Linear Method Research Articles

3-D imaging of whole-space environments with electromagnetic induction sensors based on linear approximation

Accurate mapping of the periphery of tunnel has become an essential step in underground mining and engineering. Because of high sensitivity to electric conductivity and magnetic permeability, the whole-space electromagnetic (EM) method has played an important role in geological forecast of groundwater and exploration of mineral ore. However, obtaining a three-dimensional (3-D) image of the whole-space EM method by 3-D inversion is challenging due to the limited observation space and inefficiencies in 3-D forward modeling algorithms. While 1-D inversion algorithms have long been used to map the periphery of the tunnel, various 3-D inversion algorithms have been successfully applied in other scenarios. Recently, the portable frequency domain loop-loop EM induction (EMI) measurement has been considered as an active source magnetic method in near surface surveys, because of the dominance of magnetic permeability in the in-phase (IP) response. The 3-D linear forward modeling method is developed based on the location-specific magnetic sensitivity function, and the 3-D inversion algorithm has been effectively employed for mapping magnetic anomalies in large-scale area. In this study, we intend to demonstrate that the EMI measurement can give 3-D image of the periphery of the tunnel by theory and numerical experiments. The IP response of EMI in whole-space is separated into three parts, and the 3-D linear forward modeling method is examined by analytical solution. The 1-D Fourier transform is used to accelerate the 3-D forward modeling, and the 3-D inversion show a good performance of mapping the magnetic abnormal bodies in underground space.

QbD Assisted Development and Validation of UV Spectroscopic Method in Estimation of Silymarin

Background: Silymarin is a flavonoid utilised in liver dysfunction for years; new studies have emphasised its potential utility as a therapy for nanoparticulate targeting in many other disorders. In order to establish the product quality of such compounds, researchers have been trying to develop a robust method of analysis, but the methods developed till now are too expensive and time-consuming. Here we proposed quality by design-assisted development and validation of the UV spectroscopic method using Design-Expert® software in the estimation of Silymarin for fabrication of nanoparticulate formulations, which is simple, accurate, cost-effective and non-tedious. Aim: The proposed method is a simple, new, robust, accurate, and precise UV visible spectroscopic technique for estimating silymarin produced in nano-formulations utilising an analytical quality by design (AQbD) approach. Methods: A UV spectrophotometric technique was established, as maximum absorption (287.7) was measured using a Shimadzu UV-1800 double-beam UV visible spectrophotometer. The characterization of silymarin was done by melting point, DSC and FTIR techniques. The two critical method variables chosen were scanning speed and sample interval to be analysed by the design of experiment methodology utilizing the central composite design principle, which shows robustness and optimized technique involved in this work. Results: The spectroscopy technique was developed and validated as per International Conference of Harmonization recommendations. The Beer's-Lambert rule was followed in a series of 2-12 μg/ml dilution increments, with a correlation value of R2 = 0.999. The method's linearity was shown to be excellent across the concentration range. The percent recovery of the current method approach was determined to be within the confidential limitations, i.e., less than 2% expressed as % RSD, and the methodology was proved to be precise at inter and intraday variations (% RSD). The LOD and LOQ were found to be 0.264 μg/ml and 0.801 μg/ml which were also determined correctly. During specificity testing, no interfering peaks were found. Conclusion: This UV approach has been used successfully to determine the quantity of silymarin present in the nanoparticulate formulation, which can be used for testing its other pharmaceutical dosage forms.

Analysis of Damage Assessment Tweets During Disaster using Sentiment Analysis

This seems to be an abstract or summary of a paper on monitoring Twitter for damage assessments after a disaster. Using simple linear regression and Support Vector Regression methods for weighting and the random forest methodology for classification, the research provides a novel approach that makes use of low-level lexical characteristics, top-most frequency word features, and syntactic elements relevant to damage assessment. The accuracy of the suggested method for identifying damage assessment tweets is 94.62%, as measured across 14 typical disaster datasets for binary and multi-class categorization. Significant advancements were observed when comparing the proposed method to the state-of-the-art for both in-domain and cross-domain scenarios. The suggested method does not require labelled tweets or tweets of a specific disaster kind in order to be trained and implemented; instead, it can be trained on historical disaster datasets.

A machine learning approach for protected species bycatch estimation

IntroductionMonitoring bycatch of protected species is a fisheries management priority. In practice, protected species bycatch is difficult to precisely or accurately estimate with commonly used ratio estimators or parametric, linear model-based methods. Machine-learning algorithms have been proposed as means of overcoming some of the analytical hurdles in estimating protected species bycatch.MethodsUsing 17 years of set-specific bycatch data derived from 100% observer coverage of the Hawaii shallow-set longline fishery and 25 aligned environmental predictors, we evaluated a new approach for protected species bycatch estimation using Ensemble Random Forests (ERFs). We tested the ability of ERFs to predict interactions with five protected species with varying levels of bycatch in the fishery and methods for correcting these predictions using Type I and Type II error rates from the training data. We also assessed the amount of training data needed to inform a ERF approach by mimicking the sequential addition of new data in each subsequent fishing year.ResultsWe showed that ERF bycatch estimation was most effective for species with greater than 2% interaction rates and error correction improved bycatch estimates for all species but introduced a tendency to regress estimates towards mean rates in the training data. Training data needs differed among species but those above 2% interaction rates required 7-12 years of bycatch data.DiscussionOur machine learning approach can improve bycatch estimates for rare species but comparisons are needed to other approaches to assess which methods perform best for hyperrare species.

ENHANCING UNDERWATER IMAGE QUALITY THROUGH ADAPTIVE COLOR CORRECTION AND MULTI-SCALE HISTOGRAM EQUALIZATION

Due to the selective attenuation of light in water, underwater images are poorly visible and pose significant challenges in visual activities. The structural and statistical properties of different areas of degraded underwater images are damaged to different levels, resulting in an overall uneven drift of object representation and further degrading the image quality. In order to solve these problems, we introduce a method for enhancing underwater images through multi-bin histogram perspective equalization under to solve the problems caused by underwater images. We estimate the degree of feature variation in each image region by extracting the statistical features of the image and using this information to control feature enhancement to achieve adaptive feature enhancement, thereby improving the visual effect of degraded images. We first design a vibration model that exploits the difference between data elements and regular elements to improve the color correction performance of the linear transformation-based sub-interval method. In addition, a multiple threshold selection method was developed that adaptively selects a set of thresholds for interval division. Finally, a multi-bin sub-histogram equalization method is presented, which performs histogram equalization in each sub-histogram to improve image contrast. Underwater imaging experiments in various scenarios show that our method significantly outperforms many state-of-the-art methods in terms of quality and quantity. INDEX TERMS: Multiple intervals, multi-scale fusion (MF), sub histogram equalization (SHE), underwater image.

A psycholinguistic study of intergroup bias and its cultural propagation.

Intergroup bias is the tendency for people to inflate positive regard for their in-group and derogate the out-group. Across two online experiments (N = 922) this study revisits the methodological premises of research on language as a window into intergroup bias. Experiment 1 examined (i) whether the valence (positivity) of language production differs when communicating about an in- vs. out-group, and (ii) whether the extent of this bias is influenced by the positivity of input descriptors that were initially presented to participants as examples of how an in-group or out-group characterize themselves. Experiment 2 used the linear diffusion chain method to examine how biases are transmitted through cultural generations. Valence of verbal descriptions were quantified using ratings obtained from a large-scale psycholinguistic database. The findings from Experiment 1 indicated a bias towards employing positive language in describing the in-group (exhibiting in-group favoritism), particularly in cases where the input descriptors were negative. However, there was weak evidence for increased negativity aimed at the out-group (i.e., out-group derogation). The findings from Experiment 2 demonstrated that in-group positivity bias propagated across cultural generations at a higher rate than out-group derogation. The results shed light on the formation and cultural transmission of intergroup bias.

Early-life house dust mite aeroallergen exposure augments cigarette smoke-induced myeloid inflammation and emphysema in mice

BackgroundLongitudinal studies have identified childhood asthma as a risk factor for obstructive pulmonary disease (COPD) and asthma-COPD overlap (ACO) where persistent airflow limitation can develop more aggressively. However, a causal link between childhood asthma and COPD/ACO remains to be established. Our study aimed to model the natural history of childhood asthma and COPD and to investigate the cellular/molecular mechanisms that drive disease progression.MethodsAllergic airways disease was established in three-week-old young C57BL/6 mice using house dust mite (HDM) extract. Mice were subsequently exposed to cigarette smoke (CS) and HDM for 8 weeks. Airspace enlargement (emphysema) was measured by the mean linear intercept method. Flow cytometry was utilised to phenotype lung immune cells. Bulk RNA-sequencing was performed on lung tissue. Volatile organic compounds (VOCs) in bronchoalveolar lavage-fluid were analysed to screen for disease-specific biomarkers.ResultsChronic CS exposure induced emphysema that was significantly augmented by HDM challenge. Increased emphysematous changes were associated with more abundant immune cell lung infiltration consisting of neutrophils, interstitial macrophages, eosinophils and lymphocytes. Transcriptomic analyses identified a gene signature where disease-specific changes induced by HDM or CS alone were conserved in the HDM-CS group, and further revealed an enrichment of Mmp12, Il33 and Il13, and gene expression consistent with greater expansion of alternatively activated macrophages. VOC analysis also identified four compounds increased by CS exposure that were paradoxically reduced in the HDM-CS group.ConclusionsEarly-life allergic airways disease worsened emphysematous lung pathology in CS-exposed mice and markedly alters the lung transcriptome.

Robust Direct Power Control of Three-Phase PWM Rectifier with Mismatched Disturbances

To effectively eliminate the impacts of both matched and mismatched power disturbances in a three-phase PWM rectifier, this paper proposes a robust direct power control (RDPC) method with a single-loop control structure. Firstly, a nonlinear power model of the three-phase PWM rectifier is established. Then, using the exact feedback linearization method, a linearized power model including matched and mismatched power disturbances is derived and achieves the decoupling of active and reactive power. Secondly, to regulate the DC bus voltage, a sliding-mode controller (SMC) combined with a nonlinear disturbance observer (NDO) is proposed. The proposed SMC combined with an NDO (SMC + NDO) method features a single-loop control structure, which enables a faster response and simpler structure compared to the dual-loop DPC method. By incorporating estimated mismatched power disturbance into the sliding-mode surface, it overcomes the SMC’s defect in incompletely suppressing mismatched disturbances and enables the simultaneous regulation of voltage and active power. Additionally, it effectively reduces sliding-mode chattering. To regulate reactive power, a sliding-mode controller based on the exponential convergence law is designed to suppress matched reactive power disturbances. Finally, the simulation and experimental comparative results demonstrate that the proposed controller exhibits stronger robustness against matched and mismatched power disturbances, as well as a better performance under the constant power load (CPL).

NSimplex Zen : A Novel Dimensionality Reduction for Euclidean and Hilbert Spaces

Dimensionality reduction techniques map values from a high dimensional space to one with a lower dimension. The result is a space which requires less physical memory and has a faster distance calculation. These techniques are widely used where required properties of the reduced-dimension space give an acceptable accuracy with respect to the original space. Many such transforms have been described. They have been classified in two main groups: linear and topological . Linear methods such as Principal Component Analysis (PCA) and Random Projection (RP) define matrix-based transforms into a lower dimension of Euclidean space. Topological methods such as Multidimensional Scaling (MDS) attempt to preserve higher-level aspects such as the nearest-neighbour relation, and some may be applied to non-Euclidean spaces. Here, we introduce nSimplex Zen , a novel topological method of reducing dimensionality. Like MDS, it relies only upon pairwise distances measured in the original space. The use of distances, rather than coordinates, allows the technique to be applied to both Euclidean and other Hilbert spaces, including those governed by Cosine, Jensen–Shannon and Quadratic Form distances. We show that in almost all cases, due to geometric properties of high-dimensional spaces, our new technique gives better properties than others, especially with reduction to very low dimensions.

The association between circulating CD34+CD133+ endothelial progenitor cells and reduced risk of Alzheimer’s disease in the Framingham Heart Study

Aim: Endothelial dysfunction has been associated with both cerebrovascular pathology and Alzheimer’s disease (AD). However, the connection between circulating endothelial cells and the risk of AD remains uncertain. The objective was to leverage data from the Framingham Heart Study to investigate various circulating endothelial subtypes and their potential correlations with the risk of AD. Methods: The study conducted data analyses using Cox proportional hazard regression and linear regression methods. Additionally, genome-wide association study (GWAS) was carried out to further explore the data. Results: Among the eleven distinct circulating endothelial subtypes, only circulating endothelial progenitor cells (EPCs) expressing CD34+CD133+ were found to be negatively and dose-dependently associated with reduced AD risk. This association persisted even after adjusting for age, sex, years of education, apolipoprotein E (APOE) ε4 status, and various vascular diseases. Particularly noteworthy was the significant association observed in individuals with hypertension and cerebral microbleeds. Consistently, positive associations were identified between CD34+CD133+ EPCs and specific brain regions, such as higher proportions of circulating CD34+CD133+ cells correlating with increased volumes of white matter and the hippocampus. Additionally, a GWAS study unveiled that CD34+CD133+ cells influenced AD risk specifically in individuals with homozygous genotypes for variants in two stem cell-related genes: kirre like nephrin family adhesion molecule 3 (KIRREL3, rs580382 CC and rs4144611 TT) and exocyst complex component 6B (EXOC6B, rs61619102 CC). Conclusions: The findings suggest that circulating CD34+CD133+ EPCs possess a protective effect and may offer a new therapeutic avenue for AD, especially in individuals with vascular pathology and those carrying specific genotypes of KIRREL3 and EXOC6B genes.

Can surgical computer vision benefit from large-scale visual foundation models?

We investigate whether foundation models pretrained on diverse visual data could be beneficial to surgical computer vision. We use instrument and uterus segmentation in mini-invasive procedures as benchmarks. We propose multiple supervised, unsupervised and few-shot supervised adaptations of foundation models, including two novel adaptation methods. We use DINOv1, DINOv2, DINOv2 with registers, and SAM backbones, with the ART-Net surgical instrument and the SurgAI3.8K uterus segmentation datasets. We investigate five approaches: DINO unsupervised, few-shot learning with a linear decoder, supervised learning with the proposed DINO-UNet adaptation, DPT with DINO encoder, and unsupervised learning with the proposed SAM adaptation. We evaluate 17 models for instrument segmentation and 7 models for uterus segmentation and compare to existing ad hoc models for the tasks at hand. We show that the linear decoder can be learned with few shots. The unsupervised and linear decoder methods obtain slightly subpar results but could be considered useful in data scarcity settings. The unsupervised SAM model produces finer edges but has inconsistent outputs. However, DPT and DINO-UNet obtain strikingly good results, defining a new state of the art by outperforming the previous-best by 5.6 and 4.1 pp for instrument and 4.4 and 1.5 pp for uterus segmentation. Both methods obtain semantic and spatial precision, accurately segmenting intricate details. Our results show the huge potential of using DINO and SAM for surgical computer vision, indicating a promising role for visual foundation models in medical image analysis, particularly in scenarios with limited or complex data.

Quantitative and intelligent methods for economic forecasts (birth rate time series (Ntl))

The models proposed in the field of time series forecasting are numerous. However, accurate forecasting is still one of the main challenging problems faced by decision makers in various fields, especially in financial markets. Anticipatory and intelligent methods play a crucial role in economic forecasting, helping to increase the accuracy and reliability of forecasts. This is the main reason that many researchers have in the focus of their research the development of methods for improving the performance of forecasts. Using only a linear or non-linear method is not enough to recognize and model all features and functions of the data. In addition to quantitative techniques as well as intelligent techniques that have been developed recently as a result of technological developments, a new technique or hybridization of these two techniques behaves as a very efficient approach. For this reason, in the study it is proposed to improve the modeling and forecasting results by combining a linear A RIMA method with an intelligent non-linear method, the artificial neural network -ANN. Several ARIMA-ANN hybrid model architectures are known, but in this thesis a new A RIMA-ANN technique with improving features of the existing hybrid methods is proposed. The time series under study are the birth rate in Albania obtained by INSTAT and the Bank of Albania with monthly frequencies. Time series analysis goes through three phases: defining characteristics, modeling and forecasting. The analysis is performed with ARIMA, ANN and the proposed ARIMA-ANN method. Data mining, sentiment analysis, and natural language processing (NLP) techniques are used to analyze unstructured data sources. The models, either quantitative or intelligent, give satisfactory results, but the hybrid model shows much higher performance in all series. From the measurement indicators of the prediction performance, the hybrid method has resulted with high efficiency. The ARIMA-ANN model produced much better results than the ARIMA and ANN models. These results are a consequence of the combination of the two techniques and from which results a good recognition of the different structures of the data modes. This study contributes to the field of time series forecasts in Albania and intelligent or quantitative modeling by developing a new forecast model, that of hybridization.

High-speed railway infrastructure leads to species-specific changes and biotic homogenisation in surrounding bird community.

Linear infrastructure networks, including railways, are undergoing rapid development in order to connect distant urban areas. Particularly, High-Speed Railways are increasingly seen as a viable alternative to domestic flights in many countries. However, this development of linear infrastructures is known to affect the surrounding faunal communities due to the changes in the landscape and operation of said linear infrastructures. Both positive and negative effects of linear infrastructures on adjacent faunal communities have been reported. In this study, we determined the influence of the High-Speed Railway infrastructure on the bird community that surrounds it. Birds were surveyed by using both linear transect and direct counting methods, both in the area directly adjacent to the railway infrastructure and 500m away from it in a period of two years of surveys. A total of 16114 individuals belonging to 71 species were recorded. The presence of the High-Speed Railway caused species-specific changes in the bird communities that surround it, causing the attraction of some species and the rejection of others. Furthermore, we show that the presence of the infrastructure altered the natural species turnover as the landscape changes by attracting the same bird species regardless of changes in the landscape, and filtering out others. We propose that further work in mitigation and development plans should focus on species-specific measures to assess the risk bird communities are exposed to.

Co-cooling, simulation-based exploration of a new method to measure the heat transfer coefficient of a building envelope in hot climate and summer period

ABSTRACT The discrepancy between designed and as-built building performance can be quantified by a heat transfer coefficient (HTC) measurement. A reference method for measuring this coefficient is the co-heating test. However, the use of this method is limited to certain weather conditions. This work proposes a new method, complementary to the co-heating test, to be performed in summer or in hot climates, called the co-cooling test. This method consists in cooling the building instead of heating it. This article presents a numerical setup to prove the feasibility of the co-cooling test, determining ad-hoc linear regression models. To better account for solar gains, an equivalent outdoor temperature is used. In the cases studied, the simple linear regression method using an equivalent outdoor temperature enables the HTC to be determined with errors below 10%.

Quantifying Tennis Player Performance: A Linear Regression Approach

This paper uses linear regression to quantitatively analyse the performance of players in the men's singles competition at Wimbledon 2023. Firstly, the data is processed by observationally analysing the match data to ensure compliance with the tournament standards and regulations. Next, key metrics were extracted, including short-term and long-term metrics, as well as the introduction of Serve Indicator to consider the impact of serve advantage on player performance. Then, the most important independent variables were identified through Random Forest feature analysis and parameters were calculated using least squares to construct performance indicators for use in linear regression. Finally, through data visualisation and analysis, it was found that player 1 usually performs better at critical moments, showing greater stability and consistency, while player 2 shows greater variability and unpredictability. Overall, the linear regression method in this paper is valuable and practical for quantifying tennis players' performance, and can provide a reference for players and coaches to help them better analyse and improve their performance.

Pengaruh Beban Kerja, Disiplin Kerja Dan Kepuasan Kerja Terhadap Kinerja Karyawan Pada PT Volex

This study aims to analyze the effect of workload, work discipline, and job satisfaction on employee performance at PT Volex. The type of research used is descriptive with a quantitative approach. The population used is all employees who work as production operators in the ICA department at PT Volex in 2023 with a total of 112 employees with sampling based on saturated sampling, using a questionnaire distributed to PT Volex employees. The collected data were analyzed using multiple linear regression methods which were tested involving data quality tests, classic assumption tests, influence tests and hypothesis tests, to determine the effect of workload, work discipline, and job satisfaction variables on employee performance. Findings in multiple linear regression analysis can show that workload has a positive relationship with a magnitude of 26.2% on employee performance. Work discipline has a positive relationship with a magnitude of 21.2% on employee performance. Job satisfaction has a positive relationship with a magnitude of 34.1% on employee performance. The findings of the analysis of the coefficient of determination (R2) that the level of employee performance can be explained by workload, work discipline and job satisfaction of 72.5%. The research results in the hypothesis testing carried out can show that workload, work discipline and job satisfaction partially or simultaneously can provide a positive and significant relationship to employee performance.

Hysteresis modeling and analysis of piezoelectric actuators using a modified LuGre model at different preloads and frequencies

Abstract Despite a large of models proposed to characterize the nonlinear hysteresis of piezoelectric actuators, few of them can show the effects of different preloads on the nonlinear hysteresis. In this paper, a modified LuGre model is presented for piezoelectric actuators under different preloads to accurately characterize its dynamic hysteresis nonlinearity at different frequencies. Based on the classical LuGre model,
the presented model introduces infinite impulse response(IIR) filters and smoothness coefficient to simplify it. The parameters of the models were identified by the non linear least squares method. A series of experiments were conducted to verify the validity of the modified LuGre model under different preloads at different frequencies excitation signals.In the constant frequency experiments under different preloads, the modified LuGre model reduces the mean error by 68.1% − 81.9% compared with the classical LuGre model; In the constant preload experiments at different frequencies, the modified LuGre model reduces the mean error by 72.2%−83.0% and 65.9%−83.2% compared with the classical Bouc-Wen model and the classical LuGre model.

Land Use Sustainability: Assessment of the Dynamic Response of Typical Bedrock-Buried-Hill Earth Fissure Sites in the Su-Xi-Chang Area

Disaster prevention and the mitigation of earth fissures is a key issue in the sustainable development of urban land. Structures directly avoiding earth fissures are not conducive to the rational planning and efficient utilization of urban construction. The Su-Xi-Chang area, which consists of the cities of Suzhou, Wuxi, and Changzhou, surrounded by Taihu Lake, has developed bedrock buried-hill earth fissures that are rare in the rest of the country. Existing research results have identified the genesis mechanisms, distribution patterns, and developmental characteristics of this type of fissure. Not only does the slow-variable activity of earth fissures cause direct damage to surface and underground structures, but in addition, when an earthquake occurs, the presence of earth fissures may cause the seismic response of the site to be altered or even strengthened, leading to unknown damage or the possible destruction of structures near the fissures. However, no studies have been conducted to assess the dynamic effects of bedrock-buried-hill earth fissure sites. Therefore, in this research, based on six typical bedrock-buried-hill-type earth fissures in the Su-Xi-Chang area, and in order to accurately reveal the dynamic amplification effect law of the earth fissure sites, systematic spectral analyses and comparisons of the microtremor signals were carried out by using the linear analysis method (Direct Fourier Transform Analysis) and the nonlinear analysis method (Hilbert–Huang Transform). The results show that bedrock-buried-hill-type earth fissures have a significant amplification effect on the dynamic response of the site; the amplification effect of bedrock-buried-hill fissure sites follows the same attenuation pattern, and the furthest range of the dynamic response on the site is about 25 m, beyond which the original seismic fortification level can be maintained; the extreme value of the amplification factor of the two sides of this type of site, as derived from the Fourier and HHT methods, is about double, and the nearest earth fissure region should be considered to have a raised seismic fortification intensity of more than double the original. The Hilbert–Huang transform method has good applicability for processing microtremor data, and nonlinear signal analysis methods can be considered comprehensive for future microtremor signal processing.

Investigating the properties of octane isomers by novel neighborhood product degree-based topological indices

A topological index is a real number calculated from the structure of a chemical compound to describe its topology. The use of molecular descriptors has been increasing in recent years, helping to determine the physicochemical and biological properties of drugs. The main purpose of this article is to investigate the properties of the octane isomers using the theoretical method. To study the structures of octane isomers, we have introduced a new approach called “neighborhood product degree” to calculate all the classical degree-based topological indices. The np-degree approach is applied to approximate eight properties of octane isomers, such as the acentric factor, density, refractive index, critical volume, molar volume, radius of curvature, critical pressure, and LogP. The np-degree-based topological indices are the estimated values of the properties of octane structures, so the linear and quadratic regression models and correlation coefficients are applied to check the validity of the estimated results. The quantitative structure property relation are obtained by using the linear, quadratic, exponential, logarithmic and sinusoidal regression methods with the help of SPSS. Two models are applied to all the compuations and three regression models are applied to the np-degree Randic index. The computation showed that quadratic regression model is suitable for study octane isomers and np-degree based graph invariants. If the values of the correlation coefficient r ⩾ 0.7, p-values ⩽ 0.05, and F-values ⩾ 2.5, then the results are significant. The results of np-degree-based topological indices satisfy all the criteria for being significant, so these newly introduced indices are valid to study octane isomers. The information determined in this article is beneficial for chemists and pharmacists.

Study on Dynamic Post-Buckling Stability of Thin-Walled Cylinders Subjected to Horizontal Vibration

Abstract As a lesson learned from the Fukushima nuclear accident, the importance of accident mitigation for beyond design basis events (BDBEs) is recognized. Excessive earthquake is a typical BDBE. During such events, the fast reactor main vessel (FRMV) is vulnerable to buckling because of its thin-walled cylindrical structure. Buckling deformation without causing immediate collapse should not be considered as a limit state in BDBEs. Instead, the objective is to achieve a stable post-buckling state. In the present study, dynamic post-buckling behavior of thin-walled cylinders under intense horizontal vibration is experimentally studied, which simulates loop-type FRMV design. Finite element method (FEM) simulation is also conducted and validated by experimental data. Based on results analysis, a global response stability after buckling is confirmed. The buckled model shows resilience to withstand catastrophic collapse even when intense excitation continues to apply. Using equivalent linear response method, dynamic mechanisms behind the stability are revealed. Due to substantial degradation of structural stiffness, the post-buckling response shifts into the out-of-phase domain such that response divergence is prevented. Meanwhile, the response is notably delayed from the input, which impedes the conversion of input energy into kinetic energy, reducing the inertial impact to the structure. These mechanisms are found to be independent of input waveforms and can be applied to a general seismic scenario. Moreover, the limit for global response stability after buckling and the effect of input frequency on the post-buckling behavior are clarified.