Search results for: deep soil

Authors: Ouaaz Oum Essaad, Melbouci Bachir

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The experimental study conducted has a dual purpose: to valorize the clay material in the road domain and improve the lift of the shape layers by strengthening with plastic waste (in the form of aggregates). To do this, six mixtures of Clay and sand of different percentages were studied: 100% Clay, 95% Clay + 05% Sand, 90% Clay + 10% Sand, 85% Clay + 15% Sand, 80% Clay + 20% Sand, 75% Clay + 25% Sand. Proctor compaction and simple compression tests have been carried out on mixtures (sand + clay + plastic waste). The results obtained show a clear evolution of the characteristics of the Proctor test and the compressive strength of the mixtures according to the different types and percentages of the recycled plastic Plasticity and consistency index are important parameters that play a role in the toughness of plastic soil.

Keywords: valorization, recycling, soil mixture, mechanical tests

Procedia PDF Downloads 95

Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

Procedia PDF Downloads 71

Authors: Frieda Eivazi, Nikita L. Mullings

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Soils are recipient for surfactants in herbicide formulations. Surfactants entering the soil environment can possibly disrupt different chemical, physical and biological interactions. Therefore, it is critical that we understand the fate, behavior and transport of surfactants upon entering the soil. A comprehensive study was conducted to examine effect of surfactants on nutrient uptake, microbial community, and enzyme activity. The research was conducted in the greenhouse growing corn (Zea mays) as a test plant in a factorial experiment (three surfactants at two different rates with control, and three herbicides) organized as randomized blocked design. Surfactants evaluated were Activator 90, Agri-Dex, and Thrust; herbicides were glyphosate, atrazine, and bentazon. Treatments examined were surfactant only, herbicide only, and surfactant + herbicide combinations. Corn was planted in fertilized soils (silt loam and silty clay) with moisture content maintained at the field capacity for optimum growth. This paper will report results of above mentioned treatments on acid phosphatase, beta-glucosidase, arylsulfatase, beta-glucosaminidase, and dehydrogenase activities. In general, there were variations in the enzyme activities with some inhibition and some being enhanced by the treatments. Activator 90 appeared to have the highest inhibitory effect on enzymatic activities. Atrazine application significantly decreased the activities of acid phosphatase, beta-glucosidase, and dehydrogenase in both soils; however, combination of Atrazine + Agridex increased the acid phosphatase activity while significantly inhibiting the other enzyme activities in soils. It was concluded that long-term field studies are needed to validate changes in nutrient uptake, microbial community and enzyme activities due to surfactant-herbicide combination effects.

Keywords: herbicides, nutrient cycling, soil enzymes, surfactant

Procedia PDF Downloads 245

Authors: G. K. Madhu, S. Bhaskar, M. S. Dinesh, R. Manii, C. A. Srinivasamurthy

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A field experiment was conducted in the premises of M/s Sri Chamundeshwari Sugars Ltd., Bharathinagar, Mandya District Pvt. Ltd., during 2014 to study the effect of sugar mill effluent (SME) on growth, yield and soil properties of ratoon cane with eight treatments replicated thrice using RCBD design. Significantly higher growth parameters like cane height (249.77 cm) and number of tillers per clump (12.22) were recorded in treatment which received cycle of 3 irrigations with freshwater + 1 irrigation with sugar mill effluent + RDF as compared to other treatments. Significantly lower growth attributes were recorded in treatment which received irrigation with sugar mill effluent alone. Significantly higher cane yield (104. 93 t -1) was recorded in treatment which received cycle of 3 irrigations with freshwater + 1 irrigation with sugar mill effluent + RDF as compared to other treatments. Significantly lower cane yield (87.40 t ha-1) was observed in treatment which received irrigation with sugar mill effluent alone. Soil properties like pH (7.84) was higher in treatment receiving Alternate irrigation with freshwater and sugar mill effluent + RDF. But EC was significantly higher in treatment which received Cycle of1 irrigation with freshwater + 2 irrigations with sugar mill effluent + RDF as compared to other treatments.

Keywords: sugar mill effluent, sugarcane, irrigation, cane yield

Procedia PDF Downloads 326

Authors: Wann-Yun Shieh, Chin-Man Wang, Ya-Cheng Shieh

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This study aims to develop an objective scoring system to evaluate hand-eye coordination and hand dexterity for Parkinson’s disease. This system contains three boards, and each of them is implemented with the sensors to sense a user’s finger operations. The operations include the peg test, the block test, and the blind block test. A user has to use the vision, hearing, and tactile abilities to finish these operations, and the board will record the results automatically. These results can help the physicians to evaluate a user’s reaction, coordination, dexterity function. The results will be collected to a cloud database for further analysis and statistics. A researcher can use this system to obtain systematic, graphic reports for an individual or a group of users. Particularly, a deep learning model is developed to learn the features of the data from different users. This model will help the physicians to assess the Parkinson’s disease symptoms by a more intellective algorithm.

Keywords: deep learning, hand-eye coordination, reaction, hand dexterity

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Authors: Melanie Redding

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Historic widespread nitrate contamination of the Lower Yakima Valley aquifer in Washington State initiated a community-based effort to reduce nitrate concentrations to below-drinking water standards. This group commissioned studies on characterizing local nitrogen sources, deep soil assessments, drinking water, and assessing nitrate concentrations at the water table. Nitrate is the most prevalent groundwater contaminant with common sources from animal and human waste, fertilizers, plants and precipitation. It is challenging to address groundwater contamination when common sources, such as agriculture, on-site sewage systems, and animal production, are widespread. Remediation is not possible, so mitigation is essential. The Lower Yakima Valley is located over 175,000 acres, with a population of 56,000 residents. Approximately 25% of the population do not have access to safe, clean drinking water, and 20% of the population is at or below the poverty level. Agriculture is the primary economic land-use activity. Irrigated agriculture and livestock production make up the largest percentage of acreage and nitrogen load. Commodities include apples, grapes, hops, dairy, silage corn, triticale, alfalfa and cherries. These commodities are important to the economic viability of the residents of the Lower Yakima Valley, as well as Washington State. Mitigation of nitrate in groundwater is challenging. The goal is to ensure everyone has safe drinking water. There are no easy remedies due to the extensive and pervasiveness of the contamination. Monitoring at the water table indicates that 45% of the 30 spatially distributed monitoring wells exceeded the drinking water standard. This indicates that there are multiple sources that are impacting water quality. Washington State has several areas which have extensive groundwater nitrate contamination. The groundwater in these areas continues to degrade over time. However, the Lower Yakima Valley is being successful in addressing this health issue because of the following reasons: the community is engaged and committed; there is one common goal; there has been extensive public education and outreach to citizens; and generating credible data using sound scientific methods. Work in this area is continuing as an ambient groundwater monitoring network is established to assess the condition of the aquifer over time. Nitrate samples are being collected from 170 wells, spatially distributed across the aquifer. This research entails quarterly sampling for two years to characterize seasonal variability and then continue annually afterward. This assessment will provide the data to statistically determine trends in nitrate concentrations across the aquifer, over time. Thirty-three of these wells are monitoring wells that are screened across the aquifer. The water quality from these wells are indicative of activities at the land surface. Additional work is being conducted to identify land use management practices that are effective in limiting nitrate migration through the soil column. Tracking nitrate in the soil column every season is an important component of bridging land-use practices with the fate and transport of nitrate through the subsurface. Patience, tenacity, and the ability to think outside the box are essential for dealing with widespread nitrate contamination of groundwater.

Keywords: community, groundwater, monitoring, nitrate

Procedia PDF Downloads 172

Authors: Mehran Naghizaderokni, Asscar Janalizadechobbasty

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This paper explores probabilistic method for assessing the liquefaction potential of sandy soils. The current simplified methods for assessing soil liquefaction potential use a deterministic safety factor in order to determine whether liquefaction will occur or not. However, these methods are unable to determine the liquefaction probability related to a safety factor. A solution to this problem can be found by reliability analysis.This paper presents a reliability analysis method based on the popular certain liquefaction analysis method. The proposed probabilistic method is formulated based on the results of reliability analyses of 190 field records and observations of soil performance against liquefaction. The results of the present study show that confidence coefficient greater and smaller than 1 does not mean safety and/or liquefaction in cadence for liquefaction, and for assuring liquefaction probability, reliability based method analysis should be used. This reliability method uses the empirical acceleration attenuation law in the Chalos area to derive the probability density distribution function and the statistics for the earthquake-induced cyclic shear stress ratio (CSR). The CSR and CRR statistics are used in continuity with the first order and second moment method to calculate the relation between the liquefaction probability, the safety factor and the reliability index. Based on the proposed method, the liquefaction probability related to a safety factor can be easily calculated. The influence of some of the soil parameters on the liquefaction probability can be quantitatively evaluated.

Keywords: liquefaction, reliability analysis, chalos area, civil and structural engineering

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Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo

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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.

Keywords: conversational AI, chatbot, dialog management, semantic analysis

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Authors: Yu-Min Chang, Kuo-Sheng Tsai, Hung-Te Tsai, Chia-Hsin Li

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This paper presents the early-warning lights classification management system for industrial parks promoted by the Taiwan Environmental Protection Administration (EPA) since 2011, including the definition of each early-warning light, objectives, action program and accomplishments. All of the 151 industrial parks in Taiwan were classified into four early-warning lights, including red, orange, yellow and green, for carrying out respective pollution management according to the monitoring data of soil and groundwater quality, regulatory compliance, and regulatory listing of control site or remediation site. The Taiwan EPA set up a priority list for high potential polluted industrial parks and investigated their soil and groundwater qualities based on the results of the light classification and pollution potential assessment. In 2011-2013, there were 44 industrial parks selected and carried out different investigation, such as the early warning groundwater well networks establishment and pollution investigation/verification for the red and orange-light industrial parks and the environmental background survey for the yellow-light industrial parks. Among them, 22 industrial parks were newly or continuously confirmed that the concentrations of pollutants exceeded those in soil or groundwater pollution control standards. Thus, the further investigation, groundwater use restriction, listing of pollution control site or remediation site, and pollutant isolation measures were implemented by the local environmental protection and industry competent authorities; the early warning lights of those industrial parks were proposed to adjust up to orange or red-light. Up to the present, the preliminary positive effect of the soil and groundwater quality management system for industrial parks has been noticed in several aspects, such as environmental background information collection, early warning of pollution risk, pollution investigation and control, information integration and application, and inter-agency collaboration. Finally, the work and goal of self-initiated quality management of industrial parks will be carried out on the basis of the inter-agency collaboration by the classified lights system of early warning and management as well as the regular announcement of the status of each industrial park.

Keywords: industrial park, soil and groundwater quality management, early-warning lights classification, SOP for reporting and treatment of monitored abnormal events

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

Procedia PDF Downloads 129

Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

Procedia PDF Downloads 287

Authors: M. Boumaiza, S. Mohamadi, B. Moussai

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In this article the study of the seismic response of an earth dam with sealing walls has been made by introducing the effect of the change of position and depth of the sealing wall and the effect of non-linear behavior of soil of the foundation by taking into account the variation of the viscous damping and shear modulus in each layer of soil on the seismic response of the dam. As a case study, we take the Algerian dam Kef-Eddir which lies in the far west of the territory of the Wilaya of Tipaza (wadi Eddamous), classified according to the RPA 2003 as a high seismicity zone (zone III). With a height of 71m above the foundation and a width of 478m. The seismic event applied to the rock, is the earthquake of Chenoua (29 October, 1989), with a magnitude Mw=6 that hit the region.

Keywords: earth dam, earthquake, sealing walls, viscous damping

Procedia PDF Downloads 602

Authors: Majid Yazdandoust

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This study, tries to suggest a design method based on displacement using finite difference numerical modeling in reinforcing soil retaining wall with steel strip. In this case, dynamic loading characteristics such as duration, frequency, peak ground acceleration, geometrical characteristics of reinforced soil structure and type of the site are considered to correct the pseudo static method and finally introduce the pseudo static coefficient as a function of seismic performance level and peak ground acceleration. For this purpose, the influence of dynamic loading characteristics, reinforcement length, height of reinforced system and type of the site are investigated on seismic behavior of reinforcing soil retaining wall with steel strip. Numerical results illustrate that the seismic response of this type of wall is highly dependent to cumulative absolute velocity, maximum acceleration, and height and reinforcement length so that the reinforcement length can be introduced as the main factor in shape of failure. Considering the loading parameters, mechanically stabilized earth wall parameters and type of the site showed that the used method in this study leads to most efficient designs in comparison with other methods which are generally suggested in cods that are usually based on limit-equilibrium concept. The outputs show the over-estimation of equilibrium design methods in comparison with proposed displacement based methods here.

Keywords: pseudo static coefficient, seismic performance design, numerical modeling, steel strip reinforcement, retaining walls, cumulative absolute velocity, failure shape

Procedia PDF Downloads 482

Authors: Saud S. AL Oud

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Soil contamination can have dire consequences, such as loss of ecosystem and agricultural productivity, diminished food chain quality, tainted water resources, economic loss, and human and animal illness. In recent years, attention has focused on the development of in situ immobilization methods that are generally less expensive and disruptive to the natural landscape, hydrology, and ecosystems than are conventional excavation treatments, and disposal methods. Soft, inexpensive, and efficient agents were used in the present research to immobilize Pb in polluted sandy soil. Five agents, either naturally occurring or chemically prepared, were used for this purpose. These agents include; iron ore (72% Fe2O3), cement, a mixture of calcite and shale rich in aluminum (CASH), and two chemically prepared amorphous materials of Al- and Fe-gel. These agents were selected due to their ability to specifically adsorb heavy metals onto their surface OH functional groups, which provide permanent immobilization of metal pollutants and reduce the fraction that is potentially mobile or bioavailable. The efficiency of these agents in immobilizing Pb were examined in a laboratory experiment, in which two rates (0.5 and 1.0 %) of tested agents were added to the polluted soils containing total contents of Pb ranging from 17.4-49.8 mg/kg. The results show that all immobilizing agents were succeed in minimizing the mobile form of Pb as extracted by 0.5 N HNO3. The extracted Pb decreased with increasing addition rate of immobilizing agents. At addition rate of 0.5%, HNO3 extractable-Pb varied widely depending on the agents type and were found to represent 21-67% of the initial values. All agents were able to reduce mobile Pb to levels lower than that (2.0 mg/kg) reported for non polluted soil, particularly for soils had initials of mobile Pb less than 10 mg/kg. Both iron oxide and CASH had the highest efficiency in immobilizing Pb, followed by cement, then amorphous materials of Fe and Al hydroxides.

Keywords: soil, synthetic chemical, lead, immobilization, polluted

Procedia PDF Downloads 233

Authors: R. Ziaie Moayed, M. Alibolandi

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Dynamic behavior of soil are evaluated relative to a number of factors including: strain level, density, number of cycles, material type, fine content, geosynthetic inclusion, saturation, and effective stress. This paper investigate the dynamic behavior of saturated reinforced sand under cyclic stress condition. The cyclic triaxial tests are conducted on remolded specimens under various CSR which reinforced by different arrangement of non-woven geotextile. Aforementioned tests simulate field reinforced saturated deposits during earthquake or other cyclic loadings. This analysis revealed that the geotextile arrangement played dominant role on dynamic soil behavior and as geotextile close to top of specimen, the liquefaction resistance increased.

Keywords: dynamic behavior, reinforced sand, triaxial test, non-woven geotextile

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Authors: Ashraf Eid

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Many formulas are set to estimate the pile load capacity based on the in-situ pile load tests. However, discrepancy is obvious between the results of these applications. Some formulas are more adequate than others with respect to soil formation and its characteristics. In this research, attempts were undertaken to evaluate the adequacy of the most well-known formulas based on a series of pile load tests carried out in Port Said city in the northeast of Egypt for major residential projects. Comparisons were undertaken between the different formulas supported by the results of in-situ Cone Penetration Tests (CPT). Based on this study, a guide for engineers for using the proper formula can be adopted with consideration of soil type and characteristics. The Egyptian Code which relies on the results of some formulas is involved in the study as a guiding aspect in the pile design.

Keywords: pile load formula, load test of piles, CPT, Egyptian code

Procedia PDF Downloads 142

Authors: Mehdi Shekarbeigi

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The consolidation of loose substrates as well as substrate layers through promoting stabilizing materials is one of the most commonly used road construction techniques. Cement, lime, and flax, as well as asphalt emulsion, are common materials used for soil stabilization to enhance the soil’s strength and durability properties. Cement could be simply used to stabilize permeable materials such as sand in a relatively short time threshold. In this research, typical Portland cement is selected for the stabilization of isotropic sand; the effect of static and cyclic loading on the behavior of these soils has been examined with various percentages of Portland cement. Thus, firstly, a soil’s general features are investigated, and then static tests, including direct cutting, density and single axis tests, and California Bearing Ratio, are performed on the samples. After that, the dynamic behavior of cement on silica sand with the same grain size is analyzed. These experiments are conducted on cement samples of 3, 6, and 9 of the same rates and ineffective limiting pressures of 0 to 1200 kPa with 200 kPa steps of the face according to American Society for Testing and Materials D 3999 standards. Also, to test the effect of temperature on molds and frost samples, 0, 5, 10, and 20 are carried out during 0, 5, 10, and 20-second periods. Results of the static tests showed that increasing the cement percentage increases the soil density and shear strength. The single-axis compressive strength increase is higher for samples with higher cement content and lower densities. The results also illustrate the relationship between single-axial compressive strength and cement weight parameters. Results of the dynamic experiments indicate that increasing the number of loading cycles and melting and freezing cycles enhances permeability and decreases the applied pressure. According to the results of this research, it could be stated that samples containing 9% cement have the highest amount of shear modulus and, therefore, decrease the permeability of soil. This amount could be considered as the optimal amount. Also, the enhancement of effective limited pressure from 400 to 800kPa increased the shear modulus of the sample by an average of 20 to 30 percent in small strains.

Keywords: cement, isotropic sands, static load, three-axis cycle, melting and freezing cycles

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

Procedia PDF Downloads 123

Authors: Lucie Bielská, Lucia Škulcová, Martina Hvězdová, Jakub Hofman, Zdeněk Šimek

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The currently used pesticides represent a broad group of chemicals with various physicochemical and environmental properties which input has reached 2×106 tons/year and is expected to even increases. From that amount, only 1% directly interacts with the target organism while the rest represents a potential risk to the environment and human health. Despite being authorized and approved for field applications, the effects of pesticides in the environment can differ from the model scenarios due to the various pesticide-soil interactions and resulting modified fate and behavior. As such, a direct monitoring of pesticide residues and evaluation of their impact on soil biota, aquatic environment, food contamination, and human health should be performed to prevent environmental and economic damages. The present project focuses on fluvisols as they are intensively used in the agriculture but face to several environmental stressors. Fluvisols develop in the vicinity of rivers by the periodic settling of alluvial sediments and periodic interruptions to pedogenesis by flooding. As a result, fluvisols exhibit very high yields per area unit, are intensively used and loaded by pesticides. Regarding the floods, their regular contacts with surface water arise from serious concerns about the surface water contamination. In order to monitor pesticide residues and assess their environmental and biological impact within this project, 70 fluvisols were sampled over the Czech Republic and analyzed for the total and bioaccessible amounts of 40 various pesticides. For that purpose, methodologies for the pesticide extraction and analysis with liquid chromatography-mass spectrometry technique were developed and optimized. To assess the biological risks, both the earthworm bioaccumulation tests and various types of passive sampling techniques (XAD resin, Chemcatcher, and silicon rubber) were optimized and applied. These data on chemical analysis and bioavailability were combined with the results of soil analysis, including the measurement of basic physicochemical soil properties as well detailed characterization of soil organic matter with the advanced method of diffuse reflectance infrared spectrometry. The results provide unique data on the residual levels of pesticides in the Czech Republic and on the factors responsible for increased pesticide residue levels that should be included in the modeling of pesticide fate and effects.

Keywords: currently used pesticides, fluvisoils, bioavailability, Quechers, liquid-chromatography-mass spectrometry, soil properties, DRIFT analysis, pesticides

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Authors: Jiang Zhenbo, Ishikura Ryohei, Yasufuku Noriyuki

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Improvement of soft clay deposits by the combination of surface stabilization and floating type cement-treated columns is one of the most popular techniques worldwide. On the basis of one dimensional consolidation model, a time-dependent skin friction model for the column-soil interaction is proposed. The nonlinear relationship between column shaft shear stresses and effective vertical pressure of the surrounding soil can be described in this model. The influence of column-soil surface roughness can be represented using a roughness coefficient R, which plays an important role in the design of column length. Based on the homogenization method, a part of floating type improved ground will be treated as an unimproved portion, which with a length of αH1 is defined as a time-dependent equivalent skin friction length. The compression settlement of this unimproved portion can be predicted only using the soft clay parameters. Apart from calculating the settlement of this composited ground, the load transfer mechanism is discussed utilizing model tests. The proposed model is validated by comparing with calculations and laboratory results of model and ring shear tests, which indicate the suitability and accuracy of the solutions in this paper.

Keywords: floating type improved foundation, time-dependent skin friction, roughness, consolidation

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Authors: A. Rabin, G. Mazor, I. Ladizhenski, R. Shneck, Z.

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The hardness and hardenability of AISI D2 cold work tool steel with conventional quenching (CQ), deep cryogenic quenching (DCQ) and rapid deep cryogenic quenching heat treatments caused by temporary porous coating based on magnesium sulfate was investigated. Each of the cooling processes was examined from the perspective of the full process efficiency, heat flux in the austenite-martensite transformation range followed by characterization of the temporary porous layer made of magnesium sulfate using confocal laser scanning microscopy (CLSM), surface and core hardness and hardenability using Vickr’s hardness technique. The results show that the cooling rate (CR) at the austenite-martensite transformation range have a high influence on the hardness of the studied steel.

Keywords: AISI D2, controllable cooling, magnesium sulfate coating, rapid cryogenic heat treatment, temporary porous layer

Procedia PDF Downloads 126

Authors: Boualla Nabila

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The problems created by the water in Civil Engineering are sometimes neglected or often badly posed when they are not completely ignored, and yet they are fundamental as regards both the conditions of execution of the worksites and the stability. Several damages were caused by the infiltration of water in the soils, in particular in clay regions which can swell under the effect of an increase in their water content as in the case of the Oued Tlelat clay which is made up of yellow-colored marly clays and red-colored El Maleh area. This study was carried out on soil from a site, located near the city of Oran and the city of Ain Tmouchent (northern Algeria) where we encounter many problems of cracking of buildings and bottom uplift of excavations. The study consists first of all in determining the mechanical and physical characteristics of the clay, namely the parameters of sheer, simple compression, and that of the odometer. Then the study focused on a comparison of the influence of water type on the mechanical and physical properties of swelling clay soil.

Keywords: clay, water, liquidity limit, plastic limit

Procedia PDF Downloads 96

Authors: Mengdi Zhang, Xufeng Liu, Zhenji Gao, Ying Li, Zhu Rao, Yi Huang

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As an open system, the earth is constantly undergoing the transformation and release of matter and energy. Fault zones are relatively discontinuous and fragile geological structures, and the release of material and energy inside the Earth is strongest in relatively weak fault zones. Earthquake events frequently occur in fault zones and are closely related to tectonic activity in these zones. In earthquake precursor observation, monitoring the spatiotemporal changes in the release of related gases near fault zones (such as radon gas, hydrogen, carbon dioxide, helium), and analyzing earthquake precursor anomalies, can be effective means to forecast the occurrence of earthquake events. Radon gas, as an inert radioactive gas generated during the decay of uranium and thorium, is not only a indicator for monitoring tectonic and seismic activity, but also an important topic for ecological and environmental health, playing a crucial role in uranium exploration. At present, research on soil radon gas mainly focuses on the measurement of soil gas concentration and flux in fault zone profiles, while research on the correlation between spatiotemporal concentration changes in the same region and its geological background is relatively little. In this paper, Tangshan area in north China is chosen as research area. An analysis was conducted on the seismic geological background of Tangshan area firstly. Then based on quantitative analysis and comparison of measurement radon concentrations of 2023 and 2010, combined with the study of seismic activity and environmental changes during the time period, the spatiotemporal distribution characteristics and influencing factors were explored, in order to analyze the gas emission characteristics of the Tangshan fault zone and its relationship with fault activity, which aimed to be useful for the future work in earthquake monitor of Tangshan area.

Keywords: radon, Northern China, soil gas, earthquake

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Authors: S. Joseph Antony, Z. K. Jahanger

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Soils are subjected to cyclic loading in situ in situations such as during earthquakes and in the compaction of pavements. Investigations on the local scale measurement of the displacements of the grain and failure patterns within the soil bed under the cyclic loading conditions are rather limited. In this paper, using the digital particle image velocimetry (DPIV), local scale displacement fields of a dense sand medium interacting with a rigid footing are measured under the plane-strain condition for two commonly used types of cyclic loading, and the quasi-static loading condition for the purposes of comparison. From the displacement measurements of the grains, the failure envelopes of the sand media are also presented. The results show that, the ultimate cyclic bearing capacity (qultcyc) occurred corresponding to a relatively higher settlement value when compared with that of under the quasi-static loading. For the sand media under the cyclic loading conditions considered here, the displacement fields in the soil media occurred more widely in the horizontal direction and less deeper along the vertical direction when compared with that of under the quasi-static loading. The 'dead zone' in the sand grains beneath the footing is identified for all types of the loading conditions studied here. These grain-scale characteristics have implications on the resulting bulk bearing capacity of the sand media in footing-sand interaction problems.

Keywords: cyclic loading, DPIV, settlement, soil-structure interactions, strip footing

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: A. B. Alarape, O. D. Aba

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The use of legume tree pruning as mulch in agroforestry system is a common practice to maintain soil organic matter and improve soil fertility in the tropics. The study was conducted to determine the influence of agroforestry trees leafy biomass and nitrogen fertilizer on crop growth rate and relative growth rate of maize. The experiments were laid out as 3 x 4 x 2 factorial in a split-split plot design with three replicates. Control, biomass species (Parkia biglobosa and Albizia lebbeck) as main plots were considered, rates of nitrogen considered include (0, 40, 80, 120 kg N ha⁻¹) as sub-plots, and maize varieties (DMR-ESR-7 and 2009 EVAT) were used as sub-sub plots. Data were analyzed using descriptive and inferential statistics (ANOVA) at α = 0.05. Incorporation of leafy biomass was significant in 2015 on Relative Growth Rate (RGR), while nitrogen application was significant on Crop Growth Rate (CGR). 2009 EVAT had higher CGR in 2015 at 4-6 and 6-8 WAP. Incorporation of Albizia leaves enhanced the growth of maize than Parkia leaves. Farmers are, therefore, encouraged to use Albizia leaves as mulch to enrich their soil for maize production and most especially, in case of availability of inorganic fertilizers. Though, production of maize with biomass and application of 120 kg N ha⁻¹ will bring better growth of maize.

Keywords: agroforestry trees, fertilizer, growth, incorporation, leafy biomass

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Authors: Mihai-Ionut Firescu, Mark A. P. Carson

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We present a case of inferior vena cava agenesis (IVCA) associated with bilateral deep venous thrombosis (DVT) in a patient with Beckwith-Wiedemann syndrome (BWS). In adult patients with BWS presenting with bilateral lower limb oedema, specific aetiological factors should be considered. These include cardiomyopathy and intraabdominal tumours. Congenital malformations of the IVC, through causing relative venous stasis, can lead to lower limb oedema either directly or indirectly by favouring lower limb venous thromboembolism; however, they are yet to be reported as an associated feature of BWS. Given its life-threatening potential, the prompt initiation of treatment for bilateral DVT is paramount. In BWS patients, however, this can prove more complicated. Due to overgrowth, the above-average birth weight can continue throughout childhood. In this case, the patient’s weight reached 170 kg, impacting on anticoagulation choice, as direct oral anticoagulants have a limited evidence base in patients with a body mass above 120 kg. Furthermore, the presence of IVCA leads to a long-term increased venous thrombosis risk. Therefore, patients with IVCA and bilateral DVT warrant specialist consideration and may benefit from multidisciplinary team management, with hematology and vascular surgery input. Conclusion: Here, we showcased a rare cause for bilateral lower limb oedema, respectively bilateral deep venous thrombosis complicating IVCA in a patient with Beckwith-Wiedemann syndrome. The importance of this case lies in its novelty, as the association between IVC agenesis and BWS has not yet been described. Furthermore, the treatment of DVT in such situations requires special consideration, taking into account the patient’s weight and the presence of a significant, predisposing vascular abnormality.

Keywords: Beckwith-Wiedemann syndrome, bilateral deep venous thrombosis, inferior vena cava agenesis, venous thromboembolism

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Authors: Grzegorz Szaciłowski, Marta Konop, Małgorzata Dymecka, Jakub Ośko

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In 1996, the European Council Directive 96/29/EURATOM pointed phosphate fertilizers to have a potentially negative influence on the environment from the radiation protection point of view. Fertilizers along with irrigation and crop rotation were the milestones that allowed to increase agricultural productivity. Firstly based on natural materials such as compost, manure, fish processing waste, etc., and since the 19th century created synthetically, fertilizers caused a boom in crop yield and helped to propel global food production, especially after World War II. In this work the concentrations of ²¹⁰Po, ²²⁶Ra, ²³²Th, ⁴⁰K, and ¹³⁷Cs in selected fertilizers and soil samples were determined. The results were used to calculate the annual addition of natural radionuclides and increment of the external radiation exposure caused by the use of studied fertilizers. Soils intended for different types of crops were sampled in early spring when no vegetation had occurred yet. Analysed fertilizers were those with which the soil was previously fertilized. For gamma radionuclides, a high purity germanium detector GX3520 from Canberra was used. The polonium concentration was determined by radiochemical separation followed by measurement by means of alpha spectrometry. The spectrometer used in this study was equipped with 450 cm² PIPS detector from Canberra. Obtained results showed significant differences in radionuclide composition between phosphate and nitrogenous fertilizers (e.g. the radium equivalent activity for phosphate fertilizer was 207.7 Bq/kg in comparison to <5.6 Bq/kg for nitrogenous fertilizer). The calculated increase of external radiation exposure due to use of phosphate fertilizer ranged between 3.4 and 5.4 nG/h, which represents up to 10% of the polish average outdoor exposure due to terrestrial gamma radiation (45 nGy/h).

Keywords: ²¹⁰Po, alpha spectrometry, exposure, gamma spectrometry, phosphate fertilizer, soil

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Authors: Ethan James

Abstract:

Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Maryna Barushka, Aleh Meshyk

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The research analyzes absolute maximum soil temperatures registered at 36 gauge stations in Belarus from 1950 to 2013. The main method applied in the research is cartographic, in particular, trend surface analysis. Warming that had never been so long and intensive before started in 1988. The average temperature in January and February of that year exceeded the norm by 7-7.5 С, in March and April by 3-5С. In general, that year, as well as the year of 2008, happened to be the hottest ones in the whole period of instrumental observation. Yearly average air temperature in Belarus in those years was +8.0-8.2 С, which exceeded the norm by 2.0 – 2.2 С. The warming has been observed so far. The only exception was in 1996 when the yearly average air temperature in Belarus was below normal by 0.5 С. In Belarus the value of trend line of standard temperature deviation in the warmest months (July-August) has been positive for the past 25 years. In 2010 absolute maximum air and soil temperature exceeded the norm at 15 gauge stations in Belarus. The structure of natural processes includes global, regional, and local constituents. Trend surface analysis of the investigated characteristics makes it possible to determine global, regional, and local components. Linear trend surface shows the occurrence of weather deviations on a global scale, outside Belarus. Maximum soil temperature appears to be growing in the south-west direction with the gradient of 5.0 С. It is explained by the latitude factor. Polynomial trend surfaces show regional peculiarities of Belarus. Extreme temperature regime is formed due to some factors. The prevailing one is advection of turbulent flow of the ground layer of the atmosphere. In summer influence of the Azores High producing anticyclones is great. The Gulf Stream current forms the values of temperature trends in a year period. The most intensive flow of the Gulf Stream in the second half of winter and the second half of summer coincides with the periods of maximum temperature trends in Belarus. It is possible to estimate a local component of weather deviations in the analysis of the difference in values of the investigated characteristics and their trend surfaces. Maximum positive deviation (up to +4 С) of averaged soil temperature corresponds to the flat terrain in Pripyat Polesie, Brest Polesie, and Belarusian Poozerie Area. Negative differences correspond to the higher relief which partially compensates extreme heat regime of soils. Another important factor for maximum soil temperature in these areas is peat-bog soils with the least albedo of 8-15%. As yearly maximum soil temperature reaches 40-60 С, this could be both negative and positive factors for Belarus’s environment and economy. High temperature causes droughts resulting in crops dying and soil blowing. On the other hand, vegetation period has lengthened thanks to bigger heat resources, which allows planting such heat-loving crops as melons and grapes with appropriate irrigation. Thus, trend surface analysis allows determining global, regional, and local factors in accumulating heat in the soils of Belarus.

Keywords: soil, temperature, trend surface analysis, warming

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