Search results for: water pipe networks

Authors: Abhishek Saha, Serene Tay, Gerard Pijcke

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The availability of high-resolution topography and rainfall information in urban areas has made it necessary to revise modeling approaches used for simulating flood risk assessments. Lidar derived elevation models that have 1m or lower resolutions are becoming widely accessible. The classical approaches of 1D-2D flow models where channel flow is simulated and coupled with a coarse resolution 2D overland flow models may not fully utilize the information provided by high-resolution data. In this context, a study was undertaken to compare three different modeling approaches to simulate flooding in an urban area. The first model used is the base model used is Sobek, which uses 1D model formulation together with hydrologic boundary conditions and couples with an overland flow model in 2D. The second model uses a full 2D model for the entire area with shallow water equations at the resolution of the digital elevation model (DEM). These models are compared against another shallow water equation solver in 2D, which uses a subgrid method for grid refinement. These models are simulated for different horizontal resolutions of DEM varying between 1m to 5m. The results show a significant difference in inundation extents and water levels for different DEMs. They are also sensitive to the different numerical models with the same physical parameters, such as friction. The study shows the importance of having reliable field observations of inundation extents and levels before a choice of model and data can be made for spatial flood risk assessments.

Keywords: flooding, DEM, shallow water equations, subgrid

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Authors: Gul Rahim Rahimi

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The IoT-based soil moisture monitoring system for indoor plants is designed to address the challenges of maintaining optimal moisture levels in soil for plant growth and health. The system utilizes sensor technology to collect real-time data on soil moisture levels, which is then processed and analyzed using machine learning algorithms. This allows for accurate and timely monitoring of soil moisture levels, ensuring plants receive the appropriate amount of water to thrive. The main objectives of the system are twofold: to keep plants fresh and healthy by preventing water deficiency and to provide users with comprehensive insights into the water content of the soil on a daily and hourly basis. By monitoring soil moisture levels, users can identify patterns and trends in water consumption, allowing for more informed decision-making regarding watering schedules and plant care. The scope of the system extends to the agriculture industry, where it can be utilized to minimize the efforts required by farmers to monitor soil moisture levels manually. By automating the process of soil moisture monitoring, farmers can optimize water usage, improve crop yields, and reduce the risk of plant diseases associated with over or under-watering. Key technologies employed in the system include the Capacitive Soil Moisture Sensor V1.2 for accurate soil moisture measurement, the Node MCU ESP8266-12E Board for data transmission and communication, and the Arduino framework for programming and development. Additionally, machine learning algorithms are utilized to analyze the collected data and provide actionable insights. Cloud storage is utilized to store and manage the data collected from multiple sensors, allowing for easy access and retrieval of information. Overall, the IoT-based soil moisture monitoring system offers a scalable and efficient solution for indoor plant care, with potential applications in agriculture and beyond. By harnessing the power of IoT and machine learning, the system empowers users to make informed decisions about plant watering, leading to healthier and more vibrant indoor environments.

Keywords: IoT-based, soil moisture monitoring, indoor plants, water management

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Authors: J. O. Idoko, I. Nwajiaku

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Unripe and ripe plantain were dried and milled into flour and used with wheat flour in biscuit production to determine the best plantain-wheat composite flour for biscuit production. The blends as follows: 100% wheat flour, 100% ripe plantain flour, 100% unripe plantain flour, 50% wheat flour and 50% ripe plantain flour and 50% wheat flour and 50% unripe plantain flour. The Biscuit samples were stored at ambient temperature for 8 weeks after which the equilibrium moisture content and water activity were determined. The sensory evaluation of the biscuit samples was also determined. The results of these analyses showed 100% unripe plantain flour as the most stable of the biscuit samples judging from its equilibrium moisture content level of 0.32% and water activity of 0.62. The sensory evaluation results showed Biscuit made from 150:50 ripe plantain and wheat flour as most generally accepted at 5% level of significance.

Keywords: biscuit, equilibrium moisture content, performance, plantain, water activity

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Authors: Pei-Chih Wu, Hsin-Chih Lai, Yung-Lung Lee, Yun-Yao Chi, Ching-Yi Horng, Hsien-Chang Wang

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The impact of climate change to health has always been well documented. Amongst them, water-borne infectious diseases, chronic adverse effects or cancer risks due to chemical contamination in flooding or drought events are especially important in river basin. This study therefore utilizes GIS and different models to integrate demographic, land use, disaster prevention, social-economic factors, and human health assessment in the Erren River basin. Therefore, through the collecting of climatic, demographic, health surveillance, water quality and other water monitoring data, potential risks associated with the Erren River Basin are established and to understand human exposure and vulnerability in response to climate extremes. This study assesses the temporal and spatial patterns of melioidosis (2000-2015) and various cancer incidents in Tainan and Kaohsiung cities. The next step is to analyze the spatial association between diseases incidences, climatic factors, land uses, and other demographic factors by using ArcMap and GeoDa. The study results show that amongst all melioidosis cases in Taiwan, 24% cases (115) residence occurred in the Erren River basin. The relationship between the cases and in Tainan and Kaohsiung cities are associated with population density, aging indicator, and residence in Erren River basin. Risks from flooding due to heavy rainfall and fish farms in spatial lag regression are also related. Through liver cancer, the preliminary analysis in temporal and spatial pattern shows an increases pattern in annual incidence without clusters in Erren River basin. Further analysis of potential cancers connected to heavy metal contamination from water pollution in Erren River is established. The final step is to develop an assessment tool for human exposure from water contamination and vulnerability in response to climate extremes for the second year.

Keywords: climate change, health impact, health adaptation, Erren River Basin

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Authors: C. S. Subhashini, H. L. Premaratne

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Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model.

Keywords: landslides, influencing factors, neural network model, hidden markov model

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Authors: Mst Ilme Faridatul, Bo Wu

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Multi-temporal urban land cover mapping is of paramount importance for monitoring urban sprawl and managing the ecological environment. For diversified urban activities, it is challenging to map land covers in a complex urban environment. Spectral indices have proved to be effective for mapping urban land covers. To improve multi-temporal urban land cover classification and mapping, we evaluate the performance of three spectral indices, e.g. modified normalized difference bare-land index (MNDBI), tasseled cap water and vegetation index (TCWVI) and shadow index (ShDI). The MNDBI is developed to evaluate its performance of enhancing urban impervious areas by separating bare lands. A tasseled cap index, TCWVI is developed to evaluate its competence to detect vegetation and water simultaneously. The ShDI is developed to maximize the spectral difference between shadows of skyscrapers and water and enhance water detection. First, this paper presents a comparative analysis of three spectral indices using Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM) and Operational Land Imager (OLI) data. Second, optimized thresholds of the spectral indices are imputed to classify land covers, and finally, their performance of enhancing multi-temporal urban land cover mapping is assessed. The results indicate that the spectral indices are competent to enhance multi-temporal urban land cover mapping and achieves an overall classification accuracy of 93-96%.

Keywords: land cover, mapping, multi-temporal, spectral indices

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Authors: Thayse Marques Passos, Brid Quilty, Mary Pryce

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The interest in developing alternative techniques to obtain safe water, free from pathogens and hazardous substances, is growing in recent times. The photodynamic inactivation of microorganisms (PDI) is a promising ecologically-friendly and multi-target approach for water disinfection. It uses visible light as an energy source combined with a photosensitiser (PS) to transfer energy/electrons to a substrate or molecular oxygen generating reactive oxygen species, which cause cidal effects towards cells. PDI has mainly been used in clinical studies and investigations on its application to disinfect water is relatively recent. The majority of studies use planktonic cells. However, in their natural environments, bacteria quite often do not occur as freely suspended cells (planktonic) but in cell aggregates that are either freely floating or attached to surfaces as biofilms. Microbes can form aggregates and biofilms as a strategy to protect them from environmental stress. As aggregates, bacteria have a better metabolic function, they communicate more efficiently, and they are more resistant to biocide compounds than their planktonic forms. Among the bacteria that are able to form aggregates are members of the genus Pseudomonas, they are a very diverse group widely distributed in the environment. Pseudomonas species can form aggregates/biofilms in water and can cause particular problems in water distribution systems. The aim of this study was to evaluate the effectiveness of photodynamic inactivation in killing a range of planktonic cells including Escherichia coli DSM 1103, Staphylococcus aureus DSM 799, Shigella sonnei DSM 5570, Salmonella enterica and Pseudomonas putida DSM 6125, and aggregating cells of Pseudomonas fluorescens DSM 50090, Pseudomonas aeruginosa PAO1. The experiments were performed in glass Petri dishes, containing the bacterial suspension and the photosensitiser, irradiated with a multi-LED (wavelengths 430nm and 660nm) for different time intervals. The responses of the cells were monitored using the pour plate technique and confocal microscopy. The study showed that bacteria belonging to Pseudomonads group tend to be more tolerant to PDI. While E. coli, S. aureus, S. sonnei and S. enterica required a dosage ranging from 39.47 J/cm2 to 59.21 J/cm2 for a 5 log reduction, Pseudomonads needed a dosage ranging from 78.94 to 118.42 J/cm2, a higher dose being required when the cells aggregated.

Keywords: bacterial aggregation, photoinactivation, Pseudomonads, water disinfection

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Authors: Mengfan Li, Filip Van Bockstaele, Wenyong Lou, Frank Devlighere

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The development of probiotics-encapsulated emulsions that maintain the viability of probiotics during processing, storage and human gastrointestinal (GI) tract environment receives great scientific and commercial interest. In this study, typical W/O and O/W emulsions with and without oil gelation were used to encapsulate L. plantarum. The effects of emulsion types on the viability of L. plantarum during storage and GI tract were investigated. Besides, the position of L. plantarum in emulsion system and its number of viable cells when threating by adverse environment was correlated in order to figure out which type of emulsion is more suitable as food carrier for probiotics encapsulation and protection. As a result, probiotics tend to migrate from oil to water phase due to the natural hydrophilicity; however, it’s harmful for cells viability when surrounding by water for a long time. Oil gelation in emulsions is one of the promising strategies for inhibiting the cells mobility and decreasing the contact with adverse factors (e.g., water, exogenous enzymes and gastric acid), thus enhancing the number of viable cells that enough to exert its beneficial effects in host.

Keywords: emulsion, gelation, encapsulation, probiotics

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Authors: Ki-Ho Nam, Haksoo Han

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Tetrapod zinc oxide whiskers (TZnO-Ws) were successfully synthesized by a thermal oxidation method. A series of transparent polyimide (PI)/TZnO-W composites were successfully synthesized via a solution-blending method. The structural and morphological features of TZnO-Ws and PI/TZnO-W composites were characterized by Fourier transform infrared spectroscopy (FT-IR), wide-angle X-Ray diffraction (WAXD), and field emission scanning electron microscope (FE-SEM). Dynamic stress behaviors were investigated in-situ during thermal imidization of the soft-baked PI/TZnO-W composite precursor and thermally cured composite films using a thin film stress analyzer (TFSA) by wafer bending technique. The PI/TZnO-W composite films exhibited an optical transparency greater than 80% at 550 nm (≤ 0.5 wt% TZnO-W content), a low coefficient of thermal expansion (CTE), and enhanced glass transition temperature. However, the thermal decomposition temperature decreased as the TZnO-W content increased. The water diffusion coefficient and water uptake of the PI/TZNO-W composite films were obtained by best fits to a Fickian diffusion model. The water resistance capacity of PI was greatly enhanced and moisture diffusion in the pure PI was retarded by incorporating the TZnO-W. The PI composite films based on TZNO-W resultantly may have potential applications in optoelectronic manufacturing processes as a flexible transparent substrate.

Keywords: polyimide (PI), tetrapod ZnO whisker (TZnO-W), transparent, dynamic stress behavior, water resistance

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Authors: B. L. Avanthi Isaka, P. G. Ranjith

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The development of technically-sound enhanced geothermal systems (EGSs) is identified as a viable solution for world growing energy demand with immense potential, low carbon dioxide emission and importantly, as an environmentally friendly option for renewable energy production. The use of supercritical carbon dioxide (ScCO₂) as the working fluid in EGSs by replacing traditional water-based method is promising due to multiple advantages prevail in ScCO₂-injection for underground reservoir stimulation. The evolution of reservoir stimulation using ScCO₂ and the understanding of the flow behavior of a ScCO₂-stimulated geothermal reservoir is vital in applying ScCO₂-EGSs as a replacement for water-based EGSs. The study is therefore aimed to investigate the flow behavior of a ScCO₂-fractured rock medium at in-situ stress and temperature conditions. A series of permeability tests were conducted for ScCO₂ fractured Harcourt granite rock specimens at 90ºC, under varying confining pressures from 5–60 MPa using the high-pressure and high-temperature tri-axial set up which can simulate deep geological conditions. The permeability of the ScCO₂-fractured rock specimens was compared with that of water-fractured rock specimens. The results show that the permeability of the ScCO₂-fractured rock specimens is one order higher than that of water-fractured rock specimens and the permeability exhibits a non-linear reduction with increasing confining pressure due to the stress-induced fracture closure. Further, the enhanced permeability of the ScCO₂-induced fracture with multiple secondary branches was explained by exploring the CT images of the rock specimens. However, a single plain fracture was induced under water-based fracturing.

Keywords: supercritical carbon dioxide, fracture permeability, granite, enhanced geothermal systems

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Authors: Andrew N. Saylor, James R. Peters

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Scoliosis is a complex 3D deformity of the thoracic and lumbar spines, clinically diagnosed by measurement of a Cobb angle of 10 degrees or more on a coronal X-ray. The Cobb angle is the angle made by the lines drawn along the proximal and distal endplates of the respective proximal and distal vertebrae comprising the curve. Traditionally, Cobb angles are measured manually using either a marker, straight edge, and protractor or image measurement software. The task of measuring the Cobb angle can also be represented by a function taking the spine geometry rendered using X-ray imaging as input and returning the approximate angle. Although the form of such a function may be unknown, it can be approximated using artificial neural networks (ANNs). The performance of ANNs is affected by many factors, including the choice of activation function and network architecture; however, the effects of these parameters on the accuracy of scoliotic deformity measurements are poorly understood. Therefore, the objective of this study was to systematically investigate the effect of ANN architecture and activation function on Cobb angle measurement from the coronal X-rays of scoliotic subjects. The data set for this study consisted of 609 coronal chest X-rays of scoliotic subjects divided into 481 training images and 128 test images. These data, which included labeled Cobb angle measurements, were obtained from the SpineWeb online database. In order to normalize the input data, each image was resized using bi-linear interpolation to a size of 500 × 187 pixels, and the pixel intensities were scaled to be between 0 and 1. A fully connected (dense) ANN with a fixed cost function (mean squared error), batch size (10), and learning rate (0.01) was developed using Python Version 3.7.3 and TensorFlow 1.13.1. The activation functions (sigmoid, hyperbolic tangent [tanh], or rectified linear units [ReLU]), number of hidden layers (1, 3, 5, or 10), and number of neurons per layer (10, 100, or 1000) were varied systematically to generate a total of 36 network conditions. Stochastic gradient descent with early stopping was used to train each network. Three trials were run per condition, and the final mean squared errors and mean absolute errors were averaged to quantify the network response for each condition. The network that performed the best used ReLU neurons had three hidden layers, and 100 neurons per layer. The average mean squared error of this network was 222.28 ± 30 degrees2, and the average mean absolute error was 11.96 ± 0.64 degrees. It is also notable that while most of the networks performed similarly, the networks using ReLU neurons, 10 hidden layers, and 1000 neurons per layer, and those using Tanh neurons, one hidden layer, and 10 neurons per layer performed markedly worse with average mean squared errors greater than 400 degrees2 and average mean absolute errors greater than 16 degrees. From the results of this study, it can be seen that the choice of ANN architecture and activation function has a clear impact on Cobb angle inference from coronal X-rays of scoliotic subjects.

Keywords: scoliosis, artificial neural networks, cobb angle, medical imaging

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Authors: Khwaja Naweed Seddiqi, Shigeo Honma

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Relative permeability is the important parameter controlling the immiscible displacement of multiphase fluids flow in porous medium. The relative permeability for immiscible displacement of two-phase fluids flow (oil and water) in porous medium has been measured in this paper. As a result of the experiment, irreducible water saturation, Swi, residual oil saturation, Sor, and relative permeability curves for Kerosene, Heavy oil and Lubricant oil were determined successfully.

Keywords: relative permeability, two-phase flow, immiscible displacement, porous medium

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Authors: Seyyed Naeim Saber, Javad Usefi

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This study was conducted to determine the effect of salbutamol (beta-adrenergic agonist) on carcass characteristics and some blood parameters in male broiler chicks. Four hundred and twenty day-old (Coob-500) male broiler were used in this experiment for six weeks. All birds were randomly divided into 7 treatment groups with 4 replicates of 15 birds per pen. Treatment groups included: control, 5, 10, and 15 mg salbutamol per liter water and 10, 20 and 30 mg salbutamol per kg diet. The data obtained from this study indicated that supplementation of salbutamol in water and diets have significant effect on live body weight, abdominal fat, and gizzard weight (p<0.05). Also adding salbutamol in broiler water and feed did not have significant effect on thigh and breast dry matter, thigh and breast crude protein, and thigh and breast crude fat (p>0.05). The results from this study demonstrated that salbutamol has significant (p<0.05) effect on hemoglobin content and RV/TV but it does not have significant effect (p>0.05) on hematocrit amount.

Keywords: salbutamol, beta-adrenergic agonist, broiler chicks, carcass characteristic

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Authors: Ahmed Alutaibi, Ganti Sudhakar

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Providing better Quality-of-Service (QoS) to end users has been a challenging problem for researchers and service providers. Building applications relying on best effort network protocols hindered the adoption of guaranteed service parameters and, ultimately, Quality of Service. The introduction of Software Defined Networking (SDN) opened the door for a new paradigm shift towards a more controlled programmable configurable behavior. Openflow has been and still is the main implementation of the SDN vision. To facilitate better QoS for applications, the network must calculate and measure certain parameters. One of those parameters is the delay between the two ends of the connection. Using the power of SDN and the knowledge of application and network behavior, SDN networks can adjust to different conditions and specifications. In this paper, we use the capabilities of SDN to implement multiple algorithms to measure delay end-to-end not only inside the SDN network. The results of applying the algorithms on an emulated environment show that we can get measurements close to the emulated delay. The results also show that depending on the algorithm, load on the network and controller can differ. In addition, the transport layer handshake algorithm performs best among the tested algorithms. Out of the results and implementation, we show the limitations of Openflow and develop suggestions to solve them.

Keywords: software defined networking, quality of service, delay measurement, openflow, mininet

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Authors: Ashraf Aly

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The aim of this study is to examine the impact of 5G communication technology radiation on human health and airport safety. The term 5G refers to the fifth generation of wireless mobile technology. The 5G wireless technology will increase the number of high-frequency-powered base stations and other devices and browsing and download speeds, as well as improve the network connectivity and play a big part in improving the performance of integrated applications, such as self-driving cars, medical devices, and robotics. 4G was the latest embedded version of mobile networking technology called 4G, and 5G is the new version of wireless technology. 5G networks have more features than 4G networks, such as lower latency, higher capacity, and increased bandwidth compared to 4G. 5G network improvements over 4G will have big impacts on how people live, business, and work all over the world. But neither 4G nor 5G have been tested for safety and show harmful effects from this wireless radiation. This paper presents biological factors on the effects of 5G radiation on human health. 5G services use C-band radio frequencies; these frequencies are close to those used by radio altimeters, which represent important equipment for airport and aircraft safety. The aviation industry, telecommunications companies, and their regulators have been discussing and weighing these interference concerns for years.

Keywords: wireless communication, radiofrequency, Electromagnetic field, environmental issues

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Authors: Ahmed Cherif Megri, HossamEldin ElSherif

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In a power plant, the header pipe plays a pivotal role in optimizing the performance of diverse systems by serving as a central conduit for the collection and distribution of steam within the plant. This paper investigates the significance of header pipes within power plant setups, highlighting their critical influence on reliability, efficiency, and the performance of the power plant as a whole. The concept of shape optimization emerges as a crucial factor in power plant design and operation, with the potential to maximize performance while minimizing the use of materials. Shape optimization not only enhances efficiency but also contributes to reducing the environmental footprint of power plant installations. In this paper, we initially developed a methodology designed for optimizing header shapes with the primary goal of reducing the usage of costly new alloy materials and lowering the overall maintenance operation expenses. Secondly, we conducted a case study based on an authentic header sourced from an operational power plant.

Keywords: shape optimization, header, power plant, inconel alloy, CFD, structural optimization

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Authors: Mark Pizzato

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How do ideas, images, and emotions in stage-plays and videos affect us? Do they evoke a greater awareness (or cognitive reappraisal of emotions) through possible shifts between left-cortical, right-cortical, and subcortical networks? To address these questions, this presentation summarizes the research of various neuroscientists, especially Bernard Baars and others involved in Global Workspace Theory, Matthew Lieberman in social neuroscience, Iain McGilchrist on left and right cortical functions, and Jaak Panksepp on the subcortical circuits of primal emotions. Through such research, this presentation offers an ‘inner theatre’ model of the brain, regarding major hubs of neural networks and our animal ancestry. It also considers recent experiments, by Mario Beauregard, on the cognitive reappraisal of sad, erotic, and aversive film clips. Finally, it applies the inner-theatre model and related research to survey results of theatre students who read and then watched the ‘To be or not to be’ speech in 8 different video versions (from stage and screen productions) of William Shakespeare’s Hamlet. Findings show that students become aware of left-cortical, right-cortical, and subcortical brain functions—and shifts between them—through staging and movie-making choices in each of the different videos.

Keywords: cognitive reappraisal, Hamlet, neuroscience, Shakespeare, theatre

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

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Authors: Maged El Osta, Milad Masoud

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Al Jabal Al Akhdar in the north-eastern part of Libya represents a region with promising ecological underpinning for grazing and other agricultural developments. The groundwater potential of both Upper Cretaceous and Eocene aquifers was studied based the available literature and a complete database for about 112 water wells drilled in the period 2003-2009. In this research, the hydrogeological methods will be integrated with the Geographic Information System (GIS) that played a main role in highlighting the spatial characteristics of the groundwater system. The results indicate that the depth to water for the Upper Cretaceous aquifer ranges from 150 to 458 m, and the piezometric surface decreases from over 500 m (m.s.l) in the northern parts to -20 m (m.s.l) in southeastern part. Salinity ranges between 303 and 1329 mg/l indicating that groundwater belongs to the slightly fresh water class. In the Eocene aquifer, the depth to groundwater ranges from 120 to 290.5 m and the potentiometric level decreases gradually southwards from 220 to -51 m (m.s.l) and characterized by steep slope in the southeastern part of the study area, where the aquifer characterized by relatively high productivity (specific capacity ranges between 10.08 and 332.3 m2/day). The groundwater salinity within this aquifer ranges between 198 and 2800 mg/l (fresh to brackish water class). The annual average rainfall (from 280 to 500 mm) plays a significant role in the recharge of the two aquifers. The priority of groundwater quality and potentiality increases towards the central and northern portions of the concerned area.

Keywords: Eocene and Upper Cretaceous aquifers, rainfall, potentiality, Geographic Information System (GIS)

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Authors: África Vicario, Fernando J. Álvarez, Felipe Parralejo, Fernando Aranda

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The irrigation systems of traditional agriculture, such as gravity-fed irrigation, produce a great waste of water because, generally, there is no control over the amount of water supplied in relation to the water needed. The AIRON Project tries to solve this problem by implementing an IoT-based system to sensor the irrigation plots so that the state of the crops and the amount of water used for irrigation can be known remotely. The IoT system consists of a sensor network that measures the humidity of the soil, the weather conditions (temperature, relative humidity, wind and solar radiation) and the irrigation water flow. The communication between this network and a central gateway is conducted by means of long-range wireless communication that depends on the characteristics of the irrigation plot. The main objective of the AIRON project is to deploy an IoT sensor network in two different plots of the irrigation community of Aranjuez in the Spanish region of Madrid. The first plot is 2 km away from the central gateway, so LoRa has been used as the base communication technology. The problem with this plot is the absence of mains electric power, so devices with energy-saving modes have had to be used to maximize the external batteries' use time. An ESP32 SOC board with a LoRa module is employed in this case to gather data from the sensor network and send them to a gateway consisting of a Raspberry Pi with a LoRa hat. The second plot is located 18 km away from the gateway, a range that hampers the use of LoRa technology. In order to establish reliable communication in this case, the long-term evolution (LTE) standard is used, which makes it possible to reach much greater distances by using the cellular network. As mains electric power is available in this plot, a Raspberry Pi has been used instead of the ESP32 board to collect sensor data. All data received from the two plots are stored on a proprietary server located at the irrigation management company's headquarters. The analysis of these data by means of machine learning algorithms that are currently under development should allow a short-term prediction of the irrigation water demand that would significantly reduce the waste of this increasingly valuable natural resource. The major finding of this work is the real possibility of deploying a remote sensing system for irrigated plots by using Commercial-Off-The-Shelf (COTS) devices, easily scalable and adaptable to design requirements such as the distance to the control center or the availability of mains electrical power at the site.

Keywords: internet of things, irrigation water control, LoRa, LTE, smart farming

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Authors: Hira Lal Gope, Hidekazu Fukai

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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: convolutional neural networks, coffee bean, peaberry, sorting, support vector machine

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Authors: Ruslan Salmurzauli, Sabir Nurtazin, Buho Hoshino, Niels Thevs, A. B. Yeszhanov, Aiman Imentai

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This article presents the results of a research on the transformation of the diverse ecosystems of the Ili delta during the period 1979-2014 based on the analysis of the hydrological regime dynamics, weather conditions and satellite images. Conclusions have been drawn on the decisive importance of the water runoff of the Ili River in the negative changes and environmental degradation in delta areas over the past forty-five years. The increase of water consumption in the Chinese and Kazakhstan parts of the Ili-Balkhash basin caused desiccation and desertification of many hydromorphic delta ecosystems and the reduction of water flow into Lake Balkhash. We demonstrate that a significant reduction of watering of the delta areas could drastically accelerate the aridization and degradation of the hydromorphic ecosystems. Under runoff decrease, a transformation process of the delta ecosystems begins from the head part and gradually spread northward to the periphery of the delta. The desertification is most clearly expressed in the central and western parts of the delta areas.

Keywords: Ili-Balkhash basin, Ili river delta, runoff, hydrological regime, transformation of ecosystems, remote sensing

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Authors: Harche Rima, Laoufi Nadia Aicha

Abstract:

This work focuses on the study and modeling of the fouling phenomenon in a vertical pipe. In the first step, milk is one of the fluids obeying the phenomenon of fouling because of the denaturation of these proteins, especially lactoglobulin, which is the active element of milk, and to facilitate its use, we chose to study milk as a fouling fluid. In another step, we consider the test section of our installation as a tubular-type heat exchanger that works against the current and in a closed circuit. A simple mathematical model of Kern & Seaton, based on the kinetics of the fouling resistance, was used to evaluate the influence of the operating parameters (fluid flow velocity and exchange wall temperature) on the fouling resistance. The influence of the variation of the fouling resistance with the operating conditions on the efficiency of the heat exchanger and the importance of the dirty state exchange coefficient as an exchange quality control parameter were discussed and examined. On the other hand, an electronic scanning microscope analysis was performed on the milk deposit in order to obtain its actual image and composition, which allowed us to calculate the thickness of this deposit.

Keywords: fouling, milk, tubular heat exchanger, fouling resistance

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Authors: Naveed Ghani

Abstract:

With the arrival of higher speed communication links and mature application running over the internet, the requirement for reliable, efficient and robust network designs rising day by day. Multi-Protocol Label Switching technology (MPLS) Virtual Private Networks (VPNs) have committed to provide optimal network services. They are gaining popularity in industry day by day. Enterprise customers are moving to service providers that offer MPLS VPNs. The main reason for this shifting is the capability of MPLS VPN to provide built in security features and any-to-any connectivity. MPLS VPNs improved the network performance due to fast label switching as compare to traditional IP Forwarding but traffic classification and policing was still required on per hop basis to enhance the performance of real time traffic which is delay sensitive (particularly voice and video). QoS (Quality of service) is the most important factor to prioritize enterprise networks’ real time traffic such as voice and video. This thesis is focused on the study of QoS parameters (e.g. delay, jitter and MOS (Mean Opinion Score)) for the real time traffic over MPLS VPNs. DiffServ (Differentiated Services) QoS model will be used over MPLS VPN network to get end-to-end service quality.

Keywords: network, MPLS, VPN, DiffServ, MPLS VPN, DiffServ QoS, QoS Model, GNS2

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Authors: Amin Mojiri, Hamidi Abdul Aziz

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Leachate is created while water penetrates through the waste in a landfill, carrying some forms of pollutants. In literature, for treatment of wastewater and leachate, different ways of biological treatment were used. Sequencing batch reactor (SBR) is a kind of biological treatment. This study investigated the co-treatment of landfill leachate and domestic waste water by SBR and powdered activated carbon augmented (PAC) SBR process. The response surface methodology (RSM) and central composite design (CCD) were employed. The independent variables were aeration rate (L/min), contact time (h), and the ratio of leachate to wastewater mixture (%; v/v)). To perform an adequate analysis of the aerobic process, three dependent parameters, i.e. COD, color, and ammonia-nitrogen (NH3-N or NH4-N) were measured as responses. The findings of the study indicated that the PAC-SBR showed a higher performance in elimination of certain pollutants, in comparison with SBR. With the optimal conditions of aeration rate (0.6 L/min), leachate to waste water ratio (20%), and contact time (10.8 h) for the PAC-SBR, the removal efficiencies for color, NH3-N, and COD were 72.8%, 98.5%, and 65.2%, respectively.

Keywords: co-treatment, landfill Leachate, wastewater, sequencing batch reactor, activate carbon

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Authors: Sana Dridi, Jalel Aouissi, Rafla Attia, Taoufik Hermassi, Thouraya Sahli

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Soil erosion is an increasing issue in Mediterranean countries. In Tunisia, the capacity of dam reservoirs continues to decrease as a consequence of soil erosion. This study aims to predict sediment yield to enrich soil management practices using Soil and Water Assessment Tools model (SWAT) in the Siliana watershed (1041.6 km²), located in the northwest of Tunisia. A database was constructed using remote sensing and Geographical Information System. Climatic and flow data were collected from water resources directorates in Tunisia. The SWAT model was built to simulate hydrological processes and sediment transport. A sensitivity analysis, calibration, and validation were performed using SWAT-CUP software. The model calibration of stream flow simulations shows a good performance with NSE and R² values of 0.77 and 0.79, respectively. The model validation shows a very good performance with values of NSE and R² for 0.8 and 0.88, respectively. After calibration and validation of stream flow simulation, the model was used to simulate the soil erosion and sediment load transport. The spatial distributions of soil loss rate for determining the critical sediment source areas show that 63 % of the study area has a low soil loss rate less than 7 t ha⁻¹y⁻¹. The annual average soil loss rate simulated with the SWAT model in the Siliana watershed is 4.62 t ha⁻¹y⁻¹.

Keywords: water erosion, SWAT model, streamflow, SWATCUP, sediment yield

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Authors: Mehran Asadi, Afrand Agah

Abstract:

In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Jaime Pizarro, Ximena Castillo

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The intensive use of water in agriculture, industry, human consumption and increasing pollution are factors that reduce the availability of water for future generations; the challenge is to advance in sustainable and low-cost solutions to reuse water and to facilitate the availability of the resource in quality and quantity. The use of new low-cost materials with sorbent capacity for pollutants is a solution that contributes to the improvement and expansion of water treatment and reuse systems. Fly ash, a residue from the combustion of coal in power plants that is produced in large quantities in newly industrialized countries, contains a high amount of silicon oxides and aluminum oxides, whose properties can be used for the synthesis of mesoporous materials. Properly functionalized, this material allows obtaining matrixes with high sorption capacity. The mesoporous materials have a large surface area, thermal and mechanical stability, uniform porous structure, and high sorption and functionalization capacities. The goal of this study was to develop hexagonal mesoporous siliceous material (HMS) for the adsorption of sulphate from industrial and mining waters. The silica was extracted from fly ash after calcination at 850 ° C, followed by the addition of water. The mesoporous structure has a surface area of 282 m2 g-1 and a size of 5.7 nm and was functionalized with ethylene diamine through of a self-assembly method. The material was characterized by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS). The capacity of sulphate sorption was evaluated according to pH, maximum load capacity and contact time. The sulphate maximum adsorption capacity was 146.1 mg g-1, which is three times higher than commercial sorbents. The kinetic data were fitted according to a pseudo-second order model with a high coefficient of linear regression at different initial concentrations. The adsorption isotherm that best fitted the experimental data was the Freundlich model.

Keywords: fly ash, mesoporous siliceous, sorption, sulphate

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Authors: Manal Alhazmi, Artem Mishchenko

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A molecular fluids' behavior and interaction with other materials at the nanoscale is a complex process. Nanoscale fluids behave so differently than macroscale fluids and interact with other materials in unique ways. It is, therefore, feasible to understand the molecular behavior of H₂O in such two-dimensional nanoscale systems by studying (CaSO4-2H2O), commonly known as gypsum. In the present study, spectroscopic measurements on a 2D structure of exfoliated gypsum crystals are carried out by Raman and IR spectroscopy. An array of gypsum flakes with thicknesses ranging from 8nm to 100nm were observed and analyzed for their Raman and IR spectrum. Water molecules stretching modes spectra lines were also measured and observed in nanoscale gypsum flakes and compared with those of bulk crystals. CaSO4-2H2O crystals have Raman and infrared bands at 3341 cm-1 resulting from the weak hydrogen bonds between the water molecules. This internal vibration of water molecules, together with external vibrations with other atoms, are responsible for these bands. There is a shift of about 70 cm-1 In the peak position of thin flakes with respect to the bulk crystal, which is a result of the different atomic arrangement from bulk to thin flake on the nano scale. An additional peak was observed in Raman spectra around 2910-3137 cm⁻¹ in thin flakes but is missing in bulk crystal. This additional peak is attributed to a combined mode of water internal (stretching mode at 3394cm⁻¹) and external vibrations. In addition to Raman and infra- red analysis of gypsum 2D structure, electrical measurements were conducted to reveal the water molecules transport behavior in such systems. Electrical capacitance of the fabricated device is measured and found to be (0.0686 *10-12) F, and the calculated dielectric constant (ε) is (12.26).

Keywords: gypsum, infra-red spectroscopy, raman spectroscopy, H₂O behavior

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Authors: Kamran Baksh Soomro, Amin Talei, Sina Alaghmand

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Due to the declining freshwater availability to agriculture in many areas, the utilization of saline irrigation requires more consideration. For this purpose, the effects of saline irrigation on the economics of crop yield and soil salinity should be understood. A two-year field experiment was carried out during 2017-18 with three replications to investigate the effect of saline groundwater on the economics of bitter gourd production and soil salinity status after harvesting the crop. Two irrigation treatments, i.e., fresh quality irrigation water (IT₁ EC 0.56 dS.m⁻¹ (control) and other is saline groundwater ( IT₂ EC 2.56 dS.m⁻¹) were used under drip system of irrigation. Cost-benefit analysis is often used to assess adaptation approaches. In this study, it has been observed that the salts under IT₁ (fresh quality water) and IT₂ (saline groundwater) did not accumulate in the wetted zone. However, the salts were observed deposited at wetted periphery under both the treatments after the crop end at all the three sampling depths under drip system of irrigation. Moreover, the costs and benefits associated with different irrigation treatments for two consecutive seasons for bitter-gourd cultivation were also investigated, and it was found that the average gross returns per hectare in season 1 were USD 5008.22 and 4454.78 under irrigation treatment IT₁ and IT₂ respectively. Whereas in season 2 the average gross returns per hectare were 3713.47 and 3140.51 under IT₁ and IT₂ respectively.

Keywords: ground-water, soil salinity, drip irrigation, wetted zone, wetted periphery, cost benefit analysis

Procedia PDF Downloads 135