Search results for: water network efficiency

Authors: Amir Gerayeli, Babak Moradi

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The continuously declining oil reservoirs and reservoirs aging have created a huge demand for utilization of Enhanced Oil Recovery (EOR) methods recently. Primary and secondary oil recovery methods have various limitations and are not practical for all reservoirs. Therefore, it is necessary to use chemical methods to improve oil recovery efficiency by reducing oil and water surface tension, increasing sweeping efficiency, and reducing displacer phase viscosity. One of the well-known methods of oil recovery is Alkaline-Surfactant-Polymer (ASP) flooding that shown to have significant impact on enhancing oil recovery. As some of the biggest oil reservoirs including those of Iran’s are fractional reservoirs with substantial amount of trapped oil in their fractures, the use of Alkaline-Surfactant-Polymer (ASP) flooding method is increasingly growing, the method in which the impact of several parameters including type and concentration of the Alkaline, Surfactant, and polymer are particularly important. This study investigated the use of Nano particles to improve Enhanced Oil Recovery (EOR). The study methodology included performing several laboratory tests on drill cores extracted from Karanj Oil field Asmary Formation in Khuzestan, Iran. In the experiments performed, Sodium dodecyl benzenesulfonate (SDBS) and 1-dodecyl-3-methylimidazolium chloride ([C12mim] [Cl])) were used as surfactant, hydrolyzed polyacrylamide (HPAM) and guar gum were used as polymer, Sodium hydroxide (NaOH) as alkaline, and Silicon dioxide (SiO2) and Magnesium oxide (MgO) were used as Nano particles. The experiment findings suggest that water viscosity increased from 1 centipoise to 5 centipoise when hydrolyzed polyacrylamide (HPAM) and guar gum were used as polymer. The surface tension between oil and water was initially measured as 25.808 (mN/m). The optimum surfactant concentration was found to be 500 p, at which the oil and water tension surface was measured to be 2.90 (mN/m) when [C12mim] [Cl] was used, and 3.28 (mN/m) when SDBS was used. The Nano particles concentration ranged from 100 ppm to 1500 ppm in this study. The optimum Nano particle concentration was found to be 1000 ppm for MgO and 500 ppm for SiO2.

Keywords: alkaline-surfactant-polymer, ionic liquids, relative permeability, reduced surface tension, tertiary enhanced oil recovery, wettability change

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Authors: Samaneh Abolli, Mahmood Alimohammadi

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The survey analyzed 71 bottled water brands in Tehran, Iran, examining 10 physicochemical parameters and 16 heavy metals. The water quality index (WQI) approach was used to assess water quality, and methods such as carcinogen risk (CR) and hazard index (HI) were employed to evaluate health risks. The results indicated that the bottled water had good quality overall, but some brands were of poor or very poor quality. The study also revealed significant human health risks, especially for children, due to the presence of minerals and heavy metals in bottled water. Correlation analyses and risk assessments for various substances were conducted, providing valuable insights into the potential health impacts of the analyzed bottled water.

Keywords: bottled wate, rwater quality index, health risk assessment, contamination degree, heavy metal evaluation index

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Authors: Radhia Toujani, Jalel Akaichi

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In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorporate the value of structural and semantic similarity based modularity and a metaheuristic namely bees colonies algorithm to optimize our objective function on both hierarchical level divisive and agglomerative. In order to assess the efficiency and the accuracy of the introduced hybrid bee colony model, we perform an extensive experimental evaluation on both synthetic and real networks.

Keywords: social network, community detection, agglomerative hierarchical clustering, divisive hierarchical clustering, similarity, modularity, metaheuristic, bee colony

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Authors: Hooman Fallah

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Seawater injection and produced water re-injection are presently the most commonly used approach to enhanced recovery. The dominant factors for total oil recovery are the reservoir temperature, reservoir pressure, crude oil and water composition. In this study, the production under water injection in Soroosh, one of the southern Iranian heavy oil field has been simulated (the fluid properties are focused). In order to reveal the dominant factors in this production process, the sensitivity analysis has been done for the following effective factors, fluid viscosity, initial water saturation, gravity force and injection well strategy. It is crystal clear that the study of the dominant factors in production processes will help the engineers to design the best production mechanisms in our numerous hydrocarbon reservoirs.

Keywords: water injection, initial water saturation, oil viscosity, gravity force, injection well strategy

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Authors: T. Tahri, H. Zahloul, K. E. Meddah, H. Lazergue

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This paper presents a theoretical study that leads to the design of a photovoltaic pumping system to irrigate six hectares of oranges in the valley of Chlef using the software "PVSYST". It was shown that the site of Chlef presents a favorable climate to this type of energy with an irradiation of over 5 kWh/m2/day, and significant resources underground water. Another very important coincidence still promotes the use of this type of energy for pumping water in Chlef is that the demand for water, especially in agriculture, peaked in hot and dry where it is precisely when one has access to the maximum of solar energy.

Keywords: solar energy, irradiation, water pumping, design, Valley of Chlef

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Authors: Tarek S. Jamil, Ahmed M. Shaban, Eman S. Mansor, Ahmed A. Karim, Azza M. Abdel Aty

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In this manuscript, 35 m³/d NF unit was designed and applied for surface water treatment of river Nile water. Intake of Embaba drinking water treatment plant was selected to install that unit at since; it has the lowest water quality index value through the examined 6 sites in greater Cairo area. The optimized operating conditions were feed and permeate flow, 40 and 7 m³/d, feed pressure 2.68 bar and flux rate 37.7 l/m2.h. The permeate water was drinkable according to Egyptian Ministerial decree 458/2007 for the tested parameters (physic-chemical, heavy metals, organic, algal, bacteriological and parasitological). Single and double sand filters were used as pretreatment for NF membranes, but continuous clogging for sand filters moved us to use UF membrane as pretreatment for NF membrane.

Keywords: River Nile, NF membrane, pretreatment, UF membrane, water quality

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Authors: Khalid S. Hashim, Andy Shaw, Rafid Alkhaddar, Montserrat Ortoneda Pedrola

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A flow column has been innovatively used in the design of a new electrocoagulation reactor (ECR1) that will reduce the temperature of water being treated; where the flow columns work as a radiator for the water being treated. In order to investigate the performance of ECR1 and compare it to that of traditional reactors; 600 mL water samples with an initial temperature of 35 0C were pumped continuously through these reactors for 30 min at current density of 1 mA/cm2. The temperature of water being treated was measured at 5 minutes intervals over a 30 minutes period using a thermometer. Additional experiments were commenced to investigate the effects of initial temperature (15-35 0C), water conductivity (0.15 – 1.2 S) and current density (0.5 -3 mA/cm2) on the performance of ECR1. The results obtained demonstrated that the ECR1, at a current density of 1 mA/cm2 and continuous flow model, reduced water temperature from 35 0C to the vicinity of 28 0C during the first 15 minutes and kept the same level till the end of the treatment time. While, the temperature increased from 28.1 to 29.8 0C and from 29.8 to 31.9 0C in the batch and the traditional continuous flow models respectively. In term of initial temperature, ECR1 maintained the temperature of water being treated within the range of 22 to 28 0C without the need for external cooling system even when the initial temperatures varied over a wide range (15 to 35 0C). The influent water conductivity was found to be a significant variable that affect the temperature. The desirable value of water conductivity is 0.6 S. However, it was found that the water temperature increased rapidly with a higher current density.

Keywords: water temperature, flow column, electrocoagulation

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Authors: Virender Ranga, Mayank Dave, Anil Kumar Verma

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Recently wireless sensor networks (WSNs) are used in many real life applications like environmental monitoring, habitat monitoring, health monitoring etc. Due to power constraint cheaper devices used in these applications, the energy consumption of each device should be kept as low as possible such that network operates for longer period of time. One of the techniques to prolong the network lifetime is an intelligent grouping of sensor nodes such that they can perform their operation in cooperative and energy efficient manner. With this motivation, we propose a novel approach by organize the sensor nodes in cooperative multihop mini-groups so that the total global energy consumption of the network can be reduced and network lifetime can be improved. Our proposed approach also reduces the number of transmitted messages inside the WSNs, which further minimizes the energy consumption of the whole network. The experimental simulations show that our proposed approach outperforms over the state-of-the-art approach in terms of stability period and aggregated data.

Keywords: clustering, cluster-head, mini-group, stability period

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Azam Mohammadbagheri, Bahram Fathi

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DEA has become a very popular method of performance measure, but it still suffers from some shortcomings. One of these shortcomings is the issue of having multiple optimal solutions to weights for efficient DMUs. The cross efficiency evaluation as an extension of DEA is proposed to avoid this problem. Lam (2010) is also proposed a mixed-integer linear programming formulation based on linear discriminate analysis and super efficiency method (MILP model) to avoid having multiple optimal solutions to weights. In this study, we modified MILP model to determine more suitable weight sets and also evaluate the energy efficiency of MENA countries as an application of the proposed model.

Keywords: data envelopment analysis, discriminate analysis, cross efficiency, MILP model

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Authors: Redvan Ghasemlounia, Elham Ansari, Hikmet Kerem Cigizoglu

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Hydrological modeling in arid and semi-arid regions is very important. Iran has many regions with these climate conditions such as Chaharmahal and Bakhtiari province that needs lots of attention with an appropriate management. Forecasting of hydrological parameters and estimation of hydrological events of catchments, provide important information that used for design, management and operation of water resources such as river systems, and dams, widely. Discharge in rivers is one of these parameters. This study presents the application and comparison of some estimation methods such as Feed-Forward Back Propagation Neural Network (FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) to predict the daily flow discharge of the Soolegan River, located at Chaharmahal and Bakhtiari province, in Iran. In this study, Soolegan, station was considered. This Station is located in Soolegan River at 51° 14՜ Latitude 31° 38՜ longitude at North Karoon basin. The Soolegan station is 2086 meters higher than sea level. The data used in this study are daily discharge and daily precipitation of Soolegan station. Feed Forward Back Propagation Neural Network(FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) models were developed using the same input parameters for Soolegan's daily discharge estimation. The results of estimation models were compared with observed discharge values to evaluate performance of the developed models. Results of all methods were compared and shown in tables and charts.

Keywords: ANN, multi linear regression, Bayesian network, forecasting, discharge, gene expression programming

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Authors: Erhan Berk

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The purpose of this study is to examine the efficiency and productivity of the health care sector in Turkey based on four years of health care cross-sectional data. Efficiency measures are calculated by a nonparametric approach known as Data Envelopment Analysis (DEA). Productivity is measured by the Malmquist index. The research shows how DEA-based Malmquist productivity index can be operated to appraise the technology and productivity changes resulted in the Turkish hospitals which are located all across the country.

Keywords: data envelopment analysis, efficiency, health care, Malmquist Index

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Authors: Brian H. Marsh

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Nitrogen fertility is an important component for optimum potato yield and quality. Best management practices are necessary in regards to N applications to achieve these goals without applying excess N with may contribute to ground water contamination. Eight potato fields in the Southern San Joaquin Valley were sampled for nitrogen inputs and uptake, tuber and vine dry matter and residual soil nitrate-N. The fields had substantial soil nitrate-N prior to the potato crop. Nitrogen fertilizer was applied prior to planting and in irrigation water as needed based on in-season petiole sampling in accordance with published recommendations. Average total nitrogen uptake was 237 kg ha^-1 on 63.5 Mg ha^-1 tuber yield and nitrogen use efficiency was very good at 81 percent. Sixty-nine percent of the plant nitrogen was removed in tubers. Soil nitrate-N increased 14 percent from pre-plant to post-harvest averaged across all fields and was generally situated in the upper soil profile. Irrigation timing and amount applied did not move water into the lower profile except for a single location where nitrate also moved into the lower soil profile. Pre-plant soil analysis is important information to be used. Rotation crops having deeper rooting growth would be able to utilize nitrogen that remained in the soil profile.

Keywords: potato, nitrogen fertilization, irrigation management, leaching potential

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Authors: Unnop Panpuang

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The objectives of this project are to study on the work efficiency of the employees, sorted by their profiles, and to study on the relation between job attributes and work efficiency of employees of Suan Sunandha Rajabhat University. The samples used for this study are 292 employees. The statistics used in this study are frequencies, standard deviations, One-way ANOVA and Pearson’s correlation coefficient. Majority of respondent were male with an undergraduate degree, married and lives together. The average age of respondents was between 31-41 years old, married and the educational background are higher than bachelor’s degree. The job attribute is correlated to the work efficiency with the statistical significance level of .01. This concurs with the predetermined hypothesis. The correlation between the two main factors is in the moderate level. All the categories of job attributes such as the variety of skills, job clarity, job importance, freedom to do work are considered separately.

Keywords: employees, job attributes, work efficiency, university

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Authors: Cecilia Kunseh Betek

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Managing rural water supply systems effectively and efficiently is a challenge in the O R Tambo District Municipality due to the long distances between consumers and municipal centres. This is a couple with the low income of most residents and the government's policy of free basic water which is making rural water provision very difficult. With regard to cartage, the results reveal that the majority (84.4%) of the population covers distances of about 1kilometre to fetch water, and 15.6% travel up kilometer to access water facilities. This means that the water sources are located very far from households, outside the officially legislated array of 200metres. These are many reasons to account for this situation. Firstly, this implies that there are inadequate stand pipes to cater for all the homesteads scattered across the rugged terrain of OR Tambo District municipality. Secondly, and following from the first explanation, it would be seen that funding that is made available is not adequate, or is not efficiently spent on the targeted projects. The situation in the rural areas of South Africa is fraught with cumbersome complexity when it comes to service delivery.

Keywords: water, management, government, rural

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Authors: Sadia Aziz, Mahwish Ali, Safia Ahmed

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Comparing other methods of waste water treatment, constructed wetlands are one of the most fascinating practices because being a natural process they are eco-friendly have low construction and maintenance cost and have considerable capability of wastewater treatment. The current research was focused mainly on comparison of Ranunculus muricatus and Typha latifolia as wetland plants for domestic wastewater treatment by designing and constructing efficient pilot scale HSSF mesocosms. Parameters like COD, BOD5, PO4, SO4, NO3, NO2, and pathogenic indicator microbes were studied continuously with successive treatments. Treatment efficiency of the system increases with passage of time and with increase in temperature. Efficiency of T. latifolia planted setups in open environment was fairly good for parameters like COD and BOD5 which was showing up to 82.5% for COD and 82.6% for BOD5 while DO was increased up to 125%. Efficiency of R. muricatus vegetated setup was also good but lowers than that of T. latifolia planted showing 80.95% removal of COD and BOD5. Ranunculus muricatus was found effective in reducing bacterial count in wastewater. Both macrophytes were found promising in wastewater treatment.

Keywords: wastewater treatment, wetland, mesocosms study, wetland plants

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Authors: Luyao Liu, Dekui Yuan, Xu Li

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As one of the most important bays of China, the water exchange capacity of Bohai Bay can influence the economic development and urbanization of surrounding cities. However, the rapid reclamation has influenced the weak water exchange capacity of this semi-enclosed bay in recent years. This paper sets two hydrodynamic models of Bohai Bay with two shorelines before and after reclamation. The mean value and distribution of Turn-over Time, the distribution of residual current, and the feature of the tracer path are compared. After comparison, it is found that Bohai Bay keeps these characteristics; the spending time of water exchange in the northern is longer than southern, and inshore is longer than offshore. However, the mean water exchange time becomes longer after reclamation. In addition, the material spreading is blocked because of the inwardly extending shorelines, and the direction changed from along the shoreline to towards the center after reclamation.

Keywords: Bohai Bay, water exchange, reclamation, turn-over time

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Authors: Abdelghani Qadem

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Urbanization in Morocco has ushered in profound shifts in hydrological dynamics, presenting a spectrum of challenges and avenues for sustainable water management. This abstract delves into the nuances of urban hydrology in Morocco, spotlighting the ramifications of rapid urban expansion, the imprint of climate change, and the imperative for cohesive water management strategies. The swift urban sprawl across Morocco has engendered a surge in impermeable surfaces, reshaping the natural hydrological cycle and amplifying quandaries such as urban inundations and water scarcity. Moreover, the specter of climate change looms large, heralding alterations in precipitation regimes and a heightened frequency of extreme meteorological events, thus compounding the hydrological conundrum. However, amidst these challenges, urban hydrology in Morocco also unfolds vistas of innovation and sustainability. The integration of green infrastructure, encompassing solutions like permeable pavements and vegetated roofs, emerges as a linchpin in ameliorating the hydrological imbalances wrought by urbanization, fostering infiltration, and curbing surface runoff. Additionally, embracing the tenets of water-sensitive urban design promises to fortify water efficiency and resilience in urban landscapes. Effectively navigating urban hydrology in Morocco mandates a cross-disciplinary approach that interweaves urban planning, water resource governance, and climate resilience strategies. A collaborative ethos, bridging governmental entities, academic institutions, and grassroots communities, assumes paramount importance in crafting and executing comprehensive solutions that grapple with the intricate interplay of urbanization, hydrology, and climate dynamics. In summation, confronting the labyrinthine challenges of urban hydrology in Morocco necessitates proactive strides toward fostering sustainable urban growth and bolstering resilience to climate vagaries. By embracing cutting-edge technologies and embracing an ethos of integrated water management, Morocco can forge a path toward a more water-secure and resilient urban future.

Keywords: urban hydrology, Morocco, urbanization, climate change, water management, green infrastructure, sustainable development

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Authors: S. A. Che Osmi, W. M. F. W. Ishak, S. F. Che Osmi

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Nowadays the issues of water quality and water pollution have been a major problem across the country. A lot of management attempt to develop their own TMDL database in order to control the river pollution. Over the past decade, the mathematical modeling has been used as the tool for the development of TMDL. This paper presents the application of water quality modeling to develop the total maximum daily load (TMDL) information. To obtain the reliable database of TMDL, the appropriate water quality modeling should choose based on the available data provided. This paper will discuss on the use of several water quality modeling such as QUAL2E, QUAL2K, and EFDC to develop TMDL. The attempts to integrate several modeling are also being discussed in this paper. Based on this paper, the differences in the application of water quality modeling based on their properties such as one, two or three dimensional are showing their ability to develop the modeling of TMDL database.

Keywords: TMDL, water quality modeling, QUAL2E, EFDC

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Authors: Emery Jr. Coppola

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Water resources are being depleted and degraded at an alarming and non-sustainable rate worldwide. In some areas, it is progressing more slowly. In other areas, irreversible damage has already occurred, rendering regions largely unsuitable for human existence with destruction of the environment and the economy. Today, 2.5 billion people or 36 percent of the world population live in water-stressed areas. The convergence of factors that created this global water crisis includes local, regional, and global failures. In this paper, a survey of some of these factors is presented. They include abuse of political power and regulatory acquiescence, improper planning and design, ignoring good science and models, systemic failures, and division between the powerful and the powerless. Increasing water demand imposed by exploding human populations and growing economies with short-falls exacerbated by climate change and continuing water quality degradation will accelerate this growing water crisis in many areas. Without regional measures to improve water efficiencies and protect dwindling and vulnerable water resources, environmental and economic displacement of populations and conflict over water resources will only grow. Perhaps more challenging, a global commitment is necessary to curtail if not reverse the devastating effects of climate change. Factors will be illustrated by real-world examples, followed by some partial solutions offered by water experts for helping to mitigate the growing water crisis. These solutions include more water efficient technologies, education and incentivization for water conservation, wastewater treatment for reuse, and improved data collection and utilization.

Keywords: climate change, water conservation, water crisis, water technologies

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Authors: Eiman Kattan

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This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing.

Keywords: conventional neural network, remote sensing, land cover, land use

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Authors: Rodrigo S. Moreira, Nelson F. F. Ebecken

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This paper proposes a maritime vessel tracker composed of an ensemble of WiSARD weightless neural network classifiers. A failure detector analyzes vessel movement with a Kalman filter and corrects the tracking, if necessary, using FFT matching. The use of the WiSARD neural network to track objects is uncommon. The additional contributions of the present study include a performance comparison with four state-of-art trackers, an experimental study of the features that improve maritime vessel tracking, the first use of an ensemble of classifiers to track maritime vessels and a new quantization algorithm that compares the values of pixel pairs.

Keywords: ram memory, WiSARD weightless neural network, object tracking, quantization

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Authors: Prachi Desai, Ankita Gandhi, Mitali Acharya

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Rainfall forecast is mainly used for predictions of rainfall in a specified area and determining their future rainfall conditions. Rainfall is always a global issue as it affects all major aspects of one's life. Agricultural, fisheries, forestry, tourism industry and other industries are widely affected by these conditions. The studies have resulted in insufficient availability of water resources and an increase in water demand in the near future. We already have a new forecast system that uses the deep Convolutional Neural Network (CNN) to forecast monthly rainfall and climate changes. We have also compared CNN against Artificial Neural Networks (ANN). Machine Learning techniques that are used in rainfall predictions include ARIMA Model, ANN, LR, SVM etc. The dataset on which we are experimenting is gathered online over the year 1901 to 20118. Test results have suggested more realistic improvements than conventional rainfall forecasts.

Keywords: ANN, CNN, supervised learning, machine learning, deep learning

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Authors: Rosdyana Mangir Irawan Kusuma, Wei-Chun Kao, Ho-Thi Trang, Yu-Yen Ou, Kai-Lung Hua

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Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively.

Keywords: candlestick chart, deep learning, neural network, stock market prediction

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Authors: Yalong Jiang, Zheru Chi

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In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.

Keywords: CNN, convolutional neural network, capsule network, capacity optimization, character recognition, data augmentation, semantic segmentation

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Authors: Zeinab Syed Abdel Rehim

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Water shortage is considered as one of the huge problems the world encounter now. Water desalination is considered as one of the more suitable methods governments can use to substitute the increased need for potable water. The humidification-dehumidification process for water desalination is viewed as a promising technique for small capacity production plants. The process has several attraction features which include the use of sustainable energy sources, low technology, and low-temperature dehumidification. A pilot experimental set-up plant was constructed with the conventional HVAC components such as air blower that supplies air to an air duct inside which air preheater, steam injector and cooling coil of a small refrigeration unit are placed. The present work evaluates the characteristics of humidification-dehumidification process for water desalination as a function of air flow rate, total power input and air inlet temperature in order to study the optimum conditions required to produce distilled water.

Keywords: condensation, dehumidification, evaporation, humidification, water desalination

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Authors: V. Sobotková, B. Šarapatka, M. Dumbrovský, J. Uhrová, M. Bednář

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The paper deals with the influence of implemented conservation measures on the quality of surface water resources. Recently, a new process of complex land consolidation in the Czech Republic has provided a unique opportunity to improve the quality of the environment and sustainability of crop production by means of better soil and water conservation. The most important degradation factor in our study area in the Hubenov drinking water reservoir catchment basin was water erosion together with loss of organic matter. Hubenov Reservoir water resources were monitored for twenty years (1990–2010) to collect water quality data for nitrate nitrogen (N-NO3-), total P, and undissolved substances. Results obtained from measurements taken before and after land consolidation indicated a decrease in the linear trend of N-NO3- and total P concentrations, this was achieved through implementation of conservation measures limiting soil degradation in the Hubenov reservoir catchment area.

Keywords: complex land consolidation, degradation, land use, soil and water conservation, surface water resources

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Authors: Furkan Ozkan, M. Selcuk Arslan, Hatice Mercan

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Electric vehicles are becoming increasingly popular asa sustainable alternative to traditional combustion engine vehicles. However, to fully realize the potential of EVs in reducing environmental impact and energy consumption, efficient control strategies are essential. This study explores the application of green wave control using model predictive control for electric vehicles, coupled with energy consumption modeling using neural networks. The use of MPC allows for real-time optimization of the vehicles’ energy consumption while considering dynamic traffic conditions. By leveraging neural networks for energy consumption modeling, the EV's performance can be further enhanced through accurate predictions and adaptive control. The integration of these advanced control and modeling techniques aims to maximize energy efficiency and range while navigating urban traffic scenarios. The findings of this research offer valuable insights into the potential of green wave control for electric vehicles and demonstrate the significance of integrating MPC and neural network modeling for optimizing energy consumption. This work contributes to the advancement of sustainable transportation systems and the widespread adoption of electric vehicles. To evaluate the effectiveness of the green wave control strategy in real-world urban environments, extensive simulations were conducted using a high-fidelity vehicle model and realistic traffic scenarios. The results indicate that the integration of model predictive control and energy consumption modeling with neural networks had a significant impact on the energy efficiency and range of electric vehicles. Through the use of MPC, the electric vehicle was able to adapt its speed and acceleration profile in realtime to optimize energy consumption while maintaining travel time objectives. The neural network-based energy consumption modeling provided accurate predictions, enabling the vehicle to anticipate and respond to variations in traffic flow, further enhancing energy efficiency and range. Furthermore, the study revealed that the green wave control strategy not only reduced energy consumption but also improved the overall driving experience by minimizing abrupt acceleration and deceleration, leading to a smoother and more comfortable ride for passengers. These results demonstrate the potential for green wave control to revolutionize urban transportation by enhancing the performance of electric vehicles and contributing to a more sustainable and efficient mobility ecosystem.

Keywords: electric vehicles, energy efficiency, green wave control, model predictive control, neural networks

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Authors: Abdullah Ay, Şehnaz Şener

Abstract:

It is of great importance to reveal their current status and conduct research in this direction for the sustainable use and protection of lakes, which are among the most important water resources for meeting water needs and ensuring ecological balance. In this context, the purpose of this study is to determine the hydrogeochemical properties, as well as water quality and usability characteristics of Işıklı Lake within the Lakes Region of Turkey. Işıklı Lake is a tectonic lake located in the Aegean Region of Turkey. The lake has a surface area of approximately 36 km². Temperature (T), electrical conductivity (EC) and hydrogen ion concentration (pH), dissolved oxygen (%, mg/l), Oxidation Reduction Potential (ORP; mV), and amount of dissolved solids in water (TDS; mg/l) of water samples taken from the lake values were determined by in situ analysis. Major ion and heavy metal analyses were carried out under laboratory conditions. Additionally, the relationship between major ion concentrations and TDS values of Işıklı Lake water samples was determined by correlation analysis. According to the results obtained, it is seen that especially Mg, Ca and HCO₃ ions are dominant in the lake water, and it has been determined that the lake water is in the Ca-Mg-HCO₃ water facies. According to statistical analysis, a strong and positive relationship was found between the TDS value and bicarbonate and calcium (R² = 0.61 and 0.7, respectively). However, no significant relationship was detected between the TDS value and other chemical elements. Although the waters are generally in water quality class I, they are in class IV in terms of sulfur and aluminum. It is included in the water quality class. This situation is due to the rock-water interaction in the region. When the analysis results of the lake water were compared with the drinking water limit values specified by TSE-266 (2005) and WHO (2017), it was determined that it was not suitable for drinking water use in terms of Pb, Se, As, and Cr. When the waters were evaluated in terms of pollution, it was determined that 50% of the samples carried pollution loads in terms of Al, As, Fe, NO3, and Cu.

Keywords: Işıklı Lake, water chemistry, water quality, pollution, arsenic, Denizli

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Authors: K. Gomathi

Abstract:

Mobile Ad hoc Networks (MANET) is defined as collection of routable wireless mobile nodes with no centralized administration and communicate each other using radio signals. Especially MANETs deployed in hostile environments where hackers will try to disturb the secure data transfer and drain the valuable network resources. Since MANET is battery operated network, preserving the network resource is essential one. For resource constrained computation, efficient routing and to increase the network stability, the network is divided into smaller groups called clusters. The clustering architecture consists of Cluster Head(CH), ordinary node and gateway. The CH is responsible for inter and intra cluster routing. CH election is a prominent research area and many more algorithms are developed using many different metrics. The CH with longer life sustains network lifetime, for this purpose Secondary Cluster Head(SCH) also elected and it is more economical. To nominate efficient CH, a Enhanced Distributed Weighted Clustering Algorithm (EDWCA) has been proposed. This approach considers metrics like battery power, degree difference and speed of the node for CH election. The proficiency of proposed one is evaluated and compared with existing algorithm using Network Simulator(NS-2).

Keywords: MANET, EDWCA, clustering, cluster head

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