Search results for: lexical complexity

Authors: Sasan Aryaee, Mahdi Ravanshadnia

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Naturally, any activity is associated with risk, and humans have understood this concept from very long times ago and seek to identify its factors and sources. On the one hand, proper risk management can cause problems such as delays and unforeseen costs in the development projects, temporary or permanent loss of services, getting lost or information theft, complexity and limitations in processes, unreliable information caused by rework, holes in the systems and many such problems. In the present study, a model has been presented to rank the environmental risks of oil and gas projects. The statistical population of the study consists of all executives active in the oil and gas fields, that the statistical sample is selected randomly. In the framework of the proposed method, environmental risks of oil and gas projects were first extracted, then a questionnaire based on these indicators was designed based on Likert scale and distributed among the statistical sample. After assessing the validity and reliability of the questionnaire, environmental risks of oil and gas projects were ranked using the VIKOR method of multiple-criteria decision-making. The results showed that the best options for HSE planning of oil and gas projects that caused the reduction of risks and personal injury and casualties and less than other options is costly for the project and it will add less time to the duration of implementing the project is the entering of dye to the environment when painting the generator pond and the presence of the rigger near the crane.

Keywords: ranking, multi-criteria decision making, oil and gas projects, HSEmanagement, environmental risks

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Authors: Gregory Sullivan

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The evolution of global warming has escalated the number and complexity of forest fires around the world. As an example, the United States and Brazil combined generated more than 30,000 forest fires last year. The impact to our environment, structures and individuals is incalculable. The world has learned to try to take this in stride, trying multiple ways to contain fires. Some countries are trying to use cameras in limited areas. There are discussions of using hundreds of low earth orbit satellites and linking them together, and, interfacing them through ground networks. These are all truly noble attempts to defeat the forest fire phenomenon. But there is a better, simpler answer. A bigger piece of the solutions puzzle is to see the fires while they are small, soon after initiation. The approach is to see the fires while they are very small and report their location (latitude and longitude) to local first responders. This is done by placing a sensor at geostationary orbit (GEO: 26,000 miles above the earth). By placing this small satellite in GEO, we can “stare” at the earth, and sense temperature changes. We do not “see” fires, but “measure” temperature changes. This has already been demonstrated on an experimental scale. Fires were seen at close to initiation, and info forwarded to first responders. it were the first to identify the fires 7 out of 8 times. The goal is to have a small independent satellite at GEO orbit focused only on forest fire initiation. Thus, with one small satellite, focused only on forest fire initiation, we hope to greatly decrease the impact to persons, property and the environment.

Keywords: space detection, wildfire early warning, demonstration wildfire detection and action from space, space detection to first responders

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Authors: Azeez Shaik, Amit Salvi, B. P. Gautham

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Fiber reinforced polymer resin composite materials are finding wide variety of applications in automotive and aerospace industry because of their high specific stiffness and specific strengths when compared to metals. New class of 2D and 3D textile and woven fabric composites offer excellent fracture toughens as they bridge the cracks formed during fracture. Due to complexity of their fiber architectures and its resulting composite microstructures, optimized design and analysis of these structures is very complicated. A traditional homogenization approach is typically used to analyze structures made up of these materials. This approach usually fails to predict damage initiation as well as damage propagation and ultimate failure of structure made up of woven and textile composites. This study demonstrates a methodology to analyze woven and textile composites by using the multi-level multi-scale modelling approach. In this approach, a geometric repetitive unit cell (RUC) is developed with all its constituents to develop a representative volume element (RVE) with all its constituents and their interaction modeled correctly. The structure is modeled based on the RUC/RVE and analyzed at different length scales with desired levels of fidelity incorporating the damage and failure. The results are passed across (up and down) the scales qualitatively as well as quantitatively from the perspective of material, configuration and architecture.

Keywords: cohesive zone, multi-scale modeling, rate dependency, RUC, woven textiles

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Authors: Bastien Batardière, Joon Kwon

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For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past gradients and thereby adapt to the geometry of the objective function. Variants such as RMSprop and Adam demonstrate outstanding practical performance that have contributed to the success of deep learning. In this work, we present AdaLVR, which combines the AdaGrad algorithm with loopless variance-reduced gradient estimators such as SAGA or L-SVRG that benefits from a straightforward construction and a streamlined analysis. We assess that AdaLVR inherits both good convergence properties from VR methods and the adaptive nature of AdaGrad: in the case of L-smooth convex functions we establish a gradient complexity of O(n + (L + √ nL)/ε) without prior knowledge of L. Numerical experiments demonstrate the superiority of AdaLVR over state-of-the-art methods. Moreover, we empirically show that the RMSprop and Adam algorithm combined with variance-reduced gradients estimators achieve even faster convergence.

Keywords: convex optimization, variance reduction, adaptive algorithms, loopless

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Authors: Yu Zhao, Rainer Gruenheid, Gerhard Bauch

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In the microwave and millimeter-wave transmission region, substrate-integrated waveguide (SIW) is a very promising candidate for the development of circuits and components. It facilitates the transmission at the data rates in excess of 200 Gbit/s. An SIW mimics a rectangular waveguide by approximating the closed sidewalls with a via fence. This structure suppresses the low frequency components and makes the channel of the SIW a bandpass or high pass filter. This channel characteristic impedes the conventional baseband transmission using non-return-to-zero (NRZ) pulse shaping scheme. Therefore, mixers are commonly proposed to be used as carrier modulator and demodulator in order to facilitate a passband transmission. However, carrier modulation is not an energy efficient solution, because modulation and demodulation at high frequencies consume a lot of energy. For the first time to our knowledge, this paper proposes a spectrum shaping scheme of low complexity for the channel of SIW, namely spread reshaping code. It aims at matching the spectrum of the transmit signal to the channel frequency response. It facilitates the transmission through the SIW channel while it avoids using carrier modulation. In some cases, it even does not need equalization. Simulations reveal a good performance of this scheme, such that, as a result, eye opening is achieved without any equalization or modulation for the respective transmission channels.

Keywords: bandpass channel, eye-opening, switching frequency, substrate-integrated waveguide, spectrum shaping scheme, spread reshaping code

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Authors: Nicolae Bold, Daniel Nijloveanu

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The idea of cropping-system is a method used by farmers. It is an environmentally-friendly method, protecting the natural resources (soil, water, air, nutritive substances) and increase the production at the same time, taking into account some crop particularities. The combination of this powerful method with the concepts of genetic algorithms results into a possibility of generating sequences of crops in order to form a rotation. The usage of this type of algorithms has been efficient in solving problems related to optimization and their polynomial complexity allows them to be used at solving more difficult and various problems. In our case, the optimization consists in finding the most profitable rotation of cultures. One of the expected results is to optimize the usage of the resources, in order to minimize the costs and maximize the profit. In order to achieve these goals, a genetic algorithm was designed. This algorithm ensures the finding of several optimized solutions of cropping-systems possibilities which have the highest profit and, thus, which minimize the costs. The algorithm uses genetic-based methods (mutation, crossover) and structures (genes, chromosomes). A cropping-system possibility will be considered a chromosome and a crop within the rotation is a gene within a chromosome. Results about the efficiency of this method will be presented in a special section. The implementation of this method would bring benefits into the activity of the farmers by giving them hints and helping them to use the resources efficiently.

Keywords: chromosomes, cropping, genetic algorithm, genes

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Authors: Ganesh Dattatraya Saratale, Min Kyu Oh

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Rice straw is one of the most abundant lignocellulosic waste materials and its annual production is about 731 Mt in the world. This study treats the subject of effective utilization of this waste biomass for biofuels production. We have showed a comparative assessment of numerous pretreatment strategies for rice straw, comprising of major physical, chemical and physicochemical methods. Among the different methods employed for pretreatment alkaline pretreatment in combination with sodium chlorite/acetic acid delignification found efficient pretreatment with significant improvement in the enzymatic digestibility of rice straw. A cellulase dose of 20 filter paper units (FPU) released a maximum 63.21 g/L of reducing sugar with 94.45% hydrolysis yield and 64.64% glucose yield from rice straw, respectively. The effects of different pretreatment methods on biomass structure and complexity were investigated by FTIR, XRD and SEM analytical techniques. Finally the enzymatic hydrolysate of rice straw was used for ethanol production using developed Saccharomyces cerevisiae SR8. The developed yeast strain enabled efficient fermentation of xylose and glucose and produced higher ethanol production. Thus development of bioethanol production from lignocellulosic waste biomass is generic, applicable methodology and have great implication for using ‘green raw materials’ and producing ‘green products’ much needed today.

Keywords: rice straw, pretreatment, enzymatic hydrolysis, FPU, Saccharomyces cerevisiae SR8, ethanol fermentation

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Authors: Taysir Soliman

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One of the current popular clustering techniques is Spectral Clustering (SC) because of its advantages over conventional approaches such as hierarchical clustering, k-means, etc. and other techniques as well. However, one of the disadvantages of SC is the time consuming process because it requires computing the eigenvectors. In the past to overcome this disadvantage, a number of attempts have been proposed such as the Power Iteration Clustering (PIC) technique, which is one of versions from SC; some of PIC advantages are: 1) its scalability and efficiency, 2) finding one pseudo-eigenvectors instead of computing eigenvectors, and 3) linear combination of the eigenvectors in linear time. However, its worst disadvantage is an inter-class collision problem because it used only one pseudo-eigenvectors which is not enough. Previous researchers developed Deflation-based Power Iteration Clustering (DPIC) to overcome problems of PIC technique on inter-class collision with the same efficiency of PIC. In this paper, we developed Parallel DPIC (PDPIC) to improve the time and memory complexity which is run on apache spark framework using sparse matrix. To test the performance of PDPIC, we compared it to SC, ESCG, ESCALG algorithms on four small graph benchmark datasets and nine large graph benchmark datasets, where PDPIC proved higher accuracy and better time consuming than other compared algorithms.

Keywords: spectral clustering, power iteration clustering, deflation-based power iteration clustering, Apache spark, large graph

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Authors: Abdessalam Hijab, Eric Henry, Hafida Boulekbache

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The complexity of the city makes sustainable management of the urban environment more difficult. Managers are required to make significant human and technical investments, particularly in household waste collection (focus of our research). The aim of this communication is to propose a collaborative geographic multi-actor device (MGCD) based on the link between information and communication technologies (ICT) and geo-web tools in order to involve urban residents in household waste collection processes. Our method is based on a collaborative/motivational concept between the city and its residents. It is a geographic collaboration dedicated to the general public (citizens, residents, and any other participant), based on real-time allocation and geographic location of topological, geographic, and multimedia data in the form of local geo-alerts (location-specific problems) related to household waste in an urban environment. This contribution allows us to understand the extent to which residents can assist and contribute to the development of household waste collection processes for a better protected urban environment. This suggestion provides a good idea of how residents can contribute to the data bank for future uses. Moreover, it will contribute to the transformation of the population into a smart inhabitant as an essential component of a smart city. The proposed model will be tested in the Lamkansa sampling district in Casablanca, Morocco.

Keywords: information and communication technologies, ICTs, GeoWeb, geo-collaboration, city, inhabitant, waste, collection, environment

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Authors: Alvaro Javier Ortega

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A large number of companies face the challenge of matching highly-skilled professionals to high-end positions by human resource deployment professionals. However, when the professional list and tasks to be matched are larger than a few dozens, this process result is far from optimal and takes a long time to be made. Therefore, an automated assignment algorithm for this workforce management problem is needed. The majority of companies are divided into several sectors or departments, where trained employees with different experience levels deal with a large number of tasks daily. Also, the execution order of all tasks is of mater consequence, due to some of these tasks just can be run it if the result of another task is provided. Thus, a wrong execution order leads to large waiting times between consecutive tasks. The desired goal is, therefore, creating accurate matches and a near-optimal execution order that maximizes the number of tasks performed and minimizes the idle time of the expensive skilled employees. The problem described before can be model as a mixed-integer non-linear programming (MINLP) as it will be shown in detail through this paper. A large number of MINLP algorithms have been proposed in the literature. Here, genetic algorithm solutions are considered and a comparison between two different mutation approaches is presented. The simulated results considering different complexity levels of assignment decisions show the appropriateness of the proposed model.

Keywords: employees, genetic algorithm, industry management, workforce

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Authors: Assaad Ghazouani, Hedia Teraoui

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The purpose of this article is to try to see if the FDI actually contributes to technology transfer in Tunisia or are there other sources that can guarantee this transfer? The answer to this problem was gradual as we followed an approach using economic theory, the reality of Tunisia and econometric and statistical tools. We examined the relationship between technology transfer and FDI in Tunisia over a period of 40 years from 1970 to 2010. We estimated in two stages: first, a growth equation, then we have learned from this regression residue (proxy technology), secondly, we regressed on European FDI, exports of manufactures, imports of goods from the European Union in addition to other variables to test the robustness of the results and describing the level of infrastructure in the country. It follows from our study that technology transfer does not originate primarily and exclusively in the FDI and the latter is econometrically weakly with technology transfer and spill over effect of FDI does not seem to occur according to our results. However, the relationship between technology transfer and imports is negative and significant. Although this result is cons-intuitive, is recurrent in the literature of panel data. It has also given rise to intense debate on the microeconomic modelling as well as on the empirical applications. Technology transfer through trade or foreign investment has become a catalyst for growth recognized by numerous empirical studies in particular. However, the relationship technology transfer FDI is more complex than it appears. This complexity is due, primarily, but not exclusively to the close link between FDI and the characteristics of the host country. This is essentially the host's responsibility to establish general conditions, transparent and conducive to investment, and to strengthen human and institutional capacity necessary for foreign capital flows that can have real effects on growth.

Keywords: technology transfer, foreign direct investment, economics, finance

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: José de Jesús Rivero Oliva, Jesús Salomón Llanes, Manuel Perdomo Ojeda, Antonio Torres Valle

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Combinatorial explosion is a common problem to both predominant methods for solving fault trees: Minimal Cut Set (MCS) approach and Binary Decision Diagram (BDD). High memory consumption impedes the complete solution of very complex fault trees. Only approximated non-conservative solutions are possible in these cases using truncation or other simplification techniques. The paper proposes a method (CSolv+) for solving complex fault trees, without any possibility of combinatorial explosion. Each individual MCS is immediately discarded after its contribution to the basic events importance measures and the Top gate Upper Bound Probability (TUBP) has been accounted. An estimation of the Top gate Exact Probability (TEP) is also provided. Therefore, running in a computer cluster, CSolv+ will guarantee the complete solution of complex fault trees. It was successfully applied to 40 fault trees from the Aralia fault trees database, performing the evaluation of the top gate probability, the 1000 Significant MCSs (SMCS), and the Fussell-Vesely, RRW and RAW importance measures for all basic events. The high complexity fault tree nus9601 was solved with truncation probabilities from 10-²¹ to 10-²⁷ just to limit the execution time. The solution corresponding to 10-²⁷ evaluated 3.530.592.796 MCSs in 3 hours and 15 minutes.

Keywords: system reliability analysis, probabilistic risk assessment, fault tree analysis, basic events importance measures

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Authors: Noorlizawati Abd Rahim, Zainai Mohamed, Zaidatun Tasir, Astuty Amrin, Haliyana Khalid, Nina Diana Nawi

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Entrepreneurial leaders have been sought as in-demand talents to lead profit-driven organizations during turbulent and unprecedented times. However, research regarding the pertinence of their roles in the public sector has been limited. This paper examined the characteristics of the challenging experiences encountered by senior leaders in public universities that require them to embrace entrepreneurialism in their leadership. Through a focus group interview with five Malaysian university top senior leaders with experience being Vice-Chancellor, we explored and developed a framework of institutional adversity characteristics and exemplary entrepreneurial leadership competency in the face of adversity. Complexity of diverse stakeholders, multiplicity of academic disciplines, unfamiliarity to lead different and broader roles, leading new directions, and creating change in high velocity and uncertain environment are among the dimensions that characterise institutional adversities. Our findings revealed that learning agility, opportunity recognition capacity, and bridging capability are among the characteristics of entrepreneurial university leaders. The findings reinforced that the presence of specific attributes in institutional adversity and experiences in overcoming those challenges may contribute to the development of entrepreneurial leadership capabilities.

Keywords: bridging capability, entrepreneurial leadership, leadership development, learning agility, opportunity recognition, university leaders

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Authors: Yuguang Gao, Qiang Yang, Yanpeng Zhang, Mingtao Deng

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To enhance the safety, quality and efficiency of healthcare services provided by surgical doctors, we propose a comprehensive approach to the performance evaluation of individual doctors by incorporating insights from performance data as well as views of different stakeholders in the hospital. Exploratory factor analysis was first performed on collective multidimensional performance data of surgical doctors, where key factors were extracted that encompass assessment of professional experience and service performance. A two-level indicator system was then constructed, for which we developed a weighted interval-valued spherical fuzzy analytic hierarchy process to analyze the relative importance of the indicators while handling subjectivity and disparity in the decision-making of multiple parties involved. Our analytical results reveal that, for the key factors identified as instrumental for evaluating surgical doctors’ performance, the overall importance of clinical workload and complexity of service are valued more than capacity of service and professional experience, while the efficiency of resource consumption ranks comparatively the lowest in importance. We also provide a retrospective case study to illustrate the effectiveness and robustness of our quantitative evaluation model by assigning meaningful performance ratings to individual doctors based on the weights developed through our approach.

Keywords: analytic hierarchy processes, factor analysis, fuzzy logic, performance evaluation

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Authors: Esperanza C. Santos

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Japanese Language was traditionally considered as a means of imparting culture and training aesthetic experience in students and therefore as something beyond the practical aims of language teaching and learning. Due to the complexity of foreign languages, lots of language learners and teachers shared deep reservations about the potentials of foreign language in enhancing the communication skills of the students. In spite of the arguments against the use of Foreign Language (Nihon-go) in the classroom, the researcher strongly support the use of Nihon-go in teaching communication skills as the researcher believes that Nihon-go is a valuable resource to be exploited in the classroom in order to help the students explore the language in an interesting and challenging way. The focus of this research is to find out the relationship between the preferences, opinions, and perceptions with the communication skills. This study also identifies the significance of the relationship between preferences, opinions and perceptions and communications skills in the activities employed in Foreign language (Nihon-go) among the junior and senior students in Foreign Language 2 at the Imus Institute, Imus Cavite during the academic year 2013-2014. The results of the study are expected to encourage further studies that particularly focused on the communication skills as brought about by the identified factors namely: preferences, opinions, and perceptions on the benefits factor namely the language acquisition; access to Japanese culture and students' interpretative ability. Therefore, this research is in its quest for the issues and concerns on how to effectively teach different learning activities in a Nihon-go class.

Keywords: preferences, opinions, perceptions, language acquisition

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Authors: Spandan Sahu, Amiya Kumar Pati, Kishanjit Kumar Khatua

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River is the main source of water. It is a form of natural open channel which gives rise to many complex phenomenon of sciences that needs to be tackled such as the critical flow conditions, boundary shear stress and depth averaged velocity. The development of society more or less solely depends upon the flow of rivers. The rivers are major sources of many sediments and specific ingredients which are much essential for human beings. During floods, part of a river is carried by the simple main channel and rest is carried by flood plains. For such compound asymmetric channels, the flow structure becomes complicated due to momentum exchange between main channel and adjoining flood plains. Distribution of boundary shear in subsections provides us with the concept of momentum transfer between the interface of main channel and the flood plains. Experimentally, to get better data with accurate results are very complex because of the complexity of the problem. Hence, Conveyance Estimation System (CES) software has been used to tackle the complex processes to determine the shear stresses at different sections of an open channel having asymmetric flood plains on both sides of the main channel and the results are compared with the symmetric flood plains for various geometrical shapes and flow conditions. Error analysis is also performed to know the degree of accuracy of the model implemented.

Keywords: depth average velocity, non prismatic compound channel, relative flow depth , velocity distribution

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Authors: Ellen Van Den Oord, Julien Marie Jan Ferdinand Van Campen

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This article introduces a computationally efficient method for stacking sequence blending of composite structures. The computational efficiency makes the presented method especially interesting for composite structures with a large number of design regions. Optimization of composite structures with an unequal load distribution may lead to locally optimized thicknesses and ply orientations that are incompatible with one another. Blending constraints can be enforced to achieve structural continuity. In literature, many methods can be found to implement structural continuity by means of stacking sequence blending in one way or another. The complexity of the problem makes the blending of a structure with a large number of adjacent design regions, and thus stacking sequences, prohibitive. In this work the local stacking sequence optimization is preconditioned using a method found in the literature that couples the mechanical behavior of the laminate, in the form of lamination parameters, to blending constraints, yielding near-optimal easy-to-blend designs. The preconditioned design is then fed to the scheme using cellular automata that have been developed by the authors. The method is applied to the benchmark 18-panel horseshoe blending problem to demonstrate its performance. The computational efficiency of the proposed method makes it especially suited for composite structures with a large number of design regions.

Keywords: composite, blending, optimization, lamination parameters

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Jiajia Yao, Guanlin Wu, Fang Liu, Junshuai Xue, Yue Hao

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This study reports on the epitaxial growth of a GaN-based resonant tunneling diode (RTD) structure with stable and repeatable double negative differential resistance (NDR) characteristics at room temperature on a c-plane GaN-on-sapphire template using plasma-assisted molecular beam epitaxy (PA-MBE) technology. In this structure, two independent AlN/GaN RTDs are epitaxially connected in series in the vertical growth direction through a silicon-doped GaN layer. As the collector electrode bias voltage increases, the two RTDs respectively align the ground state energy level in the quantum well with the 2DEG energy level in the emitter accumulation well to achieve quantum resonant tunneling and then reach the negative differential resistance (NDR) region. The two NDR regions exhibit similar peak current densities and peak-to-valley current ratios, which are 230 kA/cm² and 249 kA/cm², 1.33 and 1.38, respectively, for a device with a collector electrode mesa diameter of 1 µm. The consistency of the NDR is much higher than the results of on-chip discrete RTD device interconnection, resulting from the smaller chip area, fewer interconnect parasitic parameters, and less process complexity. The methods and results presented in this paper show the brilliant prospects of GaN RTDs in the development of multi-value logic digital circuits.

Keywords: MBE, AlN/GaN, RTDs, double NDR

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Authors: Tran Duong Nguyen, Marwan Shagar, Qinghao Zeng, Aras Maqsoodi, Pardis Pishdad, Eunhwa Yang

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Construction supply chain management (CSCM) involves the collaboration of many disciplines and actors, which generates vast amounts of data. However, inefficient, fragmented, and non-standardized data storage often hinders this data exchange. The industry has adopted building information modeling (BIM) -a digital representation of a facility's physical and functional characteristics to improve collaboration, enhance transmission security, and provide a common data exchange platform. Still, the volume and complexity of data require tailored information categorization, aligning with stakeholders' preferences and demands. To address this, artificial intelligence (AI) can be integrated to handle this data’s magnitude and complexities. This research aims to develop an integrated and efficient approach for data exchange in CSCM by utilizing AI. The paper covers five main objectives: (1) Investigate existing framework and BIM adoption; (2) Identify challenges in data exchange; (3) Propose an integrated framework; (4) Enhance data transmission security; and (5) Develop data exchange in CSCM. The proposed framework demonstrates how integrating BIM and other technologies, such as cloud computing, blockchain, and AI applications, can significantly improve the efficiency and accuracy of data exchange in CSCM.

Keywords: construction supply chain management, BIM, data exchange, artificial intelligence

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Authors: Ramonika Sengupta, Stuti Kachhwaha, Asha Adhiya, K. Satya Raja Sekhar, Rajwinder Kaur

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Numerous efforts have been done in the past decade to develop the methods of secure communication that are free from interception and eavesdropping. In fiber optic communication, chaotic optical carrier signals are used for data encryption in secure data transmission. Mach-Zehnder Modulators (MZM) are the key components for generating the chaotic signals to be used as optical carriers. This paper presents the dynamics of a lithium niobate MZM modulator under various biasing conditions. The chaotic fluctuations of the intensity of a laser diode have been generated using the electro-optic MZM modulator operating in a highly nonlinear regime. The modulator is driven in closed loop by its own output at an earlier time. When used as an electro-optic oscillator employing delayed feedback, the MZM displays a wide range of output waveforms of varying complexity. The dynamical behavior of the system ranges from periodic to nonlinear oscillations. The nonlinearity displayed by the system is reproducible and is easily controllable. In this paper, we demonstrate a wide variety of optical signals generated by MZM using easily controllable device parameters in both open and close loop bias conditions.

Keywords: chaotic carrier, fiber optic communication, Mach-Zehnder modulator, secure data transmission

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Authors: Marina Shakleina, Konstantin Shaklein, Stanislav Yakiro

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The digitalization of modern human life over the last decade has facilitated the acquisition, storage, and processing of data, which are used to detect changes in consumer preferences and to improve the internal efficiency of the production process. This emerging trend has attracted academic interest in the use of big data in research. The study focuses on modeling the social comfort of the Russian population for the period 2010-2021 using big data. Big data provides enormous opportunities for understanding human interactions at the scale of society with plenty of space and time dynamics. One of the most popular big data sources is Google Trends. The methodology for assessing social comfort using big data involves several steps: 1. 574 words were selected based on the Harvard IV-4 Dictionary adjusted to fit the reality of everyday Russian life. The set of keywords was further cleansed by excluding queries consisting of verbs and words with several lexical meanings. 2. Search queries were processed to ensure comparability of results: the transformation of data to a 10-point scale, elimination of popularity peaks, detrending, and deseasoning. The proposed methodology for keyword search and Google Trends processing was implemented in the form of a script in the Python programming language. 3. Block and summary integral indicators of social comfort were constructed using the first modified principal component resulting in weighting coefficients values of block components. According to the study, social comfort is described by 12 blocks: ‘health’, ‘education’, ‘social support’, ‘financial situation’, ‘employment’, ‘housing’, ‘ethical norms’, ‘security’, ‘political stability’, ‘leisure’, ‘environment’, ‘infrastructure’. According to the model, the summary integral indicator increased by 54% and was 4.631 points; the average annual rate was 3.6%, which is higher than the rate of economic growth by 2.7 p.p. The value of the indicator describing social comfort in Russia is determined by 26% by ‘social support’, 24% by ‘education’, 12% by ‘infrastructure’, 10% by ‘leisure’, and the remaining 28% by others. Among 25% of the most popular searches, 85% are of negative nature and are mainly related to the blocks ‘security’, ‘political stability’, ‘health’, for example, ‘crime rate’, ‘vulnerability’. Among the 25% most unpopular queries, 99% of the queries were positive and mostly related to the blocks ‘ethical norms’, ‘education’, ‘employment’, for example, ‘social package’, ‘recycling’. In conclusion, the introduction of the latent category ‘social comfort’ into the scientific vocabulary deepens the theory of the quality of life of the population in terms of the study of the involvement of an individual in the society and expanding the subjective aspect of the measurements of various indicators. Integral assessment of social comfort demonstrates the overall picture of the development of the phenomenon over time and space and quantitatively evaluates ongoing socio-economic policy. The application of big data in the assessment of latent categories gives stable results, which opens up possibilities for their practical implementation.

Keywords: big data, Google trends, integral indicator, social comfort

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Authors: Philip Edinger, Christian Ludwig

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The formation and control of tars remain one of the major challenges in the implementation of biomass gasification technologies. Robust, on-line analytical methods are needed to investigate the fate of tar compounds when different measures for their reduction are applied. This work establishes an on-line UV-Vis method, based on a liquid quench sampling system, to monitor tar compounds in biomass gasification processes. Recorded spectra from the liquid phase were analyzed for their tar composition by means of a classical least squares (CLS) and partial least squares (PLS) approach. This allowed for the detection of UV-Vis active tar compounds with detection limits in the low part per million by volume (ppmV) region. The developed method was then applied to two case studies. The first involved a lab-scale reactor, intended to investigate the decomposition of a limited number of tar compounds across a catalyst. The second study involved a gas scrubber as part of a pilot scale wood gasification plant. Tar compound quantification results showed good agreement with off-line based reference methods (GC-FID) when the complexity of tar composition was limited. The two case studies show that the developed method can provide rapid, qualitative information on the tar composition for the purpose of process monitoring. In cases with a limited number of tar species, quantitative information about the individual tar compound concentrations provides an additional benefit of the analytical method.

Keywords: biomass gasification, on-line, tar, UV-Vis

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Authors: Feng Tao, Han Ye, Shaoyi Liao

Abstract:

City constructions collapse after earthquake and people will be buried under ruins. Search and rescue should be conducted as soon as possible to save them. Therefore, according to the complicated environment, irregular aftershocks and rescue allow of no delay, a kind of target localization method based on RSSI (Received Signal Strength Indication) is proposed in this article. The target localization technology based on RSSI with the features of low cost and low complexity has been widely applied to nodes localization in WSN (Wireless Sensor Networks). Based on the theory of RSSI transmission and the environment impact to RSSI, this article conducts the experiments in five scenes, and multiple filtering algorithms are applied to original RSSI value in order to establish the signal propagation model with minimum test error respectively. Target location can be calculated from the distance, which can be estimated from signal propagation model, through improved centroid algorithm. Result shows that the localization technology based on RSSI is suitable for large-scale nodes localization. Among filtering algorithms, mixed filtering algorithm (average of average, median and Gaussian filtering) performs better than any other single filtering algorithm, and by using the signal propagation model, the minimum error of distance between known nodes and target node in the five scene is about 3.06m.

Keywords: signal propagation model, centroid algorithm, localization, mixed filtering, RSSI

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Authors: Noora Al Muftah, Reda Rawi, Richard Thompson, Halima Bensmail

Abstract:

Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers of response to targeted agents. There is an urgent need to identify biomarkers that predict which patients with are most likely to respond to treatment. Systematic efforts to correlate tumor mutational data with biologic dependencies may facilitate the translation of somatic mutation catalogs into meaningful biomarkers for patient stratification. To identify genomic features associated with drug sensitivity and uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we have screened and integrated a panel of several hundred cancer cell lines from different databases, mutation, DNA copy number, and gene expression data for hundreds of cell lines with their responses to targeted and cytotoxic therapies with drugs under clinical and preclinical investigation. We found mutated cancer genes were associated with cellular response to most currently available Glioma cancer drugs and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.

Keywords: cancer, gene network, Lasso, penalized regression, P-values, unbiased estimator

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Authors: Muqdad Al-Juboori, Bithin Datta

Abstract:

Hydraulic structures such as gravity dams are classified as essential structures, and have the vital role in providing strong and safe water resource management. Three major aspects must be considered to achieve an effective design of such a structure: 1) The building cost, 2) safety, and 3) accurate analysis of seepage characteristics. Due to the complexity and non-linearity relationships of the seepage process, many approximation theories have been developed; however, the application of these theories results in noticeable errors. The analytical solution, which includes the difficult conformal mapping procedure, could be applied for a simple and symmetrical problem only. Therefore, the objectives of this paper are to: 1) develop a surrogate model based on numerical simulated data using SEEPW software to approximately simulate seepage process related to a hydraulic structure, 2) develop and solve a linked simulation-optimization model based on the developed surrogate model to describe the seepage occurring under a concrete gravity dam, in order to obtain optimum and safe design at minimum cost. The result shows that the linked simulation-optimization model provides an efficient and optimum design of concrete gravity dams.

Keywords: artificial neural network, concrete gravity dam, genetic algorithm, seepage analysis

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Authors: Patrick Cummings, Laura Cassani, Deirdre Kelliher

Abstract:

In this paper we present findings from a research effort to apply a hybrid collaborative-context approach for a system focused on Marine Corps civil affairs data collection, aggregation, and analysis called the Marine Civil Information Management System (MARCIMS). The goal of this effort is to provide operators with information to make sense of the interconnectedness of entities and relationships in their area of operation and discover existing data to support civil military operations. Our approach to build a recommendation engine was designed to overcome several technical challenges, including 1) ensuring models were robust to the relatively small amount of data collected by the Marine Corps civil affairs community; 2) finding methods to recommend novel data for which there are no interactions captured; and 3) overcoming confirmation bias by ensuring content was recommended that was relevant for the mission despite being obscure or less well known. We solve this by implementing a combination of collective matrix factorization (CMF) and graph-based random walks to provide recommendations to civil military operations users. We also present a method to resolve the challenge of computation complexity inherent from highly connected nodes through a precomputed process.

Keywords: Recommendation engine, collaborative filtering, context based recommendation, graph analysis, coverage, civil affairs operations, Marine Corps

Procedia PDF Downloads 125

Authors: Jacky Hau

Abstract:

The prevalence of emerging technologies disrupts various industries as well as consumer behavior. Data collection has been in the fingertip and inherited through enabled Internet-of-things (IOT) devices. Big data analytics (BDA) becomes possible and allows real-time demand network management (DNM) through leagile supply chain. To enhance further on its resilience and predictability, governance is going to be examined to promote supply chain transparency and trust in an efficient manner. Leagility combines lean thinking and agile techniques in supply chain management. It aims at reducing costs and waste, as well as maintaining responsiveness to any volatile consumer demand by means of adjusting the decoupling point where the product flow changes from push to pull. Leagility would only be successful when collaborative planning, forecasting, and replenishment (CPFR) process or alike is in place throughout the supply chain business entities. Governance and procurement of the supply chain, however, is crucial and challenging for the execution of CPFR as every entity has to walk-the-talk generously for the sake of overall benefits of supply chain performance, not to mention the complexity of exercising the polices at both of within across various supply chain business entities on account of organizational behavior and mutual trust. Empirical survey results showed that the effective timespan on demand forecasting had been drastically shortening in the magnitude of months to weeks planning horizon, thus agility shall come first and preferably following by lean approach in a timely manner.

Keywords: governance, leagility, procure-to-pay, source-to-contract

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Authors: S. Anuradha, V. Sandeep Kumar

Abstract:

The high Peak to Average Power Ratio (PAR) in Filter Bank Multicarrier with Offset Quadrature Amplitude Modulation (FBMC-OQAM) can significantly reduce power efficiency and performance. In this paper, we address the problem of PAPR reduction for FBMC-OQAM systems using Tone Reservation (TR) technique. Due to the overlapping structure of FBMCOQAM signals, directly applying TR schemes of OFDM systems to FBMC-OQAM systems is not effective. We improve the tone reservation (TR) technique by employing sliding window with Active Constellation Extension for the PAPR reduction of FBMC-OQAM signals, called sliding window tone reservation Active Constellation Extension (SW-TRACE) technique. The proposed SW-TRACE technique uses the peak reduction tones (PRTs) of several consecutive data blocks to cancel the peaks of the FBMC-OQAM signal inside a window, with dynamically extending outer constellation points in active(data-carrying) channels, within margin-preserving constraints, in order to minimize the peak magnitude. Analysis and simulation results compared to the existing Tone Reservation (TR) technique for FBMC/OQAM system. The proposed method SW-TRACE has better PAPR performance and lower computational complexity.

Keywords: FBMC-OQAM, peak-to-average power ratio, sliding window, tone reservation Active Constellation Extension

Procedia PDF Downloads 447