Search results for: input randomization

Authors: Kishor Chandra Kandpal, Amit Kumar

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The Indian Himalaya region harbours approximately 1748 plants of medicinal importance, and as per International Union for Conservation of Nature (IUCN), the 112 plant species among these are threatened and endangered. To ease the pressure on these plants, the government of India is encouraging its in-situ cultivation. The Saussurea costus, Valeriana jatamansi, and Picrorhiza kurroa have also been prioritized for large scale cultivation owing to their market demand, conservation value and medicinal properties. These species are found from 1000 m to 4000 m elevation ranges in the Indian Himalaya. Identification of these plants in the field requires taxonomic skills, which is one of the major bottleneck in the conservation and management of these plants. In recent years, Hyperspectral remote sensing techniques have been precisely used for the discrimination of plant species with the help of their unique spectral signatures. In this background, a spectral library of the above 03 medicinal plants was prepared by collecting the spectral data using a handheld spectroradiometer (325 to 1075 nm) from farmer’s fields of Himachal Pradesh and Uttarakhand states of Indian Himalaya. The Random forest (RF) model was implied on the spectral data for the classification of the medicinal plants. The 80:20 standard split ratio was followed for training and validation of the RF model, which resulted in training accuracy of 84.39 % (kappa coefficient = 0.72) and testing accuracy of 85.29 % (kappa coefficient = 0.77). This RF classifier has identified green (555 to 598 nm), red (605 nm), and near-infrared (725 to 840 nm) wavelength regions suitable for the discrimination of these species. The findings of this study have provided a technique for rapid and onsite identification of the above medicinal plants in the field. This will also be a key input for the classification of hyperspectral remote sensing images for mapping of these species in farmer’s field on a regional scale. This is a pioneer study in the Indian Himalaya region for medicinal plants in which the applicability of hyperspectral remote sensing has been explored.

Keywords: himalaya, hyperspectral remote sensing, machine learning; medicinal plants, random forests

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Authors: Rawan A. Abdelrazeq, Ahmed M. Khalafallah, Nabil A. Kartam

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Selection of competent contractors for construction projects is usually accomplished through competitive bidding or negotiated contracting in which the contract bid price is the basic criterion for selection. The evaluation of contractor’s safety performance is still not a typical criterion in the selection process, despite the existence of various safety prequalification procedures. There is a critical need for practical and automated systems that enable owners and decision makers to evaluate contractor safety performance, among other important contractor selection criteria. These systems should ultimately favor safety-conscious contractors to be selected by the virtue of their past good safety records and current safety programs. This paper presents an exploratory sequential mixed-methods approach to develop a framework for an automated decision support system that evaluates contractor safety performance based on a multitude of indicators and metrics that have been identified through a comprehensive review of construction safety research, and a survey distributed to domain experts. The framework is developed in three phases: (1) determining the indicators that depict contractor current and past safety performance; (2) soliciting input from construction safety experts regarding the identified indicators, their metrics, and relative significance; and (3) designing a decision support system using relational database models to integrate the identified indicators and metrics into a system that assesses and rates the safety performance of contractors. The proposed automated system is expected to hold several advantages including: (1) reducing the likelihood of selecting contractors with poor safety records; (2) enhancing the odds of completing the project safely; and (3) encouraging contractors to exert more efforts to improve their safety performance and practices in order to increase their bid winning opportunities which can lead to significant safety improvements in the construction industry. This should prove useful to decision makers and researchers, alike, and should help improve the safety record of the construction industry.

Keywords: construction safety, contractor selection, decision support system, relational database

Procedia PDF Downloads 280

Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

Procedia PDF Downloads 359

Authors: A. Aulinger, A. M. Backes, J. Bieser, V. Matthias, M. Quante

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High concentrations of particles pose a threat to human health. Thus, legal maximum concentrations of PM10 and PM2.5 in ambient air have been steadily decreased over the years. In central Europe, the inorganic species ammonium sulphate and ammonium nitrate make up a large fraction of fine particles. Many studies investigate the influence of emission reductions of sulfur- and nitrogen oxides on aerosol concentration. Here, we focus on the influence of ammonia (NH3) emissions. While emissions of sulphate and nitrogen oxides are quite well known, ammonia emissions are subject to high uncertainty. This is due to the uncertainty of location, amount, time of fertilizer application in agriculture, and the storage and treatment of manure from animal husbandry. For this study, we implemented a crop growth model into the SMOKE emission model. Depending on temperature, local legislation, and crop type individual temporal profiles for fertilizer and manure application are calculated for each model grid cell. Additionally, the diffusion from soils and plants and the direct release from open and closed barns are determined. The emission data was used as input for the Community Multiscale Air Quality (CMAQ) model. Comparisons to observations from the EMEP measurement network indicate that the new ammonia emission module leads to a better agreement of model and observation (for both ammonia and ammonium). Finally, the ammonia emission model was used to create emission scenarios. This includes emissions based on future European legislation, as well as a dynamic evaluation of the influence of different agricultural sectors on particle formation. It was found that a reduction of ammonia emissions by 50% lead to a 24% reduction of total PM2.5 concentrations during winter time in the model domain. The observed reduction was mainly driven by reduced formation of ammonium nitrate. Moreover, emission reductions during winter had a larger impact than during the rest of the year.

Keywords: ammonia, ammonia abatement strategies, ctm, seasonal impact, secondary aerosol formation

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Authors: Dovile Petkeviciute-Barysiene

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Legal technologies not only create new ways for accessing and providing legal services but also transform the role of legal practitioners. Both lawyers and users of legal services expect automated solutions to outperform people with objectivity and impartiality. Although fairness of the automated decisions is crucial, research on assessing various characteristics of automated processes related to the perceived fairness has only begun. One of the major obstacles to this research is the lack of comprehensive understanding of what legal actions are automated and could be meaningfully automated, and to what extent. Neither public nor legal practitioners oftentimes cannot envision technological input due to the lack of general without illustrative examples. The aim of this study is to map decision making stages and automation levels which are and/or could be achieved in legal actions related to pre-trial and trial processes. Major legal decisions and judgments are identified during the consultations with legal practitioners. The dual-process model of information processing is used to describe cognitive processes taking place while making legal decisions and judgments during pre-trial and trial action. Some of the existing legal technologies are incorporated into the analysis as well. Several published automation level taxonomies are considered because none of them fit well into the legal context, as they were all created for avionics, teleoperation, unmanned aerial vehicles, etc. From the information processing perspective, analysis of the legal decisions and judgments expose situations that are most sensitive to cognitive bias, among others, also help to identify areas that would benefit from the automation the most. Automation level analysis, in turn, provides a systematic approach to interaction and cooperation between humans and algorithms. Moreover, an integrated map of legal decisions and judgments, information processing characteristics, and automation levels all together provide some groundwork for the research of legal technology perceived fairness and acceptance. Acknowledgment: This project has received funding from European Social Fund (project No 09.3.3-LMT-K-712-19-0116) under grant agreement with the Research Council of Lithuania (LMTLT).

Keywords: automation levels, information processing, legal judgment and decision making, legal technology

Procedia PDF Downloads 144

Authors: R. Premasiri, W. A. H. A. Abeygunasekara, S. M. Hewavidana, T. Jananthan, R. M. S. Madawala, K. Vaheeshan

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Forecasting the potential for disastrous events such as landslides has become one of the major necessities in the current world. Most of all, the landslides occurred in Sri Lanka are found to be triggered mostly by intense rainfall events. The study area is the landslide near Gerandiella waterfall which is located by the 41st kilometer post on Nuwara Eliya-Gampala main road in Kotmale Division in Sri Lanka. The landslide endangers the entire Kotmale town beneath the slope. Geographic Information System (GIS) platform is very much useful when it comes to the need of emulating the real-world processes. The models are used in a wide array of applications ranging from simple evaluations to the levels of forecast future events. This project investigates the possibility of developing a dynamic model to map the spatial distribution of the slope stability. The model incorporates several theoretical models including the infinite slope model, Green Ampt infiltration model and Perched ground water flow model. A series of rainfall values can be fed to the model as the main input to simulate the dynamics of slope stability. Hydrological model developed using GIS is used to quantify the perched water table height, which is one of the most critical parameters affecting the slope stability. Infinite slope stability model is used to quantify the degree of slope stability in terms of factor of safety. DEM was built with the use of digitized contour data. Stratigraphy was modeled in Surfer using borehole data and resistivity images. Data available from rainfall gauges and piezometers were used in calibrating the model. During the calibration, the parameters were adjusted until a good fit between the simulated ground water levels and the piezometer readings was obtained. This model equipped with the predicted rainfall values can be used to forecast of the slope dynamics of the area of interest. Therefore it can be investigated the slope stability of rainfall induced landslides by adjusting temporal dimensions.

Keywords: factor of safety, geographic information system, hydrological model, slope stability

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Authors: Marah Joy A. Nanglegan

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A survey was conducted to describe knowledge, attitude, practices, information needs, and information seeking behavior of small-scale farmers on Organic Agriculture Production (OAP) in a rural community in Ifugao, Philippines. Respondents’ age ranged from 23-67 years old. Most of them are male, married, and have reached high school level. The major source of income is farming with an average monthly income of less than Php 5,000 for a household size of seven. More than fifty percent of the respondents are members of a farmer’s organization. Farm size is less than one hectare. Majority of them own their farms and have been farming for more than twenty years. Very few attended training on Organic Agriculture Production (OAP). Most of them are not aware of any OAP program in their community. Hence, their farming practices are mostly conventional. The overall level of knowledge on OAP among all respondents was below the average. On attitude, most of the respondents agreed that organic farming would decrease production costs by reducing input purchases. They believe it benefits both the consumer and the producer. In fact, they are aware of the many benefits of organic farming, especially on health. Likewise, many of them agreed on the benefits of organic farming to soil fertility, to the environment, and to increase the income of farmers. Many of them, however, see organic farming as troublesome and difficult in terms of time and effort, obtaining organic inputs, limited production, and marketing aspects. They also have heavy reliance on pesticides and herbicides to control pests and diseases. On practices, majority of the respondents stated that they practiced crop rotation, manual weeding, and the use of animal manure. Most of them desired to do organic farming but needed information such as production techniques, costs, and marketing opportunities. Their most preferred communication channel is through extension agents and contact farmers. Their most preferred communication method is through trainings and seminars as well as through farm demonstrations. Results of this study will serve as a basis for developing appropriate communication strategies to improve knowledge, attitude, and practices of respondents on organic agriculture as well as enhance the promotion of organic agriculture production in the community.

Keywords: Ifugao, knowledge attitude practices, organic agriculture, Philippines

Procedia PDF Downloads 165

Authors: Thi Thanh Nga Pham, Damien Philippon, Alexis Drogoul, Thi Thu Thuy Nguyen, Tien Cong Nguyen

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Mekong River Delta region of Vietnam is recognized as one of the most vulnerable to climate change due to flooding and seawater rise and therefore an increased burden of climate change-related diseases. Changes in temperature and precipitation are likely to alter the incidence and distribution of vector-borne diseases such as dengue fever. In this region, the peak of the dengue epidemic period is around July to September during the rainy season. It is believed that climate is an important factor for dengue transmission. This study aims to enhance the capacity of dengue prediction by the relationship of dengue incidences with climate and environmental variables for Mekong River Delta of Vietnam during 2005-2015. Mathematical models for vector-host infectious disease, including larva, mosquito, and human being were used to calculate the impacts of climate to the dengue transmission with incorporating geospatial data for model input. Monthly dengue incidence data were collected at provincial level. Precipitation data were extracted from satellite observations of GSMaP (Global Satellite Mapping of Precipitation), land surface temperature and land cover data were from MODIS. The value of seasonal reproduction number was estimated to evaluate the potential, severity and persistence of dengue infection, while the final infected number was derived to check the outbreak of dengue. The result shows that the dengue infection depends on the seasonal variation of climate variables with the peak during the rainy season and predicted dengue incidence follows well with this dynamic for the whole studied region. However, the highest outbreak of 2007 dengue was not captured by the model reflecting nonlinear dependences of transmission on climate. Other possible effects will be discussed to address the limitation of the model. This suggested the need of considering of both climate variables and another variability across temporal and spatial scales.

Keywords: infectious disease, dengue, geospatial data, climate

Procedia PDF Downloads 384

Authors: Saleh Kasiri Bidhendi, Shiva Tashakori

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With the increasing application of six-wheel robots in various industries, including agriculture and environmental monitoring, there is a growing demand for efficient and reliable control systems that can improve manoeuvrability and at the same time reduce energy consumption. Moving on uneven terrains, various factors such as obstacles or soil heterogeneity can cause the robot to slip. There is limited research addressing this issue. Although the robot is supposed to track a predetermined path, sudden lateral deviation necessitates path planning. To further address this issue, explicit steering is added by activating actuators on steerable wheels, while the SMC controller still commands differential traction forces on all wheels. This integration improves energy efficiency and obstacle traversability while maintaining the merits of skid-steering, such as tight turning manoeuvrability. However, achieving the desired steer angles presents certain challenges. Inverse kinematics was initially employed to achieve the needed steering angles from the desired position, but this approach led to excessive steering without yawing the body. Switching to desired velocity values instead of position limited over-steering but caused zero lateral velocity on horizontal paths, which was problematic for unforeseen skidding. To overcome this, a proportional controller has been employed, using lateral error as its input and providing a proportional yaw angle as output, the P-controller contributes to modifying the steering angles. The controller's robustness has been verified through sensitivity analyses under critical speeds and turning radius conditions. Our findings offer valuable insights into designing more efficient steering controls for rocker-bogie mechanisms in challenging situations, emphasizing the importance of reducing energy¬ consumption.

Keywords: six-wheel robots, inverse kinematics, integrated steering, path following, manoeuvrability, energy efficiency, uneven terrains

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Authors: Rim Ben Amor, Myriam Abidi, Moncef Gueddari

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The purpose of the present study is to assess the level and the distribution of CaO, SO3, Cd, Cu, Pb and Zn incore sediments of Ghannouch-Gabes coast, Gulf of Gabes, Tunisian Mediterranean coast. The XRD analyses indicate that the sediments of Ghannouch-Gabes coast are mainly composed of quartz, calcite, gypsum and fluorine reflecting the impact of the phosphate fertilizer industrial waste. The vertical distribution of surface sediments shows for all the elements analyzed, that the area located between the commercial and the fishing port of Gabes, is the most polluted zone, where the two harbors acted as barriers and limited the dispersion of phosphogypsum discharge. The abundance order of metals was found to be Zn > Cd > Cu >Pb and that the highest levels of heavy metals were found in the uppermost segment of the sediment core compared to lower depth subsurface due to a continuous input of PG release and showed that the area between the two harbor suffered from several types of pollutants compared to reference core C1, collected from non-industrialized area. The level of pollution was evaluated using contamination factor (Cf), pollution load index (PLI) and the geoaccumulation index (Igeo). The obtained results of Igeo allowed us to distinguish that the area between the commercial harbor of Ghannouch and the fishing harbor of Gabes is the most polluted where sediments are strongly contaminated for Pb, Cu and Cd. The pollution load index (PLI) of all sediments collected classified them as "polluted". According to contamination factor (Cf), the sediments can be considered as ‘considerable’ to ‘very high’ contaminated for Pb, ‘very high to moderate’ for Cd, ‘ moderate’ for Zn, between ‘moderate’ and ‘considerable’ for Cu. Statistical analyses show that heavy metals, fluoride, calcium and sulphate are resulting from the same anthropogenic origin. The metallic pollution status of sediments of Ghanouch -Gabes coast is worrying and requires a serious intervention.

Keywords: trace metals, phosphogypsum, core sediments, accumulation factor, contamination factor

Procedia PDF Downloads 142

Authors: Dheyaa W. Abbood, Rafa H. AL-Suhaili, May S. Saleh

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A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs length sand their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy.The optimization carried out subjected to constraints that ensure a safe structure against the uplift pressure force and sufficient protection length at the downstream side of the structure to overcome an excessive exit gradient. The Geo-studios oft ware, was used to analyze 1200 different cases. For each case the length of protection and volume of structure required to satisfy the safety factors mentioned previously were estimated. An ANN model was developed and verified using these cases input-output sets as its data base. A MatLAB code was written to perform a genetic algorithm optimization modeling coupled with this ANN model using a formulated optimization model. A sensitivity analysis was done for selecting the cross-over probability, the mutation probability and level ,the number of population, the position of the crossover and the weights distribution for all the terms of the objective function. Results indicate that the most factor that affects the optimum solution is the number of population required. The minimum value that gives stable global optimum solution of this parameters is (30000) while other variables have little effect on the optimum solution.

Keywords: inclined cutoff, optimization, genetic algorithm, artificial neural networks, geo-studio, uplift pressure, exit gradient, factor of safety

Procedia PDF Downloads 325

Authors: Tia Kristian Tajnšek, Matjaž Mazaj, Nataša Zabukovec Logar

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Metal-organic frameworks (MOFs) with their specific properties and the possibility of tuning the structure represent excellent candidates for use in the biomedical field. Their advantage lies in large pore surfaces and volumes, as well as the possibility of using bio-friendly or bioactive constituents. So-called bioMOFs are representatives of MOFs, which are constructed from at least one biomolecule (metal, a small bioactive molecule in metal clusters and/or linker) and are intended for bio-application (usually in the field of medicine; most commonly drug delivery). When designing a bioMOF for biomedical applications, we should adhere to some guidelines for an improved toxicological profile of the material. Such as (i) choosing an endogenous/nontoxic metal, (ii) GRAS (generally recognized as safe) linker, and (iii) nontoxic solvents. Design and synthesis of bioNICS-1 (bioMOF of National Institute of Chemistry Slovenia – 1) consider all these guidelines. Zinc (Zn) was chosen as an endogenous metal with an agreeable recommended daily intake (RDI) and LD50 value, and ascorbic acid (Vitamin C) was chosen as a GRAS and active linker. With these building blocks, we have synthesized a bioNICS-1 material. The synthesis was done in ethanol using a solvothermal method. The synthesis protocol was further optimized in three separate ways. Optimization of (i) synthesis parameters to improve the yield of the synthesis, (ii) input reactant ratio and addition of specific modulators for production of larger crystals, and (iii) differing of the heating source (conventional, microwave and ultrasound) to produce nano-crystals. With optimization strategies, the synthesis yield was increased. Larger crystals were prepared for structural analysis with the use of a proper species and amount of modulator. Synthesis protocol was adjusted to different heating sources, resulting in the production of nano-crystals of bioNICS-1 material. BioNICS-1 was further activated in ethanol and structurally characterized, resolving the crystal structure of new material.

Keywords: ascorbic acid, bioMOF, MOF, optimization, synthesis, zinc ascorbate

Procedia PDF Downloads 143

Authors: Manish Chand, Subhrojit Bagchi, R. Kumar

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A new dry tube (DT) has been installed in the tank of KAMINI research reactor, Kalpakkam India. This tube will be used for neutron activation analysis of small to large samples and testing of neutron detectors. DT tube is 375 cm height and 7.5 cm in diameter, located 35 cm away from the core centre. The experimental thermal flux at various axial positions inside the tube has been measured by irradiating the flux monitor (¹⁹⁷Au) at 20kW reactor power. The measured activity of ¹⁹⁸Au and the thermal cross section of ¹⁹⁷Au (n,γ) ¹⁹⁸Au reaction were used for experimental thermal flux measurement. The flux inside the tube varies from 10⁹ to 10¹⁰ and maximum flux was (1.02 ± 0.023) x10¹⁰ n cm⁻²s⁻¹ at 36 cm from the bottom of the tube. The Au and Zr foils without and with cadmium cover of 1-mm thickness were irradiated at the maximum flux position in the DT to find out the irradiation specific input parameters like sub-cadmium to epithermal neutron flux ratio (f) and the epithermal neutron flux shape factor (α). The f value was 143 ± 5, indicates about 99.3% thermal neutron component and α value was -0.2886 ± 0.0125, indicates hard epithermal neutron spectrum due to insufficient moderation. The measured flux profile has been validated using theoretical model of KAMINI reactor through Monte Carlo N-Particle Code (MCNP). In MCNP, the complex geometry of the entire reactor is modelled in 3D, ensuring minimum approximations for all the components. Continuous energy cross-section data from ENDF-B/VII.1 as well as S (α, β) thermal neutron scattering functions are considered. The neutron flux has been estimated at the corresponding axial locations of the DT using mesh tally. The thermal flux obtained from the experiment shows good agreement with the theoretically predicted values by MCNP, it was within ± 10%. It can be concluded that this MCNP model can be utilized for calculating other important parameters like neutron spectra, dose rate, etc. and multi elemental analysis can be carried out by irradiating the sample at maximum flux position using measured f and α parameters by k₀-NAA standardization.

Keywords: neutron flux, neutron activation analysis, neutron flux shape factor, MCNP, Monte Carlo N-Particle Code

Procedia PDF Downloads 164

Authors: Menuka Maharjan

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In the name of food security, agriculture intensification through conventional farming is being implemented in Nepal. Government focus on increasing agriculture production completely ignores soil as well human health. This leads to create serious soil degradation, i.e., reduction of soil fertility and microbial activity and health hazard in the country. On this note, organic farming is sustainable agriculture approach which can address challenge of sustaining food security while protecting the environment. This creates a win-win situation both for people and the environment. However, people have limited knowledge on significance of organic farming for environment conservation and food security especially developing countries like Nepal. Thus, the objective of the study was to assess the impacts of organic farming on soil fertility and microbial activity compared to conventional farming and forest in Chitwan, Nepal. Total soil organic carbon (C) was highest in organic farming (24 mg C g⁻¹ soil) followed by conventional farming (15 mg C g⁻¹ soil) and forest (9 mg C g⁻¹ soil) in the topsoil layer (0-10 cm depth). A similar trend was found for total nitrogen (N) content in all three land uses with organic farming soil possessing the highest total N content in both 0-10 cm and 10-20 cm depth. Microbial biomass C and N were also highest under organic farming, especially in the topsoil layer (350 and 46 mg g⁻¹ soil, respectively). Similarly, microbial biomass phosphorus (P) was higher (3.6 and 1.0 mg P kg⁻¹ at 0-10 and 10-20 cm depth, respectively) in organic farming compared to conventional farming and forest at both depths. However, conventional farming and forest soils had similar microbial biomass (C, N, and P) content. After conversion of forest, the P stock significantly increased by 373% and 170% in soil under organic farming at 0-10 and 10-20 cm depth, respectively. In conventional farming, the P stock increased by 64% and 36% at 0-10 cm and 10-20 cm depth, respectively, compared to forest. Overall, organic farming practices, i.e., crop rotation, residue input and farmyard manure application, significantly alters soil fertility and microbial activity. Organic farming system is emerging as a sustainable land use system which can address the issues of food security and environment conservation by increasing sustainable agriculture production and carbon sequestration, respectively, supporting to achieve goals of sustainable development.

Keywords: organic farming, soil fertility, micobial biomas, food security

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Authors: Benjamin K. Ghansah, Richard K. Appoh, Iliya Nababa, Eric K. Forkuo

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The Nasia River is an important source of water for domestic and agricultural purposes to the inhabitants of its catchment. Major farming activities takes place within the floodplain of the river and its network of tributaries. The actual inundation extent of the river system is; however, unknown. Reasons for this lack of information include financial constraints and inadequate human resources as flood modelling is becoming increasingly complex by the day. Knowledge of the inundation extent will help in the assessment of risk posed by the annual flooding of the river, and help in the planning of flood recession agricultural activities. This study used a simple terrain based algorithm, Height Above Nearest Drainage (HAND), to delineate the floodplain of the Nasia River and its tributaries. The HAND model is a drainage normalized digital elevation model, which has its height reference based on the local drainage systems rather than the average mean sea level (AMSL). The underlying principle guiding the development of the HAND model is that hillslope flow paths behave differently when the reference gradient is to the local drainage network as compared to the seaward gradient. The new terrain model of the catchment was created using the NASA’s SRTM Digital Elevation Model (DEM) 30m as the only data input. Contours (HAND Contour) were then generated from the normalized DEM. Based on field flood inundation survey, historical information of flooding of the area as well as satellite images, a HAND Contour of 2m was found to best correlates with the flood inundation extent of the river and its tributaries. A percentage accuracy of 75% was obtained when the surface area created by the 2m contour was compared with surface area of the floodplain computed from a satellite image captured during the peak flooding season in September 2016. It was estimated that the flooding of the Nasia River and its tributaries created a floodplain area of 1011 km².

Keywords: digital elevation model, floodplain, HAND contour, inundation extent, Nasia River

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Authors: Mohammed Seyam, Faridah Othman, Ahmed El-Shafie

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The early warning of high stream flow events (HSF) and floods is an important aspect in the management of surface water and rivers systems. This process can be performed using either process-based models or data driven-based models such as artificial intelligence (AI) techniques. The main goal of this study is to develop efficient AI-based model for predicting the real-time hourly stream flow (Q) and apply it as early warning tool of HSF and floods in the downstream area of the Selangor River basin, taken here as a paradigm of humid tropical rivers in Southeast Asia. The performance of AI-based models has been improved through the integration of the lag time (Lt) estimation in the modelling process. A total of 8753 patterns of Q, water level, and rainfall hourly records representing one-year period (2011) were utilized in the modelling process. Six hydrological scenarios have been arranged through hypothetical cases of input variables to investigate how the changes in RF intensity in upstream stations can lead formation of floods. The initial SF was changed for each scenario in order to include wide range of hydrological situations in this study. The performance evaluation of the developed AI-based model shows that high correlation coefficient (R) between the observed and predicted Q is achieved. The AI-based model has been successfully employed in early warning throughout the advance detection of the hydrological conditions that could lead to formations of floods and HSF, where represented by three levels of severity (i.e., alert, warning, and danger). Based on the results of the scenarios, reaching the danger level in the downstream area required high RF intensity in at least two upstream areas. According to results of applications, it can be concluded that AI-based models are beneficial tools to the local authorities for flood control and awareness.

Keywords: floods, stream flow, hydrological modelling, hydrology, artificial intelligence

Procedia PDF Downloads 248

Authors: M. Lim Haslip

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This quantitative study explores the correlation between child gender and father-child interactions. The author analyzes data from videotaped interactions between African-American fathers and their boy or girl toddler to explain how African-American fathers and toddlers interact with each other and whether these interactions differ by child gender. The purpose of this study is to investigate the research question: 'How, if at all, do fathers’ speech and gestures differ when interacting with their two-year-old sons versus daughters during free play?' The objectives of this study are to describe how child gender impacts African-American fathers’ verbal communication, examine how fathers gesture and speak to their toddler by gender, and to guide interventions for low-income African-American families and their children in early language development. This study involves a sample of 41 low-income African-American fathers and their 24-month-old toddlers. The videotape data will be used to observe 10-minute father-child interactions during free play. This study uses the already transcribed and coded data provided by Dr. Meredith Rowe, who did her study on the impact of African-American fathers’ verbal input on their children’s language development. The Child Language Data Exchange System (CHILDES program), created to study conversational interactions, was used for transcription and coding of the videotape data. The findings focus on the quantity of speech, diversity of speech, complexity of speech, and the quantity of gesture to inform the vocabulary usage, number of spoken words, length of speech, and the number of object pointings observed during father-toddler interactions in a free play setting. This study will help intervention and prevention scientists understand early language development in the African-American population. It will contribute to knowledge of the role of African-American fathers’ interactions on their children’s language development. It will guide interventions for the early language development of African-American children.

Keywords: parental engagement, early language development, African-American families, quantity of speech, diversity of speech, complexity of speech and the quantity of gesture

Procedia PDF Downloads 105

Authors: Vuyayo Tsheleza, Simbarashe Ndhleve, Christopher Mpundu Musampa

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Mismanagement of waste is continuously emerging as a rising malpractice in most developing countries, especially in fast growing cities. Household solid waste in Mthatha has been reported to be one of the problems facing the city and is overwhelming local authorities, as it is beyond the environment and management capacity of the existing waste management system. This study estimates per capita waste generation, quantity of different waste types generated by inhabitants of formal and informal settlements in Mthatha as well as waste management practices in the aforementioned socio-economic stratums. A total of 206 households were systematically selected for the study using stratified random sampling categorized into formal and informal settlements. Data on household waste generation rate, composition, awareness, and household waste management behaviour and practices was gathered through mixed methods. Sampled households from both formal and informal settlements with a total of 684 people generated 1949kg per week. This translates to 2.84kg per capita per week. On average, the rate of solid waste generation per capita was 0.40 kg per day for a person living in informal settlement and 0.56 kg per day person living in formal settlement. When recorded in descending order, the proportion food waste accounted for the most generated waste at approximately 23.7%, followed by disposable nappies at 15%, papers and cardboards 13.34%, glass 13.03%, metals at 11.99%, plastics at 11.58%, residue at 5.17, textiles 3.93%, with leather and rubber at 2.28% as the least generated waste type. Different waste management practices were reported in both formal and informal settlements with formal settlements proving to be more concerned about environmental management as compared to their counterparts, informal settlement. Understanding attitudes and perceptions on waste management, waste types and per capita solid waste generation rate can help evolve appropriate waste management strategies based on the principle of reduce, re-use, recycle, environmental sound disposal and also assist in projecting future waste generation rate. These results can be utilized as input when designing growing cities’ waste management plans.

Keywords: awareness, characterisation, per capita, quantification

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Authors: Saima Haq

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This paper presents the contribution of the Sunflowers Vocational Center, Karachi, Pakistan, by providing a platform for the students of special needs to work with recycled materials and express themselves in a more extravagant form. The concept was to create products that would generate enough income to sustain the program while keeping the students cognitively engaged through arts and crafts and tactile instructions due to their severe intellectual disabilities. Papier-mâché is an art form that is hands-on, repetitive, economical as well as beneficial for the environment. The process of tearing paper into long strips then covering them with paste and laying the strips atop the mold provides constant sensory input for our autistic students as well as the rest of our student population. Given the marginalized stance the society has on special needs, we have marketed the paper-mâché products on social media platforms and have set up booths in carnivals, festivities, open markets that are aimed towards a cause to sell. Our students in the vocational center have also made bins, baskets, and trays that are used in all classrooms. This has cut our costs on classroom materials considerably and has added a sense of accomplishment and furthered the teamwork skills in our sunflowers. The other achievement is our long clientele; orders have been placed from several persons for birthdays, parties, events, and the like. This exposure has raised awareness of the capabilities of persons of special needs and has started a conversation on the topic. And additional achievement is that we have made our teachers, their families, our students, and their families conscientious of the environment and incorporated reusing newspapers into classrooms. Situations where plastic would be bought, for example, bin, dustbins, containers, basket, trays, the paper-mâché products made by our students have been used instead. Due to the low cost of materials, this project is easily replicable and very easy to start. Piñatas are a very popular item for children’s parties everywhere and are gaining popularity through social media. This is also easily replicable in any environment and can have a great impact on the use of plastic in any work or home environment.

Keywords: vocational training, special needs, cognitive skills, teamwork

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Authors: Abdulaziz F. Ettir, Ghait Bashir, Tarek S. Duzan

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A proper well planning is very vital to achieve any successful drilling program on the basis of preventing, overcome all drilling problems and minimize cost operations. Since the hydraulic system plays an active role during the drilling operations, that will lead to accelerate the drilling effort and lower the overall well cost. Likewise, an improperly designed hydraulic system can slow drill rate, fail to clean the hole of cuttings, and cause kicks. In most cases, common sense and commercially available computer programs are the only elements required to design the hydraulic system. Drilling optimization is the logical process of analyzing effects and interactions of drilling variables through applied drilling and hydraulic equations and mathematical modeling to achieve maximum drilling efficiency with minimize drilling cost. In this paper, practical software adopted in this paper to define drilling optimization models including four different optimum keys, namely Opti-flow, Opti-clean, Opti-slip and Opti-nozzle that can help to achieve high drilling efficiency with lower cost. The used data in this research from vertical and horizontal wells were recently drilled in Waha Oil Company fields. The input data are: Formation type, Geopressures, Hole Geometry, Bottom hole assembly and Mud reghology. Upon data analysis, all the results from wells show that the proposed program provides a high accuracy than that proposed from the company in terms of hole cleaning efficiency, and cost break down if we consider that the actual data as a reference base for all wells. Finally, it is recommended to use the established Optimization calculations software at drilling design to achieve correct drilling parameters that can provide high drilling efficiency, borehole cleaning and all other hydraulic parameters which assist to minimize hole problems and control drilling operation costs.

Keywords: optimum keys, namely opti-flow, opti-clean, opti-slip and opti-nozzle

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Authors: Solomon Yokamo, Dianjun Lu, Xiaoqin Chen, Huoyan Wang

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The current ‘high input-high output’ nutrient management model based on homogenous spreading over the entire soil surface remains a key challenge in China’s farming systems, leading to low fertilizer use efficiency and environmental pollution. Localized placement of fertilizer (LPF) to crop root zones has been proposed as a viable approach to boost crop production while protecting environmental pollution. To assess the potential benefits of LPF on three major crops—wheat, rice, and maize—a comprehensive meta-analysis was conducted, encompassing 85 field studies published from 2002-2023. We further validated the practicability and feasibility of one-time root zone N management based on LPF for the three field crops. The meta-analysis revealed that LPF significantly increased the yields of the selected crops (13.62%) and nitrogen recovery efficiency (REN) (33.09%) while reducing cumulative nitrous oxide (N₂O) emission (17.37%) and ammonia (NH₃) volatilization (60.14%) compared to the conventional surface application (CSA). Higher grain yield and REN were achieved with an optimal fertilization depth (FD) of 5-15 cm, moderate N rates, combined NPK application, one-time deep fertilization, and coarse-textured and slightly acidic soils. Field validation experiments showed that localized one-time root zone N management without topdressing increased maize (6.2%), rice (34.6%), and wheat (2.9%) yields while saving N fertilizer (3%) and also increased the net economic benefits (23.71%) compared to CSA. A soil incubation study further proved the potential of LPF to enhance the retention and availability of mineral N in the root zone over an extended period. Thus, LPF could be an important fertilizer management strategy and should be extended to other less-developed and developing regions to win the triple benefit of food security, environmental quality, and economic gains.

Keywords: grain yield, LPF, NH₃ volatilization, N₂O emission, N recovery efficiency

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

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Authors: Asma Rebaya, Kaouther Gasmi, Imen Amari, Salem Hasnaoui

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Over these last years, applications such as telecommunications, signal processing, digital communication with advanced features (Multi-antenna, equalization..) witness a rapid evaluation accompanied with an increase of user exigencies in terms of latency, the power of computation… To satisfy these requirements, the use of hardware/software systems is a common solution; where hardware is composed of multi-cores and software is represented by models of computation, synchronous data flow (SDF) graph for instance. Otherwise, the most of the embedded system designers utilize Simulink for modeling. The issue is how to simplify the c code generation, for a multi-cores platform, of an application modeled by Simulink. To overcome this problem, we propose a workflow allowing an automatic transformation from the Simulink model to the SDF graph and providing an efficient schedule permitting to optimize the number of cores and to minimize latency. This workflow goes from a Simulink application and a hardware architecture described by IP.XACT language. Based on the synchronous and hierarchical behavior of both models, the Simulink block diagram is automatically transformed into an SDF graph. Once this process is successfully achieved, the scheduler calculates the optimal cores’ number needful by minimizing the maximum density of the whole application. Then, a core is chosen to execute a specific graph task in a specific order and, subsequently, a compatible C code is generated. In order to perform this proposal, we extend Preesm, a rapid prototyping tool, to take the Simulink model as entry input and to support the optimal schedule. Afterward, we compared our results to this tool results, using a simple illustrative application. The comparison shows that our results strictly dominate the Preesm results in terms of number of cores and latency. In fact, if Preesm needs m processors and latency L, our workflow need processors and latency L'< L.

Keywords: hardware/software system, latency, modeling, multi-cores platform, scheduler, SDF graph, Simulink model, workflow

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Authors: Nikhil Kumar, Asish Bandyopadhyay

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Laser welding process is a capable technology for forming the automobile, microelectronics, marine and aerospace parts etc. In the present work, a mathematical and statistical approach is adopted to study the laser welding of AISI 304 stainless steel. A robotic control 500 W pulsed Nd:YAG laser source with 1064 nm wavelength has been used for welding purpose. Butt joints are made. The effects of welding parameters, namely; laser power, scanning speed and pulse width on the seam width and depth of penetration has been investigated using the empirical models developed by response surface methodology (RSM). Weld quality is directly correlated with the weld geometry. Twenty sets of experiments have been conducted as per central composite design (CCD) design matrix. The second order mathematical model has been developed for predicting the desired responses. The results of ANOVA indicate that the laser power has the most significant effect on responses. Microstructural analysis as well as hardness of the selected weld specimens has been carried out to understand the metallurgical and mechanical behaviour of the weld. Average micro-hardness of the weld is observed to be higher than the base metal. Higher hardness of the weld is the resultant of grain refinement and δ-ferrite formation in the weld structure. The result suggests that the lower line energy generally produce fine grain structure and improved mechanical properties than the high line energy. The combined effects of input parameters on responses have been analyzed with the help of developed 3-D response surface and contour plots. Finally, multi-objective optimization has been conducted for producing weld joint with complete penetration, minimum seam width and acceptable welding profile. Confirmatory tests have been conducted at optimum parametric conditions to validate the applied optimization technique.

Keywords: ANOVA, laser welding, modeling and optimization, response surface methodology

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Authors: Ali Mamizadeh, Ires Iskender, Saeid Aghaei

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Energy is one of the important issues with high priority property in the world. Energy demand is rapidly increasing depending on the growing population and industry. The useable energy sources in the world will be insufficient to meet the need for energy. Therefore, the efficient and economical usage of energy sources is getting more importance. In a survey conducted among electric consuming machines, the electrical machines are consuming about 40% of the total electrical energy consumed by electrical devices and 96% of this consumption belongs to induction motors. Induction motors are the workhorses of industry and have very large application areas in industry and urban systems like water pumping and distribution systems, steel and paper industries and etc. Monitoring and the control of the motors have an important effect on the operating performance of the motor, driver selection and replacement strategy management of electrical machines. The sensorless monitoring system for monitoring and calculating efficiency of induction motors are studied in this study. The equivalent circuit of IEEE is used in the design of this study. The terminal current and voltage of induction motor are used in this motor to measure the efficiency of induction motor. The motor nameplate information and the measured current and voltage are used in this system to calculate accurately the losses of induction motor to calculate its input and output power. The efficiency of the induction motor is monitored online in the proposed method without disconnecting the motor from the driver and without adding any additional connection at the motor terminal box. The proposed monitoring system measure accurately the efficiency by including all losses without using torque meter and speed sensor. The monitoring system uses embedded architecture and does not need to connect to a computer to measure and log measured data. The conclusion regarding the efficiency, the accuracy and technical and economical benefits of the proposed method are presented. The experimental verification has been obtained on a 3 phase 1.1 kW, 2-pole induction motor. The proposed method can be used for optimal control of induction motors, efficiency monitoring and motor replacement strategy.

Keywords: induction motor, efficiency, power losses, monitoring, embedded design

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Authors: Mengsha Yin

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Extractable organic matter (EOM) from thirty-six biogenic gas source rocks from the Sanhu Depression in Qaidam Basin in northwestern China were obtained via Soxhlet extraction. Twenty-nine of them were conducted SARA (Saturates, Aromatics, Resins and Asphaltenes) separation for bulk composition analysis. Saturated and aromatic fractions of all the extractions were analyzed by Gas Chromatography-Mass Spectrometry (GC-MS) to investigate the compound compositions. More abundant n-alkanes, naphthalene, phenanthrene, dibenzothiophene and their alkylated products occur in samples in shallower depths. From 2000m downward, concentrations of these compounds increase sharply, and concentration ratios of more-over-less biodegradation susceptible compounds coincidently decrease dramatically. ∑iC15-16, 18-20/∑nC15-16, 18-20 and hopanoids/∑n-alkanes concentration ratios and mono- and tri-aromatic sterane concentrations and concentration ratios frequently fluctuate with depth rather than trend with it, reflecting effects from organic input and paleoenvironments other than biodegradation. Saturated and aromatic compound distributions on the saturates and aromatics total ion chromatogram (TIC) traces of samples display different degrees of biodegradation. Dramatic and simultaneous variations in compound concentrations and their ratios at 2000m and their changes with depth underneath cooperatively justified the crucial control of burial depth on organic matter biodegradation scales in source rocks and prompted the proposition that 2000m is the bottom depth boundary for active microbial activities in this study. The study helps to better curb the conditions where effective source rocks occur in terms of depth in the Sanhu biogenic gas fields and calls for additional attention to source rock pore size estimation during biogenic gas source rock appraisals.

Keywords: pore space, Sanhu depression, saturated and aromatic hydrocarbon compound concentration, source rock organic matter biodegradation, total ion chromatogram

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Authors: Hafizah Harun, Habibah Harun, Azlina Kamaruddin

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Virtual reality has become a powerful and promising tool in education because of their unique technological characteristics that differentiate them from the other ICT applications. Despite the numerous interpretations of its definition, virtual reality can be concisely and precisely described as the integration of computer graphics and various input and display technologies to create the illusion of immersion in a computer generated reality. Generally, there are two major types based on the level of interaction and immersive environment that are immersive and non-immersive virtual reality. In the study of the role of virtual reality in built environment education, Horne and Thompson were reported as saying that the benefits of using visualization technologies were seen as having the potential to improve and extend the learning process, increase student motivation and awareness, and add to the diversity of teaching methods. Youngblut reported that students enjoy working with virtual worlds and this experience can be highly motivating. The impact of virtual reality on youth learning in Malaysia is currently not well explored because the technology is still not widely used here. Only a handful of the universities, such as University Malaya, MMU, and Unimas are applying virtual reality strategy in some of their undergraduate programs. From the literature, it has been identified that there are several virtual reality learning strategies currently available. Therefore, this study aims to investigate the impact of Virtual Reality strategy on Youth Learning Motivation in Malaysian higher learning institutions. We will explore the relationship between virtual reality (gaming, laboratory, simulation) and youth leaning motivation. Another aspect that we will explore is the framework for virtual reality implementation at higher learning institution in Malaysia. This study will be carried out quantitatively by distributing questionnaires to respondents from sample universities. Data analysis are descriptive and multiple regression. Researcher will carry out a pilot test prior to distributing the questionnaires to 300 undergraduate students who are undergoing their courses in virtual reality environment. The respondents come from two universities, MMU CyberJaya and University Malaya. The expected outcomes from this study are the identification of which virtual reality strategy has most impact on students’ motivation in learning and a proposed framework of virtual reality implementation at higher learning.

Keywords: virtual reality, learning strategy, youth learning, motivation

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Authors: Rohan Bhasin

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Agrammatism in non-fluent Aphasia Cases can be defined as a language disorder wherein a patient can only use content words ( nouns, verbs and adjectives ) for communication and their speech is devoid of functional word types like conjunctions and articles, generating speech of with extremely rudimentary grammar . Past approaches involve Speech Therapy of some order with conversation analysis used to analyse pre-therapy speech patterns and qualitative changes in conversational behaviour after therapy. We describe this approach as a novel method to generate functional words (prepositions, articles, ) around content words ( nouns, verbs and adjectives ) using a combination of Natural Language Processing and Deep Learning algorithms. The applications of this approach can be used to assist communication. The approach the paper investigates is : LSTMs or Seq2Seq: A sequence2sequence approach (seq2seq) or LSTM would take in a sequence of inputs and output sequence. This approach needs a significant amount of training data, with each training data containing pairs such as (content words, complete sentence). We generate such data by starting with complete sentences from a text source, removing functional words to get just the content words. However, this approach would require a lot of training data to get a coherent input. The assumptions of this approach is that the content words received in the inputs of both text models are to be preserved, i.e, won't alter after the functional grammar is slotted in. This is a potential limit to cases of severe Agrammatism where such order might not be inherently correct. The applications of this approach can be used to assist communication mild Agrammatism in non-fluent Aphasia Cases. Thus by generating these function words around the content words, we can provide meaningful sentence options to the patient for articulate conversations. Thus our project translates the use case of generating sentences from content-specific words into an assistive technology for non-Fluent Aphasia Patients.

Keywords: aphasia, expressive aphasia, assistive algorithms, neurology, machine learning, natural language processing, language disorder, behaviour disorder, sequence to sequence, LSTM

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Authors: Sonya Barzgar, J. Patrick, A. Hettiaratchi

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Methane (CH4) is reputed as the second largest contributor to greenhouse effect with a global warming potential (GWP) of 34 related to carbon dioxide (CO2) over the 100-year horizon, so there is a growing interest in reducing the emissions of this gas. Methane biofiltration (MBF) is a cost effective technology for reducing low volume point source emissions of methane. In this technique, microbial oxidation of methane is carried out by methane-oxidizing bacteria (methanotrophs) which use methane as carbon and energy source. MBF uses a granular medium, such as soil or compost, to support the growth of methanotrophic bacteria responsible for converting methane to carbon dioxide (CO₂) and water (H₂O). Even though the biofiltration technique has been shown to be an efficient, practical and viable technology, the design and operational parameters, as well as the relevant microbial processes have not been investigated in depth. In particular, limited research has been done on the effects of sulphur on methane bio-oxidation. Since bacteria require a variety of nutrients for growth, to improve the performance of methane biofiltration, it is important to establish the input quantities of nutrients to be provided to the biofilter to ensure that nutrients are available to sustain the process. The study described in this paper was conducted with the aim of determining the influence of sulphur on methane elimination in a biofilter. In this study, a set of experimental measurements has been carried out to explore how the conversion of elemental sulphur could affect methane oxidation in terms of methanotrophs growth and system pH. Batch experiments with different concentrations of sulphur were performed while keeping the other parameters i.e. moisture content, methane concentration, oxygen level and also compost at their optimum level. The study revealed the tolerable limit of sulphur without any interference to the methane oxidation as well as the particular sulphur concentration leading to the greatest methane elimination capacity. Due to the sulphur oxidation, pH varies in a transient way which affects the microbial growth behavior. All methanotrophs are incapable of growth at pH values below 5.0 and thus apparently are unable to oxidize methane. Herein, the certain pH for the optimal growth of methanotrophic bacteria is obtained. Finally, monitoring methane concentration over time in the presence of sulphur is also presented for laboratory scale biofilters.

Keywords: global warming, methane biofiltration (MBF), methane oxidation, methanotrophs, pH, sulphur

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Authors: Kausar Harun, Ahmad Azmin Mohamad

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Zinc oxide (ZnO) has been extensively used in optoelectronic devices, with recent interest as photoanode material in dye-sensitize solar cell. Numerous methods employed to experimentally synthesized ZnO, while some are theoretically-modeled. Both approaches provide information on ZnO properties, but theoretical calculation proved to be more accurate and timely effective. Thus, integration between these two methods is essential to intimately resemble the properties of synthesized ZnO. In this study, experimentally-grown ZnO nanoparticles were prepared by sol-gel storage method with zinc acetate dihydrate and methanol as precursor and solvent. A 1 M sodium hydroxide (NaOH) solution was used as stabilizer. The optimum time to produce ZnO nanoparticles were recorded as 12 hours. Phase and structural analysis showed that single phase ZnO produced with wurtzite hexagonal structure. Further work on quantitative analysis was done via Rietveld-refinement method to obtain structural and crystallite parameter such as lattice dimensions, space group, and atomic coordination. The lattice dimensions were a=b=3.2498Å and c=5.2068Å which were later used as main input in first-principles calculations. By applying density-functional theory (DFT) embedded in CASTEP computer code, the structure of synthesized ZnO was built and optimized using several exchange-correlation functionals. The generalized-gradient approximation functional with Perdew-Burke-Ernzerhof and Hubbard U corrections (GGA-PBE+U) showed the structure with lowest energy and lattice deviations. In this study, emphasize also given to the modification of valence electron energy level to overcome the underestimation in DFT calculation. Both Zn and O valance energy were fixed at Ud=8.3 eV and Up=7.3 eV, respectively. Hence, the following electronic and optical properties of synthesized ZnO were calculated based on GGA-PBE+U functional within ultrasoft-pseudopotential method. In conclusion, the incorporation of Rietveld analysis into first-principles calculation was valid as the resulting properties were comparable with those reported in literature. The time taken to evaluate certain properties via physical testing was then eliminated as the simulation could be done through computational method.

Keywords: density functional theory, first-principles, Rietveld-refinement, ZnO nanoparticles

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