Search results for: time workflow network

Authors: Vahid Nourani, Atefeh Ashrafi

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Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

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Authors: Ke Zhang, Yongli Zhu

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Due to rapid load growths in today’s highly electrified societies and the requirement for green energy sources, large-scale wind farm power transmission system is constantly developing. This system is a typical multi-terminal power supply system, whose structure of the network topology of transmission lines is complex. What’s more, it locates in the complex terrain of mountains and grasslands, thus increasing the possibility of transmission line faults and finding the fault location with difficulty after the faults and resulting in an extremely serious phenomenon of abandoning the wind. In order to solve these problems, a fault location method for multi-terminal transmission line based on wind farm characteristics and improved single-ended traveling wave positioning method is proposed. Through studying the zero sequence current characteristics by using the characteristics of the grounding transformer(GT) in the existing large-scale wind farms, it is obtained that the criterion for judging the fault interval of the multi-terminal transmission line. When a ground short-circuit fault occurs, there is only zero sequence current on the path between GT and the fault point. Therefore, the interval where the fault point exists is obtained by determining the path of the zero sequence current. After determining the fault interval, The location of the short-circuit fault point is calculated by the traveling wave method. However, this article uses an improved traveling wave method. It makes the positioning accuracy more accurate by combining the single-ended traveling wave method with double-ended electrical data. What’s more, a method of calculating the traveling wave velocity is deduced according to the above improvements (it is the actual wave velocity in theory). The improvement of the traveling wave velocity calculation method further improves the positioning accuracy. Compared with the traditional positioning method, the average positioning error of this method is reduced by 30%.This method overcomes the shortcomings of the traditional method in poor fault location of wind farm transmission lines. In addition, it is more accurate than the traditional fixed wave velocity method in the calculation of the traveling wave velocity. It can calculate the wave velocity in real time according to the scene and solve the traveling wave velocity can’t be updated with the environment and real-time update. The method is verified in PSCAD/EMTDC.

Keywords: grounding transformer, multi-terminal transmission line, short circuit fault location, traveling wave velocity, wind farm

Procedia PDF Downloads 263

Authors: Rebecca Richards Steed, James Van Derslice, Ken Smith, Richard Medina, Amanda Bakian

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Elucidating gene-environment interactions for heritable disease outcomes is an emerging area of disease research, with genetic studies informing hypotheses for environment and gene interactions underlying some of the most confounding diseases of our time, like autism spectrum disorder (ASD). Geography has thus far played a key role in identifying environmental factors contributing to disease, but its use can be broadened to include genetic and environmental factors that have a synergistic effect on disease. Through the use of family pedigrees and disease outcomes with life-course residential histories, space-time clustering of generations at critical developmental windows can provide further understanding of (1) environmental factors that contribute to disease patterns in families, (2) susceptible critical windows of development most impacted by environment, (3) and that are most likely to lead to an ASD diagnosis. This paper introduces a retrospective case-control study that utilizes pedigree data, health data, and residential life-course location points to find space-time clustering of ancestors with a grandchild/child with a clinical diagnosis of ASD. Finding space-time clusters of ancestors at critical developmental windows serves as a proxy for shared environmental exposures. The authors refer to geographic life-course exposures as geographic legacies. Identifying space-time clusters of ancestors creates a bridge for researching exposures of past generations that may impact modern-day progeny health. Results from the space-time cluster analysis show multiple clusters for the maternal and paternal pedigrees. The paternal grandparent pedigree resulted in the most space-time clustering for birth and childhood developmental windows. No statistically significant clustering was found for adolescent years. These results will be further studied to identify the specific share of space-time environmental exposures. In conclusion, this study has found significant space-time clusters of parents, and grandparents for both maternal and paternal lineage. These results will be used to identify what environmental exposures have been shared with family members at critical developmental windows of time, and additional analysis will be applied.

Keywords: family pedigree, environmental exposure, geographic legacy, medical geography, transgenerational inheritance

Procedia PDF Downloads 116

Authors: Dararat Tongdee, Surapong Chirarattananon, Somchai Maneewan, Chantana Punlek

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Recently, the Internet of Things (IoT) is the important technology that connects devices to the network and people can access real-time communication. This technology is used to report, collect, and analyze the big data for achieving a purpose. For a smart building, there are many IoT technologies that enable management and building operators to improve occupant thermal comfort, indoor air quality, and building energy efficiency. In this research, we propose monitoring and controlling performance of a smart dedicated outdoor air system (SDOAS) based on IoT platform. The SDOAS was specifically designed with the desiccant unit and thermoelectric module. The designed system was intended to monitor, notify, and control indoor environmental factors such as temperature, humidity, and carbon dioxide (CO₂) level. The SDOAS was tested under the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE 62.2) and indoor air quality standard. The system will notify the user by Blynk notification when the status of the building is uncomfortable or tolerable limits are reached according to the conditions that were set. The user can then control the system via a Blynk application on a smartphone. The experimental result indicates that the temperature and humidity of indoor fresh air in the comfort zone are approximately 26 degree Celsius and 58% respectively. Furthermore, the CO₂ level was controlled lower than 1000 ppm by indoor air quality standard condition. Therefore, the proposed system can efficiently work and be easy to use for buildings.

Keywords: internet of things, indoor air quality, smart dedicated outdoor air system, thermal comfort

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Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

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Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

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Authors: Ma Yuzhe, Burra Venkata Durga Kumar

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The computer is a great invention. As people use computers more and more frequently, the demand for PCs is growing, and the performance of computer hardware is also rising to face more complex processing and operation. However, the operating system, which provides the soul for computers, has stopped developing at a stage. In the face of the high price of UNIX (Uniplexed Information and Computering System), batch after batch of personal computer owners can only give up. Disk Operating System is too simple and difficult to bring innovation into play, which is not a good choice. And MacOS is a special operating system for Apple computers, and it can not be widely used on personal computers. In this environment, Linux, based on the UNIX system, was born. Linux combines the advantages of the operating system and is composed of many microkernels, which is relatively powerful in the core architecture. Linux system supports all Internet protocols, so it has very good network functions. Linux supports multiple users. Each user has no influence on their own files. Linux can also multitask and run different programs independently at the same time. Linux is a completely open source operating system. Users can obtain and modify the source code for free. Because of these advantages of Linux, it has also attracted a large number of users and programmers. The Linux system is also constantly upgraded and improved. It has also issued many different versions, which are suitable for community use and commercial use. Linux system has good security because it relies on a file partition system. However, due to the constant updating of vulnerabilities and hazards, the using security of the operating system also needs to be paid more attention to. This article will focus on the analysis and discussion of Linux security issues.

Keywords: Linux, operating system, system management, security

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Authors: Imran Khan Pathan, Anil Bhandari, Peeyush K. Sharma, Rakesh K. Patel, Suresh Purohit

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Floating tablets of Marichyadi Vati were developed with an aim to prolong its gastric residence time and increase the bioavailability of drug. Rapid gastrointestinal transit could result in incomplete drug release from the drug delivery system above the absorption zone leading to diminished efficacy of the administered dose. The tablets were prepared by wet granulation technique, using HPMC E50 LV act as Matrixing agent, Carbopol as floating enhancer, microcrystalline cellulose as binder, sodium bi carbonate as effervescent agent with other excipients. The simplex lattice design was used for selection of variables for tablets formulation. Formulation was optimized on the basis of floating time and in vitro drug release. The results showed that the floating lag time for optimized formulation was found to be 61 second with about 97.32 % of total drug release within 3 hours. The in vitro release profiles of drug from the formulation could be best expressed zero order with highest linearity r2 = 0.9943. It was concluded that the gastroretentive drug delivery system can be developed for Marichyadi Vati containing piperine to increase the residence time of the drug in the stomach and thereby increasing bioavailability.

Keywords: piperine, Marichyadi Vati, gastroretentive drug delivery, floating tablet

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Authors: Osama Terra, Hatem Hussein

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Calibration of Optical Time Domain Reflectometer (OTDR) is crucial for the accurate determination of loss budget for long optical fiber links. In this paper, the calibration of the attenuation scale of an OTDR using two different techniques is discussed and implemented. The first technique is the external modulation method (EM). A setup is proposed to calibrate an OTDR over a dynamic range of around 15 dB based on the EM method. Afterwards, the OTDR is calibrated using two standard reference fibers (SRF). Both SRF are calibrated using cut-back technique; one of them is calibrated at our home institute (the National Institute of Standards – NIS) while the other at the National Physical Laboratory (NPL) of the United Kingdom to confirm our results. In addition, the parameters contributing the calibration uncertainty are thoroughly investigated. Although the EM method has several advantages over the SRF method, the uncertainties in the SRF method is found to surpass that of the EM method.

Keywords: optical time domain reflectometer, fiber attenuation measurement, OTDR calibration, external source method

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: Charline David, Alexandre Blondin Massé, Arnaud Zinflou

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In 2016, Clements, Hurn, and Li proposed a multiple equation time series approach for the short-term load forecasting, reporting an average mean absolute percentage error (MAPE) of 1.36% on an 11-years dataset for the Queensland region in Australia. We present an adaptation of their model to the electrical power load consumption for the whole Quebec province in Canada. More precisely, we take into account two additional meteorological variables — cloudiness and wind speed — on top of temperature, as well as the use of multiple meteorological measurements taken at different locations on the territory. We also consider other minor improvements. Our final model shows an average MAPE score of 1:79% over an 8-years dataset.

Keywords: short-term load forecasting, special days, time series, multiple equations, parallelization, clustering

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Authors: Malte Brettel, Niklas Friederichsen, Michael Keller, Marius Rosenberg

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The German manufacturing industry has to withstand an increasing global competition on product quality and production costs. As labor costs are high, several industries have suffered severely under the relocation of production facilities towards aspiring countries, which have managed to close the productivity and quality gap substantially. Established manufacturing companies have recognized that customers are not willing to pay large price premiums for incremental quality improvements. As a consequence, many companies from the German manufacturing industry adjust their production focusing on customized products and fast time to market. Leveraging the advantages of novel production strategies such as Agile Manufacturing and Mass Customization, manufacturing companies transform into integrated networks, in which companies unite their core competencies. Hereby, virtualization of the process- and supply-chain ensures smooth inter-company operations providing real-time access to relevant product and production information for all participating entities. Boundaries of companies deteriorate, as autonomous systems exchange data, gained by embedded systems throughout the entire value chain. By including Cyber-Physical-Systems, advanced communication between machines is tantamount to their dialogue with humans. The increasing utilization of information and communication technology allows digital engineering of products and production processes alike. Modular simulation and modeling techniques allow decentralized units to flexibly alter products and thereby enable rapid product innovation. The present article describes the developments of Industry 4.0 within the literature and reviews the associated research streams. Hereby, we analyze eight scientific journals with regards to the following research fields: Individualized production, end-to-end engineering in a virtual process chain and production networks. We employ cluster analysis to assign sub-topics into the respective research field. To assess the practical implications, we conducted face-to-face interviews with managers from the industry as well as from the consulting business using a structured interview guideline. The results reveal reasons for the adaption and refusal of Industry 4.0 practices from a managerial point of view. Our findings contribute to the upcoming research stream of Industry 4.0 and support decision-makers to assess their need for transformation towards Industry 4.0 practices.

Keywords: Industry 4.0., mass customization, production networks, virtual process-chain

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Authors: Shobha Ramalingam

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The majority of the infrastructure projects are affected by time overrun, resulting in project delays and subsequently cost overruns. Time overrun may vary from a few months to as high as five or more years, placing the project viability at risk. One of the probable reasons noted in the literature for this outcome in projects is due to poor productivity. Researchers contend that productivity in construction has only marginally increased over the years. While studies in the literature have extensively focused on time and cost parameters in projects, there are limited studies that integrate time and cost with productivity to assess project performance. To this end, a study was conducted to understand the project delay factors concerning cost, time and productivity. A case-study approach was adopted to collect rich data from a nuclear power plant project site for two months through observation, interviews and document review. The data were analyzed using three different approaches for a comprehensive understanding. Foremost, a root-cause analysis was performed on the data using Ishikawa’s fish-bone diagram technique to identify the various factors impacting the delay concerning time. Based on it, a questionnaire was designed and circulated to concerned executives, including project engineers and contractors to determine the frequency of occurrence of the delay, which was then compiled and presented to the management for a possible solution to mitigate. Second, a productivity analysis was performed on select activities, including rebar bending and concreting through a time-motion study to analyze product performance. Third, data on cost of construction for three years allowed analyzing the cost performance using earned value management technique. All three techniques allowed to systematically and comprehensively identify the key factors that deter project performance and productivity loss in the construction of the nuclear power plant project. The findings showed that improper planning and coordination between multiple trades, concurrent operations, improper workforce and material management, fatigue due to overtime were some of the key factors that led to delays and poor productivity. The findings are expected to act as a stepping stone for further research and have implications for practitioners.

Keywords: earned value analysis, time performance, project costs, project delays, construction productivity

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Authors: Mursal Ibrahim Zada

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Since 2001, the new Government of Afghanistan has put the improvement of transportation in rural area as one of the key issues for the development of the country. Since then, about 17,000 km of rural roads were planned to be constructed in the entire country. This thesis will assess the impact of rural road improvement on the development of rural communities and housing facilities. Specifically, this study aims to show that the improved road has leads to an improvement in the community, which in turn has a positive effect on the lives of rural people. To obtain this goal, a questionnaire survey was conducted in March 2015 to the residents of four different districts of Kabul province, Afghanistan, where the road projects were constructed in recent years. The collected data was analyzed using on a regression analysis considering different factors such as land price, waiting time at the station, travel time to the city, number of employed family members and so on. Three models are developed to demonstrate the relationship between different factors before and after the improvement of rural transportation. The results showed a significant change positively in the value of land price and housing facilities, travel time to the city, waiting time at the station, number of employed family members, fare per trip to the city, and number of trips to the city per month after the pavement of the road. The results indicated that the improvement of transportation has a significant impact on the improvement of the community in different parts, especially on the price of land and housing facility and travel time to the city.

Keywords: accessibility, Afghanistan, housing facility, rural area, land price

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Authors: Abdul Ghani Rajput

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Although monitoring and evaluation concept adopted by Technical Vocational Education and Training (TVET) organization to track the progress over the continuous interval of time based on planned interventions and subsequently, evaluating it for the impact, quality assurance and sustainability. In digital world, TVET providers are giving preference to have real time information to do monitoring of training activities. Identifying the benefits and challenges of digitized monitoring & evaluation real time information system has not been sufficiently tackled in this date. This research paper looks at the impact of digitized M&E in TVET sector by analyzing two case studies and describe the benefits and challenges of using digitized M&E system. Finally, digitized M&E have been identified as carriers for high potential of TVET sector.

Keywords: digitized M&E, innovation, quality assurance, TVET

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Authors: Yooncheol Lee, Jeongmin Kim, Kwang Ryel Ryu

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This paper deals with the establishment of a short-term operational plan for an air conditioner for efficient energy management of exhibition hall. The short-term operational plan is composed of a time series of operational schedules, which we have searched using genetic algorithms. Establishing operational schedule should be considered the future trends of the variables affecting the exhibition hall environment. To reflect continuously changing factors such as external temperature and occupant, short-term operational plans should be updated in real time. But it takes too much time to evaluate a short-term operational plan using EnergyPlus, a building emulation tool. For that reason, it is difficult to update the operational plan in real time. To evaluate the short-term operational plan, we designed prediction models based on machine learning with fast evaluation speed. This model, which was created by learning the past operational data, is accurate and fast. The collection of operational data and the verification of operational plans were made using EnergyPlus. Experimental results show that the proposed method can save energy compared to the reactive control method.

Keywords: exhibition hall, energy management, predictive model, simulation-based optimization

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Authors: Sujit Kumar Pradhan, Anil Kumar, Vijay Kumar

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The testing process of the software during the software development time is a crucial step as it makes the software more efficient and dependable. To estimate software’s reliability through the mean value function, many software reliability growth models (SRGMs) were developed under the assumption that operating and testing environments are the same. Practically, it is not true because when the software works in a natural field environment, the reliability of the software differs. This article discussed an SRGM comprising change-point and imperfect debugging in a simulated testing environment. Later on, we extended it in a multi-release direction. Initially, the software was released to the market with few features. According to the market’s demand, the software company upgraded the current version by adding new features as time passed. Therefore, we have proposed a generalized multi-release SRGM where change-point and imperfect debugging concepts have been addressed in a simulated testing environment. The failure-increasing rate concept has been adopted to determine the change point for each software release. Based on nine goodness-of-fit criteria, the proposed model is validated on two real datasets. The results demonstrate that the proposed model fits the datasets better. We have also discussed the optimal release time of the software through a cost model by assuming that the testing and debugging costs are time-dependent.

Keywords: software reliability growth models, non-homogeneous Poisson process, multi-release software, mean value function, change-point, environmental factors

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Authors: Ying Han, Yuanxiang Chen, Yongtao Huang, Jia Fu, Kaile Li, Shangjing Lin, Jianguo Yu

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In long-haul and high-speed optical transmission system, the orthogonal frequency division multiplexing (OFDM) signal suffers various linear and non-linear impairments. In recent years, researchers have proposed compensation schemes for specific impairment, and the effects are remarkable. However, different impairment compensation algorithms have caused an increase in transmission delay. With the widespread application of deep neural networks (DNN) in communication, multi-impairment compensation based on DNN will be a promising scheme. In this paper, we propose and apply DNN to compensate multi-impairment of 16-QAM coherent optical OFDM signal, thereby improving the performance of the transmission system. The trained DNN models are applied in the offline digital signal processing (DSP) module of the transmission system. The models can optimize the constellation mapping signals at the transmitter and compensate multi-impairment of the OFDM decoded signal at the receiver. Furthermore, the models reduce the peak to average power ratio (PAPR) of the transmitted OFDM signal and the bit error rate (BER) of the received signal. We verify the effectiveness of the proposed scheme for 16-QAM Coherent Optical OFDM signal and demonstrate and analyze transmission performance in different transmission scenarios. The experimental results show that the PAPR and BER of the transmission system are significantly reduced after using the trained DNN. It shows that the DNN with specific loss function and network structure can optimize the transmitted signal and learn the channel feature and compensate for multi-impairment in fiber transmission effectively.

Keywords: coherent optical OFDM, deep neural network, multi-impairment compensation, optical transmission

Procedia PDF Downloads 143

Authors: Jianwei Qian, Rob Law

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With the expansion of globalization and the increment of market competition, the festival, especially the traditional one, has demonstrated its vitality under the new context. As a new tourist attraction, festivals play a critically important role in promoting the tourism economy, because the organization of a festival can engage more tourists, generate more revenues and win a wider media concern. However, in the current stage of China, traditional festivals as a way to disseminate national culture are undergoing the challenge of foreign festivals and the related culture. Different from those special events created solely for developing economy, traditional festivals have their own culture and connotation. Therefore, it is necessary to conduct a study on not only protecting the tradition, but promoting its development as well. This study conducts a comparative study of the development of China’s Valentine’s Day and Western Valentine’s Day under the Chinese context and centers on newspaper reports in China from 2000 to 2016. Based on the literature, two main research focuses can be established: one is concerned about the festival’s impact and the other is about tourists’ motivation to engage in a festival. Newspaper reports serve as the research discourse and can help cover the two focal points. With the assistance of content mining techniques, semantic networks for both Days are constructed separately to help depict the status quo of these two festivals in China. Based on the networks, two models are established to show the key component system of traditional festivals in the hope of perfecting the positive role festival tourism plays in the promotion of economy and culture. According to the semantic networks, newspaper reports on both festivals have similarities and differences. The difference is mainly reflected in its cultural connotation, because westerners and Chinese may show their love in different ways. Nevertheless, they share more common points in terms of economy, tourism, and society. They also have a similar living environment and stakeholders. Thus, they can be promoted together to revitalize some traditions in China. Three strategies are proposed to realize the aforementioned aim. Firstly, localize international festivals to suit the Chinese context to make it function better. Secondly, facilitate the internationalization process of traditional Chinese festivals to receive more recognition worldwide. Finally, allow traditional festivals to compete with foreign ones to help them learn from each other and elucidate the development of other festivals. It is believed that if all these can be realized, not only the traditional Chinese festivals can obtain a more promising future, but foreign ones are the same as well. Accordingly, the paper can contribute to the theoretical construction of festival images by the presentation of the semantic network. Meanwhile, the identified features and issues of festivals from two different cultures can enlighten the organization and marketing of festivals as a vital tourism activity. In the long run, the study can enhance the festival as a key attraction to keep the sustainable development of both the economy and the society.

Keywords: Chinese context, comparative study, festival tourism, semantic network analysis, valentine’s day

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Authors: Homa Ghave, Parmis Shahmaleki

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This paper presents a new multi-criteria stochastic mathematical model for a single machine scheduling with outsourcing allowed. There are multiple jobs processing in batch. For each batch, all of job or a quantity of it can be outsourced. The jobs have stochastic processing time and lead time and deterministic due dates arrive randomly. Because of the stochastic inherent of processing time and lead time, we use the chance constrained programming for modeling the problem. First, the problem is formulated in form of stochastic programming and then prepared in a form of deterministic mixed integer linear programming. The objectives are considered in the model to minimize the maximum tardiness and outsourcing cost simultaneously. Several procedures have been developed to deal with the multi-criteria problem. In this paper, we utilize the concept of satisfaction functions to increases the manager’s preference. The proposed approach is tested on instances where the random variables are normally distributed.

Keywords: single machine scheduling, multi-criteria mathematical model, outsourcing strategy, uncertain lead times and processing times, chance constrained programming, satisfaction function

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Authors: Omar Abdalla, Abdelfatah Ahmed, Ahmed Mustafa, Abdelazem Eldouma

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The purpose of this study is evaluating the effect of extremely low frequency magnetic field on Waster rats brain. The number of rats used in this study were 25, which were divided into five groups, each group containing five rats as follows: Group 1: The control group which was not exposed to energized field; Group 2: Rats were exposed to a magnetic field with an intensity of 0.6 mT (2 hours/day); Group 3: Rats were exposed to a magnetic field of 1.2 mT (2 hours/day); Group4: Rats were exposed to a magnetic field of 1.8 mT (2 hours/day); Group 5: Rats were exposed to a magnetic field of 2.4 mT (2 hours/day) and all groups were exposed for seven days, by designing a maze and calculating the time average for arriving to the decoy at special conditions. We found the time average before exposure for the all groups was G2=330 s, G3=172 s, G4=500 s and G5=174 s, respectively. We exposed all groups to ELF-MF and measured the time and we found: G2=465 s, G3=388 s, G4=501 s, and G5=442 s. It was observed that the time average increased directly with field strength. Histological samples of frontal lop of brain for all groups were taken and we found lesion, atrophy, empty vacuoles and disorder choroid plexus at frontal lope of brain. And finally we observed the disorder of choroid plexus in histological results and Alzheimer's symptoms increase when the magnetic field increases.

Keywords: nonionizing radiation, biophysics, magnetic field, shrinkage

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Authors: Meena Agrawal, Chaitanya P. Agrawal

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The power systems are transforming and becoming smarter with innovations in technologies to enable embark simultaneously upon the sustainable energy needs, rising environmental concerns, economic benefits and quality requirements. The advantages provided by inter-connection of renewable energy resources are becoming more viable and dependable with the smart controlling technologies. The limitation of most renewable resources have their diversity and intermittency causing problems in power quality, grid stability, reliability, security etc. is being cured by these efforts. A necessitate of optimal energy management by intelligent Micro-Grids at the distribution end of the power system has been accredited to accommodate sustainable renewable Distributed Energy Resources on large scale across the power grid. All over the world Smart Grids are emerging now as foremost concern infrastructure upgrade programs. The hardware setup includes NI cRIO 9022, Compact Reconfigurable Input Output microcontroller board connected to the PC on a LAN router with three hardware modules. The Real-Time Embedded Controller is reconfigurable controller device consisting of an embedded real-time processor controller for communication and processing, a reconfigurable chassis housing the user-programmable FPGA, Eight hot-swappable I/O modules, and graphical LabVIEW system design software. It has been employed for signal analysis, controls and acquisition and logging of the renewable sources with the LabVIEW Real-Time applications. The employed cRIO chassis controls the timing for the module and handles communication with the PC over the USB, Ethernet, or 802.11 Wi-Fi buses. It combines modular I/O, real-time processing, and NI LabVIEW programmable. In the presented setup, the Analog Input Module NI 9205 five channels have been used for input analog voltage signals from renewable energy sources and NI 9227 four channels have been used for input analog current signals of the renewable sources. For switching actions based on the programming logic developed in software, a module having Electromechanical Relays (single-pole single throw) with 4-Channels, electrically isolated and LED indicating the state of that channel have been used for isolating the renewable Sources on fault occurrence, which is decided by the logic in the program. The module for Ethernet based Data Acquisition Interface ENET 9163 Ethernet Carrier, which is connected on the LAN Router for data acquisition from a remote source over Ethernet also has the module NI 9229 installed. The LabVIEW platform has been employed for efficient data acquisition, monitoring and control. Control logic utilized in program for operation of the hardware switching Related to Fault Relays has been portrayed as a flowchart. A communication system has been successfully developed amongst the sources and loads connected on different computers using Hypertext transfer protocol, HTTP or Ethernet Local Stacked area Network TCP/IP protocol. There are two main I/O interfacing clients controlling the operation of the switching control of the renewable energy sources over internet or intranet. The paper presents experimental results of the briefed setup for intelligent control of the micro-grid for renewable energy sources, besides the control of Micro-Grid with data acquisition and control hardware based on a microcontroller with visual program developed in LabVIEW.

Keywords: data acquisition and control, LabVIEW, microcontroller cRIO, Smart Micro-Grid

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Authors: Mehrnaz Alibeikloo, Hajar Share Isfahani, Hadi Khabbaz

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In recent years, by developing cities and increasing population, reconstructing on closed landfill sites in some regions is unavoidable. Long-term settlement is one of the major concerns associated with reconstruction on landfills after closure. The purpose of this research is evaluating the effect of preloading in various patterns of height and time on long-term settlements of closed landfills. In this regard, five scenarios of surcharge from 1 to 3 m high within 3, 4.5 and 6 months of preloading time have been modeled using PLAXIS 2D software. Moreover, the numerical results have been compared to those obtained from analytical methods, and a good agreement has been achieved. The findings indicate that there is a linear relationship between settlement and surcharge height. Although, long-term settlement decreased by applying a longer and higher preloading, the time of preloading was found to be a more effective factor compared to preloading height.

Keywords: preloading, long-term settlement, landfill, PLAXIS 2D

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Authors: N. Messai, A. Boumali

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In this work, we aim to solve the two dimensional Kemmer equation including Dirac oscillator interaction term, in the background space-time generated by a cosmic string which is submitted to an uniform magnetic field. Eigenfunctions and eigenvalues of our problem have been found and the influence of the cosmic string space-time on the energy spectrum has been analyzed.

Keywords: Kemmer oscillator, cosmic string, Dirac oscillator, eigenfunctions

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Authors: Pablo De Macedo Silveira Vallejos

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This article aims to observe autobiographical and visual narrative practices among users on Instagram. In this way, the work proposes to reflect on how image resources are used to develop edited representations of the self in that social network. The research aims to explore the uses of editing and the degrees of fictionalization present on Instagram.

Keywords: autobiography, visual narratives, representation, fiction, social media

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Authors: Xianbao Sun, Jinlong Zhao, Shudong He, Jing Li

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Volutharpa ampullacea perryi is a high-protein marine shellfish. However, few data are available on the effects of boiling temperatures and time on quality of the meat. In this study, colour, texture and sensory characteristics of Volutharpa ampullacea perryi meat during the boiling cooking processes (75-100 °C, 5-60 min) were investigated by colors analysis, texture profile analysis (TPA), scanning electron microscope (SEM) and sensory evaluation. The ratio of cooking loss gradually increased with the increase of temperature and time. The colour of meat became lighter and more yellower from 85 °C to 95 °C in a short time (5-20 min), but it became brown after a 30 min treatment. TPA results showed that the Volutharpa ampullacea perryi meat were more firm and less cohesive after a higher temperature (95-100 °C) treatment even in a short period (5-15 min). Based on the SEM analysis, it was easily found that the myofibrils structure was destroyed at a higher temperature (85-100 °C). Sensory data revealed that the meat cooked at 85-90 °C in 10-20 min showed higher scores in overall acceptance, as well as color, hardness and taste. Based on these results, it could be constructed that Volutharpa ampullacea perryi meat should be heated on a suitable condition (such as 85 °C 15 min or 90 °C 10 min) in the boiling cooking to be ensure a better acceptability.

Keywords: Volutharpa ampullacea perryi meat, boiling cooking, colour, sensory, texture

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Authors: Yasuyuki Takahashi, Hayato Ishimura, Masao Miyagawa, Teruhito Mochizuki

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Quantitative measurement of myocardium perfusion is possible with single photon emission computed tomography (SPECT) using a semiconductor detector. However, accumulation of ^99mTc-tetrofosmin in the liver may make it difficult to assess that accurately in the inferior myocardium. Our idea is to reduce the high accumulation in the liver by using dynamic SPECT imaging and a technique called time subtraction. We evaluated the performance of a new SPECT system with a cadmium-zinc-telluride solid-state semi- conductor detector (Discovery NM 530c; GE Healthcare). Our system acquired list-mode raw data over 10 minutes for a typical patient. From the data, ten SPECT images were reconstructed, one for every minute of acquired data. Reconstruction with the semiconductor detector was based on an implementation of a 3-D iterative Bayesian reconstruction algorithm. We studied 20 patients with coronary artery disease (mean age 75.4 ± 12.1 years; range 42-86; 16 males and 4 females). In each subject, 259 MBq of ^99mTc-tetrofosmin was injected intravenously. We performed both a phantom and a clinical study using dynamic SPECT. An approximation to a liver-only image is obtained by reconstructing an image from the early projections during which time the liver accumulation dominates (0.5~2.5 minutes SPECT image-5~10 minutes SPECT image). The extracted liver-only image is then subtracted from a later SPECT image that shows both the liver and the myocardial uptake (5~10 minutes SPECT image-liver-only image). The time subtraction of liver was possible in both a phantom and the clinical study. The visualization of the inferior myocardium was improved. In past reports, higher accumulation in the myocardium due to the overlap of the liver is un-diagnosable. Using our time subtraction method, the image quality of the ^99mTc-tetorofosmin myocardial SPECT image is considerably improved.

Keywords: 99mTc-tetrofosmin, dynamic SPECT, time subtraction, semiconductor detector

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Authors: Mohammad Pourmohammadi Fallah, Maziar Salahi

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In the literature, a mixed-integer linear programming model for the integrated production planning and warehouse layout problem is proposed. To solve the model, the authors proposed a Lagrangian relax-and-fix heuristic that takes a significant amount of time to stop with gaps above 5$\%$ for large-scale instances. Here, we present two heuristic algorithms to solve the problem. In the first one, we use a greedy approach by allocating warehouse locations with less reservation costs and also less transportation costs from the production area to locations and from locations to the output point to items with higher demands. Then a smaller model is solved. In the second heuristic, first, we sort items in descending order according to the fraction of the sum of the demands for that item in the time horizon plus the maximum demand for that item in the time horizon and the sum of all its demands in the time horizon. Then we categorize the sorted items into groups of 3, 4, or 5 and solve a small-scale optimization problem for each group, hoping to improve the solution of the first heuristic. Our preliminary numerical results show the effectiveness of the proposed heuristics.

Keywords: capacitated lot-sizing, warehouse layout, mixed-integer linear programming, heuristics algorithm

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Authors: Domenico Chizzoniti, Monica Moscatelli, Letizia Cattani, Piero Favino, Luca Preis

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A settlement strategy is to anticipate and respond the needs of existing and future communities through the provision of primary health care facilities in marginalized areas. Access to a health care network is important to improving healthcare coverage, often lacking, in developing countries. The study explores that a good sanitary system strategy of rural contexts brings advantages to an existing settlement: improving transport, communication, water and social facilities. The objective of this paper is to define a possible methodology to implement primary health care facilities in disadvantaged areas of emerging countries. In this research, we analyze the case study of Lauro de Freitas, a municipality in the Brazilian state of Bahia, part of the Metropolitan Region of Salvador, with an area of 57,662 km² and 194.641 inhabitants. The health localization system in Lauro de Freitas is an integrated process that involves not only geographical aspects, but also a set of factors: population density, epidemiological data, allocation of services, road networks, and more. Data were collected also using semi-structured interviews and questionnaires to the local population. Synthesized data suggest that moving away from the coast where there is the greatest concentration of population and services, a network of primary health care facilities is able to improve the living conditions of small-dispersed communities. Based on the health service needs of populations, we have developed a methodological approach that is particularly useful in rural and remote contexts in emerging countries.

Keywords: healthcare, settlement strategy, urban health, rural

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Fatima S. Rodriguez, Victorino T. Taylan, Manolito C. Bulaong, Helen F. Gavino, Vitaliana U. Malamug

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In sundrying, the quality of the grains are greatly reduced when paddy grains were caught by the rain unsacked and unstored resulting to reduced profit. The objectives of this study were to design and fabricate a quick covering machine for grain drying pavement to test and evaluate the operating characteristics of the machine according to its deployment speed, recovery speed, deployment time, recovery time, power consumption, aesthetics of laminated sack, conducting partial budget, and cost curve analysis. The machine was able to cover the grains in a 12.8 m x 22.5 m grain drying pavement at an average time of 17.13 s. It consumed 0 .53 W-hr for the deployment and recovery of the cover. The machine entailed an investment cost of $1,344.40 and an annual cost charge of $647.32. Moreover, the savings per year using the quick covering machine was $101.83.

Keywords: quick, covering machine, grain, drying pavement

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