Search results for: RNG model

Authors: S. Mousavian, A. Abedianpour, A. Khanmohammadi, S. Hematian, Gh. Eidi Veisi

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Distillation is one of the most important and utilized separation methods in the industrial practice. There are different ways to design of distillation column. One of these ways is short cut method. In short cut method, material balance and equilibrium are employed to calculate number of tray in distillation column. There are different methods that are classified in short cut method. One of these methods is Fenske-Underwood-Gilliland method. In this method, minimum reflux ratio should be calculated by underwood equation. Underwood proposed an equation that is useful for simple distillation column with one feed and one top and bottom product. In this study, underwood method is developed to predict minimum reflux ratio for column with one side stream upper than feed. The result of this model compared with McCabe-Thiele method. The result shows that proposed method able to calculate minimum reflux ratio with very small error.

Keywords: minimum reflux ratio, side stream, distillation, Underwood’s method

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Authors: Valentine Seymour

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Environmental volunteering organisations use questionnaires to explore the relationship between environmental volunteers and their health. To the author’s best knowledge, no one has explored volunteers’ health perception, which could be considered when designing a health impact measurement tool used to increase effective communication. This paper examines environmental volunteers' perceptions of health, knowledge which can be used to design a health impact measurement tool. This study uses focus group interviews, content analysis, and a general inductive approach to explore the health perceptions of volunteers who engage in environmental volunteering activities from the perspective of UK charity The Conservation Volunteers. Findings showed that volunteer groups presented were relatively similar in how they defined the term health, with their overall conceptual model closely resembling that of the World Health Organization 1948 definition. This suggests that future health impact measurement tools in the environmental volunteering sector could base their design around the World Health Organization’s definition.

Keywords: health perception, impact measurement, mental models, tool development

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Authors: Haodong Huang, Zida Zhao, Shilong Sun, Chiyao Li, Wenfu Xu

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The control methods for legged robots based on deep reinforcement learning have seen widespread application; however, the inherent black-box nature of neural networks presents challenges in understanding the decision-making motives of the robots. To address this issue, we propose a fully interpretable deep reinforcement learning training method to elucidate the underlying principles of legged robot motion. We incorporate the dynamics of legged robots into the policy, where observations serve as inputs and actions as outputs of the dynamics model. By embedding the dynamics equations within the multi-layer perceptron (MLP) computation process and making the parameters trainable, we enhance interpretability. Additionally, Bayesian optimization is introduced to train these parameters. We validate the proposed fully interpretable motion control algorithm on a legged robot, opening new research avenues for motion control and learning algorithms for legged robots within the deep learning framework.

Keywords: deep reinforcement learning, interpretation, motion control, legged robots

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Authors: Tuqa Abdulrazzaq, Hussein Togun, M. K. A. Ariffin, S. N. Kazi, A. Badarudin, N. M. Adam, S. Masuri

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Numerical study of heat transfer and fluid flow over vertical double forward facing step were presented. The k-w model with finite volume method was employed to solve continuity, momentum, and energy equations. Different step heights were adopted for range of Reynolds number varied from 10000 to 40000, and range of temperature varied from 310K to 340 K. The straight side of duct is insulated while the side of double forward facing step is heated. The result shows augmentation of heat transfer due to the recirculation region created after and before steps. Effect of step length and Reynolds number observed on increase of local Nusselt number particularly at recirculation regions. Contour of streamline velocity is plotted to show recirculation regions after and before steps. Numerical simulation in this paper done by used ANSYS Fluent 14.

Keywords: turbulent flow, double forward, heat transfer, separation flow

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Authors: Mohsen Najarchi

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This research was carried out in five fields (5-15 hectares) in Arak located in center of Iran, to determine optimum level of water consumed for Barely in four stages growth (vegetative, yield formation, flowering, and ripening). Actual evapotranspiration was calculated using measured water requirement in the fields. Five levels of water requirement equal to 50, 60, 70, 80, and 90 percents formed the treatments. To determine the optimum level of water requirement linear programming was used. The study showed 60 percent water requirement (40 percent deficit irrigation) has been the optimum level of irrigation for winter wheat in four stages of growth. Comparison between all of the treatments indicated above with normal condition (100% water requirement) shows increasing in water use efficiency. Although 40% deficit irrigation treatment lead to decrease of 38% in yield, net benefit was increasing in 11.37%. Furthermore, in comparison with normal condition, 70% of water requirement increased water use efficiency as 30%.

Keywords: optimum, deficit irrigation, water use efficiency, evapotranspiration

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Authors: Hadush Kidane Meresa

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The hydrological extremes frequency analysis is the foundation for the hydraulic engineering design, flood protection, drought management and water resources management and planning to utilize the available water resource to meet the desired objectives of different organizations and sectors in a country. This spatial variation of the statistical characteristics of the extreme flood and drought events are key practice for regional flood and drought analysis and mitigation management. For different hydro-climate of the regions, where the data set is short, scarcity, poor quality and insufficient, the regionalization methods are applied to transfer at-site data to a region. This study aims in regional high and low flow frequency analysis for Poland River Basins. Due to high frequent occurring of hydrological extremes in the region and rapid water resources development in this basin have caused serious concerns over the flood and drought magnitude and frequencies of the river in Poland. The magnitude and frequency result of high and low flows in the basin is needed for flood and drought planning, management and protection at present and future. Hydrological homogeneous high and low flow regions are formed by the cluster analysis of site characteristics, using the hierarchical and C- mean clustering and PCA method. Statistical tests for regional homogeneity are utilized, by Discordancy and Heterogeneity measure tests. In compliance with results of the tests, the region river basin has been divided into ten homogeneous regions. In this study, frequency analysis of high and low flows using AM for high flow and 7-day minimum low flow series is conducted using six statistical distributions. The use of L-moment and LL-moment method showed a homogeneous region over entire province with Generalized logistic (GLOG), Generalized extreme value (GEV), Pearson type III (P-III), Generalized Pareto (GPAR), Weibull (WEI) and Power (PR) distributions as the regional drought and flood frequency distributions. The 95% percentile and Flow duration curves of 1, 7, 10, 30 days have been plotted for 10 stations. However, the cluster analysis performed two regions in west and east of the province where L-moment and LL-moment method demonstrated the homogeneity of the regions and GLOG and Pearson Type III (PIII) distributions as regional frequency distributions for each region, respectively. The spatial variation and regional frequency distribution of flood and drought characteristics for 10 best catchment from the whole region was selected and beside the main variable (streamflow: high and low) we used variables which are more related to physiographic and drainage characteristics for identify and delineate homogeneous pools and to derive best regression models for ungauged sites. Those are mean annual rainfall, seasonal flow, average slope, NDVI, aspect, flow length, flow direction, maximum soil moisture, elevation, and drainage order. The regional high-flow or low-flow relationship among one streamflow characteristics with (AM or 7-day mean annual low flows) some basin characteristics is developed using Generalized Linear Mixed Model (GLMM) and Generalized Least Square (GLS) regression model, providing a simple and effective method for estimation of flood and drought of desired return periods for ungauged catchments.

Keywords: flood , drought, frequency, magnitude, regionalization, stochastic, ungauged, Poland

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Authors: Divine Agozie, Muesser Nat, Eric Afful-Dadzie

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This examination attempts an analysis of the effect of business intelligence and analytics (BI&A) capabilities on organizational role stressors and the implications of such an effect on performance. Two hundred twenty-eight responses gathered from seventy-six firms across Ghana were analyzed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach to validate the hypothesized relationships identified in the research model. Findings suggest both endogenous and exogenous dependencies of the sensing capability on the multiple role requirements of personnel. Further, transforming capability increases role conflict, whereas driving capability of BI&A systems impacts role conflict and role ambiguity. This study poses many practical insights to firms seeking to acquire analytics capabilities to drive performance and data-driven decision-making. It is important for firms to consider balancing role changes and task requirements before implementing and post-implementation stages of BI&A innovations.

Keywords: business intelligence and analytics, dynamic capabilities view, organizational stressors, structural equation modelling

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Authors: Sonal H. Singh, Bhaskar Bhowmick

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The research hypothesis formulated in this paper examines the importance of microfinance provision for entrepreneurship development by engendering a high level of entrepreneurial orientation among the grassroots entrepreneurs. A theoretically well supported empirical framework is proposed to identify the influence of financial services and non-financial services provided by microfinance institutes in strengthening the impact of grassroots innovation on entrepreneurial orientation under resource constraints. In this paper, Grassroots innovation is perceived in three dimensions: new learning practice, localized solution, and network development. The study analyzes the moderating effect of microfinance provision on the relationship between grassroots innovation and entrepreneurial orientation. The paper employed structural equation modelling on 400 data entries from the grassroots entrepreneurs in India. The research intends to help policymakers, entrepreneurs and microfinance providers to promote the innovative design of microfinance services for the well-being of grassroots entrepreneurs and to foster sustainable entrepreneurship development.

Keywords: entrepreneurship development, grassroots innovation, India, structural equation model

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Blessing Igbadumhe

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The continuous long- and short-term delivery of value to customers continues to be the overarching objective of software organizations. Software engineering professionals and organizations face challenges in the maintenance and evolution of software as a result of architectural, technical debt. The issues of architectural, technical debt continue to receive a significant amount of attention because of its important impact on successful system implementation. The cost of doing nothing as far as architectural, technical debt is concerned can be significant both in financial terms and impacts on customers. Different architectural, technical debt issues exist, and this qualitative research design reviewed existing literature on the subject to identify and categorize them. This research intends to contribute to the existing bludgeoning body of knowledge on categorizations and descriptive model of technical debt related issues related to system architecture. The results identify the most common characteristics of architectural and technical debt categories. Raising awareness of the intricacies of architectural and technical debt helps technology stakeholders reduce negative consequences and increase the system success rate.

Keywords: architecture, categorizing TD, system design, technical debt

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Authors: Mesut BALIBEY, Serpil TÜRKYILMAZ

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A popular topic in the econometrics and time series area is the cointegrating relationships among the components of a nonstationary time series. Engle and Granger’s least squares method and Johansen’s conditional maximum likelihood method are the most widely-used methods to determine the relationships among variables. Furthermore, a method proposed to test a unit root based on the periodogram ordinates has certain advantages over conventional tests. Periodograms can be calculated without any model specification and the exact distribution under the assumption of a unit root is obtained. For higher order processes the distribution remains the same asymptotically. In this study, in order to indicate advantages over conventional test of periodograms, we are going to examine a possible relationship between tourism and economic growth during the period 1999:01-2010:12 for Turkey by using periodogram method, Johansen’s conditional maximum likelihood method, Engle and Granger’s ordinary least square method.

Keywords: cointegration, economic growth, periodogram ordinate, tourism

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Authors: Kornchanok Chidchaisuwan

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This project is a study of learning activities through experience to enhance career skills and technical abilities on the creative strategies for advertising course of undergraduate students. This instructional model consisted of study learning approaches: 1) Simulation-based learning: used to create virtual learning activities plans for work like working at advertising companies. 2) Project-based learning: Actual work based on the processed creating and focus on producing creative works to present on new media channels. The results of learning management found that there were effects on the students in various areas, including 1) The learners have experienced in the step by step of advertising work process. 2) The learner has the skills to work from the actual work (Learning by Doing), allowing the ability to create, present, and produce the campaign accomplished achievements and published on online media at a better level.

Keywords: technical, advertising, presentation, career skills, experience, simulation based learning

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Authors: Yaxin Bi

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Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Garry Miller, Thomas Alexander, Cameron Lee

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There is belief that Building Information Modelling (BIM) will improve performance of the New Zealand (NZ) Architecture, Engineering and Construction (AEC) sector, however, widespread use of BIM is yet to be seen. Previous research indicates there are many issues affecting the uptake of BIM in NZ; nevertheless the underlying benefits, drawbacks, and barriers preventing more widespread uptake are not fully understood. This investigation aimed to understand these factors more clearly and make suggestions on how to improve the uptake of BIM in NZ. Semi-structured interviews were conducted with a range of industry professionals to gather a qualitative understanding. Findings indicated the ability to incorporate better information into a BIM model could drive many benefits. However scepticism and lack of positive incentives in NZ are affecting its widespread use. This concluded that there is a need for the government to produce a number of BIM case studies and develop a set of BIM standards to resolve payment issues surrounding BIM use. This study provides useful information for those interested in BIM and members of government interested in improving the performance of the construction industry. This study may also be of interest to small, developed countries such as NZ where the level of BIM maturity is relatively low.

Keywords: BIM, New Zealand, AEC sector, building information modelling

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Authors: Rahul Arora, S. S. Dhami

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This paper implements the design structure of industrial robot along with the different transmission components like gear assembly and analysis of complete industrial robot. In this paper, it gives the overview on the most efficient types of modeling and different analysis results that can be obtained for an industrial robot. The investigation is executed in regards to two classifications i.e. the deformation and the stress tests. SolidWorks is utilized to design and review the 3D drawing plan while ANSYS Workbench is utilized to execute the FEA on an industrial robot and the designed component. The CAD evaluation was conducted on a disentangled model of an industrial robot. The study includes design and drafting its transmission system. In CAE study static, modal and dynamic analysis are presented. Every one of the outcomes is divided in regard with the impact of the static and dynamic analysis on the situating exactness of the robot. It gives critical data with respect to parts of the industrial robot that are inclined to harm under higher high force applications. Therefore, the mechanical structure under different operating conditions can help in optimizing the manipulator geometry and in selecting the right material for the same. The FEA analysis is conducted for four different materials on the same industrial robot and gear assembly.

Keywords: CAD, CAE, FEA, robot, static, dynamic, modal, gear assembly

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Authors: M. H. Aissa, Amar Bouzid Dj

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As known, many countries are now planning to build new wind farms with high capacity up to 5MW. Consequently, the size of the foundation increase. These kinds of structures are subject to fatigue damage from environmental loading mainly due to wind and waves as well as from cyclic loading imposed through the rotational frequency (1P) through mass and aerodynamic imbalances and from the blade passing frequency (3P) of the wind turbine which make them behavior dynamically very sensitive. That is why natural frequency must be determined with accuracy from the existing data of the soil and the foundation stiffness sources of uncertainties, to avoid the resonance of the system. This paper presents analytical expressions of stiffness foundation with large diameter in linear soil behavior in different soil stiffness profile. To check the accuracy of the proposed formulas, a mathematical model approach based on non-dimensional parameters is used to calculate the natural frequency taking into account the soil structure interaction (SSI) compared with the p-y method and measured frequency in the North Sea Wind farms.

Keywords: offshore wind turbines, semi analytical FE analysis, p-y curves, piles foundations

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Authors: Arnold I. Malag

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The study wants to establish the data on the characteristics of coastal solid waste in main Island of Masbate as a model for technology interventions. The research utilized the Google Maps to measure the coastal length and Fishbowl Method for area identification. The solid wastes gathered were classified as residual, non-biodegradable, recyclable wastes, and special wastes, based on the waste analysis and characterization manual of Philippine Environmental Governance Project. The wastes were evaluated by weight in kg., dimension in cm., and characteristics as floating or non-floating. Based on the dimension of coastal solid waste, the biodegradable, recyclable, residual and special waste have the average of 40.95 cm., 16.25 cm., 31.37 cm., and 0.725cm. respectively. The waste in the coastal areas is dominated by biodegradable, followed by residual, then recyclable and special wastes with the data of 0.566 kg/m, 0.533 kg/m, 0.114 kg/m and .0007 kg/m respectively. The 97.15% of solid wastes collected is characterized as “floating”, where in the sources are the nearest rivers and waterways and/or the nearest populated areas adjacent to the island. This accumulation of solid wastes can be minimized and controlled by utilizing a floating equipment.

Keywords: solid waste, coastal waste, waste characterization, waste collector

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Authors: Joelle Al Fakhoury, Emilio Sassine, Yassine Cherif, Joseph Dgheim, Emmanuel Antczak

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Hollow block masonry is the most used building technology in the Lebanese context. These blocks are manufactured in an artisanal way and have unknown thermal properties; their overall thermos-physical performance is thus unknown and also poorly investigated scientifically in both single wall and also double wall configurations. In this work, experimental measurements and numerical simulations are performed for a better understanding of the heat transfer in masonry walls. This study was realized using an experimental setup consisting of a masonry hollow block wall (0.1m x 1m x 1m) and two heat boxes, such that each covers one side of the wall. The first is a reference box having a constant interior temperature, and the other is a control box having an adjustable interior temperature. At first, the numerical model is validated using an experimental setup; then 3D numerical analyzes are held in order to investigate the effect of the air gap, the mortar joints, and the plastering on the thermal performance of masonry walls for a better understanding of the heat transfer process and the recommendation of suitable thermal improvements.

Keywords: masonry wall, hollow blocks, heat transfer, wall instrumentation, thermal improvement

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Authors: Hamed Rahmani, Wim Groot

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The term "youth not in employment, education or training (NEET)" refers to a combination of youth unemployment and school dropout. This study investigates the variables that increase the risk of becoming NEET in Iran. A selection bias-adjusted Probit model was employed using machine learning to identify these risk factors. We used cross-sectional data obtained from the Statistical Centre of Iran and the Ministry of Cooperatives Labour and Social Welfare that was taken from the labour force survey conducted in the spring of 2021. We look at years of education, work experience, housework, the number of children under the age of six in the home, family education, birthplace, and the amount of land owned by households. Results show that hours spent performing domestic chores enhance the likelihood of youth becoming NEET, and years of education and years of potential work experience decrease the chance of being NEET. The findings also show that female youth born in cities were less likely than those born in rural regions to become NEET.

Keywords: NEET youth, probit, CART, machine learning, unemployment

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Authors: Ennios Eros Giogos, Nefeli Katsarou, Giota Mantziorou, Elena Panou, Nikolaos Kourniatis, Socratis Giannoudis

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In the context of the postgraduate course Productive Design, Department of Interior Architecture of the University of West Attica in Athens, under the guidance of Professors Nikolaos Koyrniatis and Socratis Giannoudis, kinetic mechanisms with parametric models were examined for their further application in the design of objects. In the first phase, the students studied a motion mechanism that they chose from daily experience and then analyzed its geometric structure in relation to the geometric transformations that exist. In the second phase, the students tried to design it through a parametric model in Grasshopper3d for Rhino algorithmic processor and plan the design of its application in an everyday object. For the project presented, our team began by studying the movement of living beings, specifically the snake. By studying the snake and the role that the environment has in its movement, four basic typologies were recognized: serpentine, concertina, sidewinding and rectilinear locomotion, as well as its ability to perform spiral formations. Most typologies are characterized by ripples, a series of sinusoidal curves. For the application of the snake movement in a polymorphic space divider, the use of a coil-type joint was studied. In the Grasshopper program, the simulation of the desired motion for the polymorphic surface was tested by applying a coil on a sinusoidal curve and a spiral curve. It was important throughout the process that the points corresponding to the nodes of the real object remain constant in number, as well as the distances between them and the elasticity of the construction had to be achieved through a modular movement of the coil and not some elastic element (material) at the nodes. Using mesh (repeating coil), the whole construction is transformed into a supporting body and combines functionality with aesthetics. The set of elements functions as a vertical spatial network, where each element participates in its coherence and stability. Depending on the positions of the elements in terms of the level of support, different perspectives are created in terms of the visual perception of the adjacent space. For the implementation of the model on the scale (1:3), (0.50m.x2.00m.), the load-bearing structure that was studied has aluminum rods for the basic pillars Φ6mm and Φ 2.50 mm, for the secondary columns. Filling elements and nodes are of similar material and were made of MDF surfaces. During the design process, four trapezoidal patterns were picketed, which function as filling elements, while in order to support their assembly, a different engraving facet was done. The nodes have holes that can be pierced by the rods, while their connection point with the patterns has a half-carved recess. The patterns have a corresponding recess. The nodes are of two different types depending on the column that passes through them. The patterns and knots were designed to be cut and engraved using a Laser Cutter and attached to the knots using glue. The parameters participate in the design as mechanisms that generate complex forms and structures through the repetition of constantly changing versions of the parts that compose the object.

Keywords: polymorphic, locomotion, sinusoidal curves, parametric

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Authors: N. Naz, A. D. Domenico, M. N. Huda

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Soft Robotics is a rapidly growing multidisciplinary field where robots are fabricated using highly deformable materials motivated by bioinspired designs. The high dexterity and adaptability to the external environments during contact make soft robots ideal for applications such as gripping delicate objects, locomotion, and biomedical devices. The actuation system of soft robots mainly includes fluidic, tendon-driven, and smart material actuation. Among them, Soft Pneumatic Actuator, also known as SPA, remains the most popular choice due to its flexibility, safety, easy implementation, and cost-effectiveness. However, at present, most of the fabrication of SPA is still based on traditional molding and casting techniques where the mold is 3d printed into which silicone rubber is cast and consolidated. This conventional method is time-consuming and involves intensive manual labour with the limitation of repeatability and accuracy in design. Recent advancements in direct 3d printing of different soft materials can significantly reduce the repetitive manual task with an ability to fabricate complex geometries and multicomponent designs in a single manufacturing step. The aim of this research work is to design and analyse the Soft Pneumatic Actuator (SPA) utilizing both conventional casting and modern direct 3d printing technologies. The mold of the SPA for traditional casting is 3d printed using fused deposition modeling (FDM) with the polylactic acid (PLA) thermoplastic wire. Hyperelastic soft materials such as Ecoflex-0030/0050 are cast into the mold and consolidated using a lab oven. The bending behaviour is observed experimentally with different pressures of air compressor to ensure uniform bending without any failure. For direct 3D-printing of SPA fused deposition modeling (FDM) with thermoplastic polyurethane (TPU) and stereolithography (SLA) with an elastic resin are used. The actuator is modeled using the finite element method (FEM) to analyse the nonlinear bending behaviour, stress concentration and strain distribution of different hyperelastic materials after pressurization. FEM analysis is carried out using Ansys Workbench software with a Yeon-2nd order hyperelastic material model. FEM includes long-shape deformation, contact between surfaces, and gravity influences. For mesh generation, quadratic tetrahedron, hybrid, and constant pressure mesh are used. SPA is connected to a baseplate that is in connection with the air compressor. A fixed boundary is applied on the baseplate, and static pressure is applied orthogonally to all surfaces of the internal chambers and channels with a closed continuum model. The simulated results from FEM are compared with the experimental results. The experiments are performed in a laboratory set-up where the developed SPA is connected to a compressed air source with a pressure gauge. A comparison study based on performance analysis is done between FDM and SLA printed SPA with the molded counterparts. Furthermore, the molded and 3d printed SPA has been used to develop a three-finger soft pneumatic gripper and has been tested for handling delicate objects.

Keywords: finite element method, fused deposition modeling, hyperelastic, soft pneumatic actuator

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Authors: A. Belayadi, A. Mougari, L. Ait-Gougam, F. Mekideche-Chafa

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The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage.

Keywords: neural network computing, continuous functions generating the input-output mapping, decreasing the training time, machines with big memories

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Authors: Jinyoung Choi, Sunmook Lee

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In general, single parameter, i.e. temperature, as an accelerating parameter is used to assess the accelerated stability of Point-of-Care Testing (POCT) diagnostic devices. However, humidity also plays an important role in deteriorating the strip performance since major components of test strips are proteins such as enzymes. 4 different Temp./Humi. Conditions were used to assess the lifetime of strips. Degradation of test strips were studied through the accelerated stability test and the lifetime was assessed using commercial POCT products. The life distribution of strips, which were obtained by monitoring the failure time of test strip under each stress condition, revealed that the weibull distribution was the most proper distribution describing the life distribution of strips used in the present study. Equal shape parameters were calculated to be 0.9395 and 0.9132 for low and high concentrations, respectively. The lifetime prediction was made by adopting Peck Eq. Model for Stress-Life relationship, and the B10 life was calculated to be 70.09 and 46.65 hrs for low and high concentrations, respectively.

Keywords: accelerated degradation, diagnostic device, lifetime assessment, POCT

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Authors: Margarita Grushanina

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After several prominent central banks including European Central Bank (ECB), Federal Reserve System (Fed), Bank of Japan and Bank of England employed unconventional monetary policies in the aftermath of the financial crisis of 2008-2009 the problem of identification of the effects from such policies became of great interest. One of the main difficulties in identification of shocks from unconventional monetary policy measures in structural VAR analysis is that they often are anticipated, which leads to a non-fundamental MA representation of the VAR model. Moreover, the unconventional monetary policy actions may indirectly transmit to markets information about the future stance of the interest rate, which raises a question of the plausibility of the assumption of orthogonality between shocks from unconventional and conventional policy measures. This paper offers a method of identification that takes into account the abovementioned issues. The author uses factor-augmented VARs to increase the information set and identification through heteroskedasticity of error terms and rank restrictions on the errors’ second moments’ matrix to deal with the cross-correlation of the structural shocks.

Keywords: factor-augmented VARs, identification through heteroskedasticity, monetary policy, structural VARs

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Authors: Eun-Joong Kim, Sankarprasad Bhuniya, Hyunseung Lee, Hyun Min Kim, Chaejoon Cheong, Su-khendu Maiti, Kwan Soo Hong, Jong Seung Kim

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Metastatic cancers have historically been difficult to treat. However, metastatic tumors have been found to have high levels of reactive oxygen species such as hydrogen peroxide (H2O2), supporting the hypothesis that a prodrug could be activated by intracellular H2O2 and lead to a potential anti-metastatic therapy. In this study, prodrug 7 was designed to be activated by H2O2-mediated boronate oxidation, resulting in activation of the fluorophore for detection and release of the therapeutic agent, SN-38. Drug release from prodrug 7 was investigated by monitoring fluorescence after addition of H2O2 to the cancer cells. Prodrug 7 activated by H2O2 selectively inhibited tumor cell growth. Furthermore, intratracheally administered prodrug 7 showed effective anti-tumor activity in a mouse model of metastatic lung disease. Thus, this H2O2-responsive prodrug has therapeutic potential as a novel treatment for metastatic cancer via cellular imaging with fluorescence as well as selective release of the anti-cancer drug, SN-38.

Keywords: hydrogen peroxide, prodrug, metastatic tumors, fluorescence

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Authors: Reyhaneh Saeedi

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Iran is a capable zone for the earthquake that follows the loss of lives and financial damages. To have sheltering for earthquake victims is one of the basic requirements although it is hard to select suitable places for temporary resettling after an earthquake happens. Before these kinds of disasters happen, the best places for resettling the victims must be designated. This matter is an important issue in disaster management and planning. Geospatial Information System(GIS) has a determining role in disaster management, it can determine the best places for temporary resettling after such a disaster. In this paper, the best criteria have been determined associated with their weights and buffers by use of research and questionnaire for locating the best places. In this paper, AHP method is used as decision model and to locate the best places for temporary resettling is done based on the selected criteria. Also, in this research are made the buffer layers of criteria and change them to the raster layers. Later on, the raster layers are multiplied on desired weights then, the results are added together. Finally, there are suitable places for resettling of victims by desired criteria by different colors with their optimum rate in ArcGIS software.

Keywords: disaster management, temporary resettlement, earthquake, criteria

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Authors: Qianxi Lv, Junying Liang

Abstract:

Among the various modes of interpreting, simultaneous interpreting (SI) is regarded as a ‘complex’ and ‘extreme condition’ of cognitive tasks while consecutive interpreters (CI) do not have to share processing capacity between tasks. Given that SI exerts great cognitive demand, it makes sense to posit that the output of SI may be more compromised than that of CI in the linguistic features. The bulk of the research has stressed the varying cognitive demand and processes involved in different modes of interpreting; however, related empirical research is sparse. In keeping with our interest in investigating the quantitative linguistic factors discriminating between SI and CI, the current study seeks to examine the potential lexical simplification, syntactic complexity and sequential organization mechanism with a self-made inter-model corpus of transcribed simultaneous and consecutive interpretation, translated speech and original speech texts with a total running word of 321960. The lexical features are extracted in terms of the lexical density, list head coverage, hapax legomena, and type-token ratio, as well as core vocabulary percentage. Dependency distance, an index for syntactic complexity and reflective of processing demand is employed. Frequency motif is a non-grammatically-bound sequential unit and is also used to visualize the local function distribution of interpreting the output. While SI is generally regarded as multitasking with high cognitive load, our findings evidently show that CI may impose heavier or taxing cognitive resource differently and hence yields more lexically and syntactically simplified output. In addition, the sequential features manifest that SI and CI organize the sequences from the source text in different ways into the output, to minimize the cognitive load respectively. We reasoned the results in the framework that cognitive demand is exerted both on maintaining and coordinating component of Working Memory. On the one hand, the information maintained in CI is inherently larger in volume compared to SI. On the other hand, time constraints directly influence the sentence reformulation process. The temporal pressure from the input in SI makes the interpreters only keep a small chunk of information in the focus of attention. Thus, SI interpreters usually produce the output by largely retaining the source structure so as to relieve the information from the working memory immediately after formulated in the target language. Conversely, CI interpreters receive at least a few sentences before reformulation, when they are more self-paced. CI interpreters may thus tend to retain and generate the information in a way to lessen the demand. In other words, interpreters cope with the high demand in the reformulation phase of CI by generating output with densely distributed function words, more content words of higher frequency values and fewer variations, simpler structures and more frequently used language sequences. We consequently propose a revised effort model based on the result for a better illustration of cognitive demand during both interpreting types.

Keywords: cognitive demand, corpus-based, dependency distance, frequency motif, interpreting types, lexical simplification, sequential units distribution, syntactic complexity

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Authors: Daniela Stephanie Kuenstle

Abstract:

Complex industries such as banking or insurance experience slow growth in mobile sales. While today’s mobile applications are sophisticated and enable location based and personalized services, consumers prefer online or even face-to-face services to complete complex transactions. A possible reason for this reluctance is that the provided service within transactional mobile applications (apps) does not adequately correspond to users’ needs. Therefore, this paper examines the impact of the user context on mobile service (m-service) in m-commerce. Motivated by the potential which context-sensitive m-services hold for the future, the impact of temporal variations as a dimension of user context, on m-service design is examined. In particular, the research question asks: Does consumer urgency function as a determinant of m-service composition in transactional apps by moderating the relation between m-service type and m-service success? Thus, the aim is to explore the moderating influence of urgency on m-service types, which includes Technology Mediated Service and Technology Generated Service. While mobile applications generally comprise features of both service types, this thesis discusses whether unexpected urgency changes customer preferences for m-service types and how this consequently impacts the overall m-service success, represented by purchase intention, loyalty intention and service quality. An online experiment with a random sample of N=1311 participants was conducted. Participants were divided into four treatment groups varying in m-service types and urgency level. They were exposed to two different urgency scenarios (high/ low) and two different app versions conveying either technology mediated or technology generated service. Subsequently, participants completed a questionnaire to measure the effectiveness of the manipulation as well as the dependent variables. The research model was tested for direct and moderating effects of m-service type and urgency on m-service success. Three two-way analyses of variance confirmed the significance of main effects, but demonstrated no significant moderation of urgency on m-service types. The analysis of the gathered data did not confirm a moderating effect of urgency between m-service type and service success. Yet, the findings propose an additive effects model with the highest purchase and loyalty intention for Technology Generated Service and high urgency, while Technology Mediated Service and low urgency demonstrate the strongest effect for service quality. The results also indicate an antagonistic relation between service quality and purchase intention depending on the level of urgency. Although a confirmation of the significance of this finding is required, it suggests that only service convenience, as one dimension of mobile service quality, delivers conditional value under high urgency. This suggests a curvilinear pattern of service quality in e-commerce. Overall, the paper illustrates the complex interplay of technology, user variables, and service design. With this, it contributes to a finer-grained understanding of the relation between m-service design and situation dependency. Moreover, the importance of delivering situational value with apps depending on user context is emphasized. Finally, the present study raises the demand to continue researching the impact of situational variables on m-service design in order to develop more sophisticated m-services.

Keywords: mobile consumer behavior, mobile service design, mobile service success, self-service technology, situation dependency, user-context sensitivity

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Authors: Abdulrahman Sumayli, Saad M. AlShahrani

Abstract:

For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: Timothee Gidenne, Xia Pinqi

Abstract:

In this paper, an investigation of the use of carbon nanotubes (CNTs) reinforced polymer as an actuator for an active flutter suppression to counter the flutter phenomena is conducted. The goal of this analysis is to establish a link between the behavior of the control surface and the actuators to demonstrate the veracity of using such a suppression system for the aeronautical field. A preliminary binary flutter model using simplified unsteady aerodynamics is developed to study the behavior of the wing while reaching the flutter speed and when the control system suppresses the flutter phenomena. The Timoshenko beam theory for bilayer materials is used to match the response of the control surface with the CNTs reinforced polymer (CNRP) actuators. According to Timoshenko theory, results show a good and realistic response for such a purpose. Even if the results are still preliminary, they show evidence of the potential use of CNRP for control surface actuation for the small-scale and lightweight system.

Keywords: actuators, aeroelastic, aeroservoelasticity, carbon nanotubes, flutter, flutter suppression

Procedia PDF Downloads 129