Search results for: cosmic expansion model

Authors: Sadegh Torfi

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Realization of anticipated energy efficiency from recuperative run-around energy recovery (RER) systems requires identification of the system components influential parameters. Because simulation modeling is considered as an integral part of the design and economic evaluation of RER systems, it is essential to calibrate the developed models and validate the performance predictions by means of comparison with data from experimental measurements. Several theoretical and numerical analyses on RER systems by researchers have been done, but generally the effect of distance between hot and cold flow is ignored. The objective of this study is to develop a thermohydroulic model for a typical RER system that accounts for energy loss from the interconnecting piping and effects of interconnecting pipes length performance of run-around energy recovery systems. Numerical simulation shows that energy loss from the interconnecting piping is change linear with pipes length and if pipes are properly isolated, maximum reduction of effectiveness of RER systems is 2% in typical piping systems.

Keywords: combined heat and power, heat recovery, effectiveness, CGS

Procedia PDF Downloads 179

Authors: M. Moharami Gargari, S. Javdani Zamani, A. Mohammadnejad, S. Abuali

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As maritime container transport is developing fast, the need arises for efficient operations at container terminals. One of the most important determinants of container handling efficiency is the productivity of quay cranes and internal transportation vehicles, which are responsible transporting of containers for unloading and loading operations for container vessels. For this reason, this paper presents an integrated mathematical model formulation for discrete berths with quay cranes and internal transportations vehicles. The problems have received increasing attention in the literature and the present paper deals with the integration of these interrelated problems. A new mixed integer linear formulation is developed for the Berth Allocation Problem (BAP), Quay Crane Assignment and Scheduling Problem (QCASP) and Internal Transportation Scheduling (ITS), which accounts for cranes and trucks positioning conditions.

Keywords: discrete berths, container terminal, truck scheduling, dynamic vessel arrival

Procedia PDF Downloads 375

Authors: Muhammad Umair, Syed Qasim Gilani

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A technique using Independent Component Analysis (ICA) for blind receiver signal processing is investigated. The problem of the receiver signal processing is viewed as of signal equalization and implementation imperfections compensation. Based on this, a model similar to a general ICA problem is developed for the received signal. Then, the use of ICA technique for blind signal equalization in the time domain is presented. The equalization is regarded as a signal separation problem, since the desired signal is separated from interference terms. This problem is addressed in the paper by over-sampling of the received signal. By using ICA for equalization, besides channel equalization, other transmission imperfections such as Direct current (DC) bias offset, carrier phase and In phase Quadrature phase imbalance will also be corrected. Simulation results for a system using 16-Quadraure Amplitude Modulation(QAM) are presented to show the performance of the proposed scheme.

Keywords: blind equalization, blind signal separation, equalization, independent component analysis, transmission impairments, QAM receiver

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Authors: Farzaneh Rajabighamchi, Stan van Hoesel, Christof Defryn

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The generalized traveling salesman problem (GTSP) is an extension of the traveling salesman problem (TSP) where the set of nodes is partitioned into clusters, and the salesman must visit exactly one node per cluster. In this research, we apply the definition of the GTSP to an order picker routing problem with multiple locations per product. As such, each product represents a cluster and its corresponding nodes are the locations at which the product can be retrieved. To pick a certain product item from the warehouse, the picker needs to visit one of these locations during its pick tour. As all products are scattered throughout the warehouse, the product clusters not separated geographically. We propose an exact LP model as well as heuristic and meta-heuristic solution algorithms for the order picking problem with multiple product locations.

Keywords: warehouse optimization, order picking problem, generalised travelling salesman problem, heuristic algorithm

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Authors: R. K. Vinnakota, D. A. Genov, Z. Dong, A. F. Briggs, L. Nordin, S. R. Bank, D. Wasserman

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We present a semiconductor-based plasmonic electro-optic modulator based on excitation and active control of surface plasmon polaritons (SPPs) at the interface of degenerately doped In₀.₅₃Ga₀.₄₇As pn++ junctions. Set of devices, which we refer to as a surface plasmon polariton diode (SPPD), are fabricated and characterized electrically and optically. Optical characterization predicts far-field voltage-aided reflectivity modulation for mid-IR wavelengths. Numerical device characterizations using a self-consistent electro-optic multiphysics model have been performed to confirm the experimental findings were predicting data rates up to 1Gbits/s and 3dB bandwidth as high as 2GHz. Our findings also show that decreasing the device dimensions can potentially lead to data rates of more than 50Gbits/s, thus potentially providing a pathway toward fast all-semiconductor-based plasmotronic devices.

Keywords: plasmonics, optoelectronics, PN junctions, surface plasmon polaritons

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

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Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

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Authors: Hamdy Ahmed Ashour

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This paper studies the parallel operation of rotary frequency converters which can be used within a ship micro-grid system and also to supply ships and equipment in a harbour during off-sail and maintenance periods with their suitable voltage and frequency requirements in order to overcome the possible associated problems of overloading on a single converter. The paper theoretically and experimentally investigated the operation of 3-ph induction motor / 3-ph synchronous generator based rotary converters set. Concept of operation and merits of such converters has been discussed. Overall dynamic simulation model of two parallel operated rotary converters has been developed. Active and reactive load sharing of the two converters has been analyzed. Experimental setup has been implemented for proof of concept and practical validation. Simulation and experimental results have been obtained and well correlated; showing how the rotary converters based setup can be manipulated to achieve different requirements of operating conditions.

Keywords: experimental, frequency-converters, load-sharing, marine-applications, simulation, synchronization

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Authors: Leena Wanganoo

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In this digital age with the mushrooming of online companies across the globe has led to an unprecedented new business model. The frequency of online purchasing varies across the globe, but trend shows a steep upward movement. From Generation X to the Millennial the consumer not only wants to order the product with the click of mouse but also very demanding service quality during pre to post-transaction stage. The existing research examines the impact of website quality on the on behavioral intentions in e-services customers and has not adequately recognized the quality of e-commerce logistics perceived by the customer.In order to address this gap, this study examines the relationship among the logistics service quality, satisfaction, and loyalty. Drawing upon a sample of 350 millennial customers from various regions of UAE will work within the framework of structural equation modeling (SEM). Finally, the study would use Importance-Performance analysis (IPA) to discuss the relations of the level of customers’ expected logistics service quality and level of customers’ perceived logistics serviced quality.

Keywords: logistics service quality, customer satisfaction, loyalty, electronic commerce

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Authors: Belkacem Laimouche

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With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

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Authors: Parul Kulshreshtha, Subrata Sinha, Rakesh Bhatnagar

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This study was conducted by taking B. anthracis as a model pathogen. On infecting a host, B. anthracis secretes three proteins, namely, protective antigen (PA, 83kDa), edema factor (EF, 89 kDa) and lethal factor (LF, 90 kDa). These three proteins are the components of two anthrax toxins. PA binds to the cell surface receptors, namely, tumor endothelial marker (TEM) 8 and capillary morphogenesis protein (CMG) 2. TEM8 and CMG2 interact with LDL-receptor related protein (LRP) 6 for endocytosis of EF and LF. On entering the cell, EF acts as a calmodulin-dependent adenylate cyclase that causes a prolonged increase of cytosolic cyclic adenosine monophosphate (cAMP). LF is a metalloprotease that cleaves most isoforms of mitogen-activated protein kinase kinases (MAPKK/MEK) close to their N-terminus. By secreting these two toxins, B.anthracis ascertains death of the host. Once the systemic levels of the toxins rise, antibiotics alone cannot save the host. Therefore, toxin-specific inhibitors have to be developed. In this wake, monoclonal antibodies have been developed for the neutralization of toxic effects of anthrax toxins. We created hybridomas by using spleen of mice that were actively immunized with rLFn (recombinant N-terminal domain of lethal factor of B. anthracis) to obtain anti-toxin antibodies. Later on, separate group of mice were immunized with rLFn to obtain a polyclonal control for passive immunization studies of monoclonal antibodies. This led to the identification of one cohort of rLFn-immunized mice that harboured disease-enhancing polyclonal antibodies. At the same time, the monoclonal antibodies from all the hybridomas were being tested. Two hybridomas secreted monoclonal antibodies (H8 and H10) that were cross-reactive with EF (edema factor) and LF (lethal factor), while the other two hybridomas secreted LF-specific antibodies (H7 and H11). The protective efficacy of H7, H8, H10 and H11 was investigated. H7, H8 and H10 were found to be protective. H11 was found to have disease enhancing characteristics in-vitro and in mouse model of challenge with B. anthracis. In this study the disease enhancing character of H11 monoclonal antibody and anti-rLFn polyclonal sera was investigated. Combination of H11 with protective monoclonal antibodies (H8 and H10) reduced its disease enhancing nature both in-vitro and in-vivo. But combination of H11 with LETscFv (an scFv with VH and VL identical to H10 but lacking Fc region) could not abrogate the disease-enhancing character of H11 mAb. Therefore it was concluded that for suppression of disease enhancement, Fc portion was absolutely essential for interaction of H10 with H11. Our study indicates that the protective potential of an antibody depends equally on its idiotype/ antigen specificity and its isotype. A number of monoclonal and engineered antibodies are being explored as immunotherapeutics but it is absolutely essential to characterize each one for their individual and combined protective potential. Although new in the sphere of toxin-based diseases, it is extremely important to characterize the disease-enhancing nature of polyclonal as well as monoclonal antibodies. This is because several anti-viral therapeutics and vaccines have failed in the face of this phenomenon. The passive –immunotherapy thus needs to be well formulated to avoid any contraindications.

Keywords: immunotherapy, polyclonal, monoclonal, antibody-dependent disease enhancement

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Authors: Salim Ahmed

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In many countries, water pollution from industrial effluents is a real problem. It may have a negative impact on the environment. To minimize the adverse effects of these contaminants, various methods are used to improve effluent purification, including physico-chemical processes such as adsorption.The present study focuses on applying a naturally biodegradable adsorbent based on argan (southern Morocco) in a physico-chemical adsorption process to reduce the harmful effects of pollutants on the environment. Tests were carried out with the cationic dye methylene blue (MB) and revealed that removal is significantly higher within the first 15 minutes. The parameters studied in this study are adsorbent mass and concentration. The Freundlich model provides an excellent example of the adsorption phenomenon of BMs over argan powder. The results of this study show that argan kernels are a highly beneficial alternative for local communities, as they help to achieve a triple objective: pollution reduction, waste recovery and water recycling.

Keywords: environmental protection, activated carbon, water treatment, adsorption

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Authors: Reza Safari, Ali Rajabzadeh Ghatari, Raheleh Hossseinzadeh Mahabadi

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In the present world, there are different pressures on firms such as competition, legislations, social etc. these pressures force the firms to follow “survival” as their primary goal and then growth. One of the main factors that helps firms to reach their goals is proper financial performance. To find out about the financial performance, a firm should monitors its financial performance. Financial performance affected by many factors. This research seeks to clear which financial performance indicators are most important according to Environmental situation of a firm and what are their priorities. To do so, environmental indicators specified as presented on OECD Key Environmental Indicators 2008 and so the financial performance indicators such as Profitability, Liquidity, Gearing, Investor ratios, and etc. At this stage, the affections questioned through questionnaires. After gaining the results, data analyzed using Promethee technique. By using decision matrixes extracted from those techniques an expert system designed. This expert system suggests the suitable financial performance indicators and their ranking by receiving the environment situation given environment indicators weight.

Keywords: environment indicators, financial performance indicators, promethee, expert system

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Authors: Jayson G. Balansag

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An integrated biology and chemistry (iBC) course for freshmen college students was developed in University of Delaware. This course will prepare students to (1) become interdisciplinary thinkers in the field of biology and (2) collaboratively work with others from multiple disciplines in the future. This paper documents and describes the implementation of the course. The information gathered from reading literature, classroom observations, and interviews were used to carry out the purpose of this paper. The major goal of the iBC course is to align the concepts between Biology and Chemistry, so that students can draw science concepts from both disciplines which they can apply in their interdisciplinary researches. This course is offered every fall and spring semesters of each school year. Students enrolled in Biology are also enrolled in Chemistry during the same semester. The iBC is composed of lectures, laboratories, studio sessions, and workshops and is taught by the faculty from the biology and chemistry departments. In addition, the preceptors, graduate teaching assistants, and studio fellows facilitate the laboratory and studio sessions. These roles are interdependent with each other. The iBC can be used as a model for higher education institutions who wish to implement an integrated biology course.

Keywords: integrated biology and chemistry, integration, interdisciplinary research, new biology, undergraduate science education

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Authors: Surita Maini, Sanjay Dhanka

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Machine learning (ML) involves developing algorithms and statistical models that enable computers to learn and make predictions or decisions based on data without being explicitly programmed. Because of its unlimited abilities ML is gaining popularity in medical sectors; Medical Imaging, Electronic Health Records, Genomic Data Analysis, Wearable Devices, Disease Outbreak Prediction, Disease Diagnosis, etc. In the last few decades, many researchers have tried to diagnose Breast Cancer (BC) using ML, because early detection of any disease can save millions of lives. Working in this direction, the authors have proposed a hybrid ML technique RBF SVM, to predict the BC in earlier the stage. The proposed method is implemented on the Breast Cancer UCI ML dataset with 569 instances and 32 attributes. The authors recorded performance metrics of the proposed model i.e., Accuracy 98.24%, Sensitivity 98.67%, Specificity 97.43%, F1 Score 98.67%, Precision 98.67%, and run time 0.044769 seconds. The proposed method is validated by K-Fold cross-validation.

Keywords: breast cancer, support vector classifier, machine learning, hyper parameter tunning

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Authors: Talebi Aliasghar, Ebrahimpour Komeleh Hooman, Maghsoudi Ali Akbar

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In reinforced concrete (RC) structures, beam-column connection region has a considerable effect on the behavior of structures. Using fiber reinforcement polymer (FRP) for the strengthening of connections in RC structures can be one of the solutions to retrofitting this zone which result in the enhanced behavior of structure. In this paper, these changes in behavior by using FRP for high strength concrete beam-column connection have been studied by finite element modeling. The concrete damage plasticity (CDP) model has been used to analyze the RC. The results illustrated a considerable development in load-bearing capacity but also a noticeable reduction in ductility. The study also assesses these qualities for several modes of strengthening and suggests the most effective mode of strengthening. Using FRP in flexural zone and FRP with 45-degree oriented fibers in shear zone of joint showed the most significant change in behavior.

Keywords: HSC, beam-column connection, Fiber Reinforcement Polymer, FRP, Finite Element Modeling, FEM

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Authors: Wataru Nakamura, Tomoaki Hashimoto, Liang-Kuang Chen

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This paper provides a state estimation method for automatic control systems of nonlinear vehicle dynamics. A nonlinear tire model is employed to represent the realistic behavior of a vehicle. In general, all the state variables of control systems are not precisedly known, because those variables are observed through output sensors and limited parts of them might be only measurable. Hence, automatic control systems must incorporate some type of state estimation. It is needed to establish a state estimation method for nonlinear vehicle dynamics with restricted measurable state variables. For this purpose, unscented Kalman filter method is applied in this study for estimating the state variables of nonlinear vehicle dynamics. The objective of this paper is to propose a state estimation method using unscented Kalman filter for nonlinear vehicle dynamics. The effectiveness of the proposed method is verified by numerical simulations.

Keywords: state estimation, control systems, observer systems, nonlinear systems

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Authors: Oussama Derdous, Lakhdar Djemili, Hamza Bouchahed

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The consequences of a dam breach can be devastating; both in terms of lives lost and damaged infrastructure and property. Hydraulic modeling provides a clear picture of the possible consequences of partial or complete failure of a dam, which is the key to carry out emergency planning and conduct reliable risk assessments. In this paper, the MIKE11 model developed by the Danish Hydrologic Institute (DHI) was used to simulate the flood wave propagation associated with a potential failure analysis failure of Zardezas dam located in the city of Skikda in the North East of Algeria. MIKE11 results including inundation maps and the representative channel/valley cross-sections depicting flow depth and maximal flow velocities showed that Zardezas reservoir presents a significant risk to downstream areas in the event of a dam failure. These results can be used as the basis of the development of an Emergency Action Plan (EAP).The main objective of this plan is to predict the appropriate steps to avoid or at least decrease the consequences of unexpected failure of Zardezas dam.

Keywords: MIKE11, dam break, inundation maps, emergency action plan

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Authors: Antoni Ivanov, Nikolay Dandanov, Nicole Christoff, Vladimir Poulkov

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Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm).

Keywords: cognitive radio, dynamic spectrum access, GNU Radio, spectrum sensing

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Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

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Authors: A. Ejah Umraeni Salam, M. Tola, M. Selintung, F. Maricar

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Clean water is an essential and fundamental human need. Therefore, its supply must be assured by maintaining the quality, quantity and water pressure. However the fact is, on its distribution system, leakage happens and becomes a common world issue. One of the technical causes of the leakage is a leaking pipe. The purpose of the research is how to use the Radial Basis Function Neural (RBFNN) model to detect the location and the magnitude of the pipeline leakage rapidly and efficiently. In this study the RBFNN are trained and tested on data from EPANET hydraulic modeling system. Method of Radial Basis Function Neural Network is proved capable to detect location and magnitude of pipeline leakage with of the accuracy of the prediction results based on the value of RMSE (Root Meant Square Error), comparison prediction and actual measurement approaches 0.000049 for the whole pipeline system.

Keywords: radial basis function neural network, leakage pipeline, EPANET, RMSE

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Authors: Rosidah Rosidah

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The paper aims to identify and examine the effectiveness of social marketing as an approach for conducting anti-corruption campaign. Social marketing has been widely used to promote social change for the benefit of individual and society; such as for promoting healthy foods consumption, encouraging breastfeeding, reducing smoking, solving alcohol problem and drunk driving. Therefore, it is believed that this approach can be promising to be used in anti-corruption campaign. It is because social marketing can be useful of prompting people to act in accordance to the existing norms that denounce corruption, or help to establish new norms that more averse to corruption. It has established into evidence and insight based approaches to social campaign that focus on changing people’s behavior. Qualitative approach will be used in this study, with the using of literature review and secondary data analysis as the research methods. This paper is still on preliminary stage, which its results is expected to provide fundamental basis for designing model of intervention (anti-corruption campaign) using social marketing approaches.

Keywords: anti-corruption campaign, behavioral change, social influence, social marketing

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Authors: Carmen W. H. Chan, Helen Y. L. Chan, Kai Chow Choi, Ka Ming Chow, Cecilia W. M. Kwan, Nancy H. Y. Ng, Jackie Robinson

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Introduction: In Hong Kong, a significant number of deaths occur in acute care wards, which requires nurses in these settings to provide end-of-life care and lead ACP implementation. However, nurses in these settings, in fact, have very low-level involvement in ACP discussions because of limited training in ACP conversations. Objective: This study aims to assess the impact of a multi-media experiential ACP (MEACP) training program, which is guided by the experiential learning model and theory of planned behaviour, on nurses' knowledge and confidence in assisting patients with ACP. Methodology: The study utilizes a cluster randomized controlled trial with a 12-week follow-up. Eligible nurses working in acute care hospital wards are randomly assigned at the ward level, in a 1:1 ratio, to either the control group (no ACP education) or the intervention group (4-week MEACP training program). The training programme includes training through a webpage and mobile application, as well as a face-to-face training workshop with enhanced lectures and role play, which is based on the Theory of Planned Behavior and Kolb's Experiential Learning Model. Questionnaires were distributed to assess nurses' knowledge (a 10-item true/false questionnaire) and level of confidence (five-point Likert scale) in ACP at baseline (T0), four weeks after the baseline assessment (T1), and 12 weeks after T1 (T2). In this interim report, data analysis was mainly descriptive in nature. Result: The interim report focuses on the preliminary results of 165 nurses at T0 (Control: 74, Intervention: 91) over a 5-month period, 69 nurses from the control group who completed the 4-week follow-up and 65 nurses from the intervention group who completed the 4-week MEACP training program at T1. The preliminary attrition rate is 6.8% and 28.6% for the control and intervention groups, respectively, as some nurses did not complete the whole set of online modules. At baseline, the two groups were generally homogeneous in terms of their years of nursing practice, weekly working hours, working title, and level of education, as well as ACP knowledge and confidence levels. The proportion of nurses who answered all ten knowledge questions correctly increased from 13.8% (T0) to 66.2% (T1) for the intervention group and from 13% (T0) to 20.3% (T1) for the control group. The nurses in the intervention group answered an average of 7.57 and 9.43 questions correctly at T0 and T1, respectively. They showed a greater improvement in the knowledge assessment at T1 with respect to T0 when compared with their counterparts in the control group (mean difference of change score, Δ=1.22). They also exhibited a greater gain in level of confidence at T1 compared to their colleagues in the control group (Δ=0.91). T2 data is yet available. Conclusion: The prevalence of nurses engaging in ACP and their level of knowledge about ACP in Hong Kong is low. The MEACP training program can enrich nurses by providing them with more knowledge about ACP and increasing their confidence in conducting ACP.

Keywords: advance directive, advance care planning, confidence, knowledge, multi-media experiential, randomised control trial

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Authors: Weiying Wang, Qi Li, Huiwen Deng, Weirong Chen

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The abrupt light change and uneven illumination will make the PV system get rid of constant output power, which will affect the efficiency of the grid connected inverter as well as the stability of the system. To solve this problem, this paper presents a strategy to control the stability of photovoltaic power system under the condition of partial shading of PV array, leading to constant power output, improving the capacity of resisting interferences. Firstly, a photovoltaic cell model considering the partial shading is established, and the backtracking search algorithm is used as the maximum power point to track algorithm under complex illumination. Then, the energy storage system based on the constant power control strategy is used to achieve constant power output. Finally, the effectiveness and correctness of the proposed control method are verified by the joint simulation of MATLAB/Simulink and RTLAB simulation platform.

Keywords: backtracking search algorithm, constant power control, hybrid system, partial shading, stability

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Authors: Hong Lu, Xin Chen, Zhengcheng Fan

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Prior research has claimed a close association between handwriting and mathematics attainment with the help of spatial cognition. However, the exact mechanism behind this relationship remains un-investigated. Focusing on visual-motor integration (VMI), one critical spatial skill, this meta-analysis aims to estimate the size of the handwriting- visual-motor integration relationship and examine the moderating effect of handwriting dimensions on the link. With a random effect model, a medium relation (r=.26, 95%CI [.22, .30]) between handwriting and VMI was summarized in 38 studies with 55 unique samples and 141 effect sizes. Findings suggested handwriting dimensions significantly moderated the handwriting- VMI relationship, with handwriting legibility showing a substantial correlation with VMI, but neither handwriting speed nor pressure. Identifying the essential relationship between handwriting legibility and VMI, this study adds to the literature about the key cognitive processing needs underlying handwriting, and spatial cognition thus highlights the cognitive mechanism regarding handwriting, spatial cognition, and mathematics performances.

Keywords: handwriting, visual-motor integration, legibility, meta-analysis

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Authors: Faezeh Aghazadeh, Mohammad Javad Sharifi

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The application of neural networks to model a full-scale industrial acetylene hydrogenation in olefin plant has been studied. The operating variables studied are the, input-temperature of the reactor, output-temperature of the reactor, hydrogen ratio of the reactor, [C₂H₂]input, and [C₂H₆]input. The studied operating variables were used as the input to the constructed neural network to predict the [C₂H₆]output at any time as the output or the target. The constructed neural network was found to be highly precise in predicting the quantity of [C₂H₆]output for the new input data, which are kept unaware of the trained neural network showing its applicability to determine the [C₂H₆]output for any operating conditions. The enhancement of [C₂H₆]output as compared with [C₂H₆]input was a consequence of low selective acetylene hydrogenation to ethylene.

Keywords: acetylene hydrogenation, Pd-Ag/Al₂O₃, artificial neural network, modeling, optimal design

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Authors: Yuhao Jin, Zhou Zhou, Katsumi Yoshida, Zhengcao Li, Tadashi Maruyama, Toyohiko Yano

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Four kinds of nuclear graphite, IG-110U, ETP-10, CX-2002U and IG-430U were neutron-irradiated at different fluences and temperatures, ranged from 1.38 x 1024 to 7.4 x 1025 n/m2 (E > 1.0 MeV) at 473K, 573K and 673K. To take into account the disorder in the microstructure, such as stacking faults and anisotropic coherent lengths, the X-ray diffraction patterns were interpreted using a comprehensive structural model and a refinement program CARBONXS. The deduced structural parameters show the changes of lattice parameters, coherent lengths along the c-axis and the basal plane, and the degree of turbostratic disorder as a function of the irradiation dose. Our results reveal neutron irradiation effects on the microstructure and macroscopic dimension, which are consistent with previous work. The methodology used in this work enables the quantification of the damage on the microstructure of nuclear graphite induced by neutron irradiation.

Keywords: nuclear graphite, neutron irradiation, thermal annealing, recovery behavior, dimensional change, CARBONX, XRD analysis

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Authors: Hyun-Woo Cho

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Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events.

Keywords: detection, monitoring, process data, noise

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Authors: S. Subrmanian, A. Siva, R. Raghul

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In modern construction industry, usage of cold form composite slab has its scope widely due to its light weight, high structural properties and economic factor. To enhance the structural integrity, mechanical interlocking or frictional interlocking was introduced. The role of mechanical interlocking or frictional interlocking is to increase the longitudinal shear between the profiled sheet and concrete. This paper deals with the experimental evaluation of three types of mechanical interlocking devices namely normal stud shear connector, J-Type shear connector, U-Type shear connector. An attempt was made to evolve the shear connector which can be suitable for the composite slab as an interlocking device. Totally six number of composite slabs have been experimented with three types of shear connectors and comparison study is made. The outcome was compared with numerical model was created by ABAQUS software and analyzed for comparative purpose. The result was U-Type shear connector provided better performance and resistance.

Keywords: composite slabs, shear connector, end slip, longitudinal shear

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Authors: Seonjae Lee, Sungjoo Lee

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Excavation is necessary to remain competitive in the current business environment. The company survived the rapidly changing industry conditions by adapting new business strategy and reducing technology challenges. Traditionally, the two methods are conducted excavations for new businesses. The first method is, qualitative analysis of expert opinion, which is gathered through opportunities and secondly, new technologies are discovered through quantitative data analysis of method patents. The second method increases time and cost. Patent data is restricted for use and the purpose of discovering business opportunities. This study presents the company's characteristics (sector, size, etc.), of new business opportunities in customized form by reviewing the value chain perspective and to contributing to creating new business opportunities in the proposed model. It utilizes the trademark database of the Korean Intellectual Property Office (KIPO) and proprietary company information database of the Korea Enterprise Data (KED). This data is key to discovering new business opportunities with analysis of competitors and advanced business trademarks (Module 1) and trading analysis of competitors found in the KED (Module 2).

Keywords: value chain, trademark, trading analysis, new business opportunity

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Authors: Galatee Levadoux, Trevor Benson, Chris Worrall

Abstract:

With the rise of public awareness on climate change comes an increasing demand for renewable sources of energy. As a result, the wind power sector is striving to manufacture longer, more efficient and reliable wind turbine blades. Currently, one of the leading causes of blade failure in service is improper cure of the resin during manufacture. The infusion process creating the main part of the composite blade structure remains a critical step that is yet to be monitored in real time. This stage consists of a viscous resin being drawn into a mould under vacuum, then undergoing a curing reaction until solidification. Successful infusion assumes the resin fills all the voids and cures completely. Given that the electrical properties of the resin change significantly during its solidification, both the filling of the mould and the curing reaction are susceptible to be followed using dieletrometry. However, industrially available dielectrics sensors are currently too small to monitor the entire surface of a wind turbine blade. The aim of the present research project is to scale up the dielectric sensor technology and develop a device able to monitor the manufacturing process of large composite structures, assessing the conformity of the blade before it even comes out of the mould. An array of flat copper wires acting as electrodes are embedded in a polymer matrix fixed in an infusion mould. A multi-frequency analysis from 1 Hz to 10 kHz is performed during the filling of the mould with an epoxy resin and the hardening of the said resin. By following the variations of the complex admittance Y*, the filling of the mould and curing process are monitored. Results are compared to numerical simulations of the sensor in order to validate a virtual cure-monitoring system. The results obtained by drawing glycerol on top of the copper sensor displayed a linear relation between the wetted length of the sensor and the complex admittance measured. Drawing epoxy resin on top of the sensor and letting it cure at room temperature for 24 hours has provided characteristic curves obtained when conventional interdigitated sensor are used to follow the same reaction. The response from the developed sensor has shown the different stages of the polymerization of the resin, validating the geometry of the prototype. The model created and analysed using COMSOL has shown that the dielectric cure process can be simulated, so long as a sufficient time and temperature dependent material properties can be determined. The model can be used to help design larger sensors suitable for use with full-sized blades. The preliminary results obtained with the sensor prototype indicate that the infusion and curing process of an epoxy resin can be followed with the chosen configuration on a scale of several decimeters. Further work is to be devoted to studying the influence of the sensor geometry and the infusion parameters on the results obtained. Ultimately, the aim is to develop a larger scale sensor able to monitor the flow and cure of large composite panels industrially.

Keywords: composite manufacture, dieletrometry, epoxy, resin infusion, wind turbine blades

Procedia PDF Downloads 136