Variation Method Research Articles

The influence of erosion on the dynamic process of landslide in Xinmo Village, Maoxian

A large-scale, high-speed, long-runout landslide occurred in Xinmo Village, Maoxian, Sichuan Province, China, on June 24, 2017. It was characterized by fast sliding speed, rapid volume growth, and large impact area. The dynamic process of such landslides and the influence of erosion on the dynamic process are studied by field investigation, numerical inversion and simulation. The results showed that entrainment erosion was a major factor of landslide volumetric change and a salient feature of the landslide process. An exponential equation relating the erosion rate and the deposition volume was established. Moreover, the study found that a slight change of the erosion rate (1e-4) also had a significant impact on the lateral spreading, longitudinal runout, and vertical erosion. As the erosion rate increased, the lateral spreading, longitudinal runout, and vertical erosion of this type of landslide became more obvious. By using the coefficient of variation method, it was obtained that the erosion rate had a greater influence on the vertical erosion than on the lateral spreading and longitudinal runout. In the study of the velocity of the rock avalanche under different erosion conditions, it was found that the erosion amount and the landslide velocity were not strictly linearly related. This study has important significance for understanding the dynamic process and erosion effect of rock avalanche, and provides useful references and insights for future research in related fields.

Population Distribution in Guizhou’s Mountainous Cities: Evolution of Spatial Pattern and Driving Factors

Guizhou is a typical mountainous province and is also one of the lowland regions in China that has attracted a population influx. Here, using population density data from 2000 to 2020 as the basic dataset and the coefficient of variation method and standard deviation ellipse analysis, we investigated the spatial characteristics across different years. The results show: Firstly, Guizhou’s population has a distinct spatial distribution, characterized by a lower population density in the southeast and a higher density in the northwest as well as an increasing polarization of population concentration toward the centers of prefecture-level cities and provincial capitals. Fluctuations in population density resemble a central siphon effect, which is particularly pronounced in the provincial capital and show a significant gravitational pull. Secondly, the coefficient of variation in population density across Guizhou’s counties is spatially divided by Guiyang, showing higher values in the east and lower values in the west. Furthermore, the ellipse of the standard deviation of population density is gradually shrinking, indicating an increasingly concentrated population distribution. Thirdly, the explanatory power of the population and socio-economic systems on the population distribution in Guizhou is significantly greater than that of the natural systems. Population distribution and migration patterns have shifted from purely “economic driven” to coexisting with “economic and comfort-oriented” trends, and there is an urgent need to improve the comfort level of public services as a typical supply, in order to boost Guizhou’s population attraction.

The uniqueness of steady sonic–subsonic solution to hydrodynamic model for semiconductors

In this paper, we study the uniqueness of the stationary sonic–subsonic solution to the isentropic hydrodynamic model of semiconductors with sonic boundary. We provide a new method to improve the proof of the uniqueness of the steady-state sonic–subsonic solution, even for the general isentropic case. In detail, we apply the exponential variation method combining a series of modifications with respect to the degeneracy of electrons at the boundary. The proposed method in the present paper is much simpler and perfecter than the existing methods.

Effects of Plyometric Exercise of Adolescent Male Volleyball Athletes: A Systemic Review

By looking at previous literature, this study aimed to analyze the construct of plyometric exercise variation. Comprehensive methods are used for these articles, such as searching for articles in database research journals. Semantic, Scopus, Scholar, and Crossref are the data sources for this database. The variety of plyometric exercises and vertical jump skills are the main themes. Fifty-one articles were verified, and then 12 articles were evaluated based on objectives, topics, sample size, research protocols, and results. Plyometric exercises are exercises in which the movements are explosive and, besides being practical, also train muscle strength, muscular endurance, flexibility, and agility. The purpose of volleyball training is so that players can acquire good basic techniques, strengthen physical strength, and develop interests and talents. To improve jumping ability, various exercises are performed from the beginner level. Therefore, the ability to jump and jump is essential for the volleyball game. The novelty in this study is the using a form of plyometric exercise that is varied according to the characteristics of athletes aged 14-17 years that have never previously been applied in volleyball clubs, which is the focus in this study of plyometric exercise variation methods, namely plyometric squat jump, skipping and hurdle hopping. The intensity of the exercise will be adjusted to the characteristics, and physiological development of male athletes aged 14-17 years.

An innovative deformation coordination method for analyzing distortion effects on box girders

A deformation coordination method is proposed in this study to account for the distortion effects on a box girder. The differential equation for distortion in vertical web box girders is derived based on the deformation coordination condition of the distortion angle, considering both external loads and internal forces. Subsequently, a comparative analysis is conducted to explore the similarities and differences between the differential equations derived from the proposed deformation coordination method, the plate element analysis method and the total potential energy variation method. The accuracy of the proposed approach is verified through bench-scale tests and numerical simulations. The findings indicate that the derived governing distortion differential equation and distortion attenuation coefficients in the proposed method align with those obtained from the plate element analysis method and the total potential energy variational method, which enhances the applicability to allow for the distortion equations to be obtained simply by calculating the distortion displacements. The analytical findings regarding the distortion warping normal stresses on the cross-sections of the box girders demonstrate favorable correspondence with the experimental results, displaying an acceptable error ranging from − 0.3% to 5.4%. Moreover, the peak of distortion warping normal stresses on the mid-span cross-section increases with higher span-to-depth ratios and height-to-thickness ratios of the web. Consequently, augmenting the thickness of the box wall proves to be an effective means of reducing the distortion effect in box girders.

Interval Iterative Decreasing Dimension Method for Interval Linear Systems and Its Implementation to Analog Circuits

The iterative decreasing dimension method (IDDM) is an iterative method used to solve the linear algebraic system Ax=f. Such systems are important in modeling many problems in applied sciences. For a number of reasons, such as estimated measurements made for modeling, errors arising from floating point calculations, and approximation methods used for solutions, it becomes necessary to study intervals in the solutions of systems of linear equations. The objective of this paper is to utilize IDDM to achieve resolution in the interval linear system (ILS). During the calculations, the Kaucher space is considered an extended classical interval space. The solutions of Barth-Nuding and Hansen interval linear systems, which are commonly used in the literature to test the solutions of ILSs, are obtained with the interval iterative decreasing dimension method for interval linear systems (I-IDDM). Since IDDM is a variation method of Gaussian elimination, a comparative analysis of the results with the interval Gaussian elimination method (I-GEM) is performed. It has been demonstrated that our approach, I-IDDM, produces better outcomes than I-GEM. I-IDDM is also used to investigate the analog circuit problem, where interval analysis is crucial.

A Novel Linear-Based Closed-Loop Control and Analysis of Solid-State Transformer

In this paper, a new linear-based closed-loop control method for a Solid-State Transformer (SST) has been proposed. In this new control method, individual current and voltage loops for each of the power conversion stages (AC-DC, DC-DC, DC-AC) are implemented. The feedback between the input and output control signals for each loop is achieved through the voltage on the DC link capacitors and the current transferred between the converters. This enables the SST to be controlled easily in a linear-based closed-loop manner without the need for complex computations. In order to evaluate the performance analysis of the proposed control system, a simulation of an SST with approximately 10 kVA apparent power was performed. Based on the obtained simulation results, the response time of the proposed control method for dynamic load variations was proved to be in the range of 40 milliseconds, and it has been observed that this method allows electrical power to be transferred from the load to the grid. The power factor value of SST under inductive load is measured to be approximately 99%, and the overall system efficiency is 96% and above, indicating that this proposed new control method has very high performance.

Research on the Impact of Digitalization on Bank Performance Based on BP Neural Network

With the promotion and development of digital finance, the degree of digitalization has an increasingly significant impact on the banking business, which cannot be ignored in the operation of commercial banks. Therefore, establishing a digitalization bank profitability system model is of great significance for the sustainable development of banks and digital finance. This article selects 6 Chinese state-owned banks from 57 listed banks in the A+H stock market as samples, constructs a digital indicator system and evaluation model for banks through the literature review method, and uses simulation training to explore the effectiveness of BP neural network in evaluating the impact of digitalization on bank profitability. The aim is to provide a reference for the evaluation of financial statement information of other banks and other factors affecting bank digitalization. The innovation of the article lies in the innovative use of the entropy weight coefficient of variation method to establish a digital evaluation system model, which utilizes the data itself to objectively establish an evaluation system, incorporate the established digital system into the black box model of the BP neural network model to study the impact of changes in digitalization on banking operations.

Regional Disaster Risk Assessment Based on ARIMA Prediction and Comprehensive Evaluation Model

The occurrence of extreme weather events affects the natural economic and social security, and has a serious impact on some industries, so the prediction and evaluation of disaster risk level is particularly important. This paper collected data on nine indicators from 25 regions and divided them into four areas: extreme weather, natural, social, and economic. the ARIMA prediction model is used to predict the probability and intensity of extreme weather in the future. The combined weights of each index were calculated by Composite Weighting Method, including principal component analysis, entropy weight method and coefficient of variation method, and the Risk Assessment Index (RAI) formula was constructed, and then the WNSE prediction and evaluation model was constructed. It is divided into four risk levels by fuzzy cluster analysis. We use WNSE prediction and evaluation model to calculate New York City and Switzerland’s RAI and coverage levels. Using this prediction, we can assess the severity of the consequences of natural disasters and prevent or remedy them. The assessment of regional disaster risk levels can give relevant reference to the government, and the government can make reasonable resource allocation.

Кластерный анализ в оценке эффективности зернового производства Тюменской области

Abstract. In recent years, there has been stable production of grain industry products in volumes that meet the needs of the Tyumen region and allow the sale of products outside the region. The article is devoted to the study of territorial differentiation of the production of grain and leguminous crops in the Tyumen region. The purpose of the study is to assess the efficiency of grain production in the context of municipalities in the south of the Tyumen region and determine the dependence on the level of fertilizer application. Methods. Statistical methods of data processing (analysis of time series, method of variation, method of correlation-regression analysis), as well as cluster analysis using the Ward method, were used for the study. The scientific novelty lies in the fact that municipalities have been clustered and ranked according to a set of parameters characterizing the development of the grain production industry in the Tyumen region. Results. An assessment was made of the scale of industry development for the period from 2018 to 2022, quantitative and qualitative indicators were considered: the size of sown areas, gross yields, yields of grain and leguminous crops differentiated by municipal areas. An assessment was made of the dynamics of development of the grain production industry, trends of changes over 5 years, parameters of the influence and dependence of the yield of grain and leguminous crops on the level of fertilizer application were identified, clusters were identified that reflect the differentiation of municipal areas in grain production.

Inverse problem method application in building physics numerical optimization: new insight into ideal envelope properties determination

ABSTRACT Building thermal design optimization is pivotal for energy efficiency and built environment improvement. The optimal thermophysical properties for building walls remain ambiguous. This ambiguity stems from the constraints of traditional building physics, which: (1) disjoins the enhancement of wall thermal performance from the building service system management; and (2) relies on the forward method that fails to identify the ideal variable thermophysical property, the solution to which is inherently a function. This study elucidates the optimal temperature-dependent volumetric specific heat ( ρ c p ( t ) ) and thermal conductivity ( k ( t ) ) through the inverse problem and variation method. Illustrative example indicates that the optimal ρ c p ( t ) closely resembles a combination of two ‘ δ ’ functions, while the optimal k ( t ) mirrors a square wave function. Utilizing these ideal thermophysical properties, the building energy consumption can be significantly reduced by 77.1% with the optimized ρ c p ( t ) and 79.9% with the optimized k ( t ) . The ideal thermophysical properties for active buildings differ significantly from those recommended for passive buildings, underscoring that minimal energy consumption does not necessarily imply minimal discomfort. Therefore, the thermophysical properties of active and passive buildings should be optimized distinctly. This research offers valuable insights for defining ideal thermophysical properties of active building walls and construction materials.

A Fluorescent Probe Derived from L‐Tryptophan for Copper (II) Ion Detection in Aqueous Media

AbstractCopper (Cu2+) is a transition metal ion extensively distributed in numerous foodstuffs of both animals and vegetables. An excess intake of Cu2+ ions cause injury, pneumonitis, a disorder in the nervous system, and gastrointestinal problems. Hence, we have developed a simple and novel Schiff base‐based fluorescence sensor for the detection of Cu2+ ions. The sensor, ‐3‐(1H‐indol‐3‐yl)‐2‐((4‐nitrobenzylidene) amino)propanoic acid (INAP), was synthesized from tryptophan and nitrobenzaldehyde by the reflux method and characterized by various analytical methods. The prepared INAP acted as a chemical sensor and displayed outstanding sensitivity and choosiness characteristics for Cu2+ ions an aqueous solvent. In the presence of Cu2+, it demonstrated a fluorescence change in an aqueous medium with a “turn‐off” phenomenon via complex‐mediated energy transfer characteristics, and the method of continuous variation suggested a 1 : 2 complex formation of the metal ion with INAP. The LOD of the sensor system was calculated to be 1.3 μM for the Cu2+ ion. In addition, the INAP fluorescent probe was applied to tap water samples and found acceptable recoveries. These results suggest that the newly fabricated sensor system would be explored as a chemosensor for the recognition of Cu2+ ions in environmental samples.

Model and performance analysis of energy conversion in functionally graded flexoelectric semiconductor nanostructures

Energy systems converting efficiently from surrounding environment for powering semiconductor electronic components at nanoscale has attracted intense research interests. Due to the strong size dependency, flexoelectricity has been demonstrated as an excellent candidate implemented as nanoscale piezoelectric semiconductor energy harvesters. In this work, through a judicious exploitation of structural symmetry and nonuniformity, we theoretically study the energy conversion behaviors of a novel asymmetric nanodisk model composed of functionally graded (FG) flexoelectric semiconductor (FS) (FG-FS) materials while being loaded with thickness-extensional mechanical vibration. In numerical analysis, the material parameters have been treated with a general variation method under the Cartesian coordinate systems for simplicity. Taking into accounts of the nonuniform structural form and multi-field coupling features, we derived an octic governing equation with variable coefficients for the current analyzed FG-FS nanodisk structure, which is, however, almost impossible to obtain analytical solutions. To successfully address this issue and also further explore the improved performance and new phenomena induced by the FG type of non-uniform structural profile, we specially proposed an efficient tensor algorithm and reduced the octic governing equation into one quartic equation with variable coefficients and other four common quadratic equations. Later, we explicitly explored the effect of each physical parameter on the energy conversion performance of the FS energy harvester by studying the variations of the output electric power density and the energy conversion efficiency. Results illustrated that the electromechanical energy conversion behaviors of the studied FG-FS nanodisk can be properly tuned by not only the related material parameters but also their gradations. We believe our work have the potential to pave new way for the designs and manufacture of novel nano-electronic semiconductor components for the future practical applications.

A fire risk assessment method for offshore platforms: applications of 24Model with BN

ABSTRACT This paper integrates the 24Model with a BN and proposes a fire risk assessment method for offshore platforms. Triangular fuzzy membership function fuzzy set theory, coefficient of variation method and the combination of node state distance function and fuzzy inference are used to address the subjectivity and uncertainty due to insufficient objective data and conflicting opinions of multiple experts. Based on the proposed method, the human, organisational and technical factors of an offshore platform are comprehensively analysed from the perspective of the system. Based on diagnostic inference and sensitivity analysis, the main affecting factors and causal path of the risk scenario are determined. The results show that the proposed method could help in identifying the root causes of the failures and provide new insights into the probabilities and relationships among different factors.

New Electric Power System Stability Evaluation Based on Game Theory Combination Weighting and Improved Cloud Model

The development of new electric power system is a requirement for China to realize its “carbon peaking and carbon neutrality goals”, so it is of great importance that we assess the stability of a new electric power system under certain conditions. Firstly, according to the factors affecting the stability of the new power system, the characteristics of the new electric power system are analyzed in depth, so as to establish a stability evaluation index system including four first-level indices of safety, adequacy, flexibility, and adaptability. Secondly, a stability evaluation model is proposed. Based on the game theory, the entropy weight method, criteria importance though intercrieria correlation (CRITIC) method, and coefficient of variation method are combined and assigned, and the cloud model is improved through the combination of weights, which is used for evaluating the stability of the new electric power system. Finally, the applicability of the proposed evaluation model is verified by an arithmetic example analysis, which can identify the vulnerability of the new electric power system and provide suggestions for improving its stability. The model can provide a theoretical basis for promoting energy transition and sustainable development.

A level set based topology optimization for elastodynamic problems using BEM

The paper presents a topology optimization methodology for 2D elastodynamic problems using the boundary element method (BEM). The topological derivative is derived based on the variation method and the adjoint variable method. The level set method is employed for the representation of the material domain and voids within a specified design domain. Thus, the boundaries can easily be generated, following the zero isocontour of the level set function. Numerical implementation is carried out to demonstrate the effectiveness of the proposed topology optimization methodology in wave isolation and waveguide problems.

Greater baseline intra-individual variation in telephone-based cognitive screening predicts cognitive and diagnostic outcomes at 2-year follow-up.

Intra-Individual Cognitive Variability (IICV) is an emerging clinical tool that has shown promise in predicting cognitive decline and dementia incidence. The present study aims to assess the predictive validity of IICV in remote cognitive screening tests, using nationally representative data. Two waves of cognitive and diagnostic data from the Health and Retirement Study (collected in 2010 and 2012) were utilized to investigate whether baseline IICV can predict cognitive decline and dementia pathology. Middle-aged and older adults who were cognitively intact and completed all cognitive tests at both baseline and follow-up were recruited in the study, resulting in a sample of 6,050 participants. With the coefficient of variation method, the IICV-dispersion was calculated based on cognitive screeners to predict follow-up mean cognitive performance, global cognition, suspected cognitive impairment, and self-reported dementia diagnosis. After accounting for demographics, depressive symptoms, and baseline cognitive performance, the results provide support for the predictive validity of IICV. Specifically, the study demonstrated that IICV-dispersion significantly predicted cognitive and diagnostic outcomes in a concave pattern where the prediction was more sensitive toward the higher end of IICV. IICV explained about 0.2%-2.3% of the variance of outcomes variables. IICV retrieved from cognitive screening tests in telemedicine settings offers insight into future cognitive functioning and neurocognitive diagnostic status, which can be cost-effective and reduce the burden on both patients and health care providers, especially benefitting individuals with low socioeconomic status and rural residents. Potential avenues for future research were also discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Adaptive orthogonal directional total variation with kernel regression for CT image denoising.

Low-dose computed tomography (CT) has been successful in reducing radiation exposure for patients. However, the use of reconstructions from sparse angle sampling in low-dose CT often leads to severe streak artifacts in the reconstructed images. In order to address this issue and preserve image edge details, this study proposes an adaptive orthogonal directional total variation method with kernel regression. The CT reconstructed images are initially processed through kernel regression to obtain the N-term Taylor series, which serves as a local representation of the regression function. By expanding the series to the second order, we obtain the desired estimate of the regression function and localized information on the first and second derivatives. To mitigate the noise impact on these derivatives, kernel regression is performed again to update the first and second derivatives. Subsequently, the original reconstructed image, its local approximation, and the updated derivatives are summed using a weighting scheme to derive the image used for calculating orientation information. For further removal of stripe artifacts, the study introduces the adaptive orthogonal directional total variation (AODTV) method, which denoises along both the edge direction and the normal direction, guided by the previously obtained orientation. Both simulation and real experiments have obtained good results. The results of two real experiments show that the proposed method has obtained PSNR values of 34.5408 dB and 29.4634 dB, which are 1.2392-5.9333 dB and 2.828-6.7995 dB higher than the contrast denoising algorithm, respectively, indicating that the proposed method has good denoising performance. The study demonstrates the effectiveness of the method in eliminating strip artifacts and preserving the fine details of the images.

Knowledge Ottoman Turkish Medicine : From Methods of Immunization Traditional to Pioneer World Vaccine

This research explores the history of immunization "vaccination" which was known as the variation method during the Ottoman Turkish period. The method used is the historical method, with the stages of source collection, source criticism, interpretation and writing. Data were collected from conventional and non-conventional libraries (internet), sources collected in the form of books, journal articles, website articles and others printed or from the internet that support this research. Furthermore, the facts obtained are narrated diachronically so that the facts described are chronological. The results of this study show that the development of vaccines that is happening now is a great contribution from Ottoman Turkey. The history of vaccination was started by the Ottoman Turkish government, Ottoman (Ottoman Turkish) physicians created and developed the variolation (inoculation) method, a traditional vaccine method by taming and using viruses (germs) applied to human skin that had previously been scratched. In this way, the virus injected into the human body (smallpox sufferers or non-smallpox sufferers) will experience illness and during the illness the immune process is formed so that the patient will get immunity (antibody). The success of vaccination from Ottoman Turkey has spread to Europe which saved many human lives and eradicated smallpox, especially England which was brought by Lady Mary Montagu, traders, sailors, ambassadors and others who learned the variolation method from Ottoman Turkey.

Short-Term Prediction Method of Transient Temperature Field Variation for PMSM in Electric Drive Gearbox Using Spatial-Temporal Relational Graph Convolutional Thermal Neural Network

Short-Term Prediction Method of Transient Temperature Field Variation for PMSM in Electric Drive Gearbox Using Spatial-Temporal Relational Graph Convolutional Thermal Neural Network