Search results for: deep soil mix

Authors: Hossein Hassani, Ali Rezaei

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In recent years, the study of soil contamination problems surrounding mines and smelting plants has attracted some serious attention of the environmental experts. These elements due to the non- chemical disintegration and nature are counted as environmental stable and durable contaminants. Variability of these contaminants in the soil and the time and financial limitation for the favorable environmental application, in order to reduce the risk of their irreparable negative consequences on environment, caused to apply the favorable grading of these contaminant for the further success of the risk management processes. In this study, we use the contaminants factor risk indices, average concentration, enrichment factor and geoaccumulation indices for evaluating the metal contaminant of including Pb, Ni, Se, Mo and Zn in the soil of Sarcheshmeh copper mine area. For this purpose, 120 surface soil samples up to the depth of 30 cm have been provided from the study area. And the metals have been analyzed using ICP-MS method. Comparison of the heavy and potentially toxic elements concentration in the soil samples with the world average value of the uncontaminated soil and shale average indicates that the value of Zn, Pb, Ni, Se and Mo is higher than the world average value and only the Ni element shows the lower value than the shale average. Expert opinions on the relative importance of each indicators were used to assign a final weighting of the metals and the heavy metals were ranked using the TOPSIS approach. This allows us to carry out efficient environmental proceedings, leading to the reduction of environmental ricks form the contaminants. According to the results, Ni, Pb, Mo, Zn, and Se have the highest rate of risk contamination in the soil samples of the study area.

Keywords: contamination coefficient, geoaccumulation factor, TOPSIS techniques, Sarcheshmeh copper complex

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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: Harsha B. R., K. S. Anil Kumar

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A profile was dug at the University of Agricultural Sciences, Bangalore, where grapes were intensively cultivated for 25 years on the dimension of 1.5 × 1.5 × 1.5 m. Demarcation was done on the basis of texture, structure, colour, and the details like depth, texture, colour, consistency, rock fragments, presence of mottles, and structure were recorded and studied according to standard performa of soil profile description. Horizons noticed were Ap, Bt1, Bt2, Bt3, Bt4C, Bt5C and BC with respective depths of 0-13, 13-37, 37-60, 60-78, 78-104, 104-130 and 130-151+ cm. The reddish-brown colour was noticed in Ap, Bt1, and Bt2 horizons. The sub-angular blocky structure was observed in all the layers with slightly acid in reaction. Clear and abrupt smooth boundaries were present between two respective layers with clayey texture in all the horizons except the Ap horizon, which was clay loam in texture. Variegated soil colours and iron concretions were observed in Bt4, Bt5, and BC horizons. Clay skins were observed in Bt and BC horizons. Soils were of highly friable consistency for grapes cultivation.

Keywords: soil morphology, horizons, clay skins, consistency, vineyards

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Authors: Fang Bo Yu, Li Bo Guan

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Biogas slurry is a low-cost source of crop nutrients and can offer extra benefits to soil fertility and fruit quality. However, its current utilization mode and low content of active ingredients limit its application scale. In this report, one growing season field research was conducted to assess the effects of concentrated biogas slurry on soil property, tomato fruit quality, and composition of the microflora in both non-rhizosphere and rhizosphere soils. The results showed that application of concentrated slurry could cause significant changes to tomato cultivation, including increases in organic matter, available N, P, and K, total N, and P, electrical conductivity, and fruit contents of amino acids, protein, soluble sugar, β-carotene, tannins, and vitamin C, together with the R/S ratios and the culturable counts of bacteria, actinomycetes, and fungi in soils. It could be concluded as the application is a practicable means in tomato production and might better service the sustainable agriculture in the near future.

Keywords: concentrated slurry, fruit quality, soil fertility, sustainable agriculture

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Authors: A. Sawangsuriya, T. B. Edil

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Microstructure and fabric of soils play an important role on structural properties e.g. stiffness and strength of compacted earthwork. Traditional quality control monitoring based on moisture-density tests neither reflects the variability of soil microstructure nor provides a direct assessment of structural property, which is the ultimate objective of the earthwork quality control. Since stiffness and strength are sensitive to soil microstructure and fabric, any independent test methods that provide simple, rapid, and direct measurement of stiffness and strength are anticipated to provide an effective assessment of compacted earthen materials’ uniformity. In this study, the soil stiffness gauge (SSG) and the dynamic cone penetrometer (DCP) were respectively utilized to measure and monitor the stiffness and strength in companion with traditional moisture-density measurements of various earthen materials used in Thailand road construction projects. The practical earthwork quality control criteria are presented herein in order to assure proper earthwork quality control and uniform structural property of compacted earthworks.

Keywords: dynamic cone penetrometer, moisture content, quality control, relative compaction, soil stiffness gauge, structural properties

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Authors: C. D. Papanikolaou, V. A. Giouvanis, E. A. Karatasiou, D. S. Dimakas, M. A. Sakellariou-Makrantonaki

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Irrigation is one of the most important cultivation practices for each crop, especially in areas where rainfall is enough to cover the crop water needs. In such areas, the farmers must irrigate in order to achieve high economical results. The precise irrigation scheduling contributes to irrigation water saving and thus a valuable natural resource is protected. Under this point of view, in the experimental field of the Laboratory of Agricultural Hydraulics of the University of Thessaly, a research was conducted during the growing season of 2012 in order to evaluate the growth, seed and oil production of sunflower as well as the water saving, by applying different methods of irrigation scheduling. Three treatments in four replications were organized. These were: a) surface drip irrigation where the irrigation scheduling based on the Penman-Monteith (PM) method (control); b) surface drip irrigation where the irrigation scheduling based on a soil moisture sensor (SMS); and c) surface drip irrigation, where the irrigation scheduling based on a soil potential sensor (WM).

Keywords: irrigation, energy production, soil moisture sensor, sunflower, water saving

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Authors: Asma Shaheen, Javed Iqbal

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The water resources are depleting due to an intrusion of industrial pollution. There are clusters of industries including leather tanning, textiles, batteries, and chemical causing contamination. These industries use bulk quantity of water and discharge it with toxic effluents. The penetration of heavy metals through irrigation from industrial effluent has toxic effect on soil and groundwater. There was strong positive significant correlation between all the heavy metals in three media of industrial effluent, soil and groundwater (P < 0.001). The metal to the metal association was supported by dendrograms using cluster analysis. The geospatial variability was assessed by using geographically weighted regression (GWR) and pollution model to identify the simulation of carcinogenic elements in soil and groundwater. The principal component analysis identified the metals source, 48.8% variation in factor 1 have significant loading for sodium (Na), calcium (Ca), magnesium (Mg), iron (Fe), chromium (Cr), nickel (Ni), lead (Pb) and zinc (Zn) of tannery effluent-based process. In soil and groundwater, the metals have significant loading in factor 1 representing more than half of the total variation with 51.3 % and 53.6 % respectively which showed that pollutants in soil and water were driven by industrial effluent. The cumulative eigen values for the three media were also found to be greater than 1 representing significant clustering of related heavy metals. The results showed that heavy metals from industrial processes are seeping up toxic trace metals in the soil and groundwater. The poisonous pollutants from heavy metals turned the fresh resources of groundwater into unusable water. The availability of fresh water for irrigation and domestic use is being alarming.

Keywords: groundwater, geostatistical, heavy metals, industrial effluent

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Authors: Fatima Mehmood, Khalid Farooq, Rabeea Bakhtawer

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For geotechnical engineers, one of the most important properties of soil to consider in various stability analyses is its shear strength which is governed by a number of factors. The objective of this research is to ascertain the effect of compaction and degree of saturation on the shear strength of fine-grained soil. For this purpose, three different dry densities such as in-situ, maximum standard proctor, and maximum modified proctor, were determined for the sandy clay soil. The soil samples were then prepared to keep dry density constant and varying degrees of saturation. These samples were tested for (UU) unconsolidated undrained shear strength in triaxial compression tests. The decrease in shear strength was observed with the decrease in density and increase in the saturation. The values of the angle of internal friction followed the same trend. However, the change in cohesion with the increase in saturation showed a different behavior, analogous to the compaction curve.

Keywords: compaction, degree of saturation, dry density, geotechnical investigation, laboratory testing, shear strength

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Authors: Geeta Negi, Pankaj, Anjana Srivastava, Anita Sharma

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In the present study, the presence of endosulfan, imidacloprid, carbendazim, in the soil /vegetables/cereals and water samples was observed in agriculture fields of Uttarakhand. In view of biodegradation of these pesticides, nine bacterial isolates were recovered from the soil samples of the fields which tolerated endosulfan, imidacloprid, carbendazim from 100 to 200 µg/ml. Three bacterial consortia used for in vitro bioremediation experiments were three bacterial isolates for carbendazim, imidacloprid and endosulfan, respectively. Maximum degradation (87 and 83%) of α and β endosulfan respectively was observed in soil slurry by consortium. Degradation of Imidacloprid and carbendazim under similar conditions was 88.4 and 77.5% respectively. FT-IR analysis of biodegraded samples of pesticides in liquid media showed stretching of various bonds. GC-MS of biodegraded endosulfan sample in soil slurry showed the presence of non-toxic intermediates. A pot trial with Bacterial treatments lowered down the uptake of pesticides in onion plants.

Keywords: biodegradation, carbendazim, consortium, endosulfan

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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: S. Bouharati, F. Allag, M. Belmahdi, M. Bounechada

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In terms of ecology forecast effects of desertification, the purpose of this study is to develop a predictive model of growth and adaptation of species in arid environment and bioclimatic conditions. The impact of climate change and the desertification phenomena is the result of combined effects in magnitude and frequency of these phenomena. Like the data involved in the phytopathogenic process and bacteria growth in arid soil occur in an uncertain environment because of their complexity, it becomes necessary to have a suitable methodology for the analysis of these variables. The basic principles of fuzzy logic those are perfectly suited to this process. As input variables, we consider the physical parameters, soil type, bacteria nature, and plant species concerned. The result output variable is the adaptability of the species expressed by the growth rate or extinction. As a conclusion, we prevent the possible strategies for adaptation, with or without shifting areas of plantation and nature adequate vegetation.

Keywords: climate changes, dry soil, phytopathogenicity, predictive model, fuzzy logic

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Authors: Chakkrit Poonpakdee, Jing-Hua Tzen, Ya-Zhen Huang, Yao-Tung Lin

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Potassium (K) is a known macro nutrient and essential element for plant growth. Single leaching and modified sequential extraction schemes have been developed to estimate the relative phase associations of soil samples. The sequential extraction process is a step in analyzing the partitioning of metals affected by environmental conditions, but it is not a tool for estimation of K bioavailability. While, traditional single leaching method has been used to classify K speciation for a long time, it depend on its availability to the plants and use for potash fertilizer recommendation rate. Clay mineral in soil is a factor for controlling soil fertility. The change of the micro-structure of clay minerals during various environment (i.e. swelling or shrinking) is characterized using Transmission X-Ray Microscopy (TXM). The objective of this study are to 1) compare the distribution of K speciation between single leaching and sequential extraction process 2) determined clay particle flocculation structure before/after suspension with K+ using TXM. Four tropical soil samples: farming without K fertilizer (10 years), long term applied K fertilizer (10 years; 168-240 kg K2O ha-1 year-1), red soil (450-500 kg K2O ha-1 year-1) and forest soil were selected. The results showed that the amount of K speciation by single leaching method were high in mineral K, HNO3 K, Non-exchangeable K, NH4OAc K, exchangeable K and water soluble K respectively. Sequential extraction process indicated that most K speciations in soil were associated with residual, organic matter, Fe or Mn oxide and exchangeable fractions and K associate fraction with carbonate was not detected in tropical soil samples. In farming long term applied K fertilizer and red soil were higher exchangeable K than farming long term without K fertilizer and forest soil. The results indicated that one way to increase the available K (water soluble K and exchangeable K) should apply K fertilizer and organic fertilizer for providing available K. The two-dimension of TXM image of clay particles suspension with K+ shows that the aggregation structure of clay mineral closed-void cellular networks. The porous cellular structure of soil aggregates in 1 M KCl solution had large and very larger empty voids than in 0.025 M KCl and deionized water respectively. TXM nanotomography is a new technique can be useful in the field as a tool for better understanding of clay mineral micro-structure.

Keywords: potassium, sequential extraction process, clay mineral, TXM

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

Abstract:

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: Abdul Malik Dawlatzai, Shafiqullah Rahmani

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Plant cultivars of the same species differ in their Fe efficiency. This paper studied the Fe influx and root-shoot relations of Fe at different growth stages in wheat. The four wheat cultivars (HD 2967, PDW 233, PBW 550 and PDW 291) were grown in pots in Badam Bagh agricultural researching farm, Kabul under two Fe treatments: (i) 0 mg Fe kg⁻¹ soil (soil with 2.7 mg kg⁻¹ of DTPA-extractable Fe) and (ii) 50 mg Fe kg⁻¹ soil. Root length (RL), shoot dry matter (SDM), Fe uptake, and soil parameters were measured at tillering and anthesis. Application of Fe significantly increased RL, root surface area, SDM, and Fe uptake in all wheat cultivars. Under Fe deficiency, wheat cv. HD 2967 produced 90% of its maximum RL and 75% of its maximum SDM. However, PDW 233 produced only 69% and 60%, respectively. Wheat cultivars HD 2967, and PDW 233 exhibited the highest and lowest value of root surface area and Fe uptake, respectively. The concentration difference in soil solution Fe between bulk soil and root surface (ΔCL) was maximum in wheat cultivar HD 2967, followed by PBW 550, PDW 291, and PDW 233. More depletion at the root surface causes steeper concentration gradients, which result in a high influx and transport of Fe towards root. Fe influx in all the wheat cultivars increased with the Fe application, but the increase was maximum, i.e., 4 times in HD 2967 and minimum, i.e., 2.8 times in PDW 233. It can be concluded that wheat cultivars HD 2967 and PBW 550 efficiently utilized Fe as compared to other cultivars. Additionally, iron efficiency of wheat cultivars depends upon uptake of each root segment, i.e., the influx, which in turn depends on depletion of Fe in the rhizosphere during vegetative phase and higher utilization efficiency of acquired Fe during reproductive phase that governs the ultimate grain yield.

Keywords: Fe efficiency, Fe influx, Fe uptake, Rhizosphere

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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: Sandeep Kumar, Mahesh Kumar Jat, Rajib Sarkar

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Soil reinforced with randomly distributed fibers is an attractive means to improve the performance of soil in a cost effective manner. Static and dynamic characterization of fiber reinforced soil have become important to evaluate adequate performance for all classes of geotechnical engineering problems. Present study investigates the behaviour of fiber reinforced cohesionless soil through numerical simulation of triaxial specimen. The numerical model has been validated with the existing literature of laboratory triaxial compression testing. A parametric study has been done to find out optimum fiber content for shear resistance. Cyclic triaxial testing has been simulated and the stress-strain response of fiber-reinforced sand has been examined considering different combination of fiber contents. Shear modulus values and damping values of fiber-reinforced sand are evaluated. It has been observed from results that for 1.0 percent fiber content shear modulus increased 2.28 times and damping ratio decreased 4.6 times. The influence of amplitude of cyclic strain, confining pressure and frequency of loading on the dynamic properties of fiber reinforced sand has been investigated and presented.

Keywords: damping, fiber reinforced soil, numerical modelling, shear modulus

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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: 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: 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

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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: Koray U. Erbas

Abstract:

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: Henok Hailemariam, Frank Wuttke

Abstract:

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: 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

Procedia PDF Downloads 90

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

Abstract:

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: Manpreet Kaur

Abstract:

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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