Search results for: low-temperature district heating network

Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Namrata Mishra

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Health of a person plays a vital role in the collective health of his community and hence the well-being of the society as a whole. But, in today’s fast paced technology driven world, health issues are increasingly being associated with human behaviors – their lifestyle. Social networks have tremendous impact on the health behavior of individuals. Many researchers have used social network analysis to understand human behavior that implicates their social and economic environments. It would be interesting to use a similar analysis to understand human behaviors that have health implications. This paper focuses on concepts of those behavioural analyses that have health implications using social networks analysis and provides possible algorithmic approaches. The results of these approaches can be used by the governing authorities for rolling out health plans, benefits and take preventive measures, while the pharmaceutical companies can target specific markets, helping health insurance companies to better model their insurance plans.

Keywords: breadth first search, directed graph, health behaviors, social network analysis

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Authors: Shin-Pin Tseng

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Nowadays, numerous spectral amplitude coding (SAC) fiber-optic code-division-multiple-access (FO-CDMA) techniques were appealing due to their capable of providing moderate security and relieving the effects of multiuser interference (MUI). Nonetheless, the performance of the previous network is degraded due to fixed in-phase cross-correlation (IPCC) value. Based on the above problems, a new SAC FO-CDMA network using partial M-sequence (PMS) code is presented in this study. Because the proposed PMS code is originated from M-sequence code, the system using the PMS code could effectively suppress the effects of MUI. In addition, two-code keying (TCK) scheme can applied in the proposed SAC FO-CDMA network and enhance the whole network performance. According to the consideration of system flexibility, simple optical encoders/decoders (codecs) using fiber Bragg gratings (FBGs) were also developed. First, we constructed a diagram of the SAC FO-CDMA network, including (N/2-1) optical transmitters, (N/2-1) optical receivers, and one N×N star coupler for broadcasting transmitted optical signals to arrive at the input port of each optical receiver. Note that the parameter N for the PMS code was the code length. In addition, the proposed SAC network was using superluminescent diodes (SLDs) as light sources, which then can save a lot of system cost compared with the other FO-CDMA methods. For the design of each optical transmitter, it is composed of an SLD, one optical switch, and two optical encoders according to assigned PMS codewords. On the other hand, each optical receivers includes a 1 × 2 splitter, two optical decoders, and one balanced photodiode for mitigating the effect of MUI. In order to simplify the next analysis, the some assumptions were used. First, the unipolarized SLD has flat power spectral density (PSD). Second, the received optical power at the input port of each optical receiver is the same. Third, all photodiodes in the proposed network have the same electrical properties. Fourth, transmitting '1' and '0' has an equal probability. Subsequently, by taking the factors of phase‐induced intensity noise (PIIN) and thermal noise, the corresponding performance was displayed and compared with the performance of the previous SAC FO-CDMA networks. From the numerical result, it shows that the proposed network improved about 25% performance than that using other codes at BER=10-9. This is because the effect of PIIN was effectively mitigated and the received power was enhanced by two times. As a result, the SAC FO-CDMA network using PMS codes has an opportunity to apply in applications of the next-generation optical network.

Keywords: spectral amplitude coding, SAC, fiber-optic code-division multiple-access, FO-CDMA, partial M-sequence, PMS code, fiber Bragg grating, FBG

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Authors: Aditi Madan, Jayant Kumar Routray

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Purpose: Over the years flood related risks have compounded due to increasing vulnerability caused by rapid urbanisation and growing population. This increase is an indication of the need for enhancing the preparedness of institutions to respond to floods. The study describes disaster management structure and its linkages with institutions involved in managing disasters. It addresses issues and challenges associated with readiness of disaster management institutions to respond to floods. It suggests policy options for enhancing the current state of readiness of institutions to respond by considering factors like institutional, manpower, financial, technical, leadership & networking, training and awareness programs, monitoring and evaluation. Methodology: The study is based on qualitative data with statements and outputs from primary and secondary sources to understand the institutional framework for disaster management in India. Primary data included field visits, interviews with officials from institutions managing disasters and the affected community to identify the challenges faced in engaging national, state, district and local level institutions in managing disasters. For focus group discussions, meetings were held with district project officers and coordinators, local officials, community based organisation, civil defence volunteers and community heads. These discussions were held to identify the challenges associated with preparedness to respond of institutions to floods. Findings: Results show that disasters are handled by district authority and the role of local institutions is limited to a reactive role during disaster. Data also indicates that although the existing institutional setup is well coordinated at the district level but needs improvement at the local level. Wide variations exist in awareness and perception among the officials engaged in managing disasters. Additionally, their roles and responsibilities need to be clearly defined with adequate budget and dedicated permanent staff for managing disasters. Institutions need to utilise the existing manpower through proper delegation of work. Originality: The study suggests that disaster risk reduction needs to focus more towards inclusivity of the local urban bodies. Wide variations exist in awareness and perception among the officials engaged in managing disasters. In order to ensure community participation, it is important to address their social and economic problems since such issues can overshadow attempts made for reducing risks. Thus, this paper suggests development of direct linkages among institutions and community for enhancing preparedness to respond to floods.

Keywords: preparedness, response, disaster, flood, community, institution

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Authors: Debneil Saha Roy

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Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Raed Hanandeh

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This empirical study examines how knowledge sharing behaviors mediate the effect Technology-driven strategy on virtual organization success in Jordanian tourism sector firms. The results reveal that Social network ties are positively related to web knowledge seeking, web knowledge contributing and interactive system, but negatively related to accidental knowledge leakage. Furthermore, all types of knowledge sharing behavior are positively related to virtual organization success. Data collected from 23 firms. The total number of questionnaires mailed, 250 questionnaires were delivered. 214 were considered valid out of 241 Responses were received. The findings provide evidence that knowledge sharing behavior play a mediating role between Social network ties and virtual organization success and show that, web knowledge seeking, web knowledge contributing and interactive system playing an important impact on virtual organization success through knowledge sharing behaviors.

Keywords: social network ties, virtual organization success, knowledge sharing behaviors, web knowledge

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Authors: Kopal Verma, Umesh C. Kulshrestha

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The mountainous regions are very crucial for a country because of their importance for weather, water supply, forests, and various other socio-economic benefits. But the increasing population and its demand for energy and infrastructure have contributed very high loadings of air pollution. Various activities such as cooking, heating, manufacturing, transport, etc. contribute various particulate and gaseous pollutants in the atmosphere. This study was focused upon indoor air pollution and was carried out in four rural households of the Baggi village located in the Hamirpur District of the Himachal Pradesh state. The residents of Baggi village use biomass as fuel for cooking on traditional stove (Chullah). The biomass types include wood (mainly Beul, Grewia Optiva), crop residue and dung cakes. This study aimed to determine the organic carbon (OC), elemental carbon (EC), major cations and anions in the indoor air of each household. During non-cooking hours, it was found that the indoor air contained OC and EC as low as 21µg/m³ and 17µg/m³ respectively. But during cooking hours (with biomass burning), the levels of OC and EC were raised significantly by 91.2% and 85.4% respectively. Then the residents were advised to switch over as per our soft approach options. In the first approach change, they were asked to prepare the meal partially on Chullah using biomass and partially with liquefied petroleum gas (LPG). By doing this change, a considerable reduction in OC (53.1%) and in EC (41.8%) was noticed. The second change of approach included the cooking of entire meal by using LPG. This resulted in the reduction of OC (84.1%) and EC (73.3%) as compared to the values obtained during cooking entirely with biomass. The carbonaceous aerosol levels were higher in the morning hours than in the evening hours because of more biomass burning activity in the morning. According to a general survey done with the residents, the study provided them an awareness about the air pollution and the harmful effects of biomass burning. Some of them correlated their ailments like weakened eyesight, fatigue and respiratory problems with indoor air pollution. This study demonstrated that by replacing biomass with clean fuel such as LPG, the indoor concentrations of EC and OC can be reduced substantially.

Keywords: biomass burning, carbonaceous aerosol, elemental carbon, organic carbon, LPG

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Authors: Yu-Chu Yeh, WeiChin Hsu, Chih-Yen Chang

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Substitution, combination, modification, putting to other uses, elimination, and rearrangement (SCAMPER) has been regarded as an effective technique that provides a structured way to help people to produce creative ideas and solutions. Although some neuroscience studies regarding creativity training have been conducted, no study has focused on SCAMPER. This study therefore aimed at examining whether the learning of SCAMPER through video tutorials would result in alternations of neural activation. Thirty college students were randomly assigned to the experimental group or the control group. The experimental group was requested to watch SCAMPER videos, whereas the control group was asked to watch natural-scene videos which were regarded as neutral stimulating materials. Each participant was brain scanned in a Functional magnetic resonance imaging (fMRI) machine while undertaking a creativity test before and after watching the videos. Furthermore, a two-way ANOVA was used to analyze the interaction between groups (the experimental group; the control group) and tasks (C task; M task; X task). The results revealed that the left precuneus significantly activated in the interaction of groups and tasks, as well as in the main effect of group. Furthermore, compared with the control group, the experimental group had greater activation in the default mode network (left precuneus and left inferior parietal cortex) and the motor network (left postcentral gyrus and left supplementary area). The findings suggest that the SCAMPER training may facilitate creativity through the stimulation of the default mode network and the motor network.

Keywords: creativity, default mode network, neural activation, SCAMPER

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Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

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Authors: Upvan Chobera

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India bears the highest burden of tuberculosis globally, facing a significant incidence rate. To combat this public health challenge, the Ministry of Health and Family Welfare in India has launched an ambitious national strategic plan with the aim of achieving END TB targets by 2025. Addressing tuberculosis requires a comprehensive, multi-sectoral approach that encompasses factors such as nutritional support, living and working conditions, and improved access to diagnostics and treatment services. This study delves into the burden of tuberculosis in India, examining the government's strategic plan to combat the disease. Additionally, it explores the role of Ni-Kshay Mitra (community support) in this fight, encompassing various entities such as cooperative societies, corporations, elected representatives, individuals, institutions, non-government organizations, and political parties or individual donors. These efforts aim to enhance the response against tuberculosis, complementing the government's initiatives and catering to district-specific requirements, all coordinated with the district administration. It is important to note that the support provided under the Ni-Kshay Mitra initiative is supplementary to the free services offered by the National TB Elimination Program (NTEP) available to all patients.

Keywords: end TB targets, Ni-kshay Mitra, NTEP, tuberculosis burden in India

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Authors: Mahdi Jalili

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Functional connectivity in the human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks – whose nodes are brain regions and edges correspond to functional links between them – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using correlation analysis and were further binarized to obtain binary functional networks. Global and local efficiency measures – as graph theory metrics– were computed for the extracted networks. We found that male brains have a significantly greater global efficiency (i.e., global communicability of the network) across all frequency bands for a wide range of cost values in both hemispheres. Furthermore, for a range of cost values, female brains showed significantly greater right-hemispheric local efficiency (i.e., local connectivity) than male brains.

Keywords: EEG, brain, functional networks, network science, graph theory

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Authors: Hossain Samani, Mohammad Ehteram

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In this paper, we consider finite volume method (FVM) in water hammer. We will simulate these techniques on a looped network with complex boundary conditions. After comparing methods, we see the FVM method as the best method. We compare the results of FVM with experimental data. Finite volume using staggered grid is applied for solving water hammer equations.

Keywords: hydraulic transient, water hammer, interpolation, non-liner interpolation

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Authors: Mahmood Iqbal, Khalid Nawab, Tachibana Satoshi

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Farmer Field School (FFS) is constantly aims to assist farmers to determine and learn about field ecology and integrated crop management. The study was conducted to examine the change in productivity of tomato crop in the study area; to determine increase in per acre yield of the crop, and find out reduction in per acre input cost. A study of tomato crop was conducted in ten villages namely Jabban, Bijligar Colony, Palonow, Heroshah, Zara Maira, Deghar Ghar, Sidra Jour, Anar Thangi, Miangano Korona and Wartair of district Malakand. From each village 15 respondents were selected randomly on the basis of identical allocation making sample size of 150 respondents. The research was based on primary as well as secondary data. Primary data was collected from farmers while secondary data were taken from Agriculture Extension Department Dargai, District Malakand. Interview schedule was planned and each farmer was interviewed personally. The study was based on comparison of cost, yield and income of tomato before and after FFS. Paired t-test and Statistical Package for Social Sciences (SPSS) was used for analysis; outcome of the study show that integrated pest management project has brought a positive change in the attitude of farmers of the project area through FFS approach. In district Malakand 66.0% of the respondents were between the age group of 31-50 years, 11.3% of respondents had primary level of education, 12.7% of middle level, 28.7% metric level, 3.3% of intermediate level and 2.0% of graduate level of education while 42.0% of respondents were illiterate and have no education. Average land holding size of farmers was 6.47 acres, cost of seed, crop protection from insect pest and crop protection from diseases was reduced by Rs. 210.67, Rs. 2584.43 and Rs. 3044.16 respectively, the cost of fertilizers and cost of farm yard manure was increased by Rs.1548.87 and Rs. 1151.40 respectively while tomato yield was increased by 1585.03 kg/acre from 7663.87 to 9248.90 kg/acre. The role of FFS initiate by integrated pest management project through department of agriculture extension for the development of agriculture was worth mentioning. It has brought enhancement in crop yield of tomato and their income through FFS approach. On the basis of results of the research studies, integrated pest management project should spread their developmental activities for maximum participation of the complete rural masses through participatory FFS approach.

Keywords: agriculture, Farmers field schools, extension education, tomato

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Authors: Sukanya Sripho

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This paper aims to examine the level of roles performed in tourism development by local people residing in Chiang Khan Sub-District Municipality, Loei Province in Northeast of Thailand. In addition, this study also tested whether personal factors had a relationship with the level of roles performed in tourism development. These personal factors included gender, age, educational level, career, position and duty in the community, average income per month, length of residence and involvement in the tourism industry. The findings revealed a high level in each role performed. These roles were ranked from the highest mean score to the lowest mean score as follows: (1) improving and rejuvenating tourist attractions; (2) improving tourist facilities; (3) promoting people participation; (4) publicizing tourist attractions; (5) protecting for safety and security; and (6) surveying and managing the information of tourist attractions. Furthermore, it was found that position and duty in the community, length of residence and involvement in tourism industry had a relationship with the level of roles performed in tourism development at a significance level of 0.05.

Keywords: Role, local municipality administration, tourism development, Thailand

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Authors: Vidya Padmakumar, N. C. Tharavathy

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The study was conducted from July 2015 to July 2017 to determine and understand the occurrence, frequency and diversity of avifauna in the Mallathahalli Lake of Bangalore Urban district. During the study period, 46 species of both terrestrial, as well as, aquatic birds belonging to 30 families were identified out of which 9 families were aquatic birds and 21 families were terrestrial birds. There were 4 species of migratory birds out of 46, showing diurnal migration. There was a significant reduce in the number of bird species both terrestrial and aquatic during the summer season and also varied greatly during winters and monsoon. Of the total 24 species of aquatic birds, Fulica atra and Tachybaptus ruficolis were the most common with 100% frequency and the least frequent species with 3.02% frequency was identified as Threskiornis melanocephalus. Among the 22 species of terrestrial birds, Acridotheres tristis had a frequency of 89% and the least frequent was Pycnonotus cafer (4.45%). The most commonly encountered bird species were from the families- Anatidae, Podicipedidae, Ardeidae, Phalacrocoracidae, Rallidae, Accipitridae, Scolopacidae, Charadridae, Laridae, Meropidae, Hirudinidae. All the birds surviving around the area are dependent on the wetland and crop vegetation surrounding the lake, which are deteriorating due to anthropogenic interventions and urbanization which are rising to its peak gradually causing the decline in the avifaunal diversity.

Keywords: Avifaunal diversity, Mallathahalli lake, seasonal migration, urbanization

Procedia PDF Downloads 180

Authors: Viktória Sugár, Orsolya Frick, Gabriella Horváth, A. Bendegúz Vöröss, Péter Leczovics, Géza Baráth

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Urban greenery has multiple positive effects both on the city and its residents. Apart from the visual advantages, it changes the micro-climate by cooling and shading, also increasing vapor and oxygen, reducing dust and carbon-dioxide content at the same time. The above are all critical factors of livability of an urban fabric. Unfortunately, in a dense, historical district there are restricted possibilities to build green surfaces. The present study collects and systemizes the applicable green solutions in the case of a historical downtown district of Budapest. The study contains a GIS-based measurement of the eligible surfaces for greenery, and also calculates the potential of oxygen production, carbon-dioxide reduction and cooling effect of an increased green surface.  It can be concluded that increasing the green surface has measurable effects on a densely built urban fabric, including air quality, micro-climate and other environmental factors.

Keywords: urban greenery, green roof, green wall, green surface potential, sustainable city, oxygen production, carbon-dioxide reduction, geographical information system

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Authors: Mohsen Azarmjoo, Yasaman Azarmjoo, Zahra Alikhani Koopaei

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The increasing global population and continued growth in energy consumption underscore the need for renewable and sustainable energy sources more than ever. Soil batteries are a method for generating electrical energy by using recycled materials. Recycled materials include galvanized and copper sheets and recycled tires. Additionally, hydrogen, being a clean and efficient fuel, has the potential to replace fossil fuels. Consequently, hydrogen production from water presents a sustainable solution for energy supply. By utilizing aged materials, hydrogen production becomes more cost-effective and environmentally friendly. This article focuses on energy-deprived agricultural lands, explaining how soil batteries and hydrogen can provide the necessary energy for agricultural equipment, such as irrigation, lighting, greenhouse ventilation, and heating. The article explores the benefits of utilizing this method, emphasizing its potential to reduce environmental pollution through the use of recyclable materials. It is worth mentioning that these technologies face challenges, but their progress toward achieving zero-energy consumer standards positions them as promising future technologies for electricity generation. This article provides detailed insights into emerging technologies using a constructed case study involving soil batteries and a hydrogen fuel production device.

Keywords: electricity generation, soil batteries, tires, hydrogen, heat supply, water, aged materials, recycling, agricultural lands

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Authors: Abraham Dayan

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Remediation of soil containing volatile contaminants is often conducted by vapor extraction (SVE) technique. The operation is based on injection of air at ambient temperatures with or without thermal soil warming. Thermal enhancements of soil vapor extraction (TESVE) processes are usually conducted by soil heating, sometimes assisted by added steam injections. The current study addresses a technique which has not received adequate attention and is based on using exclusively hot air as an alternative to the common TESVE practices. To demonstrate the merit of the hot air TESVE technique, a sandy soil containing contaminated water is studied. Numerical and analytical tools were used to evaluate the rate of decontamination processes for various geometries and operating conditions. The governing equations are based on the Darcy law and are applied to an expanding compressible flow within a sandy soil. The equations were solved to determine the minimal time required for complete soil remediation. An approximate closed form solution was developed based on the assumption of local thermodynamic equilibrium and on a linearized representation of temperature dependence of the vapor to air density ratio. The solution is general in nature and offers insight into the governing processes of the soil remediation operation, where self-similar temperature profiles under certain conditions may exist, and the noticeable role of the contaminants evaporation and recondensation processes in affecting the remediation time. Based on analyses of the hot air TESVE technique, it is shown that it is sufficient to heat the air during a certain period of the decontamination process without compromising its full advantage, and thereby, entailing a minimization of the air-heating-energy requirements. This in effect is achieved by regeneration, leaving the energy stored in the soil during the early period of the remediation process to heat the subsequently injected ambient air, which infiltrates through it for the decontamination of the remaining untreated soil zone. The characteristic time required to complete SVE operations are calculated as a function of, both, the injected air temperature and humidity. For a specific set of conditions, it is demonstrated that elevating the injected air temperature by 20oC, the hot air injection technique reduces the soil remediation time by 50%, while requiring 30% of additional energy consumption. Those evaluations clearly unveil the advantage of the hot air SVE process, which for insignificant cost of added air heating energy, the substantial cost expenditures for manpower and equipment utilization are reduced.

Keywords: Porous Media, Soil Decontamination, Hot Air, Vapor Extraction

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Maria Amodeo, Pasquale Arpaia, Marco Buzio, Vincenzo Di Capua, Francesco Donnarumma

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Different deep neural network architectures have been compared and tested to predict magnetic hysteresis in the context of pulsed electromagnets for experimental physics applications. Modelling quasi-static or dynamic major and especially minor hysteresis loops is one of the most challenging topics for computational magnetism. Recent attempts at mathematical prediction in this context using Preisach models could not attain better than percent-level accuracy. Hence, this work explores neural network approaches and shows that the architecture that best fits the measured magnetic field behaviour, including the effects of hysteresis and eddy currents, is the nonlinear autoregressive exogenous neural network (NARX) model. This architecture aims to achieve a relative RMSE of the order of a few 100 ppm for complex magnetic field cycling, including arbitrary sequences of pseudo-random high field and low field cycles. The NARX-based architecture is compared with the state-of-the-art, showing better performance than the classical operator-based and differential models, and is tested on a reference quadrupole magnetic lens used for CERN particle beams, chosen as a case study. The training and test datasets are a representative example of real-world magnet operation; this makes the good result obtained very promising for future applications in this context.

Keywords: deep neural network, magnetic modelling, measurement and empirical software engineering, NARX

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Authors: Seyed Mahdi Jameii

Abstract:

Underwater wireless sensor networks have been attracting the interest of many ‎researchers lately, and the past three decades have beheld the rapid progress of ‎underwater acoustic communication. One of the major problems in underwater wireless ‎sensor networks is how to transfer data from the moving node to the base stations and ‎choose the optimized route for data transmission. Secure routing in underwater ‎wireless sensor network (UWCNs) is necessary for packet delivery. Some routing ‎protocols are proposed for underwater wireless sensor networks. However, a few ‎researches have been done on secure routing in underwater sensor networks. In this ‎article, a secure routing protocol is provided to resist against wormhole and sybil ‎attacks. The results indicated acceptable performance in terms of increasing the packet ‎delivery ratio with regards to the attacks, increasing network lifetime by creating ‎balance in the network energy consumption, high detection rates against the attacks, ‎and low-end to end delay.‎

Keywords: attacks, routing, security, underwater wireless sensor networks

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Authors: Duc Dong Do, Ngoc Ha Tran, Thanh Hai Dang, Cao Cuong Dang, Xuan Huan Hoang

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Global aligning two protein-protein interaction networks is an essentially important task in bioinformatics/computational biology field of study. It is a challenging and widely studied research topic in recent years. Accurately aligned networks allow us to identify functional modules of proteins and/ororthologous proteins from which unknown functions of a protein can be inferred. We here introduce a novel efficient heuristic global network alignment algorithm called FASTAn, including two phases: the first to construct an initial alignment and the second to improve such alignment by exerting a local optimization repeated procedure. The experimental results demonstrated that FASTAn outperformed the state-of-the-art global network alignment algorithm namely SPINAL in terms of both commonly used objective scores and the run-time.

Keywords: FASTAn, Heuristic algorithm, biological network alignment, protein-protein interaction networks

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Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

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High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

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Authors: D. Dorota

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This paper presents a method to generate and schedule task in the architecture of embedded systems based on the simulated annealing. This method takes into account the attribute of divisibility of tasks. A proposal represents the process in the form of trees. Despite the fact that the architecture of Network-on-Chip (NoC) is an interesting alternative to a bus architecture based on multi-processors systems, it requires a lot of work that ensures the optimization of communication. This paper proposes an effective approach to generate dedicated NoC topology solving communication problems. Network NoC is generated taking into account the energy consumption and resource issues. Ultimately generated is minimal, dedicated NoC topology. The proposed solution is assumed to be a simple router design and the minimum number of lines.

Keywords: Network-on-Chip, NoC-based embedded systems, scheduling task in embedded systems, simulated annealing

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Authors: Zhifei Hu, Feng Xia

Abstract:

In recent years, Graph neural network has been widely used in knowledge graph recommendation. The existing recommendation methods based on graph neural network extract information from knowledge graph through entity and relation, which may not be efficient in the way of information extraction. In order to better propose useful entity information for the current recommendation task in the knowledge graph, we propose an end-to-end Neural network Model based on multi-stream graph attentional Mechanism (MSGAT), which can effectively integrate the knowledge graph into the recommendation system by evaluating the importance of entities from both users and items. Specifically, we use the attention mechanism from the user's perspective to distil the domain nodes information of the predicted item in the knowledge graph, to enhance the user's information on items, and generate the feature representation of the predicted item. Due to user history, click items can reflect the user's interest distribution, we propose a multi-stream attention mechanism, based on the user's preference for entities and relationships, and the similarity between items to be predicted and entities, aggregate user history click item's neighborhood entity information in the knowledge graph and generate the user's feature representation. We evaluate our model on three real recommendation datasets: Movielens-1M (ML-1M), LFM-1B 2015 (LFM-1B), and Amazon-Book (AZ-book). Experimental results show that compared with the most advanced models, our proposed model can better capture the entity information in the knowledge graph, which proves the validity and accuracy of the model.

Keywords: graph attention network, knowledge graph, recommendation, information propagation

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Authors: Naim Karasekreter, Ugur Fidan, Fatih Basciftci

Abstract:

Wireless sensor networks are networks in which more than one sensor node is organized among themselves. The working principle is based on the transfer of the sensed data over the other nodes in the network to the central station. Wireless sensor networks concentrate on routing algorithms, energy efficiency and clustering algorithms. In the clustering method, the nodes in the network are divided into clusters using different parameters and the most suitable cluster head is selected from among them. The data to be sent to the center is sent per cluster, and the cluster head is transmitted to the center. With this method, the network traffic is reduced and the energy efficiency of the nodes is increased. In this study, clustering algorithms were examined in terms of clustering performances and cluster head selection characteristics to try to identify weak and strong sides. This work is supported by the Project 17.Kariyer.123 of Afyon Kocatepe University BAP Commission.

Keywords: wireless sensor networks (WSN), clustering algorithm, cluster head, clustering

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Authors: A. Clementking, C. Jothi Venkateswaran

Abstract:

Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources.

Keywords: master-slave back propagation neural network model(MSBPNNM), water quality analysis, multivariate analysis, environmental mining

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Authors: Mohd Khairul Bazli Mohd Aziz, Fadhilah Yusof, Zulkifli Yusop, Zalina Mohd Daud, Mohammad Afif Kasno

Abstract:

Recent developed rainfall network design techniques have been discussed and compared by many researchers worldwide due to the demand of acquiring higher levels of accuracy from collected data. In many studies, rain-gauge networks are designed to provide good estimation for areal rainfall and for flood modelling and prediction. In a certain study, even using lumped models for flood forecasting, a proper gauge network can significantly improve the results. Therefore existing rainfall network in Johor must be optimized and redesigned in order to meet the required level of accuracy preset by rainfall data users. The well-known geostatistics method (variance-reduction method) that is combined with simulated annealing was used as an algorithm of optimization in this study to obtain the optimal number and locations of the rain gauges. Rain gauge network structure is not only dependent on the station density; station location also plays an important role in determining whether information is acquired accurately. The existing network of 84 rain gauges in Johor is optimized and redesigned by using rainfall, humidity, solar radiation, temperature and wind speed data during monsoon season (November – February) for the period of 1975 – 2008. Three different semivariogram models which are Spherical, Gaussian and Exponential were used and their performances were also compared in this study. Cross validation technique was applied to compute the errors and the result showed that exponential model is the best semivariogram. It was found that the proposed method was satisfied by a network of 64 rain gauges with the minimum estimated variance and 20 of the existing ones were removed and relocated. An existing network may consist of redundant stations that may make little or no contribution to the network performance for providing quality data. Therefore, two different cases were considered in this study. The first case considered the removed stations that were optimally relocated into new locations to investigate their influence in the calculated estimated variance and the second case explored the possibility to relocate all 84 existing stations into new locations to determine the optimal position. The relocations of the stations in both cases have shown that the new optimal locations have managed to reduce the estimated variance and it has proven that locations played an important role in determining the optimal network.

Keywords: geostatistics, simulated annealing, semivariogram, optimization

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Authors: Tariq Abbas, Younus Muhammad, Sayyad Aun Muhammad

Abstract:

Background : Crimean Congo hemorrhagic fever (CCHF) is a tick born viral zoonotic disease that has been notified from almost all regions of Pakistan. The aim of this study was to investigate spatial distribution of CCHF cases reported to National Institue of Health , Islamabad during year 2013. Methods : Spatial statistics tools were applied to detect extent spatial auto-correlation and clusters of the disease based on adjusted cumulative incidence per million population for each district. Results : The data analyses revealed a large multi-district cluster of high values in the uplands of Balochistan province near Afghanistan border. Conclusion : The cluster included following districts: Pishin; Qilla Abdullah; Qilla Saifullah; Quetta, Sibi; Zhob; and Ziarat. These districts may be given priority in CCHF surveillance, control programs, and further epidemiological research . The location of the cluster close to border of Afghanistan and Iran highlight importance of the findings for organizations dealing with disease at national, regional and global levels.

Keywords: Crimean Congo hemorrhagic fever, Pakistan, spatial autocorrelation, clusters , adjusted cumulative incidence

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Authors: Naveed Ghani, Samreen Javed

Abstract:

In today’s heterogeneous network environment, there is a growing demand for distrust clients to jointly execute secure network to prevent from malicious attacks as the defining task of propagating malicious code is to locate new targets to attack. Residual risk is always there no matter what solutions are implemented or whet so ever security methodology or standards being adapted. Security is the first and crucial phase in the field of Computer Science. The main aim of the Computer Security is gathering of information with secure network. No one need wonder what all that malware is trying to do: It's trying to steal money through data theft, bank transfers, stolen passwords, or swiped identities. From there, with the help of our survey we learn about the importance of white listing, antimalware programs, security patches, log files, honey pots, and more used in banks for financial data protection but there’s also a need of implementing the IPV6 tunneling with Crypto data transformation according to the requirements of new technology to prevent the organization from new Malware attacks and crafting of its own messages and sending them to the target. In this paper the writer has given the idea of implementing IPV6 Tunneling Secessions on private data transmission from financial organizations whose secrecy needed to be safeguarded.

Keywords: network worms, malware infection propagating malicious code, virus, security, VPN

Procedia PDF Downloads 358