Search results for: deep soil

Authors: Judith Partouche-Sebban, Saeedeh Rezaee Vessal

Abstract:

Because of the rising number of new cases of cancer, especially among women, it is more than essential to better understand how women experience cancer in order to bring them adapted to support and care and enhance their well-being and patient experience. Cancer stands for a traumatic experience in which the diagnosis, its medical treatments, and the related side effects lead to deep physical and psychological changes that may arouse considerable stress and anxiety. In order to reduce these negative emotions, women tend to use various defense mechanisms, among which denial has been defined as the most frequent mechanism used by breast cancer patients. This study aims to better understand the consequences of the experience of cancer and their link with the adoption of a denial strategy. The empirical research was done among female cancer survivors in France. Since the topic of this study is relatively unexplored, a qualitative methodology and open-ended interviews were employed. In total, 25 semi-directive interviews were conducted with a female with different cancers, different stages of treatment, and different ages. A systematic inductive method was performed to analyze data. The content analysis enabled to highlight three different denial-related behaviors among women with cancer, which serve a self-protective function. First, women who expressed high levels of anxiety confessed they tended to completely deny the existence of their cancer immediately after the diagnosis of their illness. These women mainly exhibit many fears and a deep distrust toward the medical context and professionals. This coping mechanism is defined by the patient as being unconscious. Second, other women deliberately decided to deny partial information about their cancer, whether this information is related to the stages of the illness, the emotional consequences, or the behavioral consequences of the illness. These women use this strategy as a way to avoid the reality of the illness and its impact on the different aspects of their life as if cancer does not exist. Third, some women tend to reinterpret and give meaning to their cancer as a way to reduce its impact on their life. To this end, they may use magical thinking or positive reframing, or reinterpretation. Because denial may lead to delays in medical treatments, this topic deserves a deep investigation, especially in the context of oncology. As denial is defined as a specific defense mechanism, this study contributes to the existing literature in service marketing which focuses on emotions and emotional regulation in healthcare services which is a crucial issue. Moreover, this study has several managerial implications for healthcare professionals who interact with patients in order to implement better care and support for the patients.

Keywords: cancer, coping mechanisms, denial, healthcare services

Procedia PDF Downloads 89

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

Procedia PDF Downloads 98

Authors: J. M. Mayoral, M. Anaya

Abstract:

The urban and economic development of cities demands the construction of water use and flood control infrastructure. Likewise, it is necessary to determine the safety level of the structures built with the current standards and if it is necessary to define the reinforcement actions. The foregoing is even more important in structures of great importance, such as dams, since they imply a greater risk for the population in case of failure or undesirable operating conditions (e.g., seepage, cracks, subsidence). This article presents a methodology for determining the seismic integrity of dams in urban areas. From direct measurements of the dynamic properties using geophysical exploration and ambient seismic noise measurements, the seismic integrity of the concrete-faced rockfill dam selected as a case of study is evaluated. To validate the results, two accelerometer stations were installed (e.g., free field and crest of the dam). Once the dynamic properties were determined, three-dimensional finite difference models were developed to evaluate the dam seismic performance for different intensities of movement, considering the site response and soil-structure interaction effects. The seismic environment was determined from the uniform hazard spectra for several return periods. Based on the results obtained, the safety level of the dam against different seismic actions was determined, and the effectiveness of ambient seismic noise measurements in dynamic characterization and subsequent evaluation of the seismic integrity of urban dams was evaluated.

Keywords: risk, seismic, soil-structure interaction, urban dams

Procedia PDF Downloads 124

Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

Abstract:

In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

Procedia PDF Downloads 104

Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

Abstract:

The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

Procedia PDF Downloads 152

Authors: Salima Agoun-Bahar, Ouzna Abrous-Belbachir, Souad Amelal

Abstract:

Industrial areas generally contain heavy metals; thus, negative consequences can appear in the medium and long term on the fauna and flora, but also on the food chain, which man constitutes the final link. The SONATRACH Company has become aware of the importance of environmental protection by setting up a rehabilitation program for polluted sites in order to avoid major ecological disasters and find both curative and preventive solutions. The aim of this work consists to study industrial pollution located around a crude oil storage tank in the Algiers refinery of Sidi R'cine and to select the plants which accumulate the most heavy metals for possible use in phytotechnology. Sampling of whole plants with their soil clod was realized around the pollution source at a depth of twenty centimeters, then transported to the laboratory to identify them. The quantification of heavy metals, lead, zinc, copper, and nickel was carried out by atomic absorption spectrophotometry with flame in the soil and at the level of the aerial and underground parts of the plants. Ten plant species were recorded in the polluted site, three of them belonging to the grass family with a dominance percentage higher than 50%, followed by three other species belonging to the Composite family represented by 12% and one species for each of the families Linaceae, Plantaginaceae, Papilionaceae, and Boraginaceae. Koeleria phleoïdes L. and Avena sterilis L. of the grass family seem to be the dominant plants, although they are quite far from the pollution source. Lead pollution of soils is the most pronounced for all stations, with values varying from 237.5 to 2682.5 µg.g⁻¹. Other peaks are observed for zinc (1177 µg.g⁻¹) and copper (635 µg.g⁻¹) at station 8 and nickel (1800 µg.g⁻¹) at station 10. Among the inventoried plants, some species accumulate a significant amount of metals: Trifolium sp and K.phleoides for lead and zinc, P.lanceolata and G.tomentosa for nickel, and A.clavatus for zinc. K.phloides is a very interesting species because it accumulates an important quantity of heavy metals, especially in its aerial part. This can be explained by its use of the phytoextraction technique, which will facilitate the recovery of the pollutants by the simple removal of shoots.

Keywords: heavy metals, industrial pollution, phytotechnology, rehabilitation

Procedia PDF Downloads 68

Authors: Minh Chien Vu, Tomoaki Satomi, Hiroshi Takahashi

Abstract:

As being lack of sufficient strength to support the loading of construction as well as service life cause the clay content and clay mineralogy, soft and highly compressible soils (sludge) constitute a major problem in geotechnical engineering projects. Geopolymer, a kind of inorganic polymer, is a promising material with a wide range of applications and offers a lower level of CO₂ emissions than conventional Portland cement. However, the feasibility of geopolymer in term of modified the soft and highly compressible soil has not been received much attention due to the requirement of heat treatment for activating the fly ash component and the existence of high content of clay-size particles in the composition of sludge that affected on the efficiency of the reaction. On the other hand, the geopolymer modified sludge could be affected by other important factors such as initial curing time, initial water content and apparent water content. Therefore, this paper describes a different potential application of geopolymer: soil stabilization in landslide areas to adapt to the technical properties of sludge so that heavy machines can move on. Sludge condition process is utilized to demonstrate the possibility for stabilizing sludge using fly ash-based geopolymer at ambient curing condition ( ± 20 °C) in term of failure strength, strain and bulk density. Sludge conditioning is a process whereby sludge is treated with chemicals or various other means to improve the dewatering characteristics of sludge before applying in the construction area. The effect of initial curing time, water content and apparent water content on the modification of sludge are the main focus of this study. Test results indicate that the initial curing time has potential for improving failure strain and strength of modified sludge with the specific condition of soft soil. The result further shows that the initial water content over than 50% total mass of sludge could significantly lead to a decrease of strength performance of geopolymer-based modified sludge. The optimum apparent water content of geopolymer modified sludge is strongly influenced by the amount of geopolymer content and initial water content of sludge. The solution to minimize the effect of high initial water content will be considered deeper in the future.

Keywords: landslide, sludge, fly ash, geopolymer, sludge conditioning

Procedia PDF Downloads 120

Authors: Harold M. Carag, April Charmaine D. Camacho, Girlie Nora A. Abrigo, Florencia G. Palis, Ma. Larissa Lelu P. Gata

Abstract:

A technology impact assessment on the use of microbial inoculants in the reforestation and rehabilitation of forest ancestral lands of the Magbukün Aytas in Morong, Bataan was conducted. This two-year rainforestation technology aimed to determine the optimum condition for the improvement of seedling survival rate in the nursery and in the field to hasten the process of forest regeneration of Magbukün Ayta’s ancestral land. A combination of qualitative methods (key informant interviews, focus groups and participant observation), participated by the farmers who were directly involved in the project, community men and women, the council of elders and the project staff, was employed to complete this impact assessment. The recorded data were transcribed, and the accounts were broadly categorized on the following aspects: social (gender, institutional, anthropological), economic and environmental. The Australian Center for International Agricultural Research (ACIAR) framework was primarily used for the impact analysis while the Harvard Analytical Framework was specifically used for the gender impact analysis. Through this technology, a wildling nursery with more than one thousand seedlings was successfully established and served as a good area for the healthy growth of seedlings that would be planted in the forest. Results showed that this technology affected positively and negatively the various gender roles present in the community although household work remained to be the women’s responsibility. The technology introduced directly added up to the workload done by the men and women (preparing and applying fertilizer, making pots etc.) but this, in turn, provided ways to increase their sources of livelihood. The gender roles that were already present were further strengthened after the project and men remained to be in control. The technology or project in turn also benefited from the already present roles since they no longer have to assign things to them, the execution of the various roles was smoothly executed. In the anthropological aspect, their assigned task to manage the nursery was an easy responsibility because of their deep connection to the environment and their fear and beliefs on ‘engkato’ and ‘anito’ was helpful in guarding the forest. As the cultural value of these trees increases, their mindset of safeguarding the forest also heightens. Meanwhile, the welfare of the whole tribe is the ultimate determinant of the swift entry of projects. The past institutions brought ephemeral reliefs on the subsistence of the Magbukün Aytas. These were good ‘conditioning’ factors for the adoption of the technology of the project. As an attempt to turn away from the dependent of harmful chemical, the project’s way of introducing organic inputs was slowly gaining popularity in the community. Economically, the project was able to provide additional income to the farmers. However, the slow mode of payment dismayed other farmers and abandoned their roles. Lastly, major environmental effects weren’t that much observed after the application of the technology. The minor effects concentrated more on the improved conditions of the soil and water in the community. Because of the introduced technology, soil conditions became more favorable specifically for the species that were planted. The organic fertilizers used were in turn not harmful for the residents living in Sitio Kanawan. There were no human diseases caused by the technology. The conservation of the biodiversity of the forest is clearly the most evident long-term result of the project.

Keywords: ancestral lands, impact assessment, microbial inculants, reforestation

Procedia PDF Downloads 145

Authors: William C. Alexander

Abstract:

The oscillating water column (OSC) wave energy converter (WEC) with deep water reactance (DWR) consists of a large hollow sphere filled with seawater at the base, referred to as the ‘stabilizer’, a hollow cylinder at the top of the device, with a said cylinder having a bottom open to the sea and a sealed top save for an orifice which leads to an air turbine, and a long, narrow rod connecting said stabilizer with said cylinder. A small amount of ballast at the bottom of the stabilizer and a small amount of floatation in the cylinder keeps the device upright in the sea. The floatation is set such that the mean water level is nominally halfway up the cylinder. The entire device is loosely moored to the seabed to keep it from drifting away. In the presence of ocean waves, seawater will move up and down within the cylinder, producing the ‘oscillating water column’. This gives rise to air pressure within the cylinder alternating between positive and negative gauge pressure, which in turn causes air to alternately leave and enter the cylinder through said top-cover situated orifice. An air turbine situated within or immediately adjacent to said orifice converts the oscillating airflow into electric power for transport to shore or elsewhere by electric power cable. Said oscillating air pressure produces large up and down forces on the cylinder. Said large forces are opposed through the rod to the large mass of water retained within the stabilizer, which is located deep enough to be mostly free of any wave influence and which provides the deepwater reactance. The cylinder and stabilizer form a spring-mass system which has a vertical (heave) resonant frequency. The diameter of the cylinder largely determines the power rating of the device, while the size (and water mass within) of the stabilizer determines said resonant frequency. Said frequency is chosen to be on the lower end of the wave frequency spectrum to maximize the average power output of the device over a large span of time (such as a year). The upper portion of the device (the cylinder) moves laterally (surge) with the waves. This motion is accommodated with minimal loading on the said rod by having the stabilizer shaped like a sphere, allowing the entire device to rotate about the center of the stabilizer without rotating the seawater within the stabilizer. A full-scale device of this type may have the following dimensions. The cylinder may be 16 meters in diameter and 30 meters high, the stabilizer 25 meters in diameter, and the rod 55 meters long. Simulations predict that this will produce 1,400 kW in waves of 3.5-meter height and 12 second period, with a relatively flat power curve between 5 and 16 second wave periods, as will be suitable for an open-ocean location. This is nominally 10 times higher power than similar-sized WEC spar buoys as reported in the literature, and the device is projected to have only 5% of the mass per unit power of other OWC converters.

Keywords: oscillating water column, wave energy converter, spar bouy, stabilizer

Procedia PDF Downloads 108

Authors: Shanujah Mathuranayagam, William Fuentes, Samanthika Liyanapathirana

Abstract:

This paper introduces an enhanced version of the coupled hydro-mechanical hypoplastic model. The model is able to simulate volumetric collapse upon wetting and incorporates suction effects on stiffness and strength. Its mechanical constitutive equation links Bishop’s effective stress with strain and suction, featuring a normal consolidation line (NCL) with a compression index (λ) presenting a non-linear dependency with the degree of saturation. The Bulk modulus has been modified to ensure that under rapid volumetric collapse, the stress state remains at the NCL. The coupled model comprises eighteen parameters, with nine for the hydraulic component and nine for the mechanical component. Hydraulic parameters are calibrated with the use of water retention curves (IWRC) across varied soil densities, while mechanical parameters undergo calibration using isotropic and triaxial tests on both unsaturated and saturated samples. The model's performance is analyzed through the back-calculation of two experimental studies: (i) wetting under different vertical stresses for Lower Cromer Till and (ii) isotropic loading and triaxial loading for undisturbed loess. The results confirm that the proposed model is able to predict the hydro-mechanical behavior of unsaturated soils.

Keywords: hypoplastic model, volumetric collapse, normal consolidation line, compression index (λ), degree of saturation, soil suction

Procedia PDF Downloads 68

Authors: Shraddha Jain Sharma, Ratnalata Gupta

Abstract:

Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

Procedia PDF Downloads 95

Authors: Zahid O. Alibrahim, Craig D. Williams, Clive L. Roberts

Abstract:

The study area (Ecton mining area) is located in the southern part of the Peak District in Derbyshire, England. It is bounded by the River Manifold from the west. This area has been mined for a long period. As a result, huge amounts of potentially toxic metals were released into the surrounding area and are most likely to be a significant source of heavy metal contamination to the local soil, water and vegetation. In order to appraise the potential heavy metal pollution in this area, 37 topsoil samples (5-20 cm depth) were collected and analysed for their total content of Cu, Pb, Zn, Mn, Cr, Ni and V using ICP (Inductively Coupled Plasma) optical emission spectroscopy. Multivariate Geospatial analyses using the GIS technique were utilised to draw geochemical maps of the metals of interest over the study area. A few hotspot points, areas of elevated concentrations of metals, were specified, which are presumed to be the results of anthropogenic activities. In addition, the soil’s environmental quality was evaluated by calculating the Mullers’ Geoaccumulation index (I geo), which suggests that the degree of contamination of the investigated heavy metals has the following trend: Pb > Zn > Cu > Mn > Ni = Cr = V. Furthermore, the potential ecological risk, using the enrichment factor (EF), was also specified. On the basis of the calculated amount or the EF, the levels of pollution for the studied metals in the study area have the following order: Pb>Zn>Cu>Cr>V>Ni>Mn.

Keywords: enrichment factor, geoaccumulation index, GIS, heavy metals, multivariate analysis

Procedia PDF Downloads 359

Authors: Mesfin Angaw Tesfay

Abstract:

L-Xylulose is a potentially valuable rare sugar used as starting material for antiviral and anticancer drug development in pharmaceutical industries. L-Xylulose exist in a very low concentration in nature and have to be synthesized from cheap starting materials such as xylitol through biotechnological approaches. In this study, cofactor engineering and deep eutectic solvent were applied to improve the efficiency of L-xylulose production from xylitol. A water-forming NAD+ regeneration enzyme (NADH oxidase) from Streptococcus mutans ATCC 25175 was introduced into E. coli with xylitol-4-dehydrogenase (XDH) of Pantoea ananatis resulting in recombinant cells harboring the vector pETDuet-xdh-SmNox. Further, three deep eutectic solvents (DES) including, Choline chloride/glycerol (ChCl/G), Choline chloride/urea (ChCl/U), and Choline chloride/ethylene glycol (ChCl/EG) have been employed to facilitate the conversion efficiency of L-xylulose from xylitol. The co-expression system exhibited optimal activity at a temperature of 37 ℃ and pH 8.5, and the addition of Mg2+ enhanced the catalytic activity by 1.19-fold. Co-expression of NADH oxidase with XDH enzyme resulted in increased L-xylulose concentration and productivity from xylitol as well as the intracellular NAD+ concentration. Two of the DES used (ChCl/U and ChCl/EG) show positive effects on product yield and the ChCl/G has inhibiting effects. The optimum concentration of ChCl/U was 2.5%, which increased the L-xylulose yields compared to the control without DES. In a 1 L fermenter the final concentration and productivity of L-xylulose from 50 g/L of xylitol reached 48.45 g/L, and 2.42 g/L.h respectively, which was the highest report. Overall, this study is a suitable approach for large-scale production of L-xylulose from xylitol using the engineered E. coli cell.

Keywords: Xylitol-4-dehydrogenase, NADH oxidase, L-xylulose, Xylitol, Coexpression, DESs

Procedia PDF Downloads 30

Authors: Hayriye Anıl, Görkem Kar

Abstract:

In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

Procedia PDF Downloads 114

Authors: Hamid Oumer Seid

Abstract:

Piled raft foundation (PRF) is a union of pile and raft working together through the interaction of soil-pile, pile-raft, soil-raft and pile-pile to provide adequate bearing capacity and controlled settlement. A uniform pile positioning is used in PRF; however, there is a wide room for optimization through parametric study under vertical load to result in a safer and economical foundation. Addis Ababa is found in seismic zone 3 with a peak ground acceleration (PGA) above the threshold of damage, which makes investigating the performance of PRF under seismic load considering the dynamic kinematic soil structure interaction (SSI) vital. The study area is located in Addis Ababa around Mexico (commercial bank) and Kirkos (Nib, Zemen and United Bank) in which input parameters (pile length, pile diameter, pile spacing, raft area, raft thickness and load) are taken. A finite difference-based numerical software, FLAC3D V6, was used for the analysis. The Kobe (1995) and Northridge (1994) earthquakes were selected, and deconvolution analysis was done. A close load sharing between pile and raft was achieved at a spacing of 7D with different pile lengths and diameters. The maximum settlement reduction achieved is 9% for a pile of 2m diameter by increasing length from 10m to 20m, which shows pile length is not effective in reducing settlement. The installation of piles results in an increase in the negative bending moment of the raft compared with an unpiled raft. Hence, the optimized design depends on pile spacing and the raft edge length, while pile length and diameter are not significant parameters. An optimized piled raft configuration (𝐴𝐺/𝐴𝑅 = 0.25 at the center and piles provided around the edge) has reduced pile number by 40% and differential settlement by 95%. The dynamic analysis shows acceleration plot at the top of the piled raft has PGA of 0.25𝑚2/𝑠𝑒𝑐 and 0.63𝑚2/𝑠𝑒𝑐 for Northridge (1994) and Kobe (1995) earthquakes, respectively, due to attenuation of seismic waves. Pile head displacement (maximum is 2mm, and it is under the allowable limit) is affected by the PGA rather than the duration of an earthquake. End bearing and friction PRF performed similarly under two different earthquakes except for their vertical settlement considering SSI. Hence, PRF has shown adequate resistance to seismic loads.

Keywords: FLAC3D V6, earthquake, optimized piled raft foundation, pile head department

Procedia PDF Downloads 34

Authors: Fatimh Alshahri, Muna Alqahtani

Abstract:

The specific activities of 238U, 226Ra, 40K and 222Rn in chemical fertilizers were measured using gamma ray spectrometer and Cr-39 detector. In this study 21 chemical fertilizers were collected from Eastern Saudi Arabia markets. The specific activities of 238U ranged from 23 ± 0.5 to 3900 ± 195 Bq kgˉ¹, 226Ra ranged from 5.6 ± 2.8 to 392 ± 18 Bq kgˉ¹ and 40K ranged from 18.4 ± 3 to 16476 ± 820 Bq kgˉ¹. The radon concentrations and the radon exhalation rates were found to vary from 3.2 ± 1.2 to 1531.6 ± 160 Bq mˉ³ and from 1.6 to 773.7 mBq mˉ² hˉ¹, respectively. Radium equivalent activities (Raeq) were calculated for the analyzed samples to assess the radiation hazards arising due to the use of these chemical fertilizers in the agriculture soil. The Raeq for Six local samples (NPK and SSP) and one imported sample (SOP) were greater than the acceptable value 370 Bq kgˉ¹. The total air absorbed doses rates in air 1 m above the ground (D) were calculated for all samples. All samples, except one imported granule sample (DAP), were higher than the estimated average global terrestrial radiation of 55 nGy hˉ¹. The highest annual effective dose was in TSP fertilizers (2.1 mSvyˉ¹). The results show that the local TSP, imported SOP and local NPK (sample 13) fertilizers were unacceptable for use as fertilizers in agricultural soil. Furthermore, the toxic elements and trace metals (Pb, Cd, Cr, Co, Ni, Hg and As) were determined using atomic absorption spectrometer. The concentrations of chromium in chemical fertilizers were higher than the global values.

Keywords: chemical fertilizers, 238U, 222Rn, trace metals, Saudi Arabia

Procedia PDF Downloads 599

Authors: S. M. T. Mustafa, D. Raes, M. Huysmans

Abstract:

Proper utilization of water resource is very important in agro-based Bangladesh. Irrigation schedule based on local environmental conditions, soil type and water availability will allow a sustainable use of water resources in agriculture. In this study, the FAO crop water model (AquaCrop) was used to simulate the different water and fertilizer management strategies in different location of Bangladesh to obtain a management guideline for the farmer. Model was calibrated and validated for wheat (Triticum aestivum L.). The statistical indices between the observed and simulated grain yields obtained were very good with R2, RMSE, and EF values of 0.92, 0.33, and 0.83, respectively for model calibration and 0.92, 0.68 and 0.77, respectively for model validations. Stem elongation (jointing) to booting and flowering stage were identified as most water sensitive for wheat. Deficit irrigation on water sensitive stage could increase the grain yield for increasing soil fertility levels both for loamy and sandy type soils. Deficit irrigation strategies provides higher water productivity than full irrigation strategies and increase the yield stability (reduce the standard deviation). The practical deficit irrigation schedule for wheat for four different stations and two different soils were designed. Farmer can produce more crops by using deficit irrigation schedule under water stress condition. Practical application and validation of proposed strategies will make them more credible.

Keywords: crop-water model, deficit irrigation, irrigation scheduling, wheat

Procedia PDF Downloads 435

Authors: Adriano Trono, Federico Pinto, Diego Turello, Marcelo A. Ceballos

Abstract:

Numerical modeling of dynamic soil-structure interaction problems requires an adequate representation of the unbounded characteristics of the ground, material non-linearity of soils, and geometrical non-linearities such as large displacements due to rocking of the structure. In order to account for these effects simultaneously, it is often required that the equations of motion are solved in the time domain. However, boundary conditions in conventional finite element codes generally present shortcomings in fully absorbing the energy of outgoing waves. In this sense, the Perfectly Matched Layers (PML) technique allows a satisfactory absorption of inclined body waves, as well as surface waves. However, the PML domain is inherently unstable, meaning that it its instability does not depend upon the discretization considered. One way to stabilize the PML domain is to use multiaxial stretching functions. This development is questionable because some Jacobian terms of the coordinate transformation are not accounted for. For this reason, the resulting absorbing layer element is often referred to as "uncorrected M-PML” in the literature. In this work, the strong formulation of the "corrected M-PML” absorbing layer is proposed using multiaxial stretching functions that incorporate all terms of the coordinate transformation. The results of the stable model are compared with reference solutions obtained from extended domain models.

Keywords: mixed finite elements, multiaxial stretching functions, perfectly matched layer, soil-structure interaction

Procedia PDF Downloads 76

Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

Abstract:

Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

Procedia PDF Downloads 194

Authors: Meisam Zargar, Yurii Pleskachov, Mostafa Abdelkader, Aldaibe Ahmed, Maryam Bayat, Malek H. Walli, Shimendi Okbagabir

Abstract:

The increased need of humankind for foodstuffs highlights the intensification of agricultural production. It is necessary either to increase the size of the sown area or to look for new approaches to improve agricultural land productivity. Developing new areas for cultivation is possible due to the intensification of soil cultivation. Nevertheless, this will decrease the effectiveness of de-carbonization programs since this approach will inevitably increase greenhouse gas emissions. Therefore, searching for new solutions to conserve natural resources while obtaining stable predicted crop yields is a vital scientific and technical task. For a long time, destructive land use methods have been used in crop production. The present stage of civilization's development and implementation of new techniques and methods of tillage and crops require the solution of technological, economic, and environmental problems simultaneously with the possibility of creating conditions for the regeneration of soil resources. Implementing these approaches became possible due to the development of new technology for the cultivation of crops based on the exact selective impact on the object of processing. This technology of particular effects of TIV combines the positive accumulated experience of traditional farming systems and resource-saving approaches. Particularly high-quality indicators and cost savings with introducing TIV can be achieved when used on row crops, including vegetables and melons.

Keywords: agricultural machinery, vegetable, irrigation, strip system

Procedia PDF Downloads 36

Authors: Mohammad Moezzibadi, Isabelle Charpentier, Adrien Wanko, Robert Mosé

Abstract:

The calibration of hydrodynamic parameters for subsurface constructed wetlands (CWs) is a sensitive process since highly non-linear equations are involved in unsaturated flow modeling. CW systems are engineered systems designed to favour natural treatment processes involving wetland vegetation, soil, and their microbial flora. Their significant efficiency at reducing the ecological impact of urban runoff has been recently proved in the field. Numerical flow modeling in a vertical variably saturated CW is here carried out by implementing the Richards model by means of a mixed hybrid finite element method (MHFEM), particularly well adapted to the simulation of heterogeneous media, and the van Genuchten-Mualem parametrization. For validation purposes, MHFEM results were compared to those of HYDRUS (a software based on a finite element discretization). As van Genuchten-Mualem soil hydrodynamic parameters depend on water content, their estimation is subject to considerable experimental and numerical studies. In particular, the sensitivity analysis performed with respect to the van Genuchten-Mualem parameters reveals a predominant influence of the shape parameters α, n and the saturated conductivity of the filter on the piezometric heads, during saturation and desaturation. Modeling issues arise when the soil reaches oven-dry conditions. A particular attention should also be brought to boundary condition modeling (surface ponding or evaporation) to be able to tackle different sequences of rainfall-runoff events. For proper parameter identification, large field datasets would be needed. As these are usually not available, notably due to the randomness of the storm events, we thus propose a simple, robust and low-cost numerical method for the inverse modeling of the soil hydrodynamic properties. Among the methods, the variational data assimilation technique introduced by Le Dimet and Talagrand is applied. To that end, a variational data assimilation technique is implemented by applying automatic differentiation (AD) to augment computer codes with derivative computations. Note that very little effort is needed to obtain the differentiated code using the on-line Tapenade AD engine. Field data are collected for a three-layered CW located in Strasbourg (Alsace, France) at the water edge of the urban water stream Ostwaldergraben, during several months. Identification experiments are conducted by comparing measured and computed piezometric head by means of the least square objective function. The temporal variability of hydrodynamic parameter is then assessed and analyzed.

Keywords: automatic differentiation, constructed wetland, inverse method, mixed hybrid FEM, sensitivity analysis

Procedia PDF Downloads 167

Authors: Qingsong Xiong, Cheng Yuan, Qingzhao Kong, Haibei Xiong

Abstract:

Deep-learning-driven approaches based on vibration responses have attracted larger attention in rapid structural condition assessment while obtaining sufficient measured training data with corresponding labels is relevantly costly and even inaccessible in practical engineering. This study proposes an integrated label propagation network for structural condition assessment, which is able to diffuse the labels from continuously-generating measurements by intact structure to those of missing labels of damage scenarios. The integrated network is embedded with damage-sensitive features extraction by deep autoencoder and pseudo-labels propagation by optimized fuzzy clustering, the architecture and mechanism which are elaborated. With a sophisticated network design and specified strategies for improving performance, the present network achieves to extends the superiority of self-supervised representation learning, unsupervised fuzzy clustering and supervised classification algorithms into an integration aiming at assessing damage conditions. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of detecting post-earthquake damage. The identifying accuracy of a present network was 0.95 in numerical validations and an average 0.86 in laboratory case studies, respectively. It should be noted that the whole training procedure of all involved models in the network stringently doesn’t rely upon any labeled data of damage scenarios but only several samples of intact structure, which indicates a significant superiority in model adaptability and feasible applicability in practice.

Keywords: autoencoder, condition assessment, fuzzy clustering, label propagation

Procedia PDF Downloads 100

Authors: Rafael do Amaral Teodoro, Leandro Augusto da Silva

Abstract:

Originated from graphite, graphene is a two-dimensional (2D) material that promises to revolutionize technology in many different areas, such as energy, telecommunications, civil construction, aviation, textile, and medicine. This is possible because its structure, formed by carbon bonds, provides desirable optical, thermal, and mechanical characteristics that are interesting to multiple areas of the market. Thus, several research and development centers are studying different manufacturing methods and material applications of graphene, which are often compromised by the scarcity of more agile and accurate methodologies to characterize the material – that is to determine its composition, shape, size, and the number of layers and crystals. To engage in this search, this study proposes a computational methodology that applies deep learning to identify graphene oxide crystals in order to characterize samples by crystal sizes. To achieve this, a fully convolutional neural network called U-net has been trained to segment SEM graphene oxide images. The segmentation generated by the U-net is fine-tuned with a standard deviation technique by classes, which allows crystals to be distinguished with different labels through an object delimitation algorithm. As a next step, the characteristics of the position, area, perimeter, and lateral measures of each detected crystal are extracted from the images. This information generates a database with the dimensions of the crystals that compose the samples. Finally, graphs are automatically created showing the frequency distributions by area size and perimeter of the crystals. This methodological process resulted in a high capacity of segmentation of graphene oxide crystals, presenting accuracy and F-score equal to 95% and 94%, respectively, over the test set. Such performance demonstrates a high generalization capacity of the method in crystal segmentation, since its performance considers significant changes in image extraction quality. The measurement of non-overlapping crystals presented an average error of 6% for the different measurement metrics, thus suggesting that the model provides a high-performance measurement for non-overlapping segmentations. For overlapping crystals, however, a limitation of the model was identified. To overcome this limitation, it is important to ensure that the samples to be analyzed are properly prepared. This will minimize crystal overlap in the SEM image acquisition and guarantee a lower error in the measurements without greater efforts for data handling. All in all, the method developed is a time optimizer with a high measurement value, considering that it is capable of measuring hundreds of graphene oxide crystals in seconds, saving weeks of manual work.

Keywords: characterization, graphene oxide, nanomaterials, U-net, deep learning

Procedia PDF Downloads 164

Authors: Faijal Ali, Darga Kumar N., Ravikant Singh, Rajnil Lal

Abstract:

Fiji has three major landforms such as plains, low mountains, and hills. The low land soils are formed on beach sand. Fiji soils contain high concentration of iron (III), aluminum oxides and hydroxides. The soil possesses reddish or yellowish colour. The characterization of lateritic soils collected from different locations along the national highway in Viti Levu, Fiji Islands. The research has been carried out mainly to understand the physical and strength properties to assess their suitability for the highway and building construction. In this paper, the field tests such as dynamic cone penetrometer test, field vane shear, field density and laboratory tests such as unconfined compression stress, compaction, grain size analysis and Atterberg limits are conducted. The test results are analyzed and presented. From the results, it is revealed that the soils are having more percentage of silt and clay which is more than 80% and 5 to 15% of fine to medium sand is noticed. The dynamic cone penetrometer results up to 3m depth had similar penetration resistance. For the first 1m depth, the rate of penetration is found 300mm per 3 to 4 blows. In all the sites it is further noticed that the rate of penetration at depths beyond 1.5 m is decreasing for the same number of blows as compared to the top soil. From the penetration resistance measured through dynamic cone penetrometer test, the California bearing ratio and allowable bearing capacities are 4 to 5% and 50 to 100 kPa for the top 1m layer and below 1m these values are increasing. The California bearing ratio of these soils for below 1m depth is in the order of 10% to 20%. The safe bearing capacity of these soils below 1m and up to 3m depth is varying from 150 kPa to 250 kPa. The field vane shear was measured within a depth of 1m from the surface and the values were almost similar varying from 60 kPa to 120 kPa. The liquid limit and plastic limits of these soils are in the range of 40 to 60% and 20 to 25%. Overall it is found that the top 1m soil along the national highway in majority places possess a soft to medium stiff behavior with low to medium bearing capacity as well low California bearing ratio values. It is recommended to ascertain these soils behavior in terms of geotechnical parameters before taking up any construction activity.

Keywords: California bearing ratio, dynamic cone penetrometer test, field vane shear, unconfined compression stress.

Procedia PDF Downloads 194

Authors: Budhvin T. Withana, Sulochana Rupasinghe

Abstract:

The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

Procedia PDF Downloads 70

Authors: Tomer Gans-Or, Shmulik Pinkert

Abstract:

The determination of lateral pile capacity per unit length is a key aspect in geotechnical engineering. Traditional approaches for assessing piles lateral capacity in cohesive soils involve the application of upper-bound and lower-bound plasticity theorems. However, a comprehensive solution encompassing the entire spectrum of soil strength parameters, particularly in frictional soils with or without cohesion, is still lacking. This research introduces an innovative implementation of the slice method limit equilibrium solution for lateral capacity assessment. For any given numerical discretization of the soil's domain around the pile, the lateral capacity evaluation is based on mobilized strength concept. The critical failure geometry is then found by a unique optimization procedure which includes both factor of safety minimization and geometrical optimization. The robustness of this suggested methodology is that the solution is independent of any predefined assumptions. Validation of the solution is accomplished through a comparison with established plasticity solutions for cohesive soils. Furthermore, the study demonstrates the applicability of the limit equilibrium method to address unresolved cases related to frictional and cohesive-frictional soils. Beyond providing capacity values, the method enables the utilization of the mobilized strength concept to generate safety-factor distributions for scenarios representing pre-failure states.

Keywords: lateral pile capacity, slice method, limit equilibrium, mobilized strength

Procedia PDF Downloads 65

Authors: Hanen Filali, Mohamed Hachicha

Abstract:

Treated grey water (TGW) reuse emerged as an alternative resource to meet the growing demand for water for agricultural irrigation and reduce the pressure on limited existing fresh water. However, this reuse needs adapted management in order to avoid environmental and health risks. In this work, the treatment of grey water (GW) was studied from a cyclic treatment system that we designed and implemented in the greenhouse of National Research Institute for Rural Engineering, Water and Forests (INRGREF). This system is composed of three levels for treatment (TGW 1, TGW 2, and TGW 3). Each level includes a sandy soil box. The use of grey water was moderated depending on the chemical and microbiological quality obtained. Different samples of soils and treated grey water were performed and analyzed for 14 irrigation cycles. TGW through cyclic treatment showed physicochemical parameters like pH, electrical conductivity (EC), chemical oxygen demand (COD), biological oxygen demand (BOD5) in the range of 7,35-7,91, 1,69-5,03 dS/m, 102,6-54,2 mgO2/l, and 31,33-15,74 mgO2/l, respectively. Results showed a reduction in the pollutant load with a significant effect on the three treatment levels; however, an increase in salinity was observed during all irrigation cycles. Microbiological results showed good grey water treatment with low health risk on irrigated soil. Treated water quality was below permissible Tunisian standards (NT106.03), and treated water is suitable for non-potable options.

Keywords: treated grey water, irrigation, cyclic treatment, soils, physico-chemical parameters, microbiological parameters

Procedia PDF Downloads 99

Authors: Loubna Hamami, Bouchaib Nassereddine

Abstract:

Due to the evolution of technologies, the need to observe and manage hostile environments, and reduction in size, wireless sensor networks (WSNs) are becoming essential and implicated in the most fields of life. WSNs enable us to change the style of living, working and interacting with the physical environment. The agricultural sector is one of such sectors where WSNs are successfully used to get various benefits. For successful agricultural production, the irrigation system is one of the most important factors, and it plays a tactical role in the process of agriculture domain. However, it is considered as the largest consumer of freshwater. Besides, the scarcity of water, the drought, the waste of the limited available water resources are among the critical issues that touch the almost sectors, notably agricultural services. These facts are leading all governments around the world to rethink about saving water and reducing the volume of water used; this requires the development of irrigation practices in order to have a complete and independent system that is more efficient in the management of irrigation. Consequently, the selection of WSNs in irrigation system has been a benefit for developing the agriculture sector. In this work, we propose a prototype for a complete and intelligent irrigation system based on wireless sensor networks and we present and discuss the design of this prototype. This latter aims at saving water, energy and time. The proposed prototype controls water system for irrigation by monitoring the soil temperature, soil moisture and weather conditions for estimation of water requirements of each plant.

Keywords: precision irrigation, sensor, wireless sensor networks, water resources

Procedia PDF Downloads 157

Authors: Mike Yaw Osei-Atweneboana, John Asiedu Larbi, Edward Jenner Tettevi

Abstract:

Background: The lymphatic filariasis (LF) control programme has been on-going in Ghana since 2000. This community-wide approach involves the use of ivermectin (IVM) and albendazole (ALB). Soil-transmitted helminth (STH) infections control is augmented within this programme; however, in areas where LF is not prevalent, albendazole alone is administered to school children. The purpose of this study was therefore, to determine the efficacy of albendazole against soils transmitted helminths and the impact of mass drug administration of albendazole and ivermectin on the health status of children of school going age and pregnant women. Material/Methods: This was a twelve months longitudinal study. A total of 412 subjects including school children (between the ages of 2-17 years) and pregnant women were randomly selected from four endemic communities in Kpandai district of the Northern region. Coprological assessment for parasites was based on the Kato–Katz technique in both dry and rainy seasons at baseline, 21 days and 3 months post-treatment. Single-dose albendazole treatment was administered to all patients at baseline. Preserved samples are currently under molecular studies to identify possible single nucleotide polymorphism (SNP) within the beta tubulin gene which is associated with benzimidazole resistance. Results: Of all the parasites found (hookworm, Trichuris trichiura, Hymenolepis nana, and Taenia sp.); hookworm was the most prevalent. In the dry season, the overall STHs prevalence at pre-treatment was 29%, while 9% and 13% prevalence was recorded at 21 days, and three months after treatment respectively. However, in the rainy season, the overall STHs prevalence was 8%, while 4% and 12% was recorded at 21 days and three months respectively after ALB treatment. In general, ALB treatment resulted in an overall hookworm egg count reduction rate of 89% in the dry season and 93% in the rainy season, while the T. trichiura egg count reduction rate was 100% in both seasons. Conclusions: STH infections still remains a significant public health burden in Ghana. Hookworm infection seems to respond poorly or sub-optimally to ALB, raising concerns of possible emergence of resistance which may lead to a major setback for the control and elimination of STH infections, especially hookworm infections.

Keywords: hookworm, sub-optimal response, albendazole, trichuriasis, soil-transmitted helminths

Procedia PDF Downloads 293

Authors: Abdelkader Anouzla, Soukaina Bouaouda, Roukaya Bouyakhsass, Salah Souabi, Abdeslam Taleb

Abstract:

During storage and under the combined action of rainwater and natural fermentation, these wastes produce over 800.000 m3 of landfill leachates. Due to population growth and changing global economic activities, the amount of waste constantly generated increases, making more significant volumes of leachate. Leachate, when leaching into the soil, can negatively impact soil, surface water, groundwater, and the overall environment and human life. The leachate must first be treated because of its high pollutant load before being released into the environment. This article reviews the different leachate treatments in September 2022 techniques. Different techniques can be used for this purpose, such as biological, physical-chemical, and membrane methods. Young leachate is biodegradable; in contrast, these biological processes lose their effectiveness with leachate aging. They are characterized by high ammonia nitrogen concentrations that inhibit their activity. Most physical-chemical treatments serve as pre-treatment or post-treatment to complement conventional treatment processes or remove specific contaminants. After the introduction, the different types of pollutants present in leachates and their impacts have been made, followed by a discussion highlighting the advantages and disadvantages of the various treatments, whether biological, physicochemical, or membrane. From this work, due to their simplicity and reasonable cost compared to other treatment procedures, biological treatments offer the most suitable alternative to limit the effects produced by the pollutants in landfill leachates.

Keywords: landfill leachate, landfill pollution, impact, wastewater

Procedia PDF Downloads 95