Stability Of Model Research Articles

Chinese Named Entity Recognition Method Combining ALBERT and a Local Adversarial Training and Adding Attention Mechanism

For Chinese NER tasks, there is very little annotation data available. To increase the dataset, improve the accuracy of Chinese NER task, and improve the model's stability, the authors propose a method to add local adversarial training to the transfer learning model and integrate the attention mechanism. The model uses ALBERT for migration pre-training and adds perturbation factors to the output matrix of the embedding layer to constitute local adversarial training. BILSTM is used to encode the shared and private features of the task, and the attention mechanism is introduced to capture the characters that focus more on the entities. Finally, the best entity annotation is obtained by CRF. Experiments are conducted on People's Daily 2004 and Tsinghua University open-source text classification datasets. The experimental results show that compared with the SOTA model, the F1 values of the proposed method in this paper are improved by 7.32 and 7.98 in the two different datasets, respectively, proving that the accuracy of the method in this paper is improved in the Chinese domain.

Use SWOT to Analyze and Study the Enterprise of Disney

The firm I am researching is a diverse global media corporation located in Walt Disney's Burbank Studios in Los Angeles, California, USA. SWOT analysis was used to research the Disney Company. During my investigation on the Disney Company, I discovered that around the start of the pandemic, the whole economic curve of the Disney Company collapsed, including total assets and earnings. However, due to the sluggish development of internet company, after adopting to the new strategy, it has gradually ushered in a pretty decent momentum, and its income has steadily returned over the last two years.If the Disney Company continues to grow at its current rate, it will become the world leader under the impact of the pandemic. This article examined the CAPM model's stability, and via my study on Disney, I discovered that the effect of two separate markets on the stock of the firm I investigated is beneficial to investors' investment decisions.

Modeling and analysis of monkeypox disease using fractional derivatives

The frequency of monkeypox outbreaks and the extent of the projected outbreaks in human populations have both steadily increased. This paper proposes Atangana-Baleanu fractional-order derivatives define in Caputo sense to investigate the kinetics of Monkeypox transmission in Ghana. We determine the stability of the recommended model's equilibrium points and basic reproduction number. The solution's existence and originality, as well as the model's Hyers-Ullam stability, are proven. The models basic reproduction number was found to be R0 = 0.1940. The numerical simulation showed the fractional operator had an influence on the various compartments of the model. The dynamics of the disease in the community were shown to be influenced by fractional-order derivatives, and infections were eradicated within the first five (5) days when π = 0.2.

Optimal site selection study of wind-photovoltaic-shared energy storage power stations based on GIS and multi-criteria decision making: A two-stage framework

Wind-photovoltaic-shared energy storage system can improve the utilization efficiency of renewable energy resources while reducing the idle rate of energy storage resources. Using the geographic information system (GIS) and the multi-criteria decision-making (MCDM) method, a two-stage evaluation model is first developed for site selection of wind-photovoltaic-shared energy storage projects. In the first stage, infeasible alternatives are ruled out due to veto identification. In the second stage, the fuzzy MCDM method is applied to further merit selection. Firstly, the comprehensive criteria system is built, and evaluation data is collected using probabilistic linguistic term sets (PLTSs) to ensure the comprehensiveness and scientificity of decision. Then, the new score function based on compromise theory are proposed. Thirdly, the criteria weights are calculated using the improved Decision-Making and Trial Evaluation Laboratory (DEMATEL) approach. Finally, the improved gained and lost dominance score (GLDS) method is used to rank alternatives. To test model's feasibility, an empirical study is conducted in Chifeng City. The alternative A1 appears to be extremely prominent in the results. The model's stability and reliability are demonstrated by sensitivity and comparative analysis. This study aids in the layout of wind-photovoltaic-shared energy storage projects and broadens the application scopes of GIS and MCDM method.

Trastuzumab deruxtecan versus trastuzumab emtansine for patients with human epidermal growth factor receptor 2-positive metastatic breast cancer: A cost-effectiveness analysis

BackgroundDESTINY-Breast03 (NCT03529110) was the first global phase III study to assess the antitumor activity of trastuzumab deruxtecan (T-DXd) compared to trastuzumab emtansine (T-DM1) in 2022. However, the balance between efficacy and cost of T-DXd remains unclear. As a result, the present study's goal is to investigate the cost-effectiveness of T-DXd vs T-DM1 as a second-line treatment for patients with HER2-positive MBC from the US and Chinese payer's perspectives. MethodsA Markov model with a 20-year time horizon was developed to evaluate the overall cost of patient treatment, incremental cost-effectiveness ratio (ICER), quality-adjusted life-years (QALYs), and life-years (LYs) in the US and China at WTP levels of 150,000/QALY and 37,653/QALY, respectively (3 times GDP per capita in 2021). Key data were gathered from the US government's official website, the Xiangya Hospital of Central South University, and published literature. To determine the model's stability, a sensitivity analysis was performed. A subgroup analysis was also implemented. ResultsCompared with T-DM1, treatment with T-DXd generated an additional 1.672 QALYs (2.796 LYs), resulting in an ICER of $13,342/QALY (US) and $186,017/QALY (China). The cost of drugs is the most influential factor in the American and Chinese models. Subgroup analysis revealed that the T-DXd and T-DM1 regimens were more cost-effective at reducing the risk of death in the US and Chinese HER2-positive MBC patients. ConclusionT-DXd as second-line treatment could gain more health benefits for HER2-positive MBC patients in comparison with T-DM1, which is considered to be cost-effective in the US but not in China.

Fractional order mathematical model of monkeypox transmission dynamics

In this paper, we present a deterministic mathematical model of monkeypox virus by using both classical and fractional-order differential equations. The model includes all of the possible interactions that contribute to disease spread in the population. We investigate the model's stability results in the disease-free case when R 0 < 1. When R 0 < 1, we show that the model is stable, otherwise it is unstable. To obtain the best fit that describes the dynamics of this disease in Nigeria, the model is fitted using the nonlinear least square method on cumulative reported cases of monkeypox virus from Nigeria between January to December 2019. Furthermore, adequate conditions for the existence and uniqueness of the solution of the model have been proved. We run numerous simulations of the proposed monkeypox model with varied input parameters to investigate the intricate dynamics of monkeypox infection under the effect of various system input parameters. We investigate the system's dynamical behavior to develop appropriate infection control policies. This allows the public to understand the significance of control parameters in the eradication of monkeypox in the population. Lowering the order of fractional derivatives has resulted in significant modifications. To the community's policymakers, we offered numerous parameters for the control of monkeypox.

주택점유 선택모형의 추정계수 안정성에 관한 연구

The housing market is difficult to predict due to the complexities of macroeconomic conditions, government policy interventions, and ever-changing supply and demand. Furthermore, the structure of a household's housing consumption decision-making can cause structural changes over time. When a market undergoes structural change, government policies based on model coefficients estimated from historical data can be misleading. In particular, permanent income is a major variable in the analysis of housing demand. However, if using current income causes an estimation bias for other variables, the model's stability cannot be guaranteed. This study explores the stability of consumer preference using the primary variables of the housing tenure choice function, such as income, housing cost, housing price volatility, and unit size. The analysis confirms that the household preference system for income, housing costs, and housing price volatility conditions was generally stable in housing consumption from 2002 to 2019. However, there was a significant change in the 2020 housing tenure choice model. The change was statistically significant. Furthermore, the estimation bias model analysis shows that using the current income variable as a proxy for the permanent income variable did not result in any significant estimation bias for other independent variables.&#x0D;

Crossing Phantom Divide in f(Q)$f(Q)$ Gravity

AbstractThe possibility of crossing a phantom divide line in the extension of symmetric teleparallel gravity or the gravity, where Q is the non‐metricity, is investigated. The cosmic evolution of the effective equation of state parameter for dark energy is studied considering exponential, logarithmic, and combined theories. Moreover, the exponential model behaves like the ΛCDM at high redshifts before deviating to or , respectively, depending on the value of model parameter. It also approaches a desitter phase asymptotically. However, the crossing of the phantom divide line, that is, , is realized in the combined theory. Furthermore, statefinder diagnostics are studied in order to differentiate between several dark energy models. To ensure the three model's stability, the stability analysis is employed using linear perturbations. It is demonstrated how to reassemble via a numerical inversion approach based on existing observational constraints on cosmographic parameters and the potential of bridging the phantom divide in the resulting model. It explicitly demonstrates that future crossings of the phantom dividing line are a generic feature of feasible gravity models.

MODIFIKASI MODEL SEIR PADA PENYAKIT CAMPAK

The epidemic models Susceptible, Exposed, Infected and Recovered (SEIR) are used for the spread of diseases that have a latent period (incubation period) which one is measles disease. Latent periods are entered into the Exposed class. Measles itself after the incubation period will experience clinical symptoms consisting of three stages, which are prodromal stage, eruption stage and healing stage. Due to these clinical symptoms, the SEIR model can be modified by dividing the Infected class into two classes, which are Infected Prodromal class and Infected Eruption class. While the healing stage enters Recovered class. The spread of measles can be made into an epidemic model with five classes which are and . The purpose of this study is to explain the modification of the model, determine and analyze the model's local stability at the equilibrium point of the model and to interpret model simulations with multiple stability-eligible parameter values. The results obtained from this study are modification of model which is model. Based on model, two equilibrium points obtained which are disease-free equilibrium points and endemic equilibrium points, which are locally asymtotics stable with conditions. Model simulations are presented to support an explanation of model stability analysis based on stability-meeting parameters

Data driven strength and strain enhancement model for FRP confined concrete using Bayesian optimization

To ensure the safe design of composite system, a precise design-based model for concrete encased in fibre-reinforced polymer (FRP) is required. An enclosure data driven model is developed in this paper to forecast the ultimate state of FRP-confined concrete using Bayesian hyperparameter optimization. For a database of 820 columns with circular cross-section, the suggested model's predictions are compared to those of six empirical models and a non-optimized machine learning regressor of XGBoost. In comparison to the previous models, statistical analysis demonstrates that the improved model (BO-XGB) more precisely forecasts the compressive strength and axial strain of concrete confined with FRP. The results further reveal that for strength and strain increase with lateral confinement, BO-XGB with weight uncertainty offers stable predictions, whereas the other models display turbulent model mistake. In comparison to previous models, the suggested model's excellent accuracy and stability are attributable to its great flexibility and resilience in capturing the impacts of lateral confinement pressure as an interaction between the concrete core and FRP shell.

An Intrusion Detection Model Based on Improved ACGAN in Big Data Environment

With the development of big data technology, network intrusion problems against server vulnerabilities emerge one after another. To improve the accuracy of intrusion detection, this paper designs an intrusion detection platform based on the ACGAN (auxiliary classifier generative adversarial network) model in a big data environment. Firstly, by introducing a self-attention mechanism, the global characteristics of attack samples are extracted to improve the quality of generated samples. Then, by adding a gradient penalty, the model's convergence speed and training stability are improved. Finally, this method enhances and expands the attack samples and verifies the dataset. The experimental results show that compared with other comparison methods, the overall detection accuracy of this system is higher, and the false-positive rate and false-negative rate are lower.

Compact stellar model in higher torsion gravitational theory

In this study, we address the issue of a spherically symmetrical interior solution to the quadratic form of f(T)=T+ϵT2 gravitational theory using a physical tetrad that provides vanishing components of the off-components of the field equation, in contrast to what exists in the current literature. To be able to formulate the resulting differential equation in a closed form, we employ the Krori-Barua (KB) ansatz. Using the KB spacetime form, we derive the analytic form of the energy-density, radial, and tangential pressures and the anisotropic form. All of these quantities are affected by the dimensional parameter ϵ, which causes them to have a noted difference from those given in the frame of Einstein general relativity. The derived model of this study exhibits a non-trivial form of torsion scalar, and it also contains three constants that we drew from the matching of the boundary condition with a line element that also features a non-trivial form of torsion scalar. Having established the physical conditions that are needed for any real stellar, we check our model and show in detail that it bypasses all of these. Finally, we analyze the model's stability utilizing the Tolman-Oppenheimer-Volkoff equation and adiabatic index and show that our model satisfies these.

Mathematical model for the estrogen paradox in breast cancer treatment

Estrogen is known to stimulate the growth of breast cancer, but is also effective in treating the disease. This is referred to as the"estrogen paradox". Furthermore, short-term treatment with estrogen can successfully eliminate breast cancer, whereas long-term treatment can cause cancer recurrence. Studies highlighted clinical correlations between estrogen and the protein p53 which plays a pivotal role in breast cancer suppression. We sought to investigate how the interplay between estrogen and p53 impacts the dynamics of breast cancer, and further explore if this could be a plausible explanation for the estrogen paradox and the paradoxical tumor recurrence that results from prolonged treatment with estrogen. For this, we propose a novel ODE based mathematical model that accounts for dormant and active cancer cells, along with the estrogen hormone and the p53 protein. We analyze the model's global stability behavior using the Poincaré-Bendixson theorem and results from differential inequalities. We also perform a bifurcation analysis and carry out numerical simulations that elucidate the roles of estrogen and p53 in the estrogen paradox and its long term estrogen paradoxical effect. The mathematical and numerical analyses suggest that the apparent paradoxical role of estrogen could be the result of an interplay between estrogen and p53, and provide explicit conditions under which the paradoxical effect of long-term treatment may be prevented.

An abnormal driving behavior recognition algorithm based on the temporal convolutional network and soft thresholding

Most traffic accidents are caused by bad driving habits. Online monitoring of the abnormal driving behaviors of drivers can help reduce traffic accidents. Recently, abnormal driving behavior recognition based on the sensors' data embedded in commodity smartphones has attracted much attention. Though much progress has been made about driving behavior recognition, the existing works cannot achieve high recognition accuracy and show poor robustness. To improve the driving behaviors recognition accuracy and robustness, we propose an algorithm based on Soft Thresholding and Temporal Convolutional Network (S-TCN) for driving behavior recognition. In this algorithm, we first introduce a soft attention mechanism to learn the importance of different sensors. The TCN has the advantages of small memory requirement and high computational efficiency. And the soft thresholding can further filter the redundant features and extract the main features. So, we fuse the TCN and soft thresholding to improve the model's stability and accuracy. Our proposed model is extensively evaluated on four real public data sets. The experimental results show that our proposed model outperforms best state-of-the-art baselines by 2.24%.

Influence of input values on the prediction model error using artificial neural network based on Taguchi's orthogonal array

AbstractRapid and accurate assessment of software project development using artificial intelligence tools can be essential for success in the software industry. This article has two objectives: to reduce the magnitude relative error (MRE) value in estimating the effort and cost of software development using the proposed artificial neural network architecture based on the Taguchi method and examine the influence of input variables on the change in relative error value. Clustering and fuzzification methods further mitigate the heterogeneous structure of the different project values of the datasets used. Taguchi method contributes to the reduction of the number of iterations by 99%, which achieves a significant reduction in estimation and value of MRE. By monitoring additional criteria, such as prediction, correlation, and comparing two activation functions, such as sigmoid and radial basis function, the proposed model's correctness, reliability, and stability are confirmed. Significantly better results are expected using the sigmoid activation function and a decrease in the value of the mean (MRE).

For All Symmetric and Asymmetric Perturbation Modes, Self-Gravitating Stability of a Fluid Cylinder Embedded in a Confined Liquid Pervaded by Magnetic Field

For all symmetric and asymmetric perturbation modes, the self-gravitating stability of a fluid cylinder contained in a limited liquid penetrated by magnetic field has been explored. The (MHD) basic equations are solved after the problem is formulated. The results of an analytical study of a broad eigen-value relation are confirmed quantitatively. The stability of a fluid cylinder is investigated under the influence of self-gravitating, inertial, and electromagnetic forces. In ax symmetric states, the electromagnetic force has both stabilizing and destabilizing effects. The model is entirely stable for all wavelength values when the magnetic field strength is quite high. As we have superposed gas-oil layer combination fluids, this phenomenon is of interest both academically and during geological drilling in the earth's crust. When the model's destabilizing activity is decreased and inhibited, the model's stability behavior emerges.

Dynamical analysis of coronavirus disease with crowding effect, and vaccination: a study of third strain

Countries affected by the coronavirus epidemic have reported many infected cases and deaths based on world health statistics. The crowding factor, which we named "crowding effects," plays a significant role in spreading the diseases. However, the introduction of vaccines marks a turning point in the rate of spread of coronavirus infections. Modeling both effects is vastly essential as it directly impacts the overall population of the studied region. To determine the peak of the infection curve by considering the third strain, we develop a mathematical model (susceptible–infected–vaccinated–recovered) with reported cases from August 01, 2021, till August 29, 2021. The nonlinear incidence rate with the inclusion of both effects is the best approach to analyze the dynamics. The model's positivity, boundedness, existence, uniqueness, and stability (local and global) are addressed with the help of a reproduction number. In addition, the strength number and second derivative Lyapunov analysis are examined, and the model was found to be asymptotically stable. The suggested parameters efficiently control the active cases of the third strain in Pakistan. It was shown that a systematic vaccination program regulates the infection rate. However, the crowding effect reduces the impact of vaccination. The present results show that the model can be applied to other countries' data to predict the infection rate.

Design and performance analysis of SiO2-MOSFET based absorber for reflected RF signal

The Fifth Generation (5G) of technology has losses in reflection from the receiver of the Massive-Multiple-Input Multiple-Output (m-MIMO) base station. For this reason, a model has been proposed as a solution to the performance improvement and efficiency in the 5G base station. This technology uses the MOSFET transistor as a basic cell. This MOSFET is made up of SiO2. Various performance parameters (gain, reflection coefficient, total power absorbed by SiO2 based MOSFET, S-parameter, efficiency, etc.) have been analyzed for the proposed model's validity, stability, and reliability. In addition to this, the comparison has been made with existing models using efficiency, RF power absorption level, and ease of implementation as a standard for comparison.

USING AQUATIC PLANTS AS TREATMENT FOR SWINE-BREEDING WASTEWATER AFTER BIOGAS TECHNOLOGY

The livestock industry is one of the important sectors of agriculture in Vietnam. The swine-breeding farms are mainly spontaneous, the common wastewater is treated by biogas technology. However, the actual operation at the farms, shows that the wastewater treatment by biogas has been high content and still exceeded the standards of national regulation QCVN 62-MT:2016/BTNMT of COD, BOD5, TN, PO43-, leading to affect the environment. An empirical study was carried out to treat swine-breeding wastewater after biogas technology using Sagittaria sagittifolia and Enydra fluctuans. The results showed that the swine-breeding farming wastewater treatment model with Enydra fluctuans has an efficiency of 73.3% of COD content, 89.2% of BOD5, 88.3% of TN, and 89.6% of PO43-. Sagittaria sagittifolia showed a yield of 66.7% of COD content, 78.7% of BOD5, 69.5% of TN, and 85.1% of PO43-. With the same conditions and the same experimental design method, Enydra fluctuans showed better treatment results than Sagittaria sagittifolia. However, wastewater is treated by Sagittaria sagittifolia and Enydra fluctuans meeting the regulations QCVN 62-MT:2016/BTNMT, column B for pH, COD, BOD5, TN, PO43- parameters after 30 days. The treatmen model shows great potential for using aquatic plants to remove pollutants from swine-breeding wastewater. However, further studies should be carried out to evaluate the model's stability and applicability in practice.

Mathematical model and stability analysis on the transmission dynamics of skin sores.

In this study, a non-linear deterministic model for the transmission dynamics of skin sores (impetigo) disease is developed and analysed by the help of stability of differential equations. Some basic properties of the model including existence and positivity as well as boundedness of the solutions of the model are investigated. The disease-free and endemic equilibrium were investigated, as well as the basic reproduction number, R0, also calculated using the next-generation matrix approach. When R0 < 1, the model's stability analysis reveals that the system is asymptotically stable at disease-free critical point globally as well as locally. If R0 > 1, the system is asymptotically stable at disease-endemic equilibrium both locally and globally. The long-term behaviour of the skin sores model's steady-state solution in a population is investigated using numerical simulations of the model.