depression. Pore structure and reservoir quality types can be divided into dominantly three types according to characteristics of NMR transverse relaxation time (T2) distribution and HPMI curves. Correlation analysis among HPMI and NMR parameters with petrophysical parameters indicate that maximum pore throat size (rmax), logarithmic mean of T2 (T2gm), irreducible water saturation (Swi), and reservoir quality index (RQI) are the most sensitive parameters for pore structure characterization. The Type Ⅰ pore structure is composed of intergranular and intragranular pores, and NMR T2 spectrum shows the highest magnitude, with the calculated mobile fluid saturation content also being the highest. The Type II and III pore structures are composed of intergranular pores, intragranular dissolved pores and intercrystalline micropores in clay minerals. T2gm of Type II and III are smaller, with the calculated mobile fluid saturation content being the lower.The fracture features and parameters including fracture density, porosity, aperture, and length are analyzed using image logs and core. The pore structure characteristics are quantificationally and qualitatively analyzed by NMR logs. Integration of pore structure and fractures by image logs and NMR logs are used for prediction of high quality reservoir and hydrocarbon productivity. High hydrocarbon productivity is obtained in well intervals with abundant natural fractures, and favorable pore structure will also contribute to high matrix porosity. Therefore, NMR logs and image logs are crucial for evaluating reservoir quality and predicting productivity when calibrated with core and analysis data. The comprehensive analysis integrating core data with geophysical well logs provide important insights for reservoir quality prediction of deep and ultra-deep tight sandstones.

Analyzing the urban–rural divide: the role of location, time, and breach characteristics in U.S. hospital security incidents, 2012–2021

This analysis describes how cybersecurity breaches vary between urban and rural hospitals, how they differ over time, and how they differ across different types and locations of breaches within hospitals. Utilizing data from the U.S. Department of Health and Human Services Office for Civil Rights on incidents from 2012 to 2021 that affected 500 or more individuals, we studied breaches in 237 community hospitals, distinguishing between 185 urban and 52 rural facilities. Using Poisson and Quasi-Poisson regression models, we found a significant yearly increase in data breaches, especially in urban hospitals. Hacking/IT incidents were the most common breaches, particularly prevalent in urban settings, while unauthorized access/disclosure occurred frequently. Email and network servers were the primary locations of breaches in urban and rural hospitals. These findings indicate differences between urban and rural hospitals in data security incidents, suggesting areas for further research.

Intelligent Matching Method for College Dormitory Roommates: Chameleon Algorithm Based on Optimized Partitioning

A chameleon algorithm based on optimized partitioning was studied to solve the intelligent matching problem of college dormitory roommates. Using quantitative research methods, data on personal preferences and lifestyle habits of college students were collected, and the K-center object chameleon algorithm was used for data analysis and roommate matching. Test the algorithm performance on the BBC dataset, compare clustering quality indicators such as entropy, purity, and RI value, and verify the effectiveness of the algorithm. This algorithm can accurately assign students to their respective dormitories, avoiding overlapping situations and achieving excellent matching results. In terms of matching accuracy and running time, the K-center object chameleon algorithm shows superior performance compared to other algorithms. In terms of clustering quality evaluation, comparisons were made from three dimensions: entropy value, purity, and RI value. The experimental results show that the closer the entropy value is to 0, the closer the purity and RI value are to 1, and the better the matching effect. This result further validates the effectiveness of the algorithm in the intelligent matching problem of college dormitory roommates. The matching accuracy of this algorithm on the BBC dataset reached 98.82%, showing better clustering quality than other algorithms in terms of entropy, purity, and RI values. The entropy value approached 0, while the purity and RI values approached 1, verifying the efficiency of matching quality. The chameleon algorithm based on optimized partitioning proposed in the study has shown excellent performance in intelligent matching of college dormitory roommates, with the characteristics of high-precision matching and fast running time. It has important practical significance for improving the quality of life and learning efficiency of college dormitory students, and provides new research methods and ideas for related fields.

Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features

Abstract Current self-attention based Transformer models in the field of fault diagnosis are limited to identifying correlation information within a single sequence and are unable to capture both time and frequency domain fault characteristics of the original signal. To address these limitations, this research introduces a two-channel Transformer fault diagnosis model that integrates time and frequency domain features through a cross-attention mechanism. Initially, the original time-domain fault signal is converted to the frequency domain using the Fast Fourier Transform, followed by global and local feature extraction via a Convolutional Neural Network. Next, through the self-attention mechanism on the two-channel Transformer, separate fault features associated with long distances within each sequence are modeled and then fed into the feature fusion module of the cross-attention mechanism. During the fusion process, frequency domain features serve as the query sequence Q, and time domain features as the key-value pairs K. By calculating the attention weights between Q and K, the model excavates deeper fault features of the original signal. Besides preserving the intrinsic associative information within sequences learned via the self-attention mechanism, the Twins Transformer also models the degree of association between different sequence features using the cross-attention mechanism. Finally, the proposed model's performance was validated using four different experiments on four bearing datasets, achieving average accuracy rates of 99.67%, 98.76%, 98.47%, and 99.41%. These results confirm the model's effective extraction of time and frequency domain correlation features, demonstrating fast convergence, superior performance, and high accuracy.

Application and Research of Multi-Scale Dynamic Diffusion Permeability Model in Different Coal Ranks

The spatial scale of coal mining and rock layer control is distributed between 10-10 and 107, spanning 17 orders of magnitude. There are multi-scale pore cracks from millimeter to micro and nanometers in the coal, and the pore size can reach one million times, which makes the coal permeability also show a multi-scale characteristics of millions and time. For the permeability of the coal seam, it is necessary to test and analyze the distribution of micro and nano pores and cracks in the coal [1]. CBM mining is accompanied by the dynamic change of adsorption pressure in coal and the complex deformation of multi-scale heterogeneous structure of coal, and the multi-scale deformation feature of pore fissure structure plays an important role in the transport of methane [2]. However, existing studies focus on treating coal as a homogeneous isotropic medium, without considering the influence of coal multiscale pore structure on permeability. The variation of heterogeneous degree of coal structure with structural scale and its influence on dynamic diffusion and permeability are rarely reported. Li Zhiqiang et al. [3] conducted an experimental-model-mechanism study of CBM gas micro-nano series multi-scale dynamic diffusion permeability. On this basis, the dynamic diffusion and permeability of different coal scales.