Search results for: Francisco J. Real

Authors: Mayada Attia Ibrahim

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Evaluating and developing the electroplating production process is a key challenge in this type of process. The process is influenced by several factors such as process parameters, process costs, and production environments. Analyzing and optimizing all these factors together requires extensive analytical techniques that are not available in real-case industrial entities. This paper presents a practice-based framework for the evaluation and optimization of some of the crucial factors that affect the costs and production times associated with this type of process, energy costs, material costs, and product flow times. The proposed approach uses Design of Experiments, Discrete-Event Simulation, and Theory of Constraints were respectively used to identify the most significant factors affecting the production process and simulate a real production line to recognize the effect of these factors and assign possible bottlenecks. Several scenarios are generated as corrective strategies for improving the production line. Following that, data envelopment analysis CCR input-oriented DEA model is used to evaluate and optimize the suggested scenarios.

Keywords: electroplating process, simulation, design of experiment, performance optimization, theory of constraints, data envelopment analysis

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Authors: Ahmed Ferhati, Arezki Adjrad, Ratiba Mitiche-Kettab, Hakim Djafer Khodja

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The porous media are omnipresent around us that they are natural as sand, clay, rocks, or manufactured like concretes, cement, and ceramics. The variety of porous environment indicates a wide material range which can be very different from each other. Their common point is to be made up of a solid matrix and a porous space. In our case of study, we made the modeling of the flows in porous environments through the massives as in the case of an earth dam. The computer code used (PLAXIS) offer the possibility of modeling of various structures, in particular, the works in lands because that it deals with the pore water pressure due to the underground flow and the calculation of the plastic deformations. To confirm results obtained by PLAXIS, GeoStudio SEEP/W code was used. This work treats modeling of flows and mechanical and hydraulic behavior of earth dam. A general framework which can fit the calculation of this kind of structures and the coupling of the soil consolidation and free surface flows was defined. In this study; we have confronted a real case modeling of an earth dam. It was shown, in particular, that it is possible to entirely lead the calculation of real dam and to get encouraging results from the hydraulic and mechanical point of view.

Keywords: analyzes, dam, flow, modeling, PLAXIS, seep/w, slope

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Authors: Pierre Kancir, Jean-Philippe Diguet, Marc Sevaux

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One of the main difficulties in developing multi-robot systems (MRS) is related to the simulation and testing tools available. Indeed, if the differences between simulations and real robots are too significant, the transition from the simulation to the robot won’t be possible without another long development phase and won’t permit to validate the simulation. Moreover, the testing of different algorithmic solutions or modifications of robots requires a strong knowledge of current tools and a significant development time. Therefore, the availability of tools for MRS, mainly with flying drones, is crucial to enable the industrial emergence of these systems. This research aims to present the most commonly used tools for MRS simulations and their main shortcomings and presents complementary tools to improve the productivity of designers in the development of multi-vehicle solutions focused on a fast learning curve and rapid transition from simulations to real usage. The proposed contributions are based on existing open source tools as Gazebo simulator combined with ROS (Robot Operating System) and the open-source multi-platform autopilot ArduPilot to bring them to a broad audience.

Keywords: ROS, ArduPilot, MRS, simulation, drones, Gazebo

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Authors: Kamellia Reshadi, Pranav Ragji, Theodoros Soldatos

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Recent advancements in cloud computing and storage, along with rapid progress in artificial intelligence (AI), have transformed approaches to developing efficient, scalable applications. However, integrating AI with cloud computing poses challenges as these fields are often disjointed, and many advancements remain difficult to access, obscured in complex documentation or scattered across research reports. For this reason, we share experiences from prototype projects combining these technologies. Specifically, we focus on Google Cloud Platform (GCP) functionalities and describe vision and speech activities applied to labeling, subtitling, and urban traffic flow tasks. We describe challenges, pricing, architecture, and other key features, considering the goal of real-time performance. We hope our demonstrations provide not only essential guidelines for using these functionalities but also enable more similar approaches.

Keywords: artificial intelligence, cloud computing, real-time applications, case studies, knowledge management, research and development, text labeling, video annotation, urban traffic analysis, public safety, prototyping, Google Cloud Platform

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Authors: K. Asanimoghadam, M. Salahi, A. Jamalian

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Data envelopment analysis is an approach to measure the efficiency of decision making units with multiple inputs and outputs. The structure of many decision making units also has decision-making subunits that are not considered in most data envelopment analysis models. Also, the inputs and outputs of the decision-making units usually are considered desirable, while in some real-world problems, the nature of some inputs or outputs are undesirable. In this thesis, we study the evaluation of the efficiency of two stage decision-making units, where some outputs are undesirable using two non-radial models, the SBM and the ASBM models. We formulate the nonlinear ASBM model as a second order cone optimization problem. Finally, we compare two models for both external and internal evaluation approaches for two real world example in the presence of undesirable outputs. The results show that, in both external and internal evaluations, the overall efficiency of ASBM model is greater than or equal to the overall efficiency value of the SBM model, and in internal evaluation, the ASBM model is more flexible than the SBM model.

Keywords: network DEA, conic optimization, undesirable output, SBM

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Authors: Sajana Suwal, Ganesh R. Nhemafuki

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Evaluation of ground response under earthquake shaking is crucial in geotechnical earthquake engineering. Damage due to seismic excitation is mainly correlated to local geological and geotechnical conditions. It is evident from the past earthquakes (e.g. 1906 San Francisco, USA, 1923 Kanto, Japan) that the local geology has strong influence on amplitude and duration of ground motions. Since then significant studies has been conducted on ground motion amplification revealing the importance of influence of local geology on ground. Observations from the damaging earthquakes (e.g. Nigata and San Francisco, 1964; Irpinia, 1980; Mexico, 1985; Kobe, 1995; L’Aquila, 2009) divulged that non-uniform damage pattern, particularly in soft fluvio-lacustrine deposit is due to the local amplification of seismic ground motion. Non-uniform damage patterns are also observed in Kathmandu Valley during 1934 Bihar Nepal earthquake and recent 2015 Gorkha earthquake seemingly due to the modification of earthquake ground motion parameters. In this study, site effects resulting from amplification of soft soil in Kathmandu are presented. A large amount of subsoil data was collected and used for defining the appropriate subsoil model for the Kathamandu valley. A comparative study of one-dimensional total-stress equivalent linear and non-linear site response is performed using four strong ground motions for six sites of Kathmandu valley. In general, one-dimensional (1D) site-response analysis involves the excitation of a soil profile using the horizontal component and calculating the response at individual soil layers. In the present study, both equivalent linear and non-linear site response analyses were conducted using the computer program DEEPSOIL. The results show that there is no significant deviation between equivalent linear and non-linear site response models until the maximum strain reaches to 0.06-0.1%. Overall, it is clearly observed from the results that non-linear site response model perform better as compared to equivalent linear model. However, the significant deviation between two models is resulted from other influencing factors such as assumptions made in 1D site response, lack of accurate values of shear wave velocity and nonlinear properties of the soil deposit. The results are also presented in terms of amplification factors which are predicted to be around four times more in case of non-linear analysis as compared to equivalent linear analysis. Hence, the nonlinear behavior of soil prevails the urgent need of study of dynamic characteristics of the soft soil deposit that can specifically represent the site-specific design spectra for the Kathmandu valley for building resilient structures from future damaging earthquakes.

Keywords: deep soil, equivalent linear analysis, non-linear analysis, site response

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Authors: Andi Intan Purnamasari, Supriyadi, Sulbadana, Aminuddin Kasim

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Law functions as social control. Providing arrangements not only for legal certainty, but also in the scope of justice and expediency. The three values ​​achieved by law essentially function to bring comfort to each individual in carrying out daily activities. However, it is undeniable that global conditions have changed the orientation of people's lifestyles. Some people want to ensure their existence in the digital world which is popularly known as the metaverse. Some countries even project their city to be a metaverse city. The order of life is no longer limited to the real space, but also to the cyber world. Not infrequently, legal events that occur in the cyber world also force the law to position its position and even prevent crime in cyberspace. Through this research, conceptually it provides a view of the legal position in crime prevention in the Metaverse world. when the law acts to regulate the situation in the virtual world, of course some people will feel disturbed, this is due to the thought that the virtual world is a world in which an avatar can do things that cannot be done in the real world, or can be called a world without boundaries. Therefore, when the law is present to provide boundaries, of course the concept of the virtual world itself becomes no longer a cyber world that is not limited by space and time, it becomes a new order of life. approach, approach, approach, approach, and approach will certainly be the method used in this research.

Keywords: crime, cyber, metaverse, law

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Authors: Khachen Kongpakwattana, Charungthai Dejthevaporn, Orapitchaya Krairit, Piyameth Dilokthornsakul, Devi Mohan, Nathorn Chaiyakunapruk

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Background: Although an increase in the burden of Alzheimer’s disease (AD) is evident worldwide, knowledge of costs and health-related quality of life (HR-QoL) associated with AD in Low- and Middle-Income Countries (LMICs) is still lacking. We, therefore, aimed to collect real-world cost and HR-QoL data, and investigate their associations with multiple disease-severity indicators among AD patients in Thailand. Methods: We recruited AD patients aged ≥ 60 years accompanied by their caregivers at a university-affiliated tertiary hospital. A one-time structured interview was conducted to collect disease-severity indicators, HR-QoL and caregiving information using standardized tools. The hospital’s database was used to retrieve healthcare resource utilization occurred over 6 months preceding the interview date. Costs were annualized and stratified based on cognitive status. Generalized linear models were employed to evaluate determinants of costs and HR-QoL. Results: Among 148 community-dwelling patients, average annual total societal costs of AD care were 8,014 US$ [95% Confidence Interval (95% CI): 7,295 US$ - 8,844 US$] per patient. Total costs of patients with severe stage (9,860 US$; 95% CI: 8,785 US$ - 11,328 US$) were almost twice as high as those of mild stage (5,524 US$; 95% CI: 4,649 US$ - 6,593 US$). The major cost driver was direct medical costs, particularly those incurred by AD prescriptions. Functional status was the strongest determinant for both total costs and patient’s HR-QoL (p-value < 0.001). Conclusions: Our real-world findings suggest the distinct major cost driver which results from expensive AD treatment, emphasizing the demand for country-specific cost evidence. Increases in cognitive and functional status are significantly associated with decreases in total costs of AD care and improvement on patient’s HR-QoL.

Keywords: Alzheimer's disease, associations, costs, disease-severity indicators, health-related quality of life

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Authors: Chih-Chun Tang

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Nostalgia, to some people, may seem foolhardy in a way. However, nostalgia is a completely and intensely private but social, collective emotion. It has continuing consequence and outgrowth for our lives as social actions. It leads people to hunt and explore remembrance of persons and places of our past in an effort to confer meaning of persons and places of present. In the ‘Poems of 1912-13’ Thomas Hardy, a British poet, composed a series of poems after the unexpected death of his long-disaffected wife, Emma. The series interprets the cognitive and emotional concussion of Emma’s death on Hardy, concerning his mind and real visit to the landscape in Cornwall, England. Both spaces perform the author’s innermost in thought to his late wife and to the landscape. They present an apparent counterpart of the poet and his afflicted conscience. After Emma had died, Hardy carried her recollections alive by roaming about in the real visit and whimsical land (space) they once had drifted and meandered. This paper highlights the nostalgias and feds that seem endlessly to crop up.

Keywords: Thomas Hardy, remembrance, psychological, poems 1912-13, Fred Davis, nostalgia

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Authors: Mahdi Rahaie

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MicroRNAs are a novel class of small RNAs which regulate gene expression by translational repression or degradation of messenger RNAs. To produce sensitive, simple and cost-effective assays for microRNAs, detection is in urgent demand due to important role of these biomolecules in progression of human disease such as Alzheimer’s, Multiple sclerosis, and some other neurodegenerative diseases. Herein, we report several novel, sensitive and specific microRNA nanobiosensors which were designed based on colorimetric and fluorescence detection of nanoparticles and hybridization chain reaction amplification as an enzyme-free amplification. These new strategies eliminate the need for enzymatic reactions, chemical changes, separation processes and sophisticated equipment whereas less limit of detection with most specify are acceptable. The important features of these methods are high sensitivity and specificity to differentiate between perfectly matched, mismatched and non-complementary target microRNAs and also decent response in the real sample analysis with blood plasma. These nanobiosensors can clinically be used not only for the early detection of neuro diseases but also for every sickness related to miRNAs by direct detection of the plasma microRNAs in real clinical samples, without a need for sample preparation, RNA extraction and/or amplification.

Keywords: hybridization chain reaction, microRNA, nanobiosensor, neurodegenerative diseases

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Authors: Emin Avcı, Çiydem Çatak

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The recent economic crisis have shown that procyclicality, which could threaten the stability and growth of the economy, is a major problem of financial and real sector. The term procyclicality refers here the cyclical behavior of banks that lead them to follow the same patterns as the real economy. In this study, leverage which demonstrate how a bank manage its debt, is chosen as bank specific variable to see the effect of changes in it over the economic cycle. The procyclical behavior of Turkish banking sector (commercial, participation, development-investment banks) is tried to explain with analyzing the relationship between leverage and asset growth. On the basis of theoretical explanations, eight different leverage ratios are utilized in eight different panel data models to demonstrate the procyclicality effect of Turkish banks leverage using monthly data covering the 2005-2014 period. It is tested whether there is an increasing (decreasing) trend in the leverage ratio of Turkish banks when there is an enlargement (contraction) in their balance sheet. The major finding of the study indicates that asset growth has a significant effect on all eight leverage ratios. In other words, the leverage of Turkish banks follow a cyclical pattern, which is in line with those of earlier literature.

Keywords: banking, economic cycles, leverage, procyclicality

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Authors: Hari Sharan Pandit

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Mathematics teaching in Nepal has been centralized and guided by the notion of transfer of knowledge and skills from teachers to students. The overemphasis on the ‘algorithm-centric’ approach to mathematics teaching and the focus on ‘role–learning’ as the ultimate way of solving mathematical problems since the early years of schooling have been creating severe problems in school-level mathematics in Nepal. In this context, the author argues that students should learn real-world mathematical problems through various interesting, creative and collaborative, as well as artistic and alternative ways of knowing. The collaboration-incorporated pedagogy is a distinct pedagogical approach that offers a better alternative as an integrated and interdisciplinary approach to learning that encourages students to think more broadly and critically about real-world problems. The paper, as a summarized report of action research designed, developed and implemented by the author, focuses on the needs and usefulness of collaboration-incorporated pedagogy in the Nepali context to make mathematics teaching more meaningful for producing creative and critical citizens. This paper is useful for mathematics teachers, teacher educators and researchers who argue on arts integration in mathematics teaching.

Keywords: peer teaching, metacognitive approach, mitigating, action research

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Authors: Ichiro Takahashi

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One conundrum that macroeconomic theory faces is to explain how an economy can revive from depression, in which the aggregate demand has fallen substantially below its productive capacity. This paper examines an autonomous stabilizing mechanism using an agent-based Wicksell-Keynes macroeconomic model. This paper focuses on the effects of the number of firms and the length of the gestation period for investment that are often assumed to be one in a mainstream macroeconomic model. The simulations found the virtual economy was highly unstable, or more precisely, collapsing when these parameters are fixed at one. This finding may even suggest us to question the legitimacy of these common assumptions. A perpetual decline in capital stock will eventually encourage investment if the capital stock is short-lived because an inactive investment will result in insufficient productive capacity. However, for an economy characterized by a roundabout production method, a gradual decline in productive capacity may not be able to fall below the aggregate demand that is also shrinking. Naturally, one would then ask if our economy cannot rely on an external stimulus such as population growth and technological progress to revive investment, what factors would provide such a buoyancy for stimulating investments? The current paper attempts to answer this question by employing the artificial macroeconomic model mentioned above. The baseline model has the following three features: (1) the multi-period gestation for investment, (2) a large number of heterogeneous firms, (3) demand-constrained firms. The instability is a consequence of the following dynamic interactions. (a) A multiple-period gestation period means that once a firm starts a new investment, it continues to invest over some subsequent periods. During these gestation periods, the excess demand created by the investing firm will spill over to ignite new investment of other firms that are supplying investment goods: the presence of multi-period gestation for investment provides a field for investment interactions. Conversely, the excess demand for investment goods tends to fade away before it develops into a full-fledged boom if the gestation period of investment is short. (b) A strong demand in the goods market tends to raise the price level, thereby lowering real wages. This reduction of real wages creates two opposing effects on the aggregate demand through the following two channels: (1) a reduction in the real labor income, and (2) an increase in the labor demand due to the principle of equality between the marginal labor productivity and real wage (referred as the Walrasian labor demand). If there is only a single firm, a lower real wage will increase its Walrasian labor demand, thereby an actual labor demand tends to be determined by the derived labor demand. Thus, the second positive effect would not work effectively. In contrast, for an economy with a large number of firms, Walrasian firms will increase employment. This interaction among heterogeneous firms is a key for stability. A single firm cannot expect the benefit of such an increased aggregate demand from other firms.

Keywords: agent-based macroeconomic model, business cycle, demand constraint, gestation period, representative agent model, stability

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Authors: Jonardan Koner, Basabi Bhattacharya, Avinash Purandare

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The study is focused to find out the impact of infrastructural investment on economic development in Indian states. The study uses panel data analysis to measure the impact of infrastructural investment on Real Gross Domestic Product in Indian States. Panel data analysis incorporates Unit Root Test, Cointegration Teat, Pooled Ordinary Least Squares, Fixed Effect Approach, Random Effect Approach, Hausman Test. The study analyzes panel data (annual in frequency) ranging from 1991 to 2012 and concludes that infrastructural investment has a desirable impact on economic development in Indian. Finally, the study reveals that the infrastructural investment significantly explains the variation of economic indicator.

Keywords: infrastructural investment, real GDP, unit root test, cointegration teat, pooled ordinary least squares, fixed effect approach, random effect approach, Hausman test

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Authors: Kulsoom S. Bughio, Naeem K. Janjua, Gordana Dermody, Leslie F. Sikos, Shamsul Islam

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The advancement in information and communication technologies (ICT) and wireless sensor networks have played a pivotal role in the design and development of real-time healthcare solutions, mainly targeting the elderly living in health-assistive smart homes. Such smart homes are equipped with sensor technologies to detect and record activities of daily living (ADL). This survey reviews and evaluates existing approaches and techniques based on real-time sensor-based modeling and reasoning in single-user and multi-user environments. It classifies the approaches into three main categories: learning-based, knowledge-based, and hybrid, and evaluates how they handle temporal relations, granularity, and uncertainty. The survey also highlights open challenges across various disciplines (including computer and information sciences and health sciences) to encourage interdisciplinary research for the detection and recognition of ADLs and discusses future directions.

Keywords: daily living activities, smart homes, single-user environment, multi-user environment

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Authors: Chin-Yuan Hsieh, Wei-Chun Lu, Yu-Hong Zeng

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The situation of a group of fish die is frequently found due to the fish disease caused by the deterioration of aquaculture water quality. The toxic ammonia is produced by animals as a byproduct of protein. The system is designed by the smart sensor technology and developed by the mathematical model to monitor the water parameters 24 hours a day and predict the relationship among twelve water quality parameters for monitoring the water quality in aquaculture. All data measured are stored in cloud server. In productive ponds, the daytime pH may be high enough to be lethal to the fish. The sudden change of the aquaculture conditions often results in the increase of PH value of water, lack of oxygen dissolving content, water quality deterioration and yield reduction. From the real measurement, the system can send the message to user’s smartphone successfully on the bad conditions of water quality. From the data comparisons between measurement and model simulation in fish aquaculture site, the difference of parameters is less than 2% and the correlation coefficient is at least 98.34%. The solubility rate of oxygen decreases exponentially with the elevation of water temperature. The correlation coefficient is 98.98%.

Keywords: aquaculture, sensor, ammonia, dissolved oxygen

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Authors: Neslihan Sahin Celik, Ali Eraslan

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As a result of PISA (Program for International Student Assessments) survey that tests how well students can apply the knowledge and skills they have learned at school to real-life challenges, the new and redesigned mathematics education programs in many countries emphasize the necessity for the students to face complex and multifaceted problem situations and gain experience in this sense allowing them to develop new skills and mathematical thinking to prepare them for their future life after school. At this point, mathematical models and modeling approaches can be utilized in the analysis of complex problems which represent real-life situations in which students can actively participate. In particular, model eliciting activities that bring about situations which allow the students to create solutions to problems and which involve mathematical modeling must be used right from primary school years, allowing them to face such complex, real-life situations from early childhood period. A qualitative study was conducted in a university foundation primary school in the city center of a big province in 2013-2014 academic years. The participants were 4th grade students in a primary school. After a four-week preliminary study applied to a fourth-grade classroom, three students included in the focus group were selected using criterion sampling technique. A focus group of three students was videotaped as they worked on the Crime Problem. The conversation of the group was transcribed, examined with students’ written work and then analyzed through the lens of Blum and Ferri’s modeling processing cycle. The results showed that primary fourth-grade students can successfully work with model eliciting problem while they encounter some difficulties in the modeling processes. In particular, they developed new ideas based on different assumptions, identified the patterns among variables and established a variety of models. On the other hand, they had trouble focusing on problems and occasionally had breaks in the process.

Keywords: primary school, modeling, mathematical modeling, crime problem

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Authors: Juan C. Domínguez, Belén Del Saz-Orozco, María V. Alonso, Mercedes Oliet, Francisco Rodríguez

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In the present study, the kinetics of thermal degradation of a phenolic and lignin reinforced phenolic foams, and the lignin used as reinforcement were studied and the activation energies of their degradation processes were obtained by a DAEM model. The average values for five heating rates of the mean activation energies obtained were: 99.1, 128.2, and 144.0 kJ.mol-1 for the phenolic foam, 109.5, 113.3, and 153.0 kJ.mol-1 for the lignin reinforcement, and 82.1, 106.9, and 124.4 kJ. mol-1 for the lignin reinforced phenolic foam. The standard deviation ranges calculated for each sample were 1.27-8.85, 2.22-12.82, and 3.17-8.11 kJ.mol-1 for the phenolic foam, lignin and the reinforced foam, respectively. The DAEM model showed low mean square errors (< 1x10-5), proving that is a suitable model to study the kinetics of thermal degradation of the foams and the reinforcement.

Keywords: kinetics, lignin, phenolic foam, thermal degradation

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Authors: Javad Sarvestan, Zdenek Svoboda

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Force-time (F-T) curvature characteristics are globally referenced as the main indicators of athletic jump performance. Nevertheless, to the best of authors’ knowledge, no investigation tried to deeply study the relationship between F-T curve variables and real-game jump performance among elite volleyball players. To this end, this study was designated to investigate the association between F-T curve variables, including movement timings, force, velocity, power, rate of force development (RFD), modified reactive strength index (RSImod), and net impulse with spike jump height during real-game circumstances. Twelve young elite volleyball players performed 3 countermovement jump (CMJ) and 3 spike jump in real-game circumstances with 1-minute rest intervals to prevent fatigue. Shapiro-Wilk statistical test illustrated the normality of data distribution, and Pearson’s product correlation test portrayed a significant correlation between CMJ height and peak RFD (0.85), average RFD (r=0.81), RSImod (r=0.88) and concentric net impulse (r=0.98), and also significant correlation between spike jump height and peak RFD (0.73), average RFD (r=0.80), RSImod (r=0.62) and concentric net impulse (r=0.71). Multiple regression analysis also reported that these factors have a strong contribution in predicting of CMJ (98%) and spike jump (77%) heights. Outcomes of this study confirm that the RFD, concentric net impulse, and RSImod values could precisely monitor and track the volleyball attackers’ explosive strength, muscular stretch-shortening cycle function efficiency, and ultimate spike jump height. To this effect, volleyball coaches and trainers are advised to have an in-depth focus on their athletes’ progression or the impacts of strength trainings by observing and chasing the F-T curve variables such as RFD, net impulse, and RSImod.

Keywords: net impulse, reactive strength index, rate of force development, stretch-shortening cycle

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Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

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Authors: Ibrahim Darwish, Noora Abu Ain

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Referring to female relatives by male Jordanian speakers in public is governed by various linguistic and social constraints. Although Jordanian society is less conservative than it was a few decades ago, women are still considered the weaker link in society and men still believe that they need to protect them. Conservative Jordanians often avoid referring to their female relatives overtly, i.e., using their real names. Instead, they use covert names, such as pseudonyms, nicknames, pet names, etc. The reason behind such language use has to do with how Arab men, in general, see women as part of their honor. This study intends to investigate to what extent Jordanian males hide their female relatives’ names in public domains. The data was collected from spontaneous informal voice-recorded interviews carried out in the village of Saham in the far north of Jordan. Saham’s dialect is part of a larger Horani dialect used by speakers along a wide area that stretches from Salt in the south to the Syrian borders in the north of Jordan. The voice-recorded interviews were originally carried out as an audio record of some customs and traditions in the village of Saham in 2013. During most of these interviews, the researchers observed how the male participants indirectly referred to their female relatives. Instead of using real names, the male speakers used broad terms to refer to their female relatives, such al-Beit ‘the home,’ al-ciyaal ‘the kids’, um-x ‘the mother of x,’ etc. All tokens related to the issue in question were collected, analyzed and quantified about three age cohorts: young, middle-aged and old speakers. The results show that young speakers are more direct in referring to their female relatives than the other two age groups. This can point to a possible change in progress in the speech community of Saham. It is argued that due to contact with other urban speech communities, the young speakers in Saham do not feel the need to hide the real names of their female relatives as they consider them as equals. Indeed, the young generation is more open to the idea of women's rights and call for expanding Jordanian women’s roles in Jordanian society.

Keywords: gender differences, Horan, proper names, social constraints

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Zhe Li, Aihua Cai

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Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.

Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM

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Authors: A. Casale, J. Alessi, C. N. Bianchi, G. Bozzini, M. Brunoldi, V. Cappanera, P. Corvisiero, G. Fanciulli, D. Grosso, N. Magnoli, A. Mandich, C. Melchiorre, C. Morri, P. Povero, N. Stasi, M. Taiuti, G. Viano, M. Wurtz

Abstract:

This work describes a real-time algorithm for detecting, tracking and classifying single motorboats, developed using the acoustic data recorded by a hydrophone array within the framework of EU LIFE + project ARION (LIFE09NAT/IT/000190). The project aims to improve the conservation status of bottlenose dolphins through a real-time simultaneous monitoring of their population and surface ship traffic. A Passive Acoustic Monitoring (PAM) system is installed on two autonomous permanent marine buoys, located close to the boundaries of the Marine Protected Area (MPA) of Portofino (Ligurian Sea- Italy). Detecting surface ships is also a necessity in many other sensible areas, such as wind farms, oil platforms, and harbours. A PAM system could be an effective alternative to the usual monitoring systems, as radar or active sonar, for localizing unauthorized ship presence or illegal activities, with the advantage of not revealing its presence. Each ARION buoy consists of a particular type of structure, named meda elastica (elastic beacon) composed of a main pole, about 30-meter length, emerging for 7 meters, anchored to a mooring of 30 tons at 90 m depth by an anti-twist steel wire. Each buoy is equipped with a floating element and a hydrophone tetrahedron array, whose raw data are send via a Wi-Fi bridge to a ground station where real-time analysis is performed. Bottlenose dolphin detection algorithm and ship monitoring algorithm are operating in parallel and in real time. Three modules were developed and commissioned for ship monitoring. The first is the detection algorithm, based on Time Difference Of Arrival (TDOA) measurements, i.e., the evaluation of angular direction of the target respect to each buoy and the triangulation for obtaining the target position. The second is the tracking algorithm, based on a Kalman filter, i.e., the estimate of the real course and speed of the target through a predictor filter. At last, the classification algorithm is based on the DEMON method, i.e., the extraction of the acoustic signature of single vessels. The following results were obtained; the detection algorithm succeeded in evaluating the bearing angle with respect to each buoy and the position of the target, with an uncertainty of 2 degrees and a maximum range of 2.5 km. The tracking algorithm succeeded in reconstructing the real vessel courses and estimating the speed with an accuracy of 20% respect to the Automatic Identification System (AIS) signals. The classification algorithm succeeded in isolating the acoustic signature of single vessels, demonstrating its temporal stability and the consistency of both buoys results. As reference, the results were compared with the Hilbert transform of single channel signals. The algorithm for tracking multiple targets is ready to be developed, thanks to the modularity of the single ship algorithm: the classification module will enumerate and identify all targets present in the study area; for each of them, the detection module and the tracking module will be applied to monitor their course.

Keywords: acoustic-noise, bottlenose-dolphin, hydrophone, motorboat

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Authors: M. Khairudin

Abstract:

This paper investigates the system identification and design quantitative feedback theory (QFT) for the robust control of a lathe spindle. The dynamic of the lathe spindle is uncertain and time variation due to the deepness variation on cutting process. System identification was used to obtain the dynamics model of the lathe spindle. In this work, real time system identification is used to construct a linear model of the system from the nonlinear system. These linear models and its uncertainty bound can then be used for controller synthesis. The real time nonlinear system identification process to obtain a set of linear models of the lathe spindle that represents the operating ranges of the dynamic system. With a selected input signal, the data of output and response is acquired and nonlinear system identification is performed using Matlab to obtain a linear model of the system. Practical design steps are presented in which the QFT-based conditions are formulated to obtain a compensator and pre-filter to control the lathe spindle. The performances of the proposed controller are evaluated in terms of velocity responses of the the lathe machine spindle in corporating deepness on cutting process.

Keywords: lathe spindle, QFT, robust control, system identification

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

Abstract:

Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

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Authors: Yasmine Essafi Zouari, Aya Nasreddine

Abstract:

Following a long inflationary period from the post-war era to the mid-1980s (+10.1% annually), France went through a moderate inflation period between 1986 and 2001 (+2.1% annually) and even lower inflation between 2002 and 2016 (+1.4% annually). In 2022, inflation in France increased rapidly and reached 4.5% over one year in March, according to INSEE estimates. Over a long period, even low inflation has an impact on portfolio value and households’ purchasing power. In such a context, inflation hedging should remain an important issue for investors. In particular, long-term investors, who are concerned with the protection of their wealth, seek to hold effective hedging assets. Considering a mixed-asset portfolio composed of housing assets (residential real estate in 150 Grand Paris communes) as well as financial assets, and using both correlation and regression analysis, results confirm the attribute of the direct housing investment as an inflation hedge especially particularly against its unexpected component. Further, cash and bonds were found to provide respectively a partial and an over hedge against unexpected inflation. Stocks act as a perverse hedge against unexpected inflation and provide no significant positive hedge against expected inflation.

Keywords: direct housing, inflation, hedging ability, optimal portfolio, Grand Paris metropolis

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

Abstract:

The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

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Authors: Xiaoyun Zhao, Rami Darwish, Anna Pernestål

Abstract:

Automated vehicles (AVs) have the potential to enhance road capacity, improving road safety and traffic efficiency. Research and development on AVs have been going on for many years. However, when the complicated traffic rules and real situations interacted, AVs fail to make decisions on contradicting situations, and are not able to have control in all conditions due to highly dynamic driving scenarios. This limits AVs’ usage and restricts the full potential benefits that they can bring. Furthermore, regulations, infrastructure development, and public acceptance cannot keep up at the same pace as technology breakthroughs. Facing these challenges, this paper proposes automated vehicle traffic control tower (AVTCT) acting as a safe, efficient and integrated solution for AV control. It introduces a concept of AVTCT for control, management, decision-making, communication and interaction with various aspects in transportation. With the prototype demonstrations and simulations, AVTCT has the potential to overcome the control challenges with AVs and can facilitate AV reaching their full potential. Possible functionalities, benefits as well as challenges of AVTCT are discussed, which set the foundation for the conceptual model, simulation and real application of AVTCT.

Keywords: automated vehicle, connectivity and automation, intelligent transport system, traffic control, traffic safety

Procedia PDF Downloads 140

Authors: Afaque Manzoor Soomro, Faheem Ahmed, Fida Hussain Memon, Kyung Hyun Choi

Abstract:

This paper proposes the bioinspired soft frog robot. All printing technology was used for the fabrication of the robot. Polyjet printing was used to print the front and back limbs, while ultrathin filament was used to print the body of the robot, which makes it a complete soft swimming robot. The dual thrust generation approach has been proposed by embedding the main muscle and antagonistic muscle in all the limbs, which enables it to attain high speed (18 mm/s), and significant control of swimming in dual modes (synchronous and asynchronous modes). To achieve the swimming motion of the frog, the design, motivated by the rigorous modelling and real frog dynamics analysis, enabled the as-developed frog robot (FROBOT) to swim at a significant level of consistency with the real frog. The FROBOT (weighing 65 g) can swim at different controllable frequencies (0.5–2Hz) and can turn in any direction by following custom-made LabVIEW software’s commands which enables it to swim at speed up to 18 mm/s on the surface of deep water (100 cm) with excellent weight balance.

Keywords: soft robotics, soft actuator, frog robot, 3D printing

Procedia PDF Downloads 101