Search results for: network coding signature

Authors: Dan Wang

Abstract:

Sandplay therapy is a Jungian psychological therapy developed by Dora Kalff in 1956. In sandplay therapy, the client first makes a sandtray with various miniatures and then has a communication with the therapist based on the sandtray. The special method makes sandplay therapy has great assessment potential. With regarding that the core treatment hypothesis of sandplay therapy - the self-healing power, is very similar to resilience. This study tries to use sandplay to evaluate psychological resilience. Participants are 107 undergraduates recruited from three public universities in China who were required to make an initial sandtray and to complete the Ego-Resiliency Scale (ER89) respectively. First, a 28- category General Sandtray Coding Manual (GSCM) was developed based on literature on sandplay therapy. Next, using GSCM to code the 107 initial sandtrays and conducted correlation analysis and regression analysis between all GSCM categories and ER89. Results show three categories (i.e., vitality, water types, and relationships) of sandplay account for 36.6% of the variance of ego-resilience and form the four-point Likert-type Sandtray Projective Test of Resilience (SPTR). Finally, it is found that SPTR dimensions and total score all have good inter-rater reliability, ranging from 0.89 to 0.93. This study provides an alternative approach to measure psychological resilience and can help to guide clinical social work.

Keywords: sandplay therapy, psychological resilience, measurement, college students

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Authors: R. N. Begum, Ambalika Sharma, G. K. Singh

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Identity theft has serious ramifications beyond data and personal information loss. This necessitates the implementation of robust and efficient user identification systems. Therefore, automatic biometric recognition systems are the need of the hour, and ECG-based systems are unquestionably the best choice due to their appealing inherent characteristics. The CNNs are the recent state-of-the-art techniques for ECG-based user identification systems. However, the results obtained are significantly below standards, and the situation worsens as the number of users and types of heartbeats in the dataset grows. As a result, this study proposes a highly accurate and resilient ECG-based person identification system using CNN's dense learning framework. The proposed research explores explicitly the calibre of dense CNNs in the field of ECG-based human recognition. The study tests four different configurations of dense CNN which are trained on a dataset of recordings collected from eight popular ECG databases. With the highest FAR of 0.04 percent and the highest FRR of 5%, the best performing network achieved an identification accuracy of 99.94 percent. The best network is also tested with various train/test split ratios. The findings show that DenseNets are not only extremely reliable but also highly efficient. Thus, they might also be implemented in real-time ECG-based human recognition systems.

Keywords: Biometrics, Dense Networks, Identification Rate, Train/Test split ratio

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Authors: Christo Nicholls

Abstract:

The National Electricity Grid (NEG) in South Africa has been under strain for the last five years. This overburden of the NEG led Eskom (the State-Owned Entity responsible for the NEG) to implement a blunt methodology to assist them in reducing the maximum demand (MD) on the NEG, when required, called Loadshedding. The challenge of this methodology is that not only does it lead to immense technical issues with the distribution network equipment, e.g., transformers, due to the frequent abrupt off and on switching, it also has a broader negative fiscal impact on the distributors, as their key consumers (commercial & industrial) are now grid defecting due to the lack of Electricity Security Provision (ESP). This paper provides a pragmatic alternative methodology utilizing specific functionalities embedded within direct-connect single and three-phase Advanced Meter Infrastructure (AMI) Solutions deployed within the distribution network, in conjunction with a Multi-Agent Systems Based AI implementation focused on Automated Negotiation Peer-2-Peer trading. The results of this research clearly illustrate, not only does methodology provide a factual percentage contribution towards the NEG MD at the point of consideration, it also allows the distributor to leverage the real-time MD data from key consumers to activate complex, yet impact-measurable Demand Response (DR) programs.

Keywords: AI, AMI, demand response, multi-agent

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Authors: Raquel Camprubí

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Cultural tourism has been postulated as relevant motivation for tourist over the world during the last decades. In this context, museums are the main attraction for cultural tourists who are seeking to connect with the history and culture of the visited place. From the point of view of an urban destination, museums and other cultural resources are essential to have a strong tourist supply at the destination, in order to be capable of catching attention and interest of cultural tourists. In particular, museums’ challenge is to be prepared to offer the best experience to their visitors without to forget their mission-based mainly on protection of its collection and other social goals. Thus, museums individually want to be competitive and have good positioning to achieve their strategic goals. The life cycle of the destination and the level of maturity of its tourism product influence the need of tourism agents to cooperate and collaborate among them, in order to rejuvenate their product and become more competitive as a destination. Additionally, prior studies have considered an approach of different models of a public and private partnership, and collaborative and cooperative relations developed among the agents of a tourism destination. However, there are no studies that pay special attention to museums and the strategic alliances developed to obtain mutual benefits. Considering this background, the purpose of this study is to analyze in what extent museums of a given urban destination have established strategic links and relations among them, in order to improve their competitive position at both individual and destination level. In order to achieve the aim of this study, the city of Girona (Spain) and the museums located in this city are taken as a case study. Data collection was conducted using in-depth interviews, in order to collect all the qualitative data related to nature, strengthen and purpose of the relational ties established among the museums of the city or other relevant tourism agents of the city. To conduct data analysis, a Social Network Analysis (SNA) approach was taken using UCINET software. Position of the agents in the network and structure of the network was analyzed, and qualitative data from interviews were used to interpret SNA results. Finding reveals the existence of strong ties among some of the museums of the city, particularly to create and promote joint products. Nevertheless, there were detected outsiders who have an individual strategy, without collaboration and cooperation with other museums or agents of the city. Results also show that some relational ties have an institutional origin, while others are the result of a long process of cooperation with common projects. Conclusions put in evidence that collaboration and cooperation of museums had been positive to increase the attractiveness of the museum and the city as a cultural destination. Future research and managerial implications are also mentioned.

Keywords: cultural tourism, competitiveness, museums, Social Network analysis

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Authors: Meng Xu, Alexis K. H. Lau, Ming Xu, Bill Barron, Narges Shahraki

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As an emerging unconventional energy, shale gas has been an economically viable step towards a cleaner energy future in U.S. China also has shale resources that are estimated to be potentially the largest in the world. In addition, China has enormous unmet for a clean alternative to substitute coal. Nonetheless, the geological complexity of China’s shale basins and issues of water scarcity potentially impose serious constraints on shale gas development in China. Further, even if China could replicate to a significant degree the U.S. shale gas boom, China faces the problem of transporting the gas efficiently overland with its limited pipeline network throughput capacity and coverage. The aim of this study is to identify the potential bottlenecks in China’s gas transmission network, as well as to examine the shale gas development affecting particular supply locations and demand centers. We examine this through application of three scenarios with projecting domestic shale gas supply by 2020: optimistic, medium and conservative shale gas supply, taking references from the International Energy Agency’s (IEA’s) projections and China’s shale gas development plans. Separately we project the gas demand at provincial level, since shale gas will have more significant impact regionally than nationally. To quantitatively assess each shale gas development scenario, we formulated a gas pipeline optimization model. We used ArcGIS to generate the connectivity parameters and pipeline segment length. Other parameters are collected from provincial “twelfth-five year” plans and “China Oil and Gas Pipeline Atlas”. The multi-objective optimization model uses GAMs and Matlab. It aims to minimize the demands that are unable to be met, while simultaneously seeking to minimize total gas supply and transmission costs. The results indicate that, even if the primary objective is to meet the projected gas demand rather than cost minimization, there’s a shortfall of 9% in meeting total demand under the medium scenario. Comparing the results between the optimistic and medium supply of shale gas scenarios, almost half of the shale gas produced in Sichuan province and Chongqing won’t be able to be transmitted out by pipeline. On the demand side, the Henan province and Shanghai gas demand gap could be filled as much as 82% and 39% respectively, with increased shale gas supply. To conclude, the pipeline network in China is currently not sufficient in meeting the projected natural gas demand in 2020 under medium and optimistic scenarios, indicating the need for substantial pipeline capacity expansion for some of the existing network, and the importance of constructing new pipelines from particular supply to demand sites. If the pipeline constraint is overcame, Beijing, Shanghai, Jiangsu and Henan’s gas demand gap could potentially be filled, and China could thereby reduce almost 25% its dependency on LNG imports under the optimistic scenario.

Keywords: energy policy, energy systematic analysis, scenario analysis, shale gas in China

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Authors: Sheng-Hui Lan, Shan-Ying Wu, Shu-Ching Lin, Wei-Chen Wang, Hsiao-Sheng Liu

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Autophagy is an essential mechanism to maintain cellular homeostasis through its degradation function, and the autophagy deficiency is related various diseases including tumorigenesis in several cancers. MicroRNAs (miRNAs) are small none coding RNAs, which regulate gene expression through degradation of mRNA or inhibition of translation. However, the relationship between autophagy deficiency and dysregulated miRNAs is still unclear. We revealed a mechanism that autophagy up-regulates miR-449a expression at the transcriptional level through activation of forkhead transcription factor family member FoxO1 and then suppresses tumorigenesis in CRC. Our data showed that the autophagic activity and miR-449a expression were lower in colorectal cancer (CRC) and has a positive correlation. We further reveal that autophagy degrades p300 expression and then suppresses acetylation of FoxO1. Under autophagic induction conditions, FoxO1 is transported from the cytoplasm to the nucleus and binds to the miR-449a promoter and then promotes miR-449a expression. In addition, either miR-449a overexpression or amiodarone-induced autophagy inhibits cell cycle progression, proliferation, colony formation migration, invasion, and tumor formation of SW480 cells. Our findings indicate that autophagy inducers may have the potential to be used for prevention and treatment of CRC through upregulation of miR-449a expression.

Keywords: autophagy, MiR-449a, FoxO1, colorectal cancer

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Authors: Khizar Z. Choudhrya, Richard Coles, Salman Qureshi, Robert Ashford, Salim Khan, Rabia R. Mir

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Abstract: The majority of existing landscape research has been derived by conducting heuristic evaluations, without having empirical insight of real participant visual response. In this research, a modern multimodal measurement approach (using narratives and eye tracking) was applied to investigate visual behaviour in perceiving naturalistic and urban environments. This research is unique in exploring gaze behaviour on environmental images possessing different levels of saliency. Eye behaviour is predominantly attracted by salient locations. The concept of methodology of this research on naturalistic and urban environments is drawn from the approaches in market research. Borrowing methodologies from market research that examine visual responses and qualities provided a critical and hitherto unexplored approach. This research has been conducted by using mixed methodological quantitative and qualitative approaches. On the whole, the results of this research corroborated existing landscape research findings, but they also identified potential refinements. The research contributes both methodologically and empirically to human-environment interaction (HEI). This study focused on initial impressions of environmental images with the help of eye tracking. Taking under consideration the importance of the image, this study explored the factors that influence initial fixations in relation to expectations and preferences. In terms of key findings of this research it is noticed that each participant has his own unique navigation style while surfing through different elements of landscape images. This individual navigation style is given the name of ‘visual signature’. This study adds the necessary clarity that would complete the picture and bring an insight for future landscape researchers.

Keywords: human-environment interaction (HEI), multimodal measurement, narratives, eye tracking

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Authors: Hosein Hamisheh Bahar, Mohammad Hossein Fazel Zarandi, Akbar Esfahanipour

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In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Keywords: fuzzy genetic network programming, hybrid expert system, technical trading signal, Tehran stock exchange

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Authors: Xiping Wang，Shuran Yao, Liqin Dai

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Many studies report the winter is getting warmer and the minimum air temperature is obviously rising as the important climate warming evidences. The exacerbated air temperature fluctuation tending to bring more severe weather variation is another important consequence of recent climate change which induced more disasters to crop growth in quite a certain regions. Hebei Province is an important winter wheat growing province in North of China that recently endures more winter freezing injury influencing the local winter wheat crop management. A winter wheat freezing injury assessment Bayesian Network framework was established for the objectives of estimating, assessing and predicting winter wheat freezing disasters in Hebei Province. In this framework, the freezing disasters was classified as three severity degrees (SI) among all the three types of freezing, i.e., freezing caused by severe cold in anytime in the winter, long extremely cold duration in the winter and freeze-after-thaw in early season after winter. The factors influencing winter wheat freezing SI include time of freezing occurrence, growth status of seedlings, soil moisture, winter wheat variety, the longitude of target region and, the most variable climate factors. The climate factors included in this framework are daily mean and range of air temperature, extreme minimum temperature and number of days during a severe cold weather process, the number of days with the temperature lower than the critical temperature values, accumulated negative temperature in a potential freezing event. The Bayesian Network model was evaluated using actual weather data and crop records at selected sites in Hebei Province using real data. With the multi-stage influences from the various factors, the forecast and assessment of the event-based target variables, freezing injury occurrence and its damage to winter wheat production, were shown better scoped by Bayesian Network model.

Keywords: bayesian networks, climatic change, freezing Injury, winter wheat

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Authors: T. Lydon, A. McNabola, P. Coughlan

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Energy consumption in the water distribution network (WDN) is a well established problem equating to the industry contributing heavily to carbon emissions, with 0.9 kg CO2 emitted per m3 of water supplied. It is indicated that 85% of energy wasted in the WDN can be recovered by installing turbines. Existing potential in networks is present at small capacity sites (5-10 kW), numerous and dispersed across networks. However, traditional turbine technology cannot be scaled down to this size in an economically viable fashion, thus alternative approaches are needed. This research aims to enable energy recovery potential within the WDN by exploring the potential of pumps-as-turbines (PATs), to realise this potential. PATs are estimated to be ten times cheaper than traditional micro-hydro turbines, presenting potential to contribute to an economically viable solution. However, a number of technical constraints currently prohibit their widespread use, including the inability of a PAT to control pressure, difficulty in the selection of PATs due to lack of performance data and a lack of understanding on how PATs can cater for fluctuations as extreme as +/- 50% of the average daily flow, characteristic of the WDN. A PAT prototype is undergoing testing in order to identify the capabilities of the technology. Results of preliminary testing, which involved testing the efficiency and power potential of the PAT for varying flow and pressure conditions, in order to develop characteristic and efficiency curves for the PAT and a baseline understanding of the technologies capabilities, are presented here: •The limitations of existing selection methods which convert BEP from pump operation to BEP in turbine operation was highlighted by the failure of such methods to reflect the conditions of maximum efficiency of the PAT. A generalised selection method for the WDN may need to be informed by an understanding of impact of flow variations and pressure control on system power potential capital cost, maintenance costs, payback period. •A clear relationship between flow and efficiency rate of the PAT has been established. The rate of efficiency reductions for flows +/- 50% BEP is significant and more extreme for deviations in flow above the BEP than below, but not dissimilar to the reaction of efficiency of other turbines. •PAT alone is not sufficient to regulate pressure, yet the relationship of pressure across the PAT is foundational in exploring ways which PAT energy recovery systems can maintain required pressure level within the WDN. Efficiencies of systems of PAT energy recovery systems operating conditions of pressure regulation, which have been conceptualise in current literature, need to be established. Initial results guide the focus of forthcoming testing and exploration of PAT technology towards how PATs can form part of an efficiency energy recovery system.

Keywords: energy recovery, pump-as-turbine, water distribution network, water distribution network

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Authors: Shawna M. Margesson

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This research study seeks to explore the lived experience of peer support workers (PSWs) in a peer-led non-governmental organization in Denver, Colorado, USA. The Colorado Mental Wellness Network offers supportive wellness recovery services such as wellness recovery action plans (WRAP), advocacy trainings for anti-stigma campaigns, and PSWs to work with and for consumers in the community. This study suggests that a peer-run environment is a unique community setting for PSWs to work given all employees are living in mental wellness recovery. Little has been documented about PSWs' personal accounts of working within a recovery-oriented organization and their first-person accounts to working with consumers. The importance of this study is to provide an ethnographic account of both subjects; the lived experiences of PSWs of both organizational and consumer-driven recovery. This study seeks to add to the literature and the social work profession the personal accounts of PSWs as they provide services to others like themselves. It also will provide an additional lens to view the peer-driven movement in mental health and wellness recovery.

Keywords: peer to peer movement, mental health, ethnography, peer support workers

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Authors: Haoyang Liang, Dandong Ge

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With the rapid development of Chinese cities, traffic congestion has become more and more serious. At the same time, there are many closed old residential area in Chinese cities, which seriously affect the connectivity of urban roads and reduce the density of urban road networks. After reopening the restricted old residential area, the internal roads in the original residential area were transformed into urban roads, which was of great help to alleviate traffic congestion. This paper uses the spatial syntactic theory to analyze the urban road network and compares the roads with the integration and connectivity degree to evaluate whether the opening of the roads in the residential areas can improve the urban traffic. Based on the road network system within the first loop road in Kunming, the Space Syntax evaluation model is established for status analysis. And comparative analysis method will be used to compare the change of the model before and after the road openness of the old urban residential district within the first-ring road in Kunming. Then it will pick out the areas which indicate a significant difference for the small dimensions model analysis. According to the analyzed results and traffic situation, the evaluation of road openness in the old urban residential district will be proposed to improve the urban residential districts.

Keywords: Space Syntax, Kunming, urban renovation, traffic jam

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Authors: G. Punithavathy

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The research proposes a statistical model for the distributed acoustic sensor interrogation units that broadcast a laser pulse into the fiber optics, where interactions within the fiber determine the localized acoustic energy that causes light reflections known as backscatter. The backscattered signal's amplitude and phase can be calculated using explicit equations. The created model makes amplitude signal spectrum and autocorrelation predictions that are confirmed by experimental findings. Phase signal characteristics that are useful for researching optical time domain reflectometry (OTDR) system sensing applications are provided and examined, showing good agreement with the experiment. The experiment was successfully done with the use of Python coding. In this research, we can analyze the entire distributed acoustic sensing (DAS) component parts separately. This model assumes that the fiber is in a static condition, meaning that there is no external force or vibration applied to the cable, that means no external acoustic disturbances present. The backscattered signal consists of a random noise component, which is caused by the intrinsic imperfections of the fiber, and a coherent component, which is due to the laser pulse interacting with the fiber.

Keywords: distributed acoustic sensing, optical fiber devices, optical time domain reflectometry, Rayleigh scattering

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Asnakew Mulualem Tegegne, Tarun Kumar Lohani, Abunu Atlabachew Eshete

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Groundwater potential delineation is essential for efficient water resource utilization and long-term development. The scarcity of potable and irrigation water has become a critical issue due to natural and anthropogenic activities in meeting the demands of human survival and productivity. With these constraints, groundwater resources are now being used extensively in Ethiopia. Therefore, an innovative convolutional neural network (CNN) is successfully applied in the Gunabay watershed to delineate groundwater potential based on the selected major influencing factors. Groundwater recharge, lithology, drainage density, lineament density, transmissivity, and geomorphology were selected as major influencing factors during the groundwater potential of the study area. For dataset training, 70% of samples were selected and 30% were used for serving out of the total 128 samples. The spatial distribution of groundwater potential has been classified into five groups: very low (10.72%), low (25.67%), moderate (31.62%), high (19.93%), and very high (12.06%). The area obtains high rainfall but has a very low amount of recharge due to a lack of proper soil and water conservation structures. The major outcome of the study showed that moderate and low potential is dominant. Geodetoctor results revealed that the magnitude influences on groundwater potential have been ranked as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The model results showed that using a convolutional neural network (CNN), groundwater potentiality can be delineated with higher predictive capability and accuracy. CNN-based AUC validation platform showed that 81.58% and 86.84% were accrued from the accuracy of training and testing values, respectively. Based on the findings, the local government can receive technical assistance for groundwater exploration and sustainable water resource development in the Gunabay watershed. Finally, the use of a detector-based deep learning algorithm can provide a new platform for industrial sectors, groundwater experts, scholars, and decision-makers.

Keywords: CNN, geodetector, groundwater influencing factors, Groundwater potential, Gunabay watershed

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Authors: Cleiton Pons Ferreira, Diana Francisca Adamatti

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Advancements in computers technology have allowed to obtain information for research in biology and neuroscience. In order to transform the data from these surveys, networks have long been used to represent important biological processes, changing the use of this tools from purely illustrative and didactic to more analytic, even including interaction analysis and hypothesis formulation. Many studies have involved this application, but not directly for interpretation of data obtained from brain functions, asking for new perspectives of development in neuroinformatics using existent models of tools already disseminated by the bioinformatics. This study includes an analysis of neurological data through electroencephalogram (EEG) signals, using the Cytoscape, an open source software tool for visualizing complex networks in biological databases. The data were obtained from a comparative case study developed in a research from the University of Rio Grande (FURG), using the EEG signals from a Brain Computer Interface (BCI) with 32 eletrodes prepared in the brain of a blind and a sighted individuals during the execution of an activity that stimulated the spatial ability. This study intends to present results that lead to better ways for use and adapt techniques that support the data treatment of brain signals for elevate the understanding and learning in neuroscience.

Keywords: neuroinformatics, bioinformatics, network tools, brain mapping

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Authors: Esra Ece Var

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Beekeeping plays essential role both in terms of agricultural yields and agricultural economy; they produce honey, wax, royal jelly, apitoxin, pollen, and propolis. Nowadays, these natural products become more importantly suitable and preferable for nutrition, food supplement, medicine, and industry. However, to produce organic honey, majority of the apiaries are located in remote or distant rural areas where utilities such as electricity and Internet network are not available. Additionally, due to colony failures, world honey production decreases year by year despite the increase in the number of beehives. The objective of this paper is to develop a smart beehive monitoring system for apiaries including those that do not have access to Internet network. In this context, temperature and humidity inside the beehive, and ambient temperature were measured with RFID sensors. Control center, where all sensor data was sent and stored at, has a GSM module used to warn the beekeeper via SMS when an anomaly is detected. Simultaneously, using the collected data, an unsupervised machine learning algorithm is used for detecting anomalies and calibrating the warning system. The results show that the smart beehive monitoring system can detect fatal anomalies up to 4 weeks prior to colony loss.

Keywords: beekeeping, smart systems, machine learning, anomaly detection, apiculture

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Authors: Raghavi Janaswamy, Saraswathi K. Vasudev

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Raga, as the soul and base, is a distinctive musical entity, in the music system, with unique structure on its construction of srutis (musical sounds) and application. One of the essential components of the music system is the ‘tala’ that defines the rhythm of a song. There are seven basic swaras (notes) Sa, Ri, Ga, Ma, Pa, Da and Ni in the carnatic music system that are analogous to the C, D, E, F, G, A and B of the western system. The carnatic music further builds on conscious use of microtones, gamakams (oscillation) and rendering styles. It has basic 72 ragas known as melakarta ragas, and a plethora of ragas have been developed from them with permutations and combinations of the basic swaras. Among them, some ragas derived from a same melakarta raga are distinctly different from each other and could evoke a profound difference in the raga bhava (emotion) during rendering. Although these could bear similar arohana and avarohana swaras, their quintessential differences in the gamakas usage and srutis present therein offer varied melodic feelings; variations in the intonation and stress given to certain swara phrases are the root causes. This article enlightens a group of such allied ragas (AR) from the perspectives of their schema and raga alapana (improvisation), ranjaka prayogas (signature phrases), differences in rendering tempo, gamakas and delicate srutis along with the range of sancharas (musical phrases). The intricate differences on the sruti frequencies and use of AR in composing kritis (musical compositions) toward emotive accomplishments such as mood of valor, kindness, love, humor, anger, mercy to name few, have also been explored. A brief review on the existing scientific research on the music therapy on some of the Carnatic ragas is presented. Studying and comprehending the AR, indeed, enable the music aspirants to gain a thorough knowledge on the subtle nuances among the ragas. Such knowledge helps leave a long-lasting melodic impression on the listeners and enable further research on the music therapy.

Keywords: Carnatic music, allied ragas, melakarta, gamakam

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Authors: Nahla S. Abdel-Azim, Khaled A. Shams, Elsayed A. Omer, Mahmoud M. Sakr

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Medicinal plants play a significant role in the health care system in Egypt. Knowledge developed over the years by people is mostly unrecorded and orally passes on from one generation to the next. This knowledge is facing the danger of becoming extinct. Therefore there is an urgent need to document the medicinal and aromatic plants associated with traditional knowledge. The Egyptian Encyclopedia of wild medicinal plants (EEWMP) is the first attempt to collect most of the basic elements of the medicinal plant resources of Egypt and their traditional uses. It includes scientific data on about 500 medicinal plants in the form of monographs. Each monograph contains all available information and scientific data on the selected species including the following: names, description, distribution, parts used, habitat, conservational status, active or major chemical constituents, folk medicinal uses and heritage resources, pharmacological and biological activities, authentication, pharmaceutical products, and cultivation. The DNA bar-coding is also included (when available). A brief Arabic summary is given for every monograph. This work revealed the diversity in plant parts used in the treatment of different ailments. In addition, the traditional knowledge gathered can be considered a good starting point for effective in situ and ex-situ conservation of endangered plant species.

Keywords: encyclopedia, medicinal plant, traditional medicine, wild flora

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Authors: Taylor Kolody, Farkhund Iqbal, Rabia Batool, Benjamin Fung, Mohammed Hussaeni, Saiqa Aleem

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The rising threat of climate change has led to an increase in public awareness and care about our collective and individual environmental impact. A key component of this impact is our use of cars and other polluting forms of transportation, but it is often difficult for an individual to know how severe this impact is. While there are applications that offer this feedback, they require manual entry of what transportation mode was used for a given trip, which can be burdensome. In order to alleviate this shortcoming, a data from the 2016 TRIPlab datasets has been used to train a variety of machine learning models to automatically recognize the mode of transportation. The accuracy of 89.6% is achieved using single deep neural network model with Gated Recurrent Unit (GRU) architecture applied directly to trip data points over 4 primary classes, namely walking, public transit, car, and bike. These results are comparable in accuracy to results achieved by others using ensemble methods and require far less computation when classifying new trips. The lack of trip context data, e.g., bus routes, bike paths, etc., and the need for only a single set of weights make this an appropriate methodology for applications hoping to reach a broad demographic and have responsive feedback.

Keywords: classification, gated recurrent unit, recurrent neural network, transportation

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Authors: Jyotismita Talukdar

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In this paper, a study has been made on the possibility and accuracy of early prediction of several Heart Disease using Artificial Neural Network. (ANN). The study has been made in both noise free environment and noisy environment. The data collected for this analysis are from five Hospitals. Around 1500 heart patient’s data has been collected and studied. The data is analysed and the results have been compared with the Doctor’s diagnosis. It is found that, in noise free environment, the accuracy varies from 74% to 92%and in noisy environment (2dB), the results of accuracy varies from 62% to 82%. In the present study, four basic attributes considered are Blood Pressure (BP), Fasting Blood Sugar (FBS), Thalach (THAL) and Cholesterol (CHOL.). It has been found that highest accuracy(93%), has been achieved in case of PPI( Post-Permanent-Pacemaker Implementation ), around 79% in case of CAD(Coronary Artery disease), 87% in DCM (Dilated Cardiomyopathy), 89% in case of RHD&MS(Rheumatic heart disease with Mitral Stenosis), 75 % in case of RBBB +LAFB (Right Bundle Branch Block + Left Anterior Fascicular Block), 72% for CHB(Complete Heart Block) etc. The lowest accuracy has been obtained in case of ICMP (Ischemic Cardiomyopathy), about 38% and AF( Atrial Fibrillation), about 60 to 62%.

Keywords: coronary heart disease, chronic stable angina, sick sinus syndrome, cardiovascular disease, cholesterol, Thalach

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Authors: K. Durairaj, I. N. Umar

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The asynchronous discussion forum is one of the most widely used activities in learning management system environment. Online forum allows participants to interact, construct knowledge, and can be used to complement face to face sessions in blended learning courses. However, to what extent do the students perceive the benefits or advantages of forum remain to be seen. Through content and social network analyses, instructors will be able to gauge the students’ engagement and knowledge construction level. Thus, this study aims to analyze the students’ level of knowledge construction and their participation level that occur through online discussion. It also attempts to investigate the relationship between the level of knowledge construction and their social interaction patterns. The sample involves 23 students undertaking a master course in one public university in Malaysia. The asynchronous discussion forum was conducted for three weeks as part of the course requirement. The finding indicates that the level of knowledge construction is quite low. Also, the density value of 0.11 indicating that the overall communication among the participants in the forum is low. This study reveals that strong and significant correlations between SNA measures (in-degree centrality, out-degree centrality) and level of knowledge construction. Thus, allocating these active students in a different groups aids the interactive discussion takes place. Finally, based upon the findings, some recommendations to increase students’ level of knowledge construction and also for further research are proposed.

Keywords: asynchronous discussion forums, content analysis, knowledge construction, social network analysis

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Authors: Sina Moradi, Christopher W. K. Chow, John Van Leeuwen, David Cook, Mary Drikas, Patrick Hayde, Rose Amal

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Chloramine is commonly used as a disinfectant in drinking water distribution systems (DWDSs), particularly in Australia and the USA. Maintaining a chloramine residual throughout the DWDS is important in ensuring microbiologically safe water is supplied at the customer’s tap. In order to simulate how chloramine behaves when it moves through the distribution system, a water quality network model (WQNM) can be applied. In this work, the WQNM was based on mono-chloramine decomposition reactions, which enabled prediction of mono-chloramine residual at different locations through a DWDS in Australia, using the Bentley commercial hydraulic package (Water GEMS). The accuracy of WQNM predictions is influenced by a number of water quality parameters. Optimization of these parameters in order to obtain the closest results in comparison with actual measured data in a real DWDS would result in both cost reduction as well as reduction in consumption of valuable resources such as energy and materials. In this work, the optimum operating conditions of water quality parameters (i.e. temperature, pH, and initial mono-chloramine concentration) to maximize the accuracy of mono-chloramine residual predictions for two water supply scenarios in an entire network were determined using response surface methodology (RSM). To obtain feasible and economical water quality parameters for highest model predictability, Design Expert 8.0 software (Stat-Ease, Inc.) was applied to conduct the optimization of three independent water quality parameters. High and low levels of the water quality parameters were considered, inevitably, as explicit constraints, in order to avoid extrapolation. The independent variables were pH, temperature and initial mono-chloramine concentration. The lower and upper limits of each variable for two water supply scenarios were defined and the experimental levels for each variable were selected based on the actual conditions in studied DWDS. It was found that at pH of 7.75, temperature of 34.16 ºC, and initial mono-chloramine concentration of 3.89 (mg/L) during peak water supply patterns, root mean square error (RMSE) of WQNM for the whole network would be minimized to 0.189, and the optimum conditions for averaged water supply occurred at pH of 7.71, temperature of 18.12 ºC, and initial mono-chloramine concentration of 4.60 (mg/L). The proposed methodology to predict mono-chloramine residual can have a great potential for water treatment plant operators in accurately estimating the mono-chloramine residual through a water distribution network. Additional studies from other water distribution systems are warranted to confirm the applicability of the proposed methodology for other water samples.

Keywords: chloramine decay, modelling, response surface methodology, water quality parameters

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Authors: B. R. Faleye, M. I. Titocan, M. P. Ibitola

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Investigation was carried out at Opic Estate in Isheri-Osun River Basin environment using Electrical Resistivity method to study saltwater intrusion into a fresh water aquifer system from the proximal estuarine water body. The investigation is aimed at aquifer characterisation using electrical resistivity method in order to provide the depth to which fresh water fit for both domestic and industrial consumption. The 2D Electrical Resistivity and Vertical Electrical Resistivity techniques alongside Laboratory analysis of water samples obtained from the boreholes were adopted. Three traverses were investigated using Wenner and Pole-Dipole array with multi-electrode system consisting of 84 electrodes and a spread of 581 m, 664 m and 830 m were attained on the traverses. The main lithologies represented in the study area are Sand, Clay and Clayey Sand of which Sand constitutes the aquifer in the study area. Vertical Electrical Sounding data obtained at different lateral distance on the traverses have indicated that the water in the aquifer in the subsurface is brackish. Brackish water is represented by lowelectrical resistivity value signature while fresh water is characterized by relatively high electrical resistivity and in some regionfresh water is existent at depth greater than 200 m. Results of laboratory analysis of samples showed that the pH, Salinity, Total Dissolved Solid and Conductivity indicated existence of water with poor quality, indicating that salinity, TDS and Conductivity is higher in the Northern part of the study area. The 2D electrical resistivity and Vertical Electrical Sounding methods indicate that fresh water region is at ≥200m depth. Aquifers not fit for domestic use in the study area occur downwards to about 200 m in depth. In conclusion, it is recommended that wells should be sunkbeyond 220 m for the possible procurement of portable fresh water.

Keywords: 2D electrical resistivity, aquifer, brackish water, lithologies

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Authors: Constant Akama, Frank Kulor, Frederick Agyemang

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All over the world, due to increasing population, electric power distribution companies are looking for more efficient ways of reading electricity meters. In Ghana, the prepaid metering system was introduced in 2007 to replace the manual system of reading which was fraught with inefficiencies. However, the prepaid system in Ghana is not capable of integration with online systems such as e-commerce platforms and remote monitoring systems. In this paper, we present a design framework for an automatic metering system that can be integrated with e-commerce platforms and remote monitoring systems. The meter was designed using ADE 7755 which reads the energy consumption and the reading is processed by a microcontroller connected to Sim900 General Packet Radio Service module containing a GSM chip provisioned with an Access Point Name. The system also has a billing server and a management server located at the premises of the utility company which communicate with the meter over a Virtual Private Network and GPRS. With this system, customers can buy credit online and the credit will be transferred securely to the meter. Also, when a fault is reported, the utility company can log into the meter remotely through the management server to troubleshoot the problem.

Keywords: access point name, general packet radio service, GSM, virtual private network

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Authors: P. Jayashree, S. Rajkumar

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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.

Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding

Procedia PDF Downloads 297

Authors: M. Moslehpour, S. Khorsandi

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Due to shortage in IPv4 addresses, transition to IPv6 has gained significant momentum in recent years. Like Address Resolution Protocol (ARP) in IPv4, Neighbor Discovery Protocol (NDP) provides some functions like address resolution in IPv6. Besides functionality of NDP, it is vulnerable to some attacks. To mitigate these attacks, Internet Protocol Security (IPsec) was introduced, but it was not efficient due to its limitation. Therefore, SEND protocol is proposed to automatic protection of auto-configuration process. It is secure neighbor discovery and address resolution process. To defend against threats on NDP’s integrity and identity, Cryptographically Generated Address (CGA) and asymmetric cryptography are used by SEND. Besides advantages of SEND, its disadvantages like the computation process of CGA algorithm and sequentially of CGA generation algorithm are considerable. In this paper, we parallel this process between network resources in order to improve it. In addition, we compare the CGA generation time in self-computing and distributed-computing process. We focus on the impact of the malicious nodes on the CGA generation time in the network. According to the result, although malicious nodes participate in the generation process, CGA generation time is less than when it is computed in a one-way. By Trust Management System, detecting and insulating malicious nodes is easier.

Keywords: NDP, IPsec, SEND, CGA, modifier, malicious node, self-computing, distributed-computing

Procedia PDF Downloads 280

Authors: Katawut Kaewbanjong

Abstract:

We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: Mushtaq Ahmad, Bashir Rahman, Taqweem-ul-Haq, Fazal Jalil, Aftab Ali Shah

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Single nucleotide polymorphisms (SNPs) in genes coding for microRNAs (miRNAs) play a pivotal role in the progression of breast cancer (BC). We investigated the association of miR-146a rs2910164 G/C polymorphism with the risk of BC in the Pakistani population. The miR-146a rs2910164 polymorphism was genotyped in 300 BC-cases and 300 age- and gender-matched healthy controls using T-ARMS-PCR. Genotype and allele frequencies were calculated, and the association between genotypes and the risk of BC was calculated by odds ratios (OR) and confidence intervals (95%). A significant difference in genotypic frequencies (χ2=63.10; p ≤ 0.0001) and allelic frequencies (OR=0.3955 (0.3132-0.4993); p ≤ 0.0001) was observed between cases and controls. Furthermore, we also found that miR-146 rs2910164 CC homozygote increased the risk of breast cancer in the dominant (OR=0.2397 (0.1629-0.3526); p=0.0001; GG vs GC+CC) and recessive (OR=2.803 (1.865- 4.213); P ≤ 0.0001; CC vs GC+GG) inheritance models. In summary, miR-146a rs2910164 G/C is significantly associated with BC in the Pakistani population. To our knowledge, this is the first study that assessed MIR146a rs2910164 G > C SNP in Pakistani population. By analyzing the secondary structure of MIR146A variant, a significant structural modification was noted. Study with a larger sample size is needed to further confirm these findings.

Keywords: breast cancer, MIR146A, microRNA, SNP

Procedia PDF Downloads 139

Authors: Rasaq Bolakale Salau, Maimuna Muhammad Abubakar, Jonathan Yisa, Muhammad Tauheed Bisiriyu, Jimoh Oladejo Tijani, Alexander Ifeanyi Ajai

Abstract:

Vegetables oils are widely consumed in Nigeria. This has led to competitive manufacture of various oil brands. This leads increasing tendencies for fraud, labelling misinformation and other unwholesome practices. A total of thirty samples including raw and corresponding branded samples of vegetable oils were collected. The Oils were extracted from raw ground nut, soya bean and oil palm fruits. The GC-MS data was subjected to chemometric techniques of PCA and HCA. The SOLO 8.7 version of the standalone chemometrics software developed by Eigenvector research incorporated and powered by PLS Toolbox was used. The GCMS fingerprint gave basis for discrimination as it reveals four predominant but unevenly distributed fatty acids: Hexadecanoic acid methyl ester (10.27- 45.21% PA), 9,12-octadecadienoic acid methyl ester (10.9 - 45.94% PA), 9-octadecenoic acid methyl ester (18.75 - 45.65%PA), and Eicosanoic acid methyl ester (1.19% - 6.29%PA). In PCA modelling, two PCs are retained at cumulative variance captured at 73.15%. The score plots indicated that palm oil brands are most aligned with raw palm oil. PCA loading plot reveals the signature retention times between 4.0 and 6.0 needed for quality assurance and authentication of the oils samples. They are of aromatic hydrocarbons, alcohols and aldehydes functional groups. HCA dendrogram which was modeled using Euclidian distance through Wards method, indicated co-equivalent samples. HCA revealed the pair of raw palm oil brand and palm oil brand in the closest neighbourhood (± 1.62 % A difference) based on variance weighted distance. It showed Palm olein brand to be most authentic. In conclusion, based on the GCMS data with chemometrics, the authenticity of the branded samples is ranked as: Palm oil > Soya oil > groundnut oil.

Keywords: vegetable oil, authenticity, chemometrics, PCA, HCA, GC-MS

Procedia PDF Downloads 36