Search results for: deep soil

Authors: Seyed Armin Hashemi, Somayeh Rahimzadeh

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Toxic metals such as lead and cadmium are among the pollutants that are created by the metal production factories and disseminated in the nature. In order to study the quantity of cadmium pollution in the environment of the metal production factories, 50 saplings of the spruce species at the peripheries of the metal production factories were examined and the samples of the leaves, roots and stems of saplings planted around the factory and the soil of the environment of the factory were studied to investigate pollution with cadmium. They were compared to the soil and saplings of the spruce trees planted outside the factory as observer region. The results showed that the quantity of pollution in the leaves, stem, and roots of the trees planted inside the factory environment were estimated at 1.1 milligram/kilogram, 1.5 milligram/kilogram and 2.5 milligram/kilogram respectively and this indicated a significant difference with the observer region (P < 0.05). The quantity of cadmium in the soil of the peripheries of the metal production factory was estimated at 6.8 milligram/kilogram in the depth of 0-10 centimeters beneath the level of the soil. The length of roots in the saplings planted around the factory of metal production stood at 11 centimeters and 14.5 centimeters in the observer region which had a significant difference with the observer region (P < 0.05). The quantity of soil resources and spruce species’ pollution with cadmium in the region has been influenced by the production processes in the factory.

Keywords: cadmium pollution, spruce, soil pollution, the factory of producing alloy metals

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Authors: Mahdi Akhbardeh

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Synchronization of neuronal firing is a hallmark of several neurological diseases. Recently, stimulation techniques have been developed which make it possible to desynchronize oscillatory neuronal activity in a mild and effective way, without suppressing the neurons' firing. As yet, these techniques are being used to establish demand-controlled deep brain stimulation (DBS) techniques for the therapy of movement disorders like severe Parkinson's disease or essential tremor. We here present a first conceptualization suggesting that the nucleus accumbens is a promising target for the standard, that is, permanent high-frequency, DBS in patients with severe and chronic obsessive-compulsive disorder (OCD). In addition, we explain how demand-controlled DBS techniques may be applied to the therapy of OCD in those cases that are refractory to behavioral therapies and pharmacological treatment.

Keywords: stereotactic neurosurgery, deep brain stimulation, obsessive-compulsive disorder, phase resetting

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Authors: Mohanad Talal Alfach

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This paper concerns the seismic behaviour of soil-piles-bridge reinforced by additional micropiles. The analysis carried out by three-dimensional finite element modelling using the FE software ABAQUS. The soil behaviour is assumed to be elastic with Rayleigh damping, while the micropiles are modeled as 3D elastic beam elements. The bridge deck slab was represented by a concentrated mass at the top of the pier column. The interaction between the added micropiles and the existing piles as well as the performance of the retrofitted soil-pile-superstructure system were investigated for different configurations of additional micropiles (number, position, inclination). Numerical simulation results show that additional micropiles constitute an efficient retrofitting solution. Analysis of results also shows that spacing between existing piles and retrofitting micropiles has little effect; while it is observed a substantial improvement (in case of weak piles/micropiles - soil interface) with reducing the inclination angle of retrofitting micropiles.

Keywords: retrofitting, seismic, finite element, micropiles, elastic

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Authors: Mohammad Khalid, Mariya Munir, Jacelyn Rice Boyaue

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Highways are considered a major source of pollution to storm-water, and its runoff can introduce various contaminants, including nutrients, Indicator bacteria, heavy metals, chloride, and phosphorus compounds, which can have negative impacts on receiving waters. This study assessed the ability of biochar for contaminants removal and to improve the water holding capacity of soil biochar mixture. For this, ten commercially available biochar has been strategically selected. Lab scale batch testing was done at 3% and 6% by the weight of the soil to find the preliminary estimate of contaminants removal along with hydraulic conductivity and water retention capacity. Furthermore, from the above-conducted studies, six best performing candidate and an application rate of 6% has been selected for the column studies. Soil biochar mixture was filled in 7.62 cm assembled columns up to a fixed height of 76.2 cm based on hydraulic conductivity. A total of eight column experiments have been conducted for nutrient, heavy metal, and indicator bacteria analysis over a period of one year, which includes a drying as well as a deicing period. The saturated hydraulic conductivity was greatly improved, which is attributed to the high porosity of the biochar soil mixture. Initial data from the column testing shows that biochar may have the ability to significantly remove nutrients, indicator bacteria, and heavy metals. The overall study demonstrates that biochar could be efficiently applied with clay soil to improve the soil's hydraulic characteristics as well as remove the pollutants from the stormwater runoff.

Keywords: biochar, nutrients, indicator bacteria, storm-water treatment, sustainability

Procedia PDF Downloads 127

Authors: Sai Maniveer Adapa, Sai Guptha Perla, Adithya Reddy P.

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Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection.

Keywords: thyroid tumor diagnosis, ultrasound images, deep learning, machine learning, convolutional auto-encoder, support vector machine

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Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

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Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma

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Authors: M. Doyeni, S. Suproniene, V. Tilvikiene

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Agricultural wastes contribute significantly to global climate change through greenhouse gas emissions if not adequately recycled and sustainably managed. A recurring agricultural waste is livestock wastes that have consistently served as feedstock for biogas systems. The objective of this study was to access the influence of digestate fertilization on soil microbial activity and greenhouse gas emissions in agricultural fields. Wheat (Triticum spp. L.) was fertilized with different types of animal wastes digestates (organic fertilizers) and mineral nitrogen (inorganic fertilizer) for three years. The 170 kg N ha⁻¹ presented in digestates were split fertilized at an application rate of 90 and 80 kg N ha⁻¹. The soil microorganism activity could be predicted significantly using the dehydrogenase activity and soil microbial biomass carbon. By combining the two different monitoring approaches, the different methods applied in this study were sensitive to enzymatic activities and organic carbon in the living component of the soil organic matter. The emissions of greenhouse gasses (carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) were monitored directly by a static chamber system. The soil and environmental variables were measured to determine their influence on greenhouse gas emissions. Emission peaks was observed in N₂O and CO₂ after the first application of fertilizers with the emissions flattening out over the cultivating season while CH₄ emission was negligible with no apparent patterns observed. Microbial biomass carbon and dehydrogenase activity were affected by the fertilized organic digestates. A significant difference was recorded between the control and the digestate treated soils for the microbial biomass carbon and dehydrogenase. Results also showed individual and cumulative emissions of CO₂, CH₄ and N₂O from the digestates were relatively low suggesting the digestate fertilization can be an efficient method for improving soil quality and reducing greenhouse gases from agricultural sources in temperate climate conditions.

Keywords: greenhouse gas emission, manure digestate, soil microbial activity, yield

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Authors: Addharu Eri, Ningsih P. Lestari, Setyorini Adheliya, Syaiputri Khaidifah

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Rawa Pening is a lake at the Ambarawa Basin in Central Java, Indonesia. It serves as a source of power (hydroelectricity), irrigation, and flood control. The potential of this lake is getting worse by the presence of aquatic plants (Eichhornia crassipes) that grows wild, and it can make the lake covered by the cumulation of rotten E. crassipes. This cumulation causes the sediment formation which has high organic material composition. Sediment formation will be lead into a shallowing of the lake and affect water’s quality. The deposition of organic material produces methane gas and hydrogen sulfide, which in rain would turn the water muddy and decompose. Decomposition occuring in the water due to microbe activity in lake's water. The shallowing of Rawa Pening Lake not only will physically can reduce water discharge, but it also has ecologically major impact on water organism. The condition of Rawa Pening Lake peats can not be considered as unimportant issue. One of the solutions that can be applied is by using the peats as a compound materials on growing horticultural crops because the organic materials content on the mineral soil is low, particularly on an old soils. The horticultural crops required organic materials for growth promoting. The horticultural crops that use in this research is mustard cabbage (Brassica sp.). Using Rawa Pening's peats as the medium of plants with high organic materials that also can ameliorate soil’s physical properties, and indirectly serves as soil conditioner. Research will be focus on the peat’s contents and mustard cabbage product’s content. The contents that will be examined is the N-available, Ca, Mg, K, P, and C-organic. The analysis of Ca, Mg, and K is use soil base saturation measurement method and extracting soil is use NH4OAC solution. The aim of this study is to use the peats of Rawa Pening Lake as soil conditioner and increase the productivity of Brassica sp.

Keywords: Brassica sp., peats, rawa pening lake, soil conditioner

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Authors: S. M. Jogdand, D. D. Jagtap, N. R. Dalal

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Sweet orange (Citrus sinensis Osbeck) is one of the most important commercially cultivated fruit crop in India. It stands on second position amongst citrus group after mandarin. Irrigation and fertigation are vital importance of sweet orange orchard and considered to be the most critical cultural operations. The soil acts as the reservoir of water and applied nutrients, the interaction between irrigation and fertigation leads to the ultimate quality and production of fruits. The increasing cost of fertilizers and scarcity of irrigation water forced the farmers for optimum use of irrigation and nutrients. The experiment was conducted with object to find out irrigation and nutrient interaction in sweet orange to optimize the use of both the factors. The experiment was conducted in medium to deep soil. The irrigation level I3,drip irrigation at 90% ER (effective rainfall) and fertigation level F3 80% RDF (recommended dose of fertilizer) recorded significantly maximum plant height, plant spread, canopy volume, number of fruits, weight of fruit, fruit yield kg/plant and t/ha followed by F2 , fertigation with 70% RDF. The interaction effect of irrigation and fertigation on growth was also significant and the maximum plant height, E-W spread, N-S spread, canopy volume, highest number of fruits, weight of fruit and yield kg/plant and t/ha was recorded in T9 i.e. I3F3 drip irrigation at 90% ER and fertigation with 80% of RDF followed by I3F2 drip irrigation at 90% ER and fertigation with 70% of RDF.

Keywords: sweet orange, fertigation, irrigation, interactions

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Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

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Authors: Zsolt Kotroczó, Katalin Juhos, Áron Béni, Gábor Várbíró, Tamás Kocsis, István Fekete

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Soil organic matter plays an extremely important role in the functioning and regulation processes of ecosystems. It follows that the C content of organic matter in soil is one of the most important indicators of soil fertility. Part of the carbon stored in them is returned to the atmosphere during soil respiration. Climate change and inappropriate land use can accelerate these processes. Our work aimed to determine how soil CO2 emissions change over ten years as a result of organic matter manipulation treatments. With the help of this, we were able to examine not only the effects of the different organic matter intake but also the effects of the different microclimates that occur as a result of the treatments. We carried out our investigations in the area of the Síkfőkút DIRT (Detritus Input and Removal Treatment) Project. The research area is located in the southern, hilly landscape of the Bükk Mountains, northeast of Eger (Hungary). GPS coordinates of the project: 47°55′34′′ N and 20°26′ 29′′ E, altitude 320-340 m. The soil of the area is Luvisols. The 27-hectare protected forest area is now under the supervision of the Bükki National Park. The experimental plots in Síkfőkút were established in 2000. We established six litter manipulation treatments each with three 7×7 m replicate plots established under complete canopy cover. There were two types of detritus addition treatments (Double Wood and Double Litter). In three treatments, detritus inputs were removed: No Litter No Roots plots, No Inputs, and the Controls. After the establishment of the plots, during the drier periods, the NR and NI treatments showed the highest CO2 emissions. In the first few years, the effect of this process was evident, because due to the lack of living vegetation, the amount of evapotranspiration on the NR and NI plots was much lower, and transpiration practically ceased on these plots. In the wetter periods, the NL and NI treatments showed the lowest soil respiration values, which were significantly lower compared to the Co, DW, and DL treatments. Due to the lower organic matter content and the lack of surface litter cover, the water storage capacity of these soils was significantly limited, therefore we measured the lowest average moisture content among the treatments after ten years. Soil respiration is significantly influenced by temperature values. Furthermore, the supply of nutrients to the soil microorganisms is also a determining factor, which in this case is influenced by the litter production dictated by the treatments. In the case of dry soils with a moisture content of less than 20% in the initial period, litter removal treatments showed a strong correlation with soil moisture (r=0.74). In very dry soils, a smaller increase in moisture does not cause a significant increase in soil respiration, while it does in a slightly higher moisture range. In wet soils, the temperature is the main regulating factor, above a certain moisture limit, water displaces soil air from the soil pores, which inhibits aerobic decomposition processes, and so heterotrophic soil respiration also declines.

Keywords: soil biology, organic matter, nutrition, DIRT, soil respiration

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Authors: Praveen S. Muthukumarana, Achala C. Aponso

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A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

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Authors: Weiwei Xie, Yichao Li

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The initial installation of the umbilical can affect the subsequent installation process and final installation. Meanwhile, the design of both ends of the ultra-deep water dynamic umbilical (UDWDU), as well as the design of the surface unit and the subsea production system connected by UDWDU,], varies in different oil and gas fields. To optimize the installation process of UDWDU, on the basis of the summary and analysis of the surface-end and the subsea-end design of UDWDU and the mainstream construction resources, the method of initial installation from the surface unit side or the subsea production system side of UDWDU is studied, and each initiation installation method is pointed out if some difficulties that may be encountered.

Keywords: dynamic umbilical, ultra-deep-water, initial installation, installation process

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Authors: Manpreet Kaur

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The effect of external and internal exposure in outdoor and indoor environment can be significantly gauged by natural radionuclides. Therefore, it is a consequential to approximate the level of radionuclide contents in soil samples of any area and the risks associated with it. Rate of radon emerging from soil is also one of the prominent parameters for the assessment of radon levels in environmental. In present study, natural radionuclide contents viz. ²³²Th, ²³⁸U and ⁴⁰K and radon/thoron exhalation rates were evaluated operating thallium doped sodium iodide gamma radiation detector and advanced Smart Rn Duo technique in the soil samples from 30 villages of Jammu district, Jammu and Kashmir, India. Radon flux rate was also measured by using surface chamber technique. Results obtained with two different methods were compared to investigate the cause of emanation factor in the soil profile. The radon mass exhalation rate in the soil samples has been found varying from 15 ± 0.4 to 38 ± 0.8 mBq kg⁻¹ h⁻¹ while thoron surface exhalation rate has been found varying from 90 ± 22 to 4880 ± 280 Bq m⁻² h⁻¹. The mean value of radium equivalent activity (99 ± 27 Bq kg⁻¹) was appeared to be well within the admissible limit of 370 Bq kg⁻¹ suggested by Organization for Economic Cooperation and Development (2009) report. The values of various parameters related to radiological hazards were also calculated and all parameters have been found to be well below the safe limits given by various organizations. The outcomes pointed out that region was protected from danger as per health risks effects associated with these radionuclide contents is concerned.

Keywords: absorbed dose rate, exhalation rate, human health, radionuclide

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Authors: Ayyaz Hussain, Tariq Sadad

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Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy.

Keywords: breast cancer, DCNN, KNN, mammography

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Authors: W. Thongwat, B. Terakulsatit

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The study area is Kham Sakaesaeng District in Nakhon Ratchasima Province, the south section of Northeastern Thailand, located in the Lower Khorat-Ubol Basin. This region is the one of saline soil area, located in a dry plateau and regularly experience standing with periods of floods and alternating with periods of drought. Especially, the drought in the summer season causes the major saline soil and saline water problems of this region. The general cause of dry land salting resulted from salting on irrigated land, and an excess of water leading to the rising water table in the aquifer. The purpose of this study is to determine the relationship of physical and chemical properties between the soil and groundwater. The soil and groundwater samples were collected in both rainy and summer seasons. The content of pH, electrical conductivity (EC), total dissolved solids (TDS), chloride and salinity were investigated. The experimental result of soil and groundwater samples show the slightly pH less than 7, EC (186 to 8,156 us/cm and 960 to 10,712 us/cm), TDS (93 to 3,940 ppm and 480 to 5,356 ppm), chloride content (45.58 to 4,177,015 mg/l and 227.90 to 9,216,736 mg/l), and salinity (0.07 to 4.82 ppt and 0.24 to 14.46 ppt) in the rainy and summer seasons, respectively. The distribution of chloride content and salinity content were interpolated and displayed as a map by using ArcMap 10.3 program, according to the season. The result of saline soil and brined groundwater in the study area were related to the low-lying topography, drought area, and salt-source exposure. Especially, the Rock Salt Member of Maha Sarakham Formation was exposed or lies near the ground surface in this study area. During the rainy season, salt was eroded or weathered from the salt-source rock formation and transported by surface flow or leached into the groundwater. In the dry season, the ground surface is dry enough resulting salt precipitates from the brined surface water or rises from the brined groundwater influencing the increasing content of chloride and salinity in the ground surface and groundwater.

Keywords: environmental geology, soil salinity, geochemistry, groundwater hydrology

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Authors: Safitri Nur Wulandari, M. Ivan Adi Perdana, Prathisto L. Panuntun Unggul, R. Dary Wira Mahadika

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In road construction on the soft soil, we need a soil improvement method to improve the soil bearing capacity of the land base so that the soil can withstand the traffic loads. Most of the land in Indonesia has a soft soil, where soft soil is a type of clay that has the consistency of very soft to medium stiff, undrained shear strength, Cu <0:25 kg/cm2, or the estimated value of NSPT <5 blows/ft. This study focuses on the analysis of the effect on preloading load (embarkment) to the amount of settlement ratio on the under of embarkment that will impact on the building cracks around of embarkment. The method used in this research is a superposition method for embarkment distribution on 27 locations with undisturbed soil samples at some borehole point in Java and Kalimantan, Indonesia. Then correlating the results of settlement plate monitoring on the field with Asaoka method. The results of settlement plate monitoring taken from an embarkment of Ahmad Yani airport in Semarang on 32 points. Where the value of Cc (index compressible) soil data based on some laboratory test results, while the value of Cc is not tested obtained from empirical formula Ardhana and Mochtar, 1999. From this research, the results of the field monitoring showed almost the same results with an empirical formulation with the standard deviation of 4% where the formulation of the empirical results of this analysis obtained by linear formula. Value empirical linear formula is to determine the effect of compression heap area as high as 4,25 m is 3,1209x + y = 0.0026 for the slope of the embankment 1: 8 for the same analysis with an initial height of embankment on the field. Provided that at the edge of the embankment settlement worth is not equal to 0 but at a quarter of embankment has a settlement ratio average 0.951 and at the edge of embankment has a settlement ratio 0,049. The influence areas around of embankment are approximately 1 meter for slope 1:8 and 7 meters for slope 1:2. So, it can cause the building cracks, to build in sustainable development.

Keywords: building cracks, influence area, settlement plate, soft soil, empirical formula, embankment

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Authors: Mandlenkosi G. R. Mahlobo, Peter A. Olubambi, Philippe Refait

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The aim of this study was to use voltammetry as a method to understand the behaviour of steel in unsaturated soil in the presence of cathodic protection (CP). Three carbon steel coupons were buried in artificial soil wetted at 65-70% of saturation for 37 days. All three coupons were left at open circuit potential (OCP) for the first seven days in the unsaturated soil before CP, which was only applied on two of the three coupons at the protection potential -0.8 V vs Cu/CuSO₄ for the remaining 30 days of the experiment. Voltammetry was performed weekly on the coupon without CP, while electrochemical impedance spectroscopy (EIS) was performed daily to monitor and correct the applied CP potential from the ohmic drop. Voltammetry was finally performed on the last day on the coupons under CP. All the voltammograms were modeled with mathematical equations in order to compute the electrochemical parameters and subsequently deduced the corrosion rate of the steel coupons. For the coupon without CP, the corrosion rate was determined at 300 µm/y. For the coupons under CP, the residual corrosion rate under CP was estimated at 12 µm/y while the corrosion rate of the coupons, after interruption of CP, was estimated at 25 µm/y. This showed that CP was efficient due to two effects: a direct effect from the decreased potential and an induced effect associated with the increased interfacial pH that promoted the formation of a protective layer on the steel surface.

Keywords: carbon steel, cathodic protection, voltammetry, unsaturated soil, Raman spectroscopy

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: Bin Feng, Zhulin Zong

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Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

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Authors: Lorenzo Gontrani, Marilena Carbone, Domenica Tommasa Donia, Elvira Maria Bauer, Pietro Tagliatesta

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One of the fields where DES shows remarkable added values is the synthesis Of inorganic materials, in particular nanoparticles. In this field, the higher- ent and highly-tunable nano-homogeneities of DES structure give origin to a marked templating effect, a precious role that has led to the recent bloom of a vast number of studies exploiting these new synthesis media to prepare Nanomaterials and composite structures of various kinds. In this contribution, the most recent developments in the field will be reviewed, and some ex-citing examples of novel metal oxide nanoparticles syntheses using non-toxic type-IV Deep Eutectic Solvents will be described. The prepared materials possess nanometric dimensions and show flower-like shapes. The use of the pre- pared nanoparticles as fluorescent materials for the detection of various contaminants is under development.

Keywords: metal deep eutectic solvents, nanoparticles, inorganic synthesis, type IV DES, lamellar

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Authors: E. Tarantino, G. Disciglio, G. Gatta, L. Frabboni, A. Libutti, A. Tarantino

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Worldwide, about two-thirds of industrial and domestic wastewater effluent is discharged without treatment, which can cause contamination and eutrophication of the water. In particular, for Mediterranean countries, irrigation with treated wastewater would mitigate the water stress and support the agricultural sector. Changing global weather patterns will make the situation worse, due to increased susceptibility to drought, which can cause major environmental, social, and economic problems. The study was carried out in open field in an intensive agricultural area of the Apulian region in Southern Italy where freshwater resources are often scarce. As well as providing a water resource, irrigation with treated wastewater represents a significant source of nutrients for soil–plant systems. However, the use of wastewater might have further effects on soil. This study thus investigated the long-term impact of irrigation with reclaimed agro-industrial wastewater on the chemical characteristics of the soil. Two crops (processing tomato and broccoli) were cultivated in succession in Stornarella (Foggia) over four years from 2012 to 2016 using two types of irrigation water: groundwater and tertiary treated agro-industrial wastewater that had undergone an activated sludge process, sedimentation filtration, and UV radiation. Chemical analyses were performed on the irrigation waters and soil samples. The treated wastewater was characterised by high levels of several chemical parameters including TSS, EC, COD, BOD₅, Na⁺, Ca²⁺, Mg²⁺, NH₄-N, PO₄-P, K⁺, SAR and CaCO₃, as compared with the groundwater. However, despite these higher levels, the mean content of several chemical parameters in the soil did not show relevant differences between the irrigation treatments, in terms of the chemical features of the soil.

Keywords: agro-industrial wastewater, broccoli, long-term re-use, tomato

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Ali̇ Si̇nan Soğanci

Abstract:

The cement stabilization is one of the ground improvement method applied worldwide to increase the strength of clayey soils. The using of cement has got lots of advantages compared to other stabilization methods. Cement stabilization can be done quickly, the cost is low and creates a more durable structure with the soil. Cement can be used in the treatment of a wide variety of soils. The best results of the cement stabilization were seen on silts as well as coarse-grained soils. In this study, blocks of clay were taken from the Apa-Hotamış conveyance channel route which is 125km long will be built in Konya that take the water with 70m3/sec from Mavi tunnel to Hotamış storage. Firstly, the index properties of clay samples were determined according to the Unified Soil Classification System. The experimental program was carried out on compacted soil specimens with 0%, 7 %, 15% and 30 % cement additives and the results of unconfined compression strength were discussed. The results of unconfined compression tests indicated an increase in strength with increasing cement content.

Keywords: cement stabilization, unconfined compression test, clayey soils, unified soil classification system.

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Authors: Ayodele Adelusi Oyedeji

Abstract:

The study was carried out under a controlled environment with the aim of examining remediation of crude oil polluted soil. The germination rate, heights and girths, number of leaves and nodulation was determined following standard procedures. Some physicochemical (organic matter, pH, nitrogen, phosphorous, potassium, calcium, magnesium and sodium) characteristics of soil used were determined using standard protocols. Results showed that at varying concentration of crude oil i.e 0 ml, 25 ml, 50 ml, 75 ml and 100 ml, Leuceana leucocephala had germination rate of 92%, 90%, 84%, 62% and 56% respectively, mean height of 73.70cm, 58.30cm, 49.50cm, 46.45cm and 41.80cm respectively after 16 weeks after planting (WAP), mean girth of 0.54mm, 0.34mm, 0.33mm, 0.21mm and 0.19mm respectively at 16 WAP, number of nodules 18, 10, 10, 6 and 2 respectively and number of leaves 24.00, 16.00, 13.00, 10.00 and 6.00 respectively. The organic matter, pH, nitrogen, phosphorous, potassium, calcium, magnesium, and sodium decreased with the increase in the concentration of crude oil. Furthermore, as the concentration of crude oil increased the germination rate, height, girth, and number of leaves and nodules decreased, suggesting the effect of crude oil on Leuceana leucocephala. The plant withstands the varying concentration of the crude oil means that it could be used for the remediation of crude oil contaminated soil in the Niger Delta region of Nigeria.

Keywords: ecosystem conservation, Leuceana leucocephala, phytoremediation, soil pollution

Procedia PDF Downloads 114

Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

Abstract:

As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: Enayatallah Najari, Ali Noorzad, Mehdi Siavoshnia

Abstract:

One of the most decent and low-cost options in soft clayey soil improvement is using stone columns to reduce the settlement and increase the bearing capacity which is used for different ways to do this in various projects with diverse conditions. In the current study, it is tried to evaluate this improvement method in 4 different weak soils with diverse properties like specific gravity, permeability coefficient, over consolidation ratio (OCR), poison’s ratio, internal friction angle and bulk modulus by using ABAQUS 3D finite element software. Increment and decrement impacts of each mentioned factor on settlement and lateral displacement of weak soil beds are analyzed. In analyzed models, the properties related to sand columns and geosynthetic cover are assumed to be constant with their optimum values, and just soft clayey soil parameters are considered to be variable. It’s also demonstrated that OCR value can play a determinant role in soil resistance.

Keywords: stone columns, geosynthetic, finite element, 3D analysis, soft soils

Procedia PDF Downloads 363

Authors: B. Palanikumaran, N. Kanagaraj, M. Sangareswari, V. Sailaja, Kapil Sihag

Abstract:

The present study aimed to estimate the carbon sequestration potential of major forest types present in the foothills of Nilgiri biosphere reserve. The total biomass carbon stock was estimated in tropical thorn forest, tropical dry deciduous forest and tropical moist deciduous forest as 14.61 t C ha⁻¹ 75.16 t C ha⁻¹ and 187.52 t C ha⁻¹ respectively. The density and basal area were estimated in tropical thorn forest, tropical dry deciduous forest, tropical moist deciduous forest as 173 stems ha⁻¹, 349 stems ha⁻¹, 391 stems ha⁻¹ and 6.21 m² ha⁻¹, 31.09 m² ha⁻¹, 67.34 m² ha⁻¹ respectively. The soil carbon stock of different forest ecosystems was estimated, and the results revealed that tropical moist deciduous forest (71.74 t C ha⁻¹) accounted for more soil carbon stock when compared to tropical dry deciduous forest (31.80 t C ha⁻¹) and tropical thorn forest (3.99 t C ha⁻¹). The tropical moist deciduous forest has the maximum annual leaf litter which was 12.77 t ha⁻¹ year⁻¹ followed by 6.44 t ha⁻¹ year⁻¹ litter fall of tropical dry deciduous forest. The tropical thorn forest accounted for 3.42 t ha⁻¹ yr⁻¹ leaf litter production. The leaf litter carbon stock of tropical thorn forest, tropical dry deciduous forest and tropical moist deciduous forest found to be 1.02 t C ha⁻¹ yr⁻¹ 2.28 t⁻¹ C ha⁻¹ yr⁻¹ and 5.42 t C ha⁻¹ yr⁻¹ respectively. The results explained that decomposition percent at the soil surface in the following order.tropical dry deciduous forest (77.66 percent) > tropical thorn forest (69.49 percent) > tropical moist deciduous forest (63.17 percent). Decomposition percent at soil subsurface was studied, and the highest decomposition percent was observed in tropical dry deciduous forest (80.52 percent) followed by tropical moist deciduous forest (77.65 percent) and tropical thorn forest (72.10 percent). The decomposition percent was higher at soil subsurface. Among the three forest type, tropical moist deciduous forest accounted for the highest bacterial (59.67 x 105cfu’s g⁻¹ soil), actinomycetes (74.87 x 104cfu’s g⁻¹ soil) and fungal (112.60 x10³cfu’s g⁻¹ soil) population. The overall observation of the study helps to conclude that, the tropical moist deciduous forest has the potential of storing higher carbon content as biomass with the value of 264.68 t C ha⁻¹ and microbial populations.

Keywords: basal area, carbon sequestration, carbon stock, Nilgiri biosphere reserve

Procedia PDF Downloads 173

Authors: Adamu Zubairu Mohammed, Fransis Oforum Arimoro, Salihu Maikudi Ibrahim, Y. I. Auta, T. I. Arowosegbe, Y. Abdullahi

Abstract:

The present study was conducted to evaluate the effect of depth on the distribution of zooplankton and some physicochemical parameters in Tungan Kawo Lake (Wushishi dam). Water and zooplankton samples were collected from the surface, 3.0 meters deep and 6.0 meters deep, for a period of 24 hours for six months. Standard procedures were adopted for the determination of physicochemical parameters. Results have shown significant differences in the pH, DO, BOD Hardness, Na, and Mg. A total of 1764 zooplankton were recorded, comprising 35 species, with cladocera having 18 species (58%), 14 species of copepoda (41%), 3 species of diptera (1.0%). Results show that more of the zooplankton were recorded in the 3.0 meters-deep region compared to the two other depts and a significant difference was observed in the distribution of Ceriodaphnia dubia, Daphnia laevis, and Leptodiaptomus coloradensis. Though the most abundant zooplankton was recorded in the 3.0 meters deep, Leptodiaptomus coloradesnsis, which was observed in the 6.0 meters deep as the most individual observed, this was followed by Daphnia laevis. Canonical correspondence analysis between physicochemical parameters and the zooplankton indicated a good relationship in the Lake. Ceriodaphnia dubia was found to have a good association with oxygen, sodium, and potassium, while Daphnia laevis and Leptodiaptomus coloradensis are in good relationship with magnesium and phosphorus. It was generally observed that this depth does not have much influence on the distribution of zooplankton in Wushishi Lake.

Keywords: zooplankton, standard procedures, canonical correspondence analysis, Wushishi, canonical, physicochemical parameter

Procedia PDF Downloads 95

Authors: Thomas F. Ducey, Kristin M. Trippe, James A. Ippolito, Jeffrey M. Novak, Mark G. Johnson, Gilbert C. Sigua

Abstract:

The Oronogo-Duenweg mining belt –approximately 20 square miles around the Joplin, Missouri area– is a designated United States Environmental Protection Agency Superfund site due to lead-contaminated soil and groundwater by former mining and smelting operations. Over almost a century of mining (from 1848 to the late 1960’s), an estimated ten million tons of cadmium, lead, and zinc containing material have been deposited on approximately 9,000 acres. Sites that have undergone remediation, in which the O, A, and B horizons have been removed along with the lead contamination, the exposed C horizon remains incalcitrant to revegetation efforts. These sites also suffer from poor soil microbial activity, as measured by soil extracellular enzymatic assays, though 16S ribosomal ribonucleic acid (rRNA) indicates that microbial diversity is equal to sites that have avoided mine-related contamination. Soil analysis reveals low soil organic carbon, along with high levels of bio-available zinc, that reflect the poor soil fertility conditions and low microbial activity. Our study looked at the use of several materials to restore and remediate these sites, with the goal of improving soil health. The following materials, and their purposes for incorporation into the study, were as follows: manure-based biochar for the binding of zinc and other heavy metals responsible for phytotoxicity, locally sourced biosolids and compost to incorporate organic carbon into the depleted soils, effective microorganisms harvested from nearby pristine sites to provide a stable community for nutrient cycling in the newly composited 'soil material'. Our results indicate that all four materials used in conjunction result in the greatest benefit to these mine-impacted soils, based on above ground biomass, microbial biomass, and soil enzymatic activities.

Keywords: locally effective microorganisms, biochar, remediation, reclamation

Procedia PDF Downloads 223