Search results for: synthetic dataset

Authors: Mala Tankam Carine, Kekeunou Sévilor, Nukenine Elias

Abstract:

Storage and preservation of agricultural products remain the only conditions ensuring the almost permanent availability of foodstuffs. However, infestations due to insects and microorganisms often occur. Callosobruchus maculatus is a pest that causes a lot of damage to cowpea stocks in the tropics. Several methods are adopted to limit their damage, but the use of synthetic chemical insecticides is the most widespread. Biopesticides in sustainable agriculture respond to several environmental, economic and social concerns while offering innovative opportunities that are ecologically and economically viable for producers, workers, consumers and ecosystems. Our main objective is to evaluate the insecticidal efficacy of binary combinations of Fossilshield with root-bark powder of Securidaca longepedunculata against Callosobruchus maculatus in stored cowpea Vigna unguiculata. Laboratory bioassays were conducted in stored grains to evaluate the toxicity of root-bark powder of Securidaca longepedunculata alone or combined with diatomaceous earth Fossil-Shield ® against C. maculatus. Twenty-hour-old adults of C. maculatus were exposed to 50g of cowpea seeds treated with four doses (10, 20, 30, and 40g/kg) of root-bark powder of S. longepedunculata, on the one hand, and (0.5, 1, 1.5, and 2 g/kg) on DE and binary combinations on the other hand. 0g/kg corresponded to untreated control. Adult mortality was recorded up to 7 days (d) post-treatment, whereas the number of F1 progeny was assessed after 30 d. Weight loss and germinative ability were conducted after 120 d. All treatments were arranged according to a completely randomized block with four replicates. The combined mixture of S. longepedunculata and DE controlled the beetle faster compared to the root-bark powder of S. longepedunculata alone. According to the Co-toxicity coefficient, additive effect of binary combinations was recorded at 3-day post-exposure time with the mixture 25% FossilShield + 75% S. longepedunculata. A synergistic action was observed after 3-d post-exposure at mixture 50% FossilShield + 50% S. longepedunculata and at 1-d and 3-d post-exposure periods at mixture 75% FossilShield + 25% S. longepedunculata. The mixture 25% FossilShield + 75% S. longepedunculata induced a decreased progeny of 6 times fewer individuals for 4.5 times less weight loss and 2, 9 times more sprouted grains than with root-bark powder of S. longepedunculata. The combination of FossilShield + S. longepedunculata was more potent than root-bark powder of S. longepedunculata alone, although the root-bark powder of S. longepedunculata caused significant reduction of F1 adults compared to the control. Combined action of botanical insecticides with FossilShield as a grain protectant in an integrated pest management approach is discussed.

Keywords: diatomaceous earth, cowpea, callosobruchus maculatus, securidaca longepedunculata, combined action, co-toxicity coefficient

Procedia PDF Downloads 66

Authors: Zeytu G. Asfaw, Serkalem K. Abrha, Demisew G. Degefu

Abstract:

Background: HIV/AIDS remains a major public health problem in Ethiopia and heavily affecting people of productive and reproductive age. We aimed to compare the performance of Parametric Survival Analysis and Bayesian Survival Analysis using simulations and in a real dataset application focused on determining predictors of HIV patient survival. Methods: A Parametric Survival Models - Exponential, Weibull, Log-normal, Log-logistic, Gompertz and Generalized gamma distributions were considered. Simulation study was carried out with two different algorithms that were informative and noninformative priors. A retrospective cohort study was implemented for HIV infected patients under Highly Active Antiretroviral Therapy in Alamata General Hospital, North Ethiopia. Results: A total of 320 HIV patients were included in the study where 52.19% females and 47.81% males. According to Kaplan-Meier survival estimates for the two sex groups, females has shown better survival time in comparison with their male counterparts. The median survival time of HIV patients was 79 months. During the follow-up period 89 (27.81%) deaths and 231 (72.19%) censored individuals registered. The average baseline cluster of differentiation 4 (CD4) cells count for HIV/AIDS patients were 126.01 but after a three-year antiretroviral therapy follow-up the average cluster of differentiation 4 (CD4) cells counts were 305.74, which was quite encouraging. Age, functional status, tuberculosis screen, past opportunistic infection, baseline cluster of differentiation 4 (CD4) cells, World Health Organization clinical stage, sex, marital status, employment status, occupation type, baseline weight were found statistically significant factors for longer survival of HIV patients. The standard error of all covariate in Bayesian log-normal survival model is less than the classical one. Hence, Bayesian survival analysis showed better performance than classical parametric survival analysis, when subjective data analysis was performed by considering expert opinions and historical knowledge about the parameters. Conclusions: Thus, HIV/AIDS patient mortality rate could be reduced through timely antiretroviral therapy with special care on the potential factors. Moreover, Bayesian log-normal survival model was preferable than the classical log-normal survival model for determining predictors of HIV patients survival.

Keywords: antiretroviral therapy (ART), Bayesian analysis, HIV, log-normal, parametric survival models

Procedia PDF Downloads 190

Authors: Samira Baghbanbari, A. B. P. Lever, Payam S. Shabestari, Donald Weaver

Abstract:

Existing signal transmission models, although undoubtedly useful, have proven insufficient to explain the full complexity of information transfer within the central nervous system. The development of transformative models will necessitate a more comprehensive understanding of neuronal lipid membrane electrophysiology. Pursuant to this goal, the role of highly organized interfacial phospholipid-water layers emerges as a promising case study. A series of phospholipids in neural-glial gap junction interfaces as well as cholesterol molecules have been computationally modelled using high-performance density functional theory (DFT) calculations. Subsequent 'charge decomposition analysis' calculations have revealed a net transfer of charge from phospholipid orbitals through the organized interfacial water layer before ultimately finding its way to cholesterol acceptor molecules. The specific pathway of charge transfer from phospholipid via water layers towards cholesterol has been mapped in detail. Cholesterol is an essential membrane component that is overrepresented in neuronal membranes as compared to other mammalian cells; given this relative abundance, its apparent role as an electronic acceptor may prove to be a relevant factor in further signal transmission studies of the central nervous system. The timescales over which this electronic charge transfer occurs have also been evaluated by utilizing a system design that systematically increases the number of water molecules separating lipids and cholesterol. Memory loss through hydrogen-bonded networks in water can occur at femtosecond timescales, whereas existing action potential-based models are limited to micro or nanosecond scales. As such, the development of future models that attempt to explain faster timescale signal transmission in the central nervous system may benefit from our work, which provides additional information regarding fast timescale energy transfer mechanisms occurring through interfacial water. The study possesses a dataset that includes six distinct phospholipids and a collection of cholesterol. Ten optimized geometric characteristics (features) were employed to conduct binary classification through an artificial neural network (ANN), differentiating cholesterol from the various phospholipids. This stems from our understanding that all lipids within the first group function as electronic charge donors, while cholesterol serves as an electronic charge acceptor.

Keywords: charge transfer, signal transmission, phospholipids, water layers, ANN

Procedia PDF Downloads 65

Authors: Ayodeji Oluleye

Abstract:

This study used Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) and reanalysis dataset of thirty years (1983-2012) to investigate the influence of the atmospheric circulation on dust transport during the Harmattan period over WestAfrica using TOMS data. The Harmattan dust mobilization and atmospheric circulation pattern were evaluated using a kernel density estimate which shows the areas where most points are concentrated between the variables. The evolution of the Inter-Tropical Discontinuity (ITD), Sea surface Temperature (SST) over the Gulf of Guinea, and the North Atlantic Oscillation (NAO) index during the Harmattan period (November-March) was also analyzed and graphs of the average ITD positions, SST and the NAO were observed on daily basis. The Pearson moment correlation analysis was also employed to assess the effect of atmospheric circulation on Harmattan dust transport. The results show that the departure (increased) of TOMS AI values from the long-term mean (1.64) occurred from around 21st of December, which signifies the rich dust days during winter period. Strong TOMS AI signal were observed from January to March with the maximum occurring in the latter months (February and March). The inter-annual variability of TOMSAI revealed that the rich dust years were found between 1984-1985, 1987-1988, 1997-1998, 1999-2000, and 2002-2004. Significantly, poor dust year was found between 2005 and 2006 in all the periods. The study has found strong north-easterly (NE) trade winds were over most of the Sahelianregion of West Africa during the winter months with the maximum wind speed reaching 8.61m/s inJanuary.The strength of NE winds determines the extent of dust transport to the coast of Gulf of Guinea during winter. This study has confirmed that the presence of the Harmattan is strongly dependent on theSST over Atlantic Ocean and ITD position. The locus of the average SST and ITD positions over West Africa could be described by polynomial functions. The study concludes that the evolution of near surface wind field at 925 hpa, and the variations of SST and ITD positions are the major large scale atmospheric circulation systems driving the emission, distribution, and transport of Harmattan dust aerosols over West Africa. However, the influence of NAO was shown to have fewer significance effects on the Harmattan dust transport over the region.

Keywords: atmospheric circulation, dust aerosols, Harmattan, West Africa

Procedia PDF Downloads 308

Authors: Azimollah Aleshzadeh, Enver Vural Yavuz

Abstract:

The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection.

Keywords: entropy weight method, evidence belief function, information content model, landslide susceptibility mapping

Procedia PDF Downloads 130

Authors: Mustafa Alhamdi

Abstract:

Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

Procedia PDF Downloads 148

Authors: Sarah Alamolhoda, Kevin J. Smith, Xiaotao Bi, Naoko Ellis

Abstract:

For millennials, biomass has been the most important source of fuel used to produce energy. Energy derived from biomass is renewable by re-growth of biomass. Various technologies are used to convert biomass to potential renewable products including combustion, gasification, pyrolysis and fermentation. Gasification is the incomplete combustion of biomass in a controlled environment that results in valuable products such as syngas, biooil and biochar. Syngas is a combustible gas consisting of hydrogen (H₂), carbon monoxide (CO), carbon dioxide (CO₂), and traces of methane (CH₄) and nitrogen (N₂). Cleaned syngas can be used as a turbine fuel to generate electricity, raw material for hydrogen and synthetic natural gas production, or as the anode gas of solid oxide fuel cells. In this work, syngas as a product of woody biomass gasification in British Columbia, Canada, was introduced to two consecutive fixed bed reactors to perform a catalytic water gas shift reaction followed by a catalytic methanation reaction. The water gas shift reaction is a well-established industrial process and used to increase the hydrogen content of the syngas before the methanation process. Catalysts were used in the process since both reactions are reversible exothermic, and thermodynamically preferred at lower temperatures while kinetically favored at elevated temperatures. The water gas shift reactor and the methanation reactor were packed with Cu-based catalyst and Ni-based catalyst, respectively. Simulated syngas with different percentages of CO, H₂, CH₄, and CO₂ were fed to the reactors to investigate the effect of operating conditions in the unit. The water gas shift reaction experiments were done in the temperature of 150 ˚C to 200 ˚C, and the pressure of 550 kPa to 830 kPa. Similarly, methanation experiments were run in the temperature of 300 ˚C to 400 ˚C, and the pressure of 2340 kPa to 3450 kPa. The Methanation reaction reached 98% of CO conversion at 340 ˚C and 3450 kPa, in which more than half of CO was converted to CH₄. Increasing the reaction temperature caused reduction in the CO conversion and increase in the CH₄ selectivity. The process was designed to be renewable and release low greenhouse gas emissions. Syngas is a clean burning fuel, however by going through water gas shift reaction, toxic CO was removed, and hydrogen as a green fuel was produced. Moreover, in the methanation process, the syngas energy was transformed to a fuel with higher energy density (per volume) leading to reduction in the amount of required fuel that flows through the equipment and improvement in the process efficiency. Natural gas is about 3.5 times more efficient (energy/ volume) than hydrogen and easier to store and transport. When modification of existing infrastructure is not practical, the partial conversion of renewable hydrogen to natural gas (with up to 15% hydrogen content), the efficiency would be preserved while greenhouse gas emission footprint is eliminated.

Keywords: renewable natural gas, methane, hydrogen, gasification, syngas, catalysis, fuel

Procedia PDF Downloads 111

Authors: Eiman Kattan, Hong Wei

Abstract:

In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

Procedia PDF Downloads 164

Authors: Bansal Ashwani

Abstract:

Cartographic depiction and visualization of wetland changes is an important tool to map spatial-temporal information about the wetland dynamics effectively and to comprehend the response of these water bodies in maintaining the groundwater and surrounding ecosystem. This is true for the states of North Western India, i.e., J&K, Himachal, Punjab, and Haryana that are bestowed upon with several natural wetlands in the flood plains or on the courses of its rivers. Thus, the present study documents, analyses and reconstructs the lost wetlands, which existed in the flood plains of the major river basins of these states, i.e., Chenab, Jhelum, Satluj, Beas, Ravi, and Ghagar, in the beginning of the 20th century. To achieve the objective, the study has used multi-temporal datasets since the 1960s using high to medium resolution satellite datasets, e.g., Corona (1960s/70s), Landsat (1990s-2017) and Sentinel (2017). The Sentinel (2017) satellite image has been used for making the wetland inventory owing to its comparatively higher spatial resolution with multi-spectral bands. In addition, historical records, repeated photographs, historical maps, field observations including geomorphological evidence were also used. The water index techniques, i.e., band rationing, normalized difference water index (NDWI), modified NDWI (MNDWI) have been compared and used to map the wetlands. The wetland types found in the north-western states have been categorized under 19 classes suggested by Space Application Centre, India. These enable the researcher to provide with the wetlands inventory and a series of cartographic representation that includes overlaying multiple temporal wetlands extent vectors. A preliminary result shows the general state of wetland shrinkage since the 1960s with varying area shrinkage rate from one wetland to another. In addition, it is observed that majority of wetlands have not been documented so far and even do not have names. Moreover, the purpose is to emphasize their elimination in addition to establishing a baseline dataset that can be a tool for wetland planning and management. Finally, the applicability of cartographic depiction and visualization, historical map sources, repeated photographs and remote sensing data for reconstruction of long term wetlands fluctuations, especially in the northern part of India, will be addressed.

Keywords: cartographic depiction and visualization, wetland changes, NDWI/MDWI, geomorphological evidence and remote sensing

Procedia PDF Downloads 260

Authors: Kanti Sapkota, Do Hyun Lee, Sung Soo Han

Abstract:

Nanocomposites have been widely used in various fields such as electronics, catalysis, and in chemical, biological, biomedical and optical fields. They display broad biomedical properties like antidiabetic, anticancer, antioxidant, antimicrobial and antibacterial activities. Moreover, nanomaterials have been used for wastewater treatment. Particularly, bimetallic hybrid nanocomposites exhibit unique features as compared to their monometallic components. Hybrid nanomaterials not only afford the multifunctionality endowed by their constituents but can also show synergistic properties. In addition, these hybrid nanomaterials have noteworthy catalytic and optical properties. Notably, Au−Ag based nanoparticles can be employed in sensor and catalysis due to their characteristic composition-tunable plasmonic properties. Due to their importance and usefulness, various efforts were developed for their preparation. Generally, chemical methods have been described to synthesize such bimetallic nanocomposites. In such chemical synthesis, harmful and hazardous chemicals cause environmental contamination and increase toxicity levels. Therefore, ecologically benevolent processes for the synthesis of nanomaterials are highly desirable to diminish such environmental and safety concerns. In this regard, here we disclose a simple, cost-effective, external additive free and eco-friendly method for the synthesis of Au-Ag@AgCl nanocomposites using Nephrolepis cordifolia root extract. Au-Ag@AgCl NCs were obtained by the simultaneous reduction of cationic Ag and Au into AgCl in the presence of plant extract. The particle size of 10 to 50 nm was observed with the average diameter of 30 nm. The synthesized nanocomposite was characterized by various modern characterization techniques. For example, UV−visible spectroscopy was used to determine the optical activity of the synthesized NCs, and Fourier transform infrared (FT-IR) spectroscopy was employed to investigate the functional groups present in the biomolecules that were responsible for both reducing and capping agents during the formation of nanocomposites. Similarly, powder X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA) and energy-dispersive X-ray (EDX) spectroscopy were used to determine crystallinity, size, oxidation states, thermal stability and weight loss of the synthesized nanocomposites. As a synthetic application, the synthesized nanocomposite exhibited excellent catalytic activity for the multicomponent synthesis of biologically interesting quinoline molecules via domino annulation-aromatization reaction of aniline, arylaldehyde, and phenyl acetylene derivatives. Interestingly, the nanocatalyst was efficiently recycled for five times without substantial loss of catalytic properties.

Keywords: nanoparticles, catalysis, multicomponent, quinoline

Procedia PDF Downloads 123

Authors: Dmitriy V. Chulkov

Abstract:

Escalation of commitment is defined as continuation of a project after receiving negative information about it. While literature in management and psychology identified various factors contributing to escalation behavior, this phenomenon has received little analysis in economics, potentially due to the apparent irrationality of escalation. In this study, we present an economic model of escalation with asymmetric information in a principal-agent setup where the agents are responsible for a project selection decision and discover the outcome of the project before the principal. Our theoretical model complements the existing literature on several accounts. First, we link the incentive to escalate commitment to a project with the turnover decision by the manager. When a manager learns the outcome of the project and stops it that reveals that a mistake was made. There is an incentive to continue failing projects and avoid admitting the mistake. This incentive is enhanced when the agent may voluntarily resign from the firm before the outcome of the failing project is revealed, and thus not bear the full extent of reputation damage due to project failure. As long as some successful managers leave the firm for extraneous reasons, outside firms find it difficult to link failing projects with certainty to managers that left a firm. Second, we demonstrate that non-CEO managers have reputation concerns separate from those of the CEO, and thus may escalate commitment to projects they oversee, when such escalation can attenuate damage to reputation from impending project failure. Such incentive for escalation will be present for non-CEO managers if the CEO delegates responsibility for a project to a non-CEO executive. If reputation matters for promotion to the CEO, the incentive for a rising executive to escalate in order to protect reputation is distinct from that of a CEO. Third, our theoretical model is supported by empirical analysis of changes in the firm’s operations measured by the presence of discontinued operations at the time of turnover among the top four members of the top management team. Discontinued operations are indicative of termination of failing projects at a firm. The empirical results demonstrate that in a large dataset of over three thousand publicly traded U.S. firms for a period from 1993 to 2014 turnover by top executives significantly increases the likelihood that the firm discontinues operations. Furthermore, the type of turnover matters as this effect is strongest when at least one non-CEO member of the top management team leaves the firm and when the CEO departure is due to a voluntary resignation and not to a retirement or illness. Empirical results are consistent with the predictions of the theoretical model and suggest that escalation of commitment is primarily observed in decisions by non-CEO members of the top management team.

Keywords: discontinued operations, escalation of commitment, executive turnover, top management teams

Procedia PDF Downloads 363

Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

Abstract:

Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

Procedia PDF Downloads 75

Authors: Alsu F. Saifitdinova, Alexey S. Komissarov, Svetlana A. Galkina, Elena I. Koshel, Maria M. Kulak, Stephen J. O'Brien, Elena R. Gaginskaya

Abstract:

The mystery of sex determination is one of the most ancient and still not solved until the end so far. In many species, sex determination is genetic and often accompanied by the presence of dimorphic sex chromosomes in the karyotype. Genomic sequencing gave the information about the gene content of sex chromosomes which allowed to reveal their origin from ordinary autosomes and to trace their evolutionary history. Female-specific W chromosome in birds as well as mammalian male-specific Y chromosome is characterized by the degeneration of gene content and the accumulation of repetitive DNA. Tandem repeats complicate the analysis of genomic data. Despite the best efforts chicken W chromosome assembly includes only 1.2 Mb from expected 55 Mb. Supplementing the information on the sex chromosome composition not only helps to complete the assembly of genomes but also moves us in the direction of understanding of the sex-determination systems evolution. A whole-genome survey to the assembly Gallus_gallus WASHUC 2.60 was applied for repeats search in assembled genome and performed search and assembly of high copy number repeats in unassembled reads of SRR867748 short reads datasets. For cytogenetic analysis conventional methods of fluorescent in situ hybridization was used for previously cloned W specific satellites and specifically designed directly labeled synthetic oligonucleotide DNA probe was used for bioinformatically identified repetitive sequence. Hybridization was performed with mitotic chicken chromosomes and manually isolated giant meiotic lampbrush chromosomes from growing oocytes. A novel chicken W specific satellite (GGAAA)n which is not co-localizes with any previously described classes of W specific repeats was identified and mapped with high resolution. In the composition of autosomes this repeat units was found as a part of upstream regions of gonad specific protein coding sequences. These findings may contribute to the understanding of the role of tandem repeats in sex specific differentiation regulation in birds and sex chromosome evolution. This work was supported by the postdoctoral fellowships from St. Petersburg State University (#1.50.1623.2013 and #1.50.1043.2014), the grant for Leading Scientific Schools (#3553.2014.4) and the grant from Russian foundation for basic researches (#15-04-05684). The equipment and software of Research Resource Center “Chromas” and Theodosius Dobzhansky Center for Genome Bioinformatics of Saint Petersburg State University were used.

Keywords: birds, lampbrush chromosomes, sex chromosomes, tandem repeats

Procedia PDF Downloads 384

Authors: Nitin Jadhav, Pradeep R. Vavia

Abstract:

Poor water soluble drugs are difficult to promote for oral drug delivery as they demonstrate poor and variable bioavailability because of its poor solubility and dissolution in GIT fluid. Nowadays lipid based formulations especially self microemulsifying drug delivery system (SMEDDS) is found as the most effective technique. With all the impressive advantages, the need of high amount of surfactant (50% - 80%) is the major drawback of SMEDDS. High concentration of synthetic surfactant is known for irritation in GIT and also interference with the function of intestinal transporters causes changes in drug absorption. Surfactant may also reduce drug activity and subsequently bioavailability due to the enhanced entrapment of drug in micelles. In chronic treatment these issues are very conspicuous due to the long exposure. In addition the liquid self microemulsifying system also suffers from stability issues. Recently one novel approach of solid stabilized micro and nano emulsion (Pickering emulsion) has very admirable properties such as high stability, absence or very less concentration of surfactant and easily converts into the dry form. So here we are exploring pickering dry emulsion system for dissolution enhancement of anti-lipemic, extremely poorly water soluble drug (Fenofibrate). Oil moiety for emulsion preparation was selected mainly on the basis of higher solubility of drug. Captex 300 was showed higher solubility for fenofibrate, hence selected as oil for emulsion. With Silica (solid stabilizer); Span 20 was selected to improve the wetting property of it. Emulsion formed by Silica and Span20 as stabilizer at the ratio 2.5:1 (silica: span 20) was found very stable at the particle size 410 nm. The prepared emulsion was further preceded for spray drying and formed microcapsule evaluated for in-vitro dissolution study, in-vivo pharmacodynamic study and characterized for DSC, XRD, FTIR, SEM, optical microscopy etc. The in vitro study exhibits significant dissolution enhancement of formulation (85 % in 45 minutes) as compared to plain drug (14 % in 45 minutes). In-vivo study (Triton based hyperlipidaemia model) exhibits significant reduction in triglyceride and cholesterol with formulation as compared to plain drug indicating increasing in fenofibrate bioavailability. DSC and XRD study exhibit loss of crystallinity of drug in microcapsule form. FTIR study exhibit chemical stability of fenofibrate. SEM and optical microscopy study exhibit spherical structure of globule coated with solid particles.

Keywords: captex 300, fenofibrate, pickering dry emulsion, silica, span20, stability, surfactant

Procedia PDF Downloads 494

Authors: Michelle Blewitt, Scott P. Wilson, Heidi Tait, Juniper Riordan

Abstract:

Rubber crumb is a physical and chemical pollutant of concern for the environment and human health, warranting immediate investigations into its pathways to the environment and potential impacts. This emerging microplastic is created by shredding end-of-life tyres into ‘rubber crumb’ particles between 1-5mm used on synthetic turf fields and soft-fall playgrounds as a solution to intensifying tyre waste worldwide. Despite having known toxic and carcinogenic properties, studies into the transportation pathways and movement patterns of rubber crumbs from these surfaces remain in their infancy. To address this deficit, AUSMAP, the Australian Microplastic Assessment Project, in partnership with the Tangaroa Blue Foundation, conducted a study to quantify crumb loss from soft-fall surfaces. To our best knowledge, this is the first of its kind, with funding for the audits being provided by the Australian Government’s Reef Trust. Sampling occurred at 12 soft-fall playgrounds within the Great Barrier Reef Catchment Area on Australia’s North-East coast, in close proximity to the United Nations World Heritage Listed Reef. Samples were collected over a 12-month period using randomized sediment cores at 0, 2 and 4 meters away from the playground edge along a 20-meter transect. This approach facilitated two objectives pertaining to particle movement: to establish that crumb loss is occurring and that it decreases with distance from the soft-fall surface. Rubber crumb abundance was expressed as a total value and used to determine an expected average of rubber crumb loss per m2. An Analysis of Variance (ANOVA) was used to compare the differences in crumb abundance at each interval from the playground. Site characteristics, including surrounding sediment type, playground age, degree of ultra-violet exposure and amount of foot traffic, were additionally recorded for the comparison. Preliminary findings indicate that crumb is being lost at considerable rates from soft-fall playgrounds in the region, emphasizing an urgent need to further examine it as a potential source of aquatic pollution, soil contamination and threat to individuals who regularly utilize these surfaces. Additional implications for the future of rubber crumbs as a fit-for-purpose recycling initiative will be discussed with regard to industry, governments and the economic burden of surface maintenance and/ or replacement.

Keywords: microplastics, toxic rubber crumb, litter pathways, marine environment

Procedia PDF Downloads 88

Authors: Mwehe Mathenge, Ben G. J. S. Sonneveld, Jacqueline E. W. Broerse

Abstract:

Smallholder farmers are important drivers of agriculture productivity, food security, and poverty reduction in Sub-Saharan Africa. However, they are faced with myriad challenges in their efforts at participating in agribusiness markets. How the geographic explicit factors existing at the local level interact to impede smallholder farmers' decision to participates (or not) in agribusiness markets is not well understood. Deconstructing the spatial complexity of the local environment could provide a deeper insight into how geographically explicit determinants promote or impede resource-poor smallholder farmers from participating in agribusiness. This paper’s objective was to identify, map, and analyze local spatial autocorrelation in factors that impede poor smallholders from participating in agribusiness markets. Data were collected using geocoded researcher-administered survey questionnaires from 392 households in Western Kenya. Three spatial statistics methods in geographic information system (GIS) were used to analyze data -Global Moran’s I, Cluster and Outliers Analysis (Anselin Local Moran’s I), and geographically weighted regression. The results of Global Moran’s I reveal the presence of spatial patterns in the dataset that was not caused by spatial randomness of data. Subsequently, Anselin Local Moran’s I result identified spatially and statistically significant local spatial clustering (hot spots and cold spots) in factors hindering smallholder participation. Finally, the geographically weighted regression results unearthed those specific geographic explicit factors impeding market participation in the study area. The results confirm that geographically explicit factors are indispensable in influencing the smallholder farming decisions, and policymakers should take cognizance of them. Additionally, this research demonstrated how geospatial explicit analysis conducted at the local level, using geographically disaggregated data, could help in identifying households and localities where the most impoverished and resource-poor smallholder households reside. In designing spatially targeted interventions, policymakers could benefit from geospatial analysis methods in understanding complex geographic factors and processes that interact to influence smallholder farmers' decision-making processes and choices.

Keywords: agribusiness markets, GIS, smallholder farmers, spatial statistics, disaggregated spatial data

Procedia PDF Downloads 136

Authors: Sophia Liang Zhou

Abstract:

For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

Procedia PDF Downloads 99

Authors: Atsuko Okazaki, Jurg Ott

Abstract:

Some genetic diseases (‘digenic traits’) are due to the interaction between two DNA variants. For example, certain forms of Retinitis Pigmentosa (a genetic form of blindness) occur in the presence of two mutant variants, one in the ROM1 gene and one in the RDS gene, while the occurrence of only one of these mutant variants leads to a completely normal phenotype. Detecting such digenic traits by genetic methods is difficult. A common approach to finding disease-causing variants is to compare 100,000s of variants between individuals with a trait (cases) and those without the trait (controls). Such genome-wide association studies (GWASs) have been very successful but hinge on genetic effects of single variants, that is, there should be a difference in allele or genotype frequencies between cases and controls at a disease-causing variant. Frequent pattern mining (FPM) methods offer an avenue at detecting digenic traits even in the absence of single-variant effects. The idea is to enumerate pairs of genotypes (genotype patterns) with each of the two genotypes originating from different variants that may be located at very different genomic positions. What is needed is for genotype patterns to be significantly more common in cases than in controls. Let Y = 2 refer to cases and Y = 1 to controls, with X denoting a specific genotype pattern. We are seeking association rules, ‘X → Y’, with high confidence, P(Y = 2|X), significantly higher than the proportion of cases, P(Y = 2) in the study. Clearly, generally available FPM methods are very suitable for detecting disease-associated genotype patterns. We use fpgrowth as the basic FPM algorithm and built a framework around it to enumerate high-frequency digenic genotype patterns and to evaluate their statistical significance by permutation analysis. Application to a published dataset on opioid dependence furnished results that could not be found with classical GWAS methodology. There were 143 cases and 153 healthy controls, each genotyped for 82 variants in eight genes of the opioid system. The aim was to find out whether any of these variants were disease-associated. The single-variant analysis did not lead to significant results. Application of our FPM implementation resulted in one significant (p < 0.01) genotype pattern with both genotypes in the pattern being heterozygous and originating from two variants on different chromosomes. This pattern occurred in 14 cases and none of the controls. Thus, the pattern seems quite specific to this form of substance abuse and is also rather predictive of disease. An algorithm called Multifactor Dimension Reduction (MDR) was developed some 20 years ago and has been in use in human genetics ever since. This and our algorithms share some similar properties, but they are also very different in other respects. The main difference seems to be that our algorithm focuses on patterns of genotypes while the main object of inference in MDR is the 3 × 3 table of genotypes at two variants.

Keywords: digenic traits, DNA variants, epistasis, statistical genetics

Procedia PDF Downloads 117

Authors: Nurten Akgun, Dilum Dissanayake, Neil Thorpe, Margaret C. Bell

Abstract:

Cycling has gained a great attention with comparable speeds, low cost, health benefits and reducing the impact on the environment. The main challenge associated with cycling is the provision of safety for the people choosing to cycle as their main means of transport. From the road safety point of view, cyclists are considered as vulnerable road users because they are at higher risk of serious casualty in the urban network but more specifically at roundabouts. This research addresses the development of an enhanced mathematical model by including a broad spectrum of casualty related variables. These variables were geometric design measures (approach number of lanes and entry path radius), speed limit, meteorological condition variables (light, weather, road surface) and socio-demographic characteristics (age and gender), as well as contributory factors. Contributory factors included driver’s behavior related variables such as failed to look properly, sudden braking, a vehicle passing too close to a cyclist, junction overshot, failed to judge other person’s path, restart moving off at the junction, poor turn or manoeuvre and disobeyed give-way. Tyne and Wear in the UK were selected as a case study area. The cyclist casualty data was obtained from UK STATS19 National dataset. The reference categories for the regression model were set to slight and serious cyclist casualties. Therefore, binary logistic regression was applied. Binary logistic regression analysis showed that approach number of lanes was statistically significant at the 95% level of confidence. A higher number of approach lanes increased the probability of severity of cyclist casualty occurrence. In addition, sudden braking statistically significantly increased the cyclist casualty severity at the 95% level of confidence. The result concluded that cyclist casualty severity was highly related to approach a number of lanes and sudden braking. Further research should be carried out an in-depth analysis to explore connectivity of sudden braking and approach number of lanes in order to investigate the driver’s behavior at approach locations. The output of this research will inform investment in measure to improve the safety of cyclists at roundabouts.

Keywords: binary logistic regression, casualty severity, cyclist safety, roundabout

Procedia PDF Downloads 172

Authors: Esmat Kamel

Abstract:

This paper aims to shed light on the extent to which the Agadir Association agreement has fostered inter regional trade between the E.U_26 and the Agadir_4 countries; once that we control for the evolution of Agadir agreement’s exports to the rest of the world. The next valid question will be regarding any remarkable variation in the spatial/sectoral structure of exports, and to what extent has it been induced by the Agadir agreement itself and precisely after the adoption of rules of origin and the PANEURO diagonal cumulative scheme? The paper’s empirical dataset covering a timeframe from [2000 -2009] was designed to account for sector specific export and intermediate flows and the bilateral structured gravity model was custom tailored to capture sector and regime specific rules of origin and the Poisson Pseudo Maximum Likelihood Estimator was used to calculate the gravity equation. The methodological approach of this work is considered to be a threefold one which starts first by conducting a ‘Hierarchal Cluster Analysis’ to classify final export flows showing a certain degree of linkage between each other. The analysis resulted in three main sectoral clusters of exports between Agadir_4 and E.U_26: cluster 1 for Petrochemical related sectors, cluster 2 durable goods and finally cluster 3 for heavy duty machinery and spare parts sectors. Second step continues by taking export flows resulting from the 3 clusters to be subject to treatment with diagonal Rules of origin through ‘The Double Differences Approach’, versus an equally comparable untreated control group. Third step is to verify results through a robustness check applied by ‘Propensity Score Matching’ to validate that the same sectoral final export and intermediate flows increased when rules of origin were relaxed. Through all the previous analysis, a remarkable and partial significance of the interaction term combining both treatment effects and time for the coefficients of 13 out of the 17 covered sectors turned out to be partially significant and it further asserted that treatment with diagonal rules of origin contributed in increasing Agadir’s_4 final and intermediate exports to the E.U._26 on average by 335% and in changing Agadir_4 exports structure and composition to the E.U._26 countries.

Keywords: agadir association agreement, structured gravity model, hierarchal cluster analysis, double differences estimation, propensity score matching, diagonal and relaxed rules of origin

Procedia PDF Downloads 314

Authors: Liane J. Ioannou, Jonathan Serpell, Joanne Dean, Cino Bendinelli, Jenny Gough, Dean Lisewski, Julie Miller, Win Meyer-Rochow, Stan Sidhu, Duncan Topliss, David Walters, John Zalcberg, Susannah Ahern

Abstract:

Background: The occurrence of thyroid cancer is increasing throughout the developed world, including Australia and New Zealand, and since the 1990s has become the fastest increasing malignancy. Following the success of a number of institutional databases that monitor outcomes after thyroid surgery, the Australian and New Zealand Endocrine Surgeons (ANZES) agreed to auspice the development of a bi-national thyroid cancer registry. Objectives: To establish a bi-national population-based clinical quality registry with the aim of monitoring and improving the quality of care provided to patients diagnosed with thyroid cancer in Australia and New Zealand. Patients and Methods: The Australian and New Zealand Thyroid Cancer Registry (ANZTCR) captures clinical data for all patients, over the age of 18 years, diagnosed with thyroid cancer, confirmed by histopathology report, that have been diagnosed, assessed or treated at a contributing hospital. Data is collected by endocrine surgeons using a web-based interface, REDCap, primarily via direct data entry. Results: A multi-disciplinary Steering Committee was formed, and with operational support from Monash University the ANZTCR was established in early 2017. The pilot phase of the registry is currently operating in Victoria, New South Wales, Queensland, Western Australia and South Australia, with over 30 sites expected to come on board across Australia and New Zealand in 2018. A modified-Delphi process was undertaken to determine the key quality indicators to be reported by the registry, and a minimum dataset was developed comprising information regarding thyroid cancer diagnosis, pathology, surgery, and 30-day follow up. Conclusion: There are very few established thyroid cancer registries internationally, yet clinical quality registries have shown valuable outcomes and patient benefits in other cancers. The establishment of the ANZTCR provides the opportunity for Australia and New Zealand to further understand the current practice in the treatment of thyroid cancer and reasons for variation in outcomes. The engagement of endocrine surgeons in supporting this initiative is crucial. While the pilot registry has a focus on early clinical outcomes, it is anticipated that future collection of longer-term outcome data particularly for patients with the poor prognostic disease will add significant further value to the registry.

Keywords: thyroid cancer, clinical registry, population health, quality improvement

Procedia PDF Downloads 189

Authors: Buddha Ratna Shrestha, Jimmy Faivre, Xavier Banquy

Abstract:

By precisely controlling the molecular interactions between anti-wear macromolecules and bottle-brush lubricating molecules in the solution state, we obtained a fluid with excellent lubricating and wear protection capabilities. The reason for this synergistic behavior relies on the subtle interaction forces between the fluid components which allow the confined macromolecules to sustain high loads under shear without rupture. Our results provide rational guides to design such fluids for virtually any type of surfaces. The lowest friction coefficient and the maximum pressure that it can sustain is 5*10-3 and 2.5 MPa which is close to the physiological pressure. Lubricating and protecting surfaces against wear using liquid lubricants is a great technological challenge. Until now, wear protection was usually imparted by surface coatings involving complex chemical modifications of the surface while lubrication was provided by a lubricating fluid. Hence, we here research for a simple, effective and applicable solution to the above problem using surface force apparatus (SFA). SFA is a powerful technique with sub-angstrom resolution in distance and 10 nN/m resolution in interaction force while performing friction experiment. Thus, SFA is used to have the direct insight into interaction force, material and friction at interface. Also, we always know the exact contact area. From our experiments, we found that by precisely controlling the molecular interactions between anti-wear macromolecules and lubricating molecules, we obtained a fluid with excellent lubricating and wear protection capabilities. The reason for this synergistic behavior relies on the subtle interaction forces between the fluid components which allow the confined macromolecules to sustain high loads under shear without rupture. The lowest friction coefficient and the maximum pressure that it can sustain in our system is 5*10-3 and 2.5 GPA which is well above the physiological pressure. Our results provide rational guides to design such fluids for virtually any type of surfaces. Most importantly this process is simple, effective and applicable method of lubrication and protection as until now wear protection was usually imparted by surface coatings involving complex chemical modifications of the surface. Currently, the frictional data that are obtained while sliding the flat mica surfaces are compared and confirmed that a particular mixture of solution was found to surpass all other combination. So, further we would like to confirm that the lubricating and antiwear protection remains the same by performing the friction experiments in synthetic cartilages.

Keywords: bottle brush polymer, hyaluronic acid, lubrication, tribology

Procedia PDF Downloads 259

Authors: Gabriel Montague, Max Vilgalys, Catherine H. Crawford, Jorge Ortiz, Dava Newman

Abstract:

There has never been so much publicly accessible atmospheric and environmental data. The possibilities of these data are exciting, but the sheer volume of available datasets represents a new challenge for researchers. The task of identifying and working with a new dataset has become more difficult with the amount and variety of available data. Datasets are often documented in ways that differ substantially from the common English used to describe the same topics. This presents a barrier not only for new scientists, but for researchers looking to find comparisons across multiple datasets or specialists from other disciplines hoping to collaborate. This paper proposes a method for addressing this obstacle: creating a knowledge graph to bridge the gap between everyday English language and the technical language surrounding these datasets. Knowledge graph generation is already a well-established field, although there are some unique challenges posed by working with Earth data. One is the sheer size of the databases – it would be infeasible to replicate or analyze all the data stored by an organization like The National Aeronautics and Space Administration (NASA) or the European Space Agency. Instead, this approach identifies topics from metadata available for datasets in NASA’s Earthdata database, which can then be used to directly request and access the raw data from NASA. By starting with a single metadata standard, this paper establishes an approach that can be generalized to different databases, but leaves the challenge of metadata harmonization for future work. Topics generated from the metadata are then linked to topics from a collection of English queries through a variety of standard and custom natural language processing (NLP) methods. The results from this method are then compared to a baseline of elastic search applied to the metadata. This comparison shows the benefits of the proposed knowledge graph system over existing methods, particularly in interpreting natural language queries and interpreting topics in metadata. For the research community, this work introduces an application of NLP to the ecological and environmental sciences, expanding the possibilities of how machine learning can be applied in this discipline. But perhaps more importantly, it establishes the foundation for a platform that can enable common English to access knowledge that previously required considerable effort and experience. By making this public data accessible to the full public, this work has the potential to transform environmental understanding, engagement, and action.

Keywords: earth metadata, knowledge graphs, natural language processing, question-answer systems

Procedia PDF Downloads 144

Authors: Ahmad Kayvani Fard, Gordon Mckay, Muataz Hussien

Abstract:

Adsorption/sorption is believed to be one of the optimal processes for the removal of heavy metals from water due to its low operational and capital cost as well as its high removal efficiency. Different materials have been reported in literature as adsorbent for heavy metal removal in waste water such as natural sorbents, organic polymers (synthetic) and mineral materials (inorganic). The selection of adsorbents and development of new functional materials that can achieve good removal of heavy metals from water is an important practice and depends on many factors, such as the availability of the material, cost of material, and material safety and etc. In this study we reported the synthesis of doped Activated carbon and Carbon nanotube (CNT) with different loading of metal oxide nanoparticles such as Fe2O3, Fe3O4, Al2O3, TiO2, SiO2 and Ag nanoparticles and their application in removal of heavy metals, hydrocarbon, and organics from waste water. Commercial AC and CNT with different loadings of mentioned nanoparticle were prepared and effect of pH, adsorbent dosage, sorption kinetic, and concentration effects are studied and optimum condition for removal of heavy metals from water is reported. The prepared composite sorbent is characterized using field emission scanning electron microscopy (FE-SEM), high transmission electron microscopy (HR-TEM), thermogravimetric analysis (TGA), X-ray diffractometer (XRD), the Brunauer, Emmett and Teller (BET) nitrogen adsorption technique, and Zeta potential. The composite materials showed higher removal efficiency and superior adsorption capacity compared to commercially available carbon based adsorbent. The specific surface area of AC increased by 50% reaching up to 2000 m2/g while the CNT specific surface area of CNT increased by more than 8 times reaching value of 890 m2/g. The increased surface area is one of the key parameters along with surface charge of the material determining the removal efficiency and removal efficiency. Moreover, the surface charge density of the impregnated CNT and AC have enhanced significantly where can benefit the adsorption process. The nanoparticles also enhance the catalytic activity of material and reduce the agglomeration and aggregation of material which provides more active site for adsorbing the contaminant from water. Some of the results for treating wastewater includes 100% removal of BTEX, arsenic, strontium, barium, phenolic compounds, and oil from water. The results obtained are promising for the use of AC and CNT loaded with metal oxide nanoparticle in treatment and pretreatment of waste water and produced water before desalination process. Adsorption can be very efficient with low energy consumption and economic feasibility.

Keywords: carbon nanotube, activated carbon, adsorption, heavy metal, water treatment

Procedia PDF Downloads 231

Authors: Wisdom Robert Duruji

Abstract:

The outbreak of SARS-CoV-2 serotype infection was recorded in January 2020 in Wuhan City, Hubei Province, China. This study examines the concepts of the COVID-19 pandemic and the implications of vaccines for health security in Nigeria and Diasporas. It challenges the widely accepted assumption that the first case of coronavirus infection in Nigeria was recorded on February 27th, 2020, in Lagos. The study utilizes a range of research methods to achieve its objectives. These include the double-layered culture technique, literature review, website knowledge, Google search, news media information, academic journals, fieldwork, and on-site observations. These diverse methods allow for a comprehensive analysis of the concepts and the implications being studied. The study finds that coronavirus infection can be asymptomatic; it may be the antigenicity of the leukocytes (white blood cells), which produce immunogenic hapten or interferons (α, β and γ) that fight infectious parasites, was an immune response that prevented severe virulence in healthy individuals; the reason healthy patients of coronavirus infection in Nigeria naturally recovered after two to three weeks of on-set of infection and test negative. However, the fatality data from the Nigerian Centre for Disease Control (NCDC) is incorrect in this study’s finding; it perused that the fatalities were primarily due to underlying ailments, hunger, and malnutrition in debilitated, comorbid, or compromised patients. This study concluded that the kits and Polymerase Chain Reaction (PCR) machine currently used by the Nigerian Centre for Disease Control (NCDC) in testing and confirming COVID-19 in Nigeria is not ideal; it is programmed and negates separating the strain to its specific serotypes amongst its genera coronavirus, and family Coronaviridae; and might have confirmed patients with the symptoms of febrile caused by cough, catarrh, typhoid and malaria parasites as Covid-19 positive. Therefore, it is recommended that the coronavirus species infected in Nigeria are opportunistic parasites that thrive in human immuno-suppressed conditions like the herpesvirus; it cannot be eradicated by vaccines; the only virucides are interferons, immunoglobulins, and probably synthetic antiviral guanosine drugs like copegus or ribavirin. The findings emphasized that COVID-19 is not the primary pandemic disease in Nigeria; the lockdown was a mirage and not necessary; but rather, pandemic diseases in Nigeria are corruption, nepotism, hunger, and malnutrition caused by ineptitude in governance, religious dichotomy, and ethnic conflicts.

Keywords: coronavirus, corruption, Covid-19 pandemic, lock-down, Nigeria, vaccine

Procedia PDF Downloads 64

Authors: Misagh Ghobadi, Rich Crane, Karen Hudson-Edwards, Clemens Vinzenz Ullmann

Abstract:

Lithium is recognized as the critical energy metal of the 21st century, comparable in importance to coal in the 19th century and oil in the 20th century, often termed 'white gold'. Current global demand for lithium, estimated at 0.95-0.98 million metric tons (Mt) of lithium carbonate equivalent (LCE) annually in 2024, is projected to rise to 1.87 Mt by 2027 and 3.06 Mt by 2030. Despite anticipated short-term stability in supply and demand, meeting the forecasted 2030 demand will require the lithium industry to develop an additional capacity of 1.42 Mt of LCE annually, exceeding current planned and ongoing efforts. Brine resources constitute nearly 65% of global lithium reserves, underscoring the importance of exploring lithium recovery from underutilized sources, especially geothermal brines. However, conventional lithium extraction from brine deposits faces challenges due to its time-intensive process, low efficiency (30-50% lithium recovery), unsuitability for low lithium concentrations (<300 mg/l), and notable environmental impacts. Addressing these challenges, direct lithium extraction (DLE) methods have emerged as promising technologies capable of economically extracting lithium even from low-concentration brines (>50 mg/l) with high recovery rates (75-98%). However, most studies (70%) have predominantly focused on synthetic brines instead of native (natural/real), with limited application of these approaches in real-world case studies or industrial settings. This study aims to bridge this gap by investigating a geothermal brine sample collected from a real case study site in the UK. A Mn-based lithium-ion sieve (LIS) adsorbent was synthesized and employed to selectively extract lithium from the sample brine. Adsorbents with a Li:Mn molar ratio of 1:1 demonstrated superior lithium selectivity and adsorption capacity. Furthermore, the pristine Mn-based adsorbent was modified through transition metals doping, resulting in enhanced lithium selectivity and adsorption capacity. The modified adsorbent exhibited a higher separation factor for lithium over major co-existing cations such as Ca, Mg, Na, and K, with separation factors exceeding 200. The adsorption behaviour was well-described by the Langmuir model, indicating monolayer adsorption, and the kinetics followed a pseudo-second-order mechanism, suggesting chemisorption at the solid surface. Thermodynamically, negative ΔG° values and positive ΔH° and ΔS° values were observed, indicating the spontaneity and endothermic nature of the adsorption process.

Keywords: adsorption, critical minerals, DLE, geothermal brines, geochemistry, lithium, lithium-ion sieves

Procedia PDF Downloads 40

Authors: Claudia García-García, Catalina B. García-García, Román Salmerón-Gómez

Abstract:

Much economic-environmental research includes the analysis of possible interactions by using Moderated Regression Analysis (MRA), which is a specific application of multiple linear regression analysis. This methodology allows analyzing how the effect of one of the independent variables is moderated by a second independent variable by adding a cross-product term between them as an additional explanatory variable. Due to the very specification of the methodology, the moderated factor is often highly correlated with the constitutive terms. Thus, great multicollinearity problems arise. The appearance of strong multicollinearity in a model has important consequences. Inflated variances of the estimators may appear, there is a tendency to consider non-significant regressors that they probably are together with a very high coefficient of determination, incorrect signs of our coefficients may appear and also the high sensibility of the results to small changes in the dataset. Finally, the high relationship among explanatory variables implies difficulties in fixing the individual effects of each one on the model under study. These consequences shifted to the moderated analysis may imply that it is not worth including an interaction term that may be distorting the model. Thus, it is important to manage the problem with some methodology that allows for obtaining reliable results. After a review of those works that applied the MRA among the ten top journals of the field, it is clear that multicollinearity is mostly disregarded. Less than 15% of the reviewed works take into account potential multicollinearity problems. To overcome the issue, this work studies the possible application of recent methodologies to MRA. Particularly, the raised regression is analyzed. This methodology mitigates collinearity from a geometrical point of view: the collinearity problem arises because the variables under study are very close geometrically, so by separating both variables, the problem can be mitigated. Raise regression maintains the available information and modifies the problematic variables instead of deleting variables, for example. Furthermore, the global characteristics of the initial model are also maintained (sum of squared residuals, estimated variance, coefficient of determination, global significance test and prediction). The proposal is implemented to data from countries of the European Union during the last year available regarding greenhouse gas emissions, per capita GDP and a dummy variable that represents the topography of the country. The use of a dummy variable as the moderator is a special variant of MRA, sometimes called “subgroup regression analysis.” The main conclusion of this work is that applying new techniques to the field can improve in a substantial way the results of the analysis. Particularly, the use of raised regression mitigates great multicollinearity problems, so the researcher is able to rely on the interaction term when interpreting the results of a particular study.

Keywords: multicollinearity, MRA, interaction, raise

Procedia PDF Downloads 100

Authors: Shiwani Bhatnagar, K. K. Srivastava, Sangeeta Singh, Ameen Ullah Khan, Bundesh Kumar, Lokendra Singh Rathore

Abstract:

From the instigation of the world medicinal plants are treated as part and parcel of human society to fight against diseases. Azadirachta indica (Neem) a herbal plant has been used as an Indian traditional medicine since ages and its products are acknowledged to solve agricultural, forestry and public health related problems, owing to its beneficial medicinal properties. Each part of the neem tree is known for its medicinal property. Bark & leaf extracts of neem have been used to control leprosy, respiratory disorders, constipation and also as blood purifier and a general health tonic. Neem is still regarded as ' rural community dispensary' in India or a tree for solving medical problems. Use of Neem as pesticides for the management of insect pest of agriculture crops and forestry has been seen as a shift in the use of synthetic pesticides to ecofriendly botanicals. Neem oil and seed extracts possess germicidal and anti-bacterial properties which when sprayed on the plant helps in protecting them from foliage pests. Azadirachtin, the main active ingredient found in neem tree, acts as an insect repellent and antifeedant. However the young plants are susceptible to many insect pest and foliar diseases. Recently, in the avenue plantation, planted by Arid Forest Research Institute, Jodhpur, around the premises of IIT Jodhpur, two years old neem plants were found to be severely infested with tip borer Laspeyresia koenigana (Family: Eucosmidae). The adult moth of L. koenigana lays eggs on the tender shoots and the young larvae tunnel into the shoot and feed inside. A small pinhole can be seen at the entrance point, from where the larva enters in to the stem. The severely attached apical shoots exhibit profuse gum exudation resulting in development of a callus structure. The internal feeding causes the stem to wilt and the leaves to dry up from the tips resulting in growth retardation. Alternaria Leaf spot and blight symptoms were also recorded on these neem plants. For the management of tip borer and Alternaria Leaf spot, foliar spray of monocrotophos @0.05% and Dithane M-45 @ 0.15% and powermin @ 2ml/lit were found efficient in managing the insect pest and foliar disease problem. No Further incidence of pest/diseases was noticed.

Keywords: azadirachta indica, alternaria leaf spot, laspeyresia koenigana, management

Procedia PDF Downloads 473

Authors: Jelena Vucicevic

Abstract:

Reliability analysis represents a very important task in different areas of work. In any industry, this is crucial for maintenance, efficiency, safety and monetary costs. There are ways to calculate reliability, unreliability, failure density and failure rate. This paper will try to introduce another way of calculating reliability by using R statistical software. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. The R programming environment is a widely used open source system for statistical analysis and statistical programming. It includes thousands of functions for the implementation of both standard and new statistical methods. R does not limit user only to operation related only to these functions. This program has many benefits over other similar programs: it is free and, as an open source, constantly updated; it has built-in help system; the R language is easy to extend with user-written functions. The significance of the work is calculation of time to failure or reliability in a new way, using statistic. Another advantage of this calculation is that there is no need for technical details and it can be implemented in any part for which we need to know time to fail in order to have appropriate maintenance, but also to maximize usage and minimize costs. In this case, calculations have been made on diesel generator fans but the same principle can be applied to any other part. The data for this paper came from a field engineering study of the time to failure of diesel generator fans. The ultimate goal was to decide whether or not to replace the working fans with a higher quality fan to prevent future failures. Seventy generators were studied. For each one, the number of hours of running time from its first being put into service until fan failure or until the end of the study (whichever came first) was recorded. Dataset consists of two variables: hours and status. Hours show the time of each fan working and status shows the event: 1- failed, 0- censored data. Censored data represent cases when we cannot track the specific case, so it could fail or success. Gaining the result by using R was easy and quick. The program will take into consideration censored data and include this into the results. This is not so easy in hand calculation. For the purpose of the paper results from R program have been compared to hand calculations in two different cases: censored data taken as a failure and censored data taken as a success. In all three cases, results are significantly different. If user decides to use the R for further calculations, it will give more precise results with work on censored data than the hand calculation.

Keywords: censored data, R statistical software, reliability analysis, time to failure

Procedia PDF Downloads 396

Authors: Sophio Kobauri, Temur Kantaria, Nina Kulikova, David Tugushi, Ramaz Katsarava

Abstract:

The elaboration of effective drug delivery vehicles is still topical nowadays since targeted drug delivery is one of the most important challenges of the modern nanomedicine. The last decade has witnessed enormous research focused on synthetic cationic polymers (CPs) due to their flexible properties, in particular as non-viral gene delivery systems, facile synthesis, robustness, not oncogenic and proven gene delivery efficiency. However, the toxicity is still an obstacle to the application in pharmacotherapy. For overcoming the problem, creation of new cationic compounds including the polymeric nano-size particles – nano-containers (NCs) loading with different pharmaceuticals and biologicals is still relevant. In this regard, a variety of NCs-based drug delivery systems have been developed. We have found that amino acid-based biodegradable polymers called as pseudo-proteins (PPs), which can be cleared from the body after the fulfillment of their function are highly suitable for designing pharmaceutical NCs. Among them, one of the most promising are NCs made of biodegradable Cationic PPs (CPPs). For preparing new cationic NCs (CNCs), we used CPPs composed of positively charged amino acid L-arginine (R). The CNCs were fabricated by two approaches using: (1) R-based homo-CPPs; (2) Blends of R-based CPPs with regular (neutral) PPs. According to the first approach NCs we prepared from CPPs 8R3 (composed of R, sebacic acid and 1,3-propanediol) and 8R6 (composed of R, sebacic acid and 1,6-hexanediol). The NCs prepared from these CPPs were 72-101 nm in size with zeta potential within +30 ÷ +35 mV at a concentration 6 mg/mL. According to the second approach, CPPs 8R6 was blended in organic phase with neutral PPs 8L6 (composed of leucine, sebacic acid and 1,6-hexanediol). The NCs prepared from the blends were 130-140 nm in size with zeta potential within +20 ÷ +28 mV depending on 8R6/8L6 ratio. The stability studies of fabricated NCs showed that no substantial change of the particle size and distribution and no big particles’ formation is observed after three months storage. In vitro biocompatibility study of the obtained NPs with four different stable cell lines: A549 (human), U-937 (human), RAW264.7 (murine), Hepa 1-6 (murine) showed both type cathionic NCs are biocompatible. The obtained data allow concluding that the obtained CNCs are promising for the application as biodegradable drug delivery vehicles. This work was supported by the joint grant from the Science and Technology Center in Ukraine and Shota Rustaveli National Science Foundation of Georgia #6298 'New biodegradable cationic polymers composed of arginine and spermine-versatile biomaterials for various biomedical applications'.

Keywords: biodegradable polymers, cationic pseudo-proteins, nano-containers, drug delivery vehicles

Procedia PDF Downloads 152