Search results for: deep saline aquifer

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: Ramezani M., Neitzert T.

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This paper investigates mechanical properties and formability of the AZ61 magnesium alloy at high temperatures. Tensile tests were performed at elevated temperatures of up to 400ºC. The results showed that as temperature increases, yield strength and ultimate tensile strength decrease significantly, while the material experiences an increase in ductility (maximum elongation before break). A finite element model has been developed to further investigate the formability of the AZ61 alloy by deep drawing a square cup. Effects of different process parameters such as punch and die geometry, forming speed and temperature as well as blank-holder force on deep drawability of the AZ61 alloy were studied and optimum values for these parameters are achieved which can be used as a design guide for deep drawing of this alloy.

Keywords: AZ61, formability, magnesium, mechanical properties

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Simon R. Melchioly, Ibrahimu C. Mjemah, Isaac M. Marobhe

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The main objective of this research was to identify new potential sources of groundwater resources using geophysical methods and also to demarcate the saltwater - freshwater interface. Kilwa Kisiwani Island geologically is covered mostly by Quaternary alluvial sediments, sand, and gravel. The geophysical techniques employed during the research include Vertical Electrical Sounding (VES), Earth Resistivity Tomography (ERT), and Transient Electromagnetics (TEM). Two-dimensional interpolated geophysical results show that there exist freshwater lenses formations that are potential aquifers on the Island with resistivity values ranging from 11.68 Ωm to 46.71 Ωm. These freshwater lenses are underlain by formation with brackish water in which the resistivity values are varying between 3.89 Ωm and 1.6 Ωm. Saltwater with resistivity less than 1 Ωm is found at the bottom being overlaid by brackish saturated formation. VES resistivity results show that 89% (16 out of 18) of the VES sites are potential for groundwater resources drilling while TEM results indicate that 75% (12 out of 16) of TEM sites are potential for groundwater borehole drilling. The recommended drilling depths for potential sites in Kilwa Kisiwani Island show that the maximum depth is 25 m and the minimum being 10 m below ground surface. The aquifer structure in Kilwa Kisiwani Island is a shallow, unconfined freshwater lenses floating above the seawater and the maximum thickness of the aquifer is 25 m for few selected VES and TEM sites while the minimum thickness being 10 m.

Keywords: groundwater, hydrogeophysical, Kilwa Kisiwani, freshwater, saltwater, resistivity

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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Authors: Seyhan Sahan-Firat, Meryem Temiz-Resitoglu, Demet Sinem Guden, Sefika Pinar Kucukkavruk, Bahar Tunctan, Ayse Nihal Sari, Zumrut Kocak

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We hypothesized that mTOR inhibition may prevent the multiple organ failures following severe multiple tissue injury associated with increased NADPH oxidase system activity occur in zymosan-induced systemic inflammation. Therefore, we investigated the role of mTOR in oxidative/nitrosative stress associated with increase in NADPH oxidase activity in zymosan-induced systemic inflammation model in rats. Male Wistar rats received saline (4 ml/kg, i.p.) and zymosan (500 mg/kg, i.p.) at time 0. Saline, or zymosan-treated rats were given rapamycin (1 mg/kg, i.p.) 1 h after saline or zymosan injections. Rats were sacrified 4 h after zymosan challenge and kidney, heart, thoracic aorta, and superior mesenteric artery were collected. NADPH oxidase activity, p22phox, gp91phox, and p47phox protein expression and nitrotyrosine levels were measured in tissue samples. Zymosan administration caused an increase in NADPH oxidase activity, p22phox, gp91phox, and p47phox protein expression and nitrotyrosine levels in kidney, heart, thoracic aorta, and superior mesenteric artery. These changes caused by zymosan reversed by rapamycin, a selective mTOR inhibitor. Rapamycin alone had no effect on the parameters measured. Our results demonstrated that zymosan-induced oxidative/nitrosative stress presumably due to enhanced activity of NADPH oxidase, expression of p22phox, gp91phox, and p47phox and production of peroxynitrite were mediated by mTOR. [This work was financially supported by Research Foundation of Mersin University (2016-2-AP3-1900)].

Keywords: oxidative stress, mTOR, nitrosative stress, zymosan

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Authors: Jun Ming Moey, Zhiyaun Chen, David Nicholson

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Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported.

Keywords: detection, machine learning, deep learning, unsupervised, outlier analysis, data science, fraud, financial crime

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Saad M. Howladar, Mostafa M. Rady, Wael M. Semida

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A field experiment was conducted during the two successive seasons of 2012 and 2013 in the Experimental Farm (newly-reclaimed saline soil; EC = 7.8 dS m-1), Faculty of Agriculture, Fayoum University, Fayoum, Egypt to investigate the effect of vitamin C foliar application at the rates of 1, 2, 3 and 4 mM on the possibility of improving growth, seed and oil yields, and some chemical constituents of Helianthus annuus L. plants under the adverse conditions of the selected soil. Significant positive influences of all vitamin C treatments were observed on growth, seed and oil yields and some chemical constituents in both seasons. Compared to unsprayed plants (control), spraying plants with various rates of vitamin C significantly increased vegetative growth traits (i.e. plant height, No. of leaves plant-1, leaf area leaf-1, total leaves area plant-1, and dry weights of leaves and shoot plant-1) and seed and oil yields and their components (i.e. head diameter, seed weight head-1, 100-seed weight, seed yield feddan-1 and oil yield feddan-1). In addition, the concentrations of chlorophyll a, chlorophyll b, total chlorophylls, total carotenoids and total phenols in fresh leaves, and total carbohydrates, total soluble sugars, free proline and some nutrients (i.e. N, P, K, Fe, Mn, and Zn) in dry leaves were also increased significantly with all vitamin C applications. Vitamin C treatment at the rate of 3 mM was generated the best results. These results are important as the potential of vitamin C to alleviate the harmful effects of salt stress offer an opportunity to increase the resistance of sunflower plants to grow under saline conditions of the newly-reclaimed soils.

Keywords: sunflower, Helianthus annuus L., ascorbic acid, salinity, growth, seed yield, oil content, chemical composition

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Authors: Miladi Yasmine, Bouhlel Salah, Garnit Hechmi

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Hydrothermal vein-type deposits of the Hammam Jedidi F-Ba(Pb-Zn-Cu) are hosted in Lower Jurassic, Cretaceous and Tertiary series, and located near a very important structural lineament (NE-SW) corresponding to the Hammam Jedidi Fault in the Tunisian Dorsale. The circulation of the ore forming fluid is triggered by a regional tectonic compressive phase which occurred during the miocène time. Mineralization occurs as stratabound and vein-type orebodies adjacent to the Triassic salt diapirs and within fault in Jurassic limestone. Fluid inclusions data show that two distinct fluids were involved in the mineralisation deposition: a warmer saline fluid (180°C, 20 wt % NaCl equivalent) and cooler less saline fluid (126°C, 5wt%NaCl equivalent). The contrasting salinities and halogen ratios suggest that this two fluid derived from one of the brine originated after the dissolution of halite as suggested by its high salinity. The other end member, as indicated by the low Cl/Br ratios, acquired its low salinity by dilution of Br enriched evaporated seawater. These results are compatible with Mississippi-Valley- type mineralization.

Keywords: Jebel Oust, fluid inclusions, North Eastern Tunisia, mineralization

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Authors: Gaokai Liu

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Automatic defect detection for nanomaterial images is widely required in industrial scenarios. Deep learning approaches are considered as the most effective solutions for the great majority of image-based tasks. In this paper, an edge guidance network for defect segmentation is proposed. First, the encoder path with multiple convolution and downsampling operations is applied to the acquisition of shared features. Then two decoder paths both are connected to the last convolution layer of the encoder and supervised by the edge and segmentation labels, respectively, to guide the whole training process. Meanwhile, the edge and encoder outputs from the same stage are concatenated to the segmentation corresponding part to further tune the segmentation result. Finally, the effectiveness of the proposed method is verified via the experiments on open nanofibrous datasets.

Keywords: deep learning, defect detection, image segmentation, nanomaterials

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Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

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In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

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Authors: Buruk Kitachew Wossenyeleh

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Groundwater is the largest freshwater reservoir in the world. Like the other reservoirs of the hydrologic cycle, it is a finite resource. This study focused on the groundwater modeling of the Ter Kamerenbos well field to understand the groundwater flow system and the impact of different scenarios. The study area covers 68.9Km2 in the Brussels Capital Region and is situated in two river catchments, i.e., Zenne River and Woluwe Stream. The aquifer system has three layers, but in the modeling, they are considered as one layer due to their hydrogeological properties. The catchment aquifer system is replenished by direct recharge from rainfall. The groundwater recharge of the catchment is determined using the spatially distributed water balance model called WetSpass, and it varies annually from zero to 340mm. This groundwater recharge is used as the top boundary condition for the groundwater modeling of the study area. During the groundwater modeling using Processing MODFLOW, constant head boundary conditions are used in the north and south boundaries of the study area. For the east and west boundaries of the study area, head-dependent flow boundary conditions are used. The groundwater model is calibrated manually and automatically using observed hydraulic heads in 12 observation wells. The model performance evaluation showed that the root means the square error is 1.89m and that the NSE is 0.98. The head contour map of the simulated hydraulic heads indicates the flow direction in the catchment, mainly from the Woluwe to Zenne catchment. The simulated head in the study area varies from 13m to 78m. The higher hydraulic heads are found in the southwest of the study area, which has the forest as a land-use type. This calibrated model was run for the climate change scenario and well operation scenario. Climate change may cause the groundwater recharge to increase by 43% and decrease by 30% in 2100 from current conditions for the high and low climate change scenario, respectively. The groundwater head varies for a high climate change scenario from 13m to 82m, whereas for a low climate change scenario, it varies from 13m to 76m. If doubling of the pumping discharge assumed, the groundwater head varies from 13m to 76.5m. However, if the shutdown of the pumps is assumed, the head varies in the range of 13m to 79m. It is concluded that the groundwater model is done in a satisfactory way with some limitations, and the model output can be used to understand the aquifer system under steady-state conditions. Finally, some recommendations are made for the future use and improvement of the model.

Keywords: Ter Kamerenbos, groundwater modelling, WetSpass, climate change, well operation

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

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Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

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Authors: Brinda Sharma, Saakshi S. Sarpotdar

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Deep-fat frying is popular standard method that has been studied basically to clarify the complicated mechanisms of fat decomposition at high temperatures and to assess their effects on human health. The aim of this paper is to point out the application of method engineering that has been recently improved our understanding of the fundamental principles and mechanisms concerned at different scales and different times throughout the process: pretreatment, frying, and cooling. It covers the several aspects of deep-fat drying. New results regarding the understanding of the frying method that are obtained as a results of major breakthroughs in on-line instrumentation (heat, steam flux, and native pressure sensors), within the methodology of microstructural and imaging analysis (NMR, MRI, SEM) and in software system tools for the simulation of coupled transfer and transport phenomena. Such advances have opened the approach for the creation of significant information of the behavior of varied materials and to the event of latest tools to manage frying operations via final product quality in real conditions. Lastly, this paper promotes an integrated approach to the frying method as well as numerous competencies like those of chemists, engineers, toxicologists, nutritionists, and materials scientists also as of the occupation and industrial sectors.

Keywords: frying, cooling, imaging analysis (NMR, MRI, SEM), deep-fat frying

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Authors: T. Zaki, Fathi S. Soliman

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Desulfurization process is required by most, if not all refineries, to achieve ultra-low sulfur fuel, that contains less than 10 ppm sulfur. A lot of research works and many effective technologies have been studied to achieve deep desulfurization process in moderate reaction environment, such as adsorption desulfurization (ADS), oxidative desulfurization (ODS), biodesulfurization and extraction desulfurization (EDS). Extraction desulfurization using deep eutectic solvents (DESs) is considered as simple, cheap, highly efficient and environmentally friend process. In this work, four DESs were designed and synthesized. Choline chloride (ChCl) was selected as typical hydrogen bond acceptors (HBA), and ethylene glycol (EG), glycerol (Gl), urea (Ur) and thiourea (Tu) were selected as hydrogen bond donors (HBD), from which a series of deep eutectic solvents were synthesized. The experimental data showed that the synthesized DESs showed desulfurization affinities towards the thiophene species in cyclohexane solvent. Ethylene glycol molecules showed more affinity to create hydrogen bond with thiophene instead of choline chloride. Accordingly, ethylene glycol choline chloride DES has the highest extraction efficiency.

Keywords: DES, desulfurization, green solvent, extraction

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Authors: Bahar Tunctan, Sefika Pinar Kucukkavruk, Meryem Temiz-Resitoglu, Demet Sinem Guden, Ayse Nihal Sari, Seyhan Sahan-Firat

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We hypothesized that rexinoids such as bexarotene, a selective retinoid X receptor α (RXRα) agonist, may be beneficial for preventing mortality due to inflammation associated with increased expression/activity of inducible nitric oxide synthase (iNOS) induced by lipopolysaccharide (LPS). Therefore, we investigated effects of bexarotene on the changes in circulating protein levels of iNOS (an index for systemic iNOS expression), myeloperoxidase (MPO) (an index for systemic inflammation), and lactate dehydrogenase (LDH) (an index for systemic tissue injury) in LPS-induced systemic inflammation model resulting in septic shock in rats. Rats were injected with saline (4 ml/kg; i.p.), LPS (10 mg/kg; i.p.), dimethylsulphoxide (4 ml/kg, 0.1%; s.c.) at time 0. Mean arterial blood pressure and heart rate were measured using a tail-cuff device. Bexarotene (0.03, 0.1, 0.3, and 1 mg/kg; s.c.) was administered to separate groups of rats 1 h after injection of saline or LPS. The rats were sacrificed 4 h after saline or LPS injection and blood was collected for measurement of serum iNOS, MPO, and LDH protein levels. Blood pressure decreased by 31 mmHg and heart rate increased by 63 bpm in the LPS-treated rats. Bexarotene at 0.3 and 1 mg/kg doses caused 20% mortality 4 h after LPS injection. In the LPS-treated rats, serum iNOS, MPO, and LDH protein levels were increased. Bexarotene only at 0.1 mg/kg dose prevented the LPS-induced hypotension and increased in iNOS, MPO, and LDH protein levels. These data are consistent with the view that a decrease in systemic iNOS levels contributes to the beneficial effect of bexarotene to prevent the hypotension associated with inflammation and tissue injury during rat endotoxemia. [This work was financially supported by The Scientific and Technological Research Council of Turkey (SBAG-109S121)].

Keywords: bexarotene, inflammation, iNOS, lipopolisaccharide, RXRa

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Hayder H. Kareem

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Groundwater recharge is a crucial parameter for any groundwater management system. The variability of the recharge rates and the difficulty in estimating this factor in many processes by direct observation leads to the complexity of estimating the recharge value. Various methods are existing to estimate the groundwater recharge, with some limitations for each method to be able for application. This paper focuses particularly on a real study area, Al-Najaf City, Iraq. In this city, there are few groundwater aquifers, but the aquifer which is considered in this study is the closest one to the ground surface, the Dibdibba aquifer. According to the Aridity Index, which is estimated in the paper, Al-Najaf City is classified as a region located in an arid climate, and this identified that the most appropriate method to estimate the groundwater recharge is Thornthwaite's formula or Thornthwaite's method. From the calculations, the estimated average groundwater recharge over the period 1980-2014 for Al-Najaf City is 40.32 mm/year. Groundwater recharge is completely affected the groundwater table level (groundwater head). Therefore, to make sure that this value of recharge is true, the MODFLOW program has been used to apply this value through finding the relationship between the calculated and observed heads where a groundwater model for the Al-Najaf City study area has been built by MODFLOW to simulate this area for different purposes, one of these purposes is to simulate the groundwater recharge. MODFLOW results show that this value of groundwater recharge is extremely high and needs to be reduced. Therefore, a further sensitivity test has been carried out for the Al-Najaf City study area by the MODFLOW program through changing the recharge value and found that the best estimation of groundwater recharge value for this city is 16.5 mm/year where this value gives the best fitting between the calculated and observed heads with minimum values of RMSE % (13.175) and RSS m² (1454).

Keywords: Al-Najaf City, groundwater modelling, recharge estimation, visual MODFLOW

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Authors: Sadi Ali, Yaser Kishawi

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As part of Palestine, Gaza Strip (365 km2 and 1.8 million habitants) is considered a semi-arid zone relies solely on the Coastal Aquifer. The coastal aquifer is only source of water with only 5-10% suitable for human use. This barely covers the domestic and agricultural needs of Gaza Strip. Palestinian Water Authority Strategy is to find non-conventional water resource from treated wastewater to irrigate 1500 hectares and serves over 100,000 inhabitants. A new WWTP project is to replace the old-overloaded Biet Lahia WWTP. The project consists of three parts; phase A (pressure line & 9 infiltration basins - IBs), phase B (a new WWTP) and phase C (Recovery and Reuse Scheme – RRS – to capture the spreading plume). Currently, phase A is functioning since Apr 2009. Since Apr 2009, a monitoring plan is conducted to monitor the infiltration rate (I.R.) of the 9 basins. Nearly 23 million m3 of partially treated wastewater were infiltrated up to Jun 2014. It is important to maintain an acceptable rate to allow the basins to handle the coming quantities (currently 10,000 m3 are pumped an infiltrated daily). The methodology applied was to review and analysis the collected data including the I.R.s, the WW quality and the drying-wetting schedule of the basins. One of the main findings is the relation between the Total Suspended Solids (TSS) at BLWWTP and the I.R. at the basins. Since April 2009, the basins scored an average I.R. of about 2.5 m/day. Since then the records showed a decreasing pattern of the average rate until it reached the lower value of 0.42 m/day in Jun 2013. This was accompanied with an increase of TSS (mg/L) concentration at the source reaching above 200 mg/L. The reducing of TSS concentration directly improved the I.R. (by cleaning the WW source ponds at Biet Lahia WWTP site). This was reflected in an improvement in I.R. in last 6 months from 0.42 m/day to 0.66 m/day then to nearly 1.0 m/day as the average of the last 3 months of 2013. The wetting-drying scheme of the basins was observed (3 days wetting and 7 days drying) besides the rainfall rates. Despite the difficulty to apply this scheme accurately a control of flow to each basin was applied to improve the I.R. The drying-wetting system affected the I.R. of individual basins, thus affected the overall system rate which was recorded and assessed. Also the ploughing activities at the infiltration basins as well were recommended at certain times to retain a certain infiltration level. This breaks the confined clogging layer which prevents the infiltration. It is recommended to maintain proper quality of WW infiltrated to ensure an acceptable performance of IBs. The continual maintenance of settling ponds at BLWWTP, continual ploughing of basins and applying soil treatment techniques at the IBs will improve the I.R.s. When the new WWTP functions a high standard effluent quality (TSS 20mg, BOD 20 mg/l, and TN 15 mg/l) will be infiltrated, thus will enhance I.R.s of IBs due to lower organic load.

Keywords: soil aquifer treatment, recovery and reuse scheme, infiltration basins, North Gaza

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Authors: Emery Coppola Jr., Marwan Sadat, Il Kim, Diane Trube, Richard Kurisko

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Contamination sites like landfills often pose significant risks to receptors like surface water bodies. Surface water bodies are often a source of recreation, including fishing and swimming, which not only enhances their value but also serves as a direct exposure pathway to humans, increasing their need for protection from water quality degradation. In this paper, a case study presents the potential effects of leachate-impacted groundwater from a large closed sanitary landfill on the surface water quality of the nearby Raritan River, situated in New Jersey. The study, performed over a two year period, included in-depth field evaluation of both the groundwater and surface water systems, and was supplemented by computer modeling. The analysis required delineation of a representative average daily groundwater discharge from the Landfill shoreline into the large, highly tidal Raritan River, with a corresponding estimate of daily mass loading of potential contaminants of concern. The average daily groundwater discharge into the river was estimated from a high-resolution water level study and a 24-hour constant-rate aquifer pumping test. The significant tidal effects induced on groundwater levels during the aquifer pumping test were filtered out using an advanced algorithm, from which aquifer parameter values were estimated using conventional curve match techniques. The estimated hydraulic conductivity values obtained from individual observation wells closely agree with tidally-derived values for the same wells. Numerous models were developed and used to simulate groundwater contaminant transport and surface water quality impacts. MODFLOW with MT3DMS was used to simulate the transport of potential contaminants of concern from the down-gradient edge of the Landfill to the Raritan River shoreline. A surface water dispersion model based upon a bathymetric and flow study of the river was used to simulate the contaminant concentrations over space within the river. The modeling results helped demonstrate that because of natural attenuation, the Landfill does not have a measurable impact on the river, which was confirmed by an extensive surface water quality study.

Keywords: groundwater flow and contaminant transport modeling, groundwater/surface water interaction, landfill leachate, surface water quality modeling

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Authors: Dingyang Hu, Dan Liu

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DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.

Keywords: adversarial sample, gradient, probability, black box

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Authors: Seng Sing Tan

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Clamping of the umbilical cord after birth is widely practiced as a part of labor management. Further improvements were proposed to produce a smaller, lighter and more comfortable clamp while still maintaining current standards of clamping. A detachable holder was also developed to facilitate the clamping process. This pilot study on the efficacy of the mini-clamp was conducted to evaluate a tightness of the seal and a firm grip of the clamp on the umbilical cord. The study was carried out at National University Hospital, using 5 sets of placental cord. 18 samples of approximate 10 cm each were harvested. The test results showed that the mini-clamp was able to stop the flow through the cord after clamping without rupturing the cord. All slip tests passed with a load of 0.2 kg. In the pressure testing, 30kPa of saline was exerted into the umbilical veins. Although there was no physical sign of fluid leaking through the end secured by the mini-clamp, the results showed the pressure was not able to sustain the pressure set during the tests. 12 out of the 18 test samples have more than 7% of pressure drop in 30 seconds. During the pressure leak test, it was observed on several samples that when pressurized, small droplets of saline were growing on the outer surface of the cord lining membrane. It was thus hypothesized that the pressure drop was likely caused by the perfusion of the injected saline through the Wharton’s jelly and the cord lining membrane. The average pressure in the umbilical vein is roughly 2.67kPa (20 mmHg), less than 10% of 30kPa (~225mmHg), set for the pressure testing. As such, the pressure set could be over-specified, leading to undesirable outcomes. The development of the mini-clamp was an attempt to increase the comfort of newly born babies while maintaining the usability and efficacy of hospital grade umbilical cord clamp. The pressure leak in this study would be unfair to fully attribute it to the design and efficacy of the mini-clamp. Considering the unexpected leakage of saline through the umbilical membrane due to over-specified pressure exerted on the umbilical veins, improvements can definitely be made to the existing experimental setup to obtain a more accurate and conclusive outcome. If proven conclusive and effective, the mini-clamp with a detachable holder could be a smaller and potentially cheaper alternative to existing umbilical cord clamps. In addition, future clinical trials could be conducted to determine the user-friendliness of the mini-clamp and evaluate its practicality in the clinical setting by labor ward clinicians. A further potential improvement could be proposed on the sustainability factor of the mini-clamp. A biodegradable clamp would revolutionise the industry in this increasingly environmentally sustainability world.

Keywords: leak test, mini-clamp, slip test, umbilical cord

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Authors: Dudul Das, Pankaj Kalita, Sangeeta Borah

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Freshwater availability in the world is as low as 1% of total water available and in many geographical locations dissolved fluoride and arsenic are serious problem. In India availability of freshwater will be stressed by 2025, so the availability saline water from sea is a hope for the people of Indian sub-continent, but saline water is not drinkable it need to be processed, which again require a huge amount of energy. So the most easy and handy option in such situation for all those problems is solar still, this investigation presents various scopes for improvement of its efficiency. Experiments showed that by increasing the absorber plate area through better design can increase the distillate output by two fold and by using jute wicks in the modified absorber plate increases the output up to three times that of conventional solar still available in the Department of Energy, Tezpur University. The experiment is carried out at constant water depth of 8.5 cm and glass cover inclination of 27o facing South. The exergy analysis carried out clearly resulted that with the use of jute wick and baffle plated basin the efficiency achieved more than the simple baffle plated basin. The Instantaneous exergy without jute wick ranges from 2.5% to 4.5% while using jute it ranges from 1.5% to 5.15%.

Keywords: fluoride, absorber plate, jute wick, instantaneous exergy

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

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Authors: Tayeb Aissaoui, Inas M. AlNashef

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In recent years, green solvents named deep eutectic solvents (DESs) have been found to possess significant properties and to be applicable in several technologies. Choline chloride (ChCl) mixed with urea at a ratio of 1:2 and 80 °C was the first discovered DES. In this article, chemical structure and combination mechanism of ChCl: urea based DES were investigated. Moreover, the implementation of this DES in water removal from natural gas was reported. Dehydration of natural gas by ChCl:urea shows significant absorption efficiency compared to triethylene glycol. All above operations were retrieved from COSMOthermX software. This article confirms the potential application of DESs in gas industry.

Keywords: COSMO-RS, deep eutectic solvents, dehydration, natural gas, structure, organic salt

Procedia PDF Downloads 256

Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

Procedia PDF Downloads 161

Authors: Abdallah M. Fayed

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The study area is located in south Darb El Arbaieen, western desert of Egypt. It occupies the area between latitudes 22° 00/ and 22° 30/ North and Longitudes 29° 30/ and 30° 00/ East, from southern border of Egypt to the area north Bir Kuraiym and from the area East of East Owienat to the area west Tushka district, its area about 2750 Km2. The famous features; southern part of Darb El Arbaieen road, G Baraqat El Scab El Qarra, Bir Dibis, Bir El Shab and Bir Kuraiym, Interpretation of soil stratification shows layers that are related to Quaternary and Upper-Lower Cretaceous eras. It is dissected by a series of NE-SW striking faults. The regional groundwater flow direction is in SW-NE direction with a hydraulic gradient is 1m / 2km. Mathematical model program has been applied for evaluation of groundwater potentials in the main Aquifer –Nubian Sandstone- in the area of study and Remote sensing technique is considered powerful, accurate and saving time in this respect. These techniques are widely used for illustrating and analysis different phenomenon such as the new development in the desert (land reclamation), residential development (new communities), urbanization, etc. The major issues concerning water development objective of this work is to determine the new development areas in western desert of Egypt during the period from 2003 to 2015 using remote sensing technique, the impacts of the present and future development have been evaluated by using the two-dimensional numerical groundwater flow Simulation Package (visual modflow 4.2). The package was used to construct and calibrate a numerical model that can be used to simulate the response of the aquifer in the study area under implementing different management alternatives in the form of changes in piezometric levels and salinity. Total period of simulation is 100 years. After steady state calibration, two different scenarios are simulated for groundwater development. 21 production wells are installed at the study area and used in the model, with the total discharge for the two scenarios were 105000 m3/d, 210000 m3/d. The drawdown was 11.8 m and 23.7 m for the two scenarios in the end of 100 year. Contour maps for water heads and drawdown and hydrographs for piezometric head are represented. The drawdown was less than the half of the saturated thickness (the safe yield case).

Keywords: remote sensing, management of aquifer systems, simulation modeling, western desert, South Darb El Arbaieen

Procedia PDF Downloads 373

Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

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In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: ANPR, CS, CNN, deep learning, NPL

Procedia PDF Downloads 279