Search results for: deep soil mix

Authors: Arian Hosseini, Mahmudul Hasan

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To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

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Authors: Seyed Abolhasan Naeini, Mohammad Hossein Zade, E. Izadi, M. Hossein Zade

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The different seismic behavior of liquid storage tanks rather than conventional structures makes their responses more complicated. Uplifting and excessive settlement due to liquid sloshing are the most frequent damages in cylindrical liquid tanks after shell bucking failure modes. As a matter of fact, uses of liquid storage tanks because of the simple construction on compact layer of soil as a foundation are very conventional, but in some cases need to retrofit are essential. The tank seismic behavior can be improved by modifying dynamic characteristic of tank with verifying seismic loads as well as retrofitting and improving base ground. This paper focuses on a typical steel tank on loose, medium and stiff sandy soil and describes an evaluation of displacement of the tank before and after retrofitting. The Abaqus program was selected for its ability to include shell and structural steel elements, soil-structure interaction, and geometrical nonlinearities and contact type elements. The result shows considerable decreasing in settlement and uplifting in the case of retrofitted tank. Also, by increasing shear strength parameter of soil, the performance of the liquid storage tank under the case of seismic load increased.

Keywords: steel tank, soil-structure, sandy soil, seismic load

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Authors: Falana Yetunde Olaitan, Ijah U. J. J, Solebo Shakirat O.

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Genetically modified crops are already phenomenally successful and are grown worldwide in more than eighteen countries on more than 67 million hectares. Nigeria, in October 2018, approved Bacillus thuringiensis (Bt) cotton and maize; therefore, the need to carry out environmental risk assessment studies. A total of 15 4L octagonal ceramic pots were filled with 4kg of soil and placed on the bench in 2 rows of 10 pots each and the 3rd row of 5 pots, 1st-row pots were used to plant GM cotton seeds, while the 2nd-row pots were used for non-GM cotton seeds and the 3rd row of 5 pots served as control, all in the screen house. Soil samples for metagenomic DNA extraction were collected at random and at the monthly interval after planting at a distance of 2mm from the plant’s root and at a depth of 10cm using a sterile spatula. Soil samples for physicochemical analysis were collected before planting and after harvesting the GM and non-GM crops as well as from the control soil. The DNA was extracted, quantified and sequenced; Sample 1A (DNA from GM cotton Soil at 1st interval) gave the lowest sequence read with 0.853M while sample 2B (DNA from GM cotton Soil at 2nd interval) gave the highest with 5.785M, others gave between 1.8M and 4.7M. The samples treatment were grouped into four, Group 1 (GM cotton soil from 1 to 3 intervals) had between 800,000 and 5,700,000 strains of microbes (SOM), Group 2 (non GM cotton soil from 1 to 3 intervals) had between 1,400,600 and 4,200,000 SOM, Group 3 (control soil) had between 900,000 and 3,600,000 SOM and Group 4 (initial soil) had between 3,700,000 and 4,000,000 SOM. The microbes observed were predominantly bacteria (including archaea), fungi, dark matter alongside protists and phages. The predominant bacterial groups were the Terrabacteria (Bacillus funiculus, Bacillus sp.), the Proteobacteria (Microvirga massiliensis, sphingomonas sp.) and the Archaea (Nitrososphaera sp.), while the fungi were Aspergillus fischeri and Fusarium falciforme. The comparative analysis between groups was done using JACCARD PERMANOVA beta diversity analysis at P-value not more than 0.76 and there was no significant pair found. The pH for initial, GM cotton, non-GM cotton and control soil were 6.28, 6.26, 7.25, 8.26 and the percentage moisture was 0.63, 0.78, 0.89 and 0.82, respectively, while the percentage Nitrogen was observed to be 17.79, 1.14, 1.10 and 0.56 respectively. Other parameters include, varying concentrations of Potassium (0.46, 1,284.47, 1,785.48, 1,252.83 mg/kg) and Phosphorus (18.76, 17.76, 16.87, 15.23 mg/kg) were recorded for the four treatments respectively. The soil consisted mainly of silt (32.09 to 34.66%) and clay (58.89 to 60.23%), reflecting the soil texture as silty – clay. The results were then tested with ANOVA at less than 0.05 P-value and no pair was found to be significant as well. The results suggest that the GM crops have no significant effect on microbial ecology and physicochemical properties of the soil and, in turn, no direct or indirect effects on human health.

Keywords: genetically modified crop, microbial ecology, physicochemical properties, metagenomics, DNA, soil

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Authors: Waddah S. Abdullah, Khaled F. Al-Omari

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Electrokinetic remediation is one of the most influential and effective methods to decontaminate contaminated soils. Electroosmosis and electromigration are the processes of electrochemical extraction of contaminants from soils. The driving force that causes removing contaminants from soils (electroosmosis process or electromigration process) is voltage gradient. Therefore, the electric field distribution throughout the soil domain is extremely important to investigate and to determine the factors that help to establish a uniform electric field distribution in order to make the clean-up process work properly and efficiently. In this study, small-sized passive electrodes (made of graphite) were placed at predetermined locations within the soil specimen, and the voltage drop between these passive electrodes was measured in order to observe the electrical distribution throughout the tested soil specimens. The electrokinetic test was conducted on two types of soils; a sandy soil and a clayey soil. The electrical distribution throughout the soil domain was conducted with different tests properties; and the electrical field distribution was observed in three-dimensional pattern in order to establish the electrical distribution within the soil domain. The effects of density, applied voltages, and degree of saturation on the electrical distribution within the remediated soil were investigated. The distribution of the moisture content, concentration of the sodium ions, and the concentration of the calcium ions were determined and established in three-dimensional scheme. The study has shown that the electrical conductivity within soil domain depends on the moisture content and concentration of electrolytes present in the pore fluid. The distribution of the electrical field in the saturated soil was found not be affected by its density. The study has also shown that high voltage gradient leads to non-uniform electric field distribution within the electroremediated soil. Very importantly, it was found that even when the electric field distribution is uniform globally (i.e. between the passive electrodes), local non-uniformity could be established within the remediated soil mass. Cracks or air gaps formed due to temperature rise (because of electric flow in low conductivity regions) promotes electrical tortuosity. Thus, fracturing or cracking formed in the remediated soil mass causes disconnection of electric current and hence, no removal of contaminant occur within these areas.

Keywords: contaminant removal, electrical tortuousity, electromigration, electroosmosis, voltage distribution

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Authors: Mohammad Saberian, Reza Porhoseini, Mohammad Ali Rahgozar

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Peat is a partially carbonized vegetable tissue which is formed in wet conditions by decomposition of various plants, mosses and animal remains. This restricted definition, including only materials which are entirely of vegetative origin, conflicts with several established soil classification systems. Peat soils are usually defined as soils having more than 75 percent organic matter. Due to this composition, the structure of peat soil is highly different from the mineral soils such as silt, clay and sand. Peat has high compressibility, high moisture content, low shear strength and low bearing capacity, so it is considered to be in the category of problematic. Since this kind of soil is generally found in many countries and various zones, except for desert and polar zones, recognizing this soil is inevitably significant. The objective of this paper is to review the classification of peats based on various properties of peat soils such as organic contents, water content, color, odor, and decomposition, scholars offer various classification systems which Von Post classification system is one of the most well-known and efficient system.

Keywords: peat soil, degree of decomposition, organic content, water content, Von Post classification

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

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Authors: Manthan Patel, Vinit Ahlawat, Anshh Singh Claire, Pijush Samui

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This paper focuses on the analysis of quicksand condition for a dam foundation. The quicksand condition occurs in cohesion less soil when effective stress of soil becomes zero. In a dam, the saturated sediment may appear quite solid until a sudden change in pressure or shock initiates liquefaction. This causes the sand to form a suspension and lose strength hence resulting in failure of dam. A soil profile shows different properties at different points and the values obtained are uncertain thus reliability analysis is performed. The reliability is defined as probability of safety of a system in a given environment and loading condition and it is assessed as Reliability Index. The reliability analysis of dams under quicksand condition is carried by Gaussian Process Regression (GPR). Reliability index and factor of safety relating to liquefaction of soil is analysed using GPR. The results of reliability analysis by GPR is compared to that of conventional method and it is demonstrated that on applying GPR the probabilistic analysis reduces the computational time and efforts.

Keywords: factor of safety, GPR, reliability index, quicksand

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Authors: Xiao-Hua Zhu, Xin Yuan, Yi-Ran Zhao

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High-cadmium pollution in rice is a serious problem in many parts of China. Many kinds of remediation technologies have been tested and applied in many farmlands. Because of the productive function of the farmland, most technologies are inappropriate due to their destruction to the tillage soil layer. And the large labours and expensive fees of many technologies are also the restrictive factors for their applications. The conception of 'Root Micro-Geochemical Barrier' was proposed to reduce cadmium (Cd) bioavailability and the concentration of the cadmium in rice. Remediation and mitigation techniques were demonstrated on contaminated farmland in the downstream of some mine. According to the rule of rice growth, Cd would be absorbed by the crops in every growth stage, and the plant-absorb efficiency in the first stage of the tillering stage is almost the highest. We should create a method to protect the crops from heavy metal pollution, which could begin to work from the early growth stage. Many materials with repair property get our attention. The materials will create a barrier preventing Cd from being absorbed by the crops during all the growing process because the material has the ability to adsorb soil-Cd and making it losing its migration activity. And we should choose a good chance to put the materials into the crop-growing system cheaply as soon as early. Per plant, rice has a little root system scope, which makes the roots reach about 15cm deep and 15cm wide. So small root radiation area makes it possible for all the Cd approaching the roots to be adsorbed with a small amount of adsorbent. Mixing the remediation materials with the seed-raising soli and adding them to the tillage soil in the process of transplanting seedlings, we can control the soil-Cd activity in the range of roots to reduce the Cd-amount absorbed by the crops. Of course, the mineral materials must have enough adsorptive capacity and no additional pollution. More than 3000 square meters farmlands have been remediated. And on the application of root micro-geochemical barrier, the Cd-concentration in rice and the remediation-cost have been decreased by 90% and 80%, respectively, with little extra labour brought to the farmers. The Cd-concentrations in rice from remediated farmland have been controlled below 0.1 ppm. The remediation of one acre of contaminated cropland costs less than $100. The concept has its advantage in the remediation of paddy field contaminated by Cd, especially for the field with outside pollution sources.

Keywords: cadmium pollution, growth stage, cost, root micro-geochemistry barrier

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Dajana Gašo-Sokač, Valentina Bušić

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Deep eutectic solvents (DES) are liquids composed of two or three safe, inexpensive components, often interconnected by noncovalent hydrogen bonds which produce eutectic mixture whose melting point is lower than that of each component. No data in literature have been found on the quaternization reaction in DES. The use of DES have several advantages: they are environmentally benign and biodegradable, easy for purification and simple for preparation. An environmentally sustainable method for preparing quaternary salts of izonicotinamide and substituted 2-bromoacetophenones was demonstrated here using choline chloride-based DES. The quaternization reaction was carried out by three synthetic approaches: conventional method, microwave and ultrasonic irradiation. We showed that the highest yields were obtained by the microwave method.

Keywords: deep eutectic solvents, izonicotinamide salts, microwave synthesis, ultrasonic irradiation

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Authors: Lidia Dzierzbicka-Glowacka, Anna Lemieszek, Mariusz Figiela

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In 2010-2011, the research on zooplankton was conducted in the southern part of the Baltic Sea to determine seasonal variability in changes occurring throughout the zooplankton in 2010 and 2011, both in the region of Gdańsk Deep, and in the western part of Gdańsk Bay. The research in the sea showed that the taxonomic composition of holoplankton in the southern part of the Baltic Sea was similar to that recorded in this region for many years. The maximum values of abundance and biomass of zooplankton both in the Deep and the Bay of Gdańsk were observed in the summer season. Copepoda dominated in the composition of zooplankton for almost the entire study period, while rotifers occurred in larger numbers only in the summer 2010 in the Gdańsk Deep as well as in May and July 2010 in the western part of Gdańsk Bay, and meroplankton – in April 2011.

Keywords: Baltic Sea, composition, Gdańsk Bay, zooplankton

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Authors: Pholosho M. Kgopa, Phatu W. Mashela

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Due to high incidents of marginal land in Limpopo Province, South Africa, and increasing demand for arable land, small-holder farmers tend to continuously cultivate the same fields and at the same time, applying fertilisers to improve yields for meeting local food security. These practices might have an impact on the distribution of trace and essential elements. Therefore, the objective of this investigation was to assess the distribution of essential elements and trace metals in a continuously cultivated and fertilised field, at the University of Limpopo Experimental Farm. Three fields, 3 ha each were identified as continuously cultivated (CC), moderately cultivated (MC) and virgin fields (VF). Each field was divided into 12 equal grids of 50 m × 50 m for sampling. A soil profile was opened in each grid, where soil samples were collected from 0-20; 20-40 and 40-60; 60-80 and 80-100 cm depths for analysis. Samples were analysed for soil texture, pH, electrical conductivity, organic matter content, selected essential elements (Ca, P and Mg), Na and trace elements (Cu, Fe, Ni, and Zn). Results suggested that most of the variables were vertically different, with high concentrations of the test elements except for magnesium. Soil pH in depth 0-20 cm was high (6.44) in CC when compared to that in VF (5.29), but lower than that of MC (7.84). There were no distinctive vertical trends of the variables, except for Mg, Na, and K which displayed a declining trend at 40-60 cm depth when compared to the 0-20 cm depth. Concentrations of Fe, Cu, Zn, and Ni were generally low which might be due to their indirect relationship with soil pH. Continuous cultivation and fertilisation altered soil chemical properties; which could explain the unproductivity of such fields.

Keywords: over-cultivation, soil chemical properties, vertical distribution, spatial distribution

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Authors: Fawad Ali

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Toxic heavy metal contamination has a negative impact on soil quality which ultimately pollutes the agriculture system. In the current work, we analyzed uptake of various heavy metals by dietary vegetables grown in wastewater irrigated areas of Swabi city. The samples of soil and vegetables were analyzed for heavy metals viz Cd, Cr, Mn, Fe, Ni, Cu, Zn and Pb using Atomic Absorption Spectrophotometer. High levels of metals were found in wastewater irrigated soil and vegetables in the study area. Especially the concentrations of Pb and Cd in the dietary vegetable crossed the permissible level of World Health Organization. Substantial positive correlation was found among the soil and vegetable contamination. Transfer factor for some metals including Cr, Zn, Mn, Ni, Cd and Cu was greater than 0.5 which shows enhanced accumulation of these metals due to contamination by domestic discharges and industrial effluents. Linear regression analysis indicated significant correlation of heavy metals viz Pb, Cr, Cd, Ni, Zn, Cu, Fe and Mn in vegetables with concentration in soil of 0.964 at P≤0.001. Abelmoschus esculentus indicated Health Risk Index (HRI) of Pb >1 in adults and children. The source identification analysis carried out by Principal Component Analysis (PCA) and Cluster Analysis (CA) showed that ground water and soil were being polluted by the trace metals coming out from industries and domestic wastes. Hierarchical cluster analysis (HCA) divided metals into two clusters for wastewater and soil but into five clusters for soil of control area. PCA extracted two factors for wastewater, each contributing 61.086 % and 16.229 % of the total 77.315 % variance. PCA extracted two factors, for soil samples, having total variance of 79.912 % factor 1 and factor 2 contributed 63.889 % and 16.023 % of the total variance. PCA for sub soil extracted two factors with a total variance of 76.136 % factor 1 being 61.768 % and factor 2 being 14.368 %of the total variance. High pollution load index for vegetables in the study area due to metal polluted soil has opened a study area for proper legislation to protect further contamination of vegetables. This work would further reveal serious health risks to human population of the study area.

Keywords: health risk, vegetables, wastewater, atomic absorption sepctrophotometer

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Authors: Khitem Amiri, Mohamed Farah

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Image classification is a challenging task and is gaining lots of interest since it helps us to understand the content of images. Recently Deep Learning (DL) based methods gave very interesting results on several benchmarks. For Hyperspectral images (HSI), the application of DL techniques is still challenging due to the scarcity of labeled data and to the curse of dimensionality. Among other approaches, Deep Belief Network (DBN) based approaches gave a fair classification accuracy. In this paper, we address the problem of the curse of dimensionality by reducing the number of bands and replacing the HSI channels by the channels representing radiometric indices. Therefore, instead of using all the HSI bands, we compute the radiometric indices such as NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), etc, and we use the combination of these indices as input for the Deep Belief Network (DBN) based classification model. Thus, we keep almost all the pertinent spectral information while reducing considerably the size of the image. In order to test our image representation, we applied our method on several HSI datasets including the Indian pines dataset, Jasper Ridge data and it gave comparable results to the state of the art methods while reducing considerably the time of training and testing.

Keywords: hyperspectral images, deep belief network, radiometric indices, image classification

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Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

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Authors: M. Attom, F. Abed, M. Elemam, M. Nazal, N. ElMessalami

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—The main objective of this paper is to study the effect of treated waste-water (TWW) on the compaction and compressibility properties of fine soil. Two types of fine soils (clayey soils) were selected for this study and classified as CH soil and Cl type of soil. Compaction and compressibility properties such as optimum water content, maximum dry unit weight, consolidation index and swell index, maximum past pressure and volume change were evaluated using both tap and treated waste water. It was found that the use of treated waste water affects all of these properties. The maximum dry unit weight increased for both soils and the optimum water content decreased as much as 13.6% for highly plastic soil. The significant effect was observed in swell index and swelling pressure of the soils. The swell indexed decreased by as much as 42% and 33% for highly plastic and low plastic soils, respectively, when TWW is used. Additionally, the swelling pressure decreased by as much as 16% for both soil types. The result of this research pointed out that the use of treated waste water has a positive effect on compaction and compression properties of clay soil and promise for potential use of this water in engineering applications. Keywords—Consolidation, proctor compaction, swell index, treated waste-water, volume change.

Keywords: consolidation, proctor compaction, swell index, treated waste-water, volume change

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Haifa El-Sadi, Maria Elektorowicz, Reed Rushing, Ammar Badawieh, Asif Chaudry

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Clay soils have particular properties that affect the assessment and remediation of contaminated sites. In clay soils, electro-kinetic transport of heavy metals has been carried out. The transport of these metals is predicated on maintaining a low pH throughout the cell, which, in turn, keeps the metals in the pore water phase where they are accessible to electro-kinetic transport. Supercritical fluid extraction and acid digestion were used for the analysis of heavy metals concentrations after the completion of electro-kinetic experimentation. Supercritical fluid (carbon dioxide) extraction is a new technique used to extract the heavy metal (lead, nickel, calcium and potassium) from clayey soil. The comparison between supercritical extraction and acid digestion of different metals was carried out. Supercritical fluid extraction, using ethylenediaminetetraacetic acid (EDTA) as a modifier, proved to be efficient and a safer technique than acid digestion technique in extracting metals from clayey soil. Mixing time of soil with EDTA before extracting heavy metals from clayey soil was investigated. The optimum and most practical shaking time for the extraction of lead, nickel, calcium and potassium was two hours.

Keywords: clay soil, heavy metals, supercritical fluid extraction, acid digestion

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Authors: Bibiana Betancur-Corredor, Juan Carlos Loaiza, Manfred Denich, Christian Borgemeister

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Alluvial gold mining generates a vast amount of deposits that cover the natural soil and negatively impacts riverbeds and valleys, causing loss of livelihood opportunities for farmers of these regions. In Colombia, more than 79,000 ha are affected by alluvial gold mining, therefore developing strategies to return this land to productivity is of crucial importance for the country. A novel restoration strategy has been created by a mining company, where the land is restored through the establishment of agroforestry systems, in which agricultural crops and livestock are combined to complement reforestation in the area. The purpose of this study is to capture the knowledge of farmers who perform agroforestry in areas with deposits created by alluvial gold mining activities. Semi structured interviews were conducted with farmers with regard to the following: indicators of soil fertility, management practices, soil heterogeneity, pest outbreaks and weeds. In order to compare the perceptions of soil fertility of farmers with physicochemical properties of soils, the farmers were asked to identify spots within their farms that have exhibited good and poor yields. Soil samples were collected in order to correlate farmer’s perceptions with soil physicochemical properties. The findings suggest that the main challenge that farmers face is the identification of fertile soil for crop establishment. They identify the fertile soil through visually analyzing soil color and compaction as well as the use of spontaneous growth of specific plants as indicator of soil fertility. For less fertile areas, nitrogen fixing plants are used as green manure to restore soil fertility for crop establishment. The findings of this study imply that if gold mining is followed by reclamation practices that involve the successful establishment of productive farmlands, agricultural productivity of these lands might improve, increasing food security of the affected communities.

Keywords: agroforestry, knowledge, mining, restoration

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Authors: Leah Lee

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This paper examines the correlation between deep learning models and emotions with noises to see whether or not noises mask emotions. The deep learning models used are plain convolutional neural networks (CNN), auto-encoder, long short-term memory (LSTM), and Visual Geometry Group-16 (VGG-16). Emotion datasets used are Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Toronto Emotional Speech Set (TESS), and Surrey Audio-Visual Expressed Emotion (SAVEE). To make it four times bigger, audio set files, stretch, and pitch augmentations are utilized. From the augmented datasets, five different features are extracted for inputs of the models. There are eight different emotions to be classified. Noise variations are white noise, dog barking, and cough sounds. The variation in the signal-to-noise ratio (SNR) is 0, 20, and 40. In summation, per a deep learning model, nine different sets with noise and SNR variations and just augmented audio files without any noises will be used in the experiment. To compare the results of the deep learning models, the accuracy and receiver operating characteristic (ROC) are checked.

Keywords: auto-encoder, convolutional neural networks, long short-term memory, speech emotion recognition, visual geometry group-16

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Authors: Muhammad Xaaceph Khan, Abida Butt, Farah Kausar

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Due to geogenic and anthropogenic factors, heavy metals concentrations increased throughout the world and deposit into soil. Thus available to different plants and travel in different food chains. The present study was designed to achieve bioaccumulation of Cd and Zn in the wheat-aphid-beetle food chain. For this purpose, wheat plants were grown in three different treatments: Cd, Zn, Cd+Zn. Data showed that Cd content in soil and wheat plant increases with increase in Cd concentration while plant weighs, panicle weight, seed number per panicle and seed weight per panicle decreases with increase in Cd content in the soil. Zn content in soil and wheat plant increases with increase in Cd concentration while plant weighs, panicle weight, seed number per panicle, and seed weight per panicle increase with an increase in Zn content in the soil. With the addition of Zn in Cd-treated soil, the uptake of Cd decreases in all parts of wheat plants. Bioaccumulation from wheat plant to aphids and then its predators were also studied. Cd concentration increases from low to high concentration in all arthropods. Same was observed in Zn concentrations, while in Cd+Zn, Cd accumulation decreases but Zn accumulates increases. Health risk index (HRI) also showed that in the presence of Zn, the HRI improves and can help to reduce health risks associated with Cd.

Keywords: aphid, beetle, bioaccumulation, cadmium, wheat, zinc

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Authors: Layan Moussa, Darine Salam, Samir Mustapha

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Heavy metal contamination in agricultural soils in Lebanon poses serious environmental and health problems. Intensive efforts are employed to improve existing quantification methods of heavy metals in contaminated environments since conventional detection techniques have shown to be time-consuming, tedious, and costly. The implication of hyperspectral remote sensing in this field is possible and promising. However, factors impacting the efficiency of hyperspectral imaging in detecting and quantifying heavy metals in agricultural soils were not thoroughly studied. This study proposes to assess the use of hyperspectral imaging for the detection of Ni in agricultural clay soil collected from the Bekaa Valley, a major agricultural area in Lebanon, under different contamination levels and soil moisture content. Soil samples were contaminated with Ni, with concentrations ranging from 150 mg/kg to 4000 mg/kg. On the other hand, soil with background contamination was subjected to increased moisture levels varying from 5 to 75%. Hyperspectral imaging was used to detect and quantify Ni contamination in the soil at different contamination levels and moisture content. IBM SPSS statistical software was used to develop models that predict the concentration of Ni and moisture content in agricultural soil. The models were constructed using linear regression algorithms. The spectral curves obtained reflected an inverse correlation between both Ni concentration and moisture content with respect to reflectance. On the other hand, the models developed resulted in high values of predicted R2 of 0.763 for Ni concentration and 0.854 for moisture content. Those predictions stated that Ni presence was well expressed near 2200 nm and that of moisture was at 1900 nm. The results from this study would allow us to define the potential of using the hyperspectral imaging (HSI) technique as a reliable and cost-effective alternative for heavy metal pollution detection in contaminated soils and soil moisture prediction.

Keywords: heavy metals, hyperspectral imaging, moisture content, soil contamination

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Authors: Zehra Aytac, Nurdilek Gulmezoglu

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Safflower has been grown for centuries for many purposes worldwide. Especially it is important for the orange-red dye from its petal and for its high-quality oil obtained from the seeds. The crop is high adaptable to areas with insufficient rainfall and poor soil conditions. The plant has a deep taproot that can draw moisture and plant nutrients from deep to the subsoil. The research was carried out to study the nitrogen (N) uptake of different safflower cultivars and lines at different stages of growth and different plant parts in the experimental field of Faculty of Agriculture, Eskişehir Osmangazi University under semi-arid conditions. Different safflower cultivars and lines of varied origins were used as the material. The cultivars and lines were planted in a Randomized Complete Block Design with three replications. Two different growth stages (flowering and harvest) and three different plant parts (head, stem+leaf and seed) were determined. The nitrogen concentration of different plant parts was determined by the Kjeldahl method. Statistical analysis were performed by analysis of variance for each growth stage and plant parts taking a level of p < 0.05 and p < 0.01 as significant according to the LSD test. As a result, N concentration showed significant differences among different plant parts and different growth stages for different safflower genotypes of varied origins.

Keywords: Carthamus tinctorius L., growth stages, head N, leaf N, N uptake, seed N, Safflower

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Authors: S. V. Vasilenkov, O. N. Demina

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Chernobyl NPP accident is the biggest in history of nuclear energetic. Bryansk region of Russia was exposed by the most intensive radiation pollution. For that, we made some researches in order to find the methods of soil rehabilitation on territories, polluted by radionuclides with the means of Cesium 137 leaching by watering. For experiments we took the soil from the upper more polluted 10 cm layer of different species. Cesium 137 leaching was made by different methods in washing columns. Washout of Cesium was made by periodical cycles in terms of 4-6 days. In experiments with easy argillaceous soil with start specific radioactivity 4158 bk/kg through 17 cycles the effective reducing was achieved and contained 1512 bk/kg. Besides, results of researches showed, that in the first 6-10 cycles we can see reducing of washing rate but after application of intensificators: ultrasound water processing, aerification, application of fertilizers (KCl), lime, freezing, we can see increasing of Cesium 137 leaching. The experimental investigations in washout of Cesium (Cs) – 137 from the soil were carried out in the field and laboratorial conditions during its freezing and melting. The experiments showed, that washout of Cesium (Cs) – 137 from the soil is rather high after freezing, than non-frozen soil is. And it conforms to washout of Cesium, made under the influence of the intensificaters. This fact allows to recommend chip and easy to construct technically arrangement for regulation of the snow-melt runoff for rehabilitation of the radioactive impoundment.

Keywords: pollution, radiation, Cesium 137 leaching, agriculture

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Authors: Ashwini V. Chavan, Sukhanand S. Bhosale

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Challenges of weak soil subgrade are often resolved either by stabilization or reinforcing it. However, it is also practiced to reinforce the granular fill to improve the load-settlement behavior of over weak soil strata. The inclusion of reinforcement in the engineered granular fill provided a new impetus for the development of enhanced Soil-Structure Interaction (SSI) models, also known as mechanical foundation models or lumped parameter models. Several researchers have been working in this direction to understand the mechanism of granular fill-reinforcement interaction and the response of weak soil under the application of load. These models have been developed by extending available SSI models such as the Winkler Model, Pasternak Model, Hetenyi Model, Kerr Model etc., and are helpful to visualize the load-settlement behavior of a physical system through 1-D and 2-D analysis considering beam and plate resting on the foundation respectively. Based on the literature survey, these models are categorized as ‘Reinforced Pasternak Model,’ ‘Double Beam Model,’ ‘Reinforced Timoshenko Beam Model,’ and ‘Reinforced Kerr Model.’ The present work reviews the past 30+ years of research in the field of SSI models for reinforced foundation systems, presenting the conceptual development of these models systematically and discussing their limitations. Special efforts are taken to tabulate the parameters and their significance in the load-settlement analysis, which may be helpful in future studies for the comparison and enhancement of results and findings of physical models.

Keywords: geosynthetics, mathematical modeling, reinforced foundation, soil-structure interaction, ground improvement, soft soil

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Authors: Chukwuebuka Vincent Azuka, Chidimma Peace Odoh

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Changes in agricultural practices and land use influence the storage and release of soil organic carbon and soil structural dynamics. To investigate this in Nsukka, southeastern Nigeria, soil samples were collected at 0-10 cm, 10-20 cm and 20-30 cm from three locations; Ovoko (OV), Obukpa (OB) and University of Nigeria, Nsukka (UNN) and three land use types; cultivated land (CL), forest land (FL) and grassland (GL)). Data were subjected to analysis of variance (ANOVA) using SPSS. Also, correlations between organic carbon stock, structural stability indices and other soil properties were established. The result showed that Ksat was significantly (p < 0.05) influenced by location with mean values of 68 cmhr⁻¹,121.63 cmhr⁻¹, 8.42 cmhr⁻¹ in OV, OB and UNN respectively. The MWD and aggregate stability (AS) were significantly (p < 0.05) influenced by land use and depth. The mean values of MWD are 0.85 (CL), 1.35 (FL) and 1.45 (GL), and 1.66 at 0-10 cm, 1.08 at 10-20 cm and 0.88 mm at 20-30 cm. The mean values of AS are; 27.66% (CL), 46.39% (FL) and 49.81% (GL), and 53.96% at 0-10cm, 40.22% at 10-20cm and 29.57% at 20-30cm. Clay flocculation (CFI) and dispersion indices (CDI) differed significantly (p < 0.05) among the land use. Soil pH differed significantly (p < 0.05) across the land use and locations with mean values ranging from 3.90-6.14. Soil organic carbon (SOC) significantly (p < 0.05) differed across locations and depths. SOC decreases as depth increases depth with mean values of 15.6 gkg⁻¹, 10.1 gkg⁻¹, and 8.6 gkg⁻¹ at 0-10 cm, 10-20 cm, and 20-30 cm respectively. SOC in the three land use was 8.8 g kg-1, 15.2 gkg⁻¹ and 10.4 gkg⁻¹ at CL, FL, and GL respectively. The highest aggregate-associated carbon was recorded in 0.5 mm across the land use and depth except in cultivated land and at 20-30 cm which recorded their highest SOC at 1mm. SOC stock, total nitrogen (TN) and CEC were significantly (p < 0.05) different across the locations with highest values of 23.43 t/ha, 0.07g/kg and 14.27 Cmol/kg respectively recorded in UNN. SOC stock was significantly (p < 0.05) influenced by depth as follows; 0-10>10-20>20-30 cm. TN was low with mean values ranging from 0.03-0.07 across the locations, land use and depths. The mean values of CEC ranged from 9.96-14.27 Cmol kg⁻¹ across the locations and land use. SOC stock showed correlation with silt, coarse sand, N and CEC (r = 0.40*, -0.39*, -0.65** and 0.64** respectively. AS showed correlation with BD, Ksat, pH in water and KCl, and SOC (r = -0.42*, 0.54**, -0.44*, -0.45* and 0.49** respectively. Thus, land use and location play a significant role in sustainable management of soil resources.

Keywords: agricultural practices, structural dynamics, sequestration, soil resources, management

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Authors: Arie Pujiwati, Kengo Nakamura, Noriaki Watanabe, Takeshi Komai

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Coal mining is well known to cause considerable environmental impacts, including trace element contamination of soil. This study aimed to assess the trace element (As, Cd, Co, Cu, Ni, Pb, Sb, and Zn) contamination of soil in the vicinity of coal mining activities, using the case study of Asam-asam River basin, South Kalimantan, Indonesia, and to assess the human health risk, incorporating total and bioavailable (water-leachable and acid-leachable) concentrations. The results show the enrichment of As and Co in soil, surpassing the background soil value. Contamination was evaluated based on the index of geo-accumulation, I_geo and the pollution index, PI. I_geo values showed that the soil was generally uncontaminated (I_geo ≤ 0), except for elevated As and Co. Mean PI for Ni and Cu indicated slight contamination. Regarding the assessment of health risks, the Hazard Index, HI showed adverse risks (HI > 1) for Ni, Co, and As. Further, Ni and As were found to pose unacceptable carcinogenic risk (risk > 1.10^-5). Farming, settlement, and plantation were found to present greater risk than coal mines. These results show that coal mining activity in the study area contaminates the soils by particular elements and may pose potential human health risk in its surrounding area. This study is important for setting appropriate countermeasure actions and improving basic coal mining management in Indonesia.

Keywords: coal mine, risk, trace elements, soil

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Authors: Boumaraf Malak

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The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions.

Keywords: deep reinforcement learning, autonomous driving, deep deterministic policy gradient, deep Q-learning

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Authors: Abdul Halim Abdul, Wan Ismail Wan Yusoff

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The geological formation at Limau Manis Besar area, are consist of low grade metamorphic rock and undulating mountaineers, rugged terrain and the quite steeply 45 degree slope gradient. The objectives of this paper are present the methods and devices used in measurement of P-wave velocity to estimate the initial Shear Modulus (Go) in steady state and critical state soil consolidation. The relationship between SPT-N values and the Shear Modulus (Go) at very small strain is widely considered to be evaluated. Based on the seismic reflection survey, the constant (K) poroelastic theory, mean effectives stress and primer wave velocity (Vs) increase as the soil depth increase. The steady state and critical state, Degree of Soil Consolidation(U) concept is used to interpret the behavior of Shear Modulus (Go). The relationship between Consolidation Test and Seismic Reflection Survey is also discussed.

Keywords: geological setting, shear modulus, poroelastic theory, steady state and none steady state degree of soil consolidation, consolidation test

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Authors: Vijay Shankar

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In the era of water scarcity, effective use of water via irrigation requires good methods for determining crop water needs. Implementation of irrigation scheduling programs requires an accurate estimate of water use by the crop. Moisture depletion from the root zone represents the consequent crop evapotranspiration (ET). A numerical model for simulating soil water depletion in the root zone has been developed by taking into consideration soil physical properties, crop and climatic parameters. The governing differential equation for unsaturated flow of water in the soil is solved numerically using the fully implicit finite difference technique. The water uptake by plants is simulated by using three different sink functions. The non-linear model predictions are in good agreement with field data and thus it is possible to schedule irrigations more effectively. The present paper describes irrigation scheduling based on moisture depletion from the different layers of the root zone, obtained using different sink functions for three cash, oil and forage crops: cotton, safflower and barley, respectively. The soil is considered at a moisture level equal to field capacity prior to planting. Two soil moisture regimes are then imposed for irrigated treatment, one wherein irrigation is applied whenever soil moisture content is reduced to 50% of available soil water; and other wherein irrigation is applied whenever soil moisture content is reduced to 75% of available soil water. For both the soil moisture regimes it has been found that the model incorporating a non-linear sink function which provides best agreement of computed root zone moisture depletion with field data, is most effective in scheduling irrigations. Simulation runs with this moisture uptake function result in saving 27.3 to 45.5% & 18.7 to 37.5%, 12.5 to 25% % &16.7 to 33.3% and 16.7 to 33.3% & 20 to 40% irrigation water for cotton, safflower and barley respectively, under 50 & 75% moisture depletion regimes over other moisture uptake functions considered in the study. Simulation developed can be used for an optimized irrigation planning for different crops, choosing a suitable soil moisture regime depending upon the irrigation water availability and crop requirements.

Keywords: irrigation water, evapotranspiration, root uptake models, water scarcity

Procedia PDF Downloads 318