Development of PLS–path model for understanding the role of precursors on ground level ozone concentration in Gulfport, Mississippi, USA

AbstractGround-level ozone (GLO) is produced by a complex chain of atmospheric chemical reactions that depend on precursor emissions from natural and anthropogenic sources. GLO concentration in a particular location is also governed by local weather and climatic factors. In this work an attempt was made to explore a Partial Least Squares Path Modeling (PLS–PM) approach to quantify the interrelationship between local conditions (weather parameters and primary air pollution) and GLO concentrations. PLS path modeling algorithm was introduced and applied to GLO concentration analyses at Gulfport, Mississippi, USA. In the present analysis, three latent variables were selected: PRC (photochemical reaction catalyst), MP (meteorological factor), and OPP (other primary air pollutants). The three latent variables included 14 indicators for the analysis; with PRC having two (extraterrestrial radiation on horizontal surface, and extraterrestrial radiation normal to the sun), MP having nine (temperature, dew point, relative humidity, pressure, visibility, maximum wind speed, average wind speed, precipitation, and wind direction) and OPP having three (NO2, PM2.5, and SO2) parameters. The resulting model revealed that PRC had significant direct impact on GLO concentration but very small overall effect. This is because PRC had significant indirect negative impact on GLO via MP. Thus, when both direct and indirect effects were taken into account, PRC emerged as having the weakest effect on GLO. The third variable (OPP) also had a positive impact on GLO concentration

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization

Investigating the hot molecular core, G10.47+0.03: A pit of nitrogen-bearing complex organic molecules

Recent observations have shown that Nitrogen-bearing complex organic species are present in large quantities in star-forming regions. Thus, investigating the N-bearing species in a hot molecular core, such as G10.47+0.03, is crucial to understanding the molecular complexity in star-forming regions. They also allow us to investigate the chemical and physical processes that determine the many phases during the structural and chemical evolution of the source in star-forming regions. The aim of this study is to investigate the spatial distribution and the chemical evolution states of N-bearing complex organic molecules in the hot core G10.47+0.03. We used the ALMA archival data of the hot molecular core G10.47+0.03. The extracted spectra were analyzed assuming LTE. Furthermore, robust methods such as MCMC and rotational diagram methods are implemented for molecules for which multiple transitions were identified to constrain the temperature and column density. Finally, we used the Nautilus gas-grain code to simulate the nitrogen chemistry in the hot molecular core. We carried out both 0D and 1D simulations of the source and compared with observational results. We report various transitions of nitrogen-bearing species (NH2CN, HC3N, HC5N, C2H3CN, C2H5CN, and H2NCH2CN) together with some of their isotopologues and isomers. Besides this, we also report the identification of CH3CCH and one of its isotopologues. The emissions originating from vinyl cyanide, ethyl cyanide, cyanoacetylene, and cyanamide are compact, which could be explained by our astrochemical modeling. Our 0D model shows that the chemistry of certain N-bearing molecules can be very sensitive to initial local conditions such as density or dust temperature. In our 1D model, simulated higher abundances of species such as HCN, HC3N, and HC5N toward the inner shells of the source confirm the observational findings.Comment: 40 pages, 30 figure

Diversifying vegetable production systems for improving the livelihood of resource poor farmers on the East Indian Plateau

Failure of the rice crop, or low rice yield has dire consequences for rice-dependent households, including food insecurity and malnutrition, for India’s poorest farmers in the East Indian Plateau region. Crop diversification could reduce the risks of rice production from the vagaries of rainfall and provide cash income which is not generated from subsistence rice. Being the primary household laborers women bear the brunt of these difficult conditions in patriarchal societies. For this reason we engaged with the women farmers in Bokaro and West Singhbhum in the State of Jharkhand, and Purulia in West Bengal who participated in experiments conducted with vegetable crops and legumes in the upland and medium uplands where the traditional crop is broadcasted paddy rice. We explored four different vegetable systems, (i) cucurbits (rainy/kharif) (season—June to September), (ii) growing tomatoes in the “off season” (rainy season—July to October), (iii) growing legume crops in rotation with direct sown rice (dry/rabi season—November to January), and (iv) intercropping beans with maize (rainy season—June to September). The results showed that all the above crops proved much better in terms of income to the farmers, return per person day, although the input cost varied it was higher with the new systems explored. The research with the small-holding women farmers enabled them to try new options and make informed decisions about these opportunities. This study showed that farmers can increase crop diversity and expand the area sown to non-paddy crops. The farmers are now looking for new crops where the demand exceeds the supply. Importantly this study has demonstrated that the direct involvement of communities’ in research enables the farmers to sustainability explore solutions to the future problems with limited support from the external agencies

Chemical evolution of some selected complex organic molecules in low-mass star-forming regions

The destiny of complex organic molecules (COMs) in star-forming regions is interlinked with various evolutionary phases. Therefore, identifying these species in diversified environments of identical star-forming regions would help to comprehend their physical and chemical heritage. We identified multiple COMs utilizing the Large Program `Astrochemical Surveys At IRAM' (ASAI) data, dedicated to chemical surveys in Sun-like star-forming regions with the IRAM 30 m telescope. It was an unbiased survey in the millimetre regime, covering the prestellar core, protostar, outflow region, and protoplanetary disk phase. Here, we have reported some transitions of seven COMs, namely, methanol (CH3OH), acetaldehyde (CH3CHO), methyl formate (CH3OCHO), ethanol (C2H5OH), propynal (HCCCHO), dimethyl ether (CH3OCH3), and methyl cyanide (CH3CN) in some sources L1544, B1-b, IRAS4A, and SVS13A. We found a trend among these species from the derived abundances using the rotational diagram method and MCMC fit. We have found that the abundances of all of the COMs, except for HCCCHO, increase from the L1544 (prestellar core) and peaks at IRAS16293-2422 (class 0 phase). It is noticed that the abundance of these molecules correlate with the luminosity of the sources. The obtained trend is also visible from the previous interferometric observations and considering the beam dilution effect.Comment: 44 pages, 25 figures, and 12 tables. Accepted for the publication in the Astrophysical Journa

Application of transfer learning of deep CNN model for classification of time-series satellite images to assess the long-term impacts of coal mining activities on land-use patterns

The study aims to analyse the long-term impacts of mining activities in Jharia coalfield (JCF) on land-use (LU) patterns using transfer learning of the deep convolutional neural network (Deep CNN) model. A new database was prepared by extracting 10,000 image samples of 6 × 6 size for five LU types (barren land, built-up area, coal mining region, vegetation and waterbody) from Landsat data to train and validate the model. The satellite data from 1987 to 2021 at an interval of two years was used for change analysis. The study results revealed that the model offers 95 and 88% accuracy on the training and the validation dataset. The results indicate that barren land, coal mining region, and waterbody have been decreased from 237.30 sq. km. (=39.88%) to 171.25 sq. km (=28.78%), 118.77 sq. km. (=19.96%) to 68.73 sq. km (=11.55%), and 35.58 sq. km (=5.98%) to 18.68 sq. km (=3.14%) during 1987–2021, respectively. On the other hand, the built-up area and vegetation have been increased from 120.14 sq. km (=20.19%) to 233.02 sq. km (=39.16%) and 83.19 sq. km (=13.98%) to 103.36 sq. km (=17.37%) during 1987–2021. The time-series correlation results indicate that coal mining is the most sensitive LU type from 1987 to 2021, whereas barren land is least sensitive up to 2011, and thereafter vegetation is the least sensitive

Optimization Techniques and their Applications to Mine Systems

This book describes the fundamental and theoretical concepts of optimization algorithms in a systematic manner, along with their potential applications and implementation strategies in mining engineering. It explains basics of systems engineering, linear programming, and integer linear programming, transportation and assignment algorithms, network analysis, dynamic programming, queuing theory and their applications to mine systems. Reliability analysis of mine systems, inventory management in mines, and applications of non-linear optimization in mines are discussed as well. All the optimization algorithms are explained with suitable examples and numerical problems in each of the chapters. Features include: Integrates operations research, reliability, and novel computerized technologies in single volume, with a modern vision of continuous improvement of mining systems. Systematically reviews optimization methods and algorithms applied to mining systems including reliability analysis. Gives out software-based solutions such as MATLAB, AMPL, LINDO for the optimization problems. All discussed algorithms are supported by examples in each chapter. Includes case studies for performance improvement of the mine systems. This book is aimed primarily at professionals, graduate students, and researchers in mining engineering

Development of machine vision-based ore classification model using support vector machine (SVM) algorithm

© 2017, Saudi Society for Geosciences. All Right Reserved. The product of the mining industry (ore) is considered to be the raw material for the metal industry. The destination policy of the raw materials of iron mine is highly dependent on the class of iron ores. Thus, regular monitoring of iron ore class is the urgent need at the mine for accurately assigning the destination policy of raw materials. In most of the iron ore mines, decisions on ore class are made based on either visual inspection by the geologist or laboratory analyses of the ores. This process of ore class estimation is time consuming and also challenging for continuous monitoring. Thus, the present study attempts to develop an online vision-based technology for classification of iron ores. A laboratory-scale transportation system is designed using conveyor belt for online image acquisition. A multiclass support vector machine (SVM) model was developed to classify the iron ores. A total of 2200 images were captured for developing the ore classification model. A set of 18 features (9-histogram-based colour features in red, green and blue (RGB) colour space and 9-texture features based on intensity (I) component of hue, saturation and intensity (HSI) colour space) were extracted from each image. The performance of the SVM model was evaluated using four confusion matrix parameters (sensitivity, accuracy, misclassification and specificity). The SVM model performance was also compared with the other methods like K-nearest neighbour, classification discriminant, Naïve Bayes, classification tree and probabilistic neural network. It was observed that the SVM classification model performs better than the other classification methods

Effect on the performance of a support vector machine based machine vision system with dry and wet ore sample images in classification and grade prediction

The aim of the present study is to analysing the effect of water absorption on iron ore samples in the performances of SVM-based machine vision system. Two types of SVM-based machine vision system (classification and regression) were designed and developed, and performances were compared with dry and wet ore sample images. The images of the ore samples were captured in both the conditions (wet and dry) to examine the proposed model performance. A total of 280 image features were extracted and optimised using sequential forward floating selection (SFFS) algorithm for model development. The iron ore samples were collected from an Indian iron ore mine (Guamine), and image capturing system was fabricated in the laboratory for executing the proposed study. The results indicated that a different set of optimised features obtained for dry and wet sample images in both the models (classification and regression). Furthermore, the performance of both the models with dry sample images was found to be relatively better than the wet sample images

Sensitivity analysis of fuzzy-analytic hierarchical process (FAHP) decision-making model in selection of underground metal mining method

This study aims to analyse the sensitivity in decision-making which results in the selection of the appropriate underground metal mining method using the fuzzy-analytical hierarchy process (FAHP) model. The proposed model considers sixteen criteria for the selection of the most appropriate mining method out of the seven. The model consists of three-layer viz. the first layer represents the criteria (factors which influence the mining method), the second layer represents the sub-criteria (categorisation of the factors) and the third layer represents the alternatives (mining methods). The priority of the different mining methods was determined based on global weights. The global weights of seven mining method were determined using a different fuzzification factor under different decision-making attitudes (optimistic, pessimistic and unbiased). The sensitivity of the decision-making results was analysed in order to understand the robustness of the model. Keywords: Decision-making, Mining methods, Fuzzy-AHP, Sensitivity analysi