Search results for: non linear dynamic analysis

Authors: Mounir Zili

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We will introduce a new extension of the Brownian motion, that could serve to get a good model of many natural phenomena. It is a linear combination of a finite number of sub-fractional Brownian motions; that is why we will call it the mixed sub-fractional Brownian motion. We will present some basic properties of this process. Among others, we will check that our process is non-markovian and that it has non-stationary increments. We will also give the conditions under which it is a semi-martingale. Finally, the main features of its sample paths will be specified.

Keywords: fractal dimensions, mixed gaussian processes, sample paths, sub-fractional brownian motion

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Authors: Sergio Celaschi, Henrique Canavarro de Alencar, Claaudecir Biazoli

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Effluent quality is of the highest priority for compliance with the permit limits of environmental protection agencies and ensures the protection of their local water system. Of the pollutants monitored, the biochemical oxygen demand (BOD) posed one of the greatest challenges. This work presents a solution for wastewater treatment plants - WWTP’s ability to react to different situations and meet treatment goals. Delayed BOD5 results from the lab take 7 to 8 analysis days, hindered the WWTP’s ability to react to different situations and meet treatment goals. Reducing BOD turnaround time from days to hours is our quest. Such a solution is based on a system of two BOD bioreactors associated with Digital Twin (DT) and Machine Learning (ML) methodologies via an Internet of Things (IoT) platform to monitor and control a WWTP to support decision making. DT is a virtual and dynamic replica of a production process. DT requires the ability to collect and store real-time sensor data related to the operating environment. Furthermore, it integrates and organizes the data on a digital platform and applies analytical models allowing a deeper understanding of the real process to catch sooner anomalies. In our system of continuous time monitoring of the BOD suppressed by the effluent treatment process, the DT algorithm for analyzing the data uses ML on a chemical kinetic parameterized model. The continuous BOD monitoring system, capable of providing results in a fraction of the time required by BOD5 analysis, is composed of two thermally isolated batch bioreactors. Each bioreactor contains input/output access to wastewater sample (influent and effluent), hydraulic conduction tubes, pumps, and valves for batch sample and dilution water, air supply for dissolved oxygen (DO) saturation, cooler/heater for sample thermal stability, optical ODO sensor based on fluorescence quenching, pH, ORP, temperature, and atmospheric pressure sensors, local PLC/CPU for TCP/IP data transmission interface. The dynamic BOD system monitoring range covers 2 mg/L < BOD < 2,000 mg/L. In addition to the BOD monitoring system, there are many other operational WWTP sensors. The CPU data is transmitted/received to/from the digital platform, which in turn performs analyses at periodic intervals, aiming to feed the learning process. BOD bulletins and their credibility intervals are made available in 12-hour intervals to web users. The chemical kinetics ML algorithm is composed of a coupled system of four first-order ordinary differential equations for the molar masses of DO, organic material present in the sample, biomass, and products (CO₂ and H₂O) of the reaction. This system is solved numerically linked to its initial conditions: DO (saturated) and initial products of the kinetic oxidation process; CO₂ = H₂0 = 0. The initial values for organic matter and biomass are estimated by the method of minimization of the mean square deviations. A real case of continuous monitoring of BOD wastewater effluent quality is being conducted by deploying an IoT application on a large wastewater purification system located in S. Paulo, Brazil.

Keywords: effluent treatment, biochemical oxygen demand, continuous monitoring, IoT, machine learning

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Authors: Lorenzo Casagrande, Antonio Bonati, Ferdinando Auricchio, Antonio Occhiuzzi

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This paper summarizes the results of a survey on smart non-structural element dynamic dissipation when installed in modern high-rise mega-frame prototypes. An innovative glazed curtain wall was designed using Shape Memory Alloy (SMA) joints in order to increase the energy dissipation and enhance the seismic/wind response of the structures. The studied buildings consisted of thirty- and sixty-storey planar frames, extracted from reference three-dimensional steel Moment Resisting Frame (MRF) with outriggers and belt trusses. The internal core was composed of a CBF system, whilst outriggers were placed every fifteen stories to limit second order effects and inter-storey drifts. These structural systems were designed in accordance with European rules and numerical FE models were developed with an open-source code, able to account for geometric and material nonlinearities. With regard to the characterization of non-structural building components, full-scale crescendo tests were performed on aluminium/glass curtain wall units at the laboratory of the Construction Technologies Institute (ITC) of the Italian National Research Council (CNR), deriving force-displacement curves. Three-dimensional brick-based inelastic FE models were calibrated according to experimental results, simulating the fac¸ade response. Since recent seismic events and extreme dynamic wind loads have generated the large occurrence of non-structural components failure, which causes sensitive economic losses and represents a hazard for pedestrians safety, a more dissipative glazed curtain wall was studied. Taking advantage of the mechanical properties of SMA, advanced smart joints were designed with the aim to enhance both the dynamic performance of the single non-structural unit and the global behavior. Thus, three-dimensional brick-based plastic FE models were produced, based on the innovated non-structural system, simulating the evolution of mechanical degradation in aluminium-to-glass and SMA-to-glass connections when high deformations occurred. Consequently, equivalent nonlinear links were calibrated to reproduce the behavior of both tested and smart designed units, and implemented on the thirty- and sixty-storey structural planar frame FE models. Nonlinear time history analyses (NLTHAs) were performed to quantify the potential of the new system, when considered in the lateral resisting frame system (LRFS) of modern high-rise MRFs. Sensitivity to the structure height was explored comparing the responses of the two prototypes. Trends in global and local performance were discussed to show that, if accurately designed, advanced materials in non-structural elements provide new sources of energy dissipation.

Keywords: advanced technologies, glazed curtain walls, non-structural elements, seismic-action reduction, shape memory alloy

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Authors: Javier Donoso-Bravo, Camila Cortés-Zambrano

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In this paper, we analyze socioterritorial inequalities in the region of Valparaiso (Chile) using secondary data to account for these inequalities drawing on economic, social, educational, and environmental dimensions regarding the thirty-six municipalities of the region. We looked over a wide-ranging set of secondary data from public sources regarding economic activities, poverty, employment, income, years of education, post-secondary education access, green areas, access to potable water, and others. We found sharp socioterritorial inequalities especially based on the economic performance in each territory. Analysis show, on the one hand, the existence of a dual and unorganized development model in some territories with a strong economic activity -especially in the areas of finance, real estate, mining, and vineyards- but, at the same time, with poor social indicators. On the other hand, most of the territories show a dispersed model with very little dynamic economic activities and very poor social development. Finally, we discuss how socioterritorial inequalities in the region of Valparaiso reflect the level of globalization of the economic activities carried on in every territory.

Keywords: socioterritorial inequalities, development model, Chile, secondary data, Region of Valparaiso

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Authors: Sudhanshu Kumar, Raghvendra Chandrakar, Arun Chakraborty

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In the ocean, the temperature extremes have the potential to devastate marine habitats, ecosystems together with ensuing socioeconomic consequences. In recent years, these extreme events are more frequent and intense globally and their increasing trend is expected to continue in the upcoming decades. It recently attracted public interest, as well as scientific researchers, which motivates us to analyze the current marine heatwave (MHW) events in the Bay of Bengal region. we have isolated 107 MHW events (above 90th percentile threshold) in this region of the Indian Ocean and investigated the variation in duration, intensity, and frequency of MHW events during our test period (1982-2021). Our study reveals that in the study region the average of three MHW events per year with an increasing linear trend of 1.11 MHW events per decade. In the analysis, we found the longest MHW event which lasted about 99 days, which is far greater than an average MHW event duration. The maximum intensity was 5.29°C (above the climatology-mean), while the mean intensity was 2.03°C. In addition, we observed net heat flux accompanied by anticyclonic eddies to be the primary cause of these events. Moreover, we concluded that these events affect sea surface height and oceanic productivity, highlighting the adverse impact of MHWs on marine ecosystems.

Keywords: marine heatwaves, global warming, climate change, sea surface temperature, marine ecosystem

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Authors: Atakan Aral Ormancı, Tuğçe Arslantaş, Murat Özcü

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This study presents the design and implementation of an experimental chassis-level system for various control applications. Specifically, the height level of the chassis is controlled using proportional integrated derivative, fuzzy logic, and deep learning control methods. Real-time data obtained from height and pressure sensors installed in a 6x2 truck chassis, in combination with pulse-width modulation signal values, are utilized during the tests. A prototype pneumatic system of a 6x2 truck is added to the setup, which enables the Smart Pneumatic Actuators to function as if they were in a real-world setting. To obtain real-time signal data from height sensors, an Arduino Nano is utilized, while a Raspberry Pi processes the data using Matlab/Simulink and provides the correct output signals to control the Smart Pneumatic Actuator in the truck chassis. The objective of this research is to optimize the time it takes for the chassis to level down and up under various loads. To achieve this, proportional integrated derivative control, fuzzy logic control, and deep learning techniques are applied to the system. The results show that the deep learning method is superior in optimizing time for a non-linear system. Fuzzy logic control with a triangular membership function as the rule base achieves better outcomes than proportional integrated derivative control. Traditional proportional integrated derivative control improves the time it takes to level the chassis down and up compared to an uncontrolled system. The findings highlight the superiority of deep learning techniques in optimizing the time for a non-linear system, and the potential of fuzzy logic control. The proposed approach and the experimental results provide a valuable contribution to the field of control, automation, and systems engineering.

Keywords: automotive, chassis level control, control systems, pneumatic system control

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Authors: Zarah M. Bello, Nathan Baddoo, Mariana Lilley, Paul Wernick

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In this paper, we present findings from a multi-part study to evaluate candidate elements reflecting the level of digital capability maturity (DCM) in higher education and the relationship between these elements. We will use these findings to propose a model of DCM for educational institutions. We suggest that the success of learning in higher education is dependent in part on the level of maturity of digital capabilities of institutions as well as the abilities of learners and those who support the learning process. It is therefore important to have a good understanding of the elements that underpin this maturity as well as their impact and interactions in order to better exploit the benefits that technology presents to the modern learning environment and support its continued improvement. Having identified ten candidate elements of digital capability that we believe support the level of a University’s maturity in this area as well as a number of relevant stakeholder roles, we conducted two studies utilizing both quantitative and qualitative research methods. In the first of these studies, 85 electronic questionnaires were completed by various stakeholders in a UK university, with a 100% response rate. We also undertook five in-depth interviews with management stakeholders in the same university. We then utilized statistical analysis to process the survey data and conducted a textual analysis of the interview transcripts. Our findings support our initial identification of candidate elements and support our contention that these elements interact in a multidimensional manner. This multidimensional dynamic suggests that any proposal for improvement in digital capability must reflect the interdependency and cross-sectional relationship of the elements that contribute to DCM. Our results also indicate that the notion of DCM is strongly data-centric and that any proposed maturity model must reflect the role of data in driving maturity and improvement. We present these findings as a key step towards the design of an operationalisable DCM maturity model for universities.

Keywords: digital capability, elements, maturity, maturity framework, university

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Authors: P. Chang, C. Lopez, C. Burbano

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Flood protection zones are riparian buffers that are formed to manage and mitigate the impact of flooding, and in turn, protect local populations. The purpose of this study was to evaluate the Guía Técnica de Criterios para el Acotamiento de las Rondas Hídricas in Colombia against international regulations in Canada and Spain, in order to determine its limitations and contribute to its improvement. The need to establish a specific corridor that allows for the dynamic development of a river is clear; however, limitations present in the Colombian Technical Guide are identified. The study shows that international regulations provide similar concepts as used in Colombia, but additionally integrate aspects such as regionalization that allows for a better characterization of the channel way, and incorporate the frequency of flooding and its probability of occurrence in the concept of risk when determining the protection zone. The case study analyzed in Dosquebradas - Risaralda aimed at comparing the application of the different standards through hydraulic modeling. It highlights that the current Colombian standard does not offer sufficient details in its implementation phase, which leads to a false sense of security related to inaccuracy and lack of data. Furthermore, the study demonstrates how the Colombian norm is ill-adapted to the conditions of Dosquebradas typical of the Andes region, both in the social and hydraulic aspects, and does not reduce the risk, nor does it improve the protection of the population. Our study considers it pertinent to include risk estimation as an integral part of the methodology when establishing protect flood zone, considering the particularity of water systems, as they are characterized by an heterogeneous natural dynamic behavior.

Keywords: environmental corridor, flood zone determination, hydraulic domain, legislation flood protection zone

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Authors: Sheng-Yu Lee

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Aim: Opioid use disorder is often characterized by repetitive drug-seeking and drug-taking behaviors with severe public health consequences. Animal model showed that opioid-induced perturbations in the gut microbiota causally relate to neuroinflammation, deficits in reward responding, and opioid tolerance, possibly due to changes in gut microbiota. Therefore, we propose that the dysbiosis of gut microbiota can be associated with pathogenesis of opioid dependence. In this current study, we explored the differences in gut microbiota between patients and normal controls and in patients before and after initiation of methadone treatment program for 12 weeks. Methods: Patients with opioid use disorder between 20 and 65 years were recruited from the methadone maintenance outpatient clinic in 2 medical centers in the Southern Taiwan. Healthy controls without any family history of major psychiatric disorders (schizophrenia, bipolar disorder and major depressive disorder) were recruited from the community. After initial screening, 15 patients with opioid use disorder joined the study for initial evaluation (Week 0), 12 of them completed the 12-week follow-up while receiving methadone treatment and ceased heroin use (Week 12). Fecal samples were collected from the patients at baseline and the end of 12th week. A one-time fecal sample was collected from the healthy controls. The microbiota of fecal samples were investigated using 16S rRNA V3V4 amplicon sequencing, followed by bioinformatics and statistical analyses. Results: We found no significant differences in species diversity in opioid dependent patients between Week 0 and Week 12, nor compared between patients at both points and controls. For beta diversity, using principal component analysis, we found no significant differences between patients at Week 0 and Week 12, however, both patient groups showed significant differences compared to control (P=0.011). Furthermore, the linear discriminant analysis effect size (LEfSe) analysis was used to identify differentially enriched bacteria between opioid use patients and healthy controls. Compared to controls, the relative abundance of Lactobacillaceae Lactobacillus (L. Lactobacillus), Megasphaera Megasphaerahexanoica (M. Megasphaerahexanoica) and Caecibacter Caecibactermassiliensis (C Caecibactermassiliensis) were increased in patients at Week 0, while Coriobacteriales Atopobiaceae (C. Atopobiaceae), Acidaminococcus Acidaminococcusintestini (A. Acidaminococcusintestini) and Tractidigestivibacter Tractidigestivibacterscatoligenes (T. Tractidigestivibacterscatoligenes) were increased in patients at Week 12. Conclusion: In conclusion, we suggest that the gut microbiome community maybe linked to opioid use disorder, such differences may not be altered even after 12-week of cessation of opioid use.

Keywords: opioid use disorder, gut microbiota, methadone treatment, follow up study

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Authors: Idris M. Olatunde, Kareem B.

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In manufacturing systems, especially job shops, service performance is a key factor that determines customer satisfaction. Service performance depends not only on the quality of the output but on the delivery lead times as well. Besides product quality enhancement, delivery lead time must be minimized for optimal patronage. Quoting accurate due dates is sine quo non for job shop operational survival in a global competitive environment. Quoting accurate due dates in job shops has been a herculean task that nearly defiled solutions from many methods employed due to complex jobs routing nature of the system. This class of NP-hard problems possessed no rigid algorithms that can give an optimal solution. Jobshop operational problem is more complex in developing nations due to some peculiar factors. Operational complexity in job shops emanated from political instability, poor economy, technological know-how, and the non-promising socio-political environment. The mentioned exogenous factors were hardly considered in the previous studies on scheduling problem related to due date determination in job shops. This study has filled the gap created in the past studies by developing a dynamic model that incorporated the exogenous factors for accurate determination of due dates for varying jobs complexity. Real data from six job shops selected from the different part of Nigeria, were used to test the efficacy of the model, and the outcomes were analyzed statistically. The results of the analyzes showed that the model is more promising in determining accurate due dates than the traditional models deployed by many job shops in terms of patronage and lead times minimization.

Keywords: due dates prediction, improved performance, customer satisfaction, dynamic model, exogenous factors, job shops

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Authors: Tarali Kalita, Karabi Dutta

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The North Eastern Region of India, with diverse climatic conditions and a wide range of ecological habitats, makes an ideal natural abode for a good number of silk-producing insects. Cocoon is the economically important life stage from where silk of economic importance is obtained. In recent years, silk-based biomaterials have gained considerable attention, which is dependent on the structure and properties of the silkworm cocoons as well as silk yarn. The present investigation deals with the morphological study of cocoons, including cocoon color, cocoon size, shell weight and shell ratio of eleven different species of silk insects collected from different regions of North East India. The Scanning Electron Microscopic study and X-ray photoelectron spectroscopy were performed to know the arrangement of silk threads in cocoons and the atomic elemental analysis, respectively. Further, collected cocoons were degummed and reeled/spun on a reeling machine or spinning wheel to know the filament length, linear density and tensile strength by using Universal Testing Machine. The study showed significant variation in terms of cocoon color, cocoon shape, cocoon weight and filament packaging. XPS analysis revealed the presence of elements (Mass %) C, N, O, Si and Ca in varying amounts. The wild cocoons showed the presence of Calcium oxalate crystals which makes the cocoons hard and needs further treatment to reel. In the present investigation, the highest percentage of strain (%) and toughness (g/den) were observed in Antheraea assamensis, which implies that the muga silk is a more compact packing of molecules. It is expected that this study will be the basis for further biomimetic studies to design and manufacture artificial fiber composites with novel morphologies and associated material properties.

Keywords: cocoon characterization, north-east India, prospects, silk characterization

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Authors: Renalda El Samra, Elie Bou-Zeid, Hamza Kunhu Bangalath, Georgiy Stenchikov, Mutasem El Fadel

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The regional-scale impact of climate change over complex terrain was examined through high-resolution dynamic downscaling conducted using the Weather Research and Forecasting (WRF) model, with initial and boundary conditions from a High-Resolution Atmospheric Model (HiRAM). The analysis was conducted over the eastern Mediterranean, with a focus on the country of Lebanon, which is characterized by a challenging complex topography that magnifies the effect of orographic precipitation. Four year-long WRF simulations, selected based on HiRAM time series, were performed to generate future climate projections of extreme temperature and precipitation over the study area under the conditions of the Representative Concentration Pathway (RCP) 4.5. One past WRF simulation year, 2008, was selected as a baseline to capture dry extremes of the system. The results indicate that the study area might be exposed to a temperature increase between 1.0 and 3ºC in summer mean values by 2050, in comparison to 2008. For extreme years, the decrease in average annual precipitation may exceed 50% at certain locations in comparison to 2008.

Keywords: HiRAM, regional climate modeling, WRF, Representative Concentration Pathway (RCP)

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Authors: A. Torokhti

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A new method of mathematical modeling of the distributed nonlinear system is developed. The system is represented by a combination of the set of spatially distributed sensors and the fusion center. Its mathematical model is obtained from the iterative procedure that converges to the model which is optimal in the sense of minimizing an associated cost function.

Keywords: mathematical modeling, non-linear system, spatially distributed sensors, fusion center

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Authors: Ana Serafimovic, Karthik Devarajan

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Non-negative matrix factorization approximates a high dimensional non-negative matrix V as the product of two non-negative matrices, W and H, and allows only additive linear combinations of data, enabling it to learn parts with representations in reality. It has been successfully applied in the analysis and interpretation of high dimensional data arising in neuroscience, computational biology, and natural language processing, to name a few. The objective of this paper is to assess a hybrid algorithm for non-negative matrix factorization with multiplicative updates. The method aims to minimize the symmetric version of Kullback-Leibler divergence known as intrinsic information and assumes that the noise is signal-dependent and that it originates from an arbitrary distribution from the exponential family. It is a generalization of currently available algorithms for Gaussian, Poisson, gamma and inverse Gaussian noise. We demonstrate the potential usefulness of the new generalized algorithm by comparing its performance to the baseline methods which also aim to minimize symmetric divergence measures.

Keywords: non-negative matrix factorization, dimension reduction, clustering, intrinsic information, symmetric information divergence, signal-dependent noise, exponential family, generalized Kullback-Leibler divergence, dual divergence

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Authors: Mohamed Yousef, Magdy Samuel, Maha El-Meligy, Taher El-Bitar

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The present work is dealing with 2% Si-steel alloy. The alloy contains 0.05% C as well as 0.85% Al. The alloy under investigation would be used for electrical transformation purposes. A heating (expansion) - cooling (contraction) dilation investigation was executed to detect the a, a+g, and g transformation temperatures at the inflection points of the dilation curve. On heating, primary a  was detected at a temperature range between room temperature and 687 ^oC. The domain of a+g was detected in the range between 687 ^oC and 746 ^oC. g phase exists in the closed g region at the range between 746 ^oC and 1043 ^oC. The domain of a phase appears again at a temperature range between 1043 and 1105 ^oC, and followed by secondary a at temperature higher than 1105 ^oC. A physical simulation of thermo-mechanical processing on the as-cast alloy was carried out. The simulation process took into consideration the hot flat rolling pilot plant parameters. The process was executed on the thermo-mechanical simulator (Gleeble 3500). The process was designed to include seven consecutive passes. The 1^st pass represents the roughing stage, while the remaining six passes represent finish rolling stage. The whole process was executed at the temperature range from 1100 ^oC to 900 ^oC. The amount of strain starts with 23.5% at the roughing pass and decreases continuously to reach 7.5 % at the last finishing pass. The flow curve of the alloy can be abstracted from the stress-strain curves representing simulated passes. It shows alloy hardening from a pass to the other up to pass no. 6, as a result of decreasing the deformation temperature and increasing of cumulative strain. After pass no. 6, the deformation process enhances the dynamic recrystallization phenomena to appear, where the z-parameter would be high.

Keywords: si- steel, hot deformability, critical transformation temperature, physical simulation, thermo-mechanical processing, flow curve, dynamic softening.

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Authors: Chaouki Ghenai

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Computational Fluid Dynamic analysis of co-combustion of coal/biomass waste fuel blends is presented in this study. The main objective of this study is to investigate the effects of biomass portions (0%, 10%, 20%, 30%: weight percent) blended with coal and oxygen concentrations (21% for air, 35%, 50%, 75% and 100 % for pure oxygen) on the combustion performance and emissions. The goal is to reduce the air emissions from power plants coal combustion. Sub-bituminous Nigerian coal with calorific value of 32.51 MJ/kg and sawdust (biomass) with calorific value of 16.68 MJ/kg is used in this study. Coal/Biomass fuel blends co-combustion is modeled using mixture fraction/pdf approach for non-premixed combustion and Discrete Phase Modeling (DPM) to predict the trajectories and the heat/mass transfer of the fuel blend particles. The results show the effects of oxygen concentrations and biomass portions in the coal/biomass fuel blends on the gas and particles temperatures, the flow field, the devolitization and burnout rates inside the combustor and the CO2 and NOX emissions at the exit from the combustor. The results obtained in the course of this study show the benefits of enriching combustion air with oxygen and blending biomass waste with coal for reducing the harmful emissions from coal power plants.

Keywords: co-combustion, coal, biomass, fuel blends, CFD, air emissions

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Authors: Krutika K. Sawant, Anil Solanki

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The present study deals with the preparation and optimization of ethyl cellulose-containing disopyramide phosphate loaded microspheres using solvent evaporation technique. A central composite design consisting of a two-level full factorial design superimposed on a star design was employed for optimizing the preparation microspheres. The drug:polymer ratio (X1) and speed of the stirrer (X2) were chosen as the independent variables. The cumulative release of the drug at a different time (2, 6, 10, 14, and 18 hr) was selected as the dependent variable. An optimum polynomial equation was generated for the prediction of the response variable at time 10 hr. Based on the results of multiple linear regression analysis and F statistics, it was concluded that sustained action can be obtained when X1 and X2 are kept at high levels. The X1X2 interaction was found to be statistically significant. The drug release pattern fitted the Higuchi model well. The data of a selected batch were subjected to an optimization study using Box-Behnken design, and an optimal formulation was fabricated. Good agreement was observed between the predicted and the observed dissolution profiles of the optimal formulation.

Keywords: disopyramide phosphate, ethyl cellulose, microspheres, controlled release, Box-Behnken design, factorial design

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Xiaoqian Cheng, Jon Kleppe, Ole Torsaeter

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One objective of this work is to analyze the effects of surfactant properties (viscosity, concentration, and adsorption) on surfactant enhanced oil recovery at laboratory scale. The other objective is to obtain the functional relationships between surfactant properties and the ultimate oil recovery and oil recovery rate. A core is cut into two parts from the middle to imitate the matrix with a horizontal fracture. An injector and a producer are at the left and right sides of the fracture separately. The middle slice of the core is used as the model in this paper, whose size is 4cm x 0.1cm x 4.1cm, and the space of the fracture in the middle is 0.1 cm. The original properties of matrix, brine, oil in the base case are from Ekofisk Field. The properties of surfactant are from literature. Eclipse is used as the simulator. The results are followings: 1) The viscosity of surfactant solution has a positive linear relationship with surfactant oil recovery time. And the relationship between viscosity and oil production rate is an inverse function. The viscosity of surfactant solution has no obvious effect on ultimate oil recovery. Since most of the surfactant has no big effect on viscosity of brine, the viscosity of surfactant solution is not a key parameter of surfactant screening for surfactant flooding in fractured reservoirs. 2) The increase of surfactant concentration results a decrease of oil recovery rate and an increase of ultimate oil recovery. However, there are no functions could describe the relationships. Study on economy should be conducted because of the price of surfactant and oil. 3) In the study of surfactant adsorption, assume that the matrix wettability is changed to water-wet when the surfactant adsorption is to the maximum at all cases. And the ratio of surfactant adsorption and surfactant concentration (Cads/Csurf) is used to estimate the functional relationship. The results show that the relationship between ultimate oil recovery and Cads/Csurf is a logarithmic function. The oil production rate has a positive linear relationship with exp(Cads/Csurf). The work here could be used as a reference for the surfactant screening of surfactant enhanced oil recovery from fractured reservoirs. And the functional relationships between surfactant properties and the oil recovery rate and ultimate oil recovery help to improve upscaling methods.

Keywords: fractured reservoirs, surfactant adsorption, surfactant concentration, surfactant EOR, surfactant viscosity

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Authors: Samaneh Nadali, Masrah Azrifah Azmi Murad, Nurfadhlina Mohammad Sharef

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Sarcasm is a common phenomenon in social media which is a nuanced form of language for stating the opposite of what is implied. Due to the intentional ambiguity, analysis of sarcasm is a difficult task not only for a machine but even for a human. Although sarcasm detection has an important effect on sentiment, it is usually ignored in social media analysis because sarcasm analysis is too complicated. While there is a few systems exist which can detect sarcasm, almost no work has been carried out on a study and the review of the existing work in this area. This survey presents a nearly full image of sarcasm detection techniques and the related fields with brief details. The main contributions of this paper include the illustration of the recent trend of research in the sarcasm analysis and we highlight the gaps and propose a new framework that can be explored.

Keywords: sarcasm detection, sentiment analysis, social media, sarcasm analysis

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Authors: Shivani, Seema Sharma, Shveta Singh, Akriti Chandra

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Business activities contribute to various environmental issues such as deforestation, waste generation, and pollution. Therefore, integration of environmental concerns within manufacturing operations is vital for the long-term survival of businesses. In this context, green entrepreneurial orientation (GEO) is recognized as a firm-level internal strategy to mitigate ecological damage through initiating green business practices. However, despite the surge in research on GEO in recent years, ambiguity remains on the genesis of GEO and the mechanism through which GEO impacts various organizational outcomes. This prompts an examination of the ongoing scholarly discourse about GEO and its domain knowledge structure within the entrepreneurship literature using bibliometric analysis and the Theories, Contexts, Characteristics, and Methodologies (TCCM) framework. The authors analyzed a dataset comprising 73 scientific documents sourced from the Scopus and Web of Science database from 2005 to 2024 to provide insights into the publication trends, prominent journals, authors, articles, countries' collaboration, and keyword analysis in GEO research. The findings indicate that the number of relevant papers and citations has increased consistently, with authors from China being the main contributors. The articles are mainly published in Business Strategy and the Environment and Sustainability. Dynamic capability view is the dominant framework applied in the GEO domain, with large manufacturing firms and SMEs constituting the majority of the sample. Further, various antecedents of GEO have been identified at an organizational level to which managers can focus their attention. The studies have used various contextual factors to explain when GEO translates into superior organizational outcomes. The Method analysis reveals that PLS-SEM is the commonly used approach for analyzing the primary data collected through surveys. Moreover, the content analysis indicates four emerging research frontiers identified as unidimensional vs. multidimensional perspectives of GEO, typologies of green innovation, environmental management in the hospitality industry, and tech-savvy sustainability in the agriculture sector. This study is one of the earliest to apply quantitative methods to synthesize the extant literature on GEO. This research holds relevance for management practice due to the escalating levels of carbon emissions, energy consumption, and waste discharges observed in recent years, resulting in increased apprehension about climate change.

Keywords: green entrepreneurship, sustainability, SLR, TCCM

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Authors: Isidro Perez, Maryam Nazari

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Numerous earthquakes, which have taken place across the world, led to catastrophic damage and collapse of structures (e.g., 1971 San Fernando; 1995 Kobe-Japan; and 2010 Chile earthquakes). Engineers are constantly studying methods to moderate the effect this phenomenon has on structures to further reduce damage, costs, and ultimately to provide life safety to occupants. However, there are regions where structures, cities, or water reservoirs are built near fault lines. When an earthquake occurs near the fault lines, they can be categorized as near-field earthquakes. In contrary, a far-field earthquake occurs when the region is further away from the seismic source. A near-field earthquake generally has a higher initial peak resulting in a larger seismic response, when compared to a far-field earthquake ground motion. These larger responses may result in serious consequences in terms of structural damage which can result in a high risk for the public’s safety. Unfortunately, the response of structures subjected to near-field records are not properly reflected in the current building design specifications. For example, in ASCE 7-10, the design response spectrum is mostly based on the far-field design-level earthquakes. This may result in the catastrophic damage of structures that are not properly designed for near-field earthquakes. This research investigates the knowledge that the effect of near-field earthquakes has on the response of structures. To fully examine this topic, a structure was designed following the current seismic building design specifications, e.g. ASCE 7-10 and ACI 318-14, being analytically modeled, utilizing the SAP2000 software. Next, utilizing the FEMA P695 report, several near-field and far-field earthquakes were selected, and the near-field earthquake records were scaled to represent the design-level ground motions. Upon doing this, the prototype structural model, created using SAP2000, was subjected to the scaled ground motions. A Linear Time History Analysis and Pushover analysis were conducted on SAP2000 for evaluation of the structural seismic responses. On average, the structure experienced an 8% and 1% increase in story drift and absolute acceleration, respectively, when subjected to the near-field earthquake ground motions. The pushover analysis was ran to find and aid in properly defining the hinge formation in the structure when conducting the nonlinear time history analysis. A near-field ground motion is characterized by a high-energy pulse, making it unique to other earthquake ground motions. Therefore, pulse extraction methods were used in this research to estimate the maximum response of structures subjected to near-field motions. The results will be utilized in the generation of a design spectrum for the estimation of design forces for buildings subjected to NF ground motions.

Keywords: near-field, pulse, pushover, time-history

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Authors: Ati Moncef

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We describe in this paper the results of a phantom of dynamics renal with MAG3. Our phantom consisted of (tow shaped of kidneys, 1 liver). These phantoms were scanned with static and dynamic protocols and compared with clinical data. in a normal conditions we use our phantoms it's possible to acquire a renal images when we can be compared with clinical scintigraphy. In conclusion, Renal phantom also can use in the quality control of a renal scintigraphy.

Keywords: Renal scintigraphy, MAG3, Nuclear medicine, Gamma Camera.

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Authors: Muhammad Afzal, Muhammad Sajjad

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Climate change has become a global environmental challenge and it has affected the world’s economy. Its impact is widespread across all major sectors of the economy i.e. agriculture, industry, and services sectors. This study attempts to measure the long run as well as the short-run dynamic between energy; environment and economic growth by using Autoregressive Distributed Lag (ARDL) bound testing approach at aggregate as well as sectoral level. We measured the causal relationship between electricity consumption, fuel consumption, CO₂ emission, and real Gross Domestic Product (GDP) for the period of 1980 to 2016 for Pakistan. Our co-integration results reveal that all the variables are co-integrated at aggregate as well as at sectoral level. Electricity consumption shows two-way casual relation at for industry, services and aggregate level. The inverted U-Curve hypothesis tested the relationship between greenhouse gas emissions and per capita GDP and results supported the Environment Kuznet Curve (EKC) hypothesis. This study cannot ignore the importance of energy for economic growth but prefers to focus on renewable and green energy to pave on the trajectory of development.

Keywords: climate change, economic growth, energy, environment

Procedia PDF Downloads 164

Authors: Avichal Malhotra, Jerome Frisch, Christoph van Treeck

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The utilization of semantic, cadastral and topological data from geographic information systems (GIS) has exponentially increased for building and urban-scale energy performance simulations. Urban planners, simulation scientists, and researchers use virtual 3D city models for energy analysis, algorithms and simulation tools. For dynamic energy simulations at city and district level, this paper provides an overview of the available GIS data models and their levels of detail. Adhering to different norms and standards, these models also intend to describe building and construction industry data. For further investigations, CityGML data models are considered for simulations. Though geographical information modelling has considerably many different implementations, extensions of virtual city data can also be made for domain specific applications. Highlighting the use of the extended CityGML models for energy researches, a brief introduction to the Energy Application Domain Extension (ADE) along with its significance is made. Consequently, addressing specific input simulation data, a workflow using Modelica underlining the usage of GIS information and the quantification of its significance over annual heating energy demand is presented in this paper.

Keywords: CityGML, EnergyADE, energy performance simulation, GIS

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Authors: Jinsiang Shaw, Sheng-Xiang Xu

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This paper presents the development of an autonomous automated guided vehicle (AGV) with a robot arm attached on top of it within the framework of robot operation system (ROS). ROS can provide libraries and tools, including hardware abstraction, device drivers, libraries, visualizers, message-passing, package management, etc. For this reason, this AGV can provide automatic navigation and parts transportation and pick-and-place task using robot arm for typical industrial production line use. More specifically, this AGV will be controlled by an on-board host computer running ROS software. Command signals for vehicle and robot arm control and measurement signals from various sensors are transferred to respective microcontrollers. Users can operate the AGV remotely through the TCP / IP protocol and perform SLAM (Simultaneous Localization and Mapping). An RGBD camera and LIDAR sensors are installed on the AGV, using these data to perceive the environment. For SLAM, Gmapping is used to construct the environment map by Rao-Blackwellized particle filter; and AMCL method (Adaptive Monte Carlo localization) is employed for mobile robot localization. In addition, current AGV position and orientation can be visualized by ROS toolkit. As for robot navigation and obstacle avoidance, A* for global path planning and dynamic window approach for local planning are implemented. The developed ROS AGV with a robot arm on it has been experimented in the university factory. A 2-D and 3-D map of the factory were successfully constructed by the SLAM method. Base on this map, robot navigation through the factory with and without dynamic obstacles are shown to perform well. Finally, pick-and-place of parts using robot arm and ensuing delivery in the factory by the mobile robot are also accomplished.

Keywords: automated guided vehicle, navigation, robot operation system, Simultaneous Localization and Mapping

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Authors: Shoukat Ali, Abdul Qadir Jandga, Amjad Hussain

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Objective: The main purpose of this study is to compare the Percent Depth dose (PDD) and In-plane and cross-plane profiles of Varian Golden beam data to the measured data of 6 and 18 MV photons for the commissioning of Eclipse treatment planning system. Introduction: Commissioning of treatment planning system requires an extensive acquisition of beam data for the clinical use of linear accelerators. Accurate dose delivery require to enter the PDDs, Profiles and dose rate tables for open and wedges fields into treatment planning system, enabling to calculate the MUs and dose distribution. Varian offers a generic set of beam data as a reference data, however not recommend for clinical use. In this study, we compared the generic beam data with the measured beam data to evaluate the reliability of generic beam data to be used for the clinical purpose. Methods and Material: PDDs and Profiles of Open and Wedge fields for different field sizes and at different depths measured as per Varian’s algorithm commissioning guideline. The measurement performed with PTW 3D-scanning water phantom with semi-flex ion chamber and MEPHYSTO software. The online available Varian Golden Beam Data compared with the measured data to evaluate the accuracy of the golden beam data to be used for the commissioning of Eclipse treatment planning system. Results: The deviation between measured vs. golden beam data was in the range of 2% max. In PDDs, the deviation increases more in the deeper depths than the shallower depths. Similarly, profiles have the same trend of increasing deviation at large field sizes and increasing depths. Conclusion: Study shows that the percentage deviation between measured and golden beam data is within the acceptable tolerance and therefore can be used for the commissioning process; however, verification of small subset of acquired data with the golden beam data should be mandatory before clinical use.

Keywords: percent depth dose, flatness, symmetry, golden beam data

Procedia PDF Downloads 489

Authors: Ekaterina Leusheva, Valentin Morenov

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Well drilling in hard rocks is coupled with high energy demands which negates the speed of the process and thus reduces overall effectiveness. Aim of this project is to develop the technique of experimental research, which would allow to select optimal washing fluid composition while adding special hardness reducing detergent reagents. Based on the analysis of existing references and conducted experiments, technique dealing with quantitative evaluation of washing fluid weakening influence on drilled rocks was developed, which considers laboratory determination of three mud properties (density, surface tension, specific electrical resistance) and three rock properties (ultimate stress, dynamic strength, micro-hardness). Developed technique can be used in the well drilling technologies and particularly while creating new compositions of drilling muds for increased destruction effectiveness of hard rocks. It can be concluded that given technique introduces coefficient of hard rocks destruction effectiveness that allows quantitative evaluation of different drilling muds on the drilling process to be taken. Correct choice of drilling mud composition with hardness reducing detergent reagents will increase drilling penetration rate and drill meterage per bit.

Keywords: detergent reagents, drilling mud, drilling process stimulation, hard rocks

Procedia PDF Downloads 547

Authors: Muhammad Naveed Tahir, Wang Yingkuan, Huang Wenjiang, Raheel Osman

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Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time.

Keywords: ANN, chlorophyll content, chlorophyll indices, satellite images, wheat

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Authors: PB Venkataraman

Abstract:

Most of the real-life problems are ill-structured. They do not have a single solution but many competing solutions. The solution paths are non-linear and ambiguous, and the problem definition itself is many times a challenge. Students of professional education learn to solve such problems through internships. The current pandemic situation has constrained on-site internship opportunities; thus the students have no option but to pursue this learning online. This research assessed the learning gain of four undergraduate students in engineering as they undertook an online internship in an organisation over a period of eight weeks. A clinical interview at the end of the internship provided the primary data to assess the team’s problem-solving skills using a tested rubric. In addition to this, change in their learning attitudes were assessed through a pre-post study using a repurposed CLASS instrument for Electrical Engineering. Analysis of CLASS data indicated a shift in the sophistication of their learning attitude. A learning engagement survey adopting a 6-point Likert scale showed active participation and motivation in learning. We hope this new research will stimulate educators to exploit online internships even beyond the time of pandemic as more and more business operations are transforming into virtual.

Keywords: ill-structured problems, learning attitudes, internship, assessment, student engagement

Procedia PDF Downloads 201