Search results for: gas distribution network

Authors: Eylem Koç, Hasan Arda Burhan

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Location selection presents a crucial decision problem in today’s business world where strategic decision making processes have critical importance. Thus, location selection has strategic importance for companies in boosting their strength regarding competition, increasing corporate performances and efficiency in addition to lowering production and transportation costs. A right choice in location selection has a direct impact on companies’ commercial success. In this study, a store location selection problem of Carglass Turkey which operates in vehicle glass branch is handled. As this problem includes both tangible and intangible criteria, Analytic Network Process (ANP) was accepted as the main methodology. The model consists of control hierarchy and BOCR subnetworks which include clusters of actors, alternatives and criteria. In accordance with the management’s choices, five different locations were selected. In addition to the literature review, a strict cooperation with the actor group was ensured and maintained while determining the criteria and during whole process. Obtained results were presented to the management as a report and its feasibility was confirmed accordingly.

Keywords: analytic network process (ANP), BOCR, multi-actor decision making, multi-criteria decision making, real-life problem, location selection

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Authors: Abdurrahim Dal, Tuncay Karaçay

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Dynamics of a rotor supported by air bearings is strongly depends on the pressure distribution between the rotor and the bearing. In this study, internal pressure in air bearings is numerical and experimental analyzed for different radial clearances. Firstly the pressure distribution between rotor and bearing is modeled using Reynold's equation and this model is solved numerically. The rotor-bearing system is also modeled in four degree of freedom and it is simulated for different radial clearances. Then, in order to validate numerical results, a test rig is designed and the rotor bearing system is run under the same operational conditions. Pressure signals of left and right bearings are recorded. Internal pressure variations are compared for numerical and experimental results for different radial clearances.

Keywords: air bearing, internal pressure, Reynold’s equation, rotor

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Authors: Bai-Chen Xie, Biao Li

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With the deepening of international exchanges and investment, the international comparison of power grid firms has become the focus of regulatory authorities. Ignoring the differences in the economic environment, resource endowment, technology, and other aspects of different countries or regions may lead to efficiency bias. Based on the Meta-frontier model, this paper divides China and Japan into two groups by using the data of China and Japan from 2006 to 2020. While preserving the differences between the two countries, it analyzes and compares the efficiency of the transmission and distribution industries of the two countries. Combined with the four-component stochastic frontier model, the efficiency is divided into transient and persistent efficiency. We found that there are obvious differences between the transmission and distribution sectors in China and Japan. On the one hand, the inefficiency of the two countries is mostly caused by long-term and structural problems. The key to improve the efficiency of the two countries is to focus more on solving long-term and structural problems. On the other hand, the long-term and structural problems that cause the inefficiency of the two countries are not the same. Quality factors have different effects on the efficiency of the two countries, and this different effect is captured by the common frontier model but is offset in the overall model. Based on these findings, this paper proposes some targeted policy recommendations.

Keywords: transmission and distribution industries, transient efficiency, persistent efficiency, meta-frontier, international comparison

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Authors: H. Bonakdari, I. Ebtehaj

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The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), bridge pier, scour depth, nonlinear regression (NLR)

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Authors: Tigabu Dagne Akal

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While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database Process Model. The Knowledge Discovery in Database Process Model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln Laboratory. After taking the data, it has been pre-processed. The major pre-processing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labelled and unlabelled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tested as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.

Keywords: intrusion detection, data mining, computer science, data mining

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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Authors: Salehi Zahabi Saleh, Rajabi Hosaien, Rasaneh Samira

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Yttrium-90 (90Y) radioembolisation(RE) is increasingly used for the treatment of patients with unresectable primary or metastatic liver tumours. Image-based approaches to assess microsphere distribution after RE have gained interest but are mostly hampered by the limited imaging possibilities of the Isotope 90Y. Quantitative 90Y-SPECT imaging has limited spatial resolution because it is based on 90Y Bremsstrahlung whereas 90Y-PET has better spatial resolution but low sensitivity. As a consequence, new alternative methods of visualizing the microspheres have been investigated, such as MR imaging of iron-labelled microspheres. It was also shown that MRI combines high sensitivity with high spatial and temporal resolution and with superior soft tissue contrast and thus can be used to cover a broad range of clinically interesting imaging parameters.The aim of the study in this article was to investigate the capability of MRI to measure the intrahepatic microsphere distribution in order to quantify the absorbed radiation dose in RE.

Keywords: radioembolisation, MRI, imaging, dosimetry

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Authors: Mahdi Rahimi, Mohammad Mahdi Mojahedian, Mohammad Reza Aref

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In this paper, a cellular communication scenario with a transmitter and an authorized user is considered to analyze its secrecy in the face of eavesdroppers and the interferences propagated unintentionally through the communication network. It is also assumed that some D2D pairs and eavesdroppers are randomly located in the cell. Assuming hardware impairment, perfect connection probability is analytically calculated, and upper bound is provided for the secrecy outage probability. In addition, a method based on random activation of D2Ds is proposed to improve network security. Finally, the analytical results are verified by simulations.

Keywords: physical layer security, stochastic geometry, device-to-device, hardware impairment

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Authors: Ayşen Bakioğlu, Gökçen Seyra Çakır

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This study aims to explore how research assistants who work in project teams experience team member autonomy and how they reconcile team member autonomy with team collaboration. The study utilizes snowball sampling. 20 research assistants who work the faculties of education in Marmara University and Yıldız Technical University have been interviewed. The analysis of data involves a content analysis MAXQDAPlus 11 which is a qualitative data analysis software is used as the data analysis tool. According to the findings of this study, emerging themes include team norm formation, team coordination management, the role of individual tasks in team collaboration, leadership distribution. According to the findings, interviewees experience team norm formation process in terms of processes, which pertain to task fulfillment, and processes, which pertain to the regulation of team dynamics. Team norm formation process instills a sense of responsibility amongst individual team members. Apart from that, the interviewees’ responses indicate that the realization of the obligation to work in a team contributes to the team norm formation process. The participants indicate that individual expectations are taken into consideration during the coordination of the team. The supervisor of the project team also has a crucial role in maintaining team collaboration. Coordination problems arise when an individual team member does not relate his/her academic field with the research topic of the project team. The findings indicate that the leadership distribution in the project teams involves two leadership processes: leadership distribution which is based on the processes that focus on individual team members and leadership distribution which is based on the processes that focus on team interaction. Apart from that, individual tasks serve as a facilitator of collaboration amongst team members. Interviewees also indicate that individual tasks also facilitate the expression of individuality.

Keywords: project teams in higher education, research assistant teams, team collaboration, team member autonomy

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Authors: Win Thandar Soe, Innocent Mpawenimana, Mathieu Di Fazio, Cécile Belleudy, Aung Ze Ya

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Intelligent electronic equipment and automation network is the brain of high-tech energy management systems in critical role of smart homes dominance. Smart home is a technology integration for greater comfort, autonomy, reduced cost, and energy saving as well. These services can be provided to home owners for managing their home appliances locally or remotely and consequently allow them to automate intelligently and responsibly their consumption by individual or collective control systems. In this study, three smart plugs are described and one of them tested on typical household appliances. This article proposes to collect the data from the wireless technology and to extract some smart data for energy management system. This smart data is to quantify for three kinds of load: intermittent load, phantom load and continuous load. Phantom load is a waste power that is one of unnoticed power of each appliance while connected or disconnected to the main. Intermittent load and continuous load take in to consideration the power and using time of home appliances. By analysing the classification of loads, this smart data will be provided to reduce the communication of wireless sensor network for energy management system.

Keywords: energy management, load profile, smart plug, wireless sensor network

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Authors: Qiao Yushuang, Shao Longyi

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This paper presents the result of plasmid assay of inhalable particulates PM10 and PM2.5 that were collected during the period of the 11th Hanyuan temple fair of ancestor worship in Kaifeng City. By use of a high-resolution Field Emission Scanning Electron Microscopy (FESEM) and image analysis (IA) technology, the morphological characteristics and Particle Size Distribution (PSD) of each were analyzed and the Bioreactivity of PM10 was evaluated by using plasmid DNA assay. The result shows that, as the dominant component of the samples taken in the urban area of Kaifeng City, the mineral particles, compared with the other components including the soot aggregates, coal ash, and unidentified particles, have a much greater amount and volume. The mineral particles exhibited a decentralized quantity - size distribution, whose presence could be available among the particles sizing 2.5μm or smaller. In contrast, the volume-size distribution of mineral particles is scattered in a relatively narrow range of between1μm and 2.5μm. According to the plasmid assay the TD50 (toxic dose of PM causing 50% of plasmid damage, expressed in μg/ml) of water-soluble PM10 and whole fraction of Kaifeng airborne PM10 was measured respectively at 220-208μg/ml and 300-400μg/ml versus 160μg/ml and 190μg/ml for PM2.5. It can be seen that the whole fraction of airborne particles caused more oxidative damage than the water-soluble fractions, and the PM2.5 has a greater oxidative capacity than the PM10.

Keywords: inhalable particulates (PM10 and PM2.5), morphological features, bioreactivity, Kaifeng

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Authors: Awatef Chniguir, Tarek Farah, Zouhair Ben Jemaa, Safya Belguith

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Optimal routing allows minimizing energy consumption in wireless sensor networks (WSN). Clustering has proven its effectiveness in organizing WSN by reducing channel contention and packet collision and enhancing network throughput under heavy load. Therefore, nowadays, with the emergence of the Internet of Things, heterogeneity is essential. Stable election protocol (SEP) that has increased the network stability period and lifetime is the first clustering protocol for heterogeneous WSN. SEP and its descendants, namely SEP, Threshold Sensitive SEP (TSEP), Enhanced TSEP (ETSSEP) and Current Energy Allotted TSEP (CEATSEP), were studied. These algorithms’ performance was evaluated based on different metrics, especially first node death (FND), to compare their stability. Simulations were conducted on the MATLAB tool considering two scenarios: The first one demonstrates the fraction variation of advanced nodes by setting the number of total nodes. The second considers the interpretation of the number of nodes while keeping the number of advanced nodes permanent. CEATSEP outperforms its antecedents by increasing stability and, at the same time, keeping a low throughput. It also operates very well in a large-scale network. Consequently, CEATSEP has a useful lifespan and energy efficiency compared to the other routing protocol for heterogeneous WSN.

Keywords: clustering, heterogeneous, stability, scalability, IoT, WSN

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Authors: Seyyed Masoud Seyyedoshohadaei, Borhanuddin Mohd Ali

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To support group mobility, the NEMO Basic Support Protocol has been standardized as an extension of Mobile IP that enables an entire network to change its point of attachment to the Internet. Using NEMO in IEEE 802.16e (WiMax) networks causes latency in handover procedure and affects seamless communication of real-time applications. To decrease handover latency and service disruption time, an integrated scheme named Optimized Fast NEMO (OFNEMO) was introduced by authors of this paper. In OFNEMO a pre-establish multi tunnels concept, cross function optimization and cross layer design are used. In this paper, an analytical model is developed to evaluate total cost consisting of signaling and packet delivery costs of the OFNEMO compared with RFC3963. Results show that OFNEMO increases probability of predictive mode compared with RFC3963 due to smaller handover latency. Even though OFNEMO needs extra signalling to pre-establish multi tunnel, it has less total cost thanks to its optimized algorithm. OFNEMO can minimize handover latency for supporting real time application in moving networks.

Keywords: fast mobile IPv6, handover latency, IEEE802.16e, network mobility

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Authors: Giuseppe Timperio, Robert de Souza

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Logistics operations in the context of disaster response are characterized by a high degree of complexity due to the combined effect of a large number of stakeholders involved, time pressure, uncertainties at various levels, massive deployment of goods and personnel, and gigantic financial flow to be managed. It also involves several autonomous parties such as government agencies, militaries, NGOs, UN agencies, private sector to name few, to have a highly collaborative approach especially in the critical phase of the immediate response. This is particularly true in the context of L3 emergencies that are the most severe, large-scale humanitarian crises. Decision-making processes in disaster management are thus extremely difficult due to the presence of multiple decision-makers involved, and the complexity of the tasks being tackled. Hence, in this paper, we look at applying ICT based solutions to enable a speedy and effective decision making in the golden window of humanitarian operations. A high-level view of ICT based solutions in the context of logistics operations for humanitarian response in Southeast Asia is presented, and their viability in a real-life case about logistics network design is explored.

Keywords: decision support, disaster preparedness, humanitarian logistics, network design

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Authors: Soungwan Kim

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This paper discusses an analysis of how the characteristics of resources at vocational rehabilitation facilities for the disabled affect the improvement of independency skills among people with severe disabilities. The analysis results indicate that more internal financial resources and more connections to local communities among network resources had greater effects on improving the independency of people with severe disabilities. Based on this result, this paper presents strategies for mobilizing resources to improve the independency of people with severe disabilities at vocational rehabilitation facilities.

Keywords: vocational rehabilitation facility for people with disabilities, types of resources, independency, network resources

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Authors: E. Petrakis, V. Karmali, K. Komnitsas

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In this study grinding experiments were performed in a laboratory ball mill using Polish ferronickel slag in order to study the effect of the particle size on alkali activation and the properties of the produced alkali activated materials (AAMs). In this regard, the particle size distribution and the specific surface area of the grinding products in relation to grinding time were assessed. The experimental results show that products with high compressive strength, e.g. higher than 60 MPa, can be produced when the slag median size decreased from 39.9 μm to 11.9 μm. Also, finer fractions are characterized by higher reactivity and result in the production of AAMs with lower porosity and better mechanical properties.

Keywords: alkali activation, compressive strength, grinding time, particle size distribution, slag, structural integrity

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Authors: Karim Matter, Heba Fayed, Ahmed Abd-Elaziz, Moustafa Hussein

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Due to the exponential growth of mobile devices and wireless services, a huge demand for radiofrequency has increased. The presence of several frequencies causes interference between cells, which must be minimized to get the lower Bit Error Rate (BER). For this reason, it is of great interest to use visible light communication (VLC). This paper suggests a VLC system that decreases the BER by applying a new LED distribution with a hexagonal shape using a Frequency Reuse (FR) concept to mitigate the interference between the reused frequencies inside the hexagonal shape. The BER is measured in two scenarios, Line of Sight (LoS) and Non-Line of Sight (Non-LoS), for each technique that we used. The recommended values of BER in the proposed model for Soft Frequency Reuse (SFR) in the case of Los at 4, 8, and 10 dB signal to noise ratio (SNR), are 3.6×10⁻⁶, 6.03×10⁻¹³, and 2.66×10⁻¹⁸, respectively.

Keywords: visible light communication (VLC), field of view (FoV), hexagonal array, frequency reuse

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Authors: R. Oleko, A. Saraceni

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The aim of this research is to provide a rigorous evaluation of the existing academic productivity in relation to value proposition and creation in networked environments. Bibliometrics is a vigorous approach used to structure existing literature in an objective and reliable manner. To that aim, a thorough bibliometric analysis was performed in order to assess the large volume of the information encountered in a structured and reliable manner. A clear distinction between networks and service networks was considered indispensable in order to capture the effects of each network’s type properties on value creation processes. Via the use of bibliometric parameters, this review was able to capture the state-of-the-art in both value proposition and value creation consecutively. The results provide a rigorous assessment of the annual scientific production, the most influential journals, and the leading corresponding author countries. By means of citation analysis, the most frequently cited manuscripts and countries for each network type were identified. Moreover, by means of co-citation analysis, existing collaborative patterns were detected through the creation of reference co-citation networks and country collaboration networks. Co-word analysis was also performed in order to provide an overview of the conceptual structure in both networks and service networks. The acquired results provide a rigorous and systematic assessment of the existing scientific output in networked settings. As such, they positively contribute to a better understanding of the distinct impact of service networks on value proposition and value creation when compared to regular networks. The implications derived can serve as a guide for informed decision-making by practitioners during network formation and provide a structured evaluation that can stand as a basis for future research in the field.

Keywords: bibliometrics, co-citation analysis, networks, service networks, value creation, value proposition

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Authors: Risti Suryantari, Flaviana

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The aim of this research is to design calibration method of Thermochromic Liquid Crystal for temperature distribution measurement based on mathematical morphology of hue image A glass of water is placed on the surface of sample TLC R25C5W at certain temperature. We use scanner for image acquisition. The true images in RGB format is converted to HSV (hue, saturation, value) by taking of hue without saturation and value. Then the hue images is processed based on mathematical morphology using Matlab2013a software to get better images. There are differences on the final images after processing at each temperature variation based on visualization observation and the statistic value. The value of maximum and mean increase with rising temperature. It could be parameter to identify the temperature of the human body surface like hand or foot surface.

Keywords: thermochromic liquid crystal, TLC, mathematical morphology, hue image

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Authors: Adrian Ionita, Ana-Maria Ghimes

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The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application.

Keywords: artificial intelligence, big data, cloud computing, DNA profile, genetic algorithms, machine learning, neural networks, optimization, recommendation system, user profiling

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Authors: Eduardo C. Guardia, Jose W. M. Lima, Afonso H. M. Santos

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This paper presents a nonparametric method to obtain the hazard rate “Bathtub curve” for power system components. The model is a mixture of the three known phases of a component life, the decreasing failure rate (DFR), the constant failure rate (CFR) and the increasing failure rate (IFR) represented by three parametric Weibull models. The parameters are obtained from a simultaneous fitting process of the model to the Kernel nonparametric hazard rate curve. From the Weibull parameters and failure rate curves the useful lifetime and the characteristic lifetime were defined. To demonstrate the model the historic time-to-failure of distribution transformers were used as an example. The resulted “Bathtub curve” shows the failure rate for the equipment lifetime which can be applied in economic and replacement decision models.

Keywords: bathtub curve, failure analysis, lifetime estimation, parameter estimation, Weibull distribution

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Authors: Alireza Lavaei, Alireza Lohrasbi, Mohammadali M. Shahlaei

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To reduce the overall time of structural optimization for earthquake loads two strategies are adopted. In the first strategy, a neural system consisting self-organizing map and radial basis function neural networks, is utilized to predict the time history responses. In this case, the input space is classified by employing a self-organizing map neural network. Then a distinct RBF neural network is trained in each class. In the second strategy, an improved genetic algorithm is employed to find the optimum design. A 72-bar space truss is designed for optimal weight using exact and approximate analysis for the El Centro (S-E 1940) earthquake loading. The numerical results demonstrate the computational advantages and effectiveness of the proposed method.

Keywords: optimization, genetic algorithm, neural networks, self-organizing map

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Authors: Sh. Foroughi, V. Karamzadeh, M. Packirisamy

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This paper presents design and analysis of an electrothermally symmetrical actuated microgripper applicable for performing micro assembly or biological cell manipulation. Integration of micro-optics with microdevice leads to achieve extremely precise control over the operation of the device. Geometry, material, actuation, control, accuracy in measurement and temperature distribution are important factors which have to be taken into account for designing the efficient microgripper device. In this work, analyses of four different geometries are performed by means of COMSOL Multiphysics 5.2 with implementing Finite Element Methods. Then, temperature distribution along the fingertip, displacement of gripper site as well as optical efficiency vs. displacement and electrical potential are illustrated. Results show in addition to the industrial application of this device, the usage of that as a cell manipulator is possible.

Keywords: electro thermal actuator, MEMS, microgripper, MOEMS

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Authors: Tirtha Raj Dhakal, Brian Davidson, Bob Farquharson

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Institutional design is an important aspect in efficient water resource management. In Nepal, the water supply in both farmers’- and agency-managed irrigation systems has become sub-standard because of the weak institutional framework. This study characterizes both forms of the schemes and links existing institution and governance of the schemes with its performance with reference to cost recovery, maintenance of the schemes and water distribution throughout the schemes. For this, two types of surveys were conducted. A management survey of ten farmers’-managed and five agency-managed schemes of Chitwan valley and its periphery was done. Also, a farm survey comprising 25 farmers from each of head, middle and tail regions of both schemes; Narayani Lift Irrigation Project (agency-managed) and Khageri Irrigation System (farmers’-managed) of Chitwan Valley as a case study was conducted. The results showed that cost recovery of agency-managed schemes in 2015 was less than two percent whereas service fee collection rate in farmers’-managed schemes was nearly 2/3rd that triggered poor maintenance of the schemes and unequal distribution of water throughout the schemes. Also, the institution on practice is unable to create any incentives for farmers for economical use of water as well as willingness to pay for its use. This, thus, compels the need of refined institutional framework which has been suggested in this paper aiming to improve the cost recovery and better water distribution throughout the irrigation schemes.

Keywords: cost recovery, governance, institution, schemes' performance

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Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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Authors: Shih-Yan Chen, Shin-Shin Kao

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In this paper, a thorough review about dual-cubes, DCn, the related studies and their variations are given. DCn was introduced to be a network which retains the pleasing properties of hypercube Qn but has a much smaller diameter. In fact, it is so constructed that the number of vertices of DCn is equal to the number of vertices of Q2n +1. However, each vertex in DCn is adjacent to n + 1 neighbors and so DCn has (n + 1) × 2^2n edges in total, which is roughly half the number of edges of Q2n+1. In addition, the diameter of any DCn is 2n +2, which is of the same order of that of Q2n+1. For selfcompleteness, basic definitions, construction rules and symbols are provided. We chronicle the results, where eleven significant theorems are presented, and include some open problems at the end.

Keywords: dual-cubes, dual-cube extensive networks, dual-cube-like networks, hypercubes, fault-tolerant hamiltonian property

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Authors: Laura Victoria Vigoya Morales, David Rolando Suarez Mora

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The problem of detecting enterococcus faecalis is receiving considerable attention with the new cases of beachgoers infected with the bacteria, which can be found in fecal matter. The process detection of this kind of bacteria would be taking a long time, which waste time and money as a result of closing recreation place, like beach or pools. Hence, new methods for automating the process of detecting and recognition of this bacteria has become in a challenge. This article describes a novel approach to detect the enterococcus faecalis bacteria in water by using an octave algorithm, which embody a network neural. This document shows result of performance, quality and integrity of the algorithm.

Keywords: Enterococcus faecalis, image treatment, octave and network neuronal

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Authors: M. Benothman, M. Stear, S. Mitchel, O. Abuargob, R. Vijayan, Sateesh Kumar

Abstract:

Parasitic gastroenteritis caused by gastrointestinal nematodes (GIN) is a serious pathological complication in lambs. The dispersion pattern of GIN influences their transmission dynamics. There is no proper study on this aspect in Scottish Blackface lambs in Scotland. This study undertaken on 758 naturally infected, weaned, straight bred Scottish Blackface lambs in high land pasture in East Scotland extending over three months (August, September and October) in a year, and for three successive years demonstrated that the distribution of faecal egg counts (FEC) followed negative binomial distribution, with the exception of a few samples. The inverse index of dispersion (k) ranged between 0.19 ± 0.51 and 1.09 ± 0.08. Expression of low k values resulting from aggregation in a few individuals, suggested that a small proportion of animals with heavy parasitic influx significantly influenced the level of pasture contamination and parasite transmission. There was no discernible trend in the mean faecal egg count (FEC) and mean herbage larval population (HLP) in different months and in different years. Teladorsagia was the highest pasture contaminant (85.14±14.30 L3/kdh) followed by Nematodirus (53.00±13.96), Ostertagia (28.21±10.18) and Cooperia (11.43±5.55). The results of this study would be useful in instituting gastrointestinal nematode control strategies for sheep in cool temperate agro-ecological zones.

Keywords: blackface lamb, faecal egg count, Gastrointestinal nematodes, herbage larval population, Scotland

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Authors: I. M. L. Nadeesha Jayaweera, Adao Alex Trindade

Abstract:

In choosing a candidate model in likelihood-based modeling via an information criterion, the practitioner is often faced with the difficult task of deciding just how far up the ranked list to look. Motivated by this pragmatic necessity, we construct an uncertainty band for a generalized (model selection) information criterion (GIC), defined as a criterion for which the limit in probability is identical to that of the normalized log-likelihood. This includes common special cases such as AIC & BIC. The method starts from the asymptotic normality of the GIC for the joint distribution of the candidate models in an independent and identically distributed (IID) data framework and proceeds by deriving the (asymptotically) exact distribution of the minimum. The calculation of an upper quantile for its distribution then involves the computation of multivariate Gaussian integrals, which is amenable to efficient implementation via the R package "mvtnorm". The performance of the methodology is tested on simulated data by checking the coverage probability of nominal upper quantiles and compared to the bootstrap. Both methods give coverages close to nominal for large samples, but the bootstrap is two orders of magnitude slower. The methodology is subsequently extended to two other commonly used model structures: regression and time series. In the regression case, we derive the corresponding asymptotically exact distribution of the minimum GIC invoking Lindeberg-Feller type conditions for triangular arrays and are thus able to similarly calculate upper quantiles for its distribution via multivariate Gaussian integration. The bootstrap once again provides a default competing procedure, and we find that similar comparison performance metrics hold as for the IID case. The time series case is complicated by far more intricate asymptotic regime for the joint distribution of the model GIC statistics. Under a Gaussian likelihood, the default in most packages, one needs to derive the limiting distribution of a normalized quadratic form for a realization from a stationary series. Under conditions on the process satisfied by ARMA models, a multivariate normal limit is once again achieved. The bootstrap can, however, be employed for its computation, whence we are once again in the multivariate Gaussian integration paradigm for upper quantile evaluation. Comparisons of this bootstrap-aided semi-exact method with the full-blown bootstrap once again reveal a similar performance but faster computation speeds. One of the most difficult problems in contemporary statistical methodological research is to be able to account for the extra variability introduced by model selection uncertainty, the so-called post-model selection inference (PMSI). We explore ways in which the GIC uncertainty band can be inverted to make inferences on the parameters. This is being attempted in the IID case by pivoting the CDF of the asymptotically exact distribution of the minimum GIC. For inference one parameter at a time and a small number of candidate models, this works well, whence the attained PMSI confidence intervals are wider than the MLE-based Wald, as expected.

Keywords: model selection inference, generalized information criteria, post model selection, Asymptotic Theory

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