Search results for: Deep mixed column

Authors: Devatha Kalyan Kumar, Aravindraj D., Sadathulla A.

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Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data.

Keywords: active learning, big data, deep learning, machine learning

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Authors: Ghorbanali Mohammadi

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New approaches to sequencing mixed-model manufacturing systems are present. These approaches have attracted considerable attention due to their potential to deal with difficult optimization problems. This paper presents Multi-Objective Simulated Annealing Algorithms (MOSAA) approaches to the Just-In-Time (JIT) sequencing problem where workload-smoothing (WL) and the number of set-ups (St) are to be optimized simultaneously. Mixed-model assembly lines are types of production lines where varieties of product models similar in product characteristics are assembled. Moreover, this type of problem is NP-hard. Two annealing methods are proposed to solve the multi-objective problem and find an efficient frontier of all design configurations. The performances of the two methods are tested on several problems from the literature. Experimentation demonstrates the relative desirable performance of the presented methodology.

Keywords: scheduling, just-in-time, mixed-model assembly line, sequencing, simulated annealing

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Authors: R. Ziaie Moayed, A. Khalili

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Improvement and reinforcement of soils with poor strength and engineering properties for constructing low height structures or structures such as liquid storage tanks, bridge columns, and heavy structures have necessitated applying particular techniques. Stone columns are among the well-known methods applied in such soils. This method provides an economically justified way for improving engineering properties of soft clay and loose sandy soils. Stone column implementation in these soils increases their bearing capacity and reduces the settlement of foundation build on them. In the present study, the finite difference based FLAC3D software was used to investigate the performance and effect of soil reinforcement through stone columns without lining and those with geosynthetic lining with different levels of stiffness in horizontal and vertical modes in clayey soils. The results showed that soil improvement using stone columns with lining in vertical and horizontal modes results in improvement of bearing capacity and foundation settlement.

Keywords: bearing capacity, FLAC3D, geosynthetic, settlement, stone column

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Authors: Sepehr Moradi, Mehdi Asadi Rad

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The aim of this study is to evaluate and compare chest weight of pure and mixed races Kabood 30-day Pigeons to investigate about their sex, race, and some auxiliary variables. In this paper, 62 pieces of pigeons as 31 male and female pairs with equal age are studied randomly. A natural incubation was done from each pair. All produced chickens were slaughtered at 30 days age after 12 hours hunger. Then their chests were weighted by a scale with one gram precision. A covariance analysis was used since there were many auxiliary variables and unequal observations. SAS software was used for statistical analysis. Mean weight of chests in pure race (Kabood-Kabood) with 8 records, 123.8±32.3g and mixed races of Kabood-Namebar, Kabood-Parvazy, Kabood-Tizpar, Namebar-Kabood, Tizpar-Kabood, and Parvazi-Kabood with 8, 8, 6, 12, 10, and 10 records were 139.4±23.5, 7/122±23.8, 124.7±30.1, 50.3±29.3, 51.4±26.4, and 137±28.6 gr, respectively. Mean weight of 30-day chests in male and female sex were 87.3±2.5 and 82.7±2.6g, respectively. Difference chest weight of 30-day chests of Kabood-Kabood race with Kabood-Namebar, Kabood-Parvazi, Tizpar-Kabood, Kabood-Tizpar, Namebar-Kabood and Parvazi-Kabood mixed races was not significant. Effect of sex was also significant in 5% level (P<0.05), but mutual effect of sex and race was not significant. Auxiliary variable of father weight was significant in 1% level (p < 0.01), but auxiliary variable of mother weight was not significant. The results showed that most and least weights belonged to Kabood-Namebar and Namebar-Kabood.

Keywords: squab, Kabood race, 30-day chest weight, pigeons

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Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

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Authors: E. Faizan, Wahab Abdul Ghafar, Tao Zhong

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Steel plate shear walls (SPSWs) are a robust lateral load resistance structure because of their high flexibility and efficient energy dissipation when subjected to seismic loads. This research investigates the seismic performance of an innovative infill web strip (IWS-SPSW) and a typical unstiffened steel plate shear wall (USPSW). As a result, two 1:3 scale specimens of an IWS-SPSW and USPSW with a single story and a single bay were built and subjected to a cyclic lateral loading methodology. In the prototype, the beam-to-column connections were accomplished with the assistance of semi-rigid end-plate connectors. IWS-SPSW demonstrated exceptional ductility and shear load-bearing capacity during the testing process, with no cracks or other damage occurring. In addition, the IWS-SPSW could effectively dissipate energy without causing a significant amount of beam-column connection distortion. The shear load-bearing capacity of the USPSW was exceptional. However, it exhibited low ductility, severe infill plate corner ripping, and huge infill web plate cracks. The FE models were created and then confirmed using the experimental data. It has been demonstrated that the infill web strips of an SPSW system can affect the system's high performance and total energy dissipation. In addition, a parametric analysis was carried out to evaluate the material qualities of the IWS, which can considerably improve the system's seismic performances. These properties include the steel's strength as well as its thickness.

Keywords: steel shear walls, seismic performance, failure mode, hysteresis response, nonlinear finite element analysis, parametric study

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Authors: Alpana Tiwari, N. K. Gaur

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We have incorporated the translational rotational (TR) coupling effects in the framework of three body force shell model (TSM) to develop an extended TSM (ETSM). The dynamical matrix of ETSM has been applied to compute the phonon frequencies of orientationally disordered mixed crystal (ND4Br)x(KBr)1-x in (q00), (qq0) and (qqq) symmetry directions for compositions 0.10≤x≤0.50 at T=300K.These frequencies are plotted as a function of wave vector k. An unusual acoustic mode softening is found along symmetry directions (q00) and (qq0) as a result of translation-rotation coupling.

Keywords: orientational glass, phonons, TR-coupling, lattice dynamics

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Authors: Kwi Young Kang

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Objective: To determine the association between inflammatory and structural lesions on sacroiliac joint (SIJ) MRI and BMD and to identify risk factors for low BMD in patients with axial spondyloarthritis (axSpA). Methods: Seventy-six patients who fulfilled the ASAS axSpA criteria were enrolled. All underwent SIJ MRI and BMD measurement at the lumbar spine, femoral neck, and total hip. Inflammatory and structural lesions on SIJ MRI were scored. Laboratory tests and assessment of radiographic and disease activity were performed at the time of MRI. The association between SIJ MRI findings and BMD was evaluated. Results: Among the 76 patients, 14 (18%) had low BMD. Patients with low BMD showed significantly higher bone marrow edema (BME) and deep BME scores on MRI than those with normal BMD (p<0.047 and 0.007, respectively). Inflammatory lesions on SIJ MRI correlated with BMD at the femoral neck and total hip. Multivariate analysis identified the presence of deep BME on SIJ MRI, increased CRP, and sacroiliitis on X-ray as risk factors for low BMD (OR: 5.6, 14.6, and 2.5, respectively). Conclusion: The presence of deep BME on SIJ MRI, increased CRP levels, and severity of sacroiliitis on X-ray were independent risk factors for low BMD.

Keywords: axial spondyloarthritis, sacroiliac joint MRI, bone mineral density, sacroiliitis

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Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

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Authors: Ziyad Abunada, Abir Al-tabbaa

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The current work is investigating the effectiveness of combined mixed materials mainly modified bentonites and organoclay in treating contaminated groundwater. Sodium bentonite was manufactured with a quaternary amine surfactant, dimethyl ammonium chloride to produce organoclay (OC). Inorgano-organo bentonite (IOB) was produced by intercalating alkylbenzyd-methyl-ammonium chloride surfactant into sodium bentonite and pillared with chlorohydrol pillaring agent. The materials efficiency was tested for both TEX compounds from model-contaminated water and a mixture of organic contaminants found in groundwater samples collected from a contaminated site in the United Kingdom. The sorption data was fitted well to both Langmuir and Freundlich adsorption models reflecting the double sorption model where the correlation coefficient was greater than 0.89 for all materials. The mixed materials showed higher sorptive capacity than individual material with a preference order of X> E> T and a maximum sorptive capacity of 21.8 mg/g was reported for IOB-OC materials for o-xylene. The mixed materials showed at least two times higher affinity towards a mixture of organic contaminants in groundwater samples. Other experimental parameters such as pH and contact time were also investigated. The pseudo-second-order rate equation was able to provide the best description of adsorption kinetics.

Keywords: modified bentobite, groundwater, adsorption, contaminats

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Authors: Farzaneh Sarbandi Farahani

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Drought and decrease in the level of lakes in recent years due to global warming and excessive use of water resources feeding lakes are of great importance, and this research has provided a structure to investigate this issue. First, the information required for simulating lake drought is provided with strong references and necessary assumptions. Entity-Component-System (ECS) structure has been used for simulation, which can consider assumptions flexibly in simulation. Three major users (i.e., Industry, agriculture, and Domestic users) consume water from groundwater and surface water (i.e., streams, rivers and lakes). Lake Mead has been considered for simulation, and the information necessary to investigate its drought has also been provided. The results are presented in the form of a scenario-based design and optimal strategy selection. For optimal strategy selection, a deep reinforcement algorithm is developed to select the best set of strategies among all possible projects. These results can provide a better view of how to plan to prevent lake drought.

Keywords: drought simulation, Mead lake, entity component system programming, deep reinforcement learning

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Authors: Ruben Lopez-Rodriguez

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In a microgrid, diverse energy sources (both renewable and non-renewable) are combined with energy storage units to form a localized power system. Microgrids function as independent entities, capable of meeting the energy needs of specific areas or communities. This paper introduces a Model Predictive Control (MPC) approach tailored for grid-connected microgrids, aiming to optimize their operation. The formulation employs Mixed-Integer Programming (MIP) to find optimal trajectories. This entails the fulfillment of continuous and binary constraints, all while accounting for commutations between various operating conditions such as storage unit charge/discharge, import/export from/towards the main grid, as well as asset connection/disconnection. To validate the proposed approach, a microgrid case study is conducted, and the simulation results are compared with those obtained using a rule-based strategy.

Keywords: microgrids, mixed logical dynamical systems, mixed-integer optimization, model predictive control

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Authors: Chaimongkol Limpianchob

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This work addresses the problem of production planning that arises in the production of aromatic coconuts from Samudsakhorn province in Thailand. The planning involves the forwarding of aromatic coconuts from the harvest areas to the factory, which is classified into two groups; self-owned areas and contracted areas, the decisions of aromatic coconuts flow in the plant, and addressing a question of which warehouse will be in use. The problem is formulated as a mixed-integer linear programming model within supply chain management framework. The objective function seeks to minimize the total cost including the harvesting, labor and inventory costs. Constraints on the system include the production activities in the company and demand requirements. Numerical results are presented to demonstrate the feasibility of coconuts supply chain model compared with base case.

Keywords: aromatic coconut, supply chain management, production planning, mixed-integer linear programming

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Authors: T. Yilmaz, N. Kirac

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Beam-column elements are defined as structural members subjected to a combination of axial and bending forces. Lateral torsional buckling is one of the major failure modes in which beam-columns that are bent about its strong axis may buckle out of the plane by deflecting laterally and twisting. This study presents a compact closed-form equation that it can be used for calculating critical lateral torsional-buckling load of beam-columns with monosymmetric sections in the presence of a known axial load. Lateral-torsional buckling behavior of beam-columns subjected to constant axial force and various transverse load cases are investigated by using Ritz method in order to establish proposed equation. Lateral-torsional buckling loads calculated by presented formula are compared to finite element model results. ABAQUS software is utilized to generate finite element models of beam-columns. It is found out that lateral-torsional buckling load of beam-columns with monosymmetric sections can be determined by proposed equation and can be safely used in design.

Keywords: lateral-torsional buckling, stability, beam-column, monosymmetric section

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Authors: Shipra Shukla, Gaurav Chaudhary, S. K. Tewari, Mahesh Pal, D. K. Upreti

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Cassia tora L. is widely distributed in tropical Asian countries, commonly known as sickle pod. Various parts of the plant are reported for their medicinal value due to presence of anthraquinones, phenolic compounds, emodin, β-sitosterol, and chrysophanol. Therefore a sensitive analytical procedure using UHPLC-ESI-MS/MS was developed and validated for simultaneous quantification of five phenolic compounds in leaf, stem and root extracts of Cassia tora. Rapid chromatographic separation of compounds was achieved on Acquity UHPLC BEH C18 column (50 mm×2.1 mm id, 1.7µm) column in 2.5 min. Quantification was carried out using negative electrospray ionization in multiple-reaction monitoring mode. The method was validated as per ICH guidelines and showed good linearity (r2 ≥ 0.9985) over the concentration range of 0.5-200 ng/mL. The intra- and inter-day precisions and accuracy were within RSDs ≤ 1.93% and ≤ 1.90%, respectively. The developed method was applied to investigate variation of five phenolic compounds in the three geographical collections. Results indicated significant variation among analyzed samples collected from different locations in India.

Keywords: Cassia tora, phenolic compounds, quantification, UHPLC-ESI-MS/MS

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Authors: Jinkoo Kim, Hyungoo Kang, Hyungjun Shin

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In this study, a hybrid energy dissipation device is developed by combining a steel slit plate and friction pads to be used for seismic retrofit of structures, and its effectiveness is investigated by comparing the life cycle costs of the structure before and after the retrofit. The seismic energy dissipation capability of the dampers is confirmed by cyclic loading tests. The probabilities of reaching various damage states are obtained by fragility analysis, and the life cycle costs of the model structures are computed using the PACT (Performance Assessment Calculation Tool) program based on FEMA P-58 methodology. The fragility analysis shows that the probabilities of reaching limit states are minimized by the seismic retrofit with hybrid dampers and increasing column size. The seismic retrofit with increasing column size and hybrid dampers results in the lowest repair cost and shortest repair time.

Keywords: slit dampers, friction dampers, seismic retrofit, life cycle cost, FEMA P-58, PACT

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Authors: Gabriel Efomeh Omolaiye, Jimoh Ajadi, Olatunji Seminu, Yusuf Ayoola Jimoh, Ubulom Daniel

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Identification and characterization of petrophysical properties of reservoirs in the Bima Formation were undertaken to understand their spatial distribution and impacts on hydrocarbon saturation in the highly heterolithic siliciclastic sequence. The study was carried out using nine well logs from Maiduguri and Baga/Lake sub-basins within the Borno Basin. The different log curves were combined to decipher the lithological heterogeneity of the serrated sand facies and to aid the geologic correlation of sand bodies within the sub-basins. Evaluation of the formation reveals largely undifferentiated to highly serrated and lenticular sand bodies from which twelve reservoirs named Bima Sand-1 to Bima Sand-12 were identified. The reservoir sand bodies are bifurcated by shale beds, which reduced their thicknesses variably from 0.61 to 6.1 m. The shale content in the sand bodies ranged from 11.00% (relatively clean) to high shale content of 88.00%. The formation also has variable porosity values, with calculated total porosity ranged as low as 10.00% to as high as 35.00%. Similarly, effective porosity values spanned between 2.00 to 24.00%. The irregular porosity values also accounted for a wide range of field average permeability estimates computed for the formation, which measured between 0.03 to 319.49 mD. Hydrocarbon saturation (Sh) in the thin lenticular sand bodies also varied from 40.00 to 78.00%. Hydrocarbon was encountered in three intervals in Ga-1, four intervals in Da-1, two intervals in Ar-1, and one interval in Ye-1. Ga-1 well encountered 30.78 m thick of hydrocarbon column in 14 thin sand lobes in Bima Sand-1, with thicknesses from 0.60 m to 5.80 m and average saturation of 51.00%, while Bima Sand-2 intercepted 45.11 m thick of hydrocarbon column in 12 thin sand lobes with an average saturation of 61.00% and Bima Sand-9 has 6.30 m column in 4 thin sand lobes. Da-1 has hydrocarbon in Bima Sand-8 (5.30 m, Sh of 58.00% in 5 sand lobes), Bima Sand-10 (13.50 m, Sh of 52.00% in 6 sand lobes), Bima Sand-11 (6.20 m, Sh of 58.00% in 2 sand lobes) and Bima Sand-12 (16.50 m, Sh of 66% in 6 sand lobes). In the Ar-1 well, hydrocarbon occurs in Bima Sand-3 (2.40 m column, Sh of 48% in a sand lobe) and Bima Sand-9 (6.0 m, Sh of 58% in a sand lobe). Ye-1 well only intersected 0.5 m hydrocarbon in Bima Sand-1 with 78% saturation. Although Bima Formation has variable saturation of hydrocarbon, mainly gas in Maiduguri, and Baga/Lake sub-basins of the research area, its highly thin serrated sand beds, coupled with very low effective porosity and permeability in part, would pose a significant exploitation challenge. The sediments were deposited in a fluvio-lacustrine environment, resulting in a very thinly laminated or serrated alternation of sand and shale beds lithofacies.

Keywords: Bima, Chad Basin, fluvio-lacustrine, lithofacies, serrated sand

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Authors: Sana Eid, Ahmad S. Darwish, Tarek Lemaoui, Maguy Abi Jaoude, Fawzi Banat, Shadi W. Hasan, Inas M. AlNashef

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Addressing the growing challenge of per- and polyfluoroalkyl substances (PFAS) pollution in water bodies, this study introduces natural deep eutectic solvents (NADESs) as a pioneering solution for the efficient extraction of perfluorooctanoic acid (PFOA), one of the most persistent and concerning PFAS pollutants. Among the tested NADESs, trioctylphosphine oxide: lauric acid (TOPO:LauA) in a 1:1 molar ratio was distinguished as the most effective, achieving an extraction efficiency of approximately 99.52% at a solvent-to-feed (S:F) ratio of 1:2, room temperature, and neutral pH. This efficiency is achieved within a notably short mixing time of only one min, which is significantly less than the time required by conventional methods, underscoring the potential of TOPO:LauA for rapid and effective PFAS remediation. TOPO:LauA maintained consistent performance across various operational parameters, including a range of initial PFOA concentrations (0.1 ppm to 1000 ppm), temperatures (15 °C to 100 °C), pH values (3 to 9), and S:F ratios (2:3 to 1:7), demonstrating its versatility and robustness. Furthermore, its effectiveness was consistently high over seven consecutive extraction cycles, highlighting TOPO:LauA as a sustainable, environmentally friendly alternative to hazardous organic solvents, with promising applications for reliable, repeatable use in combating persistent water pollutants such as PFOA.

Keywords: deep eutectic solvents, natural deep eutectic solvents, perfluorooctanoic acid, water remediation

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Authors: Panudet Saengseedam, Nanthachai Kantanantha

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This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.

Keywords: Bayesian method, linear mixed model, multivariate conditional autoregressive model, spatial time series

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Authors: Reza Khoshnood, Zahra Khoshnood, Ali Hajinajaf, Farzad Fahim, Behdokht Hajinajaf, Farhad Fahim

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In 2013, 24 fish samples were taken from two fishery regions in Bandar-Abbas and Bandar-Lengeh, the fishing grounds north of Hormoz Strait (Persian Gulf) near the Iranian coastline. The two flat fishes were oriental sole (Euryglossa orientalis) and deep flounder (Psettodes erumei). Using the ROPME method (MOOPAM) for chemical digestion, Cd concentration was measured with a nonflame atomic absorption spectrophotometry technique. The average concentration of Cd in the edible muscle tissue of deep flounder was measured in Bandar-Abbas and was found to be 0.15±.06 µg g-1. It was 0.1±.05 µg.g-1 in Bandar-Lengeh. The corresponding values for oriental sole were 0.2±0.13 and 0.13±0.11 µg.g-1. The average concentration of Cd in the liver tissue of deep flounder in Bandar-Abbas was 0.22±.05 µg g-1 and that in Bandar-Lengeh was 0.2±0.04 µg.g-1. The values for oriental sole were 0.31±0.09 and 0.24±0.13 µg g-1 in Bandar-Abbas and Bandar-Lengeh, respectively.

Keywords: trace metal, Euryglossa orientalis, Psettodes erumei, Persian Gulf

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Authors: Umair Rashid

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Normally softmax loss is used as the supervision signal in face recognition (FR) system, and it boosts the separability of features. In the last two years, a number of techniques have been proposed by reformulating the original softmax loss to enhance the discriminating power of Deep Convolutional Neural Networks (DCNNs) for FR system. To learn angularly discriminative features Cosine-Margin based softmax has been adjusted as monotonically decreasing angular function, that is the main challenge for angular based softmax. On that issue, we propose monotonically decreasing element for Cosine-Margin based softmax and also, we discussed the effect of different monotonically decreasing parameters on angular Margin softmax for FR system. We train the model on publicly available dataset CASIA- WebFace via our proposed monotonically decreasing parameters for cosine function and the tests on YouTube Faces (YTF, Labeled Face in the Wild (LFW), VGGFace1 and VGGFace2 attain the state-of-the-art performance.

Keywords: deep convolutional neural networks, cosine margin face recognition, softmax loss, monotonically decreasing parameter

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Authors: Asim Laeeq Khan, Mazhar Amjad Gilani, Tayub Raza

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The use of fossil fuels for energy generation leads to the emission of greenhouse gases particularly CO2 into the atmosphere. To date, several techniques have been proposed for the efficient removal of CO2 from flue gas mixtures. Membrane technology is a promising choice due to its several inherent advantages such as low capital cost, high energy efficiency, and low ecological footprint. One of the goals in the development of membranes is to achieve high permeability and selectivity. Mixed matrix membranes comprising of inorganic fillers embedded in polymer matrix are a class of membranes that have showed improved separation properties. One of the biggest challenges in the commercialization if mixed matrix membranes are the removal of non-selective voids existing at the polymer-filler interface. In this work, mixed matrix membranes were prepared using polysulfone as polymer matrix and ordered mesoporous MCM-41 as filler materials. A new approach to removing the interfacial voids was developed by introducing room temperature ionic (RTIL) at the polymer-filler interface. The results showed that the imidazolium based RTIL not only provided wettability characteristics but also helped in further improving the separation properties. The removal of interfacial voids and good contact between polymer and filler was verified by SEM measurement. The synthesized membranes were tested in a custom built gas permeation set-up for the measurement of gas permeability and ideal gas selectivity. The results showed that the mixed matrix membranes showed significantly higher CO2 permeability in comparison to the pristine membrane. In order to have further insight into the role of fillers, diffusion and solubility measurements were carried out. The results showed that the presence of highly porous fillers resulted in increasing the diffusion coefficient while the solubility showed a slight drop. The RTIL filled membranes showed higher CO2/CH4 and CO2/N2 selectivity than unfilled membranes while the permeability dropped slightly. The increase in selectivity was due to the highly selective RTIL used in this work. The study revealed that RTIL filled mixed matrix membranes are an interesting candidate for gas separation membranes.

Keywords: ionic liquids, CO2 separation, membranes, mixed matrix membranes

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Authors: Luciana P. Mazur, Tatiana A. Pozdniakova, Rui A. R. Boaventura, Vitor J. P. Vilar

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The electroplating industry requires a lot of process water, which generates a large volume of wastewater loaded with heavy metals. Two different wastewaters were collected in a company’s wastewater treatment plant, one after the use of zinc in the metal plating process and the other after the use of chromium. The main characteristics of the Zn(II) and Cr(VI) wastewaters are: pH = 6.7/5.9; chemical oxygen demand = 55/<5 mg/L; sodium, potassium, magnesium and calcium ions concentrations of 326/28, 4/28, 11/7 and 46/37 mg/L, respectively; zinc(II) = 11 mg/L and Cr(VI) = 39 mg/L. Batch studies showed that L. hyperborea can be established as a natural cation exchanger for heavy metals uptake mainly due to the presence of negatively charged functional groups in the surface of the biomass. Beyond that, L. hyperborea can be used as a natural electron donor for hexavalent chromium reduction to trivalent chromium at acidic medium through the oxidation of the biomass, and Cr(III) can be further bound to the negatively charged functional groups. The uptake capacity of Cr(III) by the oxidized biomass after Cr(VI) reduction was higher than by the algae in its original form. This can be attributed to the oxidation of the biomass during Cr(VI) reduction, turning other active sites available for Cr(III) binding. The brown macroalgae Laminaria hyperborea was packed in a fixed-bed column in order to evaluate the feasibility of the system for the continuous treatment of the two galvanization wastewaters. The column, with an internal diameter of 4.8 cm, was packed with 59 g of algae up to a bed height of 27 cm. The operation strategy adopted for the treatment of the two wastewaters consisted in: i) treatment of the Zn(II) wastewater in the first sorption cycle; ii) desorption of pre-loaded Zn(II) using an 1.0 M HCl solution; iii) treatment of the Cr(VI) wastewater, taking advantage of the acidic conditions of the column after the desorption cycle, for the reduction of the Cr(VI) to Cr(III), in the presence of the electrons resulting from the biomass oxidation. This cycle ends when all the oxidizing groups are used.

Keywords: biosorption, brown marine macroalgae, zinc, chromium

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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The assessment of disease levels in crop fields is an important and time-consuming task that generally relies on expert knowledge of trained individuals. Image classification in agriculture problems historically has been based on classical machine learning strategies that make use of hand-engineered features in the top of a classification algorithm. This approach tends to not produce results with high accuracy and generalization to the classes classified by the system when the nature of the elements has a significant variability. The advent of deep convolutional neural networks has revolutionized the field of machine learning, especially in computer vision tasks. These networks have great resourcefulness of learning and have been applied successfully to image classification and object detection tasks in the last years. The objective of this work was to propose a new method based on deep learning convolutional neural networks towards the task of disease level monitoring. Common RGB images of winter wheat were obtained during a growing season. Five categories of disease levels presence were produced, in collaboration with agronomists, for the algorithm classification. Disease level tasks performed by experts provided ground truth data for the disease score of the same winter wheat plots were RGB images were acquired. The system had an overall accuracy of 84% on the discrimination of the disease level classes.

Keywords: crop disease assessment, deep learning, precision agriculture, residual neural networks

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Authors: Yeong-Gyeong Choi, Kyoung-Seok Kim

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This study, literature review research, intends to deal with the problem of conceptual ambiguity among research on emotional labor, and to look into the evolutionary trends and changing aspects of defining the concept of emotional labor. In addition, in existing studies, deep acting and surface acting are highly related to a positive outcome variable and a negative outcome variable, respectively. It was confirmed that for employees performing emotional labor, deep acting and surface acting are highly related to OCB and CWB, respectively. While positive emotion that employees come to experience during job performance process can easily trigger a positive non-task behavior such as OCB, negative emotion that employees experience through excessive workload or unfair treatment can easily induce a negative behavior like CWB. The two management behaviors of emotional labor, surface acting and deep acting, can have either a positive or negative effect on non-task behavior of employees, depending on which one they would choose. Thus, the purpose of this review paper is to clarify the relationship between emotional labor and non-task behavior more specifically.

Keywords: emotion labor, non-task behavior, OCB, CWB

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Authors: Zeineb Ben Salem, Wissem Frikha, Mounir Bouassida

Abstract:

The aim of this paper is to assess the effectiveness of stone columns as a liquefaction countermeasure focusing on shear reinforcementbenefit. In fact, stone columns which have high shear modulus relative to the surrounding soils potentially can carry higher shear stress levels. Thus, stone columns provide shear reinforcement and decrease the Cyclic Shear Stress Ratio CSR to which the treated soils would be subjected during an earthquake. In order to quantify the level of shear stress reduction in reinforced soil, several approaches have been developed. Nevertheless, the available approaches do not take into account the improvement of the soil parameters, mainly the shear modulusdue to stone columns installation. Indeed, in situ control tests carried out before and after the installation of stone columns based upon the results of collected data derived from 24 case histories have given evidence of the improvement of the existing soil properties.In this paper, the assessment of shear reinforcement of stone columns that accounts such improvement of the soil parameters due to stone column installation is investigated. Comparative results indicate that considering the improvement effects considerably affect the assessment of shear reinforcement for liquefaction analysis of reinforced soil by stone columns.

Keywords: stone column, liquefaction, shear reinforcement, CSR, soil improvement

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Authors: K. R. Sreenivas, Shaurya Kaushal

Abstract:

After sunset, under calm & clear-sky nocturnal conditions, the air layer near the surface containing aerosols cools through radiative processes to the upper atmosphere. Due to this cooling, surface air-layer temperature can fall 2-6 degrees C lower than the ground-surface temperature. This unstable convection layer, on the top, is capped by a stable inversion-boundary layer. Radiative divergence, along with the convection within the surface layer, governs the vertical transport of heat and moisture. Micro-physics in this layer have implications for the occurrence and growth of the fog layer. This particular configuration, featuring a convective mixed layer beneath a stably stratified inversion layer, exemplifies a classic case of penetrative convection. In this study, we conduct numerical simulations of the penetrative convection phenomenon within the nocturnal atmospheric surface layer and elucidate its relevance to the dynamics of fog layers. We employ field and laboratory measurements of aerosol number density to model the strength of the radiative cooling. Our analysis encompasses horizontally averaged, vertical profiles of temperature, density, and heat flux. The energetic incursion of the air from the mixed layer into the stable inversion layer across the interface results in entrainment and the growth of the mixed layer, modeling of which is the key focus of our investigation. In our research, we ascertain the appropriate length scale to employ in the Richardson number correlation, which allows us to estimate the entrainment rate and model the growth of the mixed layer. Our analysis of the mixed layer and the entrainment zone reveals a close alignment with previously reported laboratory experiments on penetrative convection. Additionally, we demonstrate how aerosol number density influences the growth or decay of the mixed layer. Furthermore, our study suggests that the presence of fog near the ground surface can induce extensive vertical mixing, a phenomenon observed in field experiments.

Keywords: inversion layer, penetrative convection, radiative cooling, fog occurrence

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Authors: D. V. S. Praneeth, V. R. Sankar Cheela, Brajesh Dubey

Abstract:

Coal Combustion (CC) residues are the major by-products from thermal power plants. The disposal of ash on to land creates havoc to environment and humans. The leaching of the constituent elements pollutes ground water. Beneficiary use of coal combustion residues in structural components is being investigated as a part of this study. This application reduces stress on the convention materials in the construction industry. The present study involves determination of leaching parameters of the CC residues. Batch and column studies are performed based on Leaching Environmental Assessment Framework (LEAF) protocol. The column studies are conducted to simulate the real time percolation conditions in the field. The structural and environmental studies are performed to determine the usability of CC residues as bricks. The physical, chemical, geo environmental and mechanical properties of the alternate materials are investigated. Scanning electron microscopy (SEM), X-Ray Diffraction analysis (XRD), X-ray fluorescence (XRF) and Energy Dispersive X-ray Spectroscopy tests were conducted to determine the characteristics of CC residue ash and bricks.

Keywords: coal combustion residues, LEAF, leaching, SEM

Procedia PDF Downloads 306

Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

Abstract:

It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

Procedia PDF Downloads 333

Authors: Kazuaki Shiraishi, Narumitsu Asai, Tsukasa Kitahara, Sosuke Mieno, Takaharu Kameoka

Abstract:

The purpose of this research is to facilitate productivity advances in agriculture. To accomplish this, we developed an automatic three-dimensional (3D) recording system for growth of field crops that consists of a number of inexpensive modules: a very low-cost stereo camera, a couple of ZigBee wireless modules, a Raspberry Pi single-board computer, and a third generation (3G) wireless communication module. Our system uses an inexpensive Web stereo camera in order to keep total costs low. However, inexpensive video cameras record low-resolution images that are very noisy. Accordingly, in order to resolve these problems, we adopted a deep learning method. Based on the results of extended period of time operation test conducted without the use of an external power supply, we found that by using Super-Resolution Convolutional Neural Network method, our system could achieve a balance between the competing goals of low-cost and superior performance. Our experimental results showed the effectiveness of our system.

Keywords: 3D plant data, automatic recording, stereo camera, deep learning, image processing

Procedia PDF Downloads 271