Multivariate Nonlinear Analysis Research Articles

Non-linear association between the time required to reaching temperature targets and the neurological outcome in patients undergoing targeted temperature management after out-of-hospital cardiac arrest: Observational multicentre cohort study

PurposeWe evaluated associations between outcomes and time to achieving temperature targets during targeted temperature management of out-of-hospital cardiac arrest. MethodsUsing Comprehensive Registry of Intensive Care for out-of-hospital cardiac arrest Survival (CRITICAL) study, we enrolled all patients transported to participating hospitals from 1 July 2012 through 31 December 2017 aged ≥ 18 years with out-of-hospital cardiac arrest of cardiac aetiology and who received targeted temperature management in Osaka, Japan. Primary outcome was Cerebral Performance Category scale of 1 or 2 one month after cardiac arrest, designated as “one-month favourable neurological outcome”. Non-linear multivariable logistic regression analyses assessed the primary outcome based on time to reaching temperature targets. In patients subdivided into quintiles based on time to achieving temperature targets, multivariable logistic regression calculated adjusted odds ratios and 95% confidence intervals. ResultsWe analysed 473 patients. In non-linear multivariable logistic regression analysis, p value for non-linearity was < 0.01. In the first quintile (< 26.7 minutes), second quintile (26.8–89.9 minutes), third quintile (90.0–175.1 minutes), fourth quintile (175.2–352.1 minutes), and fifth quintile (≥ 352.2 minutes), one-month favourable neurological outcome was 32.6% (31/95), 40.0% (36/90), 53.5% (53/99), 57.4% (54/94), and 37.9% (36/95), respectively. Adjusted odds ratios with 95% confidence intervals for one-month favourable neurological outcome in the first, second, third, and fifth quintiles compared with the fourth quintile were 0.38 (0.20 to 0.72), 0.43 (0.23 to 0.81), 0.77 (0.41 to 1.44), and 0.46 (0.25 to 0.87), respectively. ConclusionNon-linear multivariable logistic regression analysis could clearly describe the association between neurological outcome in patients with out-of-hospital cardiac arrest and the time from the introduction of targeted temperature management to reaching the temperature targets.

Decomposing difference in the kidney cancer burden measures between 1990 and 2019 based on the global burden of disease study

The kidney cancer (KC) burden measures have changed dramatically in recent years due to changes in exposure to the determinants over time. We aimed to decompose the difference in the KC burden measures between 1990 and 2019. This ecological study included data on the KC burden measures as well as socio-demographic index (SDI), behavioral, dietary, and metabolic risk factors from the global burden of disease study. Non-linear multivariate decomposition analysis was applied to decompose the difference in the burden of KC. Globally, ASIR, ASMR, and ASDR of KC increased from 2.88 to 4.37, from 1.70 to 2.16, and from 46.13 to 54.96 per 100,000 people between 1990 and 2019, respectively. The global burden of KC was more concentrated in developed countries. From 1990 to 2019, the burden of KC has increased the most in Eastern European countries. More than 70% of the difference in the KC burden measures between 1990 and 2019 was due to changes in exposure to the risk factors over time. The SDI, high body mass index (BMI), and alcohol use had the greatest contribution to the difference in the KC burden measures. Changes in characteristics over time, including SDI, high BMI, and alcohol consumption, appear to be important in the evolving landscape of KC worldwide. This finding may help policymakers design policies and implement prevention programs to control and manage KC.

Non-Linear Multivariate Analysis with Artificial Neural Network in Estimating Compression Index for Cohesive Soils of Northern Jakarta Coast

This study presents a novel application of artificial neural network (ANN) to develop a model for predicting compression index (Cc) of cohesive soils from their index properties. The model was trained using data from 347 undisturbed samples on a variety of cohesive soils from Northern Jakarta. It takes up to three variables as inputs: specific gravity (Gs), liquid limit (LL), and plastic limit (PL). The model was tested on a separate dataset of 117 samples and found to have a strong capability to predict Cc values when compared to some reference correlations. The ANN model has demonstrated good performance for each set by producing overall error of 29.6%, compared to 38.1% and 30.5% for the empirical formulas. This study shows that the application of ANN offers an essential advancement in this area, helping to overcome the limitation of conventional statistical correlation.

Ground air pollutants explanation based on multiple visibility graph of complex network by temporal community division.

In air pollution studies, the correlation analysis of environmental variables has usually been challenged by parametric diversity. Such variable variations are not only from the extrinsic meteorological conditions and industrial activities but also from the interactive influences between the multiple parameters. A promising solution has been motivated by the recent development of visibility graph (VG) on multi-variable data analysis, especially for the characterization of pollutants' correlation in the temporal domain, the multiple visibility graph (MVG) for nonlinear multivariate time series analysis has been verified effectively in different realistic scenarios. To comprehensively study the correlation between pollutant data and season, in this work, we propose a multi-layer complex network with a community division strategy based on the joint analysis of the atmospheric pollutants. Compared to the single-layer-based complex networks, our proposed method can integrate multiple different atmospheric pollutants for analysis, and combine them with multivariate time series data to obtain higher temporary community division for ground air pollutants interpretation. Substantial experiments have shown that this method effectively utilizes air pollution data from multiple representative indicators. By mining community information in the data, it successfully achieves reasonable and strong interpretive analysis of air pollution data.

Identifying quasi-periodic variability using multivariate empirical mode decomposition: a case of the tropical Pacific

Abstract. A variety of statistical tools have been used in climate science to gain a better understanding of the climate system's variability on various temporal and spatial scales. However, these tools are mostly linear, stationary, or both. In this study, we use a recently developed nonlinear and nonstationary multivariate time series analysis tool – multivariate empirical mode decomposition (MEMD). MEMD is a powerful tool for objectively identifying (intrinsic) timescales of variability within a given spatio-temporal system without any timescale pre-selection. Additionally, a red noise significance test is developed to robustly extract quasi-periodic modes of variability. We apply these tools to reanalysis and observational data of the tropical Pacific. This reveals a quasi-periodic variability in the tropical Pacific on timescales ∼ 1.5–4.5 years, which is consistent with El Niño–Southern Oscillation (ENSO) – one of the most prominent quasi-periodic modes of variability in the Earth’s climate system. The approach successfully confirms the well-known out-of-phase relationship of the tropical Pacific mean thermocline depth with sea surface temperature in the eastern tropical Pacific (recharge–discharge process). Furthermore, we find a co-variability between zonal wind stress in the western tropical Pacific and the tropical Pacific mean thermocline depth, which only occurs on the quasi-periodic timescale. MEMD coupled with a red noise test can therefore successfully extract (nonstationary) quasi-periodic variability from the spatio-temporal data and could be used in the future for identifying potential (new) relationships between different variables in the climate system.

Abstract 150: Non-Linear Association Between the Time of Reaching the Targeted Temperature and the Neurological Outcome in Patients Undergoing Targeted Temperature Management After Out-of-Hospital Cardiac Arrest Observational Multicenter Cohort Study

Background: We aimed to identify the most favourable time from the introduction of targeted temperature management (TTM) to achieving the targeted temperature in patients experiencing out-of-hospital cardiac arrest. Methods: This multicenter cohort study was performed using the Comprehensive Registry of Intensive Care for OHCA Survival study, we collected patients’ information, including that on in-hospital care. We enrolled all consecutive patients transported to participating institutions in Osaka, Japan from 1 July 2012 through 31 December 2017. Eligible were persons aged ≥18 years whose cardiac arrest was of cardiac etiology and who received in-hospital TTM. Primary outcome was favourable neurological survival with a Cerebral Performance Category scale of 1 or 2 one-month after cardiac arrest, which was designated as “one-month favourable neurological survival”. After adjusting for several factors, a non-linear multivariable logistic regression analysis assessed the time from the introduction of TTM to reaching the targeted temperature and the probability of the primary outcome. Also, we subdivided patients according to five quintiles based on the time from the introduction of TTM to achieving the targeted temperature. To compare the probability of the primary outcome among quintiles, a multivariable logistic regression analysis calculated adjusted odds ratios and 95% confidence intervals. Results: We analyzed 483 patients. p for non-linearity was &lt;0.01 in the non-linear multivariable logistic regression analysis. In the first quintile (&lt;26.7 minutes), second quintile (26.8-89.9 minutes), third quintile (90.0-175.1 minutes), fourth quintile (175.2-352.1 minutes), and fifth quintile (≥352.2 minutes), one-month favourable neurological survival was 32.6% (31/95), 40.0% (36/90), 53.5% (53/99), 57.4% (54/94), and 37.9% (36/95), respectively. Adjusted odds ratios with 95% CI for the primary outcome in the first, second, third, and fifth quintiles, compared with the fourth quintile as the reference, were 0.38 (0.20 to 0.72), 0.43 (0.23 to 0.81), 0.77 (0.41 to 1.44), and 0.46 (0.25 to 0.87), respectively. Conclusions: Time to reaching the targeted temperature was associated with favourable outcome in patients with cardiac arrest.

A multivariate nonlinear dynamic programming of female figures in Qing dynasty literature

Abstract To improve the recognition of female images in Qing Dynasty literature, this paper designs a multivariate nonlinear-based image dynamic programming model. Data-driven typical correlation analysis is used to extract the key controllable variables with the strongest correlation with female image features and use them as input variables for modeling so as to make the maximum correlation between different image features and improve the modeling efficiency. The nonlinear spatial state model is established by using the subspace identification method, and the female image planning model is substituted into the multivariate nonlinear analysis, and the parameters of the nonlinear fitting function about the image planning can be obtained. The simulation analysis of the image dynamic planning model based on multivariate nonlinearity is carried out, and the results show that the average annual failure time of the model designed in this paper is 12.6 hours, and the classification accuracy in female image classification is 96.3%, and the classification time is stable at 1.162 s. The results show that the multivariate nonlinear features of the model can carry out dynamic planning of female images in Qing dynasty literature and improve the classification of image feature recognition.

Explaining the Urban-Rural Difference in Late-Life Depression in India: Evidence from a Multivariate Decomposition Analysis Based on Longitudinal Aging Study in India, Wave 2017-18

ABSTRACT Objectives The study explored the associated factors of depression among older Indian adults and the influences of individual and socio-environmental factors in explaining the rural-urban difference in the prevalence of late-life depression. Methods Data come from the Longitudinal Aging Study in India, with a sample of 30,637 older adults aged 60 and above. Multivariable logistic regression and nonlinear multivariate decomposition analyses were conducted to fulfill the objectives. Results About 6.2% older adults in urban areas and 9.5% in rural areas were depressed. Older adults in rural areas had significantly higher likelihood to be depressed than those in urban areas. Poor self-rated health, multiple chronic conditions, functional difficulty, low life satisfaction, social inactivity, low satisfaction with living arrangement, ill-treatment and being widowed increased the risk of depression. Additionally, work status similar to urban older adults, physical activity, living arrangement satisfaction, self-rated health and ill-treatment would decrease the urban-rural difference in depression. Conclusions The study showed significant rural-urban difference in late-life depression, with a rural disadvantage. Clinical implications The findings suggest the need for identifying at-risk populations and developing a framework of targeted policy interventions for mitigating the increased risk of late-life depression among older Indians and in rural areas in particular.

Prediction of water quality under the impacts of fine dust and sand storm events using an experimental model and multivariate regression analysis

Many regions of the world, especially arid and semiarid areas, occasionally experience fine dust and sandstorms, known environmental problems that make normal life difficult. Since the intrusion of large amounts of dust into treatment plants may significantly change the water quality indices, the main goal of this study was to estimate these indices during the events, which can help decision-makers to improve water quality. To achieve relationships using nonlinear multivariate regression analysis, a long-term (three years: April 2017–February 2020) experimental study of water quality parameters including total dissolved solids (TDS), hydrogen content (pH), electrical conductivity (EC), chlorine (Cl), total hardness, sodium (Na), and magnesium (Mg) for water samples from wastewater treatment plants in Sistan region (Iran) was conducted where is one of the most popular regions in the world with high amount of annual fine dust level. Analysis of ANOVA showed that of all the independent parameters considered in this study, water quality parameters strongly correlated with monthly mean sand and dust storm index (SDSI), wind speed, temperature, and the number of monthly windy days. For the regression analysis, 25 months of data were used for the simulation process and 10 months for validation. The final results showed that the relationships obtained from the nonlinear multivariate regression analysis could predict the water quality indices very well (with R2 more than 0.75) except for Mg with R2 equal to 0.55. In addition, the maximum mean relative error belongs to Mg (10.8%) and then Na (9.9%) whereas the minimum mean relative error belongs to pH (2.6%) and then EC (2.9%).

Development of ANN Models for Prediction of Physical and Chemical Characteristics of Oil-in-Aqueous Plant Extract Emulsions Using Near-Infrared Spectroscopy

The potential of applying Artificial Neural Network (ANN) models based on near-infrared (NIR) spectra for the characterization of physical and chemical features of oil-in-aqueous oregano/rosemary extract emulsions was explored in this work. Emulsions were prepared using a batch emulsification process, with pea protein as the emulsifier. NIR spectral data were connected to the results of the analysis of physical and chemical properties of the emulsions (zeta potential, Feret droplet diameter, total polyphenolic content, and antioxidant capacity) with the final aim of quantitative prediction of the physical and chemical features. For that purpose, robust non-linear multivariate analysis (Artificial Neural Network modeling) was applied. The spectra themselves were preprocessed using several approaches (raw spectra, Savitzky–Golay smoothing, standard normal variate, and multiplicative scatter corrections) after which the impact of NIR spectral preprocessing on the ANN model’s efficiency was evaluated. The results show that NIR spectroscopy integrated with ANN computation can be employed to quantitatively predict the physical and chemical properties of oil-in-plant extract emulsions (R2 &gt; 0.9).

Serum NMR metabolomics analysis of human metastatic colorectal cancer: biomarkers and pathway analysis.

We describe the use of NMR metabolomics to analyse blood serum samples from healthy individuals (n = 26) and those with metastatic colorectal cancer (CRC, n = 57). The assessment, employing both linear and non-linear multivariate data analysis techniques, revealed specific metabolite changes associated with metastatic CRC, including increased levels of lactate, glutamate, and pyruvate, and decreased levels of certain amino acids and total fatty acids. Biomarker ratios such as glutamate-to-glutamine and pyruvate-to-alanine were also found to be related to CRC. The study also found that glutamate was linked to progression-free survival and that both glutamate and 3-hydroxybutyrate were risk factors for metastatic CRC. Additionally, GC-FID was utilized to analyse the fatty acid profile and pathway analysis was performed on the profiled metabolites to understand the metabolic processes involved in CRC. A correlation was also found between the presence of certain metabolites in the blood of CRC patients and certain clinical features.

Financial Performance of Islamic Versus Conventional Banks a Comparative Analysis for Jordan

This paper contributes to the empirical literature on interest-free finance by investigating the financial performance of interest-free and conventional banks in Jordan over the period 2005–2014 covering GFC period. Three models, two sub-periods, and 11 ratios are considered to compare bank performance evolutions. We give first a univariate based t-test analysis, and then a discriminant analysis is presented in order to determine which variables differentiate between conventional and Islamic banks. Finally, a multivariate nonlinear analysis from Binary outcome panel data models such as Probit and Logit model is conducted. Based on t-test univariate analysis, there is significant evidence that Islamic Banks (IBs) are in average less stable and more risky than conventional banks (CBs) for the three considered periods: full period, pre Global Financial Crisis(GFC) and post GFC. Pre GFC, IBs are more capitalized, more liquid, and more profitable in average. However, post GFC, IBs are in average only more liquid in addition to excess of instability and credit risk. From the results of Pooled Probit model, interest free banks seem again to be less stable, but less liquid, and riskier for the total period. The failure to find more stability for IBs is due to assumption of a stable relationships. Once we introduce interaction effect variables to take into account of behavior instability (due to Subprime crisis (GFC)), we show that IBs are rather more stable, more liquid but less profitable post GFC.

Research on the standardized layout of epidemic prevention facilities in shopping malls based on regression analysis

Epidemic prevention and control facilities are an important part of the work of COVID-19 prevention and control. However, the chaotic arrangement of epidemic prevention and control facilities has seriously affected the quality of epidemic prevention and control work and people's satisfaction. Moreover, shopping malls are places with more human traffic in cities, so the standardized layout of epidemic prevention facilities is particularly important. Based on the above two considerations, in this paper, people’s satisfaction with epidemic prevention facilities and China's epidemic prevention policy requirements are collected and analyzed. The data in shopping malls, and explores a standardized layout of epidemic prevention facilities in shopping malls by means of multivariate nonlinear analysis. And in the end, the number of facilities with the highest satisfaction of people in each place is obtained, combined with the requirements of epidemic prevention, a standardized layout of epidemic prevention facilities is obtained. The application of this type of standardized layout can improve people's satisfaction with the epidemic prevention policy.

Determinants of change in unintended pregnancy in Ethiopia using the 2005 and 2016 EDHS: non-linear multivariable decomposition analysis

BackgroundUnintended pregnancy is a pregnancy either mistimed or unwanted. The main consequence of unintended pregnancy is inducing abortion. In Ethiopia, more than half of unintended pregnancies end up in abortion.ObjectiveThis study aims to measure the change in unintended pregnancy among women of reproductive age between survey years 2005 and 2016 and to identify the socio-demographic factors that most significantly contributed to the change.MethodsData from the two most recent Ethiopian Demographic and Health Surveys (EDHS) were analyzed. We quantified the contribution of socio-demographic factors in the change of unintended pregnancy, using Oaxaca-Blinder decomposition for non-linear regression models by applying the STATA command ‘mvdcmp’.ResultUnintended pregnancy decreased from 37% in 2005 to 27% in 2016 in Ethiopia. Both changes in population characteristics and coefficient were the contributing elements to the observed change in unintended pregnancy. Among population characteristics factors, being a partial decision-maker and being a slum in the Somali region contributed 10 and 14% to the change of unintended pregnancy between the 2005 and 2016. Of the coefficient factors, knowledge of modern family planning, being a partial decision-maker, media exposure, distance to health facilities, and health facility visits contributed to the change by 93, 43, 17, and 10% respectively.ConclusionThe majority of the change in unintended pregnancy from 2005 to 2016 survey was due to differences in coefficients (85%). The principal contributing factors to the change of unintended pregnancy were FP knowledge, decision making, media exposure and health facility visits. Therefore, an interventional plan will be efficient, better, and more effective if focused on the larger contributing factors.

Propagation and attenuation characteristics of shock waves in a gas–coal dust explosion in a diagonal pipeline network

In order to obtain the propagation and attenuation characteristics of a shock wave gas–coal dust explosion, a diagonal pipeline network was built to carry out a gas–coal dust explosion experiment. Methane with a volume fraction of 9.5% was mixed with 20 g of coal dust with a particle size of 50 μm. The shock wave attenuation characteristics in the diagonal pipeline network were characterized by the overpressure attenuation coefficient. The results showed that when the shock wave propagated in the diagonal pipeline network, it was offset and superposed multiple times. The overpressure attenuation in diagonal pipes was greater than that in the other pipeline branches. Secondary explosions occurred at all monitoring points. The propagation characteristics of shock waves is less affected by the pipeline network structure when it is close to the outlet of pipeline network. Nonlinear multivariate regression analysis was carried out to analyze the relationship between the overpressure attenuation coefficient and two adjacent monitoring points, the pressure before and after attenuation, and the pipeline angle. The functional relationship was in good agreement with the experiment data.

Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce.

The currently available methods for evaluating most biochemical traits of plant phenotyping are destructive and have extremely low throughput. However, hyperspectral techniques can non-destructively obtain the spectral reflectance characteristics of plants, which can provide abundant biophysical and biochemical information. Therefore, plant spectra combined with machine learning algorithms can be used to predict plant phenotyping traits. However, the raw spectral reflectance characteristics contain noise and redundant information, thus can easily affect the robustness of the models developed via multivariate analysis methods. In this study, two end-to-end deep learning models were developed based on 2D convolutional neural networks (2DCNN) and fully connected neural networks (FCNN; Deep2D and DeepFC, respectively) to rapidly and non-destructively predict the phenotyping traits of lettuces from spectral reflectance. Three linear and two nonlinear multivariate analysis methods were used to develop models to weigh the performance of the deep learning models. The models based on multivariate analysis methods require a series of manual feature extractions, such as pretreatment and wavelength selection, while the proposed models can automatically extract the features in relation to phenotyping traits. A visible near-infrared hyperspectral camera was used to image lettuce plants growing in the field, and the spectra extracted from the images were used to train the network. The proposed models achieved good performance with a determination coefficient of prediction () of 0.9030 and 0.8490 using Deep2D for soluble solids content and DeepFC for pH, respectively. The performance of the deep learning models was compared with five multivariate analysis method. The quantitative analysis showed that the deep learning models had higher than all the multivariate analysis methods, indicating better performance. Also, wavelength selection and different pretreatment methods had different effects on different multivariate analysis methods, and the selection of appropriate multivariate analysis methods and pretreatment methods increased more time and computational cost. Unlike multivariate analysis methods, the proposed deep learning models did not require any pretreatment or dimensionality reduction and thus are more suitable for application in high-throughput plant phenotyping platforms. These results indicate that the deep learning models can better predict phenotyping traits of plants using spectral reflectance.

Research progress and application of transfer entropy algorithm

In recent years, exploring the physiological and pathological mechanisms of brain functional integration from the neural network level has become one of the focuses of neuroscience research. Due to the non-stationary and nonlinear characteristics of neural signals, its linear characteristics are not sufficient to fully explain the potential neurophysiological activity mechanism in the implementation of complex brain functions. In order to overcome the limitation that the linear algorithm cannot effectively analyze the nonlinear characteristics of signals, researchers proposed the transfer entropy (TE) algorithm. In recent years, with the introduction of the concept of brain functional network, TE has been continuously optimized as a powerful tool for nonlinear time series multivariate analysis. This paper first introduces the principle of TE algorithm and the research progress of related improved algorithms, discusses and compares their respective characteristics, and then summarizes the application of TE algorithm in the field of electrophysiological signal analysis. Finally, combined with the research progress in recent years, the existing problems of TE are discussed, and the future development direction is prospected.

A multigroup fault detection and diagnosis framework for large-scale industrial systems using nonlinear multivariate analysis

In a large-scale industrial system with numerous variables, the relations among variables are often nonlinear and very complicated, due to material, energy and information flows throughout the entire system. In such systems, fault detection and diagnosis (FDD) suffer from the strong interference of fault-free variables and hence become more difficult, especially for minor faults. To achieve high-performance FDD in large-scale nonlinear industrial systems, a multigroup FDD framework is proposed in this paper. In this framework, system variables are divided into groups firstly, and then FDD are implemented in the form of variable groups. The whole framework consists of three components: a variable grouping method based on mutual information (MI), intra-group FDD methods based on gradKPCA, and inter-group FDD methods based on gradKCCA. The MI-based variable grouping method obtains optimal variable groups by maximizing the MI of variables in every group. The gradKPCA and gradKCCA are used for extracting nonlinear variable relations within each group and between groups, respectively. Except for the ability to cope with nonlinear variable relations, this multigroup FDD framework also has other advantages, such as the improved detection ability to minor faults and the ability to reveal fault transfers between variables and groups. These advantages are demonstrated with a numerical example and an industrial case study.

Spectral knowledge-based regression for laser-induced breakdown spectroscopy quantitative analysis

Laser-induced breakdown spectroscopy (LIBS) is a promising atomic emission spectroscopic technique for multi-elemental analysis and has the advantages of real-time multi-element measurement, minimal sample preparation and remote detection. However, the quality of LIBS data can be low due to matrix effects and signal uncertainty which hinders the wide application of LIBS. Recent studies attempt to improve the performance of LIBS quantitative analysis using linear and nonlinear multivariate analysis models. Linear models can easily present how key variables contribute to the prediction but suffer from performance degradation if data has a high degree of nonlinearity. Nonlinear models tend to have good performance, but they lack simple and intuitive explanations for the contribution of variables and are prone to overfitting. Moreover, nonlinear models used in LIBS quantitative analysis, such as support vector regression (SVR) and kernel extreme learning machine (K-ELM), are not designed to incorporate domain knowledge of spectral data. In this work, a new machine learning algorithm is proposed, namely spectral knowledge-based regression (SKR), which integrates linear and nonlinear models to improve the performance of LIBS quantitative analysis. The linear model is knowledge-driven and built on key variables correlated with analyte composition. The nonlinear model is data-driven and transforms the input data into a kernel matrix. The proposed SKR is tested on 4 LIBS datasets and outperforms 5 baseline methods on 12 of 18 quantification tasks. Moreover, it intuitively explains the contribution of key variables towards prediction and has the same low computational complexity as ridge regression These results demonstrate that SKR inherits the high accuracy of nonlinear modelling and the simple variable interpretability of linear models. Therefore, it can serve as a promising method for improving the accuracy and reliability of LIBS quantitative analysis.

Impact of topography on attenuation of noise from a cement plant in a hilly terrain

The influence of hill topography on noise from industrial plants is scantly documented, despite the fact that the noise profiles can vary in plains and in hilly terrain. Significant noise data from a cement plant flanked by hills were collected and analyzed for its attenuation over space in the direction of the valley and across the hills. It was observed that the noise attenuation is not similar to that experienced in plains owing to interactions with the mountains. The overall effect has been modelled over different distances between the four sources of the noise and several monitoring stations. The analyzed noise data showed that the influence of the major source of noise is obvious, yet difficult to differentiate. An iterated weighted distance from the noise sources, proved to be a better estimate for distance while modelling the attenuation of the noise. A relationship between the equivalent noise levels during the day and night could also be established. The effect of relative altitude and slope could be modelled using nonlinear multivariate analysis and the response surfaces were obtained for minimum, maximum and average values of the variables. Noise shadow zones could be identified with noise contouring which reflects reduction in the noise levels on hind side of the hills. The impact of topography and possible interference could thus be defined in such hilly terrain. The information can be used to define the noise mitigation strategies for adjacent habitats and mitigation of human response. The findings of the paper can also be used by prospective plants in hilly areas.